From 7d4c8cfdb7edce7343408a8cc98066ac2ec4e230 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:02:43 +0530
Subject: [PATCH 001/615] #3049 initial draft: metadata, dependencies, extras,
 entry points

---
 pyproject.toml | 172 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 172 insertions(+)
 create mode 100644 pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000..3e1ad42e76
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,172 @@
+# From the pip documentation:
+
+# Fallback Behaviour
+# If a project does not have a pyproject.toml file containing a build-system section,
+# it will be assumed to have the following backend settings:
+
+# [build-system]
+# requires = ["setuptools>=40.8.0", "wheel"]
+# build-backend = "setuptools.build_meta:__legacy__"
+
+# TODO: add appropriate build-system section
+[build-system]
+# TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
+# will fail to install
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "pybamm"
+# TODO: try picking up version from the package itself
+# dynamic = ["version", "readme"]
+# [tool.setuptools.dynamic]
+# version = {attr = "my_package.VERSION"}
+version = "23.5"
+# Unsure: specify BSD-3-Clause?
+# license = {text = "BSD-3-Clause"}
+license = { file = "LICENCE.txt" }
+
+# TODO: add appropriate long description
+description = "Python Battery Mathematical Modelling"
+
+# TODO: correctly specify all authors and maintainers
+# Note: these are currently missing when running `pip show pybamm`, so we should add
+# them in some form
+authors = [{name = "The PyBaMM Team"}]
+maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+requires-python = ">=3.8, <3.12"
+readme = "README.md"
+
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: BSD License",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering",
+]
+
+dependencies = [
+    "numpy>=1.16",
+    "scipy>=1.3",
+    "casadi>=3.6.0",
+    "xarray",
+]
+
+[project.optional-dependencies]
+# For the generation of documentation
+docs = [
+    "sphinx>=6",
+    "sphinx_rtd_theme>=0.5",
+    "pydata-sphinx-theme",
+    "sphinx_design",
+    "sphinx-copybutton",
+    "myst-parser",
+    "sphinx-inline-tabs",
+    "sphinxcontrib-bibtex",
+    "sphinx-autobuild",
+    "sphinx-last-updated-by-git",
+    "nbsphinx",
+    "ipykernel",
+    "ipywidgets",
+    "sphinx-gallery",
+    "sphinx-hoverxref",
+    "sphinx-docsearch",
+]
+# For example notebooks
+examples = [
+    "jupyter",
+]
+# Plotting functionality
+plot = [
+    "imageio>=2.9.0",
+    # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # on systems without an attached display, it should never be imported
+    # outside of plot() methods.
+    "matplotlib>=2.0",
+]
+# For the Citations class
+cite = [
+    "pybtex>=0.24.0",
+]
+# To generate LaTeX strings
+latexify = [
+    "sympy>=1.8",
+]
+# Battery Parameter eXchange format
+bpx = [
+    "bpx",
+]
+# Low-overhead progress bars
+tqdm = [
+    "tqdm",
+]
+# Dependencies intended for use by developers
+dev = [
+    # For code style checking
+    "pre-commit",
+    # For code style auto-formatting
+    "ruff",
+    # For running testing sessions
+    "nox",
+]
+# Reading CSV files
+pandas =  [
+    "pandas>=0.24",
+]
+# For the Jax solver
+jax = [
+    "jax==0.4.8",
+    "jaxlib==0.4.7",
+]
+# For the scikits.odes solver
+odes = [
+    "scikits.odes"
+]
+# Contains all optional dependencies, except for odes, jax, and dev dependencies
+all = [
+    "anytree>=2.4.3",
+    "autograd>=1.2",
+    "pandas>=0.24",
+    "scikit-fem>=0.2.0",
+    "imageio>=2.9.0",
+    "matplotlib>=2.0",
+    "pybtex>=0.24.0",
+    "sympy>=1.8",
+    "bpx",
+    "tqdm",
+    "jupyter",
+]
+
+# Equivalent to the console scripts in the entry_points section of the setup()
+# function in setup.py
+[project.scripts]
+pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter"
+pybamm_add_parameter = "pybamm.parameters_cli:add_parameter"
+pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter"
+pybamm_install_odes = "pybamm.install_odes:main"
+pybamm_install_jax = "pybamm.util:install_jax"
+
+# Equivalent to the "pybamm_parameter_sets" entry_points section of the setup()
+# function in setup.py
+[project.entry-points."pybamm_parameter_sets"]
+Sulzer2019 = "pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values"
+Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
+Chen2020 = "pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values"
+Chen2020_composite = "pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values"
+Ecker2015 = "pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values"
+Marquis2019 = "pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values"
+Mohtat2020 = "pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values"
+NCA_Kim2011 = "pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values"
+OKane2022 = "pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values"
+ORegan2022 = "pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values"
+Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
+Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
+Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
+ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"

From aca9a6a553022f7bbcf47960b1899da40d0c1b9a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:05:36 +0530
Subject: [PATCH 002/615] #3049 Fix LICENSE spelling

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3e1ad42e76..5ff07e93e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ name = "pybamm"
 version = "23.5"
 # Unsure: specify BSD-3-Clause?
 # license = {text = "BSD-3-Clause"}
-license = { file = "LICENCE.txt" }
+license = { file = "LICENSE.txt" }
 
 # TODO: add appropriate long description
 description = "Python Battery Mathematical Modelling"

From 3af50925654220757fc7fdfcf906784ea30cf938 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:22:10 +0530
Subject: [PATCH 003/615] #3049 Temporarily build wheels on pull requests

---
 .github/workflows/publish_pypi.yml | 79 ++++++++++++++++--------------
 1 file changed, 41 insertions(+), 38 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 6d89da1387..ba693ec88e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,13 +1,16 @@
-name: Build and publish package to PyPI
-
+# name: Build and publish package to PyPI
+name: Test building wheels on Windows, GNU/Linux and macOS
+# Temporarily disable publishing to PyPI and enable
+# building wheels on pull requests
 on:
-  push:
-    branches: main
+  # push:
+  #   branches: main
+  pull_request:
   workflow_dispatch:
     inputs:
-      target:
-        description: 'Deployment target. Can be "pypi" or "testpypi"'
-        default: "pypi"
+      # target:
+      #   description: 'Deployment target. Can be "pypi" or "testpypi"'
+      #   default: "pypi"
       debug_enabled:
           type: boolean
           description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'
@@ -153,34 +156,34 @@ jobs:
           path: ./dist/*.tar.gz
           if-no-files-found: error
 
-  publish_pypi:
-    name: Upload package to PyPI
-    needs: [build_wheels, build_windows_wheels, build_sdist]
-    runs-on: ubuntu-latest
-    steps:
-      - name: Download all artifacts
-        uses: actions/download-artifact@v3
-
-      - name: Move all package files to files/
-        run: |
-          mkdir files
-          mv windows_wheels/* wheels/* sdist/* files/
-
-      - name: Publish on PyPI
-        if: |
-          github.event.inputs.target == 'pypi' ||
-          (github.event_name == 'push' && github.ref == 'refs/heads/main')
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_TOKEN }}
-          packages_dir: files/
-
-      - name: Publish on TestPyPI
-        if: github.event.inputs.target == 'testpypi'
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-          password: ${{ secrets.TESTPYPI_TOKEN }}
-          packages_dir: files/
-          repository_url: https://test.pypi.org/legacy/
+  # publish_pypi:
+  #   name: Upload package to PyPI
+  #   needs: [build_wheels, build_windows_wheels, build_sdist]
+  #   runs-on: ubuntu-latest
+  #   steps:
+  #     - name: Download all artifacts
+  #       uses: actions/download-artifact@v3
+
+  #     - name: Move all package files to files/
+  #       run: |
+  #         mkdir files
+  #         mv windows_wheels/* wheels/* sdist/* files/
+
+  #     - name: Publish on PyPI
+  #       if: |
+  #         github.event.inputs.target == 'pypi' ||
+  #         (github.event_name == 'push' && github.ref == 'refs/heads/main')
+  #       uses: pypa/gh-action-pypi-publish@release/v1
+  #       with:
+  #         user: __token__
+  #         password: ${{ secrets.PYPI_TOKEN }}
+  #         packages_dir: files/
+
+  #     - name: Publish on TestPyPI
+  #       if: github.event.inputs.target == 'testpypi'
+  #       uses: pypa/gh-action-pypi-publish@release/v1
+  #       with:
+  #         user: __token__
+  #         password: ${{ secrets.TESTPYPI_TOKEN }}
+  #         packages_dir: files/
+  #         repository_url: https://test.pypi.org/legacy/

From 8768ed7f3d8814aa778f259030703bba0c8ba93c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 30 Aug 2023 23:37:11 +0530
Subject: [PATCH 004/615] #3049 Add CMakeBuild steps to `setup.py` instead of
 importing it

---
 setup.py | 295 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 173 insertions(+), 122 deletions(-)

diff --git a/setup.py b/setup.py
index dfdd455a16..1917b62728 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,5 @@
 import os
+import sys
 import glob
 import logging
 import subprocess
@@ -13,12 +14,173 @@
     from distutils.core import setup, find_packages
     from distutils.command.install import install
 
-import CMakeBuild
+# import CMakeBuild
+
+# ---------- cmakebuild was integrated into setup.py directly --------------------------
+
+try:
+    from setuptools.command.build_ext import build_ext
+except ImportError:
+    from distutils.command.build_ext import build_ext
 
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
 )
 
+
+def set_vcpkg_environment_variables():
+    if not os.getenv("VCPKG_ROOT_DIR"):
+        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
+    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
+        )
+    if not os.getenv("VCPKG_FEATURE_FLAGS"):
+        raise EnvironmentError(
+            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
+        )
+    return (
+        os.getenv("VCPKG_ROOT_DIR"),
+        os.getenv("VCPKG_DEFAULT_TRIPLET"),
+        os.getenv("VCPKG_FEATURE_FLAGS"),
+    )
+
+
+class CMakeBuild(build_ext):
+    user_options = build_ext.user_options + [
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
+
+    def initialize_options(self):
+        build_ext.initialize_options(self)
+        self.suitesparse_root = None
+        self.sundials_root = None
+
+    def finalize_options(self):
+        build_ext.finalize_options(self)
+        # Determine the calling command to get the
+        # undefined options from.
+        # If build_ext was called directly then this
+        # doesn't matter.
+        try:
+            self.get_finalized_command("install", create=0)
+            calling_cmd = "install"
+        except AttributeError:
+            calling_cmd = "bdist_wheel"
+        self.set_undefined_options(
+            calling_cmd,
+            ("suitesparse_root", "suitesparse_root"),
+            ("sundials_root", "sundials_root"),
+        )
+        if not self.suitesparse_root:
+            self.suitesparse_root = os.path.join(default_lib_dir)
+        if not self.sundials_root:
+            self.sundials_root = os.path.join(default_lib_dir)
+
+    def get_build_directory(self):
+        # distutils outputs object files in directory self.build_temp
+        # (typically build/temp.*). This is our CMake build directory.
+        # On Windows, distutils is too smart and appends "Release" or
+        # "Debug" to self.build_temp. So in this case we want the
+        # build directory to be the parent directory.
+        if system() == "Windows":
+            return Path(self.build_temp).parents[0]
+        return self.build_temp
+
+    def run(self):
+        if not self.extensions:
+            return
+
+        if system() == "Windows":
+            use_python_casadi = False
+        else:
+            use_python_casadi = True
+
+        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
+        cmake_args = [
+            "-DCMAKE_BUILD_TYPE={}".format(build_type),
+            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
+        ]
+        if self.suitesparse_root:
+            cmake_args.append(
+                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
+            )
+        if self.sundials_root:
+            cmake_args.append(
+                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
+            )
+
+        build_dir = self.get_build_directory()
+        if not os.path.exists(build_dir):
+            os.makedirs(build_dir)
+
+        # The CMakeError.log file is generated by cmake is the configure step
+        # encounters error. In the following the existence of this file is used
+        # to determine whether or not the cmake configure step went smoothly.
+        # So must make sure this file does not remain from a previous failed build.
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            os.remove(os.path.join(build_dir, "CMakeError.log"))
+
+        build_env = os.environ
+        if os.getenv("PYBAMM_USE_VCPKG"):
+            (
+                vcpkg_root_dir,
+                vcpkg_default_triplet,
+                vcpkg_feature_flags,
+            ) = set_vcpkg_environment_variables()
+            build_env["vcpkg_root_dir"] = vcpkg_root_dir
+            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
+            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
+
+        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
+        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
+        subprocess.run(
+            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
+        )
+
+        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
+            msg = (
+                "cmake configuration steps encountered errors, and the idaklu module"
+                " could not be built. Make sure dependencies are correctly "
+                "installed. See "
+                "https://github.com/pybamm-team/PyBaMM/tree/develop"
+                "INSTALL-LINUX-MAC.md"
+            )
+            raise RuntimeError(msg)
+        else:
+            print("-" * 10, "Building idaklu module", "-" * 40)
+            subprocess.run(
+                ["cmake", "--build", ".", "--config", "Release"],
+                cwd=build_dir,
+                env=build_env,
+            )
+
+            # Move from build temp to final position
+            for ext in self.extensions:
+                self.move_output(ext)
+
+    def move_output(self, ext):
+        # Copy built module to dist/ directory
+        build_temp = Path(self.build_temp).resolve()
+        # Get destination location
+        # self.get_ext_fullpath(ext.name) -->
+        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
+        # using resolve() with python < 3.6 will result in a FileNotFoundError
+        # since the location does not yet exists.
+        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
+        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
+        dest_directory = dest_path.parents[0]
+        dest_directory.mkdir(parents=True, exist_ok=True)
+        self.copy_file(source_path, dest_path)
+
+# ---------- end of cmakebuild steps ---------------------------------------------------
+
+# default_lib_dir = (
+#     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
+# )
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -123,6 +285,7 @@ def compile_KLU():
 
 # Build the list of package data files to be included in the PyBaMM package.
 # These are mainly the parameter files located in the input/parameters/ subdirectories.
+# TODO: might be possible to include in pyproject.toml with data configuration values
 pybamm_data = []
 for file_ext in ["*.csv", "*.py", "*.md", "*.txt"]:
     # Get all the files ending in file_ext in pybamm/input dir.
@@ -162,144 +325,32 @@ def compile_KLU():
 ext_modules = [idaklu_ext] if compile_KLU() else []
 
 # Defines __version__
+# TODO: might not be needed anymore, because we define it in pyproject.toml
+# and can therefore access it with importlib.metadata.version("pybamm") (python 3.8+)
+# The version.py file can then be imported with attr: pybamm.__version__ dynamically
 root = os.path.abspath(os.path.dirname(__file__))
 with open(os.path.join(root, "pybamm", "version.py")) as f:
     exec(f.read())
 
 # Load text for description and license
+# TODO: might not be needed anymore, because we define the description and license
+# in pyproject.toml
+# TODO: add long description there and remove it from setup()
 with open("README.md", encoding="utf-8") as f:
     readme = f.read()
 
+# Project metadata was moved to pyproject.toml (which is read by pip).
+# However, custom build commands and setuptools extension modules are still defined here
 setup(
-    name="pybamm",
-    version=__version__,  # noqa: F821
-    description="Python Battery Mathematical Modelling.",
     long_description=readme,
     long_description_content_type="text/markdown",
     url="https://github.com/pybamm-team/PyBaMM",
     packages=find_packages(include=("pybamm", "pybamm.*")),
     ext_modules=ext_modules,
     cmdclass={
-        "build_ext": CMakeBuild.CMakeBuild,
+        "build_ext": CMakeBuild,
         "bdist_wheel": bdist_wheel,
         "install": CustomInstall,
     },
     package_data={"pybamm": pybamm_data},
-    # Python version
-    python_requires=">=3.8,<3.12",
-    classifiers=[
-        "Development Status :: 5 - Production/Stable",
-        "Intended Audience :: Developers",
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: BSD License",
-        "Programming Language :: Python",
-        "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3 :: Only",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
-        "Topic :: Scientific/Engineering",
-    ],
-    # List of dependencies
-    install_requires=[
-        "numpy>=1.16",
-        "scipy>=1.3",
-        "casadi>=3.6.0",
-        "xarray",
-    ],
-    extras_require={
-        "docs": [
-            "sphinx>=6",
-            "sphinx_rtd_theme>=0.5",
-            "pydata-sphinx-theme",
-            "sphinx_design",
-            "sphinx-copybutton",
-            "myst-parser",
-            "sphinx-inline-tabs",
-            "sphinxcontrib-bibtex",
-            "sphinx-autobuild",
-            "sphinx-last-updated-by-git",
-            "nbsphinx",
-            "ipykernel",
-            "ipywidgets",
-            "sphinx-gallery",
-            "sphinx-hoverxref",
-            "sphinx-docsearch",
-        ],  # For doc generation
-        "examples": [
-            "jupyter",  # For example notebooks
-        ],
-        "plot": [
-            "imageio>=2.9.0",
-            # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
-            # on systems without an attached display, it should never be imported
-            # outside of plot() methods.
-            # Should not be imported
-            "matplotlib>=2.0",
-        ],
-        "cite": [
-            "pybtex>=0.24.0",
-        ],
-        "latexify": [
-            "sympy>=1.8",
-        ],
-        "bpx": [
-            "bpx",
-        ],
-        "tqdm": [
-            "tqdm",
-        ],
-        "dev": [
-            "pre-commit",  # For code style checking
-            "ruff", # For code style auto-formatting
-            "nox", # For running testing sessions
-        ],
-        "pandas": [
-            "pandas>=0.24",
-        ],
-        "jax": [
-            "jax==0.4.8",
-            "jaxlib==0.4.7",
-        ],
-        "odes": ["scikits.odes"],
-        "all": [
-            "anytree>=2.4.3",
-            "autograd>=1.2",
-            "pandas>=0.24",
-            "scikit-fem>=0.2.0",
-            "imageio>=2.9.0",
-            "pybtex>=0.24.0",
-            "sympy>=1.8",
-            "bpx",
-            "tqdm",
-            "matplotlib>=2.0",
-            "jupyter",
-        ],
-    },
-    entry_points={
-        "console_scripts": [
-            "pybamm_edit_parameter = pybamm.parameters_cli:edit_parameter",
-            "pybamm_add_parameter = pybamm.parameters_cli:add_parameter",
-            "pybamm_rm_parameter = pybamm.parameters_cli:remove_parameter",
-            "pybamm_install_odes = pybamm.install_odes:main",
-            "pybamm_install_jax = pybamm.util:install_jax",
-        ],
-        "pybamm_parameter_sets": [
-            "Sulzer2019 = pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values",  # noqa: E501
-            "Ai2020 = pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values",  # noqa: E501
-            "Chen2020 = pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values",  # noqa: E501
-            "Chen2020_composite = pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values",  # noqa: E501
-            "Ecker2015 = pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values",  # noqa: E501
-            "Marquis2019 = pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values",  # noqa: E501
-            "Mohtat2020 = pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values",  # noqa: E501
-            "NCA_Kim2011 = pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values",  # noqa: E501
-            "OKane2022 = pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values",  # noqa: E501
-            "ORegan2022 = pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values",  # noqa: E501
-            "Prada2013 = pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values",  # noqa: E501
-            "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values",  # noqa: E501
-            "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values",  # noqa: E501
-            "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values",  # noqa: E501
-        ],
-    },
 )

From 66e930264daefd8c1817feb3003cb853aee3c8ad Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 00:19:53 +0530
Subject: [PATCH 005/615] #3049 Temporarily install `casadi` before installing
 editable

---
 .github/workflows/test_on_push.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2fd4c92b2e..ee633bd5dc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,6 +90,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -150,6 +152,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -233,6 +237,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -293,6 +299,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -354,6 +362,8 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
+          # For some reason casadi needs to be installed first
+          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux

From 8b6a184ad261a51f4d27f4f9a1ea4690e365a65e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 00:20:48 +0530
Subject: [PATCH 006/615] #3049 Better error message if `casadi` path is not
 found

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index c3c5141d4f..889e1c1584 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -63,8 +63,9 @@ execute_process(
 
 if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
+  message("Found python casadi path: ${CASADI_DIR}")
 endif()
-message("Found python casadi path: ${CASADI_DIR}")
+message("Could not find python casadi path")
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against python casadi package")

From 3bec0ba944676009671fc92fa9cc25f769bdbf8d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:12:58 +0530
Subject: [PATCH 007/615] #3049 Rename wheel build workflow name

---
 .github/workflows/publish_pypi.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index ba693ec88e..fbdcf6fcc3 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,5 +1,5 @@
 # name: Build and publish package to PyPI
-name: Test building wheels on Windows, GNU/Linux and macOS
+name: Test building wheels
 # Temporarily disable publishing to PyPI and enable
 # building wheels on pull requests
 on:

From bfafc753db11160f010b59998dfa8429a79ceaf6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:15:37 +0530
Subject: [PATCH 008/615] #3049 Temporarily use `--no-build-isolation` in CI

---
 .github/workflows/test_on_push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ee633bd5dc..67bac67be7 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,7 +92,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -154,7 +154,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -239,7 +239,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -301,7 +301,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -364,7 +364,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs]
+          pip install -e .[all,docs] --no-build-isolation
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From f4d148aea20a3aefcb75618091c18ba26f0fdf1c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:37:37 +0530
Subject: [PATCH 009/615] #3049 add `cmake` to build-system requirements

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5ff07e93e2..46d7117164 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel"]
+requires = ["setuptools", "wheel", "cmake"]
 build-backend = "setuptools.build_meta"
 
 [project]

From 60caf0bb0505e0b1ea8df7b0849e387b63838c1a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 01:50:49 +0530
Subject: [PATCH 010/615] Revert "#3049 add `cmake` to build-system
 requirements"

This reverts commit f4d148aea20a3aefcb75618091c18ba26f0fdf1c.
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 46d7117164..5ff07e93e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel", "cmake"]
+requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]

From d8d61949bdd0c08e5871d31d929ed0d0b3b1c584 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 19:20:26 +0530
Subject: [PATCH 011/615] #3049 Clarify author emails

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5ff07e93e2..d49d887191 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ description = "Python Battery Mathematical Modelling"
 # TODO: correctly specify all authors and maintainers
 # Note: these are currently missing when running `pip show pybamm`, so we should add
 # them in some form
-authors = [{name = "The PyBaMM Team"}]
+authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
 readme = "README.md"

From bdd191e72252fd1431a2c9976ad272606fe406d3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 19:21:24 +0530
Subject: [PATCH 012/615] #3049 clarify idaklu attributes (`setuptools` API)

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 1917b62728..5072e17fad 100644
--- a/setup.py
+++ b/setup.py
@@ -310,8 +310,8 @@ def compile_KLU():
 pybamm_data.append("../CMakeBuild.py")
 
 idaklu_ext = Extension(
-    "pybamm.solvers.idaklu",
-    [
+    name="pybamm.solvers.idaklu",
+    sources=[
         "pybamm/solvers/c_solvers/idaklu.cpp"
         "pybamm/solvers/c_solvers/idaklu.hpp"
         "pybamm/solvers/c_solvers/idaklu_casadi.cpp"

From 9f0f250cda2a5fabecfc17dc1f29e60ea863130f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:14:57 +0530
Subject: [PATCH 013/615] #3049 Specify `casadi` as a build-time dependency to
 overcome venv isolated build error

---
 pyproject.toml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d49d887191..5bc365e9ad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel"]
+requires = ["setuptools", "wheel", "casadi>=3.6.0"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -170,3 +170,6 @@ Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
+
+# [tool.setuptools.packages.find]
+# include = ["pybamm", "pybamm.*"]

From 1e480eec59f5d34b7dbe540ef1a4aaf971429165 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:15:49 +0530
Subject: [PATCH 014/615] Revert "#3049 Temporarily use `--no-build-isolation`
 in CI"

This reverts commit bfafc753db11160f010b59998dfa8429a79ceaf6.
---
 .github/workflows/test_on_push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 67bac67be7..ee633bd5dc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,7 +92,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -154,7 +154,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -239,7 +239,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -301,7 +301,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -364,7 +364,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           # For some reason casadi needs to be installed first
           pip install casadi
-          pip install -e .[all,docs] --no-build-isolation
+          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From c2e2734e787a0a156b80eba54fa5d7cec33a0459 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 21:15:59 +0530
Subject: [PATCH 015/615] #3049 Remove `casadi` installation prior to editable

This reverts commit 66e930264daefd8c1817feb3003cb853aee3c8ad.
---
 .github/workflows/test_on_push.yml | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ee633bd5dc..2fd4c92b2e 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,8 +90,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -152,8 +150,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -237,8 +233,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -299,8 +293,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -362,8 +354,6 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          # For some reason casadi needs to be installed first
-          pip install casadi
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux

From 2cd36af55a91517e46622cd0beb1aedffdae6533 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 31 Aug 2023 22:35:27 +0530
Subject: [PATCH 016/615] #3049 specify `cmake`, fix `casadi` build-time
 requirements

---
 pyproject.toml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5bc365e9ad..1bef595bbe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,12 @@
 [build-system]
 # TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
 # will fail to install
-requires = ["setuptools", "wheel", "casadi>=3.6.0"]
+requires = [
+    "setuptools",
+    "wheel",
+    "casadi>=3.6.0; platform_system!='Windows'",
+    "cmake; platform_system=='Linux'",
+    ]
 build-backend = "setuptools.build_meta"
 
 [project]

From 0cdfd5a81b75c95916e8bb6206c093de4825225d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:22:36 +0530
Subject: [PATCH 017/615] #3049 Fix doctests, trigger example notebook tests

---
 docs/source/user_guide/installation/windows-wsl.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/windows-wsl.rst b/docs/source/user_guide/installation/windows-wsl.rst
index d08545edc0..6453c92211 100644
--- a/docs/source/user_guide/installation/windows-wsl.rst
+++ b/docs/source/user_guide/installation/windows-wsl.rst
@@ -22,13 +22,13 @@ Get PyBaMM's Source Code
 
      sudo apt install git-core
 
-3. Clone the PyBaMM repository::
+3. Clone the PyBaMM repository:
 
 .. code:: bash
 
      git clone https://github.com/pybamm-team/PyBaMM.git
 
-4. Enter the PyBaMM Directory by running::
+4. Enter the PyBaMM Directory by running:
 
 .. code:: bash
 

From fc222ca897a7545b3321476497ea4789beae3ede Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:48:59 +0530
Subject: [PATCH 018/615] #3049 Remove non-colour `nox` output in the CI

---
 noxfile.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index d1f119cdf1..4bc91d7b44 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,9 +16,7 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
-# Do not stdout ANSI colours on GitHub Actions
 if os.getenv("CI") == "true":
-    os.environ["NO_COLOR"] = "1"
     # The setup-python action installs and caches dependencies by default, so we skip
     # installing them again in nox environments. The dev and docs sessions will still
     # require a virtual environment, but we don't run them in the CI

From 5cfc07adcbccd083e25ca5529e432ef5ce95b156 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 00:58:05 +0530
Subject: [PATCH 019/615] #3049 Force colour output on GitHub Actions

---
 .github/workflows/test_on_push.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2fd4c92b2e..3fe00ccc0f 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -4,6 +4,9 @@ on:
   workflow_dispatch:
   pull_request:
 
+env:
+  FORCE_COLOR: 3
+
 concurrency:
   # github.workflow: name of the workflow, so that we don't cancel other workflows
   # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request

From 82197eb1addccbd54a710e6ae154e467a85e7849 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 01:09:09 +0530
Subject: [PATCH 020/615] #3049 Remove inessential editable install job in
 favour of `nox`

---
 .github/workflows/test_on_push.yml | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3fe00ccc0f..ad3ab3c6b0 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,10 +90,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -150,10 +149,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -233,10 +231,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -293,10 +290,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -354,10 +350,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 13ed52dd52851ff58278cde1b1fb7aefd27d44ef Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Sep 2023 20:23:05 +0530
Subject: [PATCH 021/615] #3049 Remove improper CMake message, add for macOS

---
 .github/workflows/test_on_push.yml | 4 ++--
 CMakeLists.txt                     | 1 -
 pyproject.toml                     | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ad3ab3c6b0..81b0908135 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas cmake
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas cmake
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 889e1c1584..a58ef66933 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -65,7 +65,6 @@ if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
   message("Found python casadi path: ${CASADI_DIR}")
 endif()
-message("Could not find python casadi path")
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against python casadi package")
diff --git a/pyproject.toml b/pyproject.toml
index 1bef595bbe..0c22507716 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,7 +16,7 @@ requires = [
     "setuptools",
     "wheel",
     "casadi>=3.6.0; platform_system!='Windows'",
-    "cmake; platform_system=='Linux'",
+    "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
 

From 24fbb8f7419cb6ed6540bec27882e04443b25b13 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 13:39:40 +0530
Subject: [PATCH 022/615] #3049 Fix installation link

---
 setup.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 5072e17fad..fc657e7791 100644
--- a/setup.py
+++ b/setup.py
@@ -145,8 +145,7 @@ def run(self):
                 "cmake configuration steps encountered errors, and the idaklu module"
                 " could not be built. Make sure dependencies are correctly "
                 "installed. See "
-                "https://github.com/pybamm-team/PyBaMM/tree/develop"
-                "INSTALL-LINUX-MAC.md"
+                "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
             )
             raise RuntimeError(msg)
         else:

From 2561a6e770d721b2b311665ed6f67efdfddb3c27 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 13:46:00 +0530
Subject: [PATCH 023/615] #3049 Remove casadi rpath fix because its shared
 object cannot be found

---
 .github/workflows/publish_pypi.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fbdcf6fcc3..864bec5cb1 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,7 @@ jobs:
           brew update
           brew reinstall gcc
           brew install libomp
-          python -m pip install cmake wget
+          python -m pip install wget
           python scripts/install_KLU_Sundials.py
 
       - name: Build wheels on Linux and MacOS
@@ -113,8 +113,7 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
-            install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py &&
+            install casadi numpy &&
             scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac

From 921010ec8cc494ff0c736cb9b5444c240edd2c15 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 2 Sep 2023 21:18:27 +0530
Subject: [PATCH 024/615] #3049 Remove `cmake` from macOS in CI

---
 .github/workflows/test_on_push.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 81b0908135..ad3ab3c6b0 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas cmake
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas cmake
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 1ee48c139a9448716c8645ebc92c866feeac6641 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 13:43:16 +0530
Subject: [PATCH 025/615] Revert "#3049 Remove casadi rpath fix because its
 shared object cannot be found"

This reverts commit 2561a6e770d721b2b311665ed6f67efdfddb3c27.
---
 .github/workflows/publish_pypi.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 864bec5cb1..fbdcf6fcc3 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,7 @@ jobs:
           brew update
           brew reinstall gcc
           brew install libomp
-          python -m pip install wget
+          python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
       - name: Build wheels on Linux and MacOS
@@ -113,7 +113,8 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
-            install casadi numpy &&
+            install cmake casadi numpy &&
+            python scripts/fix_casadi_rpath_mac.py &&
             scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac

From e4ea1995bdcf338dfd5c09b5c3132fc1d93b887d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 22:47:52 +0530
Subject: [PATCH 026/615] #3049 Fix macOS universal ABI and platform wheels
 creation bug

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ad3ab3c6b0..4913a6f5ca 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -108,8 +108,8 @@ jobs:
             ${{ env.HOME }}/.local/examples/
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
@@ -249,8 +249,8 @@ jobs:
             ${{ env.HOME }}/.local/examples/
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}

From a91885a56a4c8eb58b0541cc5571dc5bb3be01cb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:00:31 +0530
Subject: [PATCH 027/615] #3049 Add a Fortran compiler via Homebrew

---
 .github/workflows/test_on_push.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 4913a6f5ca..2040b25bfd 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -76,7 +76,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -94,9 +94,9 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files
@@ -217,7 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -235,9 +235,9 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
+      - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
+        if: matrix.os != 'windows-latest'
         with:
           path: |
             # Repository files

From ab246c658fe1e207581ac87cb76cf6547629734f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:41:22 +0530
Subject: [PATCH 028/615] #3049 Install optional solvers for macOS `nox`
 sessions

---
 .github/workflows/test_on_push.yml               |  2 +-
 .../installation/install-from-source.rst         | 16 ++++++++--------
 noxfile.py                                       | 11 ++++++-----
 3 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2040b25bfd..ddaeeb7edf 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -247,7 +247,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 787778fa01..cd846a6ec2 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -164,10 +164,10 @@ guidelines <https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md>`
 Running the tests
 -----------------
 
-Using Nox (recommended)
+Using ``Nox`` (recommended)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
-You can use Nox to run the unit tests and example notebooks in isolated virtual environments.
+You can use ``Nox`` to run the unit tests and example notebooks in isolated virtual environments.
 
 The default command
 
@@ -175,7 +175,7 @@ The default command
 
 	nox
 
-will run pre-commit, install ``Linux`` dependencies, and run the unit tests.
+will run pre-commit, install ``Linux`` and ``macOS`` dependencies, and run the unit tests.
 This can take several minutes.
 
 To just run the unit tests, use
@@ -245,7 +245,7 @@ Doctests, examples, and coverage
 - ``nox -s coverage``: Measure current test coverage and generate a coverage report.
 - ``nox -s quick``: Run integration tests, unit tests, and doctests sequentially.
 
-Extra tips while using Nox
+Extra tips while using ``Nox``
 --------------------------
 Here are some additional useful commands you can run with ``Nox``:
 
@@ -278,11 +278,11 @@ sure each command was successful.
 One possibility is that you have not set your ``LD_LIBRARY_PATH`` to
 point to the sundials library, type ``echo $LD_LIBRARY_PATH`` and make
 sure one of the directories printed out corresponds to where the
-sundials libraries are located.
+SUNDIALS libraries are located.
 
 Another common reason is that you forget to install a BLAS library such
-as OpenBLAS before installing sundials. Check the cmake output when you
-configured Sundials, it might say:
+as OpenBLAS before installing SUNDIALS. Check the cmake output when you
+configured SUNDIALS, it might say:
 
 ::
 
@@ -291,5 +291,5 @@ configured Sundials, it might say:
 
 If this is the case, on a Debian or Ubuntu system you can install
 OpenBLAS using ``sudo apt-get install libopenblas-dev`` (or
-``brew install openblas`` for Mac OS) and then re-install sundials using
+``brew install openblas`` for Mac OS) and then re-install SUNDIALS using
 the instructions above.
diff --git a/noxfile.py b/noxfile.py
index 4bc91d7b44..f9b97aa909 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -5,7 +5,7 @@
 
 # Options to modify nox behaviour
 nox.options.reuse_existing_virtualenvs = True
-if sys.platform == "linux":
+if sys.platform != "win32":
     nox.options.sessions = ["pre-commit", "pybamm-requires", "unit"]
 else:
     nox.options.sessions = ["pre-commit", "unit"]
@@ -77,8 +77,9 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
+        session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -94,7 +95,7 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
         session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--unit")
@@ -123,7 +124,7 @@ def set_dev(session):
     envbindir = session.bin
     session.install("-e", ".[all]")
     session.install("cmake")
-    if sys.platform == "linux" or sys.platform == "darwin":
+    if sys.platform != "win32":
         session.run(
             "echo",
             "export",
@@ -139,7 +140,7 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
-    if sys.platform == "linux" or sys.platform == "darwin":
+    if sys.platform != "win32":
         session.run_always("pip", "install", "-e", ".[odes]")
         session.run_always("pip", "install", "-e", ".[jax]")
     session.run("python", "run-tests.py", "--all")

From 60ac7bf1e2236fb49c1ed1861312fe196dcae6f8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 3 Sep 2023 23:55:35 +0530
Subject: [PATCH 029/615] #3049 Add remaining `pybamm-requires` caches

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ddaeeb7edf..34bd87c148 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -106,7 +106,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -164,7 +164,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires
@@ -305,7 +305,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires
@@ -365,7 +365,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: nox -s pybamm-requires

From 95d72e5dd3e401ac47af22830fe73bcb192ffbb4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 00:16:57 +0530
Subject: [PATCH 030/615] #3049 Fix failing doctests

---
 .../user_guide/installation/install-from-source.rst  | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index cd846a6ec2..2a43b15096 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -105,8 +105,8 @@ Installing PyBaMM
 
 You should now have everything ready to build and install PyBaMM successfully.
 
-Using Nox (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~
+Using ``Nox`` (recommended)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 
@@ -164,7 +164,7 @@ guidelines <https://github.com/pybamm-team/PyBaMM/blob/develop/CONTRIBUTING.md>`
 Running the tests
 -----------------
 
-Using ``Nox`` (recommended)
+Using Nox (recommended)
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 You can use ``Nox`` to run the unit tests and example notebooks in isolated virtual environments.
@@ -246,7 +246,7 @@ Doctests, examples, and coverage
 - ``nox -s quick``: Run integration tests, unit tests, and doctests sequentially.
 
 Extra tips while using ``Nox``
---------------------------
+------------------------------
 Here are some additional useful commands you can run with ``Nox``:
 
 - ``--verbose or -v``: Enables verbose mode, providing more detailed output during the execution of Nox sessions.
@@ -258,9 +258,9 @@ Here are some additional useful commands you can run with ``Nox``:
 - ``--report output.json``: Generates a JSON report of the Nox session execution and saves it to the specified file, in this case, "output.json".
 
 Troubleshooting
-===============
+---------------
 
-**Problem:** I’ve made edits to source files in PyBaMM, but these are
+**Problem:** I have made edits to source files in PyBaMM, but these are
 not being used when I run my Python script.
 
 **Solution:** Make sure you have installed PyBaMM using the ``-e`` flag,

From 5dae55fd7fbd933b5c848cd5a5638e4c85dc618c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:21:02 +0530
Subject: [PATCH 031/615] #3049 Speed up solvers installation without extras

Remove dependence on `setuptools` and `wheel`, and use `pipx` which GitHub Actions already comes with.
---
 .github/workflows/test_on_push.yml | 42 ++++++++----------------------
 noxfile.py                         | 26 ++++++++++++------
 2 files changed, 29 insertions(+), 39 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 34bd87c148..f2be240d39 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,10 +90,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -110,10 +106,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s unit
+        run: pipx run nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -149,10 +145,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -167,10 +159,10 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -231,10 +223,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -251,10 +239,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s integration
+        run: pipx run nox -s integration
 
   # Runs only on Ubuntu with Python 3.11
   run_doctests_and_example_tests:
@@ -290,10 +278,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -308,13 +292,13 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: nox -s examples
+        run: pipx run nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -350,10 +334,6 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install standard Python dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -368,7 +348,7 @@ jobs:
           key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: nox -s scripts
+        run: pipx run nox -s scripts
diff --git a/noxfile.py b/noxfile.py
index f9b97aa909..58a66b7c76 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,6 +16,12 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
+# Versions compatible with the current version of PyBaMM. Installed directly in the
+# sessions to skip redundant installation of dependencies and building wheels both in
+# the CI and locally
+JAX_VERSION = "0.4.8"
+JAXLIB_VERSION = "0.4.7"
+
 if os.getenv("CI") == "true":
     # The setup-python action installs and caches dependencies by default, so we skip
     # installing them again in nox environments. The dev and docs sessions will still
@@ -65,8 +71,9 @@ def run_coverage(session):
     session.run_always("pip", "install", "coverage")
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -78,8 +85,9 @@ def run_integration(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -96,8 +104,9 @@ def run_unit(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -141,8 +150,9 @@ def run_tests(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.run_always("pip", "install", "-e", ".[all]")
     if sys.platform != "win32":
-        session.run_always("pip", "install", "-e", ".[odes]")
-        session.run_always("pip", "install", "-e", ".[jax]")
+        session.run_always("pip", "install", "scikits.odes")
+        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
+        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
     session.run("python", "run-tests.py", "--all")
 
 

From 4c53cc53f35f3279717850048c790cf437276e32 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:21:47 +0530
Subject: [PATCH 032/615] #3049 Cleanup scheduled tests workflow

---
 .github/workflows/run_periodic_tests.yml | 42 +++++++++---------------
 1 file changed, 15 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f70a748800..fbe664abb0 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -12,24 +12,19 @@ on:
   schedule:
     - cron: "0 3 * * *"
 
-jobs:
-  pre_job:
-    runs-on: ubuntu-latest
-    # Map a step output to a job output
-    outputs:
-      should_skip: ${{ steps.skip_check.outputs.should_skip }}
-    steps:
-      - id: skip_check
-        uses: fkirc/skip-duplicate-actions@master
-        with:
-          # All of these options are optional, so you can remove them if you are happy with the defaults
-          concurrent_skipping: "never"
-          cancel_others: "true"
-          paths_ignore: '["**/README.md"]'
+env:
+  FORCE_COLOR: 3
 
+concurrency:
+  # github.workflow: name of the workflow, so that we don't cancel other workflows
+  # github.event.pull_request.number || github.ref: pull request number or branch name if not a pull request
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  # Cancel in-progress runs when a new workflow with the same group name is triggered
+  # This avoids workflow runs on both pushes and PRs
+  cancel-in-progress: true
+
+jobs:
   style:
-    needs: pre_job
-    if: ${{ needs.pre_job.outputs.should_skip != 'true' }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -66,19 +61,12 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # Added fixes to homebrew installs:
-      # rm -f /usr/local/bin/2to3
-      # (see https://github.com/actions/virtual-environments/issues/2322)
       - name: Install MacOS system dependencies
         if: matrix.os == 'macos-latest'
         run: |
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
+          brew analytics off
           brew update
-          brew install graphviz
-          brew install openblas
+          brew install graphviz openblas gcc gfortran libomp
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -88,8 +76,8 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel setuptools nox
 
-      - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        if: matrix.os == 'ubuntu-latest'
+      - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
+        if: matrix.os != 'windows-latest'
         run: nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions

From 0c611d915ea380e28ad958d4613320cecd10247c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:22:32 +0530
Subject: [PATCH 033/615] #3049 Remove dependence on deprecated `pkg_resources`

---
 pybamm/util.py                                          | 8 ++++----
 tests/unit/test_parameters/test_parameter_sets_class.py | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 5f84f37e0a..772ab8b78b 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -6,6 +6,7 @@
 #
 import argparse
 import importlib.util
+import importlib.metadata
 import numbers
 import os
 import pathlib
@@ -18,11 +19,10 @@
 from warnings import warn
 
 import numpy as np
-import pkg_resources
 
 import pybamm
 
-# versions of jax and jaxlib compatible with PyBaMM
+# versions of jax and jaxlib compatible with PyBaMM, also in noxfile.py
 JAX_VERSION = "0.4.8"
 JAXLIB_VERSION = "0.4.7"
 
@@ -272,8 +272,8 @@ def have_jax():
 def is_jax_compatible():
     """Check if the available version of jax and jaxlib are compatible with PyBaMM"""
     return (
-        pkg_resources.get_distribution("jax").version == JAX_VERSION
-        and pkg_resources.get_distribution("jaxlib").version == JAXLIB_VERSION
+        importlib.metadata.version("jax") == JAX_VERSION
+        and importlib.metadata.version("jaxlib") == JAXLIB_VERSION
     )
 
 
diff --git a/tests/unit/test_parameters/test_parameter_sets_class.py b/tests/unit/test_parameters/test_parameter_sets_class.py
index f548fd7955..309b18bbf2 100644
--- a/tests/unit/test_parameters/test_parameter_sets_class.py
+++ b/tests/unit/test_parameters/test_parameter_sets_class.py
@@ -1,10 +1,10 @@
 #
 # Tests for the ParameterSets class
 #
+import importlib.metadata
 from tests import TestCase
 
 import pybamm
-import pkg_resources
 import unittest
 
 
@@ -25,7 +25,7 @@ def test_all_registered(self):
         """Check that all parameter sets have been registered with the
         ``pybamm_parameter_sets`` entry point"""
         known_entry_points = set(
-            ep.name for ep in pkg_resources.iter_entry_points("pybamm_parameter_sets")
+            ep.name for ep in importlib.metadata.entry_points()["pybamm_parameter_sets"]
         )
         self.assertEqual(set(pybamm.parameter_sets.keys()), known_entry_points)
         self.assertEqual(len(known_entry_points), len(pybamm.parameter_sets))

From 82082f278674885b299be3b181ef9061e8695a46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 02:27:08 +0530
Subject: [PATCH 034/615] #3049 Improvements to scheduled test workflow

---
 .github/workflows/run_periodic_tests.yml | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index fbe664abb0..9420049a7b 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,36 +72,32 @@ jobs:
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
-      - name: Install standard python dependencies
-        run: |
-          python -m pip install --upgrade pip wheel setuptools nox
-
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: nox -s unit
+        run: pipx run nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: nox -s integration
+        run: pipx run nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s examples
+        run: pipx run nox -s examples
 
   #M-series Mac Mini
   build-apple-mseries:

From ee4080f1c5785caf4962239afc2c4393bdbf5d67 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 03:23:45 +0530
Subject: [PATCH 035/615] #3049 Remove separate CMakeBuild file

---
 CMakeBuild.py | 162 --------------------------------------------------
 1 file changed, 162 deletions(-)
 delete mode 100644 CMakeBuild.py

diff --git a/CMakeBuild.py b/CMakeBuild.py
deleted file mode 100644
index 5b34bb27df..0000000000
--- a/CMakeBuild.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import sys
-import subprocess
-from pathlib import Path
-from platform import system
-
-try:
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.command.build_ext import build_ext
-
-default_lib_dir = (
-    "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
-)
-
-
-def set_vcpkg_environment_variables():
-    if not os.getenv("VCPKG_ROOT_DIR"):
-        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
-    if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
-        )
-    if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise EnvironmentError(
-            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
-        )
-    return (
-        os.getenv("VCPKG_ROOT_DIR"),
-        os.getenv("VCPKG_DEFAULT_TRIPLET"),
-        os.getenv("VCPKG_FEATURE_FLAGS"),
-    )
-
-
-class CMakeBuild(build_ext):
-    user_options = build_ext.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
-
-    def initialize_options(self):
-        build_ext.initialize_options(self)
-        self.suitesparse_root = None
-        self.sundials_root = None
-
-    def finalize_options(self):
-        build_ext.finalize_options(self)
-        # Determine the calling command to get the
-        # undefined options from.
-        # If build_ext was called directly then this
-        # doesn't matter.
-        try:
-            self.get_finalized_command("install", create=0)
-            calling_cmd = "install"
-        except AttributeError:
-            calling_cmd = "bdist_wheel"
-        self.set_undefined_options(
-            calling_cmd,
-            ("suitesparse_root", "suitesparse_root"),
-            ("sundials_root", "sundials_root"),
-        )
-        if not self.suitesparse_root:
-            self.suitesparse_root = os.path.join(default_lib_dir)
-        if not self.sundials_root:
-            self.sundials_root = os.path.join(default_lib_dir)
-
-    def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
-        # (typically build/temp.*). This is our CMake build directory.
-        # On Windows, distutils is too smart and appends "Release" or
-        # "Debug" to self.build_temp. So in this case we want the
-        # build directory to be the parent directory.
-        if system() == "Windows":
-            return Path(self.build_temp).parents[0]
-        return self.build_temp
-
-    def run(self):
-        if not self.extensions:
-            return
-
-        if system() == "Windows":
-            use_python_casadi = False
-        else:
-            use_python_casadi = True
-
-        build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
-        cmake_args = [
-            "-DCMAKE_BUILD_TYPE={}".format(build_type),
-            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
-            "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
-        ]
-        if self.suitesparse_root:
-            cmake_args.append(
-                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
-            )
-        if self.sundials_root:
-            cmake_args.append(
-                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
-            )
-
-        build_dir = self.get_build_directory()
-        if not os.path.exists(build_dir):
-            os.makedirs(build_dir)
-
-        # The CMakeError.log file is generated by cmake is the configure step
-        # encounters error. In the following the existence of this file is used
-        # to determine whether or not the cmake configure step went smoothly.
-        # So must make sure this file does not remain from a previous failed build.
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            os.remove(os.path.join(build_dir, "CMakeError.log"))
-
-        build_env = os.environ
-        if os.getenv("PYBAMM_USE_VCPKG"):
-            (
-                vcpkg_root_dir,
-                vcpkg_default_triplet,
-                vcpkg_feature_flags,
-            ) = set_vcpkg_environment_variables()
-            build_env["vcpkg_root_dir"] = vcpkg_root_dir
-            build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
-            build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
-
-        cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
-        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
-        subprocess.run(
-            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
-        )
-
-        if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
-            msg = (
-                "cmake configuration steps encountered errors, and the idaklu module"
-                " could not be built. Make sure dependencies are correctly "
-                "installed. See "
-                "https://github.com/pybamm-team/PyBaMM/tree/develop"
-                "INSTALL-LINUX-MAC.md"
-            )
-            raise RuntimeError(msg)
-        else:
-            print("-" * 10, "Building idaklu module", "-" * 40)
-            subprocess.run(
-                ["cmake", "--build", ".", "--config", "Release"],
-                cwd=build_dir,
-                env=build_env,
-            )
-
-            # Move from build temp to final position
-            for ext in self.extensions:
-                self.move_output(ext)
-
-    def move_output(self, ext):
-        # Copy built module to dist/ directory
-        build_temp = Path(self.build_temp).resolve()
-        # Get destination location
-        # self.get_ext_fullpath(ext.name) -->
-        # build/lib.linux-x86_64-3.5/idaklu.cpython-37m-x86_64-linux-gnu.so
-        # using resolve() with python < 3.6 will result in a FileNotFoundError
-        # since the location does not yet exists.
-        dest_path = Path(self.get_ext_fullpath(ext.name)).resolve()
-        source_path = build_temp / os.path.basename(self.get_ext_filename(ext.name))
-        dest_directory = dest_path.parents[0]
-        dest_directory.mkdir(parents=True, exist_ok=True)
-        self.copy_file(source_path, dest_path)

From f2a8724db36da504cc1dea4f1f50a253738481b1 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 03:24:59 +0530
Subject: [PATCH 036/615] #3049 Add configuration for package data files

---
 pyproject.toml | 17 +++++++++++++++--
 setup.py       | 35 -----------------------------------
 2 files changed, 15 insertions(+), 37 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0c22507716..05c70d6cbb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -176,5 +176,18 @@ Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_v
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
 
-# [tool.setuptools.packages.find]
-# include = ["pybamm", "pybamm.*"]
+[tool.setuptools]
+include-package-data = true
+
+# List of files to include as package data. These are mainly the parameter CSV files in
+# the input/parameters/ subdirectories. Other files such as the  CITATIONS file, relevant
+# README.md files, and specific .txt files inside the pybamm/ directory are also included.
+[tool.setuptools.package-data]
+pybamm = [
+    "*.txt",
+    "*.md",
+    "*.csv",
+    "*.py",
+    "pybamm/CITATIONS.bib",
+    "pybamm/plotting/mplstyle",
+]
diff --git a/setup.py b/setup.py
index fc657e7791..5da1d2b16c 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,5 @@
 import os
 import sys
-import glob
 import logging
 import subprocess
 from pathlib import Path
@@ -14,8 +13,6 @@
     from distutils.core import setup, find_packages
     from distutils.command.install import install
 
-# import CMakeBuild
-
 # ---------- cmakebuild was integrated into setup.py directly --------------------------
 
 try:
@@ -176,10 +173,6 @@ def move_output(self, ext):
 
 # ---------- end of cmakebuild steps ---------------------------------------------------
 
-# default_lib_dir = (
-#     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
-# )
-
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -281,33 +274,6 @@ def compile_KLU():
 
     return CMakeFound and PyBind11Found
 
-
-# Build the list of package data files to be included in the PyBaMM package.
-# These are mainly the parameter files located in the input/parameters/ subdirectories.
-# TODO: might be possible to include in pyproject.toml with data configuration values
-pybamm_data = []
-for file_ext in ["*.csv", "*.py", "*.md", "*.txt"]:
-    # Get all the files ending in file_ext in pybamm/input dir.
-    # list_of_files = [
-    #    'pybamm/input/drive_cycles/car_current.csv',
-    #    'pybamm/input/drive_cycles/US06.csv',
-    # ...
-    list_of_files = glob.glob("pybamm/input/**/" + file_ext, recursive=True)
-
-    # Add these files to pybamm_data.
-    # The path must be relative to the package dir (pybamm/), so
-    # must process the content of list_of_files to take out the top
-    # pybamm/ dir, i.e.:
-    # ['input/drive_cycles/car_current.csv',
-    #  'input/drive_cycles/US06.csv',
-    # ...
-    pybamm_data.extend(
-        [os.path.join(*Path(filename).parts[1:]) for filename in list_of_files]
-    )
-pybamm_data.append("./CITATIONS.bib")
-pybamm_data.append("./plotting/pybamm.mplstyle")
-pybamm_data.append("../CMakeBuild.py")
-
 idaklu_ext = Extension(
     name="pybamm.solvers.idaklu",
     sources=[
@@ -351,5 +317,4 @@ def compile_KLU():
         "bdist_wheel": bdist_wheel,
         "install": CustomInstall,
     },
-    package_data={"pybamm": pybamm_data},
 )

From 4a3a03c513fc1d1a645771469091ac1f17a12dbf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:34:55 +0530
Subject: [PATCH 037/615] #3049 Check links in `toml`, `yaml`, and `json` files

---
 .github/workflows/lychee_url_checker.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index a6735d2806..20eff22b8c 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -49,6 +49,10 @@ jobs:
             './**/*.md'
             './**/*.py'
             './**/*.ipynb'
+            './**/*.yml'
+            './**/*.yaml'
+            './**/*.json'
+            './**/*.toml'
           # fail the action on broken links
           fail: true
         env:

From 7c64841cae0f99e694e102fb9678b9f74cb6a7bd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:40:44 +0530
Subject: [PATCH 038/615] #3049 clean up some project configuration options

---
 pyproject.toml | 49 ++++++++++++++-----------------------------------
 setup.py       | 30 +++++++-----------------------
 2 files changed, 21 insertions(+), 58 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 05c70d6cbb..76a601b46f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,17 +1,4 @@
-# From the pip documentation:
-
-# Fallback Behaviour
-# If a project does not have a pyproject.toml file containing a build-system section,
-# it will be assumed to have the following backend settings:
-
-# [build-system]
-# requires = ["setuptools>=40.8.0", "wheel"]
-# build-backend = "setuptools.build_meta:__legacy__"
-
-# TODO: add appropriate build-system section
 [build-system]
-# TODO: specify minimum version of setuptools otherwise scikits.odes, NumPy, and others
-# will fail to install
 requires = [
     "setuptools",
     "wheel",
@@ -22,26 +9,13 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-# TODO: try picking up version from the package itself
-# dynamic = ["version", "readme"]
-# [tool.setuptools.dynamic]
-# version = {attr = "my_package.VERSION"}
 version = "23.5"
-# Unsure: specify BSD-3-Clause?
-# license = {text = "BSD-3-Clause"}
 license = { file = "LICENSE.txt" }
-
-# TODO: add appropriate long description
 description = "Python Battery Mathematical Modelling"
-
-# TODO: correctly specify all authors and maintainers
-# Note: these are currently missing when running `pip show pybamm`, so we should add
-# them in some form
-authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
+authors = [{name = "The PyBaMM Team"}, {email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
-readme = "README.md"
-
+readme = {file = "README.md", content-type = "text/markdown"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
     "Intended Audience :: Developers",
@@ -56,7 +30,6 @@ classifiers = [
     "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering",
 ]
-
 dependencies = [
     "numpy>=1.16",
     "scipy>=1.3",
@@ -64,6 +37,13 @@ dependencies = [
     "xarray",
 ]
 
+[project.urls]
+Homepage = "https://pybamm.org"
+Documentation = "https://docs.pybamm.org"
+Repository = "https://github.com/pybamm-team/PyBaMM"
+Releases = "https://github.com/pybamm-team/PyBaMM/releases"
+Changelog = "https://github.com/pybamm-team/PyBaMM/blob/develop/CHANGELOG.md"
+
 [project.optional-dependencies]
 # For the generation of documentation
 docs = [
@@ -91,7 +71,7 @@ examples = [
 # Plotting functionality
 plot = [
     "imageio>=2.9.0",
-    # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # Note: matplotlib is loaded for debug plots, but to ensure pybamm runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
     "matplotlib>=2.0",
@@ -149,8 +129,6 @@ all = [
     "jupyter",
 ]
 
-# Equivalent to the console scripts in the entry_points section of the setup()
-# function in setup.py
 [project.scripts]
 pybamm_edit_parameter = "pybamm.parameters_cli:edit_parameter"
 pybamm_add_parameter = "pybamm.parameters_cli:add_parameter"
@@ -158,8 +136,6 @@ pybamm_rm_parameter = "pybamm.parameters_cli:remove_parameter"
 pybamm_install_odes = "pybamm.install_odes:main"
 pybamm_install_jax = "pybamm.util:install_jax"
 
-# Equivalent to the "pybamm_parameter_sets" entry_points section of the setup()
-# function in setup.py
 [project.entry-points."pybamm_parameter_sets"]
 Sulzer2019 = "pybamm.input.parameters.lead_acid.Sulzer2019:get_parameter_values"
 Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
@@ -180,7 +156,7 @@ ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
 include-package-data = true
 
 # List of files to include as package data. These are mainly the parameter CSV files in
-# the input/parameters/ subdirectories. Other files such as the  CITATIONS file, relevant
+# the input/parameters/ subdirectories. Other files such as the CITATIONS file, relevant
 # README.md files, and specific .txt files inside the pybamm/ directory are also included.
 [tool.setuptools.package-data]
 pybamm = [
@@ -191,3 +167,6 @@ pybamm = [
     "pybamm/CITATIONS.bib",
     "pybamm/plotting/mplstyle",
 ]
+
+[tool.setuptools.packages.find]
+include = ["pybamm", "pybamm.*"]
diff --git a/setup.py b/setup.py
index 5da1d2b16c..2af56c6ef6 100644
--- a/setup.py
+++ b/setup.py
@@ -7,10 +7,10 @@
 import wheel.bdist_wheel as orig
 
 try:
-    from setuptools import setup, find_packages, Extension
+    from setuptools import setup, Extension
     from setuptools.command.install import install
 except ImportError:
-    from distutils.core import setup, find_packages
+    from distutils.core import setup
     from distutils.command.install import install
 
 # ---------- cmakebuild was integrated into setup.py directly --------------------------
@@ -173,6 +173,8 @@ def move_output(self, ext):
 
 # ---------- end of cmakebuild steps ---------------------------------------------------
 
+# ---------- configure setup logger ----------------------------------------------------
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -213,6 +215,7 @@ def finalize_options(self):
     def run(self):
         install.run(self)
 
+# ---------- custom wheel build (non-Windows) ------------------------------------------
 
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
@@ -289,28 +292,9 @@ def compile_KLU():
 )
 ext_modules = [idaklu_ext] if compile_KLU() else []
 
-# Defines __version__
-# TODO: might not be needed anymore, because we define it in pyproject.toml
-# and can therefore access it with importlib.metadata.version("pybamm") (python 3.8+)
-# The version.py file can then be imported with attr: pybamm.__version__ dynamically
-root = os.path.abspath(os.path.dirname(__file__))
-with open(os.path.join(root, "pybamm", "version.py")) as f:
-    exec(f.read())
-
-# Load text for description and license
-# TODO: might not be needed anymore, because we define the description and license
-# in pyproject.toml
-# TODO: add long description there and remove it from setup()
-with open("README.md", encoding="utf-8") as f:
-    readme = f.read()
-
-# Project metadata was moved to pyproject.toml (which is read by pip).
-# However, custom build commands and setuptools extension modules are still defined here
+# Project metadata was moved to pyproject.toml (which is read by pip). However, custom
+# build commands and setuptools extension modules are still defined here.
 setup(
-    long_description=readme,
-    long_description_content_type="text/markdown",
-    url="https://github.com/pybamm-team/PyBaMM",
-    packages=find_packages(include=("pybamm", "pybamm.*")),
     ext_modules=ext_modules,
     cmdclass={
         "build_ext": CMakeBuild,

From 2e332b5348cc58558d737183f9924a86b0037342 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 4 Sep 2023 06:41:37 +0530
Subject: [PATCH 039/615] #3049, #3249, #2881 Update version in
 `pyproject.toml`

---
 scripts/update_version.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/scripts/update_version.py b/scripts/update_version.py
index 4a5f60d8d8..8912035889 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -32,6 +32,16 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
+    # pyproject.toml
+    with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file:
+        output = file.read()
+        replace_version = re.sub(
+            '(?<=version = ")(.+)(?=")', release_version, output
+        )
+        file.truncate(0)
+        file.seek(0)
+        file.write(replace_version)
+
     # CITATION.cff
     with open(os.path.join(pybamm.root_dir(), "CITATION.cff"), "r+") as file:
         output = file.read()

From b51bea7796bf1caac313aeb511d4c66e4d7ffbd2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 6 Sep 2023 21:29:53 +0530
Subject: [PATCH 040/615] Clean up PyPI publishing workflow jobs

---
 .github/workflows/publish_pypi.yml | 32 +++++++++---------------------
 1 file changed, 9 insertions(+), 23 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fbdcf6fcc3..1f5d39877e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -27,9 +27,6 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
 
@@ -56,8 +53,7 @@ jobs:
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
       - name: Build 64 bits wheels on Windows
-        run: |
-          python -m cibuildwheel --output-dir wheelhouse
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
@@ -82,28 +78,19 @@ jobs:
         with:
           python-version: 3.8
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.12.3
-
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
       - name: Install SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
-          # https://github.com/actions/virtual-environments/issues/1280
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
           brew update
-          brew reinstall gcc
-          brew install libomp
+          brew install gcc gfortran libomp graphviz openblas
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on Linux and MacOS
-        run: python -m cibuildwheel --output-dir wheelhouse
+      - name: Build wheels on Linux and macOS
+        run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
@@ -114,8 +101,7 @@ jobs:
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py &&
-            scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && python scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
@@ -172,7 +158,7 @@ jobs:
   #     - name: Publish on PyPI
   #       if: |
   #         github.event.inputs.target == 'pypi' ||
-  #         (github.event_name == 'push' && github.ref == 'refs/heads/main')
+  #         (github.event-name == 'push' && github.ref == 'refs/heads/main')
   #       uses: pypa/gh-action-pypi-publish@release/v1
   #       with:
   #         user: __token__
@@ -185,5 +171,5 @@ jobs:
   #       with:
   #         user: __token__
   #         password: ${{ secrets.TESTPYPI_TOKEN }}
-  #         packages_dir: files/
-  #         repository_url: https://test.pypi.org/legacy/
+  #         packages-dir: files/
+  #         repository-url: https://test.pypi.org/legacy/

From 378eed11ea3c0335a72ca97c7aac15e16a8a4b46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 04:56:21 +0530
Subject: [PATCH 041/615] Add rpath config for `casadi` directory

Co-Authored-By: Martin Robinson <martinjrobins@gmail.com>
---
 CMakeLists.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a58ef66933..2605c89b5c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,6 +81,7 @@ find_package(SUNDIALS REQUIRED)
 message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
+set_property(TARGET idaklu APPEND PROPERTY INSTALL_RPATH "${CASADI_DIR}")
 
 # link suitesparse
 # if using vcpkg, use config mode to

From 324c31677a3816532c2a595e153ca3a2aa1a901e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:07:45 +0530
Subject: [PATCH 042/615] #3049 Add gcc reinstall step again otherwise Fortran
 compiler is not found

---
 .github/workflows/publish_pypi.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 1f5d39877e..15af4ec945 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -85,7 +85,8 @@ jobs:
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gcc gfortran libomp graphviz openblas
+          brew install gfortran libomp graphviz openblas
+          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 

From 5976ec35832ab1871076c77a8b686aaffe1c7242 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:30:31 +0530
Subject: [PATCH 043/615] #3049 Fix cibuildwheel job

---
 .github/workflows/publish_pypi.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 15af4ec945..be07bcad05 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -86,7 +86,6 @@ jobs:
         run: |
           brew update
           brew install gfortran libomp graphviz openblas
-          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
@@ -102,7 +101,7 @@ jobs:
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py && python scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >

From 8d88f5b71a549018ec78a4f4a9c353fc31dd4090 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 05:42:56 +0530
Subject: [PATCH 044/615] #3049 Revert "Add rpath config for `casadi`
 directory"

This reverts commit 378eed11ea3c0335a72ca97c7aac15e16a8a4b46.
---
 CMakeLists.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2605c89b5c..a58ef66933 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -81,7 +81,6 @@ find_package(SUNDIALS REQUIRED)
 message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
-set_property(TARGET idaklu APPEND PROPERTY INSTALL_RPATH "${CASADI_DIR}")
 
 # link suitesparse
 # if using vcpkg, use config mode to

From 91efeab3f7467aaa350e05cb77e3340bf08b5f49 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Sep 2023 17:48:19 +0530
Subject: [PATCH 045/615] #3049 Fix `gcc`/`gfortran` installation (Homebrew)

---
 .github/workflows/publish_pypi.yml | 4 +++-
 .github/workflows/test_on_push.yml | 4 ++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index be07bcad05..99bf0aa10a 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -81,11 +81,13 @@ jobs:
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gfortran libomp graphviz openblas
+          brew install gcc gfortran libomp graphviz openblas
+          brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index f2be240d39..6ad30f7668 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -73,10 +73,12 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
           brew update
           brew install graphviz openblas gcc gfortran libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -206,10 +208,12 @@ jobs:
           HOMEBREW_NO_COLOR: 1
           # Speed up CI
           NONINTERACTIVE: 1
+        # sometimes gfortran cannot be found, so reinstall gcc just to be sure
         run: |
           brew analytics off
           brew update
           brew install graphviz openblas gcc gfortran libomp
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 50dcc4e11355ddd960056bb108336a6f0a936b6f Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 9 Sep 2023 12:47:42 +0530
Subject: [PATCH 046/615] Create `docker.yml`

---
 .github/workflows/docker.yml | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 .github/workflows/docker.yml

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
new file mode 100644
index 0000000000..e69de29bb2

From 0fc448013d2cf41fc985238825ef5b5ce646de9a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 02:58:54 +0530
Subject: [PATCH 047/615] Basic config

---
 .github/workflows/docker.yml | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index e69de29bb2..c9ba896425 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -0,0 +1,11 @@
+name: Build & Push Docker Images
+
+on:
+    workflow_dispatch:
+    pull_request:
+      branches:
+      - main
+
+jobs:
+    pre_job:
+        runs-on: ubuntu-latest

From 20a720a47e56ee358ae857d30f39982ceb7a1a56 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:05:06 +0530
Subject: [PATCH 048/615] Add initial Checkout step

---
 .github/workflows/docker.yml | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index c9ba896425..2655007b79 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -1,11 +1,17 @@
 name: Build & Push Docker Images
 
 on:
-    workflow_dispatch:
-    pull_request:
-      branches:
-      - main
+  workflow_dispatch:
+  pull_request:
+    branches:
+    - main
 
 jobs:
-    pre_job:
-        runs-on: ubuntu-latest
+  pre_job:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+            fetch-depth: 0

From 2be1537dcbce80484cfb188ee23ba6c1c476e829 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:14:55 +0530
Subject: [PATCH 049/615] Add docker login step

---
 .github/workflows/docker.yml | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 2655007b79..0a5ec20e73 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -15,3 +15,13 @@ jobs:
         uses: actions/checkout@v4
         with:
             fetch-depth: 0
+
+      - name: Login to DockerHub
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v2
+        with:
+            username: ${{ secrets.DOCKERHUB_USERNAME }}
+            password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: List built images
+        run: docker images

From b611dd1479ef7c1ce3b87f0d9012184cc3006564 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:32:58 +0530
Subject: [PATCH 050/615] Build & push `pybamm:latest`

---
 .github/workflows/docker.yml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 0a5ec20e73..32bc573608 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -25,3 +25,10 @@ jobs:
 
       - name: List built images
         run: docker images
+
+      - name: Build and Push Docker Image (Without Solvers)
+        env:
+          IMAGE_NAME: pybamm/pybamm:latest
+        run: |
+          docker build -t $IMAGE_NAME -f scripts/Dockerfile .
+          docker push $IMAGE_NAME

From c3991226f0dd5926c88c633d2b136596a4602e4c Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:33:26 +0530
Subject: [PATCH 051/615] Build & push `pybamm:jax`

---
 .github/workflows/docker.yml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 32bc573608..1caf030b57 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -32,3 +32,11 @@ jobs:
         run: |
           docker build -t $IMAGE_NAME -f scripts/Dockerfile .
           docker push $IMAGE_NAME
+
+      - name: Build and Push Docker Image (With JAX Solver)
+        env:
+          IMAGE_NAME: pybamm/pybamm:jax
+          ARG_NAME: JAX
+        run: |
+          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
+          docker push $IMAGE_NAME

From 174db815d2b97f6e1f7849d611d1426703c2f805 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:33:54 +0530
Subject: [PATCH 052/615] Build & push `pybamm:odes`

---
 .github/workflows/docker.yml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 1caf030b57..4636385a5a 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -40,3 +40,11 @@ jobs:
         run: |
           docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
           docker push $IMAGE_NAME
+
+      - name: Build and Push Docker Image (With ODES & DAE Solver)
+        env:
+          IMAGE_NAME: pybamm/pybamm:odes
+          ARG_NAME: ODES
+        run: |
+          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
+          docker push $IMAGE_NAME

From 835a78f8a872fa97b0bb5fa41934ce0dc80758a3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:34:34 +0530
Subject: [PATCH 053/615] Build & push `pybamm:idaklu`

---
 .github/workflows/docker.yml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 4636385a5a..3e731fe814 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -48,3 +48,11 @@ jobs:
         run: |
           docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
           docker push $IMAGE_NAME
+
+      - name: Build and Push Docker Image (With IDAKLU Solver)
+        env:
+          IMAGE_NAME: pybamm/pybamm:idaklu
+          ARG_NAME: IDAKLU
+        run: |
+          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
+          docker push $IMAGE_NAME

From 02f7936d3230a1b957e677a21444c3e9e7845d70 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 03:34:58 +0530
Subject: [PATCH 054/615] Build & push `pybamm:all`

---
 .github/workflows/docker.yml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 3e731fe814..2b5c2b764b 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -56,3 +56,11 @@ jobs:
         run: |
           docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
           docker push $IMAGE_NAME
+
+      - name: Build and Push Docker Image (With All Solvers)
+        env:
+          IMAGE_NAME: pybamm/pybamm:all
+          ARG_NAME: ALL
+        run: |
+          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
+          docker push $IMAGE_NAME

From c15f96ffd8b8795831a79ee420b60ec1bb62be9f Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 10 Sep 2023 04:05:48 +0530
Subject: [PATCH 055/615] Point to `develop`

---
 .github/workflows/docker.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 2b5c2b764b..aed3fd3d53 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -4,7 +4,7 @@ on:
   workflow_dispatch:
   pull_request:
     branches:
-    - main
+    - develop
 
 jobs:
   pre_job:
@@ -13,8 +13,6 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-        with:
-            fetch-depth: 0
 
       - name: Login to DockerHub
         if: github.event_name != 'pull_request'

From c3708ff1ad23dbd804cf5d903191044fd8e81775 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 10 Sep 2023 22:51:59 +0530
Subject: [PATCH 056/615] #3312 Update CMake, Python versions and add `nox`

Co-Authored-By: Arjun <arjxn.py@gmail.com>
---
 scripts/Dockerfile | 41 +++++++++++++++++++++++------------------
 1 file changed, 23 insertions(+), 18 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 3cfbeaa11c..afa287fa48 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -4,7 +4,7 @@ WORKDIR /
 
 # Install the necessary dependencies
 RUN apt-get update && apt-get -y upgrade
-RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake
+RUN apt-get install -y libopenblas-dev gcc gfortran graphviz git make g++ build-essential cmake pandoc texlive-latex-extra dvipng
 RUN rm -rf /var/lib/apt/lists/*
 
 RUN useradd -m -s /bin/bash pybamm
@@ -21,45 +21,50 @@ ENV CMAKE_C_COMPILER=/usr/bin/gcc
 ENV CMAKE_CXX_COMPILER=/usr/bin/g++
 ENV CMAKE_MAKE_PROGRAM=/usr/bin/make
 ENV SUNDIALS_INST=/home/pybamm/.local
-ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib:
+ENV LD_LIBRARY_PATH=/home/pybamm/.local/lib
+
+RUN conda create -n pybamm python=3.11
+RUN conda init --all
+SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
+RUN conda install -y pip
 
 ARG IDAKLU
 ARG ODES
 ARG JAX
 ARG ALL
 
-RUN conda create -n pybamm python=3.9
-RUN conda init --all
-SHELL ["conda", "run", "-n", "pybamm", "/bin/bash", "-c"]
-RUN conda install -y pip
-
 RUN if [ "$IDAKLU" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget && \
-    pip install cmake==3.22 && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev]"; \
+    pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip wget && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
-    pip install --user -e ".[all,odes,dev]"; \
+    git clone https://github.com/pybind/pybind11.git && \
+    pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    pip install --user -e ".[jax,all,dev]";\
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
+    python scripts/install_KLU_Sundials.py && \
+    git clone https://github.com/pybind/pybind11.git && \
+    pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
-    pip install cmake==3.22 && \
-    pip install --upgrade --user pip setuptools wheel wget && \
+    pip install --upgrade --user pip setuptools wheel wget nox && \
+    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
-    pip install --user -e ".[all,dev,jax,odes]"; \
+    pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi
 
-RUN pip install --user -e ".[all,dev]"
+RUN pip install --user -e ".[all,dev,docs]"
 
 ENTRYPOINT ["/bin/bash"]

From a8661a20314ef944ebcd9dd9418f8dae0a034a97 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 10 Sep 2023 22:53:37 +0530
Subject: [PATCH 057/615] #3312 Minor cleanups for Docker installation docs

Co-Authored-By: Arjun <arjxn.py@gmail.com>
---
 .../user_guide/installation/install-from-docker.rst  | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-docker.rst b/docs/source/user_guide/installation/install-from-docker.rst
index f25a57d713..34e33e9ec2 100644
--- a/docs/source/user_guide/installation/install-from-docker.rst
+++ b/docs/source/user_guide/installation/install-from-docker.rst
@@ -3,12 +3,13 @@ Install from source (Docker)
 
 .. contents::
 
-This page describes the build and installation of PyBaMM from the source code, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using pip or conda.
+This page describes the build and installation of PyBaMM using a Dockerfile, available on GitHub. Note that this is **not the recommended approach for most users** and should be reserved to people wanting to participate in the development of PyBaMM, or people who really need to use bleeding-edge feature(s) not yet available in the latest released version. If you do not fall in the two previous categories, you would be better off installing PyBaMM using ``pip`` or ``conda``.
 
 Prerequisites
 -------------
+
 Before you begin, make sure you have Docker installed on your system. You can download and install Docker from the official `Docker website <https://www.docker.com/get-started/>`_.
-Ensure Docker installation by running :
+Ensure Docker installation by running:
 
 .. code:: bash
 
@@ -16,6 +17,7 @@ Ensure Docker installation by running :
 
 Pulling the Docker image
 ------------------------
+
 Use the following command to pull the PyBaMM Docker image from Docker Hub:
 
 .. tab:: No optional solver
@@ -135,8 +137,8 @@ If you want to build the PyBaMM Docker image locally from the PyBaMM source code
 
       conda activate pybamm
 
-Building Docker images with optional args
------------------------------------------
+Building Docker images with optional arguments
+----------------------------------------------
 
 When building the PyBaMM Docker images locally, you have the option to include specific solvers by using optional arguments. These solvers include:
 
@@ -189,7 +191,7 @@ If you want to exit the Docker container's shell, you can simply type:
 
       exit
 
-Using Visual Studio Code Inside a Running Docker Container
+Using Visual Studio Code inside a running Docker container
 ----------------------------------------------------------
 
 You can easily use Visual Studio Code inside a running Docker container by attaching it directly. This provides a seamless development environment within the container. Here's how:

From c3eaba5b2f2df8f95fbf8e5ee94564d969e7fe53 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 11 Sep 2023 00:10:56 +0530
Subject: [PATCH 058/615] #3312 Install build-time requirements in a single RUN
 command

---
 scripts/Dockerfile | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index afa287fa48..ffbd320f59 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,33 +33,27 @@ ARG ODES
 ARG JAX
 ARG ALL
 
+RUN pip install --upgrade --user pip setuptools wheel wget nox cmake
+
 RUN if [ "$IDAKLU" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
-    pip install --upgrade --user pip setuptools wheel wget nox && \
-    pip install cmake && \
     python scripts/install_KLU_Sundials.py && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \

From fd0c1a5f3766d2cdfe88e467f24b084b19587edf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:24:42 +0530
Subject: [PATCH 059/615] #3049 Copy `libcasadi.3.7.dylib` to `LD_LIBRARY_PATH`

---
 scripts/fix_casadi_rpath_mac.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 9b0a181391..82c21a34a8 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -14,6 +14,7 @@
 libcpp_name = "libc++.1.0.dylib"
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
+libcasadi_37_name = "libcasadi.3.7.dylib"
 install_name_tool_args = [
     "-change",
     os.path.join("@rpath", libcpp_name),
@@ -34,3 +35,13 @@
 print(" ".join(["install_name_tool"] + install_name_tool_args))
 subprocess.run(["install_name_tool"] + install_name_tool_args)
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
+# Copy libcasadi.3.7.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
+# This is needed for the casadi python bindings to work
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcasadi_37_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)

From e7dd32a00aa8ef829625edacf2f60276562078c2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:35:15 +0530
Subject: [PATCH 060/615] #3049 Add `libc++.1.0.dylib` as well

---
 scripts/fix_casadi_rpath_mac.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 82c21a34a8..9779c88ab3 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -36,7 +36,7 @@
 subprocess.run(["install_name_tool"] + install_name_tool_args)
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
 # This is needed for the casadi python bindings to work
 
 subprocess.run(
@@ -45,3 +45,10 @@
     os.path.join(os.getenv("HOME"),".local/lib")
     ]
 )
+
+subprocess.run(
+    ["cp",
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(os.getenv("HOME"),".local/lib")
+    ]
+)

From 8388ac6e640d5959ff1c559ba0af26235355a6a2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 02:40:32 +0530
Subject: [PATCH 061/615] #3049 Refactor casadi lib rpaths

---
 scripts/fix_casadi_rpath_mac.py | 24 +++++++++++++++++++-----
 1 file changed, 19 insertions(+), 5 deletions(-)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 9779c88ab3..fb275e339a 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -1,6 +1,6 @@
 """
-Removes the rpath from libcasadi.dylib in the casadi python install
-and uses a fixed path
+Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
+install and uses a fixed path
 
 Used when building the wheels for macos
 """
@@ -15,16 +15,28 @@
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
 libcasadi_37_name = "libcasadi.3.7.dylib"
-install_name_tool_args = [
+install_name_tool_args_for_libcasadi_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_name),
 ]
+install_name_tool_args_for_libcasadi_37_name = [
+    "-change",
+    os.path.join("@rpath", libcpp_name),
+    os.path.join(casadi_dir, libcpp_name),
+    os.path.join(casadi_dir, libcasadi_37_name),
+]
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
-print(" ".join(["install_name_tool"] + install_name_tool_args))
-subprocess.run(["install_name_tool"] + install_name_tool_args)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_name)
+
+print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name))
+subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
+
 install_name_tool_args = [
     "-change",
     os.path.join("@rpath", libcppabi_name),
@@ -32,8 +44,10 @@
     os.path.join(casadi_dir, libcpp_name),
 ]
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+
 print(" ".join(["install_name_tool"] + install_name_tool_args))
 subprocess.run(["install_name_tool"] + install_name_tool_args)
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
 # Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)

From cbba6b9e72dcfc380ee8d258b30ee70d9425504a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 03:23:43 +0530
Subject: [PATCH 062/615] Update link checker with job summary

---
 .github/workflows/lychee_url_checker.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/lychee_url_checker.yml b/.github/workflows/lychee_url_checker.yml
index 20eff22b8c..dcf2b7e8ee 100644
--- a/.github/workflows/lychee_url_checker.yml
+++ b/.github/workflows/lychee_url_checker.yml
@@ -45,16 +45,17 @@ jobs:
             --accept 200,429
             --exclude-path ./CHANGELOG.md
             --exclude-path ./scripts/update_version.py
+            --exclude-path asv.conf.json
             './**/*.rst'
             './**/*.md'
             './**/*.py'
             './**/*.ipynb'
-            './**/*.yml'
-            './**/*.yaml'
             './**/*.json'
             './**/*.toml'
           # fail the action on broken links
           fail: true
+          jobSummary: true
+          format: markdown
         env:
           # to be used in case rate limits are surpassed
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

From 9432e612f0e8273ddcc332cd4b0b63d9dc047f2a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 08:11:06 +0530
Subject: [PATCH 063/615] #3049 Run rpath fixes on macOS and Linux

---
 ...sadi_rpath_mac.py => fix_casadi_rpath_macos_linux.py} | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)
 rename scripts/{fix_casadi_rpath_mac.py => fix_casadi_rpath_macos_linux.py} (90%)

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_macos_linux.py
similarity index 90%
rename from scripts/fix_casadi_rpath_mac.py
rename to scripts/fix_casadi_rpath_macos_linux.py
index fb275e339a..8b4618f2d9 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_macos_linux.py
@@ -2,7 +2,7 @@
 Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
 install and uses a fixed path
 
-Used when building the wheels for macos
+Used when building the wheels for macOS and GNU/Linux
 """
 import casadi
 import os
@@ -15,18 +15,21 @@
 libcppabi_name = "libc++abi.dylib"
 libcasadi_name = "libcasadi.dylib"
 libcasadi_37_name = "libcasadi.3.7.dylib"
+
 install_name_tool_args_for_libcasadi_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_name),
 ]
+
 install_name_tool_args_for_libcasadi_37_name = [
     "-change",
     os.path.join("@rpath", libcpp_name),
     os.path.join(casadi_dir, libcpp_name),
     os.path.join(casadi_dir, libcasadi_37_name),
 ]
+
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
 
 print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
@@ -50,8 +53,8 @@
 
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH ($HOME/.local/lib)
-# This is needed for the casadi python bindings to work
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to $HOME/.local/lib
+# This is needed for the casadi python bindings to work while repairing the wheel
 
 subprocess.run(
     ["cp",

From c099385819f80cdc77e1e0116b30d3d245b4b8b2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 08:11:30 +0530
Subject: [PATCH 064/615] #3049 Add repair script to cibuildwheel

---
 .github/workflows/publish_pypi.yml | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 99bf0aa10a..2742ec6f56 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -82,7 +82,7 @@ jobs:
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
       # sometimes gfortran cannot be found, so reinstall gcc just to be sure
-      - name: Install SUNDIALS on macOS
+      - name: Install SuiteSparse and SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
           brew update
@@ -91,19 +91,20 @@ jobs:
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on Linux and macOS
+      - name: Build wheels on ${{ matrix.os }}
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
-
-          CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
+          CIBW_BEFORE_BUILD_LINUX: >
+            python -m pip install cmake casadi numpy &&
+            python scripts/fix_casadi_rpath_macos_linux.py
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_macos_linux.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >

From 3502303bf0649f791df98a59efb07cf55b9b8c3a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 16:36:41 +0530
Subject: [PATCH 065/615] #3049 Fix casadi rpath on macOS

---
 .github/workflows/publish_pypi.yml                           | 5 ++---
 ...x_casadi_rpath_macos_linux.py => fix_casadi_rpath_mac.py} | 4 ++--
 2 files changed, 4 insertions(+), 5 deletions(-)
 rename scripts/{fix_casadi_rpath_macos_linux.py => fix_casadi_rpath_mac.py} (94%)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 2742ec6f56..16314250b6 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,12 +99,11 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_macos_linux.py
+            python -m pip install cmake casadi numpy
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_macos_linux.py && scripts/fix_suitesparse_rpath_mac.sh
+            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
           # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
           # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
diff --git a/scripts/fix_casadi_rpath_macos_linux.py b/scripts/fix_casadi_rpath_mac.py
similarity index 94%
rename from scripts/fix_casadi_rpath_macos_linux.py
rename to scripts/fix_casadi_rpath_mac.py
index 8b4618f2d9..23c8a32d59 100644
--- a/scripts/fix_casadi_rpath_macos_linux.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -2,7 +2,7 @@
 Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
 install and uses a fixed path
 
-Used when building the wheels for macOS and GNU/Linux
+Used when building the wheels for macOS
 """
 import casadi
 import os
@@ -53,7 +53,7 @@
 
 subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
 
-# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to $HOME/.local/lib
+# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH
 # This is needed for the casadi python bindings to work while repairing the wheel
 
 subprocess.run(

From a0592f84babbd2cf6eac22ea18c09bac8ae8edcf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 12 Sep 2023 23:16:58 +0530
Subject: [PATCH 066/615] Remove `nox` because it is already installed

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index ffbd320f59..b6c0a02f67 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,7 +33,7 @@ ARG ODES
 ARG JAX
 ARG ALL
 
-RUN pip install --upgrade --user pip setuptools wheel wget nox cmake
+RUN pip install --upgrade --user pip setuptools wheel wget cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \

From 51db35f0910620570a5da8ca7de2cb52c5bd1a6b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 13 Sep 2023 00:10:15 +0530
Subject: [PATCH 067/615] #3049 try to move casadi shared objects to local path

---
 .github/workflows/publish_pypi.yml | 3 ++-
 scripts/fix_casadi_rpath_linux.sh  | 7 +++++++
 2 files changed, 9 insertions(+), 1 deletion(-)
 create mode 100644 scripts/fix_casadi_rpath_linux.sh

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 16314250b6..08025195df 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,7 +99,8 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy
+            python -m pip install cmake casadi numpy &&
+            scripts/fix_casadi_rpath_linux.sh
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&
diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
new file mode 100644
index 0000000000..188bd68781
--- /dev/null
+++ b/scripts/fix_casadi_rpath_linux.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+
+LD_LIBRARY_PATH=${HOME}/.local/lib
+CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
+
+cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
+cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From 78bff9a6f817552106af1e644130d6fa95d45768 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 13 Sep 2023 00:14:21 +0530
Subject: [PATCH 068/615] #3049 fix copying script for Linux

---
 scripts/fix_casadi_rpath_linux.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
index 188bd68781..5c7e7801b7 100644
--- a/scripts/fix_casadi_rpath_linux.sh
+++ b/scripts/fix_casadi_rpath_linux.sh
@@ -3,5 +3,5 @@
 LD_LIBRARY_PATH=${HOME}/.local/lib
 CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
 
-cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
-cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}
+sudo cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
+sudo cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From 193ce5677ed5edf8cf57b52dc2788a9bdaa84b0d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 22 Sep 2023 01:16:25 +0530
Subject: [PATCH 069/615] Login on dockerhub to test

---
 .github/workflows/docker.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index aed3fd3d53..478b18d541 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -15,7 +15,6 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Login to DockerHub
-        if: github.event_name != 'pull_request'
         uses: docker/login-action@v2
         with:
             username: ${{ secrets.DOCKERHUB_USERNAME }}

From f697bfe951145d35f9a236136ec91685b779ff0a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 28 Sep 2023 14:19:18 +0530
Subject: [PATCH 070/615] Try pushing with `build-push-action`

---
 .github/workflows/docker.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 478b18d541..940f2cd072 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -24,11 +24,11 @@ jobs:
         run: docker images
 
       - name: Build and Push Docker Image (Without Solvers)
-        env:
-          IMAGE_NAME: pybamm/pybamm:latest
-        run: |
-          docker build -t $IMAGE_NAME -f scripts/Dockerfile .
-          docker push $IMAGE_NAME
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          push: true
+          tags: latest
 
       - name: Build and Push Docker Image (With JAX Solver)
         env:

From 0cd648ac8852c72de6ec297e7580a09da16cc7f6 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 28 Sep 2023 14:22:10 +0530
Subject: [PATCH 071/615] Specify dockerfile path

---
 .github/workflows/docker.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 940f2cd072..debcf75c20 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -27,8 +27,9 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          push: true
+          file: scripts/Dockerfile
           tags: latest
+          push: true
 
       - name: Build and Push Docker Image (With JAX Solver)
         env:

From bd740b7691e96ff71ba025cc03958325afe3f1ae Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 28 Sep 2023 14:29:38 +0530
Subject: [PATCH 072/615] Set up Docker Buildx

---
 .github/workflows/docker.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index debcf75c20..c6d1ba6885 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -14,8 +14,11 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
             username: ${{ secrets.DOCKERHUB_USERNAME }}
             password: ${{ secrets.DOCKERHUB_TOKEN }}

From bcf4c608e65e18b7c6723bdc3dab8032f9740819 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 28 Sep 2023 14:36:51 +0530
Subject: [PATCH 073/615] Try name in tags

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index c6d1ba6885..ef6e25745e 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -31,7 +31,7 @@ jobs:
         with:
           context: .
           file: scripts/Dockerfile
-          tags: latest
+          tags: pybamm/pybamm:latest
           push: true
 
       - name: Build and Push Docker Image (With JAX Solver)

From 6f012f73d481acec54d03041489b9309c55270a0 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 28 Sep 2023 15:02:22 +0530
Subject: [PATCH 074/615] Push all images with `build-push-action`

---
 .github/workflows/docker.yml | 56 ++++++++++++++++++++----------------
 1 file changed, 32 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index ef6e25745e..2b0cf706ae 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -35,33 +35,41 @@ jobs:
           push: true
 
       - name: Build and Push Docker Image (With JAX Solver)
-        env:
-          IMAGE_NAME: pybamm/pybamm:jax
-          ARG_NAME: JAX
-        run: |
-          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
-          docker push $IMAGE_NAME
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:jax
+          push: true
+          build-args: |
+            JAX=true
 
       - name: Build and Push Docker Image (With ODES & DAE Solver)
-        env:
-          IMAGE_NAME: pybamm/pybamm:odes
-          ARG_NAME: ODES
-        run: |
-          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
-          docker push $IMAGE_NAME
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:odes
+          push: true
+          build-args: |
+            ODES=true
 
       - name: Build and Push Docker Image (With IDAKLU Solver)
-        env:
-          IMAGE_NAME: pybamm/pybamm:idaklu
-          ARG_NAME: IDAKLU
-        run: |
-          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
-          docker push $IMAGE_NAME
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:idaklu
+          push: true
+          build-args: |
+            IDAKLU=true
 
       - name: Build and Push Docker Image (With All Solvers)
-        env:
-          IMAGE_NAME: pybamm/pybamm:all
-          ARG_NAME: ALL
-        run: |
-          docker build -t $IMAGE_NAME -f scripts/Dockerfile --build-arg $ARG_NAME=true .
-          docker push $IMAGE_NAME
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:latest
+          push: true
+          build-args: |
+            ALL=true

From cc07fc4782c1517266f7bf626e68794900183784 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:12:09 +0530
Subject: [PATCH 075/615] #3049 fix LD_LIBRARY_PATH for `nox`

---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 58a66b7c76..d9412db495 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -14,7 +14,7 @@
 homedir = os.getenv("HOME")
 PYBAMM_ENV = {
     "SUNDIALS_INST": f"{homedir}/.local",
-    "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
+    "LD_LIBRARY_PATH": f"{homedir}/.local/lib",
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in

From 1e164aeddddcb59aff815ff04f2e11d2c0a3c77e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:13:01 +0530
Subject: [PATCH 076/615] #3049 add custom wheel repair command for Linux

---
 .github/workflows/publish_pypi.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 08025195df..ba797f2b58 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -101,6 +101,9 @@ jobs:
           CIBW_BEFORE_BUILD_LINUX: >
             python -m pip install cmake casadi numpy &&
             scripts/fix_casadi_rpath_linux.sh
+          # override; point to casadi install path so that it can be found by the repair command
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
+            LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
           CIBW_BEFORE_BUILD_MACOS: >
             python -m pip
             install cmake casadi numpy &&

From f30d37288525ba4b08ea8866c7ad87686cf6b036 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 19:29:17 +0530
Subject: [PATCH 077/615] #3049 remove Linux rpath fix script

---
 .github/workflows/publish_pypi.yml | 3 +--
 scripts/fix_casadi_rpath_linux.sh  | 7 -------
 2 files changed, 1 insertion(+), 9 deletions(-)
 delete mode 100644 scripts/fix_casadi_rpath_linux.sh

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index f9df08cb33..5a012f64a8 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -99,8 +99,7 @@ jobs:
             yum -y install blas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy &&
-            scripts/fix_casadi_rpath_linux.sh
+            python -m pip install cmake casadi numpy
           # override; point to casadi install path so that it can be found by the repair command
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
             LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
diff --git a/scripts/fix_casadi_rpath_linux.sh b/scripts/fix_casadi_rpath_linux.sh
deleted file mode 100644
index 5c7e7801b7..0000000000
--- a/scripts/fix_casadi_rpath_linux.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/usr/bin/env bash
-
-LD_LIBRARY_PATH=${HOME}/.local/lib
-CASADI_PATH=$(python -c "import casadi; print(casadi.__path__[0])")
-
-sudo cp ${CASADI_PATH}/libcasadi.so.3.7 ${LD_LIBRARY_PATH}
-sudo cp ${CASADI_PATH}/libcasadi.so ${LD_LIBRARY_PATH}

From c069e44f28549fdb0fbd7f9630fc6733d14d3419 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 20:13:39 +0530
Subject: [PATCH 078/615] #3049 Add MSMR parameters entry point

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 7842ce39a4..c3c6583adb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -155,6 +155,7 @@ Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"
 Xu2019 = "pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values"
 ECM_Example = "pybamm.input.parameters.ecm.example_set:get_parameter_values"
+MSMR_Example = "pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values"
 
 [tool.setuptools]
 include-package-data = true

From 4bf4537730d487b2524e473fda43c99c8786008b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 21:12:28 +0530
Subject: [PATCH 079/615] Set up MANIFEST.in

---
 MANIFEST.in    | 6 ++++++
 pyproject.toml | 3 ++-
 2 files changed, 8 insertions(+), 1 deletion(-)
 create mode 100644 MANIFEST.in

diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000000..24ae488d04
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,6 @@
+graft pybamm
+prune tests
+
+exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md GOVERNANCE.md CMakeLists.txt
+
+global-exclude __pycache__ *.py[cod] .venv
diff --git a/pyproject.toml b/pyproject.toml
index c3c6583adb..a69a7926ca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -163,6 +163,7 @@ include-package-data = true
 # List of files to include as package data. These are mainly the parameter CSV files in
 # the input/parameters/ subdirectories. Other files such as the CITATIONS file, relevant
 # README.md files, and specific .txt files inside the pybamm/ directory are also included.
+# These are specified to be included in the SDist through MANIFEST.in.
 [tool.setuptools.package-data]
 pybamm = [
     "*.txt",
@@ -174,4 +175,4 @@ pybamm = [
 ]
 
 [tool.setuptools.packages.find]
-include = ["pybamm", "pybamm.*"]
+include = ["pybamm"]

From 1c202741835ff57282693b3de973207b82a867db Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:30:15 +0530
Subject: [PATCH 080/615] #3049 some installation cleanups (`nox`)

---
 noxfile.py | 58 ++++++++++++++++++++++++------------------------------
 setup.py   | 16 +++++++++------
 2 files changed, 36 insertions(+), 38 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 904770d952..c6d300d0b0 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -22,12 +22,6 @@
 JAX_VERSION = "0.4.8"
 JAXLIB_VERSION = "0.4.7"
 
-if os.getenv("CI") == "true":
-    # The setup-python action installs and caches dependencies by default, so we skip
-    # installing them again in nox environments. The dev and docs sessions will still
-    # require a virtual environment, but we don't run them in the CI
-    nox.options.default_venv_backend = "none"
-
 
 def set_environment_variables(env_dict, session):
     """
@@ -50,7 +44,7 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "wget", "cmake")
+        session.install("wget", "cmake" , silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(
@@ -61,19 +55,19 @@ def run_pybamm_requires(session):
                 external=True,
             )
     else:
-        session.error("nox -s pybamm-requires is only available on Linux & MacOS.")
+        session.error("nox -s pybamm-requires is only available on Linux & macOS.")
 
 
 @nox.session(name="coverage")
 def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "coverage")
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("coverage", silent=False)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -83,18 +77,18 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
-    session.run_always("pip", "install", "-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.run("python", "run-tests.py", "--doctest")
 
 
@@ -102,11 +96,11 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -115,7 +109,7 @@ def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
     notebooks_to_test = session.posargs if session.posargs else []
-    session.run_always("pip", "install", "-e", ".[all,dev]")
+    session.install("-e", ".[all,dev]", silent=False)
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
 
@@ -123,7 +117,7 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
 
 
@@ -132,8 +126,8 @@ def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
     envbindir = session.bin
-    session.install("-e", ".[all]")
-    session.install("cmake")
+    session.install("-e", ".[all]", silent=False)
+    session.install("cmake", silent=False)
     if sys.platform != "win32":
         session.run(
             "echo",
@@ -149,11 +143,11 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.run_always("pip", "install", "-e", ".[all]")
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.run_always("pip", "install", "scikits.odes")
-        session.run_always("pip", "install", f"jax=={JAX_VERSION}")
-        session.run_always("pip", "install", f"jaxlib=={JAXLIB_VERSION}")
+        session.install("scikits.odes", silent=False)
+        session.install(f"jax=={JAX_VERSION}", silent=False)
+        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
@@ -161,7 +155,7 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    session.install("-e", ".[all,docs]")
+    session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
     session.run(
         "sphinx-autobuild",
@@ -177,7 +171,7 @@ def build_docs(session):
 @nox.session(name="pre-commit")
 def lint(session):
     """Check all files against the defined pre-commit hooks."""
-    session.install("pre-commit")
+    session.install("pre-commit", silent=False)
     session.run("pre-commit", "run", "--all-files")
 
 
diff --git a/setup.py b/setup.py
index 2af56c6ef6..018bf9eee0 100644
--- a/setup.py
+++ b/setup.py
@@ -9,16 +9,15 @@
 try:
     from setuptools import setup, Extension
     from setuptools.command.install import install
+    from setuptools.command.build_ext import build_ext
 except ImportError:
     from distutils.core import setup
     from distutils.command.install import install
+    from distutils.command.build_ext import build_ext
 
-# ---------- cmakebuild was integrated into setup.py directly --------------------------
 
-try:
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.command.build_ext import build_ext
+# ---------- CMake steps for IDAKLU target (non-Windows) -------------------------------
+
 
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
@@ -171,10 +170,13 @@ def move_output(self, ext):
         dest_directory.mkdir(parents=True, exist_ok=True)
         self.copy_file(source_path, dest_path)
 
-# ---------- end of cmakebuild steps ---------------------------------------------------
+
+# ---------- end of CMake steps --------------------------------------------------------
+
 
 # ---------- configure setup logger ----------------------------------------------------
 
+
 log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 logger = logging.getLogger("PyBaMM setup")
 
@@ -215,8 +217,10 @@ def finalize_options(self):
     def run(self):
         install.run(self)
 
+
 # ---------- custom wheel build (non-Windows) ------------------------------------------
 
+
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
 

From ddac82e448fb3c31106c08621be3c9bed50aa37d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:40:38 +0530
Subject: [PATCH 081/615] #3049 #3121 sync jax, jaxlib version requirements

---
 noxfile.py     | 7 ++++---
 pyproject.toml | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index c6d300d0b0..dafa114221 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -18,9 +18,10 @@
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally
-JAX_VERSION = "0.4.8"
-JAXLIB_VERSION = "0.4.7"
+# the CI and locally. These should be updated when the version of PyBaMM is updated and
+# must be kept in sync with the constants defined in pybamm/util.py.
+JAX_VERSION = "0.4"
+JAXLIB_VERSION = "0.4"
 
 
 def set_environment_variables(env_dict, session):
diff --git a/pyproject.toml b/pyproject.toml
index a69a7926ca..5f225cb5f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -109,10 +109,11 @@ dev = [
 pandas =  [
     "pandas>=0.24",
 ]
-# For the Jax solver
+# For the Jax solver. Note: these should be kept in sync with the versions defined
+# in noxfile.py and pybamm/util.py.
 jax = [
-    "jax==0.4.8",
-    "jaxlib==0.4.7",
+    "jax>=0.4,<=0.5",
+    "jaxlib>=0.4,<=0.5",
 ]
 # For the scikits.odes solver
 odes = [

From 8016c712f417a816760f02a9b51734e825beb0b7 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 22:51:18 +0530
Subject: [PATCH 082/615] #3049 Improve docs about project installation
 infrastructure

---
 CONTRIBUTING.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 577dbd67c6..8e8fdd36e5 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -385,21 +385,22 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ## Infrastructure
 
-### Setuptools
+### Installation
 
-Installation of PyBaMM _and dependencies_ is handled via [setuptools](http://setuptools.readthedocs.io/)
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 
 ```
 setup.py
+pyproject.toml
 ```
 
-Note that this file must be kept in sync with the version number in [pybamm/**init**.py](pybamm/__init__.py).
+Note that this file must be kept in sync with the version number in [`pybamm/__init__.py`](pybamm/__init__.py).
 
-### Continuous Integration using GitHub actions
+### Continuous Integration using GitHub Actions
 
-Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub actions](https://github.com/features/actions).
+Each change pushed to the PyBaMM GitHub repository will trigger the test and benchmark suites to be run, using [GitHub Actions](https://github.com/features/actions).
 
 Tests are run for different operating systems, and for all Python versions officially supported by PyBaMM. If you opened a Pull Request, feedback is directly available on the corresponding page. If all tests pass, a green tick will be displayed next to the corresponding test run. If one or more test(s) fail, a red cross will be displayed instead.
 

From 9307ee48939464b6a2844b8fe4c75fdedef9c4c0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 23:00:51 +0530
Subject: [PATCH 083/615] #3049 Update cache hashes to include `nox` changes

---
 .github/workflows/test_on_push.yml | 10 +++++-----
 noxfile.py                         |  3 ++-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index d6a2c73f5c..5fb2e3ab15 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -104,7 +104,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -158,7 +158,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
@@ -239,7 +239,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -293,7 +293,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
@@ -349,7 +349,7 @@ jobs:
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
             ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
diff --git a/noxfile.py b/noxfile.py
index dafa114221..c6175cd183 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -18,7 +18,8 @@
 }
 # Versions compatible with the current version of PyBaMM. Installed directly in the
 # sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally. These should be updated when the version of PyBaMM is updated and
+# the CI and locally
+# Note: These should be updated when the version of PyBaMM is updated and
 # must be kept in sync with the constants defined in pybamm/util.py.
 JAX_VERSION = "0.4"
 JAXLIB_VERSION = "0.4"

From 3720f04649c30cbb6beb54914bbf7cd50b15f7d6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 28 Sep 2023 23:16:44 +0530
Subject: [PATCH 084/615] #3049 temporarily skip i686 Linux builds

See https://github.com/numpy/numpy/issues/24703 for more information
---
 .github/workflows/publish_pypi.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 5a012f64a8..b0c5f5faae 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -94,6 +94,8 @@ jobs:
       - name: Build wheels on ${{ matrix.os }}
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
+          # NumPy requires BLAS now which is no longer available on manylinux2014 i686, so skip it
+          CIBW_ARCHS_LINUX: x86_64
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&

From b0d58020ca655208e6de77e1ad369f81e7ae0d30 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 14:40:20 -0400
Subject: [PATCH 085/615] Switch quickplot tests to temp files

---
 tests/unit/test_batch_study.py              | 19 +++++++++++--------
 tests/unit/test_plotting/test_quick_plot.py | 11 +++++++----
 tests/unit/test_simulation.py               | 19 +++++++++++--------
 3 files changed, 29 insertions(+), 20 deletions(-)

diff --git a/tests/unit/test_batch_study.py b/tests/unit/test_batch_study.py
index 89e6bd62b0..f8762133a6 100644
--- a/tests/unit/test_batch_study.py
+++ b/tests/unit/test_batch_study.py
@@ -5,6 +5,7 @@
 import os
 import pybamm
 import unittest
+from tempfile import TemporaryDirectory
 
 spm = pybamm.lithium_ion.SPM()
 spm_uniform = pybamm.lithium_ion.SPM({"particle": "uniform profile"})
@@ -90,17 +91,19 @@ def test_solve(self):
             self.assertIn(output_experiment, experiments_list)
 
     def test_create_gif(self):
-        bs = pybamm.BatchStudy({"spm": pybamm.lithium_ion.SPM()})
-        bs.solve([0, 10])
+        with TemporaryDirectory() as dir_name:
+            bs = pybamm.BatchStudy({"spm": pybamm.lithium_ion.SPM()})
+            bs.solve([0, 10])
 
-        # create a GIF before calling the plot method
-        bs.create_gif(number_of_images=3, duration=1)
+            # Create a temporary file name
+            test_file = os.path.join(dir_name, "batch_study_test.gif")
 
-        # create a GIF after calling the plot method
-        bs.plot(testing=True)
-        bs.create_gif(number_of_images=3, duration=1)
+            # create a GIF before calling the plot method
+            bs.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
-        os.remove("plot.gif")
+            # create a GIF after calling the plot method
+            bs.plot(testing=True)
+            bs.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py
index 3415777ee8..670ad3d7f0 100644
--- a/tests/unit/test_plotting/test_quick_plot.py
+++ b/tests/unit/test_plotting/test_quick_plot.py
@@ -3,6 +3,7 @@
 import unittest
 from tests import TestCase
 import numpy as np
+from tempfile import TemporaryDirectory
 
 
 class TestQuickPlot(TestCase):
@@ -290,10 +291,12 @@ def test_spm_simulation(self):
         quick_plot.plot(0)
 
         # test creating a GIF
-        quick_plot.create_gif(number_of_images=3, duration=3)
-        assert not os.path.exists("plot*.png")
-        assert os.path.exists("plot.gif")
-        os.remove("plot.gif")
+        with TemporaryDirectory() as dir_name:
+            test_stub = os.path.join(dir_name, "spm_sim_test")
+            test_file = f"{test_stub}.gif"
+            quick_plot.create_gif(number_of_images=3, duration=3, output_filename=test_file)
+            assert not os.path.exists(f"{test_stub}*.png")
+            assert os.path.exists(test_file)
 
         pybamm.close_plots()
 
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index d0926e5c94..db3bf6f39b 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -6,6 +6,7 @@
 import sys
 import unittest
 import uuid
+from tempfile import TemporaryDirectory
 
 
 class TestSimulation(TestCase):
@@ -340,17 +341,19 @@ def test_plot(self):
         sim.plot(testing=True)
 
     def test_create_gif(self):
-        sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
-        sim.solve(t_eval=[0, 10])
+        with TemporaryDirectory() as dir_name:
+            sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
+            sim.solve(t_eval=[0, 10])
 
-        # create a GIF without calling the plot method
-        sim.create_gif(number_of_images=3, duration=1)
+            # Create a temporary file name
+            test_file = os.path.join(dir_name, "test_sim.gif")
 
-        # call the plot method before creating the GIF
-        sim.plot(testing=True)
-        sim.create_gif(number_of_images=3, duration=1)
+            # create a GIF without calling the plot method
+            sim.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
-        os.remove("plot.gif")
+            # call the plot method before creating the GIF
+            sim.plot(testing=True)
+            sim.create_gif(number_of_images=3, duration=1, output_filename=test_file)
 
     def test_drive_cycle_interpolant(self):
         model = pybamm.lithium_ion.SPM()

From aa86d7268d713805ae90ef80117345fc8ebebf3a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 29 Sep 2023 00:10:32 +0530
Subject: [PATCH 086/615] #3049 Fix UNKNOWN name error on SDist

See https://github.com/pypa/setuptools/issues/3269 for more details
---
 .github/workflows/publish_pypi.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b0c5f5faae..6f82c6ca2e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -124,22 +124,22 @@ jobs:
           if-no-files-found: error
 
   build_sdist:
-    name: Build sdist
+    name: Build SDist
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install dependencies
-        run: pip install wheel
+        run: pip install --upgrade pip setuptools wheel
 
-      - name: Build sdist
-        run: python setup.py sdist --formats=gztar
+      - name: Build SDist
+        run: pipx run build --sdist
 
-      - name: Upload sdist
+      - name: Upload SDist
         uses: actions/upload-artifact@v3
         with:
           name: sdist

From 05cd6ec3fbc6529b624ca8a40f585d6530e54a4f Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 15:00:50 -0400
Subject: [PATCH 087/615] Moving save and load tests to temp directories

---
 tests/unit/test_simulation.py            | 111 +++++++-------
 tests/unit/test_solvers/test_solution.py | 183 ++++++++++++-----------
 2 files changed, 153 insertions(+), 141 deletions(-)

diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index db3bf6f39b..37b58e19c1 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -249,32 +249,36 @@ def test_step_with_inputs(self):
         )
 
     def test_save_load(self):
-        model = pybamm.lead_acid.LOQS()
-        model.use_jacobian = True
-        sim = pybamm.Simulation(model)
-
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
+        with TemporaryDirectory() as dir_name:
+            test_name = os.path.join(dir_name, "tests.pickle")
+
+            model = pybamm.lead_acid.LOQS()
+            model.use_jacobian = True
+            sim = pybamm.Simulation(model)
+
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # save after solving
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # with python formats
+            model.convert_to_format = None
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            sim.save(test_name)
+            model.convert_to_format = "python"
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            with self.assertRaisesRegex(
+                    NotImplementedError, "Cannot save simulation if model format is python"
+            ):
+                sim.save(test_name)
 
-        # save after solving
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
-
-        # with python formats
-        model.convert_to_format = None
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        model.convert_to_format = "python"
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        with self.assertRaisesRegex(
-            NotImplementedError, "Cannot save simulation if model format is python"
-        ):
-            sim.save("test.pickle")
 
     def test_load_param(self):
         # Test load_sim for parameters imports
@@ -300,33 +304,36 @@ def test_load_param(self):
         os.remove(filename)
 
     def test_save_load_dae(self):
-        model = pybamm.lead_acid.LOQS({"surface form": "algebraic"})
-        model.use_jacobian = True
-        sim = pybamm.Simulation(model)
-
-        # save after solving
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
-
-        # with python format
-        model.convert_to_format = None
-        sim = pybamm.Simulation(model)
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-
-        # with Casadi solver & experiment
-        model.convert_to_format = "casadi"
-        sim = pybamm.Simulation(
-            model,
-            experiment="Discharge at 1C for 20 minutes",
-            solver=pybamm.CasadiSolver(),
-        )
-        sim.solve([0, 600])
-        sim.save("test.pickle")
-        sim_load = pybamm.load_sim("test.pickle")
-        self.assertEqual(sim.model.name, sim_load.model.name)
+        with TemporaryDirectory() as dir_name:
+            test_name = os.path.join(dir_name, "test.pickle")
+
+            model = pybamm.lead_acid.LOQS({"surface form": "algebraic"})
+            model.use_jacobian = True
+            sim = pybamm.Simulation(model)
+
+            # save after solving
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
+
+            # with python format
+            model.convert_to_format = None
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 600])
+            sim.save(test_name)
+
+            # with Casadi solver & experiment
+            model.convert_to_format = "casadi"
+            sim = pybamm.Simulation(
+                model,
+                experiment="Discharge at 1C for 20 minutes",
+                solver=pybamm.CasadiSolver(),
+            )
+            sim.solve([0, 600])
+            sim.save(test_name)
+            sim_load = pybamm.load_sim(test_name)
+            self.assertEqual(sim.model.name, sim_load.model.name)
 
     def test_plot(self):
         sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py
index 2ef01d7434..c01700267e 100644
--- a/tests/unit/test_solvers/test_solution.py
+++ b/tests/unit/test_solvers/test_solution.py
@@ -1,6 +1,7 @@
 #
 # Tests for the Solution class
 #
+import os
 from tests import TestCase
 import json
 import pybamm
@@ -9,6 +10,7 @@
 import pandas as pd
 from scipy.io import loadmat
 from tests import get_discretisation_for_testing
+from tempfile import TemporaryDirectory
 
 
 class TestSolution(TestCase):
@@ -233,95 +235,98 @@ def test_plot(self):
         solution.plot(["c", "2c"], testing=True)
 
     def test_save(self):
-        model = pybamm.BaseModel()
-        # create both 1D and 2D variables
-        c = pybamm.Variable("c")
-        d = pybamm.Variable("d", domain="negative electrode")
-        model.rhs = {c: -c, d: 1}
-        model.initial_conditions = {c: 1, d: 2}
-        model.variables = {"c": c, "d": d, "2c": 2 * c, "c + d": c + d}
-
-        disc = get_discretisation_for_testing()
-        disc.process_model(model)
-        solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
-
-        # test save data
-        with self.assertRaises(ValueError):
-            solution.save_data("test.pickle")
-
-        # set variables first then save
-        solution.update(["c", "d"])
-        with self.assertRaisesRegex(ValueError, "pickle"):
-            solution.save_data(to_format="pickle")
-        solution.save_data("test.pickle")
-
-        data_load = pybamm.load("test.pickle")
-        np.testing.assert_array_equal(solution.data["c"], data_load["c"])
-        np.testing.assert_array_equal(solution.data["d"], data_load["d"])
-
-        # to matlab
-        solution.save_data("test.mat", to_format="matlab")
-        data_load = loadmat("test.mat")
-        np.testing.assert_array_equal(solution.data["c"], data_load["c"].flatten())
-        np.testing.assert_array_equal(solution.data["d"], data_load["d"])
-
-        with self.assertRaisesRegex(ValueError, "matlab"):
-            solution.save_data(to_format="matlab")
-
-        # to matlab with bad variables name fails
-        solution.update(["c + d"])
-        with self.assertRaisesRegex(ValueError, "Invalid character"):
-            solution.save_data("test.mat", to_format="matlab")
-        # Works if providing alternative name
-        solution.save_data(
-            "test.mat", to_format="matlab", short_names={"c + d": "c_plus_d"}
-        )
-        data_load = loadmat("test.mat")
-        np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
-
-        # to csv
-        with self.assertRaisesRegex(
-            ValueError, "only 0D variables can be saved to csv"
-        ):
-            solution.save_data("test.csv", to_format="csv")
-        # only save "c" and "2c"
-        solution.save_data("test.csv", ["c", "2c"], to_format="csv")
-        csv_str = solution.save_data(variables=["c", "2c"], to_format="csv")
-
-        # check string is the same as the file
-        with open("test.csv") as f:
-            # need to strip \r chars for windows
-            self.assertEqual(csv_str.replace("\r", ""), f.read())
-
-        # read csv
-        df = pd.read_csv("test.csv")
-        np.testing.assert_array_almost_equal(df["c"], solution.data["c"])
-        np.testing.assert_array_almost_equal(df["2c"], solution.data["2c"])
-
-        # to json
-        solution.save_data("test.json", to_format="json")
-        json_str = solution.save_data(to_format="json")
-
-        # check string is the same as the file
-        with open("test.json") as f:
-            # need to strip \r chars for windows
-            self.assertEqual(json_str.replace("\r", ""), f.read())
-
-        # check if string has the right values
-        json_data = json.loads(json_str)
-        np.testing.assert_array_almost_equal(json_data["c"], solution.data["c"])
-        np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
-
-        # raise error if format is unknown
-        with self.assertRaisesRegex(ValueError, "format 'wrong_format' not recognised"):
-            solution.save_data("test.csv", to_format="wrong_format")
-
-        # test save whole solution
-        solution.save("test.pickle")
-        solution_load = pybamm.load("test.pickle")
-        self.assertEqual(solution.all_models[0].name, solution_load.all_models[0].name)
-        np.testing.assert_array_equal(solution["c"].entries, solution_load["c"].entries)
-        np.testing.assert_array_equal(solution["d"].entries, solution_load["d"].entries)
+        with TemporaryDirectory() as dir_name:
+            test_stub = os.path.join(dir_name, "test")
+
+            model = pybamm.BaseModel()
+            # create both 1D and 2D variables
+            c = pybamm.Variable("c")
+            d = pybamm.Variable("d", domain="negative electrode")
+            model.rhs = {c: -c, d: 1}
+            model.initial_conditions = {c: 1, d: 2}
+            model.variables = {"c": c, "d": d, "2c": 2 * c, "c + d": c + d}
+
+            disc = get_discretisation_for_testing()
+            disc.process_model(model)
+            solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
+
+            # test save data
+            with self.assertRaises(ValueError):
+                solution.save_data(f"{test_stub}.pickle")
+
+            # set variables first then save
+            solution.update(["c", "d"])
+            with self.assertRaisesRegex(ValueError, "pickle"):
+                solution.save_data(to_format="pickle")
+            solution.save_data(f"{test_stub}.pickle")
+
+            data_load = pybamm.load(f"{test_stub}.pickle")
+            np.testing.assert_array_equal(solution.data["c"], data_load["c"])
+            np.testing.assert_array_equal(solution.data["d"], data_load["d"])
+
+            # to matlab
+            solution.save_data(f"{test_stub}.mat", to_format="matlab")
+            data_load = loadmat(f"{test_stub}.mat")
+            np.testing.assert_array_equal(solution.data["c"], data_load["c"].flatten())
+            np.testing.assert_array_equal(solution.data["d"], data_load["d"])
+
+            with self.assertRaisesRegex(ValueError, "matlab"):
+                solution.save_data(to_format="matlab")
+
+            # to matlab with bad variables name fails
+            solution.update(["c + d"])
+            with self.assertRaisesRegex(ValueError, "Invalid character"):
+                solution.save_data(f"{test_stub}.mat", to_format="matlab")
+            # Works if providing alternative name
+            solution.save_data(
+                f"{test_stub}.mat", to_format="matlab", short_names={"c + d": "c_plus_d"}
+            )
+            data_load = loadmat(f"{test_stub}.mat")
+            np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
+
+            # to csv
+            with self.assertRaisesRegex(
+                    ValueError, "only 0D variables can be saved to csv"
+            ):
+                solution.save_data(f"{test_stub}.csv", to_format="csv")
+            # only save "c" and "2c"
+            solution.save_data(f"{test_stub}.csv", ["c", "2c"], to_format="csv")
+            csv_str = solution.save_data(variables=["c", "2c"], to_format="csv")
+
+            # check string is the same as the file
+            with open(f"{test_stub}.csv") as f:
+                # need to strip \r chars for windows
+                self.assertEqual(csv_str.replace("\r", ""), f.read())
+
+            # read csv
+            df = pd.read_csv(f"{test_stub}.csv")
+            np.testing.assert_array_almost_equal(df["c"], solution.data["c"])
+            np.testing.assert_array_almost_equal(df["2c"], solution.data["2c"])
+
+            # to json
+            solution.save_data(f"{test_stub}.json", to_format="json")
+            json_str = solution.save_data(to_format="json")
+
+            # check string is the same as the file
+            with open(f"{test_stub}.json") as f:
+                # need to strip \r chars for windows
+                self.assertEqual(json_str.replace("\r", ""), f.read())
+
+            # check if string has the right values
+            json_data = json.loads(json_str)
+            np.testing.assert_array_almost_equal(json_data["c"], solution.data["c"])
+            np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
+
+            # raise error if format is unknown
+            with self.assertRaisesRegex(ValueError, "format 'wrong_format' not recognised"):
+                solution.save_data(f"{test_stub}.csv", to_format="wrong_format")
+
+            # test save whole solution
+            solution.save(f"{test_stub}.pickle")
+            solution_load = pybamm.load(f"{test_stub}.pickle")
+            self.assertEqual(solution.all_models[0].name, solution_load.all_models[0].name)
+            np.testing.assert_array_equal(solution["c"].entries, solution_load["c"].entries)
+            np.testing.assert_array_equal(solution["d"].entries, solution_load["d"].entries)
 
     def test_get_data_cycles_steps(self):
         model = pybamm.BaseModel()

From fda46ac2169891d7c0bf204946b6fe04e3e0e64f Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 15:08:14 -0400
Subject: [PATCH 088/615] pre-commit fixes

---
 tests/unit/test_plotting/test_quick_plot.py |  3 ++-
 tests/unit/test_simulation.py               |  3 ++-
 tests/unit/test_solvers/test_solution.py    | 15 ++++++++++-----
 3 files changed, 14 insertions(+), 7 deletions(-)

diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py
index 670ad3d7f0..507125b340 100644
--- a/tests/unit/test_plotting/test_quick_plot.py
+++ b/tests/unit/test_plotting/test_quick_plot.py
@@ -294,7 +294,8 @@ def test_spm_simulation(self):
         with TemporaryDirectory() as dir_name:
             test_stub = os.path.join(dir_name, "spm_sim_test")
             test_file = f"{test_stub}.gif"
-            quick_plot.create_gif(number_of_images=3, duration=3, output_filename=test_file)
+            quick_plot.create_gif(number_of_images=3, duration=3,
+                                  output_filename=test_file)
             assert not os.path.exists(f"{test_stub}*.png")
             assert os.path.exists(test_file)
 
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index 37b58e19c1..c98586ee59 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -275,7 +275,8 @@ def test_save_load(self):
             sim = pybamm.Simulation(model)
             sim.solve([0, 600])
             with self.assertRaisesRegex(
-                    NotImplementedError, "Cannot save simulation if model format is python"
+                    NotImplementedError,
+                    "Cannot save simulation if model format is python"
             ):
                 sim.save(test_name)
 
diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py
index c01700267e..9fc93dfb26 100644
--- a/tests/unit/test_solvers/test_solution.py
+++ b/tests/unit/test_solvers/test_solution.py
@@ -279,7 +279,8 @@ def test_save(self):
                 solution.save_data(f"{test_stub}.mat", to_format="matlab")
             # Works if providing alternative name
             solution.save_data(
-                f"{test_stub}.mat", to_format="matlab", short_names={"c + d": "c_plus_d"}
+                f"{test_stub}.mat", to_format="matlab",
+                short_names={"c + d": "c_plus_d"}
             )
             data_load = loadmat(f"{test_stub}.mat")
             np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
@@ -318,15 +319,19 @@ def test_save(self):
             np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
 
             # raise error if format is unknown
-            with self.assertRaisesRegex(ValueError, "format 'wrong_format' not recognised"):
+            with self.assertRaisesRegex(ValueError,
+                                        "format 'wrong_format' not recognised"):
                 solution.save_data(f"{test_stub}.csv", to_format="wrong_format")
 
             # test save whole solution
             solution.save(f"{test_stub}.pickle")
             solution_load = pybamm.load(f"{test_stub}.pickle")
-            self.assertEqual(solution.all_models[0].name, solution_load.all_models[0].name)
-            np.testing.assert_array_equal(solution["c"].entries, solution_load["c"].entries)
-            np.testing.assert_array_equal(solution["d"].entries, solution_load["d"].entries)
+            self.assertEqual(solution.all_models[0].name,
+                             solution_load.all_models[0].name)
+            np.testing.assert_array_equal(solution["c"].entries,
+                                          solution_load["c"].entries)
+            np.testing.assert_array_equal(solution["d"].entries,
+                                          solution_load["d"].entries)
 
     def test_get_data_cycles_steps(self):
         model = pybamm.BaseModel()

From bbf60331dddedb1a7188e70aa3f3a39d47bb7a3a Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 15:13:43 -0400
Subject: [PATCH 089/615] Moving parameter printing to temporary directories

---
 .../test_parameters/test_lead_acid_parameters.py   | 12 +++++++-----
 .../test_parameters/test_lithium_ion_parameters.py | 14 ++++++++------
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/tests/unit/test_parameters/test_lead_acid_parameters.py b/tests/unit/test_parameters/test_lead_acid_parameters.py
index e0151f0e7b..ddc73f61ee 100644
--- a/tests/unit/test_parameters/test_lead_acid_parameters.py
+++ b/tests/unit/test_parameters/test_lead_acid_parameters.py
@@ -1,10 +1,11 @@
 #
 # Test for the standard lead acid parameters
 #
+import os
 from tests import TestCase
 import pybamm
 from tests import get_discretisation_for_testing
-
+from tempfile import TemporaryDirectory
 import unittest
 
 
@@ -15,10 +16,11 @@ def test_scipy_constants(self):
         self.assertAlmostEqual(constants.F.evaluate(), 96485, places=0)
 
     def test_print_parameters(self):
-        parameters = pybamm.LeadAcidParameters()
-        parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
-        output_file = "lead_acid_parameters.txt"
-        parameter_values.print_parameters(parameters, output_file)
+        with TemporaryDirectory() as dir_name:
+            parameters = pybamm.LeadAcidParameters()
+            parameter_values = pybamm.lead_acid.BaseModel().default_parameter_values
+            output_file = os.path.join(dir_name, "lead_acid_parameters.txt")
+            parameter_values.print_parameters(parameters, output_file)
 
     def test_parameters_defaults_lead_acid(self):
         # Load parameters to be tested
diff --git a/tests/unit/test_parameters/test_lithium_ion_parameters.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py
index 9d9d892300..5fb854105f 100644
--- a/tests/unit/test_parameters/test_lithium_ion_parameters.py
+++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py
@@ -1,19 +1,21 @@
 #
-# Tests lithium ion parameters load and give expected values
+# Tests lithium-ion parameters load and give expected values
 #
+import os
 from tests import TestCase
 import pybamm
-
+from tempfile import TemporaryDirectory
 import unittest
 import numpy as np
 
 
 class TestLithiumIonParameterValues(TestCase):
     def test_print_parameters(self):
-        parameters = pybamm.LithiumIonParameters()
-        parameter_values = pybamm.lithium_ion.BaseModel().default_parameter_values
-        output_file = "lithium_ion_parameters.txt"
-        parameter_values.print_parameters(parameters, output_file)
+        with TemporaryDirectory as dir_name:
+            parameters = pybamm.LithiumIonParameters()
+            parameter_values = pybamm.lithium_ion.BaseModel().default_parameter_values
+            output_file = os.path.join(dir_name, "lithium_ion_parameters.txt")
+            parameter_values.print_parameters(parameters, output_file)
 
     def test_lithium_ion(self):
         """This test checks that all the parameters are being calculated

From 3a75c16ba4ab90e9f691849a67d5700ca51d2fb7 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 15:19:50 -0400
Subject: [PATCH 090/615] Move expression tree PNG to a temp folder

---
 tests/unit/test_expression_tree/test_symbol.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 3a74375ce7..b02e7c59e1 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -4,6 +4,7 @@
 from tests import TestCase
 import os
 import unittest
+from tempfile import TemporaryDirectory
 
 import numpy as np
 from scipy.sparse import csr_matrix, coo_matrix
@@ -386,13 +387,16 @@ def test_symbol_repr(self):
         )
 
     def test_symbol_visualise(self):
-        c = pybamm.Variable("c", "negative electrode")
-        d = pybamm.Variable("d", "negative electrode")
-        sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
-        sym.visualise("test_visualize.png")
-        self.assertTrue(os.path.exists("test_visualize.png"))
-        with self.assertRaises(ValueError):
-            sym.visualise("test_visualize")
+        with TemporaryDirectory as dir_name:
+            test_stub = os.path.join(dir_name, "test_visualize")
+            test_name = f"{test_stub}.png"
+            c = pybamm.Variable("c", "negative electrode")
+            d = pybamm.Variable("d", "negative electrode")
+            sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
+            sym.visualise(test_name)
+            self.assertTrue(os.path.exists(test_name))
+            with self.assertRaises(ValueError):
+                sym.visualise(test_stub)
 
     def test_has_spatial_derivatives(self):
         var = pybamm.Variable("var", domain="test")

From 10c727093c7a6d3c5a7085f9ba0b0b928ce85db5 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Thu, 28 Sep 2023 15:30:53 -0400
Subject: [PATCH 091/615] Fixing a few typos

---
 tests/unit/test_expression_tree/test_symbol.py            | 2 +-
 tests/unit/test_parameters/test_lithium_ion_parameters.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index b02e7c59e1..3f91633fbe 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -387,7 +387,7 @@ def test_symbol_repr(self):
         )
 
     def test_symbol_visualise(self):
-        with TemporaryDirectory as dir_name:
+        with TemporaryDirectory() as dir_name:
             test_stub = os.path.join(dir_name, "test_visualize")
             test_name = f"{test_stub}.png"
             c = pybamm.Variable("c", "negative electrode")
diff --git a/tests/unit/test_parameters/test_lithium_ion_parameters.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py
index 5fb854105f..0c46eec16e 100644
--- a/tests/unit/test_parameters/test_lithium_ion_parameters.py
+++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py
@@ -11,7 +11,7 @@
 
 class TestLithiumIonParameterValues(TestCase):
     def test_print_parameters(self):
-        with TemporaryDirectory as dir_name:
+        with TemporaryDirectory() as dir_name:
             parameters = pybamm.LithiumIonParameters()
             parameter_values = pybamm.lithium_ion.BaseModel().default_parameter_values
             output_file = os.path.join(dir_name, "lithium_ion_parameters.txt")

From 588496f198ef3ebc1214028ffebffa20d2166bb1 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 02:49:23 +0530
Subject: [PATCH 092/615] #3049 keep solvers extras in sync, don't install
 yanked versions

---
 noxfile.py     | 35 ++++++++++++-----------------------
 pybamm/util.py |  5 ++---
 pyproject.toml |  3 +--
 3 files changed, 15 insertions(+), 28 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index c6175cd183..c59538d94e 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,13 +16,6 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib",
 }
-# Versions compatible with the current version of PyBaMM. Installed directly in the
-# sessions to skip redundant installation of dependencies and building wheels both in
-# the CI and locally
-# Note: These should be updated when the version of PyBaMM is updated and
-# must be kept in sync with the constants defined in pybamm/util.py.
-JAX_VERSION = "0.4"
-JAXLIB_VERSION = "0.4"
 
 
 def set_environment_variables(env_dict, session):
@@ -65,11 +58,10 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -79,11 +71,10 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -98,11 +89,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+        session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -145,11 +135,10 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("scikits.odes", silent=False)
-        session.install(f"jax=={JAX_VERSION}", silent=False)
-        session.install(f"jaxlib=={JAXLIB_VERSION}", silent=False)
+            session.install("-e", ".[all,odes,jax]", silent=False)
+    else:
+        session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
diff --git a/pybamm/util.py b/pybamm/util.py
index 4799ee0285..f31715e551 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -21,9 +21,8 @@
 import numpy as np
 import pybamm
 
-# versions of jax and jaxlib compatible with PyBaMM. These are also defined in
-# noxfile.py and in the extras dependencies in pyproject.toml, and therefore must be
-# kept in sync.
+# Versions of jax and jaxlib compatible with PyBaMM. Note: these are also defined in
+# in the extras dependencies in pyproject.toml, and therefore must be kept in sync.
 JAX_VERSION = "0.4"
 JAXLIB_VERSION = "0.4"
 
diff --git a/pyproject.toml b/pyproject.toml
index 5f225cb5f5..912f4576c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -109,8 +109,7 @@ dev = [
 pandas =  [
     "pandas>=0.24",
 ]
-# For the Jax solver. Note: these should be kept in sync with the versions defined
-# in noxfile.py and pybamm/util.py.
+# For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
 jax = [
     "jax>=0.4,<=0.5",
     "jaxlib>=0.4,<=0.5",

From 690b8145e4c683241e92c0ab3275f6e9066369d2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 04:13:16 +0530
Subject: [PATCH 093/615] #3049 Fix up authors name and email in project

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 912f4576c9..352f35725e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,6 +3,7 @@ requires = [
     "setuptools",
     "wheel",
     "casadi>=3.6.0; platform_system!='Windows'",
+    # use CMake bundled from MSVC on Windows
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
@@ -12,7 +13,7 @@ name = "pybamm"
 version = "23.5"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
-authors = [{name = "The PyBaMM Team"}, {email = "pybamm@pybamm.org"}]
+authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 requires-python = ">=3.8, <3.12"
 readme = {file = "README.md", content-type = "text/markdown"}

From 8747dd9af78cccf6e7a21787cb282998a2ac47df Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Mon, 2 Oct 2023 17:30:45 +0530
Subject: [PATCH 094/615] fix: Broken link

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index a6aa6987c2..84d7ec8c3e 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -185,7 +185,7 @@ You may also test multiple notebooks this way. Passing the path to a folder will
 nox -s examples -- docs/source/examples/notebooks/models/
 ```
 
-You may also use an appropriate [glob pattern](https://www.malikbrowne.com/blog/a-beginners-guide-glob-patterns) to run all notebooks matching a particular folder or name pattern.
+You may also use an appropriate [glob pattern](https://developers.tetrascience.com/docs/common-glob-pattern) to run all notebooks matching a particular folder or name pattern.
 
 To edit the structure and how the Jupyter notebooks get rendered in the Sphinx documentation (using `nbsphinx`), install [Pandoc](https://pandoc.org/installing.html) on your system, either using `conda` (through the `conda-forge` channel)
 

From cbec0e946f51f0d342d5e31f2fd5e2b7aa1e7bb8 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Mon, 2 Oct 2023 17:41:17 +0530
Subject: [PATCH 095/615] Update CHANGELOG.md

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 21cfb2ae87..809a721e2f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@
 
 ## Bug fixes
 
+- Fixed a bug where there was an expired link related to glob pattern in the CONTRIBUTING.md file.([#3393](https://github.com/pybamm-team/PyBaMM/pull/3393))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).

From b18d6661129ea433b4473588c5546c6b394fb810 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 17:51:20 +0530
Subject: [PATCH 096/615] Cleanup extras list, resolve conflicts

---
 pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 352f35725e..685656432e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -95,9 +95,9 @@ tqdm = [
 ]
 # Dependencies intended for use by developers
 dev = [
-    # For code style checking
+    # For working with pre-commit hooks
     "pre-commit",
-    # For code style auto-formatting
+    # For code style checks: linting and auto-formatting
     "ruff",
     # For running testing sessions
     "nox",

From 3ebb05ff985bdf503bb8038c7705758372d9c3d9 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Mon, 2 Oct 2023 18:02:26 +0530
Subject: [PATCH 097/615] Update CHANGELOG.md

---
 CHANGELOG.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 809a721e2f..21cfb2ae87 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,7 +9,6 @@
 
 ## Bug fixes
 
-- Fixed a bug where there was an expired link related to glob pattern in the CONTRIBUTING.md file.([#3393](https://github.com/pybamm-team/PyBaMM/pull/3393))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).

From a91c87b9b4e44835dd2046cdc92fcd0106e9d844 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 2 Oct 2023 18:16:46 +0530
Subject: [PATCH 098/615] #3049 add `pipx` for doctests job

---
 .github/workflows/test_on_push.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index caf019d08e..6821016e45 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -284,10 +284,10 @@ jobs:
           cache-dependency-path: setup.py
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Check if the documentation can be built for GNU/Linux with Python 3.11
-        run: nox -s docs
+        run: pipx run nox -s docs
 
   # Runs only on Ubuntu with Python 3.11
   run_example_tests:

From 278bdf4d301aed69bb8a11b7f20079d6215e1695 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Mon, 2 Oct 2023 19:22:24 +0530
Subject: [PATCH 099/615] Update CONTRIBUTING.md

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 84d7ec8c3e..98d48b6f4a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -185,7 +185,7 @@ You may also test multiple notebooks this way. Passing the path to a folder will
 nox -s examples -- docs/source/examples/notebooks/models/
 ```
 
-You may also use an appropriate [glob pattern](https://developers.tetrascience.com/docs/common-glob-pattern) to run all notebooks matching a particular folder or name pattern.
+You may also use an appropriate [glob pattern](https://linux.die.net/man/7/glob) to run all notebooks matching a particular folder or name pattern.
 
 To edit the structure and how the Jupyter notebooks get rendered in the Sphinx documentation (using `nbsphinx`), install [Pandoc](https://pandoc.org/installing.html) on your system, either using `conda` (through the `conda-forge` channel)
 

From a3e3a9132678e22f8ece42650d5ca365ac3e74d4 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 10:16:10 -0400
Subject: [PATCH 100/615] Add smooth max

---
 pybamm/expression_tree/binary_operators.py    | 20 ++++++++++++++-
 .../test_binary_operators.py                  | 25 +++++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..73a68f4b88 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1283,7 +1283,7 @@ def _heaviside(left, right, equal):
 
 def softminus(left, right, k):
     """
-    Softplus approximation to the minimum function. k is the smoothing parameter,
+    Softminus approximation to the minimum function. k is the smoothing parameter,
     set by `pybamm.settings.min_smoothing`. The recommended value is k=10.
     """
     return pybamm.log(pybamm.exp(-k * left) + pybamm.exp(-k * right)) / -k
@@ -1297,6 +1297,24 @@ def softplus(left, right, k):
     return pybamm.log(pybamm.exp(k * left) + pybamm.exp(k * right)) / k
 
 
+def smooth_minus(left, right, k):
+    """
+    Smooth_minus approximation to the minimum function. k is the smoothing parameter,
+    set by `pybamm.settings.min_smoothing`. The recommended value is k=1000.
+    """
+    sigma = (1.0 / k)**2
+    return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2
+
+
+def smooth_plus(left, right, k):
+    """
+    Smooth_plus approximation to the maximum function. k is the smoothing parameter,
+    set by `pybamm.settings.max_smoothing`. The recommended value is k=1000.
+    """
+    sigma = (1.0 / k) ** 2
+    return (pybamm.sqrt((left - right)**2 + sigma) + (left + right)) / 2
+
+
 def sigmoid(left, right, k):
     """
     Sigmoidal approximation to the heaviside function. k is the smoothing parameter,
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 6acd7c41b0..b6fa27d7f7 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -420,6 +420,31 @@ def test_softminus_softplus(self):
         pybamm.settings.min_smoothing = "exact"
         pybamm.settings.max_smoothing = "exact"
 
+    def test_smooth_minus_plus(self):
+        a = pybamm.Scalar(1)
+        b = pybamm.StateVector(slice(0, 1))
+
+        minimum = pybamm.smooth_minus(a, b, 3000)
+        self.assertAlmostEqual(minimum.evaluate(y=np.array([2]))[0, 0], 1)
+        self.assertAlmostEqual(minimum.evaluate(y=np.array([0]))[0, 0], 0)
+
+        maximum = pybamm.smooth_plus(a, b, 3000)
+        self.assertAlmostEqual(maximum.evaluate(y=np.array([2]))[0, 0], 2)
+        self.assertAlmostEqual(maximum.evaluate(y=np.array([0]))[0, 0], 1)
+
+        minimum = pybamm.smooth_minus(a, b, 1)
+        self.assertEqual(
+            str(minimum),
+            "0.5 * (1.0 + y[0:1] - sqrt(1.0 + (1.0 - y[0:1]) ** 2.0))",
+        )
+        maximum = pybamm.smooth_plus(a, b, 1)
+        self.assertEqual(
+            str(maximum),
+            "0.5 * (sqrt(1.0 + (1.0 - y[0:1]) ** 2.0) + 1.0 + y[0:1])",
+        )
+
+
+
     def test_binary_simplifications(self):
         a = pybamm.Scalar(0)
         b = pybamm.Scalar(1)

From eeef261acc5c8a8e87be6223953e63378beb70f4 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 10:27:11 -0400
Subject: [PATCH 101/615] Fix spacing

---
 tests/unit/test_expression_tree/test_binary_operators.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index b6fa27d7f7..9929f895e9 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -443,8 +443,6 @@ def test_smooth_minus_plus(self):
             "0.5 * (sqrt(1.0 + (1.0 - y[0:1]) ** 2.0) + 1.0 + y[0:1])",
         )
 
-
-
     def test_binary_simplifications(self):
         a = pybamm.Scalar(0)
         b = pybamm.Scalar(1)

From cf4ead658c556b3532faf425f91f35a58170b2b0 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Mon, 2 Oct 2023 20:29:27 +0530
Subject: [PATCH 102/615] Update CONTRIBUTING.md

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 98d48b6f4a..4612e83252 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -185,7 +185,7 @@ You may also test multiple notebooks this way. Passing the path to a folder will
 nox -s examples -- docs/source/examples/notebooks/models/
 ```
 
-You may also use an appropriate [glob pattern](https://linux.die.net/man/7/glob) to run all notebooks matching a particular folder or name pattern.
+You may also use an appropriate [glob pattern](https://docs.python.org/3/library/glob.html) to run all notebooks matching a particular folder or name pattern.
 
 To edit the structure and how the Jupyter notebooks get rendered in the Sphinx documentation (using `nbsphinx`), install [Pandoc](https://pandoc.org/installing.html) on your system, either using `conda` (through the `conda-forge` channel)
 

From a4fedaeb85b43159ccec4a9e0c418704a4632fd1 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 11 Aug 2023 16:33:31 +0000
Subject: [PATCH 103/615] Draft a serialisation method Create to_json()
 functions in corresponsing classes Working basic de/serialisation

---
 pybamm/expression_tree/array.py            |  29 +++
 pybamm/expression_tree/binary_operators.py |  96 +++++++++
 pybamm/expression_tree/broadcasts.py       |   5 +
 pybamm/expression_tree/concatenations.py   |  64 ++++++
 pybamm/expression_tree/functions.py        |  35 ++++
 pybamm/expression_tree/input_parameter.py  |  14 ++
 pybamm/expression_tree/interpolant.py      |  19 ++
 pybamm/expression_tree/parameter.py        |  10 +
 pybamm/expression_tree/scalar.py           |   9 +
 pybamm/expression_tree/state_vector.py     |  23 ++
 pybamm/expression_tree/symbol.py           |  14 ++
 pybamm/expression_tree/unary_operators.py  |  87 +++++++-
 pybamm/expression_tree/variable.py         |  19 ++
 pybamm/models/base_model.py                |  44 ++++
 pybamm/serialisation/serialisation.py      | 232 +++++++++++++++++++++
 15 files changed, 699 insertions(+), 1 deletion(-)
 create mode 100644 pybamm/serialisation/serialisation.py

diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index a9141041b3..d0ba8d1296 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -128,6 +128,35 @@ def to_equation(self):
         entries_list = self.entries.tolist()
         return sympy.Array(entries_list)
 
+    def to_json(self):
+        """
+        Method to serialise an Array object into JSON.
+        """
+
+        if isinstance(self.entries, np.ndarray):
+            matrix = self.entries.tolist()
+        elif isinstance(self.entries, csr_matrix):
+            matrix = {
+                "shape": self.entries.shape,
+                "data": self.entries.data.tolist(),
+                "row_indices": self.entries.indices.tolist(),
+                "column_pointers": self.entries.indptr.tolist(),
+            }
+        else:
+            raise TypeError(
+                f"Ah! Dense matrix! {self.entries}"
+            )  # PL: Double check this
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "entries": matrix,
+            # "entries_string": self.entries_string.decode(),
+        }
+
+        return json_dict
+
 
 def linspace(start, stop, num=50, **kwargs):
     """
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..05520a081a 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -68,6 +68,20 @@ def __init__(self, name, left, right):
         self.left = self.children[0]
         self.right = self.children[1]
 
+    @classmethod
+    def _from_json(cls, name, left, right, domains):
+        """Use to instantiate when deserialising; discretisation has
+        already occured so pre-processing of binaries is not necessary."""
+        instance = cls.__new__(cls)
+
+        super(BinaryOperator, instance).__init__(
+            name, children=[left, right], domains=domains
+        )
+        instance.left = instance.children[0]
+        instance.right = instance.children[1]
+
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         # Possibly add brackets for clarity
@@ -155,6 +169,15 @@ def to_equation(self):
             eq2 = child2.to_equation()
             return self._sympy_operator(eq1, eq2)
 
+    def to_json(self):
+        """
+        Method to serialise a BinaryOperator object into JSON.
+        """
+
+        json_dict = {"name": self.name, "id": self.id, "domains": self.domains}
+
+        return json_dict
+
 
 class Power(BinaryOperator):
     """
@@ -165,6 +188,12 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("**", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("**", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply chain rule and power rule
@@ -206,6 +235,12 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("+", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("+", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         return self.left.diff(variable) + self.right.diff(variable)
@@ -229,6 +264,12 @@ def __init__(self, left, right):
 
         super().__init__("-", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("-", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         return self.left.diff(variable) - self.right.diff(variable)
@@ -254,6 +295,12 @@ def __init__(self, left, right):
 
         super().__init__("*", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("*", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply product rule
@@ -290,6 +337,13 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("@", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        # instance = super(MatrixMultiplication, cls)._from_json("@", left, right)
+        instance = super()._from_json("@", left, right, domains)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # We shouldn't need this
@@ -337,6 +391,12 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("/", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("/", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply quotient rule
@@ -381,6 +441,12 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("inner product", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("inner product", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply product rule
@@ -450,6 +516,12 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("==", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("==", left, right, domains)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # Equality should always be multiplied by something else so hopefully don't
@@ -496,6 +568,12 @@ def __init__(self, name, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__(name, left, right)
 
+    @classmethod
+    def _from_json(cls, name, left, right):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json(name, left, right)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # Heaviside should always be multiplied by something else so hopefully don't
@@ -561,6 +639,12 @@ class Modulo(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("%", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("%", left, right, domains)
+        return instance
+
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply chain rule and power rule
@@ -599,6 +683,12 @@ class Minimum(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("minimum", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("minimum", left, right, domains)
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "minimum({!s}, {!s})".format(self.left, self.right)
@@ -635,6 +725,12 @@ class Maximum(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("maximum", left, right)
 
+    @classmethod
+    def _from_json(cls, left, right, domains):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = super()._from_json("maximum", left, right, domains)
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "maximum({!s}, {!s})".format(self.left, self.right)
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 32cf2c002b..45c37a55f0 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -50,6 +50,11 @@ def _diff(self, variable):
         # Differentiate the child and broadcast the result in the same way
         return self._unary_new_copy(self.child.diff(variable))
 
+    def to_json(self):
+        raise NotImplementedError(
+            "pybamm.Broadcast: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+        )
+
 
 class PrimaryBroadcast(Broadcast):
     """
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 2185a0fad6..5e678af95f 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -43,6 +43,16 @@ def __init__(self, *children, name=None, check_domain=True, concat_fun=None):
 
         super().__init__(name, children, domains=domains)
 
+    @classmethod
+    def _from_json(cls, *children, name, domains, concat_fun=None):
+        instance = cls.__new__(cls)
+
+        super(Concatenation, instance).__init__(name, children, domains=domains)
+
+        instance.concatenation_function = concat_fun
+
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         out = self.name + "("
@@ -182,6 +192,18 @@ def __init__(self, *children):
             concat_fun=np.concatenate
         )
 
+    @classmethod
+    def _from_json(cls, children, domains):
+        """See :meth:`pybamm.Concatenation._from_json()`."""
+        instance = super()._from_json(
+            *children,
+            name="numpy_concatenation",
+            domains=domains,
+            concat_fun=np.concatenate
+        )
+
+        return instance
+
     def _concatenation_jac(self, children_jacs):
         """See :meth:`pybamm.Concatenation.concatenation_jac()`."""
         children = self.children
@@ -250,6 +272,22 @@ def __init__(self, children, full_mesh, copy_this=None):
             self._children_slices = copy.copy(copy_this._children_slices)
             self.secondary_dimensions_npts = copy_this.secondary_dimensions_npts
 
+    @classmethod
+    def _from_json(
+        cls, children, size, slices, children_slices, secondary_dimensions_npts, domains
+    ):
+        """See :meth:`pybamm.Concatenation._from_json()`."""
+        instance = super()._from_json(
+            *children, name="domain_concatenation", domains=domains
+        )
+
+        instance._size = size
+        instance._slices = slices
+        instance._children_slices = children_slices
+        instance.secondary_dimensions_npts = secondary_dimensions_npts
+
+        return instance
+
     def _get_auxiliary_domain_repeats(self, auxiliary_domains):
         """Helper method to read the 'auxiliary_domain' meshes."""
         mesh_pts = 1
@@ -315,6 +353,32 @@ def _concatenation_new_copy(self, children):
         )
         return new_symbol
 
+    def to_json(self):
+        """
+        Method to serialise a DomainConcatenation object into JSON.
+        """
+
+        def unpack_defaultDict(slices):
+            slices = dict(slices)
+            for domain, sls in slices.items():
+                sls = [{"start": s.start, "stop": s.stop, "step": s.step} for s in sls]
+                slices[domain] = sls
+            return slices
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "slices": unpack_defaultDict(self._slices),
+            "size": self._size,
+            "children_slices": [
+                unpack_defaultDict(child_slice) for child_slice in self._children_slices
+            ],
+            "secondary_dimensions_npts": self.secondary_dimensions_npts,
+        }
+
+        return json_dict
+
 
 class SparseStack(Concatenation):
     """
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 80c2848ad9..c759cc0b51 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -211,6 +211,28 @@ def to_equation(self):
                 eq_list.append(eq)
             return self._sympy_operator(*eq_list)
 
+    # PL: think I need something here. presumably I can serialise function methods using just their names, then rehydrate them at the point they're read back in?
+    def to_json(self):
+        """
+        Method to serialise a Function object into JSON.
+        """
+
+        try:
+            func_name = self.function.__name__
+        except:
+            raise Exception
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "function": func_name,  # PL: actually put name here
+            "derivative": self.derivative,
+            "differentiated_function": self.differentiated_function,  # PL: same here (although is this defined? or is it just written out...)
+        }
+
+        return json_dict
+
 
 def simplified_function(func_class, child):
     """
@@ -254,6 +276,19 @@ def _sympy_operator(self, child):
         sympy_function = getattr(sympy, class_name)
         return sympy_function(child)
 
+    def to_json(self):
+        """
+        Method to serialise a SpecificFunction object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "function": self.function.__name__,
+        }
+
+        return json_dict
+
 
 class Arcsinh(SpecificFunction):
     """Arcsinh function."""
diff --git a/pybamm/expression_tree/input_parameter.py b/pybamm/expression_tree/input_parameter.py
index 62c08bf0fd..1f772bc325 100644
--- a/pybamm/expression_tree/input_parameter.py
+++ b/pybamm/expression_tree/input_parameter.py
@@ -101,3 +101,17 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
                     self._expected_size
                 )
             )
+
+    def to_json(self):
+        """
+        Method to serialise an InputParameter object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domain": self.domain,
+            "expected_size": self._expected_size,
+        }
+
+        return json_dict
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index cd0df4d077..9555dcaa34 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -290,3 +290,22 @@ def _function_evaluate(self, evaluated_children):
 
         else:  # pragma: no cover
             raise ValueError("Invalid dimension: {0}".format(self.dimension))
+
+    # PL: think I need something here. presumably I can serialise function methods using just their names, then rehydrate them at the point they're read back in?
+    def to_json(self):
+        """
+        Method to serialise an Interpolant object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            # "domains": self.domains,
+            "x": self.x.tolist(),
+            "y": self.y.tolist(),
+            "interpolator": self.interpolator,
+            "extrapolate": self.extrapolate,
+            # "entries_string": self.entries_string,
+        }
+
+        return json_dict
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index 10addae464..d8aa146fd9 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -49,6 +49,11 @@ def to_equation(self):
         else:
             return sympy.Symbol(self.name)
 
+    def to_json(self):
+        raise NotImplementedError(
+            "pybamm.Parameter: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+        )
+
 
 class FunctionParameter(pybamm.Symbol):
     """
@@ -221,3 +226,8 @@ def to_equation(self):
             return sympy.Symbol(self.print_name)
         else:
             return sympy.Symbol(self.name)
+
+    def to_json(self):
+        raise NotImplementedError(
+            "pybamm.FunctionParameter: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+        )
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index 3149bf7bee..ae2b63560d 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -74,3 +74,12 @@ def to_equation(self):
             return sympy.Symbol(self.print_name)
         else:
             return self.value
+
+    def to_json(self):
+        """
+        Method to serialise a Symbol object into JSON.
+        """
+
+        json_dict = {"name": self.name, "id": self.id, "value": self.value}
+
+        return json_dict
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 6ef8bee904..2c101e0a24 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -194,6 +194,29 @@ def _evaluate_for_shape(self):
         """
         return np.nan * np.ones((self.size, 1))
 
+    def to_json(self):
+        """
+        Method to serialise a StateVector object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "y_slice": [
+                {
+                    "start": y.start,
+                    "stop": y.stop,
+                    "step": y.step,
+                }  # are there ever more than 1?
+                for y in self.y_slices
+            ],
+            "evaluation_array": list(self.evaluation_array),
+            # "children": self.children,  # might not need this, the anytree exporter handles children I think
+        }
+
+        return json_dict
+
 
 class StateVector(StateVectorBase):
     """
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 5d28884ed5..037205fda0 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -985,3 +985,17 @@ def print_name(self, name):
 
     def to_equation(self):
         return sympy.Symbol(str(self.name))
+
+    def to_json(self):
+        """
+        Method to serialise a Symbol object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            # "children": self.children,  # the encoder deals with the children itself.
+        }
+
+        return json_dict
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 7f9c45775c..efd0914464 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -316,6 +316,20 @@ def _evaluates_on_edges(self, dimension):
         """See :meth:`pybamm.Symbol._evaluates_on_edges()`."""
         return False
 
+    def to_json(self):
+        """
+        Method to serialise an Index object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "check_size": False,
+        }
+
+        return json_dict
+
 
 class SpatialOperator(UnaryOperator):
     """
@@ -581,6 +595,20 @@ def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
         return sympy.Integral(child, sympy.Symbol("xn"))
 
+    def to_json(self):
+        """
+        Method to serialise an Integral object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "integration_variable": self.integration_variable,  # PL: This may be a (list of) symbols that need cycling through in a similar mannar to children
+        }
+
+        return json_dict
+
 
 class BaseIndefiniteIntegral(Integral):
     """
@@ -685,7 +713,8 @@ class DefiniteIntegralVector(SpatialOperator):
     Parameters
     ----------
     variable : :class:`pybamm.Symbol`
-        The variable whose basis will be integrated over the entire domain
+        The variable whose basis will be integrated over the entire domain (will
+        become self.children[0])
     vector_type : str, optional
         Whether to return a row or column vector (default is row)
     """
@@ -714,6 +743,20 @@ def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         return pybamm.evaluate_for_shape_using_domain(self.domains)
 
+    def to_json(self):
+        """
+        Method to serialise a DefiniteIntegralVector object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "vector_type": self.vector_type,
+        }
+
+        return json_dict
+
 
 class BoundaryIntegral(SpatialOperator):
     """
@@ -771,6 +814,20 @@ def _evaluates_on_edges(self, dimension):
         """See :meth:`pybamm.Symbol._evaluates_on_edges()`."""
         return False
 
+    def to_json(self):
+        """
+        Method to serialise a BoundaryIntegral object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,  # PL: Not sure if this exists, but might inherit from symbol
+            "region": self.region,
+        }
+
+        return json_dict
+
 
 class DeltaFunction(SpatialOperator):
     """
@@ -815,6 +872,20 @@ def evaluate_for_shape(self):
 
         return np.outer(child_eval, vec).reshape(-1, 1)
 
+    def to_json(self):
+        """
+        Method to serialise a DeltaFunction object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "side": self.side,
+        }
+
+        return json_dict
+
 
 class BoundaryOperator(SpatialOperator):
     """
@@ -867,6 +938,20 @@ def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         return pybamm.evaluate_for_shape_using_domain(self.domains)
 
+    def to_json(self):
+        """
+        Method to serialise a BoundaryOperator object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "side": self.side,
+        }
+
+        return json_dict
+
 
 class BoundaryValue(BoundaryOperator):
     """
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index f9f7d94efc..1349901a9a 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -129,6 +129,25 @@ def to_equation(self):
         else:
             return self.name
 
+    def to_json(
+        self,
+    ):  # PL: This may never be touched if once discretised, it's turned into a statevector/statevectordot type.
+        """
+        Method to serialise a BoundaryOperator object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "domains": self.domains,
+            "bounds": self.bounds,  # tuple
+            "print_name": self.print_name,  # string
+            "scale": self.scale,  # float/symbol
+            "reference": self.reference,  # float/symbol
+        }
+
+        return json_dict
+
 
 class Variable(VariableBase):
     """
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..7e1f9b060f 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -123,6 +123,50 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
+    @classmethod
+    def deserialise(cls, properties: dict):
+        """
+        Create a model instance from a serialised object.
+        """
+        instance = cls.__new__(cls)
+
+        instance.name = properties["name"]
+        instance._options = {}
+        instance._built = False
+        instance._built_fundamental = False
+
+        # Initialise model with stored variables
+        instance.submodels = {}
+        instance._rhs = {}
+        instance._algebraic = {}
+        instance._initial_conditions = {}
+        instance._boundary_conditions = {}
+        instance._variables = pybamm.FuzzyDict({})
+        instance._events = []
+        instance._concatenated_rhs = properties["concatenated_rhs"]
+        instance._concatenated_algebraic = properties["concatenated_algebraic"]
+        instance._concatenated_initial_conditions = properties[
+            "concatenated_initial_conditions"
+        ]
+        instance._mass_matrix = None
+        instance._mass_matrix_inv = None
+        instance._jacobian = None
+        instance._jacobian_algebraic = None
+        instance._parameters = None
+        instance._input_parameters = None
+        instance._parameter_info = None
+        instance._variables_casadi = {}
+
+        # Default behaviour is to use the jacobian
+        instance.use_jacobian = True
+        instance.convert_to_format = "casadi"
+
+        # Model has already been discretised
+        instance.is_discretised = True
+        instance.y_slices = None
+
+        return instance
+
     @property
     def name(self):
         return self._name
diff --git a/pybamm/serialisation/serialisation.py b/pybamm/serialisation/serialisation.py
new file mode 100644
index 0000000000..7075c7839d
--- /dev/null
+++ b/pybamm/serialisation/serialisation.py
@@ -0,0 +1,232 @@
+import pybamm
+from anytree.exporter import JsonExporter
+from anytree.importer import JsonImporter
+import json
+import numpy as np
+import pprint
+import importlib
+from scipy.sparse import csr_matrix, csr_array
+from collections import defaultdict
+
+
+class SymbolEncoder(json.JSONEncoder):
+    def default(self, node):
+        node_dict = {"py/object": str(type(node))[8:-2], "py/id": id(node)}
+        if isinstance(node, pybamm.Symbol):
+            node_dict.update(node.to_json())  # this doesn't include children
+            node_dict["children"] = []
+            for c in node.children:
+                node_dict["children"].append(self.default(c))
+
+            return node_dict
+
+        json_obj = json.JSONEncoder.default(self, node)
+        node_dict["json"] = json_obj
+        return node_dict
+
+
+## DECODE
+
+
+class _Empty:
+    pass
+
+
+def reconstruct_symbol(dct):
+    def recreate_slice(d):
+        return slice(d["start"], d["stop"], d["step"])
+
+    # decode non-symbol objects here
+    # now for pybamm
+    foo = _Empty()
+    parts = dct["py/object"].split(".")
+    try:
+        module = importlib.import_module(".".join(parts[:-1]))
+    except Exception as ex:
+        print(ex)
+
+    class_ = getattr(module, parts[-1])
+    foo.__class__ = class_
+
+    # PL: Need to finish off the various options here.
+    if isinstance(foo, pybamm.Scalar):
+        foo.__init__(dct["value"], name=dct["name"])
+
+    elif isinstance(foo, pybamm.BinaryOperator):
+        foo = foo._from_json(dct["children"][0], dct["children"][1], dct["domains"])
+
+    elif isinstance(foo, pybamm.Array):
+        if isinstance(dct["entries"], dict):
+            matrix = csr_array(
+                (
+                    dct["entries"]["data"],
+                    dct["entries"]["row_indices"],
+                    dct["entries"]["column_pointers"],
+                ),
+                shape=dct["entries"]["shape"],
+            )
+        else:
+            matrix = dct["entries"]
+        foo.__init__(
+            matrix,
+            name=dct["name"],
+            domains=dct["domains"],
+            # entries_string=dct["entries_string"],
+        )
+
+    elif isinstance(foo, pybamm.StateVectorBase):
+        y_slices = [recreate_slice(d) for d in dct["y_slice"]]
+        foo.__init__(
+            *y_slices,
+            name=dct["name"],
+            domains=dct["domains"],
+            evaluation_array=dct["evaluation_array"],
+        )
+
+    elif isinstance(foo, pybamm.IndependentVariable):
+        if isinstance(foo, pybamm.Time):
+            foo.__init__()
+        else:
+            foo.__init__(dct["name"], domains=dct["domains"])
+
+    elif isinstance(foo, pybamm.InputParameter):
+        foo.__init__(
+            dct["name"], domain=dct["domain"], expected_size=dct["expected_size"]
+        )
+
+    elif isinstance(foo, pybamm.SpecificFunction):
+        foo.__init__(dct["children"][0])
+
+    elif isinstance(foo, pybamm.Function):
+        func = getattr(
+            np, dct["function"]
+        )  # don't think this will work for self-defined functions
+        foo.__init__(
+            func,
+            name=dct["name"],
+            derivative=dct["derivative"],
+            differentiated_function=dct["differentiated_function"],
+        )
+
+    elif isinstance(foo, pybamm.DomainConcatenation):
+
+        def repack_defaultDict(slices):
+            slices = defaultdict(list, slices)
+            for domain, sls in slices.items():
+                sls = [recreate_slice(s) for s in sls]
+                slices[domain] = sls
+            return slices
+
+        main_slice = repack_defaultDict(dct["slices"])
+        child_slice = [repack_defaultDict(s) for s in dct["children_slices"]]
+
+        foo = foo._from_json(
+            dct["children"],
+            dct["size"],
+            main_slice,
+            child_slice,
+            dct["secondary_dimensions_npts"],
+            dct["domains"],
+        )
+
+    elif isinstance(foo, pybamm.NumpyConcatenation):
+        foo = foo._from_json(
+            dct["children"],
+            dct["domains"],
+        )
+    # interpolant
+    # check various Unary operators, they differ
+    # VariableBase
+    # ...
+    elif isinstance(foo, pybamm.Symbol):
+        foo.__init__(dct["name"], children=dct["children"], domains=dct["domains"])
+
+    return foo
+
+
+def reconstruct_epression_tree(node):
+    if "children" in node:
+        for i, c in enumerate(node["children"]):
+            child_obj = reconstruct_epression_tree(c)
+            node["children"][i] = child_obj
+
+    obj = reconstruct_symbol(node)
+
+    return obj
+
+
+## Run tests
+model = pybamm.lithium_ion.DFN()
+geometry = model.default_geometry
+param = model.default_parameter_values
+param.process_model(model)
+param.process_geometry(geometry)
+mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
+disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+disc.process_model(model)
+
+# # tested all individual trees in rhs
+# # tree1 = list(model.rhs.items())[2][1]
+# tree1 = (
+#     model.y_slices
+# )  # Worked: concatenated_rhs, concat_initial_conditions, concatenated_algebraic.
+# # Do we need the 'unconcatenated' rhs etc? if not, this gets much easier.
+# tree1.visualise("tree1.png")
+
+# json_tree1 = SymbolEncoder().default(tree1)
+# with open("test_tree1.json", "w") as f:
+#     json.dump(json_tree1, f)
+
+# # pprint.pprint(json_tree1, sort_dicts=False)
+
+# with open("test_tree1.json", "r") as f:
+#     data = json.load(f)
+
+# tree1_recon = reconstruct_epression_tree(data)
+
+# print(tree1 == tree1_recon)
+
+
+# tree1_recon.visualise("recon1.png")
+
+solver_initial = model.default_solver
+solution_initial = solver_initial.solve(model, [0, 3600])
+
+# pybamm.plot(solution_initial)
+# solution_initial.plot()
+
+model_json = {
+    "py/object": str(type(model))[8:-2],
+    "py/id": id(model),
+    "name": model.name,
+    "concatenated_rhs": SymbolEncoder().default(model._concatenated_rhs),
+    "concatenated_algebraic": SymbolEncoder().default(model._concatenated_algebraic),
+    "concatenated_initial_conditions": SymbolEncoder().default(
+        model._concatenated_initial_conditions
+    ),
+}
+
+# file_name = f"test_{model.name}_stored"
+with open("test_full_model.json", "w") as f:
+    json.dump(model_json, f)
+
+with open("test_full_model.json", "r") as f:
+    model_data = json.load(f)
+
+recon_model_dict = {
+    "name": model_data["name"],
+    "concatenated_rhs": reconstruct_epression_tree(model_data["concatenated_rhs"]),
+    "concatenated_algebraic": reconstruct_epression_tree(
+        model_data["concatenated_algebraic"]
+    ),
+    "concatenated_initial_conditions": reconstruct_epression_tree(
+        model_data["concatenated_initial_conditions"]
+    ),
+}
+
+new_model = pybamm.lithium_ion.DFN.deserialise(recon_model_dict)
+
+new_solver = new_model.default_solver
+new_solution = new_solver.solve(model, [0, 3600])
+
+# THIS WORKS!!!

From 70b765d6855fa25c9ba6358c696a7fdaa278d5ce Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 18 Aug 2023 16:05:20 +0000
Subject: [PATCH 104/615] Move deserialisation functions to Symbol classes
 Creates _from_json() functionality

---
 pybamm/expression_tree/array.py               |  26 +++-
 pybamm/expression_tree/binary_operators.py    |  57 +++++----
 pybamm/expression_tree/concatenations.py      |  32 +++--
 pybamm/expression_tree/functions.py           | 119 +++++++++++++++---
 .../expression_tree/independent_variable.py   |  16 +++
 pybamm/expression_tree/input_parameter.py     |  12 ++
 pybamm/expression_tree/interpolant.py         |  18 +--
 pybamm/expression_tree/scalar.py              |   8 ++
 pybamm/expression_tree/state_vector.py        |  15 +++
 pybamm/expression_tree/symbol.py              |  20 +++
 pybamm/expression_tree/unary_operators.py     |  51 ++------
 pybamm/expression_tree/variable.py            |  18 +--
 pybamm/models/base_model.py                   |  26 +---
 pybamm/serialisation/serialisation.py         |  72 +----------
 14 files changed, 266 insertions(+), 224 deletions(-)

diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index d0ba8d1296..c2fcbc4a11 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -3,7 +3,7 @@
 #
 import numpy as np
 import sympy
-from scipy.sparse import csr_matrix, issparse
+from scipy.sparse import csr_matrix, issparse, csr_array
 
 import pybamm
 
@@ -57,6 +57,30 @@ def __init__(
             name, domain=domain, auxiliary_domains=auxiliary_domains, domains=domains
         )
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        if isinstance(snippet["entries"], dict):
+            matrix = csr_array(
+                (
+                    snippet["entries"]["data"],
+                    snippet["entries"]["row_indices"],
+                    snippet["entries"]["column_pointers"],
+                ),
+                shape=snippet["entries"]["shape"],
+            )
+        else:
+            matrix = snippet["entries"]
+
+        instance.__init__(
+            matrix,
+            name=snippet["name"],
+            domains=snippet["domains"],
+        )
+
+        return instance
+
     @property
     def entries(self):
         return self._entries
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 05520a081a..30a81ee416 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -69,13 +69,16 @@ def __init__(self, name, left, right):
         self.right = self.children[1]
 
     @classmethod
-    def _from_json(cls, name, left, right, domains):
+    def _from_json(cls, name, snippet: dict):
         """Use to instantiate when deserialising; discretisation has
         already occured so pre-processing of binaries is not necessary."""
+
         instance = cls.__new__(cls)
 
         super(BinaryOperator, instance).__init__(
-            name, children=[left, right], domains=domains
+            name,
+            children=[snippet["children"][0], snippet["children"][1]],
+            domains=snippet["domains"],
         )
         instance.left = instance.children[0]
         instance.right = instance.children[1]
@@ -189,9 +192,9 @@ def __init__(self, left, right):
         super().__init__("**", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("**", left, right, domains)
+        instance = super()._from_json("**", snippet)
         return instance
 
     def _diff(self, variable):
@@ -236,9 +239,9 @@ def __init__(self, left, right):
         super().__init__("+", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("+", left, right, domains)
+        instance = super()._from_json("+", snippet)
         return instance
 
     def _diff(self, variable):
@@ -265,9 +268,9 @@ def __init__(self, left, right):
         super().__init__("-", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("-", left, right, domains)
+        instance = super()._from_json("-", snippet)
         return instance
 
     def _diff(self, variable):
@@ -296,9 +299,9 @@ def __init__(self, left, right):
         super().__init__("*", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("*", left, right, domains)
+        instance = super()._from_json("*", snippet)
         return instance
 
     def _diff(self, variable):
@@ -338,10 +341,10 @@ def __init__(self, left, right):
         super().__init__("@", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
         # instance = super(MatrixMultiplication, cls)._from_json("@", left, right)
-        instance = super()._from_json("@", left, right, domains)
+        instance = super()._from_json("@", snippet)
         return instance
 
     def diff(self, variable):
@@ -392,9 +395,9 @@ def __init__(self, left, right):
         super().__init__("/", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("/", left, right, domains)
+        instance = super()._from_json("/", snippet)
         return instance
 
     def _diff(self, variable):
@@ -442,9 +445,9 @@ def __init__(self, left, right):
         super().__init__("inner product", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("inner product", left, right, domains)
+        instance = super()._from_json("inner product", snippet)
         return instance
 
     def _diff(self, variable):
@@ -517,9 +520,9 @@ def __init__(self, left, right):
         super().__init__("==", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("==", left, right, domains)
+        instance = super()._from_json("==", snippet)
         return instance
 
     def diff(self, variable):
@@ -569,9 +572,11 @@ def __init__(self, name, left, right):
         super().__init__(name, left, right)
 
     @classmethod
-    def _from_json(cls, name, left, right):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json(name, left, right)
+        instance = super()._from_json(
+            snippet["name"], snippet["children"][0], snippet["children"][1]
+        )
         return instance
 
     def diff(self, variable):
@@ -640,9 +645,9 @@ def __init__(self, left, right):
         super().__init__("%", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("%", left, right, domains)
+        instance = super()._from_json("%", snippet)
         return instance
 
     def _diff(self, variable):
@@ -684,9 +689,9 @@ def __init__(self, left, right):
         super().__init__("minimum", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("minimum", left, right, domains)
+        instance = super()._from_json("minimum", snippet)
         return instance
 
     def __str__(self):
@@ -726,9 +731,9 @@ def __init__(self, left, right):
         super().__init__("maximum", left, right)
 
     @classmethod
-    def _from_json(cls, left, right, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("maximum", left, right, domains)
+        instance = super()._from_json("maximum", snippet)
         return instance
 
     def __str__(self):
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 5e678af95f..af3db72846 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -45,6 +45,7 @@ def __init__(self, *children, name=None, check_domain=True, concat_fun=None):
 
     @classmethod
     def _from_json(cls, *children, name, domains, concat_fun=None):
+        # PL: update this one - I guess we still want it to take 'snippet' rather than the list? to be the same as the others?
         instance = cls.__new__(cls)
 
         super(Concatenation, instance).__init__(name, children, domains=domains)
@@ -193,12 +194,12 @@ def __init__(self, *children):
         )
 
     @classmethod
-    def _from_json(cls, children, domains):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.Concatenation._from_json()`."""
         instance = super()._from_json(
-            *children,
+            *snippet["children"],
             name="numpy_concatenation",
-            domains=domains,
+            domains=snippet["domains"],
             concat_fun=np.concatenate
         )
 
@@ -273,18 +274,27 @@ def __init__(self, children, full_mesh, copy_this=None):
             self.secondary_dimensions_npts = copy_this.secondary_dimensions_npts
 
     @classmethod
-    def _from_json(
-        cls, children, size, slices, children_slices, secondary_dimensions_npts, domains
-    ):
+    def _from_json(cls, snippet: dict):
         """See :meth:`pybamm.Concatenation._from_json()`."""
         instance = super()._from_json(
-            *children, name="domain_concatenation", domains=domains
+            *snippet["children"],
+            name="domain_concatenation",
+            domains=snippet["domains"]
         )
 
-        instance._size = size
-        instance._slices = slices
-        instance._children_slices = children_slices
-        instance.secondary_dimensions_npts = secondary_dimensions_npts
+        def repack_defaultDict(slices):
+            slices = defaultdict(list, slices)
+            for domain, sls in slices.items():
+                sls = [slice(s["start"], s["stop"], s["step"]) for s in sls]
+                slices[domain] = sls
+            return slices
+
+        instance._size = snippet["size"]
+        instance._slices = repack_defaultDict(snippet["slices"])
+        instance._children_slices = [
+            repack_defaultDict(s) for s in snippet["children_slices"]
+        ]
+        instance.secondary_dimensions_npts = snippet["secondary_dimensions_npts"]
 
         return instance
 
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index c759cc0b51..17732c7ba4 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -7,6 +7,7 @@
 import numpy as np
 import sympy
 from scipy import special
+from typing import Callable
 
 import pybamm
 
@@ -211,27 +212,8 @@ def to_equation(self):
                 eq_list.append(eq)
             return self._sympy_operator(*eq_list)
 
-    # PL: think I need something here. presumably I can serialise function methods using just their names, then rehydrate them at the point they're read back in?
     def to_json(self):
-        """
-        Method to serialise a Function object into JSON.
-        """
-
-        try:
-            func_name = self.function.__name__
-        except:
-            raise Exception
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "function": func_name,  # PL: actually put name here
-            "derivative": self.derivative,
-            "differentiated_function": self.differentiated_function,  # PL: same here (although is this defined? or is it just written out...)
-        }
-
-        return json_dict
+        raise NotImplementedError()
 
 
 def simplified_function(func_class, child):
@@ -266,6 +248,25 @@ class SpecificFunction(Function):
     def __init__(self, function, child):
         super().__init__(function, child)
 
+    @classmethod
+    def _from_json(cls, function: Callable, snippet: dict):
+        """
+        Reconstructs a SpecificFunction instance during deserialisation of a JSON file.
+
+        Parameters
+        ----------
+        function : method
+            Function to be applied to child
+        snippet: dict
+            Contains the child to apply the function to
+        """
+
+        instance = cls.__new__(cls)
+
+        super(SpecificFunction, instance).__init__(function, snippet["children"][0])
+
+        return instance
+
     def _function_new_copy(self, children):
         """See :meth:`pybamm.Function._function_new_copy()`"""
         return pybamm.simplify_if_constant(self.__class__(*children))
@@ -296,6 +297,12 @@ class Arcsinh(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.arcsinh, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.arcsinh, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Symbol._function_diff()`."""
         return 1 / sqrt(children[0] ** 2 + 1)
@@ -316,6 +323,12 @@ class Arctan(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.arctan, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.arctan, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return 1 / (children[0] ** 2 + 1)
@@ -336,6 +349,12 @@ class Cos(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.cos, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.cos, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Symbol._function_diff()`."""
         return -sin(children[0])
@@ -352,6 +371,12 @@ class Cosh(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.cosh, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.cosh, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return sinh(children[0])
@@ -368,6 +393,12 @@ class Erf(SpecificFunction):
     def __init__(self, child):
         super().__init__(special.erf, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(special.erf, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return 2 / np.sqrt(np.pi) * exp(-children[0] ** 2)
@@ -389,6 +420,12 @@ class Exp(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.exp, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.exp, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return exp(children[0])
@@ -405,6 +442,12 @@ class Log(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.log, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.log, snippet)
+        return instance
+
     def _function_evaluate(self, evaluated_children):
         # don't raise RuntimeWarning for NaNs
         with np.errstate(invalid="ignore"):
@@ -435,6 +478,12 @@ class Max(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.max, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.max, snippet)
+        return instance
+
     def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         # Max will always return a scalar
@@ -455,6 +504,12 @@ class Min(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.min, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.min, snippet)
+        return instance
+
     def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         # Min will always return a scalar
@@ -480,6 +535,12 @@ class Sin(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.sin, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.sin, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return cos(children[0])
@@ -496,6 +557,12 @@ class Sinh(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.sinh, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.sinh, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return cosh(children[0])
@@ -512,6 +579,12 @@ class Sqrt(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.sqrt, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.sqrt, snippet)
+        return instance
+
     def _function_evaluate(self, evaluated_children):
         # don't raise RuntimeWarning for NaNs
         with np.errstate(invalid="ignore"):
@@ -533,6 +606,12 @@ class Tanh(SpecificFunction):
     def __init__(self, child):
         super().__init__(np.tanh, child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.SpecificFunction._from_json()`."""
+        instance = super()._from_json(np.tanh, snippet)
+        return instance
+
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
         return sech(children[0]) ** 2
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index efeb73f8bc..665bfdb344 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -34,6 +34,14 @@ def __init__(self, name, domain=None, auxiliary_domains=None, domains=None):
             name, domain=domain, auxiliary_domains=auxiliary_domains, domains=domains
         )
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        instance.__init__(snippet["name"], domains=snippet["domains"])
+
+        return instance
+
     def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         return pybamm.evaluate_for_shape_using_domain(self.domains)
@@ -58,6 +66,14 @@ class Time(IndependentVariable):
     def __init__(self):
         super().__init__("time")
 
+    @classmethod
+    def _to_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        instance.__init__("time")
+
+        return instance
+
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
         return Time()
diff --git a/pybamm/expression_tree/input_parameter.py b/pybamm/expression_tree/input_parameter.py
index 1f772bc325..e66a4c8cdc 100644
--- a/pybamm/expression_tree/input_parameter.py
+++ b/pybamm/expression_tree/input_parameter.py
@@ -35,6 +35,18 @@ def __init__(self, name, domain=None, expected_size=None):
         self._expected_size = expected_size
         super().__init__(name, domain=domain)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        instance.__init__(
+            snippet["name"],
+            domain=snippet["domain"],
+            expected_size=snippet["expected_size"],
+        )
+
+        return instance
+
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
         new_input_parameter = InputParameter(
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 9555dcaa34..16bbe88d7e 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -291,21 +291,5 @@ def _function_evaluate(self, evaluated_children):
         else:  # pragma: no cover
             raise ValueError("Invalid dimension: {0}".format(self.dimension))
 
-    # PL: think I need something here. presumably I can serialise function methods using just their names, then rehydrate them at the point they're read back in?
     def to_json(self):
-        """
-        Method to serialise an Interpolant object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            # "domains": self.domains,
-            "x": self.x.tolist(),
-            "y": self.y.tolist(),
-            "interpolator": self.interpolator,
-            "extrapolate": self.extrapolate,
-            # "entries_string": self.entries_string,
-        }
-
-        return json_dict
+        raise NotImplementedError
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index ae2b63560d..9f7d1aa368 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -29,6 +29,14 @@ def __init__(self, value, name=None):
 
         super().__init__(name)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        instance.__init__(snippet["value"], name=snippet["name"])
+
+        return instance
+
     def __str__(self):
         return str(self.value)
 
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 2c101e0a24..9a414dc049 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -73,6 +73,21 @@ def __init__(
             domains=domains,
         )
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        y_slices = [slice(s["start"], s["stop"], s["step"]) for s in snippet["y_slice"]]
+
+        instance.__init__(
+            *y_slices,
+            name=snippet["name"],
+            domains=snippet["domains"],
+            evaluation_array=snippet["evaluation_array"],
+        )
+
+        return instance
+
     @property
     def y_slices(self):
         return self._y_slices
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 037205fda0..b0747090cd 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -234,6 +234,26 @@ def __init__(
             ):
                 self.test_shape()
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """
+        Reconstructs a Symbol instance during deserialisation of a JSON file.
+
+        Parameters
+        ----------
+        snippet: dict
+            Contains the information needed to reconstruct a specific instance.
+            At minimum, should contain "name", "children" and "domains".
+        """
+
+        instance = cls.__new__(cls)
+
+        instance.__init__(
+            snippet["name"], children=snippet["children"], domains=snippet["domains"]
+        )
+
+        return instance
+
     @property
     def children(self):
         """
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index efd0914464..c2fd6c2232 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -353,6 +353,15 @@ class with a :class:`Matrix`
     def __init__(self, name, child, domains=None):
         super().__init__(name, child, domains)
 
+    def diff(self, variable):
+        """See :meth:`pybamm.Symbol.diff()`."""
+        # We shouldn't need this
+        raise NotImplementedError
+
+    def to_json(self):
+        # Will not be present in a discretised model
+        raise NotImplementedError
+
 
 class Gradient(SpatialOperator):
     """
@@ -814,20 +823,6 @@ def _evaluates_on_edges(self, dimension):
         """See :meth:`pybamm.Symbol._evaluates_on_edges()`."""
         return False
 
-    def to_json(self):
-        """
-        Method to serialise a BoundaryIntegral object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,  # PL: Not sure if this exists, but might inherit from symbol
-            "region": self.region,
-        }
-
-        return json_dict
-
 
 class DeltaFunction(SpatialOperator):
     """
@@ -872,20 +867,6 @@ def evaluate_for_shape(self):
 
         return np.outer(child_eval, vec).reshape(-1, 1)
 
-    def to_json(self):
-        """
-        Method to serialise a DeltaFunction object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "side": self.side,
-        }
-
-        return json_dict
-
 
 class BoundaryOperator(SpatialOperator):
     """
@@ -938,20 +919,6 @@ def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         return pybamm.evaluate_for_shape_using_domain(self.domains)
 
-    def to_json(self):
-        """
-        Method to serialise a BoundaryOperator object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "side": self.side,
-        }
-
-        return json_dict
-
 
 class BoundaryValue(BoundaryOperator):
     """
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index 1349901a9a..8aa2b1d707 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -131,22 +131,8 @@ def to_equation(self):
 
     def to_json(
         self,
-    ):  # PL: This may never be touched if once discretised, it's turned into a statevector/statevectordot type.
-        """
-        Method to serialise a BoundaryOperator object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "bounds": self.bounds,  # tuple
-            "print_name": self.print_name,  # string
-            "scale": self.scale,  # float/symbol
-            "reference": self.reference,  # float/symbol
-        }
-
-        return json_dict
+    ):
+        raise NotImplementedError
 
 
 class Variable(VariableBase):
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 7e1f9b060f..4def187f41 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -123,6 +123,7 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
+    # PL: Next up, how to pass in the non-standard variables, if necessary.
     @classmethod
     def deserialise(cls, properties: dict):
         """
@@ -130,40 +131,17 @@ def deserialise(cls, properties: dict):
         """
         instance = cls.__new__(cls)
 
-        instance.name = properties["name"]
-        instance._options = {}
-        instance._built = False
-        instance._built_fundamental = False
+        instance.__init__(name=properties["name"])
 
         # Initialise model with stored variables
-        instance.submodels = {}
-        instance._rhs = {}
-        instance._algebraic = {}
-        instance._initial_conditions = {}
-        instance._boundary_conditions = {}
-        instance._variables = pybamm.FuzzyDict({})
-        instance._events = []
         instance._concatenated_rhs = properties["concatenated_rhs"]
         instance._concatenated_algebraic = properties["concatenated_algebraic"]
         instance._concatenated_initial_conditions = properties[
             "concatenated_initial_conditions"
         ]
-        instance._mass_matrix = None
-        instance._mass_matrix_inv = None
-        instance._jacobian = None
-        instance._jacobian_algebraic = None
-        instance._parameters = None
-        instance._input_parameters = None
-        instance._parameter_info = None
-        instance._variables_casadi = {}
-
-        # Default behaviour is to use the jacobian
-        instance.use_jacobian = True
-        instance.convert_to_format = "casadi"
 
         # Model has already been discretised
         instance.is_discretised = True
-        instance.y_slices = None
 
         return instance
 
diff --git a/pybamm/serialisation/serialisation.py b/pybamm/serialisation/serialisation.py
index 7075c7839d..606fd82688 100644
--- a/pybamm/serialisation/serialisation.py
+++ b/pybamm/serialisation/serialisation.py
@@ -47,68 +47,9 @@ def recreate_slice(d):
 
     class_ = getattr(module, parts[-1])
     foo.__class__ = class_
+    # foo = foo._from_json(dct) -> PL: This is what we want eventually
 
-    # PL: Need to finish off the various options here.
-    if isinstance(foo, pybamm.Scalar):
-        foo.__init__(dct["value"], name=dct["name"])
-
-    elif isinstance(foo, pybamm.BinaryOperator):
-        foo = foo._from_json(dct["children"][0], dct["children"][1], dct["domains"])
-
-    elif isinstance(foo, pybamm.Array):
-        if isinstance(dct["entries"], dict):
-            matrix = csr_array(
-                (
-                    dct["entries"]["data"],
-                    dct["entries"]["row_indices"],
-                    dct["entries"]["column_pointers"],
-                ),
-                shape=dct["entries"]["shape"],
-            )
-        else:
-            matrix = dct["entries"]
-        foo.__init__(
-            matrix,
-            name=dct["name"],
-            domains=dct["domains"],
-            # entries_string=dct["entries_string"],
-        )
-
-    elif isinstance(foo, pybamm.StateVectorBase):
-        y_slices = [recreate_slice(d) for d in dct["y_slice"]]
-        foo.__init__(
-            *y_slices,
-            name=dct["name"],
-            domains=dct["domains"],
-            evaluation_array=dct["evaluation_array"],
-        )
-
-    elif isinstance(foo, pybamm.IndependentVariable):
-        if isinstance(foo, pybamm.Time):
-            foo.__init__()
-        else:
-            foo.__init__(dct["name"], domains=dct["domains"])
-
-    elif isinstance(foo, pybamm.InputParameter):
-        foo.__init__(
-            dct["name"], domain=dct["domain"], expected_size=dct["expected_size"]
-        )
-
-    elif isinstance(foo, pybamm.SpecificFunction):
-        foo.__init__(dct["children"][0])
-
-    elif isinstance(foo, pybamm.Function):
-        func = getattr(
-            np, dct["function"]
-        )  # don't think this will work for self-defined functions
-        foo.__init__(
-            func,
-            name=dct["name"],
-            derivative=dct["derivative"],
-            differentiated_function=dct["differentiated_function"],
-        )
-
-    elif isinstance(foo, pybamm.DomainConcatenation):
+    if isinstance(foo, pybamm.DomainConcatenation):
 
         def repack_defaultDict(slices):
             slices = defaultdict(list, slices)
@@ -134,12 +75,9 @@ def repack_defaultDict(slices):
             dct["children"],
             dct["domains"],
         )
-    # interpolant
-    # check various Unary operators, they differ
-    # VariableBase
-    # ...
-    elif isinstance(foo, pybamm.Symbol):
-        foo.__init__(dct["name"], children=dct["children"], domains=dct["domains"])
+
+    else:
+        foo = foo._from_json(dct)
 
     return foo
 

From 4ea81086592be4282df401bcb7f2eb6f6c1b953d Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 24 Aug 2023 16:37:39 +0000
Subject: [PATCH 105/615] Create Serialise class Stores save_/load_model
 functions. Currently working for default models.

Add errors, make accessible from Simulation
---
 pybamm/__init__.py                            |   1 +
 pybamm/expression_tree/broadcasts.py          |   8 +-
 .../expression_tree/operations/serialise.py   | 182 ++++++++++++++++++
 pybamm/expression_tree/parameter.py           |  16 +-
 pybamm/expression_tree/unary_operators.py     |  39 +---
 pybamm/models/base_model.py                   |  36 ++++
 pybamm/serialisation/serialisation.py         | 170 ----------------
 pybamm/simulation.py                          |  22 +++
 8 files changed, 271 insertions(+), 203 deletions(-)
 create mode 100644 pybamm/expression_tree/operations/serialise.py
 delete mode 100644 pybamm/serialisation/serialisation.py

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 6c2636ba51..d7b957e1c9 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -93,6 +93,7 @@
 from .expression_tree.operations.jacobian import Jacobian
 from .expression_tree.operations.convert_to_casadi import CasadiConverter
 from .expression_tree.operations.unpack_symbols import SymbolUnpacker
+from .models.base_model import load_model
 
 #
 # Model classes
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 45c37a55f0..a9bd5c2ee2 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -52,7 +52,13 @@ def _diff(self, variable):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.Broadcast: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+            "pybamm.Broadcast: Serialisation is only implemented for discretised models."
+        )
+
+    @classmethod
+    def _from_json(cls, snippet):
+        raise NotImplementedError(
+            "pybamm.Broadcast: Please use a discretised model when reading in from JSON."
         )
 
 
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
new file mode 100644
index 0000000000..c88f32e602
--- /dev/null
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -0,0 +1,182 @@
+from __future__ import annotations
+
+import pybamm
+from datetime import datetime
+import json
+import importlib
+import numpy as np
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from pybamm import BaseBatteryModel
+
+
+class Serialise:
+    """
+    Converts a discretised model to and from a JSON file.
+
+    """
+
+    def __init__(self):
+        pass
+
+    class _SymbolEncoder(json.JSONEncoder):
+        """Converts PyBaMM symbols into a JSON-serialisable format"""
+
+        def default(self, node: dict):
+            node_dict = {"py/object": str(type(node))[8:-2], "py/id": id(node)}
+            if isinstance(node, pybamm.Symbol):
+                node_dict.update(node.to_json())  # this doesn't include children
+                node_dict["children"] = []
+                for c in node.children:
+                    node_dict["children"].append(self.default(c))
+
+                return node_dict
+
+            json_obj = json.JSONEncoder.default(self, node)
+            node_dict["json"] = json_obj
+            return node_dict
+
+    class _Empty:
+        """A dummy class to aid deserialisation"""
+
+        pass
+
+    def save_model(self, model, filename=None):
+        """
+        Saves a discretised model to a JSON file.
+
+        As the model is discretised and ready to solve, only the right hand side,
+        algebraic and initial condition variables are saved.
+
+        Parameters
+        ----------
+        model: : :class:`pybamm.BaseModel`
+            The discretised model to be saved
+        filename: str, optional
+            The desired name of the JSON file. If no name is provided, one will be
+            created based on the model name, and the current datetime.
+        """
+        if model.is_discretised == False:
+            raise NotImplementedError(
+                "PyBaMM can only serialise a discretised, ready-to-solve model."
+            )
+
+        model_json = {
+            "py/object": str(type(model))[8:-2],
+            "py/id": id(model),
+            "name": model.name,
+            "options": model.options,
+            "bounds": [bound.tolist() for bound in model.bounds],
+            "concatenated_rhs": self._SymbolEncoder().default(model._concatenated_rhs),
+            "concatenated_algebraic": self._SymbolEncoder().default(
+                model._concatenated_algebraic
+            ),
+            "concatenated_initial_conditions": self._SymbolEncoder().default(
+                model._concatenated_initial_conditions
+            ),
+        }
+
+        if filename is None:
+            filename = model.name + "_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M_%S")
+
+        with open(filename + ".json", "w") as f:
+            json.dump(model_json, f)
+
+    def load_model(self, filename: str, battery_model: BaseBatteryModel = None):
+        """
+        Loads a discretised, ready to solve model into PyBaMM.
+
+        A new pybamm battery model instance will be created, which can be solved
+        and the results plotted as usual.
+
+        Currently only available for pybamm models which have previously been written
+        out using the `save_model()` option.
+
+        Warning: This only loads in discretised models. If you wish to make edits to the
+        model or initial conditions, a new model will need to be constructed seperately.
+
+        Parameters
+        ----------
+
+        filename: str
+            Path to the JSON file containing the serialised model file
+        battery_model: :class: pybamm.BaseBatteryModel, optional
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
+            any model names within the file. If None, the function will look for the saved object
+            path, present if the original model came from PyBaMM.
+        """
+
+        with open(filename, "r") as f:
+            model_data = json.load(f)
+
+        recon_model_dict = {
+            "name": model_data["name"],
+            "options": model_data["options"],
+            "bounds": tuple(np.array(bound) for bound in model_data["bounds"]),
+            "concatenated_rhs": self._reconstruct_epression_tree(
+                model_data["concatenated_rhs"]
+            ),
+            "concatenated_algebraic": self._reconstruct_epression_tree(
+                model_data["concatenated_algebraic"]
+            ),
+            "concatenated_initial_conditions": self._reconstruct_epression_tree(
+                model_data["concatenated_initial_conditions"]
+            ),
+        }
+
+        if battery_model:
+            return battery_model.deserialise(recon_model_dict)
+
+        if "py/object" in model_data.keys():
+            model_framework = self._get_pybamm_class(model_data)
+            return model_framework.deserialise(recon_model_dict)
+
+        raise TypeError(
+            """
+            The PyBaMM battery model to use has not been provided.
+            """
+        )
+
+    def _get_pybamm_class(self, snippet: dict):
+        """Find a pybamm class to initialise from object path"""
+        empty_class = self._Empty()
+        parts = snippet["py/object"].split(".")
+        try:
+            module = importlib.import_module(".".join(parts[:-1]))
+        except Exception as ex:
+            print(ex)
+
+        class_ = getattr(module, parts[-1])
+        empty_class.__class__ = class_
+
+        return empty_class
+
+    def _reconstruct_symbol(self, dct: dict):
+        """Reconstruct an individual pybamm Symbol"""
+        symbol_class = self._get_pybamm_class(dct)
+        symbol = symbol_class._from_json(dct)
+        return symbol
+
+    def _reconstruct_epression_tree(self, node: dict):
+        """
+        Loop through an expression tree creating pybamm Symbol classes
+
+        Conducts post-order tree traversal to turn each tree node into a
+        `pybamm.Symbol` class, starting from leaf nodes without children and
+        working upwards.
+
+        Parameters
+        ----------
+        node: dict
+            A node in an expression tree.
+        """
+        if "children" in node:
+            for i, c in enumerate(node["children"]):
+                child_obj = self._reconstruct_epression_tree(c)
+                node["children"][i] = child_obj
+
+        obj = self._reconstruct_symbol(node)
+
+        return obj
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index d8aa146fd9..abf50faa75 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -51,7 +51,13 @@ def to_equation(self):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.Parameter: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+            "pybamm.Parameter: Serialisation is only implemented for discretised models."
+        )
+
+    @classmethod
+    def _from_json(cls, snippet):
+        raise NotImplementedError(
+            "pybamm.Parameter: Please use a discretised model when reading in from JSON."
         )
 
 
@@ -229,5 +235,11 @@ def to_equation(self):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.FunctionParameter: Serialisation is only implemented for post-discretisation."  # PL: Come up with a better message!
+            "pybamm.FunctionParameter: Serialisation is only implemented for discretised models."
+        )
+
+    @classmethod
+    def _from_json(cls, snippet):
+        raise NotImplementedError(
+            "pybamm.FunctionParameter: Please use a discretised model when reading in from JSON."
         )
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index c2fd6c2232..a828b8442b 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -359,8 +359,15 @@ def diff(self, variable):
         raise NotImplementedError
 
     def to_json(self):
-        # Will not be present in a discretised model
-        raise NotImplementedError
+        raise NotImplementedError(
+            "pybamm.SpatialOperator: Serialisation is only implemented for discretised models."
+        )
+
+    @classmethod
+    def _from_json(cls, snippet):
+        raise NotImplementedError(
+            "pybamm.SpatialOperator: Please use a discretised model when reading in from JSON."
+        )
 
 
 class Gradient(SpatialOperator):
@@ -604,20 +611,6 @@ def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
         return sympy.Integral(child, sympy.Symbol("xn"))
 
-    def to_json(self):
-        """
-        Method to serialise an Integral object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "integration_variable": self.integration_variable,  # PL: This may be a (list of) symbols that need cycling through in a similar mannar to children
-        }
-
-        return json_dict
-
 
 class BaseIndefiniteIntegral(Integral):
     """
@@ -752,20 +745,6 @@ def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
         return pybamm.evaluate_for_shape_using_domain(self.domains)
 
-    def to_json(self):
-        """
-        Method to serialise a DefiniteIntegralVector object into JSON.
-        """
-
-        json_dict = {
-            "name": self.name,
-            "id": self.id,
-            "domains": self.domains,
-            "vector_type": self.vector_type,
-        }
-
-        return json_dict
-
 
 class BoundaryIntegral(SpatialOperator):
     """
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 4def187f41..f407a15c08 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -10,6 +10,7 @@
 
 import pybamm
 from pybamm.expression_tree.operations.latexify import Latexify
+from pybamm.expression_tree.operations.serialise import Serialise
 
 
 class BaseModel:
@@ -134,6 +135,7 @@ def deserialise(cls, properties: dict):
         instance.__init__(name=properties["name"])
 
         # Initialise model with stored variables
+        instance._options = properties["options"]  # For information only
         instance._concatenated_rhs = properties["concatenated_rhs"]
         instance._concatenated_algebraic = properties["concatenated_algebraic"]
         instance._concatenated_initial_conditions = properties[
@@ -143,6 +145,12 @@ def deserialise(cls, properties: dict):
         # Model has already been discretised
         instance.is_discretised = True
 
+        instance.len_rhs = instance.concatenated_rhs.size
+        instance.len_alg = instance.concatenated_algebraic.size
+        instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg
+
+        instance.bounds = properties["bounds"]
+
         return instance
 
     @property
@@ -1132,6 +1140,34 @@ def process_parameters_and_discretise(self, symbol, parameter_values, disc):
 
         return disc_symbol
 
+    def save_model(self, filename=None):
+        """
+        Write out a discretised model to a JSON file
+
+        Parameters
+        ----------
+        filename: str, optional
+        The desired name of the JSON file. If no name is provided, one will be created
+        based on the model name, and the current datetime.
+        """
+        Serialise().save_model(self, filename=filename)
+
+
+def load_model(filename, battery_model: BaseModel = None):
+    """
+    Load in a saved model from a JSON file
+
+    Parameters
+    ----------
+    filename: str
+        Path to the JSON file containing the serialised model file
+    battery_model: :class: pybamm.BaseBatteryModel, optional
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
+            any model names within the file. If None, the function will look for the saved object
+            path, present if the original model came from PyBaMM.
+    """
+    return Serialise().load_model(filename, battery_model)
+
 
 # helper functions for finding symbols
 def find_symbol_in_tree(tree, name):
diff --git a/pybamm/serialisation/serialisation.py b/pybamm/serialisation/serialisation.py
deleted file mode 100644
index 606fd82688..0000000000
--- a/pybamm/serialisation/serialisation.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import pybamm
-from anytree.exporter import JsonExporter
-from anytree.importer import JsonImporter
-import json
-import numpy as np
-import pprint
-import importlib
-from scipy.sparse import csr_matrix, csr_array
-from collections import defaultdict
-
-
-class SymbolEncoder(json.JSONEncoder):
-    def default(self, node):
-        node_dict = {"py/object": str(type(node))[8:-2], "py/id": id(node)}
-        if isinstance(node, pybamm.Symbol):
-            node_dict.update(node.to_json())  # this doesn't include children
-            node_dict["children"] = []
-            for c in node.children:
-                node_dict["children"].append(self.default(c))
-
-            return node_dict
-
-        json_obj = json.JSONEncoder.default(self, node)
-        node_dict["json"] = json_obj
-        return node_dict
-
-
-## DECODE
-
-
-class _Empty:
-    pass
-
-
-def reconstruct_symbol(dct):
-    def recreate_slice(d):
-        return slice(d["start"], d["stop"], d["step"])
-
-    # decode non-symbol objects here
-    # now for pybamm
-    foo = _Empty()
-    parts = dct["py/object"].split(".")
-    try:
-        module = importlib.import_module(".".join(parts[:-1]))
-    except Exception as ex:
-        print(ex)
-
-    class_ = getattr(module, parts[-1])
-    foo.__class__ = class_
-    # foo = foo._from_json(dct) -> PL: This is what we want eventually
-
-    if isinstance(foo, pybamm.DomainConcatenation):
-
-        def repack_defaultDict(slices):
-            slices = defaultdict(list, slices)
-            for domain, sls in slices.items():
-                sls = [recreate_slice(s) for s in sls]
-                slices[domain] = sls
-            return slices
-
-        main_slice = repack_defaultDict(dct["slices"])
-        child_slice = [repack_defaultDict(s) for s in dct["children_slices"]]
-
-        foo = foo._from_json(
-            dct["children"],
-            dct["size"],
-            main_slice,
-            child_slice,
-            dct["secondary_dimensions_npts"],
-            dct["domains"],
-        )
-
-    elif isinstance(foo, pybamm.NumpyConcatenation):
-        foo = foo._from_json(
-            dct["children"],
-            dct["domains"],
-        )
-
-    else:
-        foo = foo._from_json(dct)
-
-    return foo
-
-
-def reconstruct_epression_tree(node):
-    if "children" in node:
-        for i, c in enumerate(node["children"]):
-            child_obj = reconstruct_epression_tree(c)
-            node["children"][i] = child_obj
-
-    obj = reconstruct_symbol(node)
-
-    return obj
-
-
-## Run tests
-model = pybamm.lithium_ion.DFN()
-geometry = model.default_geometry
-param = model.default_parameter_values
-param.process_model(model)
-param.process_geometry(geometry)
-mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
-disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
-disc.process_model(model)
-
-# # tested all individual trees in rhs
-# # tree1 = list(model.rhs.items())[2][1]
-# tree1 = (
-#     model.y_slices
-# )  # Worked: concatenated_rhs, concat_initial_conditions, concatenated_algebraic.
-# # Do we need the 'unconcatenated' rhs etc? if not, this gets much easier.
-# tree1.visualise("tree1.png")
-
-# json_tree1 = SymbolEncoder().default(tree1)
-# with open("test_tree1.json", "w") as f:
-#     json.dump(json_tree1, f)
-
-# # pprint.pprint(json_tree1, sort_dicts=False)
-
-# with open("test_tree1.json", "r") as f:
-#     data = json.load(f)
-
-# tree1_recon = reconstruct_epression_tree(data)
-
-# print(tree1 == tree1_recon)
-
-
-# tree1_recon.visualise("recon1.png")
-
-solver_initial = model.default_solver
-solution_initial = solver_initial.solve(model, [0, 3600])
-
-# pybamm.plot(solution_initial)
-# solution_initial.plot()
-
-model_json = {
-    "py/object": str(type(model))[8:-2],
-    "py/id": id(model),
-    "name": model.name,
-    "concatenated_rhs": SymbolEncoder().default(model._concatenated_rhs),
-    "concatenated_algebraic": SymbolEncoder().default(model._concatenated_algebraic),
-    "concatenated_initial_conditions": SymbolEncoder().default(
-        model._concatenated_initial_conditions
-    ),
-}
-
-# file_name = f"test_{model.name}_stored"
-with open("test_full_model.json", "w") as f:
-    json.dump(model_json, f)
-
-with open("test_full_model.json", "r") as f:
-    model_data = json.load(f)
-
-recon_model_dict = {
-    "name": model_data["name"],
-    "concatenated_rhs": reconstruct_epression_tree(model_data["concatenated_rhs"]),
-    "concatenated_algebraic": reconstruct_epression_tree(
-        model_data["concatenated_algebraic"]
-    ),
-    "concatenated_initial_conditions": reconstruct_epression_tree(
-        model_data["concatenated_initial_conditions"]
-    ),
-}
-
-new_model = pybamm.lithium_ion.DFN.deserialise(recon_model_dict)
-
-new_solver = new_model.default_solver
-new_solution = new_solver.solve(model, [0, 3600])
-
-# THIS WORKS!!!
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index b25b76f859..da2bac841b 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -11,6 +11,8 @@
 from datetime import timedelta
 import tqdm
 
+from pybamm.expression_tree.operations.serialise import Serialise
+
 
 def is_notebook():
     try:
@@ -1186,6 +1188,26 @@ def save(self, filename):
         with open(filename, "wb") as f:
             pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
 
+    def save_model(self, filename: str = None):
+        """
+        Write out a discretised model to a JSON file
+
+        Parameters
+        ----------
+        filename: str, optional
+        The desired name of the JSON file. If no name is provided, one will be created
+        based on the model name, and the current datetime.
+        """
+        if self.built_model:
+            Serialise().save_model(self.built_model, filename=filename)
+        else:
+            raise NotImplementedError(
+                """
+                PyBaMM can only serialise a discretised model.
+                Ensure the model has been built (e.g. run `solve()`) before saving.
+                """
+            )
+
 
 def load_sim(filename):
     """Load a saved simulation"""

From 6694cb1025156e13a26fc87af91f81835623dde5 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 1 Sep 2023 16:21:28 +0000
Subject: [PATCH 106/615] Serialised models can be plotted. Option to save
 mesh, variables and geometry

Draft notebook written
Added warning if variables are not provided and try to plot
---
 .../notebooks/models/saving_models.ipynb      | 342 ++++++++++++++++++
 pybamm/__init__.py                            |   6 +-
 pybamm/expression_tree/array.py               |   2 +-
 pybamm/expression_tree/functions.py           |  14 +-
 .../expression_tree/independent_variable.py   |   4 +-
 .../expression_tree/operations/serialise.py   | 127 ++++++-
 pybamm/expression_tree/unary_operators.py     |  48 ++-
 pybamm/meshes/meshes.py                       |  23 ++
 pybamm/meshes/one_dimensional_submeshes.py    |  23 ++
 pybamm/meshes/zero_dimensional_submesh.py     |  17 +
 pybamm/models/base_model.py                   |  58 ---
 pybamm/models/event.py                        |  38 ++
 .../full_battery_models/base_battery_model.py |  96 +++++
 pybamm/plotting/quick_plot.py                 |   4 +
 pybamm/simulation.py                          |  25 +-
 15 files changed, 748 insertions(+), 79 deletions(-)
 create mode 100644 docs/source/examples/notebooks/models/saving_models.ipynb

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
new file mode 100644
index 0000000000..94799bcc48
--- /dev/null
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -0,0 +1,342 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving PyBaMM models to file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Models which are discretised (i.e. ready to solve/ previously solved, see A DIFFERENT NOTEBOOK) can be serialised and saved to a JSON file, ready to be read in again either in PyBaMM, or a different modelling library. \n",
+    "\n",
+    "In the example below, we build and solve a basic DFN model, and then save the model out to `sim_model_example.json`, which should have appear in the 'models' directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "\n",
+    "# do the example\n",
+    "dfn_model = pybamm.lithium_ion.DFN()\n",
+    "dfn_sim = pybamm.Simulation(dfn_model)\n",
+    "dfn_sim.solve([0, 3600])\n",
+    "\n",
+    "dfn_sim.save_model(\"sim_model_example\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This model file can then be read in and solved by choosing a solver, and running as below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x7feb7764a460>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Recreate the pybamm model from the JSON file\n",
+    "new_dfn_model = pybamm.load_model(\"sim_model_example.json\")\n",
+    "\n",
+    "sim_reloaded = pybamm.Simulation(new_dfn_model) # PL: will this work if anything other than the default options are used? I guess not...\n",
+    "sim_reloaded.solve([0, 3600])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It would be nice to plot the results of the two models, to confirm that they are producing the same result.\n",
+    "\n",
+    "However, notice that running the code below generates an error stating that the model variables were not provided during the reading in of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "Variables not provided by the serialised model",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m     plot_sim\u001b[39m.\u001b[39msolve([\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m     sims\u001b[39m.\u001b[39mappend(plot_sim)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m pybamm\u001b[39m.\u001b[39;49mdynamic_plot(sims, time_unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mseconds\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/dynamic_plot.py:20\u001b[0m, in \u001b[0;36mdynamic_plot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39m    The 'QuickPlot' object that was created\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     19\u001b[0m kwargs_for_class \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 20\u001b[0m plot \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39;49mQuickPlot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs_for_class)\n\u001b[1;32m     21\u001b[0m plot\u001b[39m.\u001b[39mdynamic_plot(kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m     22\u001b[0m \u001b[39mreturn\u001b[39;00m plot\n",
+      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/quick_plot.py:163\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 163\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mVariables not provided by the serialised model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    165\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m    166\u001b[0m     \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m    167\u001b[0m )\n\u001b[1;32m    168\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n",
+      "\u001b[0;31mAttributeError\u001b[0m: Variables not provided by the serialised model"
+     ]
+    }
+   ],
+   "source": [
+    "dfn_models = [dfn_model, new_dfn_model]\n",
+    "sims = []\n",
+    "for model in dfn_models:\n",
+    "    plot_sim = pybamm.Simulation(model)\n",
+    "    plot_sim.solve([0, 3600])\n",
+    "    sims.append(plot_sim)\n",
+    "\n",
+    "pybamm.dynamic_plot(sims, time_unit=\"seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To be able to plot the results from a serialised model, the mesh and model variables need to be saved alongside the model itself.\n",
+    "\n",
+    "To do this, set the `variables` option to `True` when saving the model as in the example below; notice how the models will now plot nicely."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6b4db83fd054ba4be3ee279f7024c6a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=36.0), Output()), _dom_classes=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7feb6f3b7b80>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# using the first simulation, save a new file which includes a list of the model variables\n",
+    "dfn_sim.save_model(\"sim_model_variables\", variables=True)\n",
+    "\n",
+    "# read the model back in\n",
+    "model_with_vars = pybamm.load_model(\"sim_model_variables.json\")\n",
+    "\n",
+    "# plot the pre- and post-serialisation models together to prove they behave the same\n",
+    "models = [dfn_model, model_with_vars]\n",
+    "sims = []\n",
+    "for model in models:\n",
+    "    sim = pybamm.Simulation(model)\n",
+    "    sim.solve([0, 3600])\n",
+    "    sims.append(sim)\n",
+    "\n",
+    "pybamm.dynamic_plot(sims, time_unit=\"seconds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving from Model\n",
+    "\n",
+    "Alternatively, the model can be saved directly from the Model class.\n",
+    "\n",
+    "First set up the model, as explained in detail in the SPM NOTEBOOK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.models.full_battery_models.lithium_ion.spm.SPM at 0x7feb11370250>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create the model\n",
+    "spm_model = pybamm.lithium_ion.SPM()\n",
+    "\n",
+    "# set up and discretise ready to solve\n",
+    "geometry = spm_model.default_geometry\n",
+    "param = spm_model.default_parameter_values\n",
+    "param.process_model(spm_model)\n",
+    "param.process_geometry(geometry)\n",
+    "mesh = pybamm.Mesh(geometry, spm_model.default_submesh_types, spm_model.default_var_pts)\n",
+    "disc = pybamm.Discretisation(mesh, spm_model.default_spatial_methods)\n",
+    "disc.process_model(spm_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then save the model. Note that in this case the model variables and the mesh must be provided directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Serialise the spm_model, providing the varaibles and the mesh\n",
+    "spm_model.save_model(\"example_model\", variables=spm_model.variables, mesh=mesh)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can read the model back in, solve and plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6df594b3af646599430ff322349b44f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7feb0c3f7310>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read back in\n",
+    "new_spm_model = pybamm.load_model(\"example_model.json\")\n",
+    "\n",
+    "# select a solver and solve\n",
+    "new_spm_solver = new_spm_model.default_solver\n",
+    "new_spm_solution = new_spm_solver.solve(new_spm_model, [0, 3600])\n",
+    "\n",
+    "# plot the solution\n",
+    "new_spm_solution.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index d7b957e1c9..c6376ec0b3 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -93,7 +93,6 @@
 from .expression_tree.operations.jacobian import Jacobian
 from .expression_tree.operations.convert_to_casadi import CasadiConverter
 from .expression_tree.operations.unpack_symbols import SymbolUnpacker
-from .models.base_model import load_model
 
 #
 # Model classes
@@ -189,6 +188,11 @@
     UserSupplied2DSubMesh,
 )
 
+#
+# Serialisation
+#
+from .models.full_battery_models.base_battery_model import load_model
+
 #
 # Spatial Methods
 #
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index c2fcbc4a11..270c546dbe 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -62,7 +62,7 @@ def _from_json(cls, snippet: dict):
         instance = cls.__new__(cls)
 
         if isinstance(snippet["entries"], dict):
-            matrix = csr_array(
+            matrix = csr_matrix(
                 (
                     snippet["entries"]["data"],
                     snippet["entries"]["row_indices"],
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 17732c7ba4..9743e7c754 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -212,8 +212,18 @@ def to_equation(self):
                 eq_list.append(eq)
             return self._sympy_operator(*eq_list)
 
-    def to_json(self):
-        raise NotImplementedError()
+    def to_json(
+        self,
+    ):  # PL: I think these ones might actually be present when you build your own function.
+        raise NotImplementedError(
+            "pybamm.Function: Serialisation is only implemented for discretised models."
+        )
+
+    @classmethod
+    def _from_json(cls, snippet):
+        raise NotImplementedError(
+            "pybamm.Function: Please use a discretised model when reading in from JSON."
+        )
 
 
 def simplified_function(func_class, child):
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 665bfdb344..fbf745dfca 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -67,10 +67,10 @@ def __init__(self):
         super().__init__("time")
 
     @classmethod
-    def _to_json(cls, snippet: dict):
+    def _from_json(cls, snippet: dict):
         instance = cls.__new__(cls)
 
-        instance.__init__("time")
+        instance.__init__()
 
         return instance
 
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index c88f32e602..e11049a35a 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -32,9 +32,42 @@ def default(self, node: dict):
                 for c in node.children:
                     node_dict["children"].append(self.default(c))
 
+                if hasattr(node, "initial_condition"):  # for ExplicitTimeIntegral
+                    node_dict["initial_condition"] = self.default(
+                        node.initial_condition
+                    )
+
+                return node_dict
+
+            if isinstance(node, pybamm.Event):
+                node_dict.update(node.to_json())
+                node_dict["expression"] = self.default(node._expression)
                 return node_dict
 
-            json_obj = json.JSONEncoder.default(self, node)
+            json_obj = json.JSONEncoder.default(self, node)  # pragma: no cover
+            node_dict["json"] = json_obj
+            return node_dict
+
+    class _MeshEncoder(json.JSONEncoder):
+        """Converts PyBaMM meshes into a JSON-serialisable format"""
+
+        def default(self, node: dict):
+            node_dict = {"py/object": str(type(node))[8:-2], "py/id": id(node)}
+            if isinstance(node, pybamm.Mesh):
+                node_dict.update(node.to_json())
+
+                node_dict["sub_meshes"] = {}
+                for k, v in node.items():
+                    if len(k) == 1 and "ghost cell" not in k[0]:
+                        node_dict["sub_meshes"][k[0]] = self.default(v)
+
+                return node_dict
+
+            if isinstance(node, pybamm.SubMesh):
+                node_dict.update(node.to_json())
+                return node_dict
+
+            json_obj = json.JSONEncoder.default(self, node)  # pragma: no cover
             node_dict["json"] = json_obj
             return node_dict
 
@@ -43,7 +76,18 @@ class _Empty:
 
         pass
 
-    def save_model(self, model, filename=None):
+    class _EmptyDict(dict):
+        """A dummy dictionary class to aid deserialisation"""
+
+        pass
+
+    def save_model(
+        self,
+        model: pybamm.BaseBatteryModel,
+        mesh: pybamm.Mesh = None,
+        variables: pybamm.FuzzyDict = None,
+        filename: str = None,
+    ):
         """
         Saves a discretised model to a JSON file.
 
@@ -52,8 +96,14 @@ def save_model(self, model, filename=None):
 
         Parameters
         ----------
-        model: : :class:`pybamm.BaseModel`
+        model: : :class:`pybamm.BaseBatteryModel`
             The discretised model to be saved
+        mesh: :class: `pybamm.Mesh`, optional
+            The mesh the model has been discretised over. Not neccesary to solve
+            the model when read in, but required to use pybamm's plotting tools.
+        variables: :class: `pybamm.FuzzyDict`, optional
+            The discretised model varaibles. Not necessary to solve a model, but
+            required to use pybamm's plotting tools.
         filename: str, optional
             The desired name of the JSON file. If no name is provided, one will be
             created based on the model name, and the current datetime.
@@ -76,15 +126,29 @@ def save_model(self, model, filename=None):
             "concatenated_initial_conditions": self._SymbolEncoder().default(
                 model._concatenated_initial_conditions
             ),
+            "events": [self._SymbolEncoder().default(event) for event in model.events],
+            "mass_matrix": self._SymbolEncoder().default(model.mass_matrix),
+            "mass_matrix_inv": self._SymbolEncoder().default(model.mass_matrix_inv),
         }
 
+        if mesh:
+            model_json["mesh"] = self._MeshEncoder().default(mesh)
+
+        if variables:
+            model_json["geometry"] = dict(model._geometry)
+            model_json["variables"] = {
+                k: self._SymbolEncoder().default(v) for k, v in dict(variables).items()
+            }
+
         if filename is None:
             filename = model.name + "_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M_%S")
 
         with open(filename + ".json", "w") as f:
             json.dump(model_json, f)
 
-    def load_model(self, filename: str, battery_model: BaseBatteryModel = None):
+    def load_model(
+        self, filename: str, battery_model: BaseBatteryModel = None
+    ) -> BaseBatteryModel:
         """
         Loads a discretised, ready to solve model into PyBaMM.
 
@@ -106,6 +170,11 @@ def load_model(self, filename: str, battery_model: BaseBatteryModel = None):
             PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
             any model names within the file. If None, the function will look for the saved object
             path, present if the original model came from PyBaMM.
+
+        Returns
+        -------
+        :class: pybamm.BaseBatteryModel
+            A PyBaMM model object, of type specified either in the JSON or in `battery_model`.
         """
 
         with open(filename, "r") as f:
@@ -124,8 +193,35 @@ def load_model(self, filename: str, battery_model: BaseBatteryModel = None):
             "concatenated_initial_conditions": self._reconstruct_epression_tree(
                 model_data["concatenated_initial_conditions"]
             ),
+            "events": [
+                self._reconstruct_epression_tree(event)
+                for event in model_data["events"]
+            ],
+            "mass_matrix": self._reconstruct_epression_tree(model_data["mass_matrix"]),
+            "mass_matrix_inv": self._reconstruct_epression_tree(
+                model_data["mass_matrix_inv"]
+            ),
         }
 
+        recon_model_dict["geometry"] = (
+            model_data["geometry"] if "geometry" in model_data.keys() else None
+        )
+
+        recon_model_dict["mesh"] = (
+            self._reconstruct_mesh(model_data["mesh"])
+            if "mesh" in model_data.keys()
+            else None
+        )
+
+        recon_model_dict["variables"] = (
+            {
+                k: self._reconstruct_epression_tree(v)
+                for k, v in model_data["variables"].items()
+            }
+            if "variables" in model_data.keys()
+            else None
+        )
+
         if battery_model:
             return battery_model.deserialise(recon_model_dict)
 
@@ -141,7 +237,6 @@ def load_model(self, filename: str, battery_model: BaseBatteryModel = None):
 
     def _get_pybamm_class(self, snippet: dict):
         """Find a pybamm class to initialise from object path"""
-        empty_class = self._Empty()
         parts = snippet["py/object"].split(".")
         try:
             module = importlib.import_module(".".join(parts[:-1]))
@@ -149,7 +244,13 @@ def _get_pybamm_class(self, snippet: dict):
             print(ex)
 
         class_ = getattr(module, parts[-1])
-        empty_class.__class__ = class_
+
+        try:
+            empty_class = self._Empty()
+            empty_class.__class__ = class_
+        except:
+            empty_class = self._EmptyDict()
+            empty_class.__class__ = class_
 
         return empty_class
 
@@ -176,7 +277,21 @@ def _reconstruct_epression_tree(self, node: dict):
             for i, c in enumerate(node["children"]):
                 child_obj = self._reconstruct_epression_tree(c)
                 node["children"][i] = child_obj
+        elif "expression" in node:
+            expression_obj = self._reconstruct_epression_tree(node["expression"])
+            node["expression"] = expression_obj
 
         obj = self._reconstruct_symbol(node)
 
         return obj
+
+    def _reconstruct_mesh(self, node: dict):
+        """Reconstructs a Mesh object"""
+        if "sub_meshes" in node:
+            for k, v in node["sub_meshes"].items():
+                sub_mesh = self._reconstruct_symbol(v)
+                node["sub_meshes"][k] = sub_mesh
+
+        new_mesh = self._reconstruct_symbol(node)
+
+        return new_mesh
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index a828b8442b..b4db6b6528 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -34,6 +34,21 @@ def __init__(self, name, child, domains=None):
         super().__init__(name, children=[child], domains=domains)
         self.child = self.children[0]
 
+    @classmethod
+    def _from_json(cls, name, snippet: dict):
+        """Use to instantiate when deserialising"""
+
+        instance = cls.__new__(cls)
+
+        super(UnaryOperator, instance).__init__(
+            name,
+            snippet["children"],
+            domains=snippet["domains"],
+        )
+        instance.child = instance.children[0]
+
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{}({!s})".format(self.name, self.child)
@@ -99,6 +114,12 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("-", child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = super()._from_json("-", snippet)
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{}{!s}".format(self.name, self.child)
@@ -353,11 +374,6 @@ class with a :class:`Matrix`
     def __init__(self, name, child, domains=None):
         super().__init__(name, child, domains)
 
-    def diff(self, variable):
-        """See :meth:`pybamm.Symbol.diff()`."""
-        # We shouldn't need this
-        raise NotImplementedError
-
     def to_json(self):
         raise NotImplementedError(
             "pybamm.SpatialOperator: Serialisation is only implemented for discretised models."
@@ -944,12 +960,34 @@ def __init__(self, children, initial_condition):
         super().__init__("explicit time integral", children)
         self.initial_condition = initial_condition
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        instance.__init__(snippet["children"][0], snippet["initial_condition"])
+
+        return instance
+
     def _unary_new_copy(self, child):
         return self.__class__(child, self.initial_condition)
 
     def is_constant(self):
         return False
 
+    def to_json(self):
+        """
+        Convert ExplicitTimeIntegral to json for serialisation.
+
+        Both `children` and `initial_condition` contain Symbols, and are therefore
+        dealt with by `pybamm.Serialise._SymbolEncoder.default()` directly.
+        """
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+        }
+
+        return json_dict
+
 
 class BoundaryGradient(BoundaryOperator):
     """
diff --git a/pybamm/meshes/meshes.py b/pybamm/meshes/meshes.py
index 4c86290a2f..182282319f 100644
--- a/pybamm/meshes/meshes.py
+++ b/pybamm/meshes/meshes.py
@@ -120,6 +120,21 @@ def __init__(self, geometry, submesh_types, var_pts):
         # add ghost meshes
         self.add_ghost_meshes()
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+        super(Mesh, instance).__init__()
+
+        instance.submesh_pts = snippet["submesh_pts"]
+        instance.base_domains = snippet["base_domains"]
+
+        for k, v in snippet["sub_meshes"].items():
+            instance[k] = v
+
+        # instance.add_ghost_meshes()
+
+        return instance
+
     def __getitem__(self, domains):
         if isinstance(domains, str):
             domains = (domains,)
@@ -216,6 +231,14 @@ def geometry(self):
     def geometry(self, geometry):
         self._geometry = geometry
 
+    def to_json(self):
+        json_dict = {
+            "submesh_pts": self.submesh_pts,
+            "base_domains": self.base_domains,
+        }
+
+        return json_dict
+
 
 class SubMesh:
     """
diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py
index 2beae6bc3a..147ed590cf 100644
--- a/pybamm/meshes/one_dimensional_submeshes.py
+++ b/pybamm/meshes/one_dimensional_submeshes.py
@@ -70,6 +70,17 @@ def read_lims(self, lims):
 
         return spatial_var, spatial_lims, tabs
 
+    def to_json(self):
+        json_dict = {
+            "edges": self.edges.tolist(),
+            "coord_sys": self.coord_sys,
+        }
+
+        if hasattr(self, "tabs"):
+            json_dict["tabs"] = self.tabs
+
+        return json_dict
+
 
 class Uniform1DSubMesh(SubMesh1D):
     """
@@ -95,6 +106,18 @@ def __init__(self, lims, npts):
 
         super().__init__(edges, coord_sys=coord_sys, tabs=tabs)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        tabs = snippet["tabs"] if "tabs" in snippet.keys() else None
+
+        super(Uniform1DSubMesh, instance).__init__(
+            np.array(snippet["edges"]), snippet["coord_sys"], tabs=tabs
+        )
+
+        return instance
+
 
 class Exponential1DSubMesh(SubMesh1D):
     """
diff --git a/pybamm/meshes/zero_dimensional_submesh.py b/pybamm/meshes/zero_dimensional_submesh.py
index 5b2f38e29f..dd4afe70fd 100644
--- a/pybamm/meshes/zero_dimensional_submesh.py
+++ b/pybamm/meshes/zero_dimensional_submesh.py
@@ -38,6 +38,23 @@ def __init__(self, position, npts=None):
         self.coord_sys = None
         self.npts = 1
 
+    @classmethod
+    def _from_json(cls, snippet):
+        instance = cls.__new__(cls)
+
+        instance.nodes = np.array(snippet["spatial_position"])
+        instance.edges = np.array(snippet["spatial_position"])
+        instance.coord_sys = None
+        instance.npts = 1
+
+        return instance
+
     def add_ghost_meshes(self):
         # No ghost meshes to be added to this class
         pass
+
+    def to_json(self):
+        json_dict = {
+            "spatial_position": self.nodes.tolist(),
+        }
+        return json_dict
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index f407a15c08..41192dbe1f 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -10,7 +10,6 @@
 
 import pybamm
 from pybamm.expression_tree.operations.latexify import Latexify
-from pybamm.expression_tree.operations.serialise import Serialise
 
 
 class BaseModel:
@@ -124,35 +123,6 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
-    # PL: Next up, how to pass in the non-standard variables, if necessary.
-    @classmethod
-    def deserialise(cls, properties: dict):
-        """
-        Create a model instance from a serialised object.
-        """
-        instance = cls.__new__(cls)
-
-        instance.__init__(name=properties["name"])
-
-        # Initialise model with stored variables
-        instance._options = properties["options"]  # For information only
-        instance._concatenated_rhs = properties["concatenated_rhs"]
-        instance._concatenated_algebraic = properties["concatenated_algebraic"]
-        instance._concatenated_initial_conditions = properties[
-            "concatenated_initial_conditions"
-        ]
-
-        # Model has already been discretised
-        instance.is_discretised = True
-
-        instance.len_rhs = instance.concatenated_rhs.size
-        instance.len_alg = instance.concatenated_algebraic.size
-        instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg
-
-        instance.bounds = properties["bounds"]
-
-        return instance
-
     @property
     def name(self):
         return self._name
@@ -1140,34 +1110,6 @@ def process_parameters_and_discretise(self, symbol, parameter_values, disc):
 
         return disc_symbol
 
-    def save_model(self, filename=None):
-        """
-        Write out a discretised model to a JSON file
-
-        Parameters
-        ----------
-        filename: str, optional
-        The desired name of the JSON file. If no name is provided, one will be created
-        based on the model name, and the current datetime.
-        """
-        Serialise().save_model(self, filename=filename)
-
-
-def load_model(filename, battery_model: BaseModel = None):
-    """
-    Load in a saved model from a JSON file
-
-    Parameters
-    ----------
-    filename: str
-        Path to the JSON file containing the serialised model file
-    battery_model: :class: pybamm.BaseBatteryModel, optional
-            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
-            any model names within the file. If None, the function will look for the saved object
-            path, present if the original model came from PyBaMM.
-    """
-    return Serialise().load_model(filename, battery_model)
-
 
 # helper functions for finding symbols
 def find_symbol_in_tree(tree, name):
diff --git a/pybamm/models/event.py b/pybamm/models/event.py
index e93262641d..105106c470 100644
--- a/pybamm/models/event.py
+++ b/pybamm/models/event.py
@@ -46,6 +46,28 @@ def __init__(self, name, expression, event_type=EventType.TERMINATION):
         self._expression = expression
         self._event_type = event_type
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """
+        Reconstructs an Event instance during deserialisation of a JSON file.
+
+        Parameters
+        ----------
+        snippet: dict
+            Contains the information needed to reconstruct a specific instance.
+            Should contain "name", "expression" and "event_type".
+        """
+
+        instance = cls.__new__(cls)
+
+        instance.__init__(
+            snippet["name"],
+            snippet["expression"],
+            event_type=EventType(snippet["event_type"][1]),
+        )
+
+        return instance
+
     def evaluate(self, t=None, y=None, y_dot=None, inputs=None):
         """
         Acts as a drop-in replacement for :func:`pybamm.Symbol.evaluate`
@@ -66,3 +88,19 @@ def expression(self):
     @property
     def event_type(self):
         return self._event_type
+
+    def to_json(self):
+        """
+        Method to serialise an Event object into JSON.
+
+        The expression is written out seperately,
+        See :meth:`pybamm.Serialise._SymbolEncoder.default()`
+        """
+
+        # event_type contains string name, for JSON readability, and value for deserialisation.
+        json_dict = {
+            "name": self._name,
+            "event_type": [str(self._event_type), self._event_type.value],
+        }
+
+        return json_dict
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index ad36786381..841bf53a81 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -17,6 +17,9 @@ def represents_positive_integer(s):
         return val > 0
 
 
+from pybamm.expression_tree.operations.serialise import Serialise
+
+
 class BatteryModelOptions(pybamm.FuzzyDict):
     """
     Attributes
@@ -799,6 +802,66 @@ def __init__(self, options=None, name="Unnamed battery model"):
         super().__init__(name)
         self.options = options
 
+    # PL: Next up, how to pass in the non-standard variables, if necessary.
+    @classmethod
+    def deserialise(
+        cls, properties: dict
+    ):  # PL: maybe option up here as output_mesh=true to output a tuple, (model, mesh) rather than just updating the variables and leaving it at that.
+        """
+        Create a model instance from a serialised object.
+        """
+        instance = cls.__new__(cls)
+
+        # append the model name with _saved to differentiate
+        instance.__init__(
+            options=properties["options"], name=properties["name"] + "_saved"
+        )
+
+        # Initialise model with stored variables that have already been discretised
+        instance._concatenated_rhs = properties["concatenated_rhs"]
+        instance._concatenated_algebraic = properties["concatenated_algebraic"]
+        instance._concatenated_initial_conditions = properties[
+            "concatenated_initial_conditions"
+        ]
+
+        instance.len_rhs = instance.concatenated_rhs.size
+        instance.len_alg = instance.concatenated_algebraic.size
+        instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg
+
+        instance.bounds = properties["bounds"]
+        instance.events = properties["events"]
+        instance.mass_matrix = properties["mass_matrix"]
+        instance.mass_matrix_inv = properties["mass_matrix_inv"]
+
+        # add optional properties not required for model to solve
+        if properties["variables"]:
+            instance._variables = pybamm.FuzzyDict(properties["variables"])
+
+            # assign meshes to each variable
+            for var in instance._variables.values():
+                if var.domain != []:
+                    var.mesh = properties["mesh"][var.domain]
+                else:
+                    var.mesh = None
+
+                if var.domains["secondary"] != []:
+                    var.secondary_mesh = properties["mesh"][var.domains["secondary"]]
+                else:
+                    var.secondary_mesh = None
+
+            instance._geometry = pybamm.Geometry(properties["geometry"])
+        else:
+            # Delete the default variables which have not been discretised
+            instance._variables = pybamm.FuzzyDict({})
+
+        # PL: Simulation(new_model, new_mesh)
+        # doesn't work because the model is already discretised, you can't give it a new mesh.
+
+        # Model has already been discretised
+        instance.is_discretised = True
+
+        return instance
+
     @property
     def default_geometry(self):
         return pybamm.battery_geometry(options=self.options)
@@ -1379,3 +1442,36 @@ def set_soc_variables(self):
         This function is overriden by the base battery models
         """
         pass
+
+    def save_model(self, filename=None, mesh=None, variables=None):
+        """
+        Write out a discretised model to a JSON file
+
+        Parameters
+        ----------
+        filename: str, optional
+        The desired name of the JSON file. If no name is provided, one will be created
+        based on the model name, and the current datetime.
+        """
+        if variables and not mesh:
+            raise ValueError(
+                "Serialisation: Please provide the mesh if variables are required"
+            )
+
+        Serialise().save_model(self, filename=filename, mesh=mesh, variables=variables)
+
+
+def load_model(filename, battery_model: BaseBatteryModel = None):
+    """
+    Load in a saved model from a JSON file
+
+    Parameters
+    ----------
+    filename: str
+        Path to the JSON file containing the serialised model file
+    battery_model: :class: pybamm.BaseBatteryModel, optional
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
+            any model names within the file. If None, the function will look for the saved object
+            path, present if the original model came from PyBaMM.
+    """
+    return Serialise().load_model(filename, battery_model)
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 03bfeeccd4..3f55648225 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -154,6 +154,10 @@ def __init__(
                     f"No default output variables provided for {models[0].name}"
                 )
 
+        # check variables have been provided after any serialisation
+        if any(len(m.variables) == 0 for m in models):
+            raise AttributeError(f"Variables not provided by the serialised model")
+
         self.n_rows = n_rows or int(
             len(output_variables) // np.sqrt(len(output_variables))
         )
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index da2bac841b..4a71e819bd 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -1188,18 +1188,35 @@ def save(self, filename):
         with open(filename, "wb") as f:
             pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
 
-    def save_model(self, filename: str = None):
+    def save_model(
+        self,
+        filename: str = None,
+        mesh: bool = False,
+        variables: bool = False,
+    ):
         """
         Write out a discretised model to a JSON file
 
         Parameters
         ----------
+        mesh: bool
+            The mesh used to discretise the model. If false, plotting tools will not
+            be available when the model is read back in and solved.
+        variables: bool
+            The discretised variables. Not required to solve a model, but if false
+            tools will not be availble. Will automatically save meshes as well, required
+            for plotting tools.
         filename: str, optional
-        The desired name of the JSON file. If no name is provided, one will be created
-        based on the model name, and the current datetime.
+            The desired name of the JSON file. If no name is provided, one will be created
+            based on the model name, and the current datetime.
         """
+        mesh = self.mesh if (mesh or variables) else None
+        variables = self.built_model.variables if variables else None
+
         if self.built_model:
-            Serialise().save_model(self.built_model, filename=filename)
+            Serialise().save_model(
+                self.built_model, filename=filename, mesh=mesh, variables=variables
+            )
         else:
             raise NotImplementedError(
                 """

From 7fadee320094b57eb8c1387cd90746b06a1175ed Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Tue, 19 Sep 2023 16:45:54 +0000
Subject: [PATCH 107/615] Add unit tests for to_json()

---
 pybamm/expression_tree/array.py               |  3 +-
 pybamm/expression_tree/interpolant.py         |  4 +-
 pybamm/expression_tree/state_vector.py        |  1 -
 pybamm/expression_tree/variable.py            |  4 +-
 tests/unit/test_expression_tree/test_array.py | 18 ++++++
 .../test_binary_operators.py                  | 59 +++++++++++++++++++
 .../test_expression_tree/test_broadcasts.py   |  6 ++
 .../test_concatenations.py                    | 59 +++++++++++++++++++
 .../test_input_parameter.py                   | 14 +++++
 .../test_expression_tree/test_interpolant.py  |  8 +++
 .../unit/test_expression_tree/test_matrix.py  | 24 ++++++++
 .../test_expression_tree/test_parameter.py    | 12 ++++
 .../unit/test_expression_tree/test_scalar.py  |  6 ++
 .../test_expression_tree/test_state_vector.py | 26 ++++++++
 .../unit/test_expression_tree/test_symbol.py  | 20 +++++++
 .../test_unary_operators.py                   | 52 ++++++++++++++++
 .../test_expression_tree/test_variable.py     |  5 ++
 17 files changed, 316 insertions(+), 5 deletions(-)

diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index 270c546dbe..0fc74f3209 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -3,7 +3,7 @@
 #
 import numpy as np
 import sympy
-from scipy.sparse import csr_matrix, issparse, csr_array
+from scipy.sparse import csr_matrix, issparse
 
 import pybamm
 
@@ -176,7 +176,6 @@ def to_json(self):
             "id": self.id,
             "domains": self.domains,
             "entries": matrix,
-            # "entries_string": self.entries_string.decode(),
         }
 
         return json_dict
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 16bbe88d7e..5234e5e927 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -292,4 +292,6 @@ def _function_evaluate(self, evaluated_children):
             raise ValueError("Invalid dimension: {0}".format(self.dimension))
 
     def to_json(self):
-        raise NotImplementedError
+        raise NotImplementedError(
+            "pybamm.Interpolant: Serialisation is only implemented for discretised models."
+        )
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 9a414dc049..72b1ed18a5 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -227,7 +227,6 @@ def to_json(self):
                 for y in self.y_slices
             ],
             "evaluation_array": list(self.evaluation_array),
-            # "children": self.children,  # might not need this, the anytree exporter handles children I think
         }
 
         return json_dict
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index 8aa2b1d707..8fe655d513 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -132,7 +132,9 @@ def to_equation(self):
     def to_json(
         self,
     ):
-        raise NotImplementedError
+        raise NotImplementedError(
+            "pybamm.Variable: Serialisation is only implemented for discretised models."
+        )
 
 
 class Variable(VariableBase):
diff --git a/tests/unit/test_expression_tree/test_array.py b/tests/unit/test_expression_tree/test_array.py
index da79dbb6e0..6ef1669270 100644
--- a/tests/unit/test_expression_tree/test_array.py
+++ b/tests/unit/test_expression_tree/test_array.py
@@ -3,6 +3,7 @@
 #
 from tests import TestCase
 import unittest
+import unittest.mock as mock
 
 import numpy as np
 import sympy
@@ -41,6 +42,23 @@ def test_to_equation(self):
             pybamm.Array([1, 2]).to_equation(), sympy.Array([[1.0], [2.0]])
         )
 
+    def test_to_json_array(self):
+        arr = pybamm.Array(np.array([1, 2, 3]))
+        self.assertEqual(
+            arr.to_json(),
+            {
+                "name": "Array of shape (3, 1)",
+                "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+                "domains": {
+                    "primary": [],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+                "entries": [[1.0], [2.0], [3.0]],
+            },
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 6acd7c41b0..9a66e3a639 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -3,6 +3,7 @@
 #
 from tests import TestCase
 import unittest
+import unittest.mock as mock
 
 import numpy as np
 import sympy
@@ -10,6 +11,13 @@
 
 import pybamm
 
+EMPTY_DOMAINS = {
+    "primary": [],
+    "secondary": [],
+    "tertiary": [],
+    "quaternary": [],
+}
+
 
 class TestBinaryOperators(TestCase):
     def test_binary_operator(self):
@@ -770,6 +778,57 @@ def test_to_equation(self):
         # Test NotEqualHeaviside
         self.assertEqual(pybamm.NotEqualHeaviside(2, 4).to_equation(), True)
 
+    def test_to_json(self):
+        # Test Addition
+        self.assertEqual(
+            pybamm.Addition(2, 4).to_json(),
+            {
+                "name": "+",
+                "id": mock.ANY,
+                "domains": EMPTY_DOMAINS,
+            },
+        )
+
+        # Test Power
+        self.assertEqual(
+            pybamm.Power(7, 2).to_json(),
+            {
+                "name": "**",
+                "id": mock.ANY,
+                "domains": EMPTY_DOMAINS,
+            },
+        )
+
+        # Test Division
+        self.assertEqual(
+            pybamm.Division(10, 5).to_json(),
+            {
+                "name": "/",
+                "id": mock.ANY,
+                "domains": EMPTY_DOMAINS,
+            },
+        )
+
+        # Test EqualHeaviside
+        self.assertEqual(
+            pybamm.EqualHeaviside(2, 4).to_json(),
+            {
+                "name": "<=",
+                "id": mock.ANY,
+                "domains": EMPTY_DOMAINS,
+            },
+        )
+
+        # Test notEqualHeaviside
+        self.assertEqual(
+            pybamm.NotEqualHeaviside(2, 4).to_json(),
+            {
+                "name": "<",
+                "id": mock.ANY,
+                "domains": EMPTY_DOMAINS,
+            },
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py
index 81d1210229..be6772af2d 100644
--- a/tests/unit/test_expression_tree/test_broadcasts.py
+++ b/tests/unit/test_expression_tree/test_broadcasts.py
@@ -350,6 +350,12 @@ def test_diff(self):
         self.assertIsInstance(d, pybamm.Scalar)
         self.assertEqual(d.evaluate(y=y), 0)
 
+    def test_to_json(self):
+        a = pybamm.StateVector(slice(0, 1))
+        b = pybamm.PrimaryBroadcast(a, "separator")
+        with self.assertRaises(NotImplementedError):
+            b.to_json()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py
index df5add0f98..f846220a77 100644
--- a/tests/unit/test_expression_tree/test_concatenations.py
+++ b/tests/unit/test_expression_tree/test_concatenations.py
@@ -2,6 +2,7 @@
 # Tests for the Concatenation class and subclasses
 #
 import unittest
+import unittest.mock as mock
 from tests import TestCase
 
 import numpy as np
@@ -382,6 +383,64 @@ def test_to_equation(self):
         # Test concat_sym
         self.assertEqual(pybamm.Concatenation(a, b).to_equation(), func_symbol)
 
+    def test_to_json(self):
+        # test DomainConcatenation
+        mesh = get_mesh_for_testing()
+        a = pybamm.Symbol("a", domain=["negative electrode"])
+        b = pybamm.Symbol("b", domain=["separator", "positive electrode"])
+        conc = pybamm.DomainConcatenation([a, b], mesh)
+
+        json_dict = {
+            "name": "domain_concatenation",
+            "id": mock.ANY,
+            "domains": {
+                "primary": ["negative electrode", "separator", "positive electrode"],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "slices": {
+                "negative electrode": [{"start": 0, "stop": 40, "step": None}],
+                "separator": [{"start": 40, "stop": 65, "step": None}],
+                "positive electrode": [{"start": 65, "stop": 100, "step": None}],
+            },
+            "size": 100,
+            "children_slices": [
+                {"negative electrode": [{"start": 0, "stop": 40, "step": None}]},
+                {
+                    "separator": [{"start": 0, "stop": 25, "step": None}],
+                    "positive electrode": [{"start": 25, "stop": 60, "step": None}],
+                },
+            ],
+            "secondary_dimensions_npts": 1,
+        }
+
+        self.assertEqual(
+            conc.to_json(),
+            json_dict,
+        )
+
+        # test NumpyConcatenation
+        y = np.linspace(0, 1, 15)[:, np.newaxis]
+        a_np = pybamm.Vector(y[:5])
+        b_np = pybamm.Vector(y[5:9])
+        c_np = pybamm.Vector(y[9:])
+        conc_np = pybamm.NumpyConcatenation(a_np, b_np, c_np)
+
+        self.assertEqual(
+            conc_np.to_json(),
+            {
+                "name": "numpy_concatenation",
+                "id": mock.ANY,
+                "domains": {
+                    "primary": [],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+            },
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_input_parameter.py b/tests/unit/test_expression_tree/test_input_parameter.py
index 82dd06fee5..48ad2c441f 100644
--- a/tests/unit/test_expression_tree/test_input_parameter.py
+++ b/tests/unit/test_expression_tree/test_input_parameter.py
@@ -6,6 +6,8 @@
 import pybamm
 import unittest
 
+import unittest.mock as mock
+
 
 class TestInputParameter(TestCase):
     def test_input_parameter_init(self):
@@ -49,6 +51,18 @@ def test_errors(self):
         with self.assertRaises(KeyError):
             a.evaluate()
 
+    def test_to_json(self):
+        a = pybamm.InputParameter("a")
+
+        json_dict = {
+            "name": "a",
+            "id": mock.ANY,
+            "domain": [],
+            "expected_size": 1,
+        }
+
+        self.assertEqual(a.to_json(), json_dict)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index e1547ef3fc..b6c195eccc 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -325,6 +325,14 @@ def test_processing(self):
 
         self.assertEqual(interp, interp.new_copy())
 
+    def test_to_json(self):
+        x = np.linspace(0, 1, 200)
+        y = pybamm.StateVector(slice(0, 2))
+        interp = pybamm.Interpolant(x, 2 * x, y)
+
+        with self.assertRaises(NotImplementedError):
+            interp.to_json()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_matrix.py b/tests/unit/test_expression_tree/test_matrix.py
index 39aba44483..8e466818f1 100644
--- a/tests/unit/test_expression_tree/test_matrix.py
+++ b/tests/unit/test_expression_tree/test_matrix.py
@@ -4,8 +4,10 @@
 from tests import TestCase
 import pybamm
 import numpy as np
+from scipy.sparse import csr_matrix
 
 import unittest
+import unittest.mock as mock
 
 
 class TestMatrix(TestCase):
@@ -38,6 +40,28 @@ def test_matrix_operations(self):
             (self.mat @ self.vect).evaluate(), np.array([[5], [2], [3]])
         )
 
+    def test_to_json_matrix(self):
+        arr = pybamm.Matrix(csr_matrix([[0, 1, 0, 0], [0, 0, 0, 1]]))
+        self.assertEqual(
+            arr.to_json(),
+            {
+                "name": "Sparse Matrix (2, 4)",
+                "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+                "domains": {
+                    "primary": [],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+                "entries": {
+                    "column_pointers": [0, 1, 2],
+                    "data": [1.0, 1.0],
+                    "row_indices": [1, 3],
+                    "shape": (2, 4),
+                },
+            },
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index f67ee2dd62..6001d8906b 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -31,6 +31,12 @@ def test_to_equation(self):
         # Test name
         self.assertEqual(func1.to_equation(), sympy.Symbol("test_name"))
 
+    def test_to_json(self):
+        func = pybamm.Parameter("test_string")
+
+        with self.assertRaises(NotImplementedError):
+            func.to_json()
+
 
 class TestFunctionParameter(TestCase):
     def test_function_parameter_init(self):
@@ -109,6 +115,12 @@ def test_function_parameter_to_equation(self):
         func1.print_name = None
         self.assertEqual(func1.to_equation(), sympy.Symbol("func"))
 
+    def test_to_json(self):
+        func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)})
+
+        with self.assertRaises(NotImplementedError):
+            func.to_json()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_scalar.py b/tests/unit/test_expression_tree/test_scalar.py
index af0a6e80ca..9d990e354d 100644
--- a/tests/unit/test_expression_tree/test_scalar.py
+++ b/tests/unit/test_expression_tree/test_scalar.py
@@ -3,6 +3,7 @@
 #
 from tests import TestCase
 import unittest
+import unittest.mock as mock
 
 import pybamm
 
@@ -44,6 +45,11 @@ def test_copy(self):
         b = a.create_copy()
         self.assertEqual(a, b)
 
+    def test_to_json(self):
+        a = pybamm.Scalar(5)
+
+        self.assertEqual(a.to_json(), {"name": "5.0", "id": mock.ANY, "value": 5.0})
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_state_vector.py b/tests/unit/test_expression_tree/test_state_vector.py
index d401487264..0165d1d512 100644
--- a/tests/unit/test_expression_tree/test_state_vector.py
+++ b/tests/unit/test_expression_tree/test_state_vector.py
@@ -6,6 +6,7 @@
 import numpy as np
 
 import unittest
+import unittest.mock as mock
 
 
 class TestStateVector(TestCase):
@@ -62,6 +63,31 @@ def test_failure(self):
         with self.assertRaisesRegex(TypeError, "all y_slices must be slice objects"):
             pybamm.StateVector(slice(0, 10), 1)
 
+    def test_to_json(self):
+        array = np.array([1, 2, 3, 4, 5])
+        sv = pybamm.StateVector(slice(0, 10), evaluation_array=array)
+
+        json_dict = {
+            "name": "y[0:10]",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "y_slice": [
+                {
+                    "start": 0,
+                    "stop": 10,
+                    "step": None,
+                }
+            ],
+            "evaluation_array": [1, 2, 3, 4, 5],
+        }
+
+        self.assertEqual(sv.to_json(), json_dict)
+
 
 class TestStateVectorDot(TestCase):
     def test_evaluate(self):
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 3a74375ce7..17c5f0a02f 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -4,6 +4,7 @@
 from tests import TestCase
 import os
 import unittest
+import unittest.mock as mock
 
 import numpy as np
 from scipy.sparse import csr_matrix, coo_matrix
@@ -486,6 +487,25 @@ def test_numpy_array_ufunc(self):
         x = pybamm.Symbol("x")
         self.assertEqual(np.exp(x), pybamm.exp(x))
 
+    def test_to_json(self):
+        symc1 = pybamm.Symbol("child1", domain=["domain_1"])
+        symc2 = pybamm.Symbol("child2", domain=["domain_2"])
+        symp = pybamm.Symbol("parent", domain=["domain_3"], children=[symc1, symc2])
+
+        self.assertEqual(
+            symp.to_json(),
+            {
+                "name": "parent",
+                "id": mock.ANY,
+                "domains": {
+                    "primary": ["domain_3"],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+            },
+        )
+
 
 class TestIsZero(TestCase):
     def test_is_scalar_zero(self):
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index b0513c974b..e8fc7c7be0 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -3,6 +3,7 @@
 #
 import unittest
 from tests import TestCase
+import unittest.mock as mock
 
 import numpy as np
 import sympy
@@ -668,6 +669,57 @@ def test_explicit_time_integral(self):
         self.assertEqual(expr.new_copy(), expr)
         self.assertFalse(expr.is_constant())
 
+    def test_to_json(self):
+        # UnaryOperator
+        a = pybamm.Symbol("a", domain=["test"])
+        un = pybamm.UnaryOperator("unary test", a)
+        self.assertEqual(
+            un.to_json(),
+            {
+                "name": "unary test",
+                "id": mock.ANY,
+                "domains": {
+                    "primary": ["test"],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+            },
+        )
+
+        # Index
+        vec = pybamm.StateVector(slice(0, 5))
+        ind = pybamm.Index(vec, 3)
+        self.assertEqual(
+            ind.to_json(),
+            {
+                "name": "Index[3]",
+                "id": mock.ANY,
+                "domains": {
+                    "primary": [],
+                    "secondary": [],
+                    "tertiary": [],
+                    "quaternary": [],
+                },
+                "check_size": False,
+            },
+        )
+
+        # SpatialOperator
+        spatial_vec = pybamm.SpatialOperator("name", vec)
+        with self.assertRaises(NotImplementedError):
+            spatial_vec.to_json()
+
+        # ExplicitTimeIntegral
+        expr = pybamm.ExplicitTimeIntegral(pybamm.Parameter("param"), pybamm.Scalar(1))
+        self.assertEqual(
+            expr.to_json(),
+            {
+                "name": "explicit time integral",
+                "id": mock.ANY,
+            },
+        )
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py
index be791903e2..f4f3029c75 100644
--- a/tests/unit/test_expression_tree/test_variable.py
+++ b/tests/unit/test_expression_tree/test_variable.py
@@ -63,6 +63,11 @@ def test_to_equation(self):
         # Test name
         self.assertEqual(pybamm.Variable("name").to_equation(), sympy.Symbol("name"))
 
+    def test_to_json(self):
+        func = pybamm.Variable("test_string")
+        with self.assertRaises(NotImplementedError):
+            func.to_json()
+
 
 class TestVariableDot(TestCase):
     def test_variable_init(self):

From 25cb002789ab71212653d0880b20b60d0a7f6618 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 21 Sep 2023 16:01:30 +0000
Subject: [PATCH 108/615] Allow saving of geometry where symbols are dict keys
 Put warning in for BaseModel - atm requires more model information to
 re-create the model.

---
 .../notebooks/models/saving_models.ipynb      |  25 ++--
 .../expression_tree/operations/serialise.py   | 118 ++++++++++++++++--
 pybamm/models/base_model.py                   |  11 ++
 3 files changed, 129 insertions(+), 25 deletions(-)

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index 94799bcc48..c3c9c90ea8 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Models which are discretised (i.e. ready to solve/ previously solved, see A DIFFERENT NOTEBOOK) can be serialised and saved to a JSON file, ready to be read in again either in PyBaMM, or a different modelling library. \n",
+    "Models which are discretised (i.e. ready to solve/ previously solved, see [this notebook](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb) for more information on the pybamm.Discretisation class) can be serialised and saved to a JSON file, ready to be read in again either in PyBaMM, or a different modelling library. \n",
     "\n",
     "In the example below, we build and solve a basic DFN model, and then save the model out to `sim_model_example.json`, which should have appear in the 'models' directory."
    ]
@@ -25,9 +25,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -59,7 +56,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7feb7764a460>"
+       "<pybamm.solvers.solution.Solution at 0x7f1b9f66cee0>"
       ]
      },
      "execution_count": 2,
@@ -98,7 +95,7 @@
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
       "\u001b[1;32m/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m     plot_sim\u001b[39m.\u001b[39msolve([\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m     sims\u001b[39m.\u001b[39mappend(plot_sim)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m pybamm\u001b[39m.\u001b[39;49mdynamic_plot(sims, time_unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mseconds\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
       "File \u001b[0;32m~/PyBaMM/pybamm/plotting/dynamic_plot.py:20\u001b[0m, in \u001b[0;36mdynamic_plot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39m    The 'QuickPlot' object that was created\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     19\u001b[0m kwargs_for_class \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 20\u001b[0m plot \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39;49mQuickPlot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs_for_class)\n\u001b[1;32m     21\u001b[0m plot\u001b[39m.\u001b[39mdynamic_plot(kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m     22\u001b[0m \u001b[39mreturn\u001b[39;00m plot\n",
-      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/quick_plot.py:163\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m    162\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 163\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mVariables not provided by the serialised model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    165\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m    166\u001b[0m     \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m    167\u001b[0m )\n\u001b[1;32m    168\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n",
+      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/quick_plot.py:159\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 159\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mVariables not provided by the serialised model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    161\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m    162\u001b[0m     \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m    163\u001b[0m )\n\u001b[1;32m    164\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n",
       "\u001b[0;31mAttributeError\u001b[0m: Variables not provided by the serialised model"
      ]
     }
@@ -131,7 +128,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6b4db83fd054ba4be3ee279f7024c6a",
+       "model_id": "eaf8ae8b8dd84a99b8b1aecfc132ad83",
        "version_major": 2,
        "version_minor": 0
       },
@@ -145,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x7feb6f3b7b80>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f1b95310fa0>"
       ]
      },
      "execution_count": 4,
@@ -179,7 +176,9 @@
     "\n",
     "Alternatively, the model can be saved directly from the Model class.\n",
     "\n",
-    "First set up the model, as explained in detail in the SPM NOTEBOOK"
+    "Note that at the moment, only models derived from the BaseBatteryModel class can be serialised; those built from scratch using pybamm.BaseModel() are currently unsupported.\n",
+    "\n",
+    "First set up the model, as explained in detail for the [SPM](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/models/SPM.ipynb)."
    ]
   },
   {
@@ -190,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.spm.SPM at 0x7feb11370250>"
+       "<pybamm.models.full_battery_models.lithium_ion.spm.SPM at 0x7f1b8b856040>"
       ]
      },
      "execution_count": 5,
@@ -244,7 +243,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6df594b3af646599430ff322349b44f",
+       "model_id": "a1c0b22c969b45858361b7e9de264e76",
        "version_major": 2,
        "version_minor": 0
       },
@@ -258,7 +257,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x7feb0c3f7310>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f1b322bc160>"
       ]
      },
      "execution_count": 7,
@@ -289,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index e11049a35a..d27f770451 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -5,6 +5,7 @@
 import json
 import importlib
 import numpy as np
+import re
 
 from typing import TYPE_CHECKING
 
@@ -135,7 +136,9 @@ def save_model(
             model_json["mesh"] = self._MeshEncoder().default(mesh)
 
         if variables:
-            model_json["geometry"] = dict(model._geometry)
+            model_json["geometry"] = self._deconstruct_pybamm_dicts(
+                dict(model._geometry)
+            )
             model_json["variables"] = {
                 k: self._SymbolEncoder().default(v) for k, v in dict(variables).items()
             }
@@ -184,27 +187,29 @@ def load_model(
             "name": model_data["name"],
             "options": model_data["options"],
             "bounds": tuple(np.array(bound) for bound in model_data["bounds"]),
-            "concatenated_rhs": self._reconstruct_epression_tree(
+            "concatenated_rhs": self._reconstruct_expression_tree(
                 model_data["concatenated_rhs"]
             ),
-            "concatenated_algebraic": self._reconstruct_epression_tree(
+            "concatenated_algebraic": self._reconstruct_expression_tree(
                 model_data["concatenated_algebraic"]
             ),
-            "concatenated_initial_conditions": self._reconstruct_epression_tree(
+            "concatenated_initial_conditions": self._reconstruct_expression_tree(
                 model_data["concatenated_initial_conditions"]
             ),
             "events": [
-                self._reconstruct_epression_tree(event)
+                self._reconstruct_expression_tree(event)
                 for event in model_data["events"]
             ],
-            "mass_matrix": self._reconstruct_epression_tree(model_data["mass_matrix"]),
-            "mass_matrix_inv": self._reconstruct_epression_tree(
+            "mass_matrix": self._reconstruct_expression_tree(model_data["mass_matrix"]),
+            "mass_matrix_inv": self._reconstruct_expression_tree(
                 model_data["mass_matrix_inv"]
             ),
         }
 
         recon_model_dict["geometry"] = (
-            model_data["geometry"] if "geometry" in model_data.keys() else None
+            self._reconstruct_geometry(model_data["geometry"])
+            if "geometry" in model_data.keys()
+            else None
         )
 
         recon_model_dict["mesh"] = (
@@ -215,7 +220,7 @@ def load_model(
 
         recon_model_dict["variables"] = (
             {
-                k: self._reconstruct_epression_tree(v)
+                k: self._reconstruct_expression_tree(v)
                 for k, v in model_data["variables"].items()
             }
             if "variables" in model_data.keys()
@@ -235,6 +240,8 @@ def load_model(
             """
         )
 
+    # Helper functions
+
     def _get_pybamm_class(self, snippet: dict):
         """Find a pybamm class to initialise from object path"""
         parts = snippet["py/object"].split(".")
@@ -254,13 +261,55 @@ def _get_pybamm_class(self, snippet: dict):
 
         return empty_class
 
+    def _deconstruct_pybamm_dicts(self, dct: dict):
+        """
+        Converts dictionaries which contain pybamm classes as keys
+        into a json serialisable format.
+
+        Dictionary keys present as pybamm objects are given a seperate key
+        as "symbol_<symbol name>" to store the dictionary required to reconstruct
+        a symbol, and their seperate key is used in the original dictionary. E.G:
+
+        {'rod':
+            {SpatialVariable(name='spat_var'): {"min":0.0, "max":2.0} }
+            }
+
+        converts to
+
+        {'rod':
+            {'symbol_spat_var': {"min":0.0, "max":2.0} },
+        'spat_var':
+            {"py/object":pybamm....}
+        }
+
+        Dictionaries which don't contain pybamm symbols are returned unchanged.
+        """
+
+        def nested_convert(obj):
+            if isinstance(obj, dict):
+                new_dict = {}
+                for k, v in obj.items():
+                    if isinstance(k, pybamm.Symbol):
+                        new_k = self._SymbolEncoder().default(k)
+                        new_dict["symbol_" + new_k["name"]] = new_k
+                        k = new_k["name"]
+                    new_dict[k] = nested_convert(v)
+                return new_dict
+            return obj
+
+        try:
+            _ = json.dumps(dct)
+            return dict(dct)
+        except TypeError:  # dct must contain pybamm objects
+            return nested_convert(dct)
+
     def _reconstruct_symbol(self, dct: dict):
         """Reconstruct an individual pybamm Symbol"""
         symbol_class = self._get_pybamm_class(dct)
         symbol = symbol_class._from_json(dct)
         return symbol
 
-    def _reconstruct_epression_tree(self, node: dict):
+    def _reconstruct_expression_tree(self, node: dict):
         """
         Loop through an expression tree creating pybamm Symbol classes
 
@@ -275,10 +324,10 @@ def _reconstruct_epression_tree(self, node: dict):
         """
         if "children" in node:
             for i, c in enumerate(node["children"]):
-                child_obj = self._reconstruct_epression_tree(c)
+                child_obj = self._reconstruct_expression_tree(c)
                 node["children"][i] = child_obj
         elif "expression" in node:
-            expression_obj = self._reconstruct_epression_tree(node["expression"])
+            expression_obj = self._reconstruct_expression_tree(node["expression"])
             node["expression"] = expression_obj
 
         obj = self._reconstruct_symbol(node)
@@ -295,3 +344,48 @@ def _reconstruct_mesh(self, node: dict):
         new_mesh = self._reconstruct_symbol(node)
 
         return new_mesh
+
+    def _reconstruct_geometry(self, obj: dict):
+        """
+        pybamm.Geometry can contain PyBaMM symbols as dictionary keys.
+
+        Converts
+        {"rod":
+            {"symbol_spat_var":
+                {"min":0.0, "max":2.0} },
+            "spat_var":
+                {"py/object":"pybamm...."}
+        }
+
+        from an exported JSON file to
+
+        {"rod":
+            {SpatialVariable(name="spat_var"): {"min":0.0, "max":2.0} }
+            }
+        """
+
+        def recurse(obj):
+            if isinstance(obj, dict):
+                new_dict = {}
+                for k, v in obj.items():
+                    if "symbol_" in k:
+                        new_dict[k] = self._reconstruct_symbol(v)
+                    elif isinstance(v, dict):
+                        new_dict[k] = recurse(v)
+                    else:
+                        new_dict[k] = v
+
+                pattern = re.compile("symbol_")
+                symbol_keys = {k: v for k, v in new_dict.items() if pattern.match(k)}
+
+                # rearrange the dictionary to make pybamm objects the dictionary keys
+                if symbol_keys:
+                    for k, v in symbol_keys.items():
+                        new_dict[v] = new_dict[k.lstrip("symbol_")]
+                        del new_dict[k]
+                        del new_dict[k.lstrip("symbol_")]
+
+                return new_dict
+            return obj
+
+        return recurse(obj)
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..80dda2808d 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -123,6 +123,12 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
+    @classmethod
+    def deserialise(cls, properties: dict):
+        raise NotImplementedError(
+            "BaseModel: Serialisation not yet implemented for non-battery models."
+        )
+
     @property
     def name(self):
         return self._name
@@ -1110,6 +1116,11 @@ def process_parameters_and_discretise(self, symbol, parameter_values, disc):
 
         return disc_symbol
 
+    def save_model(self, filename=None, mesh=None, variables=None):
+        raise NotImplementedError(
+            "BaseModel: Serialisation not yet implemented for non-battery models."
+        )
+
 
 # helper functions for finding symbols
 def find_symbol_in_tree(tree, name):

From efa78887a584f2733969a6a3a1b28e327897fb8d Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 21 Sep 2023 16:36:26 +0000
Subject: [PATCH 109/615] Add _from_json tests for symbols without children

---
 tests/unit/test_expression_tree/test_array.py | 33 +++++++++-------
 .../test_expression_tree/test_broadcasts.py   |  2 +-
 .../test_input_parameter.py                   |  6 ++-
 .../test_expression_tree/test_interpolant.py  |  2 +-
 .../unit/test_expression_tree/test_matrix.py  | 39 ++++++++++---------
 .../test_expression_tree/test_parameter.py    |  4 +-
 .../unit/test_expression_tree/test_scalar.py  |  7 +++-
 .../test_expression_tree/test_state_vector.py |  4 +-
 .../test_expression_tree/test_variable.py     |  2 +-
 9 files changed, 57 insertions(+), 42 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_array.py b/tests/unit/test_expression_tree/test_array.py
index 6ef1669270..885c5e0851 100644
--- a/tests/unit/test_expression_tree/test_array.py
+++ b/tests/unit/test_expression_tree/test_array.py
@@ -42,22 +42,27 @@ def test_to_equation(self):
             pybamm.Array([1, 2]).to_equation(), sympy.Array([[1.0], [2.0]])
         )
 
-    def test_to_json_array(self):
+    def test_to_from_json(self):
         arr = pybamm.Array(np.array([1, 2, 3]))
-        self.assertEqual(
-            arr.to_json(),
-            {
-                "name": "Array of shape (3, 1)",
-                "id": mock.ANY,  # The value of the ID will change, but want to check it is present
-                "domains": {
-                    "primary": [],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
-                "entries": [[1.0], [2.0], [3.0]],
+
+        json_dict = {
+            "name": "Array of shape (3, 1)",
+            "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
             },
-        )
+            "entries": [[1.0], [2.0], [3.0]],
+        }
+
+        # array to json conversion
+        created_json = arr.to_json()
+        self.assertEqual(created_json, json_dict)
+
+        # json to array conversion
+        self.assertEqual(pybamm.Array._from_json(created_json), arr)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py
index be6772af2d..b91cd7d95c 100644
--- a/tests/unit/test_expression_tree/test_broadcasts.py
+++ b/tests/unit/test_expression_tree/test_broadcasts.py
@@ -350,7 +350,7 @@ def test_diff(self):
         self.assertIsInstance(d, pybamm.Scalar)
         self.assertEqual(d.evaluate(y=y), 0)
 
-    def test_to_json(self):
+    def test_to_json_error(self):
         a = pybamm.StateVector(slice(0, 1))
         b = pybamm.PrimaryBroadcast(a, "separator")
         with self.assertRaises(NotImplementedError):
diff --git a/tests/unit/test_expression_tree/test_input_parameter.py b/tests/unit/test_expression_tree/test_input_parameter.py
index 48ad2c441f..a5fc79f2e2 100644
--- a/tests/unit/test_expression_tree/test_input_parameter.py
+++ b/tests/unit/test_expression_tree/test_input_parameter.py
@@ -51,7 +51,7 @@ def test_errors(self):
         with self.assertRaises(KeyError):
             a.evaluate()
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         a = pybamm.InputParameter("a")
 
         json_dict = {
@@ -61,8 +61,12 @@ def test_to_json(self):
             "expected_size": 1,
         }
 
+        # to_json
         self.assertEqual(a.to_json(), json_dict)
 
+        # from_json
+        self.assertEqual(pybamm.InputParameter._from_json(json_dict), a)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index b6c195eccc..7389ff183a 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -325,7 +325,7 @@ def test_processing(self):
 
         self.assertEqual(interp, interp.new_copy())
 
-    def test_to_json(self):
+    def test_to_json_error(self):
         x = np.linspace(0, 1, 200)
         y = pybamm.StateVector(slice(0, 2))
         interp = pybamm.Interpolant(x, 2 * x, y)
diff --git a/tests/unit/test_expression_tree/test_matrix.py b/tests/unit/test_expression_tree/test_matrix.py
index 8e466818f1..2c3d2379ab 100644
--- a/tests/unit/test_expression_tree/test_matrix.py
+++ b/tests/unit/test_expression_tree/test_matrix.py
@@ -40,27 +40,28 @@ def test_matrix_operations(self):
             (self.mat @ self.vect).evaluate(), np.array([[5], [2], [3]])
         )
 
-    def test_to_json_matrix(self):
+    def test_to_from_json(self):
         arr = pybamm.Matrix(csr_matrix([[0, 1, 0, 0], [0, 0, 0, 1]]))
-        self.assertEqual(
-            arr.to_json(),
-            {
-                "name": "Sparse Matrix (2, 4)",
-                "id": mock.ANY,  # The value of the ID will change, but want to check it is present
-                "domains": {
-                    "primary": [],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
-                "entries": {
-                    "column_pointers": [0, 1, 2],
-                    "data": [1.0, 1.0],
-                    "row_indices": [1, 3],
-                    "shape": (2, 4),
-                },
+        json_dict = {
+            "name": "Sparse Matrix (2, 4)",
+            "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
             },
-        )
+            "entries": {
+                "column_pointers": [0, 1, 2],
+                "data": [1.0, 1.0],
+                "row_indices": [1, 3],
+                "shape": (2, 4),
+            },
+        }
+
+        self.assertEqual(arr.to_json(), json_dict)
+
+        self.assertEqual(pybamm.Matrix._from_json(json_dict), arr)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index 6001d8906b..62441f4309 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -31,7 +31,7 @@ def test_to_equation(self):
         # Test name
         self.assertEqual(func1.to_equation(), sympy.Symbol("test_name"))
 
-    def test_to_json(self):
+    def test_to_json_error(self):
         func = pybamm.Parameter("test_string")
 
         with self.assertRaises(NotImplementedError):
@@ -115,7 +115,7 @@ def test_function_parameter_to_equation(self):
         func1.print_name = None
         self.assertEqual(func1.to_equation(), sympy.Symbol("func"))
 
-    def test_to_json(self):
+    def test_to_json_error(self):
         func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)})
 
         with self.assertRaises(NotImplementedError):
diff --git a/tests/unit/test_expression_tree/test_scalar.py b/tests/unit/test_expression_tree/test_scalar.py
index 9d990e354d..34ea1aa514 100644
--- a/tests/unit/test_expression_tree/test_scalar.py
+++ b/tests/unit/test_expression_tree/test_scalar.py
@@ -45,10 +45,13 @@ def test_copy(self):
         b = a.create_copy()
         self.assertEqual(a, b)
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         a = pybamm.Scalar(5)
+        json_dict = {"name": "5.0", "id": mock.ANY, "value": 5.0}
 
-        self.assertEqual(a.to_json(), {"name": "5.0", "id": mock.ANY, "value": 5.0})
+        self.assertEqual(a.to_json(), json_dict)
+
+        self.assertEqual(pybamm.Scalar._from_json(json_dict), a)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_state_vector.py b/tests/unit/test_expression_tree/test_state_vector.py
index 0165d1d512..9897b9a027 100644
--- a/tests/unit/test_expression_tree/test_state_vector.py
+++ b/tests/unit/test_expression_tree/test_state_vector.py
@@ -63,7 +63,7 @@ def test_failure(self):
         with self.assertRaisesRegex(TypeError, "all y_slices must be slice objects"):
             pybamm.StateVector(slice(0, 10), 1)
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         array = np.array([1, 2, 3, 4, 5])
         sv = pybamm.StateVector(slice(0, 10), evaluation_array=array)
 
@@ -88,6 +88,8 @@ def test_to_json(self):
 
         self.assertEqual(sv.to_json(), json_dict)
 
+        self.assertEqual(pybamm.StateVector._from_json(json_dict), sv)
+
 
 class TestStateVectorDot(TestCase):
     def test_evaluate(self):
diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py
index f4f3029c75..b350cb794d 100644
--- a/tests/unit/test_expression_tree/test_variable.py
+++ b/tests/unit/test_expression_tree/test_variable.py
@@ -63,7 +63,7 @@ def test_to_equation(self):
         # Test name
         self.assertEqual(pybamm.Variable("name").to_equation(), sympy.Symbol("name"))
 
-    def test_to_json(self):
+    def test_to_json_error(self):
         func = pybamm.Variable("test_string")
         with self.assertRaises(NotImplementedError):
             func.to_json()

From fbc8f6fd682725dc30f39c818d281ffab907079c Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 22 Sep 2023 13:42:30 +0000
Subject: [PATCH 110/615] (wip) testing: add draft de/serialisation tests allow
 interpolant to be serialised fix concatenation with debug mode switch msmr
 warnings

---
 pybamm/expression_tree/concatenations.py      |   4 +-
 pybamm/expression_tree/interpolant.py         |  38 +++-
 .../full_battery_models/base_battery_model.py |   4 +
 .../full_battery_models/lithium_ion/msmr.py   |   4 +-
 .../test_expression_tree/test_interpolant.py  |  40 +++-
 .../test_expression_tree/test_state_vector.py |   6 +
 tests/unit/test_serialisation/__init__.py     |   0
 .../test_serialisation/test_serialisation.py  | 182 ++++++++++++++++++
 8 files changed, 268 insertions(+), 10 deletions(-)
 create mode 100644 tests/unit/test_serialisation/__init__.py
 create mode 100644 tests/unit/test_serialisation/test_serialisation.py

diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index af3db72846..d393cc6647 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -48,10 +48,10 @@ def _from_json(cls, *children, name, domains, concat_fun=None):
         # PL: update this one - I guess we still want it to take 'snippet' rather than the list? to be the same as the others?
         instance = cls.__new__(cls)
 
-        super(Concatenation, instance).__init__(name, children, domains=domains)
-
         instance.concatenation_function = concat_fun
 
+        super(Concatenation, instance).__init__(name, children, domains=domains)
+
         return instance
 
     def __str__(self):
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 5234e5e927..20d4e0180b 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -202,6 +202,27 @@ def __init__(
         self.interpolator = interpolator
         self.extrapolate = extrapolate
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """Create an Interpolant object from JSON data"""
+        instance = cls.__new__(cls)
+
+        if len(snippet["x"]) == 1:
+            x = [np.array(x) for x in snippet["x"]]
+        else:
+            x = tuple(np.array(x) for x in snippet["x"])
+
+        instance.__init__(
+            x,
+            np.array(snippet["y"]),
+            snippet["children"],
+            name=snippet["name"],
+            interpolator=snippet["interpolator"],
+            extrapolate=snippet["extrapolate"],
+        )
+
+        return instance
+
     @property
     def entries_string(self):
         return self._entries_string
@@ -292,6 +313,17 @@ def _function_evaluate(self, evaluated_children):
             raise ValueError("Invalid dimension: {0}".format(self.dimension))
 
     def to_json(self):
-        raise NotImplementedError(
-            "pybamm.Interpolant: Serialisation is only implemented for discretised models."
-        )
+        """
+        Method to serialise an Interpolant object into JSON.
+        """
+
+        json_dict = {
+            "name": self.name,
+            "id": self.id,
+            "x": [x_item.tolist() for x_item in self.x],
+            "y": self.y.tolist(),
+            "interpolator": self.interpolator,
+            "extrapolate": self.extrapolate,
+        }
+
+        return json_dict
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 841bf53a81..deb312b379 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -604,6 +604,10 @@ def __init__(self, extra_options):
             if option in ["working electrode"]:
                 pass
             else:
+                # serialised options save tuples as lists which need to be converted
+                if isinstance(value, list) and len(value) == 2:
+                    value = tuple(value)
+
                 if isinstance(value, str) or option in [
                     "dimensionality",
                     "operating mode",
diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py
index 3ca07c4ef8..f1ec7f90bd 100644
--- a/pybamm/models/full_battery_models/lithium_ion/msmr.py
+++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py
@@ -19,7 +19,7 @@ def __init__(self, options=None, name="MSMR", build=True):
                     options["open-circuit potential"]
                 )
             )
-        elif "particle" in options and options["particle"] == "MSMR":
+        elif "particle" in options and options["particle"] != "MSMR":
             raise pybamm.OptionError(
                 "'particle' must be 'MSMR' for MSMR not '{}'".format(
                     options["particle"]
@@ -27,7 +27,7 @@ def __init__(self, options=None, name="MSMR", build=True):
             )
         elif (
             "intercalation kinetics" in options
-            and options["intercalation kinetics"] == "MSMR"
+            and options["intercalation kinetics"] != "MSMR"
         ):
             raise pybamm.OptionError(
                 "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format(
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 7389ff183a..93009adf0d 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -5,6 +5,7 @@
 import pybamm
 
 import unittest
+import unittest.mock as mock
 import numpy as np
 
 
@@ -326,12 +327,45 @@ def test_processing(self):
         self.assertEqual(interp, interp.new_copy())
 
     def test_to_json_error(self):
-        x = np.linspace(0, 1, 200)
+        x = np.linspace(0, 1, 10)
         y = pybamm.StateVector(slice(0, 2))
         interp = pybamm.Interpolant(x, 2 * x, y)
 
-        with self.assertRaises(NotImplementedError):
-            interp.to_json()
+        self.assertEqual(
+            interp.to_json(),
+            {
+                "name": "interpolating_function",
+                "id": mock.ANY,
+                "x": [
+                    [
+                        0.0,
+                        0.1111111111111111,
+                        0.2222222222222222,
+                        0.3333333333333333,
+                        0.4444444444444444,
+                        0.5555555555555556,
+                        0.6666666666666666,
+                        0.7777777777777777,
+                        0.8888888888888888,
+                        1.0,
+                    ]
+                ],
+                "y": [
+                    0.0,
+                    0.2222222222222222,
+                    0.4444444444444444,
+                    0.6666666666666666,
+                    0.8888888888888888,
+                    1.1111111111111112,
+                    1.3333333333333333,
+                    1.5555555555555554,
+                    1.7777777777777777,
+                    2.0,
+                ],
+                "interpolator": "linear",
+                "extrapolate": True,
+            },
+        )
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_state_vector.py b/tests/unit/test_expression_tree/test_state_vector.py
index 9897b9a027..18025c0aa3 100644
--- a/tests/unit/test_expression_tree/test_state_vector.py
+++ b/tests/unit/test_expression_tree/test_state_vector.py
@@ -64,6 +64,9 @@ def test_failure(self):
             pybamm.StateVector(slice(0, 10), 1)
 
     def test_to_from_json(self):
+        original_debug_mode = pybamm.settings.debug_mode
+        pybamm.settings.debug_mode = False
+
         array = np.array([1, 2, 3, 4, 5])
         sv = pybamm.StateVector(slice(0, 10), evaluation_array=array)
 
@@ -90,6 +93,9 @@ def test_to_from_json(self):
 
         self.assertEqual(pybamm.StateVector._from_json(json_dict), sv)
 
+        # Turn debug mode back to what is was before
+        pybamm.settings.debug_mode = original_debug_mode
+
 
 class TestStateVectorDot(TestCase):
     def test_evaluate(self):
diff --git a/tests/unit/test_serialisation/__init__.py b/tests/unit/test_serialisation/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
new file mode 100644
index 0000000000..72d4a1a072
--- /dev/null
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -0,0 +1,182 @@
+#
+# Tests for the serialisation class
+#
+from tests import TestCase
+import pybamm
+
+pybamm.settings.debug_mode = True
+
+import numpy as np
+import unittest
+
+
+class TestSerialise(TestCase):
+    # test lithium models
+    def test_spm_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.SPM()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_equal(solution.all_ys[x], new_solution.all_ys[x])
+
+    def test_spme_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.SPMe()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_equal(solution.all_ys[x], new_solution.all_ys[x])
+
+    def test_mpm_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.MPM()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
+
+    def test_dfn_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.DFN()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
+
+    def test_newman_tobias_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.NewmanTobias()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
+
+    def test_msmr_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x], decimal=3
+            )
+
+    # test lead-acid models
+    def test_lead_acid_full_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lead_acid.Full()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
+
+    def test_loqs_serialisation_recreation(self):
+        t = [0, 3600]
+
+        model = pybamm.lead_acid.LOQS()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
+
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
+
+    # def test_thevenin_serialisation_recreation(self):
+    #     t = [0, 3600]
+
+    #     model = pybamm.equivalent_circuit.Thevenin()
+    #     sim = pybamm.Simulation(model)
+    #     solution = sim.solve(t)
+
+    #     sim.save_model("test_model")
+
+    #     new_model = pybamm.load_model("test_model.json")
+    #     new_solver = new_model.default_solver
+    #     new_solution = new_solver.solve(new_model, t)
+
+    #     for x, val in enumerate(solution.all_ys):
+    #         np.testing.assert_array_almost_equal(
+    #             solution.all_ys[x], new_solution.all_ys[x]
+    #         )
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()

From 4745484f7b00943c8a0ff521817bde64fccc0a90 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 22 Sep 2023 16:43:21 +0000
Subject: [PATCH 111/615] (wip) tests: add _from_json tests with children allow
 BaseModel to run without rhs

---
 pybamm/expression_tree/binary_operators.py    | 24 +++--
 pybamm/expression_tree/unary_operators.py     | 49 +++++++++-
 pybamm/models/base_model.py                   | 81 ++++++++++++++--
 .../full_battery_models/base_battery_model.py |  5 +-
 pybamm/solvers/base_solver.py                 | 11 ++-
 .../test_binary_operators.py                  | 95 +++++++++++--------
 .../test_concatenations.py                    | 43 ++++++---
 .../unit/test_expression_tree/test_symbol.py  | 29 +++---
 .../test_unary_operators.py                   | 70 +++++++-------
 .../test_serialisation/test_serialisation.py  | 34 ++++---
 tests/unit/test_simulation.py                 | 20 ++++
 11 files changed, 324 insertions(+), 137 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 30a81ee416..56f3154be9 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -571,14 +571,6 @@ def __init__(self, name, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__(name, left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json(
-            snippet["name"], snippet["children"][0], snippet["children"][1]
-        )
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # Heaviside should always be multiplied by something else so hopefully don't
@@ -610,6 +602,14 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("<=", left, right)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = cls.__new__(cls)
+
+        instance.__init__(snippet["children"][0], snippet["children"][1])
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{!s} <= {!s}".format(self.left, self.right)
@@ -627,6 +627,14 @@ class NotEqualHeaviside(_Heaviside):
     def __init__(self, left, right):
         super().__init__("<", left, right)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.BinaryOperator._from_json()`."""
+        instance = cls.__new__(cls)
+
+        instance.__init__(snippet["children"][0], snippet["children"][1])
+        return instance
+
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{!s} < {!s}".format(self.left, self.right)
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index b4db6b6528..2b85309469 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -150,6 +150,12 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("abs", child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = super()._from_json("abs", snippet)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return sign(self.child) * self.child.diff(variable)
@@ -176,6 +182,12 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("sign", child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = super()._from_json("sign", snippet)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return pybamm.Scalar(0)
@@ -206,6 +218,12 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("floor", child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = super()._from_json("floor", snippet)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return pybamm.Scalar(0)
@@ -228,6 +246,12 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("ceil", child)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = super()._from_json("ceil", snippet)
+        return instance
+
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return pybamm.Scalar(0)
@@ -293,6 +317,25 @@ def __init__(self, child, index, name=None, check_size=True):
         if isinstance(index, int):
             self.clear_domains()
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        """See :meth:`pybamm.UnaryOperator._from_json()`."""
+        instance = cls.__new__(cls)
+
+        index = slice(
+            snippet["index"]["start"],
+            snippet["index"]["stop"],
+            snippet["index"]["step"],
+        )
+
+        instance.__init__(
+            snippet["children"][0],
+            index,
+            name=snippet["name"],
+            check_size=snippet["check_size"],
+        )
+        return instance
+
     def _unary_jac(self, child_jac):
         """See :meth:`pybamm.UnaryOperator._unary_jac()`."""
 
@@ -345,7 +388,11 @@ def to_json(self):
         json_dict = {
             "name": self.name,
             "id": self.id,
-            "domains": self.domains,
+            "index": {
+                "start": self.slice.start,
+                "stop": self.slice.stop,
+                "step": self.slice.step,
+            },
             "check_size": False,
         }
 
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 80dda2808d..21648f3dfc 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -10,6 +10,7 @@
 
 import pybamm
 from pybamm.expression_tree.operations.latexify import Latexify
+from pybamm.expression_tree.operations.serialise import Serialise
 
 
 class BaseModel:
@@ -123,11 +124,65 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
+    # PL: Next up, how to pass in the non-standard variables, if necessary.
     @classmethod
-    def deserialise(cls, properties: dict):
-        raise NotImplementedError(
-            "BaseModel: Serialisation not yet implemented for non-battery models."
-        )
+    def deserialise(
+        cls, properties: dict
+    ):  # PL: maybe option up here as output_mesh=true to output a tuple, (model, mesh) rather than just updating the variables and leaving it at that.
+        """
+        Create a model instance from a serialised object.
+        """
+        instance = cls.__new__(cls)
+
+        # append the model name with _saved to differentiate
+        instance.__init__(name=properties["name"] + "_saved")
+
+        # PL: what to do with the options?
+
+        # Initialise model with stored variables that have already been discretised
+        instance._concatenated_rhs = properties["concatenated_rhs"]
+        instance._concatenated_algebraic = properties["concatenated_algebraic"]
+        instance._concatenated_initial_conditions = properties[
+            "concatenated_initial_conditions"
+        ]
+
+        instance.len_rhs = instance.concatenated_rhs.size
+        instance.len_alg = instance.concatenated_algebraic.size
+        instance.len_rhs_and_alg = instance.len_rhs + instance.len_alg
+
+        instance.bounds = properties["bounds"]
+        instance.events = properties["events"]
+        instance.mass_matrix = properties["mass_matrix"]
+        instance.mass_matrix_inv = properties["mass_matrix_inv"]
+
+        # add optional properties not required for model to solve
+        if properties["variables"]:
+            instance._variables = pybamm.FuzzyDict(properties["variables"])
+
+            # assign meshes to each variable
+            for var in instance._variables.values():
+                if var.domain != []:
+                    var.mesh = properties["mesh"][var.domain]
+                else:
+                    var.mesh = None
+
+                if var.domains["secondary"] != []:
+                    var.secondary_mesh = properties["mesh"][var.domains["secondary"]]
+                else:
+                    var.secondary_mesh = None
+
+            instance._geometry = pybamm.Geometry(properties["geometry"])
+        else:
+            # Delete the default variables which have not been discretised
+            instance._variables = pybamm.FuzzyDict({})
+
+        # PL: Simulation(new_model, new_mesh)
+        # doesn't work because the model is already discretised, you can't give it a new mesh.
+
+        # Model has already been discretised
+        instance.is_discretised = True
+
+        return instance
 
     @property
     def name(self):
@@ -1117,9 +1172,21 @@ def process_parameters_and_discretise(self, symbol, parameter_values, disc):
         return disc_symbol
 
     def save_model(self, filename=None, mesh=None, variables=None):
-        raise NotImplementedError(
-            "BaseModel: Serialisation not yet implemented for non-battery models."
-        )
+        """
+        Write out a discretised model to a JSON file
+
+        Parameters
+        ----------
+        filename: str, optional
+        The desired name of the JSON file. If no name is provided, one will be created
+        based on the model name, and the current datetime.
+        """
+        if variables and not mesh:
+            raise ValueError(
+                "Serialisation: Please provide the mesh if variables are required"
+            )
+
+        Serialise().save_model(self, filename=filename, mesh=mesh, variables=variables)
 
 
 # helper functions for finding symbols
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index deb312b379..ac7d16f3ed 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -6,6 +6,8 @@
 from functools import cached_property
 import warnings
 
+from pybamm.expression_tree.operations.serialise import Serialise
+
 
 def represents_positive_integer(s):
     """Check if a string represents a positive integer"""
@@ -17,9 +19,6 @@ def represents_positive_integer(s):
         return val > 0
 
 
-from pybamm.expression_tree.operations.serialise import Serialise
-
-
 class BatteryModelOptions(pybamm.FuzzyDict):
     """
     Attributes
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 7740006310..13f8a22f34 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -707,9 +707,14 @@ def solve(
         # Make sure model isn't empty
         if len(model.rhs) == 0 and len(model.algebraic) == 0:
             if not isinstance(self, pybamm.DummySolver):
-                raise pybamm.ModelError(
-                    "Cannot solve empty model, use `pybamm.DummySolver` instead"
-                )
+                # check a discretised model without original paramaters is not being used
+                if not (
+                    model.concatenated_rhs is not None
+                    or model.concatenated_algebraic is not None
+                ):
+                    raise pybamm.ModelError(
+                        "Cannot solve empty model, use `pybamm.DummySolver` instead"
+                    )
 
         # t_eval can only be None if the solver is an algebraic solver. In that case
         # set it to 0
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 9a66e3a639..18f654566b 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -780,54 +780,69 @@ def test_to_equation(self):
 
     def test_to_json(self):
         # Test Addition
-        self.assertEqual(
-            pybamm.Addition(2, 4).to_json(),
-            {
-                "name": "+",
-                "id": mock.ANY,
-                "domains": EMPTY_DOMAINS,
-            },
-        )
+        add_json = {
+            "name": "+",
+            "id": mock.ANY,
+            "domains": EMPTY_DOMAINS,
+        }
+        add = pybamm.Addition(2, 4)
+
+        self.assertEqual(add.to_json(), add_json)
+
+        add_json["children"] = [pybamm.Scalar(2), pybamm.Scalar(4)]
+        self.assertEqual(pybamm.Addition._from_json(add_json), add)
 
         # Test Power
-        self.assertEqual(
-            pybamm.Power(7, 2).to_json(),
-            {
-                "name": "**",
-                "id": mock.ANY,
-                "domains": EMPTY_DOMAINS,
-            },
-        )
+        pow_json = {
+            "name": "**",
+            "id": mock.ANY,
+            "domains": EMPTY_DOMAINS,
+        }
+
+        pow = pybamm.Power(7, 2)
+        self.assertEqual(pow.to_json(), pow_json)
+
+        pow_json["children"] = [pybamm.Scalar(7), pybamm.Scalar(2)]
+        self.assertEqual(pybamm.Power._from_json(pow_json), pow)
 
         # Test Division
-        self.assertEqual(
-            pybamm.Division(10, 5).to_json(),
-            {
-                "name": "/",
-                "id": mock.ANY,
-                "domains": EMPTY_DOMAINS,
-            },
-        )
+        div_json = {
+            "name": "/",
+            "id": mock.ANY,
+            "domains": EMPTY_DOMAINS,
+        }
+
+        div = pybamm.Division(10, 5)
+        self.assertEqual(div.to_json(), div_json)
+
+        div_json["children"] = [pybamm.Scalar(10), pybamm.Scalar(5)]
+        self.assertEqual(pybamm.Division._from_json(div_json), div)
 
         # Test EqualHeaviside
-        self.assertEqual(
-            pybamm.EqualHeaviside(2, 4).to_json(),
-            {
-                "name": "<=",
-                "id": mock.ANY,
-                "domains": EMPTY_DOMAINS,
-            },
-        )
+        equal_json = {
+            "name": "<=",
+            "id": mock.ANY,
+            "domains": EMPTY_DOMAINS,
+        }
+
+        equal_h = pybamm.EqualHeaviside(2, 4)
+        self.assertEqual(equal_h.to_json(), equal_json)
+
+        equal_json["children"] = [pybamm.Scalar(2), pybamm.Scalar(4)]
+        self.assertEqual(pybamm.EqualHeaviside._from_json(equal_json), equal_h)
 
         # Test notEqualHeaviside
-        self.assertEqual(
-            pybamm.NotEqualHeaviside(2, 4).to_json(),
-            {
-                "name": "<",
-                "id": mock.ANY,
-                "domains": EMPTY_DOMAINS,
-            },
-        )
+        not_equal_json = {
+            "name": "<",
+            "id": mock.ANY,
+            "domains": EMPTY_DOMAINS,
+        }
+
+        ne_h = pybamm.NotEqualHeaviside(2, 4)
+        self.assertEqual(ne_h.to_json(), not_equal_json)
+
+        not_equal_json["children"] = [pybamm.Scalar(2), pybamm.Scalar(4)]
+        self.assertEqual(pybamm.NotEqualHeaviside._from_json(not_equal_json), ne_h)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py
index f846220a77..2da745158a 100644
--- a/tests/unit/test_expression_tree/test_concatenations.py
+++ b/tests/unit/test_expression_tree/test_concatenations.py
@@ -383,7 +383,7 @@ def test_to_equation(self):
         # Test concat_sym
         self.assertEqual(pybamm.Concatenation(a, b).to_equation(), func_symbol)
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         # test DomainConcatenation
         mesh = get_mesh_for_testing()
         a = pybamm.Symbol("a", domain=["negative electrode"])
@@ -420,26 +420,41 @@ def test_to_json(self):
             json_dict,
         )
 
-        # test NumpyConcatenation
+        # manually add children
+        json_dict["children"] = [a, b]
+
+        # check symbol re-creation
+        self.assertEqual(pybamm.pybamm.DomainConcatenation._from_json(json_dict), conc)
+
+        # -----------------------------
+        # test NumpyConcatenation -----
+        # -----------------------------
+
         y = np.linspace(0, 1, 15)[:, np.newaxis]
         a_np = pybamm.Vector(y[:5])
         b_np = pybamm.Vector(y[5:9])
         c_np = pybamm.Vector(y[9:])
         conc_np = pybamm.NumpyConcatenation(a_np, b_np, c_np)
 
-        self.assertEqual(
-            conc_np.to_json(),
-            {
-                "name": "numpy_concatenation",
-                "id": mock.ANY,
-                "domains": {
-                    "primary": [],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
+        np_json = {
+            "name": "numpy_concatenation",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
             },
-        )
+        }
+
+        # test to_json
+        self.assertEqual(conc_np.to_json(), np_json)
+
+        # add children
+        np_json["children"] = [a_np, b_np, c_np]
+
+        # test _from_json
+        self.assertEqual(pybamm.NumpyConcatenation._from_json(np_json), conc_np)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 17c5f0a02f..a2cea1801e 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -487,24 +487,27 @@ def test_numpy_array_ufunc(self):
         x = pybamm.Symbol("x")
         self.assertEqual(np.exp(x), pybamm.exp(x))
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         symc1 = pybamm.Symbol("child1", domain=["domain_1"])
         symc2 = pybamm.Symbol("child2", domain=["domain_2"])
         symp = pybamm.Symbol("parent", domain=["domain_3"], children=[symc1, symc2])
 
-        self.assertEqual(
-            symp.to_json(),
-            {
-                "name": "parent",
-                "id": mock.ANY,
-                "domains": {
-                    "primary": ["domain_3"],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
+        json_dict = {
+            "name": "parent",
+            "id": mock.ANY,
+            "domains": {
+                "primary": ["domain_3"],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
             },
-        )
+        }
+
+        self.assertEqual(symp.to_json(), json_dict)
+
+        json_dict["children"] = [symc1, symc2]
+
+        self.assertEqual(pybamm.Symbol._from_json(json_dict), symp)
 
 
 class TestIsZero(TestCase):
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index e8fc7c7be0..3c9de976d6 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -669,41 +669,42 @@ def test_explicit_time_integral(self):
         self.assertEqual(expr.new_copy(), expr)
         self.assertFalse(expr.is_constant())
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         # UnaryOperator
         a = pybamm.Symbol("a", domain=["test"])
         un = pybamm.UnaryOperator("unary test", a)
-        self.assertEqual(
-            un.to_json(),
-            {
-                "name": "unary test",
-                "id": mock.ANY,
-                "domains": {
-                    "primary": ["test"],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
+
+        un_json = {
+            "name": "unary test",
+            "id": mock.ANY,
+            "domains": {
+                "primary": ["test"],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
             },
-        )
+        }
+
+        self.assertEqual(un.to_json(), un_json)
+
+        un_json["children"] = [a]
+        self.assertEqual(pybamm.UnaryOperator._from_json("unary test", un_json), un)
 
         # Index
         vec = pybamm.StateVector(slice(0, 5))
         ind = pybamm.Index(vec, 3)
-        self.assertEqual(
-            ind.to_json(),
-            {
-                "name": "Index[3]",
-                "id": mock.ANY,
-                "domains": {
-                    "primary": [],
-                    "secondary": [],
-                    "tertiary": [],
-                    "quaternary": [],
-                },
-                "check_size": False,
-            },
-        )
+
+        ind_json = {
+            "name": "Index[3]",
+            "id": mock.ANY,
+            "index": {"start": 3, "stop": 4, "step": None},
+            "check_size": False,
+        }
+
+        self.assertEqual(ind.to_json(), ind_json)
+
+        ind_json["children"] = [vec]
+        self.assertEqual(pybamm.Index._from_json(ind_json), ind)
 
         # SpatialOperator
         spatial_vec = pybamm.SpatialOperator("name", vec)
@@ -712,13 +713,14 @@ def test_to_json(self):
 
         # ExplicitTimeIntegral
         expr = pybamm.ExplicitTimeIntegral(pybamm.Parameter("param"), pybamm.Scalar(1))
-        self.assertEqual(
-            expr.to_json(),
-            {
-                "name": "explicit time integral",
-                "id": mock.ANY,
-            },
-        )
+
+        expr_json = {"name": "explicit time integral", "id": mock.ANY}
+
+        self.assertEqual(expr.to_json(), expr_json)
+
+        expr_json["children"] = [pybamm.Parameter("param")]
+        expr_json["initial_condition"] = [pybamm.Scalar(1)]
+        self.assertEqual(pybamm.ExplicitTimeIntegral._from_json(expr_json), expr)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 72d4a1a072..268a4082eb 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -2,6 +2,7 @@
 # Tests for the serialisation class
 #
 from tests import TestCase
+import tests
 import pybamm
 
 pybamm.settings.debug_mode = True
@@ -10,7 +11,7 @@
 import unittest
 
 
-class TestSerialise(TestCase):
+class TestSerialiseModels(TestCase):
     # test lithium models
     def test_spm_serialisation_recreation(self):
         t = [0, 3600]
@@ -153,23 +154,28 @@ def test_loqs_serialisation_recreation(self):
                 solution.all_ys[x], new_solution.all_ys[x]
             )
 
-    # def test_thevenin_serialisation_recreation(self):
-    #     t = [0, 3600]
+    def test_thevenin_serialisation_recreation(self):
+        t = [0, 3600]
 
-    #     model = pybamm.equivalent_circuit.Thevenin()
-    #     sim = pybamm.Simulation(model)
-    #     solution = sim.solve(t)
+        model = pybamm.equivalent_circuit.Thevenin()
+        sim = pybamm.Simulation(model)
+        solution = sim.solve(t)
 
-    #     sim.save_model("test_model")
+        sim.save_model("test_model")
+
+        new_model = pybamm.load_model("test_model.json")
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, t)
+
+        for x, val in enumerate(solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                solution.all_ys[x], new_solution.all_ys[x]
+            )
 
-    #     new_model = pybamm.load_model("test_model.json")
-    #     new_solver = new_model.default_solver
-    #     new_solution = new_solver.solve(new_model, t)
 
-    #     for x, val in enumerate(solution.all_ys):
-    #         np.testing.assert_array_almost_equal(
-    #             solution.all_ys[x], new_solution.all_ys[x]
-    #         )
+class TestSerialiseExpressionTree(TestCase):
+    def test_tree_walk(self):
+        pass
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index d0926e5c94..e20d2e0460 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -327,6 +327,26 @@ def test_save_load_dae(self):
         sim_load = pybamm.load_sim("test.pickle")
         self.assertEqual(sim.model.name, sim_load.model.name)
 
+    def test_save_load_model(self):
+        model = pybamm.lead_acid.LOQS({"surface form": "algebraic"})
+        model.use_jacobian = True
+        sim = pybamm.Simulation(model)
+
+        # test exception if not discretised
+        with self.assertRaises(NotImplementedError):
+            sim.save_model("sim_save")
+
+        # save after solving
+        sim.solve([0, 600])
+        sim.save_model("sim_save")
+
+        # load model
+        saved_model = pybamm.load_model("sim_save.json")
+
+        self.assertEqual(model.options, saved_model.options)
+
+        os.remove("sim_save.json")
+
     def test_plot(self):
         sim = pybamm.Simulation(pybamm.lithium_ion.SPM())
 

From 80fc250e3c6e25b1e1ff00ba1a5ffec123ccd0e5 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Wed, 27 Sep 2023 16:02:42 +0000
Subject: [PATCH 112/615] testing: add unit tests for Serialise() functions Add
 to_from_json test for Events

---
 .../expression_tree/operations/serialise.py   |   7 +-
 pybamm/expression_tree/symbol.py              |   1 -
 tests/unit/test_models/test_event.py          |  22 ++
 .../test_serialisation/test_serialisation.py  | 364 +++++++++++++++++-
 4 files changed, 387 insertions(+), 7 deletions(-)

diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index d27f770451..2f79f0f6f7 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -207,7 +207,7 @@ def load_model(
         }
 
         recon_model_dict["geometry"] = (
-            self._reconstruct_geometry(model_data["geometry"])
+            self._reconstruct_pybamm_dict(model_data["geometry"])
             if "geometry" in model_data.keys()
             else None
         )
@@ -255,7 +255,8 @@ def _get_pybamm_class(self, snippet: dict):
         try:
             empty_class = self._Empty()
             empty_class.__class__ = class_
-        except:
+        except TypeError:
+            # Mesh objects have a different layouts
             empty_class = self._EmptyDict()
             empty_class.__class__ = class_
 
@@ -345,7 +346,7 @@ def _reconstruct_mesh(self, node: dict):
 
         return new_mesh
 
-    def _reconstruct_geometry(self, obj: dict):
+    def _reconstruct_pybamm_dict(self, obj: dict):
         """
         pybamm.Geometry can contain PyBaMM symbols as dictionary keys.
 
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index b0747090cd..dfa4a05bf0 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -1015,7 +1015,6 @@ def to_json(self):
             "name": self.name,
             "id": self.id,
             "domains": self.domains,
-            # "children": self.children,  # the encoder deals with the children itself.
         }
 
         return json_dict
diff --git a/tests/unit/test_models/test_event.py b/tests/unit/test_models/test_event.py
index 7d0d00f000..84b0dcde84 100644
--- a/tests/unit/test_models/test_event.py
+++ b/tests/unit/test_models/test_event.py
@@ -48,6 +48,28 @@ def test_event_types(self):
             event = pybamm.Event("my event", pybamm.Scalar(1), event_type)
             self.assertEqual(event.event_type, event_type)
 
+    def test_to_from_json(self):
+        expression = pybamm.Scalar(1)
+        event = pybamm.Event("my event", expression)
+
+        event_json = {
+            "name": "my event",
+            "event_type": ["EventType.TERMINATION", 0],
+        }
+
+        event_ser_json = event.to_json()
+        self.assertEqual(event_ser_json, event_json)
+
+        event_json["expression"] = expression
+
+        new_event = pybamm.Event._from_json(event_json)
+
+        # check for equal expressions
+        self.assertEqual(new_event.expression, event.expression)
+
+        # check for equal event types
+        self.assertEqual(new_event.event_type, event.event_type)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 268a4082eb..de9dfc868d 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -4,11 +4,87 @@
 from tests import TestCase
 import tests
 import pybamm
+from pybamm.expression_tree.operations.serialise import Serialise
 
 pybamm.settings.debug_mode = True
 
 import numpy as np
 import unittest
+import unittest.mock as mock
+
+from numpy import testing
+
+
+def scalar_var_dict():
+    """variable, json pair for a pybamm.Scalar instance"""
+    a = pybamm.Scalar(5)
+    a_dict = {
+        "py/id": mock.ANY,
+        "py/object": "pybamm.expression_tree.scalar.Scalar",
+        "name": "5.0",
+        "id": mock.ANY,
+        "value": 5.0,
+        "children": [],
+    }
+
+    return a, a_dict
+
+
+def mesh_var_dict():
+    """mesh, json pair for a pybamm.Mesh instance"""
+
+    r = pybamm.SpatialVariable(
+        "r", domain=["negative particle"], coord_sys="spherical polar"
+    )
+
+    geometry = {
+        "negative particle": {r: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}
+    }
+
+    submesh_types = {"negative particle": pybamm.Uniform1DSubMesh}
+    var_pts = {r: 20}
+
+    # create mesh
+    mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
+
+    mesh_json = {
+        "py/object": "pybamm.meshes.meshes.Mesh",
+        "py/id": mock.ANY,
+        "submesh_pts": {"negative particle": {"r": 20}},
+        "base_domains": ["negative particle"],
+        "sub_meshes": {
+            "negative particle": {
+                "py/object": "pybamm.meshes.one_dimensional_submeshes.Uniform1DSubMesh",
+                "py/id": mock.ANY,
+                "edges": [
+                    0.0,
+                    0.05,
+                    0.1,
+                    0.15000000000000002,
+                    0.2,
+                    0.25,
+                    0.30000000000000004,
+                    0.35000000000000003,
+                    0.4,
+                    0.45,
+                    0.5,
+                    0.55,
+                    0.6000000000000001,
+                    0.65,
+                    0.7000000000000001,
+                    0.75,
+                    0.8,
+                    0.8500000000000001,
+                    0.9,
+                    0.9500000000000001,
+                    1.0,
+                ],
+                "coord_sys": "spherical polar",
+            }
+        },
+    }
+
+    return mesh, mesh_json
 
 
 class TestSerialiseModels(TestCase):
@@ -173,9 +249,291 @@ def test_thevenin_serialisation_recreation(self):
             )
 
 
-class TestSerialiseExpressionTree(TestCase):
-    def test_tree_walk(self):
-        pass
+class TestSerialise(TestCase):
+    # test the symbol encoder
+
+    def test_symbol_encoder_symbol(self):
+        """test basic symbol encoder with & without children"""
+
+        # without children
+        a, a_dict = scalar_var_dict()
+
+        a_ser_json = Serialise._SymbolEncoder().default(a)
+
+        self.assertEqual(a_ser_json, a_dict)
+
+        # with children
+        add = pybamm.Addition(2, 4)
+        add_json = {
+            "py/id": mock.ANY,
+            "py/object": "pybamm.expression_tree.binary_operators.Addition",
+            "name": "+",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [
+                {
+                    "py/id": mock.ANY,
+                    "py/object": "pybamm.expression_tree.scalar.Scalar",
+                    "name": "2.0",
+                    "id": mock.ANY,
+                    "value": 2.0,
+                    "children": [],
+                },
+                {
+                    "py/id": mock.ANY,
+                    "py/object": "pybamm.expression_tree.scalar.Scalar",
+                    "name": "4.0",
+                    "id": mock.ANY,
+                    "value": 4.0,
+                    "children": [],
+                },
+            ],
+        }
+
+        add_ser_json = Serialise._SymbolEncoder().default(add)
+
+        self.assertEqual(add_ser_json, add_json)
+
+    def test_symbol_encoder_explicitTimeIntegral(self):
+        """test symbol encoder with initial conditions"""
+        expr = pybamm.ExplicitTimeIntegral(pybamm.Scalar(5), pybamm.Scalar(1))
+
+        expr_json = {
+            "py/object": "pybamm.expression_tree.unary_operators.ExplicitTimeIntegral",
+            "py/id": mock.ANY,
+            "name": "explicit time integral",
+            "id": mock.ANY,
+            "children": [
+                {
+                    "py/object": "pybamm.expression_tree.scalar.Scalar",
+                    "py/id": mock.ANY,
+                    "name": "5.0",
+                    "id": mock.ANY,
+                    "value": 5.0,
+                    "children": [],
+                }
+            ],
+            "initial_condition": {
+                "py/object": "pybamm.expression_tree.scalar.Scalar",
+                "py/id": mock.ANY,
+                "name": "1.0",
+                "id": mock.ANY,
+                "value": 1.0,
+                "children": [],
+            },
+        }
+
+        expr_ser_json = Serialise._SymbolEncoder().default(expr)
+
+        self.assertEqual(expr_json, expr_ser_json)
+
+    def test_symbol_encoder_event(self):
+        """test symbol encoder with event"""
+
+        expression = pybamm.Scalar(1)
+        event = pybamm.Event("my event", expression)
+
+        event_json = {
+            "py/object": "pybamm.models.event.Event",
+            "py/id": mock.ANY,
+            "name": "my event",
+            "event_type": ["EventType.TERMINATION", 0],
+            "expression": {
+                "py/object": "pybamm.expression_tree.scalar.Scalar",
+                "py/id": mock.ANY,
+                "name": "1.0",
+                "id": mock.ANY,
+                "value": 1.0,
+                "children": [],
+            },
+        }
+
+        event_ser_json = Serialise._SymbolEncoder().default(event)
+        self.assertEqual(event_ser_json, event_json)
+
+    # test the mesh encoder
+    def test_mesh_encoder(self):
+        mesh, mesh_json = mesh_var_dict()
+
+        # serialise mesh
+        mesh_ser_json = Serialise._MeshEncoder().default(mesh)
+
+        self.assertEqual(mesh_ser_json, mesh_json)
+
+    def test_deconstruct_pybamm_dicts(self):
+        """tests serialisation of dictionaries with pybamm classes as keys"""
+
+        x = pybamm.SpatialVariable("x", "negative electrode")
+
+        test_dict = {"rod": {x: {"min": 0.0, "max": 2.0}}}
+
+        ser_dict = {
+            "rod": {
+                "symbol_x": {
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
+                    "py/id": mock.ANY,
+                    "name": "x",
+                    "id": mock.ANY,
+                    "domains": {
+                        "primary": ["negative electrode"],
+                        "secondary": [],
+                        "tertiary": [],
+                        "quaternary": [],
+                    },
+                    "children": [],
+                },
+                "x": {"min": 0.0, "max": 2.0},
+            }
+        }
+
+        self.assertEqual(Serialise()._deconstruct_pybamm_dicts(test_dict), ser_dict)
+
+    def test_get_pybamm_class(self):
+        # symbol
+        _, scalar_dict = scalar_var_dict()
+
+        scalar_class = Serialise()._get_pybamm_class(scalar_dict)
+
+        self.assertIsInstance(scalar_class, pybamm.Scalar)
+
+        # mesh
+        _, mesh_dict = mesh_var_dict()
+
+        mesh_class = Serialise()._get_pybamm_class(mesh_dict)
+
+        self.assertIsInstance(mesh_class, pybamm.Mesh)
+
+    def test_reconstruct_symbol(self):
+        scalar, scalar_dict = scalar_var_dict()
+
+        new_scalar = Serialise()._reconstruct_symbol(scalar_dict)
+
+        self.assertEqual(new_scalar, scalar)
+
+    def test_reconstruct_expression_tree(self):
+        y = pybamm.StateVector(slice(0, 1))
+        t = pybamm.t
+        equation = 2 * y + t
+
+        equation_json = {
+            "py/object": "pybamm.expression_tree.binary_operators.Addition",
+            "py/id": 139691619709376,
+            "name": "+",
+            "id": -2595875552397011963,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [
+                {
+                    "py/object": "pybamm.expression_tree.binary_operators.Multiplication",
+                    "py/id": 139691619709232,
+                    "name": "*",
+                    "id": 6094209803352873499,
+                    "domains": {
+                        "primary": [],
+                        "secondary": [],
+                        "tertiary": [],
+                        "quaternary": [],
+                    },
+                    "children": [
+                        {
+                            "py/object": "pybamm.expression_tree.scalar.Scalar",
+                            "py/id": 139691619709040,
+                            "name": "2.0",
+                            "id": 1254626814648295285,
+                            "value": 2.0,
+                            "children": [],
+                        },
+                        {
+                            "py/object": "pybamm.expression_tree.state_vector.StateVector",
+                            "py/id": 139691619589760,
+                            "name": "y[0:1]",
+                            "id": 5063056989669636089,
+                            "domains": {
+                                "primary": [],
+                                "secondary": [],
+                                "tertiary": [],
+                                "quaternary": [],
+                            },
+                            "y_slice": [{"start": 0, "stop": 1, "step": None}],
+                            "evaluation_array": [True],
+                            "children": [],
+                        },
+                    ],
+                },
+                {
+                    "py/object": "pybamm.expression_tree.independent_variable.Time",
+                    "py/id": 139692083289392,
+                    "name": "time",
+                    "id": -3301344124754766351,
+                    "domains": {
+                        "primary": [],
+                        "secondary": [],
+                        "tertiary": [],
+                        "quaternary": [],
+                    },
+                    "children": [],
+                },
+            ],
+        }
+
+        new_equation = Serialise()._reconstruct_expression_tree(equation_json)
+
+        self.assertEqual(new_equation, equation)
+
+    def test_reconstruct_mesh(self):
+        mesh, mesh_dict = mesh_var_dict()
+
+        new_mesh = Serialise()._reconstruct_mesh(mesh_dict)
+
+        testing.assert_array_equal(
+            new_mesh["negative particle"].edges, mesh["negative particle"].edges
+        )
+        testing.assert_array_equal(
+            new_mesh["negative particle"].nodes, mesh["negative particle"].nodes
+        )
+
+        # reconstructed meshes are only used for plotting, geometry not reconstructed.
+        with self.assertRaisesRegex(
+            AttributeError, "'Mesh' object has no attribute '_geometry'"
+        ):
+            self.assertEqual(new_mesh.geometry, mesh.geometry)
+
+    def test_reconstruct_pybamm_dict(self):
+        x = pybamm.SpatialVariable("x", "negative electrode")
+
+        test_dict = {"rod": {x: {"min": 0.0, "max": 2.0}}}
+
+        ser_dict = {
+            "rod": {
+                "symbol_x": {
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
+                    "py/id": mock.ANY,
+                    "name": "x",
+                    "id": mock.ANY,
+                    "domains": {
+                        "primary": ["negative electrode"],
+                        "secondary": [],
+                        "tertiary": [],
+                        "quaternary": [],
+                    },
+                    "children": [],
+                },
+                "x": {"min": 0.0, "max": 2.0},
+            }
+        }
+
+        new_dict = Serialise()._reconstruct_pybamm_dict(ser_dict)
+
+        self.assertEqual(new_dict, test_dict)
 
 
 if __name__ == "__main__":

From ac928ab0fbeb52578e7e0903e8010cddf155f122 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 29 Sep 2023 09:00:57 +0000
Subject: [PATCH 113/615] testing: save/load model tests

---
 .../expression_tree/operations/serialise.py   |  11 +-
 pybamm/models/base_model.py                   |   3 +-
 .../test_serialisation/test_serialisation.py  | 117 +++++++++++++++++-
 3 files changed, 117 insertions(+), 14 deletions(-)

diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index 2f79f0f6f7..edba97a3c4 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -10,7 +10,7 @@
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from pybamm import BaseBatteryModel
+    from pybamm import BaseModel
 
 
 class Serialise:
@@ -136,9 +136,8 @@ def save_model(
             model_json["mesh"] = self._MeshEncoder().default(mesh)
 
         if variables:
-            model_json["geometry"] = self._deconstruct_pybamm_dicts(
-                dict(model._geometry)
-            )
+            if model._geometry:
+                model_json["geometry"] = self._deconstruct_pybamm_dicts(model._geometry)
             model_json["variables"] = {
                 k: self._SymbolEncoder().default(v) for k, v in dict(variables).items()
             }
@@ -149,9 +148,7 @@ def save_model(
         with open(filename + ".json", "w") as f:
             json.dump(model_json, f)
 
-    def load_model(
-        self, filename: str, battery_model: BaseBatteryModel = None
-    ) -> BaseBatteryModel:
+    def load_model(self, filename: str, battery_model: BaseModel = None) -> BaseModel:
         """
         Loads a discretised, ready to solve model into PyBaMM.
 
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 21648f3dfc..11d8661be9 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -171,7 +171,8 @@ def deserialise(
                 else:
                     var.secondary_mesh = None
 
-            instance._geometry = pybamm.Geometry(properties["geometry"])
+            if properties["geometry"]:
+                instance._geometry = pybamm.Geometry(properties["geometry"])
         else:
             # Delete the default variables which have not been discretised
             instance._variables = pybamm.FuzzyDict({})
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index de9dfc868d..d65f95fe93 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -3,16 +3,15 @@
 #
 from tests import TestCase
 import tests
-import pybamm
-from pybamm.expression_tree.operations.serialise import Serialise
-
-pybamm.settings.debug_mode = True
-
-import numpy as np
+import os
 import unittest
 import unittest.mock as mock
+from datetime import datetime
+import numpy as np
+import pybamm
 
 from numpy import testing
+from pybamm.expression_tree.operations.serialise import Serialise
 
 
 def scalar_var_dict():
@@ -535,6 +534,112 @@ def test_reconstruct_pybamm_dict(self):
 
         self.assertEqual(new_dict, test_dict)
 
+    def test_save_load_model(self):
+        model = pybamm.lithium_ion.SPM(name="test_spm")
+        geometry = model.default_geometry
+        param = model.default_parameter_values
+        param.process_model(model)
+        param.process_geometry(geometry)
+        mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
+
+        # test error if not discretised
+        with self.assertRaisesRegex(
+            NotImplementedError,
+            "PyBaMM can only serialise a discretised, ready-to-solve model",
+        ):
+            Serialise().save_model(model, filename="test_model")
+
+        disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+        disc.process_model(model)
+
+        # default save
+        Serialise().save_model(model, filename="test_model")
+        self.assertTrue(os.path.exists("test_model.json"))
+
+        # default save where filename isn't provided
+        Serialise().save_model(model)
+        filename = (
+            "test_spm_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M_%S") + ".json"
+        )
+        self.assertTrue(os.path.exists(filename))
+        os.remove(filename)
+
+        # default load
+        new_model = Serialise().load_model("test_model.json")
+
+        # check new model solves
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, [0, 3600])
+
+        # check an error is raised when plotting the solution
+        with self.assertRaisesRegex(
+            AttributeError,
+            "Variables not provided by the serialised model",
+        ):
+            new_solution.plot()
+
+        # load when specifying the battery model to use
+        newest_model = Serialise().load_model(
+            "test_model.json", battery_model=pybamm.lithium_ion.SPM
+        )
+        os.remove("test_model.json")
+
+        # check new model solves
+        newest_solver = newest_model.default_solver
+        newest_solution = newest_solver.solve(newest_model, [0, 3600])
+
+    def test_serialised_model_plotting(self):
+        # models without a mesh
+        model = pybamm.BaseModel()
+        c = pybamm.Variable("c")
+        model.rhs = {c: -c}
+        model.initial_conditions = {c: 1}
+        model.variables["c"] = c
+        model.variables["2c"] = 2 * c
+
+        # setup and discretise
+        _ = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
+
+        Serialise().save_model(
+            model,
+            variables=model.variables,
+            filename="test_base_model",
+        )
+
+        new_model = Serialise().load_model("test_base_model.json")
+        os.remove("test_base_model.json")
+
+        new_solution = pybamm.ScipySolver().solve(new_model, np.linspace(0, 1))
+
+        # check dynamic plot loads
+        new_solution.plot(["c", "2c"], testing=True)
+
+        # models with a mesh ----------------
+        model = pybamm.lithium_ion.SPM(name="test_spm_plotting")
+        geometry = model.default_geometry
+        param = model.default_parameter_values
+        param.process_model(model)
+        param.process_geometry(geometry)
+        mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
+        disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+        disc.process_model(model)
+
+        Serialise().save_model(
+            model,
+            variables=model.variables,
+            mesh=mesh,
+            filename="test_plotting_model",
+        )
+
+        new_model = Serialise().load_model("test_plotting_model.json")
+        os.remove("test_plotting_model.json")
+
+        new_solver = new_model.default_solver
+        new_solution = new_solver.solve(new_model, [0, 3600])
+
+        # check dynamic plot loads
+        new_solution.plot(testing=True)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 9e323d986653bc1374d0537110581b4e86013847 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 29 Sep 2023 16:23:36 +0000
Subject: [PATCH 114/615] testing: Add integration tests add to_json tests for
 meshes All but 3 int. tests passing, high accuracy diff failures

---
 .../notebooks/models/saving_models.ipynb      |  42 +++-
 pybamm/__init__.py                            |   2 +-
 pybamm/expression_tree/array.py               |   4 -
 pybamm/expression_tree/concatenations.py      |   2 +-
 pybamm/expression_tree/functions.py           |   4 +-
 pybamm/meshes/one_dimensional_submeshes.py    |   5 +-
 pybamm/meshes/scikit_fem_submeshes.py         |  24 +++
 pybamm/models/base_model.py                   |  35 +++-
 .../full_battery_models/base_battery_model.py |  26 +--
 .../test_models/standard_model_tests.py       |  38 ++++
 tests/unit/test_meshes/test_meshes.py         |  24 +++
 .../test_one_dimensional_submesh.py           |  26 +++
 .../test_meshes/test_scikit_fem_submesh.py    |  38 ++++
 tests/unit/test_models/test_base_model.py     |  27 +++
 .../test_serialisation/test_serialisation.py  | 190 ++++--------------
 15 files changed, 291 insertions(+), 196 deletions(-)

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index c3c9c90ea8..f2826813d4 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -56,7 +56,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f1b9f66cee0>"
+       "<pybamm.solvers.solution.Solution at 0x7f6084221cd0>"
       ]
      },
      "execution_count": 2,
@@ -68,7 +68,7 @@
     "# Recreate the pybamm model from the JSON file\n",
     "new_dfn_model = pybamm.load_model(\"sim_model_example.json\")\n",
     "\n",
-    "sim_reloaded = pybamm.Simulation(new_dfn_model) # PL: will this work if anything other than the default options are used? I guess not...\n",
+    "sim_reloaded = pybamm.Simulation(new_dfn_model)\n",
     "sim_reloaded.solve([0, 3600])"
    ]
   },
@@ -122,13 +122,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eaf8ae8b8dd84a99b8b1aecfc132ad83",
+       "model_id": "b21266c9388043dbbe06e6d93dda3009",
        "version_major": 2,
        "version_minor": 0
       },
@@ -142,10 +142,10 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f1b95310fa0>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7fcdc807aa60>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,6 +277,36 @@
     "new_spm_solution.plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making edits to a serialised model\n",
+    "\n",
+    "As mentioned at the begining of this notebook, only models which have already been discretised can be serialised and readh back in. This means that after serialisation, the model *cannot be edited*, as the non-discretised elements of the model such as the original rhs are not saved.\n",
+    "\n",
+    "If you are likely to want to save a model and then edit it in the future, you may wish to use the `Simulation.save()` functionality to pickle your simulation, as described in [tutorial 6](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before finishing we will remove the data files we saved so that we leave the directory as we found it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.remove(\"example_model.json\")\n",
+    "os.remove(\"sim_model_example.json\")\n",
+    "os.remove(\"sim_model_variables.json\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index c6376ec0b3..d3eab5cc56 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -191,7 +191,7 @@
 #
 # Serialisation
 #
-from .models.full_battery_models.base_battery_model import load_model
+from .models.base_model import load_model
 
 #
 # Spatial Methods
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index 0fc74f3209..6807cf9f94 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -166,10 +166,6 @@ def to_json(self):
                 "row_indices": self.entries.indices.tolist(),
                 "column_pointers": self.entries.indptr.tolist(),
             }
-        else:
-            raise TypeError(
-                f"Ah! Dense matrix! {self.entries}"
-            )  # PL: Double check this
 
         json_dict = {
             "name": self.name,
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index d393cc6647..7ebbb0c59a 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -45,7 +45,7 @@ def __init__(self, *children, name=None, check_domain=True, concat_fun=None):
 
     @classmethod
     def _from_json(cls, *children, name, domains, concat_fun=None):
-        # PL: update this one - I guess we still want it to take 'snippet' rather than the list? to be the same as the others?
+        """Creates a new Concatenation instance from a json object"""
         instance = cls.__new__(cls)
 
         instance.concatenation_function = concat_fun
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 9743e7c754..a6c47f1650 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -212,9 +212,7 @@ def to_equation(self):
                 eq_list.append(eq)
             return self._sympy_operator(*eq_list)
 
-    def to_json(
-        self,
-    ):  # PL: I think these ones might actually be present when you build your own function.
+    def to_json(self):
         raise NotImplementedError(
             "pybamm.Function: Serialisation is only implemented for discretised models."
         )
diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py
index 147ed590cf..d68745daec 100644
--- a/pybamm/meshes/one_dimensional_submeshes.py
+++ b/pybamm/meshes/one_dimensional_submeshes.py
@@ -34,7 +34,7 @@ def __init__(self, edges, coord_sys, tabs=None):
         self.internal_boundaries = []
 
         # Add tab locations in terms of "left" and "right"
-        if tabs:
+        if tabs and "negative tab" not in tabs.keys():
             self.tabs = {}
             l_z = self.edges[-1]
 
@@ -52,6 +52,9 @@ def near(x, point, tol=3e-16):
                         f"{tab} tab located at {tab_location}, "
                         f"but must be at either 0 or {l_z}"
                     )
+        elif tabs:
+            # tabs have already been calculated by a serialised model
+            self.tabs = tabs
 
     def read_lims(self, lims):
         # Separate limits and tabs
diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index f25dce80b1..ca86bcc11f 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -34,6 +34,9 @@ def __init__(self, edges, coord_sys, tabs):
         self.npts = len(self.edges["y"]) * len(self.edges["z"])
         self.coord_sys = coord_sys
 
+        # save tabs for serialisation
+        self.tabs = tabs
+
         # create mesh
         self.fem_mesh = skfem.MeshTri.init_tensor(self.edges["y"], self.edges["z"])
 
@@ -141,6 +144,15 @@ def between(x, interval, tol=3e-16):
         else:
             raise pybamm.GeometryError("tab location not valid")
 
+    def to_json(self):
+        json_dict = {
+            "edges": {k: v.tolist() for k, v in self.edges.items()},
+            "coord_sys": self.coord_sys,
+            "tabs": self.tabs,
+        }
+
+        return json_dict
+
 
 class ScikitUniform2DSubMesh(ScikitSubMesh2D):
     """
@@ -177,6 +189,18 @@ def __init__(self, lims, npts):
 
         super().__init__(edges, coord_sys, tabs)
 
+    @classmethod
+    def _from_json(cls, snippet: dict):
+        instance = cls.__new__(cls)
+
+        edges = {k: np.array(v) for k, v in snippet["edges"].items()}
+
+        super(ScikitUniform2DSubMesh, instance).__init__(
+            edges, snippet["coord_sys"], snippet["tabs"]
+        )
+
+        return instance
+
 
 class ScikitExponential2DSubMesh(ScikitSubMesh2D):
     """
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 11d8661be9..46645f992d 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -2,6 +2,7 @@
 # Base model class
 #
 import numbers
+import warnings
 from collections import OrderedDict
 
 import copy
@@ -124,11 +125,8 @@ def __init__(self, name="Unnamed model"):
         self.is_discretised = False
         self.y_slices = None
 
-    # PL: Next up, how to pass in the non-standard variables, if necessary.
     @classmethod
-    def deserialise(
-        cls, properties: dict
-    ):  # PL: maybe option up here as output_mesh=true to output a tuple, (model, mesh) rather than just updating the variables and leaving it at that.
+    def deserialise(cls, properties: dict):
         """
         Create a model instance from a serialised object.
         """
@@ -137,7 +135,7 @@ def deserialise(
         # append the model name with _saved to differentiate
         instance.__init__(name=properties["name"] + "_saved")
 
-        # PL: what to do with the options?
+        instance.options = properties["options"]
 
         # Initialise model with stored variables that have already been discretised
         instance._concatenated_rhs = properties["concatenated_rhs"]
@@ -177,9 +175,6 @@ def deserialise(
             # Delete the default variables which have not been discretised
             instance._variables = pybamm.FuzzyDict({})
 
-        # PL: Simulation(new_model, new_mesh)
-        # doesn't work because the model is already discretised, you can't give it a new mesh.
-
         # Model has already been discretised
         instance.is_discretised = True
 
@@ -1183,13 +1178,33 @@ def save_model(self, filename=None, mesh=None, variables=None):
         based on the model name, and the current datetime.
         """
         if variables and not mesh:
-            raise ValueError(
-                "Serialisation: Please provide the mesh if variables are required"
+            warnings.warn(
+                """
+                Serialisation: Variables are being saved without a mesh. 
+                Plotting may not be available.
+                """,
+                pybamm.ModelWarning,
             )
 
         Serialise().save_model(self, filename=filename, mesh=mesh, variables=variables)
 
 
+def load_model(filename, battery_model: BaseModel = None):
+    """
+    Load in a saved model from a JSON file
+
+    Parameters
+    ----------
+    filename: str
+        Path to the JSON file containing the serialised model file
+    battery_model: :class: pybamm.BaseBatteryModel, optional
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
+            any model names within the file. If None, the function will look for the saved object
+            path, present if the original model came from PyBaMM.
+    """
+    return Serialise().load_model(filename, battery_model)
+
+
 # helper functions for finding symbols
 def find_symbol_in_tree(tree, name):
     if name == tree.name:
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index ac7d16f3ed..5046ba801f 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -605,7 +605,7 @@ def __init__(self, extra_options):
             else:
                 # serialised options save tuples as lists which need to be converted
                 if isinstance(value, list) and len(value) == 2:
-                    value = tuple(value)
+                    value = tuple(tuple(v) if len(v) == 2 else v for v in value)
 
                 if isinstance(value, str) or option in [
                     "dimensionality",
@@ -805,11 +805,8 @@ def __init__(self, options=None, name="Unnamed battery model"):
         super().__init__(name)
         self.options = options
 
-    # PL: Next up, how to pass in the non-standard variables, if necessary.
     @classmethod
-    def deserialise(
-        cls, properties: dict
-    ):  # PL: maybe option up here as output_mesh=true to output a tuple, (model, mesh) rather than just updating the variables and leaving it at that.
+    def deserialise(cls, properties: dict):
         """
         Create a model instance from a serialised object.
         """
@@ -857,9 +854,6 @@ def deserialise(
             # Delete the default variables which have not been discretised
             instance._variables = pybamm.FuzzyDict({})
 
-        # PL: Simulation(new_model, new_mesh)
-        # doesn't work because the model is already discretised, you can't give it a new mesh.
-
         # Model has already been discretised
         instance.is_discretised = True
 
@@ -1462,19 +1456,3 @@ def save_model(self, filename=None, mesh=None, variables=None):
             )
 
         Serialise().save_model(self, filename=filename, mesh=mesh, variables=variables)
-
-
-def load_model(filename, battery_model: BaseBatteryModel = None):
-    """
-    Load in a saved model from a JSON file
-
-    Parameters
-    ----------
-    filename: str
-        Path to the JSON file containing the serialised model file
-    battery_model: :class: pybamm.BaseBatteryModel, optional
-            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
-            any model names within the file. If None, the function will look for the saved object
-            path, present if the original model came from PyBaMM.
-    """
-    return Serialise().load_model(filename, battery_model)
diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index 9341122d84..0526363a75 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -5,6 +5,7 @@
 import tests
 
 import numpy as np
+import os
 
 
 class StandardModelTest(object):
@@ -138,6 +139,42 @@ def test_sensitivities(self, param_name, param_value, output_name="Voltage [V]")
             atol=1e-6,
         )
 
+    def test_serialisation(self, solver=None, t_eval=None):
+        self.model.save_model(
+            "test_model", variables=self.model.variables, mesh=self.disc.mesh
+        )
+
+        new_model = pybamm.load_model("test_model.json")
+
+        # create new solver for re-created model
+        if solver is not None:
+            new_solver = solver
+        else:
+            new_solver = new_model.default_solver
+
+        if isinstance(new_model, pybamm.lithium_ion.BaseModel):
+            new_solver.rtol = 1e-8
+            new_solver.atol = 1e-8
+
+        Crate = abs(
+            self.parameter_values["Current function [A]"]
+            / self.parameter_values["Nominal cell capacity [A.h]"]
+        )
+        # don't allow zero C-rate
+        if Crate == 0:
+            Crate = 1
+        if t_eval is None:
+            t_eval = np.linspace(0, 3600 / Crate, 100)
+
+        new_solution = new_solver.solve(new_model, t_eval)
+
+        for x, val in enumerate(self.solution.all_ys):
+            np.testing.assert_array_almost_equal(
+                new_solution.all_ys[x], self.solution.all_ys[x]
+            )
+
+        os.remove("test_model.json")
+
     def test_all(
         self, param=None, disc=None, solver=None, t_eval=None, skip_output_tests=False
     ):
@@ -145,6 +182,7 @@ def test_all(
         self.test_processing_parameters(param)
         self.test_processing_disc(disc)
         self.test_solving(solver, t_eval)
+        self.test_serialisation(solver, t_eval)
 
         if (
             isinstance(
diff --git a/tests/unit/test_meshes/test_meshes.py b/tests/unit/test_meshes/test_meshes.py
index 6563ba232d..000ec729a5 100644
--- a/tests/unit/test_meshes/test_meshes.py
+++ b/tests/unit/test_meshes/test_meshes.py
@@ -390,6 +390,30 @@ def test_1plus1D_tabs_right_left(self):
         # positive tab should be "left"
         self.assertEqual(mesh["current collector"].tabs["positive tab"], "left")
 
+    def test_to_from_json(self):
+        r = pybamm.SpatialVariable(
+            "r", domain=["negative particle"], coord_sys="spherical polar"
+        )
+
+        geometry = {
+            "negative particle": {r: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}
+        }
+
+        submesh_types = {"negative particle": pybamm.Uniform1DSubMesh}
+        var_pts = {r: 20}
+
+        # create mesh
+        mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
+
+        mesh_json = mesh.to_json()
+
+        expected_json = {
+            "submesh_pts": {"negative particle": {"r": 20}},
+            "base_domains": ["negative particle"],
+        }
+
+        self.assertEqual(mesh_json, expected_json)
+
 
 class TestMeshGenerator(TestCase):
     def test_init_name(self):
diff --git a/tests/unit/test_meshes/test_one_dimensional_submesh.py b/tests/unit/test_meshes/test_one_dimensional_submesh.py
index 207f5f2b6f..a7cafb5e25 100644
--- a/tests/unit/test_meshes/test_one_dimensional_submesh.py
+++ b/tests/unit/test_meshes/test_one_dimensional_submesh.py
@@ -18,6 +18,32 @@ def test_exceptions(self):
         with self.assertRaises(pybamm.GeometryError):
             pybamm.SubMesh1D(edges, None, tabs=tabs)
 
+    def test_to_json(self):
+        edges = np.linspace(0, 1, 10)
+        tabs = {"negative": {"z_centre": 0}, "positive": {"z_centre": 1}}
+        mesh = pybamm.SubMesh1D(edges, None, tabs=tabs)
+
+        mesh_json = mesh.to_json()
+
+        expected_json = {
+            "edges": [
+                0.0,
+                0.1111111111111111,
+                0.2222222222222222,
+                0.3333333333333333,
+                0.4444444444444444,
+                0.5555555555555556,
+                0.6666666666666666,
+                0.7777777777777777,
+                0.8888888888888888,
+                1.0,
+            ],
+            "coord_sys": None,
+            "tabs": {"negative tab": "left", "positive tab": "right"},
+        }
+
+        self.assertEqual(mesh_json, expected_json)
+
 
 class TestUniform1DSubMesh(TestCase):
     def test_exceptions(self):
diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py
index 2e646e1085..88bde7941f 100644
--- a/tests/unit/test_meshes/test_scikit_fem_submesh.py
+++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py
@@ -180,6 +180,44 @@ def test_tab_left_right(self):
         param.process_geometry(geometry)
         pybamm.Mesh(geometry, submesh_types, var_pts)
 
+    def test_to_json(self):
+        param = get_param()
+        geometry = pybamm.battery_geometry(
+            include_particles=False, options={"dimensionality": 2}
+        )
+        param.process_geometry(geometry)
+
+        var_pts = {"x_n": 10, "x_s": 7, "x_p": 12, "y": 16, "z": 24}
+
+        submesh_types = {
+            "negative electrode": pybamm.Uniform1DSubMesh,
+            "separator": pybamm.Uniform1DSubMesh,
+            "positive electrode": pybamm.Uniform1DSubMesh,
+            "current collector": pybamm.MeshGenerator(pybamm.ScikitUniform2DSubMesh),
+        }
+
+        # create mesh
+        mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
+
+        mesh_json = mesh.to_json()
+
+        expected_json = {
+            "submesh_pts": {
+                "negative electrode": {"x_n": 10},
+                "separator": {"x_s": 7},
+                "positive electrode": {"x_p": 12},
+                "current collector": {"y": 16, "z": 24},
+            },
+            "base_domains": [
+                "negative electrode",
+                "separator",
+                "positive electrode",
+                "current collector",
+            ],
+        }
+
+        self.assertEqual(mesh_json, expected_json)
+
 
 class TestScikitFiniteElementChebyshev2DSubMesh(TestCase):
     def test_mesh_creation(self):
diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py
index 4167d5fff5..1274d1a7bf 100644
--- a/tests/unit/test_models/test_base_model.py
+++ b/tests/unit/test_models/test_base_model.py
@@ -9,6 +9,7 @@
 
 import casadi
 import numpy as np
+from numpy import testing
 
 import pybamm
 
@@ -982,6 +983,32 @@ def test_timescale_lengthscale_get_set_not_implemented(self):
         with self.assertRaises(NotImplementedError):
             model.length_scales = 1
 
+    def test_save_load_model(self):
+        model = pybamm.BaseModel()
+        c = pybamm.Variable("c")
+        model.rhs = {c: -c}
+        model.initial_conditions = {c: 1}
+        model.variables["c"] = c
+        model.variables["2c"] = 2 * c
+
+        # setup and discretise
+        solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
+
+        # save model
+        model.save_model(filename="test_base_model")
+
+        # raises warning if variables are saved
+        with self.assertWarns(pybamm.ModelWarning):
+            model.save_model(filename="test_base_model", variables=model.variables)
+
+        new_model = pybamm.load_model("test_base_model.json")
+
+        new_solution = pybamm.ScipySolver().solve(new_model, np.linspace(0, 1))
+
+        # model solutions match
+        testing.assert_array_equal(solution.all_ys, new_solution.all_ys)
+        os.remove("test_base_model.json")
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index d65f95fe93..53924b8c2b 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -87,165 +87,63 @@ def mesh_var_dict():
 
 
 class TestSerialiseModels(TestCase):
-    # test lithium models
-    def test_spm_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.SPM()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_equal(solution.all_ys[x], new_solution.all_ys[x])
-
-    def test_spme_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.SPMe()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_equal(solution.all_ys[x], new_solution.all_ys[x])
-
-    def test_mpm_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.MPM()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x]
-            )
-
-    def test_dfn_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.DFN()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x]
-            )
-
-    def test_newman_tobias_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.NewmanTobias()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x]
-            )
-
-    def test_msmr_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x], decimal=3
-            )
-
-    # test lead-acid models
-    def test_lead_acid_full_serialisation_recreation(self):
-        t = [0, 3600]
-
-        model = pybamm.lead_acid.Full()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
-
-        sim.save_model("test_model")
-
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x]
-            )
-
-    def test_loqs_serialisation_recreation(self):
-        t = [0, 3600]
+    def test_user_defined_model_recreaction(self):
+        # Start with a base model
+        model = pybamm.BaseModel()
 
-        model = pybamm.lead_acid.LOQS()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
+        # Define the variables and parameters
+        x = pybamm.SpatialVariable("x", domain="rod", coord_sys="cartesian")
+        T = pybamm.Variable("Temperature", domain="rod")
+        k = pybamm.Parameter("Thermal diffusivity")
+
+        # Write the governing equations
+        N = -k * pybamm.grad(T)  # Heat flux
+        Q = 1 - pybamm.Function(np.abs, x - 1)  # Source term
+        dTdt = -pybamm.div(N) + Q
+        model.rhs = {T: dTdt}  # add to model
+
+        # Add the boundary and initial conditions
+        model.boundary_conditions = {
+            T: {
+                "left": (pybamm.Scalar(0), "Dirichlet"),
+                "right": (pybamm.Scalar(0), "Dirichlet"),
+            }
+        }
+        model.initial_conditions = {T: 2 * x - x**2}
 
-        sim.save_model("test_model")
+        # Add desired output variables, geometry, parameters
+        model.variables = {"Temperature": T, "Heat flux": N, "Heat source": Q}
+        geometry = {"rod": {x: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(2)}}}
+        param = pybamm.ParameterValues({"Thermal diffusivity": 0.75})
 
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
-        new_solution = new_solver.solve(new_model, t)
-
-        for x, val in enumerate(solution.all_ys):
-            np.testing.assert_array_almost_equal(
-                solution.all_ys[x], new_solution.all_ys[x]
-            )
+        # Process model and geometry
+        param.process_model(model)
+        param.process_geometry(geometry)
 
-    def test_thevenin_serialisation_recreation(self):
-        t = [0, 3600]
+        # Pick mesh, spatial method, and discretise
+        submesh_types = {"rod": pybamm.Uniform1DSubMesh}
+        var_pts = {x: 30}
+        mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
+        spatial_methods = {"rod": pybamm.FiniteVolume()}
+        disc = pybamm.Discretisation(mesh, spatial_methods)
+        disc.process_model(model)
 
-        model = pybamm.equivalent_circuit.Thevenin()
-        sim = pybamm.Simulation(model)
-        solution = sim.solve(t)
+        # Solve
+        solver = pybamm.ScipySolver()
+        t = np.linspace(0, 1, 100)
+        solution = solver.solve(model, t)
 
-        sim.save_model("test_model")
+        model.save_model("heat_equation", variables=model._variables, mesh=mesh)
+        new_model = pybamm.load_model("heat_equation.json")
 
-        new_model = pybamm.load_model("test_model.json")
-        new_solver = new_model.default_solver
+        new_solver = pybamm.ScipySolver()
         new_solution = new_solver.solve(new_model, t)
 
         for x, val in enumerate(solution.all_ys):
             np.testing.assert_array_almost_equal(
                 solution.all_ys[x], new_solution.all_ys[x]
             )
+        os.remove("heat_equation.json")
 
 
 class TestSerialise(TestCase):

From 2934df462cb5f11912ae22aca0f76b435ae75d7d Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Mon, 2 Oct 2023 10:55:16 +0000
Subject: [PATCH 115/615] Add docs for serialisation update integration test to
 pass at lower accuracy Remove outputs from example notebook

---
 .../api/expression_tree/operations/index.rst  |   1 +
 .../expression_tree/operations/serialise.rst  |   5 +
 docs/source/examples/index.rst                |   1 +
 .../notebooks/models/saving_models.ipynb      | 127 ++----------------
 .../expression_tree/operations/serialise.py   |  26 ++--
 .../test_models/standard_model_tests.py       |   7 +-
 6 files changed, 36 insertions(+), 131 deletions(-)
 create mode 100644 docs/source/api/expression_tree/operations/serialise.rst

diff --git a/docs/source/api/expression_tree/operations/index.rst b/docs/source/api/expression_tree/operations/index.rst
index c084389f1a..67beaca136 100644
--- a/docs/source/api/expression_tree/operations/index.rst
+++ b/docs/source/api/expression_tree/operations/index.rst
@@ -8,4 +8,5 @@ Classes and functions that operate on the expression tree
   evaluate
   jacobian
   convert_to_casadi
+  serialise
   unpack_symbol
diff --git a/docs/source/api/expression_tree/operations/serialise.rst b/docs/source/api/expression_tree/operations/serialise.rst
new file mode 100644
index 0000000000..daa1b652f1
--- /dev/null
+++ b/docs/source/api/expression_tree/operations/serialise.rst
@@ -0,0 +1,5 @@
+Serialise
+=========
+
+.. autoclass:: pybamm.expression_tree.operations.serialise.Serialise
+  :members:
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 4bab430032..36b0d3d81f 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -62,6 +62,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/models/MSMR.ipynb
     notebooks/models/pouch-cell-model.ipynb
     notebooks/models/rate-capability.ipynb
+    notebooks/models/saving_models.ipynb
     notebooks/models/SEI-on-cracks.ipynb
     notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
     notebooks/models/SPM.ipynb
diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index f2826813d4..85ca516a59 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -18,17 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -50,20 +42,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f6084221cd0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Recreate the pybamm model from the JSON file\n",
     "new_dfn_model = pybamm.load_model(\"sim_model_example.json\")\n",
@@ -83,23 +64,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "Variables not provided by the serialised model",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m     plot_sim\u001b[39m.\u001b[39msolve([\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m     sims\u001b[39m.\u001b[39mappend(plot_sim)\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bmonkshood:pybamm/home/pliggins/PyBaMM/docs/source/examples/notebooks/models/saving_models.ipynb#W6sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m pybamm\u001b[39m.\u001b[39;49mdynamic_plot(sims, time_unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mseconds\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
-      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/dynamic_plot.py:20\u001b[0m, in \u001b[0;36mdynamic_plot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39m    The 'QuickPlot' object that was created\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     19\u001b[0m kwargs_for_class \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 20\u001b[0m plot \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39;49mQuickPlot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs_for_class)\n\u001b[1;32m     21\u001b[0m plot\u001b[39m.\u001b[39mdynamic_plot(kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m     22\u001b[0m \u001b[39mreturn\u001b[39;00m plot\n",
-      "File \u001b[0;32m~/PyBaMM/pybamm/plotting/quick_plot.py:159\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 159\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mVariables not provided by the serialised model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    161\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m    162\u001b[0m     \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m    163\u001b[0m )\n\u001b[1;32m    164\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n",
-      "\u001b[0;31mAttributeError\u001b[0m: Variables not provided by the serialised model"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "dfn_models = [dfn_model, new_dfn_model]\n",
     "sims = []\n",
@@ -122,34 +89,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b21266c9388043dbbe06e6d93dda3009",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=36.0), Output()), _dom_classes=…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x7fcdc807aa60>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# using the first simulation, save a new file which includes a list of the model variables\n",
     "dfn_sim.save_model(\"sim_model_variables\", variables=True)\n",
@@ -183,20 +125,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.spm.SPM at 0x7f1b8b856040>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# create the model\n",
     "spm_model = pybamm.lithium_ion.SPM()\n",
@@ -237,34 +168,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a1c0b22c969b45858361b7e9de264e76",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x7f1b322bc160>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# read back in\n",
     "new_spm_model = pybamm.load_model(\"example_model.json\")\n",
@@ -318,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -337,13 +243,6 @@
    "source": [
     "pybamm.print_citations()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index edba97a3c4..53e1f357d0 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -7,11 +7,6 @@
 import numpy as np
 import re
 
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from pybamm import BaseModel
-
 
 class Serialise:
     """
@@ -84,28 +79,27 @@ class _EmptyDict(dict):
 
     def save_model(
         self,
-        model: pybamm.BaseBatteryModel,
+        model: pybamm.BaseModel,
         mesh: pybamm.Mesh = None,
         variables: pybamm.FuzzyDict = None,
         filename: str = None,
     ):
-        """
-        Saves a discretised model to a JSON file.
+        """Saves a discretised model to a JSON file.
 
         As the model is discretised and ready to solve, only the right hand side,
         algebraic and initial condition variables are saved.
 
         Parameters
         ----------
-        model: : :class:`pybamm.BaseBatteryModel`
+        model : :class:`pybamm.BaseModel`
             The discretised model to be saved
-        mesh: :class: `pybamm.Mesh`, optional
+        mesh : :class:`pybamm.Mesh` (optional)
             The mesh the model has been discretised over. Not neccesary to solve
             the model when read in, but required to use pybamm's plotting tools.
-        variables: :class: `pybamm.FuzzyDict`, optional
+        variables: :class:`pybamm.FuzzyDict` (optional)
             The discretised model varaibles. Not necessary to solve a model, but
             required to use pybamm's plotting tools.
-        filename: str, optional
+        filename: str (optional)
             The desired name of the JSON file. If no name is provided, one will be
             created based on the model name, and the current datetime.
         """
@@ -148,7 +142,9 @@ def save_model(
         with open(filename + ".json", "w") as f:
             json.dump(model_json, f)
 
-    def load_model(self, filename: str, battery_model: BaseModel = None) -> BaseModel:
+    def load_model(
+        self, filename: str, battery_model: pybamm.BaseModel = None
+    ) -> pybamm.BaseModel:
         """
         Loads a discretised, ready to solve model into PyBaMM.
 
@@ -166,14 +162,14 @@ def load_model(self, filename: str, battery_model: BaseModel = None) -> BaseMode
 
         filename: str
             Path to the JSON file containing the serialised model file
-        battery_model: :class: pybamm.BaseBatteryModel, optional
+        battery_model:  :class:`pybamm.BaseModel` (optional)
             PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
             any model names within the file. If None, the function will look for the saved object
             path, present if the original model came from PyBaMM.
 
         Returns
         -------
-        :class: pybamm.BaseBatteryModel
+        :class:`pybamm.BaseModel`
             A PyBaMM model object, of type specified either in the JSON or in `battery_model`.
         """
 
diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index 0526363a75..d0e38501c9 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -155,6 +155,9 @@ def test_serialisation(self, solver=None, t_eval=None):
         if isinstance(new_model, pybamm.lithium_ion.BaseModel):
             new_solver.rtol = 1e-8
             new_solver.atol = 1e-8
+            accuracy = 6
+        else:
+            accuracy = 5
 
         Crate = abs(
             self.parameter_values["Current function [A]"]
@@ -170,7 +173,7 @@ def test_serialisation(self, solver=None, t_eval=None):
 
         for x, val in enumerate(self.solution.all_ys):
             np.testing.assert_array_almost_equal(
-                new_solution.all_ys[x], self.solution.all_ys[x]
+                new_solution.all_ys[x], self.solution.all_ys[x], decimal=accuracy
             )
 
         os.remove("test_model.json")
@@ -182,7 +185,6 @@ def test_all(
         self.test_processing_parameters(param)
         self.test_processing_disc(disc)
         self.test_solving(solver, t_eval)
-        self.test_serialisation(solver, t_eval)
 
         if (
             isinstance(
@@ -190,6 +192,7 @@ def test_all(
             )
             and not skip_output_tests
         ):
+            self.test_serialisation(solver, t_eval)
             self.test_outputs()
 
 

From 66d8045b29a26fc595ff82e386b5c89c1a082357 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Mon, 2 Oct 2023 14:53:17 +0000
Subject: [PATCH 116/615] Increase test coverage

---
 pybamm/expression_tree/binary_operators.py    |  87 +------------
 .../expression_tree/operations/serialise.py   |  12 +-
 pybamm/expression_tree/unary_operators.py     |   9 +-
 .../test_expression_tree/test_broadcasts.py   |   5 +-
 .../test_expression_tree/test_functions.py    | 115 ++++++++++++++++++
 .../test_expression_tree/test_interpolant.py  |  65 +++++-----
 .../test_expression_tree/test_parameter.py    |   6 +
 .../test_unary_operators.py                   |  64 ++++++++++
 .../test_serialisation/test_serialisation.py  |  28 ++++-
 9 files changed, 262 insertions(+), 129 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 56f3154be9..5bff7419d0 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -69,14 +69,14 @@ def __init__(self, name, left, right):
         self.right = self.children[1]
 
     @classmethod
-    def _from_json(cls, name, snippet: dict):
+    def _from_json(cls, snippet: dict):
         """Use to instantiate when deserialising; discretisation has
         already occured so pre-processing of binaries is not necessary."""
 
         instance = cls.__new__(cls)
 
         super(BinaryOperator, instance).__init__(
-            name,
+            snippet["name"],
             children=[snippet["children"][0], snippet["children"][1]],
             domains=snippet["domains"],
         )
@@ -191,12 +191,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("**", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("**", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply chain rule and power rule
@@ -238,12 +232,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("+", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("+", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         return self.left.diff(variable) + self.right.diff(variable)
@@ -267,12 +255,6 @@ def __init__(self, left, right):
 
         super().__init__("-", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("-", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         return self.left.diff(variable) - self.right.diff(variable)
@@ -298,12 +280,6 @@ def __init__(self, left, right):
 
         super().__init__("*", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("*", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply product rule
@@ -340,13 +316,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("@", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        # instance = super(MatrixMultiplication, cls)._from_json("@", left, right)
-        instance = super()._from_json("@", snippet)
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # We shouldn't need this
@@ -394,12 +363,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("/", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("/", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply quotient rule
@@ -444,12 +407,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("inner product", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("inner product", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply product rule
@@ -519,12 +476,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("==", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("==", snippet)
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         # Equality should always be multiplied by something else so hopefully don't
@@ -602,14 +553,6 @@ def __init__(self, left, right):
         """See :meth:`pybamm.BinaryOperator.__init__()`."""
         super().__init__("<=", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = cls.__new__(cls)
-
-        instance.__init__(snippet["children"][0], snippet["children"][1])
-        return instance
-
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{!s} <= {!s}".format(self.left, self.right)
@@ -627,14 +570,6 @@ class NotEqualHeaviside(_Heaviside):
     def __init__(self, left, right):
         super().__init__("<", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = cls.__new__(cls)
-
-        instance.__init__(snippet["children"][0], snippet["children"][1])
-        return instance
-
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{!s} < {!s}".format(self.left, self.right)
@@ -652,12 +587,6 @@ class Modulo(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("%", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("%", snippet)
-        return instance
-
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
         # apply chain rule and power rule
@@ -696,12 +625,6 @@ class Minimum(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("minimum", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("minimum", snippet)
-        return instance
-
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "minimum({!s}, {!s})".format(self.left, self.right)
@@ -738,12 +661,6 @@ class Maximum(BinaryOperator):
     def __init__(self, left, right):
         super().__init__("maximum", left, right)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.BinaryOperator._from_json()`."""
-        instance = super()._from_json("maximum", snippet)
-        return instance
-
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "maximum({!s}, {!s})".format(self.left, self.right)
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index 53e1f357d0..aa84db631e 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -40,9 +40,8 @@ def default(self, node: dict):
                 node_dict["expression"] = self.default(node._expression)
                 return node_dict
 
-            json_obj = json.JSONEncoder.default(self, node)  # pragma: no cover
-            node_dict["json"] = json_obj
-            return node_dict
+            node_dict["json"] = json.JSONEncoder.default(self, node)  # pragma: no cover
+            return node_dict  # pragma: no cover
 
     class _MeshEncoder(json.JSONEncoder):
         """Converts PyBaMM meshes into a JSON-serialisable format"""
@@ -63,9 +62,8 @@ def default(self, node: dict):
                 node_dict.update(node.to_json())
                 return node_dict
 
-            json_obj = json.JSONEncoder.default(self, node)  # pragma: no cover
-            node_dict["json"] = json_obj
-            return node_dict
+            node_dict["json"] = json.JSONEncoder.default(self, node)  # pragma: no cover
+            return node_dict  # pragma: no cover
 
     class _Empty:
         """A dummy class to aid deserialisation"""
@@ -137,7 +135,7 @@ def save_model(
             }
 
         if filename is None:
-            filename = model.name + "_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M_%S")
+            filename = model.name + "_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M")
 
         with open(filename + ".json", "w") as f:
             json.dump(model_json, f)
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 2b85309469..cc2b2a434e 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -184,9 +184,7 @@ def __init__(self, child):
 
     @classmethod
     def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.UnaryOperator._from_json()`."""
-        instance = super()._from_json("sign", snippet)
-        return instance
+        raise NotImplementedError()
 
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
@@ -421,6 +419,11 @@ class with a :class:`Matrix`
     def __init__(self, name, child, domains=None):
         super().__init__(name, child, domains)
 
+    def diff(self, variable):
+        """See :meth:`pybamm.Symbol.diff()`."""
+        # We shouldn't need this
+        raise NotImplementedError
+
     def to_json(self):
         raise NotImplementedError(
             "pybamm.SpatialOperator: Serialisation is only implemented for discretised models."
diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py
index b91cd7d95c..be8fe1a677 100644
--- a/tests/unit/test_expression_tree/test_broadcasts.py
+++ b/tests/unit/test_expression_tree/test_broadcasts.py
@@ -350,12 +350,15 @@ def test_diff(self):
         self.assertIsInstance(d, pybamm.Scalar)
         self.assertEqual(d.evaluate(y=y), 0)
 
-    def test_to_json_error(self):
+    def test_to_from_json_error(self):
         a = pybamm.StateVector(slice(0, 1))
         b = pybamm.PrimaryBroadcast(a, "separator")
         with self.assertRaises(NotImplementedError):
             b.to_json()
 
+        with self.assertRaises(NotImplementedError):
+            pybamm.PrimaryBroadcast._from_json({})
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index ac5410d9e1..07bfa7efe8 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -3,6 +3,7 @@
 #
 from tests import TestCase
 import unittest
+import unittest.mock as mock
 
 import numpy as np
 import sympy
@@ -145,8 +146,30 @@ def test_to_equation(self):
         # Test Function
         self.assertEqual(pybamm.Function(np.log, 10).to_equation(), 10.0)
 
+    def test_to_from_json_error(self):
+        a = pybamm.Symbol("a")
+        funca = pybamm.Function(test_function, a)
+
+        with self.assertRaises(NotImplementedError):
+            funca.to_json()
+
+        with self.assertRaises(NotImplementedError):
+            pybamm.Function._from_json({})
+
 
 class TestSpecificFunctions(TestCase):
+    def test_to_json(self):
+        a = pybamm.InputParameter("a")
+        fun = pybamm.cos(a)
+
+        expected_json = {
+            "name": "function (cos)",
+            "id": mock.ANY,
+            "function": "cos",
+        }
+
+        self.assertEqual(fun.to_json(), expected_json)
+
     def test_arcsinh(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.arcsinh(a)
@@ -180,6 +203,15 @@ def test_arcsinh(self):
             pybamm.PrimaryBroadcast(pybamm.PrimaryBroadcast(fun, "test"), "test2"),
         )
 
+        # test creation from json
+        input_json = {
+            "name": "arcsinh",
+            "id": mock.ANY,
+            "function": "arcsinh",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Arcsinh._from_json(input_json), fun)
+
     def test_arctan(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.arctan(a)
@@ -196,6 +228,15 @@ def test_arctan(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "arctan",
+            "id": mock.ANY,
+            "function": "arctan",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Arctan._from_json(input_json), fun)
+
     def test_cos(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.cos(a)
@@ -213,6 +254,15 @@ def test_cos(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "cos",
+            "id": mock.ANY,
+            "function": "cos",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Cos._from_json(input_json), fun)
+
     def test_cosh(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.cosh(a)
@@ -230,6 +280,15 @@ def test_cosh(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "cosh",
+            "id": mock.ANY,
+            "function": "cosh",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Cosh._from_json(input_json), fun)
+
     def test_exp(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.exp(a)
@@ -247,6 +306,15 @@ def test_exp(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "exp",
+            "id": mock.ANY,
+            "function": "exp",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Exp._from_json(input_json), fun)
+
     def test_log(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.log(a)
@@ -276,6 +344,17 @@ def test_log(self):
             places=5,
         )
 
+        # test creation from json
+        a = pybamm.InputParameter("a")
+        fun = pybamm.log(a)
+        input_json = {
+            "name": "log",
+            "id": mock.ANY,
+            "function": "log",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Log._from_json(input_json), fun)
+
     def test_max(self):
         a = pybamm.StateVector(slice(0, 3))
         y_test = np.array([1, 2, 3])
@@ -307,6 +386,15 @@ def test_sin(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "sin",
+            "id": mock.ANY,
+            "function": "sin",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Sin._from_json(input_json), fun)
+
     def test_sinh(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.sinh(a)
@@ -324,6 +412,15 @@ def test_sinh(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "sinh",
+            "id": mock.ANY,
+            "function": "sinh",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Sinh._from_json(input_json), fun)
+
     def test_sqrt(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.sqrt(a)
@@ -340,6 +437,15 @@ def test_sqrt(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "sqrt",
+            "id": mock.ANY,
+            "function": "sqrt",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Sqrt._from_json(input_json), fun)
+
     def test_tanh(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.tanh(a)
@@ -370,6 +476,15 @@ def test_erf(self):
             places=5,
         )
 
+        # test creation from json
+        input_json = {
+            "name": "erf",
+            "id": mock.ANY,
+            "function": "erf",
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Erf._from_json(input_json), fun)
+
     def test_erfc(self):
         a = pybamm.InputParameter("a")
         fun = pybamm.erfc(a)
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 93009adf0d..0b5ca5f64a 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -331,41 +331,46 @@ def test_to_json_error(self):
         y = pybamm.StateVector(slice(0, 2))
         interp = pybamm.Interpolant(x, 2 * x, y)
 
-        self.assertEqual(
-            interp.to_json(),
-            {
-                "name": "interpolating_function",
-                "id": mock.ANY,
-                "x": [
-                    [
-                        0.0,
-                        0.1111111111111111,
-                        0.2222222222222222,
-                        0.3333333333333333,
-                        0.4444444444444444,
-                        0.5555555555555556,
-                        0.6666666666666666,
-                        0.7777777777777777,
-                        0.8888888888888888,
-                        1.0,
-                    ]
-                ],
-                "y": [
+        print(interp.children)
+        expected_json = {
+            "name": "interpolating_function",
+            "id": mock.ANY,
+            "x": [
+                [
                     0.0,
+                    0.1111111111111111,
                     0.2222222222222222,
+                    0.3333333333333333,
                     0.4444444444444444,
+                    0.5555555555555556,
                     0.6666666666666666,
+                    0.7777777777777777,
                     0.8888888888888888,
-                    1.1111111111111112,
-                    1.3333333333333333,
-                    1.5555555555555554,
-                    1.7777777777777777,
-                    2.0,
-                ],
-                "interpolator": "linear",
-                "extrapolate": True,
-            },
-        )
+                    1.0,
+                ]
+            ],
+            "y": [
+                0.0,
+                0.2222222222222222,
+                0.4444444444444444,
+                0.6666666666666666,
+                0.8888888888888888,
+                1.1111111111111112,
+                1.3333333333333333,
+                1.5555555555555554,
+                1.7777777777777777,
+                2.0,
+            ],
+            "interpolator": "linear",
+            "extrapolate": True,
+        }
+
+        # check correct writing to json
+        self.assertEqual(interp.to_json(), expected_json)
+
+        expected_json["children"] = [y]
+        # check correct re-creation
+        self.assertEqual(pybamm.Interpolant._from_json(expected_json), interp)
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index 62441f4309..deab4a0cff 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -37,6 +37,9 @@ def test_to_json_error(self):
         with self.assertRaises(NotImplementedError):
             func.to_json()
 
+        with self.assertRaises(NotImplementedError):
+            pybamm.Parameter._from_json({})
+
 
 class TestFunctionParameter(TestCase):
     def test_function_parameter_init(self):
@@ -121,6 +124,9 @@ def test_to_json_error(self):
         with self.assertRaises(NotImplementedError):
             func.to_json()
 
+        with self.assertRaises(NotImplementedError):
+            pybamm.FunctionParameter._from_json({})
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index 3c9de976d6..f11c5d5d10 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -53,6 +53,20 @@ def test_negation(self):
             pybamm.PrimaryBroadcast(pybamm.PrimaryBroadcast(nega, "test"), "test2"),
         )
 
+        # Test from_json
+        input_json = {
+            "name": "-",
+            "id": -2659857727954094888,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Negate._from_json(input_json), nega)
+
     def test_absolute(self):
         a = pybamm.Symbol("a")
         absa = pybamm.AbsoluteValue(a)
@@ -80,6 +94,20 @@ def test_absolute(self):
             pybamm.PrimaryBroadcast(pybamm.PrimaryBroadcast(absa, "test"), "test2"),
         )
 
+        # Test from_json
+        input_json = {
+            "name": "abs",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [a],
+        }
+        self.assertEqual(pybamm.AbsoluteValue._from_json(input_json), absa)
+
     def test_smooth_absolute_value(self):
         a = pybamm.StateVector(slice(0, 1))
         expr = pybamm.smooth_absolute_value(a, 10)
@@ -116,6 +144,11 @@ def test_sign(self):
             ),
         )
 
+        # Test from_json
+        with self.assertRaises(NotImplementedError):
+            # signs are always scalar/array types in a discretised model
+            pybamm.Sign._from_json({})
+
     def test_floor(self):
         a = pybamm.Symbol("a")
         floora = pybamm.Floor(a)
@@ -130,6 +163,20 @@ def test_floor(self):
         floorc = pybamm.Floor(c)
         self.assertEqual(floorc.evaluate(), -4)
 
+        # Test from_json
+        input_json = {
+            "name": "floor",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Floor._from_json(input_json), floora)
+
     def test_ceiling(self):
         a = pybamm.Symbol("a")
         ceila = pybamm.Ceiling(a)
@@ -144,6 +191,20 @@ def test_ceiling(self):
         ceilc = pybamm.Ceiling(c)
         self.assertEqual(ceilc.evaluate(), -3)
 
+        # Test from_json
+        input_json = {
+            "name": "ceil",
+            "id": mock.ANY,
+            "domains": {
+                "primary": [],
+                "secondary": [],
+                "tertiary": [],
+                "quaternary": [],
+            },
+            "children": [a],
+        }
+        self.assertEqual(pybamm.Ceiling._from_json(input_json), ceila)
+
     def test_gradient(self):
         # gradient of scalar symbol should fail
         a = pybamm.Symbol("a")
@@ -711,6 +772,9 @@ def test_to_from_json(self):
         with self.assertRaises(NotImplementedError):
             spatial_vec.to_json()
 
+        with self.assertRaises(NotImplementedError):
+            pybamm.SpatialOperator._from_json({})
+
         # ExplicitTimeIntegral
         expr = pybamm.ExplicitTimeIntegral(pybamm.Parameter("param"), pybamm.Scalar(1))
 
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 53924b8c2b..7ef55bd2f3 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -3,6 +3,7 @@
 #
 from tests import TestCase
 import tests
+import json
 import os
 import unittest
 import unittest.mock as mock
@@ -305,6 +306,17 @@ def test_get_pybamm_class(self):
 
         self.assertIsInstance(mesh_class, pybamm.Mesh)
 
+        with self.assertRaises(Exception):
+            unrecognised_symbol = {
+                "py/id": mock.ANY,
+                "py/object": "pybamm.expression_tree.scalar.Scale",
+                "name": "5.0",
+                "id": mock.ANY,
+                "value": 5.0,
+                "children": [],
+            }
+            Serialise()._get_pybamm_class(unrecognised_symbol)
+
     def test_reconstruct_symbol(self):
         scalar, scalar_dict = scalar_var_dict()
 
@@ -456,9 +468,7 @@ def test_save_load_model(self):
 
         # default save where filename isn't provided
         Serialise().save_model(model)
-        filename = (
-            "test_spm_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M_%S") + ".json"
-        )
+        filename = "test_spm_" + datetime.now().strftime("%Y_%m_%d-%p%I_%M") + ".json"
         self.assertTrue(os.path.exists(filename))
         os.remove(filename)
 
@@ -480,6 +490,18 @@ def test_save_load_model(self):
         newest_model = Serialise().load_model(
             "test_model.json", battery_model=pybamm.lithium_ion.SPM
         )
+
+        # Test for error if no model type is provided
+        with open("test_model.json", "r") as f:
+            model_data = json.load(f)
+            del model_data["py/object"]
+
+        with open("test_model.json", "w") as f:
+            json.dump(model_data, f)
+
+        with self.assertRaises(TypeError):
+            Serialise().load_model("test_model.json")
+
         os.remove("test_model.json")
 
         # check new model solves

From d5dd21da07eb1b81be11eceadda0a79978ba6a9e Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Mon, 2 Oct 2023 15:36:42 +0000
Subject: [PATCH 117/615] Fix minor style issues

---
 pybamm/expression_tree/broadcasts.py                |  4 ++--
 pybamm/expression_tree/operations/serialise.py      | 11 ++++++-----
 pybamm/expression_tree/parameter.py                 | 10 ++++++----
 pybamm/expression_tree/unary_operators.py           |  6 ++++--
 pybamm/models/base_model.py                         |  8 ++++----
 pybamm/models/event.py                              |  4 +++-
 pybamm/plotting/quick_plot.py                       |  2 +-
 pybamm/simulation.py                                | 11 ++++++-----
 pybamm/solvers/base_solver.py                       |  2 +-
 tests/unit/test_expression_tree/test_array.py       |  2 +-
 tests/unit/test_expression_tree/test_matrix.py      |  2 +-
 tests/unit/test_serialisation/test_serialisation.py | 11 +++++------
 12 files changed, 40 insertions(+), 33 deletions(-)

diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index a9bd5c2ee2..50afa526e5 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -52,13 +52,13 @@ def _diff(self, variable):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.Broadcast: Serialisation is only implemented for discretised models."
+            "pybamm.Broadcast: Serialisation is only implemented for discretised models"
         )
 
     @classmethod
     def _from_json(cls, snippet):
         raise NotImplementedError(
-            "pybamm.Broadcast: Please use a discretised model when reading in from JSON."
+            "pybamm.Broadcast: Please use a discretised model when reading in from JSON"
         )
 
 
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index aa84db631e..e3b3d38472 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -101,7 +101,7 @@ def save_model(
             The desired name of the JSON file. If no name is provided, one will be
             created based on the model name, and the current datetime.
         """
-        if model.is_discretised == False:
+        if model.is_discretised is False:
             raise NotImplementedError(
                 "PyBaMM can only serialise a discretised, ready-to-solve model."
             )
@@ -161,14 +161,15 @@ def load_model(
         filename: str
             Path to the JSON file containing the serialised model file
         battery_model:  :class:`pybamm.BaseModel` (optional)
-            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
-            any model names within the file. If None, the function will look for the saved object
-            path, present if the original model came from PyBaMM.
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will
+            override any model names within the file. If None, the function will look
+            for the saved object path, present if the original model came from PyBaMM.
 
         Returns
         -------
         :class:`pybamm.BaseModel`
-            A PyBaMM model object, of type specified either in the JSON or in `battery_model`.
+            A PyBaMM model object, of type specified either in the JSON or in
+            `battery_model`.
         """
 
         with open(filename, "r") as f:
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index abf50faa75..afbfe8ac37 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -51,13 +51,13 @@ def to_equation(self):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.Parameter: Serialisation is only implemented for discretised models."
+            "pybamm.Parameter: Serialisation is only implemented for discretised models"
         )
 
     @classmethod
     def _from_json(cls, snippet):
         raise NotImplementedError(
-            "pybamm.Parameter: Please use a discretised model when reading in from JSON."
+            "pybamm.Parameter: Please use a discretised model when reading in from JSON"
         )
 
 
@@ -235,11 +235,13 @@ def to_equation(self):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.FunctionParameter: Serialisation is only implemented for discretised models."
+            "pybamm.FunctionParameter:"
+            "Serialisation is only implemented for discretised models."
         )
 
     @classmethod
     def _from_json(cls, snippet):
         raise NotImplementedError(
-            "pybamm.FunctionParameter: Please use a discretised model when reading in from JSON."
+            "pybamm.FunctionParameter:"
+            "Please use a discretised model when reading in from JSON."
         )
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index cc2b2a434e..7aadae412c 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -426,13 +426,15 @@ def diff(self, variable):
 
     def to_json(self):
         raise NotImplementedError(
-            "pybamm.SpatialOperator: Serialisation is only implemented for discretised models."
+            "pybamm.SpatialOperator:"
+            "Serialisation is only implemented for discretised models."
         )
 
     @classmethod
     def _from_json(cls, snippet):
         raise NotImplementedError(
-            "pybamm.SpatialOperator: Please use a discretised model when reading in from JSON."
+            "pybamm.SpatialOperator:"
+            "Please use a discretised model when reading in from JSON."
         )
 
 
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 46645f992d..32a8a27258 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -1180,7 +1180,7 @@ def save_model(self, filename=None, mesh=None, variables=None):
         if variables and not mesh:
             warnings.warn(
                 """
-                Serialisation: Variables are being saved without a mesh. 
+                Serialisation: Variables are being saved without a mesh.
                 Plotting may not be available.
                 """,
                 pybamm.ModelWarning,
@@ -1198,9 +1198,9 @@ def load_model(filename, battery_model: BaseModel = None):
     filename: str
         Path to the JSON file containing the serialised model file
     battery_model: :class: pybamm.BaseBatteryModel, optional
-            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will override
-            any model names within the file. If None, the function will look for the saved object
-            path, present if the original model came from PyBaMM.
+            PyBaMM model to be created (e.g. pybamm.lithium_ion.SPM), which will
+            override any model names within the file. If None, the function will look
+            for the saved object path, present if the original model came from PyBaMM.
     """
     return Serialise().load_model(filename, battery_model)
 
diff --git a/pybamm/models/event.py b/pybamm/models/event.py
index 105106c470..5bba4cd14b 100644
--- a/pybamm/models/event.py
+++ b/pybamm/models/event.py
@@ -97,7 +97,9 @@ def to_json(self):
         See :meth:`pybamm.Serialise._SymbolEncoder.default()`
         """
 
-        # event_type contains string name, for JSON readability, and value for deserialisation.
+        # event_type contains string name, for JSON readability,
+        # and value for deserialisation.
+
         json_dict = {
             "name": self._name,
             "event_type": [str(self._event_type), self._event_type.value],
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 3f55648225..bfe46b8ed0 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -156,7 +156,7 @@ def __init__(
 
         # check variables have been provided after any serialisation
         if any(len(m.variables) == 0 for m in models):
-            raise AttributeError(f"Variables not provided by the serialised model")
+            raise AttributeError("Variables not provided by the serialised model")
 
         self.n_rows = n_rows or int(
             len(output_variables) // np.sqrt(len(output_variables))
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 4a71e819bd..4118118533 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -293,9 +293,10 @@ def update_new_model_events(self, new_model, op):
                 # figure out whether the voltage event is greater than the starting
                 # voltage (charge) or less (discharge) and set the sign of the
                 # event accordingly
-                if (isinstance(op.value, pybamm.Interpolant) or
-                    isinstance(op.value, pybamm.Multiplication)):
-                    inpt = {"start time":0}
+                if isinstance(op.value, pybamm.Interpolant) or isinstance(
+                    op.value, pybamm.Multiplication
+                ):
+                    inpt = {"start time": 0}
                     init_curr = op.value.evaluate(t=0, inputs=inpt).flatten()[0]
                     sign = np.sign(init_curr)
                 else:
@@ -1207,8 +1208,8 @@ def save_model(
             tools will not be availble. Will automatically save meshes as well, required
             for plotting tools.
         filename: str, optional
-            The desired name of the JSON file. If no name is provided, one will be created
-            based on the model name, and the current datetime.
+            The desired name of the JSON file. If no name is provided, one will be
+            created based on the model name, and the current datetime.
         """
         mesh = self.mesh if (mesh or variables) else None
         variables = self.built_model.variables if variables else None
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 13f8a22f34..cabe36a108 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -707,7 +707,7 @@ def solve(
         # Make sure model isn't empty
         if len(model.rhs) == 0 and len(model.algebraic) == 0:
             if not isinstance(self, pybamm.DummySolver):
-                # check a discretised model without original paramaters is not being used
+                # check for a discretised model without original parameters
                 if not (
                     model.concatenated_rhs is not None
                     or model.concatenated_algebraic is not None
diff --git a/tests/unit/test_expression_tree/test_array.py b/tests/unit/test_expression_tree/test_array.py
index 885c5e0851..b75c313f47 100644
--- a/tests/unit/test_expression_tree/test_array.py
+++ b/tests/unit/test_expression_tree/test_array.py
@@ -47,7 +47,7 @@ def test_to_from_json(self):
 
         json_dict = {
             "name": "Array of shape (3, 1)",
-            "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+            "id": mock.ANY,
             "domains": {
                 "primary": [],
                 "secondary": [],
diff --git a/tests/unit/test_expression_tree/test_matrix.py b/tests/unit/test_expression_tree/test_matrix.py
index 2c3d2379ab..055902b15e 100644
--- a/tests/unit/test_expression_tree/test_matrix.py
+++ b/tests/unit/test_expression_tree/test_matrix.py
@@ -44,7 +44,7 @@ def test_to_from_json(self):
         arr = pybamm.Matrix(csr_matrix([[0, 1, 0, 0], [0, 0, 0, 1]]))
         json_dict = {
             "name": "Sparse Matrix (2, 4)",
-            "id": mock.ANY,  # The value of the ID will change, but want to check it is present
+            "id": mock.ANY,
             "domains": {
                 "primary": [],
                 "secondary": [],
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 7ef55bd2f3..6ae39c05cc 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -2,7 +2,6 @@
 # Tests for the serialisation class
 #
 from tests import TestCase
-import tests
 import json
 import os
 import unittest
@@ -273,7 +272,7 @@ def test_deconstruct_pybamm_dicts(self):
         ser_dict = {
             "rod": {
                 "symbol_x": {
-                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",  # noqa: E501
                     "py/id": mock.ANY,
                     "name": "x",
                     "id": mock.ANY,
@@ -342,7 +341,7 @@ def test_reconstruct_expression_tree(self):
             },
             "children": [
                 {
-                    "py/object": "pybamm.expression_tree.binary_operators.Multiplication",
+                    "py/object": "pybamm.expression_tree.binary_operators.Multiplication",  # noqa: E501
                     "py/id": 139691619709232,
                     "name": "*",
                     "id": 6094209803352873499,
@@ -362,7 +361,7 @@ def test_reconstruct_expression_tree(self):
                             "children": [],
                         },
                         {
-                            "py/object": "pybamm.expression_tree.state_vector.StateVector",
+                            "py/object": "pybamm.expression_tree.state_vector.StateVector",  # noqa: E501
                             "py/id": 139691619589760,
                             "name": "y[0:1]",
                             "id": 5063056989669636089,
@@ -424,7 +423,7 @@ def test_reconstruct_pybamm_dict(self):
         ser_dict = {
             "rod": {
                 "symbol_x": {
-                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",  # noqa: E501
                     "py/id": mock.ANY,
                     "name": "x",
                     "id": mock.ANY,
@@ -506,7 +505,7 @@ def test_save_load_model(self):
 
         # check new model solves
         newest_solver = newest_model.default_solver
-        newest_solution = newest_solver.solve(newest_model, [0, 3600])
+        newest_solver.solve(newest_model, [0, 3600])
 
     def test_serialised_model_plotting(self):
         # models without a mesh

From 31e3e813ee7a8be0b0afc00956e0a58a9a4af169 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 16:11:54 -0400
Subject: [PATCH 118/615] Adding the first version of the switch, tests broken

---
 pybamm/expression_tree/binary_operators.py    |  8 +++++--
 pybamm/settings.py                            | 22 +++++++++++++++----
 .../test_binary_operators.py                  | 17 ++++++++++++++
 3 files changed, 41 insertions(+), 6 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 73a68f4b88..5ec4f47aa6 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1216,6 +1216,8 @@ def minimum(left, right):
     # (i.e. no need for smoothing)
     if k == "exact" or (left.is_constant() and right.is_constant()):
         out = Minimum(left, right)
+    elif k == "smooth":
+        out = pybamm.smooth_minus(left, right, pybamm.settings.min_max_smoothing)
     else:
         out = pybamm.softminus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1237,6 +1239,8 @@ def maximum(left, right):
     # (i.e. no need for smoothing)
     if k == "exact" or (left.is_constant() and right.is_constant()):
         out = Maximum(left, right)
+    elif k == "smooth":
+        out = pybamm.smooth_plus(left, right, pybamm.settings.min_max_smoothing)
     else:
         out = pybamm.softplus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1300,7 +1304,7 @@ def softplus(left, right, k):
 def smooth_minus(left, right, k):
     """
     Smooth_minus approximation to the minimum function. k is the smoothing parameter,
-    set by `pybamm.settings.min_smoothing`. The recommended value is k=1000.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=1000.
     """
     sigma = (1.0 / k)**2
     return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2
@@ -1309,7 +1313,7 @@ def smooth_minus(left, right, k):
 def smooth_plus(left, right, k):
     """
     Smooth_plus approximation to the maximum function. k is the smoothing parameter,
-    set by `pybamm.settings.max_smoothing`. The recommended value is k=1000.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=1000.
     """
     sigma = (1.0 / k) ** 2
     return (pybamm.sqrt((left - right)**2 + sigma) + (left + right)) / 2
diff --git a/pybamm/settings.py b/pybamm/settings.py
index bdc9c1a137..299c17074e 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -8,6 +8,7 @@ class Settings(object):
     _simplify = True
     _min_smoothing = "exact"
     _max_smoothing = "exact"
+    _min_max_smoothing = 1000
     _heaviside_smoothing = "exact"
     _abs_smoothing = "exact"
     max_words_in_line = 4
@@ -43,16 +44,17 @@ def simplify(self, value):
         self._simplify = value
 
     def set_smoothing_parameters(self, k):
-        "Helper function to set all smoothing parameters"
+        """Helper function to set all smoothing parameters"""
         self.min_smoothing = k
         self.max_smoothing = k
         self.heaviside_smoothing = k
         self.abs_smoothing = k
 
-    def check_k(self, k):
-        if k != "exact" and k <= 0:
+    @staticmethod
+    def check_k(k):
+        if k != "exact" and k != "smooth" and k <= 0:
             raise ValueError(
-                "smoothing parameter must be 'exact' or a strictly positive number"
+                "Smoothing parameter must be 'exact', 'smooth', or a positive number"
             )
 
     @property
@@ -73,6 +75,18 @@ def max_smoothing(self, k):
         self.check_k(k)
         self._max_smoothing = k
 
+    @property
+    def min_max_smoothing(self):
+        return self._min_max_smoothing
+
+    @min_max_smoothing.setter
+    def min_max_smoothing(self, k):
+        if k < 1:
+            raise ValueError(
+                "Smoothing parameter must be greater than 1"
+            )
+        self._min_max_smoothing = k
+
     @property
     def heaviside_smoothing(self):
         return self._heaviside_smoothing
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 9929f895e9..5a71a2431a 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -443,6 +443,23 @@ def test_smooth_minus_plus(self):
             "0.5 * (sqrt(1.0 + (1.0 - y[0:1]) ** 2.0) + 1.0 + y[0:1])",
         )
 
+        # Test that smooth min/max are used when the setting is changed
+        pybamm.settings.min_smoothing = "smooth"
+        pybamm.settings.max_smoothing = "smooth"
+        pybamm.settings.min_max_smoothing = 3000
+
+        self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.smooth_minus(a, b, 1)))
+        self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.smooth_plus(a, b, 1)))
+
+        a = pybamm.Scalar(1)
+        b = pybamm.Scalar(2)
+        self.assertEqual(str(pybamm.minimum(a, b)), str(a))
+        self.assertEqual(str(pybamm.maximum(a, b)), str(b))
+
+        # Change setting back for other tests
+        pybamm.settings.min_smoothing = "exact"
+        pybamm.settings.max_smoothing = "exact"
+
     def test_binary_simplifications(self):
         a = pybamm.Scalar(0)
         b = pybamm.Scalar(1)

From e3db154f09dd4bc9c554fff51c065a9ce40f2916 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 16:21:27 -0400
Subject: [PATCH 119/615] Fixing the broken test

---
 .../unit/test_expression_tree/test_binary_operators.py |  3 ++-
 tests/unit/test_settings.py                            | 10 ++++++----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 5a71a2431a..487cffe877 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -446,11 +446,12 @@ def test_smooth_minus_plus(self):
         # Test that smooth min/max are used when the setting is changed
         pybamm.settings.min_smoothing = "smooth"
         pybamm.settings.max_smoothing = "smooth"
-        pybamm.settings.min_max_smoothing = 3000
 
+        pybamm.settings.min_max_smoothing = 1
         self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.smooth_minus(a, b, 1)))
         self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.smooth_plus(a, b, 1)))
 
+        pybamm.settings.min_max_smoothing = 3000
         a = pybamm.Scalar(1)
         b = pybamm.Scalar(2)
         self.assertEqual(str(pybamm.minimum(a, b)), str(a))
diff --git a/tests/unit/test_settings.py b/tests/unit/test_settings.py
index 5e40399315..c2b19a2954 100644
--- a/tests/unit/test_settings.py
+++ b/tests/unit/test_settings.py
@@ -29,14 +29,16 @@ def test_smoothing_parameters(self):
         pybamm.settings.set_smoothing_parameters("exact")
 
         # Test errors
-        with self.assertRaisesRegex(ValueError, "strictly positive"):
+        with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.min_smoothing = -10
-        with self.assertRaisesRegex(ValueError, "strictly positive"):
+        with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.max_smoothing = -10
-        with self.assertRaisesRegex(ValueError, "strictly positive"):
+        with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.heaviside_smoothing = -10
-        with self.assertRaisesRegex(ValueError, "strictly positive"):
+        with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.abs_smoothing = -10
+        with self.assertRaisesRegex(ValueError, "greater than 1"):
+            pybamm.settings.min_max_smoothing = 0.9
 
 
 if __name__ == "__main__":

From bf9e0b6308de5777ba8cd8739c90e49bcd0b30a5 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 18:48:45 -0400
Subject: [PATCH 120/615] Switching to smoothing mode

---
 pybamm/expression_tree/binary_operators.py    | 18 ++++----
 pybamm/settings.py                            | 44 +++++++++----------
 .../test_binary_operators.py                  | 13 +++---
 tests/unit/test_settings.py                   | 17 ++++---
 4 files changed, 45 insertions(+), 47 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 5ec4f47aa6..cdc0535c0c 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1211,13 +1211,14 @@ def minimum(left, right):
     if out is not None:
         return out
 
-    k = pybamm.settings.min_smoothing
+    mode = pybamm.settings.min_max_mode
+    k = pybamm.settings.min_max_smoothing
     # Return exact approximation if that is the setting or the outcome is a constant
     # (i.e. no need for smoothing)
-    if k == "exact" or (left.is_constant() and right.is_constant()):
+    if mode == "exact" or (left.is_constant() and right.is_constant()):
         out = Minimum(left, right)
-    elif k == "smooth":
-        out = pybamm.smooth_minus(left, right, pybamm.settings.min_max_smoothing)
+    elif mode == "smooth":
+        out = pybamm.smooth_minus(left, right, k)
     else:
         out = pybamm.softminus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1234,13 +1235,14 @@ def maximum(left, right):
     if out is not None:
         return out
 
-    k = pybamm.settings.max_smoothing
+    mode = pybamm.settings.min_max_mode
+    k = pybamm.settings.min_max_smoothing
     # Return exact approximation if that is the setting or the outcome is a constant
     # (i.e. no need for smoothing)
-    if k == "exact" or (left.is_constant() and right.is_constant()):
+    if mode == "exact" or (left.is_constant() and right.is_constant()):
         out = Maximum(left, right)
-    elif k == "smooth":
-        out = pybamm.smooth_plus(left, right, pybamm.settings.min_max_smoothing)
+    elif mode == "smooth":
+        out = pybamm.smooth_plus(left, right, k)
     else:
         out = pybamm.softplus(left, right, k)
     return pybamm.simplify_if_constant(out)
diff --git a/pybamm/settings.py b/pybamm/settings.py
index 299c17074e..4dc8db8151 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -6,8 +6,7 @@
 class Settings(object):
     _debug_mode = False
     _simplify = True
-    _min_smoothing = "exact"
-    _max_smoothing = "exact"
+    _min_max_mode = "exact"
     _min_max_smoothing = 1000
     _heaviside_smoothing = "exact"
     _abs_smoothing = "exact"
@@ -45,35 +44,32 @@ def simplify(self, value):
 
     def set_smoothing_parameters(self, k):
         """Helper function to set all smoothing parameters"""
-        self.min_smoothing = k
-        self.max_smoothing = k
+        if k == "exact":
+            self.min_max_mode = "exact"
+        else:
+            self.min_max_smoothing = k
+            self.min_max_mode = "soft"
         self.heaviside_smoothing = k
         self.abs_smoothing = k
 
     @staticmethod
     def check_k(k):
-        if k != "exact" and k != "smooth" and k <= 0:
+        if k != "exact" and k <= 0:
             raise ValueError(
-                "Smoothing parameter must be 'exact', 'smooth', or a positive number"
+                "Smoothing parameter must be 'exact' or a strictly positive number"
             )
 
     @property
-    def min_smoothing(self):
-        return self._min_smoothing
+    def min_max_mode(self):
+        return self._min_max_mode
 
-    @min_smoothing.setter
-    def min_smoothing(self, k):
-        self.check_k(k)
-        self._min_smoothing = k
-
-    @property
-    def max_smoothing(self):
-        return self._max_smoothing
-
-    @max_smoothing.setter
-    def max_smoothing(self, k):
-        self.check_k(k)
-        self._max_smoothing = k
+    @min_max_mode.setter
+    def min_max_mode(self, mode):
+        if mode not in ["exact", "soft", "smooth"]:
+            raise ValueError(
+                "Smoothing mode must be 'exact', 'soft', or 'smooth'"
+            )
+        self._min_max_mode = mode
 
     @property
     def min_max_smoothing(self):
@@ -81,7 +77,11 @@ def min_max_smoothing(self):
 
     @min_max_smoothing.setter
     def min_max_smoothing(self, k):
-        if k < 1:
+        if self._min_max_mode == "soft" and k <= 0:
+            raise ValueError(
+                "Smoothing parameter must be a strictly positive number"
+            )
+        if self._min_max_mode == "smooth" and k < 1:
             raise ValueError(
                 "Smoothing parameter must be greater than 1"
             )
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 487cffe877..9ced98d6fe 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -404,8 +404,8 @@ def test_softminus_softplus(self):
         )
 
         # Test that smooth min/max are used when the setting is changed
-        pybamm.settings.min_smoothing = 10
-        pybamm.settings.max_smoothing = 10
+        pybamm.settings.min_max_mode = "soft"
+        pybamm.settings.min_max_smoothing = 10
 
         self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.softminus(a, b, 10)))
         self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.softplus(a, b, 10)))
@@ -417,8 +417,7 @@ def test_softminus_softplus(self):
         self.assertEqual(str(pybamm.maximum(a, b)), str(b))
 
         # Change setting back for other tests
-        pybamm.settings.min_smoothing = "exact"
-        pybamm.settings.max_smoothing = "exact"
+        pybamm.settings.set_smoothing_parameters("exact")
 
     def test_smooth_minus_plus(self):
         a = pybamm.Scalar(1)
@@ -444,8 +443,7 @@ def test_smooth_minus_plus(self):
         )
 
         # Test that smooth min/max are used when the setting is changed
-        pybamm.settings.min_smoothing = "smooth"
-        pybamm.settings.max_smoothing = "smooth"
+        pybamm.settings.min_max_mode = "smooth"
 
         pybamm.settings.min_max_smoothing = 1
         self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.smooth_minus(a, b, 1)))
@@ -458,8 +456,7 @@ def test_smooth_minus_plus(self):
         self.assertEqual(str(pybamm.maximum(a, b)), str(b))
 
         # Change setting back for other tests
-        pybamm.settings.min_smoothing = "exact"
-        pybamm.settings.max_smoothing = "exact"
+        pybamm.settings.set_smoothing_parameters("exact")
 
     def test_binary_simplifications(self):
         a = pybamm.Scalar(0)
diff --git a/tests/unit/test_settings.py b/tests/unit/test_settings.py
index c2b19a2954..99310a42c1 100644
--- a/tests/unit/test_settings.py
+++ b/tests/unit/test_settings.py
@@ -16,29 +16,28 @@ def test_simplify(self):
         pybamm.settings.simplify = True
 
     def test_smoothing_parameters(self):
-        self.assertEqual(pybamm.settings.min_smoothing, "exact")
-        self.assertEqual(pybamm.settings.max_smoothing, "exact")
+        self.assertEqual(pybamm.settings.min_max_mode, "exact")
         self.assertEqual(pybamm.settings.heaviside_smoothing, "exact")
         self.assertEqual(pybamm.settings.abs_smoothing, "exact")
 
         pybamm.settings.set_smoothing_parameters(10)
-        self.assertEqual(pybamm.settings.min_smoothing, 10)
-        self.assertEqual(pybamm.settings.max_smoothing, 10)
+        self.assertEqual(pybamm.settings.min_max_smoothing, 10)
         self.assertEqual(pybamm.settings.heaviside_smoothing, 10)
         self.assertEqual(pybamm.settings.abs_smoothing, 10)
         pybamm.settings.set_smoothing_parameters("exact")
 
         # Test errors
+        with self.assertRaisesRegex(ValueError, "greater than 1"):
+            pybamm.settings.min_max_mode = "smooth"
+            pybamm.settings.min_max_smoothing = 0.9
         with self.assertRaisesRegex(ValueError, "positive number"):
-            pybamm.settings.min_smoothing = -10
-        with self.assertRaisesRegex(ValueError, "positive number"):
-            pybamm.settings.max_smoothing = -10
+            pybamm.settings.min_max_mode = "soft"
+            pybamm.settings.min_max_smoothing = -10
         with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.heaviside_smoothing = -10
         with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.abs_smoothing = -10
-        with self.assertRaisesRegex(ValueError, "greater than 1"):
-            pybamm.settings.min_max_smoothing = 0.9
+        pybamm.settings.set_smoothing_parameters("exact")
 
 
 if __name__ == "__main__":

From ea830d2a28f52294242f4aa35b0e584cb277d38b Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 19:18:45 -0400
Subject: [PATCH 121/615] Reset default

---
 pybamm/settings.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/settings.py b/pybamm/settings.py
index 4dc8db8151..f7fb3c9fb0 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -7,7 +7,7 @@ class Settings(object):
     _debug_mode = False
     _simplify = True
     _min_max_mode = "exact"
-    _min_max_smoothing = 1000
+    _min_max_smoothing = 10
     _heaviside_smoothing = "exact"
     _abs_smoothing = "exact"
     max_words_in_line = 4

From 02d1e2a2e1b8865ffb2fca0cb7debc049f4d3ed9 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Mon, 2 Oct 2023 20:15:50 -0400
Subject: [PATCH 122/615] Fix coverage

---
 tests/unit/test_settings.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tests/unit/test_settings.py b/tests/unit/test_settings.py
index 99310a42c1..a3b62f8ee4 100644
--- a/tests/unit/test_settings.py
+++ b/tests/unit/test_settings.py
@@ -37,6 +37,8 @@ def test_smoothing_parameters(self):
             pybamm.settings.heaviside_smoothing = -10
         with self.assertRaisesRegex(ValueError, "positive number"):
             pybamm.settings.abs_smoothing = -10
+        with self.assertRaisesRegex(ValueError, "'soft', or 'smooth'"):
+            pybamm.settings.min_max_mode = "unknown"
         pybamm.settings.set_smoothing_parameters("exact")
 
 

From cfc2a378ee91e40f31e75222e2fc6edd3cb09071 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 3 Oct 2023 20:28:04 +0530
Subject: [PATCH 123/615] Add cron job [skip ci]

---
 .github/workflows/docker.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 2b0cf706ae..bb17564fa2 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -4,7 +4,11 @@ on:
   workflow_dispatch:
   pull_request:
     branches:
-    - develop
+    - main
+
+  # Run everyday at 3 am UTC
+  schedule:
+    - cron: "0 3 * * *"
 
 jobs:
   pre_job:

From 2a88dfea7853be9c0c3fcab88dbf920f333b2c90 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Tue, 3 Oct 2023 20:58:57 +0530
Subject: [PATCH 124/615] Push to docker on every push to develop

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/docker.yml | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index bb17564fa2..c3575a50f8 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -2,13 +2,9 @@ name: Build & Push Docker Images
 
 on:
   workflow_dispatch:
-  pull_request:
+  push:
     branches:
-    - main
-
-  # Run everyday at 3 am UTC
-  schedule:
-    - cron: "0 3 * * *"
+    - develop
 
 jobs:
   pre_job:

From 5cda9bc041dd43ca5c02b5957b6e04e730a885c5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 3 Oct 2023 23:05:59 +0530
Subject: [PATCH 125/615] Install all,dev,docs only if no build-args given

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index b6c0a02f67..7015feef5a 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -59,6 +59,11 @@ RUN if [ "$ALL" = "true" ]; then \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi
 
-RUN pip install --user -e ".[all,dev,docs]"
+RUN if [ -z "$IDAKLU" ] \
+    && [ -z "$ODES" ] \
+    && [ -z "$JAX" ] \
+    && [ -z "$ALL" ]; then \
+    pip install --user -e ".[all,dev,docs]"; \
+    fi
 
 ENTRYPOINT ["/bin/bash"]

From bd4037cbfab6c9a46f4e02dd18b01b55edf6a629 Mon Sep 17 00:00:00 2001
From: Akhilender <akhilenderb9@gmail.com>
Date: Wed, 4 Oct 2023 23:51:23 +0530
Subject: [PATCH 126/615] fix: Implement Automatic Activation of Virtual
 Environment in the 'dev' Nox Session

- Implement automatic activation of the virtual environment in the 'dev' Nox session
- Enhance the 'dev' Nox session defined in `noxfile.py`
- Simplify the development setup process for contributors and developers

This commit enhances the 'dev' Nox session to automatically activate the virtual environment when running 'nox -s dev'.

Signed-off-by: Akhilender <akhilenderb9@gmail.com>
---
 noxfile.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index c215c73ca5..469dbb60d6 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,6 +1,7 @@
 import nox
 import os
 import sys
+import pathlib
 
 
 # Options to modify nox behaviour
@@ -117,7 +118,11 @@ def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
     envbindir = session.bin
-    session.install("-e", ".[all]", silent=False)
+    venv_dir = pathlib.Path('./venv').resolve()
+    session.install("virtualenv")
+    session.run("virtualenv", os.fsdecode(venv_dir), silent=True)
+    python = os.fsdecode(venv_dir.joinpath("bin/python"))
+    session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False)
     session.install("cmake", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
         session.run(

From 6d63732f1819d50b2435a055058f46d673c444e6 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 5 Oct 2023 09:48:46 +0000
Subject: [PATCH 127/615] Remove accidental SpatialOperator.diff() addition

---
 pybamm/expression_tree/unary_operators.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 7aadae412c..8745e5f33c 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -419,11 +419,6 @@ class with a :class:`Matrix`
     def __init__(self, name, child, domains=None):
         super().__init__(name, child, domains)
 
-    def diff(self, variable):
-        """See :meth:`pybamm.Symbol.diff()`."""
-        # We shouldn't need this
-        raise NotImplementedError
-
     def to_json(self):
         raise NotImplementedError(
             "pybamm.SpatialOperator:"

From 14c3c61719e0ffc8a44e90a763e173baa8df8285 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 15:28:15 +0530
Subject: [PATCH 128/615] #3049 try to bump `pybind11`, `vcpkg` versions
 (Windows)

---
 .github/workflows/publish_pypi.yml | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 6f82c6ca2e..c38092906e 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -28,14 +28,13 @@ jobs:
           python-version: 3.8
 
       - name: Clone pybind11 repo (no history)
-        run: git clone --depth 1 --branch v2.10.4 https://github.com/pybind/pybind11.git
+        run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      # remove when a new vcpkg version is released
-      - name: Install the latest commit of vcpkg on windows
+      - name: Install vcpkg on windows
         run: |
           cd C:\
           rm -r -fo 'C:\vcpkg'
-          git clone https://github.com/microsoft/vcpkg
+          git clone https://github.com/microsoft/vcpkg --branch 2023.08.09
           cd vcpkg
           .\bootstrap-vcpkg.bat
 

From 30d76cd1994e566120054a355eaa97cde53e2be5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 15:31:43 +0530
Subject: [PATCH 129/615] #3049 add OKane2022 parameter entry points

---
 pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 685656432e..a416dfd2a2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -147,10 +147,12 @@ Ai2020 = "pybamm.input.parameters.lithium_ion.Ai2020:get_parameter_values"
 Chen2020 = "pybamm.input.parameters.lithium_ion.Chen2020:get_parameter_values"
 Chen2020_composite = "pybamm.input.parameters.lithium_ion.Chen2020_composite:get_parameter_values"
 Ecker2015 = "pybamm.input.parameters.lithium_ion.Ecker2015:get_parameter_values"
+Ecker2015_graphite_halfcell = "pybamm.input.parameters.lithium_ion.Ecker2015_graphite_halfcell:get_parameter_values"
 Marquis2019 = "pybamm.input.parameters.lithium_ion.Marquis2019:get_parameter_values"
 Mohtat2020 = "pybamm.input.parameters.lithium_ion.Mohtat2020:get_parameter_values"
 NCA_Kim2011 = "pybamm.input.parameters.lithium_ion.NCA_Kim2011:get_parameter_values"
 OKane2022 = "pybamm.input.parameters.lithium_ion.OKane2022:get_parameter_values"
+OKane2022_graphite_SiOx_halfcell = "pybamm.input.parameters.lithium_ion.OKane2022_graphite_SiOx_halfcell:get_parameter_values"
 ORegan2022 = "pybamm.input.parameters.lithium_ion.ORegan2022:get_parameter_values"
 Prada2013 = "pybamm.input.parameters.lithium_ion.Prada2013:get_parameter_values"
 Ramadass2004 = "pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values"

From f8049a53b20d88b95cc21d52948f46dc1f84142d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 16:56:24 +0530
Subject: [PATCH 130/615] Drop support for i686 manylinux, use `build` for
 sdist

---
 .github/workflows/publish_pypi.yml | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 919a00d6ef..a0273d29cd 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -102,6 +102,8 @@ jobs:
       - name: Build wheels on Linux and MacOS
         run: python -m cibuildwheel --output-dir wheelhouse
         env:
+          # NumPy requires BLAS now which is no longer available on manylinux2014 i686, so skip it
+          CIBW_ARCHS_LINUX: x86_64
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&
@@ -135,13 +137,13 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.11
 
       - name: Install dependencies
-        run: pip install wheel
+        run: pip install --upgrade pip setuptools wheel build
 
       - name: Build sdist
-        run: python setup.py sdist --formats=gztar
+        run: python -m build --sdist
 
       - name: Upload sdist
         uses: actions/upload-artifact@v3
@@ -171,7 +173,7 @@ jobs:
         with:
           user: __token__
           password: ${{ secrets.PYPI_TOKEN }}
-          packages_dir: files/
+          packages-dir: files/
 
       - name: Publish on TestPyPI
         if: github.event.inputs.target == 'testpypi'
@@ -179,5 +181,5 @@ jobs:
         with:
           user: __token__
           password: ${{ secrets.TESTPYPI_TOKEN }}
-          packages_dir: files/
-          repository_url: https://test.pypi.org/legacy/
+          packages-dir: files/
+          repository-url: https://test.pypi.org/legacy/

From 390b47f9b5c967361bf0e81c488db385b8cd2d82 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 17:03:08 +0530
Subject: [PATCH 131/615] Update changelog about dropping 32-bit Linux support

because it was already dropped for Windows earlier
---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index c400cc31ea..3344940521 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -42,6 +42,7 @@
 
 ## Breaking changes
 
+- Dropped support for i686 (32-bit) architectures on GNU/Linux distributions ([#3412](https://github.com/pybamm-team/PyBaMM/pull/3412))
 - The class `pybamm.thermal.OneDimensionalX` has been moved to `pybamm.thermal.pouch_cell.OneDimensionalX` to reflect the fact that the model formulation implicitly assumes a pouch cell geometry ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - The "lumped" thermal option now always used the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" to compute the cell cooling regardless of the chosen "cell geometry" option. The user must now specify the correct values for these parameters instead of them being calculated based on e.g. a pouch cell. An `OptionWarning` is raised to let users know to update their parameters ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205))

From 319d0b4aa61ce9e60c286b51056244a659506805 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 5 Oct 2023 17:12:34 +0530
Subject: [PATCH 132/615] Remove comments and notes about BLAS

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/publish_pypi.yml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index a0273d29cd..f0ab7ffd2f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -102,11 +102,9 @@ jobs:
       - name: Build wheels on Linux and MacOS
         run: python -m cibuildwheel --output-dir wheelhouse
         env:
-          # NumPy requires BLAS now which is no longer available on manylinux2014 i686, so skip it
           CIBW_ARCHS_LINUX: x86_64
-          # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
-            yum -y install blas-devel lapack-devel &&
+            yum -y install openblas-devel lapack-devel &&
             bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
 
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"

From c60ac7d292bbd08be42c7aba41df6f48de784771 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Thu, 5 Oct 2023 18:06:34 +0530
Subject: [PATCH 133/615] Improve release workflow, add a note, bump version
 manually

---
 .github/release_workflow.md          | 25 +++++++++++++++----------
 .github/workflows/update_version.yml | 10 ++++++++++
 CHANGELOG.md                         |  2 ++
 CITATION.cff                         |  2 +-
 pybamm/version.py                    |  2 +-
 vcpkg.json                           |  2 +-
 6 files changed, 30 insertions(+), 13 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 04f0667773..1af23fca25 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -4,7 +4,7 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
 
 ## rc0 releases (automated)
 
-1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `YY.MMrc0` by running `scripts/update_version.py` in the following files -
+1. The `update_version.yml` workflow will run on every 1st of January, May and September, updating incrementing the version to `vYY.MMrc0` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
@@ -13,9 +13,9 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to a new branch `YY.MM`.
+      These changes will be automatically pushed to a new branch `vYY.MM` and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-2. Create a new GitHub _pre-release_ with the tag `YY.MMrc0` from the `YY.MM` branch and a description copied from `CHANGELOG.md`.
+2. Create a new GitHub _pre-release_ with the tag `vYY.MMrc0` from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
 3. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI.
 
@@ -23,11 +23,11 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
 
 If a new release candidate is required after the release of `rc0` -
 
-1. Fix a bug in `YY.MM` (no new features should be added to `YY.MM` once `rc0` is released) and `develop` individually.
+1. Fix a bug in `vYY.MM` (no new features should be added to `vYY.MM` once `rc0` is released) and `develop` individually.
 
 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...).
 
-3. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files -
+3. This will increment the version to `vYY.MMrcX` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
@@ -36,9 +36,9 @@ If a new release candidate is required after the release of `rc0` -
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing branch `YY.MM`.
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-4. Create a new GitHub _pre-release_ with the same tag (`YY.MMrcX`) from the `YY.MM` branch and a description copied from `CHANGELOG.md`.
+4. Create a new GitHub _pre-release_ with the same tag (`vYY.MMrcX`) from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
 5. This release will automatically trigger `publish_pypi.yml` and create a _pre-release_ on PyPI.
 
@@ -48,7 +48,7 @@ Once satisfied with the release candidates -
 
 1. Run `update_version.yml` manually, leaving the `append_to_tag` field blank ("") for an actual release.
 
-2. This will increment the version to `YY.MMrcX` by running `scripts/update_version.py` in the following files -
+2. This will increment the version to `vYY.MMrcX` by running `scripts/update_version.py` in the following files -
 
    - `pybamm/version.py`
    - `docs/conf.py`
@@ -57,9 +57,9 @@ Once satisfied with the release candidates -
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing branch `YY.MM`.
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
 
-3. Next, a PR from `YY.MM` to `main` will be generated that should be merged once all the tests pass.
+3. Next, a PR from `vYY.MM` to `main` will be generated that should be merged once all the tests pass.
 
 4. Create a new GitHub _release_ with the same tag from the `main` branch and a description copied from `CHANGELOG.md`.
 
@@ -72,3 +72,8 @@ Some other essential things to check throughout the release process -
 - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id.
 - Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise
 - Make sure the URLs in `docs/_static/versions.json` are valid
+- As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
+  ```
+  git tag -f <tag_name> <commit_hash>
+  git push origin <tag_name>  # can only be carried out by the maintainers
+  ```
diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml
index 472de06f0e..0d63e68007 100644
--- a/.github/workflows/update_version.yml
+++ b/.github/workflows/update_version.yml
@@ -63,7 +63,17 @@ jobs:
         with:
           message: 'Bump to ${{ env.VERSION }}'
 
+      - name: Make a PR from ${{ env.NON_RC_VERSION }} to develop
+        uses: repo-sync/pull-request@v2
+        with:
+          source_branch: '${{ env.NON_RC_VERSION }}'
+          destination_branch: "develop"
+          pr_title: "Sync ${{ env.NON_RC_VERSION }} and develop"
+          pr_body: "**Merge as soon as possible to avoid potential conflicts.**"
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+
       - name: Make a PR from ${{ env.NON_RC_VERSION }} to main
+        id: release_pr
         if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
         uses: repo-sync/pull-request@v2
         with:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c400cc31ea..a8518ba639 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,7 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+# [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
+
 ## Features
 - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237))
diff --git a/CITATION.cff b/CITATION.cff
index f5d6fe4911..5a9e1e2ddc 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.5"
+version: "23.9rc0"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index 0e8c575aea..c8d63f83e1 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.5"
+__version__ = "23.9rc0"
diff --git a/vcpkg.json b/vcpkg.json
index 2609370382..6877dfa094 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.5",
+  "version-string": "23.9rc0",
   "dependencies": [
     "casadi",
     {

From 0893769e11875330b945cef06c5fe82e71cc8f7a Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Thu, 5 Oct 2023 18:28:08 +0530
Subject: [PATCH 134/615] Bump SuiteSparse to 6.0.3

---
 .github/workflows/publish_pypi.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index f0ab7ffd2f..a009828e6f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -105,7 +105,7 @@ jobs:
           CIBW_ARCHS_LINUX: x86_64
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install openblas-devel lapack-devel &&
-            bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
+            bash build_manylinux_wheels/install_sundials.sh 6.0.3 6.5.0
 
           CIBW_BEFORE_BUILD_LINUX: "python -m pip install cmake casadi numpy"
           CIBW_BEFORE_BUILD_MACOS: >

From 245a3faad9ff5dc940093ffa11d77c731ae5db82 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Thu, 5 Oct 2023 18:42:17 +0530
Subject: [PATCH 135/615] Try fixing KLU paths

---
 build_manylinux_wheels/install_sundials.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/build_manylinux_wheels/install_sundials.sh b/build_manylinux_wheels/install_sundials.sh
index 709d9c13c7..3d14bde7c7 100644
--- a/build_manylinux_wheels/install_sundials.sh
+++ b/build_manylinux_wheels/install_sundials.sh
@@ -65,8 +65,8 @@ yum -y install openblas-devel
 
 mkdir -p build_sundials
 cd build_sundials
-KLU_INCLUDE_DIR=/usr/include
-KLU_LIBRARY_DIR=/usr/lib
+KLU_INCLUDE_DIR=/usr/local/include
+KLU_LIBRARY_DIR=/usr/local/lib
 SUNDIALS_DIR=sundials-$SUNDIALS_VERSION
 cmake -DENABLE_LAPACK=ON\
       -DSUNDIALS_INDEX_SIZE=32\

From 42c6c556385db6e14cc9a37928443cd4ca4cf7bb Mon Sep 17 00:00:00 2001
From: Akhilender <akhilenderb9@gmail.com>
Date: Thu, 5 Oct 2023 23:55:16 +0530
Subject: [PATCH 136/615] refactor: Improve noxfile.py setup and install
 [jax,odes]

- Imported `Path` directly from `pathlib` for better readability.
- Moved the definition of `venv_dir` closer to `PYBAMM_ENV` and marked it as a constant.
- Installed development dependencies using `-e .[dev]` to set up the developer environment and silenced the warning with `external=True`.
- Added the installation of `[jax,odes]` extras for macOS and Linux platforms.

Signed-off-by: Akhilender <akhilenderb9@gmail.com>
---
 noxfile.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 469dbb60d6..ccbbc0d749 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,7 +1,7 @@
 import nox
 import os
 import sys
-import pathlib
+from pathlib import Path
 
 
 # Options to modify nox behaviour
@@ -17,6 +17,7 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
+VENV_DIR = Path('./venv').resolve()
 
 
 def set_environment_variables(env_dict, session):
@@ -118,12 +119,14 @@ def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
     envbindir = session.bin
-    venv_dir = pathlib.Path('./venv').resolve()
     session.install("virtualenv")
-    session.run("virtualenv", os.fsdecode(venv_dir), silent=True)
-    python = os.fsdecode(venv_dir.joinpath("bin/python"))
+    session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
+    python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False)
     session.install("cmake", silent=False)
+    session.install("-e", ".[dev]", external=True)
+    if session.platform.startswith("macos") or session.platform.startswith("linux"):
+        session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
         session.run(
             "echo",

From 713da15376285851f11a89afb4ec33664bd80b3a Mon Sep 17 00:00:00 2001
From: Akhilender <akhilenderb9@gmail.com>
Date: Fri, 6 Oct 2023 15:20:58 +0530
Subject: [PATCH 137/615] refactor: Improve noxfile.py

- Installed `virtualenv` and `cmake` before other dependencies.
- Installed all and dev dependencies together using a single `pip` command to avoid redundant wheel building.
- Corrected sys.platform to maintain consistency in the code

Signed-off-by: Akhilender <akhilenderb9@gmail.com>
---
 noxfile.py | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index ccbbc0d749..df00bfadc2 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -118,24 +118,13 @@ def run_scripts(session):
 def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    envbindir = session.bin
-    session.install("virtualenv")
+    session.install("virtualenv","cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    session.run(python, "-m", "pip", "install", "-e", ".[all]", silent=False)
-    session.install("cmake", silent=False)
-    session.install("-e", ".[dev]", external=True)
-    if session.platform.startswith("macos") or session.platform.startswith("linux"):
+    session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False)
+    if sys.platform == "linux" or sys.platform == "macos":
         session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False)
-    if sys.platform == "linux" or sys.platform == "darwin":
-        session.run(
-            "echo",
-            "export",
-            f"LD_LIBRARY_PATH={PYBAMM_ENV['LD_LIBRARY_PATH']}",
-            ">>",
-            f"{envbindir}/activate",
-            external=True,  # silence warning about echo being an external command
-        )
+
 
 
 @nox.session(name="tests")

From 148ea1870320cc293a1a8a89e94c1e16f405a36f Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 6 Oct 2023 21:57:05 +0530
Subject: [PATCH 138/615] Test `pybamm_install_odes` on macOS on CI

---
 .github/workflows/test_on_push.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b740da2e1b..8e315c6950 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -299,6 +299,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: nox -s doctests
 

From 9b07583f50dbb2bcc31fea84d519ca7af63e023d Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Fri, 6 Oct 2023 17:27:38 -0400
Subject: [PATCH 139/615] Fix notebook

---
 .../notebooks/solvers/speed-up-solver.ipynb   | 318 ++++++++++++++----
 pybamm/expression_tree/binary_operators.py    |   8 +-
 2 files changed, 249 insertions(+), 77 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 2bd7f47ae1..7cf74f156e 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -17,13 +17,17 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-06T15:43:06.684699Z",
+     "start_time": "2023-10-06T15:43:02.151747Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zsh:1: no matches found: pybamm[plot,cite]\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -43,7 +47,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -115,13 +118,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 125.714 ms\n",
-      "Fast: 77.698 ms\n"
+      "Safe: 143.843 ms\n",
+      "Fast: 84.697 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -168,10 +171,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 7.791 s\n",
-      "Solving fast mode, error occured: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n",
+      "Safe: 5.881 s\n",
+      "Solving fast mode, error occurred: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n",
       "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n",
-      ".../casadi/interfaces/sundials/idas_interface.cpp:604: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
+      ".../casadi/interfaces/sundials/idas_interface.cpp:596: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
      ]
     },
     {
@@ -183,7 +186,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -222,7 +225,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d84d30bf7d8d4df1a330e8c9a69267a1",
+       "model_id": "eed7984c8b474c1d84244101566e2d79",
        "version_major": 2,
        "version_minor": 0
       },
@@ -245,7 +248,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -264,9 +266,16 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "At t = 159.925 and h = 7.17391e-08, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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eeYSbb76Z7t27c+jQoUxDQkBAAEuXLuWGG26gffv23HrrrYwZM8Z5Fqlt27a88MILPP3009x+++2cPXuWqKiodMcYPnw4vr6+1KxZk9KlS6ebUfVXQUFBLFmyhNOnT3P77bfTtWtXWrVqxcSJE7P+w3dxDuvvF9xcUEJCAqGhocTHxxMSEmJfIZMnmzEyhQtDbCzcfLN9tYiIZNPFixc5cOAAN954o/NLV3Ln6NGjhIWFsXz5clq1amV3ObbK6u9Xfn9/60xMTgwYAK1bw4UL5rJSDka/i4iI+/r+++/56quvOHDgAGvXrqV79+5UrlyZZs2a2V2aV1OIyQmHAz76CIoWhbVrzawlERHxeJcuXeJf//oXtWrVonPnzpQuXZqYmJh0g2yl4Gl2Uk6Fh8Pbb0O/fmYl3w4doEYNu6sSEZF81LZtW9q2bWt3GfI3OhNzPR59FNq1g6Qk6NMHsjFvX0RERPKWQsz1cDjggw8gNBTWr4dnn7W7IhEREa+ToxAzadIkIiIiCAkJISQkhMjISBYvXpxp+2nTpjl31ky7eczI+IoVIW2TsbfegnnzbC1HRORa3GAyqrghO/9e5SjEVKxYkTFjxrB582Y2bdpEy5Yt6dSpEzt37sz0d0JCQpy7X8bFxXHo0KFcF+0y7r8fRoww9x95BH75xdZyREQykrbuSE6W0RfJrvP/21fQjkHOORrY27Fjx3SPX331VSZNmsRPP/1ErVq1Mvwdh8NB2bJlr79CV/faa+aS0o8/Qteu8NNPEBRkd1UiIk5+fn4EBQXxxx9/4O/vf9VeRyLXw7Iszp8/z4kTJyhWrFimWz3kp+uenZSSksKcOXM4d+4ckZGRmbZLTEykUqVKpKamUq9ePV577bVMA49b8vODWbOgbl3Yvh0ee8xcZvrfUtQiInZzOByUK1eOAwcOeNbZcHEJxYoVs+1kRY5DzPbt24mMjOTixYsEBwezYMECatasmWHb6tWr8/HHHxMREUF8fDxvvPEGjRs3ZufOnVSsWDHT10hKSiIpKcn5OCEhIadlFqxy5eCLL6BlS5g+HZo0MQvjiYi4iICAAKpVq6ZLSpKn/P39bTkDkybH2w4kJydz+PBh4uPjmTt3Lh9++CE//PBDpkHmry5dusQtt9xCjx49GDVqVKbtXnrpJV5++eWrnrd924Fref11M1MpIMBcXmrY0O6KREREbJPf2w7keu+k1q1bU6VKFSZPnpyt9g888AB+fn58/vnnmbbJ6ExMWFiY64cYy4L/+z9YuBDKlDFjZTLYEl1ERMQbuPzeSampqekCR1ZSUlLYvn075cqVy7JdYGCgcxp32s0tOBzmclJEBPz+O3TsCK5+KUxERMRN5SjEREdH8+OPP3Lw4EG2b99OdHQ0MTExPPTQQwBERUURHR3tbP/KK6+wdOlSfv31V/773//y8MMPc+jQIfr165e3vXAlRYvCokVQtqwZ6NujB1y+bHdVIiIiHidHA3tPnDhBVFQUcXFxhIaGEhERwZIlS2jTpg0Ahw8fTjd1788//6R///4cP36c4sWLU79+fdauXZut8TNuLSwMvvoKmjWDb7+F4cO1WaSIiEgey/WYmIKQ39fU8s3cufDAA+b+f/5jpl+LiIh4CZcfEyNZ6NoVXn3V3H/ySfjuO3vrERER8SAKMfktOhp694aUFBNqNmywuyIRERGPoBCT3xwOmDIF7r4bzp2D9u3h55/trkpERMTtKcQUhIAAs8v17bfDqVPQti0cPWp3VSIiIm5NIaagBAfDN9/AzTfD4cPQrh2cPm13VSIiIm5LIaYglS4NS5ZA+fKwcyfcdx/8bwtzERERyRmFmIJWubKZpVSsGKxZY6Zga0M2ERGRHFOIscOtt8LXX0OhQmYxvJ49taqviIhIDinE2OXOO81GkWmDfvv0MdOwRUREJFsUYuzUti3MmQN+fjBjBgwaZHbCFhERkWtSiLHbffeZAOPjAx9+CEOHKsiIiIhkg0KMK+jWDaZONfcnTIBnn1WQERERuQaFGFcRFQXvv2/ujx0Lzz2nICMiIpIFhRhXMnAgvPuuuT96tIKMiIhIFhRiXM2TT8L48ea+goyIiEimFGJc0ZAhCjIiIiLXoBDjqhRkREREsqQQ48r+HmSioxVkRERE/kchxtX9Nci8/jqMGKEgIyIigkKMexgyBCZONPfffBP+8Q8FGRER8XoKMe7i8cevrCMzfryZxaQgIyIiXkwhxp0MHGi2JnA44L33YPBgSE21uyoRERFbKMS4m759zRYFDoc5MzNggHa/FhERr6QQ445694bp082mkR99BI88Apcv212ViIhIgVKIcVcPPwwzZ4KvL3z6qXl86ZLdVYmIiBQYhRh39uCDMGcO+PvDF1+Yx8nJdlclIiJSIBRi3F3nzjB/PgQEwIIF0KULXLxod1UiIiL5TiHGE9x7L3z9NRQqBIsWQadOcP683VWJiIjkK4UYT3H33fDttxAUBEuXQrt2kJBgd1UiIiL5RiHGk9x1lwkwISGwahW0aQOnT9tdlYiISL5QiPE0TZrA999DiRKwYQO0bAknTthdlYiISJ5TiPFE9evDDz9AmTKwdSs0bw6//WZ3VSIiInlKIcZT1a4NP/4IFSvCzz9D06awf7/dVYmIiOQZhRhPdvPNZmxMlSpw4ADceSds3253VSIiInlCIcbTVa5sgsytt8Lx4+bS0k8/2V2ViIhIrinEeINy5SAmBho1gj//hFatYNkyu6sSERHJFYUYb1GiBCxfbqZdnz8PHTrAvHl2VyUiInLdFGK8SZEiZmXfrl3NZpHdusGUKXZXJSIicl0UYrxNYCDMmgX9+kFqKgwcCC+/DJZld2UiIiI5ohDjjXx9zRmY5583j196CR57DFJSbC1LREQkJxRivJXDAaNGwXvvmfuTJ5vLTBcu2F2ZiIhItijEeLvBg2H2bAgIgIULoW1bM4NJRETExSnEiDkDs2TJlY0jmzSBgwftrkpERCRLCjFitGhhAkyFCrB7t1lTZvNmu6sSERHJlEKMXBERYVbzjYiA33+HZs1g0SK7qxIREcmQQoykV7GiOSOTtihep04waZLdVYmIiFxFIUauFhIC33wDjz5q1pIZPBiGD9cUbBERcSkKMZIxf3/48EMzDRvgzTfh//4PEhPtrUtEROR/FGIkcw6HWRBv5kyz0u9XX8Gdd8Lhw3ZXJiIiohAj2dCjh9kFu0wZ2LoV7rgD1q+3uyoREfFyCjGSPY0awYYNV2YuNW9u9mASERGxiUKMZF94OKxeDffeC0lJ5gxNdLQG/IqIiC0UYiRnihY12xOMGGEejxkDHTvCmTN2ViUiIl5IIUZyztcXxo6FGTOgUCFYvNiMk9m92+7KRETEiyjEyPXr2RPWrIGwMNi7Fxo2NDOYRERECoBCjOROvXqwaZPZouDsWbPC74svapyMiIjkO4UYyb0bboDly+Hxx83jUaOgfXs4edLeukRExKMpxEje8PeHiRPh00+hcGFYutScpdmwwe7KRETEQynESN56+GGzEF61anDkCDRtCu+/D5Zld2UiIuJhFGIk7916K2zcCJ07Q3IyPPaYCTdnz9pdmYiIeBCFGMkfoaEwb56Ziu3ra/Zfql8fYmPtrkxERDyEQozkH4fDLIr3ww9QsaKZht2oEfznP7q8JCIiuaYQI/mvSRNzBqZjR7NdweOPQ7duWuVXRERyRSFGCkbJkvDll/DWW2Ym09y5cNttZrE8ERGR66AQIwXH4YB//MNsInnjjXDokFkkb+RIuHzZ7upERMTN5CjETJo0iYiICEJCQggJCSEyMpLFixdn63dnzZqFw+Hg/vvvv546xZPccYe5vBQVBamp8MorZir2/v12VyYiIm4kRyGmYsWKjBkzhs2bN7Np0yZatmxJp06d2LlzZ5a/d/DgQYYPH07Tpk1zVax4kJAQ+OQT+PxzM5Ppp5/M5aVp0zToV0REssVhWbn7xihRogTjxo2jb9++Gf48JSWFZs2a8eijj7Jq1SrOnDnDwoULc/QaCQkJhIaGEh8fT0hISG7KFVd0+DD06gU//mge33+/WSCvTBlbyxIRkdzJ7+/v6x4Tk5KSwqxZszh37hyRkZGZtnvllVe44YYbMg05GUlKSiIhISHdTTxYeDh8/z289poZ9LtwIdSubQb/ioiIZCLHIWb79u0EBwcTGBjIoEGDWLBgATVr1syw7erVq/noo4/44IMPcvQao0ePJjQ01HkLCwvLaZnibnx9ITra7Ihdp47ZPPKBB+Chh+D0aburExERF5TjEFO9enViY2NZv349jz32GL1792bXrl1XtTt79iy9evXigw8+oFSpUjl6jejoaOLj4523I0eO5LRMcVcREWbTyOeeAx8fs9Jv7drw1Vd2VyYiIi4m12NiWrduTZUqVZg8eXK652NjY6lbty6+vr7O51JTUwHw8fFhz549VKlSJVuvoTExXmr9eujdG/bsMY+7d4d334XSpe2tS0REssVlx8SkSU1NJSkp6arna9Sowfbt24mNjXXe7rvvPu666y5iY2N1iUiurWFD2LIFnn7anJWZNQtq1jQzmjSDSUTE6/nlpHF0dDT33HMP4eHhnD17lpkzZxITE8OSJUsAiIqKokKFCowePZpChQpRu3btdL9frFgxgKueF8lU4cLw+utmfEzfvrBtG/TsaS4zTZpk9mQSERGvlKMzMSdOnCAqKorq1avTqlUrNm7cyJIlS2jTpg0Ahw8fJi4uLl8KFS/XoAFs3GgWxvP3h0WL4JZb4J13tNqviIiXyvWYmIKgMTGSzs6dMGAArF1rHterB5Mnm6AjIiIuw+XHxIgUuFq1YNUqE1yKFYP//teMnxk6FLSmkIiI11CIEffk42POxvz8sxkjk5pqZi7VqGHGy7j+CUYREcklhRhxb2XKwIwZsGQJVKkCcXFmgbwWLWD7drurExGRfKQQI57h7rthxw7497/NjKYff4S6dc0lpjNn7K5ORETygUKMeI5ChcxKvz//DF26QEqKucR0880wZYp5LCIiHkMhRjxPeLjZPHLpUjNG5o8/YOBAM4tp5Uq7qxMRkTyiECOeq00bszjeO++YWUzbtkHLltC5M+zbZ3d1IiKSSwox4tn8/c24mH374IknzG7ZCxea7Qv+8Q84dcruCkVE5DopxIh3KFkSJkwwZ2PatoVLl8wZmipVzLYGFy7YXaGIiOSQQox4l5o14bvvzJTsOnUgPh6efdYM/v3kEw3+FRFxIwox4p3uvtus9Dt9OoSFwdGj0KcP3HYbfPmlFssTEXEDCjHivXx8oFcv+OUXGDvWDP7dsQPuvx8aNYIVK+yuUEREsqAQI1KoEIwYAQcOmHVmgoJgwwZo3drcfvrJ7gpFRCQDCjEiaYoVMyv+/vorDBkCAQHmbExkJNxzjwk2IiLiMhRiRP6uTBkYP95cZnr0UTMt+7vvzE7ZHTrAxo12VygiIijEiGSuUiX46CPYs8cM+vX1hW+/hTvuMGFm3Tq7KxQR8WoKMSLXUqUKTJ1q9mSKijIDgr/9Fho3NmNmYmI0m0lExAYKMSLZVbWqWUvm55/NZSY/PzNm5q674M47YfFihRkRkQKkECOSU9WqmctM+/bB4MEQGAhr10L79lC3LsycCZcv212liIjHU4gRuV6VKsF775nZTP/8JxQpAlu3wkMPmaAzcSKcP293lSIiHkshRiS3ypeHN9+Ew4fhlVegVCk4eBCefNIEnZdeghMn7K5SRMTjKMSI5JUSJeCFF+DQIXMWpnJlOHkSXn4ZwsNhwADYvdvuKkVEPIZCjEheCwqCxx+HvXth1iy4/XZISoIPPjAbUHboAMuXaxCwiEguKcSI5Bc/P3jwQVi/HlatMnsyORxmenabNlC7NkyerHEzIiLXSSFGJL85HGYK9oIFZhXgJ54wg4B37YJBg6BiRXj6aXMZSkREsk0hRqQgVa0KEybAb7/B22/DTTfBn3/CuHHmfqdOsGQJpKbaXamIiMtTiBGxQ2goDBtmzsx89RW0amWCy1dfQbt2UL06vPUWnD5td6UiIi5LIUbETr6+0LGjGei7ezcMHWoCzr598NRTUKEC9O5tFtPTQGARkXQUYkRcRY0a8M475lLTlClQpw5cvAjTp0OTJhARYaZunzljd6UiIi5BIUbE1RQpAv37w5YtZqfsPn2gcGHYscMsoFe+vHlu1SqdnRERr6YQI+KqHA5o1MjsoH3smBkQXLs2XLhgNqJs1sycvRk7Fo4ft7taEZEC57As1/9fuYSEBEJDQ4mPjyckJMTuckTsY1nw009mA8pZs+DcOfO8r69ZRK9PH/PfgABbyxQRgfz//laIEXFXZ8/C7Nkm0Kxbd+X5kiXNJpR9+sBtt5kzOiIiNlCIQSFG5Jp27zaXmKZPh7i4K8/feitERUHPnmYsjYhIAVKIQSFGJNsuXzbTtadNg4ULzZ5NAD4+Zi2aXr2gc2cIDrazShHxEgoxKMSIXJc//4Q5c8zZmTVrrjxfpIjZx6lnT7OHk7+/bSWKiGdTiEEhRiTX9u+Hzz6DTz8199OULg3dupkxNI0aafyMiOQphRgUYkTyjGWZXbVnzIAvvoA//rjys8qVoXt36NHDjKVRoBGRXFKIQSFGJF+kjZ+ZMcPssJ02XRugZk0TZrp3N5tWiohcB4UYFGJE8t3587BokVl75ptvIDn5ys/q1zdhpls3CA+3r0YRcTsKMSjEiBSo+HhzZubzz2HFCkhJufKzxo3hwQfhgQegXDn7ahQRt6AQg0KMiG3++APmzTNnaH788cpeTQ4HNG9uAk2XLmaAsIjI3yjEoBAj4hJ++81M2f7iC7P1QRpfX2jZ0gSazp2hRAn7ahQRl6IQg0KMiMs5dMhsefDFF7B585Xn/f3h7rvNGJr77gN9XkW8mkIMCjEiLm3fviuBZtu2K88HBprNKLt3h3vvhcKF7atRRGyhEINCjIjb2L3bhJkvvoCff77yfHAwdOpkAs3dd2uXbREvoRCDQoyI27Esc1Zm1ixzO3jwys9KlDCzm3r2hDvvNPs6iYhHUohBIUbErVkWbNhgpmzPnp1+l+2KFc2iej17Qp06WiVYxMMoxKAQI+IxUlLghx/MKsHz5pk1adLUrm122X7oIahQwb4aRSTPKMSgECPikS5ehMWLTaD5+usrqwQ7HGbKdq9eZg2a4GB76xSR66YQg0KMiMc7c8asQfPpp7Bq1ZXnixQx2x306QNNm+pyk4ibUYhBIUbEqxw4YM7OfPKJmb6d5qabTJjp0wfCwuyqTkRyQCEGhRgRr2RZsHYtTJ1qpmwnJprnHQ5o1w769YOOHc0CeyLikvL7+1tzG0XENTkc0KQJfPghHD8O06eb/Zosy4yl6dLFzG56+mn45Re7qxURG+hMjIi4l7174eOPYdo0E27S3HUXDBxo9m/SYnoiLkFnYkRE/qpaNRg9Gg4fhoULzdYGDgesXGlWBK5YEZ55Bvbvt7tSEclnCjEi4p78/c1WBosWmRWBX3gBypeHP/6AsWOhalVo29YEncuX7a5WRPKBQoyIuL/wcHjlFbO79oIFZuCvwwFLl5rLSzfeaH5+7JjdlYpIHlKIERHP4ecH999vBv7u22cuK5UqBUePwsiRJuw88IC59OT6wwFF5BoUYkTEM910E4wZYwLMjBlms8mUFJg716wIXKsWTJwICQl2Vyoi10khRkQ8W2Cg2WBy1SrYuhUGDTIrAe/eDU8+afZpGjwYdu60u1IRySGFGBHxHhERMGkS/PYbTJgANWqYRfQmTTIbUN51l9mYUgOBRdyCQoyIeJ/QUHjiCdi1C1asgP/7P/DxgZgY6NoVKleGV181M51ExGUpxIiI90rbMXvePLNn07/+BaVLmzM1zz9v9mh65BHYssXuSkUkAzkKMZMmTSIiIoKQkBBCQkKIjIxk8eLFmbafP38+DRo0oFixYhQpUoTbbruNTz/9NNdFi4jkufBwc/blyBGzxUGDBpCUZFYGrlfP7KI9d64uNYm4kBxtO/D111/j6+tLtWrVsCyLTz75hHHjxrFlyxZq1ap1VfuYmBj+/PNPatSoQUBAAIsWLeKpp57im2++oW3bttkuUtsOiEiBsyxYvx7efRfmzLkSXipVMgOC+/Uzl6VEJFMuv4t1iRIlGDduHH379s1W+3r16tGhQwdGjRqV7ddQiBERWx07Zgb/vv8+nDxpngsOhkcfhSFDoEoVe+sTcVEuu3dSSkoKs2bN4ty5c0RGRl6zvWVZrFixgj179tCsWbMs2yYlJZGQkJDuJiJim/LlYdQos1/TBx9AzZpmVtO775q9nLp2NWdtRKRA5TjEbN++neDgYAIDAxk0aBALFiygZs2ambaPj48nODiYgIAAOnTowIQJE2jTpk2WrzF69GhCQ0Odt7CwsJyWKSKS9woXNpeRduyAJUvM9gaWZQYGN2oEzZubvZxSU+2uVMQr5PhyUnJyMocPHyY+Pp65c+fy4Ycf8sMPP2QaZFJTU/n1119JTExkxYoVjBo1ioULF9KiRYtMXyMpKYmkpCTn44SEBMLCwnQ5SURcz44d8MYbMHMmXLpknqtZE0aMgIceMhtVingplx8T07p1a6pUqcLkyZOz1b5fv34cOXKEJUuWZPs1NCZGRFze0aMwfjxMngxnz5rnwsNh+HDo2xeCguytT8QGLjsmJk1qamq6syZ53V5ExC1UrAjjxpkp2mPGQJkyZgzNkCFXFs87c8buKkU8So5CTHR0ND/++CMHDx5k+/btREdHExMTw0MPPQRAVFQU0dHRzvajR49m2bJl/Prrr+zevZs333yTTz/9lIcffjhveyEi4ipCQ83u2QcOwH/+AzfeaFb+ff55Mz37+eevzHASkVzJUYg5ceIEUVFRVK9enVatWrFx40aWLFniHKh7+PBh4uLinO3PnTvH4MGDqVWrFk2aNGHevHl89tln9OvXL297ISLiagoXhsceg19+Mbto165tdsx+9VVzZmbECDh+3O4qRdxarsfEFASNiRERt5eaCl9+aaZqp21jUKgQ9O9vztxUqGBvfSL5wOXHxIiISDb4+EDnzrB5s5mG3bAhXLxodtOuUsVsSHn0qN1VirgVhRgRkYLkcECHDrBuHSxdCnfeafZoeu89E2YGDzaDg0XkmhRiRETs4HBAmzbw44+wYgU0awbJyWZ7gypVzHgahRmRLCnEiIjYyeGAli3hhx8gJgbuusssmvf++1C1Kjz+uC4ziWRCIUZExFU0bw7ff2/CTIsW5szMf/5zZczMb7/ZXaGIS1GIERFxNc2bw8qV5pZ2mSltzMw//gG//253hSIuQSFGRMRVtWhhzsqsWAFNmpgBwO+8AzfdZKZlnzplc4Ei9lKIERFxZWljZlatgu++g9tvh/PnYexYs2jeiy9CfLzdVYrYQiFGRMQdOBzQti2sXw9ffQW33QaJiWbxvJtuMvs2nT9vd5UiBUohRkTEnTgc0LGjWTRv7lyoUQNOn4annzazmSZNMmNoRLyAQoyIiDvy8YEuXWD7dpg61WwuGRdnFsu75Rb44guz1YGIB1OIERFxZ35+0KcP7NljtjAoUwZ+/RW6dzdbG6xcaXeFIvlGIUZExBMEBpq1ZPbtg5dfhuBg2LTJDApu396csRHxMAoxIiKeJDjYzFjav9+s9uvnB4sXQ506Zsfs48ftrlAkzyjEiIh4ohtugIkTYdcueOABsCz48EOoVg1eew0uXLC7QpFcU4gREfFk1arB7NmwZg3ccYeZlv3cc2ZW06xZJtyIuCmFGBERb9C4MaxbB599BhUrwuHD0KMHNG0K//2v3dWJXBeFGBERb+HjAw89ZGYyjRoFQUHmDE2DBjBwIPzxh90ViuSIQoyIiLcJCoLnnzdhpmdPc0lpyhS4+WYzTfvyZbsrFMkWhRgREW9VsSLMmAE//mi2MThzBoYMgXr1zF5NIi5OIUZExNs1bWrWlHn/fShRwqwp06wZREVpSra4NIUYEREBX18zLuaXX2DAALNH06efQvXq8O67usQkLkkhRkRErihZEiZPhp9+MgN+ExJg6FBzf906u6sTSUchRkRErnbHHSbIvP8+FC8OW7eaadoDBphds0VcgEKMiIhkLO0S0549ZpNJgA8+MJeYpk3TQnliO4UYERHJWunSMHWqmcVUqxacPAmPPALNm5ttDURsohAjIiLZ07QpbNkCY8eatWZWrTJTs59/XnsxiS0UYkREJPv8/WHECNi9G+67Dy5dgldfhVtvhWXL7K5OvIxCjIiI5Fx4OCxcCPPnQ4UKsH8/3H232dbgxAm7qxMvoRAjIiLXx+GAzp3NuJghQ8zjmTPhlltg+nQN/JV8pxAjIiK5ExIC48fD+vVQp46Zgt27N7RrBwcP2l2deDCFGBERyRu33w4bN8Jrr0FgICxdCrVrm4CTkmJ3deKBFGJERCTv+PtDdDRs22b2Xzp3DoYNgzvvhJ9/trs68TAKMSIikvduvhlWrjRbGISEmNV/b7sN3nhDZ2UkzyjEiIhI/vDxMdsU7NgBbdtCUpKZnq2zMpJHFGJERCR/hYXB4sXw4Yfpz8q8+SakptpdnbgxhRgREcl/Dgf07Zv+rMzw4dCqFRw+bHd14qYUYkREpOCknZX54AMoUgRiYiAiwqwvI5JDCjEiIlKwHA7o1w9iY6FRI4iPNyv99uwJf/5pd3XiRhRiRETEHlWrmk0kX34ZfH3h88/NYnk//mh3ZeImFGJERMQ+fn7w4ouwZo0JNUeOwF13wUsvweXLdlcnLk4hRkRE7NewIWzZAo88YmYsvfwytGypQb+SJYUYERFxDcHB8PHHZpBv0aLmUtNtt5mdskUyoBAjIiKupUcPM+j3jjvMQN8uXeDxx+HiRbsrExejECMiIq7npptg9Wp45hnz+D//gSZNYP9+e+sSl6IQIyIirsnfH8aMMevKlCwJ//0v1KsHc+bYXZm4CIUYERFxbe3amctLTZpAQgJ06wZPPGFW/RWvphAjIiKur2JFsyv2s8+ax++9Z0LNgQP21iW2UogRERH34O8Po0fDN99AiRKweTPUrw+LFtldmdhEIUZERNxL+/ZmTZm02UsdO8K//qXF8byQQoyIiLif8HCzjswTT5jHo0dDmzZw/Li9dUmBUogRERH3FBAAEyaYPZfSdsSuV89sYSBeQSFGRETcW/fusHEj1KwJcXHQogW8+y5Ylt2VST5TiBEREfd3yy2wfj08+KAZGzN0KDz8MJw7Z3dlko8UYkRExDMEB5tLS2+/Db6+Zg+myEjYt8/uyiSfKMSIiIjncDhg2DD4/nsoUwa2b4cGDeDbb+2uTPKBQoyIiHieZs3MNgWNG0N8PNx7L7z+usbJeBiFGBER8Uzly5tVfgcMMOHl2WehZ084f97uyiSPKMSIiIjnCgiAyZNh0iTw84NZs+DOO+HwYbsrkzygECMiIp5v0CBYsQJKlzar/TZoAKtX212V5JJCjIiIeIdmzWDTJqhbF/74A1q1ghkz7K5KckEhRkREvEd4uDkD83//B8nJZi2Zl1/WgF83pRAjIiLeJSgI5syBESPM45degqgoSEqytSzJOYUYERHxPj4+MHYsTJliFsb77DNo3RpOnrS7MskBhRgREfFe/fvD4sUQEmIuMzVuDAcP2l2VZFOOQsykSZOIiIggJCSEkJAQIiMjWbx4cabtP/jgA5o2bUrx4sUpXrw4rVu3ZsOGDbkuWkREJM+0aQNr15rxMnv3miCzbZvdVUk25CjEVKxYkTFjxrB582Y2bdpEy5Yt6dSpEzt37sywfUxMDD169GDlypWsW7eOsLAw7r77bn777bc8KV5ERCRP1Kplgkzt2mYn7GbN4Icf7K5KrsFhWbkbkl2iRAnGjRtH3759r9k2JSWF4sWLM3HiRKKiorL9GgkJCYSGhhIfH09ISEhuyhUREcncn3/CffeZS0uBgWZDyc6d7a7KbeX39/d1j4lJSUlh1qxZnDt3jsjIyGz9zvnz57l06RIlSpTIsl1SUhIJCQnpbiIiIvmueHFYuhQ6dTKzlbp2NYN/xSXlOMRs376d4OBgAgMDGTRoEAsWLKBmzZrZ+t1nnnmG8uXL07p16yzbjR49mtDQUOctLCwsp2WKiIhcn8KFYe5c6NcPUlNh4EAYN87uqiQDOb6clJyczOHDh4mPj2fu3Ll8+OGH/PDDD9cMMmPGjGHs2LHExMQQERGRZdukpCSS/jJfPyEhgbCwMF1OEhGRgmNZ8NxzMHq0efz88/DKK+Bw2FuXG8nvy0m5HhPTunVrqlSpwuTJkzNt88Ybb/Dvf/+b5cuX06BBgxy/hsbEiIiIbcaMgehoc3/oUHj7bQWZbHLZMTFpUlNT0501+buxY8cyatQovvvuu+sKMCIiIrZ69lmYONHcHz/erC2TkmJvTQKAX04aR0dHc8899xAeHs7Zs2eZOXMmMTExLFmyBICoqCgqVKjA6P+denv99dd58cUXmTlzJpUrV+b48eMABAcHExwcnMddERERySePPw7BwfDoo/DRR3DuHEyfDv7+dlfm1XIUYk6cOEFUVBRxcXGEhoYSERHBkiVLaNOmDQCHDx/Gx+fKyZ1JkyaRnJxM165d0x1n5MiRvPTSS7mvXkREpKD07g1FikDPnjBrFly8CF98AQEBdlfmtXI9JqYgaEyMiIi4jMWLzdoxSUnQoYOZyVSokN1VuSSXHxMjIiLiVe65BxYtMlOxv/nGrClz4YLdVXklhRgREZGcat0avv3WXF5auhTuvdeMk5ECpRAjIiJyPVq0gO++g6JF4fvvzRmas2ftrsqrKMSIiIhcrzvvNGdiQkNh1SoFmQKmECMiIpIbjRrB8uVQrBisWaMgU4AUYkRERHKrQQNYtuxKkGnfXkGmACjEiIiI5IW/BpnVqxVkCoBCjIiISF5JCzKhoVeCTGKi3VV5LIUYERGRvPT3INOhA5w/b3dVHkkhRkREJK/dfrsJMiEh8OOPV1b4lTylECMiIpIfbr/dbFGQtiBet25w6ZLdVXkUhRgREZH80rgxfPUVBAaa//bqBSkpdlflMRRiRERE8lPLljB/Pvj7m12v+/eH1FS7q/IICjEiIiL5rX17mDkTfHxg6lQYOhQsy+6q3J5CjIiISEHo2hWmTQOHAyZOhNdes7sit6cQIyIiUlB69YJ33zX3n3/ehBq5bgoxIiIiBemJJ+DZZ839fv3MDCa5LgoxIiIiBe21167MVHrgAdi0ye6K3JJCjIiISEFzOODDD6FNGzh3zqzqu3+/3VW5HYUYEREROwQEwLx5ULcunDgBbdvCyZN2V+VWFGJERETsUrQofPstVK5szsQ88IBW9c0BhRgRERE7lS0L33xjAk1MDAwbZndFbkMhRkRExG41a8KMGWaszH/+A5Mn212RW1CIERERcQUdO8K//23uP/GE2f1asqQQIyIi4iqio+HBB+HyZejSBQ4dsrsil6YQIyIi4iocDvj4YzNj6eRJ6NTJTMGWDCnEiIiIuJKgIFi4EG64AbZuNbtea7PIDCnEiIiIuJrwcJg7F3x94fPP4f337a7IJSnEiIiIuKKmTeH11839YcNg40Zby3FFCjEiIiKu6p//hPvvh+RksxDe6dN2V+RSFGJERERclcMBU6dClSpmplJUFKSm2l2Vy1CIERERcWXFipnxMYGBZmXfsWPtrshlKMSIiIi4uttug/feM/efe85sTyAKMSIiIm7h0UehTx9zOalHD7PztZdTiBEREXEHDoc5G1OrFhw/fiXQeDGFGBEREXcRFASzZkGhQrB4Mbz9tt0V2UohRkRExJ3Urg3jx5v7zz7r1evHKMSIiIi4m/79zboxly9D9+6QkGB3RbZQiBEREXE3DgdMmQKVKsGvv8LAgV65v5JCjIiIiDsqVsyMj/H1Nf+dOtXuigqcQoyIiIi7atQI/v1vc/+JJ2DPHnvrKWAKMSIiIu7s6aehVSu4cAEefhguXbK7ogKjECMiIuLOfHxg2jQoXhw2bYJRo+yuqMAoxIiIiLi7ihVh8mRz/9VXYe1ae+spIAoxIiIinuCBB6BXL7OKb69ecPas3RXlO4UYERERTzFhwpVp18OG2V1NvlOIERER8RShofDpp2YdmY8/hvnz7a4oXynEiIiIeJKmTeGZZ8z9/v0hLs7eevKRQoyIiIinefllqFsXTp8268d4KIUYERERTxMQYKZd+/mZS0rz5tldUb5QiBEREfFEERFml2uAxx83Z2U8jEKMiIiIp3r+eahRA37/HZ56yu5q8pxCjIiIiKcKDISPPjKzlaZNg6VL7a4oTynEiIiIeLLGjeHJJ839AQMgMdHeevKQQoyIiIine/VVswjeoUPw3HN2V5NnFGJEREQ8XXAwTJli7k+Y4DF7KynEiIiIeIO774bevcGyzNoxKSl2V5RrCjEiIiLeYuxYCAmBLVvgk0/sribXFGJERES8xQ03wIsvmvv/+hckJNhbTy4pxIiIiHiTJ5+EqlXN2jGvvWZ3NbmiECMiIuJNAgLgzTfN/bffhl9/tbeeXFCIERER8TYdO0Lr1pCcDCNG2F3NdfOzuwC7nTt3LtOf+fr6UqhQoWy19fHxoXDhwtfV9vz581iWlWFbh8NBUFDQdbW9cOECqampmdZRpEiR62p78eJFUrIY1Z6TtkFBQTgcDgCSkpK4fPlynrQtXLgwPj4moycnJ3Pp0qU8aVuoUCF8fX1z3PbSpUskJydn2jYwMBA/P78ct718+TJJSUmZtg0ICMDf3z/HbVNSUrh48WKmbf39/QkICMhx29TUVC5cuJAnbf38/AgMDATAsizOnz+fJ21z8rnXvxEZt9W/EW7yb8SrrxKwYgX+8+dDTAy0aJHpMV2W5Qbi4+MtwIqPj8/zYwOZ3tq3b5+ubVBQUKZtmzdvnq5tqVKlMm3boEGDdG0rVaqUaduaNWuma1uzZs1M21aqVCld2wYNGmTatlSpUunaNm/ePNO2QUFB6dq2b98+yz+3v+ratWuWbRMTE51te/funWXbEydOONsOHjw4y7YHDhxwth0+fHiWbXfs2OFsO3LkyCzbbtiwwdl27NixWbZduXKls+3EiROzbLto0SJn26lTp2bZdvbs2c62s2fPzrLt1KlTnW0XLVqUZduJEyc6265cuTLLtmPHjnW23bBhQ5ZtR44c6Wy7Y8eOLNsOHz7c2fbAgQNZth08eLCz7YkTJ7Js27t3b2fbxMTELNt27do13d/hrNrq3whz078RV25u92/EXXdZFlhWnTqWdfmyldfy8/vbsiwrR5eTJk2aREREBCEhIYSEhBAZGcnixYszbb9z5066dOlC5cqVcTgcvPPOOzl5OREREclPnTtDsWKwdSt89pnd1eSYw7IyOe+Yga+//hpfX1+qVauGZVl88sknjBs3ji1btlCrVq2r2m/cuJHZs2dTv359/vGPf/DMM88wbNiwHBeZkJBAaGgo8fHxhISE5Pj3s6JTxTlvq1PFbnKqGF1Oup62upx0hf6NyHlbt/w34qOP4ORJs8v1X/7e5YX8/P6GHIaYjJQoUYJx48bRt2/fLNtVrlyZYcOGuVyIERERkfyR39/f1z2wNyUlhTlz5nDu3DkiIyPzsiaSkpLSJccEN1+MR0RERPJejqdYb9++neDgYAIDAxk0aBALFiygZs2aeVrU6NGjCQ0Ndd7CwsLy9PgiIiLi/nIcYqpXr05sbCzr16/nscceo3fv3uzatStPi4qOjiY+Pt55O3LkSJ4eX0RERNxfji8nBQQEULVqVQDq16/Pxo0bGT9+PJMnT86zogIDA52D8EREREQykusVe1NTU7Mc+SwiIiKSH3J0JiY6Opp77rmH8PBwzp49y8yZM4mJiWHJkiUAREVFUaFCBUaPHg2Y6WVpl5qSk5P57bffiI2NJTg42Hk2R0REROR65CjEnDhxgqioKOLi4ggNDSUiIoIlS5bQpk0bAA4fPuycSw9w7Ngx6tat63z8xhtv8MYbb9C8eXNiYmLypgciIiLilXK9TkxB0DoxIiIi7ie/v7+1i7WIiIi4JYUYERERcUsKMSIiIuKWFGJERETELSnEiIiIiFu67g0gC1LaBCptBCkiIuI+0r6382sitFuEmLNnzwJoI0gRERE3dPbsWUJDQ/P8uG6xTkxqairHjh2jaNGiOByOPDtuQkICYWFhHDlyxOPXn/GWvnpLP8F7+uot/QTv6au39BO8p6+Z9dOyLM6ePUv58uXTLYabV9ziTIyPjw8VK1bMt+OHhIR49F+uv/KWvnpLP8F7+uot/QTv6au39BO8p68Z9TM/zsCk0cBeERERcUsKMSIiIuKWvDrEBAYGMnLkSAIDA+0uJd95S1+9pZ/gPX31ln6C9/TVW/oJ3tNXu/rpFgN7RURERP7Oq8/EiIiIiPtSiBERERG3pBAjIiIibkkhRkRERNySW4eY9957j8qVK1OoUCEaNmzIhg0bsmw/Z84catSoQaFChbj11lv59ttv0/3csixefPFFypUrR+HChWndujV79+5N1+b06dM89NBDhISEUKxYMfr27UtiYmKe9+3v7Ohr5cqVcTgc6W5jxozJ8779VV73c/78+dx9992ULFkSh8NBbGzsVce4ePEijz/+OCVLliQ4OJguXbrw+++/52W3MmRHX1u0aHHVezpo0KC87NZV8rKfly5d4plnnuHWW2+lSJEilC9fnqioKI4dO5buGJ7wOc1uXz3hc/rSSy9Ro0YNihQpQvHixWndujXr169P18YT3lPIXl894T39q0GDBuFwOHjnnXfSPZ8n76nlpmbNmmUFBARYH3/8sbVz506rf//+VrFixazff/89w/Zr1qyxfH19rbFjx1q7du2ynn/+ecvf39/avn27s82YMWOs0NBQa+HChdbWrVut++67z7rxxhutCxcuONu0a9fOqlOnjvXTTz9Zq1atsqpWrWr16NHDI/taqVIl65VXXrHi4uKct8TERLfq5/Tp062XX37Z+uCDDyzA2rJly1XHGTRokBUWFmatWLHC2rRpk9WoUSOrcePG+dVNy7Ls62vz5s2t/v37p3tP4+Pj86ubed7PM2fOWK1bt7a++OIL6+eff7bWrVtn3XHHHVb9+vXTHccTPqfZ7asnfE5nzJhhLVu2zNq/f7+1Y8cOq2/fvlZISIh14sQJZxtPeE+z21dPeE/TzJ8/36pTp45Vvnx56+233073s7x4T902xNxxxx3W448/7nyckpJilS9f3ho9enSG7bt162Z16NAh3XMNGza0Bg4caFmWZaWmplply5a1xo0b5/z5mTNnrMDAQOvzzz+3LMuydu3aZQHWxo0bnW0WL15sORwO67fffsuzvv2dHX21LPNB+vtfuvyU1/38qwMHDmT4xX7mzBnL39/fmjNnjvO53bt3W4C1bt26XPQma3b01bJMiBk6dGiuas+J/Oxnmg0bNliAdejQIcuyPOdzmpG/99WyPOtzmiY+Pt4CrOXLl1uW5dnv6d/7alme854ePXrUqlChgrVjx46r+pRX76lbXk5KTk5m8+bNtG7d2vmcj48PrVu3Zt26dRn+zrp169K1B2jbtq2z/YEDBzh+/Hi6NqGhoTRs2NDZZt26dRQrVowGDRo427Ru3RofH5+rTgfmFbv6mmbMmDGULFmSunXrMm7cOC5fvpxXXUsnP/qZHZs3b+bSpUvpjlOjRg3Cw8NzdJycsKuvaWbMmEGpUqWoXbs20dHRnD9/PsfHyI6C6md8fDwOh4NixYo5j+EJn9OM/L2vaTzpc5qcnMyUKVMIDQ2lTp06zmN44nuaUV/TuPt7mpqaSq9evRgxYgS1atXK8Bh58Z66xQaQf3fy5ElSUlIoU6ZMuufLlCnDzz//nOHvHD9+PMP2x48fd/487bms2txwww3pfu7n50eJEiWcbfKaXX0FGDJkCPXq1aNEiRKsXbuW6Oho4uLieOutt3Ldr7/Lj35mx/HjxwkICLjqSyGnx8kJu/oK0LNnTypVqkT58uXZtm0bzzzzDHv27GH+/Pk560Q2FEQ/L168yDPPPEOPHj2cm855yuf07zLqK3jO53TRokV0796d8+fPU65cOZYtW0apUqWcx/Ck9zSrvoJnvKevv/46fn5+DBkyJNNj5MV76pYhRgrGP//5T+f9iIgIAgICGDhwIKNHj/b4JbQ91YABA5z3b731VsqVK0erVq3Yv38/VapUsbGynLt06RLdunXDsiwmTZpkdzn5Kqu+esrn9K677iI2NpaTJ0/ywQcf0K1bN9avX3/VF50nuFZf3f093bx5M+PHj+e///0vDocjX1/LLS8nlSpVCl9f36tmkPz++++ULVs2w98pW7Zslu3T/nutNidOnEj388uXL3P69OlMXze37OprRho2bMjly5c5ePBgTrtxTfnRz+woW7YsycnJnDlzJlfHyQm7+pqRhg0bArBv375cHScj+dnPtC/1Q4cOsWzZsnRnJjzlc5omq75mxF0/p0WKFKFq1ao0atSIjz76CD8/Pz766CPnMTzpPc2qrxlxt/d01apVnDhxgvDwcPz8/PDz8+PQoUM89dRTVK5c2XmMvHhP3TLEBAQEUL9+fVasWOF8LjU1lRUrVhAZGZnh70RGRqZrD7Bs2TJn+xtvvJGyZcuma5OQkMD69eudbSIjIzlz5gybN292tvn+++9JTU11fhnkNbv6mpHY2Fh8fHzy5f+M8qOf2VG/fn38/f3THWfPnj0cPnw4R8fJCbv6mpG0adjlypXL1XEykl/9TPtS37t3L8uXL6dkyZJXHcMTPqdw7b5mxFM+p6mpqSQlJTmP4SnvaUb+2teMuNt72qtXL7Zt20ZsbKzzVr58eUaMGMGSJUucx8iT9zTbQ4BdzKxZs6zAwEBr2rRp1q5du6wBAwZYxYoVs44fP25ZlmX16tXLevbZZ53t16xZY/n5+VlvvPGGtXv3bmvkyJEZTjsuVqyY9eWXX1rbtm2zOnXqlOEU67p161rr16+3Vq9ebVWrVq1ApvkVdF/Xrl1rvf3221ZsbKy1f/9+67PPPrNKly5tRUVFuVU/T506ZW3ZssX65ptvLMCaNWuWtWXLFisuLs7ZZtCgQVZ4eLj1/fffW5s2bbIiIyOtyMjIfOunXX3dt2+f9corr1ibNm2yDhw4YH355ZfWTTfdZDVr1sxt+pmcnGzdd999VsWKFa3Y2Nh0U1CTkpKcx/GEz2l2+uoJn9PExEQrOjraWrdunXXw4EFr06ZN1iOPPGIFBgZaO3bscB7HE97T7PTVE97TjGQ04yov3lO3DTGWZVkTJkywwsPDrYCAAOuOO+6wfvrpJ+fPmjdvbvXu3Ttd+9mzZ1s333yzFRAQYNWqVcv65ptv0v08NTXVeuGFF6wyZcpYgYGBVqtWraw9e/aka3Pq1CmrR48eVnBwsBUSEmI98sgj1tmzZ/Otj2kKuq+bN2+2GjZsaIWGhlqFChWybrnlFuu1116zLl686Fb9nDp1qgVcdRs5cqSzzYULF6zBgwdbxYsXt4KCgqzOnTunCzn5paD7evjwYatZs2ZWiRIlrMDAQKtq1arWiBEj8nWdmLzuZ9r08YxuK1eudLbzhM9pdvrqCZ/TCxcuWJ07d7bKly9vBQQEWOXKlbPuu+8+a8OGDemO4QnvaXb66gnvaUYyCjF58Z46LMuysn/eRkRERMQ1uOWYGBERERGFGBEREXFLCjEiIiLilhRiRERExC0pxIiIiIhbUogRERERt6QQIyIiIm5JIUZERETckkKMiIiIuCWFGBEREXFLCjEiIiLilhRiRERExC39P0Y5pILi1TRQAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -296,7 +305,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -346,12 +355,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 610.021 ms (integration time: 534.636 ms)\n",
-      "With dt_max=20, took 686.939 ms (integration time: 536.861 ms)\n",
-      "With dt_max=100, took 338.657 ms (integration time: 291.815 ms)\n",
-      "With dt_max=1000, took 83.867 ms (integration time: 51.518 ms)\n",
-      "With dt_max=3700, took 52.384 ms (integration time: 32.960 ms)\n",
-      "With 'fast' mode, took 44.846 ms (integration time: 32.949 ms)\n"
+      "With dt_max=10, took 670.587 ms (integration time: 597.206 ms)\n",
+      "With dt_max=20, took 666.842 ms (integration time: 594.925 ms)\n",
+      "With dt_max=100, took 364.074 ms (integration time: 320.450 ms)\n",
+      "With dt_max=1000, took 85.601 ms (integration time: 56.237 ms)\n",
+      "With dt_max=3700, took 54.820 ms (integration time: 36.811 ms)\n",
+      "With 'fast' mode, took 47.771 ms (integration time: 36.758 ms)\n"
      ]
     }
    ],
@@ -396,20 +405,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 541.980 ms (integration time: 447.827 ms)\n",
-      "With dt_max=20, took 518.415 ms (integration time: 428.332 ms)\n",
-      "With dt_max=100, took 300.344 ms (integration time: 245.695 ms)\n",
-      "With dt_max=1000, took 101.787 ms (integration time: 60.608 ms)\n",
-      "With dt_max=3600, took 516.396 ms (integration time: 32.718 ms)\n"
+      "With dt_max=10, took 572.819 ms (integration time: 484.879 ms)\n",
+      "With dt_max=20, took 573.989 ms (integration time: 485.942 ms)\n",
+      "With dt_max=100, took 325.108 ms (integration time: 273.404 ms)\n",
+      "With dt_max=1000, took 109.595 ms (integration time: 69.396 ms)\n",
+      "With dt_max=3600, took 748.976 ms (integration time: 38.108 ms)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 460.712 and h = 4.16966e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
-      "At t = 460.712 and h = 5.11965e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
-      "At t = 460.712 and h = 8.91111e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 460.712, , mxstep steps taken before reaching tout.\n",
+      "At t = 460.712 and h = 4.67695e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 460.712 and h = 9.20727e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     }
    ],
@@ -462,7 +471,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  813.671 ms\n"
+      "Took  792.337 ms\n"
      ]
     }
    ],
@@ -496,7 +505,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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BAOAKdp+bGRmeHGlyJd0TYQQAgCtwz4wkEUY6A2EEAIA2FFXU6WhxtSwWaWRqlNnldEuEEQAA2rDxaIkkaViSg+bVTkIYAQCgDRuPnJUkTewXa3Il3RdhBACANrjDSH/CSGchjAAAcBn5JTU6cqZaVot0bd8Ys8vptggjAABcxso9hZKkCf1iFRlKv0hnIYwAAHAZ7+9uDiO3Dk80uZLujTACAMAlnK6o07a8UknSLcMSTK6meyOMAABwCW9szpNhSNf2jVbvyBCzy+nWCCMAAHxGQ5NLb2zOkyR9ZWIfk6vp/ggjAAB8xqrc0zpd4VSv8CD6Ra4CwggAAJ/xlw3HJEl3X5smuy3A3GJ6AMIIAAAXOHC6UhuPlMhqke6dkGZ2OT0CYQQAgAv8Y0u+JGnqNQlKiqJx9WogjAAAcI7LZejfuwokSXeNTTG5mp6DMAIAwDmbj5WosKJOEcE23TQ4zuxyegzCCAAA56zIaZ4VuW14Io2rV1GPDiOGYWjPqXKzywAA+In1h5vv0DvlGnZcvZp6bBhxNjbpnj9s1H+9sE57CyrMLgcAYLLiKqcOFlVJksZzh96rqseGEbstQLFhdhmG9KN/7lJRZZ3ZJQEATLT5aIkkaUhihKLDgkyupmfpsWFEkn5462CF223aeaJct/7mE72385QMwzC7LACACTYdaV6imdCPWZGrrUeHkT6xYXpn3iQNSYxQSXW9vvvGDn3jz1tVUF5rdmkAgKvsk0PFkqSJ/WNNrqTn6dFhRJLS4yP03oPX6+FpgxQYYNHqfUWa9txavbbxuFwuZkkAoCc4frZaR85Uy2a16LqBvcwup8fp8WFEkoJsVn13ykAt/+4NGpMWpSpnox59Z7fu/sNGHTlTZXZ5AIBOtjynUJJ0bd8YOYIDTa6m5yGMXGBgQoTefGCSHps5VKFBAdp8tES3Pv+JFq05rMYml9nlAQA6gWEYemtb8xbws0YlmVxNz0QY+YwAq0X3X9dPKx+6UTcM7KX6Rpd++f4+zXrxU+06UWZ2eQAAH1tz4IwOn6lWWFCAbs/obXY5PRJh5DJSY0L1l/8Zr199caQiQwK151SF7njxUz3+rz2qcjaaXR4AwAcamlx6+v39kqR7xqexRGMSwkgbLBaL7hqbotXfn6xZo5LkMqRXPz2mac99rA/2FJpdHgDAQydKa3T4TJUMw5DLZejJZXu1t6BCjmCbvvO5dLPL67EsRhfYWKOiokKRkZEqLy+Xw+EwrY61B87o0Xd2K6+kRpI0fViCHv/8cCVGBptWEwCgfdYdLNbXXt2sRpeh/nFhstsC3Dtw/372GN0+giUaX2vv729mRjxw46A4rXzoRs29aYBsVotW7jmtqc99rD99elRNXAYMAH7tuVX71Xju/9VHzlRrb0GF7Darnv5CBkHEZMyMeGlfYYV+/HaOtueVSZJGpkbpqTuHa1hSpLmFAQAu0tjk0rDHVsrZ6NJ7D16nk6W1qm9y6br0XuoVbje7vG6LmZFONiTRobcemKSf3zFcEXabduaX6fO/+1RPLd+rmnoaXAHAnxw7Wy1no0uhQQEanhSp20b01qxRyQQRP0EY6QCr1aKvTuyj1d+frBkjeqvJZeiltUc07bm1en93Afe5AQA/cfB08waWgxIiZLVaTK4Gn0UY8YF4R7BenD1Gr3xtnJKjQnSyrFYPvLZdc17ZzA6uAOAHymsbJEmx3I3XLxFGfOjmIQn6z8OT9X9uTldQgFWfHCzW9N+s1S/f38fSDQCYqGV/qDC7zeRKcCmEER8LCQrQ928ZrA++d6M+NzhODU2GFq05rCm/+lj/3nWKpRsAMEG1s0mSFGYPMLkSXIpHYWTRokXKyMiQw+GQw+FQZmamVqxY0ebn/OY3v9HgwYMVEhKi1NRUfe9731NdXV2Hiu4K+vYK0ytfu1Z/nDNOqTEhKiiv04Ov79BXXt6kQ0WVZpcHAD1K9bnZ6bAgZkb8kUdhJCUlRQsXLtS2bdu0detW3XzzzZo1a5b27NlzyfGvv/66fvSjH+mxxx7T3r179fLLL+vvf/+7fvzjH/ukeH9nsVg0dWiCVn1vsh6aOlB2m1WfHjqrW3/ziZ74V67KaxrMLhEAegSWafybR2Fk5syZuv322zVw4EANGjRITz75pMLDw7Vx48ZLjl+/fr2uu+463Xvvverbt69uueUW3XPPPdq8ebNPiu8qggMD9NDUQfrPw5M19ZoENboMvfLpUU1+9iO9+ulRNXBHYADoVDXnwkg4YcQved0z0tTUpCVLlqi6ulqZmZmXHDNp0iRt27bNHT6OHDmi5cuX6/bbb/f2sF1aakyo/njfOP3lf8ZrcEKEymoa9Pi/cjX912u1Kvc0/SQA0Emq3D0jhBF/5PHfSk5OjjIzM1VXV6fw8HAtXbpUQ4cOveTYe++9V8XFxbr++utlGIYaGxv1wAMPXHGZxul0yul0up9XVFR4WqZfu3FQnCYNiNU/tp7Qc6v260hxtb75l62aNCBWP5lxDbu4AoCPVbuXaWhg9Ucez4wMHjxY2dnZ2rRpk+bOnav77rtPubm5lxy7Zs0aPfXUU/r973+v7du36+2339ayZcv085//vM1jZGVlKTIy0v1ITU31tEy/Zwuw6t4Jafro/96k79w0QEE2q9YfPqv/emGdfvDmTp0sqzW7RADoNloaWFmm8U8dvjfN1KlTNWDAAC1evPii92644QZNnDhRzzzzjPu11157Td/61rdUVVUlq/XSWehSMyOpqal+dW8aX8svqdHTK/frXztPSZKCAqz6amYffeemAYplu2IA6JCbf7VGR85Ua8m3Jmpi/1izy+kxrtq9aVwuV6vgcKGampqLAkdAQPMUWVsZyG63uy8fbnl0d6kxoXrhntF6+zuTNLF/jOqbXHp53VHd+PRH+vWqA6qs48obAPCWe5mGS3v9kkd/KwsWLNBtt92mtLQ0VVZW6vXXX9eaNWu0cuVKSdKcOXOUnJysrKwsSc1X3zz33HMaPXq0JkyYoEOHDumnP/2pZs6c6Q4laG1MWrTe+OZEfXKwWE+v3KfdJyv0/OqD+suGY5r3uXR9ZWIfBQdy7gDAEw1Nzf8ADrKx16c/8iiMFBUVac6cOSooKFBkZKQyMjK0cuVKTZs2TZKUl5fXaibk0UcflcVi0aOPPqqTJ08qLi5OM2fO1JNPPunb76KbsVgsunFQnK5P76UVuwv1qw+am1x/sWyvXll3VN/5XLq+OC5FdhuhBADao2U2nnvk+acO94xcDe1dc+quGptc+uf2E/rNfw6qoLx599rekcGae9MAfWlcKjMlAHAFIx//QOW1DfrPw5OVHh9udjk9xlXrGUHnswVY9eVrm6+8eWzmUCU47Coor9PP3t2jyc80b5xW19BkdpkA4LdczIz4NcJIFxIcGKD7r+unj3/wOT0xa5h6RwbrdIVTj/8rVzc8/ZH++MkR7g4MAJfQsgZgsZBG/BFhpAsKDgzQnMy+WvODm/TkncOVHBWiM5VO/WLZXk1a+KF+9cF+FVdd+gonAOiJ6Bnxb4SRLsxuC9DsCX300f+9SVn/PUJ9YkNVVtOgFz48pOsWfqifLM3RseJqs8sEANO5zs2MWJkZ8UuEkW4gyGbVPePT9OH3b9Ki2WM0MiVSzkaX/rYpT5/71RrNfW2bduSVml0mAJjG5f/XavRo7P7SjQRYLbptRG/dOjxRm46W6KW1R/ThviKt2F2oFbsLNTotSl+b1Fe3De/NtfYAepSWKGJlncYvEUa6IYvFoon9YzWxf6wOnK7US2uP6N3sk9qRV6Ydedn6RcRe3Ts+TbMnpCneEWx2uQDQ6egZ8W/887ibG5QQoWe/OFLrfzRFD08bpPgIu85UOvX86oO67pcf6rtv7NC246Vtbs8PAF0dPSP+jZmRHiIuwq7vThmouTcN0Pu7C/Wn9ce07Xip3tt5Su/tPKXBCRH68rWp+u8xyYoKDTK7XADwqZaeEaKIfyKM9DCBAVbNHJmkmSOTtPtkuf60/pj+tfOU9p+u1BP/ztXC9/fp1mGJunt8qib2i2V9FUC3wD4j/o0w0oMNT47Us18cqZ/+11C9l31Sb2zOV25BhXu2pE9sqL58baruGpOiBHpLAHRRFy5D8+8r/8S9aeBmGIZ2n6zQki15ejf7lKrO3XLbYpGuG9BLd4xO1vRhCYoIDjS5UgBovyaXoQE/Xi5J2vHTaYoOYyn6amnv729mRuBmsVg0IiVSI1JG6CczrtGyXQX6x9Z8bTlWqnWHirXuULEefceqaUMTdefoJN0wME6BAfRAA/BvF+4xwiqNfyKM4JJCg2z64rhUfXFcqvJLavRu9km9veOkjpyp1r92ntK/dp5STFiQ/iujt24f0VvX9o1RAPOfAPzQhfP/9Iz4J8IIrig1JlQP3jxQ8z6Xrt0nK7R0x0m9t/OUiquc+suG4/rLhuPqFR6k6cMSdfuI3prQL0Y2ZkwA+AkXPSN+jzCCdju/jBOpH98+ROsOFWvZrgJ9kHtaxVX1+tumPP1tU56iQwM1fViibhvRW5n9Y9ntFYCpLpwZYZ8R/0QYgVdsAVbdNDheNw2O11NNLm04fFYrdhdo5Z7TKqmu15It+VqyJV8RdptuHBSnm4fE63ND4hVD4xiAq8wQPSP+jjCCDgsMsOrGQXG6cVCcfj7LpU1HS7Q8pzmYFFc5tSynQMtyCmS1SGPSojXlmgRNvSZe6fHhrN8C6HQuZkb8Hpf2otO4XIZ2nSzX6r2n9Z+9RdpbUNHq/bSYUE0eFKcbBvZS5oBYLhkG0Ckq6hqU8b8fSJL2/+JW2W0BJlfUc3BpL0xntVo0KjVKo1Kj9P1bButkWa0+3Fek1XtPa/3hs8orqdFfNx7XXzceV4DVotGpUbp+YC/dMDBOI1MiaYIF4BOG6/yfLWwI75cII7hqkqNC9NWJffTViX1UU9+oTw+d1ScHz+iTg8U6WlytrcdLtfV4qX7zn4OKCLZp0oBYXZ/eSxP7x7KkA8BrF/aMcDWNfyKMwBShQTZNG5qgaUMTJEn5JTVad6hYnxw8o08PnVV5bYNW7jmtlXtOS5JiwoI0vm+MJvSP0YR+sRqSGMF9cwC0Cz0j/o8wAr+QGhOqe8an6Z7xaWpyGco5Wa5154LJ9rxSlVTX6/09hXp/T6EkyRFs0/h+zcFkfL8YDU1ysBssgEtiB1b/RxiB3wm4oNfkwZsHytnYpJwT5dp0tEQbj5zVtuOlqqhr1H/2Fuk/e4skScGBVmUkR2l0nyiNSYvW6LQoxUdwcz8A7MDaFRBG4PfstgCN6xujcX1jNO9z6Wpscmn3qQptPnpWm46UaMuxElXUNWrzsRJtPlbi/ryU6BCNTovWmLTmgHJNbwcbsAE9UMtFo6zs+i/CCLocW4DVPXPyrRsHyOUydKS4StuPl2lHfqm2Hy/TgaJKnSit1YnSWv1r5ylJkt1m1fDkSPfnjkqNUkp0CP9SArq5lp4R+kX8F2EEXZ7ValF6fITS4yP0pWtTJUmVdQ3amV+uHXml2p5Xqh35ZSqradC246XadrzU/bm9woPcwWTkuYeD/U6AbsXlnhkhjPgrwgi6pYjgQF0/sJeuH9hLUvM07dHiamXnl7kfuacqVFxV36r3RJIGxIVpVGq0RqVFaXRqlAYnRtAcC3Rh7pYRsojfIoygR7BYLOofF67+ceH67zEpkqS6hibtOVVxQUApVX5JrQ6fqdbhM9X65/YTkpqbY4cnnVveSWueRUmOYnkH6CpcLnpG/B1hBD1WcGCAxvaJ1tg+0e7Xiquc2nnB7El2fpkq6xrdG7K16BVu16jUKI0+F05GpESyvAP4KYOeEb9HGAEu0CvcrinXJGjKNc2bsTU3x1a7Z0525pdrb0GFiquc+s/e0/rP3uZN2SwWaUBceKst7blDMeAfWnZgJYz4L8II0Ibm5thwpceH6wtjL1zeKdeOvPOzJydKa3WoqEqHiqr05rYTslikkSlR+tzgeN08JF7Dkx0s6wAmabmahp9A/0UYATzUvLwTo7F9YtyvnalsXt7ZcrxEH+8/o32Fle6g8uv/HFBaTKj+K6O3ZmT01tDeBBPgamq5moYfO/9lMYwL96bzT+29BTHgLwrKa/Xx/jP6aH+R1h4oVm1Dk/u9AXFh+tK4VN01NkW9wu0mVgn0DIeKKjX1ubWKCg1U9s9uMbucHqW9v7+ZGQE6Qe/IEN09Pk13j09TTX2jVu8t0r93ndJH+8/o8JlqZa3Yp2c/2K9pQxN07/g+ui49ltkSoJMYLNP4PcII0MlCg2yaOTJJM0cmqbKuQf/eVaAlm/O080S5lucUanlOoYYkRugbN/TX50cmsWU94GPswOr/+L8ecBVFBAfqnvFpevfB67X8uzdoTmYfhQYFaF9hpf7vmzt1w9MfatGawyqvbTC7VKDbON8zQhjxV4QRwCRDkxx6YtZwbfjRFD1y6xDFR9h1usKpX76/T9f/8kP9etUBQgngAy5ulOf3CCOAySJDAzX3pgFa98jNevaLIzUoIVyVdY16fvVBXb/wQz236oDKawglgLfcPSOEEb9FGAH8RJDNqi+MTdH782/U72eP0eCECFU6G/Xb1Qd1/S8JJYC32IHV/xFGAD9jtVp0+4jeWjH/hkuGkmdX7ldpdb3ZZQJdBnft9X8ehZFFixYpIyNDDodDDodDmZmZWrFixWXH33TTTbJYLBc9ZsyY0eHCge7uwlCyaPYYDUlsDiW/++iQrv/lh8pasVfFVU6zywT8XstmWmQR/+XRpb0pKSlauHChBg4cKMMw9Oc//1mzZs3Sjh07NGzYsIvGv/3226qvP/8vuLNnz2rkyJH64he/2PHKgR7CarXothG9NX1Yoj7IPa3frj6o3IIKLf74iP68/pi+MqGPvnVjf8U7gs0uFfBL7MDq/zwKIzNnzmz1/Mknn9SiRYu0cePGS4aRmJiYVs+XLFmi0NBQwgjgBavVoluHJ2r6sAR9uK9Iv119UDtPlOuP647qLxuP685Rybr/+r4aksguxcCFDJZp/J7Xm541NTXpzTffVHV1tTIzM9v1OS+//LLuvvtuhYWFtTnO6XTK6Tw//VxRUeFtmUC3Y7FYNOWaBN08JF4fHzij364+qO15Zfr71nz9fWu+Jg2I1f3X9dPNQ+IVwLWMAJuedQEeh5GcnBxlZmaqrq5O4eHhWrp0qYYOHXrFz9u8ebN2796tl19++Ypjs7Ky9Pjjj3taGtCjWCwW3TQ4XpMHxWnb8VK9+ukxvb+nUOsPn9X6w2cVH2HXnWOS9YUxKRqYEGF2uYBpuLTX/3l8o7z6+nrl5eWpvLxcb731lv74xz/q448/vmIg+fa3v60NGzZo165dVzzGpWZGUlNTuVEecAUny2r11w3H9fcteSq94DLgkSmRun1Eb90yLFH9erU9Mwl0NxuPnNXdL23UgLgwrf7+TWaX06O090Z5Hb5r79SpUzVgwAAtXrz4smOqq6uVlJSkJ554QvPnz/f4GNy1F/BMfaNLH+0v0lvbTuijfUVqdJ3/MR8YH65pQxN0w8A4jU6LUnBggImVAp1v/eFi3fuHTRoYH65VD082u5we5ardtdflcrWaxbiUN998U06nU1/5ylc6ejgA7RBks2r6sERNH5ao4iqnVuQU6IPc09pw+KwOFlXpYFGVfr/msOw2q8b2iVZm/1iN7ROtESmRiggONLt8wLfoGfF7HoWRBQsW6LbbblNaWpoqKyv1+uuva82aNVq5cqUkac6cOUpOTlZWVlarz3v55Zd1xx13KDY21neVA2iXXuF2fTWzr76a2VfltQ1as79IH+4r0vrDZ3Wm0unuMZGa19QHxIVrZEqURiQ7NCgxQoMTIhQbbjf5uwC856JnxO95FEaKioo0Z84cFRQUKDIyUhkZGVq5cqWmTZsmScrLy5PV2noftf3792vdunX64IMPfFc1AK9EhgRq1qhkzRqVLMMwdPhMldYfPqtNR0qUnV+mk2W1OlRUpUNFVfrn9vOfFxsWpIEJ4RqcEKH+ceFKiw1V39gwJUeFKMjGRs7wb9y11/91uGfkaqBnBLg6iquc2nWiTNl5ZcotqNSB05XKL63R5f4vYbVISVEh6hMbqrSYMPWJDVXvyGAlR4Wod1SIEiLssgUQVmCuNfuL9LVXt2hYkkPLvnuD2eX0KFetZwRA99Er3K6bhyTo5iEJ7tdq6ht1qKhK+wsrdbCoSkeLq5V3tkbHS6pV1+DSidJanSit1ac6e9HXs1qk+Ihg9Y4KVlJkiJKigtX73Md4R7Diwu2Ki7DTRItO1ZKl6RnxX4QRAG0KDbIpIyVKGSlRrV43DENFlU4dP1uj42erlVdSo7ySGhWU1elUea1OV9SpoclQYUWdCivqtENllz1GZEig4iLsio+wf+ZjcKvnjuBAWdnIDR4y2A7e7xFGAHjFYrEowRGsBEewxveLueh9l8tQcZVTp8rrVFBW6/5YUN4cVooqnDpT5VR9o0vltQ0qr23QoaKqNo9ptUjRoUGKDgtSTGiQosMCFRMWpOjQoNYfw4IUHRooR3CgIoJtLBX1cC5X80d6RvwXYQRAp7BaLYp3NC/HjEqNuuQYwzBUUduooso6nal0qqjSee7jZ587VV7bIJchna2u19nq+kt+vcsJCQxQRLBN4cE2RQQHyhFsU0SwTRH25rAScS60hAfb5Ai2KdweeG6sTRH25tdDAgP4ZdZFudz3pjG5EFwWYQSAaSwWiyJDAxUZGnjFLevrG10qq6lXSU29SqrrVVrdoJKaepVWn3tec/5jaXWDSqrrVdvQJEmqbWhSbUOTiirb3hOpLQFWi8LtzQGl5WNEcKD7z46QQEWHBioq9PysTVRo82xNZEgg9wkyET0j/o8wAqBLCLJZ3TMt7dXQ5FJVXaMq6xpVUdegyrpGVX72o/P8n6ucje7xVeder3I2ymVITS7DvZzkKYtFcgQ3LylFhQYqOjRIsWFBSoxsXuZKPLfclRBpV68wO30xPubuGTG5DlweYQRAtxUYYG3uHwkL8vprGIahmvqmcwGlJcicDy4tIaeirkGl1fUqrWlQWU3zx9KaelXWNcow1O4gY7NalBQVov5xYerXK0z948I1oFeYhiVFKjKU3XG9wV17/R9hBADaYLFYFGa3Kcxuk9T+WZkWDU0ulX0moJRW16u4ytl8pVF5c49MYXmdzlQ51egy3Fcmrdl/ptXX6tcrTCNTIjW2b4xuHNhLfWK56WF7cNde/0cYAYBOFBhgVdy5S5OvpLHJpTNVzZdLHzlTraPFVTpyploHiiqVX1Kro8XVOlpcrXeyT0mS+sSGasqQBM0alaSMlEgabC/jfAMr58dfEUYAwE/YAqzqHRmi3pEhmti/9b28SqvrtfNEmXbml+vTw8XafrxUx8/W6JVPj+qVT4+qX68wfWlcqu4dn8Zyzme42GfE7xFGAKALiA4L0k2D43XT4HjNnzpQVc5GfXqoWP/eVaBVuYU6WlytX76/T79dfVBfGpeib00eoOSoELPL9gsGPSN+jzACAF1QuN2m6cMSNX1YoqqdjVqWU6BX1h3VvsJK/XnDcb2xJV/3T+qr79yU3uNnSgwxM+Lv2JYQALq4MLtNXxqXqhXzb9Dr35igif1jVN/o0uK1RzTluTVanlNgdommYgdW/0cYAYBuwmKxaFJ6L73xzYl69f5rlR4fruKqen3nb9s197VtKq/xfI+U7oAdWP0fYQQAuhmLxaLPDY7Xsu9er+9OGSib1aIVuwv1+RfXaV9hhdnlXXXswOr/CCMA0E3ZbQF6eNogvTPvOqVEh+j42Rrd+eJ6fbCn0OzSriqDmRG/RxgBgG5ueHKk/vXg9bphYC/VNjRp7t+265/bTphd1lXTsgMrG8L7L8IIAPQA0WFBevVr1+oLY1PU5DL0/Td36q0eEkjoGfF/hBEA6CFsAVY9fVeGvjapryTpkX/u6hFLNuwz4v8IIwDQg1itFv3sv4bqrjHNMyTfXbJDu0+Wm11Wp3L3jPAbz2/xVwMAPYzVatEv7xqhyYPiVNfg0rf/uk0l1fVml9VpWnpGLPSM+C3CCAD0QLYAq35792j1jQ3VybJafe/v2e4ZhO7G4N40fo8wAgA9VGRooF6aM052m1UfHzij1zblmV1Sp3DRM+L3CCMA0IMNSojQI7cOkSQ9tWyvjhVXm1yR73E1jf8jjABAD/e1SX01aUCsahua9LP39nS75ZqWb4d70/gvwggA9HBWq0VP3jlCQQFWrT1wRqtyT5tdkk9x117/RxgBAKhfrzB944Z+kqSfL8tVXUOTyRX5Dj0j/o8wAgCQJD14c7oSHcHKL6nVks3dp5m1pWeEKOK/CCMAAElSaJBND96cLkl6cc3hbjM7wg6s/o8wAgBw+9K4VCVHhehMpVOvbTxudjk+wT4j/o8wAgBwC7JZ9X/OzY4sXntE9Y0ukyvyHcKI/yKMAABauWtsihIcdp2pdGpZzimzy0EPQBgBALQSGGDVVyb0kSS9+umxbrfvCPwPYQQAcJF7J6QpyGbVrhPl2na81Oxy0M0RRgAAF4kNt+uOUUmSpL92k0ZW+C/CCADgku4ZnyZJWrmnUJV1DSZXg+6MMAIAuKRRqVHqHxemugaXVuQUml0OujHCCADgkiwWi+4akyJJemv7CZOrQXdGGAEAXNZ/j0mWxSJtPlqivLM1ZpeDboowAgC4rN6RIZo0IFaStHx3gcnVoLsijAAA2nTr8N6SmhtZgc7gURhZtGiRMjIy5HA45HA4lJmZqRUrVrT5OWVlZZo3b5569+4tu92uQYMGafny5R0qGgBw9Uy7JkGStCOvTEUVdSZXg+7IozCSkpKihQsXatu2bdq6datuvvlmzZo1S3v27Lnk+Pr6ek2bNk3Hjh3TW2+9pf379+sPf/iDkpOTfVI8AKDzJUYGa1RqlCTpg9zT5haDbsnmyeCZM2e2ev7kk09q0aJF2rhxo4YNG3bR+FdeeUUlJSVav369AgMDJUl9+/b1vloAgCmmD0tUdn6ZPsg9ra9M7GN2OehmvO4ZaWpq0pIlS1RdXa3MzMxLjnnvvfeUmZmpefPmKSEhQcOHD9dTTz2lpqamNr+20+lURUVFqwcAwDy3DGteqtlwuJgN0OBzHoeRnJwchYeHy26364EHHtDSpUs1dOjQS449cuSI3nrrLTU1NWn58uX66U9/ql/96lf6xS9+0eYxsrKyFBkZ6X6kpqZ6WiYAwIcGxIWrT2yoGpoMbTpSYnY56GY8DiODBw9Wdna2Nm3apLlz5+q+++5Tbm7uJce6XC7Fx8frpZde0tixY/XlL39ZP/nJT/T//t//a/MYCxYsUHl5ufuRn5/vaZkAAB+7Pr2XJGndoWKTK0F341HPiCQFBQUpPT1dkjR27Fht2bJFzz//vBYvXnzR2N69eyswMFABAQHu16655hoVFhaqvr5eQUFBlzyG3W6X3W73tDQAQCe6Pr2X/rYpjzACn+vwPiMul0tOp/OS71133XU6dOiQXC6X+7UDBw6od+/elw0iAAD/NGlAL1ks0qGiKhWU15pdDroRj8LIggULtHbtWh07dkw5OTlasGCB1qxZo9mzZ0uS5syZowULFrjHz507VyUlJZo/f74OHDigZcuW6amnntK8efN8+10AADpdZGigMpIjJUmfHjprcjXoTjxapikqKtKcOXNUUFCgyMhIZWRkaOXKlZo2bZokKS8vT1br+XyTmpqqlStX6nvf+54yMjKUnJys+fPn65FHHvHtdwEAuCquH9hLO0+Ua93BM/rC2BSzy0E34VEYefnll9t8f82aNRe9lpmZqY0bN3pUFADAP103oJde/OiwNh/lihr4DvemAQC028jUKAVYLTpVXqdTZfSNwDcIIwCAdguz23RN7whJ0va8UpOrQXdBGAEAeGRsWrQkadtxwgh8gzACAPDImD6EEfgWYQQA4JFxfWMkSXtOVaimvtHkatAdEEYAAB5JigxWoiNYTS5Du06Um10OugHCCADAIxaLRWNZqoEPEUYAAB7LSGneiXXPKWZG0HGEEQCAx4YltYSRCpMrQXdAGAEAeGxYkkOSdPxsjSrqGkyuBl0dYQQA4LHosCAlR4VIknKZHUEHEUYAAF5pmR3ZfZK+EXQMYQQA4JWWvhFmRtBRhBEAgFeGJzfPjNDEio4ijAAAvNIyM3LoTJXqGppMrgZdGWEEAOCVBIddsWFBanIZ2ldYaXY56MIIIwAAr1gsFg1OjJAkHTxNGIH3CCMAAK8NjA+XJB0qqjK5EnRlhBEAgNfSE87NjBBG0AGEEQCA11pmRg4WsUwD7xFGAABeawkjJ0prVVPfaHI16KoIIwAAr8WG2xUTFiTDkI6cqTa7HHRRhBEAQIeks1SDDiKMAAA6xN03cpomVniHMAIA6JDzTayEEXiHMAIA6JD0+ObLe9lrBN4ijAAAOqSlZySvpEYNTS6Tq0FXRBgBAHRIfIRdwYFWNbkMnSytNbscdEGEEQBAh1itFqXFhEqSjp3l8l54jjACAOiwPrFhkpqXagBPEUYAAB3Wp2VmpJgwAs8RRgAAHdanV8vMCMs08BxhBADQYX1jW3pGmBmB5wgjAIAO6xNzvmfE5TJMrgZdDWEEANBhSVHBslktqm90qbCizuxy0MUQRgAAHWYLsColOkSSdJylGniIMAIA8ImWy3uPs9cIPEQYAQD4REsT63H2GoGHCCMAAJ9IY2YEXiKMAAB8oqVn5GQZDazwDGEEAOATyVHNYeRUGTfLg2c8CiOLFi1SRkaGHA6HHA6HMjMztWLFisuO/9Of/iSLxdLqERwc3OGiAQD+J+lcGDlT6ZSzscnkatCV2DwZnJKSooULF2rgwIEyDEN//vOfNWvWLO3YsUPDhg275Oc4HA7t37/f/dxisXSsYgCAX4oODVRwoFV1DS4Vlte5r64BrsSjMDJz5sxWz5988kktWrRIGzduvGwYsVgsSkxM9L5CAECXYLFYlBQVoiNnqnWyrJYwgnbzumekqalJS5YsUXV1tTIzMy87rqqqSn369FFqaqpmzZqlPXv2XPFrO51OVVRUtHoAAPxfS9/IyVL6RtB+HoeRnJwchYeHy26364EHHtDSpUs1dOjQS44dPHiwXnnlFb377rt67bXX5HK5NGnSJJ04caLNY2RlZSkyMtL9SE1N9bRMAIAJzjexckUN2s/jMDJ48GBlZ2dr06ZNmjt3ru677z7l5uZecmxmZqbmzJmjUaNGafLkyXr77bcVFxenxYsXt3mMBQsWqLy83P3Iz8/3tEwAgAmSuKIGXvCoZ0SSgoKClJ6eLkkaO3astmzZoueff/6KAUOSAgMDNXr0aB06dKjNcXa7XXa73dPSAAAmc4eRcsII2q/D+4y4XC45nc52jW1qalJOTo569+7d0cMCAPxQUlTz9g0nmRmBBzyaGVmwYIFuu+02paWlqbKyUq+//rrWrFmjlStXSpLmzJmj5ORkZWVlSZKeeOIJTZw4Uenp6SorK9Mzzzyj48eP6xvf+IbvvxMAgOku3PjMMAy2c0C7eBRGioqKNGfOHBUUFCgyMlIZGRlauXKlpk2bJknKy8uT1Xp+sqW0tFTf/OY3VVhYqOjoaI0dO1br16+/bMMrAKBrS4wMlsUi1TW4VFrToJiwILNLQhfgURh5+eWX23x/zZo1rZ7/+te/1q9//WuPiwIAdE12W4Diwu0qqnTqZGmtbAEW/WNLvg6crlRlXaOaXIZchqEmlyHjKtV0/Cx3EfZ3HjewAgDQlqSoEBVVOnXsbLUWLN2l3Sf9Y6+oXuFcGOGvCCMAAJ9KigpWdr705/XHtPtkhUKDAvStG/srJixIVotFNqtFVqtF1qvYTxISGKCbh8RftePBM4QRAIBPxUc0X1Gz9XipJOnW4Yl6aOogM0uCn+vwpb0AAFwo3tF6OSTBwd3a0TbCCADApxIiWoePOHo1cAWEEQCAT312JqRXBGEEbSOMAAB86rPLNMyM4EoIIwAAn7pomSaCjc/QNsIIAMCnHCE2BdnO/3oJDgwwsRp0BYQRAIBPWSwWJThYmkH7EUYAAD4XH8HlvGg/wggAwOeYGYEnCCMAAJ9jZgSeIIwAAHzus5f3Am0hjAAAfO6zl/cCbSGMAAB8jpkReIIwAgDwOW6OB08QRgAAPnfhMo2z0WViJegKCCMAAJ9zhNjcfw5hB1Zcge3KQwAA8IzFYtHfvzVRJdX1SooKMbsc+DnCCACgU0zoH2t2CegiWKYBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYKoucddewzAkSRUVFSZXAgAA2qvl93bL7/HL6RJhpLKyUpKUmppqciUAAMBTlZWVioyMvOz7FuNKccUPuFwunTp1ShEREbJYLD77uhUVFUpNTVV+fr4cDofPvi7ah/NvLs6/uTj/5uHcXz2GYaiyslJJSUmyWi/fGdIlZkasVqtSUlI67es7HA7+gzQR599cnH9zcf7Nw7m/OtqaEWlBAysAADAVYQQAAJiqR4cRu92uxx57THa73exSeiTOv7k4/+bi/JuHc+9/ukQDKwAA6L569MwIAAAwH2EEAACYijACAABMRRgBAACm6tFh5MUXX1Tfvn0VHBysCRMmaPPmzWaX5PfWrl2rmTNnKikpSRaLRe+8806r9w3D0M9+9jP17t1bISEhmjp1qg4ePNhqTElJiWbPni2Hw6GoqCh9/etfV1VVVasxu3bt0g033KDg4GClpqbq6aefvqiWN998U0OGDFFwcLBGjBih5cuX+/z79SdZWVm69tprFRERofj4eN1xxx3av39/qzF1dXWaN2+eYmNjFR4errvuukunT59uNSYvL08zZsxQaGio4uPj9YMf/ECNjY2txqxZs0ZjxoyR3W5Xenq6/vSnP11UT0/7+Vm0aJEyMjLcG2VlZmZqxYoV7vc591fPwoULZbFY9NBDD7lf4/x3cUYPtWTJEiMoKMh45ZVXjD179hjf/OY3jaioKOP06dNml+bXli9fbvzkJz8x3n77bUOSsXTp0lbvL1y40IiMjDTeeecdY+fOncbnP/95o1+/fkZtba17zK233mqMHDnS2Lhxo/HJJ58Y6enpxj333ON+v7y83EhISDBmz55t7N6923jjjTeMkJAQY/Hixe4xn376qREQEGA8/fTTRm5urvHoo48agYGBRk5OTqefA7NMnz7dePXVV43du3cb2dnZxu23326kpaUZVVVV7jEPPPCAkZqaaqxevdrYunWrMXHiRGPSpEnu9xsbG43hw4cbU6dONXbs2GEsX77c6NWrl7FgwQL3mCNHjhihoaHGww8/bOTm5hovvPCCERAQYLz//vvuMT3x5+e9994zli1bZhw4cMDYv3+/8eMf/9gIDAw0du/ebRgG5/5q2bx5s9G3b18jIyPDmD9/vvt1zn/X1mPDyPjx44158+a5nzc1NRlJSUlGVlaWiVV1LZ8NIy6Xy0hMTDSeeeYZ92tlZWWG3W433njjDcMwDCM3N9eQZGzZssU9ZsWKFYbFYjFOnjxpGIZh/P73vzeio6MNp9PpHvPII48YgwcPdj//0pe+ZMyYMaNVPRMmTDC+/e1v+/R79GdFRUWGJOPjjz82DKP5XAcGBhpvvvmme8zevXsNScaGDRsMw2gOk1ar1SgsLHSPWbRokeFwONzn+4c//KExbNiwVsf68pe/bEyfPt39nJ+fZtHR0cYf//hHzv1VUllZaQwcONBYtWqVMXnyZHcY4fx3fT1ymaa+vl7btm3T1KlT3a9ZrVZNnTpVGzZsMLGyru3o0aMqLCxsdV4jIyM1YcIE93ndsGGDoqKiNG7cOPeYqVOnymq1atOmTe4xN954o4KCgtxjpk+frv3796u0tNQ95sLjtIzpSX9/5eXlkqSYmBhJ0rZt29TQ0NDqvAwZMkRpaWmtzv+IESOUkJDgHjN9+nRVVFRoz5497jFtnVt+fqSmpiYtWbJE1dXVyszM5NxfJfPmzdOMGTMuOkec/66vS9woz9eKi4vV1NTU6j9KSUpISNC+fftMqqrrKywslKRLnteW9woLCxUfH9/qfZvNppiYmFZj+vXrd9HXaHkvOjpahYWFbR6nu3O5XHrooYd03XXXafjw4ZKaz01QUJCioqJajf3s+b/UeWt5r60xFRUVqq2tVWlpaY/9+cnJyVFmZqbq6uoUHh6upUuXaujQocrOzubcd7IlS5Zo+/bt2rJly0Xv8d9+19cjwwjQ1c2bN0+7d+/WunXrzC6lRxk8eLCys7NVXl6ut956S/fdd58+/vhjs8vq9vLz8zV//nytWrVKwcHBZpeDTtAjl2l69eqlgICAizqtT58+rcTERJOq6vpazl1b5zUxMVFFRUWt3m9sbFRJSUmrMZf6Ghce43JjesLf34MPPqh///vf+uijj5SSkuJ+PTExUfX19SorK2s1/rPn39tz63A4FBIS0qN/foKCgpSenq6xY8cqKytLI0eO1PPPP8+572Tbtm1TUVGRxowZI5vNJpvNpo8//li//e1vZbPZlJCQwPnv4npkGAkKCtLYsWO1evVq92sul0urV69WZmamiZV1bf369VNiYmKr81pRUaFNmza5z2tmZqbKysq0bds295gPP/xQLpdLEyZMcI9Zu3atGhoa3GNWrVqlwYMHKzo62j3mwuO0jOnOf3+GYejBBx/U0qVL9eGHH160lDV27FgFBga2Oi/79+9XXl5eq/Ofk5PTKhCuWrVKDodDQ4cOdY9p69zy83Oey+WS0+nk3HeyKVOmKCcnR9nZ2e7HuHHjNHv2bPefOf9dnNkdtGZZsmSJYbfbjT/96U9Gbm6u8a1vfcuIiopq1WmNi1VWVho7duwwduzYYUgynnvuOWPHjh3G8ePHDcNovrQ3KirKePfdd41du3YZs2bNuuSlvaNHjzY2bdpkrFu3zhg4cGCrS3vLysqMhIQE46tf/aqxe/duY8mSJUZoaOhFl/babDbj2WefNfbu3Ws89thj3f7S3rlz5xqRkZHGmjVrjIKCAvejpqbGPeaBBx4w0tLSjA8//NDYunWrkZmZaWRmZrrfb7m88ZZbbjGys7ON999/34iLi7vk5Y0/+MEPjL179xovvvjiJS9v7Gk/Pz/60Y+Mjz/+2Dh69Kixa9cu40c/+pFhsViMDz74wDAMzv3VduHVNIbB+e/qemwYMQzDeOGFF4y0tDQjKCjIGD9+vLFx40azS/J7H330kSHposd9991nGEbz5b0//elPjYSEBMNutxtTpkwx9u/f3+prnD171rjnnnuM8PBww+FwGPfff79RWVnZaszOnTuN66+/3rDb7UZycrKxcOHCi2r5xz/+YQwaNMgICgoyhg0bZixbtqzTvm9/cKnzLsl49dVX3WNqa2uN73znO0Z0dLQRGhpq3HnnnUZBQUGrr3Ps2DHjtttuM0JCQoxevXoZ3//+942GhoZWYz766CNj1KhRRlBQkNG/f/9Wx2jR035+/ud//sfo06ePERQUZMTFxRlTpkxxBxHD4NxfbZ8NI5z/rs1iGIZhzpwMAABAD+0ZAQAA/oMwAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABT/X9WOtWdN0uL0AAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -525,12 +534,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  629.273 ms\n"
+      "Took  689.698 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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uoqm5RWv2lksijHQVwggAABex7Uilqk43Ki4qTJcPiDO7nF6JMAIAwEW8v6e1V+T67GSFhvCx2RU4qwAAXMTq3a3zRaYyRNNlCCMAAFzAwRM1OlhRq7AQiyYPu/BlTOAfwggAABfw/metQzQTh/RTbESYydX0XoQRAAAuYNWZJb1ThyebXEnvRhgBAKADlbUN2nakUhLzRboaYQQAgA7k7ytXc4uh4amxykyIMrucXo0wAgBAB1Z/xkZn3YUwAgDAv2loatHavSckSVNHMF+kqxFGAAD4N5sPnVKNu0mJMTaNyYgzu5xejzACAMC/WX3OKhqr1WJyNb0fYQQAgHMYhnE2jDBE0y0IIwAAnGPv8WodrayTLdSqqy9h19XuQBgBAOAcbdeiuSorUVHhoSZX0zcQRgAAOMMwDC3bUSJJunEkS3q7C2EEAIAzNh86pYMnahUVHqJbx6SbXU6fQRgBAOCMP28ukiTdNiZdMTaGaLpLnw4jzS2GdpY4zS4DABAEqk436N2dZZKkb105wORq+pY+HUaeem+vZv7+Iy1ef0iGYZhdDgDARMu2l6ihqUUj0+zKyXCYXU6f0mfDSEuLoWNVdWpuMfTzf+7Ww0sKdLqhyeyyAAAmMAzDM0TzrSszZbGw0Vl36rNhxGq16DffvEw/mzFSoVaL3v7kmL76hw06XFFrdmkAgG62vahS+8trFBFm1cyx/c0up8/ps2FEkiwWi+6+arDe+M5EJcbYtKesWjOeXa/3z+y8BwDoG974uFiSdGtOuuwRYSZX0/f06TDS5srBCXrnoas1bmC8quubdM8rW/XUe3vV2NxidmkAgC7mrGvUO4XHJDFx1SyEkTNS7BH683cmak7uQEnS7z44oNue/UifHq0ytzAAQJd6q6BE9Y0tyk6J1eUD4swup08ijJwjPNSqJ2aO1jPfGqu4qDB9VurSl3//kX7xzm4mtwJAL2QYht74uHXi6n8wcdU0hJEO3DYmXasfnaKZl6WrxZBe+PCQbvrtOq3fX2F2aQCAAPrkqFN7yqplC7XqK0xcNQ1h5AISY2xa+B9j9dJdVyjdEaHiU3X6P4s/1vf/+olO1TaYXR4AIAD+fKZX5JZL0xQXFW5yNX0XYeQLXDc8We89OkV3TRoki0Vauv2opj29Vsu2H2WjNADowarrG/X2J0xcDQaEkU6IsYXq8dtGael9k5SdEqtTtQ169K+faPbizTpykn1JAKAnWlFYprrGZg1NitYVg+LNLqdPI4x44fIB8frnQ1frhzdlyxZq1foDFbrxN+v0+zUHWAYMAD3MoTN/TF5zSRITV01GGPFSWIhV91+XpX89MllXZfWTu6lFT/5rr6Y/86E2HzpldnkAgE6qrm+UJNkjuDqv2QgjPhqUGK3X7pmgp28fo4TocO07XqPbn9uoH77JBFcA6Amq61u3bIhlx1XTEUb8YLFY9NXLM/T+o1P0rSszJUlvbjuq65/K15LNRWppYYIrAASrs2GEnhGzEUYCID46XHlfzdHS+yZpeGqsqk436sfLCvWN5zZq1zGn2eUBADrgGaaJpGfEbISRABo3MF7/fPBq/X/TRyg6PETbjlRqxu/W66dv7ZTzdKPZ5QEAzkHPSPAgjARYaIhV914zRKu/P0W35qSpxZBe3XhE1zF0AwBBhTkjwcOrMLJo0SLl5OTIbrfLbrcrNzdXK1asuOhzXn/9dY0ZM0ZRUVFKS0vT3XffrZMnT/pVdE+Q5ojUs3dcrje+M0HDUmJ0qrZBP15WqK/84SPtKKo0uzwA6PNcZ4Zp6Bkxn1dhJCMjQwsWLNDWrVu1detWXX/99Zo5c6Z27drVYfv169frzjvv1D333KNdu3bpzTff1JYtW3TvvfcGpPieYNLQRL3z0DX6ya0jFWsL1SdHnfrKHzbo0b8UqMxZb3Z5ANAntbQYqnEzTBMsvAojM2bM0C233KJhw4Zp2LBh+sUvfqGYmBht2rSpw/abNm3SoEGD9NBDD2nw4MG6+uqr9b3vfU9bt24NSPE9RViIVfdcPVjv/2CKvnZ5hiRp2Y4SXffrfC1cvV91Dc0mVwgAfUttQ5ParuhhZ5jGdD7PGWlubtaSJUtUW1ur3NzcDttMmjRJR48e1bvvvivDMHT8+HH97W9/0/Tp030uuCdLjo3QU7eP0Vv3X6VxA+NV19is36zep+ufytffd5QwnwQAuknbfJGwEItsoUyfNJvX70BhYaFiYmJks9k0d+5cLV++XCNHjuyw7aRJk/T666/rm9/8psLDw5Wamqq4uDj97ne/u+gx3G63XC5Xu1tvMiYzTn+bm6tn7xir/nGRKnXW65G/FOgrizZo08HeP58GAMx27uRVtoI3n9dhJDs7WwUFBdq0aZPuu+8+zZkzR7t37+6w7e7du/XQQw/ppz/9qbZt26aVK1fq0KFDmjt37kWPkZeXJ4fD4bllZmZ6W2bQs1gsujUnXe9/f4p+eFO2osND9Elxlf7j+U369stbtLes2uwSAaDXqmbyalCxGIbh19jAtGnTNHToUD333HPnPTZ79mzV19frzTff9Ny3fv16XXPNNTp27JjS0tI6/J1ut1tut9vzs8vlUmZmppxOp+x2uz/lBq3y6no98/5+/XlzsZpbDFkt0tfHZei/bhimNEek2eUBQK+yZk+57n55i0b3t+ufD15jdjm9lsvlksPh+MLPb78HygzDaBccznX69GlZre0PERIS4nnehdhsNs/y4bZbb5ccG6H/+fKlWvVfk3Xz6FS1GNJftx7VtU/mK+/dz7jeDQAEkGdZr43Jq8HAqzAyf/58ffjhhzp8+LAKCwv12GOPKT8/X7NmzZIkzZs3T3feeaen/YwZM7Rs2TItWrRIBw8e1EcffaSHHnpIV155pdLT0wP7SnqJIUkxWvR/xmnpfZN0xaB4uZta9Ny6g5r8qzV6etU+zz8gAIDv2H01uHj1Lhw/flyzZ89WaWmpHA6HcnJytHLlSt1www2SpNLSUhUVFXna33XXXaqurtazzz6r73//+4qLi9P111+vX/7yl4F9Fb3QuIHx+uv3cvXBnnI99d4+7S516Zn39+uVDYf13clDdNekQYq28Y8IAHzB7qvBxe85I92hs2NOvVVLi6GVu8r0m1X7tL+8RpLULzpc35k8RLMnDiSUAICXfrlyjxblf667Jg3S47eNMrucXqvb5oyg61mtFt1yaZpWPjJZv/3mZRrYL0onaxu0YMUeXfXLD/S79/czfAMAXvBcsZdhmqBAGOlBQqwWfXlsf61+dIqe/HqOBidGq+p0o55atU9XLfhAT7+3V1WnmegKAF+EYZrgQhjpgcJCrPrG+EytfnSKFv7HZbokOUbV9U165oMDmrTgA/3//9itkqo6s8sEgKDFBNbgQhjpwUKsFs28rL/+9chkLZp1uUam2XW6oVkvfnRIk3+1Rv/1lwJ9Vtq7dq8FgEA4u+kZPSPBgEjYC1itFt18aZq+NDpV6/ZX6Lm1n2vD5ye1fEeJlu8o0eRhSfruNUN0VVY/tj0GANEzEmx4F3oRi8WiKcOSNGVYkgqPOvXcus/1bmGp1u07oXX7TuiS5BjdddUgfWVsf0WF89YD6LsII8GFYZpe6tIMh56943Ll/+A6zckdqOjwEO0vr9Fjy3cqN+8D5b37mY5Wnja7TAAwhYthmqDCPiN9hKu+UW9uPapXNhxW0anWEGK1SNcPT9GsiQM0+ZIkhVgZwgHQ+7W0GBr62LsyDGnz/KlKtkeYXVKv1dnPb/qn+gh7RJjuuXqw7po0SPl7y/XSR4e1/kCFVn92XKs/O67+cZG6Y8IAfWN8hpJj+YcJoPeqbWhS25/h9kh6RoIBYaSPCbFaNHVEiqaOSNGB8mq98XGxlm4/qpKqOj35r736zap9unFUir4xPpPeEgC9Utt8kbAQi2yhzFYIBoSRPiwrOVY/nTFS//2lbL3zaane2FykbUcq9W5hmd4tLFOK3aavXp6hr4/L0NCkGLPLBYCAOHfDM1YYBgfCCBQRFqKvjcvQ18Zl6LNSl/6ypVhvFZTouMutRfmfa1H+57p8QJy+Pi5Tt1yaqriocLNLBgCfnd1jhI/AYME7gXZGpNn1+G2jNP+WEXr/s+P627ajyt93QtuLqrS9qEo/e3unrs1O1szL0jVtRIoiwkLMLhkAvMKy3uDDO4EOhYdadfOlabr50jSVu+o9G6jtKavWqt3HtWr3ccXYQnXjqBTdNiZdk4YmKpyxVwA9gGdZr43Jq8GCMIIvlGyP0PemDNX3pgzV3rJqvVVQorcKjqmkqk7Ltpdo2fYSOSLDdMPIFN1yaaquzkoimAAIWvSMBB/eCXglOzVW//2l4frBjdnaXlSptwqOacXOMlXUuPW3bUf1t21HFRsRqhtGpOjGUamaPCyR3V4BBBWu2Bt8+JSAT6xWi8YPStD4QQl6/LZR2nL4lFYUlmrFzjKVV7u1bEeJlu0okS3UqquzEnXDyNblxEmxNrNLB9DHMYE1+PBOwG8hVosmDumniUP66WczRmlbUaVWFJZp1WdlKj5Vp/f3lOv9PeWyWAp1WWacrs9O1nXDkzUyzS4r+5gA6GZtPSN2wkjQ4J1AQFmtFl0xKEFXDErQT24dob3Hq7VqV+sur58cdWpHUZV2FFXpqVX7lBhj07XZSbo2O0nXZCXJEUWXKYCux3Vpgg9hBF3GYrFoeKpdw1PtenDqJSpz1uuDPeXK31uujw5UtJtnYrVIORlxuuaSRF2dlaixA+KZBAugSzCBNfjwTqDbpDoidMeEAbpjwgA1NLVo6+FTWrO3XGv2ntCB8hoVFFepoLhKv/vggKLCQzRhcIKuykrUxCH9NCLNztb0AAKimp6RoEMYgSnCQ62alJWoSVmJemy6dKyqTusPVGj9/gp9dKBCJ2sbtGbvCa3Ze0JS69juhDPzUiYOSdDwVMIJAN/QMxJ8eCcQFNLjInX7+EzdPj5TLS2G9pRVa/2BE9p08JQ2HzolV32TZ7M1qfV/IuMGxnvmp+RkONgNFkCnEEaCD+8Ego7VatHIdLtGptv13clD1dTcol3HXNp48KQ2HTypLYdOqbq+Sfl7Tyj/TM9JeIhVl2Y4dPmAOF0+IF6XD4xXij3C5FcCIBgxgTX4EEYQ9EJDrBqTGacxmXGaO6U1nOwpq9bmQ6e09cgpbT5UqYoat7YdqdS2I5WSDkmS+sdFauyAOI0dEK/LMh0alU7vCdDXtbQYqnGztDfY8E6gxwkNsWp0f4dG93fo21cPlmEYOnLytLYeqdT2okrtKKrS3jKXSqrqVFJVp39+Wtr6PKtFw9NiNSajNdiMyYjT0KRohYawagfoK2obmmQYrd/TMxI8CCPo8SwWiwYlRmtQYrS+Pi5DklTjbtKnxVXaXlSpgmKnCoqrVFHj1s4Sl3aWuPT6x0WSpMiwEI1Kt+vSDIdyMhy6tH+chiRGsxkb0Eu1zRcJtVoUEcYfIsGCMIJeKcYW6lmtI0mGYajUWa9PiqtUcLRKBUVV2lniVG1Ds7YeqdTWI5Xtnjsq3d4aTjLilNPfoYH9omSxEFCAnu7cyav8mw4ehBH0CRaLRelxkUqPi9TNl6ZJah07PlhRq8KSKn161KnCo07tOuZSjbtJHx86pY8PnfI83x4RqjGZcRqb2TYHJU7x0eFmvRwAPmKPkeBEGEGfZbValJUco6zkGH1lbOvwTlNziz4/UatPj1apsMSpT486tbvUJVd9kz7cX6EP91d4nj+oX5QuHxDvuS5PZkIkf2kBQc5zXZpIPv6CCe8GcI7QEKuyU2OVnRqrb4zPlCQ1Nrdob1m1dhRXaUdRpQqKq3TwRK0OnzytwydPa9mOEklSmiPCsynb1ZckqX9cpJkvBUAHPMt6bfSMBBPCCPAFws5ZvTN74kBJkvN0owqOVmnr4VPadPCkCoqrVOqs1/IdJVp+JpyMTLPrhpEpumFkikal2+k1AYIAG54FJ94NwAeOqDBNGZakKcOSJEl1Dc3aXlSpTQdPasPnJ7WjqFK7S13aXerSwvf3K80RoWkjUjTzsnSNGxhPMAFMcjaM0DMSTAgjQABEhofoqqxEXZWVqO9LOlnj1pq9J7Rqd5nW7atQqbNe/7vpiP530xENSYrW7eMz9dWx/ZXMLrFAtzo7gZWPv2DCuwF0gX4xNn19XIa+Pi5D9Y3N2vj5Sb1TWKp3C0t18EStFqzYoyf/tVfXDkvS7VdkatqIFC78B3QDzwRWwkhQ4d0AulhEWIiuG56s64Yn6/HbRundT0v1163F2nqkUu/vKdf7e8o1sF+U7r16sL4+LlOR4WxZD3QVlvYGJ8II0I1ibKG6/YpM3X5Fpj4/UaO/bi3WX7cU68jJ0/rJW7v0m9X7NXviQN2ZO1D9Ymxmlwv0OkxgDU7shQuYZGhSjObdPEIf/fh6PXHbKGUmROpUbYMWvr9fV/3yA/30rZ0qddaZXSbQqzCBNTgRRgCTRYWHas6kQVrz/Wv17B1jdWl/h+obW/TqxiOa8qt8/fStnSpz1ptdJtAruJjAGpQII0CQCA2x6tacdL39wFV6494JunJwghqaW0PJ5CfX6PG3d+m4i1AC+INhmuDEuwEEGYvFoklZicod2k8bD57Ub1ft1+bDp/TyhsN6Y3OR7rhygL43ZYjSHOzwCnjLxQTWoORVz8iiRYuUk5Mju90uu92u3NxcrVix4oLt77rrLlkslvNuo0aN8rtwoLezWCyaNDRRf/neRL1+7wSNHxivhqYWvbzhsKb8Kl+PLS/U0crTZpcJ9BgtLYZq3CztDUZehZGMjAwtWLBAW7du1datW3X99ddr5syZ2rVrV4ftFy5cqNLSUs+tuLhYCQkJ+sY3vhGQ4oG+wGKx6KqsRL05N1ev3XN2+Ob1j4t07ZP5+u+/faIjJ2vNLhMIerUNTTKM1u/pGQkuFsNoe2t8k5CQoCeffFL33HPPF7b9+9//rq9+9as6dOiQBg4c2OljuFwuORwOOZ1O2e12f8oFeoWPD57U7z44oPUHWq8iHGK16KZRKbozd5AmDE5gu3mgA8eq6jRpwQcKtVq0/xc38++kG3T289vnfqrm5ma9+eabqq2tVW5ubqees3jxYk2bNu0Lg4jb7Zbb7fb87HK5fC0T6JUmDOmnCUP6aduRSj37wX6t2XtC7xaW6d3CMmWnxOrOSQP15cv6K9pGVzTQ5tzJqwSR4OL1aprCwkLFxMTIZrNp7ty5Wr58uUaOHPmFzystLdWKFSt07733fmHbvLw8ORwOzy0zM9PbMoE+YdzAeL1095X61yOTdceEAYoMC9He49V6bPlOTfy/7+vxt3dpR1Gl/OwABXoFdl8NXl4P0zQ0NKioqEhVVVVaunSp/vSnP2nt2rVfGEjy8vL01FNP6dixYwoPD79o2456RjIzMxmmAb6As65Rf9t2VP+78bAOnzw7ubV/XKRuuTRV03PSNSbDwV+F6JPW7CnX3S9v0ah0u9556Bqzy+kTOjtM4/eckWnTpmno0KF67rnnLtjGMAwNGzZMt956q37zm994fQzmjADeaWkxtG7/CS3dXqL3Pzuu0w3Nnsf6x0XqxlEpmjQ0UVcOTpAjkr8S0Te8VVCih5cUaOKQBC35buemF8A/XT5npI1hGO16MTqydu1aHThwoFOTXAH4z2q16NrsZF2bnaz6xmbl7y3XO4Vlev+z4yqpqtNLHx3WSx8dltUijUp3KHdoP+UO6afLB8YTTtBrsRV88PIqjMyfP18333yzMjMzVV1drSVLlig/P18rV66UJM2bN08lJSV69dVX2z1v8eLFmjBhgkaPHh24ygF0SkRYiL40Ok1fGp3mCSbr9ldo0+cndbCiVoUlThWWOPX8uoOSpAEJURrd365R6Q6N7u/QqHS7ErloH3oBdl8NXl69I8ePH9fs2bNVWloqh8OhnJwcrVy5UjfccIOk1kmqRUVF7Z7jdDq1dOlSLVy4MHBVA/DJucFEksqc9dp08KQ2fn5Smw6d1JGTp1V0qvX2bmGZ53kJ0eEamhStoUkxrbfkaA1JjFH/+EiFhXBVCfQMbRNY7fSMBB2/54x0B+aMAN3DebpRu445tfOYUztLXNp5zKlDFbW60P8lrBYpPS5SmfFRGpAQpQH9opQRH6n+cZFKi4tUSqxNoYQVBImf/H2n/nfTET10fZYevTHb7HL6hG6bMwKg93BEhWlSVqImZSV67jvd0KSDJ2r1+Ykafd72tbxGhypq5W5q0dHKOh2trNPGgyfP+31Wi5Rqj1B6XKRSHRFKtUco1RGhlDNfU+0RSoq1KSIspDtfJvoolvYGL8IIgIuKCg/V6P6t80fOZRiGTlS7PcM6xafqdORUrUoq61TqrFeps06NzYaOOet1zHnxqw07IsOUHGtTst2m5NgIJcfalBhjU9KZr4mx4UqMsSk+KlwhVpYlwzfMGQlevCMAfGKxWJRsj1CyPULjByWc93hLi6GKGrdKqup0rKo1nBx31avM5VaZs05lrnodd7rV0NwiZ12jnHWN2l9ec9FjWi1SfFS4EqJbb/1iWr/GR4UrLipc8VFhZ75v/WqPDJM9IpShIkhiNU0wI4wA6BJW69mwMnZAx20Mw5CzrlHl1W6Vu9wqr673fF9Rc/Z2otqtytONajGkk7UNOlnb4FUt0eEhZ4JJmOyRoYq2hSrGFqrYiFBFh4cqJqL15+i2W3iIp01bu9iIMIWHEmp6MpdnmIaPvmDDOwLANBaLRXFnejWGpcRetG1jc4sqzwSRU21fa9w6VdugytONqjzdoKpzvladblDtmc3eahuaVdvQrNIvGC76IuGhVtnPBBN7ROg5Aac15DgiwxQX2dozExcZJkdUmOKiwpUQFa7IcObFmI1hmuDFOwKgRwgLsXp6WjqrqblF1fVNctU3ylXX9rVRNe6m1lt9k2oaWr/WuptU425WrbtJtQ3nPO5u8uxg29DUooqaBlXUeNczI0kRYVb1i7adHWKKDleS3dY6qffM60p1tM6XYbl013AxgTVoEUYA9FqhIVbFR4crPvri18P6Ik3NLap1N8tV3xpkXHWN54ScRjnPBB1nXaOqTjfKWXemd6autYemsdlQfWOLSqrqVFJVd9FjWS1S//hIDUmM0ZCkaA1JitHQxGhlpcQoObbzQQzttbQYqnG39ozY6RkJOrwjAPAFQkOsckRZ5Yjy/i9qwzBU29CsUzUNOlnr9gwxnaxpUHl1feukXme9jp+ZM9PYbKj4VJ2KT9Vp7b4T7X7XwH5RmjA4QRMG99OEIQnKiI8K1Evs9Wobmjz75dAzEnwIIwDQhSwWi2ci7IB+Fw8PbSuQDlXU6mBFrQ6eqNHBE7U6VFGrwydrdeTkaR05eVp/3XpUUutFD6/NTtK3rhxw3tJrtNc2XyTUalFEGMNgwYYwAgBB4twVSBOG9Gv3mKu+UVsPn9LHB09p06FT2lniVElVnV7/uEivf1ykS/s79K0rB+i2y9IVY+N/7f/u3MmrFgt71QQb/osFgB7AHhGm64en6PrhKZKkGneTthw+peXbS7RyZ1nrBQ+XF+p/3tmt28ak695rhigrOcbkqoMHu68GN8IIAPRAMbZQXZedrOuyk3WqtkHLth/VG5uLdPBErZZsKdbS7Ud137VZ+s9rh7LdvljWG+wYOAOAHi4hOlz3XjNE7z86RX/57kRdl52kxmZDz7y/X7c886E2dXDdoL6GDc+CG2EEAHoJi8WiCUP66cW7rtAfZl2upFibDp6o1X88v0k/XvqpnKcbzS7RNGwFH9wIIwDQy1gsFt1yaZpWPzpFd0xo3Yt/yZZiTX06Xx/30V4ShmmCG2EEAHopR2SY/u9XLtWbc3OVlRyjipoG3fXSFm0+dMrs0rpd2wRWOz0jQYkwAgC93BWDEvTPB6/WNZckqq6xWXe9tFlbD/etQELPSHAjjABAHxARFqIX7hyvq7MSdbqhWXNe3KxtRyrNLqvbVDOBNagRRgCgj2gLJJOG9lPtmUCyo6hvBBImsAY3wggA9CGR4SH605zxmjgkQTXuJt25eLM+Ka4yu6wuxzBNcCOMAEAfExUeqhfvukJXDkpQtbtJsxd/rKKTp80uq0u5mMAa1AgjANAHRYWH6qW7r9BlmXFy1TfpiX/sMrukLkXPSHAjjABAHxVtC9WvvzFGYSEWvb+nXKt3Hze7pC7DtWmCG2EEAPqwrOQY3XvNEEnS4//YpfrGZpMrCryWFkM17taeETs9I0GJMAIAfdyD12cp3RGho5V1+kP+52aXE3C1DU1qMVq/p2ckOBFGAKCPiwoP1U9uHSlJ+uPaz3W4otbkigKrbb5IqNWiiDA+9oIR7woAQF8anaprLklUQ1OLHv/HLhmGYXZJAXPu5FWLxWJyNegIYQQAIIvFoiduG6WwEIvy957Qql40mZXJq8GPMAIAkCQNSYrRdye3TmZ94h+7VdfQOyazsqw3+BFGAAAe91+Xpf5xkSqpqtMf8g+YXU5AVJ9ZSRNjI4wEK8IIAMCjdTLrCEnSc+sOylnXaHJF/ms5s5QmLISPvGDFOwMAaOemUakalhKjhqYWrdxZanY56AMIIwCAdiwWi748tr8k6e87jplcDfoCwggA4Dy3jUmXJG06dFJlznqTq0FvRxgBAJwnIz5KVw5KkGFIb39SYnY56OUIIwCADs0c29o7spyhGnQxwggAoEPTL01TWIhFn5W6tO94tdnloBcjjAAAOhQXFa5rs5MlSX/fwVANug5hBABwQV++rHVVzVsFxzz7dQCBRhgBAFzQ1BHJirGFqqSqTtuKKs0uB70UYQQAcEERYSH60uhUSdJyhmrQRbwKI4sWLVJOTo7sdrvsdrtyc3O1YsWKiz7H7Xbrscce08CBA2Wz2TR06FC9+OKLfhUNAOg+bUM17xaWqqGpxeRq0Bt5ddWgjIwMLViwQFlZWZKkV155RTNnztSOHTs0atSoDp9z++236/jx41q8eLGysrJUXl6upqYm/ysHAHSL3KH9lBxrU3m1W2v3ndANI1PMLgm9jFdhZMaMGe1+/sUvfqFFixZp06ZNHYaRlStXau3atTp48KASEhIkSYMGDfK9WgBAtwuxWnTbmHT9af0h/b2ghDCCgPN5zkhzc7OWLFmi2tpa5ebmdtjm7bff1vjx4/WrX/1K/fv317Bhw/SDH/xAdXV1F/3dbrdbLper3Q0AYJ62a9Ws3n1c1fU9/0q+CC5e9YxIUmFhoXJzc1VfX6+YmBgtX75cI0eO7LDtwYMHtX79ekVERGj58uWqqKjQf/7nf+rUqVMXnTeSl5enJ554wtvSAABdZFS6XUOTovX5iVqt3Fmmb4zPNLsk9CJe94xkZ2eroKBAmzZt0n333ac5c+Zo9+7dHbZtaWmRxWLR66+/riuvvFK33HKLnn76ab388ssX7R2ZN2+enE6n51ZcXOxtmQCAALJYLO0msgKB5HUYCQ8PV1ZWlsaPH6+8vDyNGTNGCxcu7LBtWlqa+vfvL4fD4blvxIgRMgxDR48eveAxbDabZ8VO2w0AYK623Vh3FFfJMNgADYHj9z4jhmHI7XZ3+NhVV12lY8eOqaamxnPfvn37ZLValZGR4e+hAQDdKDs1VuEhVlWdblTRqdNml4NexKswMn/+fH344Yc6fPiwCgsL9dhjjyk/P1+zZs2S1Dq8cuedd3ra33HHHerXr5/uvvtu7d69W+vWrdMPf/hDffvb31ZkZGRgXwkAoEuFh1o1Ir21p7qguMrcYtCreBVGjh8/rtmzZys7O1tTp07Vxx9/rJUrV+qGG26QJJWWlqqoqMjTPiYmRqtWrVJVVZXGjx+vWbNmacaMGXrmmWcC+yoAAN1iTEbrsPunR50mV4LexKvVNIsXL77o4y+//PJ59w0fPlyrVq3yqigAQHDKyYiTdESfHq0yuRL0JlybBgDQaW09IztLXGpqZmt4BAZhBADQaUOSYhRjC1VdY7MOnKj54icAnUAYAQB0WojVotH9WyexflrMvBEEBmEEAOCVMRlxkqRPmDeCACGMAAC8knMmjLCiBoFCGAEAeCXnzCTWPWUuuZuaTa4GvQFhBADglYz4SCVEh6ux2dBnpdVml4NegDACAPCKxWLx9I6w3wgCgTACAPBa27yRT1hRgwAgjAAAvDaGnhEEEGEEAOC1tp6RAydqVONuMrcY9HiEEQCA15JibUp3RMgwpJ0lDNXAP4QRAIBPzu43UmVqHej5CCMAAJ/kZLbOG2ESK/xFGAEA+IRt4REohBEAgE9G92/tGTlaWaeTNW6Tq0FPRhgBAPjEERmmIYnRkqRPmcQKPxBGAAA+8+zEyrwR+IEwAgDwGStqEAiEEQCAz8a0rag56pRhGCZXg56KMAIA8NnINIdCrBZV1LhV6qw3uxz0UIQRAIDPIsNDNCwlVhJDNfAdYQQA4JecM0t8dx9zmVwJeirCCADALwP6RUmSjlbVmVwJeirCCADALxnxkZKkkkrCCHxDGAEA+KV/3JkwQs8IfEQYAQD4Jf1MGClz1qu5heW98B5hBADglxR7hEKtFjW1GCqvZnkvvEcYAQD4JcRqUaojQhLzRuAbwggAwG/pzBuBHwgjAAC/ZRBG4AfCCADAb/3PLO89RhiBDwgjAAC/eYZpmDMCHxBGAAB+Y68R+IMwAgDwW/9zdmE1DPYagXcIIwAAv6U7WsNIbUOzXHVNJleDnoYwAgDwW2R4iPpFh0uSjladNrka9DSEEQBAQJxdUcMurPAOYQQAEBBtQzUllfSMwDuEEQBAQHgmsbKiBl4ijAAAAqJtrxGGaeAtr8LIokWLlJOTI7vdLrvdrtzcXK1YseKC7fPz82WxWM677dmzx+/CAQDBpW2vkaP0jMBLod40zsjI0IIFC5SVlSVJeuWVVzRz5kzt2LFDo0aNuuDz9u7dK7vd7vk5KSnJx3IBAMEqgy3h4SOvwsiMGTPa/fyLX/xCixYt0qZNmy4aRpKTkxUXF+dTgQCAnqFtmOZEtVv1jc2KCAsxuSL0FD7PGWlubtaSJUtUW1ur3Nzci7YdO3as0tLSNHXqVK1Zs+YLf7fb7ZbL5Wp3AwAEt/ioMEWeCSClTuaNoPO8DiOFhYWKiYmRzWbT3LlztXz5co0cObLDtmlpaXr++ee1dOlSLVu2TNnZ2Zo6darWrVt30WPk5eXJ4XB4bpmZmd6WCQDoZhaLhav3wideDdNIUnZ2tgoKClRVVaWlS5dqzpw5Wrt2bYeBJDs7W9nZ2Z6fc3NzVVxcrF//+teaPHnyBY8xb948Pfroo56fXS4XgQQAeoD0uEgdKK/h6r3witdhJDw83DOBdfz48dqyZYsWLlyo5557rlPPnzhxol577bWLtrHZbLLZbN6WBgAwGStq4Au/9xkxDENut7vT7Xfs2KG0tDR/DwsACEKsqIEvvOoZmT9/vm6++WZlZmaqurpaS5YsUX5+vlauXCmpdXilpKREr776qiTpt7/9rQYNGqRRo0apoaFBr732mpYuXaqlS5cG/pUAAEyXHhchSQzTwCtehZHjx49r9uzZKi0tlcPhUE5OjlauXKkbbrhBklRaWqqioiJP+4aGBv3gBz9QSUmJIiMjNWrUKL3zzju65ZZbAvsqAABBoX9clCS2hId3LIZhGGYX8UVcLpccDoecTme7zdMAAMGlpKpOVy34QGEhFu39+c2yWi3tHm9pMbRo7efacvhUt9VU5qzXnrJqXZ2VqNfundBtx0XnP7+9nsAKAMCFpMTaFGK1qLHZ0Ikat1LsEe0ef3bNAT29ap8ptSXFsjAiWBFGAAABExpiVao9QiVVdSqpqmsXRtbvr9BvVrcGkfuvG6rBiTHdVldYiEXXDkvutuPBO4QRAEBA9Y+LbA0jlXW6fEC8pNahkoeX7JBhSN8cn6kf3jTc5CoRTPxe2gsAwLk8K2rOTGJtbG7Rg3/erpO1DRqRZtcTMy98LTP0TYQRAEBA/fuW8E/+a6+2HK5UrC1Ui2ZdzgX0cB7CCAAgoDzLeyvr9K9dZXp+3UFJ0pPfyNGgxGgzS0OQYs4IACCg2oZpNh48qc1nlvDee/VgfWk0u2+jY4QRAEBAtW0Jf7qhWZI0bmC8fnQzE1ZxYQzTAAACKv3MxfLa/Ne0YQoL4eMGF8Z/HQCAgIoKD1V8VJjn59AQy0VaA4QRAEAXaFtRA3QGYQQAEHDpDsIIOo8wAgAIOHpG4A3CCAAg4PrHEUbQeYQRAEDAEUbgDcIIACDgGKaBNwgjAICAO7dnpLG5xcRK0BMQRgAAAZcQHe75vrSq3sRK0BMQRgAAAWexnN3oLNURYWIl6Am4Ng0AoEu889DV2lXi0jWXJJpdCoIcYQQA0CVGpTs0Kt1hdhnoARimAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGCqHnHVXsMwJEkul8vkSgAAQGe1fW63fY5fSI8II9XV1ZKkzMxMkysBAADeqq6ulsPhuODjFuOL4koQaGlp0bFjxxQbGyuLxRKw3+tyuZSZmani4mLZ7faA/V50DuffXJx/c3H+zcX57x6GYai6ulrp6emyWi88M6RH9IxYrVZlZGR02e+32+38x2gizr+5OP/m4vybi/Pf9S7WI9KGCawAAMBUhBEAAGCqPh1GbDabfvazn8lms5ldSp/E+TcX599cnH9zcf6DS4+YwAoAAHqvPt0zAgAAzEcYAQAApiKMAAAAUxFGAACAqfp0GPnDH/6gwYMHKyIiQuPGjdOHH35odklBb926dZoxY4bS09NlsVj097//vd3jhmHo8ccfV3p6uiIjI3Xttddq165d7dq43W49+OCDSkxMVHR0tG677TYdPXq0XZvKykrNnj1bDodDDodDs2fPVlVVVbs2RUVFmjFjhqKjo5WYmKiHHnpIDQ0NXfGyg0JeXp6uuOIKxcbGKjk5WV/+8pe1d+/edm04/11n0aJFysnJ8WySlZubqxUrVnge59x3n7y8PFksFj3yyCOe+zj/PZzRRy1ZssQICwszXnjhBWP37t3Gww8/bERHRxtHjhwxu7Sg9u677xqPPfaYsXTpUkOSsXz58naPL1iwwIiNjTWWLl1qFBYWGt/85jeNtLQ0w+VyedrMnTvX6N+/v7Fq1Spj+/btxnXXXWeMGTPGaGpq8rT50pe+ZIwePdrYsGGDsWHDBmP06NHGrbfe6nm8qanJGD16tHHdddcZ27dvN1atWmWkp6cbDzzwQJefA7PcdNNNxksvvWTs3LnTKCgoMKZPn24MGDDAqKmp8bTh/Hedt99+23jnnXeMvXv3Gnv37jXmz59vhIWFGTt37jQMg3PfXTZv3mwMGjTIyMnJMR5++GHP/Zz/nq3PhpErr7zSmDt3brv7hg8fbvz4xz82qaKe59/DSEtLi5GammosWLDAc199fb3hcDiMP/7xj4ZhGEZVVZURFhZmLFmyxNOmpKTEsFqtxsqVKw3DMIzdu3cbkoxNmzZ52mzcuNGQZOzZs8cwjNZQZLVajZKSEk+bP//5z4bNZjOcTmeXvN5gU15ebkgy1q5daxgG598M8fHxxp/+9CfOfTeprq42LrnkEmPVqlXGlClTPGGE89/z9clhmoaGBm3btk033nhju/tvvPFGbdiwwaSqer5Dhw6prKys3Xm12WyaMmWK57xu27ZNjY2N7dqkp6dr9OjRnjYbN26Uw+HQhAkTPG0mTpwoh8PRrs3o0aOVnp7uaXPTTTfJ7XZr27ZtXfo6g4XT6ZQkJSQkSOL8d6fm5mYtWbJEtbW1ys3N5dx3k/vvv1/Tp0/XtGnT2t3P+e/5esSF8gKtoqJCzc3NSklJaXd/SkqKysrKTKqq52s7dx2d1yNHjnjahIeHKz4+/rw2bc8vKytTcnLyeb8/OTm5XZt/P058fLzCw8P7xHtoGIYeffRRXX311Ro9erQkzn93KCwsVG5ururr6xUTE6Ply5dr5MiRng8qzn3XWbJkibZv364tW7ac9xj/7fd8fTKMtLFYLO1+NgzjvPvgPV/O67+36ai9L216qwceeECffvqp1q9ff95jnP+uk52drYKCAlVVVWnp0qWaM2eO1q5d63mcc981iouL9fDDD+u9995TRETEBdtx/nuuPjlMk5iYqJCQkPNSbHl5+XmJF52XmpoqSRc9r6mpqWpoaFBlZeVF2xw/fvy833/ixIl2bf79OJWVlWpsbOz17+GDDz6ot99+W2vWrFFGRobnfs5/1wsPD1dWVpbGjx+vvLw8jRkzRgsXLuTcd7Ft27apvLxc48aNU2hoqEJDQ7V27Vo988wzCg0N9bxuzn/P1SfDSHh4uMaNG6dVq1a1u3/VqlWaNGmSSVX1fIMHD1Zqamq789rQ0KC1a9d6zuu4ceMUFhbWrk1paal27tzpaZObmyun06nNmzd72nz88cdyOp3t2uzcuVOlpaWeNu+9955sNpvGjRvXpa/TLIZh6IEHHtCyZcv0wQcfaPDgwe0e5/x3P8Mw5Ha7OfddbOrUqSosLFRBQYHnNn78eM2aNUsFBQUaMmQI57+n6975ssGjbWnv4sWLjd27dxuPPPKIER0dbRw+fNjs0oJadXW1sWPHDmPHjh2GJOPpp582duzY4VkSvWDBAsPhcBjLli0zCgsLjW9961sdLq/LyMgwVq9ebWzfvt24/vrrO1xel5OTY2zcuNHYuHGjcemll3a4vG7q1KnG9u3bjdWrVxsZGRm9enndfffdZzgcDiM/P98oLS313E6fPu1pw/nvOvPmzTPWrVtnHDp0yPj000+N+fPnG1ar1XjvvfcMw+Dcd7dzV9MYBue/p+uzYcQwDOP3v/+9MXDgQCM8PNy4/PLLPUskcWFr1qwxJJ13mzNnjmEYrUvsfvaznxmpqamGzWYzJk+ebBQWFrb7HXV1dcYDDzxgJCQkGJGRkcatt95qFBUVtWtz8uRJY9asWUZsbKwRGxtrzJo1y6isrGzX5siRI8b06dONyMhIIyEhwXjggQeM+vr6rnz5purovEsyXnrpJU8bzn/X+fa3v+35/0VSUpIxdepUTxAxDM59d/v3MML579kshmEY5vTJAAAA9NE5IwAAIHgQRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgqv8HDSaE+v4ba+EAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -572,19 +581,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 1262.29 and h = 1.0534e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 1262.29, , mxstep steps taken before reaching tout.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  539.358 ms\n"
+      "Took  689.714 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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A4DzyKhokSUlRobKyFbzPEUYAADiP/NNhhCEa/yCMAABwHp09I8lRhBF/IIwAAHAeefSM+BVhBACA88iv7FjWm0wY8QvCCAAA59E5ZyQ5mj1G/IEwAgBAD+rcrZ5zaegZ8Q/CCAAAPejsFRkRZpMzxGZyNUMTYQQAgB4wedX/CCMAAPTgi/kihBF/IYwAANCDfPYY8TvCCAAAPWCYxv8IIwAA9KBzjxHCiP8QRgAAOAfDMNhjZAAQRgAAOIdTtW65W9tltUiJIwgj/kIYAQDgHDrniyS4QmUL4ivTX7iyAACcA5NXBwZhBACAc8ivYPLqQCCMAABwDnlMXh0QwzqMNLe269DJGrPLAAAEKHZfHRjDOow89b+f6ZanPtTT732m1rZ2s8sBAASY/ErmjAyEYRtGDMNQbnm9WtsNPbHxiP7xmW36/FSd2WUBAAJEU0ubTtY0SaJnxN+GbRixWCz6/bcu1u9uu0iRIcHam1+l+U99oJe25qq93TC7PACAyQqrGmUYUpg9SDHhdrPLGdKGbRiROgLJ1y4erY3/MkdXXRCrppZ2/fStA1r8wscqqmo0uzwAgInyz1jWa7FYTK5maBvWYaRTgitUL989Sz9fOFWhtiB9dLRcN/52i9buKpBh0EsCAMNRZxhJ4rRevyOMnGaxWLQ4I0XvPHSVLhkzQrXuVv3gjU+05NVdKq9zm10eAGCAcUDewCGMfMm42HC9sWS2fvSVVNmCLHr3QIlu+O0WvXvgpNmlAQAGUF45e4wMFMJIN4KsFn3vmon67/uv1OT4SJXXN+u7r+zSD/7rE9U0tZhdHgBgALAV/MAhjPQgLdGp/37gCt13zQRZLdLa3QX6ym+3aOvRMrNLAwD4kWEYXSawwr8II+fhCA7Sj78yWW8sydDYmDAVVTfp289/rJ+9dUCNzW1mlwcA8IPqxhbVulslMYF1IBBGemnG2Gi98+BV+ufLx0iSVm/N1fynPtDe/CpzCwMA+FznEE1cpEOh9iCTqxn6CCNeCHcE6xdfu1Av3T1Lo5wOHSur1zdWbtVvNh5WcyvbyQPAUMFpvQOLMNIHV0+K08aHr9bCixLV1m7o9+8d1c1PfaCPmEsCAEOC57TeKFbSDATCSB+5wmxa8U8X64+LLlFMuF1HS+u06PmP9b3XdqmQ3VsBYFDjgLyBRRjpp5svTNB7P7xGd85OkdUivZNzUnN/k6Wn3/tMTS1McAWAwciz+yphZEAQRnzAFWrTz746VX978CrNGhetppZ2PbHxiG747Rb976clZpcHAPASe4wMLMKID01JcOo//8/lWvFPF2mU06G8igbd89JO3fXidh0vqze7PABAL7S1GypkK/gBRRjxMYvFooUXjdZ7P7hGS66eIFuQRe8fPqUbf7tFv373kBqaW80uEQDQg+LqRrW2G7IFWTTKGWJ2OcOCV2Fk5cqVSk9Pl9PplNPpVEZGhtavX9/ja1577TVNnz5dYWFhSkhI0F133aXy8vJ+FT0YhDuC9W83TdaGh+dozqQ4Nbe16w/vf665v9ms/9lXxGnAABCg8s44rTfIajG5muHBqzCSlJSk5cuXa+fOndq5c6euu+46LVy4UAcOHOi2/Ycffqjbb79d99xzjw4cOKA33nhDO3bs0He+8x2fFD8YTIiL0Et3Xao/LZ6hpKhQFVc36YHX9+jbz32sIyW1ZpcHAPiSgtN7jCQzRDNgvAojCxYs0M0336xJkyZp0qRJevzxxxUREaHs7Oxu22dnZyslJUUPPvigxo0bpyuvvFLf/e53tXPnTp8UP1hYLBbdMDVef3/kav3LvElyBFu17Vi5blrxgf7v2wc5fA8AAgh7jAy8Ps8ZaWtr05o1a1RfX6+MjIxu28yePVsFBQV65513ZBiGSkpK9Je//EXz58/vc8GDWYgtSA/Nu0B/f+Rq3Th1lNraDb3w0XFd90SW/rKrQO3tDN0AgNnYY2TgeR1GcnJyFBERIYfDoSVLlmjdunVKS0vrtu3s2bP12muv6bbbbpPdbld8fLxGjBih3//+9z2+h9vtVk1NTZfbUJIcHaZnF8/Uy3fP0vi4cJXVNeuHb3yir/3xI205cor5JABgIk/PCGFkwHgdRlJTU7V3715lZ2frvvvu0x133KGDBw922/bgwYN68MEH9R//8R/atWuXNmzYoOPHj2vJkiU9vkdmZqZcLpfnlpyc7G2Zg8KcSXHa8NAcLb1pssLtQdpXUK3bX9iu257N1sfHhv4kXwAIRPnsMTLgLEY//xk+b948TZgwQc8+++xZzy1evFhNTU164403PI99+OGHuuqqq1RUVKSEhIRuf6bb7Zbb7fbcr6mpUXJysqqrq+V0OvtTbsAqq3NrZdbneiX7hOfQvasuiNUj10/SxWOiTK4OAIaHhuZWpf3Hu5KkT356g1yhNpMrGtxqamrkcrnO+/0d3N83MgyjS3A4U0NDg4KDu75FUFCQ53Xn4nA45HA4+lvaoBIb4dBPbknTvVeN19Pvf6b/3JGvDz4r0weflWnelJH6l+snaWqiy+wyAWBI6zyt1xkSTBAZQF4N0yxbtkwffPCBcnNzlZOTo0cffVRZWVlatGiRJGnp0qW6/fbbPe0XLFigN998UytXrtSxY8f00Ucf6cEHH9SsWbOUmJjo208yRMS7QvSLr12o935wjb45I0lWi/T3T0s1/6kPdf9ru3W0lOXAAOAvnm3gYxiiGUhe9YyUlJRo8eLFKi4ulsvlUnp6ujZs2KDrr79eklRcXKy8vDxP+zvvvFO1tbV6+umn9YMf/EAjRozQddddp1/+8pe+/RRDUHJ0mH79zelacs0Erfj7Z3p7X5H+llOs9fuL9bWLRuuheRdobEy42WUCwJDCfBFz9HvOyEDo7ZjTUHboZI1+u+mI3j3QcfBesNWib85M1vevm6jEEayFBwBf+NlbB7R6a66+O2e8lt48xexyBr3efn9zNs0gMTneqWcXz9RbD1yha1Lj1Npu6M/b83TNr7P0s7cOqLS2yewSAWDQK6hkWa8ZCCODTHrSCK2+a5b+siRDl4+PVnNbu1ZvzdWcX72vzPWfqrK+2ewSAWDQymOYxhSEkUFqZkq0/nzv5XrtO5fp4jEj1NTSrmc3H9Ps5e/p3/+ao6OldWaXCACDimEYntU09IwMrH4v7YV5LBaLrpgYq9kTYpR1+JR+s+mw9hfW6NXsPL2anac5k+J01+wUXT0pTlZOngSAHpXVNauxpU0WizSauXgDijAyBFgsFl07eaSuSY3TtmPlWv1RrjZ9WqItR05py5FTGhcbrjsyxuofZyYrwsFvOQB0p3OIJsEZInswAwcDiW+mIcRisWj2hFjNnhCr/IoGvbwtV2t25Ot4Wb1+9vZBPbHxiL45M0l3zk5hWTAAfEk+Z9KYhug3RCVHh+nR+WnKXjpXP184VePjwlXnbtWLH+XqmieydM/qHfrwszIO5QOA09hjxDz0jAxx4Y5gLc5I0aLLxuqDo2Va/dFxvX/4lP73UKn+91CpLhgZoTuvSNHXL05SqD3I7HIBwDSc1msewsgwYbVadPWkOF09KU7HTtXppa25+suuAn1WWqdH1+3XrzYc1j9dmqzFGWOVFMX/iACGn/xKekbMwjDNMDQ+LkKPLZymbcvm6ie3pGlMdJiqG1v07JZjmvOr97XklV36+8ESz+nBADAcsKzXPPSMDGPOEJvuuXKc7pydovcPlWr11lx9eLRMGw6c1IYDJzUizKabL0zQwumJujQlmuXBAIas5tZ2FVV3hhGW9Q40wggUZLVoXtoozUsbpSMltfrPHfl6+5Milda69frHeXr94zyNHhGqBdMTtfCiRE1JGJ7nAwEYuoqqGmUYUojNqrgIh9nlDDsclIdutbUbyj5Wrr/uKdSG/SdV6271PJc6KlJfvagjmDC/BMBQsOXIKd3+wnZdMDJCmx652uxyhozefn/TM4JuBVk7dne9YmKsfv61aXr/UKn+e2+R3jtUqsMltfr1u4f163cPa+bYKC28eLTmX5ig6HC72WUDQJ9wJo25CCM4rxBbkG66MEE3XZig6sYWbdhfrP/eW6Rtx8q180Sldp6o1GNvHdCcSXFaeFGirk8bpTA7f7QADB75nNZrKr4x4BVXqE23XTpGt106Rierm/T2J0X6708Ktb+wRu8dKtV7h0oVZg/SDWmjdOPUeF1xQaycITazywaAHrH7qrkII+izeFeI7p0zXvfOGa+jpbV6a2+R/rq3SHkVDfrr6V8HWy2aMTZK104eqWtTR2rSqAhZLKzKARBYOpf1MkxjDiawwqcMw9De/Cr9z75ivX+4VMdO1Xd5PsEVomtSR+ra1DhdMTFW4RzcByAATH9so6obW/Tuw3OUGh9pdjlDRm+/vwkj8Ku88gZlHSnV+4dKtfXzcrnP2EjNFmTRrHHRujZ1pK5JHakJceH0mgAYcNWNLZr+2EZJ0oHHbuQfST5EGEHAaWppU/axcmUdPqX3DpV6Zq93SooK1bWpI3Xt5DhljI/lrBwAA2J/YbVu+f2Hio2wa+e/X292OUMKS3sRcEJsQbrmdC/ITxek6XhZvbIOn9L7h0v18bEKFVQ26pXsE3ol+4TswVZdPj5GV06M0aUp0Zo22iVbEKcXAPC9zsmr7JtkHsIITGGxWDQ+LkLj4yJ095Xj1NDcqq1Hy/X+4VJlHT6lwqpGbTlySluOnJLUsSvixclRunRctC5NidIlY6LoSgXgE+wxYj7+NkdACLMHe7akNwxDR0vrlHX4lD4+XqGdJypU1dCibcfKte1YuaSOTdnSEpy6NKUjnMxMiVZcJFs4A/Aep/WajzCCgGOxWHTBqEhdMCpS984Zr/Z2Q5+fqtP23ArtzK3U9uMVKqxqVE5htXIKq/XCR8clSeNiwz3BZFZKtMbGhDEhFsB55VVwQJ7ZCCMIeFbrF+Fk0WVjJXUcarXjdDjZkVuhwyW1Ol5Wr+Nl9fqvnQWSpLhIh2dIZ3ryCE1NdLIzLICzFLDhmen4mxmDUuKIUC28aLQWXjRaklTd0KJdeRXakVupHccrtK+gWqdq3Xon56TeyTkpSbJapEmjIpWe5NKFSSM0PcmlyfFO2YOZGAsMV23thgoq2fDMbIQRDAmuMJuumzxK100eJaljGfG+gmrtyK3Qnrwq7SuoUmmtW4dO1urQyVpP74k9yKrJCR0BJT1phNKTXLpgZKSCrAzvAMNBSU2TmtvaFWy1KMHFMI1ZCCMYkkJsQZo1LlqzxkV7HjtZ3aR9BVXaV1CtTwqqlFNYraqGFu0rqNa+gmpJeZKkUFuQpo126sLRIzQ9uSOkpDD/BBiSOpf1jo4K5R8hJiKMYNiId4Uo3hWvG6bGS+rYuj6/otETTD7Jr9L+wmrVN7d1DPfkVnpe6wq16bJx0Zo9IUazJ8bqgpGcsQMMBSzrDQyEEQxbFotFY2LCNCYmTAumJ0rqGD8+XlanT/Krta+gSp8UVOtgcY2qG1u08WCJNh4skSTFRtiVMSG2I5xMiNGYaHpOgMGIDc8CA2EEOEOQ1aKJIyM1cWSkvjEjSZLU0tau/YXVHfucfF6uHbkVKqtr1tufFOntT4okSaNHhOry8TGne05iGHsGBol8Jq8GBMIIcB62IKsuHhOli8dE6XvXTJS7tU1786q09fOOcLInv1KFVY1au7tAa3d3TIwdFxuujNO9JpePj1FsBBuyAYEoz7Osl39AmIkwAnjJERyky8bH6LLxMfqX66WG5lbtzK3UtmPl2vp5uXIKqjx7nrz+ccek2MnxkcqYEKNvXJKkaaNdJn8CAJ3ymTMSEAgjQD+F2YM1Z1Kc5kyKkyTVNLVo+7EKbf28XFs/L/MsJz50slYvfpSry8dH696rxuva1JGyMnsfME1jc5tKa92SCCNmI4wAPuYMsXnO2ZGk8jq3so9VaMOBk3onp1jZxyqUfaxC4+PCdc+V4/SNS5IUYgsyuWpg+Ck4fSZNpCNYrlCbydUMb2w9CfhZTIRD89MT9PtvXawPfnSt/s+c8Yp0BOvYqXo9um6/Zi9/T09uOqJTp/+FBmBgdB6Ql8xqONMRRoABlDgiVMtunqJty+bqJ7ekafSIUFXUN+up//1MV/zyPf34L/v0WUmt2WUCw0JeOfNFAgVhBDBBhCNY91w5Tpv/9Rr94duX6KLkEWpubdd/7szX9b/dojtf3K4PPyuTYRhmlwoMWZzWGziYMwKYKDjIqvnpCbr5wnjtzqvUc1uO692DJ5V1+JSyDp/SlASnvnPlOC2YnsiBfoCPdQ7T0DNiPv52AwKAxWLRjLHRembxDGX98BrdOTtFYfYgfVpcox+88Ymu/OV7+sP7R1XV0Gx2qcCQ4dl9lTBiOsIIEGDGxoTrZ1+dqm3/Nlc/+kqqRjkdKq1169fvHlZG5nv697/m6PBJ5pUA/dFxNhU9I4HCqzCycuVKpaeny+l0yul0KiMjQ+vXrz9n+zvvvFMWi+Ws29SpU/tdODDUucJs+t41E/XBj67Tk7dO15QEpxpb2vRqdp5u/N0W3frMNr39SZGaW9vNLhUYdCrqm1Xf3CaLpeM4B5jLqzkjSUlJWr58uSZOnChJeumll7Rw4ULt2bOn24CxYsUKLV++3HO/tbVV06dP1ze/+c1+lg0MH/Zgq75+SZL+4eLR2nasXK9mn9C7B0q0PbdC23MrFBvh0LdmJetbs8Yokb9UgV7p3AZ+VGQI+/wEAIvRz+n60dHR+vWvf6177rnnvG3/+te/6utf/7qOHz+usWPH9vo9ampq5HK5VF1dLafT2Z9ygSHhZHWT1uzI0+sf53l2kLRapOvTRmnx5SmaPSGG3V2BHrz1SZEe/PMezUqJ1n8tyTC7nCGrt9/ffV5N09bWpjfeeEP19fXKyOjdb+SqVas0b9688wYRt9stt/uLDaBqamr6WiYwJMW7QvTwvEm6/9qJ2nSwRK9sO6Ftx8r17oESvXugRONjw7Xo8rH6x0uS5ApjZ0ngy/IrvtjwDObzOozk5OQoIyNDTU1NioiI0Lp165SWlnbe1xUXF2v9+vV6/fXXz9s2MzNTjz32mLelAcOOLciqmy9M0M0XJuizklq99nGe1u4q0LGyev38fw7q1+8e0sLpo7U4YywH9AFn6NzwjD1GAoPXwzTNzc3Ky8tTVVWV1q5dq+eff16bN28+byDJzMzUb37zGxUVFclut/fYtruekeTkZIZpgF6od7fqr3sL9cq2Ezp0xqqbi8eM0OLLx+rmCxMYI8ew9+3nsrX183I9eet0ff2SJLPLGbJ6O0zT7zkj8+bN04QJE/Tss8+es41hGJo0aZJuueUW/fa3v/X6PZgzAnjPMAztOlGpV7JP6J2cYrW0dfyvHhVm0y3pibooeYTSk1waHxehIOaXYJi58pfvqaCyUX9ZkqGZKdFmlzNk+X3OSCfDMLr0YnRn8+bNOnr0aK8muQLwDYvFopkp0ZqZEq1/n5+m/9qZr9eyT6ioukmvZJ/QK9knJElh9iBNTXTqwtEd4WTaaJfGx4YzARZDVktbu4qrmyQxZyRQeBVGli1bpptuuknJycmqra3VmjVrlJWVpQ0bNkiSli5dqsLCQr388stdXrdq1SpddtllmjZtmu8qB9BrcZEO3X/tRC25eoKyDpdq6+flyimo1v6iajU0t2lHbqV25FZ62kc4gk8HFJcuTHIpPWmExkaHEVAwJBRXNamt3ZAj2Kq4CIfZ5UBehpGSkhItXrxYxcXFcrlcSk9P14YNG3T99ddL6pikmpeX1+U11dXVWrt2rVasWOG7qgH0SZDVorlTRmnulFGSpLZ2Q8fL6rSvoFr7CqqVU1itA0XVqnO36uPjFfr4eIXntZGOYE0b7fL0nkxNdGp0VKgcwcw/weDSucdIUlQoATtA9HvOyEBgzggwcFrb2vX5qXrtK6hSTmFHQDlYVCP3OXZ6jYt0KHFEqEaPCNHoEaFKPH0bffo2Iswmi4W/8BE4/rw9T0vfzNG1qXF68a5ZZpczpA3YnBEAQ0twkFWp8ZFKjY/UN2cmS+oYY/+spE77C6u1r7BKOQXVOlxSq6aWdp2qdetUrVuf5Hf/88LsQWcElLMDS1ykg9U9GFB5nEkTcAgjAM7LFmRVWqJTaYlO3XppR0AxDEOVDS0qqmpUQWWjiqo6boWe/zaprM6thuY2HS2t09HSunP+/AhHsOIiHYqNsCs2wvHFLfKL+yMjO/4baie4oH/y2PAs4BBGAPSJxWJRdLhd0eH2c26o1tTSpuLqpo5wUnlmUDkdXqqb1Nzarjp3q+rcrTpeVn/e9w23Byk2sjOwdISVqDC7nKHBcobY5Aq1yRlqkzPE5nksMiRYwUEcUo4OBYSRgEMYAeA3IbYgjYsN17jY8G6fNwxDNU2tKqtzq6zWrbK6Zp2qbVJZXXPHY3VunaprPv2cW+7WdtU3t6m+vEEnTu+g2VsRjmA5Q4LPCCpfhJWOx4IVGRKsyBCbIhxf/Dry9OOhtiDmvgwRDNMEHsIIANNYLBa5Qjt6MybERfTY1jAM1blbPUHl1OmAUlbrVnVji2qaWlXT2KKappaO+42tqmlqUUNzmyR5el+KTu8v4a0gq8UTUjqCjU0RngATrAhHx+eICbcrJsKumAiHYsLtDC0FmNqmFlU2tEiiZySQEEYADAoWi+V0T4XtnD0t3Wlpa1ft6aBSfTqsdAaVM8NLbVOraptaVdfU8VxtU0d4qW1qUbvRsQy6+vTP8FaYPagjoIR3DC3FhDs8gaXrfbuiw+wMKflRfkWjJCk63K4IB1+BgYLfCQBDmi3I6pnb0heGYaixpe10WPkitHSEla73qxqbVVHfrPK6ZpXXuVVW36zm1nY1NLepoaLR80XYkyCrRdOTXLpyYqyumBiri8dEyR5MOPEVz+TVKA7ICySEEQDogcViUZg9WGH2YI1yhnj12s6hpfK6ZpXXdwSUzv+WnflYXbPK692qqG9WW7uh3XlV2p1XpafeO6pQW5AuGx/tCSepoyLZqKsfCiqZvBqICCMA4CdnDi2l9GJoqa3dUFFVo7Z9Xq4Pj5Zp6+dlKqtrVtbhU8o6fEqSFBNu1+yJsbpyYoyumBirpCi+VL3B5NXARBgBgAARZLUoOTpMydFhuvXSZBmGocMltfrwszJ9dLRMHx+vUHl9s97+pEhvf1IkSUqJCdMVp3tNMsbHKKqPw1HDBXuMBCbCCAAEKIvFosnxTk2Od+o7V41Xc2u79uZX6cOjHeFkb36VcssblFuep9c+zpPFIk1LdOmKibFadNkYvnC7kU/PSEAijADAIGEPtmrWuGjNGhetR66fpNqmFm0/XuEJJ0dK6jznCa3dXaC1S2ZrTAxfup3a2w3lV3ZMIiaMBBbCCAAMUpEhti6nMJfWNOmjz8u0MutzHSmp0+0vfKy/3DdbsREOkysNDKfq3GpubVeQ1aIEl3eTkeFfrBcDgCFipDNE/3Bxkl695zIlRYUqt7xBd724Q3XuVrNLCwid80USR4Swl0uA4XcDAIaYkc4QvXLPZYoJtyunsFpLXtml5tZ2s8syXV555x4jDNEEGsIIAAxB42LD9eJdlyrMHqQPj5bpB298ovZ2w+yyTJVfyeTVQEUYAYAhKj1phJ5dPEO2IIve/qRI//d/Dsowhm8gYVlv4CKMAMAQdtUFcXrim9MlSau35uqPWZ+bXJF58gkjAYswAgBD3MKLRuunC9IkSb9+97D+c0eeyRWZo/NsIIZpAg9hBACGgbuuGKfvXTNBkrT0zRxtOlhickUDq6mlTSdrmiQRRgIRYQQAhol/vTFVt85MUrshPfD6bu3IrTC7pAFTcHqzs3B7kKLCbCZXgy8jjADAMGGxWPT//uFCzZsyUu7Wdt2zeocOn6w1u6wBkX/Gab0WC6ceBxrCCAAMI8FBVv3+W5do5tgo1TS16vYXPlbB6S/qoYwzaQIbYQQAhplQe5Cev2OmJo2KUEmNW7e/sF0V9c1ml+VXrKQJbIQRABiGRoTZ9dLds5ToCtGxU/W6a/UONTQP3W3j8+gZCWiEEQAYphJcoXr5nlkaEWbTJ/lVuu/V3WppG5rbxuedXtabHB1qciXoDmEEAIaxiSMj9eKdlyrUFqTNR07pR3/ZN+S2jTcMQwX0jAQ0wggADHMXj4nSH//5EgVZLVq3p1CZ6z81uySfqmpoUe3pk4uTOCQvIBFGAAC6NnWkfvWNdEnScx8c15+2DJ1t4zvni4yMdCjEFmRyNegOYQQAIEn6xowkLbt5siTp/71zSGt3FZhckW8UVnXOF6FXJFARRgAAHv9nzgTde9U4SdJP/nu/3K1tJlfUf82tHZNyw+z0igQqwggAoIulN02RK9SmhuY2HS2tM7scDAOEEQBAF1arRWkJTknSgaIak6vBcEAYAQCcJS2xI4wcJIxgABBGAABn6ewZOVhMGIH/EUYAAGfp7Bn5tKhGhjG0NkFD4CGMAADOMiEuQvYgq2rdrSqobDS7HAxxhBEAwFnswVZdMCpCEpNY4X+EEQBAt5g3goFCGAEAdIsVNRgohBEAQLc6e0Y+pWcEfuZVGFm5cqXS09PldDrldDqVkZGh9evX9/gat9utRx99VGPHjpXD4dCECRP0wgsv9KtoAID/TTndM1JY1aiqhmaTq8FQFuxN46SkJC1fvlwTJ06UJL300ktauHCh9uzZo6lTp3b7mltvvVUlJSVatWqVJk6cqNLSUrW2tva/cgCAXzlDbEqODlV+RaMOFtdo9oRYs0vCEOVVGFmwYEGX+48//rhWrlyp7OzsbsPIhg0btHnzZh07dkzR0dGSpJSUlL5XCwAYUGkJzo4wUkQYgf/0ec5IW1ub1qxZo/r6emVkZHTb5q233tLMmTP1q1/9SqNHj9akSZP0wx/+UI2NPa9Zd7vdqqmp6XIDAAy8tASXJFbUwL+86hmRpJycHGVkZKipqUkRERFat26d0tLSum177NgxffjhhwoJCdG6detUVlam733ve6qoqOhx3khmZqYee+wxb0sDAPjYVFbUYAB43TOSmpqqvXv3Kjs7W/fdd5/uuOMOHTx4sNu27e3tslgseu211zRr1izdfPPNevLJJ7V69eoee0eWLl2q6upqzy0/P9/bMgEAPtC5vPdoaZ3crW0mV4OhyuswYrfbNXHiRM2cOVOZmZmaPn26VqxY0W3bhIQEjR49Wi6Xy/PYlClTZBiGCgoKzvkeDofDs2Kn8wYAGHgJrhCNCLOptd3QZyV1ZpeDIarf+4wYhiG3293tc1dccYWKiopUV/fFH+AjR47IarUqKSmpv28NAPAzi8XCTqzwO6/CyLJly/TBBx8oNzdXOTk5evTRR5WVlaVFixZJ6hheuf322z3tv/3tbysmJkZ33XWXDh48qC1btuhf//Vfdffddys0NNS3nwQA4BeeMMK8EfiJVxNYS0pKtHjxYhUXF8vlcik9PV0bNmzQ9ddfL0kqLi5WXl6ep31ERIQ2bdqk73//+5o5c6ZiYmJ066236he/+IVvPwUAwG8828LTMwI/8SqMrFq1qsfnV69efdZjkydP1qZNm7wqCgAQODrDyKdFNTIMQxaLxeSKMNRwNg0AoEcT4iJkD7Kq1t2qgsqe94kC+oIwAgDokS3IqknxEZKkA8wbgR8QRgAA58WKGvgTYQQAcF6sqIE/EUYAAOeVltixeeWn9IzADwgjAIDzmpwQKUkqrGpUVUOzydVgqCGMAADOyxli05joMEnMG4HvEUYAAL3CvBH4C2EEANArnp1YCSPwMcIIAKBXWN4LfyGMAAB6pbNn5GhpnZpa2kyuBkMJYQQA0CsJrhCNCLOptd3Q0dI6s8vBEEIYAQD0isViYRIr/IIwAgDoNeaNwB8IIwCAXmNFDfyBMAIA6DVPGCmuUXu7YXI1GCoIIwCAXpsQFyF7kFV17lYVVDaaXQ6GCMIIAKDXbEFWTYqPkCQdLK42uRoMFYQRAIBXWFEDXyOMAAC8wooa+BphBADglamjXZLoGYHvEEYAAF6ZHB8pSSqqblJlfbPJ1WAoIIwAALwSGWLT2JgwSdKnDNXABwgjAACvMW8EvkQYAQB4jRU18CXCCADAa2fuxAr0F2EEAOC1zjBytLROTS1tJleDwY4wAgDwWrwzRFFhNrW2GzpaWmd2ORjkCCMAAK9ZLBZO8IXPEEYAAH3Cihr4CmEEANAn9IzAVwgjAIA+SUs4vS18cY3a2w2Tq8FgRhgBAPTJ+Lhw2YOtqnO3qqCy0exyMIgRRgAAfWILsip1VMc5NQeLq02uBoMZYQQA0Gedk1gPMG8E/UAYAQD0GZNY4QuEEQBAn7EtPHyBMAIA6LPJ8R1zRoqrm1RR32xyNRisCCMAgD6LDLFpbEyYJOlTekfQR4QRAEC/eHZiZd4I+ogwAgDoF7aFR395FUZWrlyp9PR0OZ1OOZ1OZWRkaP369edsn5WVJYvFctbt0KFD/S4cABAYWFGD/gr2pnFSUpKWL1+uiRMnSpJeeuklLVy4UHv27NHUqVPP+brDhw/L6XR67sfFxfWxXABAoOkMI0dP1amppU0htiCTK8Jg41UYWbBgQZf7jz/+uFauXKns7Owew8jIkSM1YsSIPhUIAAhs8c4QRYXZVNnQos9K6nRhksvskjDI9HnOSFtbm9asWaP6+nplZGT02Pbiiy9WQkKC5s6dq/fff/+8P9vtdqumpqbLDQAQmCwWyxn7jbAtPLzndRjJyclRRESEHA6HlixZonXr1iktLa3btgkJCfrTn/6ktWvX6s0331Rqaqrmzp2rLVu29PgemZmZcrlcnltycrK3ZQIABhAratAfXg3TSFJqaqr27t2rqqoqrV27VnfccYc2b97cbSBJTU1Vamqq535GRoby8/P1xBNPaM6cOed8j6VLl+qRRx7x3K+pqSGQAEAAm5rYMTTDihr0hddhxG63eyawzpw5Uzt27NCKFSv07LPP9ur1l19+uV599dUe2zgcDjkcDm9LAwCYpHOY5tPiWrW3G7JaLSZXhMGk3/uMGIYht9vd6/Z79uxRQkJCf98WABBAxseGyx5sVZ27VfmVDWaXg0HGq56RZcuW6aabblJycrJqa2u1Zs0aZWVlacOGDZI6hlcKCwv18ssvS5J+97vfKSUlRVOnTlVzc7NeffVVrV27VmvXrvX9JwEAmCY4yKrJ8ZHaV1Ctg0U1GhsTbnZJGES8CiMlJSVavHixiouL5XK5lJ6erg0bNuj666+XJBUXFysvL8/Tvrm5WT/84Q9VWFio0NBQTZ06VX/729908803+/ZTAABMl5bg7AgjxTW66UJ6wNF7XoWRVatW9fj86tWru9z/0Y9+pB/96EdeFwUAGHx8vRNrY3Ob1u0p1HuHStTSZvT555TUNPmkHviP1xNYAQDojq/OqCmubtQr207o9e15qmpo8UVpkqTYCBZGBCrCCADAJyafDiPF1U2qqG9WdLjdq9fvyavUCx/lan1OsVrbO3pCkqJC9e3LxmhkZEi/arMFWXTNpJH9+hnwH8IIAMAnIhzBSokJU255gz4trtEVE2PP+5qWtnZt2H9SL3x0XHvyqjyPXzYuWndfOU7zpoxSEMuEhzzCCADAZ9ISncotb9DBop7DSGV9s/68I08vbz2hk6fndNiDrPrqRYm664oUzyZqGB4IIwAAn0lLcOqdnJPnnDfyWUmtXtyaqzd3F6ippV2SFBth1z9fPlaLLhuruEjmdQxHhBEAgM90t6Kmvd3Q5s9O6cWPcrXlyKkv2iY4dfeV47RgeoIcwUEDXisCB2EEAOAzaQkdwytHT9WpuqFFb+0r0osfHdexU/WSJItFuiFtlO6+YpxmjYuWxcJ8EBBGAAA+NMrpUHS4XRX1zbrhd5tVUtNxXEikI1i3XpqsOzJSNCYmzOQqEWgIIwAAn7FYLEpLcOrDo2WeIHLfNRN0/7UTFeHgKwfd6/dBeQAAnKlz3kinqyfFEUTQI8IIAMCnOndiBXqLMAIA8Kkv94wA50MYAQD41PjYcNmD+XpB7/GnBQDgU8FBVk2OjzS7DAwihBEAgM8xbwTeIIwAAHyOeSPwBmEEAOBz9IzAG4QRAIDPTT4jjFTUN5tYCQYDwggAwOfO3ORsRJjNxEowGLAlHgDAL/724JU6UFijjPExZpeCAEcYAQD4xdREl6YmuswuA4MAwzQAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATDUoTu01DEOSVFNTY3IlAACgtzq/tzu/x89lUISR2tpaSVJycrLJlQAAAG/V1tbK5XKd83mLcb64EgDa29tVVFSkyMhIWSwWn/3cmpoaJScnKz8/X06n02c/F73D9TcX199cXH9zcf0HhmEYqq2tVWJioqzWc88MGRQ9I1arVUlJSX77+U6nkz+MJuL6m4vrby6uv7m4/v7XU49IJyawAgAAUxFGAACAqYZ1GHE4HPrpT38qh8NhdinDEtffXFx/c3H9zcX1DyyDYgIrAAAYuoZ1zwgAADAfYQQAAJiKMAIAAExFGAEAAKYa1mHkj3/8o8aNG6eQkBDNmDFDH3zwgdklBbwtW7ZowYIFSkxMlMVi0V//+tcuzxuGoZ/97GdKTExUaGiorrnmGh04cKBLG7fbre9///uKjY1VeHi4vvrVr6qgoKBLm8rKSi1evFgul0sul0uLFy9WVVVVlzZ5eXlasGCBwsPDFRsbqwcffFDNzc3++NgBITMzU5deeqkiIyM1cuRIfe1rX9Phw4e7tOH6+8/KlSuVnp7u2SQrIyND69ev9zzPtR84mZmZslgsevjhhz2Pcf0HOWOYWrNmjWGz2YznnnvOOHjwoPHQQw8Z4eHhxokTJ8wuLaC98847xqOPPmqsXbvWkGSsW7euy/PLly83IiMjjbVr1xo5OTnGbbfdZiQkJBg1NTWeNkuWLDFGjx5tbNq0ydi9e7dx7bXXGtOnTzdaW1s9bb7yla8Y06ZNM7Zu3Wps3brVmDZtmnHLLbd4nm9tbTWmTZtmXHvttcbu3buNTZs2GYmJicYDDzzg92tglhtvvNF48cUXjf379xt79+415s+fb4wZM8aoq6vztOH6+89bb71l/O1vfzMOHz5sHD582Fi2bJlhs9mM/fv3G4bBtR8o27dvN1JSUoz09HTjoYce8jzO9R/chm0YmTVrlrFkyZIuj02ePNn4t3/7N5MqGny+HEba29uN+Ph4Y/ny5Z7HmpqaDJfLZTzzzDOGYRhGVVWVYbPZjDVr1njaFBYWGlar1diwYYNhGIZx8OBBQ5KRnZ3tabNt2zZDknHo0CHDMDpCkdVqNQoLCz1t/vznPxsOh8Oorq72y+cNNKWlpYYkY/PmzYZhcP3NEBUVZTz//PNc+wFSW1trXHDBBcamTZuMq6++2hNGuP6D37AcpmlubtauXbt0ww03dHn8hhtu0NatW02qavA7fvy4Tp482eW6OhwOXX311Z7rumvXLrW0tHRpk5iYqGnTpnnabNu2TS6XS5dddpmnzeWXXy6Xy9WlzbRp05SYmOhpc+ONN8rtdmvXrl1+/ZyBorq6WpIUHR0ties/kNra2rRmzRrV19crIyODaz9A7r//fs2fP1/z5s3r8jjXf/AbFAfl+VpZWZna2to0atSoLo+PGjVKJ0+eNKmqwa/z2nV3XU+cOOFpY7fbFRUVdVabztefPHlSI0eOPOvnjxw5skubL79PVFSU7Hb7sPg9NAxDjzzyiK688kpNmzZNEtd/IOTk5CgjI0NNTU2KiIjQunXrlJaW5vmi4tr7z5o1a7R7927t2LHjrOf4sz/4Dcsw0slisXS5bxjGWY/Be325rl9u0137vrQZqh544AHt27dPH3744VnPcf39JzU1VXv37lVVVZXWrl2rO+64Q5s3b/Y8z7X3j/z8fD300EPauHGjQkJCztmO6z94DcthmtjYWAUFBZ2VYktLS89KvOi9+Ph4SerxusbHx6u5uVmVlZU9tikpKTnr5586dapLmy+/T2VlpVpaWob87+H3v/99vfXWW3r//feVlJTkeZzr7392u10TJ07UzJkzlZmZqenTp2vFihVcez/btWuXSktLNWPGDAUHBys4OFibN2/WU089peDgYM/n5voPXsMyjNjtds2YMUObNm3q8vimTZs0e/Zsk6oa/MaNG6f4+Pgu17W5uVmbN2/2XNcZM2bIZrN1aVNcXKz9+/d72mRkZKi6ulrbt2/3tPn4449VXV3dpc3+/ftVXFzsabNx40Y5HA7NmDHDr5/TLIZh6IEHHtCbb76p9957T+PGjevyPNd/4BmGIbfbzbX3s7lz5yonJ0d79+713GbOnKlFixZp7969Gj9+PNd/sBvY+bKBo3Np76pVq4yDBw8aDz/8sBEeHm7k5uaaXVpAq62tNfbs2WPs2bPHkGQ8+eSTxp49ezxLopcvX264XC7jzTffNHJycoxvfetb3S6vS0pKMv7+978bu3fvNq677rpul9elp6cb27ZtM7Zt22ZceOGF3S6vmzt3rrF7927j73//u5GUlDSkl9fdd999hsvlMrKysozi4mLPraGhwdOG6+8/S5cuNbZs2WIcP37c2Ldvn7Fs2TLDarUaGzduNAyDaz/QzlxNYxhc/8Fu2IYRwzCMP/zhD8bYsWMNu91uXHLJJZ4lkji3999/35B01u2OO+4wDKNjid1Pf/pTIz4+3nA4HMacOXOMnJycLj+jsbHReOCBB4zo6GgjNDTUuOWWW4y8vLwubcrLy41FixYZkZGRRmRkpLFo0SKjsrKyS5sTJ04Y8+fPN0JDQ43o6GjjgQceMJqamvz58U3V3XWXZLz44oueNlx//7n77rs9f1/ExcUZc+fO9QQRw+DaD7QvhxGu/+BmMQzDMKdPBgAAYJjOGQEAAIGDMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAU/1/Np/AtKA9dEwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -626,12 +635,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  289.618 ms\n"
+      "Took  292.674 ms\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -689,7 +698,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -713,7 +721,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some functions, such as `minimum`, `maximum`, `heaviside`, and `abs`, are discontinuous and/or non-differentiable (their derivative is discontinuous). Adaptive solvers can deal with this discontinuity, but will take many more steps close to the discontinuity in order to resolve it. Therefore, using smooth approximations instead can reduce the number of steps taken by the solver, and hence the integration time. See [this post](https://discourse.julialang.org/t/handling-instability-when-solving-ode-problems/9019/5) for more details.\n",
+    "Some functions, such as `minimum`, `maximum`, `heaviside`, and `abs`, are discontinuous and/or non-differentiable (their derivative is discontinuous). Adaptive solvers can deal with this discontinuity, but will take many more steps close to the discontinuity in order to resolve it. Therefore, using soft approximations instead can reduce the number of steps taken by the solver, and hence the integration time. See [this post](https://discourse.julialang.org/t/handling-instability-when-solving-ode-problems/9019/5) for more details.\n",
     "\n",
     "Here is an example using the `maximum` function. The function `maximum(x,1)` is continuous but non-differentiable at `x=1`, where its derivative jumps from 0 to 1. However, we can approximate it using the [`softplus` function](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)#Softplus), which is smooth everywhere and is sometimes used in neural networks as a smooth approximation to the RELU activation function. The `softplus` function is given by\n",
     "$$\n",
@@ -734,7 +742,7 @@
      "output_type": "stream",
      "text": [
       "Exact maximum: maximum(x, y)\n",
-      "Softplus (k=10): 0.1 * log(exp(10.0 * x) + exp(10.0 * y))\n",
+      "Softplus (k=10):  0.1 * log(exp(10.0 * x) + exp(10.0 * y))\n",
       "Softplus (k=20): 0.05 * log(exp(20.0 * x) + exp(20.0 * y))\n",
       "Softplus (k=30): 0.03333333333333333 * log(exp(30.0 * x) + exp(30.0 * y))\n",
       "Exact maximum: maximum(x, y)\n"
@@ -746,22 +754,23 @@
     "y = pybamm.Variable(\"y\")\n",
     "\n",
     "# Normal maximum\n",
-    "print(\"Exact maximum:\", pybamm.maximum(x,y))\n",
+    "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Softplus\n",
-    "print(\"Softplus (k=10):\", pybamm.softplus(x,y,10))\n",
+    "print(f\"Softplus (k=10): \", pybamm.softplus(x,y,10))\n",
     "\n",
     "# Changing the setting to call softplus automatically\n",
-    "pybamm.settings.max_smoothing = 20\n",
-    "print(\"Softplus (k=20):\", pybamm.maximum(x,y))\n",
+    "pybamm.settings.min_max_mode = \"soft\"\n",
+    "pybamm.settings.min_max_smoothing = 20\n",
+    "print(f\"Softplus (k=20): {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# All smoothing parameters can be changed at once\n",
     "pybamm.settings.set_smoothing_parameters(30)\n",
-    "print(\"Softplus (k=30):\", pybamm.maximum(x,y))\n",
+    "print(f\"Softplus (k=30): {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Change back\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")\n",
-    "print(\"Exact maximum:\", pybamm.maximum(x,y))"
+    "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")"
    ]
   },
   {
@@ -781,7 +790,7 @@
      "output_type": "stream",
      "text": [
       "Exact: 1.0\n",
-      "Softplus: 1.0341598589863317\n"
+      "Softplus: 1.0\n"
      ]
     }
    ],
@@ -809,17 +818,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x28223f940>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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jS3OYkht0MpMh2baMHpZM+zarJbctPSFv37TTue0Xy07LbctOv6bSoqKi6NChA/Xr16dLly507tyZPn36EBgYSFJSEidOnKB169Z2fVq3bs3WrVuv6XwtW7bM83ratGnXdCyAUaNG8fDDD7Ns2TI6duxI7969adCgwWX3T09PzxmluXh7mzZtGDBgANOnT8/TXr169auuqWbNmtSsWTPndcuWLTl69ChTpkyxCzrnw1FaWtpVH1tERESkKEvY8jW3b5lHivM+XKvu5i234Xx6Sx0a3dLM0aU5VMmduubuC75htoezu32bk3Num2dA3r5eZXPbL+bqldvm6pm3/So4OzuzfPlyfvrpJ+rUqcOMGTOoWbOm3WpgF45sABiGkWfb9bjcsZycnHLOd152drbdPsOGDePgwYMMGjSI7du307Rp0yte91O2bFnOnTuXZ7u7uzsdO3Zk6dKlHDt2LE/7tUxdu1CLFi1ypvudd/bsWQDKlSt3xb4iIiIixUHGL5MxFj2Mn/kgAE7OWfxfn+qlPuRASR7RafWo7XEpgRF5p6RdaODXl29rdI/tcZ1MJhOtW7emdevWPPfcc1SuXJlFixYxZswYwsLCWLt2rd1IxPr162ne/PJrnru5uWGxWC7Z9scff+R5XatWrUvuez4AxMbGEhgYCFx6Cll4eDgjRoxgxIgRjB8/no8//pjHHnvsksds1KgRO3fuzLPdycmJ2bNnM3DgQNq3b8+qVasIC8sNl/mZunYpW7ZsoXz58nbbduzYgaurK3Xr1r1iXxEREZGiLuXwbk5N+hQnUyBzbznMnRUaMrrNy/Su28rRpRUJJTfoFGEbN25kxYoVdO7cmeDgYDZu3MipU6eoXbs2AOPGjWPixIlUq1aNhg0bMnPmTKKjo5kzZ85ljxkREUFKSgorVqwgKioKLy8vvLy8AFi3bh2vv/46PXv2ZPny5cyfP5+lS5de8jjVq1cnPDyc559/nkmTJrFv3z6mTp1qt8/o0aPp2rUrkZGRnDt3jpUrV+bUfildunRh2LBhWCyWPKugOTs7M2fOHAYMGJATds5fZ5SfqWvTpk0jIiKCunXrkpWVxZdffsmCBQtYsGCB3X5r1qyhbdu2/xmSRERERIqylEwzv454npqnXAE4uL4eMz5bSMNKgQ6urOgouVPXijA/Pz9Wr15Nt27diIyM5Nlnn2Xq1Kk5U7FGjRrF2LFjGTt2LPXr1+fnn39myZIldveEuVirVq0YMWIE/fr1o1y5crz++us5bWPHjmXz5s00atSIl156ialTp9KlS5dLHsfV1ZW5c+eye/duoqKieO2115g0aZLdPhaLhZEjR1K7dm1uu+02atasyXvvvXfZ2rp164arqyu//vrrJdtdXFyYO3cudevWpX379sTHx1/2WJeTlZXFk08+SYMGDWjbti1r165l6dKl9OrVy26/uXPn8uCDD+b7+CIiIiJFxcmUBLrPepH3w9uS5uJOmqsHZce/rpBzEZNx4cUYRVRSUhL+/v4kJibi5+dn15aRkUFMTAxVqlS55AXvpV1ERASjR49m9OjRDq3jvffeY/Hixfzyyy8Oq2Hp0qWMGzeObdu24eJSuIOZel+KiIhIgTMM4g+u5/bf3yDD+QDZCU2ou78FL/RqRL1bb3J0dTfMlbLBhTR1TW6I4cOHc+7cOZKTk/H19XVIDampqcycObPQQ46IiIhIgTMMMn54mn3/zCIjNBgAV99djHz8aepVv/wlBKWZPvHJDeHi4sKECRMcWkPfvn0den4RERGRa5WybhanX11A3WrwmtNpxpetxCut36G9Qs5lKeiUcIcOHXJ0CSIiIiJyHc6dTeSv//ua8DNupJ9xJc65KTNfnErj8BBHl1akaTECEREREZEi6mjCGfrNXcRmn4oApLl70nLYGIWcq6ARHRERERGRosZq4Wj8IXr++BiZXnF8ctN94OnFXWOHUqtVQ0dXVywo6IiIiIiIFCUWM1kLRvDpyY1k+YEJ8Kq4kDuHf0et8lpC+mpp6pqIiIiISBGSOv9J4j/+laePHqNRqgksvrx9yzvUUcjJF43oiIiIiIgUEaePx7NjxlZCzniScc6V8I6389CIIbSOqOno0oodjeiIiIiIiBQBB8+e5OGZq7CmZwOQYPZlSJ97FHKukYJOMbV7925atGiBh4cHDRs2vK5jmUwmvvvuuwKp63L27NlDaGgoycnJAHz++ecEBAQU6jmvRZ8+fXjzzTcdXYaIiIiUJhYz+0+doPd3g9kRNJun297PnuCqBHw6ixpN6zm6umJLQaeYmjhxIt7e3uzZs4cVK1YU2eBw3oQJExg5ciS+vr6FcvzY2FgGDhxIzZo1cXJyYvTo0Zfcb8GCBdSpUwd3d3fq1KnDokWL7Nqfe+45Xn75ZZKSkgqlThERERE72elkzu7DY4vuxux8AhevQ2TXWkX9b7+mehOFnOuhoFNMHThwgDZt2lC5cmWCgoIcXc4VHTt2jCVLljB06NBCO0dmZiblypVjwoQJREVFXXKfDRs20K9fPwYNGsTWrVsZNGgQffv2ZePGjTn7NGjQgIiICObMmVNotYqIiIgAYLWQ9mFvUn7+kymn9uBqccZkDuDdbv9HteDC+XK4NFHQcZBvv/2W+vXr4+npSVBQEB07diQ1NRUAq9XKiy++SMWKFXF3d6dhw4b8/PPPOX1NJhObN2/mxRdfxGQyccsttzB06FASExMxmUyYTCaef/55ACIiInjppZcYOHAgPj4+hIWFMWPGjMvWtWrVKkwmEwkJCTnboqOjMZlMHDp0CIDDhw/TvXt3AgMD8fb2pm7duvz444+XPea8efOIioqiYsWKl93nzJkzNG/enB49epCRkXEVv0F7ERERTJ8+ncGDB+Pv73/JfaZNm0anTp0YP348tWrVYvz48XTo0IFp06bZ7dejRw/mzp2b7xpERERE8iPucCw7Z58iPtof/z99qHOqOx92/ITWlWs5urQSQUHHAWJjYxkwYAD3338/u3btYtWqVfTq1QvDMACYPn06U6dOZcqUKWzbto0uXbrQo0cP9u3bl9O/bt26jB07ltjYWJYsWcK0adPw8/MjNjaW2NhYnnzyyZzzvfHGGzRo0IC///6b8ePH88QTT7B8+fJrrn/kyJFkZmayevVqtm/fzmuvvYaPj89l91+9ejVNmza9bPuxY8do27YttWrVYuHChXh4eADg4+NzxUfXrl3zVfeGDRvo3Lmz3bYuXbqwfv16u23Nmzfnzz//JDMzM1/HFxEREblax84lMvWNr/FOTAHg+NFyTOozgpaVtfBAQSmxy0t/8c8XzNo5C4BX275Ks9BmOW3Hko9x38/3AdChUgf+76b/s+v72IrH2Hl2JwAr7l5h1/bd/u+YscU2IjK++Xg6Vu6Y79piY2Mxm8306tWLypUrA1C/fv2c9ilTpvD000/Tv39/AF577TV+++03pk2bxrvvvktoaCguLi74+PgQGhoKgL+/PyaTKef1hVq3bs0zzzwDQGRkJOvWreOtt96iU6dO+a4d4MiRI/Tu3Tun5qpVq15x/0OHDtGkSZNLtu3du5dOnTpx5513Mn36dEwmU05bdHT0FY/r6emZr7rj4uIICQmx2xYSEkJcXJzdtgoVKpCZmUlcXFzOn4+IiIhIQfnn5FHu+WEoqZVakdKwDwP3rSR85kwiqlVwdGklSokNOqnZqcSnxQOQZcmya7Ma1py2pKy8F52fzTyb036xdHN6Tlu6Of2aaouKiqJDhw7Ur1+fLl260LlzZ/r06UNgYCBJSUmcOHGC1q1b2/Vp3bo1W7duvabztWzZMs/ri6dr5ceoUaN4+OGHWbZsGR07dqR37940aNDgsvunp6fnjNJcvL1NmzYMGDCA6dOn52mvXr36Ndd4ORcGKQDDMPJsOx+g0tLSCvz8IiIiUoqlJ3B63hCGmM9icTmHR/lFbPYYxPipS6hUoWhfc10cldipa96u3gR7BRPsFYybs5tdm5PJKafNz80vT98y7mVy2i/m6eKZ0+bpkr8RhfOcnZ1Zvnw5P/30E3Xq1GHGjBnUrFmTmJiYnH2u5gP59bjcsZycnHLOd152drbdPsOGDePgwYMMGjSI7du307Rp0yte91O2bFnOnTuXZ7u7uzsdO3Zk6dKlHDt2LE97QU9dCw0NzTN6Ex8fn2eU5+zZswCUK1cuX8cXERERuay0s6TP6Ib35rXcmRILgMkcxPt391bIKSQldkTnvrr3cV/d+y7ZVtG3Yp4paRea0eHyH9p7Vu9Jz+o9r7c8TCYTrVu3pnXr1jz33HNUrlyZRYsWMWbMGMLCwli7di3t2rXL2X/9+vU0b978ssdzc3PDYrFcsu2PP/7I87pWrUtf5Hb+w31sbCyBgYHApaeQhYeHM2LECEaMGMH48eP5+OOPeeyxxy55zEaNGrFz5848252cnJg9ezYDBw6kffv2rFq1irCwsJz2gp661rJlS5YvX84TTzyRs23ZsmW0atXKbr8dO3ZQsWJFypYtm6/ji4iIiFzO8QPHSZyXgHNmEPcbGayJ7MirvZ+iUVgVR5dWYpXYoFOUbdy4kRUrVtC5c2eCg4PZuHEjp06donbt2gCMGzeOiRMnUq1aNRo2bMjMmTOJjo6+4pLHERERpKSksGLFCqKiovDy8sLLywuAdevW8frrr9OzZ0+WL1/O/PnzWbp06SWPU716dcLDw3n++eeZNGkS+/btY+rUqXb7jB49mq5duxIZGcm5c+dYuXJlTu2X0qVLF4YNG4bFYsHZ2dmuzdnZmTlz5jBgwICcsHP+OqP8Tl07H4xSUlI4deoU0dHRuLm5UadOHQAef/xx2rVrx2uvvcadd97J4sWL+fXXX1m7dq3dcdasWZNn0QIRERGRa3U8IZ2fx79JqxQDK07s+rsqn704lQqBXo4urWQzioHExEQDMBITE/O0paenGzt37jTS09MdUNm12blzp9GlSxejXLlyhru7uxEZGWnMmDEjp91isRgvvPCCUaFCBcPV1dWIiooyfvrpJ7tjREVFGRMnTrTbNmLECCMoKMgActoqV65svPDCC0bfvn0NLy8vIyQkxJg2bZpdP8BYtGhRzuu1a9ca9evXNzw8PIy2bdsa8+fPNwAjJibGMAzDePTRR41q1aoZ7u7uRrly5YxBgwYZp0+fvuzPazabjQoVKhg///xzzraZM2ca/v7+Oa+zs7ONXr16GbVr1zZOnjx5Fb/FvIA8j8qVK9vtM3/+fKNmzZqGq6urUatWLWPBggV27enp6Yafn5+xYcOGa6rhwuMUt/eliIiIFLzNxw8YDT+8y6j19KfGN626GquatjWO7YlxdFnF2pWywYVMhnHBxRhFVFJSEv7+/iQmJuLnZ39NTUZGBjExMVSpUuWSF7yXdhEREYwePZrRo0c7tI733nuPxYsX88svvzi0jv/y7rvvsnjxYpYtW3Zdx9H7UkREpJRLPM7elZO5++x2rC5nsWaVIezsw3zRuxVh1Ss5urpi7UrZ4EKauiY3xPDhwzl37hzJycn4+hbdO/26urpecWEFERERkf+UcJSMGd0IsByjTIUITgOuTi68dV9rwkLDHV1dqaGgIzeEi4sLEyZMcHQZ/2n48OGOLkFERESKudi/1pKyKBOPgACmtknhkYpRfNZzGnWCKzq6tFJFQaeEO3TokKNLEBERESk1Dscns//ZjwhLcyYlzZmt/7RkyZPvEeynqew3Wom9j46IiIiIyI204chues17gWn1e5Lh7EpcQChdp7yokOMgGtEREREREbke8bvZvG8dD+2ZieGbyN66rXi/zCheeqQbIRFh/91fCoWCjoiIiIjItTr5D9mfdCfWLROjXBkAvPximDDkFUICgxxcXOmmqWsiIiIiItfozHdvcWiBM+12mbn/lCuu5op823MWlRVyHE4jOiIiIiIi12D/zoOc+mA7AekuHNtQhpN+9/DTQyMI8fN2dGmCRnRERERERPLttwPb6f/DCjaWqwVAXJkwnnzyXoWcIkRBp5javXs3LVq0wMPDg4YNG17XsUwmE999912B1HU5e/bsITQ0lOTkZAA+//xzAgICCvWchaVZs2YsXLjQ0WWIiIiIIxzbzKpdaxj1+3Ayyn7IjJY38VOzHkTNm0vZiiGOrk4uoKBTTE2cOBFvb2/27NnDihUrinxwmDBhAiNHjsTX17dQjh8bG8vAgQOpWbMmTk5OjB49+pL7LViwgDp16uDu7k6dOnVYtGhRnn3ee+89qlSpgoeHB02aNGHNmjV27f/73/945plnsFqthfGjiIiISFEVswbr57czd+VIcE7B5JxJYKWVDP3gRYIqBDu6OrmIgk4xdeDAAdq0aUPlypUJCiraF7sdO3aMJUuWMHTo0EI7R2ZmJuXKlWPChAlERUVdcp8NGzbQr18/Bg0axNatWxk0aBB9+/Zl48aNOft88803jB49mgkTJrBlyxbatm1L165dOXLkSM4+t99+O4mJifzyyy+F9vOIiIhIEWPJJvnTRzi6zIupx45RMc0HN0slFvX+gEBvd0dXJ5egoOMg3377LfXr18fT05OgoCA6duxIamoqAFarlRdffJGKFSvi7u5Ow4YN+fnnn3P6mkwmNm/ezIsvvojJZOKWW25h6NChJCYmYjKZMJlMPP/88wBERETw0ksvMXDgQHx8fAgLC2PGjBmXrWvVqlWYTCYSEhJytkVHR2MymTh06BAAhw8fpnv37gQGBuLt7U3dunX58ccfL3vMefPmERUVRcWKFS+7z5kzZ2jevDk9evQgIyPjKn6D9iIiIpg+fTqDBw/G39//kvtMmzaNTp06MX78eGrVqsX48ePp0KED06ZNy9nnzTff5IEHHmDYsGHUrl2badOmER4ezvvvv5+zj7OzM926dWPu3Ln5rlNERESKp10bt7Pne1fS4t05sLICgUlPsrjXbMIDyjq6NLkMBR0HiI2NZcCAAdx///3s2rWLVatW0atXLwzDAGD69OlMnTqVKVOmsG3bNrp06UKPHj3Yt29fTv+6desyduxYYmNjWbJkCdOmTcPPz4/Y2FhiY2N58sknc873xhtv0KBBA/7++2/Gjx/PE088wfLly6+5/pEjR5KZmcnq1avZvn07r732Gj4+Ppfdf/Xq1TRt2vSy7ceOHaNt27bUqlWLhQsX4uFhu3uwj4/PFR9du3bNV90bNmygc+fOdtu6dOnC+vXrAcjKymLz5s159uncuXPOPuc1b948z5Q2ERERKZl+3b+dJxZvJ93kBkC8ewXeHXoLFQPKOLYwuaISu7z0mZmfc/bzzwEIe/11vG9qntOWdewYh++5FwDfjh0J/d+zdn2PPvwIGTt3AlDj91V2bQkLF3Fq+nQAQib8H34XfSi+GrGxsZjNZnr16kXlypUBqF+/fk77lClTePrpp+nfvz8Ar732Gr/99hvTpk3j3XffJTQ0FBcXF3x8fAgNDQXA398fk8mU8/pCrVu35plnngEgMjKSdevW8dZbb9GpU6d81w5w5MgRevfunVNz1apVr7j/oUOHaNKkySXb9u7dS6dOnbjzzjuZPn06JpMppy06OvqKx/X09MxX3XFxcYSE2F8kGBISQlxcHACnT5/GYrFccZ/zKlSowJEjR7BarTg56fsCERGREun0PpaeTuSZ9SMxV6rA022H8cSBlXT8/B0CgxVyiroSG3SsKSmYT54EwMjKsm+0WHLaLElJefpazp7Nac9z3PS03ONewxQrgKioKDp06ED9+vXp0qULnTt3pk+fPgQGBpKUlMSJEydo3bq1XZ/WrVuzdevWazpfy5Yt87y+cLpWfo0aNYqHH36YZcuW0bFjR3r37k2DBg0uu396enrOKM3F29u0acOAAQOY/m94vFD16tWvucbLuTBIARiGkWfb1ezj6emJ1WolMzMz34FLREREioFt88la9BAvVawKLpm4+OzDucEuurzxBX4ero6uTq5Cif0q2snHB5eQEFxCQjC5udk3OjvntDn7+eXp61ymTE57nuN6euUe9xIf3q+Gs7Mzy5cv56effqJOnTrMmDGDmjVrEhMTk7PP1XzYvh6XO9b50Ynz0+gAsrOz7fYZNmwYBw8eZNCgQWzfvp2mTZte8bqfsmXLcu7cuTzb3d3d6dixI0uXLuXYsWN52gt66lpoaGiekZn4+PicEZyyZcvi7Ox8xX3OO3v2LF5eXgo5IiIiJVH8LtI/fYTknZ68H38YJ6sLHpZqzO//P4WcYiTfQWf16tV0796dsLCwq77/ypw5c4iKisLLy4vy5cszdOhQzpw5cy31XrWgoUOo8fsqavy+ym7aGoBbxYo5bRdPWwMIf/+9nPaLBfS6K6ftWqatnWcymWjdujUvvPACW7Zswc3NjUWLFuHn50dYWBhr166123/9+vXUrl37ssdzc3PDYrFcsu2PP/7I87pWrVqX3LdcuXKAbXrdeZeaQhYeHs6IESNYuHAhY8eO5eOPP75sbY0aNWLnv1MBL+Tk5MTs2bNp0qQJ7du358SJE3bt0dHRV3x88sknlz3npbRs2TLPtUnLli2jVatWgO132KRJkzz7LF++PGef83bs2EHjxo3zdX4REREpHrbvSGLfylDit/px9q9ahGc+yfd3f06ob4CjS5N8yPfUtdTUVKKiohg6dCi9e/f+z/3Xrl3L4MGDeeutt+jevTvHjx9nxIgRDBs27JL3MCkNNm7cyIoVK+jcuTPBwcFs3LiRU6dO5QSZcePGMXHiRKpVq0bDhg2ZOXMm0dHRzJkz57LHjIiIICUlhRUrVuSESi8vLwDWrVvH66+/Ts+ePVm+fDnz589n6dKllzxO9erVCQ8P5/nnn2fSpEns27ePqVOn2u0zevRounbtSmRkJOfOnWPlypVXDGFdunRh2LBhWCwWnJ2d7dqcnZ2ZM2cOAwYMoH379qxatSrnOqP8Tl07H8hSUlI4deoU0dHRuLm5UadOHQAef/xx2rVrx2uvvcadd97J4sWL+fXXX+1C5ZgxYxg0aBBNmzalZcuWfPTRRxw5coQRI0bYnWvNmjV5Fi0QERGR4m9DzFG+/fQHhmWbAchKCGTuvb3w9dEsjmLHuA6AsWjRoivu88YbbxhVq1a12/b2228bFStWvOrzJCYmGoCRmJiYpy09Pd3YuXOnkZ6eftXHc7SdO3caXbp0McqVK2e4u7sbkZGRxowZM3LaLRaL8cILLxgVKlQwXF1djaioKOOnn36yO0ZUVJQxceJEu20jRowwgoKCDCCnrXLlysYLL7xg9O3b1/Dy8jJCQkKMadOm2fW7+M9x7dq1Rv369Q0PDw+jbdu2xvz58w3AiImJMQzDMB599FGjWrVqhru7u1GuXDlj0KBBxunTpy/785rNZqNChQrGzz//nLNt5syZhr+/f87r7Oxso1evXkbt2rWNkydPXsVvMS8gz6Ny5cp2+8yfP9+oWbOm4erqatSqVctYsGBBnuO8++67RuXKlQ03NzejcePGxu+//27XfuzYMcPV1dU4evToZWspju9LERGRUi3tnPHt9rVGvU+bGdVefsl4vPdTxo/texiJZ/N+/hTHulI2uJDJMC64GCOfTCYTixYtomfPnpfdZ/369dx6660sWrSIrl27Eh8fT9++falduzYffPDBJftkZmaSmZmZ8zopKYnw8HASExPxu+iamoyMDGJiYnLuZC/2IiIiGD16NKNHj3ZoHe+99x6LFy8uETfZHDduHImJiXz00UeX3UfvSxERkWJk7Vsc/ON9egb5YjhlYRgmKmWPYt7Ae/Hx1r/jRU1SUhL+/v6XzAYXKvTFCFq1asWcOXPo168fbm5uhIaGEhAQcMWL1ydPnoy/v3/OIzw8vLDLlEI2fPhw2rVrR3JysqNLuW7BwcG89NJLji5DRERECsLmz0n/5mXCjp3h9mTbzdt9jJp8eU8/hZxirtCDzs6dOxk1ahTPPfccmzdv5ueffyYmJibPNQ8XGj9+PImJiTmPo0ePFnaZUshcXFyYMGECvr6+ji7luo0bNy7PKmwiIiJSPG09Vo5Dv5XjyG9BhO9rTPnsAfzY7zPKeBX/zyylXaHfR2fy5Mm0bt2acePGAdCgQQO8vb1p27YtkyZNonz58nn6uLu74+7uXtillQqHDh1ydAkiIiIiRdJfB09z4M2PiDKDxexMuaNuLJ76NJ5uzv/dWYq8Qh/RSUtLy3Pn+PMrb13H5UEiIiIiIvlnGGC1MHvLSoYuH8Sk5nexJyCcg5Xr0m3Wuwo5JUi+R3RSUlLYv39/zuuYmBiio6MpU6YMlSpVYvz48Rw/fpxZs2YB0L17dx588EHef/99unTpQmxsLKNHj6Z58+aEhYUV2A+i0CRFid6PIiIiRZDVCkvHsOLccV6zHMDkkY2lxiwWRTzO+4M74uXr7egKpQDlO+hs2rSJW2+9Nef1mDFjALjvvvv4/PPPiY2N5ciRIzntQ4YMITk5mXfeeYexY8cSEBBA+/btee211wqgfHB1td2dNi0tTXeplyIjLS0NyH1/ioiISBGwdAwZK2ZTs4yBf1hlkpwg0K08MwZ3xstDnyNLmutaXvpG+a8l5GJjY0lISCA4OBgvLy9MJpMDqhSxjeSkpaURHx9PQEDAJa9BExEREcfY9eHLmN6ehXdYJi/ddCf7qltY3P8N/BRyipWrXV660BcjuBFCQ0MBiI+Pd3AlIjYBAQE570sRERFxvPXbDuPyzgJ8LU6kHPWkYY0gZtw7AXcXXZNTUpWIoGMymShfvjzBwcFkZ2c7uhwp5VxdXXMW3BARERHH++ivn5i25hfqNhnA//78giNV6nP/lHEKOSVciQg65zk7O+sDpoiIiIhAdjosGMaXAQ15O/YrnIPMbK1/K/MiJ/Ds6J54eGm6WklXooKOiIiIiAhZaTC3P5Y9qzGVWYOpnD8A5csmMWFAHzzctFhQaVDo99EREREREbmhnF05vjeT/d+H0HmPlZC4mwiiGT8O+BBPhZxSQ0FHREREREqUNT/9wZlFx7FmOxG3zp/Wps4su+djvN3cHV2a3EAKOiIiIiJSYry9YTHDN+1iffn6AMTUbMqEUXfipoUHSh1doyMiIiIixVvqafj1eaYFtOSTmNdxDvdgSqv7MSU3YOirY3Dz0EhOaaSgIyIiIiLFV3IczLoTa/xu1pVfg8nTCi5pVIvczQMDp+LirAlMpZX+5EVERESk+Eo7w7ntpziyoiyvHEnCOS2MEKfWLOn/ukJOKac/fREREREptn7/aQ/Hf/ck/bQb+36L4FbPp1nafwburlpdrbRT0BERERGRYumd9b/w/KYzJLvabv5prlSDN+5pp5AjgK7REREREZHi5NQeOPIHLye7MPfQa5hr1eZp63AeTdtFr4/fwMVVH2/FRu8EERERESke4rbDrJ6cyzjL/PDKmJwMXH13Ur5tC3r3exNnJ5OjK5QiRFPXRERERKR42LeMpN0pWHd68lScgWF1IdylPXP6jlLIkTw0oiMiIiIixcLKreUIXV8GE7DNfCs39+3M9N5dcHHWzUAlL43oiIiIiEiR98Wf0SxfuYXz4zYtPc283fs2hRy5LI3oiIiIiEjRdPB3CKjE/7asZNGRGWQ0GYDJ2pmb/Kzc8dk0nHWfHLkCBR0RERERKXr2/AzzBrPapyzflXHG5AQeFb4iudYr3NGzq0KO/Ce9Q0RERESkaLFa4LdJJB50otmxOGon+QNQ3aML0+9SyJGroxEdERERESlanJxZda4XIRs+ItvThdjWQ2h+i5WPew3DyUkhR66O3ikiIiIiUqR8tX4PKd8tBUy4plsYZTrKJ70fVMiRfNG7RUREREQc7+DvYLUy5ud3mbT9MSa0vZcYv1D2tr2D3tMnYjLpPjmSP5q6JiIiIiKOtX4GLHuW9yrfwnKngzh7QHaNufx900v8X+9WGsmRa6J3jYiIiIg4zqk9sPw5Uk+6cdvxtbiaPQBoUKalQo5cF43oiIiIiIjjlKvJXyl34/PbGo5GBJPQbAito+L5otc4hRy5Lnr3iIiIiIjDzP1uAx4/rAMg/FA8z/vArN5PKeTIddM7SERERERuHKvVNl0NeOSHKUzct4DXmt6DxeTEvk69uXfcfVp4QAqEpq6JiIiIyI1hMcOSR2HnEsZG9mNN2q+4B8OfDW5jXYe3eHBwR43kSIHRO0lEREREboyNH8DWuWQnplPt6JKcza2qBTH8vk4KOVKgNKIjIiIiIjdGs2Ec/HgOWX+e40yrW8mM9KF1tbLM7PW0oyuTEkhBR0RERERuiG/fmU/dDQmAiTvWryO40/s81Ku1o8uSEkrjgyIiIiJSODKTITMFwzB4cPEb/N/pDP4IrQPA8a59eaivQo4UHo3oiIiIiEjBSz8HX/bBcPPkft/GbEpehGtVHyZzPy8H3sHdTw51dIVSwinoiIiIiEjBMgyYNxiObyLDauJ4hRPgAU4uKXRukc3d3R90dIVSCmjqmoiIiIgULJMJOkzk5M5Ajv0WjOvB/ljSK9C2zIO80/0RR1cnpYRGdERERESkwH0341dqbvME4L61v3O2y1RGtK/r4KqkNNGIjoiIiIhcv8wUAKxWKyMXf8o7iX4ku9qCjk/79go5csNpREdERERErk/8Lph9F9ZbJzDg4D/sTF1Kdp2GTDCG8XgFM3c8N8rRFUoppKAjIiIiItcu6QR8fjuknWHfT2PYVSEMTODit5U2fe7mjk49HF2hlFKauiYiIiIi1863PNa6vYnf6kvWzlBMx3pjWF24LeQxJirkiANpREdERERErpnVMPj+Bxcid/nigpmWlmzqt/uYke2aOro0KeUUdEREREQkfyzZ4OyKxWph/PfL8TmWQOS/TT0blOc2hRwpAhR0REREROTq7VwMKydhuXchvX6eyoH030lvNggMaN6mAbc987CjKxQBFHRERERE5Grt+h7mDwHDyqwvunEwAExO4Bk+h7COn3Nbq/qOrlAkhxYjEBEREZGrU6EJVv9wTv/jg9+hMMxJdTEME73CRzNMIUeKGI3oiIiIiMhVsfqEsmxLSypvX03ggRR8rPfQ967BjLulm6NLE8lDIzoiIiIicmmGYXsAZouZ579ejfM/uwAISTvHpEhnhRwpsjSiIyIiIiJ5GQYs/x9YzGR1fJE754/icOJRfmg7lMlrPsf53iF0GXmPo6sUuSwFHRERERHJ66en4M+PABh9PJpjrsdx9oKMyEWkDphJr+ZVHVygyJVp6pqIiIiI5FW+IYYBCYc94Vg4VrMXhuHMoNqDFXKkWNCIjoiIiIjkYanfnz9fnkPAjhjCT0Km8QDDO5TlqXa9HF2ayFXRiI6IiIiI2LFYrLw5bQF+Ow4B0D1mPe81jFTIkWJFQUdERESktMtIgq/6wbFNZGRn0XnuMD6yRPNW475km5w59fgEOt91i6OrFMkXTV0TERERKc3Sz8HsXnDib8xH/qBX+ebEsxuPUFhj6knfYV/SuX1DR1cpkm8KOiIiIiKlmYsnuPtgWCHltEGSqxeUBcPqwsg2N9G5VUNHVyhyTRR0REREREozVw+ye33Bnv5dyD6chU+r5pwmiEdbdeTRlroZqBRfCjoiIiIipZjZYmX2+HdoeSAFZ+D5P2aSPHwenZpqCWkp3rQYgYiIiEhpErcDvn0AsjNIyUyj65xHed23AlvK1SDb5Ix57ASFHCkR8h10Vq9eTffu3QkLC8NkMvHdd9/9Z5/MzEwmTJhA5cqVcXd3p1q1anz22WfXUq+IiIiIXKtjm+DzbrDjW9Lm3Ue3r4cSZ6zBudpMXmnXi8RJb9HmvrscXaVIgcj31LXU1FSioqIYOnQovXv3vqo+ffv25eTJk3z66adUr16d+Ph4zGZzvosVERERketgWMFixrDCnsOHOFfWG9zB5JzOEz0r07bZrY6uUKTA5DvodO3ala5du171/j///DO///47Bw8epEyZMgBERETk97QiIiIicr3Cm5PdezZ7Hx/H7vRgMlv1wzViLmObPsn9TRVypGQp9Gt0lixZQtOmTXn99depUKECkZGRPPnkk6Snp1+2T2ZmJklJSXYPEREREbk+WWYrS/73NU6H02kYv5/xfy7m3VtnKuRIiVToq64dPHiQtWvX4uHhwaJFizh9+jSPPPIIZ8+evex1OpMnT+aFF14o7NJERERESrZNM8HFHRoOJCE9hXvnTcca0ICXXP7C2Wqh1kNDaFkz2NFVihQKk2EYxjV3NplYtGgRPXv2vOw+nTt3Zs2aNcTFxeHv7w/AwoUL6dOnD6mpqXh6eubpk5mZSWZmZs7rpKQkwsPDSUxMxM/P71rLFRERESk91k2H5c+ByYmEnh/QLfobktlH1rmbqL27IePbV+Omvld/OYJIUZGUlIS/v/9/ZoNCH9EpX748FSpUyAk5ALVr18YwDI4dO0aNGjXy9HF3d8fd3b2wSxMREREpmQwDEo/bnlqtfLZ6EUm++zGZwNVvG0MffZyb6tV3cJEihavQr9Fp3bo1J06cICUlJWfb3r17cXJyomLFioV9ehEREZHSx2SC214lu3Y//vojiu1/1CHjRD8MszfPNpnGnQo5UgrkO+ikpKQQHR1NdHQ0ADExMURHR3PkyBEAxo8fz+DBg3P2HzhwIEFBQQwdOpSdO3eyevVqxo0bx/3333/JaWsiIiIicv3SM7L45YtEfA+fYuzmr7llj4m328yjf1QrR5cmckPkO+hs2rSJRo0a0ahRIwDGjBlDo0aNeO655wCIjY3NCT0APj4+LF++nISEBJo2bco999xD9+7defvttwvoRxAREREp5cyZ8ONTkBQLQHzqOe6du4hdhjcA2c6ujOjVnPY1KzmySpEb6roWI7hRrvaCIxEREZFSJzMFvrkHDq6CcrU52XcO3ZeOIs2IJePwUIb//Q8dhvWlSY/2jq5UpEAUmcUIRERERKQQZaXA2YMAGAmHeeS7F0l3O4LJBB4VF9B28DyaVNUS0lL6FPpiBCIiIiJSiHxDYfBizP6RLP27HQd3dsCcUhPD7Msrrd6kpUKOlFIa0REREREp5lKcyrL6x4pUO7qNVw/H8aL7CJ4d0oCuteo6ujQRh9GIjoiIiEhxcmov/P667V45wPGkeEZ/+itep08CUDYziXc6VFLIkVJPIzoiIiIixcWJLfBlb0g7A4bBkUZDuGvhINI8Pdne9gGe//Mrwl58kahOrR1dqYjDKeiIiIiIFBfxu2whBzDv/oG7D64nyzkWF29Ii1xD2Nj5RFUu4+AiRYoGBR0RERGR4qLhQEg7S/bW75mxrSWnyoXgVflTTIYnb3V5SiFH5AK6RkdERESkGEmMvIeV81zpuvIbBm35B6e4h5h+80d0rFHH0aWJFCkKOiIiIiJF1V+fwLHNOS9PpSQz5eVZVIo9AMAdhzbw1e230r56bUdVKFJkKeiIiIiIFDWGYVtZbelYmNMb4nex98wRuszrwdzgc7zT4C6S3b3xeucD6kVVd3S1IkWSgo6IiIhIUWM1Q8xq2/P0c8RuX0S/JYPJdj6NR/mFrG0eiM83i6jbrplj6xQpwhR0RERERIoaZ1fo/xWENSLlpv/jubVVSTtXCwCTuSzv9e5FnVrhDi5SpGjTqmsiIiIiRZGHH+dun8tfA4Yy7NwpDrQeztnaAXzQ8yFaVK7i6OpEijyN6IiIiIg4WkYiLH8OzJk5mxLSslg06gXC4w/jl53G/22Zyzd3P6OQI3KVFHREREREHCnlFHx+B6ybDgseAKuFbSf30XFuf6ZUv4l/ykSQ4OFLpbenUzMswNHVihQbmromIiIi4kgJR+DMftvzQ+vYvm8Vg9ZPxOqSiKnaLKZ5DmNm9yhqNKnn2DpFihmN6IiIiIg4UsUm0G82BEZw9va5PPVjEmaz7btoZ5Mbbw+7WSFH5Boo6IiIiIg4WvWOnLrze7YMf5Y+S74iM+Z+nNIa8EW3z2hcsaKjqxMpljR1TURERORG2r8CUk9DVL+cTaeSM9g8+CEqnz1OGMd5YkcAXZ/4kKrlfBxYqEjxpqAjIiIicqPsWAALHwLDCh5+ULMrfxzbzhM/zCKkeieePz2TVA8fbnthjEKOyHVS0BERERG5UY7+CdZs2/MdC1jnGcbDK4ZjeKZxtl4bpvuM4H/3d6Bqw1qOrVOkBFDQEREREblRuky2TVtz8+Jk60k8NfcjDN80ADx8jzP+2UlUDQ5ycJEiJYMWIxARERG5UZyc4K4Pias7ju3de1PpTysZsb1wzqzGNz0+JVIhR6TAKOiIiIiIFAZzFvwyAZJi7TbHHj/NroGDqXD2OP/31yw6HAviu96zqRVSzkGFipRMCjoiIiIiBS0zBeb2gw3vwJe9IT0BgF8PbqTvtz+yw8+2ZHSCdyDjH+5GRJCvA4sVKZl0jY6IiIhIQctKgTP7bc/PHoCT/7DMYmLs6scwApyY3vJ+LAGB9HphNJXqVHNsrSIllEZ0RERERAqabyjcuwiCqsPgxZwIaMwzK2aAUxYm5wz8KvzB3R+/rpAjUogUdEREREQKQ9nq8MhGjqaHsKrfULJ334E5tQqumXVY2Gc6FQI8HV2hSImmoCMiIiJyvY78ASteAsOw37z3MDH3DKLR4a1MXvcZEWfvY1Gfj4gICnBMnSKliK7REREREbkee3+BefeBOR1cPaHdkwAsP7CR92fvZYzZdoNQH8PMp/2aEVbG35HVipQaGtERERERuR6pp20hB+DQWrCYWbBrGWPWPMSusCU83eZB9obWoObc2YTVqOTYWkVKEY3oiIiIiFyPRvdAajzEboO7PmTP6bO88Mf/wMmCi+9uLHVq0fKVeQT7eTi6UpFSRUFHRERE5Hq1Hg2GwaEd+5k95UtSq9+LZ/hMPLLrs+iepxVyRBxAQUdERETkapkz4Ycx0HQoVGyau91kImbbXk4MHcI9aQlkpnTjL/8n+XrInYT6ezuuXpFSTNfoiIiIiFyNzGSYczdEf2n77+l9OU1/HT3IR2/OpUxaAgBdY7cwb2A3hRwRB1LQEREREbkaTq5gWG3Ps9Mh4QgAM7cu4P4VvZgf6cZndbpxokwY9b6ZQ3C5AMfVKiIKOiIiIiJXxdUD+s+BKu3gvu+hegeW7t3Am1teAJMFjwrfsPnWhkQtWUhwpfKOrlak1NM1OiIiIiJXy8MfBi8Bk4l9f+3go/n7yAprilvgX/hktmbe0Lsp66OFB0SKAo3oiIiIiFzKvuXwVT/bAgQXMpnY9+d2Tj94P0/98g7l9rWmTOpQfrznLYUckSJEQUdERETkYtvm2ULO3p9h0UNgteY0/XPiNJueeZ6AjGTKZCYzZt/PfHffo5TxcXdgwSJyMU1dExEREblYmarg4g7ZabYFCKzZ4OTOmxs/4fPtX2M0uYfJqQm4u7lwyxfvEejt5uiKReQiCjoiIiIiF6vYFO7+HA6ugs4vg5MT7/w1m5m7p4MrWKrP4YvgUXw0qA2BoUGOrlZELkFBR0RERMRqASdn+22RXWwPYNeW3cz8xRlrqD9OrokEmZry8ciuBHhpJEekqNI1OiIiIlK6pZ2Fmd1gy5xLNu9cs4nkIffywKofyDj0AEEZffhh0EsKOSJFnEZ0REREpPTKSIRPO8OZfXB8E/iGQvUOABiGwT8xJ0kaNZLAzFRuPbaF7GOVGTLhBfw9XR1cuIj8F43oiIiISOnl7gfVbrU99woC77KALeT8b/UbDPz+daY36E22yZkjoVUZ8sY4/D01kiNSHGhER0REREovkwluexWc3aD5cAisDMCE31/l+8NfQQBsqd+J2RWe4NkxvfAvG+jYekXkqinoiIiISOmSmQLuPrmvnZyhy8s5L7cdiOP7zWlgG9wh3L8sEx+7Dx93fWwSKU40dU1ERERKB8OA39+A91tB8slL7rLt1/Wk9L6TGjsCyTh5O6FZ97J48FMKOSLFkIKOiIiIlA5r34TfJkHCYfjqbsjOsGveui6a7CdGEpiRxMSNM+meWpfvBo9VyBEpphR0REREpHRoeA/4h9ue1+sNLu4AWKwWnlzxIvet3MTf5WoAcDysOi8+cSfeCjkixZb+7xUREZHSwTcU7vkW4ndCvV4AmK1mHln2NBtOLsMa5s3rrR4g+0R1Br/5DD4Bfg4uWESuh4KOiIiIlEznDtlGcJycc7cF17I9/rXxYCzrD+/B5AEm53QiI8wMfe4lPN2c8x5PRIoVTV0TERGRkidmNXzQDpY9e9ldNv/wG0cfeBTr/gGY0ypTxfIw3w4erpAjUkIo6IiIiEjJknIKvuoHmYnwx3uw9Zs8u2z+/jdMz4wm6uReXlo3m5bGGOYPflAhR6QEUdARERGRksWnHHR9zfa8RheodXtOU6Ylk4m/v82zy/eT5WSbwe/h78c7g27Cw1UhR6Qk0TU6IiIiUvI0Hgze5aB6J3C2fdxJN6dz39JH2JWwiewa9Rmf/SAj4/6k2+x38PLxcnDBIlLQNKIjIiIixVt6Ahxen3d7za45IQdg4fa/2Hk2GgAXnz2ENA/mjnkfK+SIlFAKOiIiIlJ8JRyBz26DL3vDiS2X3W3jtz+z6fVfSD82CKvZm9qmsXwx6E5NVxMpwfIddFavXk337t0JCwvDZDLx3XffXXXfdevW4eLiQsOGDfN7WhEREZG81r0Np3ZBdhp8NxKs1jy7bJz/M+7PjWP4loV03X6a5i5v8OW9/XB3UcgRKcnyHXRSU1OJiorinXfeyVe/xMREBg8eTIcOHfJ7ShEREZFL6zwJKrWEMtWg32xwyv1ok5iZyId//sQPc3/B3WoGoEvGET4Y2EohR6QUyPdiBF27dqVr1675PtFDDz3EwIEDcXZ2ztcokIiIiMhluXpA/69sz73K5Gw+l3GOgd/fz9HUGNIb3QPmjjTnHJ2/+QR3TVcTKRVuyDU6M2fO5MCBA0ycOPGq9s/MzCQpKcnuISIiIqWcxQyr37AtPnAhrzJ2IQfg5bUfcSxtPyaTBY/QJZzsPZAu8z/D3dPjxtUrIg5V6EFn3759PPPMM8yZMwcXl6sbQJo8eTL+/v45j/Dw8EKuUkRERIq0jCSY2x9WToL594El+7K7rpu7lD+/DyU7MQprth9NPZ7m3Xtvws3d7QYWLCKOVqhBx2KxMHDgQF544QUiIyOvut/48eNJTEzMeRw9erQQqxQREZEiL+0MHN9se35oXe7zi6ydtQjfl57mpdUfELCvAy09XuCTgd1wddZCsyKlTaHeMDQ5OZlNmzaxZcsWHn30UQCsViuGYeDi4sKyZcto3759nn7u7u64u7sXZmkiIiJSnJSpYrsWZ/4Q6PMpVGph13w85Th/7UvC5b13CbJaCE5PYHTSNvoMuA8XhRyRUqlQg46fnx/bt2+32/bee++xcuVKvv32W6pUqVKYpxcREZGSpHJLeDwaXD3tNh9OOsw9PwzlXLIrrq3u4bU1n5NZuRq9P3pNIUekFMt30ElJSWH//v05r2NiYoiOjqZMmTJUqlSJ8ePHc/z4cWbNmoWTkxP16tWz6x8cHIyHh0ee7SIiIiIAGAasmwZWC7R70r7topBjGAYjfhlDYvYpnDwgo/JvrKj3Ei/f0wJXXZMjUqrlO+hs2rSJW2+9Nef1mDFjALjvvvv4/PPPiY2N5ciRIwVXoYiIiJQehgFLHoUtX9pel6kC9XpfdvfflqzmwI47cK30EYbZhw4hDzC5b1ucnUw3qGARKapMhmEYji7ivyQlJeHv709iYiJ+fn6OLkdEREQK09pp8Ou/t6To+Dy0eeKSu636+BuC3nyRZZWb816zNnStG8n0vm0UckRKuKvNBoV6jY6IiIhIvrV+HBKPQkQbqHtXnuaTqSfZ8lc85d+ahIthpduhP/Bq3JjhCjkicgEFHREREXGsjCTwuOBbWZMJbp96yV03n9zMQ8seIfnkzbRo3I9xm77iQOObefCFhxVyRMSOliIRERERx/nzY3i7EZze/5+7xqXG8eCyh8i0puFW7ifW1Xbh5+Evcsesd3Bx1Xe3ImJPQUdEREQcY/MX8OOTkHYa5vaDjMQr7v7X32dJjbsFAHNKJHfV7MiY0b1xdnG+AcWKSHGjoCMiIiKOUa8XBNexPa91B7j5XnbXFW9/TvmH76H6nsqkH+9P99AJvNarCU6ariYil6FxXhEREXEMd18Y8DUc+QOi+l1yl7TsNFZ/9SsV33sdZwxe2vAJy5q/zlN3NVLIEZEr0oiOiIiI3BiH1kJWmv22wMqXDTlLDiyh47xuPL4vnr+DIwE42uxWnhrWSSFHRP6Tgo6IiIgULsOADe/CF91h8SO21//h18O/MmHtsySbz+ASMZNJrfrw110P0v2zN3Fy0scXEflv+ptCREREClfSCfjtFTCs8M8i+Gfhf3Y5fjwUS0YoAObkugxoGcWgV55QyBGRq6a/LURERKRw+VeA3p+AyQnaPQV18t4E9ELLXvsAt6fGYz04iIyT3RhQbRTP96iHyaTpaiJy9UyGcRXjxw6WlJSEv78/iYmJ+Pn5/XcHERERKXpO74OyNS7ZZBgGVsPK8qmfUfnTNwGILluNg0++zP/d2UAhR0RyXG020KprIiIiUrC2zYPMJGg2zH77FULOlE1T2HT0MOf21+YVFw+8zRl41a3L+B71FXJE5Joo6IiIiEjBMAz4dSKsmw4mZwiqAVVv/s9ur//1Ol/u+hKArNppTDAeZITHSe6cNlHX5IjINVPQERERkYJhMtmuwwEwLLBv2VUFndSEKhiGE2BgzQjn1rva0/O2mhrJEZHroqAjIiIiBaf9/+DUXqh2a96pa5fw44tvE7frDBn1+oPJwvDGfRjXRSFHRK6fgo6IiIhcu4xE8PDPfe3kDP3n2EZ3LsMwDEwmEz8+/xZVvv6IxzFhpS+1hgxgbOdIhRwRKRAKOiIiIpJ/Viv8/ips/hwe/M22hPR5VwgqadlpjFs9jjLmW/H4azdVACcMeoRAd4UcESlACjoiIiKSf6vfgN9fsz2fNwiG/gwublfskpadxsO/Pszf8X9jWNeT3nwoToZB3cgw7njzOYUcESlQCjoiIiKSf82GwZYvIekY1LkTnF3/s4ubsxuJKR62F4YLBi54PjGWHp1qFnKxIlIaKeiIiIhI/nkHQf8vIe0MVGt/VV1+mPQBx860JrtqBlnnWjO67a2M6nDpe+uIiFwvBR0RERG5MqsF/v4CGt5rPz2tfNRVH+KHpydTc/EsJnuX5Rke5tE7m/Foe4UcESk8CjoiIiJyeWlnYcEDcGAlnPwHbp961V2PJB3hzc1vEpHaj/q/LgWgYuppng06w10KOSJSyBR0RERE5PISj8Khdbbnm2bars0Jrv2f3fad28fw5cM5nX4ac9p+vNs8wOtrPiWtS3fueu6xQi5aRAScHF2AiIiIFGHlo+CON8G7HAxefFUhB8DJ5ERyZiYAJqcMzvh4cnDy+9wx+enCrFZEJIdGdERERCSXxQzOF308aHQv1LoDPAOu6hBWq5V1X/zF2bP34R78C+nHBzDhtiYMa1e14OsVEbkMjeiIiIiITcIR+LQjRM/N25aPkLP0iRdo9v4L3L/pb9KP3M+zXZvwoEKOiNxgGtERERERSI6DD9tB+jn4YTSE1IXyDa66+/LDy4lNiSVjQxBtf5kHQO8Dq6nZpzt3t1XIEZEbT0FHREREwDcU6vSEzTNtz01XP+lj8f7FPLf+OayGlYzTd7G54d2Miv6WIwMf4u6hdxRezSIiV6CgIyIiIja3vQruPtB2LHgGXnW3uNQ4rIYVACfPo/wS0ZsOfTrQt8/NhVWpiMh/UtAREREpjQ5vAFdPCGuYu83VAzpPytdhrFYradtqknWmHZiyyTzZnZd61qdvi8oFW6+ISD5pMQIREZHSxDBg3dvw+e0wb7DtmpxrZLVa+f7h8bR57Qkq76lH5skevHxXAwYp5IhIEaCgIyIiUppYsmHnYjAskHAY/vggf92tFqb8NYWYxBjmPjudyN+X4GPOYNL6j3mtUwT33KSQIyJFg4KOiIhIaeLiBnd/Dl5B0PZJuPmpq+6aZcli3OpxfLHzC/ovuZ+XrAFsC7KtqJY4+CH6dahXSEWLiOSfrtEREREpyQwDslJtiwycFxAOj24CrzL5OlSGJYPDSYcBSLWcI9vnLBNbPcBb1c10fahvQVYtInLdNKIjIiJSUqWdhbkD4Jt7wWq1b8tnyAHwcfGhRuZjWDLKk370PqyptXipXzOFHBEpkjSiIyIiUhIZBsy5G45vsr1e+ya0e/KaD2cxW/hh2BhqxRxlQfOHsbq4MaVPFL2bVCyggkVECpZGdEREREoikwk6PAeYbNfjlI/K9yGOJh9lyl9TyDabWfzQ00T+sYybTu5i/KY5vHl3A4UcESnSNKIjIiJSUlW9GXq+b/uvX1i+uu49t5eHlj/E6fTTrNp3HJMpghedXXG1mIm4qzvtG4cXUtEiIgVDQUdERKQk2PcrxG6BduPstzcccE2Hi0uN41yG7R47B1O2kRbcmudbPsDjjcrQfuSg661WRKTQKeiIiIgUdytehDVTbc/LN4IaHa/7kG3C2lLX7UG2nPuRtGP34YwXwx67m/ZR+RsZEhFxFF2jIyIiUtx5l8t9vmPBdR/OnG1myaBHcf4hgbTDI3A2fJjevyHdFXJEpBjRiI6IiEhxd9MIOLwOKrWCFg9f0yHm7p5LRZ+KtAxpydJBI6kZvZqnTE7g5ES/J4dwe4PyBVy0iEjhUtAREREpTtITIG47VGmbu81kgr6zbf/NJ8MweDf6XT7c9iEezh7Ud3qSeucyiQQM4IG2VblZIUdEiiEFHRERkeLi0FpYNALSz8GINVCmam7bNYQcAAODQ0mHAMiwZLA29i9+a9gbnJxo0rsLNw/rVwCFi4jceLpGR0REpLjY+jUkHoWsFPhx3H/vfxWcTE682HISgaZ6ZMTdQdaZW3F2dqbulFcUckSkWNOIjoiISHFx22TbqI5/RbhjWoEcMjszi0WPTCTRrwfZ7n64Opt4d2BjOtcNLZDji4g4ikZ0REREiiLDgJR4+23uvjBkKQxeDAHXdsPO0+mnmbB2AslZyWRnZvHTgOE0XreEV9d+SJA5lffvaaKQIyIlgkZ0REREiprkOFj8KJw9AA+tAXef3Db/Ctd82KNJR3no14c4mnyU48knKBvTl+4xewCokHqaac18aFsn5HqrFxEpEjSiIyIiUtR8/zjsXw5nD8Iv/1dgh7UYFlKyUgDYEX+QBbGneabNCGK9g0j+v1do269bgZ1LRMTRFHRERESKmi6vgKsX+IRArdsL7LAR/hFMu/VtfKjKmX3DMbLLcMY/GKcvvqH1vT0K7DwiIkWBpq6JiIg4miUbnF1zXwdVg/5zoHxD8CpTYKfJTM/gt7fWEMuDgAk3Fyc+HtyUmyPLFdg5RESKCo3oiIiIOEpmCvzwBMztb1t84ELV2l9XyDEMg0+2f8In2z+xnSotg2V9H6Dbd+8xcPdy3F2c+EQhR0RKMI3oiIiIOMpX/eDwWtvzTZ9BswcK5LCGYfDqn6/y1e6vAAh0D2LvrHh67vsbgL57V3LH2AdopZAjIiWYRnREREQcpe0Ttv+6eoGzW4Ed1mQyUc4rN8R8sn47H5or8mG9HmQ4u5H50hRatW1QYOcTESmKTIZx8Vh50ZOUlIS/vz+JiYn4+fk5uhwREZFrYxhgMtlv2/Au1OwKZaoW8KkMJm98jQ273Ni+pyYAHq5OzLwjgpY31S7Qc4mI3EhXmw00dU1ERKSwZSTB8ufAzRu6vGzf1nJkgZzCYrXg7OSc8zo9NZ34tXXYftq2zdPVmc+GNKNltaACOZ+ISFGnqWsiIiKFyZINH7eHzTNtozdHNhb4Kbaf2k6vJb2ISYwBIC05ld/63Ef/WS9SOSkWLzdnPh+qkCMipYuCjoiISGFydoXGg23PXb0g8WiBHv7vk39z/y/3czDxIA//+jDHk+L55uFnqXpoB/5ZqTy/8XM+H9SYm6oq5IhI6aKpayIiIgXp/KWvF16L03IkJJ2AFiMgMKJATxcZGEklv0rsPbeXEK9Qxs7bwbaQ1rwc+A+Vk+Pxe+ElmtQILtBziogUB/ke0Vm9ejXdu3cnLCwMk8nEd999d8X9Fy5cSKdOnShXrhx+fn60bNmSX3755VrrFRERKboSjsJXfWHLbPvtTs7Q9dUCDzkAPm4+vNfhPfpHDiTz2AP8sT+dNFdPJt/yMLz5Lk3uuLXAzykiUhzkO+ikpqYSFRXFO++8c1X7r169mk6dOvHjjz+yefNmbr31Vrp3786WLVvyXayIiEiRlRQL77WEfctg2bOQHFcop0k3p5NuTrfb5m32Ymd0W/44kASAj7sL74+4hcadWxdKDSIixUG+p6517dqVrl27XvX+06ZNs3v9yiuvsHjxYr7//nsaNWqU39OLiIgUTX7lodbtsO1rcPGEhCPgG1qgpzidfppRK0cR6h3KlJun4GRyIiUhiTV9BnOz1Z0/mt6Dl6c7XzzQnMaVAgv03CIixc0Nv0bHarWSnJxMmTJlLrtPZmYmmZmZOa+TkpJuRGkiIiJXz5JtW2jgQl1eAa8ycPPT4BlQsKezWnhw2YPsT9jP9tPbeS/6PYbUfoiV/R6gxrE9RABZO7xo8+FbNAwv2HOLiBRHN3zVtalTp5Kamkrfvn0vu8/kyZPx9/fPeYSHh9/ACkVERP7D7qXwduO8S0V7B8Ftkws85AA4OznzRJMncDI5EeodSpuw9gz9/C++CGtBtpMzqa4etBvzoEKOiMi/TIZxfnmYa+hsMrFo0SJ69ux5VfvPnTuXYcOGsXjxYjp27HjZ/S41ohMeHv6fdz8VEREpdLuXwtcDbc+D68JDv+cd2SlEvxz6hZoBDXhybgybDp8DoN3ZvYzr14L6HVresDpERBwlKSkJf3///8wGN2zq2jfffMMDDzzA/PnzrxhyANzd3XF3d79BlYmIiORDjS5QPgpit4JvCGQk2UZyCpjFamHrqa00Dmlst71lyK0M/XwTm/8NOf6erjw18X7qVfAv8BpERIqzGzJ1be7cuQwZMoSvvvqK22+//UacUkREpGAknbB/7ewC3adDn8/g3oWFEnJSslJ4bOVjDP1lKGuPr83ZnnDqLGtvvxvnjesACPByZc6wmxRyREQuId9BJyUlhejoaKKjowGIiYkhOjqaI0eOADB+/HgGDx6cs//cuXMZPHgwU6dOpUWLFsTFxREXF0diYmLB/AQiIiKFIeEofDMI3mlmWzr6QmGNoF5v+5uCFqDv9n/HmuNrsBpWnlnzDClZKSScSWBjn0FUObGXZ//8glvO7VXIERG5gnwHnU2bNtGoUaOcpaHHjBlDo0aNeO655wCIjY3NCT0AH374IWazmZEjR1K+fPmcx+OPP15AP4KIiEghWP827FoCWSmw/H839NQDaw+kQ6UO+Ln5MfXmqVgs7gz9ahsHXG2hJt3Vg6fubUfdMIUcEZHLua7FCG6Uq73gSEREpMCknYUZjcHJBTq+AI3uubGnz07jTPoZ/FxCGfTZRrYdS8TJamH0ru/p9MzD1G7d+L8PIiJSAhW5xQhERESKrPhdtkUFKt2Uu82rDAz4GsrVKpTlos/Ltmbz7pZ36VezH+V9yuee3tWLrGwX7vn0D3Yct91PLtDXky4fv0nNUN9Cq0dEpKS44ffRERERKTKyM2DpWHi/NSx+BMxZ9u2VWhRqyEnMTOThXx/m0x2fMuq3UaRlp+W0nY09xeKBD3MoJg6Asj5uzB3eQiFHROQqKeiIiEjp5eIOJ/8BwwJn9sOW2Tf09CaTidgU20IH+xP2s+30NgDOHI/n77vvofnu9by8/iMquVmY+2ALIkMUckRErpaCjoiIlF4mE9w2Gdx8of3/oOHAG3p6Pzc/pt86nYo+Ffmsy2e0KN+Cs6lZjPl4FV7JtvvkBGck8emdVamhkCMiki9ajEBEREqHY5th5Ytw+5sQVM2+LSMJPAr/3xez1YzVsOLm7JZnu4uTC2dSMrnnk43sjkumUlIc/7fla6rNeIsaTesVem0iIsXF1WYDjeiIiEjJ988i+KQ9HFwFK17I234DQk5CRgIP//owL2x4gYu/Y3RxcuF0SiYDP7aFHICMCpWpvnihQo6IyDXSqmsiIlLyVe8I3sGQGm+7JiczGdxv3FQwi9XC0F+Gsj9hPwD1ytZjQK0BOe3xR2KZ8fJM9gRGARDi587cB1tQtZzPDatRRKSk0YiOiIiULFmpELfdfpu7L3SeBN2mwCN/3NCQA+Ds5MzwBsMBKONRhhoBNXLaTh46wY5+9zDw99nctf93Qv08+Hp4S4UcEZHrpBEdEREpGQwD/vwIVk8BZ1d4bDO4eua2R/VzXG1A1ypdScxM5JbwWwj1DgUgPjmDd1/+jAHnbCuv9Y5Zy6ipY6lS1tuRpYqIlAga0RERkZLBZIKDv9umpyUdh40fOKyUY8nH+PnQz3m296/VPzfkJGUw4KM/mBXUiC9q38YZ70AqzvycKpVDbnS5IiIlkoKOiIgUT5daNLT9BDA5QZ2eUPP2G14SwKqjq+j7Q1/GrxnPtlPbLrnPyaQM+n/0BwdOpQKw5qbuVFq4iIgGkTewUhGRkk1BR0REiherFXYugQ/awolo+7aQuvD4Vuj7BZRzTGjYfHIzyVnJmK1m3t7ydp72E/uOMOqNxRw8bQs5FQI8+Xp4CyppJEdEpEDpGh0RESlediyAhcNsz1dOgnu/tW8PqHTja7rAqMaj2HZqG0GeQbzQyn4p6+N7D7H/3sE8mpXJ0TYP4xReia+Ht6BioJeDqhURKbl0w1ARESlezFnwdiNIOgZhjeG+78HdcSuUJWYm4u/ub7ctJSsFb1dvTCZTzrYTCen81v9+Gh+KBmBvSDWafDdPIUdEJJ+uNhtoREdERIomq8V2o09zBjS6N3e7ixt0ex2c3Wz3x7kgTNxIWZYspv09jR8P/si3Pb6lrGfZnDYfN/vgdTwhnQEf/UFC7bt49exJfIxsmnw0QyFHRKQQaURHRESKHnMWfNAGTu8Bz0AYvf2G3/vmv7zx1xvM2jkLgFZhrXi/4/s4mfJe+no8IZ3+H23g6Nl0AOp6WfiofwMqREbcyHJFREqMq80GWoxARESKHhc3qNDY9jz9nG3xgSLm/nr3U9azLK5OrrSr2A4TeUeWjhw4yoAP1uWEnCplvfn08c4KOSIiN4CmromIiGOlnoad30HTB+ynobUeDUknoO0YqHKzo6q7rCDPIN5o9wY+bj7UKlMrT/vhf/Zz+L4h9C5ThemN+lK5nC9zH2xBqL+HA6oVESl9FHRERMRxNrwLK14CczqUrQlV2ua2BdeC+4rGSM6muE3M3jmbKTdPwdXZNWd709Cml9z/yMlEYobcT0jKGTqnnMEcFMy9E14mxE8hR0TkRtHUNRERcRyfEFvIAVg33bG1XMaXO7/k/l/uZ+XRlby/9f3/3P/ImTT6f7aJj2p3w2JyIi4glHteHq2QIyJygynoiIhI4bNkw7b5tqloF6pzJ5SrBTc9DN2nOaS0/9I0tCnOTs4ARJ+Kxmw1X3bfw2dS6ffRBk4kZrA+rD4fd3mY2nNnE1o1/EaVKyIi/9KqayIiUriObIRvh0LScdt1N53sb6KJ1QL/Bomi6vMdn2Mymbi39r05oediMXGJDPhsE3FJGQDUCPbhqwdbUM7X/UaWKiJS4mnVNRERKRrKVIXUU7bnm2dCVqp9exEKOQcTD15yetqQekO4r+59lw05B7bs4lD37oQc2A5AZIgPc4cr5IiIOJKCjoiIFIzsDNj6Nez5yX67Tzmo1xsiu0L/r8C1aN4k8+vdX3P3krt5L/o9fo75+ar77d++j5P3DyU0+RTP//EZXY2TzH2wBWV9FHJERBxJq66JiMj1Sz0D7zaHtNMQUh8ib7NfKvrOd4vUyM2lBHoEkmXNAuDLXV/SJaILJlPee+NcaH98CoMWHeBB/4q0TE/krH8wLz7alSCFHBERh9OIjoiIXD/vIChTxfb85HY48bd9exEPOQCdK3emU+VO3FfnPj7u/PFVhJxkBnz8B7FpFl5pPojfojoRNW8O5cLL36CKRUTkSjSiIyIiV+/0fvj7c0hPgDvfsW9rNgwCKkHz4RDW2BHVXbUNJzYQkxjDwNoDc7aZTCam3DwFJ9N/fwe472QyAz7eyOmUTAAiK5RhwMQpBHq7FVrNIiKSPwo6IiJydaxWmNXDtnqayRlu/T/wC8ttj+pvexRxL214iXl75+FicqFpaFMiAyNz2q4m5OzZuJWNz04mtcHd4OpB3TA/5gy7iQAvhRwRkaJEU9dERCQvqwVO7bXf5uQEDe/597kzHNt04+sqAGW9ygJgNsws2LsgX313r4/m3PBhNDu6lRf/+JQm5dwVckREiiiN6IiIiL01U+GvzyAzCZ7cC66euW2NB4OHP0QNsF2XUwwNrTuU3478Rt+afelVo9dV99sdl8T/zf+bZ6wWAAKcrHxyTyOFHBGRIkojOiIiYu/MAUg6Zgs6u5fatwWEQ6tHi0XIiU+L5+nVT7PkwBK77R4uHnxzxzf0iexzVVPVAHbFJjHw44387R7C+NYPsTe8Dk0XzCEwtOj/HkRESisFHRGR0ijtLGyaCfPus117c6GGA23X4ETeZn8NTjESlxpH90Xd+THmR97c9CbJWcl27f+1otqFdp5IYuDHf3A21bb0tE/9etz6/dcEBivkiIgUZZq6JiJSGn33MOz996aYRx+Cyq1y2yq1gjG7wDfEMbUVgFDvUFqFteLXI79iNszsT9hPo+BG+T7OP7//ycef/cS58k0BaBgewKwHmuPn4VrQJYuISAHTiI6ISEmWegZ2LMy7ve4F16YcWGnf5uRU7ELOvnP78mwb12wc/Wv254eeP1xTyNnx20bSHnuYhzZ+TdeYDTSuFMBshRwRkWJDIzoiIiXVT0/Dnx+DYYHQBlC2em5brW7QahTU6w3loxxX43Xae24vb25+k3XH1zH39rnUK1svpy3MJ4wJLSZc03G3H0tk4Xvz6JuVBkCPU9tofd+z+CrkiIgUGxrREREp7gzDtoDAxfwq2EIOwK7F9m3uvtD5JQhrCPm4XqWo2XxyM+uOrwNg6qapGIZx3cfcdiyBez75g5lVb2V+jVs4FFaD1t/Oxs/b47qPLSIiN46CjohIcbZ2GkxrAO80hdTT9m11ekBgFWj9uG1hgRKoT2QfKvlWorx3+XwtFX05W48mcM8nG0nKMIPJxI5ug2jz3Vz8yvgXQLUiInIjaeqaiEhxkXoavMvab0s/C4lHbM/3LbOtmHZeYASM2lKsR2zOO5R4iI+3f0yEXwQPNngwZ7urkyvvdHiHMJ8w3J3dr+sc0cvWMWbZYZJdbKGmeZUyzBzSDG93/VMpIlIcaURHRKSoWz0FZjSFt+pBdrp9W83bwckVqrUH7+C8fUtAyEnISKD3kt4sObCEmf/MzLNUdBX/Ktcdcrb8uBrLmJH8b+U7lEs7R4uqZfh8qEKOiEhxpqAjIlJUWC1w9mDe7edi4Mw+MKfDoXX2bRWbwlMHYdAiqNHxxtR5gwV4BNC1SlcAnExOl1xh7XpsPnSGY5NexsucSWjaOZ44vprPhjTDy00hR0SkONPf4iIiRcF3j8DuH8DJBZ7cb1vi+bzqnSD6K6jYPO8IjZMzePjd2FoLidlq5tcjv7Lm2BomtZ5kd1PP4Q2GE+EfwYBaA/B29S6wc246dJb7PvsLt+ZDeH3t+2QGBXPnzLcUckRESgCTURBL1BSypKQk/P39SUxMxM+vZPyDLiKlkNUK8f9A2lmoerN929yBsGep7flDa6B8g9y27HQwZ4Bn4I2r1QFG/zaaFUdWAPBJ50+4qfxNhXq+P2POMmTmn6Rl2Vam61LehTeHtsHbr+CClIiIFLyrzQaauiYiciNYsmFqTfigDSwdk7e96i22IFPnzryjNq6eJT7kANwWkbsy3PnAU1j++v1vhny2MSfktK1RlumPdFTIEREpQTQ2LyJSULLSYP+vcHQjlKkKzR7IbXN2ta2ClhoPZ/ZDchz4hua2N7kPmg2zn7JWAqVmp/JTzE8s3LeQV9u+SiW/SjltHSt35M5qd9K9WneahzYvtBr+WvgLrv8bx32VmvF+g7toVzOYjwY1wcPVudDOKSIiN56CjojItchMsd2M0+OC+6tkJsO8QbbnlVrZBx2AWt3AJxgqtwZnN/s2l+tbNay4mLdnHm9ufhOAhfsWMrrJ6Jw2FycXJrWZVKjn3/D3Ptyfexp3SzY9YtbjElmTkYO6KuSIiJRAJfurQxGRgnZ4PbzXCl4Nh79n2bf5hthGbQBO/G2brnahNk9A/znQ8hHwKnNDynWko8lHScxMtNvWvVp3XEy279h2n9t9Q+tZv/80Qxft550GvbBiYl+NxjwyeZRCjohICaURHRGRi53cCQdXQexWuOVp2zS089z9bAsKABzfnLdv+/+BqxeEN7dNVyuFouOjeeOvN9h2ehvPNH+Ge2rfk9NW1rMs428aT92ydakbVPeG1bR232ke+OIvMs1WVlZqQkSNcJ56ZgAenh43rAYREbmxFHREpHSymOHsAdv1MrVut2/bsxRW/juFqkYn+6BTrpYt7ARWhqAaeY9bv0/h1VxEGYZhtxS0j6sP205vA+DHmB/tgg5A35p9b2h9a/8+wAML95JptgLQsXYIT99zG+4uGskRESnJFHREpGSzmCEjAbzL2m+fdSccXmt7/swR+2ttQqNyn8dG24cXZxcYdwBcLrrGppSJS41j8f7FrDq6ioG1B9K9WvectuqB1YkMjASgfXh7rIYVJ5NjZkpv/GA27u++SdUWw9gVFEGnOiG8O7Axbi6auS0iUtIp6IhIyWC12q9YZs6Cj26BM/sgrDE88Iv9/uUic4NO/C6o1CK3rWJT6D4dykdBcJ285yrlIQfgRMoJ3ol+B4Dfjv5mF3QAZt42Ez83x973bP1XS/CfNhknDF7a8Anzhk9ikkKOiEipob/tRaT4OrEFZnaDN2rAmqn2bS5utpEcSxac2g0X3xs5oi3U6Qm3Pmu/zDPYFgpoMgTCGpWa1dAu5WjyUT7a9hH3/3I/vx35za4tqlwUge62e/ucTj/NxfeednTI+W13PA9ug+hy1QHY37Atkx7uopAjIlKKaERHRIqOi0dlALZ8CZu/gHMxMHAeVGic2+bkCofX2Z6f2Z/3eCH1wN3Xdl2NOcN2483z6vWyPQSAbGs2Jky4OOX+sxCTGMOMLTMAqOpflVsr3ZrT5uzkzGvtXqOyX2XCfMJueL1XsnL3SUbM/pssnHnxpqE8bt3HsNfH4qZrckREShUFHRG5cTKTbVPKvIPsty8YZrvJpjkLntxj35Z6Co79aXt+LsY+6ARVA0zgXQ7cvPKeb+A3cMFF8pLXyiMrmbVzFv+c/ocZHWbQonzuFL4mIU1wMblgNswcSTqSp2/LsJY3stT/ZM3K4rcthxmxZB/ZFtsIU8fGlXmw3524OmskR0SktFHQEZGCkZEECYchKRbKN7CfDnbuEHzYDjISIWoA3PWBfd/E45Dw7wfpzBRw98ltC6hk+69PqC0IXcjV89+FBC4zTUohB4CEjAR2nt3JrjO7GFx3MK5Ouctep2ansvmkbZns6Phou6Dj7erN1FumEhkYSUXfije87vywJCSw/f4RJJ5MhFYPgbMr3aPCeKtvFC4KOSIipZKCjohcvR0Lbde7pJ2F26fYt/09C5ZNsD3v8xnU653b5hVkCzkAicfyHte/IngE2EJNRoJ90KnZDf4v9tIjNnD5kFMKZVuyOZV+Ks9Uslc2vsJPh34CoE2FNtQsUzOnrWFwQwAq+FTAzTnvIgvtK7UvvIIL0LZhj+Cxcyu1gceiF7D//jG8qZAjIlKqKeiIlDapp23Xs6Sdsa0q5l/Rvm1OH9t/I9rkHXnZ9BkcWmN73vF5+0DiE5L7POmEfT93XwiuC56BENYwb00937v8zTUvvK5GAFugcb3o9zX4p8FsO7WNII8gVvRdYddWO6h2TtDZfXa3XdCp6FORFXevINgruPALLyQ/74jjzeBbeNX5HzJc3Ei+7U6FHBERUdARKZbMWXBql226mLtv3vCw4iXbimQZiTDsV/spXDsWwk/jbM/v+hCi+ue2uXrZ+sGlR158LvgwnHLSPugE14ZGg8AvDMJvytv3kfWX/3kuF3JKqQxzBiaTCXfn3BXfTqSc4IlVT3A0+Sjtw9szqc0kuz4mTFgMC/Hp8SRlJdmtetY8tDn31r6X2kG1aR7a3L6fyVSsQ85P22N5bO4WzH4VeLHFUBo0q8vE4R1xdtK0RRGR0k5BR6SwGQZkp4NhtQ8GAPt/tY1+mDOh+YP2bTsWwsYPbBfwd54E1TvktqWdsV3zAlDrDug/x77v8c1w8N/lgDOT7G+G6VXG/jgXcvMCNx9w8QD3S0wJa/EINOgPviH2I0EAofXgzncu/TuQHImZicSlxnEm/Qwtw1piuiCELjmwhKmbpnI24yyvt3udrlW65rT5u/uz88xOwLbs88XqBNUhNTuVqgFVSc9Otws6dcvWpW7ZuoX4U91Y6dt3cO6br9nSazijvt2BxWpbeKBql1uY2CdKIUdERAAFHREbS7ZtupY5HVy9bR/kL7R7qW31L8OApkPt2/75DrbPh+w0aP8/+1XBzhyAGU0AwxYQen1o3/e3yXB8E2CCpg/YL62cdsa2EhnYRk8udGFwSU/I+/N4Bvz7xATp5+z3D64DLUaCVyCEt8jb95mjeZd4Pq9i00tvL4UMwyDdnI6TyQkPF4+c7RnmDGbtnMW5jHOU9y7P4LqD7fo9+fuT/BH7BwDrBqyzCyRuzm6czTgLQGxqrF0/b1dvynmWw83ZjRCvi96fwNPNny6wn60oO/Xuu5yeYQvUP+w3YYmwvYf7NKnIa70bKOSIiEiOfAed1atX88Ybb7B582ZiY2NZtGgRPXv2vGKf33//nTFjxvDPP/8QFhbGU089xYgRI661ZikJkk/abuTo5Gyb6nShU3sg6bhtelZEG/tRkISjsHOxrW94c1v7hZY+CWmnbRe2d59m37ZmKmz+3DZ6MuBr+0Bycgd8dIvtebNhcPtFN59c8aLtInw337xB5+wB2P1Dbt8LuXoB/95IMSsl7+8h52czbO0XXlh/Ppy4eNp+XrvjekLT+23T1spG5j3u7W/CHW+Bu3/e0BJSB257JW+f8y4XckoYs9XMwcSDpJvT8XD2sLtuBWDRvkXsObeHlKwUnmv5nN2F+ksOLGHiuomYDTMTW06kT2SfnDYnk1POvWcalmuYJ+iU8yyX8/x02mm7oFPBuwKh3qGU9y6fczPOCy3rs8zuPjelzeEzqXyTGUL3f1/fenQzP0W0oG/TirzaqwFOCjkiInKBfP+LmZqaSlRUFEOHDqV3797/uX9MTAzdunXjwQcf5Msvv2TdunU88sgjlCtX7qr6F0mpp8FqAZMT+JTL25aRaPvm378iuOZ+00tWKpzea5vC5F0ud9nc847+afuw6+QCVdrZt8Xvhvidtr6VW4Nf+dy2jCTY9o2trUw1qNHRvu/fs2wBwbDYRhwuvF7j0FrbaITVDI0GQ6ULrq3ISoX5Q21t5RvYLj6/0I9PQczvtvYHlttPidq5BBY9ZBsp6fQitHzEvu9bdf49bhQ8tNq+bd3bEP2l7fnIP6HcBR9Azx7MXdmrzZi8QWf3D5AcC76XuIFhRlLuEsZZqfZtLhdc8J6dkbfv+W/ss9Pytrl65z6/+LjuPhDWGNy8bdewXKz5Q1Cnpy2wXLziVd27bI9LXb9iMtmCzOVc+GdRCAzD+LeM3PeSxWohJTsFi2HBzckNHzf7aXpHko6Qbk7HwKBWmVp52vYn7MdsNdMwuKHdNSPJWcl8u/dbsq3ZRPhF0Dmis13f96LfY3/CfjItmcxoPwMnU25QW3JgCW9tfotMcybPtniWblW75bSlmdPovcT2d1CrsFZ82Ml+tG3lkZWsOrYKgCeaPEGQZ+69fzxdPDEbZgCSspLs+rk5u+Hj6kNKdgoJmQl5fnfNQpvh7ORMOc9yeLnaryRXv1x9lvdZnqfPeaUl5FhSUklcspjso8dwqxROYqcezFi5n++ij2OxeuJauTn7AiqyIbIV49rX5OGbqynkiIhIHvn+V7Nr16507dr1v3f81wcffEClSpWYNm0aALVr12bTpk1MmTKl2AWdHccTmb3hMK3n3Y2T2YrZ05UNd31tt0+jtY/jc8R2Efe+dsOIq5j7uyp//Geq//4xAMnhFYluO92ub8vvhuCSlo3V2Yl1/ebbtdX/awIB+3YDcPSmOzlULfdbYv9zW6n/0+sApIeUYXMH+wuLb1o6BdfEdADWxnexW8Wq9rYPCNphmx4VvyWVvfVy29wyztJ8oe3C9MzAGP5KsL+LfNPlv+FxKgGAaO8VpPjlBpKqe5cRttn2Qfdc2jL+OW4fSFptD8LJYpB94Awbg7bZtTVavQ2vY7apOfu+/on48pk5bWFHV1Nlm60tOXE12xLs+9602YRzRghWVyt/fGvfVvvPv/HfXx7DZOLYgl84HpE7ncvnbDQ1d1TGwETyuRj2me37Vt/kgmdCJIbJiR3zt9qFRZ/dR6m4qz4GcGjZYTL35vY1MhOpudKMQSLngv/h9Fn74watmU2Zk7brLfbeURaTR25AcTm8jIg/FwJwtEF7Mmv2tetbdcFDmKwGyQFliO/0ql2bz59vEHx4D2BwsPN48K+e2xi3liprPgUMTtS8icwGD9v1Lb/kQdwyM8j09CLuDvsP/y7RUyi/bxMAh9s9hlNI65w269loKq+w1RFfuS6Zzf9n1zfgl4fwTUrE6uTMzN721xSZdn5AxX9WAbDipnuh0h05bZa0Y0QsfQOAP0IjWNE21K6v96r5VD1lm9o3/uQftmuM/mUc/IPem21tqw+v4vcaudcVWS1ZPPSjBYCkwN08nXjRn836wzx03NY+2fo7zr65v0OnE3t5bJ0XTrhw9NAunt5t33fkLzVwy7aQ6VOWpy32bVW2xdH0wBngDB/fvJ2UMvE5bT7n4mmwyvZnHlu1HjFR9v/fNFr+NZ4pCVhc3dh4x/12bRX3/E2lnbYbq+5qcRvnykfktLmnJdPkF9vv/HSFauxt3smub73Vi/E7Y5sqt6HHgxjOzjltITE7qbbF9mXE/sa3EB+RG1KdzNm0WPIpAInlwvinbQ+749be8BNBxw8A8OftQ8nyzP1SICRmJ1G/LcDJYmZ3iy4cqt8qp80tPYWBL78EwJHKdXh4mx//XoYDwMyWA7m/TRUmt66Cv6cWshARkUsr9K8HN2zYQOfO9t/AdunShU8//ZTs7GxcXfP+I5WZmUlmZu6H26SkpDz7OMLxhHS+2XSUPnud8MlwIj7Q4JtN9hcF1z2eSLWdtl/rkgqHWBaX294h7Qhd/m3b5JSQp2/XAwYh51xIcydPW8TxMzT5t++6kMN8cy63vW7mEe76t+2f7LQ8fW8+YqbKcVv7uC1HyHDK/RZ5ZHwcN/3bd2eZE3yTkdvX15rAwH/bDlbMzHPc5scyqH7A1v52lRj2XfDtdN+EU7T7t+8v3uf4Bvu+/Xc745ENx4Py/g6rH8smaqftg9bMiqfYcjy3vXlKAp3/bfvJxZKnb7f9TgQnQYKXKU/bkKNW+u20BZQ5wRlsPp3bXjHzNH12ZAPwW1YS33ja952+9zSRJ7OwAv+rar8aWc/Y/fTZaruof53vHlacrpPT5m0+w9Do0wD8XSmdd73sj/vSnv00PWSb0ja94iGSXXJHhG49s4d7t54D4BP3PSy46AL077cm4GKF/cHpzAi0b3tyzyHa7Um2/azlD3LEI3f1rkZJhxiy1TYyNd90mFlZ9n3n/JNMmVSDU75mBofatz0Yc5IOW22fOJ8ue4RtR3NHJaukn+D+f9uWZZ3iLSf7vu/tTqXKKYMsFzN3VrZvu/t47nEn+x9hdXxue5nsWOb827Yx6QzPX/Rn8+qeZKKO2trvqhxDhlNATlvn+FMM/bfvux4n+SExt6/JsPLjv227gi2MCbA/7tM7XLjliK19SHAiJ71z25ueTGfwVtvfS7Mzs/gm3b5vr7//wS87jWPeZflfGfu//x7atoWbD9qW6H7Pux57yuROS6xx7ih9NtsWkdh9Notvsivb9e2weS3hKadIcfFgTKh9WOm/Zysdd9n6znGtwp+huWElOO0sA/49bmzcOb5xsh9Ra7J5A5FnYgAYU+E2LE65fbse+oeu0ba+PxjlWH46N6y4m7MY/LetLbpsNb7xbGR33Gf/3kT12B0ATAy5hbOeuV8utD4eQ9eDtrYffKqxIDPcrm8PFw98zBm4norLCTn+nq480KYKQ1pH4OehgCMiIldW6EEnLi6OkBD7C2dDQkIwm82cPn2a8uXL5+kzefJkXnjhhcIu7boZ5J0qkY4bYBs9ScXDri2F3NGSNNy5mBUnwIqRpwUSDW/A9qH3rGG/GlbyBcdNNezPCZBpuAK2D1MW7K+/OGEEAccBOGYta9+PC+6eTt7j2mqy3QTyLAGXOK5N3AXPL2bGOc+2FDxyjpuM/dSetAt+1kzy3tzQuOi/RZ5RWNNtrnBcw+nSz3M4AZZ//3tRV0vu+9YwLvqgaXW7YL+8974xLB5ANsYlzmlk545kWc3216bY+v3blnXRVFHAmhkMxPz7wt2ubGtGhQueX7RK3IXHMPvmrcl64Xs+7/tU/tul/o48z3xBmHKxWvO0v9W4H2muHsR6lSHAy5VhbapwX6sIfBVwRETkKpmM85Ptr6WzyfSfixFERkYydOhQxo8fn7Nt3bp1tGnThtjYWEJDQ/P0udSITnh4OImJifj5Oe4u6MkZ2cQmZhC3az2G1YyzqwfBkfb3pEg4sY/0c7YpIGUiGuDuHZDTlpmWxNmYaAA8A0IJqGB/EXn83j+xZGdgMjkRWsd+ykpifAxpp2zfGgeE18bTL/cDX3ZmGmf2bwbAzTeIMpXq2PU9fTAac7rt2/3g2q1xuuBi85Qzx0iJs31I9C1fDe8yude2WCxmTu3eAICrlx9BVaLsjnv2yA6ykm3hq2yNZri45X4wTE+MJ+nYXgC8givhW87+eqSTuzaAYcXZzZOy1RvbtSWe2EdGgm06T5kqDXD1zP0QmpmaSOLhf2y/wzKh+IZWtet7Zt9mrOYsTM4ulI1sZteWEn+YjH+n5/iG18TdJ/cDtTkjjaQY27fLbn5l8KlQ3a5v0qF/sGTYAmxgLfuVxzLOxJF52naDTI/QSrj75wZGizmbtIO2ep28fPCuaH/cjOMxWNNSMJlMuFWOxNk1NyxYkhIwx8dhApyDyuEcaB8YrQcP2GbQubljqmD/Id44FQ8ptj9zKoRjcrsghKSlwcl/V/TyD8BUxv64xpHDYDGDiwumcPsRBePMaUiyhVBCwzB55gYaIzMTTvw72uXrh6msfSgxjh+DrEwwmTBF2P+5GQkJcO7fpa6DQzB5XzD9zGyGo4dtL7y8MYXY/51hxJ6Af/9sqBSB6YJpV0ZyMpz+d2pYUFlMfrkjCoZhwKGDthfuHpjCckMRgBF/ElL/XUCiYiVMF4w+G2mpcDLO9iIgEFOg/TVRxuFDtuv4XF0xVbR/79v9DstXwOSR+/+NkZkBJ2xfPODnjynI/ssH49gRyM7m/9u7t9io6i2O478pnXbEQ6vIrU2xFGPaVBNTCtrKLQQo3kh4sfpgLQYTMaIpjTFVH8QnMfES0KohKfQJRG2rJKLSxF5ULglkqjEgXoGeYwmHEy6lHAK16zyMnZ5Np+3stjPt7H4/yTzMnjW7e//4dyVrpnsjX5J8c3Kcr50/J50L3bFNM2fJN7nvmxe7dk3659/Xp934D/lmOD98so5/SVf+vjZtzlzHtVd28YL0n9C3kpo2Xb4pfT3YenqkUydCT1ID8mU4r42zs/+W/ns59KeeszLlS+77bM26u6WrVyW/P7TWfJGHIp+k2VMnK+Bn2AQAhFy8eFHp6elDzgYxH3SWLFmigoICbdnSdz1KQ0ODSktLdfny5Yh/una9aE8GAAAAgLdFOxvE/D6yxcXFamx03kVo3759mj9/flRDDgAAAAC45XrQuXTpktra2tTW1iYpdPvotrY2nToV+rOIF198UY8/3ndHsPXr1+vkyZOqrKzUsWPHtH37dtXU1Oj5558fnTMAAAAAgOu4vhnB4cOHtWzZsvDzyspKSVJ5eblqa2vV0dERHnokKScnR3v37tXGjRtVXV2tzMxMbd26NeFuLQ0AAAAgcYzoGp144RodAAAAANI4ukYHAAAAAOKNQQcAAACA5zDoAAAAAPAcBh0AAAAAnsOgAwAAAMBzGHQAAAAAeA6DDgAAAADPYdABAAAA4DkMOgAAAAA8J3msDyAaZiYp9L+gAgAAAJi4emeC3hlhIAkx6HR2dkqSZs+ePcZHAgAAAGA86OzsVHp6+oCv+2yoUWgc6Onp0Z9//qkpU6bI5/ON6bFcvHhRs2fPVnt7u9LS0sb0WLyIfGOLfGOLfGOLfGOLfGOLfGOPjGNrPOVrZurs7FRmZqaSkga+EichvtFJSkpSVlbWWB+GQ1pa2pj/I3sZ+cYW+cYW+cYW+cYW+cYW+cYeGcfWeMl3sG9yenEzAgAAAACew6ADAAAAwHMYdFxKTU3VK6+8otTU1LE+FE8i39gi39gi39gi39gi39gi39gj49hKxHwT4mYEAAAAAOAG3+gAAAAA8BwGHQAAAACew6ADAAAAwHMYdAAAAAB4DoMOAAAAAM+Z8IPOe++9p5ycHAUCARUWFuqbb74ZtL6lpUWFhYUKBAKaO3euPvjgg341dXV1ys/PV2pqqvLz89XQ0BCrw08IbjKur6/XypUrNX36dKWlpam4uFhfffWVo6a2tlY+n6/f48qVK7E+lXHJTb7Nzc0Rs/vpp58cdazhPm7yXbt2bcR877jjjnAN6zektbVVq1evVmZmpnw+nz799NMh30P/jZ7bfOm97rnNmP7rjtt86b/Re+2117RgwQJNmTJFM2bM0Jo1a3T8+PEh35eIPXhCDzq7d+9WRUWFXn75ZQWDQS1evFj333+/Tp06FbH+jz/+0AMPPKDFixcrGAzqpZde0nPPPae6urpwzYEDB/TII4+orKxM33//vcrKylRaWqpDhw7F67TGFbcZt7a2auXKldq7d6+OHDmiZcuWafXq1QoGg466tLQ0dXR0OB6BQCAepzSuuM231/Hjxx3Z3X777eHXWMN93Oa7ZcsWR67t7e2aOnWqHn74YUcd61fq6urSXXfdpXfffTeqevqvO27zpfe65zbjXvTf6LjNl/4bvZaWFj3zzDM6ePCgGhsb1d3drZKSEnV1dQ34noTtwTaB3X333bZ+/XrHtry8PKuqqopY/8ILL1heXp5j21NPPWVFRUXh56WlpXbfffc5alatWmWPPvroKB11YnGbcST5+fn26quvhp/v2LHD0tPTR+sQE5rbfJuamkySnTt3bsB9sob7jHT9NjQ0mM/nsxMnToS3sX77k2QNDQ2D1tB/hy+afCOh90Yvmozpv8M3nDVM/43emTNnTJK1tLQMWJOoPXjCfqNz9epVHTlyRCUlJY7tJSUl2r9/f8T3HDhwoF/9qlWrdPjwYV27dm3QmoH26WXDyfh6PT096uzs1NSpUx3bL126pOzsbGVlZemhhx7q96njRDCSfAsKCpSRkaHly5erqanJ8RprOGQ01m9NTY1WrFih7Oxsx3bWr3v03/ii98YO/Tc+6L/Ru3DhgiT1+33/f4nagyfsoHP27Fn99ddfmjlzpmP7zJkzdfr06YjvOX36dMT67u5unT17dtCagfbpZcPJ+Hpvvvmmurq6VFpaGt6Wl5en2tpa7dmzR7t27VIgENDChQv1yy+/jOrxj3fDyTcjI0Pbtm1TXV2d6uvrlZubq+XLl6u1tTVcwxoOGen67ejo0BdffKEnn3zSsZ31Ozz03/ii944++m/80H+jZ2aqrKzUokWLdOeddw5Yl6g9OHnMfvI44fP5HM/NrN+2oeqv3+52n1433Dx27dqlTZs26bPPPtOMGTPC24uKilRUVBR+vnDhQs2bN0/vvPOOtm7dOnoHniDc5Jubm6vc3Nzw8+LiYrW3t+uNN97QkiVLhrVPrxtuFrW1tbrpppu0Zs0ax3bW7/DRf+OD3hsb9N/4of9Gb8OGDfrhhx/07bffDlmbiD14wn6jM23aNE2aNKnflHnmzJl+02ivWbNmRaxPTk7WLbfcMmjNQPv0suFk3Gv37t1at26dPvroI61YsWLQ2qSkJC1YsGDCfSIzknz/X1FRkSM71nDISPI1M23fvl1lZWVKSUkZtHairl+36L/xQe+NL/rv6KP/Ru/ZZ5/Vnj171NTUpKysrEFrE7UHT9hBJyUlRYWFhWpsbHRsb2xs1L333hvxPcXFxf3q9+3bp/nz58vv9w9aM9A+vWw4GUuhTxPXrl2rnTt36sEHHxzy55iZ2tralJGRMeJjTiTDzfd6wWDQkR1rOGQk+ba0tOjXX3/VunXrhvw5E3X9ukX/jT16b/zRf0cf/XdoZqYNGzaovr5eX3/9tXJycoZ8T8L24Pje+2B8+fDDD83v91tNTY0dPXrUKioq7MYbbwzfoaOqqsrKysrC9b///rtNnjzZNm7caEePHrWamhrz+/32ySefhGu+++47mzRpkm3evNmOHTtmmzdvtuTkZDt48GDcz288cJvxzp07LTk52aqrq62joyP8OH/+fLhm06ZN9uWXX9pvv/1mwWDQnnjiCUtOTrZDhw7F/fzGmtt83377bWtoaLCff/7ZfvzxR6uqqjJJVldXF65hDfdxm2+vxx57zO65556I+2T9hnR2dlowGLRgMGiS7K233rJgMGgnT540M/rvSLnNl97rntuM6b/uuM23F/13aE8//bSlp6dbc3Oz4/f98uXL4Rqv9OAJPeiYmVVXV1t2dralpKTYvHnzHLfWKy8vt6VLlzrqm5ubraCgwFJSUmzOnDn2/vvv99vnxx9/bLm5ueb3+y0vL8/RxCYiNxkvXbrUJPV7lJeXh2sqKirs1ltvtZSUFJs+fbqVlJTY/v3743hG44ubfF9//XW77bbbLBAI2M0332yLFi2yzz//vN8+WcN93PaI8+fP2w033GDbtm2LuD/Wb0jvrXYH+l2n/46M23zpve65zZj+685wegT9NzqRcpVkO3bsCNd4pQf7zP6+kggAAAAAPGLCXqMDAAAAwLsYdAAAAAB4DoMOAAAAAM9h0AEAAADgOQw6AAAAADyHQQcAAACA5zDoAAAAAPAcBh0AAAAAnsOgAwAAAMBzGHQAAAAAeA6DDgAAAADP+R9cSGfoUOG6VwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -836,14 +835,14 @@
     "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,5).evaluate(), \":\", lw=2, label=\"softplus (k=5)\")\n",
     "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,10).evaluate(), \":\", lw=2, label=\"softplus (k=10)\")\n",
     "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,100).evaluate(), \":\", lw=2, label=\"softplus (k=100)\")\n",
-    "ax.legend()"
+    "ax.legend();"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Solving a model with the exact maximum, and smooth approximations, demonstrates a clear speed-up even for a very simple model"
+    "Solving a model with the exact maximum and soft approximation, demonstrates a clear speed-up even for a very simple model"
    ]
   },
   {
@@ -855,16 +854,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 172.240 us\n",
-      "Smooth, k=5: 161.790 us\n",
-      "Smooth, k=10: 150.367 us\n",
-      "Smooth, k=100: 193.054 us\n"
+      "Exact: 190.871 us\n",
+      "Soft, k=5: 175.473 us\n",
+      "Soft, k=10: 150.303 us\n",
+      "Soft, k=100: 188.151 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13ea3acf77af43019375fb4f53395b28",
+       "model_id": "58008e3b72c34a4684a5b3bd343a813d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -907,10 +906,180 @@
     "    for _ in range(100):\n",
     "        sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n",
     "        time += sol.integration_time\n",
+    "    print(f\"Soft, k={k}:\", time/100)\n",
+    "    sols.append(sol)\n",
+    "\n",
+    "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the minimum and maximum functions, an alternative smoothing functions (smooth_plus, smooth_minus) are provided.\n",
+    "$$\n",
+    " \\textrm{min}(x, y) = 0.5 * (\\sqrt((x - y)^2 + \\sigma) + (x + y))\n",
+    " \\quad , \\quad\n",
+    " \\textrm{max}(x, y) = 0.5 * ((x + y) - \\sqrt((x - y)^2 + \\sigma))\n",
+    "$$\n",
+    "where\n",
+    "$$\n",
+    " \\sigma = \\frac{1}{k^2}\n",
+    "$$\n",
+    "For the smooth minimum and maximum functions, the recommended value of k is 100, where the function closely approximates the exact function, but is differentiable.\n",
+    "\n",
+    "Changing between the soft, smooth, and exact functions can be done by setting the `min_max_mode` and the value of `k` stored in `min_max_smoothing`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exact maximum: maximum(x, y)\n",
+      "Smooth plus (k=100):  0.5 * (sqrt(0.0001 + (x - y) ** 2.0) + x + y)\n",
+      "Smooth plus (k=200): 0.5 * (sqrt(2.5e-05 + (x - y) ** 2.0) + x + y)\n",
+      "Softplus (k=10): 0.1 * log(exp(10.0 * x) + exp(10.0 * y))\n",
+      "Exact maximum: maximum(x, y)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = pybamm.Variable(\"x\")\n",
+    "y = pybamm.Variable(\"y\")\n",
+    "\n",
+    "# Normal maximum\n",
+    "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
+    "\n",
+    "# Smooth plus can be called explicitly\n",
+    "print(f\"Smooth plus (k=100): \", pybamm.smooth_plus(x,y,100))\n",
+    "\n",
+    "# Smooth plus and smooth minus will be used when the mode is set to \"smooth\"\n",
+    "pybamm.settings.min_max_mode = \"smooth\"\n",
+    "pybamm.settings.min_max_smoothing = 200\n",
+    "print(f\"Smooth plus (k=200): {pybamm.maximum(x,y)}\")\n",
+    "\n",
+    "# Setting the smoothing parameters with set_smoothing_parameters() defaults to softplus\n",
+    "pybamm.settings.set_smoothing_parameters(10)\n",
+    "print(f\"Softplus (k=10): {pybamm.maximum(x,y)}\")\n",
+    "\n",
+    "# Change back\n",
+    "pybamm.settings.set_smoothing_parameters(\"exact\")\n",
+    "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the plot of smooth_plus with different values of `k`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Xb7bfLin2ZltS3H2VFhoaSnJyMm3btuWJJ54AoFy5cjZ9/P39+fLLL3FwcKBEiRKMHTuW2NhY3nnnHQCGDRvGp59+yqZNm+jQoQOzZs0iLi6OGTNmkCOH5Q/xpEmTaNmyJWPGjCEoKIhx48bx9ttv06FDBwDGjBnDunXrmDBhApMnT7ZOsQoICCB37tw29fj5+TFp0iQcHR0pWbIkzzzzDGvXrqVXr15pes916tRh6NChABQvXpxNmzbxxRdf0Lhx4/v6HZ45c4Z27dpZf2+FCxe+a/9Tp05RpUqVVNuOHDlC48aNad26NRMnTsRgMFjb7nW/kru7u3U7ODiYb7/9lipVqpCQkMDMmTNp2LAh69evp379+tZ+efPmtdZ049+/iIiIyH1JToQF3eHwcjiwlCutZvDGgggGxls+z3jFejOtbUPy+3vYt047yLpBx9ULvCwrS+Doatvm4Hizzd035bEeOW+2387Z42abs3vqfe6hQoUKNGzYkHLlytG0aVOaNGnC888/j5+fn7VPmTJlcHC4OeAWFBRE2bJlra8dHR0JCAggPDwcgIMHD1KhQgVryAFLuDCZTBw+fBh3d3cuXLhAnTq2T7ytU6cOe/fuvWfNZcqUsZmCFRwczL59+9L8nmvVqpXi9YPcLzNgwABee+01Vq1aRaNGjWjXrh3ly5e/Y/+4uDjrKM3t++vWrUvHjh2ZOHFiivaiRYumuaYSJUpQokQJ6+tatWpx9uxZxo0bZxN0boSj2NjYNJ9bREREJFUOjuDshmVekAMfr/iXHcmlGFq3D//bv4iK335F/hKF7FykfWTdqWu1+8Hgg5afQvVs2/wK3mxr8VnKYzvNvdl+u0qdb7aVbnVfpTk6OrJ69Wp+//13SpcuzVdffUWJEiVsVuJydna2OcZgMKS6z2QyAWA2m21GIm7vl9r2vY671d2ufb/udN0bAe/WqXy33w/Us2dPTpw4QZcuXdi3bx9Vq1a9630/OXPm5Nq1ayn2u7q60qhRI5YvX865c+dStN/P1LVb1axZ0zo18IarV68CtosUiIiIiNwXB0fCm46mS+HSvOLyKr9EWG69cCpUmCpL52fbkANZOehkcgaDgTp16jBq1Cj27NmDi4sLixcvvu/zlS5dmpCQEOuCBgCbNm3CwcGB4sWL4+3tTZ48efj7779tjtu8eTOlSln+Qri4uABgNBp52LZu3ZridcmSJVPteyMAhIaGWvelNoUsf/789OnTh0WLFjF48GC+++67O16/UqVKHDhwIMV+BwcHZs6cSZUqVWjQoAEXLthOWQwJCbnrz/fff3/HawLs2bOH4OBgm3379+/H2dmZMmXK3PVYERERkXu5GHORN2d1JfeOSLblXIWDy0UK58rB3N41yeOb/aar3SrrTl3LxLZt28batWtp0qQJgYGBbNu2jUuXLlkDx/3o3Lkz77//Pt26dWPkyJFcunSJ/v3706VLF4KCggAYMmQI77//PkWKFKFixYpMmzaNkJAQZs2aBVhWC3N3d2flypXky5cPNzc3fHweztNyN23axNixY2nTpg2rV69mwYIFLF++PNW+RYsWJX/+/IwcOZLRo0dz9OhRxo8fb9Nn4MCBNG/enOLFi3Pt2jX+/PPPu/7+mjZtSs+ePTEajSlWQXN0dGTWrFl07NiRBg0asH79eus9SumZujZhwgQKFixImTJlSExM5Oeff+aXX37hl19+sem3ceNG6tWrZ3N/j4iIiEiaxEfA6hHQaCS4+xG+7zSvfxeGf4wJ54Rktpb2Y+4rNQn0SjllP7vRiI4deHt7s2HDBlq0aEHx4sV59913GT9+/D2nQd2Nh4cHf/zxB1evXqVatWo8//zzNGzYkEmTJln7DBgwgMGDBzN48GDKlSvHypUrWbZsGcWKFQPAycmJL7/8km+++YY8efLQunXrB36vNwwePJhdu3ZRqVIlPvzwQ8aPH0/Tpk1T7evs7MycOXM4dOgQFSpUYMyYMYwePdqmj9FopG/fvpQqVYpmzZpRokQJpkyZcsfrt2jRAmdnZ9asWZNqu5OTE3PmzKFMmTI0aNDAeu9TeiQmJvLWW29Rvnx56tWrx99//83y5ctp27atTb85c+akeREHEREREau4azCjDeyaDj+34/i5UOZM/hX/GMtsnIb/ejG/S0OFnP8YzLfeCJFJRUZG4uPjQ0REhM2DGAHi4+M5efIkhQoVSvVmc7G/ggULMnDgQAYOHGjXOqZMmcLSpUv5448/7FbD8uXLGTJkCP/88w9OThk7oKq/GyIiIlnM5WMwrRnEXMLo5kfHpBFsjwmi/eG1NArfT8V5M8iVL/e9z/OYu1s2uJVGdCTb6N27N/Xr1ycqKspuNcTExDBt2rQMDzkiIiKSBeUsysm2U/kgTyE6JA5ne4zl9oT9Tz9HlWULs0XISQ992pIHsnHjxrtOuYuOjn6E1dydk5MTw4cPt2sNL774ol2vLyIiIo+v49eP897SkVw1J3PK9y+IfZHy+fyZ+XINfDyc732CbEZBRx5I1apV7/lQzVOnTj2SWkRERESylIhzcHQ1VO0BwD/r/uLNn66S5AQjnj+PW353S8hxV8hJjYKOPBB3d/d0rUwmIiIiImlw7RT81BKun4GkOP7J34mkCUvxTAASoNem3DRf+BTebgo5d6KgIyIiIiKS2RxZZQk5QPyW73jlj3zEVXmJTzd9jYOnJ81nfqOQcw8KOiIiIiIimU2N3uy/doQLx9by8aW3uZTgCC4eLOw4lK+61cDL786rjYmFgo6IiIiISCYTEh7CqAN/cdzXRIzTeUgoSe0iAUzpVhUPF32ETwstLy0iIiIiYm+he+HyUevLFfMm8/6P0by+Igk3303ULRbAD92qKeSkg35TIiIiIiL2dG4X/PwcOHtAjxVsPmOgybc7cU2GJ/ebSQgqz6sTquHm7GjvSh8rGtGRe+revTtt2rTJ0GsYDAaWLFmSodc4fPgwuXPntj4wdPr06fj6+mboNe/H888/z+eff27vMkRERORRMJth9QiIj4CoUMKXvkePhYcYX6kDyQYHjhatyKufva2Qcx8UdMTq1KlTGAyGez4X53E1fPhw+vbti5eXV4acPzQ0lE6dOlGiRAkcHBwYOHBgqv1++eUXSpcujaurK6VLl2bx4sU27SNGjOCjjz4iMjIyQ+oUERGRTMRggBd/YmdQcU4HVqfJ8edJSDaxJU9ZFr70Dk0XTMPNw93eVT6WFHQkWzh37hzLli2jR48eGXaNhIQEcuXKxfDhw6lQoUKqfbZs2UL79u3p0qULe/fupUuXLrz44ots27bN2qd8+fIULFiQWbNmZVitIiIiknlsuHaAgQ5JPOvowHWTEYDmZXPz7tCOuLq72bm6x5eCjp0sXLiQcuXK4e7uTkBAAI0aNSImJga4OVXs448/JigoCF9fX0aNGkVycjJDhgzB39+ffPny8eOPP9qcc9++fTRo0MB6zt69exMdHW1tN5lMfPDBB+TLlw9XV1cqVqzIypUrre2FChUCoFKlShgMBp566imb848bN47g4GACAgLo27cvSUlJaXqvBQsW5MMPP6RTp054enqSJ08evvrqqzv2X79+PQaDgevXr1v3hYSEYDAYOHXqFACnT5+mZcuW+Pn5kSNHDsqUKcOKFSvueM758+dToUIF8uXLd8c+V65coXr16rRq1Yr4+Pg0vbfb3+fEiRPp2rUrPj4+qfaZMGECjRs3ZtiwYZQsWZJhw4bRsGFDJkyYYNOvVatWzJkzJ901iIiIyGPgwh74L9AkGhNZ+O1QJk5OoFzoOVwCNvJMuWC+7FgJZ0d9VH8QWfa399O/P9FwQUMaLmjIjrAdNm3nos5Z2z7e9nGKY/uv7W9tv92SY0usbWtOr7mv2kJDQ+nYsSMvv/wyBw8eZP369bRt2xaz2Wzt8+eff3LhwgU2bNjA559/zsiRI3n22Wfx8/Nj27Zt9OnThz59+nD27FkAYmNjadasGX5+fuzYsYMFCxawZs0a+vXrZz3nxIkTGT9+POPGjeOff/6hadOmtGrViqNHLSt8bN++HYA1a9YQGhrKokWLrMeuW7eO48ePs27dOn766SemT5/O9OnT0/yeP/vsM8qXL8/u3bsZNmwYb775JqtXr76v3x9A3759SUhIYMOGDezbt48xY8bg6el5x/4bNmygatWqd2w/d+4c9erVo2TJkixatAg3N8u3J56ennf9ad68ebrq3rJlC02aNLHZ17RpUzZv3myzr3r16mzfvp2EhIR0nV9EREQyuYO/wveNYdkAMJnYtPgvei+4jkci/G+BmY4O9ZnYoaJCzkOQZVddi0mKITw2HLAk5VuZzCZrW2RiyvsgriZctbbfLi45ztoWlxx3X7WFhoaSnJxM27ZteeKJJwAoV66cTR9/f3++/PJLHBwcKFGiBGPHjiU2NpZ33nkHgGHDhvHpp5+yadMmOnTowKxZs4iLi2PGjBnkyJEDgEmTJtGyZUvGjBlDUFAQ48aN4+2336ZDhw4AjBkzhnXr1jFhwgQmT55Mrly5AAgICCB37tw29fj5+TFp0iQcHR0pWbIkzzzzDGvXrqVXr15pes916tRh6NChABQvXpxNmzbxxRdf0Lhx4/v6HZ45c4Z27dpZf2+FCxe+a/9Tp05RpUqVVNuOHDlC48aNad26NRMnTsRgMFjb7nW/krt7+ubMhoWFERQUZLMvKCiIsLAwm3158+YlISGBsLAw658RERERecxFh8MvvcCUBCE/s9ehFK/ufIL/5S5DndD9nCpbl/f6tcZJIeehyLJBJ4dzDgI9AgFwcXSxaXMwOFjbvF1SPlXW39Xf2n47dyd3a5u70/3dGFahQgUaNmxIuXLlaNq0KU2aNOH555/Hz8/P2qdMmTI4ONz8Qx4UFETZsmWtrx0dHQkICCA83BK6Dh48SIUKFawhByzhwmQycfjwYdzd3blw4QJ16tSxqaVOnTrs3bv3njWXKVMGR8ebq30EBwezb9++NL/nWrVqpXh9+3St9BgwYACvvfYaq1atolGjRrRr147y5cvfsX9cXJx1lOb2/XXr1qVjx45MnDgxRXvRokXvu8Y7uTVIAZjN5hT7bgSo2NjYh359ERERsRPPQHjua04s7Y1zUFOe31qQJAx8Wu0lhpmO0O3TQTg5aXW1hyXLBp1uZbrRrUy3VNvyeeVj7Qtr73jsVw3vfP9Im6JtaFO0zQPV5ujoyOrVq9m8eTOrVq3iq6++Yvjw4Wzbts16n4yzs7PNMQaDIdV9JpMJSP3D8q39Utu+13G3utu179edrnsj4N06le/2+4F69uxJ06ZNWb58OatWreKTTz5h/Pjx9O/fP9Vz5syZk2vXrqXY7+rqSqNGjVi+fDlDhgxJcQ/P3abDAdSrV4/ff//9rn1ulTt37hSjN+Hh4SlGea5evQpgHWUTERGRrGGZmwPvBQeRcCkPSSbLZ6HnaxSix3OtcHC492cySTuNi9mJwWCgTp06jBo1ij179uDi4pJimeH0KF26NCEhIdYFDQA2bdqEg4MDxYsXx9vbmzx58vD333/bHLd582ZKlSoFgIuLZeTLaDTedx13snXr1hSvS5YsmWrfGx/uQ0NDrftSm0KWP39++vTpw6JFixg8eDDffffdHa9fqVIlDhw4kGK/g4MDM2fOpEqVKjRo0IALFy7YtIeEhNz15/vvv7/jNVNTq1atFPcmrVq1itq1a9vs279/P/ny5SNnzpzpOr+IiIhkMpE3P1scuXaENZPfYeTPyXj7LsfR4zidaxTg4+fKKeRkgCw7opOZbdu2jbVr19KkSRMCAwPZtm0bly5dsgaO+9G5c2fef/99unXrxsiRI7l06RL9+/enS5cu1tGCIUOG8P7771OkSBEqVqzItGnTCAkJsS5jHBgYiLu7OytXriRfvny4ubndcfWw9Nq0aRNjx46lTZs2rF69mgULFrB8+fJU+xYtWpT8+fMzcuRIRo8ezdGjRxk/frxNn4EDB9K8eXOKFy/OtWvX+PPPP+/6+2vatCk9e/bEaDTaTMEDywjbrFmz6NixIw0aNGD9+vXWe5TSO3XtRiCLjo7m0qVLhISE4OLiQunSpQF44403qF+/PmPGjKF169YsXbqUNWvWpAigGzduTLFogYiIiDxmtkyGtR9C5/lQqD4nf9zAayssXyi/PTsH+/vWYUSbsmmaXSPppxEdO/D29mbDhg20aNGC4sWL8+677zJ+/Ph0r+B1Kw8PD/744w+uXr1KtWrVeP7552nYsCGTJk2y9hkwYACDBw9m8ODBlCtXjpUrV7Js2TKKFSsGgJOTE19++SXffPMNefLkoXXr1g/8Xm8YPHgwu3btolKlSnz44YeMHz+epk2bptrX2dmZOXPmcOjQISpUqMCYMWMYPXq0TR+j0Ujfvn0pVaoUzZo1o0SJEkyZMuWO12/RogXOzs6sWZP6SnlOTk7MmTOHMmXK0KBBA+u9T+lVqVIlKlWqxK5du5g9ezaVKlWiRYsW1vbatWszd+5cpk2bRvny5Zk+fTrz5s2jRo0a1j7x8fEsXrw4zQs9iIiISCZ08Ff44x1IjoPZ7Vn852Y+OWYm0tkDAMdi9XmvXSWFnAxkMN96I0QmFRkZiY+PDxEREXh72y4eEB8fz8mTJylUqFCqN5uL/RUsWJCBAwcycOBAu9YxZcoUli5dyh9//GHXOu5l8uTJLF26lFWrVj3QefR3Q0RExI6SE2F+VyKPreR4oT48929dwECR6+d53eUCz00YYbPwlKTd3bLBrTR1TbKN3r17c+3aNaKiovDy8rJ3OXfk7Ox81weqioiIyGPAyYVpZRvyQ/Ipzh8pBVhGbpq0qU/bpiU0kvMIKEbKA9m4ceNdH6iZmTg5OTF8+PBMHXLAEshKlChh7zJEREQkPcxmSLz5WIhFRxdxbOoEau6IwuOJb8Ehlv4NivI/hZxHRiM68kCqVq16z4dqnjp16pHUIiIiImIXJhOseAsu/gsv/QKunjh+G8JL6yyP4kiMCaZwpwoMbFTczoVmLwo68kDc3d0z5KGaIiIiIo+N1e/Bzh8s23M78lXecYQfvsaNWNM4Z2VaKuQ8cgo6IiIiIiIPovyLmPfMxJQQxSqnhoxfcwxKNcXRbKJquSdo+fH/7F1htqSgIyIiIiLyAMy5yzO2+gv8e+40G/YVsew0GMg7ZDDP1i9s3+KyMS1GICIiIiKSHiaTzcvPto8lZt5Kzl89hGvuJQC892xpeink2JWCjoiIiIhIWiXFwZz2sON7AEwmE/m+3UfHv0y8P9tIwCV/RrUqwyt1C9m5UNHUNRERERGRtEhOhDkd4MR6OLoKs6Mrnx4rQeV/zgGQKxLezVmaFrUL2rVMsdCIjtxT9+7dadOmTYZew2AwsGTJkgy9xuHDh8mdOzdRUVEATJ8+HV9f3wy9ZkapVq0aixYtsncZIiIi2YujM+SpBIDZxZMfDxr4dvclhtV5ldNeQZx9ZRAt3uxh5yLlBgUdsTp16hQGg+Gez8V5XA0fPpy+fftm2ANDQ0ND6dSpEyVKlMDBwYGBAwem2u+XX36hdOnSuLq6Urp0aRYvXpyiz5QpUyhUqBBubm5UqVKFjRs32rS/9957DB06FNNtc4RFREQkAxkMJD89nBFlnuTd/H35cJ8fANfdvYn66keaDOll5wLlVgo6ki2cO3eOZcuW0aNHxn3LkpCQQK5cuRg+fDgVKlRItc+WLVto3749Xbp0Ye/evXTp0oUXX3yRbdu2WfvMmzePgQMHMnz4cPbs2UO9evVo3rw5Z86csfZ55plniIiI4I8//siw9yMiIiK2kkxJDP1rKFe2HWdZ0gIcPY7jYIDxL1TghZpaeCCzUdCxk4ULF1KuXDnc3d0JCAigUaNGxMTEADenin388ccEBQXh6+vLqFGjSE5OZsiQIfj7+5MvXz5+/PFHm3Pu27ePBg0aWM/Zu3dvoqOjre0mk4kPPviAfPny4erqSsWKFVm5cqW1vVAhy01zlSpVwmAw8NRTT9mcf9y4cQQHBxMQEEDfvn1JSkpK03stWLAgH374IZ06dcLT05M8efLw1Vdf3bH/+vXrMRgMXL9+3bovJCQEg8HAqVOnADh9+jQtW7bEz8+PHDlyUKZMGVasWHHHc86fP58KFSqQL1++O/a5cuUK1atXp1WrVsTHx6fpvd3+PidOnEjXrl3x8fFJtc+ECRNo3Lgxw4YNo2TJkgwbNoyGDRsyYcIEa5/PP/+cV155hZ49e1KqVCkmTJhA/vz5mTp1qrWPo6MjLVq0YM6cOemuU0RERNIo5jLM7gDXLV82JiclU3bqdvr9ZuKV1Yk4OMTzRfuKtK18588XYj9ZNuhcmTado08+xdEnnyJm23abtsRz56xtYR+OTnHs2ddet7bf7vqixda2yFWr7qu20NBQOnbsyMsvv8zBgwdZv349bdu2xWw2W/v8+eefXLhwgQ0bNvD5558zcuRInn32Wfz8/Ni2bRt9+vShT58+nD17FoDY2FiaNWuGn58fO3bsYMGCBaxZs4Z+/fpZzzlx4kTGjx/PuHHj+Oeff2jatCmtWrXi6NGjAGzfbvk9rVmzhtDQUJt7QNatW8fx48dZt24dP/30E9OnT2f69Olpfs+fffYZ5cuXZ/fu3QwbNow333yT1atX39fvD6Bv374kJCSwYcMG9u3bx5gxY/D09Lxj/w0bNlC1atU7tp87d4569epRsmRJFi1ahJubGwCenp53/WnevHm66t6yZQtNmjSx2de0aVM2b94MQGJiIrt27UrRp0mTJtY+N1SvXj3FlDYRERF5SKLCYPozcOR3+KklydfOMmHiMqruuwJAwxAzk8vVoHXFvHYuVO4ky666ZoqOJvniRQDMiYm2jUajtc0YGZniWOPVq9b2FOeNi7153vv41h8sQSc5OZm2bdvyxBNPAFCuXDmbPv7+/nz55Zc4ODhQokQJxo4dS2xsLO+88w4Aw4YN49NPP2XTpk106NCBWbNmERcXx4wZM8iRIwcAkyZNomXLlowZM4agoCDGjRvH22+/TYcOHQAYM2YM69atY8KECUyePJlcuXIBEBAQQO7cuW3q8fPzY9KkSTg6OlKyZEmeeeYZ1q5dS69eaZuLWqdOHYYOHQpA8eLF2bRpE1988QWNGze+r9/hmTNnaNeunfX3Vrjw3YeLT506RZUqVVJtO3LkCI0bN6Z169ZMnDgRg8FgbbvX/Uru7u7pqjssLIygoCCbfUFBQYSFhQFw+fJljEbjXfvckDdvXs6cOYPJZMLBIct+ZyEiImIfBgcwGQEwGxP5eFkIP1715HCVjgzcM5+IwSNo0vpJOxcpd5Nlg46DpydO/31YNLi42DY6OlrbHL29Uxzr6O9vbU9xXnePm+f971v/9KpQoQINGzakXLlyNG3alCZNmvD888/j5+dn7VOmTBmbD69BQUGULVv2lrfgSEBAAOHh4QAcPHiQChUqWEMOWMKFyWTi8OHDuLu7c+HCBerUqWNTS506ddi7d+89ay5TpgyOjo7W18HBwezbty/N77lWrVopXt86XSu9BgwYwGuvvcaqVato1KgR7dq1o3z58nfsHxcXZx2luX1/3bp16dixIxMnTkzRXrRo0fuu8U5uDVIAZrM5xb609HF3d8dkMpGQkJDuwCUiIiL34BlI/EsLmLysC5fNg5hz0PK5bFPBKrz0Wlsa1S1j5wLlXrLs18ABPbpT7K/1FPtrPTlqVLdpc8mXz9qW+713Uxybf+oUa/vtfNs+Z23zvm16UVo5OjqyevVqfv/9d0qXLs1XX31FiRIlOHnypLWPs7OzzTEGgyHVfTdW3Urtg/Ct/VLbvtdxt7rbte/Xna57I+DdOpXv9vuBevbsyYkTJ+jSpQv79u2jatWqd73vJ2fOnFy7di3FfldXVxo1asTy5cs5d+5civaHPXUtd+7cKUZmwsPDrSM4OXPmxNHR8a59brh69SoeHh4KOSIiIhkgNimWfps+YN3F6yyJmwaGRFwcHfj6pSoKOY+JLBt0MjuDwUCdOnUYNWoUe/bswcXFJdVlhtOqdOnShISEWBc0ANi0aRMODg4UL14cb29v8uTJw99//21z3ObNmylVqhQALv+NfBmNxvuu4062bt2a4nXJkiVT7XtjCl1oaKh1X2pTyPLnz0+fPn1YtGgRgwcP5rvvvrvj9StVqsSBAwdS7HdwcGDmzJlUqVKFBg0acOHCBZv2kJCQu/58//33d7xmamrVqpXi3qRVq1ZRu3ZtwPLvoEqVKin6rF692trnhv3791O5cuV0XV9ERETu4NIRWPsB/PdF6+nLp3jq6518ONNImUvncPW4zDddqtCwVOqzfiTzybJT1zKzbdu2sXbtWpo0aUJgYCDbtm3j0qVL1sBxPzp37sz7779Pt27dGDlyJJcuXaJ///506dLFOhIwZMgQ3n//fYoUKULFihWZNm0aISEhzJo1C4DAwEDc3d1ZuXIl+fLlw83N7Y6rh6XXpk2bGDt2LG3atGH16tUsWLCA5cuXp9q3aNGi5M+fn5EjRzJ69GiOHj3K+PHjbfoMHDiQ5s2bU7x4ca5du8aff/55199f06ZN6dmzJ0aj0WYKHlhG2GbNmkXHjh1p0KAB69evt96jlN6pazcCWXR0NJcuXSIkJAQXFxdKly4NwBtvvEH9+vUZM2YMrVu3ZunSpaxZs8YmgA4aNIguXbpQtWpVatWqxbfffsuZM2fo06ePzbU2btyYYtECERERuQ8X/4UZrSHmEiTGEt9wNBvfm0W9Y8kADF5kIGlmM+qXDLRzoZIe6R7R2bBhAy1btiRPnjxpfpr9rFmzqFChAh4eHgQHB9OjRw+uXLlyP/VmCd7e3mzYsIEWLVpQvHhx3n33XcaPH5/uaVC38vDw4I8//uDq1atUq1aN559/noYNGzJp0iRrnwEDBjB48GAGDx5MuXLlWLlyJcuWLaNYsWIAODk58eWXX/LNN9+QJ08eWrdu/cDv9YbBgweza9cuKlWqxIcffsj48eNp2rRpqn2dnZ2ZM2cOhw4dokKFCowZM4bRo21XxzMajfTt25dSpUrRrFkzSpQowZQpU+54/RYtWuDs7MyaNWtSbXdycmLOnDmUKVOGBg0aWO99Sq9KlSpRqVIldu3axezZs6lUqRItWrSwtteuXZu5c+cybdo0ypcvz/Tp05k3bx41atSw9mnfvj0TJkzggw8+oGLFimzYsIEVK1ZYF64AOH/+PJs3b87Q5wKJiIhkG5cOW5aSBkynNjFgxiY+86vGrsDixDs64zBiDPXL5bdzkZJeBvOtN0Kkwe+//86mTZuoXLky7dq1Y/HixbRp0+aO/f/++2+efPJJvvjiC1q2bMn58+fp06cPxYoVS/NUrcjISHx8fIiIiMD7tsUD4uPjOXnypPUp8pL5FCxYkIEDBzJw4EC71jFlyhSWLl2aJR6yOWTIECIiIvj222/v2Ed/N0RERNIuYsf3bP5nGktMo/jjeAIAPg5GvnsyJ9Wb1r7H0fIo3S0b3CrdU9eaN2+erpGHrVu3UrBgQQYMGABYHkr56quvMnbs2PReWuSB9O7dm2vXrhEVFYWXl5e9y3kggYGBvPXWW/YuQ0REJEu4Fn+N106t4JgpgogrG4AaeLg48l2PWlQv5G/v8uQ+ZfhiBLVr1+bcuXOsWLECs9nMxYsXWbhwIc8888wdj0lISCAyMtLmRzKnjRs33nVVsszEycmJ4cOHP/YhBywjOrevwiYiIiJpdOIvOL3F+vLXfxbxwrcHeXuhCU+f1Xi6JTPj5eoKOY+5DF+MoHbt2syaNYv27dsTHx9PcnIyrVq1uutSwJ988gmjRo3K6NLkIahateo9H6p56tSpR1KLiIiIyD0dXQ1zO4OjC3RdQnSuiniP+IMSZwHM9F+cm4rf16dyAb97nUkyuQwf0Tlw4AADBgxgxIgR7Nq1i5UrV3Ly5MkUK0jdatiwYURERFh/zp49m9Flyn1yd3enaNGid/wRERERyVR2TgNjAiRGkbjlG7r+sI2pBZ4i1smVaBd3qr79tkJOFpHhIzqffPIJderUYciQIQCUL1+eHDlyUK9ePUaPHk1wcHCKY1xdXXF1dU3XddK5poJIlqe/EyIiIql4/gfCZj1HvIsPgy6+xO5z18H/CT55+jVGtqtE2aeq3/MU8njI8KATGxuLk5PtZW48x+RhfBBzdna2XkdPiBe5KTY2Frj5d0RERETgQsI1XnEzERZ3keth5wBf/DycGTWgA2XyPJznB0rmkO6gEx0dzbFjx6yvT548SUhICP7+/hQoUIBhw4Zx/vx5ZsyYAUDLli3p1asXU6dOpWnTpoSGhjJw4ECqV69Onjx5HvgNODo64uvra33uiYeHBwaD4YHPK/K4MpvNxMbGEh4ejq+vb4oHpIqIiGQrh1ZA4afAxQOAT/54j35Tz7KyqgNrCi/C42ofZvWqQcncd16mWB5P6Q46O3fu5Omnn7a+HjRoEADdunVj+vTphIaGcubMGWt79+7diYqKYtKkSQwePBhfX18aNGjAmDFjHkL5FjeeYn+/D3kUyYp8fX2tfzdERESype3fwYq3oPDT0HEul67G0nr8CfJehT7LTZjql2HghzUpFvT4r8gqKaX7gaH2kNaHAhmNRpKSkh5hZSKZk7Ozs0ZyREQke4u+BF9VhgTLY0qimkyg3dZCPL1mNm1ObOSquxeB30+jWJUydi5U0ivDHhiamTk6OurDnYiIiIiAZy7ovICw2c+To/zLPLelEMcuxXCkXCvw9OTFYb0oUqmUvauUDJSlgo6IiIiIyA1HvPx5pcATxJ+5zKVL0YCBPL7udJr6IU8E5LB3eZLBMvw5OiIiIiIiGc5shnM7rS9jkmIYMv9lXp95DffkDTj7/01eX3fm9q6lkJNNKOiIiIiIyOPNZIIVQ+D7RrB3HgBXj4Ux6IcEKp408/7PBorFlWfeqzUpEOBh52LlUVHQEREREZHH28GlsOM7wAxLX+fCqcO8PicEY7LlAfSOJl++7VibfH4KOdmJgo6IiIiIPN5Kt4FqPYl3cOBKg/G8MPc8+xLdeLtuH/7NV5ris2eRr3hBe1cpj5gWIxARERGRx5vBwM7KHRgcsY24rcmEX48DwPuJfNQbPYsgbzc7Fyj2oBEdEREREXm8JCdA1EXry3+v/MuoeX14et01Yv2m4uAaRrFAT+b2rqWQk41pREdEREREHh9JcTDvJbh6EnqsAK/ccDiWd2Yk4BtrwiXGnb+qPcGsXjXJ6elq72rFjjSiIyIiIiKPjxVD4NgauHoc5nTkSFgk33y/Gt9YIwDVzrgzu0tVhRzRiI6IiIiIPEaefBtO/IU57iqnq75Dh++2cTWwAkkV42hzYSfV5s3AP6evvauUTEAjOiIiIiLy+PDNz9omw+hRsi5tlidyNSYRgAt1m1Ljt0X4B+eyc4GSWSjoiIiIiEjmFR8JZrP15erTq5n826dcP/wvCf4/giGZSgV8mdmzBr7e7nYsVDIbTV0TERERkcwpKgxmtIZiTaDxB2AwkLgrjHdmJwEwoqUBtwIB/NSjOl5uznYuVjIbBR0RERERyXySE2D6s3DlKFw6BDlysTt/F65MXkbhBEuXV/a40PS9mni66iOtpKSpayIiIiKS+Ti5Qq2+lm2fAvzjXZ+uP2zn40od2RdQiBP5S9Hk568VcuSO9CdDRERERDKnqj2Yf/1fwhwL8vWCMGITjeDkyu+dhzCpczVy+Hjau0LJxBR0RERERCRzMCaD482Pp7MOzmL+zpWczmkg0a0DJFakXrGcTO1aFTdnRzsWKo8DTV0TEREREfsL/QcmV4MLIdZd51f9xQc/G3lrsREXp1CeKpGL7xRyJI0UdERERETEvi4egJ+ehasnYGYbCD/EhpDTPPnDLlySocpxMwNOBPBNlyoKOZJmCjoiIiIiYl9+T0BgGct2QDE2hDnTc8EBRlfrSryjM0dLVKXHF8NwdVLIkbQzmM23PIEpk4qMjMTHx4eIiAi8vb3tXY6IiIiIPGTm+EhmLe+F5xNvMWTxJZKMlo+oXXIm8F6/Z3Bxc7VzhZJZpDUbaDECEREREXn0zGYwGP7bNDPx3x9ZFPovV66+TTK9AS+eKR/MiPYVcXbUJCRJP/2pEREREZFH69BymPcSJCcCEJkYyZnFC/hqqpE6xy/imOMorSrkYaJCjjwA/ckRERERkUfn38Uwvysc+g0W9gBjEtt+3cnLv1zDxQgDlpp52a00X7SviJNCjjwA/ekRERERkUcnRyA4OFu2XTxZtvcCr+2MZXWBagAcq/w0/xvQBkcHgx2LlKxAixGIiIiIyCNlOraG1f9MJyr/R/xv0X5MZjCYTbzjcpqX338NR62uJnehxQhEREREJNMxmoyMCFvH8ushxJ0ah8ncFDDQuVZBXmn1LA4ayZGHRFPXRERERCTj/P0F7P/F+nLnxZ3ELVjCp9OMBLj9hYPrRbrXLsiHrcsq5MhDpREdEREREXn4zGZY9zFsGAsGR3Byg5LPcGXmXnr9YQLg3Rme7HmrIu+0LI3BoJAjD5dGdEREREQkY0RftPzTbIQrx5i+6SQfnnXlkrsPAEmVn2ZY20oKOZIhNKIjIiIiIg+fwQDPfkFiUhznchbiT1NLRi8/ADlyMrROHwZ7hPLcp2/j4KDv3SVjKOiIiIiISIaINyUx0NuRnWeWcfVYTiAvAM+3qkXbxsU1kiMZShFaRERERB6cMRnWjIToS9ZdU/dOJWjBRlqtj8Q9/0wgmTcbFWdQkxIKOZLhNKIjIiIiIg8mORF+eRkO/gpHV0O3X8HDnyI/X6XYBsvCAwmRhQl+rTT9GhSzc7GSXSjoiIiIiMiDib0M5/dYti8fwRz2D18cz8Ppc0nciDU1i5elpUKOPEIKOiIiIiLyYLzzQLdlRP/cFufmn/HV0WAmrTsKRerhYDbRqEwwLUe9ae8qJZtR0BERERGRBxbhmZM+RUpxbddsDu19jhsfM8sNfI1n6hayb3GSLWkxAhERERFJn9irsPNH60uz2Uz/tf0os+gfHM7txDX3UgA+aF2GlxVyxE40oiMiIiIiaRd9CWa2gYv7IfYK1B+C2WzmmXkGym8z0yjExNv1StK7U1k613jC3tVKNqYRHRERERFJuxPrLCEHYNu3mKKv8MHC3bgcuQiAV6yZdwoHKuSI3WlER0RERETSrvyLEH2RpK1TceyylHdXX2D27ovkqNWL0Vu+I0f7DjR9o7u9qxRR0BERERGR9Amr8AK9w1biu3Y9G0LyABDn6g6TvqVJFY3kSOagqWsiIiIicmeXDkPYPuvLK3FXeHl5N4qvO8XemK9wzHEYRwcDX7SvyHMKOZKJaERHRERERFIXtg9mtAHM0H0FBJbE05CDTnPjqXLARMnTLowtH8gXHSrxTPlge1crYkMjOiIiIiKSurUfQuxly+pqa94n2Wji0ykrKHfoKgDVjybydZU8CjmSKSnoiIiIiEjq2n0HwRUhX3WSWn/NG3NDmBbuyujq3Yh1cuX60NE0eK6BvasUSZWmromIiIhI6tx8ONpqPJ+GTML8yx7+PBAHwN68ZYh4fQENqhezc4Eid6agIyIiIiIWZ3dAnorg6AzA4auHeXXNQALOXOdgzgvg0AsXB0++7VKFp0oE2rdWkXvQ1DURERERgQNLYVoz+KUnGJMBMCSY6TMnkpGzjFQ+kYCrkwM/dKuqkCOPBQUdERERkewu5goseR1MyXBgCeyZQXySkdWj5lLhRBJOJhjwexTTnq9KvWK57F2tSJoo6IiIiIhkdzkC4IXp4OAMFTsTV/Ylev60k/Helfkrb0XinFwxjB5H7fJ6To48Pgxms9ls7yLuJTIyEh8fHyIiIvD29rZ3OSIiIiJZ0s5/57Ej8TobdlRgy4krAHg5wfRGQVR5qqqdqxOxSGs20GIEIiIiItnRpSOQq7j15dbQrby19TOSk+K5GtUMeBpPVyemv1yNKk/4269OkfukqWsiIiIi2YnZbHkQ6NRacOQP6+7Dp/fx1uwY3p1rxMvxOF5uDvzcs4ZCjjy2FHREREREspN/F8HGcZaFB+Z3hetniYxPwnPUn5Q4D0XCYPCvMKtnLSrm97V3tSL3TUFHREREJDsp3QbKtLVsN/6QCNfcdPl+GxMLNeK6Sw4iXXNQftRwyufztWeVIg9MixGIiIiIZDfGJNbs/Ar/As0YsfAK+89HAlA+6TKfPleO0nUr27lAkTtLazZI94jOhg0baNmyJXny5MFgMLBkyZJ7HpOQkMDw4cN54okncHV1pUiRIvz444/pvbSIiIiIpFdyIsRetdm1/PQqhu6bTo+VPTlw6RgAOT1d+GxIW4UcyTLSvepaTEwMFSpUoEePHrRr1y5Nx7z44otcvHiRH374gaJFixIeHk5ycnK6ixURERGRdEiKt9yHExUK3X4Fd1+MJiOLtv7ERz8l8Ve5CBYW2YVPfBvm9KpB0UAve1cs8tCkO+g0b96c5s2bp7n/ypUr+euvvzhx4gT+/pZVOwoWLJjey4qIiIhIev06AI7+t7La/C7QdRlXLkfR+atwgq9Blz9NJMXkpM+4mhTJ5WnfWkUesgxfjGDZsmVUrVqVsWPHkjdvXooXL85bb71FXFzcHY9JSEggMjLS5kdERERE0qneW+CRE1w84cm3CY9KoOPMEFblrgTAFQ8/+gzqqJAjWVKGPzD0xIkT/P3337i5ubF48WIuX77M66+/ztWrV+94n84nn3zCqFGjMro0ERERkawtV3HouoS/QrdS1KciXb7dyonLMRwv0QgXzxz0+F9XCpUrfu/ziDyGHmjVNYPBwOLFi2nTps0d+zRp0oSNGzcSFhaGj48PAIsWLeL5558nJiYGd3f3FMckJCSQkJBgfR0ZGUn+/Pm16pqIiIjI3STGgEsOm13T909n/M5xuCRU4crJ5wEH8vq6M7d3TfL7e9inTpEHkGGrrqVXcHAwefPmtYYcgFKlSmE2mzl37lyqx7i6uuLt7W3zIyIiIiJ3EXEOvqkPf0+w7joXdY7Z6ybwwUwj/rG7cPI6QAF/D+a9qpAjWV+GB506depw4cIFoqOjrfuOHDmCg4MD+fLly+jLi4iIiGR9CVEwrTlcOQZr3ofdMwEwno7jvZ+cKXkeRsx0pmJ8Ieb2rkk+P4UcyfrSHXSio6MJCQkhJCQEgJMnTxISEsKZM2cAGDZsGF27drX279SpEwEBAfTo0YMDBw6wYcMGhgwZwssvv5zqtDURERERSSdXL6jS3bLtXxgKP8XpKzH0WniQSAfLktHJzr582b0WeXz1+Uuyh3QvRrBz506efvpp6+tBgwYB0K1bN6ZPn05oaKg19AB4enqyevVq+vfvT9WqVQkICODFF19k9OjRD6F8EREREQGg3mDMzp4cz18Bx2R/On23ldB4R96p8ypDDv9Ko8ljyF0or72rFHlkHmgxgkclrTcciYiIiGQbxmRwvPmdtdls5tPtnzL/yAIcw3tw+VJhAIoFejK7V01yebnaq1KRhyrTLEYgIiIiIg/ZqU0wuRpcPmrdtez4MjZunEXnVfEk+v6IwTGKkrm9mNtbIUeypwx/jo6IiIiIPESnt8DP7SA5Dn5qBa+sAt/8FA/Lw/uzDHjHmXG+lodVjYKZ2asm/jlc7F2xiF1oREdERETkcRJYEnIWtWznLgs5cnIwNJLPZ2wgR7wJgJKRJmZ2Lq+QI9maRnREREREHifuftBlCUl/f058vcGcDk/kpR+2cd2/JHFVO9Pp3BZqzf0Jv1x+9q5UxK4UdEREREQyO7MZDAbryyQ3H952jef4yj6c3P8SkbGWj3QxtZ6kTvfB+HjonhwRTV0TERERycy2TIFFvcBktO4avW00pzavwmPfAZJy/giYqfqEHzNerq6QI/IfjeiIiIiIZFZ/fwFrRlq2ndyg1VdgMFAntCSt5hpxNMHIJ4vjUj6Aad2rkcNVH+1EbtCIjoiIiEhmlaskOPwXXrwtD/vcceoqJ75djkciuCZD91PnmN5DIUfkdvobISIiIpJZlWgObb8j+foZnOoOZOuJK7w8fQeJFV7ANSGOXG4ONJ7zPR4u+kgncjv9rRARERHJxKKLN6Hv2r4U2TCVOasLEZdkBAcnNnQaxOROlfDw9LB3iSKZkqauiYiIiGQGyYmwqDccXWPdlWhM5NXVrxKzaxe/HppMco7NADxdIhdTe9RUyBG5CwUdEREREXtLiof5XeCfeTCvM5z6GwAXRxdqHsvFiDlGhs0D56hAGpUK5OsuVXBzdrRz0SKZm4KOiIiIiL05OIHTLctCJ8UBsG7vWSpP24KzEUqdNzL4chhTOlfB1UkhR+RedI+OiIiIiL05OkHb78HgAFVfhkL1WXPgIq/N30eBmi/zyaZvCC1egS5fjsDFSd9Ti6SFgo6IiIhIZuDkQvgzY3ln4zs8ecWDDxaHk2Q0c9w3H8teHc37vRrh7Opi7ypFHhv6SkBERETkUYsMhV96QXyEdVdYTBg9Vvbg1MGtfBoykGSHKwC0qZiHka81U8gRSScFHREREZFH6dopmNYM9s2H2e0hMQYAV0dXyu+MYcI3RhqHJALQrnI+xr9YESdHfWQTSS/9rRERERF5lJLiISHKsh15AWKvArB1xV5eWnQRRzO8uiaKfj7OfPZ8eRwdDHYsVuTxpaAjIiIi8igFloSXFkG+avDySvDNz8Jd53h9ezQLiz0NwOEaTRg48AUcFHJE7pvBbDab7V3EvURGRuLj40NERATe3t72LkdERETkgR2/doxFxxZTwPwiQxfvx2wGzGaGel+i19BuOGq6mkiq0poNtOqaiIiISEY6uQEuHoCafay7Dl89TK9VvYiNukpUzCHM5jaAA93rFOLVls9gMGgkR+RBKeiIiIiIZJQjq2B+F0iOBycXyzNygHPR56j591We2WFkRLsznHZIomftkgx/ppRCjshDojFRERERkYwS9o8l5AAcXQ3/3TEQN+cYPdYYCYyA9+fG0K9qAYUckYdMIzoiIiIiGaXeYMuzciLOQdtvwWDg+40nmBzqzVjPXOSLvkRUvUYMalNZIUfkIdNiBCIiIiIZyWxmR+g2jAbYeyQXn/x+CICAuAiGeYXR7sM3FXJE0kGLEYiIiIg8SmYzbJ0CxZtBQBHr7i2hWxnw5wCSjUYiT/UACgPQtWU1nm9UzE7FimR9ukdHRERE5EGZzbDmffjjHZjRGq6ftTYtOvILrdbF0PWPeJx9twIwpGkJ3lDIEclQGtEREREReVCJMXB0jWU74iycWAeVu2I2m6m3yJVimyx3CsRfd8f/7ZK8+mSRu5xMRB4GjeiIiIiIPChXT+i6BHIWh2cnWEPOuFWHWXzFDROWe3DKVy+rkCPyiGhER0RERORh8AyEPn+zMWw7lRKj+WrtWb756wTkr4yj2UjbUgE8815/e1cpkm0o6IiIiIikV2IM7PgBavUDh5sTZJaeWsl7m94jp1NJTuzvCLgA8GT/7rSoVdA+tYpkU5q6JiIiIpIecddh5nOw+j1YMdj6ENDIxEjGb/+Ml9Ym43/qAM6+OwD4+LlydFHIEXnkFHRERERE0uPCHji307K97xe4fhqAHA45GLCiCC23mxk2F4qcyMvYduXpVKOAHYsVyb4UdERERETSo8jT8NzX4BkE3X8Dv4KYTGZGLNpL/JnrALgmmRhc1osXq+W3b60i2Zju0RERERFJr/IvQonmHIg+SwmTmWGL/mH+7lBcavRgxLbpBHd4nsavv2TvKkWyNQUdERERkbu5eABiwqHwU9ZdZrOZqQdnMnXvVEo5v8z2f4oDYHR2wefLyTSsmNdOxYrIDZq6JiIiInIn53bB9BYwpyOc2WrdveXCFr7dPYXWW0wci/kRB5eLODkYmNSxEi0VckQyBQUdERERkTvZ8R3EXYOkWPhrjHV3Vf/KDF2Wi87rTQye449bYgCTOlWmeblgOxYrIrfS1DURERGRO2k5EaIvgjEJXpwBQJLRxKivV9H22DUAyoZd5esaXtQvm9uelYrIbRR0RERERO7EyRXa/4wJuGqMwzs5B/1m72bVRUf21OrJe9t/wjR0JPWfa2DvSkXkNgo6IiIiIjf8uwSKNAA3b+suo5MbIzaPYGfYTgJjBrHxYDIAx4OKkDjzF54sr+fkiGRGukdHREREBGDTRFjQDeZ0gKQ46+6v9nzF74eWkuufc+xKGAsYcXN24Idu1RRyRDIxBR0RERGR6Evw9wTL9ulNsP8Xa1O7/G0YtsCRYfNN1N5WBHdnF37sXo26xXLap1YRSRMFHRERERHPXPDSL+DiBQ3ehYqdAYhLNLJg1CzKnk7EAXh1+06mP1+C2kUUckQyO92jIyIiIgKQtzL030mihz9OmIlPNPLy9B1s9ayAsdBxnj63B8exE6hWoZC9KxWRNFDQERERkewnKR6OrYFSz9rsjnXzpv+a1yjgVYj9/zRgx6lrYDAws9oLNBzxJpVrlbNTwSKSXgo6IiIikr0kRMGcjnBqI7T6Cip3BcBkNtF3bV/+Pb2Do0nbCEsIB5ri5ebEjFdqUDG/r13LFpH00T06IiIikr3sX2QJOQB/DIfYqwA4GBx4Jmdj3ptj4v1ZJrzCC+Dj7szsnjUVckQeQxrRERERkeylcle4dBhCZlkWIPDwByAiNgnT+8spGmYG4J0tf1FyTk/K5PWxZ7Uicp8UdERERCR7MRig6UdQ8zXMPvkwANdjE3nph21cLNKcsaEncTclU/rTDymV19fe1YrIfdLUNREREcnawg/CleO2+wwGLrm40XlFZzac3kGn77ax/3wklzz8GNNkAH7ffE+p2hXtUq6IPBwa0REREZGs69xO+LkduHrDyyvBJy8Al2Iv0eOPHoRfOkW/8NeIvtYLyEcuL1cm9apP0UAv+9YtIg9MIzoiIiKSNZnN8Mc7EH8dIs7AmpHWJm9Xbwom+PHxdCMd/zRhTnYnyNuVub1rKuSIZBEa0REREZGsyWCAF2fAj83AJx88+7m16frVeJ7/4gxBEZD3ahwJpoN0n9iOgjlz2LFgEXmYNKIjIiIiWZdXbui+HDovBFfLSE1YRDwdf9rD/IJ1AbjkGUD3Ya8o5IhkMQo6IiIiknUcXQ0mk+0+n7wciT7D/zb8j5NXrtH+2y2cuBzDikK1+bFuF56Y8RMFyxa1T70ikmE0dU1EREQef2YzrPsINnwG1XtD87GWqWvAgSsH6L26N5Hx11l96DTXrnYCnCjg78HAoW+S19fdvrWLSIbQiI6IiIg8/i7+Cxv/uwdn+7dwaqO1KdmUjG94AmN+NJIn7DIYkikY4MG8V2sq5IhkYekOOhs2bKBly5bkyZMHg8HAkiVL0nzspk2bcHJyomLFium9rIiIiMid5S4LrSeDwQGajYFC9a1NXqGuvDPdTMFwGLHwGnUN8cx7tRbBPgo5IllZuoNOTEwMFSpUYNKkSek6LiIigq5du9KwYcP0XlJERETk3ip2hL7boWYf666Tl2Povvg4590DAYjO4ce43k8T5O1mrypF5BFJ9z06zZs3p3nz5um+0KuvvkqnTp1wdHRM1yiQiIiISApx1+HSYShQw3Z/zmJsOr+JK/FXKOPdgI7fbiU8zsyImq8w5PhKnp30IYH5ctulZBF5tB7JYgTTpk3j+PHj/Pzzz4wePfqe/RMSEkhISLC+joyMzMjyRERE5HESHQ4/t4Urx6HrMshfzdq0/ux6Bq0fhNFkxOHyIa5FlQHgify5aD1qEgGernYqWkQetQxfjODo0aMMHTqUWbNm4eSUtlz1ySef4OPjY/3Jnz9/BlcpIiIij42/v4CwfZAUC0v6gMlobdoRtoM8oYn0+S2JJIcQwEzpYG/m9KqpkCOSzWRo0DEajXTq1IlRo0ZRvHjxNB83bNgwIiIirD9nz57NwCpFRETksdJoJBR6ErzzQYc54OBobWoRXZ8Rsww8tc/Mm8sSqZDbk9m9auCXw8V+9YqIXWTo1LWoqCh27tzJnj176NevHwAmkwmz2YyTkxOrVq2iQYMGKY5zdXXF1VXfuoiIiEgqnFyhwyyIjwSfvNbd+89H8NHCnfwv2fL8nDzEM/2livh6KOSIZEcZGnS8vb3Zt2+fzb4pU6bw559/snDhQgoVKpSRlxcREZGs4PRmyF0OXL1u7nP1Alcvfj3+K9VzV+fiNVde+n4bkV4FGVWjBy+f/Zv6c3/AN8DXbmWLiH2lO+hER0dz7Ngx6+uTJ08SEhKCv78/BQoUYNiwYZw/f54ZM2bg4OBA2bJlbY4PDAzEzc0txX4RERGRFPb/AotehQI1ofNCcL65LPSMf2fw2c7PCPYoQOjBHkTFW56L41y9Bk9P6YeXm7O9qhaRTCDd9+js3LmTSpUqUalSJQAGDRpEpUqVGDFiBAChoaGcOXPm4VYpIiIi2U98JKz4H5iS4NRG2PGdtSk2KZY5h+ZQ8qyZ/HtOEe+6G4DqhfyZ3qO6Qo6IYDCbzWZ7F3EvkZGR+Pj4EBERgbe3t73LERERkUfl3E74qRWUawfPTrBZeGDdgnn4jhqJk9HAx9W6Yaj3JN93q4qHyyN5eoaI2Elas4H+SyAiIiKZV76q8OoGCCgCBoN199YTV9gzfTXNkwHMtL+6lyZdByvkiIiV/msgIiIimUNyIpxYB8Wb2u7PWRST2cS603/SoEADNh+/wis/7SCxbGucE+Mp6GKk6bwfcHfVxxoRuUn/RRARERH7S4yBeV3g+FpoMxUqdrI2mcwmPtjyAb8c/YWmeTvz6/oKJCSbwOBASMf+9OxQAXcPt7ucXESyowx9YKiIiIhImuxfZAk5ACuGQMwVa9Oui7v45egvlDtpYvORWSQ5ngegUakgpnStqpAjIqlS0BERERH7q/QSVH8VXH2g03zIEWBtqpa7GgOuNWfYfBPvzvQiR5Q3zcrkZkrnyrg6Od7lpCKSnWnqmoiIiNifwQDNPoWar4G/7QPFV4WcodiMTTiZoNC1a7wVFUKnTs/j7Kjva0XkzvRfCBEREXn0zu+CK8dt9zk4gH8h4pLjOHrtKAAr94fy+vz9DK/diytu3hwuX5dOk0cp5IjIPWlER0RERB6tY2stCw/kCIBXVoNXbmtTbFIs/f7sx6Grh+hRaCyfLovEaDJz3jMXK1//mPe718fZRQ8DFZF709chIiIi8uiYTLD2A0iKgetn4K8xNs2TQiaxI2wHfucimbBvOEZTMgDtKudjVM8GCjkikmYKOiIiIvLoODhAx7ngUwBKtYSmn9g096vYj3YHczP2ByOtVhUBHGlfNT+fPV8eRwdD6ucUEUmFpq6JiIjIo+UdDK/8AZ5B4GC7atqa33bywpLzOABd920huGpD+rVtgYNCjoikk0Z0REREJOMYk2H3DMuUtVt55wEHRy7HXSbBmADA3O1neGN7FD+UeQaAQ3Wfod+Qjgo5InJfNKIjIiIiGSMpDn7pCYd+g4sHoNknlmWk/xMaHcorq16hiE8RquV4gxFLDgOwqNhTFK1blT5vvICDg76TFZH7o6AjIiIiGeP8Ljj8u2V7x/dQpTsElgTAaDLy2prXOBt1lqvhZ1gVlwS0BKBXvUK81qIUBoNGckTk/ulrEhEREckYBetCqy/BxQteWmgNOQCODo4MqzGM1tsMfPGtmaCTpQDo82QR3lHIEZGHQCM6IiIiknEqvQTFmoJnrhRNF38KofOfSQB88tcsdr4/mQHNSijkiMhDoREdEREReTjObLM8DPR2/4WcqMQo666p648zLDyAI775ALjcuDUDnquqkCMiD41GdEREROTBHf4dFnQHgyN0/xXyVrFp3n1xN/3/7M/I2iM5fLwg41YdARd3htfuzUj/yzz/7mv2qVtEsiwFHREREXlw/8yD5HjL9pYp8PwP1qYT10/QZ00f4pJieevPt4g+0wsoBMDrrSrx/FNF7VCwiGR1CjoiIiLy4Np8DdHh4BUMbabaNBX0KUiTAo3x/H4x/ldz8Gnp3GCAd1qUpHf9InYqWESyOgUdEREReXDObtBpPjh7wG3PvjFgoNocE8W3mYFIEuMW4/7+h7xSr7B9ahWRbEGLEYiIiEj6JMbCqnchPtJ2v6unNeQkGS2rqZnNZj5ecZCfY/1JMjgCULhBHYUcEclwGtERERGRtIu9CrPbw7ntEPoPdF4ITi42XRYeWcjsQ7P5vvH3fLUmlGmbTkHu0nxUvSvdyvjS4n+97VO7iGQrCjoiIiKSdrFX4MpRy/b53XD5MOQuZ21efHQxo7aMAqDVL904928PwBWDAdr07UCL6gXsULSIZEeauiYiIiJpl7MYdJwHfgWhxwqbkANQNagqga656LnSSIGdHmB2wWCAMe3K01EhR0QeIY3oiIiISPoUqAH9doKjc4qmPO7B9FuSl5IhoTzlcIAPah6hc78XaVs5nx0KFZHsTCM6IiIicmchc2DTlyn3/xdyzGYzZrMZAKPJzNBf/uHipWQAHMwmXq8erJAjInahER0RERFJ3YZx8OeHlm2vYCj/gk2zyWxi9NbR5HTPyavlX+OtBXtZHBKKY5WOmA0GSnRoQ8OeL9qhcBERBR0RERG5I/PNzQu7bYKO2WzmvU3vsez4MgBW7b9KyP5KABicnCj4xXgalAt+pNWKiNxKQUdERERSV+8tiDhvWXigzhs2TQaDgVL+pVh+ZCkvbjQz7wnLc3OcHQ1M7lSZJmVy26FgEZGbFHRERETEwmwGg+Hma4MBnv3Cdt8tXij4PO7/m0bpYxcodWgrI+tV5PNutWlYKugRFSwicmdajEBERETg8jH4oTFcOW67/w4hJzHZxPDv1pLnTAQAhSJDmVzHRyFHRDINBR0REZHsLnQv/NAIzu2An9tB9KUUXSITI+m3th9Hrh0hIdnIaz/vYmEoDKvzKuEefsSN+oy6rRvYoXgRkdRp6pqIiEh251fQsqpa3DVwyQGmJJvma/HXeHX1qxy8epB9l/cTHDuIrYcdAQjLmQ+nWb9Qq5QWHhCRzEUjOiIiItmdmw90XgDlO0CP38E7j02zi6MLTg5OuCaaKbU7mu2nLgLg7uzItO7VqaOQIyKZkEZ0REREshtjMpiN4OR6c59PPmj7TardczjnYHyVMWz74jlKnIvBpdxx1pTKz/Qe1aleyP8RFS0ikj4a0REREclOEqJhbkdY8hqYTGk6JCYhmemfzKXEuRgAuhxezYx2xRVyRCRTU9ARERHJLsxmmP0iHF0F+3+BdaNT7Xbk2hFGbh5JsimZ6IRkekzbwTfupZlVojHRLh44fTGZqhUKP+LiRUTSR1PXREREsguDAWoPgDNbwNULCj+dosv+y/t5dfWrRCZGEpUYy8mDrdh92rKE9LKKLXhhZH8qVCr2qCsXEUk3BR0REZHspEQzaDUJ8laBwJIpmqMSo4hLjsMz1szBXf9w4EpNwBUfd2dm9axB2bw+j75mEZH7oKAjIiKSlV06DLlK2O6r1PmO3WvlqcVHpUdgGDiKHFHX+F+9KOJzefJzzxqUyaOQIyKPD92jIyIikhWZjLByGEypBcf/TPNh12ISufb+Ip64FE/O+Eje2T2H2b0UckTk8aOgIyIikhXtnQNbp1iWkZ7fDaIvpdpt2fFlrD29FoAr0Ql0/G4rY4q14LRXEFfdfSg+YRylghVyROTxo6lrIiIiWVGFjnBoBRxZCU1Gg2euFF3mHprLR9s+wtnBmY9qf8GEZQYOX4wCVy/GNR3A1LalKFa5lB2KFxF5cAo6IiIiWZGDI7T7DkL/gSdqpWg2m83su7wPALfoREatWkTYxSYABHm78m2vJymcy/ORliwi8jBp6pqIiEhWcGgFRF6w3eeSI9WQA2AwGBhVexTNXGvyyXRX2i+5jsFsItjHjXm9aynkiMhjTyM6IiIijzOzGf7+HNZ+AMEVoMfvloCTBpeuxvHM50cIjIylWcR2En1z0nHKhxQI8MjgokVEMp5GdERERB5nCZGw6yfLduhe2DMr1W7JpmQm7ZnE9fjrAJy/HkeHaTv5oUQTjBi46B1Ihw/6K+SISJahER0REZHHmZsPdJoHPzSFOv2heq8UXRKNiby94W3WnFnDpvObGFn9S16Zto9z1+I4nbcifj45GDKwLfmLFrDDGxARyRgKOiIiIo+7wFIwYDfkyJlq85W4K4RcCgHg6OVDdP15ERev5QOgUM4cDBv2KsE+7o+qWhGRR0JT10RERB4nh3+Hle9Y7s251R1CDkCwZzDfNv6WslH+jJviSuCBBAAK58rB3N41FXJEJEtS0BEREXlcbP4K5nSErZNh+3fpOtThtJE3vo8lKDKCUVt+oBGXmNu7JkHebhlUrIiIfSnoiIiIPC48g4D/RnLO7Ug5qvOfsJgw5h6aa319LDyaLstOcsgnPwCX/YL4uF9zAr0UckQk69I9OiIiIo+L8i9C+EFw9oD6b4HBkKLL6cjT9FrVi9CYUJJNyVQPaE2n77ZxOdbER9W78uaZdbw44T1yBt95qpuISFagoCMiIpJZxUdYVlW7VaP373rIttBthMaEAjDj39l8ftiPq9GWtuL5A+g48jP8crhkRLUiIpmKpq6JiIhkRiFz4ItycG5nug57scSL9KnQhzrX8tLlG1dirlsWHiifz4fZPWsq5IhItqGgIyIiktkcWApL+kBCBMztBFFh6Tq8fnh5ev1wkarnDjFq649Uze3OzFdq4OPhnEEFi4hkPgo6IiIimU2JFvBEnZvb7v537Lr69GqOXjtqfb337HXe/e0wpv/WKfBydeL7blXwcVfIEZHsxWA232HJlkwkMjISHx8fIiIi8Pb2tnc5IiIiGS/6EhxbAxU73rHLwiML+WDLB+R0z8lPzX/i0jVPuv2wnaiEZIpfO0Pv85to8vNUvH29HmHhIiIZK63ZIN0jOhs2bKBly5bkyZMHg8HAkiVL7tp/0aJFNG7cmFy5cuHt7U2tWrX4448/0ntZERGRrGv/L5ZgcyvPXHcNOcmmZBYdXYQZM5fiLjF111y6/hdyAHwrV6T5L9MVckQk20p30ImJiaFChQpMmjQpTf03bNhA48aNWbFiBbt27eLpp5+mZcuW7NmzJ93FioiIZCnGZPh9KCx8GRZ0B2NSmg91cnBiSsMpFPEpQo/oJ4n+Ppro/0JO7SIBTO9RjRyuWlxVRLKvdP8XsHnz5jRv3jzN/SdMmGDz+uOPP2bp0qX8+uuvVKpUKdVjEhISSEhIsL6OjIxMb5kiIiKZX/x1OLDEsn36bzj4K5Rtm+bDfd18GRLXFc/J7+NiSibeCHGNWvBtl6q4uzhmSMkiIo+LR74YgclkIioqCn//O99Y+cknn+Dj42P9yZ8//yOsUERE5BHJkRNemA7OOaDlxLuGnERjItP3TyfZlGzdt+nYZf6c9ycu/+1rHnOCb7tUUcgREcEODwwdP348MTExvPjii3fsM2zYMAYNGmR9HRkZqbAjIiKPP7MZzCZwuCWIFKgJA/+xhJ47iEmK4Y11b7AtdBtHrx/lwzof8vfRK/SasZOEEs1wTEqktFM8zeZ8i7uLpquJiMAjDjpz5sxh5MiRLF26lMDAwDv2c3V1xdXV9RFWJiIiksESomBpX/DJD00/sm27S8gBOHrtKLsv7gYsy0mXydGakb9cITHZBAYDJ1/sxaD25XFz0/87RURueGRT1+bNm8crr7zC/PnzadSo0aO6rIiIiP0Zk+GHppYHgW6ZBP8uSdfhFQMrMrb+WPxc/Rga2YlJM45ZQg7QrExuJneuopAjInKbRxJ05syZQ/fu3Zk9ezbPPPPMo7ikiIhI5uHoBFW6W7ZdfcAp/aGk0RONGHm1O8W//I7Rf00hZ9x1nikXzFedKuHipOd/i4jcLt1T16Kjozl27Jj19cmTJwkJCcHf358CBQowbNgwzp8/z4wZMwBLyOnatSsTJ06kZs2ahIWFAeDu7o6Pj89DehsiIiKZXPVeEHsZyreHgCJ37brv0j7C48JpWKChdd/vu8/gMG0GjmYTwbFXeCPuAB07dMTJUSFHRCQ1BrPZbE7PAevXr+fpp59Osb9bt25Mnz6d7t27c+rUKdavXw/AU089xV9//XXH/mmR1qefioiIZAqXjsDV41Ai7Y9juOHv838zaP0gkk3JfN3oa6oHV+e3fy7wxtwQfGOuM/bvKUSUqkir6RNxctLqaiKS/aQ1G6Q76NiDgo6IiDw29s6D3960bPdeD7mKp+vwkZtH8svRXwB4Kv9TNPIfypvzQjD993/rziW9GdWljkZyRCTbSms20BqUIiIiD9OpDZAUY9le/7HlOTnp8G7Nd7kcdxlnB2canH+aN1btwYQBgA7V8vPhc+VwcDA85KJFRLIefR0kIiLyMDX/DHKVgkovQesp6T7cycGJ8U+Np8WWQhQd/Ta9/lkGZjOdaxTgY4UcEZE0U9ARERG5X2YzRF203efiAa+sgtaTLdt3kWxKZnLIZK7HX7fZv/zXHQTP+BqANic2MtT/GqPblFXIERFJBwUdERGR+xFzGeZ0hB8aQ3ykbZvbve8njUuO4811b/L13q/pu7YvsUmxAPy89TSDtkYwsdILABxo2I7eQzpjMCjkiIikh+7RERERuR+/vgFHfrdsrxgCbb9J1+ERCRH8e+VfAA5cPcC/V/7lwIlcvL/Msm/1E9Wp+FQ1Xu/9jEKOiMh90IiOiIjI/Wj6Ebh6g0dOKNs23YfnzpGbqY2mkjtHbr5u9DUHt5mtIQegz5NFFHJERB6Ago6IiEha3P40Br+C0H4mvLYZije9r1OW8C/Bb21+4/q4lZQZ3psSV08D0L9BUd5uVkIhR0TkASjoiIiI3I3JCH9PgJ/bgclk21b4KfAKStNpNl/YzDd7U05vWz52GgWWz8UjOYHRm7/jfzVyMbiJQo6IyIPSPToiIiJ3s6gX7Lc8wJPt30LNPuk+xW8nfuO9v98j2ZyMj6sPHUp2AODLtUeZfDWIkTmLUPHycc636cLrz1V/mNWLiGRbGtERERG5myrd/9swQOyV+zpFZEIkyeZkALaFbsNkMvH56iN8vvoICU4ujKz5MvteHUab0YMeTs0iIqIRHRERkbsqVB8avg9P1IYCNe/rFJ1KdSI8NpyoxCiGVhvKhN8P8OXG09b2t1pV5MX6hR9WxSIigoKOiIiIhckEu6bB9TPQeJRtW730jbSYzeYU99i8UfkNTEYjf/R+i1zHTuFcoztJjs6MeLY0L9ct9KDVi4jIbTR1TURExGyGOR1g+SDYNAFO/HXfpwqPDeflP17m8NXDKdqWvzaUQpv/oGr4Yd7ZMZMPWinkiIhkFAUdERERgwGKNb75+vSm+zrN2aizvLTiJXZe3Mnra18nLCYMsIzwjPr1AN9QkDhHF4wYCGzdkq61FXJERDKKpq6JiIgAVH0Zzm6HSi9B4Sfv6xQ53XOSyz0XoTGhOBmciE2OxWSyPAh05tbTEFCI92u/wuvl/Ximf5eH/AZERORWBrP59iegZT6RkZH4+PgQERGBt7e3vcsREZHHWdx1WDMSgspA9V4P/fRX46/y0daPGFZjGH4u/ry37F9mbzsDWAaOxrYrzwtV8z/064qIZBdpzQYa0RERkewjIQqm1ISoUHDxhBLNwSfffZ/OZDYRnxyPh7OHdZ+/mz/jnxqPMSGRlR16ccGlIOSvjIMBxr9Ygecq3f/1REQk7XSPjoiIZB+uXlC82c3XF/+971PFJsUyaP0g3lz/JsmmZJu25IREVnXoSeF9m3lr1xzqnw/hi/YVFXJERB4hjeiIiEjWFR8JbrdNa2g4ApLioOF7DzSa8+b6N9l8YTMA43aOY2j1oQAYTWaGLP6XPPFOFASMDo50b1yWBhXz3ve1REQk/RR0REQk6zEmw84fYN1H8MJ0KNLgZpuHP7T95oEv0atcL7aHbcfV0ZW6eesCkGw08eb8vfy69wKG8s9hdnCkeqdWNOjc8oGvJyIi6aPFCEREJOs5sBTmd7Vs5ywOr20GR+eHfpk/Tv1BEZ8iFPUrSpLRxMC5ISzfFwqAs6OByZ0q06RM7od+XRGR7Cyt2UD36IiISNZTsiXkq27ZzlsVkmIf6HRms5ndF3en2N+0YFOK+hUlPiqGxS/1Y/MOy0NCXRwd+PqlKgo5IiJ2pKAjIiKPtyvH4Z/5tvscHKDlBHhlNTw3Fdx87vv0MUkxDFo/iG4ru7Hq1KoU7XGRUfzVrgvl9qzjk03fkCs5hm+6VqFhqaD7vqaIiDw4BR0REXl8rXoXJleHpX3h+hnbtqAykL/6g1/i1CrWnFkDwLub3uVK3BVrW3ySkaHTN+F6yTJdLWdcBBOfDubpEoEPfF0REXkwCjoiIvL4MjiCKRmMifD3hAy5RJuibXi28LN4OXsx7slxBLgHAJaQ0+fnXSy9YGRo3dc45ROMceyX1H6mXobUISIi6aPFCERE5PEQdRE8AsDxlgVD467D13WhYmeo3c/ynJwMEJ8cT3hsOAW8C1heJxnpNWMnG49eBsDDxZEfu1ahZtFcGXJ9ERG5SYsRiIhI1hAVBr8PhYnlYd8C2zZ3XxiwB54e9lBCTkxSDMP/Hs7JiJM2+92c3KwhJ+rSFaa88Skbj1wCIIeLI9N7VFfIERHJZBR0REQkc7t6ArZNheR42PAZmIy27Q9p2ejQ6FA6L+/MsuPLeGPdG0QnRqfoExl2iZ1tO9L8z5/pcugPPF2dmPFKdaoX8n8oNYiIyMOjoCMiIpmH2QwJtwWMJ2pDwXrg5A4lmlsCTwbwd/fHycEyLe5S7CWORxy3aY9OSGbCpz+T+9JZAJqf3s7MdsWo8oRCjohIZuR07y4iIiIZzJgM/y6CLZPBtwC0n2nb/uwEyxLRnhk3PczV0ZVxT47jvU3v8WGdDynoU9DaFhWfRLcft7PbrTiXy7XhxWPr8Jr8DRXKF86wekRE5MFoMQIREbG/5ESYUBaiL4LBwXLfjV/BDL3k0WtHCcoRhLeL7f9XzGYzBoPB+joiLomuP25n79nrAPh6OPNz+zKULZE3Q+sTEZHUaTECERHJnEwmuHzUdp+TC1TvZdnOXQ5ir6Q87iExm80sPLKQjss7MmLTCG7/vu/WkHPlxBmGfLLAGnL8PJyZ3bOmQo6IyGNAU9dEROTR2TIZtn1jWRZ68CFw8bjZVvUVKFDbck/OLWHjYbuecJ0JuyeQYExg7Zm1/HbiN1oWaZmi36WjJzncuSuvxMVypG4fovIUZFavGpTMrZkFIiKPA43oiIjIo3PxX7h+GhIi4MAS2zYPfyhYJ0NDDoCfmx+j64wGoH2J9jQp2CRFnyvRCfzx5vsERF7GOymWN/cvZo5CjojIY0UjOiIi8nAZk+HkX3DkD2j2CTg43myr3A1CZkORBuCT/5GUE50YjaujK863LEP9VP6nWNhyISX8S6Tofzk6gc7fbeNsidaMvhKOjzGO0t9MpphCjojIY0WLEYiIyMO1qDf8M8+y3e03KFTvZpvZDJHnwSffIylla+hWRmwaQcsiLelfqf89+4dHxdP5u20cDbcscV3Izcz3HctRpMQTGV2qiIikkRYjEBGRjJWcACfWW8LLrYrdMhXs9ulpBsMjCznhseG8tuY1QmNC+WHfD+y/vP+u/S8cPEbnqX9bQ04eHzem939aIUdE5DGloCMiIum34TMYWwRmtIbwg7ZtJZpD2Xbw4gxoMto+9QGBHoH0Lt8bgCpBVfB3u/ODPc/t3Mu5jh1p//s3OJqM5PV1Z96rtXgiIMejKldERB4y3aMjIiJ3ZjLCxf0QXMF2v4sXJEZZtg8vh6DSt7TlgOd/fHQ1/udc1DnyeObBwXDzO7ye5XqS3ys/LQq1sNlvc1x4BKdf7YN/fDR1QvfT5/Q6Ogz9iPz+Hqn2FxGRx4NGdEREJHV/joaxheCb+hBx3ratZAtw94cKHSF/TfvU95+YpBg+3/U5LZe05LcTv9m0OTs482zhZ+8ccq7F0nH6LsZXeIFEBydO5CpEx/HDFHJERLIABR0RkezMZLJMPft3cco2gwPER1i2T6y3bfMtAEOOwXNf2y42YAf7Lu9j2v5pJJuS+Xzn50QnRqfpuDNXYmn/zVbOXo1jd2AJvmoxgMpzZ5AvX2AGVywiIo+Cgo6ISHY2/RmYUhMWvgwJUbZthZ8Gdz8o3QZ8U1kK+tZlo+2oZnBNGhZoiLODM22LtcUxDXWdPBlG+2+3cP56HABFcuXg4xFdCM6bM6PLFRGRR0T36IiIZGUmE+yZAed3W4LJs1/YtgeWgjObwWyCs9uhaMObbflrwJDjmSbQmM1mtoZuZefFnSmWih5afSiDqwwmv/e9n81z7I/1RLw1iCcqtyc0uCzFAj2Z1asGgV5uGVW6iIjYgYKOiEhWkBAFFw+As5vtwgEODrB+DERdAFdvaDHesu+Gog0hKhQK1IKcxWzP6ZC5Bv3f+ustVp1eBUCjAo0oFVDK2pY7R+40nePolhBiBw3Aw5jEO9tnMrnVYD7q3Yicnq4ZUrOIiNhP5vq/mIiI3F1yAiTF2+67dBg+yQc/NoFNX6Y8Jm9lyz8To+H6adu2ks9AxzlQZ4DlvptMrEpQFev24mOp3FN0D0cuRtFp9UU2BpcD4HCBMnw89AWFHBGRLEojOiIimc2NB3AaDDf3HfwVVr8P105Cm6lQocPNNr9C4OAMpiTLUtC3q/sm1OoLucuDq2fG1v4QXIy5yJJjS2hbrC25PHJZ97cr3o51Z9fRrlg7Gj/ROF3nPBgaSefvt3E1NpnPK7cnumAxXh0/BD8fPSdHRCSrUtAREbEHs9k2yABsHA8HlsLVk9B/N3je/JCPowtcPW7ZvnTY9jgnF8sDOm+ftnZDvqoPt/YM9OvxX3l307uYzCYcHRzpWa6ntc3V0ZXvmnyX7nP+e/YKL03bxbXYJADKFvDntZHD8fFwfmh1i4hI5qOpayIiGSExFuIjbfeZzTDzOZhQHr5vmPKYyAsQuhcSIuHqCdu2nMXByd0SZHLkSnls22+g5USo+vLDew8ZzGw2k2xKttlXKbASJrMJgMVHF2O+Mbp1n/Z/9zOnX+xA0nXLMtkV8/sy85UaCjkiItmARnRERNIr7hpcPwNRYVCgJrj53Gw7sw3mdIC4q1B3EDR6/2abwQBXjlmOjb+e8rz+RSzPrvHJb7mf5lZ+BeGdC5lugYD7cTbqLLMPzmbd2XV0K9ONjiU7WtvyeeWjVZFWBOcI5rliz2G4fdQrHf75YRbO4z+iEPDR5m+Z0+kdvnulOl5uCjkiItmBgo6ISGrTyHZ8bwkkAI0/sG37ayxsnWLZ7rESnqh1s83NxxJyACLPp7yW7xMQF2G58T8pDpzdb7ZV6Q7Velqmot3OYEhZ42PCbDbbBJbYpFh+PvgzAOvOrLMJOgAf1f3oga+5+8w1hv5r4n1XL/wTorhSsCTf9a6rkCMiko0o6IhI1nXpCIT/C7FXoeSz4BV0sy1sH8x7CWKuQOUu0OwT22O3TLHcE+PqnTLoeN2ylHFUqG2bT17LiIx3Xgi4bblmgM4LLffSpMbFI+3vLZM7fv04sw/OZnf4brqU7kLbYm2tbcX9ipMnRx7CY8NxcHDAZDbhYHh4I1U7T12l+7QdRLv4M6zOq7SPP063KaPwVMgREclWFHREJPMwJlmmbt36gMqYy3D8T4iPsDzcsmBd22NmvWCZDuaSA/r8bdsWMgs2TbBsBxS1DTpObnDt1M1r3M4z0BJ0EiItyznfGk7yVIbK3SyBJ2dx2+NcveDNVFY+u+FOIecxFZEQwZFrRyjmWwxfN1/r/qjEKOYfmQ/Arou7bIKOwWDg86c/J59nPnxcfW4/5X0zm0xsP3mVHj/tJDbRCED+ymV4uWt33F0yx0NPRUTk0VHQEZGHIz7SshpYYrRlRCNnUdv2P0dDdLglCDS9bWrSmpGw9WtIjoNe624+9wXg2mlY1MuyXb13yqBz7ZTlxn2XVJZN9vC/uX1jOpm1LQDcfCFHztRv7m/4PhgTwTMIHG8bCShUz/KTjcQnx+PmZBvSpu+fzvhd4wEY9+Q4mhZsam0rHVAaFwcXjGYjsUmxKc5XJqDMQ63PnJTE3v6D+T3cgdjilqWn6xXLyXddq+LmrJAjIpIdKeiIZEUJ0ZAYYwkOPvltR0giL8CZrZAcb1nBK+i2D5zLB1uO98oNjUfZtv0xHPbOsawo1mcj5LxlataF3TCjtWW73mBoOML22D0/W6Z5eedNGXTAUitAQpTtfjfvm9u3r2IG4O5vuS/G3Q9MRtv3WrAeNP7Q0panku1xHv4w9LaHZ97q1vtusgGz2UxcchwezrbT58ZsH8Oq06sIjw1nU8dNeLvc/PeR3yu/dfvw1cM2QcfF0YWfmv9EIZ9C5HDO2GfVmE0m/nm5D647NtMeuOroTmSL5/j6pSoKOSIi2ZiCjsiDMiZZbio3JlmmT906NSk5AS6EWEYG3P0gd1nbYw/+BhFnLf1q97f9kH7qb9g9E4wJlpvUCz91sy0pDr6pbzkub2V4YbrteRe/Cod+s2wPOgTewTfbzu+ChT0s241GpQw6IXMgKQZylUoZdJLiIPaKZTsxxrbt1g+zt7eB5XcDlhB1O++8EFjacj/MbaMGeOWGZmMsYSZnKve8vLzyzjfp561sOzqUTSUaEwmLCeNK/BVyuue0CSgms4nWS1oTGhNKUd+izH12rs2xcclxhMeGA3Am8gxlc978M1wyoCSVAytTwr8EVYNSPqvn1r4ZacOxKyxLDKQnkOjgRO5iT/BZlyq4OinkiIhkZwo6Yh8J0XDj+RnuvrZtkaGWD9OmZMhVwnZVqpgrcG67pc2/CASVtj1210+WEQFnd6j2im3boeWWez1MyVB7AAQUudl27RT89qYlrBRrAnUG2B478zkIP2h5+vyb+2zbtkyyTL0CaP8zlGp5sy32KvzYxLJdqqWl/VbbvoZTGy3b1XvdDAM3avrnvw+dBevZBh0HZ7h8xLLtdUuIueHWsHBjpMTadsvvM+m2NrD87pJiLD+38wyyrBbm4pkyXHjngRp9LG35a6Q89vkfLfffuHqlbKvey/KTGlcvqNkn9TZ4bFciux+JxkQiEyNJMiYR7Gn7733t6bXsvLiT6wnXGVh5IEE5bt6PtD1sO6+teQ2A3uV7079Sf2ubg8GB2KRYEowJhMWEpbhmAe8C+Ln6UcC7QIpn3uT1zMtPzX96mG8x3dYdCufVmbtILFQXp8QEPCpXYtiwzrg4Pf7LcIuIyINR0LkfCdFgttzoavP8DLB8sE2ItEyh8c5r++1+Yozlw6nJZLkvwO8J22NPb7Hc3+DgCEUa2LZd/NeySpTJaPnA65P3Zlvcdcu0ILPRcmN0iea2x27/Dq6ftly36Ue2HwxP/AUhsy3HVn0ZnqhtW+/czpa24ArQZLTteVcMsRxvSoaea2zvhzj4Gyx5zVJvwxEpP6iOLWQZ5chd3jIF6lbrRlveD8DrWy03oN8Q/q/lGSUAdd+EoJG2x67/5Ob0qNuDztntliWDwfIU+VuDTlKcJQRByn8vYLlZPSrUEjBu53jLUsDGxDu3Jd/WBuDkekt7gm3QcbylLcV5nSwjRAZH26ldN+StDEmxlsBz+whJruLQ5CPLtfNWSXls99/AwSn1e16eetvykxqfvNB8TOptYPkzlE2YzWYSjAkkm5LxvO33ePjqYc5FnSM2OZYGBRrYTOs6fPUwP+7/kdikWJoWasqzhZ+1Obb+vPrEJMVQyKcQy9oss2nbFraNOYfmANCxZEeboBPgFmDdvhJ3JUW9RXyL4OXiRbBncIoV0LqX6c7LZTPXQ0jNZjOxO3awJUcBXpu9mySj5aGike06M7pjJZwdFXJERERB5/58UdqyAlRAMei/07Zt/Sew/VvL9u03VYcfvPk09Bp9Un4oXPq65aZqdz94+5Rt28HfYP3Hlu1OC24LOtdg1XDLdtl2KYPOvgVwdptlu8mHlg/HN1w5dnPUoPDTtkEH4MS6/zZS+dY88gJcPmzZvv2DuNlkCXxgmXp1OwcnyzEmY+ptN9z2DbJN0Li97dZ2Y1LKtltvKL+9/dbzGlM5r1duS4h1dE75zBXfJyzB1NEFcgTaHueSA2r1sxx3++pcYBlZqtDRcqzLbfcxFG8C/XZa2jwCUh57+5+RW9Xqa/lJjW8BqN3vzsfeGiwfA4nGRJJMSSSbkvF28bZ5Xsv1+OuExoSSZEoir2deAtxv/h6TTEmsPrWaRFMi/m7+1M9X3+a8S48t5dDVQySZkhhYeaBNYNkRtoOv9nxFgjGBl0q9RMsiLW2OrfJzFZJMSZQOKM28Z+fZtM08MJOlx5cCsKT1Eor43gzcEQkRrDi5AsBm/w1eLl7EJMUQlRiVos3X1ffm+064btOWO0dumhdsToB7AJWDUk7l+7bJtyn23fAwl31+GGK2buXcx2MwHTnElLqvkvTfdMZnywfzRfuKCjkiImKV7qCzYcMGPvvsM3bt2kVoaCiLFy+mTZs2dz3mr7/+YtCgQfz777/kyZOH//3vf/Tpc5epKJndjf/xm1P5kH7rhwKz6c5tqX3AvxFATKaUbbc+Df326956X0dagsOt/W/dvv28hnuc19kDXLws5zCbbdvcvC0f7B3+G3m4XeGnLEHHN5XRkwK1LedL7VjfAtDgXUswSe3eixZjLTfZO6fyPJIqPaDkM5bz+hW0bfMvBEPPWM7rmMrDGjsvSLnvhlLPYi75DGbMGDDYREKzkytxDYZjxoyDwQH32w6NyFuRBGMCJrOJQEdnbv2IFu3gyCVnJ8xmIwEYuX0R3v2X92Mym3BzcqO4n22IOnrtKOGx4ZjMJqoEVbG5wfxy3GV2hO3AZDZR1LcoJfxL2By7+OhiYpNj8XDy4Lliz9m0bT6/mT2X9mA0GXmh+As206fCYsL4ft/3GM1GqgRVSTEaMWb7GM5Fn8PFwYXxT423aVt6bClzDs3BaDbyv2r/o1ruata2izEXaf9be5LNyTyZ78kUD5PstaoXu8N3A7D7pd043xJoV59ZzQdbLM/A+aD2Bzbvx2gy8vZGy+hUtdzVUgSd9WfXs+bMGgB6lutpE3SiEqPYE77HUl/sRW7n4uhCkimJ+OT4FG3ut0wdjLttWuGtozvRSSnvZaqeuzqRCZH4u/unaGtdtDW189TGx9WH4By209r83PwY++TYFMc8bs5ejWXFyr3UP3IIgA4HV7O7XjFaV8zD+Bcq4KSQIyIit0h30ImJiaFChQr06NGDdu3a3bP/yZMnadGiBb169eL/7d15eFTlvQfw75lkMkNC9n0hISgkAq1l0wQDFIEgLr20XtFWI6C9VwVrkfYhWHsr9OkteKtVVCzl3kB8asGgEDdQoQoBISBLwpawI4RkQhayL5PMzHv/OMPMvDMBMoEEMvl+nifP45x34czvvPN6fnPOe+b999/Hzp07MWfOHISHh3eq/a3ovbhkGM2tCNQF41Gnsm/9/HFoSAoABQ/DAodf7UC5l4J1QycAigZ3BoVhglPb7NtG41LcIOi89HD+Ln5XUAR2jPwJoAAP6/3g+ODeWi8t3rnrYQAKhoUNx0+d2q5OSsO52AQIAH+ABo7Lc78Li8cnaU9CKMDD4YlwvJHJqAD/NeEpCAC3Bw3GM079rkpKxcFg9QT6jzpf6US8sH8Q/p48GgICPwuJQrpT2znhwTCajYjz94fTcnes9mrGN941EBD4s2JBvENZsaker9TthYDAT0JCkeHU9j/ObcDF5osI1gXjvSFTpbL3LvwLa4rXwAILXh33qvTN9vnGUmR8kQEhBKYOnIqXU16W2s78YiaKLxVDgYI9j++Ryv5Z/E8s/W4pAODVca/i/kH328rqjHUYl6M+hnhc7Di8O/ldqW3mjkzsLN0JAPj2sW+l3xTJu5CHhTsWAgAW3rUQj9/xuNT28U2PwyIsGB46HGsfXCuVZR/Nxqen1VubPpv+GQYGDrSVnag5gQXbFwBQ12s4JzrLDixDdWs1YvvHuiY6ZbvwXpG6JiMtNk1KdGqNtcg5rl69UKC4JDq7DbtxqvaUdKJ/WXVrNY5WHwUA1F++EmilKAqqW9XbrZo6WDekdbga125plxId57IrtuvgCqCPQ8LrXK6z3laoUTQwd/AlwIiIETCajYjxi3EpmzhgIqL8otDPux+i/KKkstuDb8fGn26Er9YX/j6ua5mckzxHsf1jEds/9orlvYmwWGA8fhzm+gboBiWiVOOL5VtPYcOBUljMMRjYPwJGLy02Jf8YL0y8Db+ekgQvTd9Zq0VERJ3jdqIzbdo0TJs27doVrVasWIH4+Hi8+eabAIA77rgD+/btw2uvvdbrEp0jpXX4R/45xGw0QNvejpL+zcj0OiTV8dl3FNGnSwAAr9cdhBJuXx9hqTqAxK9PAQC+HqTHJoPcNnzjAfg2NsLk7Y3MNrnM6/BOxBXtBwCsHDscyoAge78NZ5G4aTcA4FDcRew+P0lqG/ivrxBRXQkAeOlSIRSHdSHKyS8Qf+BrAMC6Uf2x7nb7ibalvRGJG74CAFSHHUTmJPn3S/x2fIikMvXxvH8qmQqNX5ytTJzfhB/k5wEAtg7X4Oth8gld8vod8DaZ0eQfgMwyeRzovtuI1LNFAIC3S7ZBE/oj+z5V7MTEreqPMZ4YrEPmSHndxx2fHcDI5hYYdTpkNskx1B7Mw/Rj6rHJKduOnBj78LfUHcNjX6oxuhi/F5kX5LZ3bD6NlJpGCACZOrlMc+w7zDmonuzmVexGXqJDHIyXMOdztexS5Elk1sptY/NOY065Wv4nr/3Q6MPshd/vxZw9atmRku+QmfwDqe0zn5ugsQjUBZ1HZqvcr353EeZ+r7Z9t30PlCB78iAMezE3Ty27cHI/Mk/Lbad/Vgd9qxktvpXIFE7ju+Ag5h5X265tzEdOpEMMaw5h7ldqWXliITJL5bZjN5Vjar0ZFk2zSwy9ig7g+UMWAAo2V+Vjc4LD79o0X8SvP9NCAVAZcw6Z1XLbod9cQlqFPxQoWKQ9AMXh8ce+Z84gc284AA2OlZ1B5mCHthYLfv95AgAF9aE6ZDbL/Sbma7G4ZCgUaPB3ywm0+9fYyoLLKrHk27sAKDh3VoPMk3LbqR97QdumQUv/VmSWy2W3HdiP2JOFAIAVk8PQEGr/bPhXl2Pkv9RbSUtvvxOnRk2U2o7Z9B5862tg8tFh58+ek8rij+7BoENq0nx4/HRUxw6ylema6pH6yf8CACoSklB0j5yEjty8FkGVFwAAW3/+GwiHK8ixJwqRvEedB4pSp8Fw+w9tZV7tbbj3/b9AgUB19EDsv+8Jqd8RWz5A1NmjUITA108sgNEvQOr37s9XQWOx4NCE6TgxZrKtTDGbMfOVXwAA9qY8gMUx98JssV41VjT4071zMX3SD/FG2iAE+3VwBZaIiAg9sEYnPz8f6eny9/lTp05FVlYW2tvbodW6Lu42Go0wGu3rOurrO/jtjJugtLYFOftKsK6oHf6tAmVBzXg6skSqM+dUJSYeUf+HPD/qAoodvtEd0lyKpw6rZRstFXhHkdv+37FmxNYINOna8e8JctnjJRcx0dp2cWgpdl+0l0caL9j63dFyCX/WyW1fO1GPYWVq+YOJF2DW2E8MHrxosPX7pn8Zvqq1t/WxNOETa9nhuFosCJT7/cPJGqSeUcuz4i+gWmu/fS2t5gJmW9u+pzMgp0Vum3vEBH07cC60Cc9GyGW/PlWFiUVq2/XRF3DaYX3KsEZ7DHNRiX9Y5LbZxUZE1gvU+LbhF3Fy2azvq/Bja9vfh5Vhf5m9PK61zNbvN611+ItWbrvsWAuGVAhYADxwm1w2vbTG1u9f/KvxTbW9vH97LT60lu2vacHv+8tt/3jMiDElavm78aVo0NpvZfrxxTrMsrZd6dOA3Ea57WeHBbyFwMlQM14Ik8t+U2TCBOux+Wd0JUr87Ldd/ehSA2Zbx2iOxYz3TXLb948qCG0RqPBTMDNGLnuq2AsTjqttPw9pxpEQe/nA+kY8Ze33iyY9sr3ktsuP6jCoTsCoUfA/g+Syh0/2w/ij6i2b2/t5YWelvTy4tR6zjqhx2VXtjRW+ctv/PmzCyEo1CVkysAKt3nW2ssnn6zHjsEFtq2nApjp7W0VYMOvwaQDA0RATcoLkfhccPIM7rAnv0shSXPSz34Y2pvwU/u3QLrXfNj/ktMhXbn52MB8B7c244BeGnAg5WXnm0CGMO6O2XRYyGieC7VeLBteU4OHD+QCAw40a5Aj5R1cnHdqLAY2VaND2Q068nKw8drwI9xarVxtX+w/Ddwb73BrRfAk/L/oOAHCurg05OvkLgtFHDyK++iwAYN2+87A43NI67ewJTDmu3hr4oe/t2FJrv51UZzIiw5ooV9S1IMdpHA4+cRKRBvXpgJ8fOI9L/exfpIwtK8OUS+ptf8WnyvCR03z4iLcOviYjhu/dAr/0u1Cv80OA3htPpSVi9thEBPp28GAQIiIiB92e6JSXlyMyMlLaFhkZCZPJhKqqKkRHuz4ad8mSJVi82PmGpluHsPgAMEKYXZ9KZTZGASgFAFjag+R2Dq/NRtf3bTEFAKix9u/Ub2sMAPWEwWKUF7wLi/2+fotRvnICAKItHMDlkwj5HnZzaywA9UqRpdXpthfh8G29UT6G9rbqSaUw+QKO6/lb4hz+e6DrPll8ALTB0u66yN7cPBCAegJkaQ+T2zm8P0tLomu/l2Nodv2BQnNLAoCT1vcT7dQuyN6vcxwA6342o6OHMqj191r7dYq/sB9LS5v8XuzbKq11dVKZcIiNpc3pIQeAdV8EhNn1FieLw/txHB8ApHErTM4rfwAILYAWCEsHJ5IWh9vOLPL+Oo4XdDCGIbh+ole66uO7pRVpLqXCoVzjVG700qLOxw9mRYO2Dp5muDFR/cHWJu9+iNKa8J/pQ/Dk2IEI0DPBISKizlGEcF5F7kZjRbnmwwiGDBmC2bNn46WXXrJt27lzJ9LS0mAwGBAV5Xpi3tEVnQEDBqCurg4BAR08SreHNLS2w1DXClFyDrAIwNsbSmycVEdcqgYarE9EioqCorPfuiaMrUC59Xcq/P2hhMgn+aKsFGhvBzQKlAHyIn1RWwvU1aovwiOg+NoXlwuTCShVbzuBry+UcKdEqNwAXI5nfIL0VCrR0ABcsj5uNiQUir/9pFlYLEDJefWFXg8lUj5WouIi0GK9AhEbB8XbfqIrmpuASusJfFAQlMAgue35c+oDB3y0UKLlxEJUVwGN1oXYUdFQdPYTatHaCly0xjAgAEqwvChblF4AzGZAo4ESN0Auq60F6q3f9odHQOlnP2kX7e2AoUx94ecHJdQpwSo3AG3qk+WUeKdj01AP1FhvawoNheJw9USYzfZjo9dDiZATRlFxEWi1XimIjYPiZf8mXTQ1AtXWYxMcDMVfHvvivHrbILRaKNHyFQVRXQU0W9ezRMVAcbhyKlpbgUrrAnr/QChBQXLb0gvqwye8vKHEOB2b2hqgwXqFNTwSit5hfLe1ARcN6omxr5/r+C43AKZ2AIrrsWmot4/vkDDX8V1uPTb6flDCwuW2lRX28R0TC8XhtivR1Gg/NkHBUPo7HBshgDL1Swn4+Lh+bi5V28d3ZJQ8vltb7Z8bf3/XY2MoAyDUGDof87pa+7EJCZPHd1ubvV9fXygBciIqqirV8a0orv02NQJN1n4DA+W5x2QCamvs79W53/o6wGR92mBwiDxHGFuBllY1p9H3k/dXCPW9KArg5SX9m4B1/APq57GLv3ekABgQ4gu9lj/+SUREqvr6egQGBl4zN+j2RGf8+PEYMWIEli1bZtuWm5uLGTNmoLm5ucNb15x19s0QEREREZFn62xu0O33kqSmpmLLli3Sts2bN2P06NGdSnKIiIiIiIjc5Xai09jYiMLCQhQWFgJQHx9dWFiI8+fVW5xeeuklPPnkk7b6zz77LM6dO4f58+ejuLgYq1atQlZWFn7729/emHdARERERETkxO2HEezbtw8TJ9qfIjR//nwAwMyZM5GdnQ2DwWBLegAgMTERmzZtwosvvojly5cjJiYGb731Vq97tDQREREREfUe17VGp6dwjQ4REREREQG30BodIiIiIiKinsZEh4iIiIiIPA4THSIiIiIi8jhMdIiIiIiIyOMw0SEiIiIiIo/DRIeIiIiIiDwOEx0iIiIiIvI4THSIiIiIiMjjMNEhIiIiIiKP432zd6AzhBAA1F9BJSIiIiKivutyTnA5R7iSXpHoNDQ0AAAGDBhwk/eEiIiIiIhuBQ0NDQgMDLxiuSKulQrdAiwWC8rKyuDv7w9FUW7qvtTX12PAgAEoKSlBQEDATd0XT8T4di/Gt3sxvt2L8e1ejG/3Yny7H2PcvW6l+Aoh0NDQgJiYGGg0V16J0yuu6Gg0GsTFxd3s3ZAEBATc9IPsyRjf7sX4di/Gt3sxvt2L8e1ejG/3Y4y7160S36tdybmMDyMgIiIiIiKPw0SHiIiIiIg8DhMdN+l0OrzyyivQ6XQ3e1c8EuPbvRjf7sX4di/Gt3sxvt2L8e1+jHH36o3x7RUPIyAiIiIiInIHr+gQEREREZHHYaJDREREREQeh4kOERERERF5HCY6RERERETkcZjoEBERERGRx+nzic67776LxMRE6PV6jBo1Cjt27Lhq/by8PIwaNQp6vR6DBg3CihUrXOqsX78eQ4cOhU6nw9ChQ5Gbm9tdu98ruBPjDRs2YMqUKQgPD0dAQABSU1Px1VdfSXWys7OhKIrLX2tra3e/lVuSO/Hdtm1bh7E7duyYVI9j2M6d+M6aNavD+A4bNsxWh+NXtX37djz00EOIiYmBoij4+OOPr9mG82/nuRtfzr3uczfGnH/d4258Of923pIlSzBmzBj4+/sjIiIC06dPx/Hjx6/ZrjfOwX060cnJycG8efPw8ssvo6CgAOPGjcO0adNw/vz5DuufPXsW999/P8aNG4eCggL87ne/wwsvvID169fb6uTn5+PRRx9FRkYGDh48iIyMDMyYMQN79uzpqbd1S3E3xtu3b8eUKVOwadMm7N+/HxMnTsRDDz2EgoICqV5AQAAMBoP0p9fre+It3VLcje9lx48fl2I3ePBgWxnHsJ278V22bJkU15KSEoSEhOCRRx6R6nH8Ak1NTbjzzjvxzjvvdKo+51/3uBtfzr3uczfGl3H+7Rx348v5t/Py8vIwd+5c7N69G1u2bIHJZEJ6ejqampqu2KbXzsGiD7vrrrvEs88+K21LTk4WCxcu7LD+ggULRHJysrTtmWeeESkpKbbXM2bMEPfdd59UZ+rUqeKxxx67QXvdu7gb444MHTpULF682PZ69erVIjAw8EbtYq/mbny3bt0qAIiampor9skxbHe94zc3N1coiiK+//572zaOX1cARG5u7lXrcP7tus7EtyOcezuvMzHm/Nt1XRnDnH87r6KiQgAQeXl5V6zTW+fgPntFp62tDfv370d6erq0PT09Hbt27eqwTX5+vkv9qVOnYt++fWhvb79qnSv16cm6EmNnFosFDQ0NCAkJkbY3NjYiISEBcXFxePDBB12+dewLrie+I0aMQHR0NCZNmoStW7dKZRzDqhsxfrOysjB58mQkJCRI2zl+3cf5t2dx7u0+nH97BuffzqurqwMAl8+7o946B/fZRKeqqgpmsxmRkZHS9sjISJSXl3fYpry8vMP6JpMJVVVVV61zpT49WVdi7Oz1119HU1MTZsyYYduWnJyM7OxsfPrpp1i7di30ej3uuecenDx58obu/62uK/GNjo7GypUrsX79emzYsAFJSUmYNGkStm/fbqvDMay63vFrMBjwxRdf4Je//KW0neO3azj/9izOvTce59+ew/m384QQmD9/PtLS0jB8+PAr1uutc7D3TfuXbxGKokivhRAu265V33m7u316uq7GY+3atVi0aBE++eQTRERE2LanpKQgJSXF9vqee+7ByJEj8fbbb+Ott966cTveS7gT36SkJCQlJdlep6amoqSkBK+99hrGjx/fpT49XVdjkZ2djaCgIEyfPl3azvHbdZx/ewbn3u7B+bfncP7tvOeffx6HDh3Ct99+e826vXEO7rNXdMLCwuDl5eWSZVZUVLhko5dFRUV1WN/b2xuhoaFXrXOlPj1ZV2J8WU5ODp5++mmsW7cOkydPvmpdjUaDMWPG9LlvZK4nvo5SUlKk2HEMq64nvkIIrFq1ChkZGfDx8blq3b46ft3F+bdncO7tWZx/bzzOv533q1/9Cp9++im2bt2KuLi4q9btrXNwn010fHx8MGrUKGzZskXavmXLFowdO7bDNqmpqS71N2/ejNGjR0Or1V61zpX69GRdiTGgfps4a9YsrFmzBg888MA1/x0hBAoLCxEdHX3d+9ybdDW+zgoKCqTYcQyrrie+eXl5OHXqFJ5++ulr/jt9dfy6i/Nv9+Pc2/M4/954nH+vTQiB559/Hhs2bMA333yDxMTEa7bptXNwzz774NbywQcfCK1WK7KyskRRUZGYN2+e8PPzsz2hY+HChSIjI8NW/8yZM8LX11e8+OKLoqioSGRlZQmtVis++ugjW52dO3cKLy8vsXTpUlFcXCyWLl0qvL29xe7du3v8/d0K3I3xmjVrhLe3t1i+fLkwGAy2v9raWludRYsWiS+//FKcPn1aFBQUiNmzZwtvb2+xZ8+eHn9/N5u78X3jjTdEbm6uOHHihDhy5IhYuHChACDWr19vq8MxbOdufC974oknxN13391hnxy/qoaGBlFQUCAKCgoEAPHXv/5VFBQUiHPnzgkhOP9eL3fjy7nXfe7GmPOve9yN72Wcf6/tueeeE4GBgWLbtm3S5725udlWx1Pm4D6d6AghxPLly0VCQoLw8fERI0eOlB6tN3PmTDFhwgSp/rZt28SIESOEj4+PGDhwoPjb3/7m0ueHH34okpKShFarFcnJydIk1he5E+MJEyYIAC5/M2fOtNWZN2+eiI+PFz4+PiI8PFykp6eLXbt29eA7urW4E99XX31V3HbbbUKv14vg4GCRlpYmNm7c6NInx7Cdu3NEbW2t6Nevn1i5cmWH/XH8qi4/avdKn3XOv9fH3fhy7nWfuzHm/OuerswRnH87p6O4AhCrV6+21fGUOVgRwrqSiIiIiIiIyEP02TU6RERERETkuZjoEBERERGRx2GiQ0REREREHoeJDhEREREReRwmOkRERERE5HGY6BARERERkcdhokNERERERB6HiQ4REREREXkcJjpERERERORxmOgQEREREZHHYaJDREREREQe5/8BTxWjC7I81dEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pts = pybamm.linspace(0, 2, 100)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10,5))\n",
+    "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,5).evaluate(), \":\", lw=2, label=\"smooth_plus (k=5)\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,10).evaluate(), \":\", lw=2, label=\"smooth_plus (k=10)\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,100).evaluate(), \":\", lw=2, label=\"smooth_plus (k=100)\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Solving a model with the exact maximum and smooth approximation, demonstrates a clear speed-up even for a very simple model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exact: 187.987 us\n",
+      "Smooth, k=10: 173.686 us\n",
+      "Smooth, k=50: 189.009 us\n",
+      "Smooth, k=100: 152.141 us\n",
+      "Smooth, k=1000: 222.295 us\n",
+      "Smooth, k=10000: 194.155 us\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "330ed4c43f374964b97dea4e45b0e240",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=2.0, step=0.02), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_exact = pybamm.BaseModel()\n",
+    "model_exact.rhs = {x: pybamm.maximum(x, 1)}\n",
+    "model_exact.initial_conditions = {x: 0.5}\n",
+    "model_exact.variables = {\"x\": x, \"max(x,1)\": pybamm.maximum(x, 1)}\n",
+    "\n",
+    "model_smooth = pybamm.BaseModel()\n",
+    "k = pybamm.InputParameter(\"k\")\n",
+    "model_smooth.rhs = {x: pybamm.smooth_plus(x, 1, k)}\n",
+    "model_smooth.initial_conditions = {x: 0.5}\n",
+    "model_smooth.variables = {\"x\": x, \"max(x,1)\": pybamm.smooth_plus(x, 1, k)}\n",
+    "\n",
+    "\n",
+    "# Exact solution\n",
+    "timer = pybamm.Timer()\n",
+    "time = 0\n",
+    "solver = pybamm.CasadiSolver(mode=\"fast\")\n",
+    "for _ in range(100):\n",
+    "    exact_sol = solver.solve(model_exact, [0, 2])\n",
+    "    # Report integration time, which is the time spent actually doing the integration\n",
+    "    time += exact_sol.integration_time\n",
+    "print(\"Exact:\", time/100)\n",
+    "sols = [exact_sol]\n",
+    "\n",
+    "ks = [10, 50, 100, 1000, 10000]\n",
+    "solver = pybamm.CasadiSolver(mode=\"fast\")\n",
+    "for k in ks:\n",
+    "    time = 0\n",
+    "    for _ in range(100):\n",
+    "        sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n",
+    "        time += sol.integration_time\n",
     "    print(f\"Smooth, k={k}:\", time/100)\n",
     "    sols.append(sol)\n",
     "\n",
-    "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"smooth (k={k})\" for k in ks]);"
+    "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);"
    ]
   },
   {
@@ -929,24 +1098,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Smooth minimum (softminus):\t -0.1 * log(exp(-10.0 * x) + exp(-10.0 * y))\n",
+      "Soft minimum (softminus):\t -0.1 * log(exp(-10.0 * x) + exp(-10.0 * y))\n",
       "Smooth heaviside (sigmoid):\t 0.5 + 0.5 * tanh(10.0 * (y - x))\n",
-      "Smooth absolute value: \t\t x * (exp(10.0 * x) - exp(-10.0 * x)) / (exp(10.0 * x) + exp(-10.0 * x))\n"
+      "Smooth absolute value: \t\t x * (exp(10.0 * x) - exp(-10.0 * x)) / (exp(10.0 * x) + exp(-10.0 * x))\n",
+      "Smooth minimum:\t\t\t 0.5 * (x + y - sqrt(0.010000000000000002 + (x - y) ** 2.0))\n"
      ]
     }
    ],
    "source": [
     "pybamm.settings.set_smoothing_parameters(10)\n",
-    "print(\"Smooth minimum (softminus):\\t {!s}\".format(pybamm.minimum(x,y)))\n",
+    "print(\"Soft minimum (softminus):\\t {!s}\".format(pybamm.minimum(x,y)))\n",
     "print(\"Smooth heaviside (sigmoid):\\t {!s}\".format(x < y))\n",
     "print(\"Smooth absolute value: \\t\\t {!s}\".format(abs(x)))\n",
+    "pybamm.settings.min_max_mode = \"smooth\"\n",
+    "print(\"Smooth minimum:\\t\\t\\t {!s}\".format(pybamm.minimum(x,y)))\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")"
    ]
   },
@@ -961,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -987,7 +1159,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1001,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.9.18"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index cdc0535c0c..f67819bda6 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1290,7 +1290,7 @@ def _heaviside(left, right, equal):
 def softminus(left, right, k):
     """
     Softminus approximation to the minimum function. k is the smoothing parameter,
-    set by `pybamm.settings.min_smoothing`. The recommended value is k=10.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=10.
     """
     return pybamm.log(pybamm.exp(-k * left) + pybamm.exp(-k * right)) / -k
 
@@ -1298,7 +1298,7 @@ def softminus(left, right, k):
 def softplus(left, right, k):
     """
     Softplus approximation to the maximum function. k is the smoothing parameter,
-    set by `pybamm.settings.max_smoothing`. The recommended value is k=10.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=10.
     """
     return pybamm.log(pybamm.exp(k * left) + pybamm.exp(k * right)) / k
 
@@ -1306,7 +1306,7 @@ def softplus(left, right, k):
 def smooth_minus(left, right, k):
     """
     Smooth_minus approximation to the minimum function. k is the smoothing parameter,
-    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=1000.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
     sigma = (1.0 / k)**2
     return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2
@@ -1315,7 +1315,7 @@ def smooth_minus(left, right, k):
 def smooth_plus(left, right, k):
     """
     Smooth_plus approximation to the maximum function. k is the smoothing parameter,
-    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=1000.
+    set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
     sigma = (1.0 / k) ** 2
     return (pybamm.sqrt((left - right)**2 + sigma) + (left + right)) / 2

From 5c18e229b5f9a111dcbf46c0bd9c9fece2bfe069 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 03:27:38 +0530
Subject: [PATCH 140/615] #3049 correctly specify inclusion of packages

---
 pyproject.toml | 2 +-
 setup.py       | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a416dfd2a2..c91d275789 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -178,4 +178,4 @@ pybamm = [
 ]
 
 [tool.setuptools.packages.find]
-include = ["pybamm"]
+include = ["pybamm", "pybamm.*"]
diff --git a/setup.py b/setup.py
index 018bf9eee0..a0180cb3e8 100644
--- a/setup.py
+++ b/setup.py
@@ -299,6 +299,8 @@ def compile_KLU():
 # Project metadata was moved to pyproject.toml (which is read by pip). However, custom
 # build commands and setuptools extension modules are still defined here.
 setup(
+    # silence "Package would be ignored" warnings
+    include_package_data=True,
     ext_modules=ext_modules,
     cmdclass={
         "build_ext": CMakeBuild,

From 961ad29ce659bdb8eccf911ccb6c6263e25448ef Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Fri, 6 Oct 2023 18:00:32 -0400
Subject: [PATCH 141/615] Rerunning the notebook

---
 .../notebooks/solvers/speed-up-solver.ipynb   | 81 +++++++++----------
 1 file changed, 40 insertions(+), 41 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 7cf74f156e..1f72b0cf15 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -118,8 +118,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 143.843 ms\n",
-      "Fast: 84.697 ms\n"
+      "Safe: 138.986 ms\n",
+      "Fast: 84.050 ms\n"
      ]
     },
     {
@@ -164,14 +164,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 506.167, , mxstep steps taken before reaching tout.\n"
+      "At t = 506.167 and h = 5.37919e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 5.881 s\n",
+      "Safe: 336.690 ms\n",
       "Solving fast mode, error occurred: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n",
       "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n",
       ".../casadi/interfaces/sundials/idas_interface.cpp:596: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
@@ -225,7 +225,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eed7984c8b474c1d84244101566e2d79",
+       "model_id": "3d9ea56ac0fb4b7f8d325414876b1a00",
        "version_major": 2,
        "version_minor": 0
       },
@@ -266,13 +266,6 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "At t = 159.925 and h = 7.17391e-08, the corrector convergence failed repeatedly or with |h| = hmin.\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -355,12 +348,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 670.587 ms (integration time: 597.206 ms)\n",
-      "With dt_max=20, took 666.842 ms (integration time: 594.925 ms)\n",
-      "With dt_max=100, took 364.074 ms (integration time: 320.450 ms)\n",
-      "With dt_max=1000, took 85.601 ms (integration time: 56.237 ms)\n",
-      "With dt_max=3700, took 54.820 ms (integration time: 36.811 ms)\n",
-      "With 'fast' mode, took 47.771 ms (integration time: 36.758 ms)\n"
+      "With dt_max=10, took 654.513 ms (integration time: 586.519 ms)\n",
+      "With dt_max=20, took 655.676 ms (integration time: 587.639 ms)\n",
+      "With dt_max=100, took 363.174 ms (integration time: 319.367 ms)\n",
+      "With dt_max=1000, took 84.630 ms (integration time: 55.838 ms)\n",
+      "With dt_max=3700, took 54.536 ms (integration time: 36.817 ms)\n",
+      "With 'fast' mode, took 47.632 ms (integration time: 36.758 ms)\n"
      ]
     }
    ],
@@ -405,11 +398,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 572.819 ms (integration time: 484.879 ms)\n",
-      "With dt_max=20, took 573.989 ms (integration time: 485.942 ms)\n",
-      "With dt_max=100, took 325.108 ms (integration time: 273.404 ms)\n",
-      "With dt_max=1000, took 109.595 ms (integration time: 69.396 ms)\n",
-      "With dt_max=3600, took 748.976 ms (integration time: 38.108 ms)\n"
+      "With dt_max=10, took 572.246 ms (integration time: 484.119 ms)\n",
+      "With dt_max=20, took 581.830 ms (integration time: 491.914 ms)\n",
+      "With dt_max=100, took 326.363 ms (integration time: 274.358 ms)\n",
+      "With dt_max=1000, took 110.161 ms (integration time: 69.523 ms)\n"
      ]
     },
     {
@@ -417,8 +409,15 @@
      "output_type": "stream",
      "text": [
       "At t = 460.712, , mxstep steps taken before reaching tout.\n",
-      "At t = 460.712 and h = 4.67695e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
-      "At t = 460.712 and h = 9.20727e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 460.712, , mxstep steps taken before reaching tout.\n",
+      "At t = 460.712 and h = 1.04372e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "With dt_max=3600, took 894.873 ms (integration time: 37.240 ms)\n"
      ]
     }
    ],
@@ -471,7 +470,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  792.337 ms\n"
+      "Took  799.154 ms\n"
      ]
     }
    ],
@@ -534,7 +533,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  689.698 ms\n"
+      "Took  682.905 ms\n"
      ]
     },
     {
@@ -588,7 +587,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  689.714 ms\n"
+      "Took  679.216 ms\n"
      ]
     },
     {
@@ -635,7 +634,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  292.674 ms\n"
+      "Took  291.654 ms\n"
      ]
     },
     {
@@ -854,16 +853,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 190.871 us\n",
-      "Soft, k=5: 175.473 us\n",
-      "Soft, k=10: 150.303 us\n",
-      "Soft, k=100: 188.151 us\n"
+      "Exact: 193.123 us\n",
+      "Soft, k=5: 175.344 us\n",
+      "Soft, k=10: 148.491 us\n",
+      "Soft, k=100: 191.960 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58008e3b72c34a4684a5b3bd343a813d",
+       "model_id": "13a25450d75d4f59b654751e9b83e57b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1022,18 +1021,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 187.987 us\n",
-      "Smooth, k=10: 173.686 us\n",
-      "Smooth, k=50: 189.009 us\n",
-      "Smooth, k=100: 152.141 us\n",
-      "Smooth, k=1000: 222.295 us\n",
-      "Smooth, k=10000: 194.155 us\n"
+      "Exact: 191.759 us\n",
+      "Smooth, k=10: 171.753 us\n",
+      "Smooth, k=50: 191.591 us\n",
+      "Smooth, k=100: 152.561 us\n",
+      "Smooth, k=1000: 228.356 us\n",
+      "Smooth, k=10000: 196.291 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "330ed4c43f374964b97dea4e45b0e240",
+       "model_id": "4c9502b601ef4174ac002f6bf0f5e85b",
        "version_major": 2,
        "version_minor": 0
       },

From b5aa9098a979bcce7152e02fc90313030667f02e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 6 Oct 2023 22:00:59 +0000
Subject: [PATCH 142/615] style: pre-commit fixes

---
 docs/source/examples/notebooks/solvers/speed-up-solver.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 1f72b0cf15..276f60f68a 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -756,7 +756,7 @@
     "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Softplus\n",
-    "print(f\"Softplus (k=10): \", pybamm.softplus(x,y,10))\n",
+    "print(\"Softplus (k=10): \", pybamm.softplus(x,y,10))\n",
     "\n",
     "# Changing the setting to call softplus automatically\n",
     "pybamm.settings.min_max_mode = \"soft\"\n",
@@ -955,7 +955,7 @@
     "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Smooth plus can be called explicitly\n",
-    "print(f\"Smooth plus (k=100): \", pybamm.smooth_plus(x,y,100))\n",
+    "print(\"Smooth plus (k=100): \", pybamm.smooth_plus(x,y,100))\n",
     "\n",
     "# Smooth plus and smooth minus will be used when the mode is set to \"smooth\"\n",
     "pybamm.settings.min_max_mode = \"smooth\"\n",

From a7cc3bf36dd55f00710fdee64940c1d9d5edaf8c Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Fri, 6 Oct 2023 20:53:02 -0400
Subject: [PATCH 143/615] Running on M2

---
 .../notebooks/solvers/speed-up-solver.ipynb   | 87 ++++++++++---------
 1 file changed, 47 insertions(+), 40 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 276f60f68a..60335d9e02 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -19,8 +19,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2023-10-06T15:43:06.684699Z",
-     "start_time": "2023-10-06T15:43:02.151747Z"
+     "end_time": "2023-10-07T00:46:40.608248Z",
+     "start_time": "2023-10-07T00:46:38.357851Z"
     }
    },
    "outputs": [
@@ -118,8 +118,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 138.986 ms\n",
-      "Fast: 84.050 ms\n"
+      "Safe: 161.060 ms\n",
+      "Fast: 78.290 ms\n"
      ]
     },
     {
@@ -164,24 +164,24 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 506.167 and h = 5.37919e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 506.167, , mxstep steps taken before reaching tout.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 336.690 ms\n",
+      "Safe: 7.181 s\n",
       "Solving fast mode, error occurred: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n",
       "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n",
-      ".../casadi/interfaces/sundials/idas_interface.cpp:596: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
+      ".../casadi/interfaces/sundials/idas_interface.cpp:596: IDASolve returned \"IDA_TOO_MUCH_WORK\". Consult IDAS documentation.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 4051.62 and h = 2.10435e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 4051.62, , mxstep steps taken before reaching tout.\n"
      ]
     },
     {
@@ -225,12 +225,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3d9ea56ac0fb4b7f8d325414876b1a00",
+       "model_id": "a679a5fa8fc34bf782956e3d2c040fcb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3617.9769611086454, step=36.179769611086456)…"
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3617.9769611091, step=36.179769611091004), O…"
       ]
      },
      "metadata": {},
@@ -266,6 +266,13 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "At t = 159.516 and h = 1.33464e-07, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+     ]
+    },
     {
      "data": {
       "image/png": 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",
@@ -348,12 +355,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 654.513 ms (integration time: 586.519 ms)\n",
-      "With dt_max=20, took 655.676 ms (integration time: 587.639 ms)\n",
-      "With dt_max=100, took 363.174 ms (integration time: 319.367 ms)\n",
-      "With dt_max=1000, took 84.630 ms (integration time: 55.838 ms)\n",
-      "With dt_max=3700, took 54.536 ms (integration time: 36.817 ms)\n",
-      "With 'fast' mode, took 47.632 ms (integration time: 36.758 ms)\n"
+      "With dt_max=10, took 575.783 ms (integration time: 508.473 ms)\n",
+      "With dt_max=20, took 575.500 ms (integration time: 510.705 ms)\n",
+      "With dt_max=100, took 316.721 ms (integration time: 275.459 ms)\n",
+      "With dt_max=1000, took 76.646 ms (integration time: 49.294 ms)\n",
+      "With dt_max=3700, took 48.773 ms (integration time: 32.436 ms)\n",
+      "With 'fast' mode, took 42.224 ms (integration time: 32.177 ms)\n"
      ]
     }
    ],
@@ -398,10 +405,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 572.246 ms (integration time: 484.119 ms)\n",
-      "With dt_max=20, took 581.830 ms (integration time: 491.914 ms)\n",
-      "With dt_max=100, took 326.363 ms (integration time: 274.358 ms)\n",
-      "With dt_max=1000, took 110.161 ms (integration time: 69.523 ms)\n"
+      "With dt_max=10, took 504.163 ms (integration time: 419.740 ms)\n",
+      "With dt_max=20, took 504.691 ms (integration time: 421.396 ms)\n",
+      "With dt_max=100, took 286.620 ms (integration time: 238.390 ms)\n",
+      "With dt_max=1000, took 98.500 ms (integration time: 60.880 ms)\n"
      ]
     },
     {
@@ -409,15 +416,15 @@
      "output_type": "stream",
      "text": [
       "At t = 460.712, , mxstep steps taken before reaching tout.\n",
-      "At t = 460.712, , mxstep steps taken before reaching tout.\n",
-      "At t = 460.712 and h = 1.04372e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 460.712 and h = 1.26752e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 460.712 and h = 9.51455e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=3600, took 894.873 ms (integration time: 37.240 ms)\n"
+      "With dt_max=3600, took 645.118 ms (integration time: 32.601 ms)\n"
      ]
     }
    ],
@@ -470,7 +477,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  799.154 ms\n"
+      "Took  693.344 ms\n"
      ]
     }
    ],
@@ -533,7 +540,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  682.905 ms\n"
+      "Took  598.237 ms\n"
      ]
     },
     {
@@ -580,14 +587,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 1262.29, , mxstep steps taken before reaching tout.\n"
+      "At t = 1262.29 and h = 3.51225e-14, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  679.216 ms\n"
+      "Took  532.042 ms\n"
      ]
     },
     {
@@ -634,7 +641,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  291.654 ms\n"
+      "Took  257.314 ms\n"
      ]
     },
     {
@@ -853,16 +860,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 193.123 us\n",
-      "Soft, k=5: 175.344 us\n",
-      "Soft, k=10: 148.491 us\n",
-      "Soft, k=100: 191.960 us\n"
+      "Exact: 172.706 us\n",
+      "Soft, k=5: 160.098 us\n",
+      "Soft, k=10: 133.737 us\n",
+      "Soft, k=100: 168.833 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13a25450d75d4f59b654751e9b83e57b",
+       "model_id": "d29d9970463440e9a4f57c708b961ed5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1021,18 +1028,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 191.759 us\n",
-      "Smooth, k=10: 171.753 us\n",
-      "Smooth, k=50: 191.591 us\n",
-      "Smooth, k=100: 152.561 us\n",
-      "Smooth, k=1000: 228.356 us\n",
-      "Smooth, k=10000: 196.291 us\n"
+      "Exact: 168.348 us\n",
+      "Smooth, k=10: 149.515 us\n",
+      "Smooth, k=50: 170.092 us\n",
+      "Smooth, k=100: 137.928 us\n",
+      "Smooth, k=1000: 200.991 us\n",
+      "Smooth, k=10000: 175.300 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4c9502b601ef4174ac002f6bf0f5e85b",
+       "model_id": "711bb9dbf6634320ba57199bf925b84a",
        "version_major": 2,
        "version_minor": 0
       },

From c016a64a4c41138acd962ab1c36bacbcc69ec2e3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:04:30 +0530
Subject: [PATCH 144/615] #3049 Remove unneeded files (used for old manylinux)

Not needed anymore after we have been using `cibuildwheel`, which uses the Dockerfile from PyPA
---
 build_manylinux_wheels/Dockerfile    | 18 -----------------
 build_manylinux_wheels/action.yml    | 17 ----------------
 build_manylinux_wheels/entrypoint.sh | 30 ----------------------------
 3 files changed, 65 deletions(-)
 delete mode 100644 build_manylinux_wheels/Dockerfile
 delete mode 100644 build_manylinux_wheels/action.yml
 delete mode 100644 build_manylinux_wheels/entrypoint.sh

diff --git a/build_manylinux_wheels/Dockerfile b/build_manylinux_wheels/Dockerfile
deleted file mode 100644
index a6c2dcc41c..0000000000
--- a/build_manylinux_wheels/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-FROM quay.io/pypa/manylinux2014_x86_64:2020-11-11-bc8ce45
-
-ENV PLAT manylinux2014_x86_64
-
-RUN yum -y update
-RUN yum -y remove cmake
-RUN yum -y install wget openblas-devel
-RUN /opt/python/cp37-cp37m/bin/pip install --upgrade pip cmake
-RUN ln -s /opt/python/cp37-cp37m/bin/cmake /usr/bin/cmake
-
-COPY install_sundials.sh /install_sundials.sh
-RUN chmod +x /install_sundials.sh
-COPY entrypoint.sh /entrypoint.sh
-RUN chmod +x /entrypoint.sh
-
-RUN ./install_sundials.sh
-
-ENTRYPOINT ["/entrypoint.sh"]
diff --git a/build_manylinux_wheels/action.yml b/build_manylinux_wheels/action.yml
deleted file mode 100644
index 7264606b30..0000000000
--- a/build_manylinux_wheels/action.yml
+++ /dev/null
@@ -1,17 +0,0 @@
-# action.yml
-# Based on  RalfG/python-wheels-manylinux-build/action.yml by Ralf Gabriels
-
-name: "Python wheels manylinux build"
-author: "Thibault Lestang"
-description: "Build manylinux wheels for PyBaMM"
-inputs:
-  python-versions:
-    description: "Python versions to target, space-separated"
-    required: true
-    default: "cp36-cp36m cp37-cp37m"
-
-runs:
-  using: "docker"
-  image: "Dockerfile"
-  args:
-    - ${{ inputs.python-versions }}
diff --git a/build_manylinux_wheels/entrypoint.sh b/build_manylinux_wheels/entrypoint.sh
deleted file mode 100644
index 203e5471d3..0000000000
--- a/build_manylinux_wheels/entrypoint.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-set -e -x
-
-# GitHub runners add "-e LD_LIBRARY_PATH" option to "docker run",
-# overriding default value of LD_LIBRARY_PATH in manylinux image. This
-# causes libcrypt.so.2 to be missing (it lives in /usr/local/lib)
-export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-
-# CLI arguments
-PY_VERSIONS=$1
-
-git clone https://github.com/pybind/pybind11.git /github/workspace/pybind11
-# Compile wheels
-arrPY_VERSIONS=(${PY_VERSIONS// / })
-for PY_VER in "${arrPY_VERSIONS[@]}"; do
-    # Update pip
-    /opt/python/"${PY_VER}"/bin/pip install --upgrade --no-cache-dir pip
-
-    # Build wheels
-    /opt/python/"${PY_VER}"/bin/pip wheel /github/workspace/ -w /github/workspace/wheelhouse/ --no-deps || { echo "Building wheels failed."; exit 1; }
-done
-ls -l /github/workspace/wheelhouse/
-
-# Bundle external shared libraries into the wheels
-for whl in /github/workspace/wheelhouse/*-linux*.whl; do
-    auditwheel repair "$whl" --plat "${PLAT}" -w /github/workspace/dist/ || { echo "Repairing wheels failed."; auditwheel show "$whl"; exit 1; }
-done
-
-echo "Succesfully built wheels:"
-ls -l /github/workspace/dist/

From b70e0a64b44acf0e3596efe9dd1cd27d48f1d7c5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:06:01 +0530
Subject: [PATCH 145/615] #3049 move SUNDIALS installation to `scripts/`

---
 .github/workflows/publish_pypi.yml                      | 2 +-
 {build_manylinux_wheels => scripts}/install_sundials.sh | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)
 rename {build_manylinux_wheels => scripts}/install_sundials.sh (93%)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index c38092906e..671cf4a0b0 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -98,7 +98,7 @@ jobs:
           # TODO: openblas no longer available on centos 7 i686 image, use blas instead for now
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install blas-devel lapack-devel &&
-            bash build_manylinux_wheels/install_sundials.sh 5.8.1 6.5.0
+            bash scripts/install_sundials.sh 5.8.1 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
             python -m pip install cmake casadi numpy
           # override; point to casadi install path so that it can be found by the repair command
diff --git a/build_manylinux_wheels/install_sundials.sh b/scripts/install_sundials.sh
similarity index 93%
rename from build_manylinux_wheels/install_sundials.sh
rename to scripts/install_sundials.sh
index 709d9c13c7..56435066b4 100644
--- a/build_manylinux_wheels/install_sundials.sh
+++ b/scripts/install_sundials.sh
@@ -1,10 +1,10 @@
 #!/bin/bash
 
 # This script installs both SuiteSparse
-# (https://people.engr.tamu.edu/davis/suitesparse.html) and Sundials
+# (https://people.engr.tamu.edu/davis/suitesparse.html) and SUNDIALS
 # (https://computing.llnl.gov/projects/sundials) from source. For each
 # two library:
-# - Archive downloaded and source code extrated in current working
+# - Archive downloaded and source code extracted in current working
 #   directory.
 # - Library is built and installed.
 #

From 5583d01844a8f7e4eac6ce7e8be6ea81c61233aa Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 7 Oct 2023 17:08:36 +0530
Subject: [PATCH 146/615] #3049 delete older SUNDIALS CMake files

these were used for finding KLU earlier but are for now outdated versions of SUNDIALS (<6). We currently use a custom vcpkg registry which comes with modified portfiles for this purpose.
---
 scripts/replace-cmake/README.md               |    1 -
 .../sundials-3.1.1/CMakeLists.txt             | 1597 -----------------
 .../sundials-4.1.0/CMakeLists.txt             | 1151 ------------
 .../sundials-5.0.0/CMakeLists.txt             | 1151 ------------
 4 files changed, 3900 deletions(-)
 delete mode 100644 scripts/replace-cmake/README.md
 delete mode 100644 scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
 delete mode 100644 scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
 delete mode 100644 scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt

diff --git a/scripts/replace-cmake/README.md b/scripts/replace-cmake/README.md
deleted file mode 100644
index e578a96abb..0000000000
--- a/scripts/replace-cmake/README.md
+++ /dev/null
@@ -1 +0,0 @@
-A modified sundials cmake file which finds the KLU solvers correctly
diff --git a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt b/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
deleted file mode 100644
index 81f4267c22..0000000000
--- a/scripts/replace-cmake/sundials-3.1.1/CMakeLists.txt
+++ /dev/null
@@ -1,1597 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer:  Radu Serban @ LLNL
-# ---------------------------------------------------------------
-# LLNS Copyright Start
-# Copyright (c) 2014, Lawrence Livermore National Security
-# This work was performed under the auspices of the U.S. Department
-# of Energy by Lawrence Livermore National Laboratory in part under
-# Contract W-7405-Eng-48 and in part under Contract DE-AC52-07NA27344.
-# Produced at the Lawrence Livermore National Laboratory.
-# All rights reserved.
-# For details, see the LICENSE file.
-# LLNS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-CMAKE_MINIMUM_REQUIRED(VERSION 2.8.1)
-
-# Set CMake policy to allow examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 3.1.1")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "3")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "1")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-#
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "2.1.1")
-SET(arkodelib_SOVERSION "2")
-
-SET(cvodelib_VERSION "3.1.1")
-SET(cvodelib_SOVERSION "3")
-
-SET(cvodeslib_VERSION "3.1.1")
-SET(cvodeslib_SOVERSION "3")
-
-SET(idalib_VERSION "3.1.1")
-SET(idalib_SOVERSION "3")
-
-SET(idaslib_VERSION "2.1.0")
-SET(idaslib_SOVERSION "2")
-
-SET(kinsollib_VERSION "3.1.1")
-SET(kinsollib_SOVERSION "3")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "3.1.1")
-SET(nveclib_SOVERSION "3")
-
-SET(sunmatrixlib_VERSION "1.1.1")
-SET(sunmatrixlib_SOVERSION "1")
-
-SET(sunlinsollib_VERSION "1.1.1")
-SET(sunlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(SundialsCMakeMacros)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (int64_t) or signed 32-bit (int32_t) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "int64_t")
-
-# prepare substitution variable INDEX_TYPE for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_INDEX_TYPE} SUNDIALS_INDEX_TYPE)
-SET(INDEX_TYPE "#define SUNDIALS_${SUNDIALS_INDEX_TYPE} 1")
-
-# prepare substitution variable FINDEX_TYPE for sundials_fconfig.h
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-  SET(FINDEX_TYPE "4")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT32_T")
-IF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  SET(FINDEX_TYPE "8")
-ENDIF(SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran-C support")
-SHOW_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(FCMIX_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran" "Disabling FCMIX")
-    FORCE_VARIABLE(FCMIX_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(FCMIX_ENABLE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-## clock-monotonic, see if we need to link with rt
-include(CheckSymbolExists)
-set(CMAKE_REQUIRED_LIBRARIES_SAVE ${CMAKE_REQUIRED_LIBRARIES})
-set(CMAKE_REQUIRED_LIBRARIES rt)
-CHECK_SYMBOL_EXISTS(_POSIX_TIMERS "unistd.h;time.h" SUNDIALS_POSIX_TIMERS)
-set(CMAKE_REQUIRED_LIBRARIES ${CMAKE_REQUIRED_LIBRARIES_SAVE})
-if(SUNDIALS_POSIX_TIMERS)
-  find_library(SUNDIALS_RT_LIBRARY NAMES rt)
-  mark_as_advanced(SUNDIALS_RT_LIBRARY)
-  if(SUNDIALS_RT_LIBRARY)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HAVE_POSIX_TIMERS TRUE)
-    set(EXTRA_LINK_LIBS ${EXTRA_LINK_LIBS} ${SUNDIALS_RT_LIBRARY})
-  endif()
-endif()
-
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_TYPE MATCHES "INT64_T")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_TYPE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(FCMIX_ENABLE)
-  OPTION(EXAMPLES_ENABLE_F77 "${DOCSTR}" ON)
-  # Fortran examples do not support single or extended precision
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "Fortran examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but FCMIX is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# C++ examples (off by default) are an option only if ARKode is enabled
-SET(DOCSTR "Build ARKode C++ examples")
-IF(BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_CXX)
-    PRINT_WARNING("EXAMPLES_ENABLE_CXX is ON but BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CXX")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CXX BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_CXX)
-ENDIF()
-
-# F90 examples (off by default) are an option only if ARKode is
-# built and the Fortran interface is enabled
-SET(DOCSTR "Build ARKode F90 examples")
-IF(FCMIX_ENABLE AND BUILD_ARKODE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  # Fortran90 examples do not support single or extended precision
-  # NOTE: This check can be removed after Fortran configure file is integrated into examples
-  IF(SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE")
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "Fortran90 examples are disabled" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but FCMIX or BUILD_ARKODE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# RAJA examples (off by default)
-SET(DOCSTR "Build SUNDIALS RAJA examples")
-IF(RAJA_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_RAJA)
-    PRINT_WARNING("EXAMPLES_ENABLE_RAJA is ON but RAJA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_RAJA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_RAJA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA OR
-    EXAMPLES_ENABLE_RAJA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLED)
-
-  # If examples are enabled, set different options
-
-  # The examples will be linked with the library corresponding to the build type.
-  # Whenever building shared libraries, use them to link the examples.
-  IF(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "shared")
-  ELSE(BUILD_SHARED_LIBS)
-    SET(LINK_LIBRARY_TYPE "static")
-  ENDIF(BUILD_SHARED_LIBS)
-
-  # Enable installing examples by default
-  SHOW_VARIABLE(EXAMPLES_INSTALL BOOL "Install example files" ON)
-
-  # If examples are to be exported, check where we should install them.
-  IF(EXAMPLES_INSTALL)
-
-    SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-      "Output directory for installing example files" "${CMAKE_INSTALL_PREFIX}/examples")
-
-    IF(NOT EXAMPLES_INSTALL_PATH)
-      PRINT_WARNING("The example installation path is empty"
-                    "Example installation path was reset to its default value")
-      SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-        "Output directory for installing example files" FORCE)
-    ENDIF(NOT EXAMPLES_INSTALL_PATH)
-
-    # create test_install target and directory for running smoke tests after
-    # installation
-    ADD_CUSTOM_TARGET(test_install)
-
-    SET(TEST_INSTALL_DIR ${PROJECT_BINARY_DIR}/Testing_Install)
-
-    IF(NOT EXISTS ${TEST_INSTALL_DIR})
-      FILE(MAKE_DIRECTORY ${TEST_INSTALL_DIR})
-    ENDIF()
-
-
-  ELSE(EXAMPLES_INSTALL)
-
-    HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-ELSE(EXAMPLES_ENABLED)
-
-  # If examples are disabled, hide all options related to
-  # building and installing the SUNDIALS examples
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Include development examples in regression tests?
-# ---------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any other necessary compiler flags & definitions
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed if:
-# (a) FCMIX is enabled
-# (b) BLAS is enabled (for the name-mangling scheme)
-# (c) LAPACK is enabled (for the name-mangling scheme)
-# ---------------------------------------------------------------
-
-IF(FCMIX_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  INCLUDE(SundialsFortran)
-  IF(NOT F77_FOUND AND FCMIX_ENABLE)
-    PRINT_WARNING("Fortran compiler not functional"
-                  "FCMIX support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A Fortran90 compiler is needed if:
-# (a) F90 ARKODE examples are enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_F90)
-  INCLUDE(SundialsFortran90)
-  IF(NOT F90_FOUND)
-    PRINT_WARNING("Fortran90 compiler not functional"
-                  "F90 support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ ARKODE examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# ---------------------------------------------------------------
-
-IF(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE)
-  INCLUDE(SundialsCXX)
-  IF(NOT CXX_FOUND)
-    PRINT_WARNING("C++ compiler not functional"
-                  "C++ support will not be provided")
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Check if we need an alternate way of specifying the Fortran
-# name-mangling scheme if we were unable to infer it using a
-# compiler.
-# Ask the user to specify the case and number of appended underscores
-# corresponding to the Fortran name-mangling scheme of symbol names
-# that do not themselves contain underscores (recall that this is all
-# we really need for the interfaces to LAPACK).
-# Note: the default scheme is lower case - one underscore
-# ---------------------------------------------------------------
-
-IF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-  # Specify the case for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_CASE STRING
-    "case of Fortran function names (lower/upper)"
-    "lower")
-  # Specify the number of appended underscores for the Fortran name-mangling scheme
-  SHOW_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-    "number of underscores appended to Fortran function names"
-    "one")
-  # Based on the given case and number of underscores,
-  # set the C preprocessor macro definition
-  IF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "mysub__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ELSE(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "none")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB_")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "one")
-    IF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-      SET(CMAKE_Fortran_SCHEME_NO_UNDERSCORES "MYSUB__")
-    ENDIF(${SUNDIALS_F77_FUNC_UNDERSCORES} MATCHES "two")
-  ENDIF(${SUNDIALS_F77_FUNC_CASE} MATCHES "lower")
-  # Since the SUNDIALS codes never use symbol names containing
-  # underscores, set a default scheme (probably wrong) for symbols
-  # with underscores.
-  SET(CMAKE_Fortran_SCHEME_WITH_UNDERSCORES "my_sub_")
-  # We now "have" a scheme.
-  SET(F77SCHEME_FOUND TRUE)
-ENDIF(BLAS_ENABLE OR LAPACK_ENABLE AND NOT F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# If we have a name-mangling scheme (either automatically
-# inferred or provided by the user), set the SUNDIALS
-# compiler preprocessor macro definitions.
-# ---------------------------------------------------------------
-
-SET(F77_MANGLE_MACRO1 "")
-SET(F77_MANGLE_MACRO2 "")
-
-IF(F77SCHEME_FOUND)
-  # Symbols WITHOUT underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "mysub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-    SET(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MYSUB__")
-  # Symbols with underscores
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "my_sub__")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB_")
-  IF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-    SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-  ENDIF(${CMAKE_Fortran_SCHEME_NO_UNDERSCORES} MATCHES "MY_SUB__")
-ENDIF(F77SCHEME_FOUND)
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes.
-# ---------------------------------------------------------------
-
-IF(MPI_ENABLE)
-  # show command to run MPI codes (defaults to mpirun)
-  SHOW_VARIABLE(MPI_RUN_COMMAND STRING "MPI run command" "mpirun")
-
-  INCLUDE(SundialsMPIC)
-  IF(MPIC_FOUND)
-    IF(CXX_FOUND AND EXAMPLES_ENABLE_CXX)
-      INCLUDE(SundialsMPICXX)
-    ENDIF()
-    IF(F77_FOUND AND EXAMPLES_ENABLE_F77)
-      INCLUDE(SundialsMPIF)
-    ENDIF()
-    IF(F90_FOUND AND EXAMPLES_ENABLE_F90)
-      INCLUDE(SundialsMPIF90)
-    ENDIF()
-  ELSE()
-    PRINT_WARNING("MPI not functional"
-                  "Parallel support will not be provided")
-  ENDIF()
-
-  IF(MPIC_MPI2)
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  ELSE()
-    SET(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(MPI_INCLUDE_PATH)
-  HIDE_VARIABLE(MPI_LIBRARIES)
-  HIDE_VARIABLE(MPI_EXTRA_LIBRARIES)
-  HIDE_VARIABLE(MPI_MPICC)
-  HIDE_VARIABLE(MPI_MPICXX)
-  HIDE_VARIABLE(MPI_MPIF77)
-  HIDE_VARIABLE(MPI_MPIF90)
-
-ENDIF(MPI_ENABLE)
-
-# ---------------------------------------------------------------
-# If using MPI with C++, disable C++ extensions (for known wrappers)
-# ---------------------------------------------------------------
-
-# IF(MPICXX_FOUND)
-#   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DMPICH_SKIP_MPICXX -DOMPI_SKIP_MPICXX -DLAM_BUILDING")
-# ENDIF(MPICXX_FOUND)
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-IF(OPENMP_ENABLE)
-  FIND_PACKAGE(OpenMP)
-  IF(NOT OPENMP_FOUND)
-    message(STATUS "Disabling OpenMP support, could not determine compiler flags")
-  ENDIF(NOT OPENMP_FOUND)
-ENDIF(OPENMP_ENABLE)
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find CUDA
-# -------------------------------------------------------------
-
-# disable CUDA if a working C++ compiler is not found
-IF(CUDA_ENABLE AND (NOT CXX_FOUND))
-  PRINT_WARNING("C++ compiler required for CUDA support" "Disabling CUDA")
-  FORCE_VARIABLE(CUDA_ENABLE BOOL "CUDA disabled" OFF)
-ENDIF()
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-
-  if (CUDA_FOUND)
-    #message("CUDA found!")
-    set(CUDA_NVCC_FLAGS "-lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-  endif()
-endif(CUDA_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-IF(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-ENDIF()
-
-# Check if C++11 compiler is available
-IF(RAJA_ENABLE)
-  include(CheckCXXCompilerFlag)
-  CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
-
-  IF(COMPILER_SUPPORTS_CXX11)
-    set(CMAKE_CXX_STANDARD 11)
-  ELSE()
-    PRINT_WARNING("C++11 compliant compiler required for RAJA support" "Disabling RAJA")
-    FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-  ENDIF()
-ENDIF()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA CONFIGS)
-  if (RAJA_FOUND)
-    #message("RAJA found!")
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found" "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT LAPACK_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  IF(NOT BLAS_ENABLE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_CASE)
-    HIDE_VARIABLE(SUNDIALS_F77_FUNC_UNDERSCORES)
-  ENDIF()
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-
-# ===============================================================
-# Add source and configuration files
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Add selected modules to the build system
-# ---------------------------------------------------------------
-
-# Shared components
-
-ADD_SUBDIRECTORY(src/sundials)
-ADD_SUBDIRECTORY(src/nvec_ser)
-ADD_SUBDIRECTORY(src/sunmat_dense)
-ADD_SUBDIRECTORY(src/sunmat_band)
-ADD_SUBDIRECTORY(src/sunmat_sparse)
-ADD_SUBDIRECTORY(src/sunlinsol_band)
-ADD_SUBDIRECTORY(src/sunlinsol_dense)
-IF(KLU_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_klu)
-ENDIF(KLU_FOUND)
-IF(SUPERLUMT_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_superlumt)
-ENDIF(SUPERLUMT_FOUND)
-IF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackband)
-  ADD_SUBDIRECTORY(src/sunlinsol_lapackdense)
-ENDIF(LAPACK_FOUND)
-ADD_SUBDIRECTORY(src/sunlinsol_spgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spfgmr)
-ADD_SUBDIRECTORY(src/sunlinsol_spbcgs)
-ADD_SUBDIRECTORY(src/sunlinsol_sptfqmr)
-ADD_SUBDIRECTORY(src/sunlinsol_pcg)
-IF(MPIC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_par)
-ENDIF(MPIC_FOUND)
-
-IF(HYPRE_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_parhyp)
-ENDIF(HYPRE_FOUND)
-
-IF(OPENMP_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_openmp)
-ENDIF(OPENMP_FOUND)
-
-IF(PTHREADS_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_pthreads)
-ENDIF(PTHREADS_FOUND)
-
-IF(PETSC_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_petsc)
-ENDIF(PETSC_FOUND)
-
-IF(CUDA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_cuda)
-ENDIF(CUDA_FOUND)
-
-IF(RAJA_FOUND)
-  ADD_SUBDIRECTORY(src/nvec_raja)
-ENDIF(RAJA_FOUND)
-
-# ARKODE library
-
-IF(BUILD_ARKODE)
-  ADD_SUBDIRECTORY(src/arkode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/arkode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_ARKODE)
-
-# CVODE library
-
-IF(BUILD_CVODE)
-  ADD_SUBDIRECTORY(src/cvode)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/cvode/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_CVODE)
-
-# CVODES library
-
-IF(BUILD_CVODES)
-  ADD_SUBDIRECTORY(src/cvodes)
-ENDIF(BUILD_CVODES)
-
-# IDA library
-
-IF(BUILD_IDA)
-  ADD_SUBDIRECTORY(src/ida)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/ida/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_IDA)
-
-# IDAS library
-
-IF(BUILD_IDAS)
-  ADD_SUBDIRECTORY(src/idas)
-ENDIF(BUILD_IDAS)
-
-# KINSOL library
-
-IF(BUILD_KINSOL)
-  ADD_SUBDIRECTORY(src/kinsol)
-  IF(FCMIX_ENABLE AND F77_FOUND)
-    ADD_SUBDIRECTORY(src/kinsol/fcmix)
-  ENDIF(FCMIX_ENABLE AND F77_FOUND)
-ENDIF(BUILD_KINSOL)
-
-# CPODES library
-
-IF(BUILD_CPODES)
-  ADD_SUBDIRECTORY(src/cpodes)
-ENDIF(BUILD_CPODES)
-
-# ---------------------------------------------------------------
-# Include the subdirectories corresponding to various examples
-# ---------------------------------------------------------------
-
-# If building and installing the examples is enabled, include
-# the subdirectories for those examples that will be built.
-# Also, if we will generate exported example Makefiles, set
-# variables needed in generating them from templates.
-
-# For now, TestRunner is not being distributed.
-# So:
-#  - Don't show TESTRUNNER variable
-#  - Don't enable testing if TestRunner if not found.
-#  - There will be no 'make test' target
-
-INCLUDE(SundialsAddTest)
-HIDE_VARIABLE(TESTRUNNER)
-
-IF(EXAMPLES_ENABLED)
-
-  # enable regression testing with 'make test'
-  IF(TESTRUNNER)
-    ENABLE_TESTING()
-  ENDIF()
-
-  # set variables used in generating CMake and Makefiles for examples
-  IF(EXAMPLES_INSTALL)
-
-    SET(SHELL "sh")
-    SET(prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(exec_prefix "${CMAKE_INSTALL_PREFIX}")
-    SET(includedir "${prefix}/include")
-    SET(libdir "${exec_prefix}/lib")
-    SET(CPP "${CMAKE_C_COMPILER}")
-    SET(CPPFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(CC "${CMAKE_C_COMPILER}")
-    SET(CFLAGS "${CMAKE_C_FLAGS_RELEASE}")
-    SET(LDFLAGS "${CMAKE_EXE_LINKER_FLAGS_RELEASE}")
-    LIST2STRING(EXTRA_LINK_LIBS LIBS)
-
-    IF(CXX_FOUND)
-      SET(CXX "${CMAKE_CXX_COMPILER}")
-      SET(CXX_LNKR "${CMAKE_CXX_COMPILER}")
-      SET(CXXFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      SET(CXX_LDFLAGS "${CMAKE_CXX_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS CXX_LIBS)
-    ENDIF(CXX_FOUND)
-
-    IF(F77_FOUND)
-      SET(F77 "${CMAKE_Fortran_COMPILER}")
-      SET(F77_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(FFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F77_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F77_LIBS)
-    ENDIF(F77_FOUND)
-
-    IF(F90_FOUND)
-      SET(F90 "${CMAKE_Fortran_COMPILER}")
-      SET(F90_LNKR "${CMAKE_Fortran_COMPILER}")
-      SET(F90FLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      SET(F90_LDFLAGS "${CMAKE_Fortran_FLAGS_RELEASE}")
-      LIST2STRING(EXTRA_LINK_LIBS F90_LIBS)
-    ENDIF(F90_FOUND)
-
-    IF(SUPERLUMT_FOUND)
-      LIST2STRING(SUPERLUMT_LIBRARIES SUPERLUMT_LIBS)
-      SET(SUPERLUMT_LIBS "${SUPERLUMT_LINKER_FLAGS} ${SUPERLUMT_LIBS}")
-    ENDIF(SUPERLUMT_FOUND)
-
-    IF(KLU_FOUND)
-      LIST2STRING(KLU_LIBRARIES KLU_LIBS)
-      SET(KLU_LIBS "${KLU_LINKER_FLAGS} ${KLU_LIBS}")
-    ENDIF(KLU_FOUND)
-
-    IF(BLAS_FOUND)
-      LIST2STRING(BLAS_LIBRARIES BLAS_LIBS)
-    ENDIF(BLAS_FOUND)
-
-    IF(LAPACK_FOUND)
-      LIST2STRING(LAPACK_LIBRARIES LAPACK_LIBS)
-    ENDIF(LAPACK_FOUND)
-
-    IF(MPIC_FOUND)
-      IF(MPI_MPICC)
-        SET(MPICC "${MPI_MPICC}")
-        SET(MPI_INC_DIR ".")
-        SET(MPI_LIB_DIR ".")
-        SET(MPI_LIBS "")
-        SET(MPI_FLAGS "")
-      ELSE(MPI_MPICC)
-        SET(MPICC "${CMAKE_C_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICC)
-      SET(HYPRE_INC_DIR "${HYPRE_INCLUDE_DIR}")
-      SET(HYPRE_LIB_DIR "${HYPRE_LIBRARY_DIR}")
-      SET(HYPRE_LIBS "${HYPRE_LIBRARIES}")
-    ENDIF(MPIC_FOUND)
-
-    IF(MPICXX_FOUND)
-      IF(MPI_MPICXX)
-        SET(MPICXX "${MPI_MPICXX}")
-      ELSE(MPI_MPICXX)
-        SET(MPICXX "${CMAKE_CXX_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPICXX)
-    ENDIF(MPICXX_FOUND)
-
-    IF(MPIF_FOUND)
-      IF(MPI_MPIF77)
-        SET(MPIF77 "${MPI_MPIF77}")
-        SET(MPIF77_LNKR "${MPI_MPIF77}")
-      ELSE(MPI_MPIF77)
-        SET(MPIF77 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF77_LNKR "${CMAKE_Fortran_COMPILER}")
-        SET(MPI_INC_DIR "${MPI_INCLUDE_PATH}")
-        SET(MPI_LIB_DIR ".")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF77)
-    ENDIF(MPIF_FOUND)
-
-    IF(MPIF90_FOUND)
-      IF(MPI_MPIF90)
-        SET(MPIF90 "${MPI_MPIF90}")
-        SET(MPIF90_LNKR "${MPI_MPIF90}")
-      ELSE(MPI_MPIF90)
-        SET(MPIF90 "${CMAKE_Fortran_COMPILER}")
-        SET(MPIF90_LNKR "${CMAKE_Fortran_COMPILER}")
-        LIST2STRING(MPI_LIBRARIES MPI_LIBS)
-      ENDIF(MPI_MPIF90)
-    ENDIF(MPIF90_FOUND)
-
-  ENDIF(EXAMPLES_INSTALL)
-
-  # add ARKode examples
-  IF(BUILD_ARKODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/arkode/C_serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/C_parhyp)
-      ENDIF()
-    ENDIF()
-    # C++ examples
-    IF(EXAMPLES_ENABLE_CXX)
-      IF(CXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_serial)
-      ENDIF()
-      IF(MPICXX_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/CXX_parallel)
-      ENDIF()
-    ENDIF()
-    # F77 examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F77_parallel)
-      ENDIF()
-    ENDIF()
-    # F90 examples
-    IF(EXAMPLES_ENABLE_F90)
-      IF(F90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_serial)
-      ENDIF()
-      IF(MPIF90_FOUND)
-        ADD_SUBDIRECTORY(examples/arkode/F90_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_ARKODE)
-
-  # add CVODE examples
-  IF(BUILD_CVODE)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvode/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parallel)
-      ENDIF()
-      IF(HYPRE_ENABLE AND HYPRE_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/parhyp)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-    # cuda examples
-    IF(EXAMPLES_ENABLE_CUDA)
-      IF(CUDA_ENABLE AND CUDA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/cuda)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_CUDA)
-    # raja examples
-    IF(EXAMPLES_ENABLE_RAJA)
-      IF(RAJA_ENABLE AND RAJA_FOUND)
-        ADD_SUBDIRECTORY(examples/cvode/raja)
-      ENDIF()
-    ENDIF(EXAMPLES_ENABLE_RAJA)
-  ENDIF(BUILD_CVODE)
-
-  # add CVODES Examples
-  IF(BUILD_CVODES)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cvodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/parallel)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/cvodes/C_openmp)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CVODES)
-
-  # add IDA examples
-  IF(BUILD_IDA)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/ida/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/parallel)
-      ENDIF()
-      IF(PETSC_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/petsc)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_serial)
-      ENDIF()
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_openmp)
-      ENDIF()
-      IF(PTHREADS_FOUND)
-	ADD_SUBDIRECTORY(examples/ida/fcmix_pthreads)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/ida/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDA)
-
-  # add IDAS examples
-  IF(BUILD_IDAS)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/idas/serial)
-      IF(OPENMP_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/idas/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_IDAS)
-
-  # add KINSOL examples
-  IF(BUILD_KINSOL)
-    # C examples
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/kinsol/serial)
-      IF(OPENMP_FOUND)
-        # the only example here need special handling from testrunner (not yet implemented)
-        ADD_SUBDIRECTORY(examples/kinsol/C_openmp)
-      ENDIF()
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/parallel)
-      ENDIF()
-    ENDIF()
-    # Fortran examples
-    IF(EXAMPLES_ENABLE_F77)
-      IF(F77_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_serial)
-      ENDIF()
-      IF(MPIF_FOUND)
-        ADD_SUBDIRECTORY(examples/kinsol/fcmix_parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_KINSOL)
-
-  # add CPODES examples
-  IF(BUILD_CPODES)
-    IF(EXAMPLES_ENABLE_C)
-      ADD_SUBDIRECTORY(examples/cpodes/serial)
-      IF(MPIC_FOUND)
-        ADD_SUBDIRECTORY(examples/cpodes/parallel)
-      ENDIF()
-    ENDIF()
-  ENDIF(BUILD_CPODES)
-
-  # Always add the nvector serial examples
-  ADD_SUBDIRECTORY(examples/nvector/serial)
-
-  # # Always add the serial sunmatrix dense/band/sparse examples
-  ADD_SUBDIRECTORY(examples/sunmatrix/dense)
-  ADD_SUBDIRECTORY(examples/sunmatrix/band)
-  ADD_SUBDIRECTORY(examples/sunmatrix/sparse)
-
-  # # Always add the serial sunlinearsolver dense/band/spils examples
-  ADD_SUBDIRECTORY(examples/sunlinsol/band)
-  ADD_SUBDIRECTORY(examples/sunlinsol/dense)
-  IF(KLU_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/klu)
-  ENDIF(KLU_FOUND)
-  IF(SUPERLUMT_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/superlumt)
-  ENDIF(SUPERLUMT_FOUND)
-  IF(LAPACK_FOUND)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackband)
-    ADD_SUBDIRECTORY(examples/sunlinsol/lapackdense)
-  ENDIF(LAPACK_FOUND)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/serial)
-  ADD_SUBDIRECTORY(examples/sunlinsol/pcg/serial)
-
-  IF(MPIC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spfgmr/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/spbcgs/parallel)
-      ADD_SUBDIRECTORY(examples/sunlinsol/sptfqmr/parallel)
-      #ADD_SUBDIRECTORY(examples/sunlinsol/pcg/parallel)
-  ENDIF(MPIC_FOUND)
-
-  IF(HYPRE_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/parhyp)
-  ENDIF()
-
-  IF(PTHREADS_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/pthreads)
-  ENDIF()
-
-  IF(OPENMP_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/C_openmp)
-  ENDIF()
-
-  IF(PETSC_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/petsc)
-  ENDIF()
-
-  IF(CUDA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/cuda)
-  ENDIF(CUDA_FOUND)
-
-  IF(RAJA_FOUND)
-      ADD_SUBDIRECTORY(examples/nvector/raja)
-  ENDIF(RAJA_FOUND)
-
-ENDIF(EXAMPLES_ENABLED)
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install license file
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION .)
diff --git a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt b/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-4.1.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )
diff --git a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt b/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
deleted file mode 100644
index fc8acbddc9..0000000000
--- a/scripts/replace-cmake/sundials-5.0.0/CMakeLists.txt
+++ /dev/null
@@ -1,1151 +0,0 @@
-# ---------------------------------------------------------------
-# Programmer: Radu Serban, David J. Gardner, Cody J. Balos,
-#             and Slaven Peles @ LLNL
-# ---------------------------------------------------------------
-# SUNDIALS Copyright Start
-# Copyright (c) 2002-2019, Lawrence Livermore National Security
-# and Southern Methodist University.
-# All rights reserved.
-#
-# See the top-level LICENSE and NOTICE files for details.
-#
-# SPDX-License-Identifier: BSD-3-Clause
-# SUNDIALS Copyright End
-# ---------------------------------------------------------------
-# Top level CMakeLists.txt for SUNDIALS (for cmake build system)
-# ---------------------------------------------------------------
-
-# ---------------------------------------------------------------
-# Initial commands
-# ---------------------------------------------------------------
-
-# Require a fairly recent cmake version
-cmake_minimum_required(VERSION 3.1.3)
-
-# Libraries linked via full path no longer produce linker search paths
-# Allows examples to build
-if(COMMAND cmake_policy)
-  cmake_policy(SET CMP0003 NEW)
-endif(COMMAND cmake_policy)
-
-# MACOSX_RPATH is enabled by default
-# Fixes dynamic loading on OSX
-if(POLICY CMP0042)
-  cmake_policy(SET CMP0042 NEW) # Added in CMake 3.0
-else()
-  if(APPLE)
-    set(CMAKE_MACOSX_RPATH 1)
-  endif()
-endif()
-
-# Project SUNDIALS (initially only C supported)
-# sets PROJECT_SOURCE_DIR and PROJECT_BINARY_DIR variables
-PROJECT(sundials C)
-
-# Set some variables with info on the SUNDIALS project
-SET(PACKAGE_BUGREPORT "woodward6@llnl.gov")
-SET(PACKAGE_NAME "SUNDIALS")
-SET(PACKAGE_STRING "SUNDIALS 4.1.0")
-SET(PACKAGE_TARNAME "sundials")
-
-# set SUNDIALS version numbers
-# (use "" for the version label if none is needed)
-SET(PACKAGE_VERSION_MAJOR "4")
-SET(PACKAGE_VERSION_MINOR "1")
-SET(PACKAGE_VERSION_PATCH "0")
-SET(PACKAGE_VERSION_LABEL "")
-
-IF(PACKAGE_VERSION_LABEL)
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}-${PACKAGE_VERSION_LABEL}")
-ELSE()
-  SET(PACKAGE_VERSION "${PACKAGE_VERSION_MAJOR}.${PACKAGE_VERSION_MINOR}.${PACKAGE_VERSION_PATCH}")
-ENDIF()
-
-SET_PROPERTY(GLOBAL PROPERTY USE_FOLDERS ON)
-
-# Prohibit in-source build
-IF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-  MESSAGE(FATAL_ERROR "In-source build prohibited.")
-ENDIF("${CMAKE_SOURCE_DIR}" STREQUAL "${CMAKE_BINARY_DIR}")
-
-# Hide some cache variables
-MARK_AS_ADVANCED(EXECUTABLE_OUTPUT_PATH LIBRARY_OUTPUT_PATH)
-
-# Always show the C compiler and flags
-MARK_AS_ADVANCED(CLEAR
-  CMAKE_C_COMPILER
-  CMAKE_C_FLAGS)
-
-# Specify the VERSION and SOVERSION for shared libraries
-
-SET(arkodelib_VERSION "3.1.0")
-SET(arkodelib_SOVERSION "3")
-
-SET(cvodelib_VERSION "4.1.0")
-SET(cvodelib_SOVERSION "4")
-
-SET(cvodeslib_VERSION "4.1.0")
-SET(cvodeslib_SOVERSION "4")
-
-SET(idalib_VERSION "4.1.0")
-SET(idalib_SOVERSION "4")
-
-SET(idaslib_VERSION "3.1.0")
-SET(idaslib_SOVERSION "3")
-
-SET(kinsollib_VERSION "4.1.0")
-SET(kinsollib_SOVERSION "4")
-
-SET(cpodeslib_VERSION "0.0.0")
-SET(cpodeslib_SOVERSION "0")
-
-SET(nveclib_VERSION "4.1.0")
-SET(nveclib_SOVERSION "4")
-
-SET(sunmatrixlib_VERSION "2.1.0")
-SET(sunmatrixlib_SOVERSION "2")
-
-SET(sunlinsollib_VERSION "2.1.0")
-SET(sunlinsollib_SOVERSION "2")
-
-SET(sunnonlinsollib_VERSION "1.1.0")
-SET(sunnonlinsollib_SOVERSION "1")
-
-# Specify the location of additional CMAKE modules
-SET(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/config)
-
-# Get correct build paths automatically, but expose CMAKE_INSTALL_LIBDIR
-# as a regular cache variable so that a user can more easily see what
-# the library dir was set to be by GNUInstallDirs.
-INCLUDE(GNUInstallDirs)
-MARK_AS_ADVANCED(CLEAR CMAKE_INSTALL_LIBDIR)
-
-# ---------------------------------------------------------------
-# Which modules to build?
-# ---------------------------------------------------------------
-
-# For each SUNDIALS solver available (i.e. for which we have the
-# sources), give the user the option of enabling/disabling it.
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/arkode")
-  OPTION(BUILD_ARKODE "Build the ARKODE library" ON)
-ELSE()
-  SET(BUILD_ARKODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvode")
-  OPTION(BUILD_CVODE "Build the CVODE library" ON)
-ELSE()
-  SET(BUILD_CVODE OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cvodes")
-  OPTION(BUILD_CVODES "Build the CVODES library" ON)
-ELSE()
-  SET(BUILD_CVODES OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/ida")
-  OPTION(BUILD_IDA "Build the IDA library" ON)
-ELSE()
-  SET(BUILD_IDA OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/idas")
-  OPTION(BUILD_IDAS "Build the IDAS library" ON)
-ELSE()
-  SET(BUILD_IDAS OFF)
-ENDIF()
-
-IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/kinsol")
-  OPTION(BUILD_KINSOL "Build the KINSOL library" ON)
-ELSE()
-  SET(BUILD_KINSOL OFF)
-ENDIF()
-
-# CPODES is always OFF for now.  (commented out for Release); ToDo: better way to do this?
-#IF(IS_DIRECTORY "${sundials_SOURCE_DIR}/src/cpodes")
-#  OPTION(BUILD_CPODES  "Build the CPODES library"  OFF)
-#ELSE()
-#  SET(BUILD_CPODES OFF)
-#ENDIF()
-
-# ---------------------------------------------------------------
-# MACRO definitions
-# ---------------------------------------------------------------
-INCLUDE(CMakeParseArguments) # can be removed when CMake 3.5+ is required
-INCLUDE(SundialsCMakeMacros)
-INCLUDE(SundialsAddF2003InterfaceLibrary)
-INCLUDE(SundialsAddTest)
-INCLUDE(SundialsAddTestInstall)
-
-# ---------------------------------------------------------------
-# Check for deprecated SUNDIALS CMake options/variables
-# ---------------------------------------------------------------
-INCLUDE(SundialsDeprecated)
-
-# ---------------------------------------------------------------
-# xSDK specific options
-# ---------------------------------------------------------------
-INCLUDE(SundialsXSDK)
-
-# ---------------------------------------------------------------
-# Build specific C flags
-# ---------------------------------------------------------------
-
-# Hide all build type specific flags
-MARK_AS_ADVANCED(FORCE
-  CMAKE_C_FLAGS_DEBUG
-  CMAKE_C_FLAGS_MINSIZEREL
-  CMAKE_C_FLAGS_RELEASE
-  CMAKE_C_FLAGS_RELWITHDEBINFO)
-
-# Only show flags for the current build type if it is set
-# NOTE: Build specific flags are appended those in CMAKE_C_FLAGS
-IF(CMAKE_BUILD_TYPE)
-  IF(CMAKE_BUILD_TYPE MATCHES "Debug")
-    MESSAGE("Appending C debug flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_DEBUG)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "MinSizeRel")
-    MESSAGE("Appending C min size release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_MINSIZEREL)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "Release")
-    MESSAGE("Appending C release flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELEASE)
-  ELSEIF(CMAKE_BUILD_TYPE MATCHES "RelWithDebInfo")
-    MESSAGE("Appending C release with debug info flags")
-    MARK_AS_ADVANCED(CLEAR CMAKE_C_FLAGS_RELWITHDEBINFO)
-  ENDIF()
-ENDIF()
-
-# ---------------------------------------------------------------
-# Option to specify precision (realtype)
-# ---------------------------------------------------------------
-
-SET(DOCSTR "single, double, or extended")
-SHOW_VARIABLE(SUNDIALS_PRECISION STRING "${DOCSTR}" "double")
-
-# prepare substitution variable PRECISION_LEVEL for sundials_config.h
-STRING(TOUPPER ${SUNDIALS_PRECISION} SUNDIALS_PRECISION)
-SET(PRECISION_LEVEL "#define SUNDIALS_${SUNDIALS_PRECISION}_PRECISION 1")
-
-# prepare substitution variable FPRECISION_LEVEL for sundials_fconfig.h
-IF(SUNDIALS_PRECISION MATCHES "SINGLE")
-  SET(FPRECISION_LEVEL "4")
-ENDIF(SUNDIALS_PRECISION MATCHES "SINGLE")
-IF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-  SET(FPRECISION_LEVEL "8")
-ENDIF(SUNDIALS_PRECISION MATCHES "DOUBLE")
-IF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-  SET(FPRECISION_LEVEL "16")
-ENDIF(SUNDIALS_PRECISION MATCHES "EXTENDED")
-
-# ---------------------------------------------------------------
-# Option to specify index type
-# ---------------------------------------------------------------
-
-SET(DOCSTR "Signed 64-bit (64) or signed 32-bit (32) integer")
-SHOW_VARIABLE(SUNDIALS_INDEX_SIZE STRING "${DOCSTR}" "64")
-SET(DOCSTR "Integer type to use for indices in SUNDIALS")
-SHOW_VARIABLE(SUNDIALS_INDEX_TYPE STRING "${DOCSTR}" "")
-MARK_AS_ADVANCED(SUNDIALS_INDEX_TYPE)
-include(SundialsIndexSize)
-
-# ---------------------------------------------------------------
-# Enable Fortran interface?
-# ---------------------------------------------------------------
-
-# Fortran interface is disabled by default
-SET(DOCSTR "Enable Fortran 77 interfaces")
-OPTION(F77_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 77 interface is built
-IF(NOT BUILD_ARKODE AND NOT BUILD_CVODE AND NOT BUILD_IDA AND NOT BUILD_KINSOL)
-  IF(F77_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 77 interface" "Disabling F77 interface")
-    FORCE_VARIABLE(F77_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F77_INTERFACE_ENABLE)
-ENDIF()
-
-# Fortran 2003 interface is disabled by default
-SET(DOCSTR "Enable Fortran 2003 interfaces")
-OPTION(F2003_INTERFACE_ENABLE "${DOCSTR}" OFF)
-
-# Check that at least one solver with a Fortran 2003 interface is built
-IF(NOT BUILD_CVODE)
-  IF(F2003_INTERFACE_ENABLE)
-    PRINT_WARNING("Enabled packages do not support Fortran 2003 interface" "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(F2003_INTERFACE_ENABLE)
-ENDIF()
-
-IF(F2003_INTERFACE_ENABLE)
-  # F2003 interface only supports double precision
-  IF(NOT (SUNDIALS_PRECISION MATCHES "DOUBLE"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # F2003 interface only supports 64-bit indices
-  IF(NOT (SUNDIALS_INDEX_SIZE MATCHES "64"))
-    PRINT_WARNING("F2003 interface is not compatible with ${SUNDIALS_INDEX_SIZE}-bit indicies"
-                  "Disabling F2003 interface")
-    FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-  ENDIF()
-
-  # Put all F2003 modules into one build directory
-  SET(CMAKE_Fortran_MODULE_DIRECTORY "${CMAKE_BINARY_DIR}/fortran")
-
-  # Allow a user to set where the Fortran modules will be installed
-  SET(DOCSTR "Directory where Fortran module files are installed")
-  SHOW_VARIABLE(Fortran_INSTALL_MODDIR DIRECTORY "${DOCSTR}" "fortran")
-ENDIF()
-
-# ---------------------------------------------------------------
-# Options to build static and/or shared libraries
-# ---------------------------------------------------------------
-
-OPTION(BUILD_STATIC_LIBS "Build static libraries" ON)
-OPTION(BUILD_SHARED_LIBS "Build shared libraries" ON)
-
-# Make sure we build at least one type of libraries
-IF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-  PRINT_WARNING("Both static and shared library generation were disabled"
-                "Building static libraries was re-enabled")
-  FORCE_VARIABLE(BUILD_STATIC_LIBS BOOL "Build static libraries" ON)
-ENDIF(NOT BUILD_STATIC_LIBS AND NOT BUILD_SHARED_LIBS)
-
-# ---------------------------------------------------------------
-# Option to use the generic math libraries (UNIX only)
-# ---------------------------------------------------------------
-
-IF(UNIX)
-  OPTION(USE_GENERIC_MATH "Use generic (std-c) math libraries" ON)
-  IF(USE_GENERIC_MATH)
-    # executables will be linked against -lm
-    SET(EXTRA_LINK_LIBS -lm)
-    # prepare substitution variable for sundials_config.h
-    SET(SUNDIALS_USE_GENERIC_MATH TRUE)
-  ENDIF(USE_GENERIC_MATH)
-ENDIF(UNIX)
-
-# ---------------------------------------------------------------
-# Check for POSIX timers
-# ---------------------------------------------------------------
-INCLUDE(SundialsPOSIXTimers)
-
-# ===============================================================
-# Options for Parallelism
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable MPI support?
-# ---------------------------------------------------------------
-OPTION(MPI_ENABLE "Enable MPI support" OFF)
-
-# ---------------------------------------------------------------
-# Enable OpenMP support?
-# ---------------------------------------------------------------
-OPTION(OPENMP_ENABLE "Enable OpenMP support" OFF)
-
-# provide OPENMP_DEVICE_ENABLE option
-OPTION(OPENMP_DEVICE_ENABLE "Enable OpenMP device offloading support" OFF)
-
-# Advanced option to skip OpenMP device offloading support check.
-# This is needed for a specific compiler that doesn't correctly
-# report its OpenMP spec date (with CMake >= 3.9).
-OPTION(SKIP_OPENMP_DEVICE_CHECK "Skip the OpenMP device offloading support check" OFF)
-MARK_AS_ADVANCED(FORCE SKIP_OPENMP_DEVICE_CHECK)
-
-# ---------------------------------------------------------------
-# Enable Pthread support?
-# ---------------------------------------------------------------
-OPTION(PTHREAD_ENABLE "Enable Pthreads support" OFF)
-
-# -------------------------------------------------------------
-# Enable CUDA support?
-# -------------------------------------------------------------
-OPTION(CUDA_ENABLE "Enable CUDA support" OFF)
-
-# -------------------------------------------------------------
-# Enable RAJA support?
-# -------------------------------------------------------------
-OPTION(RAJA_ENABLE "Enable RAJA support" OFF)
-
-
-# ===============================================================
-# Options for external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable BLAS support?
-# ---------------------------------------------------------------
-OPTION(BLAS_ENABLE "Enable BLAS support" OFF)
-
-# ---------------------------------------------------------------
-# Enable LAPACK/BLAS support?
-# ---------------------------------------------------------------
-OPTION(LAPACK_ENABLE "Enable Lapack support" OFF)
-
-# LAPACK does not support extended precision
-IF(LAPACK_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling LAPACK")
-  FORCE_VARIABLE(LAPACK_ENABLE BOOL "LAPACK is disabled" OFF)
-ENDIF()
-
-# LAPACK does not support 64-bit integer index types
-IF(LAPACK_ENABLE AND SUNDIALS_INDEX_SIZE MATCHES "64")
-  PRINT_WARNING("LAPACK is not compatible with ${SUNDIALS_INDEX_SIZE} integers"
-                "Disabling LAPACK")
-  SET(LAPACK_ENABLE OFF CACHE BOOL "LAPACK is disabled" FORCE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable SuperLU_MT support?
-# ---------------------------------------------------------------
-OPTION(SUPERLUMT_ENABLE "Enable SUPERLUMT support" OFF)
-
-# SuperLU_MT does not support extended precision
-IF(SUPERLUMT_ENABLE AND SUNDIALS_PRECISION MATCHES "EXTENDED")
-  PRINT_WARNING("SuperLU_MT is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling SuperLU_MT")
-  FORCE_VARIABLE(SUPERLUMT_ENABLE BOOL "SuperLU_MT is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable KLU support?
-# ---------------------------------------------------------------
-OPTION(KLU_ENABLE "Enable KLU support" OFF)
-
-# KLU does not support single or extended precision
-IF(KLU_ENABLE AND
-  (SUNDIALS_PRECISION MATCHES "SINGLE" OR SUNDIALS_PRECISION MATCHES "EXTENDED"))
-  PRINT_WARNING("KLU is not compatible with ${SUNDIALS_PRECISION} precision"
-                "Disabling KLU")
-  FORCE_VARIABLE(KLU_ENABLE BOOL "KLU is disabled" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable hypre Vector support?
-# ---------------------------------------------------------------
-OPTION(HYPRE_ENABLE "Enable hypre support" OFF)
-
-# Using hypre requres building with MPI enabled
-IF(HYPRE_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling hypre"
-                "Set MPI_ENABLE to ON to use parhyp")
-  FORCE_VARIABLE(HYPRE_ENABLE BOOL "Enable hypre support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable PETSc support?
-# ---------------------------------------------------------------
-OPTION(PETSC_ENABLE "Enable PETSc support" OFF)
-
-# Using PETSc requires building with MPI enabled
-IF(PETSC_ENABLE AND NOT MPI_ENABLE)
-  PRINT_WARNING("MPI not enabled - Disabling PETSc"
-                "Set MPI_ENABLE to ON to use PETSc")
-  FORCE_VARIABLE(PETSC_ENABLE BOOL "Enable PETSc support" OFF)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Enable Trilinos support?
-# ---------------------------------------------------------------
-OPTION(Trilinos_ENABLE "Enable Trilinos support" OFF)
-
-
-# ===============================================================
-# Options for examples
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Enable examples?
-# ---------------------------------------------------------------
-
-# Enable C examples (on by default)
-OPTION(EXAMPLES_ENABLE_C "Build SUNDIALS C examples" ON)
-
-# C++ examples (off by default, unless Trilinos is enabled)
-SET(DOCSTR "Build C++ examples")
-OPTION(EXAMPLES_ENABLE_CXX "${DOCSTR}" ${Trilinos_ENABLE})
-
-# F77 examples (on by default) are an option only if the Fortran
-# interface is enabled
-SET(DOCSTR "Build SUNDIALS Fortran examples")
-IF(F77_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" ON)
-  # Fortran 77 examples do not support single or extended precision
-  IF(EXAMPLES_ENABLE_F77 AND (SUNDIALS_PRECISION MATCHES "EXTENDED" OR SUNDIALS_PRECISION MATCHES "SINGLE"))
-    PRINT_WARNING("F77 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F77)
-    PRINT_WARNING("EXAMPLES_ENABLE_F77 is ON but F77_INTERFACE_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_F77")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F77 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F77)
-ENDIF()
-
-# F90 examples (on by default) are an option only if a Fortran interface is enabled.
-SET(DOCSTR "Build SUNDIALS F90 examples")
-IF(F77_INTERFACE_ENABLE OR F2003_INTERFACE_ENABLE)
-  SHOW_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" ON)
-  # Fortran 90 examples do not support extended precision
-  IF(EXAMPLES_ENABLE_F90 AND (SUNDIALS_PRECISION MATCHES "EXTENDED"))
-    PRINT_WARNING("F90 examples are not compatible with ${SUNDIALS_PRECISION} precision"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ELSE()
-  # set back to OFF (in case was ON)
-  IF(EXAMPLES_ENABLE_F90)
-    PRINT_WARNING("EXAMPLES_ENABLE_F90 is ON but both F77 and F2003 interfaces are OFF"
-                  "Disabling EXAMPLES_ENABLE_F90")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_F90 BOOL "${DOCSTR}" OFF)
-  ENDIF()
-  HIDE_VARIABLE(EXAMPLES_ENABLE_F90)
-ENDIF()
-
-# CUDA examples (off by default)
-SET(DOCSTR "Build SUNDIALS CUDA examples")
-IF(CUDA_ENABLE)
-  OPTION(EXAMPLES_ENABLE_CUDA "${DOCSTR}" OFF)
-ELSE()
-  IF(EXAMPLES_ENABLE_CUDA)
-    PRINT_WARNING("EXAMPLES_ENABLE_CUDA is ON but CUDA_ENABLE is OFF"
-                  "Disabling EXAMPLES_ENABLE_CUDA")
-    FORCE_VARIABLE(EXAMPLES_ENABLE_CUDA BOOL "${DOCSTR}" OFF)
-  ENDIF()
-ENDIF()
-
-# If any of the above examples are enabled set EXAMPLES_ENABLED to TRUE
-IF(EXAMPLES_ENABLE_C OR
-    EXAMPLES_ENABLE_F77 OR
-    EXAMPLES_ENABLE_CXX OR
-    EXAMPLES_ENABLE_F90 OR
-    EXAMPLES_ENABLE_CUDA)
-  SET(EXAMPLES_ENABLED TRUE)
-ELSE()
-  SET(EXAMPLES_ENABLED FALSE)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install examples?
-# ---------------------------------------------------------------
-
-# Enable installing examples by default
-SET(DOCSTR "Install SUNDIALS examples")
-IF(EXAMPLES_ENABLED)
-  OPTION(EXAMPLES_INSTALL "${DOCSTR}" ON)
-ELSE()
-  FORCE_VARIABLE(EXAMPLES_INSTALL BOOL "${DOCSTR}" OFF)
-  HIDE_VARIABLE(EXAMPLES_INSTALL)
-ENDIF()
-
-# If examples are to be exported, check where we should install them.
-IF(EXAMPLES_INSTALL)
-
-  SHOW_VARIABLE(EXAMPLES_INSTALL_PATH PATH
-    "Output directory for installing example files"
-    "${CMAKE_INSTALL_PREFIX}/examples")
-
-  IF(NOT EXAMPLES_INSTALL_PATH)
-    PRINT_WARNING("The example installation path is empty"
-      "Example installation path was reset to its default value")
-    SET(EXAMPLES_INSTALL_PATH "${CMAKE_INSTALL_PREFIX}/examples" CACHE STRING
-      "Output directory for installing example files" FORCE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(EXAMPLES_INSTALL_PATH)
-
-ENDIF()
-
-
-# ==============================================================================
-# Advanced (hidden) options
-# ==============================================================================
-
-# ------------------------------------------------------------------------------
-# Manually specify the Fortran name-mangling scheme
-#
-# The build system tries to infer the Fortran name-mangling scheme using a
-# Fortran compiler and defaults to using lower case and one underscore if the
-# scheme can not be determined. If a working Fortran compiler is not available
-# or the user needs to override the inferred or default scheme, the following
-# options specify the case and number of appended underscores corresponding to
-# the Fortran name-mangling scheme of symbol names that do not themselves
-# contain underscores. This is all we really need for the FCMIX and LAPACK
-# interfaces. A working Fortran compiler is only necessary for building Fortran
-# example programs.
-# ------------------------------------------------------------------------------
-
-# The case to use in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_CASE STRING
-  "case of Fortran function names (lower/upper)"
-  "")
-
-# The number of underscores of appended in the name-mangling scheme
-show_variable(SUNDIALS_F77_FUNC_UNDERSCORES STRING
-  "number of underscores appended to Fortran function names (none/one/two)"
-  "")
-
-# Hide the name-mangling varibales as advanced options
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_CASE)
-mark_as_advanced(FORCE SUNDIALS_F77_FUNC_UNDERSCORES)
-
-# If used, both case and underscores must be set
-if((NOT SUNDIALS_F77_FUNC_CASE) AND SUNDIALS_F77_FUNC_UNDERSCORES)
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_UNDERSCORES is set, SUNDIALS_F77_FUNC_CASE must also be set.")
-endif()
-
-if(SUNDIALS_F77_FUNC_CASE AND (NOT SUNDIALS_F77_FUNC_UNDERSCORES))
-  message(FATAL_ERROR
-    "If SUNDIALS_F77_FUNC_CASE is set, SUNDIALS_F77_FUNC_UNDERSCORES must also be set.")
-endif()
-
-# ------------------------------------------------------------------------------
-# Include development examples in regression tests?
-#
-# NOTE: Development examples are currently used for internal testing and may
-# produce erroneous failures when run on different systems as the pass/fail
-# status is determined by comparing the output against a saved output file.
-# ------------------------------------------------------------------------------
-OPTION(SUNDIALS_DEVTESTS "Include development tests in make test" OFF)
-MARK_AS_ADVANCED(FORCE SUNDIALS_DEVTESTS)
-
-# ===============================================================
-# Add any platform specifc settings
-# ===============================================================
-
-IF(APPLE)
-  SET(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "${CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS} -undefined dynamic_lookup")
-ENDIF(APPLE)
-
-# ===============================================================
-# Fortran and C++ settings
-# ===============================================================
-
-# ---------------------------------------------------------------
-# A Fortran compiler is needed to:
-# (a) Determine the name-mangling scheme if FCMIX, BLAS, or
-#     LAPACK are enabled
-# (b) Compile example programs if F77 or F90 examples are enabled
-# ---------------------------------------------------------------
-
-# Do we need a Fortran name-mangling scheme?
-if(F77_INTERFACE_ENABLE OR BLAS_ENABLE OR LAPACK_ENABLE)
-  set(NEED_FORTRAN_NAME_MANGLING TRUE)
-endif()
-
-# Did the user provide a name-mangling scheme?
-if(SUNDIALS_F77_FUNC_CASE AND SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_CASE} SUNDIALS_F77_FUNC_CASE)
-  STRING(TOUPPER ${SUNDIALS_F77_FUNC_UNDERSCORES} SUNDIALS_F77_FUNC_UNDERSCORES)
-
-  # Based on the given case and number of underscores, set the C preprocessor
-  # macro definitions. Since SUNDIALS never uses symbols names containing
-  # underscores we set the name-mangling schemes to be the same. In general,
-  # names of symbols with and without underscore may be mangled differently
-  # (e.g. g77 mangles mysub to mysub_ and my_sub to my_sub__)
-  if(SUNDIALS_F77_FUNC_CASE MATCHES "LOWER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## _")
-      SET(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) name ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) name ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  elseif(SUNDIALS_F77_FUNC_CASE MATCHES "UPPER")
-    if(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "NONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "ONE")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## _")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## _")
-    elseif(SUNDIALS_F77_FUNC_UNDERSCORES MATCHES "TWO")
-      set(F77_MANGLE_MACRO1 "#define SUNDIALS_F77_FUNC(name,NAME) NAME ## __")
-      set(F77_MANGLE_MACRO2 "#define SUNDIALS_F77_FUNC_(name,NAME) NAME ## __")
-    else()
-      message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_UNDERSCORES option.")
-    endif()
-  else()
-    message(FATAL_ERROR "Invalid SUNDIALS_F77_FUNC_CASE option.")
-  endif()
-
-  # name-mangling scheme has been manually set
-  set(NEED_FORTRAN_NAME_MANGLING FALSE)
-
-endif()
-
-# Do we need a Fortran compiler?
-if(F2003_INTERFACE_ENABLE OR EXAMPLES_ENABLE_F77 OR EXAMPLES_ENABLE_F90 OR NEED_FORTRAN_NAME_MANGLING)
-  include(SundialsFortran)
-endif()
-
-# Ensure that F90 compiler is found if F90 examples are enabled
-if (EXAMPLES_ENABLE_F90 AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F90 Examples")
-  SET(DOCSTR "Build F90 examples")
-  FORCE_VARIABLE(EXAMPLES_ENABLE_F90 "${DOCSTR}" OFF)
-endif()
-
-# Ensure that F90 compiler found if F2003 interface is enabled
-if (F2003_INTERFACE_ENABLE AND (NOT F90_FOUND))
-  PRINT_WARNING("Compiler with F90 support not found" "Disabling F2003 Interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-endif()
-
-# F2003 interface requires ISO_C_BINDING
-IF(F2003_INTERFACE_ENABLE AND (NOT Fortran_COMPILER_SUPPORTS_ISOCBINDING))
-  PRINT_WARNING("Fortran compiler does not provide ISO_C_BINDING support"
-                "Disabling F2003 interface")
-  SET(DOCSTR "Enable Fortran 2003 interfaces")
-  FORCE_VARIABLE(F2003_INTERFACE_ENABLE BOOL "${DOCSTR}" OFF)
-ENDIF()
-
-
-# ---------------------------------------------------------------
-# A C++ compiler is needed if:
-# (a) C++ examples are enabled
-# (b) CUDA is enabled
-# (c) RAJA is enabled
-# (d) Trilinos is enabled
-# ---------------------------------------------------------------
-
-if(EXAMPLES_ENABLE_CXX OR CUDA_ENABLE OR RAJA_ENABLE OR Trilinos_ENABLE)
-  include(SundialsCXX)
-endif()
-
-# ---------------------------------------------------------------
-# Setup CUDA. Since CUDA is its own language we do this
-# separate from the TPLs.
-# ---------------------------------------------------------------
-
-if(CUDA_ENABLE)
-  find_package(CUDA)
-  if (CUDA_FOUND)
-    set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} -lineinfo")
-  else()
-    message(STATUS "Disabling CUDA support, could not find CUDA.")
-    set(CUDA_ENABLE OFF)
-  endif()
-endif(CUDA_ENABLE)
-
-# ---------------------------------------------------------------
-# Now that all languages are setup, we can configure them more.
-# ---------------------------------------------------------------
-
-# C++11 is needed if:
-#   (a) CUDA is enabled
-# C++11 should not be enabled if
-#   (a) RAJA is enabled (they provide a std flag)
-if (CXX_FOUND AND CUDA_ENABLE AND CUDA_FOUND AND (NOT RAJA_ENABLE))
-  USE_CXX_STD(11)
-endif()
-
-# ---------------------------------------------------------------
-# Decide how to compile MPI codes. We must check for MPI if
-# MPI is enabled or if Trilinos is enabled because the Trilinos
-# examples may need MPI without us turning on the MPI SUNDIALS
-# components.
-# ---------------------------------------------------------------
-
-if(MPI_ENABLE OR Trilinos_ENABLE)
-  include(SundialsMPI)
-endif()
-
-if(MPI_ENABLE)
-  if(NOT MPI_C_FOUND)
-    print_warning("MPI not functional" "Parallel support will not be provided")
-  else()
-    set(IS_MPI_ENABLED "#ifndef SUNDIALS_MPI_ENABLED\n#define SUNDIALS_MPI_ENABLED 1\n#endif")
-  endif()
-endif()
-
-# always define FMPI_COMM_F2C in sundials_fconfig.h file
-if(MPIC_MPI2)
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 1")
-  set(FMPI_COMM_F2C ".true.")
-else()
-  set(F77_MPI_COMM_F2C "#define SUNDIALS_MPI_COMM_F2C 0")
-  set(FMPI_COMM_F2C ".false.")
-endif()
-
-# -------------------------------------------------------------
-# Find OpenMP
-# -------------------------------------------------------------
-
-if(OPENMP_ENABLE OR OPENMP_DEVICE_ENABLE)
-
-  include(SundialsOpenMP)
-
-  # turn off OPENMP_ENABLE and OPENMP_DEVICE_ENABLE if OpenMP is not found
-  if(NOT OPENMP_FOUND)
-    print_warning("Could not determine OpenMP compiler flags" "Disabling OpenMP support")
-    force_variable(OPENMP_ENABLE BOOL "Enable OpenMP support" OFF)
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-  # turn off OPENMP_DEVICE_ENABLE if offloading is not supported
-  if(OPENMP_DEVICE_ENABLE AND (NOT OPENMP_SUPPORTS_DEVICE_OFFLOADING))
-    print_warning("OpenMP found does not support device offloading"
-                  "Disabling OpenMP device offloading support")
-    force_variable(OPENMP_DEVICE_ENABLE BOOL "Enable OpenMP device offloading support" OFF)
-  endif()
-
-endif()
-
-# -------------------------------------------------------------
-# Find PThreads
-# -------------------------------------------------------------
-
-IF(PTHREAD_ENABLE)
-  FIND_PACKAGE(Threads)
-  IF(CMAKE_USE_PTHREADS_INIT)
-    message(STATUS "Using Pthreads")
-    SET(PTHREADS_FOUND TRUE)
-    # SGS
-  ELSE()
-    message(STATUS "Disabling Pthreads support, could not determine compiler flags")
-  endif()
-ENDIF(PTHREAD_ENABLE)
-
-# -------------------------------------------------------------
-# Find RAJA
-# -------------------------------------------------------------
-
-# disable RAJA if CUDA is not enabled/working
-if(RAJA_ENABLE AND (NOT CUDA_FOUND))
-  PRINT_WARNING("CUDA is required for RAJA support" "Please enable CUDA and RAJA")
-  FORCE_VARIABLE(RAJA_ENABLE BOOL "RAJA disabled" OFF)
-endif()
-
-if(RAJA_ENABLE)
-  # Look for CMake configuration file in RAJA installation
-  find_package(RAJA)
-  if (RAJA_FOUND)
-    include_directories(${RAJA_INCLUDE_DIR})
-    set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} ${RAJA_NVCC_FLAGS})
-  else()
-    PRINT_WARNING("RAJA configuration not found"
-                  "Please set RAJA_DIR to provide path to RAJA CMake configuration file.")
-  endif()
-endif(RAJA_ENABLE)
-
-# ===============================================================
-# Find (and test) external packages
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Find (and test) the BLAS libraries
-# ---------------------------------------------------------------
-
-# If BLAS is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(BLAS_ENABLE)
-
-  # find BLAS
-  INCLUDE(SundialsBlas)
-
-  # show after include so FindBlas can locate BLAS_LIBRARIES if necessary
-  SHOW_VARIABLE(BLAS_LIBRARIES STRING "Blas libraries" "${BLAS_LIBRARIES}")
-
-  IF(BLAS_LIBRARIES AND NOT BLAS_FOUND)
-    PRINT_WARNING("BLAS not functional"
-                  "BLAS support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(BLAS_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the Lapack libraries
-# ---------------------------------------------------------------
-
-# If LAPACK is needed, first try to find the appropriate
-# libraries and linker flags needed to link against them.
-
-IF(LAPACK_ENABLE)
-
-  # find LAPACK and BLAS Libraries
-  INCLUDE(SundialsLapack)
-
-  # show after include so FindLapack can locate LAPCK_LIBRARIES if necessary
-  SHOW_VARIABLE(LAPACK_LIBRARIES STRING "Lapack and Blas libraries" "${LAPACK_LIBRARIES}")
-
-  IF(LAPACK_LIBRARIES AND NOT LAPACK_FOUND)
-    PRINT_WARNING("LAPACK not functional"
-                  "Blas/Lapack support will not be provided")
-  ELSE()
-    #set sundials_config.h symbol via sundials_config.in
-    SET(SUNDIALS_BLAS_LAPACK TRUE)
-  ENDIF()
-
-ELSE()
-
-  HIDE_VARIABLE(LAPACK_LIBRARIES)
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the SUPERLUMT libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for SuperLU_MT integer type
-
-# If SUPERLUMT is needed, first try to find the appropriate
-# libraries to link against them.
-
-IF(SUPERLUMT_ENABLE)
-
-  # Show SuperLU_MT options and set default thread type (Pthreads)
-  SHOW_VARIABLE(SUPERLUMT_THREAD_TYPE STRING "SUPERLUMT threading type: OpenMP or Pthread" "Pthread")
-  SHOW_VARIABLE(SUPERLUMT_INCLUDE_DIR PATH "SUPERLUMT include directory" "${SUPERLUMT_INCLUDE_DIR}")
-  SHOW_VARIABLE(SUPERLUMT_LIBRARY_DIR PATH "SUPERLUMT library directory" "${SUPERLUMT_LIBRARY_DIR}")
-
-  INCLUDE(SundialsSuperLUMT)
-
-  IF(SUPERLUMT_FOUND)
-    # sundials_config.h symbols
-    SET(SUNDIALS_SUPERLUMT TRUE)
-    SET(SUNDIALS_SUPERLUMT_THREAD_TYPE ${SUPERLUMT_THREAD_TYPE})
-    INCLUDE_DIRECTORIES(${SUPERLUMT_INCLUDE_DIR})
-  ENDIF()
-
-  IF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-    PRINT_WARNING("SUPERLUMT not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(SUPERLUMT_LIBRARIES AND NOT SUPERLUMT_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(SUPERLUMT_THREAD_TYPE)
-  HIDE_VARIABLE(SUPERLUMT_LIBRARY_DIR)
-  HIDE_VARIABLE(SUPERLUMT_INCLUDE_DIR)
-  SET (SUPERLUMT_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the KLU libraries
-# ---------------------------------------------------------------
-
-# If KLU is requested, first try to find the appropriate libraries to
-# link against them.
-
-IF(KLU_ENABLE)
-
-  SHOW_VARIABLE(KLU_INCLUDE_DIR PATH "KLU include directory"
-    "${KLU_INCLUDE_DIR}")
-  SHOW_VARIABLE(KLU_LIBRARY_DIR PATH
-    "Klu library directory" "${KLU_LIBRARY_DIR}")
-
-  set(KLU_FOUND TRUE)
-  get_filename_component(PYBAMM_DIR ${PROJECT_SOURCE_DIR} DIRECTORY)
-  set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PYBAMM_DIR}) # use FindSuiteSparse.cmake that is in PyBaMM root
-  set(SuiteSparse_ROOT ${PYBAMM_DIR}/SuiteSparse-5.6.0)
-  find_package(SuiteSparse OPTIONAL_COMPONENTS KLU AMD COLAMD BTF)
-  include_directories(${SuiteSparse_INCLUDE_DIRS})
-  set(KLU_LIBRARIES ${SuiteSparse_LIBRARIES})
-
-
-  IF(KLU_LIBRARIES AND NOT KLU_FOUND)
-    PRINT_WARNING("KLU not functional - support will not be provided"
-                  "Double check spelling of include path and specified libraries (search is case sensitive)")
-  ENDIF(KLU_LIBRARIES AND NOT KLU_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(KLU_LIBRARY_DIR)
-  HIDE_VARIABLE(KLU_INCLUDE_DIR)
-  SET (KLU_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF(KLU_ENABLE)
-
-# ---------------------------------------------------------------
-# Find (and test) the hypre libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for hypre precision and integer type
-
-IF(HYPRE_ENABLE)
-  SHOW_VARIABLE(HYPRE_INCLUDE_DIR PATH "HYPRE include directory"
-    "${HYPRE_INCLUDE_DIR}")
-  SHOW_VARIABLE(HYPRE_LIBRARY_DIR PATH
-    "HYPRE library directory" "${HYPRE_LIBRARY_DIR}")
-
-  INCLUDE(SundialsHypre)
-
-  IF(HYPRE_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_HYPRE TRUE)
-    INCLUDE_DIRECTORIES(${HYPRE_INCLUDE_DIR})
-  ENDIF(HYPRE_FOUND)
-
-  IF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-    PRINT_WARNING("HYPRE not functional - support will not be provided"
-                  "Found hypre library, test code does not work")
-  ENDIF(HYPRE_LIBRARIES AND NOT HYPRE_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(HYPRE_INCLUDE_DIR)
-  HIDE_VARIABLE(HYPRE_LIBRARY_DIR)
-  SET (HYPRE_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# ---------------------------------------------------------------
-# Find (and test) the PETSc libraries
-# ---------------------------------------------------------------
-
-# >>>>>>> NOTE: Need to add check for PETSc precision and integer type
-
-IF(PETSC_ENABLE)
-  SHOW_VARIABLE(PETSC_INCLUDE_DIR PATH "PETSc include directory"
-    "${PETSC_INCLUDE_DIR}")
-  SHOW_VARIABLE(PETSC_LIBRARY_DIR PATH
-    "PETSc library directory" "${PETSC_LIBRARY_DIR}")
-
-  INCLUDE(SundialsPETSc)
-
-  IF(PETSC_FOUND)
-    # sundials_config.h symbol
-    SET(SUNDIALS_PETSC TRUE)
-    INCLUDE_DIRECTORIES(${PETSC_INCLUDE_DIR})
-  ENDIF(PETSC_FOUND)
-
-  IF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-    PRINT_WARNING("PETSC not functional - support will not be provided"
-                  "Double check spelling specified libraries (search is case sensitive)")
-  ENDIF(PETSC_LIBRARIES AND NOT PETSC_FOUND)
-
-ELSE()
-
-  HIDE_VARIABLE(PETSC_LIBRARY_DIR)
-  HIDE_VARIABLE(PETSC_INCLUDE_DIR)
-  SET (PETSC_DISABLED TRUE CACHE INTERNAL "GUI - return when first set")
-
-ENDIF()
-
-# -------------------------------------------------------------
-# Find Trilinos
-# -------------------------------------------------------------
-
-if(Trilinos_ENABLE)
-  include(SundialsTrilinos)
-  if(NOT Trilinos_FUNCTIONAL)
-    PRINT_WARNING("Trilinos not functional" "Verify the path to Trilinos and check the Trilinos installation")
-  endif()
-endif(Trilinos_ENABLE)
-
-
-# ===============================================================
-# At this point all the configuration options are set.
-# ===============================================================
-
-# ---------------------------------------------------------------
-# Configure the header file sundials_config.h
-# ---------------------------------------------------------------
-
-# All required substitution variables should be available at this point.
-# Generate the header file and place it in the binary dir.
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_config.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  )
-CONFIGURE_FILE(
-  ${PROJECT_SOURCE_DIR}/include/sundials/sundials_fconfig.in
-  ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  )
-
-# Add the include directory in the source tree and the one in
-# the binary tree (for the header file sundials_config.h)
-INCLUDE_DIRECTORIES(${PROJECT_SOURCE_DIR}/include ${PROJECT_BINARY_DIR}/include)
-
-# ---------------------------------------------------------------
-# Enable testing and add source and example files to the build.
-# ---------------------------------------------------------------
-
-# Enable testing
-IF(EXAMPLES_ENABLED)
-  INCLUDE(SundialsTesting)
-ENDIF()
-
-# Add selected packages and modules to the build
-ADD_SUBDIRECTORY(src)
-
-# Add selected examples to the build
-IF(EXAMPLES_ENABLED)
-  ADD_SUBDIRECTORY(examples)
-ENDIF()
-
-# ---------------------------------------------------------------
-# Install configuration header files and license file
-# ---------------------------------------------------------------
-
-# install configured header file
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_config.h
-  DESTINATION include/sundials
-  )
-
-# install configured header file for Fortran 90
-INSTALL(
-  FILES ${PROJECT_BINARY_DIR}/include/sundials/sundials_fconfig.h
-  DESTINATION include/sundials
-  )
-
-# install shared Fortran 2003 modules
-IF(F2003_INTERFACE_ENABLE)
-  # While the .mod files get generated for static and shared
-  # libraries, they are identical. So only install one set
-  # of the .mod files.
-  IF(BUILD_STATIC_LIBS)
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_STATIC/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ELSE()
-    INSTALL(
-      DIRECTORY ${CMAKE_Fortran_MODULE_DIRECTORY}_SHARED/
-      DESTINATION ${Fortran_INSTALL_MODDIR}
-      )
-  ENDIF()
-ENDIF()
-
-# install license and notice files
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/LICENSE
-  DESTINATION include/sundials
-  )
-INSTALL(
-  FILES ${PROJECT_SOURCE_DIR}/NOTICE
-  DESTINATION include/sundials
-  )

From d9743ec3c453a57d6e2f20278f9997e27854f6e8 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:01:45 +0530
Subject: [PATCH 147/615] Add parallel job

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 8e315c6950..235ae91202 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -155,6 +155,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -299,10 +303,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
-
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: nox -s doctests
 

From 7c7420a41ebf17f17057f39736f627a0e9d38ccc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:14:28 +0530
Subject: [PATCH 148/615] Test before unit tests in mac

---
 .github/workflows/test_on_push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 235ae91202..3589a52e78 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -95,6 +95,10 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
+      - name: Test pybamm_install_odes on MacOS (for only this PR)
+        if: matrix.os == 'macos-latest'
+        run: pybamm_install_odes
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         if: matrix.os == 'ubuntu-latest'
@@ -155,10 +159,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:

From 906683b2f21589122c36d1e75cbafd961e8e37c7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 7 Oct 2023 19:27:51 +0530
Subject: [PATCH 149/615] Install `wget` before odes

---
 .github/workflows/test_on_push.yml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3589a52e78..aff1cfbe49 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -97,7 +97,9 @@ jobs:
 
       - name: Test pybamm_install_odes on MacOS (for only this PR)
         if: matrix.os == 'macos-latest'
-        run: pybamm_install_odes
+        run: |
+          pip install wget
+          pybamm_install_odes
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From eae0bbb3bbcd974a1505404a1023b686d8f5b1ad Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Sun, 8 Oct 2023 14:16:03 +0530
Subject: [PATCH 150/615] fix: Resolved indentaions

- Corrected the space between virtualenv and cmake
- Changed macos to darwin
---
 noxfile.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index df00bfadc2..736c5be6c2 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -118,11 +118,11 @@ def run_scripts(session):
 def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("virtualenv","cmake")
+    session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False)
-    if sys.platform == "linux" or sys.platform == "macos":
+    if sys.platform == "linux" or sys.platform == "darwin":
         session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False)
 
 

From 570b96f7315ceb3ded9f3595c03ec8e2c996bc8d Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Sun, 8 Oct 2023 14:21:52 +0530
Subject: [PATCH 151/615] Updated install-from-source.rst

- Updated the docs regarding the virtual environment
---
 docs/source/user_guide/installation/install-from-source.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 0aa50cf8a8..fb448950bf 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -116,7 +116,7 @@ Using Nox (recommended)
 .. note::
 	It is recommended to use ``--verbose`` or ``-v`` to see outputs of all commands run.
 
-This creates a virtual environment ``.nox/dev`` inside the ``PyBaMM/`` directory.
+This creates a virtual environment ``venv/`` inside the ``PyBaMM/`` directory.
 It comes ready with PyBaMM and some useful development tools like `pre-commit <https://pre-commit.com/>`_ and `ruff <https://beta.ruff.rs/docs/>`_.
 
 You can now activate the environment with
@@ -125,13 +125,13 @@ You can now activate the environment with
 
 	.. code:: bash
 
-		source .nox/dev/bin/activate
+		source venv/bin/activate
 
 .. tab:: Windows
 
 	.. code:: bash
 
-		.nox\dev\Scripts\activate.bat
+		venv\Scripts\activate.bat
 
 and run the tests to check your installation.
 

From 94753958dd97888bc964e8b9d4d1066cfa098bd5 Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Sun, 8 Oct 2023 16:27:54 +0530
Subject: [PATCH 152/615] fix: Changed few minor instances

- Added external = True instead of silent = False as it is not required for session.run()
---
 noxfile.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 736c5be6c2..9d13656894 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -121,9 +121,9 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", silent=False)
+    session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
     if sys.platform == "linux" or sys.platform == "darwin":
-        session.run(python, "-m", "pip", "install", ".[jax,odes]", silent=False)
+        session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True)
 
 
 

From 96eef72344451e532636167eafa3ef279149e7aa Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Sun, 8 Oct 2023 17:51:37 +0530
Subject: [PATCH 153/615] Fix URL

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index a4bb69ac7c..9f45d34327 100644
--- a/README.md
+++ b/README.md
@@ -233,7 +233,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chuckliu1979"><img src="https://avatars.githubusercontent.com/u/13491954?v=4?s=100" width="100px;" alt="Chuck Liu"/><br /><sub><b>Chuck Liu</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achuckliu1979" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chuckliu1979" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/partben"><img src="https://avatars.githubusercontent.com/u/88316576?v=4?s=100" width="100px;" alt="partben"/><br /><sub><b>partben</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=partben" title="Documentation">📖</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://wigging.me"><img src="https://avatars.githubusercontent.com/u/6828967?v=4?s=100" width="100px;" alt="Gavin Wiggins"/><br /><sub><b>Gavin Wiggins</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Awigging" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=wigging" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://gavinw.me"><img src="https://avatars.githubusercontent.com/u/6828967?v=4?s=100" width="100px;" alt="Gavin Wiggins"/><br /><sub><b>Gavin Wiggins</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Awigging" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=wigging" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/dion-w"><img src="https://avatars.githubusercontent.com/u/91852142?v=4?s=100" width="100px;" alt="Dion Wilde"/><br /><sub><b>Dion Wilde</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Adion-w" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=dion-w" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://www.ehtec.co"><img src="https://avatars.githubusercontent.com/u/48386220?v=4?s=100" width="100px;" alt="Elias Hohl"/><br /><sub><b>Elias Hohl</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=ehtec" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/KAschad"><img src="https://avatars.githubusercontent.com/u/93784399?v=4?s=100" width="100px;" alt="KAschad"/><br /><sub><b>KAschad</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AKAschad" title="Bug reports">🐛</a></td>

From f5ddab39fb673d0ffb297d49e4c4f6e5f42d65e8 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Sun, 8 Oct 2023 17:52:10 +0530
Subject: [PATCH 154/615] Update .all-contributorsrc

---
 .all-contributorsrc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 2456085b32..405ff36569 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -442,7 +442,7 @@
       "login": "wigging",
       "name": "Gavin Wiggins",
       "avatar_url": "https://avatars.githubusercontent.com/u/6828967?v=4",
-      "profile": "https://wigging.me",
+      "profile": "https://gavinw.me",
       "contributions": [
         "bug",
         "code"

From bc909c3212f0b7d7974b6e11525b1f7108b37489 Mon Sep 17 00:00:00 2001
From: Agnik Bakshi <77234005+Agnik7@users.noreply.github.com>
Date: Sun, 8 Oct 2023 18:08:48 +0530
Subject: [PATCH 155/615] Update CONTRIBUTING.md

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 4612e83252..bec0fee02a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -44,7 +44,7 @@ You now have everything you need to start making changes!
 
 ### B. Writing your code
 
-6. PyBaMM is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](http://blog.hackerearth.com/how-can-r-users-learn-python-for-data-science)).
+6. PyBaMM is developed in [Python](https://en.wikipedia.org/wiki/Python_(programming_language)), and makes heavy use of [NumPy](https://en.wikipedia.org/wiki/NumPy) (see also [NumPy for MatLab users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html) and [Python for R users](https://www.rebeccabarter.com/blog/2023-09-11-from_r_to_python)).
 7. Make sure to follow our [coding style guidelines](#coding-style-guidelines).
 8. Commit your changes to your branch with [useful, descriptive commit messages](https://chris.beams.io/posts/git-commit/): Remember these are publicly visible and should still make sense a few months ahead in time. While developing, you can keep using the GitHub issue you're working on as a place for discussion. [Refer to your commits](https://stackoverflow.com/questions/8910271/how-can-i-reference-a-commit-in-an-issue-comment-on-github) when discussing specific lines of code.
 9. If you want to add a dependency on another library, or re-use code you found somewhere else, have a look at [these guidelines](#dependencies-and-reusing-code).

From ba2365bdc29eb85bb09bc6bb07492a891267cdc0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 8 Oct 2023 18:34:09 +0530
Subject: [PATCH 156/615] Don't bundle IDAKLU with Jax solver, remove pybind11

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 scripts/Dockerfile | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 7015feef5a..c3d12bb7fe 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -37,24 +37,25 @@ RUN pip install --upgrade --user pip setuptools wheel wget cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs]"; \
     fi
 
 RUN if [ "$ODES" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 
 RUN if [ "$JAX" = "true" ]; then \
-    python scripts/install_KLU_Sundials.py && \
-    git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax]"; \
     fi
 
 RUN if [ "$ALL" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
+    rm -rf pybind11 && \
     git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,jax,odes]"; \
     fi

From 2be8313396da2a894b5c19c4a7f648092e204c16 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Mon, 9 Oct 2023 08:33:13 +0100
Subject: [PATCH 157/615] docs: add Agnik7 as a contributor for doc (#3399)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
Co-authored-by: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
---
 .all-contributorsrc | 11 ++++++++++-
 README.md           |  3 ++-
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 405ff36569..226c0cfc07 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -708,7 +708,16 @@
       "contributions": [
         "infra"
       ]
-    }
+    },
+    {
+      "login": "Agnik7",
+      "name": "Agnik Bakshi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/77234005?v=4",
+      "profile": "https://github.com/Agnik7",
+      "contributions": [
+        "doc"
+      ]
+    },
   ],
   "contributorsPerLine": 7,
   "projectName": "PyBaMM",
diff --git a/README.md b/README.md
index 9f45d34327..1354f3f81f 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-64-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-65-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -268,6 +268,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     </tr>
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://aitorres.com"><img src="https://avatars.githubusercontent.com/u/26191851?v=4?s=100" width="100px;" alt="Andrés Ignacio Torres"/><br /><sub><b>Andrés Ignacio Torres</b></sub></a><br /><a href="#infra-aitorres" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
     </tr>
   </tbody>
 </table>

From 774d56d453c54f22a1a95ad81b33f444734b6363 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 9 Oct 2023 13:03:58 +0530
Subject: [PATCH 158/615] Try adding Plausible script in theme options (#3419)

---
 docs/conf.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/conf.py b/docs/conf.py
index a96d139b12..74d1f76488 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -155,6 +155,10 @@
     # add Algolia to the persistent navbar, this removes the default search icon
     "navbar_persistent": "algolia-searchbox",
     "use_edit_page_button": True,
+    "analytics": {
+        "plausible_analytics_domain": "docs.pybamm.org",
+        "plausible_analytics_url": "https://plausible.io/js/script.js",
+    },
     "pygment_light_style": "xcode",
     "pygment_dark_style": "monokai",
     "footer_start": [

From 342c70312bad7780fd9b4c28b4983d2dc0ccc417 Mon Sep 17 00:00:00 2001
From: John Brittain <john.brittain@dtc.ox.ac.uk>
Date: Mon, 9 Oct 2023 13:12:22 +0100
Subject: [PATCH 159/615] Fix for jax-gpu solver

---
 pybamm/solvers/jax_solver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 8e7b1b5cc5..beeb932597 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -215,7 +215,7 @@ def _integrate(self, model, t_eval, inputs=None):
 
         y = []
         platform = jax.lib.xla_bridge.get_backend().platform.casefold()
-        if platform.startswith("cpu"):
+        if len(inputs) <= 1 or platform.startswith("cpu"):
             # cpu execution runs faster when multithreaded
             async def solve_model_for_inputs():
                 async def solve_model_async(inputs_v):

From 9748a1b9957ef955839e7517785881b97ca3a3c2 Mon Sep 17 00:00:00 2001
From: John Brittain <john.brittain@dtc.ox.ac.uk>
Date: Mon, 9 Oct 2023 16:36:01 +0100
Subject: [PATCH 160/615] Add changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 421d3bfa29..17a54d41a8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,6 +18,7 @@
 
 ## Bug fixes
 
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).

From 4a8b7ebf973fc950c2de593defbac6cfe443908f Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 9 Oct 2023 19:42:11 +0000
Subject: [PATCH 161/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/pre-commit/pre-commit-hooks: v4.4.0 → v4.5.0](https://github.com/pre-commit/pre-commit-hooks/compare/v4.4.0...v4.5.0)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 61e7bf74c4..8d253a49ee 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -23,7 +23,7 @@ repos:
          additional_dependencies: [black==22.12.0]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v4.5.0
     hooks:
         - id: check-added-large-files
         - id: check-case-conflict

From 9fa63c320a874b7910003594a7bf93c49a1881ab Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:09:34 +0530
Subject: [PATCH 162/615] Set up parallel build and push with matrices and tags

#3316 fix discrepancy with tagging the images, add a step to append tags to GitHub output, add support for multi-architecture images by setting up QEMU.
---
 .github/workflows/docker.yml | 83 ++++++++++++++++--------------------
 1 file changed, 36 insertions(+), 47 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index c3575a50f8..fc7e52ec36 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -1,75 +1,64 @@
-name: Build & Push Docker Images
+name: Build and push Docker images to Docker Hub
 
 on:
   workflow_dispatch:
   push:
     branches:
     - develop
+  # temporary
+  pull_request:
 
 jobs:
-  pre_job:
+  build_docker_images:
+    # This workflow is only of value to PyBaMM and would always be skipped in forks
+    if: github.repository_owner == 'pybamm-team'
+    name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }})
     runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
+        architectures: [amd64, arm64]
 
     steps:
       - name: Checkout
         uses: actions/checkout@v4
 
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
-      - name: Login to DockerHub
+      - name: Login to Docker Hub
         uses: docker/login-action@v3
         with:
             username: ${{ secrets.DOCKERHUB_USERNAME }}
             password: ${{ secrets.DOCKERHUB_TOKEN }}
 
-      - name: List built images
-        run: docker images
-
-      - name: Build and Push Docker Image (Without Solvers)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:latest
-          push: true
-
-      - name: Build and Push Docker Image (With JAX Solver)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:jax
-          push: true
-          build-args: |
-            JAX=true
-
-      - name: Build and Push Docker Image (With ODES & DAE Solver)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:odes
-          push: true
-          build-args: |
-            ODES=true
+      - name: Create tags for Docker images based on build-time arguments
+        id: tags
+        run: |
+          if [ "${{ matrix.build-args }}" = "" ]; then
+            echo "::set-output name=tag::latest" >> $GITHUB_OUTPUT
+          elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then
+            echo "::set-output name=tag::jax" >> $GITHUB_OUTPUT
+          elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then
+            echo "::set-output name=tag::odes" >> $GITHUB_OUTPUT
+          elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then
+            echo "::set-output name=tag::idaklu" >> $GITHUB_OUTPUT
+          elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then
+            echo "::set-output name=tag::all" >> $GITHUB_OUTPUT
+          fi
 
-      - name: Build and Push Docker Image (With IDAKLU Solver)
+      - name: Build and push Docker image to Docker Hub
         uses: docker/build-push-action@v5
         with:
           context: .
           file: scripts/Dockerfile
-          tags: pybamm/pybamm:idaklu
-          push: true
-          build-args: |
-            IDAKLU=true
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: false # temporary
+          build-args: ${{ matrix.build-args }}
+          platforms: linux/${{ matrix.architectures }}
 
-      - name: Build and Push Docker Image (With All Solvers)
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:latest
-          push: true
-          build-args: |
-            ALL=true
+      - name: List built image(s)
+        run: docker images

From 14ffa0236f752f72fae5e3a61ef8f0f6a53dd8fc Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:15:22 +0530
Subject: [PATCH 163/615] Don't add architectures to the matrix

---
 .github/workflows/docker.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index fc7e52ec36..e69492e3d6 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -17,7 +17,6 @@ jobs:
     strategy:
       matrix:
         build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
-        architectures: [amd64, arm64]
 
     steps:
       - name: Checkout
@@ -58,7 +57,7 @@ jobs:
           tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
           push: false # temporary
           build-args: ${{ matrix.build-args }}
-          platforms: linux/${{ matrix.architectures }}
+          platforms: linux/amd64, linux/arm64
 
       - name: List built image(s)
         run: docker images

From 192dc9dbd081cc94a0b2da84aaa12f0854051509 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:17:07 +0530
Subject: [PATCH 164/615] Run on forks, temporarily

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index e69492e3d6..c39befe5af 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -11,7 +11,7 @@ on:
 jobs:
   build_docker_images:
     # This workflow is only of value to PyBaMM and would always be skipped in forks
-    if: github.repository_owner == 'pybamm-team'
+    # if: github.repository_owner == 'pybamm-team'
     name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }})
     runs-on: ubuntu-latest
     strategy:

From 7cb7b20538bcc4d599f470ef0c5bb8ea373fd66f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:18:26 +0530
Subject: [PATCH 165/615] Fix name scheme for build job

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index c39befe5af..47388e3830 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -12,7 +12,7 @@ jobs:
   build_docker_images:
     # This workflow is only of value to PyBaMM and would always be skipped in forks
     # if: github.repository_owner == 'pybamm-team'
-    name: Build image ({{ matrix.build-args }} / {{ matrix.architectures }})
+    name: Build image ({{ matrix.build-args }})
     runs-on: ubuntu-latest
     strategy:
       matrix:

From b9db45c37c5e118b2fdc171ea09c952a92ed11f6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:22:03 +0530
Subject: [PATCH 166/615] Enable fail fast to not push any broken images

---
 .github/workflows/docker.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 47388e3830..e45104b2fc 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -17,6 +17,7 @@ jobs:
     strategy:
       matrix:
         build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
+      fail-fast: true
 
     steps:
       - name: Checkout

From a3d561a25e286c6d91345259cb5239eca593e310 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:49:33 +0530
Subject: [PATCH 167/615] Properly set tags into GitHub step outputs

---
 .github/workflows/docker.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index e45104b2fc..8031d86203 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -39,15 +39,15 @@ jobs:
         id: tags
         run: |
           if [ "${{ matrix.build-args }}" = "" ]; then
-            echo "::set-output name=tag::latest" >> $GITHUB_OUTPUT
+            echo "tag=latest" >> "$GITHUB_OUTPUT"
           elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then
-            echo "::set-output name=tag::jax" >> $GITHUB_OUTPUT
+            echo "tag=jax" >> "$GITHUB_OUTPUT"
           elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then
-            echo "::set-output name=tag::odes" >> $GITHUB_OUTPUT
+            echo "tag=odes" >> "$GITHUB_OUTPUT"
           elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then
-            echo "::set-output name=tag::idaklu" >> $GITHUB_OUTPUT
+            echo "tag=idaklu" >> "$GITHUB_OUTPUT"
           elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then
-            echo "::set-output name=tag::all" >> $GITHUB_OUTPUT
+            echo "tag=all" >> "$GITHUB_OUTPUT"
           fi
 
       - name: Build and push Docker image to Docker Hub

From ec52a9ff1a4209359aad51173dc63185c02c1951 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 03:51:47 +0530
Subject: [PATCH 168/615] Rename job step name to include build args

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 8031d86203..1f50801a0e 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -12,7 +12,7 @@ jobs:
   build_docker_images:
     # This workflow is only of value to PyBaMM and would always be skipped in forks
     # if: github.repository_owner == 'pybamm-team'
-    name: Build image ({{ matrix.build-args }})
+    name: Build image (${{ matrix.build-args }})
     runs-on: ubuntu-latest
     strategy:
       matrix:

From 6506dfc066a68b1e2cfcf0a920b24f016a483c4b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 04:13:30 +0530
Subject: [PATCH 169/615] Do not fail fast until `Jax` issue is resolved

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 1f50801a0e..28d958dadb 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -17,7 +17,7 @@ jobs:
     strategy:
       matrix:
         build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
-      fail-fast: true
+      fail-fast: false
 
     steps:
       - name: Checkout

From 96a74a4eeb9106979415fa0b9051afb7ca611f5c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 05:31:55 +0530
Subject: [PATCH 170/615] Clean up push and remove PR builds

---
 .github/workflows/docker.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 28d958dadb..82bc0006fe 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -5,8 +5,6 @@ on:
   push:
     branches:
     - develop
-  # temporary
-  pull_request:
 
 jobs:
   build_docker_images:
@@ -17,7 +15,7 @@ jobs:
     strategy:
       matrix:
         build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
-      fail-fast: false
+      fail-fast: true
 
     steps:
       - name: Checkout
@@ -56,7 +54,7 @@ jobs:
           context: .
           file: scripts/Dockerfile
           tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
-          push: false # temporary
+          push: true
           build-args: ${{ matrix.build-args }}
           platforms: linux/amd64, linux/arm64
 

From 34b7288173744922d161bab9b23aa15e8938739b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 10 Oct 2023 18:52:50 +0530
Subject: [PATCH 171/615] Add note about ALL and JAX for now, fix arguments,
 prettify matrix job name

---
 .github/workflows/docker.yml | 42 ++++++++++++++++++++++++++++--------
 1 file changed, 33 insertions(+), 9 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 82bc0006fe..9c16f95705 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -10,11 +10,11 @@ jobs:
   build_docker_images:
     # This workflow is only of value to PyBaMM and would always be skipped in forks
     # if: github.repository_owner == 'pybamm-team'
-    name: Build image (${{ matrix.build-args }})
+    name: Image (${{ matrix.build-args }})
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        build-args: ["", "JAX=true", "ODES=true", "IDAKLU=true", "ALL=true"]
+        build-args: ["No solvers", "JAX", "ODES", "IDAKLU", "ALL"]
       fail-fast: true
 
     steps:
@@ -36,27 +36,51 @@ jobs:
       - name: Create tags for Docker images based on build-time arguments
         id: tags
         run: |
-          if [ "${{ matrix.build-args }}" = "" ]; then
+          if [ "${{ matrix.build-args }}" = "No solvers" ]; then
             echo "tag=latest" >> "$GITHUB_OUTPUT"
-          elif [ "${{ matrix.build-args }}" = "JAX=true" ]; then
+          elif [ "${{ matrix.build-args }}" = "JAX" ]; then
             echo "tag=jax" >> "$GITHUB_OUTPUT"
-          elif [ "${{ matrix.build-args }}" = "ODES=true" ]; then
+          elif [ "${{ matrix.build-args }}" = "ODES" ]; then
             echo "tag=odes" >> "$GITHUB_OUTPUT"
-          elif [ "${{ matrix.build-args }}" = "IDAKLU=true" ]; then
+          elif [ "${{ matrix.build-args }}" = "IDAKLU" ]; then
             echo "tag=idaklu" >> "$GITHUB_OUTPUT"
-          elif [ "${{ matrix.build-args }}" = "ALL=true" ]; then
+          elif [ "${{ matrix.build-args }}" = "ALL" ]; then
             echo "tag=all" >> "$GITHUB_OUTPUT"
           fi
 
-      - name: Build and push Docker image to Docker Hub
+      - name: Build and push Docker image to Docker Hub (no solvers)
+        if: matrix.build-args == 'No solvers'
         uses: docker/build-push-action@v5
         with:
           context: .
           file: scripts/Dockerfile
           tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
           push: true
-          build-args: ${{ matrix.build-args }}
           platforms: linux/amd64, linux/arm64
 
+      - name: Build and push Docker image to Docker Hub (with ODES and IDAKLU solvers)
+        if: matrix.build-args == 'ODES' || matrix.build-args == 'IDAKLU'
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: true
+          build-args: ${{ matrix.build-args }}=true
+          platforms: linux/amd64, linux/arm64
+
+      - name: Build and push Docker image to Docker Hub (with ALL and JAX solvers)
+        if: matrix.build-args == 'ALL' || matrix.build-args == 'JAX'
+        uses: docker/build-push-action@v5
+        with:
+          context: .
+          file: scripts/Dockerfile
+          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
+          push: true
+          build-args: ${{ matrix.build-args }}=true
+          # exclude arm64 for JAX and ALL builds for now, see
+          # https://github.com/google/jax/issues/13608
+          platforms: linux/amd64
+
       - name: List built image(s)
         run: docker images

From 35ac761862b44bd3c31b24544fad2ace42e79310 Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Tue, 10 Oct 2023 15:14:03 +0100
Subject: [PATCH 172/615] #3431 update sundials to 6.0.0 for windows wheels

---
 vcpkg-configuration.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 27c1b0bcb1..1fe14cdd44 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "2aaffb6bba7bc0b50cb74ddad636832d673851a1",
+      "baseline": "5a6a8c4daf1e898809a19e60ea6e6cece85fe08e",
       "packages": ["sundials"]
     },
     {

From 00d661167d422458c05f1b82a8174c0c04924e3e Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Tue, 10 Oct 2023 15:38:17 +0000
Subject: [PATCH 173/615] #3431 update windows wheels to sundials 6.5.0

---
 vcpkg-configuration.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 1fe14cdd44..1d728d704d 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "5a6a8c4daf1e898809a19e60ea6e6cece85fe08e",
+      "baseline": "7d19be6c0713589465e2eb67ef3f5ffbf9f5d5c1",
       "packages": ["sundials"]
     },
     {

From 4f5d8051c2acfa18112a95fc554377605844f1d0 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Tue, 10 Oct 2023 16:50:34 +0100
Subject: [PATCH 174/615] Fix typo .all-contributorsrc (#3424)

There's a typo in the .all-contributorsrc which causes the bot to fail. This hopefully fixes it.
---
 .all-contributorsrc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 226c0cfc07..d9f4ec5f7f 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -717,7 +717,7 @@
       "contributions": [
         "doc"
       ]
-    },
+    }
   ],
   "contributorsPerLine": 7,
   "projectName": "PyBaMM",

From 6f1531be842022802901d08c8c39aaadf3186c85 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 10 Oct 2023 15:51:19 +0000
Subject: [PATCH 175/615] docs: update README.md [skip ci]

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1354f3f81f..c7ca111873 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-65-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-66-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -269,6 +269,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://aitorres.com"><img src="https://avatars.githubusercontent.com/u/26191851?v=4?s=100" width="100px;" alt="Andrés Ignacio Torres"/><br /><sub><b>Andrés Ignacio Torres</b></sub></a><br /><a href="#infra-aitorres" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
     </tr>
   </tbody>
 </table>

From 19e8256587d9009ac2fa1f3d87dcf58ef8f2e710 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 10 Oct 2023 15:51:20 +0000
Subject: [PATCH 176/615] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index d9f4ec5f7f..5cbd6f5ebd 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -717,6 +717,16 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "RuiheLi",
+      "name": "RuiheLi",
+      "avatar_url": "https://avatars.githubusercontent.com/u/84007676?v=4",
+      "profile": "https://github.com/RuiheLi",
+      "contributions": [
+        "code",
+        "test"
+      ]
     }
   ],
   "contributorsPerLine": 7,

From e1585f79daf891ca420cd3ab5b2fec645166610b Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Tue, 10 Oct 2023 16:11:34 +0000
Subject: [PATCH 177/615] update windows wheels to sundials 6.5.0

---
 vcpkg-configuration.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 1d728d704d..8ab4e738fc 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "7d19be6c0713589465e2eb67ef3f5ffbf9f5d5c1",
+      "baseline": "af9f5e4bc730bf2361c47f809dcfb733e7951faa",
       "packages": ["sundials"]
     },
     {

From 34cb58877ba2c22df8a6d33e16739ae04bbe115e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 10 Oct 2023 22:05:11 +0530
Subject: [PATCH 178/615] Build wheels on the 1st and 15th of every month

---
 .github/workflows/publish_pypi.yml | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index a009828e6f..a957562a2b 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -3,6 +3,9 @@ name: Build and publish package to PyPI
 on:
   release:
     types: [published]
+  schedule:
+    # Run at 10 am UTC on day-of-month 1 and 15.
+    - cron: "0 10 1,15 * *"
   workflow_dispatch:
     inputs:
       target:
@@ -151,6 +154,7 @@ jobs:
           if-no-files-found: error
 
   publish_pypi:
+    if: github.event_name != 'schedule'
     name: Upload package to PyPI
     needs: [build_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
@@ -164,9 +168,7 @@ jobs:
           mv windows_wheels/* wheels/* sdist/* files/
 
       - name: Publish on PyPI
-        if: |
-          github.event.inputs.target == 'pypi' ||
-          (github.event_name == 'push' && github.ref == 'refs/heads/main')
+        if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           user: __token__

From ad703bf74fb460e4a733ddf059322ec61018ef02 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 10 Oct 2023 14:12:59 -0400
Subject: [PATCH 179/615] Removing outputs

---
 ...olution-data-and-processed-variables.ipynb | 1025 +++++++++--------
 1 file changed, 523 insertions(+), 502 deletions(-)

diff --git a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
index 87e5e44730..2849fca58c 100644
--- a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
+++ b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
@@ -1,536 +1,557 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# A look at solution data and processed variables"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "9ad1d544145646a3ae6c71a74f1dd41d",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
-        "import pybamm\n",
-        "import numpy as np\n",
-        "import os\n",
-        "import matplotlib.pyplot as plt\n",
-        "os.chdir(pybamm.__path__[0]+'/..')\n",
-        "\n",
-        "# load model\n",
-        "model = pybamm.lithium_ion.SPMe()\n",
-        "\n",
-        "# set up and solve simulation\n",
-        "simulation = pybamm.Simulation(model)\n",
-        "dt = 90\n",
-        "t_eval = np.arange(0, 3600, dt)  # time in seconds\n",
-        "solution = simulation.solve(t_eval)\n",
-        "\n",
-        "quick_plot = pybamm.QuickPlot(solution)\n",
-        "quick_plot.dynamic_plot();"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])"
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data.keys()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(20, 40)"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data['Negative particle surface concentration [mol.m-3]'].shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "(40,)"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.t.shape"
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A look at solution data and processed variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have run a simulation the first thing you want to do is have a look at the data. Most of the examples so far have made use of PyBaMM's handy QuickPlot function but there are other ways to access the data and this notebook will explore them. First off we will generate a standard SPMe model and use QuickPlot to view the default variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "tags": [
-          "outputPrepend"
-        ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9ad1d544145646a3ae6c71a74f1dd41d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n"
-          ]
-        }
-      ],
-      "source": [
-        "keys = list(model.variables.keys())\n",
-        "keys.sort()\n",
-        "print(keys)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you want to find a particular variable you can search the variables dictionary"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Time\n",
-            "Time [h]\n",
-            "Time [min]\n",
-            "Time [s]\n"
-          ]
-        }
-      ],
-      "source": [
-        "model.variables.search(\"time\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We'll use the time in hours"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.solvers.processed_variable.ProcessedVariable at 0x7f69966b6210>"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This created a new processed variable and stored it on the solution object"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution.data.keys()"
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3510.0, step=35.1), Output()), _dom_classes=…"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can see the data by simply accessing the entries attribute of the processed variable"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([0.   , 0.025, 0.05 , 0.075, 0.1  , 0.125, 0.15 , 0.175, 0.2  ,\n",
-              "       0.225, 0.25 , 0.275, 0.3  , 0.325, 0.35 , 0.375, 0.4  , 0.425,\n",
-              "       0.45 , 0.475, 0.5  , 0.525, 0.55 , 0.575, 0.6  , 0.625, 0.65 ,\n",
-              "       0.675, 0.7  , 0.725, 0.75 , 0.775, 0.8  , 0.825, 0.85 , 0.875,\n",
-              "       0.9  , 0.925, 0.95 , 0.975])"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]'].entries"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can also call the method with specified time(s) in SI units of seconds"
-      ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "# load model\n",
+    "model = pybamm.lithium_ion.SPMe()\n",
+    "\n",
+    "# set up and solve simulation\n",
+    "simulation = pybamm.Simulation(model)\n",
+    "dt = 90\n",
+    "t_eval = np.arange(0, 3600, dt)  # time in seconds\n",
+    "solution = simulation.solve(t_eval)\n",
+    "\n",
+    "quick_plot = pybamm.QuickPlot(solution)\n",
+    "quick_plot.dynamic_plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Behind the scenes the QuickPlot classed has created some processed variables which can interpolate the model variables for our solution and has also stored the results for the solution steps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])"
+     "data": {
+      "text/plain": [
+       "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]'])"
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([0.        , 0.16666667, 0.25      , 0.47222222, 0.83333333])"
-            ]
-          },
-          "execution_count": 11,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "solution['Time [h]'](time_in_seconds)"
+     "data": {
+      "text/plain": [
+       "(20, 40)"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data['Negative particle surface concentration [mol.m-3]'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If the variable has not already been processed it will be created behind the scenes"
+     "data": {
+      "text/plain": [
+       "(40,)"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.t.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": [
+     "outputPrepend"
+    ]
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "array([298.15, 298.15, 298.15, 298.15, 298.15])"
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "var = 'X-averaged negative electrode temperature [K]'\n",
-        "solution[var](time_in_seconds)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ambient temperature', 'Ambient temperature [K]', 'Average negative particle concentration', 'Average negative particle concentration [mol.m-3]', 'Average positive particle concentration', 'Average positive particle concentration [mol.m-3]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Change in measured open-circuit voltage', 'Change in measured open-circuit voltage [V]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner SEI concentration [mol.m-3]', 'Inner SEI interfacial current density', 'Inner SEI interfacial current density [A.m-2]', 'Inner SEI thickness', 'Inner SEI thickness [m]', 'Inner positive electrode SEI concentration [mol.m-3]', 'Inner positive electrode SEI interfacial current density', 'Inner positive electrode SEI interfacial current density [A.m-2]', 'Inner positive electrode SEI thickness', 'Inner positive electrode SEI thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local ECM resistance', 'Local ECM resistance [Ohm]', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to SEI [mol]', 'Loss of lithium to positive electrode SEI [mol]', 'Maximum negative particle concentration', 'Maximum negative particle concentration [mol.m-3]', 'Maximum negative particle surface concentration', 'Maximum negative particle surface concentration [mol.m-3]', 'Maximum positive particle concentration', 'Maximum positive particle concentration [mol.m-3]', 'Maximum positive particle surface concentration', 'Maximum positive particle surface concentration [mol.m-3]', 'Measured battery open-circuit voltage [V]', 'Measured open-circuit voltage', 'Measured open-circuit voltage [V]', 'Minimum negative particle concentration', 'Minimum negative particle concentration [mol.m-3]', 'Minimum negative particle surface concentration', 'Minimum negative particle surface concentration [mol.m-3]', 'Minimum positive particle concentration', 'Minimum positive particle concentration [mol.m-3]', 'Minimum positive particle surface concentration', 'Minimum positive particle surface concentration [mol.m-3]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active material volume fraction change', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode extent of lithiation', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open-circuit potential', 'Negative electrode open-circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open-circuit potential', 'Negative electrode oxygen open-circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'SEI film overpotential', 'SEI film overpotential [V]', 'SEI interfacial current density', 'SEI interfacial current density [A.m-2]', 'Negative electrode surface area to volume ratio', 'Negative electrode surface area to volume ratio [m-1]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle radius', 'Negative particle radius [m]', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative SEI concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer SEI concentration [mol.m-3]', 'Outer SEI interfacial current density', 'Outer SEI interfacial current density [A.m-2]', 'Outer SEI thickness', 'Outer SEI thickness [m]', 'Outer positive electrode SEI concentration [mol.m-3]', 'Outer positive electrode SEI interfacial current density', 'Outer positive electrode SEI interfacial current density [A.m-2]', 'Outer positive electrode SEI thickness', 'Outer positive electrode SEI thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active material volume fraction change', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode extent of lithiation', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open-circuit potential', 'Positive electrode open-circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open-circuit potential', 'Positive electrode oxygen open-circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode SEI film overpotential', 'Positive electrode SEI film overpotential [V]', 'Positive electrode SEI interfacial current density', 'Positive electrode SEI interfacial current density [A.m-2]', 'Positive electrode surface area to volume ratio', 'Positive electrode surface area to volume ratio [m-1]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle radius', 'Positive particle radius [m]', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive SEI concentration [mol.m-3]', 'Pressure', 'R-averaged negative particle concentration', 'R-averaged negative particle concentration [mol.m-3]', 'R-averaged positive particle concentration', 'R-averaged positive particle concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Voltage', 'Voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total concentration in electrolyte [mol]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total lithium in negative electrode [mol]', 'Total lithium in positive electrode [mol]', 'Total SEI thickness', 'Total SEI thickness [m]', 'Total positive electrode SEI thickness', 'Total positive electrode SEI thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged Ohmic heating', 'Volume-averaged Ohmic heating [W.m-3]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged irreversible electrochemical heating', 'Volume-averaged irreversible electrochemical heating[W.m-3]', 'Volume-averaged reversible heating', 'Volume-averaged reversible heating [W.m-3]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged Ohmic heating', 'X-averaged Ohmic heating [W.m-3]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open-circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner SEI concentration [mol.m-3]', 'X-averaged inner SEI interfacial current density', 'X-averaged inner SEI interfacial current density [A.m-2]', 'X-averaged inner SEI thickness', 'X-averaged inner SEI thickness [m]', 'X-averaged inner positive electrode SEI concentration [mol.m-3]', 'X-averaged inner positive electrode SEI interfacial current density', 'X-averaged inner positive electrode SEI interfacial current density [A.m-2]', 'X-averaged inner positive electrode SEI thickness', 'X-averaged inner positive electrode SEI thickness [m]', 'X-averaged irreversible electrochemical heating', 'X-averaged irreversible electrochemical heating [W.m-3]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode active material volume fraction change', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode extent of lithiation', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open-circuit potential', 'X-averaged negative electrode open-circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open-circuit potential', 'X-averaged negative electrode oxygen open-circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode resistance [Ohm.m2]', 'X-averaged SEI concentration [mol.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged SEI interfacial current density', 'X-averaged SEI interfacial current density [A.m-2]', 'X-averaged negative electrode surface area to volume ratio', 'X-averaged negative electrode surface area to volume ratio [m-1]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open-circuit voltage', 'X-averaged open-circuit voltage [V]', 'X-averaged outer SEI concentration [mol.m-3]', 'X-averaged outer SEI interfacial current density', 'X-averaged outer SEI interfacial current density [A.m-2]', 'X-averaged outer SEI thickness', 'X-averaged outer SEI thickness [m]', 'X-averaged outer positive electrode SEI concentration [mol.m-3]', 'X-averaged outer positive electrode SEI interfacial current density', 'X-averaged outer positive electrode SEI interfacial current density [A.m-2]', 'X-averaged outer positive electrode SEI thickness', 'X-averaged outer positive electrode SEI thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode active material volume fraction change', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode extent of lithiation', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open-circuit potential', 'X-averaged positive electrode open-circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open-circuit potential', 'X-averaged positive electrode oxygen open-circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode resistance [Ohm.m2]', 'X-averaged positive electrode SEI concentration [mol.m-3]', 'X-averaged positive electrode SEI film overpotential', 'X-averaged positive electrode SEI film overpotential [V]', 'X-averaged positive electrode SEI interfacial current density', 'X-averaged positive electrode SEI interfacial current density [A.m-2]', 'X-averaged positive electrode surface area to volume ratio', 'X-averaged positive electrode surface area to volume ratio [m-1]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged reversible heating', 'X-averaged reversible heating [W.m-3]', 'X-averaged SEI film overpotential', 'X-averaged SEI film overpotential [V]', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total SEI thickness', 'X-averaged total SEI thickness [m]', 'X-averaged total positive electrode SEI thickness', 'X-averaged total positive electrode SEI thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n"
+     ]
+    }
+   ],
+   "source": [
+    "keys = list(model.variables.keys())\n",
+    "keys.sort()\n",
+    "print(keys)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to find a particular variable you can search the variables dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this example the simulation was isothermal, so the temperature remains unchanged."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time\n",
+      "Time [h]\n",
+      "Time [min]\n",
+      "Time [s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.variables.search(\"time\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll use the time in hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Saving the solution\n",
-        "\n",
-        "The solution can be saved in a number of ways:"
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.processed_variable.ProcessedVariable at 0x7f69966b6210>"
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This created a new processed variable and stored it on the solution object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# to a pickle file (default)\n",
-        "solution.save_data(\n",
-        "    \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n",
-        ")\n",
-        "# to a matlab file\n",
-        "# need to give variable names without space\n",
-        "solution.save_data(\n",
-        "    \"outputs.mat\", \n",
-        "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n",
-        "    to_format=\"matlab\",\n",
-        "    short_names={\n",
-        "        \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
-        "    }\n",
-        ")\n",
-        "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n",
-        "solution.save_data(\n",
-        "    \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n",
-        ")"
+     "data": {
+      "text/plain": [
+       "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Voltage [V]', 'Time [h]'])"
       ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution.data.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the data by simply accessing the entries attribute of the processed variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Stepping the solver\n",
-        "\n",
-        "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step."
+     "data": {
+      "text/plain": [
+       "array([0.   , 0.025, 0.05 , 0.075, 0.1  , 0.125, 0.15 , 0.175, 0.2  ,\n",
+       "       0.225, 0.25 , 0.275, 0.3  , 0.325, 0.35 , 0.375, 0.4  , 0.425,\n",
+       "       0.45 , 0.475, 0.5  , 0.525, 0.55 , 0.575, 0.6  , 0.625, 0.65 ,\n",
+       "       0.675, 0.7  , 0.725, 0.75 , 0.775, 0.8  , 0.825, 0.85 , 0.875,\n",
+       "       0.9  , 0.925, 0.95 , 0.975])"
       ]
-    },
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]'].entries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also call the method with specified time(s) in SI units of seconds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Time 0\n",
-            "[3.77047806 3.71250693]\n",
-            "Time 360\n",
-            "[3.77047806 3.71250693 3.68215218]\n",
-            "Time 720\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574]\n",
-            "Time 1080\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n",
-            "Time 1440\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n",
-            "Time 1800\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451]\n",
-            "Time 2160\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334]\n",
-            "Time 2520\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055]\n",
-            "Time 2880\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055 3.55158694]\n",
-            "Time 3240\n",
-            "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
-            " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n"
-          ]
-        }
-      ],
-      "source": [
-        "dt = 360\n",
-        "time = 0\n",
-        "end_time = solution[\"Time [s]\"].entries[-1]\n",
-        "step_simulation = pybamm.Simulation(model)\n",
-        "while time < end_time:\n",
-        "    step_solution = step_simulation.step(dt)\n",
-        "    print('Time', time)\n",
-        "    print(step_solution[\"Voltage [V]\"].entries)\n",
-        "    time += dt"
+     "data": {
+      "text/plain": [
+       "array([0.        , 0.16666667, 0.25      , 0.47222222, 0.83333333])"
       ]
-    },
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution['Time [h]'](time_in_seconds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the variable has not already been processed it will be created behind the scenes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can plot the voltages and see that the solutions are the same"
+     "data": {
+      "text/plain": [
+       "array([298.15, 298.15, 298.15, 298.15, 298.15])"
       ]
-    },
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var = 'X-averaged negative electrode temperature [K]'\n",
+    "solution[var](time_in_seconds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example the simulation was isothermal, so the temperature remains unchanged."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving the solution\n",
+    "\n",
+    "The solution can be saved in a number of ways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to a pickle file (default)\n",
+    "solution.save_data(\n",
+    "    \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n",
+    ")\n",
+    "# to a matlab file\n",
+    "# need to give variable names without space\n",
+    "solution.save_data(\n",
+    "    \"outputs.mat\", \n",
+    "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n",
+    "    to_format=\"matlab\",\n",
+    "    short_names={\n",
+    "        \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
+    "    }\n",
+    ")\n",
+    "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n",
+    "solution.save_data(\n",
+    "    \"outputs.csv\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\"], to_format=\"csv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stepping the solver\n",
+    "\n",
+    "The previous solution was created in one go with the solve method, but it is also possible to step the solution and look at the results as we go. In doing so, the results are automatically updated at each step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7f69964178d0>"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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+vR9zzDH+3HPPHRR7ixYtfM+ePe7uvn37dnd3v+WWW/y+++5zd/d///vf3qlTJ3d3f+qpp/zmm292d/f+/fv7G2+84e7ukyZN8muvvdbd3Xv37u0rVqxwd/e5c+d6r169io41duxYf/jhhyP9+Q5bpM8dsMDLyKuqoZehcWO47jp4911Is8jT3Lmmv5NIKmG6xMLRFsePH09KSgqDBg1iwoQJRftzcnLIzMxkzpw5PPzww0Xbox1tsXPnzixfvjzizDnFR1vs2LEjr732Gj//+c+ZPXs2xxxzzEHl586dWzSyYGZmJhMnTuSzYpMWFI4pM2TIEN55552i7QMHDiQpKYl27drRpk0bli9fzvTp03n66afJzMzkrLPOYtu2baxcuZJZs2YxZMgQkpOTadmyZYmBtYrLyMggJyeHv/zlL0V9vd96662icdx79+7Ntm3bDro+MGjQICZPngxQNOzwrl27ePvttxkwYACZmZlcf/31JZqwCkdMjKdye7ObWQNgFlC/oPzz7v7LUmUeAXoVrDYCmrt709iGGj+WmkqkWTQ+81SG9Qj926+6KtypKkIZn5cjnS6xOoy2eMopp7Bo0SJefvllfvGLX3D++edz7733HvS+hxpZsPhs9mU9L1x3dx577DEuuuiiEvtefvnlQ8Zc6KWXXmLWrFm8+OKLjBs3jmXLlkX1uv79+3PPPffw5ZdfsnDhQnr37s3u3btp2rQpixcvjviaWI2YeCSiqaHvBXq7eycgE+hrZt2KF3D32909090zgceAF2IdaFxFmP4uv2EjFg8YxxdfhDtTTzwxzL8xbRp8912c4pTqIcLnhUaNwvbDVF1GW9ywYQONGjVi6NChjBo1ikWLFgElR08sb2TBwprv5MmTS4wJ/txzz5Gfn8+nn37KqlWrOPXUU7nooov4wx/+UDTBxYoVK9i9ezfnnXcekydPJi8vj40bNzJjxoyD4s7Pz+fzzz+nV69ePPjgg+zcuZNdu3aRnZ1NbsH1jJkzZ9KsWbODLvg2btyYM888k9tuu41LLrmE5ORkjj76aE4++WSee+45IHxxFZ+UIlYjJh6JcmvoBW02uwpW6xYshxrRawgQ+epKTZWTEx5Hjw4/m1NTSRo3jstycrjUw8XUiRPDmDHPPht6y1xxRRhyoFevcNeq1CIRPi+MG3dg+2GoLqMtLlu2jFGjRpGUlETdunX5wx/+AMCIESPo27cvLVu2ZMaMGWWOLAiwfft2MjIyqF+/folafGpqKl27duWrr77iiSeeoEGDBvzkJz9hzZo1dO7cGXcnJSWFf/zjH1x++eW88cYbtG/fntTU1BJfDIXy8vIYOnQoO3fuxN0ZOXIkTZs2LZriLiMjg0aNGhWNiFjaoEGDGDBgADNnzizalpuby4033sjYsWPZt28fgwcPplOnTkCY2an0r6AqV1bjevEFSAYWExL7g4colwZsBJLL2D8CWAAsSE1NjeGlg+ph7173adPcf/Qj9yZNwnWwlBT3G25wnznTff/+eEcoh6tCF0UTzIYNG7xPnz4xea/iF0+LGzZsWMSLmjXFokWLfOjQoTF/30q5KOrueR6aU1oDXc2srN8Vgwlt7BHHNnT38e6e5e5ZKSkp0X3j1CD16kG/fuGmpc2b4YUXoHfvsN6zZxhuYMQIePlliGKyFZFqQaMtlm/r1q386le/incYFR8P3czuBb5x94cj7HsPuNnd3y7vfSpjPPTqavfu0Lb+97+HZP7116EXzQ9+AJddFoYeaNo03lHKodTm8dAlfmI+HrqZpZhZ04LnDYELgOURyn0fOBZ4p/S+2u6oo2DQIJg0CbZsgX/9KzSnzp4dHlNS4IILwuTDH39c7O5U0B2q1UhFKz8iR+JwPm/RNLm0AGaY2VLgXeA1d59mZvebWf9i5QYDk1yf+kOqXx/69g0Dg61fH4YX+OlPYd06uP12+P734XvfC1PpLbpTd6hWFw0aNGDbtm1K6lIl3J1t27bRoEGDCr0uoaagq+lWrw6jPv7rX/Dvf8MH36STToT+zGlpsGZNlcdXm+3bt49169ZFNdO8SCw0aNCA1q1bU7du3RLbD9XkooReTe3dC/UaJmER/j75GE88nk/v3mH4X02GLVJ7HCqhq4d0NVW/PmXecbghOZWbbw7PW7YMPWnOOw+6dw9NNkrwIrWTxnKpzsq447DVxHF88gmMHx8S+fTpoWm9fftwgfXSS+Hhh2HuXN21KlKbqIZenZVxx6Hl5PA9wsXT664L10tXroS33jqwTJ0aXtqgAXTtCmeddWBp1Uq1eJFEpDb0BLVpE8yZE5L7nDmwePGB2nqLFiGxFyb6rKwyBhbLzY3p7esicuR0UVTYuxeWLAnjzsyfHx6Lj5Tati107nxg6bYqlyZ3jIDis9A0ahTaeZTUReJGCV0i+vLLkNwXLTqwrF4d9q0mcpdJT03DPltTtYGKSBH1cpGIjjsu3OTUt++BbV9+GZpn0s4ve1KP7O7QqVNYMjKgQ4cwlIGIxJcSupRw3HGhGyRpkbtMbm+cSlIS/OUv8L//e2B7mzYhuXfseOCxbVtITq662EVqOyV0iWzcuNAXslQb+vFPjGNWzoGRCJYsgWXLwrJ0aehdUzC3Lg0ahMSemXlgychQbV6ksqgNXcp2GL1cvv0WPvooJPelS0PCf+892L497DeDdu1Ccj/jDOjWDc48MwxgdiTHFaktdFFU4so9DD723nuhfb5wKbwAm5wcEvw558DgvFy6PTWCpG/Vu0YkEiV0qZa+/DLczfr222GZN08DkomUR71cpFo67rgwucfFF4f1/fshud7aiDPW5n+2locegEsugdNP152uIpFoLBepNurUAUtNjbjvi7qp3H13uMianh7Gi589u9RkICK1nBK6VC9lDEjW8qlxrFsXmtIzM2HChDAwWdu2MGbMgfZ4kdpMCV2ql5yckLXT0kK7Slpa0QXRVq3CYGT//GcYq2bixFBbHzMm9IPv2ROeeirM2SpSG+miqNR4a9fCM8+EWvsnn4QK/pVXwo9/DD16qL1dEssRTRItUt2lpoZu6ytWhJElhw4NtfhevUKf93HjQrdJkUSnhC4Jwyz0ZX/ySdi4MdTaU1PhF78ILTcXXwzPPx9GnhRJRErokpAaNQo19TfeCM0w99wThicYMCBM8HHjjWGmpxIzOuXmhkb5pKTwmJsbp+hFDo/a0KXWyMuD114LF06nTQvD1Bx9NPTrB7cen0u3/xuB6Q5VqeZ0p6hIKd9+C6+/Dv/4RxhQ7N2tukNVagbdKSpSSsOG8B//EZa8PEiqW/Ydqv9vLJx9dpiyr0mTqo9VJFrlJnQzawDMAuoXlH/e3X8ZodxA4D7Cf4sl7v7D2IYqUjmSkwlXTyOM//5F3VT+67/C86SkcKdqVha0bw+nnRYeTzop7BOJt2hq6HuB3u6+y8zqAm+Z2b/cfW5hATNrB9wNnOvu282seSXFK1I5yhj/veX4cWzvFwYOe+edMIjY1Knwf/9XohinnXZgadsWTj45LMcfH2U/eA0ZLDFQbkL30Mi+q2C1bsFS+sfpdcDj7r694DWbYxmkSKUrTJ4RkmpT4KKLwlJo69Yw7vtHH8GHH4bHmTPDTE7FNWkSOswUJviTTw41+tTU8JiSAkl/zS35ZfLZZ2G9eFwiUYjqoqiZJQMLgbaExP3zUvv/AawAzgWSgfvc/ZUI7zMCGAGQmpra5bMIP3FFarKvvw7jyqxeDatWHXheuBT/AQBQrx6syk+n1f6D/y/sPTGNbQvXcMIJmspPDohZLxczawr8HbjV3d8vtn0asA8YCLQmtLl3dPcdZb2XerlIbeMeavaffx5+BHz+eVge+HUSSRGuyOZjJJNPUhKceGLoP9+yZVhatDj4MSXlMNry1dRT48Ssl4u77zCzGUBf4P1iu9YB89x9H7DazFYA7YB3DzNmkYRjFpJuSgp07lxsx5TIF2T3pKTyv2Ng/foDy6efwltvwbZtB79/cjI0bx6S/wknhMfiz084Iexv3jyMRZ88SU09iSaaXi4pwL6CZN4QuAB4sFSxfwBDgKfMrBlwCrAqxrGKJKYyLsg2emQcN5aRV/fuhS++gA0bwjAHhY+bNoXtmzbB+++H5/v3H/z6pCRYw2hOyi/VBvTNN3x162heTs6hWTNKLA0axO6UpXJEU0NvAUwsaEdPAqa4+zQzux9Y4O5TgVeBC83sQyAPGOXuEeoQInKQQ1yQLUv9+uGep7S0Q791fn6YoPuLL2Dz5pJL67FrI76m8fa1DBly8PaGDUOvncKlWbOSz5s1C78+in8JlB7avgQ198Sc7hQVqa3S0yM29eS1TuPjV9ewdStFy7ZtYSn+vHDZvj18cUTSsGFo4mnRomR7/7lrcun+zAjq7NVQCxWlO0VF5GBlNPUkPzCO9u2jf5u8PNix40Dy37KFEl8GmzaF5qCPPw5dO7dvh9WMpg4HN/dsvX40j3+SQ9u2Yejjdu3g2GNjcbK1g2roIrVZHJo9vv0WGhyVhEXIPfkYdSy/xFyx3/senHXWgSUzMzQ51VYanEtEqpcymntIS+Pbj9awenUY9vjDD2H+fJg7N9TyIfTdP+OMkNx79YK+fWvXBVsldBGpXnJzIzb3lNWG7h5mnZo378CyYEGo7TdpApdeCgMHwoUXJn7tXQldRKqfI2zu2bcPZsyAyZPh738PbfPHHAOXXx6Se58+ULduJcYfJ0roIpLQvvsujG8/ZUoY437nznDz1B13wO23l9N9sobRJNEiktDq1Qtzxk6YEHrVTJ0K3buH+WRPPRWefrrsrpWJRAldRBJK/fph4pJ//hNmzQr93ocNgy5dwhyziTx3rBK6iCSs7OzQQ+bZZ0Mb+5/Oz2XPsBGhh437gfFrEiSpqw1dRGqFPXtgb4t0jtlRs+eO1Z2iIlLrNWgADXZGHr+GtWVsr2HU5CIitUdqasTNflLk7TWNErqI1B7jxh3Uh3E3jfhd83F8912cYoohJXQRqT1ycsLdqGlpYcaRtDRmDR3PHQtyGDiQGp/UldBFpHbJyQkXQPPzYc0afvBMDo89Fro5XnVVmDykplJCF5Fa75Zb4PHH4cUX4cora25SV0IXEQFuugmeeAJeegmuuCJ0c6xplNBFRApcf31oYn/55TAOTE2jhC4iUsx114Xa+h//CKtXxzuailFCFxEp5Z57IDkZxo6NdyQVo4QuIlJKq1Zwww0wcWKYOammUEIXEYngrrvCsLz33x/vSKKnhC4iEsGJJ4bujLm5sHx5vKOJjhK6iEgZRo2Chg3hvvviHUl0yk3oZtbAzOab2RIz+8DMxkQoM9zMtpjZ4oLlJ5UTrohI1UlJgdtuC1PbLVsW72jKF00NfS/Q2907AZlAXzPrFqHcZHfPLFj+FMsgRUTi5ac/hSZNakYtvdyE7sGugtW6BUt8ZsUQEalixx0XJpp+4QV47714R3NoUbWhm1mymS0GNgOvufu8CMWuNLOlZva8mZ1UxvuMMLMFZrZgy5Ythx+1iEgVuv12aNoUfvnLeEdyaFEldHfPc/dMoDXQ1cw6lCryIpDu7hnAa8DEMt5nvLtnuXtWSkrKEYQtIlJ1jjkG7rwzDN717rvxjqZsFerl4u47gBlA31Lbt7l74fhkfwK6xCQ6EZFqYuRIOP54uPfeeEdStmh6uaSYWdOC5w2BC4Dlpcq0KLbaH/gohjGKiMRdkybw85/DK6/A22/HO5rIoqmhtwBmmNlS4F1CG/o0M7vfzPoXlBlZ0KVxCTASGF454YqIxM9NN0Hz5tW3lm7u8emwkpWV5QsWLIjLsUVEDtcjj4ShdZcsgYyMqj++mS1096xI+3SnqIhIBVx1VXicOTOuYUSkhC4iUgEnnRTmmH7rrXhHcjAldBGRCureHWbPhji1WJdJCV1EpIKys+GLL2DVqnhHUpISuohIBXXvHh5nz45vHKUpoYuIVNBpp4UxXpTQRURquKSkUEuvbhdGldBFRA5D9+6wYgVs2hTvSA5QQhcROQzZ2eFxzpz4xlGcErqIyGHo3DlMT1ed2tGV0EVEDkO9enDWWUroIiIJITs7zGL09dfxjiRQQhcROUzdu0N+PsydG+9IAiV0EZHDdPbZoQtjdem+qIQuInKYmjSBzMzq046uhC4icgSys0OTy7598Y5ECV1E5IhkZ8O338KiRfGORAldROSIVKeBupTQRUSOwAknQLt21ePCqBK6iMgRKhyoKz8/vnEooYuIHKHsbNi2DT7+OL5xKKGLiByh6tKOroQuInKE2rYNbenxbkdXQhcROUJmodml2tfQzayBmc03syVm9oGZjTlE2SvNzM0sK7ZhiohUb927w5o1sG5d/GKIpoa+F+jt7p2ATKCvmXUrXcjMmgC3AfNiGqGISA1QOOFFPJtdyk3oHuwqWK1bsHiEor8CHgT2xC48EZGaISMDGjeOb7NLVG3oZpZsZouBzcBr7j6v1P7OwEnu/lI57zPCzBaY2YItW7YcbswiItVOnTpwzjnVvIYO4O557p4JtAa6mlmHwn1mlgT8FvhpFO8z3t2z3D0rJSXlMEMWEamesrNh2TLYsSM+x69QLxd33wHMAPoW29wE6ADMNLM1QDdgqi6Mikht0707uMdv4uhoermkmFnTgucNgQuA5YX73X2nuzdz93R3TwfmAv3dfUHlhCwiUj117Qp168av2SWaGnoLYIaZLQXeJbShTzOz+82sf+WGJyJSczRqBF26xO/CaJ3yCrj7UuCMCNvvLaN8zyMPS0SkZrrjDtgTp75+5SZ0ERGJ3oAB8Tu2bv0XEUkQSugiIglCCV1EJEEooYuIJAgldBGRBKGELiKSIJTQRUQShBK6iEiCUEIXEUkQSugiIglCCV1EJEEooYuIJAgldBGRBKGELiKSIJTQRUQShBK6iEiCUEIXEUkQSugiIglCCV1EJEEooYuIJAgldBGRBKGELiKSIMpN6GbWwMzmm9kSM/vAzMZEKHODmS0zs8Vm9paZta+ccEVEpCzR1ND3Ar3dvROQCfQ1s26lyjzr7h3dPRN4CPhtTKMUEZFy1SmvgLs7sKtgtW7B4qXKfFVs9ajS+0VEpPKVm9ABzCwZWAi0BR5393kRytwM3AHUA3rHMkgRESlfVBdF3T2voDmlNdDVzDpEKPO4u38P+Dnwi0jvY2YjzGyBmS3YsmXLEYQtIiKlVaiXi7vvAGYAfQ9RbBJwWRmvH+/uWe6elZKSUpFDi4hIOaLp5ZJiZk0LnjcELgCWlyrTrthqP2BlDGMUEZEoRNOG3gKYWNCOngRMcfdpZnY/sMDdpwK3mFkfYB+wHRhWaRGLiEhE0fRyWQqcEWH7vcWe3xbjuEREpIJ0p6iISIJQQhcRSRBK6CIiCUIJXUQkQSihi4gkCCV0EZEEoYQuIpIglNBFRBKEErqISIJQQhcRSRBK6CIiCUIJXUQkQSihi4gkCCV0EZEEoYQuIpIglNBFRBKEErqISIJQQhcRSRBK6CIiCUIJXUQkQSihi4gkCCV0EZEEoYQuIpIglNBFRBJEuQndzBqY2XwzW2JmH5jZmAhl7jCzD81sqZn928zSKidcEREpSzQ19L1Ab3fvBGQCfc2sW6ky7wFZ7p4BPA88FNMoRUSkXOUmdA92FazWLVi8VJkZ7v5NwepcoHVMoxQRkXJF1YZuZslmthjYDLzm7vMOUfxa4F8xiE1ERCogqoTu7nnunkmoeXc1sw6RypnZUCAL+HUZ+0eY2QIzW7Bly5bDDFlERCKpUC8Xd98BzAD6lt5nZn2A0UB/d99bxuvHu3uWu2elpKQcRrgiIlKWaHq5pJhZ04LnDYELgOWlypwBPElI5psrIU4RESlHnSjKtAAmmlky4QtgirtPM7P7gQXuPpXQxNIYeM7MANa6e//KClpERA5WbkJ396XAGRG231vseZ8YxyUiIhWkO0VFRBKEErqISIJQQhcRqSq5uZCeDklJ4TE3N6ZvH81FUREROVK5uTBiBHxTcFP9Z5+FdYCcnJgcQjV0EZGqMHr0gWRe6JtvwvYYUUIXEakKa9dWbPthUEIXEakKqakV234YlNBFRKrCuHHQqFHJbY0ahe0xooQuIlIVcnJg/HhISwOz8Dh+fMwuiIJ6uYiIVJ2cnJgm8NJUQxcRSRBK6CIiCUIJXUQkQSihi4gkCCV0EZEEYe4enwObbQE+O8yXNwO2xjCcmkDnXDvonGuHIznnNHePOIdn3BL6kTCzBe6eFe84qpLOuXbQOdcOlXXOanIREUkQSugiIgmipib08fEOIA50zrWDzrl2qJRzrpFt6CIicrCaWkMXEZFSqnVCN7O+ZvaxmX1iZndF2F/fzCYX7J9nZulxCDOmojjnO8zsQzNbamb/NrO0eMQZS+Wdc7FyV5qZm1mN7xERzTmb2cCCv/UHZvZsVccYa1F8tlPNbIaZvVfw+b44HnHGipn92cw2m9n7Zew3M3u04N9jqZl1PuKDunu1XIBk4FOgDVAPWAK0L1XmJuCJgueDgcnxjrsKzrkX0Kjg+Y214ZwLyjUBZgFzgax4x10Ff+d2wHvAsQXrzeMddxWc83jgxoLn7YE18Y77CM/5PKAz8H4Z+y8G/gUY0A2Yd6THrM419K7AJ+6+yt2/AyYBl5YqcykwseD588D5ZmZVGGOslXvO7j7D3QsnJpwLtK7iGGMtmr8zwK+AB4E9VRlcJYnmnK8DHnf37QDuvrmKY4y1aM7ZgaMLnh8DbKjC+GLO3WcBXx6iyKXA0x7MBZqaWYsjOWZ1TuitgM+Lra8r2BaxjLvvB3YCx1dJdJUjmnMu7lrCN3xNVu45F/wUPcndX6rKwCpRNH/nU4BTzGyOmc01s75VFl3liOac7wOGmtk64GXg1qoJLW4q+v+9XJrgooYys6FAFtAj3rFUJjNLAn4LDI9zKFWtDqHZpSfhV9gsM+vo7jviGVQlGwJMcPffmNnZwDNm1sHd8+MdWE1RnWvo64GTiq23LtgWsYyZ1SH8TNtWJdFVjmjOGTPrA4wG+rv73iqKrbKUd85NgA7ATDNbQ2hrnFrDL4xG83deB0x1933uvhpYQUjwNVU053wtMAXA3d8BGhDGPElUUf1/r4jqnNDfBdqZ2clmVo9w0XNqqTJTgWEFz68C3vCCqw01VLnnbGZnAE8SknlNb1eFcs7Z3Xe6ezN3T3f3dMJ1g/7uviA+4cZENJ/tfxBq55hZM0ITzKoqjDHWojnntcD5AGZ2GiGhb6nSKKvWVODqgt4u3YCd7r7xiN4x3leCy7lKfDGhZvIpMLpg2/2E/9AQ/uDPAZ8A84E28Y65Cs75dWATsLhgmRrvmCv7nEuVnUkN7+US5d/ZCE1NHwLLgMHxjrkKzrk9MIfQA2YxcGG8Yz7C8/0rsBHYR/jFdS1wA3BDsb/x4wX/Hsti8bnWnaIiIgmiOje5iIhIBSihi4gkCCV0EZEEoYQuIpIglNBFRBKEErqISIJQQhcRSRBK6CIiCeL/AxAdFsZB2aDwAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "voltage = solution[\"Voltage [V]\"].entries\n",
-        "step_voltage = step_solution[\"Voltage [V]\"].entries\n",
-        "plt.figure()\n",
-        "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n",
-        "plt.plot(\n",
-        "    step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n",
-        ")\n",
-        "plt.legend()"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time 0\n",
+      "[3.77047806 3.71250693]\n",
+      "Time 360\n",
+      "[3.77047806 3.71250693 3.68215218]\n",
+      "Time 720\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574]\n",
+      "Time 1080\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942]\n",
+      "Time 1440\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857]\n",
+      "Time 1800\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451]\n",
+      "Time 2160\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334]\n",
+      "Time 2520\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055]\n",
+      "Time 2880\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055 3.55158694]\n",
+      "Time 3240\n",
+      "[3.77047806 3.71250693 3.68215218 3.66125574 3.64330942 3.61166857\n",
+      " 3.59709451 3.58821334 3.58056055 3.55158694 3.16842636]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt = 360\n",
+    "time = 0\n",
+    "end_time = solution[\"Time [s]\"].entries[-1]\n",
+    "step_simulation = pybamm.Simulation(model)\n",
+    "while time < end_time:\n",
+    "    step_solution = step_simulation.step(dt)\n",
+    "    print('Time', time)\n",
+    "    print(step_solution[\"Voltage [V]\"].entries)\n",
+    "    time += dt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the voltages and see that the solutions are the same"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f69964178d0>"
       ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.0"
+   ],
+   "source": [
+    "voltage = solution[\"Voltage [V]\"].entries\n",
+    "step_voltage = step_solution[\"Voltage [V]\"].entries\n",
+    "plt.figure()\n",
+    "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n",
+    "plt.plot(\n",
+    "    step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n",
+    ")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "As a final step, we will clean up the output files created by this notebook:"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "os.remove(\"outputs.csv\")\n",
+    "os.remove(\"outputs.mat\")\n",
+    "os.remove(\"outputs.pickle\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }

From 5c868c658434cfb2e6d185634cc674b972c5990f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 11 Oct 2023 00:22:01 +0530
Subject: [PATCH 180/615] Open an issue if the build fails

---
 .github/release_reminder.md        |  1 +
 .github/wheel_failure.md           |  6 ++++++
 .github/workflows/publish_pypi.yml | 12 ++++++++++++
 3 files changed, 19 insertions(+)
 create mode 100644 .github/wheel_failure.md

diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 69696d03ec..94066e80c8 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,5 +1,6 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
+labels: priority:high
 ---
 Quarterly reminder to create a -
 
diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
new file mode 100644
index 0000000000..107b4dd6d6
--- /dev/null
+++ b/.github/wheel_failure.md
@@ -0,0 +1,6 @@
+---
+title: Fortnightly build for wheels failed
+labels: priority:high, bug
+---
+
+The build is failing with the following logs - {{ env.LOGS }}
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index a957562a2b..c82a03fed5 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -183,3 +183,15 @@ jobs:
           password: ${{ secrets.TESTPYPI_TOKEN }}
           packages-dir: files/
           repository-url: https://test.pypi.org/legacy/
+
+  open_failure_issue:
+    needs: [build_windows_wheels, buld_wheels, build_sdist]
+    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    runs-on: ubuntu-latest
+    steps:
+    - uses: JasonEtco/create-an-issue@v2
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      with:
+        filename: .github/wheel_failure.md

From d1bd29c9b9725191b17241755081c1204384b1b3 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 11 Oct 2023 00:23:31 +0530
Subject: [PATCH 181/615] Typo

---
 .github/workflows/publish_pypi.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index c82a03fed5..c48de9c4fd 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -185,7 +185,7 @@ jobs:
           repository-url: https://test.pypi.org/legacy/
 
   open_failure_issue:
-    needs: [build_windows_wheels, buld_wheels, build_sdist]
+    needs: [build_windows_wheels, build_wheels, build_sdist]
     if: ${{ always() && contains(needs.*.result, 'failure') }}
     runs-on: ubuntu-latest
     steps:

From 0e83ee5041bdb14aeafd9068d157d2216548c27f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 11 Oct 2023 00:57:29 +0530
Subject: [PATCH 182/615] checkout

---
 .github/workflows/publish_pypi.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index c48de9c4fd..25fbafc0af 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -186,9 +186,11 @@ jobs:
 
   open_failure_issue:
     needs: [build_windows_wheels, build_wheels, build_sdist]
+    name: Open an issue if build fails
     if: ${{ always() && contains(needs.*.result, 'failure') }}
     runs-on: ubuntu-latest
     steps:
+    - uses: actions/checkout@v4
     - uses: JasonEtco/create-an-issue@v2
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

From eeb08564e4303a5289ec17edf96ebe945f7c971b Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Wed, 11 Oct 2023 15:54:50 +0530
Subject: [PATCH 183/615] Updated noxfile.py

- Removed redundant code

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 noxfile.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 9d13656894..b9cc6366e2 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -121,7 +121,6 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
     if sys.platform == "linux" or sys.platform == "darwin":
         session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True)
 

From 3cdc937b6f02e9a117e9c8c729e89020ea8a645b Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Wed, 11 Oct 2023 15:55:39 +0530
Subject: [PATCH 184/615] Updated noxfile.py

- Added all the installation in a single line

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index b9cc6366e2..e338ecf7cf 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -122,7 +122,7 @@ def set_dev(session):
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     if sys.platform == "linux" or sys.platform == "darwin":
-        session.run(python, "-m", "pip", "install", ".[jax,odes]", external=True)
+        session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True)
 
 
 

From 94e3fdc3f05a4c53ef225533efdbdfd1d48fe7f5 Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Wed, 11 Oct 2023 15:56:25 +0530
Subject: [PATCH 185/615] Updated noxfile.py

- Added else block for efficient running of the code

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 noxfile.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index e338ecf7cf..262af5892a 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -123,7 +123,8 @@ def set_dev(session):
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     if sys.platform == "linux" or sys.platform == "darwin":
         session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True)
-
+    else:
+        session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
 
 
 @nox.session(name="tests")

From ce8e56b52e4598c5183ee2492b5744d16d9299a1 Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 11 Oct 2023 11:42:00 +0100
Subject: [PATCH 186/615] simplify expression of cooling terms in x-lumped
 thermal models

---
 .../notebooks/models/pouch-cell-model.ipynb   | 12 ++--
 pybamm/models/submodels/thermal/lumped.py     |  5 +-
 .../pouch_cell_1D_current_collectors.py       | 64 ++++++++-----------
 .../pouch_cell_2D_current_collectors.py       | 47 +++++++-------
 4 files changed, 60 insertions(+), 68 deletions(-)

diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index 8e84374fbe..a9431211af 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -49,7 +49,11 @@
                               "name": "stdout",
                               "output_type": "stream",
                               "text": [
-                                    "zsh:1: no matches found: pybamm[plot,cite]\n",
+                                    "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
+                                    "\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
                                     "Note: you may need to restart the kernel to use updated packages.\n"
                               ]
                         }
@@ -82,7 +86,7 @@
                               "name": "stderr",
                               "output_type": "stream",
                               "text": [
-                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:835: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
+                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
                                     "  options = BatteryModelOptions(extra_options)\n"
                               ]
                         }
@@ -619,7 +623,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": "iVBORw0KGgoAAAANSUhEUgAABHoAAAKSCAYAAACtCLygAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydeXwURdrHf9UzmZwkgXAkgQSi3IiCAREQFI0cIorigaILyiuuAoJ44InHLqJ4IV7oqqCryOoqqKyiiHKoERFE7ku5FAJqOHJOZqbr/aOnu6v6mCSQg4Tny2eY6bqe56mu7kw983QV45xzEARBEARBEARBEARBEHUepbYVIAiCIAiCIAiCIAiCIKoGcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1hBPa0fPXX3+hadOm2LVrV4XK33PPPRg/fnz1KkUQBEEQBFFPEb97LV26FIwxHD582LX8okWL0KVLF6iqWnNKEgRBEAQRkRPa0TN16lRceumlaNWqVYXK33nnnXjzzTfx66+/Vq9iBEEQBEEQ9ZDKfvcaOHAgoqKi8M4771SvYgRBEARBVBhvbSvgRnFxMV5//XV8/vnnFa7TuHFjDBgwAC+//DKefPLJatSOIAiCIAiifnEs370AYNSoUZg5cyauv/76atLMmVAohEAgUKMyCYIgCOJY8fl8UJSaibU5YR09n376KaKjo3H22WcD0P6YjxkzBl999RXy8vKQmZmJW2+9FRMmTJDqDRkyBPfffz85egiiEsyZMwetWrXCeeedV9uqVCsni50EQRDHgvW7l863336Le++9F9u2bUOXLl3w2muv4bTTTjPyhwwZgnHjxuGXX37BqaeeWu16cs6Rl5cX8ZEygiAIgjjRUBQFWVlZ8Pl81S7rhHX0rFixAtnZ2caxqqpo0aIF3n//faSkpOC7777DmDFjkJaWhquuusood9ZZZ+G3337Drl27Khx2TBAnK3PnzoXH4wGgfXF+/vnn0bFjR1xwwQW1rFnVcrLYSRAEcTxYv3vp3HXXXXjuueeQmpqK++67D0OGDMG2bdsQFRUFAMjMzESzZs2wYsWKGnH06E6epk2bIi4uDoyxapdJEARBEMeDqqrYt28f9u/fj8zMzGr/23XCOnp2796N9PR04zgqKgqPPPKIcZyVlYXc3Fy89957kqNHr7N7925y9BAnPdnZ2cjMzMT8+fMd86+++mq88MILmD17NmJjY3HrrbfWS+fH8dg5atQovPnmmwCATp06YcOGDcekw4wZM3D77bcbx3/88QcaN258TG0RBEFUB9bvXjoPPfQQLrzwQgDAm2++iRYtWmD+/Pm271+7d++udh1DoZDh5ElJSal2eQRBEARRVTRp0gT79u1DMBg0fiypLk7YxZhLSkoQExMjpb344ovIzs5GkyZNkJCQgFdffRV79uyRysTGxgLQnjMniJMZzjm2bNmCjh07Riyne5MZY0bUy4nKDTfcgJiYGIRCIdcygwYNQlxcHH777Tcp/XjsbNy4Mf7973/j8ccfBwBccskliIuLQ0FBgWudESNGwOfz4a+//gKgLVj673//G5dddlmlZBMEQdQUTt+9AKBnz57G50aNGqFdu3bYvHmzVCY2NrZGvnvpa/LExcVVuyyCIAiCqEr0R7YizWWqihPW0dO4cWMcOnTIOJ43bx7uvPNOjB49Gl988QXWrl2LG264AWVlZVK9/Px8AJq3jCBOZnbt2oXi4uKIjp7//Oc/aNq0KSZOnIipU6fizz//xJIlS2pQy8rRoUMH+P1+7Ny50zF/xYoVWLRoEW677Ta0aNHCSD9eO+Pj43Hdddfh4osvBqA5cUpKSlwjpYqLi/HRRx9h4MCBxi/O7du3x3XXXYfTTz+9wnIJgiBqEut3r8qQn59fo9+96HEtgiAIoq5Rk3+7TlhHT9euXbFp0ybj+Ntvv0WvXr1w6623omvXrmjdujV++eUXW70NGzYgKioKnTp1qkl1CeKEQ79+Ijl6rr32WgwfPhyAduO57bbbTuhHt3RbtmzZ4ph/7733olGjRrjnnnuk9Kq285JLLkGDBg0wd+5cx/yPPvoIRUVFGDFixDHLIAiCqGms3710vv/+e+PzoUOHsG3bNnTo0MFIKy0txS+//IKuXbvWiJ4EQRAEQUTmhHX0DBgwABs3bjR+WWrTpg1+/PFHfP7559i2bRsefPBBrFq1ylZvxYoV6NOnj/EIF0GcbMyfPx/Z2dnGI0J9+vTBiBEjcOTIEdc6o0aNqhM7UemOHusjAwDwv//9z9gZJjk52bF+VdkZGxuLyy+/HEuWLMHBgwdt+XPnzkWDBg1wySWXHLcsgiCImsL63Uvn0UcfxZIlS7BhwwaMGjUKjRs3xtChQ43877//HtHR0dIjXic6oVAIS5cuxbvvvoulS5fWSBg9oC0kPX78eJxyyimIjo5GRkYGhgwZIkWZfvfdd7jooovQsGFDxMTEoHPnznjmmWdsOjLGwBiTHHEA4Pf7kZKSAsYYli5daqQvW7YM559/Pho1aoS4uDi0adMGI0eOlKLjQ6EQnn32WXTu3BkxMTFo2LAhBg0ahG+//VaSMWfOHNe/tUT9Yvny5RgyZAjS09PBGMOCBQtqRcaoUaOMMR8VFYVmzZrhwgsvxBtvvAFVVatcJ+LEoKLnvVWrVkY5/SVG9+v51vvlxIkTbXODo0eP4v7770f79u0RExOD1NRU5OTk4MMPPwTn3Ci3Y8cO3HDDDWjRogWio6ORlZWFa665Bj/++GP1dEYlOWEdPZ07d8aZZ56J9957DwBw88034/LLL8fVV1+NHj164K+//sKtt95qqzdv3jzcdNNNNa0uQZwQPPnkk7j88svRrl07tG/fHi1btsR1112HuXPn4pZbbqlt9Y6bVq1aITY21hbRwznHAw88gIyMDIwbN65GdBkxYgSCwaBxj9LJz8/H559/jssuu4wczgRB1Cms3710Hn/8cUyYMAHZ2dnIy8vDJ598Im0N++6772LEiBF1Zt2cDz/8EK1bt0a/fv1w7bXXol+/fmjdujU+/PDDapW7a9cuZGdn46uvvsKTTz6J9evXY9GiRejXrx/Gjh0LQPux5txzz0WLFi3w9ddfY8uWLZgwYQL++c9/Yvjw4dIkAwAyMjIwe/ZsKW3+/PlISEiQ0jZt2oSBAweiW7duWL58OdavX4/nn38ePp/PcCBxzjF8+HA8+uijmDBhAjZv3oylS5ciIyMD5513XrVM8IkTn6KiIpxxxhl48cUXK133vPPOw5w5c6pMxsCBA7F//37s2rULn332Gfr164cJEybg4osvRjAYrLR+RN2gouf90Ucfxf79+43XTz/9JLUTExODyZMnR5R1+PBh9OrVC2+99RbuvfderFmzBsuXL8fVV1+Nu+++2/jh/Mcff0R2dja2bduGV155BZs2bcL8+fPRvn173HHHHVXfCccCP4FZuHAh79ChAw+FQhUq/+mnn/IOHTrwQCBQzZoRxInHDz/8wBlj/M477+Scc962bVt+zTXXcM45v/DCC7nX6+VFRUW1qWKV0LVrV96zZ08p7d133+UA+OzZs6tc3siRI3nLli1t6cFgkKelpdl0mTVrFgfAP//8c8f2HnroIQ6A//HHH1WuK0EQxPFS2e9ef/zxB2/UqBH/9ddfq1kzjZKSEr5p0yZeUlJyTPU/+OADzhjjQ4YM4bm5ubygoIDn5ubyIUOGcMYY/+CDD6pYY5NBgwbx5s2b88LCQlveoUOHeGFhIU9JSeGXX365Lf/jjz/mAPi8efOMNAD8gQce4ImJiby4uNhIv/DCC/mDDz7IAfCvv/6ac875s88+y1u1ahVRv3nz5nEA/OOPP7blXX755TwlJcXQffbs2TwpKakiZhP1CAB8/vz5FS5/7rnnVvq7mZuMkSNH8ksvvdSWvmTJEg6A/+tf/6qUHKJuUNHz3rJlS/7ss8+6ttOyZUt+2223cZ/Px//3v/8Z6RMmTODnnnuucXzLLbfw+Ph4/vvvv9vaKCgo4IFAgKuqyjt16sSzs7Md/1YeOnTIVY/j/RtWGU7YiB4AGDx4MMaMGYPff/+9QuWLioowe/ZseL0n7K7xBFFtPPHEE2jSpAkeffRRlJSUYMeOHTjjjDMAAL1790YwGHR8zKimUFUVpaWlFXpxyy+WIh07dsTWrVuN42AwiClTpqBz587429/+VhOmAAA8Hg+GDx+O3Nxc7Nq1y0ifO3cumjVrdkKvdUQQBOFGZb977dq1Cy+99BKysrKqWbPjJxQK4Y477sDFF1+MBQsW4Oyzz0ZCQgLOPvtsLFiwABdffDHuvPPOanmMKz8/H4sWLcLYsWMRHx9vy09OTsYXX3yBv/76C3feeactf8iQIWjbti3effddKT07OxutWrXCBx98AADYs2cPli9fjuuvv14ql5qaiv3792P58uWuOs6dOxdt27bFkCFDbHl33HEH/vrrLyxevLhC9hLlwzlHUVFRjb8ifceqi5x//vk444wzqj0ir77iNC7KyspQVFQEv9/vWFZ8ZCoQCKCoqAilpaUVKltVHMt5z8rKwt///nfce++9jo/7qaqKefPmYcSIEUhPT7flJyQkwOv1Yu3atdi4cSPuuOMOKIrdnXKiPNZ6Qjt6AO25uYyMjAqVveKKK9CjR49q1oggTjyCwSAWLVqEQYMGITY2Fhs2bICqqsYOT0VFRQCAhg0b1pqOy5cvR2xsbIVeoiPHSocOHZCfn284rWbPno3t27dj2rRpjjfb6kRfbFlflPm3337DihUrMHz48BN+q3qCIAg3KvPdq1u3brj66qurWaOqYcWKFdi1axfuu+8+298LRVFw7733YufOnVixYkWVy96xYwc452jfvr1rmW3btgGAtNC1SPv27Y0yIjfeeCPeeOMNANraORdddJFtB7Qrr7wS11xzDc4991ykpaXhsssuwwsvvICjR49K8t1k6+lO8oljo7i4GAkJCTX+Ki4urm3Tq5z27dtLP7oRFUcfF3/++aeR9uSTTyIhIcG2HELTpk2RkJCAPXv2GGkvvvgiEhISMHr0aKlsq1atkJCQIK2rWZHH+CqD9bxPnjxZGuszZ8601XnggQewc+dOvPPOO7a8P//8E4cOHYp4nwaA7du3G/JPZCj0hSDqATt27EBRURE6d+4MAFi3bh0AGBE9a9euRcuWLZGUlFRrOrZv3962joAbaWlprnnigsxJSUn4xz/+gb59+2Lw4MFVomdlyM7ORvv27fHuu+/ivvvuw7vvvgvOOe22RRAEcQKyf/9+AMBpp53mmK+n6+WqkspEUVQ24uK6667DPffcg19//RVz5sxxnNx4PB7Mnj0b//znP/HVV19h5cqVeOyxx/DEE0/ghx9+MP7u1rdoD6Jmeeyxx/DYY48ZxyUlJfj+++8lh8GmTZuQmZlZpXI55zW6bTVxYmA973fddRdGjRplHDdu3NhWp0mTJrjzzjsxZcoU248UFb3/1ZX7JDl6CKIeoO+QooeD//zzz2jcuDHS09Px559/YtmyZfj73/9emyoiNTVVuvkeK+IW62vWrMHevXvx/vvvH3e7x8qIESPw4IMPYt26dZg7dy7atGmD7t2715o+BEEQhDO6M2PDhg04++yzbfkbNmyQylUlbdq0AWPMtpmASNu2bQFoP2T06tXLlr9582bjb6BISkoKLr74YowePRqlpaUYNGgQCgoKHGU0b94c119/Pa6//nr84x//QNu2bTFr1iw88sgjaNu2reOulrpsUUfi+ImLi0NhYWGtyK0u/v73v+Oqq64yjkeMGIFhw4bh8ssvN9KcHok5XjZv3lwnHh89EdHHoDgu7rrrLkycONG2HIoeTS9uNjJ27FjcdNNNtkh2PdJGLFsV8wAR63lv3LgxWrduXW69SZMm4aWXXsJLL70kpTdp0gTJyckR79OAeR/csmULunbtegya1wwn/KNbBEGUT/PmzQEAubm5ALSIHj2a5/bbb4eiKJg4cWJtqVeltG7dGj6fD6tWrcK0adNw+eWX1+ojm3r0zpQpU7B27VqK5iEIgjhB6dOnD1q1aoXHHnvMtj6DqqqYNm0asrKy0KdPnyqX3ahRIwwYMAAvvvii8Ti1yOHDh9G/f380atQITz/9tC3/448/xvbt23HNNdc4tn/jjTdi6dKl+Nvf/lbhR4cbNmyItLQ0Q5/hw4dj+/bt+OSTT2xln376aaSkpODCCy+sUNtE+TDGEB8fX+Ov6ox8adSoEVq3bm28YmNj0bRpUymtqtdS/eqrr7B+/XoMGzasSts9WXAaFz6fD/Hx8YiOjnYsKz76GhUVhfj4eMTExFSobFVxPOc9ISEBDz74IKZOnSo5xRVFwfDhw/HOO+9g3759tnqFhYUIBoPo0qULOnbsiKefftpxrZ/Dhw9XWqfqgBw9BFEPyMzMxHnnnYd///vfuPvuu/Hzzz/D7/djyJAhePfdd/Haa68hKysLqqritttuQ+PGjZGcnIzu3btLz+Q6sXPnTgwePBgpKSlIS0uTQsIZY3j++eeRmZmJ1NRUPPnkk9VtKjweD9q2bYs5c+bg0KFDUohwbZCVlYVevXrho48+AgBy9BAEQZygeDwePP3001i4cCGGDh2K3NxcFBQUIDc3F0OHDsXChQvx1FNPVdsaay+++CJCoRDOOussfPDBB9i+fTs2b96MmTNnomfPnoiPj8crr7yCjz76CGPGjMG6deuwa9cuvP766xg1ahSuuOIKKVpCZODAgfjjjz/w6KOPOua/8soruOWWW/DFF1/gl19+wcaNGzF58mRs3LjRWHx5+PDhuOyyyzBy5Ei8/vrr2LVrF9atW4ebb74ZH3/8MV577TVpIelQKIS1a9dKL7eIIKLuUlhYaJxfQPteuHbtWmmdlpqS4ff7kZeXh99//x1r1qzBY489hksvvRQXX3xxjW7IQdQs1XHex4wZg6SkJGOdTZ2pU6ciIyMDPXr0wFtvvYVNmzZh+/bteOONN9C1a1cUFhaCMYbZs2dj27Zt6NOnDz799FP8+uuvWLduHaZOnYpLL720Ksw+fqp9Xy+CIGqE/fv384svvpjHxMRwANzn8/HevXvzJUuWGGU+++wznp2dzY8cOcKDwSBfvXo1LygocG0zEAjwDh068IceeoiXlJTwI0eO8B9//NHIB8D79+/Pjxw5wjdv3sxTU1P5l19+Wa12cs75VVddxQHwMWPGVLsst+3VRV588UUOgJ911lnltkfbqxMEQRw7VbE17QcffMBbtWrFARivrKysat1aXWffvn187NixvGXLltzn8/HmzZvzSy65xNgGnXPOly9fzgcMGMATExO5z+fjnTp14k899RQPBoNSW4iw1fWhQ4ek7dXXrFnDr7vuOp6VlcWjo6N5SkoK79u3r20r9UAgwJ988kneqVMn7vP5eGJiIh8wYAD/5ptvpHKzZ8+W+k9/nXrqqcfdR8SJxddff+14rkeOHFlu3Ypur14RGSNHjjTSvV4vb9KkCc/JyeFvvPGG4xbXRP2goue9IturW/Pnzp3LAUjbq3PO+eHDh/k999zD27Rpw30+H2/WrBnPycnh8+fP56qqGuW2bt3K//a3v/H09HTu8/l4y5Yt+TXXXMPXrFnjqkdNbq/OOK8jqwkRBFEhFi5ciCFDhuCnn35Cly5dpLwlS5bg1ltvxb///W9079693PDdb7/9FsOHD8fu3bsdd7RijOHrr7/GeeedB0Bbyf7gwYN49dVXq8qcWmfUqFH46quvsGbNGni93mPeMrG0tBSFhYWYPn06nnzySfzxxx+Oi8QRBEEQ7pSWlmLnzp3IysqyPSpQGUKhEFasWIH9+/cjLS0Nffr0od0SCYIgiGqlqv6GVQRajJkg6hlbtmwBYwzt2rWz5V1wwQX4+9//jjFjxiAvLw/XXXcdpk2b5vrM7G+//YaWLVtG3LZc3II3IyMDP//88/EbcYKxd+9eNGnSBJ06dTIW66wss2bNwu23317FmhEEQRDHgsfjMX6kIAiCIIj6Bjl6CKKesWXLFmRmZkqr3IvcfvvtuP3227F3715cdNFFOO2001xXwc/IyMDu3bsjblu5d+9enHrqqcbn6titpDa5++67cd111wHQFm87VoYNGyZt6VubW90TBEEQBEEQBFF/IUcPQdQztmzZgvbt2zvm/fjjj+Cco2vXrmjQoAGioqKkUHXd4TNnzhwAwFlnnYUGDRrgH//4B+6++26UlZVh+/btyM7ONuo88cQTOPPMM7F//3688cYbeOutt6rNttqgY8eOjtvZVpaMjAwp+okgCIIgCIIgCKI6oF23CKKe8c0332DRokWOeUeOHMGNN96I5ORktGvXDr1798a1115r5P/222/o3bu3cez1erFw4UJ89913SEtLQ7t27Ywt3HX0qKC+ffvitttuQ05OTvUYRhAEQRAEQRAEQZQLLcZMEAQAIBgM4vTTT8fPP//sumaPFcYY9u7dixYtWlSzdgRBEARRswtZEgRBEERVQosxEwRR43i9XmzatKm21SAIgiCIcqHfKQmCIIi6Rk3+7aJHtwiCIAiCIIg6gR5xWlxcXMuaEARBEETlKCsrAwBpjdTqgiJ6CII4ZugXVYIgCKIm8Xg8SE5OxsGDBwEAcXFxrrtCEgRBEMSJgqqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwpajJkgCIIgCIKoc4RCIQQCgdpWgyAIgiAqhM/ng6LUzOo55OghCIIgCIIgCIIgCIKoJ9BizARBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD21xPLlyzFkyBCkp6eDMYYFCxZUSbtLly7FmWeeiejoaLRu3Rpz5sxxLfv444+DMYaJEydWiezymDZtGrp3744GDRqgadOmGDp0KLZu3Volbb///vto3749YmJi0LlzZ3z66aeuZf/+97+DMYYZM2ZUiexIvPzyyzj99NORmJiIxMRE9OzZE5999tlxt3ui2mulKsfYiWzzww8/DMaY9Grfvv1xt3si2/z777/juuuuQ0pKCmJjY9G5c2f8+OOPx93uiXoPa9Wqle0cM8YwduzY42r3RD7HBEEQBEEQRN2EHD21RFFREc444wy8+OKLVdbmzp07MXjwYPTr1w9r167FxIkT8X//93/4/PPPbWVXrVqFV155BaeffnqVyS+PZcuWYezYsfj++++xePFiBAIB9O/fH0VFRcfV7nfffYdrrrkGo0ePxk8//YShQ4di6NCh2LBhg63s/Pnz8f333yM9Pf24ZFaUFi1a4PHHH8fq1avx448/4vzzz8ell16KjRs3HnObJ7K9IlU5xuqCzZ06dcL+/fuN1zfffHNc7Z3INh86dAi9e/dGVFQUPvvsM2zatAlPP/00GjZseFztnsj3sFWrVknnd/HixQCAK6+88pjbPJHPMUEQBEEQBFGH4UStA4DPnz9fSistLeV33HEHT09P53Fxcfyss87iX3/9dcR27r77bt6pUycp7eqrr+YDBgyQ0goKCnibNm344sWL+bnnnssnTJhQBVZUnoMHD3IAfNmyZUbaoUOH+OjRo3njxo15gwYNeL9+/fjatWsjtnPVVVfxwYMHS2k9evTgN998s5T222+/8ebNm/MNGzbwli1b8meffbbKbKkMDRs25K+99hrnvP7aG2mM1UebH3roIX7GGWe45tc3mydPnszPOeeciGXq+z1swoQJ/NRTT+WqqnLO6985JgiCIAiCIOouFNFzgjJu3Djk5uZi3rx5WLduHa688koMHDgQ27dvd62Tm5uLnJwcKW3AgAHIzc2V0saOHYvBgwfbytY0R44cAQA0atTISLvyyitx8OBBfPbZZ1i9ejXOPPNMXHDBBcjPz3dtpyJ2q6qK66+/HnfddRc6depUxZZUjFAohHnz5qGoqAg9e/YEUH/tjTTG6qvN27dvR3p6Ok455RSMGDECe/bsMfLqm80ff/wxunXrhiuvvBJNmzZF165d8a9//UsqU5/vYWVlZXj77bdx4403gjEGoP6dY4IgCIIgCKLu4q1tBQg7e/bswezZs7Fnzx4jTP/OO+/EokWLMHv2bDz22GOO9fLy8tCsWTMprVmzZjh69ChKSkoQGxuLefPmYc2aNVi1alW12xEJVVUxceJE9O7dG6eddhoA4JtvvsEPP/yAgwcPIjo6GgDw1FNPYcGCBfjvf/+LMWPGOLblZndeXp5x/MQTT8Dr9eK2226rJovcWb9+PXr27InS0lIkJCRg/vz56NixY721N9IYq6829+jRA3PmzEG7du2wf/9+PPLII+jTpw82bNiAn3/+ud7Z/Ouvv+Lll1/GpEmTcN9992HVqlW47bbb4PP5MHLkyHp/D1uwYAEOHz6MUaNGAai/45ogCIIgCIKom5Cj5wRk/fr1CIVCaNu2rZTu9/uRkpICAEhISDDSr7vuOsyaNavcdvfu3YsJEyZg8eLFiImJqVqlK8nYsWOxYcMGaR2Tn3/+GYWFhYaNOiUlJfjll1+wZ88edOzY0Ui/7777cN9995Ura/Xq1XjuueewZs0a49f3mqRdu3ZYu3Ytjhw5gv/+978YOXIkli1bVi/tLW+M1UebAWDQoEHG59NPPx09evRAy5Yt8d5776G0tLTe2ayqKrp162Y4bLp27YoNGzZg1qxZGDlyZL2/h73++usYNGiQ4cSqr+OaIAiCIAiCqJuQo+cEpLCwEB6PB6tXr4bH45Hy9MnR2rVrjbTExEQAQGpqKg4cOCCVP3DgABITExEbG4vVq1fj4MGDOPPMM438UCiE5cuX44UXXoDf77fJqw7GjRuHhQsXYvny5WjRooWRXlhYiLS0NCxdutRWJzk5GcnJyZLd+iNfbnanpqYCAFasWIGDBw8iMzPTyA+FQrjjjjswY8YM7Nq1q+qMc8Dn86F169YAgOzsbKxatQrPPfccTjnllHpnb3ljbOrUqfXOZieSk5PRtm1b7NixA8nJyfXO5rS0NMlxAQAdOnTABx98AKB+38N2796NL7/8Eh9++KGRVl/vXQRBEARBEETdhBw9JyBdu3ZFKBTCwYMH0adPH8cyuuNApGfPnrateRcvXmysB3PBBRdg/fr1Uv4NN9yA9u3bY/LkydU+QeKcY/z48Zg/fz6WLl2KrKwsKf/MM89EXl4evF4vWrVq5diGm91LliyRtlgW7b7++usd18G4/vrrccMNNxyfUceAqqrw+/310t7yxtj+/fvrnc1OFBYW4pdffsH111+PDh061Dube/fuja1bt0pp27ZtQ8uWLQHU33sYAMyePRtNmzbF4MGDjbT6eC0TBEEQBEEQdZjaXg36ZKWgoID/9NNP/KeffuIA+DPPPMN/+uknvnv3bs455yNGjOCtWrXiH3zwAf/111/5ypUr+WOPPcYXLlzo2uavv/7K4+Li+F133cU3b97MX3zxRe7xePiiRYtc69TkjjW33HILT0pK4kuXLuX79+83XsXFxZxzzlVV5eeccw4/44wz+Oeff8537tzJv/32W37ffffxVatWubb77bffcq/Xy5966im+efNm/tBDD/GoqCi+fv161zo1tXPNPffcw5ctW8Z37tzJ161bx++55x7OGONffPFFvbTXCXGM1Veb77jjDr506VLDnpycHN64cWN+8ODBemnzDz/8wL1eL586dSrfvn07f+edd3hcXBx/++23jTL18R4WCoV4ZmYmnzx5spReH88xQRAEQRAEUXchR08t8fXXX3MAttfIkSM555yXlZXxKVOm8FatWvGoqCielpbGL7vsMr5u3bpy2+3SpQv3+Xz8lFNO4bNnz45YviYnSU72ApB0PHr0KB8/fjxPT0/nUVFRPCMjg48YMYLv2bMnYtvvvfceb9u2Lff5fLxTp078f//7X8TyNTVZuvHGG3nLli25z+fjTZo04RdccAH/4osvjPz6Zq8T1jFWH22++uqreVpaGvf5fLx58+b86quv5jt27DDy66PNn3zyCT/ttNN4dHQ0b9++PX/11Vel/Pp4D/v88885AL5161ZbXn08xwRBEARBEETdhHHOea2EEhEEQRAEQRDEMRIKhRAIBGpbDYIgCIKoED6fD4qi1IgsWqOHIAiCIAiCqDNwzpGXl4fDhw/XtioEQRAEUWEURUFWVhZ8Pl+1y6KIHoIgCIIgCKLOsH//fhw+fBhNmzZFXFwcGGO1rRJBEARBRERVVezbtw9RUVHIzMys9r9dFNFDEARBEARB1AlCoZDh5ElJSaltdQiCIAiiwjRp0gT79u1DMBhEVFRUtcqqmQfECIIgCIIgCOI40dfkiYuLq2VNCIIgCKJy6I9shUKhapdFjh6CIAiCIAiiTkGPaxEEQRB1jZr820WOHoIgCIIgCIIgCIIgiHoCOXrqOH6/Hw8//DD8fn9tq1JjnGw2n2z2AmTzyQLZTBDEycK0adPQvXt3NGjQAE2bNsXQoUOxdetWqUxpaSnGjh2LlJQUJCQkYNiwYThw4IBUZs+ePRg8eDDi4uLQtGlT3HXXXQgGgzVpClGP+f3333HdddchJSUFsbGx6Ny5M3788Ucjn3OOKVOmIC0tDbGxscjJycH27dulNvLz8zFixAgkJiYiOTkZo0ePRmFhYU2bQtQzli9fjiFDhiA9PR2MMSxYsMBWpqrG57p169CnTx/ExMQgIyMD06dPr07Tqg1y9NRx/H4/HnnkkZNq0nCy2Xyy2QuQzScLZDNBECcLy5Ytw9ixY/H9999j8eLFCAQC6N+/P4qKiowyt99+Oz755BO8//77WLZsGfbt24fLL7/cyA+FQhg8eDDKysrw3Xff4c0338ScOXMwZcqU2jCJqGccOnQIvXv3RlRUFD777DNs2rQJTz/9NBo2bGiUmT59OmbOnIlZs2Zh5cqViI+Px4ABA1BaWmqUGTFiBDZu3IjFixdj4cKFWL58OcaMGVMbJhH1iKKiIpxxxhl48cUXXctUxfg8evQo+vfvj5YtW2L16tV48skn8fDDD+PVV1+tVvuqBU7UaY4cOcIB8CNHjtS2KjXGyWbzyWYv52TzyQLZTBBEZSkpKeGbNm3iJSUlta3KcXHw4EEOgC9btoxzzvnhw4d5VFQUf//9940ymzdv5gB4bm4u55zzTz/9lCuKwvPy8owyL7/8Mk9MTOR+v99Rjt/v52PHjuWpqak8OjqaZ2Zm8scee6waLSPqKpMnT+bnnHOOa76qqjw1NZU/+eSTRtrhw4d5dHQ0f/fddznnnG/atIkD4KtWrTLKfPbZZ5wxxn///XfXdh966CGekZHBfT4fT0tL4+PHj68iq4j6CAA+f/58Ka2qxudLL73EGzZsKN1TJ0+ezNu1a+eqT35+Pr/22mt548aNeUxMDG/dujV/4403HMvW5N8w2l6dIAiCIAiCqLNwzlFcXFzjcuPi4o55Yc0jR44AABo1agQAWL16NQKBAHJycowy7du3R2ZmJnJzc3H22WcjNzcXnTt3RrNmzYwyAwYMwC233IKNGzeia9euNjkzZ87Exx9/jPfeew+ZmZnYu3cv9u7de0w6E8cG5xzBkrJake2N9VV4jH788ccYMGAArrzySixbtgzNmzfHrbfeiptuugkAsHPnTuTl5UljNCkpCT169EBubi6GDx+O3NxcJCcno1u3bkaZnJwcKIqClStX4rLLLrPJ/eCDD/Dss89i3rx56NSpE/Ly8vDzzz8fp+VEReGcA6Gav38CADzHfg+1UlXjMzc3F3379jV2xwK0++wTTzyBQ4cOSRFuOg8++CA2bdqEzz77DI0bN8aOHTtQUlJSJXYdD+ToOU5KS0tRVlY7N29ACy8T308GTjabTzZ7AbL5ZIFsPvHw+XyIiYmpbTUIolIUFxcjISG5xuUWFh5GfHx8peupqoqJEyeid+/eOO200wAAeXl58Pl8SE5Olso2a9YMeXl5RhnRyaPn63lO7NmzB23atME555wDxhhatmxZaX2J4yNYUoZXuk6oFdk3//QcouKiK1T2119/xcsvv4xJkybhvvvuw6pVq3DbbbfB5/Nh5MiRxhhzGoPiGG3atKmU7/V60ahRo4hjNDU1FTk5OYiKikJmZibOOuusyppKHCuhYqjvNS2/XDWgXHUQ8Fb+HupEVY3PvLw8ZGVl2drQ85wcPXv27EHXrl0NB1KrVq2O36AqgBw9x0FpaSmSYhuiDKXlF65mMjIyaluFGudks/lksxcgm08WyOYTh9TUVOzcuZOcPQRRjYwdOxYbNmzAN998U+2yRo0ahQsvvBDt2rXDwIEDcfHFF6N///7VLpeoe6iqim7duuGxxx4DAHTt2hUbNmzArFmzMHLkyGqTe+WVV2LGjBk45ZRTMHDgQFx00UUYMmQIvF6aphJ1g1tuuQXDhg3DmjVr0L9/fwwdOhS9evWqbbXI0XM8lJWVoQylOAcXwcs0bzlTGMAU8zMA6CFpCjM+M0Ux88TP4XcWbgPWNhzLW2RIeYo9DcyxPDfSYGnDLC+VEeWLeYI+jmlC+5yZn21tKfZ2reU5TJN0W7hgpq08E2VEyFPc2zBgsiz39i39YMlzLu/QLlzSmKUfymvDRaZjmsUmHdc0S1uR9AHjDmkO7YvlYUEqz+1tCXXtesjlmVsbsJYztWAR2mDGu5Msbqoolgu/K7Y2uGueAm6qK+TZyonlrWlCeUXQzS1PYRwKrGmqUNeUped5mD1Nv/14oOuqGm2a5YU04TMAeJhqyPIYddVwm+JnQaa1jfCxh6mGbkZbUI3boSlbFerIOnrE9oW2PJb+8Ah6Mase4IJuXEiD3H+6XgzwhM+WmcagGGnyu5anWNIUKGA4WqCiZfYulJWVkaOHqFPExcWhsPBwrcitLOPGjTMWAG3RooWRnpqairKyMhw+fFiK6jlw4ABSU1ONMj/88IPUnr4rl17GyplnnomdO3fis88+w5dffomrrroKOTk5+O9//1tp3Yljwxvrw80/PVdrsitKWloaOnbsKKV16NABH3zwAQBzjB04cABpaWlGmQMHDqBLly5GmYMHD0ptBINB5Ofnu47RjIwMbN26FV9++SUWL16MW2+9FU8++SSWLVuGqKioCutPHCOeOC2yppZkVxVVNT5TU1Ntux2Wd58dNGgQdu/ejU8//RSLFy/GBRdcgLFjx+Kpp56qEtuOFXL0VAFeRMHLtBsRY8x09IjOFi3BdPRIaRbHjVJZRw+T6sp5Do4el/LH7uhxz6t2R484Ia8hR0+5DhknR0xVOXrcykMuX22OHqc0HJtNx+ToscisVUcPq7ijx17OydHj7swp19FjXM5V5+gRHTmVdfQ45Tk7emSHhnIMjh6bc4aJDhO7o8fWRjmOHo9RjoXrMeOzqaN+zASnCxfSrA4hCDrq7Tul2R09nko6esw8JuQ5OXpoE06i7sIYO6ZHqGoSzjnGjx+P+fPnY+nSpbZHA7KzsxEVFYUlS5Zg2LBhAICtW7diz5496NmzJwCgZ8+emDp1Kg4ePGg8frB48WIkJibaJugiiYmJuPrqq3H11VfjiiuuwMCBA5Gfn2+sD0RUL4yxCj8+VZv07t0bW7duldK2bdtmPO6XlZWF1NRULFmyxJg4Hz16FCtXrsQtt9wCQBujhw8fxurVq5GdnQ0A+Oqrr6CqKnr06OEqOzY2FkOGDMGQIUMwduxYtG/fHuvXr8eZZ55ZDZYSIoyxKnt8qjapqvHZs2dP3H///QgEAoajcfHixWjXrp3jY1s6TZo0wciRIzFy5Ej06dMHd911Fzl6CIIgCIIgCKI+M3bsWMydOxcfffQRGjRoYKwHkZSUhNjYWCQlJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OhGeeeQZpaWno2rUrFEXB+++/j9TUVNtaQARx++23o1evXnjsscdw1VVX4YcffsCrr75qbCvNGMPEiRPxz3/+E23atEFWVhYefPBBpKenY+jQoQC0CKCBAwfipptuwqxZsxAIBDBu3DgMHz4c6enpjnLnzJmDUCiEHj16IC4uDm+//TZiY2NpPSlCorCwEDt27DCOd+7cibVr16JRo0bIzMyssvF57bXX4pFHHsHo0aMxefJkbNiwAc899xyeffZZV92mTJmC7OxsdOrUCX6/HwsXLkSHDh2qtT8qAjl6CIIgCIIgCKIaefnllwEA5513npQ+e/ZsjBo1CgDw7LPPQlEUDBs2DH6/HwMGDMBLL71klPV4PFi4cCFuueUW9OzZE/Hx8Rg5ciQeffRRV7kNGjTA9OnTsX37dng8HnTv3h2ffvopFIWi+AiZ7t27Y/78+bj33nvx6KOPIisrCzNmzMCIESOMMnfffTeKioowZswYHD58GOeccw4WLVokPe77zjvvYNy4cbjggguM8Txz5kxXucnJyXj88ccxadIkhEIhdO7cGZ988glSUlKq1V6ibvHjjz+iX79+xvGkSZMAACNHjsScOXMAVM34TEpKwhdffIGxY8ciOzsbjRs3xpQpUzBmzBhX3Xw+H+69917s2rULsbGx6NOnD+bNm1fFPVB5GOfctuwFUTGOHj2KpKQknIdL4VUirdEjPIZVkTV66NEtenTLqd1I5SGXp0e3ZD3o0S16dMvQ45gf3arYGj2eiGv0qFL78mNaFVujpzof3TpaEELDtr/iyJEjSExMBEGciJSWlmLnzp3IysqitaQIgiCIOkVN/g0jdz5BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BPI0UMQBEEQBEEQBEEQBFFPIEcPQRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQRA3x+OOPgzGGiRMnSumlpaUYO3YsUlJSkJCQgGHDhuHAgQNSmT179mDw4MGIi4tD06ZNcddddyEYDNag9kR9JRQK4cEHH0RWVhZiY2Nx6qmn4h//+Ac450YZzjmmTJmCtLQ0xMbGIicnB9u3b5fayc/Px4gRI5CYmIjk5GSMHj0ahYWFNW0OQZz0kKOHIAiCIAiCIGqAVatW4ZVXXsHpp59uy7v99tvxySef4P3338eyZcuwb98+XH755UZ+KBTC4MGDUVZWhu+++w5vvvkm5syZgylTptSkCUQ95YknnsDLL7+MF154AZs3b8YTTzyB6dOn4/nnnzfKTJ8+HTNnzsSsWbOwcuVKxMfHY8CAASgtLTXKjBgxAhs3bsTixYuxcOFCLF++HGPGjKkNkwjipIYcPQRBEARBEARRzRQWFmLEiBH417/+hYYNG0p5R44cweuvv45nnnkG559/PrKzszF79mx89913+P777wEAX3zxBTZt2oS3334bXbp0waBBg/CPf/wDL774IsrKyhxllpWVYdy4cUhLS0NMTAxatmyJadOmVbutRN3ju+++w6WXXorBgwejVatWuOKKK9C/f3/88MMPALRonhkzZuCBBx7ApZdeitNPPx1vvfUW9u3bhwULFgAANm/ejEWLFuG1115Djx49cM455+D555/HvHnzsG/fPke5nHM8/PDDyMzMRHR0NNLT03HbbbfVlNkEUW8hRw9BEARBEARRZ+Gco6TIX+Mv8ZGWijB27FgMHjwYOTk5trzVq1cjEAhIee3bt0dmZiZyc3MBALm5uejcuTOaNWtmlBkwYACOHj2KjRs3OsqcOXMmPv74Y7z33nvYunUr3nnnHbRq1apSehPHB+ccamlJrbwqM0Z79eqFJUuWYNu2bQCAn3/+Gd988w0GDRoEANi5cyfy8vKkMZqUlIQePXpIYzQ5ORndunUzyuTk5EBRFKxcudJR7gcffIBnn30Wr7zyCrZv344FCxagc+fOle5ngiBkvLWtAEEQBEEQBEEcK6XFZbi46cQal7vw4AzExkdXqOy8efOwZs0arFq1yjE/Ly8PPp8PycnJUnqzZs2Ql5dnlBGdPHq+nufEnj170KZNG5xzzjlgjKFly5YV0peoOri/FLuutTv3aoJWc78Ei4mtUNl77rkHR48eRfv27eHxeBAKhTB16lSMGDECgDnGnMagOEabNm0q5Xu9XjRq1CjiGE1NTUVOTg6ioqKQmZmJs846q1J2EgRhhxw9VUAQAYBrwVGMM+iBUtpnAAi/c2Z8Zlwx88TPAKAyMGZJY+K7YkkTPtvKK/Y0MPMzN/O4kQZLG2Z5qYwoX8xTYS8v6Giay2B2kUNbCmxtWMsbv1MwAIouE+7lzVMQOU9xb8OAybLc27f0gyXPubxDu3BJY5Z+KK8NF5mOaRabdFzTLG1F0geMO6Q5tC+WhwWpPLe3JdS16yGXZ25twFrO1IJFaIMZ706yuKmiWC78zm1tcNc8Dm6oqwp5iqWcfqyA29OE8oqgm1uewjgUWNNUoa4pS8/zMHta+JKFB7quqtGmWV5IEz4DgIephiyPUVcNtyl+FmRa2wgfe5hq6Ga0BdUIezVlq0IdWUeP2L7QlsfSHx5BL2bVA1zQjQtpkPtP14sBnvDZMtMYFCNNftfyYEnT2j1aoIIgiKpn7969mDBhAhYvXoyYmJgalT1q1ChceOGFaNeuHQYOHIiLL74Y/fv3r1EdiLrBe++9h3feeQdz585Fp06dsHbtWkycOBHp6ekYOXJktcm98sorMWPGDJxyyikYOHAgLrroIgwZMgReL01TCeJ4oCvoOPD5fEhNTcU3eZ+aM+1QrapEEARBEMdMamoqfD5fbatBEJUiJs6HhQdn1IrcirB69WocPHgQZ555ppEWCoWwfPlyvPDCC/D7/UhNTUVZWRkOHz4sRfUcOHAAqampALTrU18vRczX85w488wzsXPnTnz22Wf48ssvcdVVVyEnJwf//e9/K2MqcRyw6Bi0mvtlrcmuKHfddRfuueceDB8+HADQuXNn7N69G9OmTcPIkSONMXbgwAGkpaUZ9Q4cOIAuXboA0MbhwYMHpXaDwSDy8/Ndx2hGRga2bt2KL7/8EosXL8att96KJ598EsuWLUNUVFRlzCUIQoAcPcdBTEwMdu7c6boAHkEQBEHUJXw+X41HHBDE8cIYq/AjVLXBBRdcgPXr10tpN9xwA9q3b4/JkyfD4/EgOzsbUVFRWLJkCYYNGwYA2Lp1K/bs2YOePXsCAHr27ImpU6fi4MGDxuMxixcvRmJiIjp27OgqPzExEVdffTWuvvpqXHHFFRg4cCDy8/PRqFGjarKYEGGMVfjxqdqkuLgYiiIv3+rxeKCqWrRnVlYWUlNTsWTJEsOxc/ToUaxcuRK33HILAG2MHj58GKtXr0Z2djYA4KuvvoKqqujRo4er7NjYWAwZMgRDhgzB2LFj0b59e6xfv15yjhIEUTnI0XOcxMTE0JdigiAIgiAIwpEGDRrgtNNOk9Li4+ORkpJipCclJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OrmeeeQZpaWno2rUrFEXB+++/j9TUVNtaQAQxZMgQTJ06FZmZmejUqRN++uknPPPMM7jxxhsBaA6riRMn4p///CfatGmDrKwsPPjgg0hPT8fQoUMBAB06dMDAgQNx0003YdasWQgEAhg3bhyGDx+O9PR0R7lz5sxBKBRCjx49EBcXh7fffhuxsbG0nhRBHCfk6CEIgiAIgiCIWubZZ5+FoigYNmwY/H4/BgwYgJdeesnI93g8WLhwIW655Rb07NkT8fHxGDlyJB599FHXNhs0aIDp06dj+/bt8Hg86N69Oz799FNb5AZBPP/883jwwQdx66234uDBg0hPT8fNN9+MKVOmGGXuvvtuFBUVYcyYMTh8+DDOOeccLFq0SPrR+5133sG4ceNwwQUXGON55syZrnKTk5Px+OOPY9KkSQiFQujcuTM++eQTpKSkVKu9BFHfYbyye0MSBEEQBEEQRC1QWlqKnTt3IisriyKqCYIgiDpFTf4NI3c+QRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQRJ2CNo0lCIIg6ho1+beLHD0EQRAEQRBEnSAqKgoAUFxcXMuaEARBEETlKCsrAwB4PJ5ql+WtdgkEQRAEQRAEUQV4PB4kJyfj4MGDAIC4uDgwxmpZK4IgCIKIjKqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwrG6SFngiAIgiAIoo4RCoUQCARqWw2CIAiCqBA+nw+KUjOr55CjhyAIgiAIgiAIgiAIop5AizETBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BNOSEfP8uXLMWTIEKSnp4MxhgULFhh5gUAAkydPRufOnREfH4/09HT87W9/w759+6Q28vPzMWLECCQmJiI5ORmjR49GYWGhVGbdunXo06cPYmJikJGRgenTp9eEeQRBEARBEARBEARBENXCCenoKSoqwhlnnIEXX3zRlldcXIw1a9bgwQcfxJo1a/Dhhx9i69atuOSSS6RyI0aMwMaNG7F48WIsXLgQy5cvx5gxY4z8o0ePon///mjZsiVWr16NJ598Eg8//DBeffXVarePIAiCIAiCIAiCIAiiOmCcc17bSkSCMYb58+dj6NChrmVWrVqFs846C7t370ZmZiY2b96Mjh07YtWqVejWrRsAYNGiRbjooovw22+/IT09HS+//DLuv/9+5OXlwefzAQDuueceLFiwAFu2bKkJ0wiCIAiCIAiCIAiCIKqUEzKip7IcOXIEjDEkJycDAHJzc5GcnGw4eQAgJycHiqJg5cqVRpm+ffsaTh4AGDBgALZu3YpDhw7VqP4EQRAEQRAEQRAEQRBVgbe2FTheSktLMXnyZFxzzTVITEwEAOTl5aFp06ZSOa/Xi0aNGiEvL88ok5WVJZVp1qyZkdewYUObLL/fD7/fbxyrqor8/HykpKSAMValdhEEQRBEdcM5R0FBAdLT06Eo9eK3H6Keo6oq9u3bhwYNGtB3L4IgCKJOUZPfu+q0oycQCOCqq64C5xwvv/xytcubNm0aHnnkkWqXQxAEQRA1yd69e9GiRYvaVoMgymXfvn3IyMiobTUIgiAI4pipie9dddbRozt5du/eja+++sqI5gGA1NRUHDx4UCofDAaRn5+P1NRUo8yBAwekMvqxXsbKvffei0mTJhnHR44cQWZmJvbu3SvJJwiCIIi6wNGjR5GRkYEGDRrUtioEUSH0sUrfvQiCIIi6Rk1+76qTjh7dybN9+3Z8/fXXSElJkfJ79uyJw4cPY/Xq1cjOzgYAfPXVV1BVFT169DDK3H///QgEAoiKigIALF68GO3atXN8bAsAoqOjER0dbUtPTEykLxsEQRBEnYUegSHqCvpYpe9eBEEQRF2lJr53nZAP5BcWFmLt2rVYu3YtAGDnzp1Yu3Yt9uzZg0AggCuuuAI//vgj3nnnHYRCIeTl5SEvLw9lZWUAgA4dOmDgwIG46aab8MMPP+Dbb7/FuHHjMHz4cKSnpwMArr32Wvh8PowePRobN27Ef/7zHzz33HNSxA5BEARBEARRPykpLMDS+0fjm4kDsfT+0SgpLKhtlQiCIAiiSjght1dfunQp+vXrZ0sfOXIkHn74Ydsiyjpff/01zjvvPABAfn4+xo0bh08++QSKomDYsGGYOXMmEhISjPLr1q3D2LFjsWrVKjRu3Bjjx4/H5MmTK6zn0aNHkZSUhCNHjtCvSgRBEESdg/6OEXWNqhqzyyddgu7tV8CXUGaklRX6sGpLH/R95uOqUNWgpLAAK6dNhLfodwTjm6PHvTMQm0CPSxIEQZxs1OT3rhPS0VNXoC/IBEEQRF2G/o4RdY2qGLPLJ12CXtlLkL+3ETaXXYY2w27C9g/+hQ6++WiUkY/vVl9QZc6emnQoEQRBECc2Nfm9q06u0XOisXvLPxEfHwXOtS3TwAGVq+FjCMdaAg+/jGfzmPacHgPAFAYwQGFMS1OgpTMFigJA5QghCMZ8UAFwVQXAoKoqODR5WhqHyllYpgqucugePcY4oDBo/2DKYQBDWA60Y0VRwHkIQTAw5oXKObiq28jBodmkckBVOcAAzlVwNfwOLU8xbFEAcDAFUBTdfgUK0/qDMQWMcYRCAYQUHxi8Whth+6CG+xLQZIBD5VofcFUN68IBxsAZC/ejdg6YwrT+DdusKAwcHB5FAQMQ5CGEEAsW7kdV1ezjnCGkqmCcQwWggoOr4fOKEMAZQioHYx6Aca3PAK1/w3ZzCOeUwej/QDAEVYkDY1p/qqqmPwOg6n3IOTgYOFehqoBmNRBSGRgUMEXLZwoD4xxQwicTHJ5wPzAwMA8DwFCmcjAWG+5DDs5VhLjWt1pfayNF1ceyqgJM6/eAqmh2wOxLMLNPGWNAuN85AKYoYApQGgAUJUbrV64CYTtCuj3ha0IfXzwsX0vzaCJYePSwsGyuXRdcv26gjVcwDjAPSgMcXiVaGDuqJjYUPofhi1PvZxYe0yo4oBoXHsC0C0Ix7BQvEC1d5YCieOAPcEQpPnBVG/tc5eF2tfsBVABM62PjfgBADYXvB9y8H4ABgKJdpYrQvwAUpmjXPecIBYFo5g2fT9XsT6NvEb7vqOHrFoZcpobHpnQeFUO+bisL5zNFgRoKwRtQ4PF6jPMWPrHaPSl8rGqDVZOl93F4zClMAWeadZJMaHlgDEw7sYDCEAwEEc098ChKePzoYwaAGgpfH2EdwvcDcBUq51A0pUxbGAO4fj9iYdu0fubQrl9whlAwhFjmCeusX4faOOLhe542bsL3nPD9Amr4vIvnKnxPYtCHlfFJG2KcQVEUBEr9iGFarxh/QxCWrYbv5Vy/L4Wv1bB9ajBg3l8QvgY5g7RzJ9d00C4fBQqAZpnyGncEUd8pKSxA9/YrkL+3EZLHb0Df2HgwxYv0TjNRVvI48p9rh+7tvkFJYcFxR91IDqU82aHUK3sJlk+6pEqdPVwNAX98C16SBxabCjTpDaZ4qqx9giAIou5Ajp4qoODPuVCLtdm84WwxXuHJgSVP5SpKS7WJpjmp0tpj4IbTRZ8ZKOF3Bo6Y2LADSW9Pb5+bx7qDiYcnepwDQZWjtEzVpjZMa0Cfq+vymf5ZTGOANxqGA4TDSQ6MiZeuj8qBQJCjNMCN1aD0uTMc5ADmwlRM4fBEM8lGs0+ZYb/ucIKQ7w8A/iAzJlYQ5BkydVnhY6YA8DDA67XJRFie6XCx9D/nKCrzIqgyzeHCdMeOMW+XOlafiEEBEOWFqnjAuaaVGu5PcLE/mSBPn2wyHC31QOWmQQza5NnsX2aTzxQgFBUNlXnMcyfZKp9D8/xqxhwpUTRdjUYZxEOu97lwchkDyqJiwaGE7TDHpGSr5iWUrxeVo7BY0fKMNctEI+W+1a4QzfaANwacK+H2uClPOMGqIEvvBFXlKCkWZsfm4ISQCG6Ty6B6YrT+EfrSNEYXInzW7wchgJdoDgvJJvHcSf0KQD/fiDbbNE6kaaORyeU0HlDBSkPh+wEAbuljyWZZF6/qC6dwmy0A128vklzOARYIgZWpQteaFwnnppPCcHgJukWFvNpHONvFhLEkdDKYPwSUhaTbgHGRCjYyvUrYM+tRGaJCHtORZTl3XLhQWPheq/c7K/FD82KG2+bGJ3D9LHNzPGlOLQaPn0MJcbN96Pc26YYTVkO6+SJYVBAeSCycznSDDMnMMCHsSAMPOwcJ4uRh5bSJ6NOpDJvzLkP8Uzega5vPoYYYggEvQgEPYpMBXwM/jrzeBn8GGkJlMVA98WC+RChxyYhu2BTxjdMR0ygNLK4RWGxDsKgEIKoB4I0HvA0ATwxKiwpNh9KErTg3Ng4AqsWhBAB870cIrb4HrHiPdgyAx2XCk/04WMalx90+QRAEUbcgR08V0K61D4kNKreudTi24JgI8pDxmUcoZyUcq1BuHaf8AI+ssVubqkM9t7JiekgF/BHKWNuQ6nIGFR5beqQ6ABBQgVJEhfOYQ1nm2l6Z6oUKxdE2a5+Ln/0hL0p5tCVP/7U/Ul2G0lCUoadTGbMtSOVKQz741SihvDkFdpInHieEfNAn4pLfxEE/0YaioA8B1bzdGHNxUSaHzR4VgC/kg4joqzHOCbfrXRyIQTCkOOinOxDlcyza5Al5AS6fb8k+Yd4tJvlLo8FVS7tOWBrWnD0eu0525eUsFWClHoBb7j8R6oidq4SYkcas+WCWY7OepwSVlskAMBVQQoJTxSbT5TioIqrEni+145QGgIVUeAIubYvlrMdlQUSVhGxlmSRPcP4IdRVPKOywsRjleMFwU7ZSBk9J+PEOyYtukQUgHNKIsJcJ3Bsb9hTDfBccQTY9wo6qIHO60xJE/cVb9DsAoM2wm/D7a38HACgeDp8nAMSYN4ukJgVIgsPizEXh127t0Ol7FVcBT0iBJ0FFPC9Cwb9aIxiKQlkoDgFvCrzJLVHETkVKg1VY/cwodL97FhCdYkaMVhK+9yOEll+Lgt+TUbStDQJHYhCVVIr4tn+gQdG18PSdS84egiCIkwxy9NQS+m+tVdGOE85tM/NX53LLusiKUNgpSwEQKqeMW16k6XKk/jN+9RdaqMi8V6sru3R0x4PeJhOcPaYOzFUfLrThrCuDIpwVsx1Tf7G22J7Cwo/nIVJf6blmKYWrUJhqccrIn8z+Eif7ChSmQuWKGQmll+OiJF2eXaYx12W6Q0frU1NDa7+H8xw7V7Zaj3kx5sSca48pOvaJVl53rIR9RlKkhdWpI0qyOgtMZxO3RP64iLcWcXA6lduGeOAm0jowRdmWPN0Ga7SJk1yOCGZGkmnTXTiOdHFa29CDVkR5zPB5SDrYpk3i8HSSBZjnUg/LEy4jW9dwbgswY+LN0jp4bXbKdyApekpy8FiuTik0Mqwgd3HHixeqJNapIwiifhOMbw4A2P7Bv1CaMRrvvrsEPlaCGFaC6KgytGj2B9r3/gW/rW2OksJY+KKDiPJpL29UEJ6okPFSvCqYNwQlSgXzqlC82jXIFMCjaJ9jGvgR00B0qO4B8BOQoR1lt14E9cNWUEMMZaU+lJbEoCQQjwBrBJbQHA1adkLSqWdAaXgKWGwaENMETBF+OFFDOPr5GCiHE/HzF51wpNuF6Hbn5fhxzodI+mIxsgeuh7roZiSOvpge4yIIgjiJoMWYjwN9MaU/t2bVWkSPG85zpvKlug2GYCUiesQJsmqRWZHBxqFF9AQcyrtNS8S0oBDRI+ZFmtcBWkRPGbzgwtRQrCs6bKx1/aoHKjzO/QBAjAYSHSz+kAcB+Gx53FLHWg8ASkNaFJGbTlxwW4ltlgajEIBX94UYZd31NPOLglGAQ1m9vP6omWw3Q3HQhyA8RqCBOUaY5Exysr84aEb0SPlcPi/S+eEMxQEfQlxxbVeasYtlOFAW9CJStJSTM4iDwe/3godlRsTqVOBAKOiBq8fGZeBzlYH5FdiiawC4XrB6OyEzoscpMgZ6jlVuCPD4UWmZDAALAR4nz285FycLqvD64XgyGHc51usGVHiC7m07tQEASlkQXj8XLxLJFtkJI39WSsWIHm7TW4qeCtdlAHhpGTylQS3ReI5TLyvIEqN9wnm8uBiwypQeY+OCPFNuMOTH0rL/0mLMRJ3heBeyLCksAOZmoiA/AckTtsIXfqQKAMpKinH4uXZo0LAIGLHb9kgV5xyFRwpxcM8B/Lk3D4d/O4jC/QdRkn8EgcNHoBYVwBsohI8XoXWTnTgzZxM2Lm+NwsJ4xPgCiI8vRXyCH7HxpYhJKkFMSjHUMgWKr+LfCTkHykqj4C+JQYk/DvBGoVmT37BvfRpSrr4XMekdweIzgLh0BPxlWH9DP3QZsg6B3gsQ0/LCSveXE2qgDGWr/w1+eBdYciv4sq+HEuUrvyJBEMRJDi3GfBJQVRE9ViK3acajuDskKtAut+e5tcEqUMZJlrW8+NkpYMAqkwGuDqZIc0rts+rQpjyxN+1iUr5VD60197grzRZVakvOd44ggi1NliyncUljhBfJdp5j6+PDGi0T1kYPcBAmtpy79Y2gDVc1B4hNT3MCym12hfuW6Wv6mHJER43sfgtHOImLBNvQH5XR9JG/XsvRFLYACD3Tmh7uFGkdICdc85ixXo1N5YgD1qGCfcA412Ww22l1Qri0y93KuVycUkCPHoDiNABdZNr6Vex/vT3hGDDTmGCnm44cchvg4buAtM6RoI9RUb4iDTsNmZbJm1TfLGvGWnL5pQ8uyX6X822ENHEXWy0dIziJCOJkIjahAZZv6aMtkvxcO2z2D0Xry0Zjx/zX0SF6gbnrlsO6OYwxNEhugAbJDXDq6a0jyikpLEDZ3EyktspH8oRcyaFUfPQoCl/sCMUXxPurb0Pozz8QFzyAZN8RJMUVITGhGAnxfsQmlCI63g9vbACemAA8MUEwBkTHBhAdG0Ci8GhZeuf9wKbbwDdpl3coqKD4aBxST48FAPz27j04ZWQ0WMP2QHQTc0OQSlKyZCqUXc8iKjb8PG0+ENg4GWqr2xF7wf3H1CZBEARR9ZCjp5aorq/WkR1IdidP+XXMMkYrzDnPyRlknQNWBN1JY3d7OOvpNLfUHwlyUCniUySGQyNcytEJ4FDP6ZE4ey2Ls8D4pO/SZbXRHlFi7RV3Z43pBBGjdbQ3bccmcaIuR69Ye94e5SPNm3W/h0sXaAspmzspGbZy85EtY91Ya3SO4OAR1u0N1+M2vblw3sydjUTdhHPIrHmS0rJtsI8zs0VmOj9EH5DbYBXzxML6gtT27pcHhpDPFGY+XmSVWd7FIrQpfd+X5OgXMpP0sM0PnAei2f1cEB3pQnS5OJ2cNcztKSUnP0hFbrjh9pmhBzMHG2CseyO5I/U+stzvTEeWw83S6vySyun9rJ9XVfZcOS60bbWDCc4bF6+WkcVtKhLEyUDfZz42tj0/J+ENYMMbSG0DlBVEV9nW6uU6lDIP4bvVF+Bvrz7i2gbnHPt/+R1bvvsZB37eipK9exBTmockzyEkxRUiKaEEzdLzkdLmDxQfSIDiVeGNCcATG4DHq6JBo0I0aFQIAMjK3AL+9SBwAMEyDwqPxqPQn4JQwqlofEY/JLTuBZbcDsyX5KpPyZKpiMp7DIcONsKPy3vgt9+boEXzP9Ct7zo0jHkMJUtAzh6CIIgTBHp06zg4nke3uEPUSEVxe3SrvPbCm/Iax5V5eKysnF+m3WRzqCjnqQnHPP3RrfLq6PlSXc4QgubMcMq3tqnnawtAex3KWFfusabbF2O2y7Q7Szi0R4S0x8WsTh3rY1EWBwiAUjVKWy8njBg5xG31zHZLgh4EYD6CJZW1Oej0xZK1siXCAtAAM9d8dZAp2l4U8CHAHR6nM+adou7mZxUMJUF5gWxzlzBnp5cecVMY8EHlCqzn2njsStJdeASOA2XhxZgleZIwh/OpAv5AFBwfaSpnEHLOoAb1evKuck7ljSQV2qNb8FTsQhHfVUAJMimfRVoriJsvpVRzUFZEpugvYiFhMWaxzfJkhlREWdcMdrqgIPtmwAElqEIRb0KqpZ6op5geCMBX6jDAAWHgcqOuZKc/BCUk3DNV7n4uRdmlfnhKA/oAt8sU30WnDefgxSWAqprp1ke/4KQDRzBQiqWBD+jRLaLOUJVh7yWFBVg5bSK8Rb8jGN8cPe6dUSU7YInoDiVfQpmRVlYQjVVbz6kSh9J/xz+EIafPxB9lLfFbixnYu3ozCrZtRVLpLjSLP4gzem1DdLwf/kNx8CX44YkLRFxKrqwkCoUFCSgMNAGS26JptwsR26oHeGwmyv6dhaK/4vHc6/3R/m/not91Ofj67S+x5a1lmDD6C8SnFCF6VB49xkUQBOFCTT66RY6e4+BEXKMnsiPFedetigyAgMXR4zQXA2B7CkCFCqu2FZEXFBw9bvXcnCqao8d5wcFItgdVoAxRkuNBfvzLKcJGOw6oHoQsa/RY64nHhoMo5IGf+xzzxM9OOviFNXqsTiVhA2uLDgwlQW/Y0WM6TJwdWXrfmvoXhbyGI8M851YHE4PVQVQU9CHEzZ3Q9HmsrpOTnRyaI6NEdVijh5vlRLsNWzhQFPDJzi/rLlqq3VcAaPPjslCU4eiR5tOGfXbbVQB+vxfS8r9uF4olj6sRdt1yasuQD23XLV2m0wVura+/h0xHD3MqZ6tn9oenFJWWycIyvcEI5dx0DaqIKrPkhwe56GCxtRF29HhCcO178bMippcFEeXnZhl50IbrhRdetfSB4g9BCQqJTg4bvZ6YXxqAx19mpumP5nEjQbgpiY4bDl5Yog0k25pC3Cwr6a+lB4N+cvQQdYqa/JJcVVSnQ6mkqBh5d5yNjD6/AGkD4el8N5DcETi8CaH104H9i7B3xako/dvb2PTFtyjYsB4NQ7vRrMFfaJxSgKSGRYhNKkZUgh/eWKcbtMzRgwnIL8uEN7UzUs8aCF/mOQh6UvDhpUNxxd+WoyTjGST0ublKbCMIgqhv0Bo9JwFuj4EcK5Vp61hku82jrD8SO8mqbLt6uqvMCqDvReXmDHJqSysjrpUhP/qk95v4SBiH+ZCU/qu+PWDA+oiRsD4OF48gSGfC/4DpPND10h7xMB+Fsm+NLsvmpn3caqW+FpC1tixTkyI8pmSLOrJaoZ0BfQWiSD5l0069n8I9y8yzyCGfD3OdIP1Y7gEO67pAHLZFlAWZpo2WJ6zCT9OY0Uvc8ckZ7hQpIh67ORoA41ko8VdWwY/gXk8FHJWpCIKhRlcaWQ6PgwnvnNsfkSpPlP6uPwVmtOnUL07v1nLhQaOfI92ZJN1zGKTNqSI5e/QnmaRE65i1F7KMHvHAUtdhvR+935mRrwq2OlzR3GqEcGjcoIwL1O70cYSe3SKI6iY2oQHOm/p69bQdH4fvj/ZDTG4IiZ2/RkzeIiOvtCAaBetb4fuj/XBNr9PRodfptvqhUAibvl2HjQuXwb9jAxpjL5olHULjlAI0aFiE2MQSeBv44fFpP9slNi1EIjYB2ASs+Q/UNUCwJAp9z9ccV/u/eR2ndhkAltDymNcBEuFqCPjjW/CSPLDYVKBJb9o9jCAIogKQo6eWqEonD1Bx5015zo5I7VvrsPB/0g/HFWhf1NXtK4BtwubSrrujSK6tt8ctaWIbWhl93Ri7Q8Bp1y1n54bofpAdFPKcU1vcmBnOBybUcZKv1eRygiCdSbJ1B5HNjcE0K8U1cuQeMZuXnS+AsY6MLlWIQLHKFN1D4PpCzpby3GITLP3M5TQupMlL7DBpDR+uajba1unRy7j4RnQXmHW+L9uH8MLJljFmPnkl1bMdOwrmsI5ZaRA5HQOaL44x5xtAJAeK/lmwzVbZmqh7aTjsa+aUI1MckzYHUaQbgjig7ReU4eyUrwVZB+m+ZL04dVPF9oxGrP3qfEKt17pZQh93QqreuNO5BNMGkW6rKnivXBcWF4UzuYxkt+gVE20nJw9B1Aeumfsc3r12Ajp9shYpzQ/BExNEqNSLv35viI2JXXDN3Odc63o8HnTu2xWd+3a15QXKAljzxffY8cU3yPjrE/Qe/DN2/ZQBn8KRkFyEmOQSRCWWwhcbQNMW+QCAU1puBP+kE8r8XhzOT0Yhy0JK9iVI6nIxWGJrMFbxCHi+9yOEVt8DVrxHOwbA4zLhyX4cLOPSynUSQRDESQY5emqJijpmKkJl2inP2VERGbayESpHcuQ4/F4tNek8Ea+QWGjxKY7TV9e6mkyzhPXRKdEBZJ8vMkebRHlyFI32SeV6zAuTpMu7fCmCTNMdxJm5lotbdI24o5cunYd33QKsTg3R8SPLNDRiTHLQ6OsAy3NQff0pZkgxo2vknndyNBmWhJ1DokXMLAauMqkvRT30fcWkKB/ro1uCTFkf0eFmFjHm20DYMWOZ+Lus/VIhmB5TJehjHVBObekRPRWVZ3Wq2G4AYWeE05Z1ohjRD1SOTCbI1H1FpjPFQY6TTKcLiwtFdIcPLG2LNrrJFP0uoi/E5lyxO0pssrg5imWjIQ8g0XkkneOQy/l0MMKoo8vRPVaCPABGlJD1BAIAVAfPG0EQdZFr5j6HkqJi/O+eJ1H2ex58aakY/J+7cHp8XPmVXYjyRaHHxX3Q4+I+eP3uRHQv3IyGzQNIHr8V/pIAvnt/MfYuXIxmgc3o2+8n+BLKECiMhi+pFFHRQTRJ+xNN8Cfw+yrw3x9EMKDgcH4SjgZbIrFzfzQ+axhYcnswxT4d4Xs/Qmj5tSj4PRlF29ogcCQGUUmliG/7BxoUXQtP37nk7CEIgogArdFzHNTWGj2B41ijJ2RzNkQqL8pUbWliObdRpBquhsrJVB3W6BHz3RwqgH0xZqf6TulBFfALixRbyzsvdqyhr9HjrqvimKev0SM6OwSXhq3vxMiWspDXaJcbL1FHc5Yrtlkc9CLAo2zppmPGap/pICkJhdeg4YLTA8yin11mYdCHoMNizABzWOxY0EMF/GqUKYM76eYQAcT1NXrkRYqNdi2OAmksccsaPTa5LHws2qm9G2v0uDplHNLDdqohr20+LinuksxKPHBdANrpXf+sVnKNHh0V8JSySstkYZmVWqNHJ+C+GDML62TLC+cba/RY2xV0tPo6GAcQCCKqVChk6wfTS2Tdml3xB831ftycO1YbOIAScY0es33pxghrO9rFyItLw4s+c0ueIEhMC3/W1uj5b51a74Q4uamLa/TUB0pL/Mgd1R99LvkRZcWt4DnzAXjbnI/g9q8QWvNP+OJ2YcXH3eC57D7s/HQxGhVtRMuUA2iWdhgJjQvgSy6B4rH/MQsFFRw91ABH/M0R26YfUvtcDSR1RMGbraAcVrB6UWcc6XYhuo26HD/O+RBJPy5G9sD1UJM4Ekf/To9xEQRRp6A1ek4Cjjei51jqcrhH2Di1KeroNFeU6rk0zLjoRrDPsyLNaZ1cYeXbzaBaJv9iPbsTwyQIhBeONnvKXtbZ2ROCYmtPzHfeY40hJEWGAGbUD4O4qK/TEy2ig0VPtT5UYl3zR3NyyD0vr6jj7LA02hWdH0yUqM8bxb3dzHV/OFdhXcTZlG1GD4WbkJ5e4WItUbxlLmufV6uavtzpXIqxWDJ6RI+kp8UTYl1bCWCyI6cSzhqtuiWSx6meq/OIu+dbVBROtfmS6rHIkUThdJWHz1o5MplFpusjX+XJdGpfMNsaNSS1WV4b3HJvNMaVoJBDlA2zCYTQEBPqSKE+MB6xkhwxershSS+b0uIjWEY9y0VhS9PbU2WZ0iLPBEEQkYmJjcaa6EHwfQKcef46eLf+H7BVm0ioajRWftINa6IH4Y7hA9B3+ACjHuccP3+1Cuve/QSJ+WuR2Wg/0lIPoUGTAkQnl8ATpaJhkyNoiCNAySbwL14EV4GEOODwn8loNepUZFzQC0rTFrj08btQVjoe62/ohy5D1sG/9yvEtLyw9jqFIAjiBIYcPbXE8X61rozDRqwTIaggYhvM8i6Ws87RrPlueWJ7TvNRNxvLm7tFcqKJ80xrOS3Pbqmoi+bEkB0HAKCAh6OInO0QV8oRI4MUxqQtrXUnhNPTJtZ5MLf0ku5W4YLTSdOBSS2K68s4uTvc+lWrywHOBN+Cnis7pVjY2Wb0AVOgP15m9TcY9vBwChNs5cLc1U1HxsIORW23MX0uzKDJ1Ktp41/oCQZ57VuhdevuV/pTN6bOzGjPKKMHkTkNXg55bWtbJ9sdCTacLojyZJbn7HByJlhPjvWEqfoYKl+maJbRh5GcQ042MkDauo/Zs402HHwXut/FdrFLioU/q25lmE1HbqyJEx6ZkmfawVDjPHDhRmRxyDAFQEjQSXAScaZdDKpQXoregeWdCyfAYrx4btVIf00IgiBM7njrHjz9N+Db6a3RteNvaJBYjIKjcfhpUwt4erXBHW/dY6vDGEOXC85ClwvOktK3rtqEVXM+RMy+1WiZ/DvSUg8hqUkBohsVGws/J2ceRjLeAr5+C8EgQ/6fDXFUOQ3+Nq0BrMPa2a/h7IfJ0UMQBOEEOXpqiUjOiIpwLHX1OVh5sq3zt/KcNeXpUlmHTaR2nRxCTsdONnLLZ+d5r2a9NVpDd8BAShVtsEuUZXDBKWCmamv02NwMgMO26WJt63nkUilukcQl/bktikj/3x6VZB8vzDY3tfaXGO1i7pClQuVOj6+ZUUlMzjA+cocM0/GieR64oIPZJ+auW5K1Dmv1mDaGLWJ2Z4/ugDJat3gUuO6tchu4rheKPvHmwmehnqmWPcxN99yWd7E45TneDByuVrfHoiooUzx9uo8x4rIwFb3ZcAdtLd0X8SbjtA6R00VlccJJbl6xfHh4Mw7Yt08LZ+h5HDC2aZNsUmV5Vo+ZbTet8Elk3MzT9RLvAvrCSEZUj95GxIFJEARh44637kFpiR9vPzwHf+08iJQOTXHbf0YhJja6Uu20694R7bp3lNJ2b9mJ7177EK3z5+HMnE34Y3sTxMX7EZNSBE90CI1T89EYy3FKU618x8ZfYfvT5yMt5ybEd7wYLKpqtqwnCIKoD5Cjp5Y43q/WkX6DdWvb+sN4Zes6/Xiv51V0PlUZLBtju+pltcnNiWOt5+Qo0uM9ZJvkB8HCQQ3GZ+2dw+2xJx3FKGk6QBQGYdctvS1mlNP1EHVxssnJySWulyO6fxhjULi5Do5ZgtnasK7M5BQgIPd3eLersDwj2IRp8TVidTMQRZsAG9NO0aFiNC73kYwoEzD7zlzMWepHxk2/Svjd7IfwItwOziCrTrI95mLVNkSnio4l+kPyD0VyoFidQHpEj92H6IzYjlN4n9NiW8xyoMuriEzBLsY0P4PNyeN0cTrh0rW2NiwXhqNTyTzhZtvWGwfTKzOpDgdgeh/DI044gVw/dnIGiU4avV1xjXJFMR1BTGhQb8SILBLa5xY5Ul9YB7m1E5nzmCUIgohATGw0/u+Jm6u83Zbts9DyqTvw3/FHcXrRDoSaNUTpRZ/i81feg2fL12jTZDfSM/9EYsYhKB6O+EbFOAUrgY0rEVoPHPkrEfn+1mhy7igkdR0G5kuusOySwgKsnDYR3qLfEYxvjh73zkBsAjmOCIKou5Cjp5YoL6omEsdTz81Zo+M2d3OTWZH6btMI69y1PJm2OYxQvyLynORb2zMjepxaY7Y6Zl153ytn3TR3kOg8UbkHYiSKcx9ID1456mbW1f+XJctzP6eIHga5hbAjymgrLDFcjDOxrl2mHtUjRvRwi0NHdpMJFlkcOyycqM995XNmzm5Vw2FmrkWkrUfk3meQ2pO9KNb1h6Q5eliOavU0qKJ2clYk3HZId0Qs5OisKUeug9PI6d5gKyB8dnXWON0QHHweFbpGrRe1xWPsFhUkrQtUnrBIdlh33RJssJVnlrNn3c480o1L9PKpwgmVdBOcOvr1YdgmyLKOR4XJY1KSF65A+zEQBHGCMfjxydh3x3xk9NkG7BiHKybeDSTfCBzehLKfp4EdWIy8VRn4oygeLTL+RIPUo/DGlyG5yVEkYw3wyxqEdtyGgvwE/FmchYY9rkXK2deCxTR2lLd80iXo3n4F+nQqM9LK5n6I5Vv6oO8zH9eU2QRBEFVK5baKqiGWL1+OIUOGID09HYwxLFiwQMrnnGPKlClIS0tDbGwscnJysH37dqlMfn4+RowYgcTERCQnJ2P06NEoLCyUyqxbtw59+vRBTEwMMjIyMH369Oo2zbThOOoyl1dlZLs5FdwcGW6yIkX66Pnc5eUm02Ee6qiD875aVg3c+9q5H5nFTllrJrxM15DoZtEXgtYjZOTWNL2Z+WKQJIvynXqXO/aivQWxJSb1FoPCnCw2W7I6OLj4zzJHRHixYxZ+wVj8mEFfI0eyijEw4yU6cbilSQ7OdOnMWFSZW/RmegXo7bGwZEVb/0iQo5nNjRcDB2PaS5Osai/OIZko9IXYVQyAh2nrLClMkypF2DChYDkXrDnXd/NeuHx2ugjKuznox9ySZBMt9AATX4D1qbaK3pBsfpdIN0KxLQ7NVqF/9civ8l4VlmG7uRiDBvrYBRPvABbHrySYR2ifWWxnglxFkGVVVK8rCOIqzMe93O7MTK6vhF9MOCYIgjiBiI2Pw/dH++GP3Fbwb/8a6uLzob6fCnXx+Qj8shx/5LbC8gMD0OXV1Ui+Zwe+9r+M/35wCdYu6Yi/tjdFoCAajAGJKYU4JWM9Gu67F+qHLXHkxSbY8Xg28j57DGrxfgCak6dX9hIU5Cfgmx2jceCM7/HNjtEoyE9Ar+wlWD7pklruDYIgiGPjhIzoKSoqwhlnnIEbb7wRl19+uS1/+vTpmDlzJt58801kZWXhwQcfxIABA7Bp0ybExMQAAEaMGIH9+/dj8eLFCAQCuOGGGzBmzBjMnTsXgLa1Wf/+/ZGTk4NZs2Zh/fr1uPHGG5GcnIwxY8ZUu4361/Vj5XgdRU7tWL/uOzlfypPvVK4y0wi9vJvzJ9IP5CbMduSkr7Ut7bd002Fjd7IwqbwsTf/ftheTUIcZravhY32dFzk/Es4zfr1d0ya55xnM6BqVc7jJdHbymXYzZs4pI8lk+rHxiJgWOeAsU94tzLo4NQOXHQtcGB/WyIkwKjhUKaLHei7FtiwjlElvRlkm5IsbXekOMIADqrDNa6STacljYuNOA9ZtALtdKE51rAPeyVlj8+A417duJ16uTBeR5d4IRV2FiB5nx1Q4r7I3RyeddWeNGG0j+G4cnWLSILaMS+t50huy3vBCXBhcLu/SANXHi9NCSmIdLr1JXjCnKDSCIIha5pq5z+Hdayeg0ydrkdL8EDwxQYRKvfjr94bYmNgF18x9DgDgjYrCwHHDgXHDAQChUAgr3v4f9s//L05J2IqWGX8gKe0IfEmlSGhYjISGW4BDU8EXTEXBkVj0PMOPor/ikDR6Mc5t0h4AkN5pJspKHkf+c+3Qvd03KCksoMe4CIKoczDOT+y4bcYY5s+fj6FDhwIAOOdIT0/HHXfcgTvvvBMAcOTIETRr1gxz5szB8OHDsXnzZnTs2BGrVq1Ct27dAACLFi3CRRddhN9++w3p6el4+eWXcf/99yMvLw8+nw8AcM8992DBggXYsmVLhXQ7evQokpKS8OfWLCQ2qFxwlHqMK9dwcARtX+or5vhRETKkVtRxoxMQZEZymliPOecIurQZSQdVBQIR6rg7gRiCHAjBY8svT4eACpTB5yoH4fad5m5B1YugESBndcDI9cV0f9CDMvhsNrg5lUSZpaEocEGm1XkifuZCWmnAizJEWdpkrraJehQHfaZ9FoeMqDM32tLSigJeBC0y7fNga3vafyWhaNs8FVJ9Ib6Km8clgSiEuEeqZ60v5elOKRUoC0XZHUBG+zpMaoNzoLTMC3BP+ReURSFVBdSQB2bfll9H/8hKFEAY75HKa8LMdyUoOAPddHaor5QyMO5wz4sgkwFACPCGHMqXJzOgIspvb9OpDhOPOaAEVHicFvKyYHNelQXhK1XlNGsZLoxywSmklAWhhCwnCmZ5eeALI80fgKckYJaVHh1TTZniBaRHoJWUmE4bo5pQRtyhy6jPEeQBLPW/jyNHjiAxMdHeMQRxgqF/96Ixe3JQUlSM/93zJMr258GXlorBj9+F2Pi4CtfnnGPlgq/x63/eRSvfRrTMPIiGaYfhSy6xLVFWWhCNA/ktEHvm35B6/s1Y/th9OKfNG1ixcTjOm/p6FVtGEMTJSE3+DTshI3oisXPnTuTl5SEnJ8dIS0pKQo8ePZCbm4vhw4cjNzcXycnJhpMHAHJycqAoClauXInLLrsMubm56Nu3r+HkAYABAwbgiSeewKFDh9CwYUObbL/fD7/fnG0cPXoUgOZ8se6cVD7cdU5UHmqla4hSnQVGiuZxqWLLc5ubusUIWH/Ml34Qd2nTyT1mLcMsEt3acnYWcVh3vDLL2Z0vVp25Qw3rkX6sPSjEw+dTdHI495rVGSLrzi1p+rG50qse1WOOV+Zgo2yb6Kyx2cPEh9ZkmfoCywjLVGFG11jlmTbJjhPJNiaWF9/lyAnjUTDOpIgeUY4k22kbdT2Yy+k6YYZZMCOUBH3cPKlWLPnakzqCfpEiXUSVQ2GFreUregGH6+nBIVrZcmLwuOAUqaRMp7WAI+qn5zn0h6OW3CE9kjluOqsQwtfkPCmih0Fy8MhCuUP/MEtZbnaKyoGQsOuWzTEjtCFGAxkZukzxIhKcQkD43HK5P9VjXS6fIAii+omNj8MVzz90zPUZYzj7svNx9mXnG2lrl/yADW+8id4tFiPzjN9Rmh+L6OQSxDTwo2WDX4C/HkJw3sPokJAMAEgIbjteMwiCIGqcE3KNnkjk5eUBAJo1ayalN2vWzMjLy8tD06ZNpXyv14tGjRpJZZzaEGVYmTZtGpKSkoxXRkbGcVjivrBvdWFO0M2XroP1ZYVZXtY8OKTraW4yKiLbKte6BIrTy8npVtF+1trQXDDmu74ihxp+yWv0aC4E62NXeoSM+W5dv0cBgwf6SjbW/tPblXtGli0vMizK0FeqkdsVV+2R1yISY3HsZ8OUabVaXJ9HW6NHsZxIrWUPtPWI5DVxTJug9294HRgl/NL3z+LcXKeHcQaoukxAXCcI4XIMCK/RA6NNxlSAqWBMk8PAoYSPrXbb4hz1Obcx97auvxSWL27zVZlXuHcr7DA2h4DpdXG6UMTT4HQRCz4B0x8g9AWXTqb2URF8QZWU6egTsTpDnLqAa0NL6i8mvPRjxZJeGSePqLMCgKvhtWzCieF3zi1r9IjeK72oo5MnbJw4/pnQzwzmrluwyJVusoIzR0+U2hd1EduxltcPI3USQRBE/aPLBWfhundexG5vHwDANzsvwX+W3IzlC7ORtzENgYJoKB6OlLRDAICuZ6xBwazG2DatBw798DZ4qCxS8wRBECcEdS6ipza59957MWnSJOP46NGjx+HskWMzKuPwEX+MrQymI8QU6vQV363tSOmR8o51GuEy1y63nLzVtp7mXt7MFx0z9rJO0TSmXm77bkWWzxm3bYrj5Hpx08Gcr3Phs5kmz/rCbgSFSUt6iM4im35W7Zk51wTMtXNku/WJpHAWFAY15DYSwv1udToY71yai3JACLZgQim9tfB5YAxclW2SHg9z8AIYa+iGm7X4q0yNwxNzab6thGVX5OJ0KMMYs83fXS8s6WQzezmnAewkW2iH2WQyuw4M2mNYThdjBWQaaxuL5Zjls5OODGBi4AmXi4uDhlnKuOni2L/CMQOTo10c27ddvNpHbnH96p1rkxdWQtQlUkSP3hgDjEe5rJ3FhTaMQWxVXpdNEARx8tLj3hkom/shujZZjOR/boUvVnskbMXcT5H34Wu46Pxl8CX4wRQgLrEEpyZuAHbcjMDmW3Bgf1OUpQ9E1rD7ocSn17IlBEEQduqcoyc1NRUAcODAAaSlpRnpBw4cQJcuXYwyBw8elOoFg0Hk5+cb9VNTU3HgwAGpjH6sl7ESHR2N6OjoKrFDm9wei7vm2Jw8kkxLAxVtjwGOk1inuZmOAu3pEjcq4yAKzzErRIT5W4Sytn10hPmXOXGzPgKkBQCwCHaazharE4VxBkUaCbpE3XXkju7s4IJOdpvkEAsGACo3ZJqOHP2hLtlJpsA2jRQcU0I/cHeZ0GUy1ZiDWiOgFMFppGsDwIieEJ1AopGG3Y7OBT16yFJJ6A3d2WOcSwZoUUGOVYQ0QX8u9gEvP0LCaXBzJj1mVu5Fae0Idx9aZGeR1U+hN2VzIOiHZn+5mlmeJ/pYnFLWPrM64sJpzH57s28qZV5i7nrp51Jh9nTbueO2vmDijc9607TZKSoteMKMftQHv+VOawgNGw4G+6LMkQRzOHcEQRDEyUFsQgMs39IHvbKXIP+5dtjsH4rWl42Guv0znNttFaKTSrHi+3Oxr7gjsoLfofUp+5DU4hC8sUGkZ+YBmAP+0Rwc+rMBDpR0RMaw+xB36vlgzP2BiZLCAqycNhHeot8RjG+OHvfOoIWeCYKoFuqcoycrKwupqalYsmSJ4dg5evQoVq5ciVtuuQUA0LNnTxw+fBirV69GdnY2AOCrr76Cqqro0aOHUeb+++9HIBBAVJS2SOzixYvRrl07x/V5IuH0I3X58OOKzDFbqQxOv+pWrD1dV+uExuoEkNJ4eKmLSulotiX9yG3RIxLWOaHTsSzLqqH5B1qW726JaWfknbPk4AUGNexYkPOZmR+hJd0xUNGxZ9igMIihOFaZen+JfY5wGe4wXphQyPHRHLDwzFd7rEtf50Z26DjUMdo3nSf28x9ed0ia+OuRWYopzNK20Rf6nNqit+jgcgt8MHTRHQxMezxNBSKfDKsRxmSeyWVgLQN7vv64mFN+eV5RQSQTjyVVmH0gqDAiniotE5Z7iKNM2GQakUAWmUwsq9/eKuBXEds26lgGveJ0ZQnjRTKIC4lc87cwSXlzgBlipQGm/z3gZnm9L/VHt4xIH+biPLKcUOeLUXYqiRcvQRDESUjfZz7G8kmXoHv7FTgn4Q1gwxtIbQOUFUTju9UXoN/zHxtlQ6EQPp/5NkJfvIeOLX5Faqs/EZNSjMTGBUjESuCHS1G6LAp5B5rD23E4mg8aDyU62aivy+nTyXz0q2zuh1i+pQ/6PvMxCIIgqpITctetwsJC7NixAwDQtWtXPPPMM+jXrx8aNWqEzMxMPPHEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvfqRI0fQrl079O/fH5MnT8aGDRtw44034tlnn63w9ur6qtl/bG0VYdct5+5VUYF4HpcCgYgxMpGa48ZCzg5rjEYkIC4B7VDB7XdhDm7+sO1Qx42gCmm3Lrcf/eVjbcIS5AwqFDg86RFR56AKYzcquzzTnSfPZbW0gOpFyMHJIzuI7PXLgh74rbtRCRMvDnsUkO6IKlWjjJ2iVMtkTdZbbrckvOuWmxPLKlNL02QWh3ejMh+bEuafEWQWBaIQ4B4pTbbLSX8FnAPFapQ2T+ayDFEHcPs5Lgz4oHK3hbUBqIrDuNXaLQt5NTvdHnETbObCh9KyKMBpNyp7I1Ia5yy86xZcd8p2OuYqwPyWXbfsA8a5HRVQQky+EERHntXDJZRRSgAmLvFWjkzjCggBivXRL7eLW0wLWnbdsjp8HG5Aut9FCapQgi46Wn04YnogAF8pj9CH3AhvM+zT7fWHtF239JNZzg3MuOJKyuApDULe2l3QwRpqxs00XlyiPWomXShCG8Zb+LOq1Q2GSrE08AHtYETUGWjXLaI6OJZIm22rNmH1jGfRNu4nZJ2ShwbpR6BECbvUqgz5B5Lxl6c7CvMOo2vnH5C/txE2l12GNsNuwvYP/oUOvvlolJGP71ZfQM4egjgJqMm/YSeko2fp0qXo16+fLX3kyJGYM2cOOOd46KGH8Oqrr+Lw4cM455xz8NJLL6Ft27ZG2fz8fIwbNw6ffPIJFEXBsGHDMHPmTCQkJBhl1q1bh7Fjx2LVqlVo3Lgxxo8fj8mTJ1dYT/1EHdza8hi2V4/Q7eWckaDg6KnMyePhTd0d67j8+GvKtGsccd5kzCV4xB/53WSGhO3VxTLl77rFEOQMIcs6407zLGua6ehRXMozW12dgOoRZLrtZmV3gPhDCsq4dUt3ezmnvNKQF1zQ1Vmug6MnGGU6tLiTHKeFpbW8olCU4QCwOogcH+kKR9cUB30IwmPOTfV8bpcntquqDCWq1REGZ92NY61Pist8hgPMdEYJ55A7O4k4B8qCXlgf0ZN0E+fNup0c8Pu94LqjJ9IFZTlRnANq0OtexcGRoaUzwK+YziVeTnmxTCjs6IElysR2oQsOHw5ABTx+wIh8q6BMhrCjJ2SJIXGUaakbUuH1O6Q7XNDWiB4WUOEJymmuzigu6FYWRJRfOEHckm8MYLszSCkNO3rsA8UmT8or9cPjDwgOHOGCEQeL6MwJ5/HiYvMilBb+4pa6kPKCoVIsLSNHD1F3IEcPcSJSWlyKhf98AUm7PkX7VnvROPMv+BL9Uhk1qGDnnjZI7nsLmvS+HswTg7KSYhx+rh0aNCwCRuymx7gIop5z0jt66grH4+hx3F+ngmeiKiJ6dHEVPftlVheLZf7iPr/jkrZOjhYnxIieitQRJ/AhzhC0PH7lNI+0thsqN6LH6uwxPwdUrxRVI++ybUb0iHI5tIieMltEj1nWKkuUWaJGGU4FbtNNbkdy9AS8CFjstOomyzJ1KQ75ID5uJS5obJUj1i0O+BAwtk2yy7Gfn/CuRhwoCQn9w4XyovPGYT5dGPBJ/SPlW9bhMXfQghHRYzi0xLm2WN/SJlcBf5nmKLTh6qjR7WJQg4Kj0OnCcqgvRfSUdy1b21QBJSg7cZhbFI9oAweU0nBETwVkipEyNkdPBZw8+sUZ5XcpZ3k3z2RYVz2iR0e11BP1FNP1iB5HeVx6N2TqOvhDUELCPVO1O4McZZf64SkVHD0Wp4xRQdJByzMiesAFeRZBNh04ggGK6CHqFuToIeoCqxYux+63X0aHlE1o1WEfYhoVS/mhgIJ9vzeHt8v/4dcff0Xv1m9ixcbhOG/q67WkMUEQNUFN/g2rc2v01Bds38ErgdNSFhWTad822mmpjfJkApAWzpV0sTRmbZtZPrvJFndq1stFmCdDs04rHUkmF9LEdpmltL6+htyWlmp/ssZcZIRLJUWZzPF/hZmTax6uZ7bvtN6PUNYybxOlc0sNc/FjBsYYpLV4xIWFy3msSlvMW+itcPiE7vAR+1F0/HDGjEd9hLmprX2rnfI+3tqbMaFmWl1zmREWnsvquijG1te6nWLzTHQaSfZaPjPTLuu5lBbMVoxE58Yi5XEutQ2L2dIgFeoxBZC2dBcvlEgXtuWCMvvQ4jGT5Ivn3WJTBJn6fcG8SgTcLk6nY/H0WW8IVluEdJvTzHYzM9OYpKDtxAM8PLrEhZGZRY7eBBdPOHM8l7a1gfR1pQCYq61z84IHIjuNjPYtJ0hc6Ft3+NBvPARBENVG94v7ovvFfQEAK8ZfiF49v8PKxafhlJYH0TAjH97YIDJa7QUOP4Qmmdrj143xE7gaBFNoekYQxPFTuTAUosowJ1aVhwuvyspkzHluVRmZ4rzJmIe4KORkol5Uhb1dPV11+Oxmk/nZ6myw6+4kH1I+D//THzoztdT/SRED0Fwa3Cabh9dhsrYqtsQR4uYxwiWYYD0zdJCtYdDmkeZ0joU1sTouzJa11lVwzrWFnPV3qIJbRjXkOsfqMG0MMUGmfRsiQyoTrRbkahNms4w1nsiId2NcskzawN4YgFpVrurtqwDXzp8uU7cTgt0cVtv1vnTGGONCm+JLVbm9ktvFZi3KmDGupPElD0HbRcPD66w4XiiODVryBHWMuT8PZ3LLS5Ctd2dFZeqLKVt9XeVenE55+hBiQhKD5SzCcKhIPg8nudZ2Dd+K9ebGw75EDs7Mq9a1Wxm39IWlrfAY5MYjWtq4NS9/QxGzdf3cWG/kkhKis5A722ooy+HeEEEQBFEVhBIzAQCBrB5oOuUXrFBfw6JP+mL/xjQES7zw+rTY9w6dtsI/OwXb/3kG/sj9N3jEnRQJgiAiQ46eWkL87n4sHEs1cVJ0LPLsDg5hAufSppuDxtqm9aVY3p3mqk7SxAAD25zG5QXpMwv/U4xXJOm6DGtgA6BfXKYDSGvP/AcweBiEY1Gm9uLGZ9PVors7xLVILPEggraKIUuzSosYYGFvjSlL721RNoRW9celuDn3hRwh5iQT0GWw8Fhh8ktw4+gtWB+j0vNV3XbdByQ1wcAU7aXv8KU5pRgYU7QXFMFup1EluuDCtstzfNNWxkwZ+j99H2/HC0USY4dzacxycWA5DVTpQmHlXyjc8m61TX8x2BOlArr9qJRMUTwTDdVtgvC5/ItUqqP7K/Rsaz9KgStubQmfdR8K0w/KOaHGOLGYZZQzEi0OGpud4bFrXJLMPMdGJzoMHslwwWDRSWTIlW4a0Lb9IwiCIKqTHvfOQFmhDx188xH0lyLnpisx+N3P0GLqDiwtfRGFB+MRCigI+T2Iig3ilFN2oNHOv6PktRRs/Wc35P/0EWilDYIgKgs5emqJ4/16fSy3+/KnLJHlufy4bv5QXUmZTj80WyN5nH7YL0/PSHa5RRGZLzGiR5zyO2sg2uLmCjJtU23thrgpC0a+HlEjR/NY3SB6FIOWJ0s3z5MpU/ukgnPVjOgRZFojiETXiyFfn/uGUxTh2OwdvT2hL7kqROCYLzOaRpweh6N3LI4HI51pL2PchdvlnIOrHEY0D/RoG9Nm027386n3pm6v/hLt5FLfhdvTI3pcL5QICBE9Uue7XSjSBcPt8lzlWD7bLpSwodZoHuMVFuNkYwSZom+D22RWwE63G5D+0c3xI8py09PSrhEkZo3mgfUY9nECcwwZDUr9zIXmhGvBiN4JmWliWScPl6EzN29AoveTKdqzoRDkQKgDANC3QCPqIy+++CJatWqFmJgY9OjRAz/88EPE8u+//z7at2+PmJgYdO7cGZ9++qmUzznHlClTkJaWhtjYWOTk5GD79u1Smfz8fIwYMQKJiYlITk7G6NGjUVhYaGvnqaeeQtu2bREdHY3mzZtj6tSpVWM0QZyAxCY0wKotfdAoIx+Hn2uHZY+Mx+/r1mLZI+PRteBexDcpwvdrz8OS/BlY+ulZ+HN7E6hlCqLjy9D6lM1I2nwtil9NwZZ/9sSRLV+S04cgiApBD4HWEtItmlkTqlemMTGqhExxriQeo5y2Is0FIzmY9B+13VQsT3W3fKc25R/YnV1E1jpWe1jYPWOFCZ/MdXLMuh7GEOSK0K/mWjpi+7IbJDyvEwroi2yLj5Bx6JErZtsAwJgizYyZ0APWdWoFF4+9Ty3ryugy9Ra5YTXAxLVHbO1Y22fG3FaPWtb7xRqdYcq0nDUelq07LcJijHHMpaIW+c4Lpctj3zyfRllFKOB4ocChE0WF5EWmDSJ5EvXIDzd5Tu+iLm6GOsnS00Lh6KxKyNTHq3XcSvUiyRSdXhaZDJCebuSWD0ys43LxM+5wzKyGMYut3DhfUnQdBxjngldL6AQwuzBJNw9gLF/P7TpbQ+iMfhNlhD8Yg0mXKd7Fw2PGci8g6g//+c9/MGnSJMyaNQs9evTAjBkzMGDAAGzduhVNmza1lf/uu+9wzTXXYNq0abj44osxd+5cDB06FGvWrMFpp50GAJg+fTpmzpyJN998E1lZWXjwwQcxYMAAbNq0CTExMQCAESNGYP/+/Vi8eDECgQBuuOEGjBkzBnPnzjVkTZgwAV988QWeeuopdO7cGfn5+cjPz6+ZjiGIWqLvMx9j+aRL0L39CpyT8Aaw4Q2ktgHKCqK1rdWf1bdWvxGhUAj/e2wWkrbPQ8d2u5GccQgxDfxo02AdsOZSFH4di735HdHy2icQn9XTUd6xbBdPEET9gnbdOg7q2q5bWjSHu6hI4vVdtypbj3N5e3WnuaZTe+L26uXJktPM7dXd9HLZdEfYXl1rRyxjOjSYVEd/t+66Jefbd7TS2ykNKgjA51hHm/hb2zSPS0NeqNLuYmaek8NGzy8JRCEo7LolO1h0uVa7NYpDURB3uRL7yTmgRcsvCvgQ4l5JJ7F9x23Pw3aIu25J9TmgclNfQ2Z4jlsciILKPeFEB8cZt59LQGszEPLa1h7iRr7F4aTbYN11y1omgqNH33XL2t+mUs51y911y0kHoU0lyKQ0KTLM7ULh5ey6ZfFFSMcqtK3Oj+HijCqz5LnIsckMqPCEHNq0yGTWvLIAfOL26tZ2I3gLldIQFNsW50IZcVcsLsgu9cPjD4X7WRgwQlnj3RKhw4tLtAGhVzMcPTx8cXJBf24M5KBaRrtu1UN69OiB7t2744UXXgAAqKqKjIwMjB8/Hvfcc4+t/NVXX42ioiIsXLjQSDv77LPRpUsXzJo1C5xzpKen44477sCdd94JADhy5AiaNWuGOXPmYPjw4di8eTM6duyIVatWoVu3bgCARYsW4aKLLsJvv/2G9PR0bN68Gaeffjo2bNiAdu3aHZNttOsWUZeprAMmGAjgk4efRdN9C9Ch7R40aHEYise8/xfmx+O3o6fj1BueQUza6QBgOJR8CWVGubJCH1Zt6YO+z3xsk0EQRM1Rk3/D6NGtWsJxnlMTP6yGAxzEJzEizD8l1Risq7iYaW6q6227PYYlPdUgpFmX/3AqZ9VbdjC447T+j4Lwj95cCb/kjmJcAQtvya1yJr14OJ07pusvBeAegHugci9U7oHKPWDwgFteKjzgxvo8+vo0SvjFEAq/nJw4MD4zcOlMeaCvx8OEdUfMf+Z6M27nkOtHQoL5OJQZu6OvBcTgCb+H18jR18cR1uhhxrFgR7jb9eVDzDVdzPOh7RRmrvtjrD8EYT0ecQ0dFt7hTJSr2O3TbbQvMG0W0FcAUqQrQl8fSCjvNoCdLhZjVyRmV8jpYpEGrks+IMuK4Hgyq4S9WFpYivZSnD5XTKawVrZppptzSOgC+8UplLXIZJY+tTwd6Ixbe9xaRh+EgrDwi0Mxrn0A8g0OHLbFiIxDLtjFLC/Foh8T3hzGF3jYuWNxRBkOIW72q7UDGAOUSHdvoq5SVlaG1atXIycnx0hTFAU5OTnIzc11rJObmyuVB4ABAwYY5Xfu3Im8vDypTFJSEnr06GGUyc3NRXJysuHkAYCcnBwoioKVK1cCAD755BOccsopWLhwIbKystCqVSv83//9X8SIHr/fj6NHj0ovgqirxCY0wHlTX8c5MxbhvKmvlxtl442KwmVT70bv2d8h/vZf8PHau7BqyWko+C0ZXGVIaFSE9q1yEfV1T+Q/l4qdj7RBr+wlKMhPwDc7RuPAGd/jmx2jUZCfgF7ZS7B80iU1ZClBELUNOXpqCenX44p6W6oKYd5WkTlouIq0bo51Z6xIqlfWYWNNd+siq65MqOc4l7a8nNbrgbDminVh3kjOKbdtus2+EVszezDkKs1p9RqrbOsixvpLd5nYt2tiMHf5kf+pjufBOueWJp7G/JQJA0rY3QuquS4Rt+yAZXw2J/+GbcL4BGCu6SJkMCb2uCnTXB9IlwtjCRQ17JhCeHMjcTML2T4Gx+eLTD8TONN7zDyXXJ9wOw1aq6fT2rD4LJTTABMdEeUN4orcU0SnilUX60uVDIfNQ1yOTEMMB4Qn6dxxskvME162hZ1h8ZuI9awyxHrmZSeXge4s4cZnxjkYD6+jxQQXa9jnZyw6zi39aDjCwsdWjxTnMCNyRMUgyZfTmSmUw3LqxE4QxphVnusgIeoqf/75J0KhEJo1ayalN2vWDHl5eY518vLyIpbX38srY30szOv1olGjRkaZX3/9Fbt378b777+Pt956C3PmzMHq1atxxRVXuNozbdo0JCUlGa+MjIzyuoAg6iXRMdEY9vRDOPv1lYi6eSs+XHkbflraAYX7E8FVIKlJATLb7NP8+NFBpMT9jqYZSTh3ykwkT9iK/L2N0L3dNygpLKhtUwiCqAHI0VNLcOO/Gparzw8ivJzQJ/zWaB67I8CO21y0PJxkOb3c5oOIcGzV23w5/ZPdLqJDxr7tt1Nr2idFal+zTBFKQpJone+bskV3jh3rjlJyL/JwRI+smRnRI/YPYLgwzH/WibEwBzain6AYkTxmRA+TXmY0D6TZOgeHyrQX19OF9rkY1SPpK8bviLtuQYjmCc91FQ6mmIs6i+fWcI5x2dEmja+wg0Hcc0uP8RG2bJIvCuupkNC3e7f3Laxp1natG6ZFulCsjiOnC9G6+LBurO1VQZmimLBvw+ZDc7o43frQ8rL5nXT/hX7MHNp3kmONjAKEc6kPHO2d69E8UMwrX3dO6cE1Tv0oGcyN9qTBqSiCY0a0n5k6GQrq14cqyBNEGR0iyHWKIHJbO4sgqgFVVeH3+/HWW2+hT58+OO+88/D666/j66+/xtatWx3r3HvvvThy5Ijx2rt3bw1rTRAnHvFJCbjq+cfQ7dUfwW7Yig9XjMHe9ekAtFt/w2ZH0SF9EfBxB+x7oiX2zH8MW/yXwNfAj5XTJtau8gRB1Ajk6KkljO/ukSZj1SXY+l3fZW4q4jZtsU5hnOpXZM5ZnkynNN2B5CSvIjjZZI/nMV0AsktGkyS6etxaFd1DqvRJFZxgYinZpSP2EXPpRS58kltQ7cdctsxqrbh+lOycYlKAgD7RBuNGlIs1qsewl4f1cIzmsVjHAcYZWDjdnGdbd+nS9dTsUjmHqu+ype/6FZZjbGpkcRRxLkZFiZ4LbutlJnc0AG6X6RbR4zbn19vWbawIYhsVjHxxzIsoz82jor2cImkcZVoui0h+F1c79XYstlr9TqJPxjguz86I58kYoNpL3ymOC1c9D49a603U6uWy3jT1ItaIHlVw2kj1wpUMncLnQnLWMKFzwx2jCCfA8IiJF4Tw2BdRb2jcuDE8Hg8OHDggpR84cACpqamOdVJTUyOW19/LK3Pw4EEpPxgMIj8/3yiTlpYGr9eLtm3bGmU6dOgAANizZ4+jbtHR0UhMTJReBEGYJDZKxFWvPIvfSk4FAHy0aBg2f9caJX/GgylAs4w/kYVncVaLtwEACYEttakuQRA1BDl6agluPYgwCawW+Zb5RXkqVMZJwy35kea6bvNFvW4kPdxXl+ARjpzbMtuUVnYR7HPqGdPxwIzZqP2l/8/A4BE+66vK2Eu59yt3OVPM+N8ej6SE8/TIE8Wmn+i6ktfp0WWKET3czNCcMeGJv8JhOgOgr9Nj6UlLNI+4fbphVdhppEf06A4ZSE6ZcKt6Bejr8IhrBMlyjOgh/fEvCFu1h51FZkiGy/zcclIUxoyXFtFjOf2APc3h5JrrybiMVutFZyjg3F4kWcaxfThbDty9Kq5bpJcj0+Z3iXS/E9visEUQVfgpsorKsEUnGYMG4ti1xvGJvhWbUMdzL3aG2DbCET16mkMHGp5W3RmkP4cYPjeOhjG5vsLkKCKK6Kl3+Hw+ZGdnY8mSJUaaqqpYsmQJevZ03qGnZ8+eUnkAWLx4sVE+KysLqampUpmjR49i5cqVRpmePXvi8OHDWL16tVHmq6++gqqq6NGjBwCgd+/eCAaD+OWXX4wy27ZtAwC0bNnyeMwmiJOeYHxzAEDjNsk47YWfUTB4DRYsugx7fm6BYKkXUbFBAEDXLmvxx9PNsXnWLVADxbWpMkEQ1QjtunUcHPeuW8fY88e66xaHilAlZIpF9V233PLd0lTLrlvlldePVRUoc0i3vtvbYAhxhqDFhxlJV/29LASUCTtgAeK6O8xSRz4us+y6ZdXJqq8+SfSHPJZdt8R2re3J7ZTqO2DBuT+sbej6lQS8CCLKYgszPtvbMmWUBO0y7fK0fJWbOhcFvAhxq0ynz4C4QxY37BTzRQeMOWnmgLGIsgqguEzfdctp9zJI9aR+4kBZ0AvrOZfqq2Y74uZK/tIoaFtku1S0KRA+5EAo6JHsiVTemOdzmLtuWcu5tSG8KwHTRailOUz6rfVUwFMKOO4uFkEmA8xdt5zKRJIZUhFVYmYzMc9Sh1n0MXbdcpMp+GQkZ00gAJ9ftekiybYPJK1fy0JQQpa6NkeaqHO4fkkZPP7wfoPGc7bWdyeZKnhRiXMdbi2rXyxaiFQwRLtu1Uf+85//YOTIkXjllVdw1llnYcaMGXjvvfewZcsWNGvWDH/729/QvHlzTJs2DYC2vfq5556Lxx9/HIMHD8a8efPw2GOPSdurP/HEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvbqqqujevTsSEhIwY8YMqKqKsWPHIjExEV988UWFbKNdtwjCmZLCAmBuJgryE5A8YSt8sXFG3pJZb+OsstsRk1wC5uGGjz/o92D3niw0uuQxNO46uJY0J4iTh5r8G+at1tYJV8JzYEtCzckUf3SuCNbpH4ez+tZgAQZ3MeXNZSPJd2qXO3yK1K7eht62HjBgczxIn/SJMbekcogBclb9mOA0YEKKB0BQaNds0+lXdm5pV27Neg6YYAsXdNAjeuyBHUyoJ8uxzWuB8KNVIqYsLmhhSGPMZpW2ARGX6zChb3k43XISWbiY3lfcksugRdtwi5VSH0mDmIcf4zILOfofpIEdPod6VIk+BCpzLVvm5a6CnQa8NeJFT3ORYTvhjhdQBOWFgcUZMx0qlZBp2wSqvL4S6wt9G1FVi8xy41XE8mJ/OkXW6GPO6HN9BOo3VssJFesyMV9sUyjrsUTYcC4cW502usNGqG+0y6Q3U1fh5OsLOCvl9hBRB7n66qvxxx9/YMqUKcjLy0OXLl2waNEiYzHlPXv2QFHMv1m9evXC3Llz8cADD+C+++5DmzZtsGDBAsPJAwB33303ioqKMGbMGBw+fBjnnHMOFi1aZDh5AOCdd97BuHHjcMEFF0BRFAwbNgwzZ8408hVFwSeffILx48ejb9++iI+Px6BBg/D000/XQK8QRP0mNqEBlm/pg17ZS5D/XDts9g9F68tGY8f819E5egFiM4rxzQ/98GdJBk5PWI7MDvsQFV+GU9vsADZfhUPLG+C34LnoOHYWPDENa9scgiCOE4roOQ6OJ6JHjTSbLIfjiehRgQpHEonFnCJ6rOWcmq1sRI+eJkb0iGWc2rJO8p0iepxkWefWwRBQCh/sThcYabITw8wvUz1QXaKI3KJJVDAhokds35QHy7HY1/5wRE9ku+yRSCUBLwKIsrTJHO2yOlJK9CgirtvgJFfrB3NOy1AQjuhxipIxdbRE54RfpaEoSYahmzHjNusgrK/KgSIhosepL7Q27JNcPaLHet4kO7ksU59vGxE95V1jlnzOATXolZMjnVg9KQSwUg/AlPIHuPUzd4joAWCL6rG2GxIieiohU4/oUYIOzpfy2qloRA+32gKwoBo5ikj0yZgXJxAKwVccjFwOML1XQrriD0FR7dFAthMstqlyoMQPjz8olFUNuyyD3OJU4uDFJebaO1K+IFgVPoefFwyG/BTRQ9QpKKKHICKzfNIl6N5+BXwJZmx8WUE0Vm09B32f+dhIW/3x1zj036no0n4rkjMPgSna3wc1qGDv7haI7jUZ6eeP1DbRIAiiSqCInpOJGozqMeYLlh+MK4JTRI+Yx4V3a71IIioiXpRdXsBEeMpTaTliVI+4xK8Y46IfaxEoslPEOl22z+uY7ZP+wE1QKMml2B97v0b6U2t1EGlH1mNztyh7+069ZMYF6c4M0SbrDkqyc4QZOnnAwuupmJNPbvSa3rfh/5kZWGE8/sXFVhGOBBJkho9VoZQW0aOV4JKccCscYCwcTVTunt+6zk52WiKETCEVRttVuyJn2dTFKKqfhIpE1oioDsVEp4D0UWiAab4gx4WOrTcGS57j/aY8h1a4T7limsqtss2L09HB5IjbcAe0izPItWgX/cap62F11rBwPJ10MXGH9qVBG5bH5fb1NXq4nsfMwWW079BB4nZ4UkSSKF7Ik84TfYEnCIKoT/R95mOUFBZgxbSJ8Bb9jmB8c/S4dwb6JjSQymVf0g+4pB8CZWX4+O6H0Ta4EKee9ht8iX60PHUPcGAsCl++EzsPdUeHcf9CVFILR3klhQVYaZEVa5FFEETNQxE9x0GVRPToVOIsHHdETzkynZLLi+hxU/9YInoAIKQC/grKkdO0iJ4QlHLLWvMDIaAU0a7tRoqeCagehCoV0aOl+UMK/Igx0u3OGqf1fRCu6wXXo2ci2KXroKeXBqPg5z5LeTOiR6yvWvQpDnm1GbfNRottgiOHAygKRiGgWmWaNqpClJBoI4cc0SPJ0p1A4UTRAcUBFPqjoXK5fyQHnct6NCqAspDXMd967Yh3Tw6gtCQajuvcuDZkthMKOvjdyzmxXAVYiQe2dfUj1dOPVUAJmRFRzFaeWY7Nep5iVFqmEdETcoqKcdFRJyhH9Oj5ks5OaQBYSIUn4NK+IMemU1kQvpKQrY5RTnSKWW5OSlkIStDhzud4wXCzzdIyeErCv8Bad98SZQEIeweNMrxEj+jR65p59rpmuaDqx9Ky/1J0BFFnoIgegqg+tn2/DjteuBdnttmIlKy/oHi1v2U8xLBvbzME2t2MUy6/E4xp3wEco4cKfVi1pY8UPUQQhAZF9JwEiD/81qRU7vBLc0VUMH6Idihs/ZFaRAEiuqXcZOs/XlvlRHIaRWo3ko3Sj9tSTIjVsWNdI0d2yDjFDlgdN9a5rRZbo7tT9F189FLW2CG5LcZgibARnTZOOuqFGBSI27frUTuaNPF8WiOomKWHAMhPiEgyBV05g8JUYw5q7VsmeBrMGCKY259bZRjHLs4IAAoTN4o35Zi9K8owVWZcjiBy9pwxSRfjWnbbz9vahi2ST4/KcLfHdqKtnVFRxNMjOqqsjgwpE5IDywg0ORbcLs5INlu7VY+ugZzGxLRwGVcXfKSbhG6/wmzp+uky07gtKMbWN1aPoFWwpLR0IZgVuHhlAsYOXHp4FWMwVgl3xMlg7pBOEARBnKy0Pft0tD37fwiFQvj0H8+ged48tOu0BzEpxWjeKg/wP4KSfz2OX/efhsJCH3pl5yJ/byNszrsMbYbdhO0f/AsdfPPRK3sJlk+6hJw9BFGL1Mnt1UOhEB588EFkZWUhNjYWp556Kv7xj39ADE7inGPKlClIS0tDbGwscnJysH37dqmd/Px8jBgxAomJiUhOTsbo0aNRWFhYIzbYvtdXYtZ0DFUMqQyQdgyuaHvG3JDJL7GyU5pqab+yL1NzZ+ePs5X2Ntza1fLM7b/1Lcitey1zh3cutWx1CYly5dKmVCWsqyyP2bSVZRvHXJQBoX2n2CNTrvUEMqEVJ9ni/xzm/NJpTOi2c2svMEV7QdF+BTK2WHcYNJLNMMasvjM005tj0LZGZ9CCjBjCW24z8LAsa9vm+VOMl66LaJUi2uiinlFFF6MwbV3byl+YmrMAwoN2kQasmA/mvJiuPiTL83C63QOcjBeGuuPTPhWRGS4nbc/udJFa9GAu/cAAfcd38wk2DnEneEe/ilWeYarQsGJTBrau0MamJRG6v8VpsDDzH9ffFSPd6FxRcWNLdIRvE4JOoiOMW2SK+dayhj3HOmAJgiCI+ozH48GQh+/CmbNW48igNVi46CLs35QGtUxBdIIfHdqsRrcuuQiWenGw0RXo+8DTSO/UGedOmYnkCVuRv7cRurf7RtsJjCCIWqFOOnqeeOIJvPzyy3jhhRewefNmPPHEE5g+fTqef/55o8z06dMxc+ZMzJo1CytXrkR8fDwGDBiA0tJSo8yIESOwceNGLF68GAsXLsTy5csxZsyYGrFB+mpdyR9Vj/13WN0NIDdQkfbEMuE1PI0nC/SXUxpzqh9+qS7pYp6seeQf4cWy1jmntX3ZLj3yQ39XoT1cZ9bQHi5ShWPdmSK6e7iUb8qwb++tyVLDepryRF2sPcSMNN1Op52zTJmy5YLFnIOHX5DkmTLlNPvqMbZW9XMPaZopyFTBjZdFB+40EoSHxlhYB86hGvX0dWeZNs7CTWgTfA7GVa1frQNSPJ9WPay9yOTq5mdmH/cAoHJtTaHKXpgcxuReujbdLgZpYHN5f3cxP9L83ZJvPMkjtcHsdbhZvkIynbqVGafULCOWdbDVdnq4kM1kVfVjacxanUfW9i1qg4cfr7V2jFCeM9jHDg83r/ttw9e2+RiWcJUz8x0s/K43Ij22xc1HssTFlq2dYdgm6CPZbXVIMbN9olpYsWIFAODbb7+tZU0IgiCOnfTWGbj0rffR4p87sKzwefzw1ekoPRQLxoCo2CA6JLwK/+wm2PjQ2Sjer23rvtk/FL4GfqycNrG21SeIk5Y6+ejWd999h0svvRSDBw8GALRq1QrvvvsufvjhBwDal+8ZM2bggQcewKWXXgoAeOutt9CsWTMsWLAAw4cPx+bNm7Fo0SKsWrUK3bp1AwA8//zzuOiii/DUU08hPT29Wm2QvlpbJx7lUMnizqWFRhi35TrINCvokypreenX8/DkQXePVFAr49ic6jvO71zb0etZ15Zxk2n9xZ8JThnZWWRzXQgtAFZnjtwmN/43UYRABj2yx9525CAJ+4NdslZ6KUvoBBMWY+ZyDSdb9RRDD64fm1rrj5GZMi21mQIWttlmD3fqW10/cxbvFEVi6BBu2IzYAcxQG1GgOI7D7bpM9A27LH1gRR8tXJHLV+jiFB02kmfCJsC8IKyTd/HxIv2UlHehWPQTd/A2Zeg6MSFNS5bOhSjT6aZgkcWt9cU2rDrqTiHRewvzs3UxcMCexo3/HHDQj4n/GXK5oSOT+toYeEZlruvFAdvDj4INhrnhPjYeKxQ9U/qCzHpB43lNsUMtnS/pJHaa5UZvuS0QVc9nn30Gr9eL//3vf+jdu3dtq0MQBHHc5IwbBWAUVozvj149v8Xe9elIb3MQUbEBtG+3HvzLbOzalY7Gbf4PAOAt+r1W9SWIk5k6GdHTq1cvLFmyBNu2bQMA/Pzzz/jmm28waNAgAMDOnTuRl5eHnJwco05SUhJ69OiB3NxcAEBubi6Sk5MNJw8A5OTkQFEUrFy5stptcPxuXUGvDQcc51QVlWr8oMth/yXfBTHmQlOC2yIi9EgN7Vdr+9zNqjtH5KieSPNGKzxCGXGaU758eynmmma2bi8T2R4ONfzSP1ujedx0EA21O0h0beRjLv8Tomr0tmX95eglvQWjZSFkR4rusvS3lMI5uKpKY4TpY8jFVm0eLc6atXoqFwIb9OYMdbUDxoVICi7bpqczroYrWkNmIA8iLtupRxRJ8jkHV7l7cER5k+nw5Fy8NkV1Ja+n9CrnImYOLwedjKYA+zm1XkAQutR6Y3CQFUGss95c/iydQmtR5tAllr62db37RWnYpvWFw+A2qojXk9107UN47Eo3W3PQcC5f88a1L54AcWgaulp7VOgMsdMgXhiWDjn2XwuICvLII48gGAzi/PPPRygUwqOPPlrbKhEEQVQZocQMAMDu2EFYEz8X3315JooPJoB5ODJO/R3t8QiA/2fv2wO1Ksq9f7PevTcgV0EBUfBKooigiIjXPiWp6GLaOXqOpRlpX2GJaCWaeiwV9aippVF+mdXJNCs9ZmkRBt4QETSv4K0UL4CK3Pd+b2u+P9aamWeembXed29gv8CeH679rjVr5rnMmrV85/c+6xmg9w6rIeOOLSITEBCwadgmI3ouuOACrF27FiNGjEChUEC1WsUVV1yBU089FQCwfPlyAMCgQYOsdoMGDdLnli9fjoEDB1rnm5qa0L9/f12Ho1gsolg0a0GtXbu2wz5I/af9yCIyaut0J6K1fvzP0hkTkoHLsHIl1ZDj+UEdAia3j6WzDjt9mur9wTqZ/9grSknrLK+vKAkVs2PoHx6AIEF9NRE1yd/I0w8k8oSVaUogzesi4R8TdpmJpkmia0y72JoZG5KIlyXl0ppJizTcgo4r6qdUJULovtV9Ik09bjtNxqwmt2qOG7F6amItgSSRspZnInqEkuUYmb2SmuovmjLF0DGmro52i2BfdE8whVdR2hlWP/iiLTwykkUvhF3PFwrnA79mmTeKNB2RHgpuX45O3R3Sc9rnH78ZPH1hvfrlE0ns0txI3s2Zltu5tIVrn6TxfmkhDaBRpI9jn7D9EqSeuTGhB5FA0slxTNqkAmNfR8J0sLpW+lMXGgNSXwK2HC699FLceuut+P73v49+/frhK1/5SqNNCggICNhsGD/jBpTu+AP2a7kH/T5zFVpOnoz331qJeRd9DYcOX4j+e34AIYADDngOG3+6M159bzxGTv8Vmnru1GjTAwK6DLbJiJ7f/va3+PWvf4077rgDixcvxi9+8Qtce+21+MUvfrFF9c6cORN9+/bV29ChQzssK39ilQ/fj9H16hSeuUtHdGoZPJKHTR58LlI5NJ6i1pbxO3bmRJ0eZ2WBcfXY0TW+aB57hmhH99D6/tpIJScRPTGEpdNnHY+4kUoaiZgwvgpHX5z6Fau8QyxPjkitoFfElFFPElJDJ19Or4g7XyQ20t5kY0V4/WNUk7DTJwtIK+BBh6HoQJ3EP6Q5eqxoMxLhQ8t4tJQv+oTrpFE9SlbMJ+B5Nxc/J8xLg3qc0xsk4yZJkv5Ku5wSI5ZArtM2RyKVp5Pc8M3YLbltVKZHp3reOWZk3STcX1ZXSvijebhsqtMhbVzZPHKLXfiUUHTj75QM7TbvB3VBpe2Eui/0xVRLpscwuZe0gvQ6pMSpC/VAoBeWyKD3i7aRDpiAzY1KpYLzzz8f1Wr4NTsgIGD7Qo9evbFwyVHoP3QVVt+4L+Zd9g0UV72D3nsNAZqS/9Gs+ld/VIsFdO9TxAF7P4z47r2w5NIx+HDJEw22PiCga2CbJHq+9a1v4YILLsApp5yCUaNG4Ytf/CLOPfdczJw5EwAwePBgAMCKFSusditWrNDnBg8ejJUrV1rnK5UKVq1apetwzJgxA2vWrNHbsmXLOuwD/e7eEXSk2abo9M0XE+JIzfr96zzxMmd+286Nm+/TmfxAbk/CfLbb62qpja/AZTa+6pUqsykC2o7rpBoSCxLywv0HTWuYjXohifPcT9eaJJomQpInR68ypaNsVE9E1uauLAZo2kemfSxtfQkij68izQUr7M3pW9j7EqSf7TxIiYh0Jh8JiHTTq3shSlf2Slb6UscmRxEfFf4X6KwJvLqSwvZPQOlG9pYHFgUnabfTjS8Ip1ZhqqUr6+Y0fJpeodtetirdIrVv+t65gbJ0SmvXfpNO+Qmyn3fjex4C2mSoe5/xGtR3nyxSbg9Pe4y6Rvpd5s8D64JSpl2YIuh7I0r6tQBTNwLsHszwxXFYkjrCNVkRSgFbDF/72tcAAF/96lcbbElAQEDA5sfR19+Hxxcdh9791+PI4bdh8PNH4Mjht6H3jhvw+KLjsPOFy/B4+WY88/AIlNZ2Q6GliuH7voLeTx2Ht67cE6/e+0PnB+KAgIDNh22S6Nm4cSOiyDa9UCggTsPc99xzTwwePBhz5szR59euXYsFCxZgwoQJAIAJEyZg9erVWLRoka7z0EMPIY5jjB8/3qu3W7du6NOnj7V1FLXmfbXQkceiT2c9c1ClL+PHdc9sKlunO7Vu3wbU9l39Ru2b19TWa+fMoFN+wSSol20EK5Okvt1fXG6MKrOGnjMRL4YOUV4IWIotG2wfk78mnsdEuSQRBWrFLxPJQ1NZCyYTEGR+KfRy53YtE4Kio5dkTCJwzGZHTCm5abkiHtg1ECLZIJH6kchVeXJ0NI/21az2Zfy2ddr9KOzrSifkyitporL0NVMRPXmDl3en1pFxF1LmwhfdE6s+zdCVBy9xosiJdIvJPlnKysqZU0sn4zZyiaH2PAQk2c0ifqiuvOtA/HIieqhyxlI54wRmDGnBFlckiQ9Sj1uddEpWyfOUXFdFGPk6Tl1/gYQo0gNWvbpIbee+S3gzWgdsVnz44YeNNiEgICBgi+Do6+8D/vNNPPLCKZj/5FF45IVTgFPfSMoBfPSsL2LsrEV4d+zDmPvXCVj3Th+ICNhlj5XYc+MFWH3TYDxz/RmIq6UGexIQsP1hm8zR8+lPfxpXXHEFhg0bhpEjR+Lpp5/G9ddfjy9/+csAkl9ip02bhssvvxzDhw/HnnvuiYsvvhhDhgzBCSecAADYb7/98PGPfxxnnnkmZs2ahXK5jLPPPhunnHLKFl9xC2DzIsELtqxOPW9oh046V6LHACCFyGTkfaU2bZCtr1ZgQpb5eTK5/dSvRKe96pbR5YtY4mXCuZTCU5fqLECgQvhWvRoW2PUiMugcjfpldBtvFCLA+CUEmRkL6y9PtSLJWS1bL7tmKC1jh63XxM6QFbA4yCxd6dNjNDZyFE+k6wmqk5AIaSclr5kRX4l635xXkhLnWpP5uSGD2PXQS2rDXAImI3PQSnPlHMMcMoac9+Xo4e0zdfrOkUGlPiXbl8LwB9wurpNcL92kPTcuHdwZOnk+HOf+4PZzfdJD1kiw8ZoaoduQ68W4EimRJBkXgFkRS30I6Fw6VL6uVgCgXvORrs2+ztNkqLphJOuEVAhfWk2mZBDPXh2w2TF8+HBcfvnl+OpXv5pGAwYEBARsP+jRqzc+esXPcuvsNe4A7HX737Bh9Tr86fypOGTXRzFwn5Xos/N6jMJvUfz5PXhl2Wh85JxfoXv/YV4ZcbmE0qJfQa7+F0S/PdAy9ouImlu2hEsBAdsFhNwGY+bWrVuHiy++GPfccw9WrlyJIUOG4D/+4z9wySWXoKUlueGllLj00kvx05/+FKtXr8aRRx6JW265BR/5yEe0nFWrVuHss8/GH//4R0RRhJNOOgk33XQTevXqVZcda9euRd++fbFy6e7o07t9wVEqpoAU1I0yOva+f6xXefLrzDOhRCgEjvz5q0Ql1yY/KjFQzpKZpw9AVQrE6azbV4/rVHUqVaCEJqsdJVm4PPoaUSkuIE6XSedyEwLF/nKvzrVVC6igWct25QurPrWhWG2CJDppW58+ZW9ruSnVyfvSLEvuI3oAYEO1CUBkJq2kHk0pYtondTaUW1CVTY6dhthhZE86+YwBtFWb/f0qVSJpu+9UgMPGcjNiWUgLXQLNJZgSxFKgXG2ykzeTOnEqxB5bAjIGiqVm6EBJn9EZN1AMAVmOrH6w4Fk8CUjm9aIUAcpPn0O8HbElKhNSjl1T782Z+h61peRd3o0IwkEoHTEQVTKCSOyBY5dXYjSX2DkqN09nOUah6pGpoMlEdr5URktRDWrptBOeMrUfFWNEVdKBnFmMiVxJbG4rolCqELuoDmk+6blUjtzYaidvlqROzAggHbYEVOIi5pZ/jzVr1mxSlGpANmbOnImZM2dir732wg9/+EMcddRRjTZpm4b67hXGbEDAto37L70C+6z9DfY8YBmaeiT/74srEd54fSh6Tr4Wu4z7pK7bOucKRP/6AZp6tOqySmsPxHucix7HXdTptgcEdBSd+f+wbZLo2VqwKURP7J211dd2U4geZ3GcunV6ohyIiKxzVSm91tZS6yN6+Pwua64YS4EKCp6z+XrLVaAI+5cBm+gxLzVxn8sW0eMSCJKsOEUn8+VqZOnkxAtvQ8+XGNGjYGykdpj2bZUmlGUzs1Hoeu5825BLrdUmSGkm+DZJJJxjVWdDpQVVWci4doIsJMT8lEBbpdk65xJ1hnjS41sqoididQmJRwgba64ugVLF/YXImqtrIsomZtqKTQDTaTmdMQBlDFQrTXb9Wkjn6qJNJXXx6PPJ04wVEFWEIRpy9FioAlFRQHA/a+gWadsmH/ObdTMrlGM0F/3nte18CfoUUTlGwUeUMRsFlQEA5Qpa2pgwTurE5O4kVaNiBRFNfGyJccu1D20lFNrSJ1/MDNQkj7TlqFfAWtsM0UOJJF1XMvuTg0qliLnl34VJ8xbGu+++iwsvvBC/+tWv8G//9m+49tprseuuuzbarG0SgegJCNi+sPAPD6DywPdw4JhX0H1HQ+S8t2wAVg3+MvbctQnNK2biwzf746mHD8Rbb++M3XZ9D4cc/Sx2HLYK5cEXBrInYJtBZ/4/bJvM0bM9QH+xl3AmJltcr4TJswp7y4LM2GqtmpX3GhV9kYBvvnJuS56tNs2R74+9eJC96pZ3dR24/WWTHElt2lbJTWoajdXUAp8mn+/8WmX3C89/E+sytcqP/S92dFF95q0kYZ2wEiKngyqRZvLkxLBz5NAVv2h6J+1bOjYjNVdlJ1SOHr0qmDQ6Vb+aHD2J/FiqNCipTjbx5y+ceUNO0rfApDAa6OiRWTl6AHOT+CCTjtRjNuuG5INVwn/zwXPsA309y3p3iWx85a04jaDKuuGJTufZIpPxUvNNIZ9f9FyOTgCwEiorQ3g/ZPWXdVOnhXz1LSkhZJxsQtpdp99QVI6SjfexR65+V5ESNOpYbTRkSaby1DLq+pp6OoHn6/HdCAFbDLvssgt+/vOfY8GCBXjrrbew77774vLLL0exyBnMgICAgK6FcSd+AhNunY/1H38as//6f7D6zR0hJbDz0A+wb/N/o/ndq9D64Q64+Zf/B6sOPxmT//dKrDr8ZNx02/FYvaw/8Pp1iMshx09AAEcgehoEqf90sl4yP8iZK3nhI4UUCeBfwao2IaNk1KOPz0vz2rmpk13ChOtJboZkxSj3n+klnnDZjmWxLbbX8KKrPSW9ViA6werYfhrdZt/vvZ1KmK6klSxS7l0xSvtt9w+lTaxVtsigMbljk8ktXeFL76crfakNeh8JeUP8i4XZEgVKl0giadLN5BwC8SDxNRKpP+n8ViWNTjYJEXHCyFzPRKdNk+nxk+6odb3UCmnQK3o5Q8A3LNglS4kvNYrybkguJ+vGy9LHbwqqTx04S4qnG8ynMG5n6tRDQolXXISPdMnz0XezUj1EB3kjSevPRJ4eQDE2ZuDADKIkki6yr1lKwEnnIrKLqVky3+BUkVl8LAnzqQ1VYzVVynO/6A6hBdSPCCZhc0BnYezYsXjkkUfws5/9DD/72c+w33774Z577mm0WQEBAQENx6C9h+Ljt9+P3ue+hj8/cQbeen4I4oqAKEjs0H8jZnzrfzG69CP0ij7EqRefhkte+DHmzD0ALT2L2PjEzxttfkDAVodA9DQI6kfWmpONLaHYmvSSOUSOCVnEUL0kUT1mbapOOm910yR75nFMVvLbNo/oMRSAIW+ULN+y37aFdA0vlSVJ7ct01S13pS+b0qH2+5cFp33BJcTuseT2uR4reREYJaRm6YZvSYgaAUiyHLeKsDEEUWqHL5pHkzapdxIQUkCkERFm+Wl3BEjtfYxYSsR6ha101a9Uj17USFqGQxLdNnPBF7T3jVGJmK7qpWb5HblZUgIqF8oYd9DWr4+TJpl6yMOBXmxF5vlk17o5ZTsfb1QW99sMNTsykT/Paj2YqK3OPlOoo3nIXS/TUes8RMnIyeoTAVikGl11C55xBFJfXRNN2AimI+0YvSyeBE2ijnR1Ok0SBXQ6Tj75ZCxZsgRTpkzB6aefjo997GONNikgICBgq0BLt274zA9/hN2vfAWLFx0EAChvaEZT9wr22+8F9Hh4PF6+ZCTWvLoY4rAkj8+S2X9ppMkBAVsltslVt7YHSGenc5W3Vy2foElPGT1HP/Nk5tVpr04jTzrn6tGhojRcmohSH4LIlFpn0sLlTQXbK4DmdRHWK1HUPzqvVYhzvDHUBO+jhDyJIXQdGqNk6iv9IlenTlAs7Xa2DckZtdKXBJLoHSl8C2DpFdt0Th9B+lvNaz1XXqjzyhIh9NxZ01MsWsGKg5ImmMFEYUBPiH1cBR+0EZEv1cSbN6rjRqGvsGXCvmDQQ66eG8V3U2ZlJXcPGEGUXEfBq2XpVOd8z516b056e0nro+aDJpfs4cQX7RPFGrF+l55+Et78N9LVoQr0kuiCXC8JRIS0EdxRENJGJsZycoc7xgeWIqRouxDR06kolUpYsmQJnn/+eb316NEDDz30UKNNCwgICNjq8H5xFwDAq/JsfDDnERx84EvYYecN2HvEvyAXTcRR3QcAAFasDFPagACODt0V9913X7vbfOxjH0OPHj06om67hJmQdq7OzLmHB7KO/Zr62gE+/6hXv2uPiQ7hc17fj+pqbmfy9JiZnbRq8pbUVpsBkKwuf9Eq8VMihkpQzMgOps1HwlCbpPM3q58kkldOJLMd+iw9ptSRIrQkksmikefqEyA5YwGTn8er02hJ5re2X4ISP2mhHid64pvaopMkq2gbQ8O5/UWa0uvLyCgfv0KNiamwmPV61sWgEHY/ZzKgdAhm5bCpVyeVZ90onruWy5OMPKnn5szhPHLtpJ1PSBiHZKIi2/tc9dksYZMknLfxPUgseYzZ8l0n2u+K+KlSnVmfaWMRETlZrF3GeIy1k53/P6IuiMsuu0yTOq+99hoqlQr69u2LAw44AAceeCA++clP4sADD2y0mQEBAQFbHZb3OQal9bMxZONvsP9Pl2LNytX424Wn47BRi9Fr8FoMHPY+pAT2H7QYbz3+J+x2+ORGmxwQsNWgQ6tuRVH73vgSQuCVV17BXnvt1V5VWzU266pb7bgKm23VrXbozFte3a8r/cxYdYvL4nKrZNWt2sSOPY+qSiBGAYBwghby5CTLqzdbJIQ7fbKnqup8OW5Cla30lUfKKDklveqWIOdcPb6+SpYdV+POv5S8vVpXst9WbkIJZtUttUoXlcHnpWp/Y8XYyiNfuC5KoGwgS7pbbax5NO+j5E9rpZvTTh0oHXRuro5by82opsuOe6e90i6TKi9PDJSq9jjQTaxOYfZKoK3UBL3Ued4AZ47GEogryZjNrcuOJWBW3aqX8NE3ChBVU4rJR0Rk6Y/bseqWQpyOhCpQqOYTNd725XR5dd6nnj4W7HykllfPkw9ikzpXqqClLbbbOPola5scR6Uyoiqrq/c5AURGWrGMQmvZ1LPIIkk2mHMqIo6uuqX1kTrWK2GGPKrIEuYWw6pbWxIHHHAARo0ahQMPPFB/Dhs2rNFmbbMIq24FBHQdtLUWMf9Lx+OozzyF0sY9UDj4u2gafizW/+N+yMUXoddO60x0dgy888/BiI+4Cnsc+2+NNTwgIAOd+f+wDse5LV++HAMHDqyrbu/evTuqZvtHJ/6Yql6qoYuz5ME3T63X3Hrk0x/zabQE15VH9Pjt4REp/vo8QMJ8Sk9LNwLFlZvVO3ZEj6obQ6Q5eoxO33ybEiYys4zLh6d+Eo1j8gRlk0l29ArpT8VDSABCWkQYzewj9ASV6JRGp20jjeixiY6Et7F7RM9xhWmlJ/nkjRUa0UP1uGSRPV7o62X8Wlj+E491II/FpHKjMyCgc71IpdjXhperwRJnGFvrRhEZ+3x8e8gVIT3n6tApsnTWIn3qjejxPdtqPYx8NseAXsmKnbN002gcCxH0q1W0vu8C00FbJTlzLDKH7JsQML8T3gggafch9Y1HoQVsdjz//PONNiEgICBgm0T3Ht2wuNsn0PJH4OBjn0XT0q8AS4FeAErdu2HBHw/Bu6074NjDn0Xfoaux697LId/9Et664tsoHvRf2PuTpzfahYCAhqFDRM/pp5/ertewvvCFL4RfXbJQ72RnM0DPF4Q5rldnPcQN1ZMnOmN+5uiy3ijJscudp7kaas27lY6IyDC6DVHjs8XIcl8N8p2jugoAKqRVkkPHtZATYj74LBTOMfR6WGo8mD70kUWafknq07kjhPOajE2CmaieAoSbe8fpXUO0KFIyTgeslWYktcNMl01USkxqRcKs5UX1aFpJH5hXvmxf3AJzXe36kUDy9kw9gzZDrtULtW4SEB3809cmf+DSrvAogs1+ifQaZfmZpVPavInPDu+x4vIiQzBJqruGj5mrfOWRXxGAikwvLKwbxcnRk+aG0oPSImV4XW4zJXJg5+hRStWF0TZIWwYUaUOYKAm4+aKoE9Tn9gzWgICAgICAzsV5v7wA150GPHbNPjho/7fQu89GrFu7A55+cTcUDh+O8+68AMW2Ih745hQctuc87Lj7Kuyy50pg9dfxzsxLsHbfCzDixK812o2AgE5Hh17dCkiwrb26JdNXt+ohePjpYiaFki8j79UtLo/uV2OAv6lRq41uK0X66lbtOSQtr1SBNrRk1FWkgT8KphwXUGXJmLNIAUqiFKsFFGFeTTKvUdl6aVt1XCSvF+X1Ca/TVmlGUXI/BYyPXJ+xZ2O1Gc4rSx6/1PLkCusqzajE2X2rE1SnJ2hgTFu12aqtdRKyiM9dJQTWF7shTl8v8l0LH7mDlCwqVZqc62bZTQkHamtrN3jX9M67cVLyoFptqm/AUp0xINoKCQuS1S5Ld5y+uiUNl2BAfOCDIgYKbWifzlSHiIGomkO+ZB1XYjS3uvJ8NgpaBkBUYhQqsOEhe5w+KFXQ0lp12lj1PDl8ACAqVhFVPS+Ocr381a+2EgqtJSZbDW5O8EiSaweQG1vT1bRIW3jaUiJKSlTiIuaWwqtbAdsOwqtbAQFdE22tRfzPf92OD/65EgP2HIgv/NeX0L1HN6tOuVTCg9O+hvFDZqP/nh/o3zLeW9Yfq4aeg/3/8/wGWB4QYLBNvLql0NraCikldthhBwDAG2+8gXvuuQf7778/jj/++E02cHsF/eG3M7VKzy/N9ZhQz2++XE4SPQJkparwlflktBdZbbL0WD9u1+gN+yUs/h5KFjHgRtWoc4ZeUcd2jJKdGhmeGr594dS3jRI6csn21rRW5b43RLj/dNJu6zcEUyQFIiGtOSZto/tdJGSMXmBJkTmeOa7yTsnUUUdplUiYBePdWCvTM5KSUsIEWXiakDLh1pGqMzoYIUHDVOq5UWhncJW0M7LMIRfaugd8DyYVapXKk8gJBOE62XEuyZN1TsKs9JVR1bl2qX9RPQ8E2kafk3ZEj69ZGmHD+0I4oW4ZzxVnsPHniLSuk03iII3+YRUsW1QED39qCGS/ZxgQEBAQELD1oXuPbvjK1V/NrdPc0oJP3/IzVKtVPDD9GzhkwJ+x017vYeehq7AzLsXK//4Blu/0VRx4xiWdZHVAQOPQvjAUDz772c/il7/8JQBg9erVGD9+PK677jp89rOfxY9//ONNNnB7hTUZ8XMEmehAE61VIJ2gEQH1yKNT96z6vjL+m7avPS+zaYJ88Drk923nvI+W8etMSgQ5K9mnu++jTLKPjDxoPVSjz0puA0137FIrUvcF3/QSy4K2Ufuu7/QvhOTNk4k/MZVfg6ROZPpLJMue0w1MH/VZjVkhknm3EICI1DhOJrYySnmItLIUEdTa49SXZIsgyQaPDZGw/fNtwikT2aRCLUhjqVcnhy4X2UxGrfk7IWAsVdkHyYeEn+QRGTpZXcn98t2Y7Fhdfy6TDAFNIOkymWGSx0ZBz6XnI+43u/bmsqtCU11KTwM1vmV6r8skobVQg5d2rtVnZOBHwrze5VwDYW4CRcqpCB9I6Ne7aOZtfY0DAgICAgK2HxQKBXzqxluw80Wv4S/PfBXLlw6CjIEBu67GyG5X44PrhuCZWRfA92JL6/p1mHvRFDw67eOYe9EUtK5f1wAPAgI2HZtM9CxevBhHHXUUAOB3v/sdBg0ahDfeeAO//OUvcdNNN22ygdsrrK/WbmhFLqxJewe06h/sya/5teTZPyhLvanVXLLK6Lwti3jw6a/Xv6x5pT/TjSuf607okWShdUqVCGc9a1UvaZlQCrS+scU/kTILmUutT6VJ5vKVTFVmPKV9S8ul0+tUNblWlj6jk9NEUksWfql0HunzW6Y+ymSzbJBZfavmoakN0iyZniRbTifUEhCpiGSCLyFknPar70rHEIgh2Nh1+knYzXQVNYkm5TLt1w7ltZXQE3fr3qzn5lAdweVJ5M/f2Xnlg6Tn6StozA7ne1GeTt6ttF/BPjN8dS4PfXYJf/eYMcvs8hBMzmWT6eu1vGOUPWqXjx3J+DE1ls3gMf+E+dTMlBLCV8hShE0Mt/Np9JDWKR0fLaZMGankB3Q6Xn75ZVQq/J3CgICAgIDNiUKhgMnXXo9dL3sdf3nxHLz94i6QVYF+u6zBqD4/xOobd8Him87RhM/D0z8D3DEMR428ExMOfQRHjbwTuGNYUh4QsI1hk4mejRs36lW1/vrXv+LEE09EFEU47LDD8MYbb2yygdsrrK/Web/ce9DO6n6t9MfmOmRFaZ0I6S/YbNORGenG527cbkHleXzK8pFPSWrVywgQ8NqQnFclEVQkSEKx2JZKpjnxk56nOn3ZXgTUC0pC6zM6/TZwb6TVv7Tcto71akYkDTx+2rSZISI4jWZHeChiiFwFEUGICFH6aY0fb9+muXVMmA6LAkoDWXQ4R7Il1SMT0aN12P0KRJBq7PpGESUuTLd5B1AiJjGIR8vVDbpyU9Yghe982hH85qlLJxGp/LB0sNFFdDh+WgOEgdkjWb/m3vTCuOncyKmJQrqnaHVNnoEWuD5ZRSId9bR/qT26nrqPzHmLrPNeTGH/kwKQUfIJpVzppsewH1baH9b5tZY284ztgM7Hfvvth9dff73RZgQEBAR0GUy+8koMu/xV/O31b2PZ80MQVwX6DFyH0Tv9P6z94WC8cMGBOHzsHKxb1QuPvjoFK0Y/gUdfnYJ1q3rh8LFzAtkTsM1hk4meffbZB/feey+WLVuGv/zlLzovz8qVK0OSvBw43639M3YvfD98t0er/m7v+cE6Cyo2wiRzlnrzRfPYGl27JZPHfcry0ZnjZNRTJVkyqX7bBreWyCijZIehKAzlk2dn8lt+nJaZSJ78aB7+S77wzP3MzM20kPY/GZtrldEjdgSToW74pJtecjPd9HgvY8g4tsdIhg10rSwhZLKp82k0D29qi4kh0ggiV4cp1xE93r4lH44OaZmciJGQsbRV1QNVLw1zofemNUBpOhVLh3TssQZ8rk67SIvhtzG/gZAxdHLgpR6y2jJ9yiafjnryXgv1J4sIY35q/8E7gthj3U/0GUD0RZKQZfag1c9Mcs/rcSjJuI3h9IffS19npLJ8Y9vD/wR0LsI6GAEBAQGNwccvvQR7XPkK5r3zX/jXP3ZDXInQa6f1GHHga4grEf5VPQZHffcHGDJyFI655Cb0O2cpVi3rj3H7Phpe4wrYprDJRM8ll1yC888/H3vssQfGjx+PCRMmAEiiew466KBNNnB7hZqjtWtSmML3Y3h9OqUziatXBg0siADrV+esaJ5EZ7bdVJ7/d29783VVtv02qZW1ubrNP0Pd5EtRNARgR91wu+gZoSN4ABW5Y0eWREw+p3SEvoj2VM5+bcxsRocQEcuPEzG9ae4az0hTr00phUKYRLSS6QXpFxXRY0WDEf/s/oy0F1KKNFlyWi+N5uHRNSYlT3rAInqMn7Sc95LrBwA61FO9wtat+jKyy2veWNaNImHRefwGybhJhPKB1ldoh37tluo+q49TwoL45ARJ1aNKsvs3q5/qKKdmcViXlerkDyNPQ6sfVAdbncGfE7DGv7VAlr4xycUS9n5yP/KLnZ6PIrsflNNZ7Lyqp1lXdWPwJwLsTqk1TgICAgICArZDTPzO+dj76qV4dPU1WPnqzgCAQnOMsXveg42zBmHh909BXK2gpccOeKl4Alp6F7Fg5rTGGh0Q0A5s8qpbn//853HkkUfi3XffxejRo3X5cccdh8997nObKn67RToH6RA62o7PnToSeEBlqV8ks4gdVS9PVl7QQeY8poZtPpk8KbQv6EH9rk6Ps6+TOUMpAlqaFVxBI0hiNCHJJcN7Suoy1+9Ei1pIx6yglU2yKR3JZDTWfkpdStf+ojojq0+s11SkyMxJw0cGjXywvXD9s3pWUBotnT9LRQL5nDV9KxEDMmJXFDryQaYzcjuttX0ljE7bO5lO4KmfMQ+eaM+NKtzV0aRndW4uU8apFZzE4GyVr73wFMcpwcHbcL0ZJItPnyDnvJxC9qDNPKdf/8oielR9Yeu3bkiPbHtMSVaQyPBFYigOxi5glaR0dCpSSGjlMbFN6nrmUzFtVAA3Rhi7JZfBDBCANywqICAgICCgi+D/fPNreHTa/2Ig3sNrTw/FHiPfQY9+rTi43x+x/se74OXiv2OfE74CvHAbmja83WhzAwLqxiYTPQAwePBgDB482Co79NBDN4fo7Rb6O3sHWJsswqNenR2JGPfSEOmk354DS6de3nHWFMM3T9J6weY+3nqUQvCTCX6dyV93HskXXqf0TrbFLplhVpWSQBq74ralpI1N8BDZ0swJpW4lHB9lGnsglX+CHFvLhHMbXCor6VmRzjFlOqc0BInRTfsw7Vmh9lNJWjEnqOw61jHMPFZFsyTjmuQRUgma0zw82jo1L868gSTrT1PPXo5eaL8tUsiXP0UbzYQ6NwanxmCUUnDbVRSTT2etMuKoMBfO31jbn/atQG2fWLGqTl/zs2XD9o+7xc9JUkxJIKY883nH9UrWlSpyS7f3GenXa90Rup+FMZb4p+1XUWcqQipWRiFhGhN23e8HkBA29LzTry7RZBFKAQEBAQEBXRSVnrsCAN7tfTxWNR+J3k9ejuFj/oWe/TfiINyODQ/fDQww9QICtgV06NWtZ599FnHs+7nZjxdeeGGzry7x9ttv4wtf+AIGDBiAHj16YNSoUXjqqaf0eSklLrnkEuyyyy7o0aMHJk6ciFdeecWSsWrVKpx66qno06cP+vXrhylTpmD9+vWb1c4sCGenfjgT0XbqdBZfaYdO30Zz9WTppMc2bdC+DajPd+8P6nXptXNm2Ctv2VZIXQ6rNa0vmT4jN0m8UrXKqM5Y61O+OHl6cq4jJ4BinQco1itXJa8L0RXGVG6emMm0p7DC2hPOq1tIZVm+6le+pN5oTiPv1ZFqru1eA7VykySJZWScbJBqBbOY5I9K/HYT/NhQE3Rr/AhYA0pFJzm5ldTKSLUGrW8Akxw9DpQcmq+HlmXl6OEyfM4Ksq926BLdMftM5UjuWw2dlhrWn46f9T4EJNk1piWiU17ESuJsD2O/LGuI0HHCHZeWKArnGlr+SqKXPDf1VtXjWdeVqRDplW6uv5CEbUzrRcTReh6cAQEBAQEBXRDjZ9yA0voW7NdyDw4+8VMYecOzeDK+Gf/8x1DIqkDPARsAAMN3+BtefeCOBlsbEFAfOkT0HHTQQfjggw/qrj9hwgS8+eabHVHlxYcffogjjjgCzc3NeOCBB/Diiy/iuuuuw4477qjrXHPNNbjpppswa9YsLFiwAD179sSkSZPQ1tam65x66ql44YUXMHv2bNx///14+OGHcdZZZ202O/NgfefuANmzKTrpvKHe7/58XmbNXQSPdnF1ZpWJDmx5soHs7vTZ76Y/oXtuvh4uxX5diufZEblyAYFCaoWtTeXJsekdhxqRtl+UcOI6qYbImgzSHlC5eSJnKktJIzPnlSnZQvVTOYkvEc074ttIv0p6dQSVnfZFSkCoV3dMzpwkR46I0nw56p9IcxIhQkTyEklHJywfnXuDFKi+jXhuJV+OHj5wM28U6Z62mAvY+XrsZfA6dqN4HwASoMt9R+RTJxeGu7BTlk7CV+jiLELK5wPPTeSpS4kdSzzlaVBbBi1LhpVHsR6zxFXmC02RY7VVD159w5J7IFJyC8QQZm/WE1t1gGWMNJ2gZbm2u8uZBWxPuPnmm7HHHnuge/fuGD9+PJ588snc+nfffTdGjBiB7t27Y9SoUfjzn/9snd/cP6C9+uqr6N27N/r167dJfgYEBARsKnr06o2FS45C/6GrsPrGfTHvsm9gz0NG4e0dPoY1y/vo7xSD93wfe3xwJl67dF+88/QjjTY7ICAXQnZg6YcoinDWWWdhhx12qKv+LbfcghdffBF77bVXuw304YILLsBjjz2GRx7x32BSSgwZMgTnnXcezj//fADAmjVrMGjQINx+++045ZRT8NJLL2H//ffHwoULccghhwAAHnzwQXzyk5/EW2+9hSFDhtS0Y+3atejbty9WLt0dfXq3jzNTcRukoG6U0ziQ9iKJ6yCqJD+fjZKZ5nvk5s1fJfJiubLiwioxUM6SmacPQFUKxOkM0VcvK19PpQqU0GS1s3+3z37FqBQXECNi5009HquhzrVVCyijhemC1sVfW6LcbGu1mfhiy4+ZD9SPjeUmVNDs2OLTmZy3derJOusPnTvH0SuwsdyMqmxy/FTnHT9TWXGqE06bZCeW9nXRE3wAG8vNiGU6cSa2mfa8b40f5WqTfiWN14kZeQHlQwwUS83Q18g/GFzIpM/jcuT0vTHK31bGgChFgPLT5xBvR2yJykafUEQE1emxFRKI2hL6MPdGtOSm+zFQqPh9sS4Ot78So7nk2q/lkmPBz1Viv059QUk7eq5URkuRKGMPHsHz35D9qBgjqpIOpP+bVZFfqpyQVGgtolCqELuoDml/WquBSciNrWmiKDjy9epb9s0DSKASFzG3/HusWbMmrJTZiYiiCEuWLMFHPvKRLSL/rrvuwmmnnYZZs2Zh/PjxuOGGG3D33Xdj6dKlGDhwoFP/8ccfx9FHH42ZM2fiU5/6FO644w5cffXVWLx4MQ444AAAwNVXX42ZM2fiF7/4Bfbcc09cfPHFeO655/Diiy+ie/fuAIBPfOITePfdd/GTn/wE5XIZZ5xxBsaNG4c77rB/AS+Xyzj88MOx88474/HHH8fq1avr9k199wpjNiAgYHPj4emfwbgRj6ClV0mXldZ1w8KlR2KdGIbxO/8vdhy2CgAQVwReXrIPdvv679B3t30aZXLANobO/H9Yh4iej370o3ZS1jpwxx13YJdddmmvKi/2339/TJo0CW+99RbmzZuHXXfdFV//+tdx5plnAgBef/117L333nj66acxZswY3e6YY47BmDFjcOONN+K2227Deeedhw8//FCfr1Qq6N69O+6+++66EklvCtETe2dt9bXtKNETp0SPpapunTKzah7xUpXSsbYelT6ih8+nsuaKsRSooOA5m6+7XAWKKeni1jfEgx3hkrbVRI+fHFBkCSdSytUIRbRkEkhmfi+scxJAsVqAWR2Lz5M5AWJkbqw0oSJtoofrtH2mOpuAdCUsu73RyfVJABsrLajKQgbfIcjcl/kigbZKs3XOJeoiTZbQ+W1C9NjLUFl9RAgbq/8lUKo2gyeptefVdt8otBWbAMmeBS7z5UDGQLXSZNevBZnYJNoigI533sk5BEdUETbRkKHHQhWIigKC+1lDt0jbNvmY36ybWaEco7noP69t50vQp4gqMQpVjw5mo6AyAKBcQUsbE8YfQjG5e0jVQrEKQV9vdgaeXa7bF0sotKVPvpgZqMgduu69Jnwk5MY2Q/TErE4GOQQAlUoRc8u/C5PmTsaMGTNw/vnnY8CAAVtE/vjx4zFu3Dj86Ec/AgDEcYyhQ4fiG9/4Bi644AKn/sknn4wNGzbg/vvv12WHHXYYxowZg1mzZm32H9C+853v4J133sFxxx2HadOmBaInICBgq0Hr+nVYMHMamja8jUrPXTF+xg3o0as3AKBcKmHOOV/E4fvORc+BSbRitVjACy8fiJEX/REtvXZspOkB2wA68/9hHUrGPHfu3M1sRvvw+uuv48c//jGmT5+OCy+8EAsXLsQ3v/lNtLS04PTTT8fy5csBAIMGDbLaDRo0SJ9bvny586tWU1MT+vfvr+twFItFFItmtrF27doO+yBgvn93JgTgJkMlaC+Zk0fyKH15ZXnts8prZYeSUFN6N6JH9btvDhynf90oGtXSlApwosNegcmUG63qDM3dU02P7PmjBH1lxFAfvDcFqa9KsuywJ3h8dTGu05wRTkmiiGpRdkol3tKYzC9jSOmLeCIRJUyJQEp0iYwWitzRrz1xn9JrKe0+UcmkKehrP75RK9NizZHQybbai0XHbhQp0jd60jHL1RNixkFGpE/NGxMgrxBRPUx5Rvhb5qpbnNhh54RI3c2zrd6HQkYd4RuwNUgecnMyQoYSK+acXulMsLapb8kl5a9JqTZqnMSur2qpNT6+6DF/iCk9kpyjibO0QcQXQhB1+v+EAgAAM2fO3GKyS6USFi1ahBkzZuiyKIowceJEzJ8/39tm/vz5mD59ulU2adIk3HvvvQCAf/7zn1i+fDkmTpyoz/ft2xfjx4/H/Pnzccopp2D+/Pno16+fJnkAYOLEiYiiCAsWLNA/oD300EO4++678cwzz+APf/hDTX8253evgICAgFro0as3PnrFz7znmlta8PEf34V1q1Zj3ndOxmEHPYVu/dpw4KinUfrNXnjyzaMw9pLfodDc4m0fENCZ6FCOnkYjjmMcfPDBuPLKK3HQQQfhrLPOwplnnolZs2ZtUb0zZ85E37599TZ06NAOy2oEyQOw7/meLQ+CbarMzXNjtlrzOSWjHn3CU57Vji81rvT5iB+1JTcDz5ej/pleon+VJnutK7PZWXvoUdJrBVIurFp2QmAJYaU3NqSFr0doNE+anyb9FIisHDbI0WkiYIhOxULQhL1SJKt3pblzVJ4fQT9JfhyhcuqINJGzygeT6omFhBTJJ9SEWiY6JdGpCDmRTqojmPxGkSDXUZgUKMkmISIJIcz1o9dS+cnvC33PSmjPBLmWIitXDh+4HOkEXI+irJvSJzfrxvPpB5NnMXGkkCYHpvl6YD6FcTtTp1TDRIkXGQRR3k3J7ffpITq06TD6c5GlBzBkCc0lle7LNA+Vdc3SRNk66XjWhdTnUnnW4CQ5eizbiC1In6yKuJFKKYPuEFpg+wERAe2Mzg3Y+vH++++jWq3m/tjFsXz58po/jqmyvDq1fkD74IMP8KUvfQm333573b9kbs7vXgEBAQGbA73798Oxt/4Fq49biCcfHoPyxma09Cxh7H5zsPGnQ/DkVWd6F6kJCOhMbJNEzy677IL999/fKttvv/10wme11PuKFSusOitWrNDnBg8ejJUrV1rnK5UKVq1a5SwVrzBjxgysWbNGb8uWLeuwD0L9yZvUbAnw7/lqq6E+ixgi85tM4iiPkMlDnk4faUPnrd55m2ef+8FXUqJra5kUx3aCZjsHj20hXcMLRLbqtSqp5a7zZYgcM6e3B4tgOl0CKtETg61EZVnm1wlLC+vRlIxJPmNIkRAnyaZW8iLLREmz2pfZ1MQ8na2rnqXkEYSZ21LSQbAF6IVeUyz1Me1rmbzFEus5sTCbNNcw+TRJpBP5nluTkDZS2NdSrS7mvUGyBq1iKhTpZTrdRfagrf9GoTKy9CliQW2M1NMsTt6Nn+rUq18pngjw+8bBZQm/DmtIULNB9NajA3D7iq/SplaLk3GyIYaQ0vgUUU6IMVH6U51T/UAHp0Sy6lZqiLZPkjIJE/XDyRp6fyqbSLn2k+rLIIkCArYQzjzzTPznf/4njj766LrbbM7vXgEBAQGbE0P23QsTZj2Gfw3/M55/4iOoFgvYYcdWjB12Bz78wRAs/slljTYxoAtjmyR6jjjiCCxdutQqe/nll7H77rsDAPbcc08MHjwYc+bM0efXrl2LBQsWYMKECQCSlcBWr16NRYsW6ToPPfQQ4jjG+PHjvXq7deuGPn36WFtHIelOHnuxJeCbv9RQ7+Oi9FyCbbxePS5lzWuzdPqIKbNvyAul3+cbl2PiT3g0TyLFjuqRVpl67YZvPE4nskoj69jVSb0xC5bbxA7XF2mNhoAi0TxkrSjbRv6iiSR9x3vQEDHJZ6T3DWljjwohhLXqlYrmocun654VZhNQy7JnEA5Q54z3EekHHjCBKI3miSgxpa5hury8Wk0M7qYHjiYXzLUUeqbPBqdv0OrOTkkqtcw2VcxBul+390XUZOnkMnw3CCcXeFSPYk7UJc7yjXAb6vKBiHZ84uCyJLwPGCrfMp24UrcOtjK5JxQMOppH0IieVHkMwskwNsoMGOY7lQ0gIqSNtoUYaa2cpYiaGBY5RH3WHSCNTI9PAdsXdtppJxQKhdwfuzgGDx5c88cxVZZXp9YPaA899BCuvfZaNDU1oampCVOmTMGaNWvQ1NSE2267zWvb5vzuFRAQELAlMOKYwzH6pqfxdPf/h9efGYa4KtB38FqM7n0N3r1yd7x0r//51rp+HeZeNAWPTvs45l40Ba3r13Wy5QHbM7ZJoufcc8/FE088gSuvvBKvvvoq7rjjDvz0pz/F1KlTAQBCCEybNg2XX3457rvvPjz33HM47bTTMGTIEJxwwgkAkgigj3/84zjzzDPx5JNP4rHHHsPZZ5+NU045pa4VtzYVgu7ksRebWaeaN3hXus4xwTsXzNg2xT6li+tsX+CCTVzkdauaQyodKjaDR7lkzaSl9ekSQYYw8UXRxBZ5Y5915/Ui9cyduXOZvFdiq07s2GF0+vrHIoUE9ISfR/RIGm0j7CuWTIB90TzQfah7NiWRhHR1Wq94KZt1WRKtFMu0F7Iih8irX1InlibEVDrx5WNGH0vT84BMI5XSiB6VVbqdN0pyD7p0m+di2CRNvTdG3o0pDOdgb4oEoKyKsLgLjVp6pXHB8SkPhKOgEUROJE+NzZHp0+E8eCQhUMwmINNIHpJ+3Nd3vn6hOZXoowTpuKlSncQOUDvSRiqaJ/JE9PCLrn1TpJTtU0BjEccxfvnLX+Kss87CV7/6VfzqV79CtdqxRRcAoKWlBWPHjrV+7IrjGHPmzNE/dnFMmDDBqg8As2fP1vU31w9o8+fPxzPPPKO3733ve+jduzeeeeaZuhbBCAgICNiaMf4//h3Dr3kJj6y4DO8sGQwpgYF7vI/h67+Bf176ESx7wjxDH57+GeCOYThq5J2YcOgjOGrkncAdw5LygIDNgA4lY240xo0bh3vuuQczZszA9773Pey555644YYbcOqpp+o63/72t7FhwwacddZZWL16NY488kg8+OCDeglQAPj1r3+Ns88+G8cddxyiKMJJJ52Em266qVN8cL5ad8J3bWfe1Q6dfApRa35WK2EyV0/3BfusBUqxGGmJlXku8vmWmur7V86ya/tTHhveVID7xL2h+XDoX3vPnaNLtudrRY9oH5jYkyydvv6ykhxLU1fnlnVsNiV0H8LthSR/rImPUoK1RkXQMCjux2pneQlEgi9rb5NZwhrIUi8PbxM5rr3KALUceUGQ+XhEGtZzo1g8Qo0G/CZR+mrdnL52rFzwek5D0CEFKYT9WlSWTmEf1xXN49PJ+paOj9w2viruzemXQckTIlPfAuQhqke2ZI66tz0ha+DWLShyjShTyZsBmLw85LxuT+WaewnUNuumTeVG9T5pA7YUpkyZglKphFNOOQVCCNx1112YM2cObr/99g7LnD59Ok4//XQccsghOPTQQ3HDDTdgw4YNOOOMMwAAp512GnbddVedFPqcc87BMcccg+uuuw6TJ0/GnXfeiaeeego//elPAcD6AW348OF6efWsH9BmzZqFcrns/IC23377WXY+9dRTiKJIL+EeEBAQsD3g2PPPA3Ae/nLheTi492/Rf/dVGLbv24hf/ixevG8vrGodhMMPeRyrlvXHS8s/h+EnnYlXfn8r9mu5B4ePnYOHp38GR19/X6PdCNjG0aHl1QMSbJbl1TvQ+x1dXl1HkNSpk1YrOsRGffAtr56lQx1LJKsEl3LqZLVPdArE6Qw4ry1FDKBaBdqglh3nxIc59q3KVUqXV/frEh6bk+w5xWoBJb2ku/DqA9FJdbdVm8Ay1bC6Rg6Vv7HchJLXT9dn7m+rWl4dNmSW7Wnd9eVmVGWTRwfvT2Hmr+nWVm0GRZwKsPwjc1yZ6txQakYsC6kf7jUz7eyJbiyBUqUJvvFj/LJ1qtWsi23N0Eudt4PUkDEQV5oc27IbQL8qJIrp8urtvVHU8urgRA+b+NMBluotFAGky9rXpVOm3EkMRJU6o2zofjVGc6sp8hJT0uix9FZiFPiS7h47LZnpA6GlteIQY45uStykn1Gpiqgau6SaO/iITAlsLKJQrBi59GagA47+/4Mvr07JJl4/Ju2QkEaVahFzy78PS1U3EPvvvz9efPHFmmXtxY9+9CP893//N5YvX44xY8bgpptu0pE1H/3oR7HHHntYZNLdd9+N7373u/jXv/6F4cOH45prrsEnP/lJfV5KiUsvvRQ//elP9Q9ot9xyCz7ykY/oOqtWrcLZZ5+NP/7xj9YPaL169fLaePvtt4fl1QMCArZrVCoV/G3a6Ziw9xz0GpS8miUl0LamO6qfewZ9dzEJ5kutG7H6xn3Re8cNwKlv6GXdA7YfdOb/w9pN9DzyyCM46qij8Nhjj+GII47YUnZtE9gsRA9FnVeio0RPnBI9lro6dZbaQfTQOU2cQfTUImCqMVDO0FdzTiiBGGqCX58+AKhUgRKaWZtaxESyleMmVNUEH/DodX81lxAoViOU0GKRM5yg8RFGEkBrtYURLD4bk09z3QVay00oMz/r1llpsf23lPt1SghsLDehQgIIzStbrj56HEugtdLNvX6kQMuRts7WcjOqeUu6W3NlY0tC9DQ7BBAnkzhkDLSVmgBZcM7VIm3iGIgrBce2TBlkLi/aUqLHVz9PbwWIqiSmK68+9TcGoiIgpOeZlyVDpvKrQBN/INRzc5ZjNJVgoohqEEqWreUYhaqnPtPDCSKUKmgpsiXQnbbpiNKES1IUlSqIKtYgtRXS9jHR3VZGoa1sETFJNbbPbZExZGtrSuRIT/+k7S2WNCV64hLmln4XJs0NxMknn4wLLrgABx10EADgmWeewdVXX43f/OY3DbZs60QgegICArZFbFy7HvO/9e84/OD5aOmd/Jxd3tCMZ18/HAdfdg+aWroBAOZd9g0cOfw2PPLCKZnLvAdsu+jM/4e1O0fPAw88gPnz5+NPf/rTlrCna6LWhGwzgbyIoCdeAv4ty8R6tnbbsgk6/QsauV749Pn8TeTR1bQMOZCk643YeQFDhhhL7CTN9jm+RHpM9PgIF9sWuvoXMq01epJMNqZOnJbyfuOki19n8h/xUyUzFiBJlFX2HNVaRZKZnDg2iUKWq07Px+kmkbwjZSVOFhKIkk+p3uGCZCt/qdw5AqB6JdURIZYRpCRZeGXWHWAPGJrEWW+RBGTUvpsl3Uhcja3Lp59WlelB1o0BZOv1vJlkfBSwc/QQSEIKUX21dMIvruZxWsbSPtk5emRy3srdowiULPl5Dx0FlcsG5tO6+2WyWRmgfcK1IWyTgvU1mD4Y2Zaw5F5M6sS2Ot968yp3lLKD2hOCehuOJUuWYNy4cRgxYgRGjBiBsWPH4sUXX8S4ceNw6KGHNtq8gICAgIDNgB369MJxP/kznnr+YAAJydPcs4yxo+Zh4093w8IbLgAA7PO5KQCApg1vN8zWgO0D7crRc9lll6FSqeDYY4/FN7/5TXzve9/DJZdcsqVs266hv6N3slZfAFc9ZmRMfXPl+OZ0Pl28jB7Xo5cjq42sY9+N3+GyVZ4XTiT5CQJ/XXNOwLCtZp4odU1qj4QgxBF0mc9KEqdhSZTpWaoz+bR1qvW8HJ3S1in1H1cnlV9ASgRpGaauIqJUU0ntU/psEwxxoFqmE346105y9Egzv7UsIr1LX0VLeCV3BPCJNq+jyRNb2yYh78ZQxyLd8ZEZlNDxQdVhooXkJapueh1SLstarIkO6lpdkOdX1gNCppxIXlV+7VL/MlPQ+HRZBJqElVTZ1yx9kHNCyYmz8z7spXUN/CSRrw5xUihyUtr1dXPaabxzYlddQENw330hD0NAQEBAV0G1zx4AnsCjSz+DAcXnMfLgV9Gz/0YcjB9i2ffvwj/XH4LBo4FKz10bbWrANo52RfRceumlGD58OL7//e9j+PDhgeTZBKRzY3urEx1oorUKj8565NWjyycni/zhwQm+YIV6dXLYZIm/rue3dVKu/pqzkn3aGrIInuwjWx6ldmBpt0u4N3xxdaNB6l7g8UOAEMJaobxdOgX0Cm3acwFAR/MYnTQAAyIy1jpLrIsMnSIlmuCuEBdRvUgCabRPAjJZS117Za9WppbGTjZlD+8Jy3frtEQSZQS7IyLR8by2Hq6mvptSdDyZLiFkrDHkO5CkIuE/OqSW++W7MWGXOcFF1G5pPq0yxZFkEUtEj6Dn0vORUkztMkPYrFTIly0E0lf7WAM1tmQ6vmSkN6tTmCxLYZQOfhH5B4y1slzqPItIst7R09c4oJG4+eab0bdvX+y+++7Yfffd0adPH/z4xz/WxwEBAQEB2w/Gz7gBpfUtOHCnv2PkNY/i6eYf462XdoGUwJC9V+LwUX9GpVjAgV+/tNGmBmzjaPerW5VKBeeff/4mLf0ZwL5a01l4HZBodxNXKxNQS54+Z62LbTbJNlVG5yuyjg2ezzxkBTmIGhKydSuKwn39KLuVoiRofVVu/nILVCSLf1H1Wr1kS7VbitQiU9O6DvzatVen9Oikk0umM5lsp30qkw3WWOEvr7F+FKZuzJdml0K/pqPmsOp1Gko12cvdxxCI9Ss32g7ikSDRR+5GJ9KkVSx1rtt2QSKdyDOBvpvC7Xg4Sun5rDm8tM9Jplb7mSHXS55k6WR1hYDrD5Xh8VVm9Ie+HLwJKXNeF/P0iXPZZJpHzZsPJ/2QYGMn+bDIMiTjm5It+p8wn3aCJEkMIoNdvx7HOkk43pvzlt+c2SJkUEBDMXv2bPTr108f77jjjvjrX//aOIMCAgICArYYevTqjYVLjkL/oauw+sZ90fbaE2j6t99hwWMHo1osQERAU7cqut8/BvMvO937NkZAQD1oN9Hzta99DQDw1a9+dbMb05Vg3bLWL+i10Y6q2VqZzlrydAAFicagIRa8jM/duEq1RRnl9frp6zpDEbgvVOXZAN0iWWhdtU7oGNtSyTRTnTbJwo9MqUCUzrd51IlqxVu6OumZPH2EwrCvF2jsiokwMvK4TqnlWdaKrP8JpVE5IoIQEaL0E3S8IGJ9R/ZTUoWOOzt9jLRMTKqnEQ9ptI4b95Sck2rsekefGu/slGfTUUmR8OegqQdSwhNiZZtsGUYMjEQ2kZH33YA+DpQfqrnqW98zA3D9VPucqOH2qDp5NzyTa10HJpNG7uhTrEwTVx6/sx4ySV+QgUZstLtf3UfmvEUQKhl0sFh3fBLhA6meN2D6iA3pkHb7k3iaOwAZm+fr74CGII5jrFu3Th+vXbsW5XK5gRYFBAQEBGxJHH39fXh80XHo3X89jhx+GwY/fwQOO3IxqqUI/3p2N1Q2NqNb7yIOHf47vPffQ/HC70NS5oD2o105egI2HwTYfKgdZG3Hed1Eq54WyPplmdWMSOM67FHzCN/8L69tVv162hsaxLesefZ+opPOgmx50ilTGXsMJWQ05yzNbekpwKzBZs/QFQVB21C5kUgTwYL2l9Ce0ytkiCiJSFYhHI5XkBrQPimdai8CkqTHgImo0RJsPVZfyZi8zmL3BQcdpUJI2xdJRjARYP/YEZtPGbn3GhJSxc3FkzHjZUEdqr11mkQatesG1SyoAIRMJv3UHy7Lka0iPjx18m4uwHLXIkOsYBDhlScpuVKHv4If0H7K81ES2zx69Btl0i4TRI4mrlQZHxDcDt2d6oJ67NENkgKHLxFIk3MDTlQQXLHqDkvkxaZdpp1ZTz5J/PPY7yMBO/4/lIDNhHPOOQdHHnkkTj75ZADAXXfdhXPPPbfBVgUEBAQEbEkcff19aF2/Do/MnIamDW+j0nNXjJ9xA/bu1Rsv/m0uCn/5OvY+8E0M2O1D9G/9Jl668AcY/NV7sePu+zTa9IBtBO1eXp3j8MMPx4MPPtgll7jc5OXVO9jzFbZgef1kTZz5akktGWVWI/bU0fMGMqRioK7F4Ln+Srq8ei2djm4AsRTWUuc+Hb59tby6mg3FVj3htFOkioRAOY4Qo5CjQ7D95LhUFSiimz5Hl0HPKlNETLFaSFcB43BJJknqtVWaUSIcbyy5bX4/AWBDpQkqvIT2jT0nVTKEtnVjpQUVsuy47lvp2mstNR8LtMbNHpvoPJjUJ0t+byi1IKb9Llm/SE8ZkjehytUmZwl1M8YM+WHpl0BbsQmgy47nTaYpsSCBaqUG7+4jOySAtgKskJ+Ync/SG6fLq0tGUniJF2G1KxTh15lDLgmZ1PMur55lp745YzSXmDz+QLAIKlKvEqNQ8ejwEFtWYFqpguYiKYil3VeUgWKyorYqoioxMOPB4CwX31ZCoY04qkmftJK1zLpth9y4kawSRs+TjvScq1TbMLf0+7BUdYPx/PPP4+9//zsA4Nhjj8XIkSMbbNHWi7C8ekBAQFfBQ1f+Fw6M/h92HPYhAKDS2oRnXj0SY793D5qaWxpsXUBH0Jn/D9vkiJ4nnngCbW1tjqFr167FFVdcgauvvnpTVWyXcKMM6kdH2/FfnbPmV/XoTCI6pHPOVy9PVs68MFNW1nwt77jGHI/U4xRHlo009sVIMMEKNmFi6zGaYjQhySXjvI+hy1y/09//hXlFJZHlJyaS/URHQoYYCkvqUhqPRHVGVp9Yr6lIUYM4lORYgnPKtG/5fqz2BKWLUj951IulKiZFSUSP5JWs9mZ1Mau/pd3vNpEl0//sPottZrB9N6oQzr2Zy6aqwzi9crScd2ZWex7Rk+rkKbh9N5CO6Kl1w1NiRLinHZvcGzLznLXYlM8ETvyoHU5KERlWJBUlVYgM6Rl4DknkPGwZwaJ8SD+FrsOIHcdOItQJSVMRPcJeFl635fWlR0ZAo3DAAQfggAMOaLQZAQEBAQFbEY698L9QKV+EueeciAkHPo5ufdpwyKi5WHfzULzaMhWHfD0sjBSQjXbn6FH4/Oc/j6uuugpCCKxcudI5v2HDBlx77bWbZNz2DP6dvT1wXhNop872EDxcJ9Xty9Xj1dkOubU2oLbv6jUI6ifvs2wdJmcLz93i5npR5a5U21aafUdpiNJ/Ms0GRP+lOWQgyEZzBKWyyRxOUTW8n6hO/Umum7ASfwiYRCBqVSreW5IQH9LKzWP3r9uHfKxk5uUhlpskwaYv9EJIIiWeBJL8OOmm8vMIsqKWEJHx1VrCifYYTZRr5vpqIk56Hiq3j/VP5crJGrQOk0OPDWWkT/kSWPGySHWERydHDgOq89oIeuDbTN/XpZP5RPuUDFwDX5/5+lCwJuQaWeJ9ZBGXQeTSVd2s8Zp7MW0/jQphTqg2fMk6JV4pR5Rc0wIxJkqFCC3MhuW4NCxcRGz3PZ878j+CgICAgICAgE5FU3Mzjrvlj1h11KN4fsG+iMsReg9cj4P6XY1/XTocbz31SKNNDNhK0WGiZ9iwYbj//vshpcTo0aMxcOBAfOxjH8P555+P//mf/8Ett9yCXXbZZXPaul1BODv1o6Pfz635RDvV87mZmUhJPbnwvQXok8/nu+3ZADjzNp+tvvmnL1jA3exVsOyVt2wraEYaXqbqm+m7+atW9pKIUbU00YXJzUpUlO6xvMi4jm6fyXQNsVQ6XSHNWWFMLe0Dyy+qSVh7hniRpL5a8Ur7Q1ZjUxtfqczxQFIOw74GauUmqZkgCRknG2Ss+9esBBcj5qvFeUaPmqDrsUMGknXlpURs9arUr/ZkDlrfANaKBfHP7Qrr8jjHHr0cXp0gDwVSSJfojtlxKsdLoOTotNRkkUPtfQhIskuuj0X8pGY7Ngp47beGCDzjxVn+y89TWd1h+SuJD/S5qfRU0+tKy1Ih6pND1YmQMFScuQK13YcO/E8ooC488kjy5fuxxx5rV7t3330XxWJxS5gUEBAQELCNYreR+2H0jYuxsHwdlr8yEAAwdN93MPD5T2LRt49EaePaBlsYsLWhw69uXX/99QCAlpYWPPbYY3jnnXfw9NNP45lnnsE999yDOI5xzTXXbDZDtzc4E4Gs7+BbQKeeN7RDJ58r6eN0lp+V6qmWiqwpRj2BCfnTFuHUod3M57dS6/StPOWdggNOmcjuJ8eypKwAgQroyldGrs9Pyw7p+qU8EaQ+kMS3qDqRiFCFSkZMbRIZfUv7UpqZs5RWG0VVKP22JSqSwTdR9VFKaf3YbmO9ASOoTkJgpjN/AbOymCKNlHzfK2e0D6XnhPEJ4By5VEW1Bm0WWF86AwieYz25z1BaU6epY/ySLnNhTpq+5c8sPvDZpxIhAfsVJ4osVtjcnN5+4YuD8WsX+W9G65gT74KTKuoVJ81qktEiYUXXaXkChIEihA2XZaEAiCr0A9rq54yO0wSSAMhrmVqvEqLZUadxwBbCAw88gKamJvzpT3/CEUccUXe7L37xi3jttddw0kknhcjogICAgAALh3/lLABn4a/nno4Jez2AHQZswJgxT6P19r2waPW/YcKFP260iQFbCTY5R8+GDRvQ3JwkS/3sZz+7yQZ1FXQSt5Or0/eDehZkxn49OvNsyZKVd64WNCGRoZ3LpXNSe60q3l8u2WNr4Bl+VKmdy4XuVYkWe09Jt8kjW59LZiWl0tEpYVYNozl6bJuVdN9KVS4Z5OSV0RNLO64pKU3y5fA+4ro5GZDMk0ktQfpBCqLT9J+KxkgietTk1tdXvt5MbLY4ATaMEp2xK6ueHD2ZJAdjVPjQ5bKtcj55r8MOnw5emJHsGDAcRM2bVNj19FtQvsGVlURako2XgXAmSgeT643oYXKse0t1py9siRxyPcYYpVgZxBRYtx+7drJq2jnkORkblKnUg5QwTITfMbIk06uO89LcB3QUl112GSqVCo499lh885vfxPe+9z1cckl9uRT+9re/AQCWLFmyJU0MCAgICNiGcfwPfoE1K9/Hwss+i4PGPo8e/VpxaL9f4t0r/4Q1o6/BiMmn6Lqt69dhAVvdq0ev3g20PqAzsMmrbnVlbPKqWxx1XolyXetY+XTGnmXS69XpJzGUiKxzVSkda+tR6Vt1i7bN0imRJOuteFbdqqW7XAWKsDPYcyJCvdTE9ZdjtQIWp37Up1mpitYpVyNLpy2XRtgI53yp2gS6mpaCsZFGypj91koTyrLZe859Qc22o7WS6KSTbGMrTZts27yBrbrFSRL11hO4nzJZJYyei1lbtQqWRSxJYGO5GTFdAYv7Rua+1lxbAqWKWX2Nlmu/yD7tqyJfdYs6m3OjyBioVgoAkV0T6VxdtKmkLkynfRFde2IgqgjDD+TosVAFoqKA4H7Suh7dIm3bVKlDBz8ux2jmb7JwYoc+3Ej7qBKjUM3RQfgY+6auoKWNFXKyKSajjlSNilVEMVt1yxp4drn2oa2EQlvZ1FM3ga4rSXtyXkrIjUUgTp+2sa8O153sVypFzC3/LqxgtAVw6623Ys2aNejXrx++8pWvZNabPXs2JkyYgF69euGWW27BM888g/POOw/77rtvJ1q77SCsuhUQEBBg49n7/4Rej03HsJFvQURAXBVY+vw+2P3c+/H0NV/HuBGPoKWXWdWztL4FC5cchaOvv6+BVndNdOb/wzqcoydg00B/Pc5lSjY31MRCbbC3Gs2cTWV0yTqf9TYG1eeTmaUTyA5uoLZm+ePTGZMNLO8Kz9SjWrs+0AiaZKOra9FjQTRWUwtcTVSb+Wvr5cuS81bcy9Qeaeyj+YKUdt5fAuStJJ0JOdmshMjCyEvy11TNvjR5elSuHpM/x9alxqZ65UayE0JtxB/TdyrnEPEpnRtbuYnYILLHi3AGj0yLZWqHkU5GT705ejgkdHSUfuXHd1PGbLMva/aWBUE+BdhBuvEcPXFS7iRU9uj0uZH1lpkdGpbjQx1+Wjm/lWjeDzLj07IjFezJ0yNknGxC2l0Xkagl3gN6N93hOaOkRDIwU92KoNEf6uHNbVfyYrujBdHLO9jSlzdIAjYFlUoF559/PqrV/B9nzj//fPTq1QtPPPEEfv3rX2PixIn48pe/3ElWBgQEBARs6zjwU5Ox18ylmPvq2Vj7bh9EBYn9Rr+Clj+PxOFj52Ddqp549NUpWDH6CTz66hSsW9ULh4+dg4enf6bRpgdsQQSip0Gg3+Ebpb89c0LAnbhREsC3QBB9cyDLBiWX68nSx8vzZPvmdj57qB/wrIGV/KMEjvmrKB47U4290T0Bu9cKRCeINmWN7adk2v29K4heuopWQgxFOm8NX+lLkMeB0klpE0WaWJ0pQRIi2yuKCRT0vln9iqz4pfclIYkkYiEhRfJpdCVEgySkA83NozxQK2tFglxHOvFXWyQhInP96LVMnPKniVYFal0vc60iQnh5Nvdi0guWEF9qFGXdlD6ZWTdeFlnESROqTx1Q4oGuuAXzKYzbmTr1ZaOqfEPWd2Nm2e/TQ3TQN5K0/lpkV9YDxV7ezVQSAlKoVenINUsJOKkM8V1Iek6vqmUNTGOYdwyRA0HIGimNvVa/StO3OjkS05fJvgVsKr72ta8BAL761a/WVf/ee+/F//2//xf//u//jo0bN25J0wICAgICtkNM/K+r0fPrr+ORR49EeUMLmrpXIATQ1FxFn6F7YMjIUTjmkpvQ75ylWLWsP8bt+yha169rtNkBWwiB6GkQ9I+seZOyLaXYN/Gtod43baFBBVmBBbVMyUMWGZUl3zcn5vronJvrSHzwRfQYOscmY+h6WD6JtH2igUeeVJ1adM/QD5QqUtbTDDn2/J22tENAhFqJKuMfJbMAm9CwUkZ7x64iBUxETZx+Il31SlqbmpgLQhIJCGk2CTW3lc5mvTQlTCyRTCOIFDEVp5vWFQvIdAPMdZXpkvLJDSId96yBIwAp3Cgi7wpY/Obgo1ImN6Feqj6PEFIXW8mpJ6KH3xhKRhYBJWA/HBS5BvqJ/BtfuSLN0IAkz708CI8sXxkfEtRsEL31PNQAt6+8ETdpNE8cQyCGkNL4FBFOiDNR+hOMWLEGpyFtQMeRJGVpHUg9bjRZQ3gcvUXk4mo/ffoCGokhQ4bgi1/8In7zm9/gU5/6FIrFYs0ooICAgICAAB9aevTAR2/5Cx5/6TgAgKwK9N1lLUY1X4xXLtoPq/75Clp67ICXiiegpXcRC2ZOa6zBAVsMgehpECTdaQ87spmUO/OXGuq9Py5Dz28yf9yvF1nz2iydPh2U9qCvIeXNea05EZDGtfBol0SKHdUjrTKhZ6P2ZqgZ9x+SWBdN28A6Q30mrykRfW4aZZeIsq9QGtFDfLTr015JYBZft/vUdEVCyNBjSNOLkepRIRCxqB4zKTfkkoSJ5pEpmSO1XLbpCaxISQQVl0SuorADJtQ7YSIir4CpKBUkk3d1Q+TemppcMFdM6Jk+60bfoBW2IPoKW+aNyCfyQPZN59MnmQyfPk4u0Kge5bRqX0c0ER0WIKIdnzh8tnseMnroCWY61VuvDrYyuRttk2wSkYnoESZHlB460upIu++UbN3ndHACiMi7X9oWYiStC0XU+Mgh2r+qQMl0fQpoLH73u9/hc5/7HP72t79hxx13xKpVq8JqWwEBAQEBm4SWeD0AYN4b38T7/xwAEQF7jXwTO/ztUDx2yRewz+emAACaNrzdSDMDtiC2C6LnqquughAC06ZN02VtbW2YOnUqBgwYgF69euGkk07CihUrrHZvvvkmJk+ejB122AEDBw7Et771LVQqvkyhmx+C7uSxF5tZp5o3eF9nyTHBN+nd0hxVe3Ta+k28DXJ84npM7IsvW44tRf010SB0Ju1Gx9BIHpobx2TIoHWz8hBxAsd/ldwonZjY5Hpn96P9QphNBQnrDZZkoi3JBtAQi8RD9cqXZLl5CJegtaRiSUQPtE6bcLCIJ12WRCvFUuUF8kcO0Ve/pHodjNKV6c3Ae5gfa61SphFEOXl6atwsKtdR7q3PiQ+g/ogeCXe4ELmS7JtNkQCUVREWd8E6I1sn4Yccn7LA5ZAIIieSh5ut9n0PJV6W2WeKtaGDlcbwScPdOA9Q4ZevVkmzBpPSA6BKdZJ2oHakHaeieSLf61dpfdb/mnGLbZ8COg/PPvssbr75Ztx666144YUXAAA9e/bEiSeeiOHDhwMAdtllFxx//PGNNDMgICAgYBtHpeeuAICmeD12uuCfmDv/4yitb0FzzxIOG3EPuv/1k1a9gO0P2zzRs3DhQvzkJz/BgQceaJWfe+65+OMf/4i7774b8+bNwzvvvIMTTzxRn69Wq5g8eTJKpRIef/xx/OIXv8Dtt99e9/KnmwrJD3ImgZtTp/6+Lz1bjgmcWsjip+g8tJYbtc63R6c9kBV1InJ9UsdURvKjvh0TY2rbEtzcLsYiOzMPLIlKR6T3XZ18yq88kpkeCUcSf+WKltt1lQbh6DVaFWFD1atoHkLMpESAlB4LrNw8dG7MSDVCHgHSIWgs6im1g7/eJkAjiJRqmyEQoFE9huLjET2+MQOhxkyiJxKpj05USI0tFZrcg9LR5yjnBAqPqsnSSex2nUmL84gOxq5IKsenk59TKn3kUBb4A4X4SoaafxM54vNsj+gnOUFIL5vqoTqoUpkvnxugzheILiubtDD1LNInBmKVo8fnFPWD6I6UbJG+3hXQGbjxxhsxZswYXHTRRbjgggswatQojB49Gs8880yjTQsICAgI2M4wfsYNKK1vwX4t96BaKuK4H/4eyw/6G177xzDIGOg3ZA2kBHq0/ROVcqm2wIBtDts00bN+/XqceuqpuPXWW7Hjjjvq8jVr1uBnP/sZrr/+ehx77LEYO3Ysfv7zn+Pxxx/HE088AQD461//ihdffBH/8z//gzFjxuATn/gEvv/97+Pmm29GqdSJg70Tf0zVX/elPffIm4NSM/UP0nVsPhk+e3zHknzW0mmvmGVLquUTn0eagAEezQNLgp2XR03+qMUuGePm4VFZbEBKqb9Gs/R4Yy+JDlCLs3LvUL32GcDQLT5ugJAoFplgXrFKthgQcTovlsl+qiGGiepxyUVKqgmLQJIQNjmjdCmZqR36FTqq08oHZBI5q30T0RNBSpONSDnpG796zi1V3xNdkO2LsJFGqND9nKGYDwF14am+jubr4bKd13rIpok9YkOWDo9OkaUzD+qBQvzLjObxnPOCnvDZriJvQAerGq0yzc0jrVcP9YMVgP2uGtlidY4YQK9nlbz/JWNLrxXNA8DKz2P5o+qT9qpDFNNm2UQHRcDmxm233YbFixejWCziiiuuwFVXXYUPP/wQH3zwAV5//XV84hOfwFFHHYXHH3+80aYGBAQEBGxH6NGrNxYuOQr9h67C6hv3xbzLvoGWbgW8u8NErFvRW6/jcMjYBVj7w93xjzt+0miTAzYztmmiZ+rUqZg8eTImTpxolS9atAjlctkqHzFiBIYNG4b58+cDAObPn49Ro0Zh0KBBus6kSZOwdu1aHU7NUSwWsXbtWmvbZNQ72dkM0PMYOmdD9pZlZr0EUd70getg817nM2vzBTT4NGfpU5uSZeSpSZ2hd/QkD+5v+pxaoq90caImiXVBui6V3cqV4q4KZcrsQcPJJ6XLH0lkonfs/jakEJ2xm6XMaf/ZkTzJfkQ61kiOYEfW6MTDpK+1Hr6EOiVnHH0wr3jp17BSncK8aiZEnJBAIrZkm2ge129nFNE5vwAkIUEikdpFB2K9N4w02nPhIYhq6kLGeeagpDs81I9WIM+OTL05Op3AE2aH9zgtkzSiB3CjeoRn84iq60GnInoo2ZUV0SO440CSsMejmOY7AqBXzlK6rRw97JN3kJbPHNXXRjAf6EmYshDRs0Vx7bXXYvz48ejVqxc++OADLFy4EDfeeCPmzZuHHXfcEVdddRWuuuoqnH/++Y02NSAgICBgO8PR19+Hxxcdh9791+PI4bdh8PNH4Mjht6F7rxIee/JoPLtgBOJKhL6D12Jk9Tw8+62xaF27utFmB2wmNDXagI7izjvvxOLFi7Fw4ULn3PLly9HS0oJ+/fpZ5YMGDcLy5ct1HUryqPPqnA8zZ87EZZddthmsZ+ikH1Str/O+CZd92nu8uUzlc02RoTNjzufdt6ULp4Tr88mMyVlbtk3p8HacGlD+CN1GOm3UPo/O8RE4dn16ZM4Jx4rkSFr+mEgi1x7/iKD6hZDpa1mpbOHrA8UEmJZJRA/3k1ourBMxrSekO83VBIlhShTPok7G0uiklJWW47x/lM67vb0A3T1CC2TUTISEjag1aLNFuwW+CbwzgDyMRu2bxCKaXEOIRZLVY1xFXToVl5EODcF11joWSALEiDz+PNM6uA28c+2bM9sHwI524fIBOBEyWkcEK85QAFaUD5XpPIyk+2nZpQaiNPZZN4gwbRWLqD6VHUKm5okQ0bOF8eKLL6KtrQ3PPfccjjrqKERRhDvvvBMXXXQR2trasPvuu2OvvfbCokWL8Kc//QkjR47EHnvs0WizAwICAgK2Exx9/X1oXb8Oj8ychqYNb6PSc1eMn3EDjunVGwDw2M0/wIgN16Lfbqsx8qAlaP2ffTB/45cx4fxrGmx5wKZimyR6li1bhnPOOQezZ89G9+7dO03vjBkzMH36dH28du1aDB06tEOyBMh38U6D8Cqtxwyh4vtqwDM/q6krb67Vkd+as9rUMQ/1Ujy2bPqCE9fqavaRKHS+rmIEzLFLCpnz5hWnLAs5EaUkUkSkzO4Tn1Sh9QNIX3myPaGTdrtvTG8WINJXrmDmnmmdhIoyE1YJoUMNdbJmrkNXF/pY9YuqGgmzNpmPYtJ0FfVJQC/rbsEz0fbmm8ldz7sWWLtaJIQeQJ7rRjsjyxw2kPQ9QDuYN5AAUi7LeRzUo9Nnbi1SKpVpiLyMqoIRdak9mQErGS6ac+SZ5+kno4Mssa6K+XOWHut+9wxaywhWh9qlLxYn+TgDRuynnUOJrhDQs8XRvXt3jBs3DkcccQRGjx6Nu+66C3EcY8mSJXjmmWfw8MMP46GHHsJpp52GDz/8EL169do8EcMBAQEBAQFIXuP66BU/8547Yuq5KLV9DY+fNxmHjn0KPfq1Ylzfm/Hqd/8Xfb74Bwzcd2QnWxuwubBNvrq1aNEirFy5EgcffDCamprQ1NSEefPm4aabbkJTUxMGDRqEUqmE1atXW+1WrFiBwYMHAwAGDx7srMKljlUdjm7duqFPnz7W1lFIwHADfo4gEx1oYrR6dNYjT9S5BC+Xw+dSPpd9x/XCV1eSv1l1s3QmZeqvqSXZp9Hgp17yyDMzr7Nf1uJranEbTHv72PZLSaeZeWBtfIUg+5rwf3af8PpOZwrPvBUARGTsdpZY5xZQb1LNAtabLEK/xiM9q66L9HUaX2rtpFySTdvj6Xmrk4XRCUj7jZpUXYffgpGwXmezdNJjfh7ouFIyybeuqT6gF5VUrI/zzVab55fvQaSuecZDQufjMVyhlacnn7Rj4sjgjSwlZqPjMdmnB0oMuylIXwqpEolHerMa+24wpSNKB38Uuf2krhN9b02trgXySZd51xc8oDNw3XXX4ZprrsFXvvIVLF68GMOHD8enP/1p9OrVC0OGDMEHH3yAN998E7/97W8bbWpAQEBAQBdCS/fuOOrmOXh54K/w7suDIASw5/5voc8jR+Cxi05utHkBHcQ2SfQcd9xxeO655/DMM8/o7ZBDDsGpp56q95ubmzFnzhzdZunSpXjzzTcxYcIEAMCECRPw3HPPYeXKlbrO7Nmz0adPH+y///5b3Afrq7V3ZpwNa9LeTq2+CU09Juhz3uW6aNJbs4y2+qVbEBm1NktXHcgMcqijnV93kuZXr8CUbsKT6ZauuZNM1eg5CkrSUBtUPXtRd55AmeqjOrN9FI4FtvPs2gFEj5uq2fw1E2c+XmjOWG//y9RHmWywxoovizBg+pGPL2q+i4R+bAAA0iFJREFUsNKeJPtJslx/quvER5Fu1tilfUuij9yNTKQlsTjehLdgBLlLsm4Ke/CYT66U1s27Ecg5vuqe94FAypzAwDydrK7gZdxHfo6MLd9Dgr+5JlmZw2V4yCLnssnkTrAeXumnNkXCjGHdMYwsQzq+Cdmi/wn1GcN5F06SHTXYdbZ41SGuH04nWn4z4kqzsh0dtAHtxZgxY7Bo0SK88cYbOOyww9C9e3f069cPP/zhD3H11VcDAHbbbTd8/OMfb7ClAQEBAQFdEaM+9Rns9l+vY+6Tn0ZpXTc071DGYSPvx7tX7o5X5vyp0eYFtBPb5KtbvXv3xgEHHGCV9ezZEwMGDNDlU6ZMwfTp09G/f3/06dMH3/jGNzBhwgQcdthhAIDjjz8e+++/P774xS/immuuwfLly/Hd734XU6dORbdu3ba4D9ZXa5F1wg8+92iPVt2G6fT+8u3RSScL0nPenvzbKX3b+7txPX76uo5SLVyG8jPLFhXVIUldTqtI1oLr5PqURipTxYwkZb7F4bN01tOLxnvfq1zJf+aVJyPX88qStpl6YvRoTSqSwcx307+pb0KlgDZ9qvuC5cqhvkqVaDnDbf+LWVHaOonWceexJJIqDcTwzXXpXDgPIvVBpnl07X6tA+lkO7FBZF9ifsOpd5kiQzoZg8CXovPoJOK8NwoTSOr468Pvt+/h4iODfDezgHkbTrC6Ev631jy8l5dYInY4wVSC0OKasCH20IqANYCkkkevJ+snm5sxd6vUMpU+Ss6Qm8vrj8wZgAJ6QPBxsinhWQHtxt57743Zs2djxYoVeOKJJ1AqlTBhwgTstttujTYtICAgICAAAHDcDXfireeew/pfnoJ9Rv8LA/d4HwOWnYInzx+Lg2f+FU3NLY02MaAObJNETz34wQ9+gCiKcNJJJ6FYLGLSpEm45ZZb9PlCoYD7778fX/va1zBhwgT07NkTp59+Or73ve91in3O3KcdE8OO//5q8q2ouUC9siT75BObrPpqfpY1B8zSn1W/nvaKsiCZX5y62fuUhjDyLFLDaUfpIdXar9t8Kj0FqNTI3N6khBNElDyxJ51+Qsr8VYmLTfSK8U/VEh45po7Q/AKghoAv4bHtQ/IRa9KG9wkHJZbU6lhS6yOEmppIk3MJVH/GgIzg3GuQ6cpdfJy5E17p9K85sPwlkUbtJnkAQK0QJrkvTJ7voeFJGFyfTlLk0alHu+fCWqtn1aFT8APaTzX81byFR49+o0xmlwn9BxZJxHXQJeMTdZ6LYT0zzXmLM1E6IiWIEDSqJRMrlD5IJBFu8Edq6c+sJ5+0/ctkMGvUCdjiGDRoED772c822oyAgICAgAAvdhs1CvjvF/DQZd/CITv+Aj132oCxBy/E2h8Ow+v9L8TYL32z0SYG1ICQMnzL6yjWrl2Lvn37YuXS3dGnd/vegovbw7IwVFDV++0RESPOfLWklpyyPb3JJW3okIoBYm02uLxKDJR9smu0lwCqUiBGIVeHb79SBYpohh2lo5BdBgCluAkxohwdgu0nx6WqQBEmgswOwhDeMiW3WE105ukyZca2tkozSoTj1QmPQT/9OjdUmgDmZxJ54+srEzmzsdKCijTXRL1Upcgan/0SgIwFWuNmj00gOgU5NvfhhlILYtrvPMmy9JQhmV+XK01OP5prb8gPrr+t2IRkHXDQCjVumKRttVKDd/fdeBJAWwGa/ON18vTGQFQVhNTJaJNeI9quUIRfZ84NKmTStok/EPIeKPrmjNFc9Mjz2O1E55RjFKrI7hdKCNG+KFXQXCQVY2mfpwwU8z9qqyKqkjs3o38EL28rodBWIu1i0g/pQOHEUXpObtxocvJY/pGOtNom9SrVNswt/R5r1qzZpLxzAQGdBfXdK4zZgICAgM7B+vdXYcn3JmH02CWImmPIqsBLz+2Dvb49Gz132lnXa12/DgvY6l490tW9AhJ05v/DtskcPdsDNiVYXpKtvTrpRmW1V6cAkpVlPHl5uM48WTrtRI0tT149tmfp4OX0yM6Jw8sBYVlnzgGSlXK/k/wxyT8BSfLiZOvzzBSFfT2NZVwfoPLVSEidI0ddO1h6kytichKxPkmTzqpXW6R0X43y9WWi1x4rQrq+Klt0Dwpjm+5baeeYhQRJ8xPrTbIcPHq0Wb5zX0l0Btm3onV0e7WbyIpjPsFG/RCetNsx2zzdlQR/SPfmpM44gt16WmQMONmtpbD1g/SF3XVefXpXZNy/vJ/yHgBED0mTZFdlvgp+QfNvTqi0UdYzTQ1yAZNbR/9zXXf7wXfxzH1hLmZMbkPfeCJCnc5UHZ6eM46Qccv6UzAdAQEBAQEBAQEMvXbqj0NuWohFpavw4bIdIQoS+495Bbh7BB678hwAwMPTPwPcMQxHjbwTEw59BEeNvBO4Y1hSHtAQBKKnQdDf2TuArHlbvTqz5k716tR6yepJalUY35ytPXJrbYDrN9ehXoPgc9+8+a/Z+ApNZnNW0iF0D5dq22r/Tf5F6T+pS8xqXxFMeme1sZV5iOPKT3tNMFqbraNFV71KdSUtI7YZisWaJat5KaS1UpStl/qS7pNxkoyVWv2s5qbsCqXzWKH+CEBEgIgEhFqVCElOIONrZPrVWsKJ9hghpYSZG2vOiXoq3PXJEt0el8D2ybUzxzY1BuhLYG+8LDJ94OjkyCFU9CpVAuSAb0a28NmTo9biUzjnwdvyPss6psXEPEs84Wi0Ab4HCfFLrepmjVeq2Fk2LC2mPIrzsE3bqGvFirRyREmfFqgx1EbPE9VynDgc2febk4unI/8jCAgICAgICOiyOOzMqeg3/Z947PHDUWlrQve+bThsj/+H96/ZFYePnYN1q3rh0VenYMXoJ/Doq1OwblUvHD52TiB7GoTtNkfP1g71vdzs1I+OfjdXqqjKetVnEjjSjl7x6cyyI6uNKs87l2WXKvPNqX3teBnNXeP8+K2PhVXT0DqS2WOy9kinTvK3iiarHT2nvHB9Eea0tHf9ftl2xZJGImSNBJMsms+OhUCaoyUrBxL1R61epSIXqAbXVssOqUgd2wrdB5KWQif/VvolYkj9GpWR6cJlGrRtpKvVa48yJbucRNA8uqXWQLVUJp7RRd0NYZAjR6/CVEN+1s0pyGkyeKzF5T03o5WjJ0s+KbO4iizCy70Z6zqmz1HtEh3WvoEmYN6JJOX2EOUskVNBi3L2fWSSai/JPkhfSsC8qiWtOrYGbhM5JaLUbnpC2vVyPQgICAgICAgIyEZTczOO/tFsvPjXv6DXY1/Hrvsux467rUZcFVi6aiyOvvpGCCEwZORNKLVehVU37otx+z6K1vXrwmtcnYwQ0dMgNOKHVDU3ksI+rgeZAQpZvxYTnfXKrXerJZvSI3xux/W6QRN0z460kY4F9ktVYPVdu4VTp8DaqWgfE2XDXyAjfhGSh1pE/YOWGCFS+yKNdNE9EFl6aTQP1aDpFsPyeSbzQstDKlNH9Qi4EQYsssay3mJD0/PqVaK0WBCZKqJHRSoJ2NE8ApGOZLKjhLQ3jCiyT9hjPzI6lK9ZET3OZB8upPviHu0Ka8CaS5Qd0VPP3J0ospoKwInmici+qkORpTPVQSkH4ThZw1efT6RMm8vc0nyJj/TxyFBlJpCGjk913n3WsdXV2WJl6XjVdYgy770QkYvA7M3K9K2vi4D1jqE1iDz+OwoCAgICAgICAurD/sdPwrDLXsMzT+wPAIgKEoePmY0VV+2Ol/78ewBAS48d8FLxBLT0LmLBzGkNtLZrIhA9DYLIPNiyOq0JDS/LgczY9PmMnN5Zsr2Tsjq3emzNq5kvn+7RLBzumljKD0oV0PpcIphsQKLq1UeTstg6rE9B/aU20R5QsmOTCUjl7YBEkhbcbP500Pa+RWzROao+a+TFVLaaBZNNssmrprbI3JX2l0hPJKtxwZKpc+VIafTKxF+aH4jrtHWnfsG+V+iWtOb9Jk1OldwbxVGb6uDxUZ5O9+XsoRE97dbpq6KcFECcbjxfDwiZUUunJk20m/6HgvDIquUT4TNooJZLWrE2FJwTAR2ehDRRldm7YFq8c5MK0gfSNor6E0uzSQmganQ6kVqKlWI3vu4AaTtNSVR9nzAn43qfqgHbIm6++Wbsscce6N69O8aPH48nn3wyt/7dd9+NESNGoHv37hg1ahT+/Oc/W+ellLjkkkuwyy67oEePHpg4cSJeeeUVq86qVatw6qmnok+fPujXrx+mTJmC9evX6/Nz587FZz/7Weyyyy7o2bMnxowZg1//+tebz+mAgICAgE5FazQAAPDqM8MgY2Dn3T/A3u99CfPP+xjiOMY+n5sCAGja8HYjzeySCERPg+B8te4EsodNWewfmmsg44dwPW/x/jrtydnD7eCyam2qTZ7NgvzNOk83GjhAQwj4P75ylm/+6cvvQ6VR2YBAQVtgR/OYqBiqg+lknWATMa6X2hNh22GiU1T0jT1OLBvUXDKdOMp08pjMfamuCAIFnYlIiMjJ0WPleNI60ggmxTNIcs0Y4eAbd0nEEvFLmEgepQ9EJ/XN9LN0rqsqSObsptdoPiWoiB56QazJfw6ktMaXM7joQCUqRYGc990seTcKOef0horioZE81nGGXq6T8hU2R+LaknXTm5vTNpjwGdby6tLeNHfpuwbcdlJPOKwUG7+Qlm/2Ju2m9JzlXzpuVD6dJDmP0aWNgdZJE0PrG5IOFE3q+AYR7bRUb83BGbAt4q677sL06dNx6aWXYvHixRg9ejQmTZqElStXeus//vjj+I//+A9MmTIFTz/9NE444QSccMIJeP7553Wda665BjfddBNmzZqFBQsWoGfPnpg0aRLa2tp0nVNPPRUvvPACZs+ejfvvvx8PP/wwzjrrLEvPgQceiN///vd49tlnccYZZ+C0007D/fffv+U6IyAgICBgi6HSc1cAwPJeH8Ojy6Zhw3u9UGiOcejYx/He1XvglV9dadUL6DyE5dU3AZuyvLpMJwkdQbmuBctd5C2vrpB1uqSnwD65OfKkRKWGPi5TAohjoOSpn9WGnqPLq9cimmidchUow15W29Tjy5jb62Aly6v72hnygtsAAG3VAipohvVKlv6MWH27Dl1e3dUJjx+Jza3lplQnb6eSPnM7jJyN1ebk9SnSxugzk3oqMwawsdyCqmzy9jtdrj2ZNBv5MYDWajMoJNmREIil3S9AQhBtLDcjlgUtz9It/dcjaStQrjZZdXhfxZ5BL2OBYqkZQOQf1DmDVkIgLvNX5rhSn05AFFX2Xk+brBsg/YzKhBbVZB0B16l4oDYgkhnPPEogMf0iBgplfzOXVSTH1XR5dc8AEr5jIktUYhT4Q4j3j3TloFRGSzE1hulNdEivLAFAtFUR0YctfdhLmEga1Z+qvFhEoVg1NxOoDml/Svuc3NgKezl2Wkeac7S9lKjEJcwth+XVtzeMHz8e48aNw49+9CMAQBzHGDp0KL7xjW/gggsucOqffPLJ2LBhg0W4HHbYYRgzZgxmzZoFKSWGDBmC8847D+effz4AYM2aNRg0aBBuv/12nHLKKXjppZew//77Y+HChTjkkEMAAA8++CA++clP4q233sKQIUO8tk6ePBmDBg3CbbfdVpdvYXn1gICAgK0HrevXAXcMw7pVvdDvnKUobSzi1cuPwwFjX4aIJGRVoFIqQJ78VsjRg7C8epeAnlP5fhHfkkj1WMERyJ+Heppb5vpSaagtT6bnd2b9Az4vz7Ijy0aegDhPJw2SsCJfrH80YMGN5rGn526uHhqzI4nGAtEJS19EdAqtgW/+PjF6bQ+TfRWpwPMCCfI4oMEZrq+mQJKoHrUakcoJlMQrqYgeYW32im1JcIEmo9JxGadl+jUuJ6In9VBAe6AibCJBI5hIEIbO5wPNx7g+uiSs6g9lS4GMFB2BRSN6fAM0bzCTHD2ZF9kT0eO98YBsO3xMm6WHKacRPSqqB9JdtIzrFq40APkRPbQ/fA8E3zmuW8KO6MlQ59jqe2AJUok6LASkSO5j3zWTeRdRESxkLOpoHr1iHLHPZ686SMeNXkKdg5A5tkyiL4qInwHbC0qlEhYtWoSJEyfqsiiKMHHiRMyfP9/bZv78+VZ9AJg0aZKu/89//hPLly+36vTt2xfjx4/XdebPn49+/fppkgcAJk6ciCiKsGDBgkx716xZg/79+7ff0YCAgICAhqNHr95YuOQo9B+6Cqtv3BeLfnQJdj7jNsxfMA7VcgRRkGjuUcH71x+IZU/nv0IcsHkRiJ4Gwfpq3R6mZXPpp/M31OaafBNimiYkzqiTa0OG/NhTlmUHt1F9qil4HqjtxgedyYb8s+3y9ZX0eGO3TyRIprVKaiJDr4+cArL7j7YU7AoJlbfG+Rdb7ZROTuJBTZ7NnJcYZgaVk6NHxslqX9ZmT8o9YvS8VqYnJDlJY6psPxJ9KmrOSoGSdodMu8TtS+UYvKBckxopqn917iOH3YBbxoUKYfEKmYPXm6PHIz9LJ2XwcvWRC2s5rco8OjJ0qmsKcj1t/z1+clnCUyaNXHpzWm/z+VzL6h/ngSK9m5AxRBxDIIaQ0vgUwSYUaf/pT3WOGBJT+TEsckbVsfL2pBdd3YhpcnXvgyKiugQhh5TeDJIoYJvG+++/j2q1ikGDBlnlgwYNwvLly71tli9fnltffdaqM3DgQOt8U1MT+vfvn6n3t7/9LRYuXIgzzjgj059isYi1a9daW0BAQEDA1oOjr78Pjy86Dr37r8eRw2/D4OePwOETnkS1rYD3/jkAUgJD9lmJnRcdj4cvOr3R5nYZBKKnQZDOTucqr2N+ZiHvh3seZFCLNMqSz/fzdPp02NMne8lyn29cjsqM48b0JFJoTA1N0GzWxXKto3E9rtSIpB+xa9G0JHTdLarXnb/bMtwrlEQhmOgXWp/HUSVIKQzyj3eqSHPnmGNI04uR6lWR5s6xNjUxpvRUQubEitSBNCttOUmBUzsl94SsJCZMwIReTSqSEFGa0DndzLVUE+2cCKr0gEZpqZGTuQJW3o0haJJorozAN8TyInr4RuUKsu8liAj5YEKqoN/FytLtsUUNC7Vv+VbvA0fC+5DRQ08w04neTB4jry8BOINH5x2LIEWUfqaRZSnZZFLjEAaO9p2SrfucDk6QCBsyjqiRVuZzpZSQNZnklTT73tW+AgI6H3//+99xxhln4NZbb8XIkSMz682cORN9+/bV29ChQzvRyoCAgICAenD09fcB//kmHnnhFMx/8ig88sIpwKnLMPiiN/Hwi19EcW03NO9QxhEjf4fXv7sv3nvt5UabvN0jED0NgqA7tSaCm1Gnmjd4cyfnmJBHDNUiibJQq357dHL9gu1ndSuVo0gNQ2xQubYU9ZdSPVQaj44xEiltEpO1tWgNuuaWDVHHVXLjgmJik+ud3XcugWTRU5owQTrRlmQD7KiepC8Tska94uWL5lFaUrEpeSRUOY3iEXYfJ3NlciwlYklX3JK2Hvbql5Tpku2Urkyz+/Ie5sdKa6xW9kLsrrxVZ6ibev0s99b3kTZZ8n2bQzCZT0nL9OZcbM2oCO5HLZ2EH7Lcz3OYyyE3Rl5EovUs891E9dgtQVkbOljtu15xN/wBqv7XymWqVdKswaT0AKhSnaSdMlq/j6aIn4iQQ/xpSByV7DO2fQrYvrDTTjuhUChgxYoVVvmKFSswePBgb5vBgwfn1lefterwZM+VSgWrVq1y9M6bNw+f/vSn8YMf/ACnnXZarj8zZszAmjVr9LZs2bLc+gEBAQEBjUGPXr3x0St+hiNveBAfveJnOifPsVfMwodHzMW/XtgNALD7/m+h598Ow6NXnNNIc7d7BKKnQZD8oMZEcHPp1N/3pWfLMYFTC1n8lPOD9SagPTrt+aKZYdbqVtqexIHoSbeR65NgJPOIHmHt22SJigSJIEgQgYqBMZEiXFPymeWRrZtG7bixO7wul8G9JP/IPNRE8xBiRpEo0mOBlZuHzo0ZqUbII8CO6JGS2Z7aYV6dI/77IofYq18mqsfELtHkLr4rrroquVaJHrXSlxsVUmNLhSYqrZgpFz4ChUfVZOkkdmvErDiP6GDsCstlXeuGNC5IcqrWQ4ITW8RXX36xrM0rN8tuK/cROUFILx7Hp3X4iJks+dwgdb5AdNHrqQ74hZJxkhnc6lhqNz2GkavySemVtwK2J7S0tGDs2LGYM2eOLovjGHPmzMGECRO8bSZMmGDVB4DZs2fr+nvuuScGDx5s1Vm7di0WLFig60yYMAGrV6/GokWLdJ2HHnoIcRxj/Pjxumzu3LmYPHkyrr76amtFrix069YNffr0sbaAgICAgG0Lu446EHtfsRTzFn8W5Q0t6Na7iMP2+H9YcsFIrFnxTqPN2y7R1GgDujw68cdUPWeSnrlGBvgPwXw/T1c9dThVYZFRGTpr25JI8sVH+Ags+mN7rGsJVp9SPllys3rGftGKzgdpRA+siSOdzQP+HqU22vJ91ugf81MtUtumzic6BasvSB0h0rl/OuGkizJJYrNQx1JlCUpIA64zaUd1JrN3ayyQsAyB9BopLiaNZBDpsRBSr5alcgIl1W1Om07O6fUUgHkrzHM51bxZxtRnsww86xD/vkeokKpvRaZuawhQffXeHL7B79vnY02SYhJR0xGdKpUM5S5qPlBUg5hZ52vXjmeb10arXxWZYp8TtJ56oFLDkoFoKjkPNeY4vd5V+hpWxqdqYC3/Hptypz6gbxDf64G1lmIM2CYxffp0nH766TjkkENw6KGH4oYbbsCGDRt0LpzTTjsNu+66K2bOnAkAOOecc3DMMcfguuuuw+TJk3HnnXfiqaeewk9/+lMAgBAC06ZNw+WXX47hw4djzz33xMUXX4whQ4bghBNOAADst99++PjHP44zzzwTs2bNQrlcxtlnn41TTjlFr7j197//HZ/61Kdwzjnn4KSTTtK5e1paWkJC5oCAgIAugGOvvQOvPvYous0+DUM+sgLDD/wXWn93IB4vT8Xh0y5rtHnbFQLR02j4Jm9bCGbiTNTVqdOaw9Shp5bojDmapUt4yvLsMp+29DxdVI5ZectM4A2ZIslRlizhlPnOGR5BoACQ5ed577re5M2NOQElyF+JxDcJE9Gj+tf+tHXyOvaKzpxK85NjKrYnFnYNRcgI0ruCz9QlwJdHd8nKNJ5KUp4libJRY14QCk9pMt1t7LEJIAY1V9auCV0psggwENl1ILb7NhN55A+9UFltnH61DwV1niuhnS+SSyJoWZ06HZK5nptTtY1sXsWpn3FzOsV5DyhVmUb0cB8cBWlkGWeMvPLpDaSqkgP1Gpa+6RShQ2X6ZJACPRZ8F0YYfYq1DRE92yVOPvlkvPfee7jkkkuwfPlyjBkzBg8++KBOpvzmm28iigwBfvjhh+OOO+7Ad7/7XVx44YUYPnw47r33XhxwwAG6zre//W1s2LABZ511FlavXo0jjzwSDz74ILp3767r/PrXv8bZZ5+N4447DlEU4aSTTsJNN92kz//iF7/Axo0bMXPmTE0yAcAxxxyDuXPnbsEeCQgICAjYWrDPEUcCR7yOeed8GoeNfhg9dmzFofG1+Mf5f8RHLp2LHr1D5ObmgJCy5vQiIANr165F3759sXLp7ujTu31vwcV8ltUOlFFtXwOtJjYta+jkp4teCqW2jFjKXGuduUqKagyUMvRltdFtpUCskw9nt+XllSrQhma4hAW0LE6mqDqluJDqdM9ltQEEWqsFlNGidcZpuV3fJpBU+2K1CckSz64vvD0t21huRtnxU4D2F/2k+lurzZqg4fZYBI8Ulv/ryk2oSn/fSl1mCAW68lpbtdnxTc1rtW3S1h9LYEOpGbFsYu2I7c77R6luCZQqTc51s+xWc23iSCyBYlszIAuu0LwbJyVAqpUCMqNpfGUSkDEg2pLF3jPbZemOgahqIqjsesQOfoGrQKGIhHmpV2eqQ8RAVIWbO6fWcSVGc6srz6pPeA4qQ1RiFCqw4SF7nD4oVdDSWnXaWPUyEmhHpSqiCg3v8utU7bXMjW0oFCtMdhbBI0k0joTc2JY8OGlbSg5xm9O2lWob5pZ/jzVr1oRXYgK2CajvXmHMBgQEBGz7eO7+ezHw2W9ipz0+AACsf68XXu55IcZ95ZwGW7Zl0Jn/Dws5ehoE/cW+U2k24eiUGZvTUprktHkb0+bAp4frpPv16PTbkDNJ99ihzim6I2uLkC6r7JzzW0Nzx6iNZsYpkJqKhuAZQESqE9oGTt1Qv7gN9ipb9j5v6+8Vao+dyyn1x5Ojx/YZKECkCxjZCZXp1RCIk1w5QiJSG6ROmCxpnp442ac6hT6fei5MImeaiyfZJCIRIxIxhMdnp3etKulVIvmCtP56V93yDtwaJI/vRhGANyOxvWxb9ubjKSRgJ+qVdsUoPeqATrWIV6afvgdQWia5LL5F6UaPhf855OhVsB4iEk5Can7nC3InkmrO/clzHUECiM2+Oh95ki5RBzRpk96Ilm2pbDoeQOzXcohcuhJYQEBAQEBAQEAnY9SnTsCAb72G+fMPQ7VUQK+d12NMy0V4avrhqJSKjTZvm0YgehoEZ6LUDviJjTq1euYQ9cgTdS7BW8smny7fcb3IruvO4gTb9/vtn6FK9mlrEOD68vg7TqHkzcSFx0JNsHj98pEo9uJMnIygyZqpXpcKEk4PqQLpmmm3jmxf7SXW+WTaUD8SNJky2fR82IxpZYORZxZAt61WVFmyRLZOEG31AvPeEpFaxwdtlJBLHYJESoB5dHo7lBz7Xr2ph0BWRBFXpf0k10WSiin/0RGdgo4Vqo/rhl1GcxODV5fk07PvBG7Vd3OmrzgyuwTpGlXkWaJcclaKjG/9T0bJZp23m9kdINIkymqlrdRg6x06cr0oKQRps9nWwO3gmA0ICAgICAgI2Axoam7GkT+cg+eab8Tqt/siapI46JB/YO0P98Szv/tlo83bZrFNEj0zZ87EuHHj0Lt3bwwcOBAnnHACli5datVpa2vD1KlTMWDAAPTq1QsnnXSSsyzom2++icmTJ2OHHXbAwIED8a1vfQuVCo/x3zKwvlrXMylj1fOib/K0qrmB84NzDXnm137p3STbzFLExldfvIjPl/b45Pvxv56pC9Vp57ONyWZqCXasaAiFRKf/nE3SUBtUPSrb1i+9Mq0Zm8c3e0UgyWvya6f1mMgh9Y9HJqj0Ic5YIfNJn6/JayIxIGNIGVtjBlJCSNtP1Z+K9lF1Y9/S7M5AUn5lrT2uorJie/xSCk6Q9NmOCDKRlqQv4uRVxQ6B52Hx3RT24DGfPJmuf+h5dJImTK2jX9jHmXlyfDpZXcHLuI/8nPToo6eFp4mwTfcSS3l9I9PXa+nDK/20h5pK+G3qan3KUakSLNtjTQp672XdPGSw68cC7RD2tLOisBishz91pINjNiAgICAgICBgM2LsqWeg99mvYdGC0YgrEfrusgb7bZiK+eceh0q5rOu1rl+HuRdNwaPTPo65F01B6/p1DbR668U2SfTMmzcPU6dOxRNPPIHZs2ejXC7j+OOPx4YNG3Sdc889F3/84x9x9913Y968eXjnnXdw4okn6vPVahWTJ09GqVTC448/jl/84he4/fbbcckll3SKD9ZXa+cX3Hx0/DfYdPLKAxzqkEd/vVYbDbHwlfnmivVu9frpa+fTSetn2ZKUqwXQ1WLn5qUtWpvSEUanfR7OEbUl0ZOQMCbyJOsVMIBH/yitvj6RzALWYyK5jtITksL91OQg8dM3XHlQg7TsS6IQhIjIJxkvzDfaWyoyQo2viCyXrl4FowYlHIyAFOni9cIXNZX4qaJ5IHi/Q8/brUm73mydOtokEnaUT71IJ9tS3Zx5N4XucFVXdQQ7J2DfCF6dpAnxQ/tsMTu2HY6feTqZ7VnPIP/ASot8dQErckc3YWWauLIMsHU63StURA+9wMQetalxzMVr9ljYxgt1T9mRPVCRPUq5itKhz1SVLZ4ba/mTNQAFrGsJ2EusBwQEBAQEBARsBWjp0QOH3vg4Fq6/DOtX9kbUHOPQcU/gg2v3wtLZf8bD0z8D3DEMR428ExMOfQRHjbwTuGNYUh5gYbtIxvzee+9h4MCBmDdvHo4++misWbMGO++8M+644w58/vOfBwAsWbIE++23H+bPn4/DDjsMDzzwAD71qU/hnXfe0atQzJo1C9/5znfw3nvvoaWlpabeTUnGrH7HJQV1o+PJmKVOAq0mP/WqLVnt7FY+GfRH+iqy55xZ+isxUM44lzVnU8exFIhR8OqU7JPuV6tAUS9EJzz1fMmdk4lVOS6gmvKm0jpn73P9pWoBJTRnygfcOB6kBFExNomRqa9+O42stnJTmow5y1b72NghsLHabHzR0R3cN8HapQmgdZJiAX5t3ITVKekj0wTQVB8hYxxbiIwN5RbEkl8TgjhidhhZpWpTmheI2kTqSSYzDaQolprhJCl2FDPEiR9xJUkqkxstw85JCYi2CEm2J4+eGkRPVBH+m1MTJcLtPAlEbUiItFo6FV+RHooqULDeI/TXd44rMZr5a9qUqPL4oLiSqBIjqrA2Phv5uXIZLW1sAFjtpB54Fv8kAVGsIqqmDXQy5Wz9+o5tK6HQlj75qp6LrRrQB3i6Lze2AnGajFlFeUmaEFq6fkuJSiUkYw7YthCSMQcEBAR0DWxcvRovX3YcDjh4KURBIi5HEE0xVi3rj5dKn8Pwk87EK7+/Ffu13IP+Q1fh8UXH4ejr72u02bkIyZjbiTVr1gAA+vfvDwBYtGgRyuUyJk6cqOuMGDECw4YNw/z58wEA8+fPx6hRozTJAwCTJk3C2rVr8cILL2xxm535V9Yv9x60oyrTyWdC5sfiWlA/JCerDpOGGdE8Rqfx1Rek4MaP5Acz8H7L6ge6HHpW0IA/qMBYxdMw09p2mSFH3Kgbo9f+jCB0FhA7goj3ji2XrLIlvS99ePxWUUppBIEgkSxOolkaNkB1MjKHTrLZBaB6dUkayZNsdJxkj4JkHirMldQ2k6Zp6IbJ2ZMeiNQPx8/IbDqawTOK1OSfD2uy6Ry3QkBEyZY5cLNAOzTrRvFdEqIbXG/dOkkZ9dPqFgkn2bN1nunMGIhUpfPWkXDr8xtGAI4eQTZLJStTnEgm6cV16X7gF5+baPJMaV2EgzHKvYNG54Uyzxx2kQuRm+CaCnb80cwr8c0R4PZFPWMmICAgICAgIKCTsUO/fhjzg0V4/J1paF21A6LmGEIApVITdp90CoaMHIVjLrkJ/c5ZilXL+mPcvo+G17gItnmiJ45jTJs2DUcccQQOOOAAAMDy5cvR0tKCfv36WXUHDRqE5cuX6zqU5FHn1TkfisUi1q5da20dhf7O7ttqoB1VM3UK9UOzRF0pGviP8MkcMGnM8/LQIDE+h+CuZmVRyeqS+uexwjrOztbCy8weX/0KnnPUOmt1qgx/zRYjTuOrkjgrnqvD1uWbnUs2+aQvbFHdiVcm904sY5P2Q9I6UtsCyyL3Sqi5r5SCyHD7krblOZxoXh73aiumxRzTlb9iNW7VsNMXMj2QceIz1amuOB2nUuUlckH5BnsBKtVe6U/zB9GOsC+VH9bAZvSYGpx8Y10lVUfwm5M74SOBOLcloVcz05s6Zg8A2p3ODcpJIdhUg6XW9yDzDAkJOHpYqiSzefRbNtW4OfUQ0gfq4kOTVtL657quL6Ig+2wVM52rhypWzsYAvONJmE/G39hMFYgjanwweZDp/wgQEBAQEBAQELDV4ujvXI6Fb04CkHyd2WWflRj83Ccxb8YXAAAtPXbAS8UT0NK7iAUzpzXQ0q0L2zzRM3XqVDz//PO48847t7iumTNnom/fvnobOnRoh2Wp7+odQXt+vPfpVFON9sjwzhc90Ty+ORuXkyc3b8uS4dNhp9bNDxywNztXjpsa2N0kk6oWWze22n9VHqBIZwFR0TzJr/pSb2Yhd/XTfqxXjBIJuQABs+C7uramXazbRPpTiijNY2NWnorJeSNP2RChmm4AoJY6j9O1q1W/2am31b6JSnJzOGVfBfuVLLvcjjoRJIhHRdWoCCmyopbK2ZOZl4fRU8LMjTXnRK+zMLmL9D9fRE8Ww+HcKFIXM5ftrnSDrUAuQv5NnUOo6Jw2NExGSCCix0a2NxgrR63FrXBOz/dQyPqkupTtcMyjfJxNZEu4csm+Hp5RcmWdUC4JT+NUNOXbpDT1NTGjNtsfYdmTRvEUiDGWygxWRg9h4nBEFPhCN30kW0BAQEBAQEDAVojm0vsAgHn/OAnFtd3RvEMZR466B69cOAJrVryDfT43BQDQtOHtRpq5VWGbJnrOPvts3H///fj73/+O3XbbTZcPHjwYpVIJq1evtuqvWLECgwcP1nX4KlzqWNXhmDFjBtasWaO3ZcuWddj2nHlRTfB5Unt1dkR3xo/fTjRPls6ssky5no228e3TMt+cl7fzb/y3ekqf2JZIXU5LbIulVRNEQxJxUoUgupKwDUPT2DoTv0jcjjD0Cu9X218jX4JF1qR2IEOnak+TO6tlwNl8m/Wt8tLELVnjRGZFS9m9lcyLTT2rP/TENimSsUyjIGLtl/HVRPiYVbn8dxGlgOhk3JAVaQQPHy2xhOOOxXA4qohSATWWLN1cFo/qiVPhWTdLHih/QTiJpF+FZzOyHbKmhk5rjGYRUrV8zfBRm+u6ZfMb3EbPtbGGKOhzzbMpcoj5qe5N15hUmdabjkMemiaR5tjxXUzPE1VVUw5bjJzSKV1ReTIDAgICAgICArYiVHruCgBo6tUPa495FP96Ppn773XAMkS/HY3Xfj7DqhewjRI9UkqcffbZuOeee/DQQw9hzz33tM6PHTsWzc3NmDNnji5bunQp3nzzTUyYMAEAMGHCBDz33HNYuXKlrjN79mz06dMH+++/v1dvt27d0KdPH2vrsA9qp4PfsTeJrBH2cb366NzMmtBkRPMoHfXKrXerBz66wjch868xpfbc6B4uxSQ+ttvYklQrXidKfri3rDFhG4ZeMXl5KD2S8C2UZrJh64z0xnPW6HKm08ikfkpACs1BkPmup+ci/Q9p9A9PesOjeqQlSSkhUVWKdCCidIROJNKVr5RfdjSPgMkP5MulZKbWjAKy5/VpKyWTXO+siB4+bLw3ilnMXuuWcGXwqJ5I+Z+jL1Mn8U3a5EhuZI+uQ2Tl6ZTWrkmc7Buweb769CnXyfVRZJX+5AyQR5cgck13CrjEiSoz9tOoL8stkf6xSDKijCeASsetuQi8PzOe2NQvPXCkbUjmuPDoCQgICAgICAjYyjB+xg0orW9JEi/vsTv2vnIpHnnq46gWC9hhwEYcPvZhVNoKGD/jhkabutWgqXaVrQ9Tp07FHXfcgf/93/9F7969dU6dvn37okePHujbty+mTJmC6dOno3///ujTpw++8Y1vYMKECTjssMMAAMcffzz2339/fPGLX8Q111yD5cuX47vf/S6mTp2Kbt26bXEf9ETZOagP7ayu1WhVAm7C0jr1+eepfglZ0whlR5beekiovPMip07+D/vSKudEEV8ditMEfn2EJLF0yjSiJ4Y7CLL0CKtQklkstYXqNJYlOpIEx5LU9esEjM9Uv0l0LfQ8UcDO9wMgjeRJdSJGshpV1lUT5K/UE9Dkg1goDP8hY/sqWxN89U9HSwjYklx/jTRhjx9SzQzz2O03nq/Gt5/pvkh71r6+xrGMtjH80Rq1blgqX/UpddZnL31eKGIl7wZW4snQ1ryJj+RhC0Q5n9wWQq7Q55l1VR0SxGOnNGMKINfY994XkyH4Dn2o67w+rBH1xbl2VVundY7cbNZKXpJVoTcJzM0JT5ss8iggICAgICAgYCtCj1698fCSo3D42DlYdeO+eKl4AoZ/6WI89vMqDj/wITR1q6KpexXvXDcahc/fiyEjD2y0yQ3HNrm8ushYJurnP/85vvSlLwEA2tracN555+E3v/kNisUiJk2ahFtuucV6LeuNN97A1772NcydOxc9e/bE6aefjquuugpNTfXxX5uyvHrs+3Jd55Xo6PLqyWs0TFXdOjlVYZBnTSylc74elVnLq6u2/vS5yflqury6T1fevLRSBYpoyagv2DzUjmAqx02I01mlOzcmOWfYuXK1gCJayHzWHtuxI88cl6oFxGRZHtu+iOwDdPq7odKEimy2fKM6YmYr1VmsNCX1Pcu6WyuCMV82VFpQlYUMvoMmdLblyjhZRj4p9KWlZn2bnowlsLHckowDSgjQz9gcW3ZLoFRthhXtIO3+UOwCt6dYbAIQZZM8GWSDlEC10uS2yYMiKFrJ8uq0fY4+AECcLq+O+kkb1a7QJpC5jHwGOSRk0rapAhe1btRy9vLqIrXJaZt+RpUYhSo756knuK3lCprbOKNEEMduW6WzWEVEl+CyHljS1a32W8soFEvpOdJefciY2EGIKCkhN7aZLOWSKJAg9dy2lUoRc8u/C0tVB2wzCMurBwQEBHRdPDz9Mxg34hG09CrpstK6Zqxa2Q+D9noPQgDFtd2weN3/xZHfurKBlvrRmf8P2yaJnq0Fm0L0JNEGHdO7SURPDZ1Zp0s5RE8W6QIAUkr45nW12lZjoJRxLu836FpEj0+nqlOuAmU0WQQFJTHsY2HN1Uqa6HHl8tWw6Lm2agHllFxySRKX5KGETWuV6zT7sdbLbQHays0oo9lrJ9cJZntrtUmTPKaNkWOWQ7d1biy3oCqbMubzkd1G2n60Vps9bZDOYYXWafEpEthYbkYsC+DRPFyHJRNALAXKjp923dgzcGUsUCw1wyF6tFFuG+pHXC74qxBixtUJiGIG0cMN95yLypzoYSS672aRgGgDIh/Rw3Q6S5/HQMHH4BLZjt0SQDUlejxEjfAdEzmiEqPAH0Ie8o3LQamMlmJagelNdEirjH5GbVVE9GHL66pzKZmjbS4WUShWCTlDCRppf0r7nNzYaoggSdpoPbHbXkpU4hLmln8fJs0B2wwC0RMQEBDQtdG6fh0WzJyGpg1vo9JzV4yfcQN69OqNv3/3LEzY/bdo7lmGrAo8s3gUxlzzMJqam2sL7SR05v/DtslXt7YH+OZU5sSWg3r1xVLTDp1OigeYRE/tIYFU3bwMEfQcl533tpt6Wcgn3VeqjqP0b55NSd+50TnCkmwmz6qUvvZFyZkCBCqktekXSnbwF8S47Ty6RrC/wrIFQkB4SAz75S8WWeN0hO0jJYhUimhHp4dAs95OUjcFHaPSkAV+Ak75o97+oSuepTrp62XqvArKiX3+uSSs9jPVE1n9k/ZFBPuic0OzXiGir9ZowzzgvIq6+Xw3ZZ4dYGV5DwP+tp0AUM2OquQ6edNMcjvvgZDqzDxPZRAuhDb3yrQMS/fpA0uQTrRvzlSHej1LmrxD3BivjZLoF/a1EhGSDlbN0x2R6hPS2Kg7VLrXUhM6pIDK1A+ljMisgICAgICAgICtFD169cZHr/iZU/5/Lv8pXv77yej7xJew0+6rcNC4Z/HBdXth7YTbsc8xxzXA0sYifMNrEKzJR+ZsegshnTzzVZPteBGnSeYWI3uBnCxwXVweL/PV89moPm1aIdunmG2etZQcnb6+kh5v7PamjGqsejRxnVkLkdMZtb0CmHuFYrUKVbryVC2dlDKim9STRAmpN0AKCZFuSqekPSslYrLal9kMx6H1kLEJQC/6pHTC0gOo1cRipldFzSkdcTrJljHUwlwawtoTmTcCtSXWvZbqk2pVL6sj6ZDIGLgCVnbjvJvRHqz+G4/ry7pZKC9JiQe+0ZW3YlXm0ZGhU11TyMTNuvL/clnCUybhJnZOu5KvZO4F7TvA7SdrNSyzL2ScbHqlOij2j+RqZv1HO9kiyaT9upUeQ8yodIU8JzmSJIrVOb2JdIl1ag/RpVaok1l0fEBAQEBAQEDAtoWP/J/jMOD817FowWjEVYEBw1Zht1dOwryLz2y0aZ2OQPQ0CNLZ6Vy9dczPLGSRQek0w7tATj3zOSqH7/tk+QkPV4aagptj1zcuJ/FBeP5RCZSwMWtg0XgWutE1uOySRFtEykwNN6rIzOm5HYresXXa9BBdWSvSPtpt/JFMkuvUu2lUUPqp9qW0e1P3qhCIhFoFi27QpI1ekl0kmxRUp3A2TeVJob1Ua32ZVbfsiT8EICKZbIycSnpMTbTdhd+l3SnWtVN97F0ByzfYKERKeunJO1dI9PoGbb03CiNEnIcBQAgHsmnWTdryfbo9tqjLB2HEOX5x+Gz36LOWhVebPUz98n196fShb/AISERmUyu4ER4m4Wgy2CjBbACVDyCKiC5iC4x+014pjQkxxPylg1Z6fBJMZkBAQEBAQEDANo6m5mYceuPjeOLdc9C2pjuadyjjiBF34PnzR6N13dpGm9dpCERPg8C/v+dOBDejTpHueL/v56jPI4ZqkURZyKpfDxnFdXL9zlzaU8Z1kDgQ8k/JtXtH/aVUD5VIyRFKYygNKvIkdmqYmtx+RSy4dtD2cRrZwj0zNsVk395zoWggM9FMa1kRPbHeh464MeESavUrmc6CVSSPCZRQWlJNEoY8SskaLdfq19RmYeuJZRq95OiyiSKd34eTU0KkuvzcCR07sY5USiOJVFZpu+tr3ij0/ssErVBLvm/z3RTpp6RleiMEh+432NwF7Yw8nYTnqPs5weUwX7MiEq1nWRbJ49Ph+KEHKOiAte56xd04D1Dhkad8UPcQMUKVValOalt6XidoVvdjZJNDui7pcOqz+oxtnwICAgICAgICtjcc9e0rsObouXhr6WAIAex38Ktou3VfPPu7XzfatE5BIHoaBMkPakwEN5dO/X1ferYc9VmT3jyOqqNu+HTU0mnPYc2spla30vb+iB4qk0uwiQcqTVj7sCQqwibS8nkN+7Uz2we+J5z2kSWFkkLmvN0Gjk7bS2KBDs0wkTx8I+83WToUcWAieQxRZV0jAUMcQSY+EnLGkitBdCGRnfoYcV3W+4rpRN2K6klJOLJ0tY/PUBAACmmkUqRyENGoE5D9vEFr3X85dw01RrXlUS5ZOn0g70gKKt9HdLC+c16/qlMn5X5qPuuoLOn66o3m8WxeuVl2R/STnCCkl6Z5WBJwLzGTJZ8bpM4XiC4VaSOI0fxCyThd/YsPDjYgtKxUV6R0CvJ6V0BAQEBAQEDA9oUhI0dh98tew/wFRyAuR+gzeC1GrPsaHj7/xEabtsURiJ5GoxN/TKVzhFrzTz5Xq2dCxSdWtaYPvrmi0tUenTF4N1JiI3/uSeeRJvWJm5eHSjARPDx7TnaP8HgdlVPGPmsm+9n9yAkUu1UMO8uQOkfrxaS+0ZNHMdAJb1qXRPBItvmiemIAvmgeZR2haiwSSfcrJxqUnwKwE7WYTEt2TiBhRfRIK6IngpSG5stL7qLmxnavpbog2xdhQ+blVm6XrBvHN5Dr1QfySeVxud7XegjZoV/XI3LawbQIWpRHQlEodij1NS+/mBoONfgmVz63V0fe0MFq7iUhFUEIc29Z3Irwy44BhyWjh1Xy/pfKnWPZQRoIARPRwwWRNtQxzuhLoOZSjAEBAQEBAQEB2ziOvPGvWFy8Ahve74lCtyqOOPgveO2ifbH6rWWNNm2LIRA9jUbds5FNh54jpFu980NuZnvIoTxbfHNFJb89OnkQBdecMee0ypUck2uI0jmmtmCbTaQYqygRJJkHKqpHZc3xx9/wvnB1Jv9sS01kji1VEkk8W1AW4eYjiZL/1ACyyRNfVI+yJUoFq8gaIahF5urQKBtN4EhXl9EHQwiR17AEVESP0hsnxFO6CSuiJ9aklLT8dAeMzmErSD/AE9FjX/L8jfIIeci6MflnFsvBy4kcqeVLd6O6TVf75dYotxacYjbkQgIpF+ema1LmCfJJzvlkWT5RWA8VPXgAX0SPGrk0kbaW7RtAMAyVZQ85Vq9hWRE96pg5oEmpVJeQ9nnaFgLueEgLQkRPQEBAQEBAQBfA+LO+ifjzz+C1Z3cHAOwx8i0U7j0YT/z42gZbtmUQiJ6tAZ30gyqfYNVD1pDqdW+5ekmZj9DgOmsFLPAVv2xpnGLx20DLE3kqLa9NQQACMSKHXFFUBbVMWJ/2OZoXx+QF8vchoT8cnSJlCVwSCERP7NTh8T48IipXpyDEDHntSbCcPXaUjdQBEiqyRr32AhjChObMiUnUjYkQkmaiHCWfasUvbg+gInqgCSAVuZNs6TUmZZQEyxq0dj4r28cORfRILdoei3kDlsJiOMjmu6h8gBF5ghuRl6iXchVZOrP8zCKJ8qAuCVttzInqoWXknLcffeXcbrWSlS+iRxGFMtlcYocJpxfWtwqXImPi2NFn2EXVjx7mjD7yrI3KASGPYHSHiJ6AgICAgICALoJ+Q4bgI1e9iEcWT0alrQk79N+IsT0vw/xzj0Ucb18rkQaip0EQQDYzsiW1Mp05c0/esq6Nt1E6KLIJDXe/Xr2uDfaRT5/PZzuax35JC5CIECPSVBDd/NbQuBVK0KijAvQLQzl9SOOC7FW+fJbaeox0E3ckOqwzmc/a/ilyxkT0ROS1K2H8FIC9BLuRn0hVUTcSkdogCVGTTJJlLJJlvtUrRCTCJ6mbaDYRPXbkjiARPZGgdB4hbTIGrZkze/pbL5PuHQo1N0oLap0eG6z91D9HJ88Fk3WjSFe0RRDoHD3kXETqZunM8pGSRD4/fQ8g1ee+HD1qQ3JeeiJ++Pj26vX5I2M1aJMCFdFDX/0TIo3osdsKJ2qHOKLHPyFEVT/rxMpgepkwndCZXVDnfTZqP3WSyAqrbgUEBAQEBAR0MXz02t9iyY63Ys07fRE1xTh03AK8c/neePvZpxtt2mZDU6MN6KrQP8xaBfVBtK+6rYR/x/fXympdt968ej63BduvZUdtXWoW7JeZNbWJyRl7/ik8ZVS63Tu2X8J7TlEclDuWzDIeEwTnrAu7vrFEWSGEsH7Ez9Pp6pVWXwrAitBI5rN230sAiCLEVX+va30O6ZCWC2ZheiD1TeR6muxH6atcvE/SMp4rReaMQVZVpGwStRtRqts3IPNuCM2lSPta+Aatz0Ah7EFE29S6YYXSzcroDh3aAgk/4ZOdp5PwEpp8qffmVzyFfaOY6jKnDPb4tGT7HqTkWEAkETY+nfpYOueBZGx5+1SyQiWD2qJflyOEDpWjOlDl8KED1745zDkqR3jqBwQEBAQEBAR0MYz+3L+jdf0n8PwlR2PkwS9jl31Wojj/ODzyp6/gqBnXAABa16/DgpnT0LThbVR67orxM25Aj169G2x5fQhET4Ogv9d3gLHpGMmTaFVRGR2RmUygsmvyuRn9zNKRV1bPFMSmFPLbZZEXtt2xdWT22CQclMTx22Dn5/HrTQIhzGtUeT5TfTw0w0dK+fwAACml1lkfjE4hhEOeJAcsGoWRbDKOIYTQ805DxpAAChIYocgxrYuQQJSAkGSfQyKGEL6gRSWF+aIJCGG/0eLIFk6ZifSRHYyQUJN3vy9eQkJvnKmxxWZeaErQcT7BlzCYcR6ZbnKdbGBnRvTk+Z1xc1CuT2SUOc2y+pnZLCGTiB56c0pGHKUdpwNrlB0RkqXSSR2vHl9Bep9oxkr3AWOLdD1N4RLyx+Mzt4UTSwEBAQEBAQEBXQw9evXG6OufxtxLvo7xQ3+Dbr2LmLDDLVh07lyslzvj0P0fx1EjS7p+6Y4/4OElR+Ho6+9roNX1Iby61SBYP6jac/aaaEdVR6ueBDGdtWTq6nrJagG+Og8v43MprjZvq9dP3s7odAkYn2xXJ02VnJxJX/KxarupmoVzHs6RTX+INN+PqmXHACgKKKtnANq7dkve89Lql2RSKvTs1KZk8nVagQFUr5NkVuq/EgJCRAnpoj/JeIH7GpT2RCVXTuur17HsJdOhX+tJXtcRiR5EMDNwukWQ6abHLtdNSQJHhHTKhAAQmeTP7UJK1ui+zbsprM5PFaulsvn5LNII7jk795DykVRSxIMw9evW6bXd45vPV3KtfbeAlYsH/rJMXoVeP2anEMkTwBhA7KFN1Tgm9kogJcXo4LA361/6vpmwlCiCh3SAzhTPOlSSncwBKGBdS+WkGkcBAQEBAQEBAV0YH/3eLXhnv//Fe/8cAFGQGDPuBRwxbi42rO6BR1+dghWjn8Cjr07BulW9cPjYOXh4+mcabXJNhIieBoF87U7Qjh9VNzWiR8tg3/vr0Zm0k95zvjI17ag3UIGWt8cuijydPHjAPW9+vqc+JGdsSsS0F7Dz5ij9pr6rT6VDFnrP9kBRJLYc01pNGG1bkVFfEKuktDUaYqy2zmROmOpUc1HNHrrQfSdjkoBZlVIL/JNTlUsn5UJs/6TZV/NiIzlNRC39ET2JG8k5EwNhT9gh/QEQZjZPyilZ05EbVOUTYjqZYnac7sReIz0PGa6TNGMEnmlHK9n1hafc0SlNsVPHdxN69k3/es4RwkefYmUWiWNuA0uW5DJk+izgSZatQ3OxGP9ESClyQTMuU0IeqTuSdYw98H2tbUGQGded3GOCyQsRPQEBAQEBAQEB2PvIo1EZ/xoWnf9RHHzoMxAC6NV/I+Tr6zBk5CgMGXkTSq1XYdWN+2Lcvo+idf26rfo1rhDR0yA4X62zfrn3oB1VvVqtOZz6wbkGVDxL8oMy+ZU5K8KHaOSkD92ijHK+2R4YZPWDmjb5AgGy9Cfn7VKThtgX0WOyzyqKRHqt9kwE9eLqic4o3bhe13Ka8VYtr877xFdCZIrIjsxqh061pLviFnRAgPM+ji1DRfREqW47TMM3CqJ0jmvIIaHHG2mqQjd0RI+AFBGkSMMfnHCQSG8mka5nFCn/SHNvcJBI7gcRCRNZ056bk062s24UHcnhnheiA3o9BItz69L+VeQBke/0BZetBScbVemNXIKnTNjF3De6upav3DIpj4BjN6ca00JddHVtHRPVPWTrMuSn+iQDSFitk39SQGeSpsZEUXrtlR3KuVSwl8zJco7UUUYqOfWO1YCAgICAgICA7RxNzc3Y2GsEhABK61vQvEMZR478LV789ihUymW09NgBLxVPQEvvIhbMnNZoc3MRiJ4GQX3H9m410I6q3sZqImQtrFMDfAnzZA4o0yiGZAM5BqmXZbtE/atRZ8nLcNGqmafHLTd77rLl7jlqnb2Eud9fs8WI01gSmS6wThc89+mkEgVkQnowaoacJf7b3koZ29fNo1Nk6YRI9tO5p1mYye5vs4R76ptUUT1mnAjp71t6Ffk63gIwq02n1aVEkppEsQgyBmSMJKKH+pj2gZRWPeFkMlbXk4wiok+RMkZ/oiNWS2DXC2tgG23WsyFmG+sqqTqC35zcCR8JxAkKdR3pFqtPWDoktY/foJwUIqocM3wPMs9QkICjx1pxi24e/ZZNNW5OPTT0gbrY0KSVpP/MKbakuyR2UDmpBHUPUsXK2RhmSXXaL5o1Ew5BZT0F1I0i1XiH+aQCaX8EBAQEBAQEBASgacPbAIDXd7sdby3ZBUIAPVqKaGpuBgDs87kpVr2tFYHoaRDUd/WOoL1BA1ynmmq0R4Z3vuiJ5vHN2bicPLl5G2+Tp4PSJr422Xoi/Us7pW4AHq1jNkkk0sXWjb+C1DDLnkc6hocudq7yx/h0wtYhTUQP99NuYfIOAcLKkSOEOUd18sge45fUs2spYed1sWygcUoi1RexsZLXp4JcXLuvaWoRFVGRpP/RO9ARU9rPNDdQZl4eMgGHNAQC6VzjY2KAEKZ3BdKonqyBW+tmk0q34RScC0oje2gZPHrrASE3TE4bdZBuET02sp1grDrU6H1O5PgeClmf1D9lOxzzDK9BOBrrwQcmK93XwzNdQc2JCNPXhnZGKp7xbRYLZckyIpwoKqRRPAWQpd099nLoIUzInIg65GnsI9kCAgICAgICAro4Kj13BQC8/9Qc7P69V/HIoknodcYD+vyr9/zMqre1IhA9DUK9czEf+DypvTo7opv/8K3LWDRPls565eZtvE2ezIw5oVXHH+VjomskO+NLcgxNSfA6lGiSpAaNdInTOBoezRMTasXVqWiQZJVlX6JnY53ak2lISBKBEJtoAmnO1aMzIYoMteXrW6UR2sfUczZW/NFSds9B++heg0SMmV3LWEKkUTpKt/Ez1pFMOvKMyLKJKkMBUQLA6k8pEUsrriONroG7+TqJd5hQvcvm5VwWH7RxKizrZrEvCtNpNisyRgLeqB4i2yFrasAao76bk9qY5SvfCBcoyfWxOC+HSPHoIcf0UWZF9KgKgu/zsUOOHfKNdViqQ0ecmVCi1HdPB1uHtAO4w5zUybtQm/J/o4CAgICAgICA7QvjZ9yA0voW7NdyD0qtG/HR6/6AnffeGwBQat2I/brdi9K6bhg/44bGGloDgegBcPPNN2OPPfZA9+7dMX78eDz55JNbXKf+2t3B79ibRNYIu6xefXz6kMwrzC/GPlm+Mt/EqD1bPfKzIl1oHV+AhLD2zD9DqZhZI6UqqBZVn+f/4XKBKI2jodaY6BpDr0TaV0unlJZ03zze1mlHuShdqjxLJ7hOKTQHAWlP4jUZpPUmkUtqyXIrwkC4ET2SSdLEArkGakl0O9jC5MkRkYBaNU1H8yg/nZw9lKZTmx0Npuf1Vt+q/qwjoocPPu+NYkdnqb51ZPBBq1Zh6siNwjgLSo44UT00skfXsTokW6e0+RRhOZkhJ+sGBWujXCfXR5FV+lN62rGbk5JBpjv5WCEVKOkFe1+qPZHWtx72gt0wgm0RUSdMPUUE+dhCWp8SU9xv7zXiBQEBAQEBAQEBXRs9evXGwiVHof/QVVh9476Yd9k38Pazz2DeZd/A6hv3Rf+hq7Bw6ZFbdSJmIBA9uOuuuzB9+nRceumlWLx4MUaPHo1JkyZh5cqVW1SvyDyoD/USNLXU+L77+/Vl/+N5eWrppOV5P9hnbSBt/bbm+5Uv357u02gbnvnG0A92mWTWunuqTowqAOkkYvHn6HFeqhI0GsidyymdJn4o3VO5ckAiX3J0gusUKiWzUajmvjHpOSNZRdcAdsiEL89RSucoTkF3LukDkWy2qWnkTpzkyol5biAS3WOieWzYRBVRzXw0V9D0qoQ0OVVqDVwfhIBDlSojVHtfrh4V+dFefUp++im5s040D9nAOIcaN6ciTrRK381J/aQbj+qh8gmfQfkKi++qRbIpGaTckESUXCEVpbQ4GD1OLZ8io8sidrh/qTyda6lq69R+e5ga3f9pPUhY7KcgRkpaD8ZhyZMsBQQEBAQEBAQEHH39fXh80XHo3X89jhx+GwY/fwSOHH4beu+4AY8vOg5HX39fo02siS6/vPr111+PM888E2eccQYAYNasWfjTn/6E2267DRdccMEW0+t8tRa+ws0gl51TE1xZhz7fvM3XhL42weGu/CzZsR/6F3WPTQJ0SWx/2/acU12RnIsy6vKUvULbU0uDIH/p3E8CKECgkq4wxdsndfj039iRJDM2kz9jnwTS/D+mFZErIggpiC2C2cV9E5ZsARNVY+pJCMYbR0xnVu8L2HNQQJg3hWJiF48+8LAG6iiWiWQh3Am3utZ6rgt+HT00kFS9avrHGQP2gmjufh4Bk0Zn6duS1hPsk9iEQnrC17V5Ny0r51fT0skJixhJZBQv9+ki11Vxc/6HCLKGh6uDHYvUJscE5UqtBw1vKBR5IzwnaP3YJGJ2SCjpPtN9/UUZIwkkF7RKGqQn1FiXsd1U6yFsEw9jkrRBWqAyqUdRQuYFBAQEBAQEBARYOPr6+9C6fh0emTkNTRveRqXnrhg/4wYcvZVH8ih0aaKnVCph0aJFmDFjhi6LoggTJ07E/Pnzt6huOtHsCDrSVCJ/0sNP0XlKLtGTo5NOhT3Tpg7JreV7e8+r41j/FV67PDEXZP6uzrlElr0Ol32uChNdYv81P/8LIgewqSjahpI5SaQJ9YPYxSJapEen8k3p0DoVQSTMVTT2xea8JRdpfpwIbt8b6ZTk0IQNIWf44KXEAeVQpN7ihBySdBSqNjY5ZNskrLHJqidWS04HCXsFKt8gy71RGGnECYis9jqXi0efzw5LZ41Cn860LJbJ21xeeAkkWEEmXrX8InrkWcfSfAhywbSKvAcMHyzsXDJGyEkPcaJ5Ri6XP+gssgbJYKJROJaNVWYXcVIJp9FsoCy7tO5L60ELEJJItZe2joCAgICAgICAAAs9evXGR6/4WaPN6BC6NNHz/vvvo1qtYtCgQVb5oEGDsGTJEqd+sVhEsVjUx2vWrAEArFufF1/iR5z15bqO79zl3HiWbDHmhZbalXlR2deOwWdVLM3v01xDnrw4BspsFlUv6RRLgQoA30w2T2elChRZLU7PZM3lKzFQzViCSNEmiqiJSZ1yHKNERoNvPkrrU/KmWAVoRIuZlgrnk8ouVqqoEubCXDcqx943OqVFntj+URn2+VK1kiYv9kGk143bkPwpVygR5c7NJXlNRteRQLUMxNIefTGzi86zdbkEKpUWx0pr3i3dawkAcTGGNwlWHnGR6owrhcT+eufdMmkn2tQyTdnyvfpjABVhcRBZeixUgagkUJWeN3+5Hk76VIH05szXwY8rMaKSv462PePmjCqxuww816P6gD7EKmVUKi7Bo0ESJgvma1SpIopZA63LQxrpa1KGjMvpvofRgiQ2pgMgJWykLANx1dimzuvGnsEugIqspGYFwidg24Aaq2vXrm2wJQEBAQEBAe2D+n9XZ3zv6tJET3sxc+ZMXHbZZU753mOXNcCagICAgICAzYN169ahb9++jTYjIKAm1q1bBwAYOnRogy0JCAgICAjoGDrje1eXJnp22mknFAoFrFixwipfsWIFBg8e7NSfMWMGpk+fro/jOMaqVaswYMCAdBWjbR9r167F0KFDsWzZMvTp06fR5jQcoT8MQl/YCP1hEPrCxrbUH1JKrFu3DkOGDGm0KQEBdWHIkCFYtmwZevfu7Xz32pbuvc2F4HPX8Bnomn4Hn7uGz0DX8bszv3d1aaKnpaUFY8eOxZw5c3DCCScASMibOXPm4Oyzz3bqd+vWDd26dbPK+vXr1wmWdj769OmzXd9k7UXoD4PQFzZCfxiEvrCxrfRHiOQJ2JYQRRF222233Drbyr23ORF87jroin4Hn7sOuoLfnfW9q0sTPQAwffp0nH766TjkkENw6KGH4oYbbsCGDRv0KlwBAQEBAQEBAQEBAQEBAQEB2wq6PNFz8skn47333sMll1yC5cuXY8yYMXjwwQedBM0BAQEBAQEBAQEBAQEBAQEBWzu6PNEDAGeffbb3Va2uiG7duuHSSy91XlHrqgj9YRD6wkboD4PQFzZCfwQENAZd8d4LPncddEW/g89dB13V7y0JIcOaqgEBAQEBAQEBAQEBAQEBAQHbBaJGGxAQEBAQEBAQEBAQEBAQEBAQsHkQiJ6AgICAgICAgICAgICAgICA7QSB6AkICAgICAgICAgICAgICAjYThCInoCAgICAgICAgICAgICAgIDtBIHo6aKYOXMmxo0bh969e2PgwIE44YQTsHTpUqtOW1sbpk6digEDBqBXr1446aSTsGLFigZZ3Hm46qqrIITAtGnTdFlX6ou3334bX/jCFzBgwAD06NEDo0aNwlNPPaXPSylxySWXYJdddkGPHj0wceJEvPLKKw20eMuhWq3i4osvxp577okePXpg7733xve//33QHPbbc388/PDD+PSnP40hQ4ZACIF7773XOl+P76tWrcKpp56KPn36oF+/fpgyZQrWr1/fiV5sHuT1Rblcxne+8x2MGjUKPXv2xJAhQ3DaaafhnXfesWRsL30RELA14uabb8Yee+yB7t27Y/z48XjyyScbbdJmQ/jO1rW+m3W172Fd5btWV/xOFb47NRaB6OmimDdvHqZOnYonnngCs2fPRrlcxvHHH48NGzboOueeey7++Mc/4u6778a8efPwzjvv4MQTT2yg1VseCxcuxE9+8hMceOCBVnlX6YsPP/wQRxxxBJqbm/HAAw/gxRdfxHXXXYcdd9xR17nmmmtw0003YdasWViwYAF69uyJSZMmoa2trYGWbxlcffXV+PGPf4wf/ehHeOmll3D11VfjmmuuwQ9/+ENdZ3vujw0bNmD06NG4+eabvefr8f3UU0/FCy+8gNmzZ+P+++/Hww8/jLPOOquzXNhsyOuLjRs3YvHixbj44ouxePFi/OEPf8DSpUvxmc98xqq3vfRFQMDWhrvuugvTp0/HpZdeisWLF2P06NGYNGkSVq5c2WjTNgu6+ne2rvTdrCt+D+sq37W64neq8N2pwZABAVLKlStXSgBy3rx5UkopV69eLZubm+Xdd9+t67z00ksSgJw/f36jzNyiWLdunRw+fLicPXu2POaYY+Q555wjpexaffGd73xHHnnkkZnn4ziWgwcPlv/93/+ty1avXi27desmf/Ob33SGiZ2KyZMnyy9/+ctW2YknnihPPfVUKWXX6g8A8p577tHH9fj+4osvSgBy4cKFus4DDzwghRDy7bff7jTbNzd4X/jw5JNPSgDyjTfekFJuv30RELA14NBDD5VTp07Vx9VqVQ4ZMkTOnDmzgVZtOXSl72xd7btZV/we1hW/a3XF71Thu1PnI0T0BAAA1qxZAwDo378/AGDRokUol8uYOHGirjNixAgMGzYM8+fPb4iNWxpTp07F5MmTLZ+BrtUX9913Hw455BD827/9GwYOHIiDDjoIt956qz7/z3/+E8uXL7f6om/fvhg/fvx21xcAcPjhh2POnDl4+eWXAQD/+Mc/8Oijj+ITn/gEgK7XHxT1+D5//nz069cPhxxyiK4zceJERFGEBQsWdLrNnYk1a9ZACIF+/foB6Np9ERCwJVEqlbBo0SLrWRRFESZOnLjdPoe70ne2rvbdrCt+DwvftcJ3KoXw3WnzoqnRBgQ0HnEcY9q0aTjiiCNwwAEHAACWL1+OlpYWfaMpDBo0CMuXL2+AlVsWd955JxYvXoyFCxc657pSX7z++uv48Y9/jOnTp+PCCy/EwoUL8c1vfhMtLS04/fTTtb+DBg2y2m2PfQEAF1xwAdauXYsRI0agUCigWq3iiiuuwKmnngoAXa4/KOrxffny5Rg4cKB1vqmpCf3799+u+6etrQ3f+c538B//8R/o06cPgK7bFwEBWxrvv/8+qtWq91m0ZMmSBlm15dCVvrN1xe9mXfF7WPiuFb5TAeG705ZAIHoCMHXqVDz//PN49NFHG21KQ7Bs2TKcc845mD17Nrp3795ocxqKOI5xyCGH4MorrwQAHHTQQXj++ecxa9as/9/efYdFcb1vA7+X3osgTaq9gIhiQWOLxt6N3dhNVKwYY69RscQUY4vmq8ZEY+wmRqNGRdTYBcGGDUURxEKRDrvz/uHL/FxB2i677O79ua693D07Z+aZcWEenj1zBkOHDlVzdKq3a9cubN++HTt27ECdOnUQHh6OyZMnw8XFRSePBxUtJycHffv2hSAIWL9+vbrDISItoys5m67mZrqYhzHXIuZOZYOXbum48ePH49ChQzh16hRcXV3FdicnJ2RnZyMpKUlu+efPn8PJyUnFUZatq1evIiEhAfXr14eBgQEMDAxw+vRprF69GgYGBnB0dNSZY+Hs7IzatWvLtdWqVQsxMTEAIO7v+3e10MZjAQDTpk3DjBkz0L9/f/j4+OCzzz7DlClTEBwcDED3jse7irPvTk5O+SZDzc3NxevXr7Xy+OQlKo8fP8bx48fFb6QA3TsWRKpib28PfX19nfg9rEs5m67mZrqYhzHX0u2cirlT2WGhR0cJgoDx48dj//79OHnyJLy8vOTeb9CgAQwNDXHixAmxLSoqCjExMQgICFB1uGWqTZs2iIyMRHh4uPjw9/fHoEGDxOe6ciyaNWuW75atd+/ehYeHBwDAy8sLTk5OcsciJSUFFy9e1LpjAby9I4CenvyvSX19fchkMgC6dzzeVZx9DwgIQFJSEq5evSouc/LkSchkMjRu3FjlMZelvETl3r17+Pfff2FnZyf3vi4dCyJVMjIyQoMGDeR+F8lkMpw4cUJrfg/rYs6mq7mZLuZhzLV0N6di7lTG1DsXNKnL2LFjBWtrayEkJESIi4sTH+np6eIyY8aMEdzd3YWTJ08KV65cEQICAoSAgAA1Rq06797ZQRB051hcunRJMDAwEJYsWSLcu3dP2L59u2BmZib89ttv4jLLli0TbGxshIMHDwoRERFC9+7dBS8vLyEjI0ONkZeNoUOHCpUqVRIOHTokREdHC/v27RPs7e2Fr776SlxGm4/HmzdvhLCwMCEsLEwAIHz77bdCWFiYeDeE4ux7hw4dBD8/P+HixYvC2bNnhWrVqgkDBgxQ1y6VWmHHIjs7W+jWrZvg6uoqhIeHy/1OzcrKEtehLceCqLzZuXOnYGxsLGzdulW4deuW8Pnnnws2NjZCfHy8ukNTCuZsb+lCbqaLeZiu5Fq6mFMxd1IvFnp0FIACH1u2bBGXycjIEMaNGyfY2toKZmZmQs+ePYW4uDj1Ba1C7ycTunQs/vrrL8Hb21swNjYWatasKWzcuFHufZlMJsydO1dwdHQUjI2NhTZt2ghRUVFqirZspaSkCJMmTRLc3d0FExMToXLlysLs2bPlTkDafDxOnTpV4O+JoUOHCoJQvH1/9eqVMGDAAMHCwkKwsrIShg8fLrx580YNe6OYwo5FdHT0B3+nnjp1SlyHthwLovLoxx9/FNzd3QUjIyOhUaNGwoULF9QdktIwZ3tLV3IzXcvDdCXX0sWcirmTekkEQRCUP06IiIiIiIiIiIhUjXP0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqISKkEQQAALFiwQO41ERERESkfcy8iep9E4G8CIlKidevWwcDAAPfu3YO+vj46duyIli1bqjssIiIiIq3E3IuI3scRPUSkVOPGjUNycjJWr16Nrl27FivRaNWqFSQSCSQSCcLDw8s+yPcMGzZM3P6BAwdUvn0iIiKi0mLuRUTvY6GHiJRqw4YNsLa2xsSJE/HXX3/hzJkzxeo3evRoxMXFwdvbu4wjzO+HH35AXFycyrdLREREpCjmXkT0PgN1B0BE2uWLL76ARCLBggULsGDBgmJfJ25mZgYnJ6cyjq5g1tbWsLa2Vsu2iYiIiBTB3IuI3scRPURUIkuXLhWH2r77+P777wEAEokEwP9NCJj3uqRatWqFCRMmYPLkybC1tYWjoyM2bdqEtLQ0DB8+HJaWlqhatSqOHDmilH5ERERE5RFzLyIqKRZ6iKhEJkyYgLi4OPExevRoeHh44NNPP1X6tn755RfY29vj0qVLmDBhAsaOHYs+ffqgadOmuHbtGtq1a4fPPvsM6enpSulHREREVN4w9yKikuJdt4io1ObOnYtff/0VISEh8PT0LPV6WrVqhXr16onfTOW1SaVS8TpzqVQKa2tr9OrVC9u2bQMAxMfHw9nZGefPn0eTJk0U6ge8/QZs//796NGjR6n3hYiIiKisMPciouLgiB4iKpV58+YpJdEoTN26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIqLyirkXERUXCz1EVGLz58/Htm3byjTRAABDQ0O51xKJRK4t7xp0mUymlH5ERERE5RFzLyIqCRZ6iKhE5s+fj19++aXMEw0iIiIiYu5FRCXH26sTUbEtXrwY69evx59//gkTExPEx8cDAGxtbWFsbKzm6IiIiIi0C3MvIioNFnqIqFgEQcDKlSuRkpKCgIAAufcuXbqEhg0bqikyIiIiIu3D3IuISouFHiIqFolEguTkZJVtLyQkJF/bo0eP8rW9f+PA0vYjIiIiKk+YexFRaXGOHiIqF9atWwcLCwtERkaqfNtjxoyBhYWFyrdLREREpC7MvYi0l0RgaZWI1Cw2NhYZGRkAAHd3dxgZGal0+wkJCUhJSQEAODs7w9zcXKXbJyIiIlIl5l5E2o2FHiIiIiIiIiIiLcFLt4iIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0ENEREREREREpCVY6CEiIiIiIiIi0hIs9BARERERERERaQkWeoiIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi1Rrgs9r169goODAx49elTksjNmzMCECRPKPigiIiIiLVVU7hUSEgKJRIKkpCQAwD///IN69epBJpOpLkgiIiIqVLku9CxZsgTdu3eHp6dnkct++eWX+OWXX/Dw4cOyD4yIiIhIC5Uk9wKADh06wNDQENu3by/bwIiIiKjYDNQdwIekp6fjf//7H44ePVqs5e3t7dG+fXusX78eK1euLOPoiKg8kEqlyMnJUXcYRBrJ0NAQ+vr66g6DypGS5l55hg0bhtWrV+Ozzz4ro8iIqDxg3kWkGCMjI+jpqWasTbkt9Bw+fBjGxsZo0qSJ2Hbz5k1Mnz4doaGhEAQB9erVw9atW1GlShUAQNeuXTF79mwWeoi0nCAIiI+PFy8dIKLSsbGxgZOTEyQSibpDoXKgoNzr8OHDmDx5Mp48eYImTZpg6NCh+fp17doV48ePx4MHD8ScjIi0B/MuIuXQ09ODl5cXjIyMynxb5bbQc+bMGTRo0EB8HRsbixYtWqBVq1Y4efIkrKyscO7cOeTm5orLNGrUCE+fPsWjR4+KPeSYiDRPXrLh4OAAMzMz/pFKVEKCICA9PR0JCQkAAGdnZzVHROXB+7nXkydP0KtXLwQGBuLzzz/HlStXMHXq1Hz93N3d4ejoiDNnzrDQQ6SFmHcRKU4mk+HZs2eIi4uDu7t7mf8cldtCz+PHj+Hi4iK+Xrt2LaytrbFz504YGhoCAKpXry7XJ2/5x48fs9BDpKWkUqmYbNjZ2ak7HCKNZWpqCgBISEiAg4MDL+OifLnX+vXrUaVKFaxatQoAUKNGDURGRmL58uX5+rq4uODx48cqi5WIVIN5F5HyVKxYEc+ePUNubq5Y0ygr5XYy5oyMDJiYmIivw8PD0bx580IPSF7Smp6eXubxEZF65F0bbmZmpuZIiDRf3s8R51wgIH/udfv2bTRu3FhumYCAgAL7mpqaMv8i0kLMu4iUJ++SLalUWubbKreFHnt7eyQmJoqv84o4hXn9+jWAt5UyItJuHDZMpDj+HNG73s+9SuL169fMv4i0GM8XRIpT5c9RuS30+Pn54datW+LrunXr4syZM4V+63jjxg0YGhqiTp06qgiRiIiISGu8n3vVqlULly5dklvmwoUL+fplZmbiwYMH8PPzK/MYiYiIqGjlttDTvn173Lx5U/xmafz48UhJSUH//v1x5coV3Lt3D7/++iuioqLEPmfOnEHz5s2LNfqHiEjVQkND0bVrV7i4uEAikeDAgQNq2cawYcMgkUggkUhgaGgIR0dHfPLJJ9i8eTNkMpnSY9ImxT12np6e4nJ5D1dX13zvv/9H8+TJk9GqVSu5tpSUFMyePRs1a9aEiYkJnJyc0LZtW+zbtw+CIIjL3b9/H8OHD4erqyuMjY3h5eWFAQMG4MqVK2VzMEjrvJ97jRkzBvfu3cO0adMQFRWFHTt2YOvWrfn6XbhwAcbGxh+8rIuISF2Ye2k25l2lV24LPT4+Pqhfvz527doFALCzs8PJkyeRmpqKli1bokGDBti0aZPcnD07d+7E6NGj1RUyEVGh0tLS4Ovri7Vr15a4b6tWrQr8A6u02+jQoQPi4uLw6NEjHDlyBK1bt8akSZPQpUsXubsZUn7FPXaLFi1CXFyc+AgLC5Nbj4mJCaZPn17otpKSktC0aVNs27YNM2fOxLVr1xAaGop+/frhq6++QnJyMgDgypUraNCgAe7evYuffvoJt27dwv79+1GzZs0C75JEVJD3cy93d3fs3bsXBw4cgK+vLzZs2IClS5fm6/f7779j0KBBnMODiMod5l6aj3lXKQnl2KFDh4RatWoJUqm0yGUPHz4s1KpVS8jJyVFBZESkLhkZGcKtW7eEjIwMdYeiEADC/v37i718y5YthS1btihlG0OHDhW6d++er/3EiRMCAGHTpk0l2o4uKe6x8/DwEL777rsPrsfDw0OYOHGiYGRkJPz9999i+6RJk4SWLVuKr8eOHSuYm5sLsbGx+dbx5s0bIScnR5DJZEKdOnWEBg0aFHi+TExM/GAc2vLzRMpTktxLEAThxYsXQoUKFYSHDx+WcWREpA7adJ5g7qV5mHeVXrm9vToAdO7cGffu3UNsbCzc3NwKXTYtLQ1btmyBgUG53iUiUjJBENR2pxczMzOtmpzw448/hq+vL/bt24dRo0apJYa0tDQA8sc2OzsbOTk5MDAwgLGxcb5lTU1Noaf3doBqTk4OsrOzoa+vL3f3oIKWVabSHDsvLy+MGTMGM2fORIcOHfLFJZPJsHPnTgwaNEjultd5LCwsAABhYWG4efMmduzYUeC+2djYlHyHSGeVJPcCgEePHmHdunXw8vJSQXREVB4w91Iededeqsy7cnJylHZLceZdRSu3l27lmTx5crESjU8//TTfLUCJSPulp6fDwsJCLQ9tvJVwzZo18ejRI7VtP+/Yvnz5UmxbuXIlLCwsMH78eLllHRwcYGFhgZiYGLFt7dq1sLCwwMiRI+WW9fT0hIWFBW7fvl1msb9/7KZPny73eVm9enW+PnPmzEF0dDS2b9+e772XL18iMTERNWvWLHS79+7dE7dPpAzFzb0AwN/fH/369SvjiIioPGHupVzqzL1UmXcV5zK4kmDeVbhyX+ghItJFS5culTtZnTlzBmPGjJFre/dEqyyCIGjVN2Wq9P6xmzZtGsLDw8XHkCFD8vWpWLEivvzyS8ybNw/Z2dn51lfc7RIREZFimHtpFuZdheN1TkSk0czMzJCamqq2bZeVMWPGoG/fvuLrQYMGoXfv3ujVq5fYVtCwUkXdvn1brZdg5P1fvntsp02bhsmTJ+e7NDchIQEA5O60GBgYiNGjR0NfX19u2bxvfMryrozvHzt7e3tUrVq1yH5BQUFYt24d1q1bJ9desWJF2NjY4M6dO4X2r169OgDgzp07vL01ERGVOeZeyqXO3EuVedewYcOUGTrzriKw0ENEGk0ikcDc3FzdYShdhQoVUKFCBfG1qakpHBwcinUCK62TJ08iMjISU6ZMKbNtFKWg/0sjIyMYGRkVa1lDQ8MCr/8u68+IIsfOwsICc+fOxYIFC9CtWzexXU9PD/3798evv/6K+fPn50suU1NTYWJignr16qF27dpYtWoV+vXrl+968aSkpHJzvTgREWk+5l7Ko+7cS5V5l7Lm5wGYdxUHL90iIlKR1NRUcTgpAERHRyM8PFypw4CLu42srCzEx8cjNjYW165dw9KlS9G9e3d06dKlwKGu9H/K4th9/vnnsLa2xo4dO+TalyxZAjc3NzRu3Bjbtm3DrVu3cO/ePWzevBl+fn5ITU2FRCLBli1bcPfuXTRv3hyHDx/Gw4cPERERgSVLlqB79+7K2G0iIiKNw9xL8zHvKh2O6CEiUpErV66gdevW4uugoCAAwNChQ5U2QV1xt/HPP//A2dkZBgYGsLW1ha+vL1avXo2hQ4eWyV2ptElZHDtDQ0N8/fXXGDhwoFx7hQoVcOHCBSxbtgyLFy/G48ePYWtrCx8fH6xcuRLW1tYAgEaNGuHKlStYsmQJRo8ejZcvX8LZ2RlNmzbF999/r+guExERaSTmXpqPeVfpSARNmU2IiAhAZmYmoqOj4eXlJXcbRyIqOf48ERFRYXieIFIeVf48sXRIRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4i0ki8YSCR4vhzRERExcHzBZHiVPlzxEIPEWkUQ0NDAEB6erqaIyHSfHk/R3k/V0RERO9i3kWkPNnZ2QAAfX39Mt+WQZlvgYhIifT19WFjY4OEhAQAgJmZGSQSiZqjItIsgiAgPT0dCQkJsLGxUUnCQUREmod5F5FyyGQyvHjxAmZmZjAwKPsyDAs9RKRxnJycAEBMOoiodGxsbMSfJyIiooIw7yJSDj09Pbi7u6ukWCoReMElEWkoqVSKnJwcdYdBpJEMDQ05koeIiIqNeReRYoyMjKCnp5rZc1joISIiIiIiIiLSEpyMWUlCQ0PRtWtXuLi4QCKR4MCBA2W+zdjYWAwePBh2dnYwNTWFj48Prly5UubbJSIiIlI35l5EREQFY6FHSdLS0uDr64u1a9eqZHuJiYlo1qwZDA0NceTIEdy6dQurVq2Cra2tSrZPREREpE7MvYiIiArGS7fKgEQiwf79+9GjRw+xLSsrC7Nnz8bvv/+OpKQkeHt7Y/ny5WjVqlWptjFjxgycO3cOZ86cUU7QRERERBqKuRcREdH/4YgeFRk/fjzOnz+PnTt3IiIiAn369EGHDh1w7969Uq3vzz//hL+/P/r06QMHBwf4+flh06ZNSo6aiIiISDMx9yIiIl3FET1l4P1vlWJiYlC5cmXExMTAxcVFXK5t27Zo1KgRli5dWuJtmJiYAACCgoLQp08fXL58GZMmTcKGDRswdOhQpewHERERkSZg7kVERPR/DNQdgC6IjIyEVCpF9erV5dqzsrJgZ2cHALhz5w5q1apV6HqmT5+OZcuWAQBkMhn8/f3FRMXPzw83btxgskFEREQ6j7kXERHpMhZ6VCA1NRX6+vq4evUq9PX15d6zsLAAAFSuXBm3b98udD15iQkAODs7o3bt2nLv16pVC3v37lVS1ERERESaibkXERHpMhZ6VMDPzw9SqRQJCQlo3rx5gcsYGRmhZs2axV5ns2bNEBUVJdd29+5deHh4KBQrERERkaZj7kVERLqMhR4lSU1Nxf3798XX0dHRCA8PR4UKFVC9enUMGjQIQ4YMwapVq+Dn54cXL17gxIkTqFu3Ljp37lzi7U2ZMgVNmzbF0qVL0bdvX1y6dAkbN27Exo0blblbREREROUScy8iIqKCcTJmJQkJCUHr1q3ztQ8dOhRbt25FTk4OFi9ejG3btiE2Nhb29vZo0qQJFi5cCB8fn1Jt89ChQ5g5cybu3bsHLy8vBAUFYfTo0YruChEREVG5x9yLiIioYCz0EBERERERERFpCT11B0BERERERERERMrBQg8RERERERERkZbgZMwKkMlkePbsGSwtLSGRSNQdDhERUYkIgoA3b97AxcUFenr87ofKP+ZeRESkqVSZd7HQo4Bnz57Bzc1N3WEQEREp5MmTJ3B1dVV3GERFYu5FRESaThV5l9YUeoKDg7Fv3z7cuXMHpqamaNq0KZYvX44aNWp8sM/WrVsxfPhwuTZjY2NkZmYWa5uWlpYA3v5HWVlZlT54IiIiNUhJSYGbm5t4PiMq75h7ERGRplJl3qU1hZ7Tp08jMDAQDRs2RG5uLmbNmoV27drh1q1bMDc3/2A/KysrREVFia9LMgw4b1krKysmG0REpLF4CQxpCuZeRESk6VSRd2lNoeeff/6Re71161Y4ODjg6tWraNGixQf7SSQSODk5lXV4RERERERERERlTmtnXkxOTgYAVKhQodDlUlNT4eHhATc3N3Tv3h03b9784LJZWVlISUmRexARERERERERlRdaWeiRyWSYPHkymjVrBm9v7w8uV6NGDWzevBkHDx7Eb7/9BplMhqZNm+Lp06cFLh8cHAxra2vxwckAiYiIiIiIiKg8kQiCIKg7CGUbO3Ysjhw5grNnz5ZoNuucnBzUqlULAwYMwNdff53v/aysLGRlZYmv8yZTSk5OVtp14ps3b4aDgwM+/vhjmJmZKWWdREREBUlJSYG1tbVSz2NEZUnZn9nY2FicPn0a/fv3L/Nb3RIRkW5TZd6lNXP05Bk/fjwOHTqE0NDQEt+yzNDQEH5+frh//36B7xsbG8PY2FgZYRYoOzsbo0aNgiAIeP78uVjo2b59O/bu3YuePXvis88+E5fP+4BwEk0iIiKikhs8eDBCQkIQExODGTNmqDscIiIipdCary4EQcD48eOxf/9+nDx5El5eXiVeh1QqRWRkJJydncsgwqKlpqaiR48eaNy4MSpWrCi2X7hwAfv378etW7fEtuzsbNja2sLGxgavXr0S269cuYK9e/fiwYMHKo2diIiISJNkZ2cjJCQEADBr1ixo4SB3IiLSUVpT6AkMDMRvv/2GHTt2wNLSEvHx8YiPj0dGRoa4zJAhQzBz5kzx9aJFi3Ds2DE8fPgQ165dw+DBg/H48WOMGjVKHbuAChUqYN++fbhw4YLcKJ0hQ4ZgzZo16Natm9gWGxsLQRCQnZ0tN+H0tm3b8Omnn+Lnn38W23JyctC5c2eMGzdO7ngQERER6apLly6JzwVBwK+//qrGaIiIiJRHay7dWr9+PQCgVatWcu1btmzBsGHDAAAxMTFy118nJiZi9OjRiI+Ph62tLRo0aID//vsPtWvXVlXYxdKwYUM0bNhQrs3LywupqamIj4+XKwq5u7ujSZMmcpNQP3nyBIcPH4aJiQnWrl0rti9fvhynTp1CYGAgunbtWvY7QkRERFROnDp1Su71mDFjYG9vj06dOqkpIiIiIuXQysmYVUVTJrF8/fo19u3bh5SUFAQFBYntLVu2RGhoKDZu3IjRo0cDAF69eoU1a9agZcuW+YpmRESkXTTlPEaUR5mf2aSkJISGhsLa2horVqzA4cOHAQBffPEFZs+ezburEhGRUqky72KhRwGaniBHRkYiJCQE3bt3h7u7OwDgwIED6NmzJ2rXro2bN2+Ky964cQMeHh6wtLRUV7hERKRkmn4eI91TVp/ZrKwsTJs2DT/++CMAQE9PD61bt0bv3r3RokUL1KpVi3flIiIihfCuW6QSPj4+8PHxkWtzcHDAgAEDULVqVbn2Ll264OnTpzhz5gwCAgIAvL2enXf8IiIiIk1nbGyM1atXo3fv3li4cCFOnTqFEydO4MSJEwAACwsLVK9eHdWqVYOnpyccHBxQsWJFODg4oEKFCjA3N4eZmZn4r6mpKQtDVC5kZ2fj5s2buHbtGq5du4br16/j5cuXePPmDYyNjeHo6Ahvb2/Uq1cPzZs3h7e3Nz+7RFqAI3oUoCvfhCYlJcHPzw9Pnz5FYmIiLCwsAACrV6/G5s2bMW7cOHz++edqjpKIiEpKV85jpD1U9Zl9+PAhdu3ahWPHjuHSpUtIS0sr8TqMjY2hp6cHiUSS71+JRAKZTAZBEMR/330ukUhQsWJFODs7w9nZGS4uLqhUqZL4cHFxgZWVFUxMTMSHgYEBv4DTccnJybh+/br4CAsLQ2RkJHJycoq9DltbW3z00Udo0aIFWrRoAT8/PxgaGha4rCAIyMjIQEZGBmQyGYC3n3tLS0t+FokKwEu3NISuJcjPnz+Ho6Oj+LpXr17Yv38/li1bhunTpwMAMjMzERQUhObNm6Nv377Q19dXV7hERFQEXTuPkeZTx2c2NzcX9+7dw71793D37l08ffoUL168QEJCAhISEvD69WtkZGQgPT1drXc31dPTE4s+xsbGMDY2hkwmQ+3atVGzZk1kZmYiLi5OXFZPTw/6+vowMDCAoaEhDAwM8j3PK0ABEJ+/+6dDXvHqQwWtov7NKwbkPTcwMICNjQ0yMzORlJSEnJycfDG8G0tRzxV9v7iPd5c3MDCAlZWV+LC2toaDgwMcHR3h6OgIBwcHGBsbl/j/NzMzU7yrcN7jyZMnePTokfh49uxZgX1tbGxQv3591K9fH35+fnB1dYWFhQWys7Px+PFjRERE4PLly/jvv//yFTWNjY1hZ2cHKysryGQy5OTkICsrC6mpqUhNTRULPO/S19dHhQoVUKFCBVhYWMDMzEx85I16MzMzEwuU7z/e/QzmPYyMjOT65eTkIDMzE1lZWcjMzERKSgqSk5PFf/OeC4IAQ0NDGBkZwdjYWPw/eff/J++5jY0NXFxcYG1tzUIVlQkWejSErifIcXFxOHPmDOrXry9e6hUaGoqWLVvC0dERcXFx4i/JM2fOwNLSEt7e3jAw4BWDRETlga6fx0jzlPfPrEwmkyv6vDtK5/1/Cyp65D3Pzc1FQkIC4uLiEBcXh2fPniE2NhaxsbF49uwZnj17hrS0NGRnZ6t7l6kUbGxs5Io/jo6OsLe3h1QqFT8/iYmJckWdpKSkYq3b3d0dvr6+8PX1Rb169VC/fn14enoWq3CRk5ODsLAwhIaG4syZMzhz5gwSExMV3FvNY2lpCTc3N3h4eKBq1aqoUqWK+PDy8oKJiYm6QyQNxUKPhijvyYY63LlzBxs3boSpqSmWLFkittevXx9hYWHYtWsX+vTpAwB48+YNsrKyYG9vr65wiYh0Gs9jVFrBwcHYt28f7ty5A1NTUzRt2hTLly9HjRo1Pthn06ZN2LZtG27cuAEAaNCgAZYuXYpGjRoVe7v8zMqTyWTiiIb3H1lZWRAEARcuXEBCQgIMDAxQqVIl6OvrQyaTQSqVQiqVIjc3F7m5ucjJycn3b14BqqDHuyNaZDLZBwtahf0LyI+iyc7ORlJSEkxMTGBjYyOOfHl/9E9BbSV5v6TLFueRt3xOTg7evHmDlJQUpKSkIDExEc+fP0dCQgKeP3+O3NzcUv9/Gxsbw8nJCU5OTuIlfV5eXvD09ISnpyeqVKkCW1vbUq//fTKZDDExMUhMTERKSoo4AszIyAiWlpawsLCApaWl3JxUGRkZSExMxKtXr/D69Wukp6fLPdLS0sTnmZmZkEql4meusEdmZqZYBMvMzIShoaE4es3ExASWlpawtrYWR+jk/auvr4/s7GxkZ2eLI3/eHf3z7vPXr18XWVCTSCSoVKmSXPGnatWqcHJykhuxZG5uDnNzc5iYmHB0EIlY6NEQTDaKRyaToXPnzjh37hxu3boFV1dXAMAvv/yCYcOGoV+/fti5c6e4PCd5JiJSDZ7HqLQ6dOiA/v37o2HDhsjNzcWsWbNw48YN3Lp1C+bm5gX2GTRoEJo1a4amTZvCxMQEy5cvx/79+3Hz5k1UqlSpWNvlZ5Y0mSAIYuEn75FXAHr58iUMDQ1hamoKMzMzWFtbiwWdvOIOLykqe2lpaXj69CliYmLw6NEj3L9/Hw8ePBAfb968KdH6DAwM8hWgPvSwtbUVHxUqVICtrS2srKw4ObYW4V23SKvo6enhyJEjkEqlcnP2PHjwAADg5uYmtslkMnGY5O+//w4nJyeVx0tERESF++eff+Reb926FQ4ODrh69SpatGhRYJ/t27fLvf7555+xd+9enDhxAkOGDCmzWInKC4lEIs5dU6tWLXWHQwUwNzdHjRo1ChydKAgCXr58KVf4ySsEvXr1ShyxlJaWhqysLABv5/h69eoVXr16Vap49PT0xCJQ3iiqvNFCRT3PG+1kYmIiFhBNTU1hamoqthkaGrJ4qKVY6CGVeX9i5kWLFmHKlCly15dHRUXh6dOnePXqFezs7MT2tWvX4sKFCxg2bBjatGmjspiJiIioaMnJyQCAChUqFLtPeno6cnJyCu2TlZUl/sEEvP02lIhIHfLuhlexYkU0adKk0GWlUinS0tLkJocu6pGYmCj3yLubWd7rstqnvAJRcYpHVlZWcHFxEecwcnV15YijcoqFHlKr968jrl69Om7cuIGHDx/K3crx4MGDOH78OJo2bSoWel69eoVVq1YhICAAXbt2VWncRERE9JZMJsPkyZPRrFkzeHt7F7vf9OnT4eLigrZt235wmeDgYCxcuFAZYRIRqYy+vr54N6+8aStKKisrS67wk3ens7S0NPHfwp7nzd+VkZEhPvLmOMojCIK43tIwMTFBtWrVULduXXEScF9fX7k7NZN6cI4eBfA6cdU5deoUQkJCMHjwYFSrVg0A8Pfff6NLly6oUaMG7ty5Iy4bGxsLZ2dnVpeJiIrA8xgpw9ixY3HkyBGcPXu22H/QLFu2DCtWrEBISAjq1q37weUKGtHj5ubGzywRUSkJgoDs7Gy54k9xCkepqalITk7G06dPxXmMcnJyCtyGo6OjWPTJKwLVrFlT7ot8XcQ5eoje07p1a7Ru3VquzdHRESNHjsw3gePHH3+MlJQU/PXXX/D391dlmERERDpl/PjxOHToEEJDQ4td5Pnmm2+wbNky/Pvvv4UWeYC3dxnKu/MSEREpTiKRiL9bbWxsSr2e3NxcxMTE4ObNm4iIiMD169dx/fp13Lt3D8+fP8exY8dw7NgxcXkjIyPUrl0btWrVgqenJ7y8vMSHu7t7uSwCxcbGYvbs2Rg7diwaN26s7nBKhCN6FMBvQsuf58+fo1q1asjMzMTLly/F/5cTJ07g0aNH6NKlC4cSEhH9fzyPUWkJgoAJEyZg//79CAkJEUfbFmXFihVYsmQJjh49WuQcFwXhZ5aIqHxLS0vDjRs3cP36dbEAFBERUegca3p6enB1dc1XAMp7uLi4qPRqjaysLHHkaXp6Opo1a4YzZ84oPHG1Vo/oCQoKKnGfOXPmlGhyP9Jdjo6OePnyJSIiIuR+eNasWYMDBw5g4cKFmDdvHoC3SSoAzjRPRERarSxyr8DAQOzYsQMHDx6EpaUl4uPjAQDW1tYwNTUFAAwZMgSVKlVCcHAwAGD58uWYN28eduzYAU9PT7GPhYUFLCwsShwjERGVP+bm5mjcuLHcCBhBEPDo0SNcv34d9+/fR3R0tPh49OgRMjMzERMTg5iYGISGhuZbp7W1NZo1a4aPPvoI7du3h5+fX5n9DSeTyTBixAjs2LEDANCsWTN8++23Gvc3o8pH9Ojp6SEgIABGRkbFWv7s2bOIiopC5cqVyziykuO3Sprjm2++wR9//IFNmzahXr16AIALFy5g0KBBGDRoEBYtWqTeAImI1IDnMd1QFrnXhxLeLVu2YNiwYQCAVq1awdPTE1u3bgUAeHp64vHjx/n6zJ8/HwsWLChWbPzMEhFpF0EQEB8fLxZ93i0CRUdHIyYmBlKpVK5P5cqV8emnn2L06NGoWrWq0mKJiYnByJEj8e+//0JfXx+//PILBg4cqLQijyrPYWop9MTHx8PBwaFYy1taWuL69ess9JDSzZ49G0uXLkW/fv2wc+dOsf3ff/9Fw4YNYW1trcboiIjKHs9juoG5FxERaaqcnBxERETg3LlzOHXqFI4ePYqMjAwAb7906N69OxYuXFjknG9FefHiBZo2bYr79+/D2NgYGzduxJAhQ5SxCyKtvnRry5YtJfoD+qeffuKcKhpEJpMhNTUVSUlJSEpKwps3b2BsbCwOyzY3N4eFhUW5mGxr1qxZ8Pf3h5OTk9j26tUrdOjQAXp6enj69Gmxk2IiUp+8u0e8e3eIvFuFZmVlwcrKCjY2NuLD1NRU44bfEimCuRcREWkqQ0NDNGjQAA0aNMDEiRORlpaGw4cPY/Pmzfjnn39w4MAB/Pnnnxg7diwWL15cqgmm09LS0KVLF9y/fx+enp44evQoqlevrvydUSG1TMYslUqhr6+v6s0qnTZ+qyQIglyh5t1HYmJige3vPpKTkyGTyYrcjqGhYb7iT96/H3penDZDQ0OF/oALCwvDoEGDYGhoiOvXr4vty5Ytw5s3bzB8+HClDg/UZIIgICsrC2/evEFKSgrevHkj97ygf3Nzc1USm4GBAaysrGBpaQlLS0vxeUFtlpaWMDEx4R/+KpD3mcm7TWdBhZnSPi/JZ8vIyEiu8FOch62trfjcxMSkDI+SamnjeYwKxtyLiIi0za1btzBv3jzs3bsXAODh4YFdu3ahUaNGxV6HVCpFr1698Oeff6JChQr477//UKNGjTKJV6sv3QIAJycnDBs2DCNGjNDoSpmqkw2ZTIasrCxkZmYW65GRkfHB99LS0j5YrClOoaYoRkZGsLW1hYWFBbKyssQ/ysr6D319fX2Ym5vD3NwcZmZmpX4uk8ng6OgIc3NzmJqaonHjxoiLi8Pff/+NTp06lek+KINMJkN2djZycnKQk5MjPi+oLe//p6giTUH/5uTkqHtXlcLAwKDYRaF3n1tYWMDIyAiGhobiv+8/f/+1Ku8YUBo5OTliESY9PV2uKPP+6+K2vftaGb9fCpM3gjCvCGxkZIQ3b96IhWplbD/vdqTvP8zMzGBiYqK0h6KF6+LgH826g7kXERFpq1OnTmHUqFF4+PAhDA0NsWbNGnz++efF6jt+/HisXbsWJiYmOHHiBJo2bVpmcWp9oefrr7/GL7/8gujoaDRt2hQjR45E3759YWZmpupQFKLM/6ikpCT06NGj0CJNdna2kiIvmqGhodw32MX9pruob7w/dHnF+23Fee/dZVR1bPT09GBiYgJ9fX3k5uYiNzcX5ubmMDExgZ6eHvT19aGvr6+U5xKJpMgizYfayvqP6feZm5vnK5AUVDAp7kSgisrOzv7gSKN3n6empqoknnfp6+sXqyD0/nuCIEAqlUIqlUImkxX4XBnvqeqzY2RkVOhIvuKM8ivouYHBh69ILmzEYnFHLqrylCmRSAosAFlYWODSpUtK2Qb/aNYdzL2IiEibJScnY9SoUdizZw+At+e92bNnF/ql2caNG/HFF19AIpFg9+7d6N27d5nGqPWFnjwhISHYsmUL9u7dC319ffTt2xejRo2SuxVbeabM/6jk5OQSXU8okUhgamoqJv7vPi/Ow9TUtNBCjqbNYZGTkyM3aqCkz4taTtNHrkgkkg8WEvL+4C6qUPOhfy0sLDT2coC8OaWKUxT6UKHo/YLb+6811YdGx32orajX77aZmZmVi3m6Sur9OcjeLwoVNoqyuI+srKwi4zA3N1dakZJ/NGsGW1vbYp+TX79+Xej7zL2IiEhbCYKABQsWiHdUnjhxIr777rsCR9RfuHABLVq0QE5ODpYuXYqZM2eWeXw6U+jJk5qaip07d2Lr1q3477//UKtWLYwcORJBQUHqDq1QyvyPkkql2LdvX7ELNQYGBhpViNF0eZezpKenIysrC1KpFFFRUfj999/x+eefw8bGBlKpFNeuXcOuXbvQq1cv1KlT54MjKIrzXBCEDxZmStJmZGSksYUYTScIAnJzcwssAJX0uZ6entzIr8JGhSny2sTEBGZmZjAyMuLvGDXIu+yysGJQbm4u2rZtq5Tt8Y9mzfDLL7+Iz1+9eoXFixejffv2CAgIAACcP38eR48exdy5czFlypRirZO5FxERaavVq1dj0qRJAID+/ftj69atMDY2Ft9/9uwZ/P39ERcXh969e2P37t0qyXvLdaGnJAnAt99+W+KA/v77bwwZMgRJSUmQSqUl7q9KTDbofQMHDsTvv/+O0aNHY+PGjeoOh4ioUDyPaZ7evXujdevWGD9+vFz7mjVr8O+//+LAgQMlXidzLyIi0jY7duzAsGHDkJOTgzZt2mDPnj2wtLTEwYMHMWXKFMTExMDb2xvnz5+HhYWFSmIq17dXDwsLk3t97do15ObmijNT3717F/r6+mjQoEGx15meno5du3Zhy5YtOHv2LKpUqYJp06aVNDQitfvyyy9hYWGBsWPHim1Pnz7FvHnzMGbMmBLNAE9ERPS+o0ePYvny5fnaO3TogBkzZhR7Pcy9iIhImw0cOBD29vbo1asXTpw4gcqVK8PU1BTPnj0DAHh5eeHAgQMqK/KoWokLPadOnRKff/vtt7C0tMQvv/wCW1tbAEBiYiKGDx+O5s2bF7mu//77D5s3b8bu3buRm5uLTz/9FF9//TVatGhR0rCIyoX69evnG8nz888/Y8uWLXj48CFCQkLUExgREWkFOzs7HDx4EFOnTpVrP3jwIOzs7Irsz9yLiIh0Rbt27XD69GkMHDgQd+/eRWJiImxtbTF27FjMnj1b425IUBIlLvS8a9WqVTh27JhY5AHeThi4ePFitGvXLl8SkmfFihXYsmUL7t69C39/f6xcuRIDBgyApaWlIuEQlUudO3fGw4cP0b17d7EtKysLM2bMwLBhw+Dr66vG6IiISJMsXLgQo0aNQkhIiDiB8sWLF/HPP/9g06ZNH+zH3IuIiHRRgwYNcOPGDYSGhkImk6F58+YfvEO0NlFoMmZLS0v89ddfaNWqlVz7qVOn0K1bN7x586bAfhUrVsTgwYMxcuRIeHt7l3bzasfrxKm0duzYgUGDBsHNzQ3R0dGcLJmI1ILnMc108eJFrF69Grdv3wYA1KpVCxMnTiz0zlnMvYiIiNSrXM/R866ePXti+PDhWLVqlTj3yMWLFzFt2jT06tXrg/2ePXumkbfWJVKW6tWro2/fvvDz8xOLPIIgYOXKlejWrRtq1qyp5giJiKi8aty4MbZv316iPsy9iIiIdEf+G8qXwIYNG9CxY0cMHDgQHh4e8PDwwMCBA9GhQwesW7euwD6rV68u0R0dNmzY8MGRQUSayt/fH3/88YfcxJlXrlzB9OnTUa9ePaSkpKgxOiIiKs8ePHiAOXPmYODAgUhISAAAHDlyBDdv3ixweeZeREREukWhQo+ZmRnWrVuHV69eISwsDGFhYXj9+jXWrVsHc3PzAvtMmTKlRMnDV199hRcvXigSJpFGMDQ0RLdu3dC/f3+5oXyXL19Gbm6uGiMjIqLy4vTp0/Dx8cHFixexd+9epKamAgCuX7+O+fPnF9iHuRcREZFuUejSrTxxcXGIi4tDixYtYGpqCkEQIJFIClxWEAS0adMGBgbF23RGRoYyQiQq9+rVq4eDBw9CJpOJbc+ePUPz5s3h5uaGc+fOwcHBQY0REhGRus2YMQOLFy9GUFCQ3ETKH3/8MdasWVNgH+ZeREREukWhQs+rV6/Qt29fnDp1ChKJBPfu3UPlypUxcuRI2NraYtWqVfn6fOjbpg/p3r07KlSooEiYRBpFT+//Btrdvn0bFhYWcHR0RMWKFdUYFRERlQeRkZHYsWNHvnYHBwe8fPmywD7MvYiIiHSLQoWeKVOmwNDQEDExMahVq5bY3q9fPwQFBSml0EOky9q0aYNHjx4hISFBHCWXnZ2NDh06YNCgQRgyZAgn1yQi0iE2NjaIi4uDl5eXXHtYWBgqVapUYB/mXkRERLpFoULPsWPHcPToUbi6usq1V6tWDY8fP1YoMCJ6y8LCAhYWFuLr7du349SpU7h9+zYGDhzIQg8RkQ7p378/pk+fjt27d0MikUAmk+HcuXP48ssvMWTIEHWHR0REROWAQpMxp6WlwczMLF/769evYWxsrMiqSyw4OBgNGzaEpaUlHBwc0KNHD0RFRRXZb/fu3ahZsyZMTEzg4+ODw4cPqyBaotLr27cvVq1ahSVLlsDU1FRs/+uvv5Cdna3GyIiIqKwtXboUNWvWhJubG1JTU1G7dm20aNECTZs2xZw5c9QdHhEREZUDChV6mjdvjm3btomv875ZWrFiBVq3bq1wcCVx+vRpBAYG4sKFCzh+/DhycnLQrl07pKWlfbDPf//9hwEDBmDkyJEICwtDjx490KNHD9y4cUOFkROVjLm5OYKCgjBixAix7fz58+jWrRtq166NzMxMNUZHRERlycjICJs2bcKDBw9w6NAh/Pbbb7hz5w5+/fVX6Ovrqzs8IiIiKgckgiAIpe1848YNtGnTBvXr18fJkyfRrVs33Lx5E69fv8a5c+dQpUoVZcZaIi9evICDgwNOnz6NFi1aFLhMv379kJaWhkOHDoltTZo0Qb169bBhw4Yit5GSkgJra2skJyfL3Q6bSNX+/PNPjBkzBh07dsT//vc/sb2wO+AREfE8RpqGn1kiItJUqjyHKTRHj7e3N+7evYs1a9bA0tISqamp6NWrFwIDA+Hs7Fxo35ycHNSsWROHDh2Sm8hZWZKTkwGg0LtGnD9/HkFBQXJt7du3x4EDB5QeD1FZ6tatGz755BOkp6eLbc+fP0eLFi0wceJEjBkzht/0EhFpgffzljwSiQQmJiaoWrXqB++aVda5FxEREZUPChV6AMDa2hqzZ88ucT9DQ8Myu8REJpNh8uTJaNasGby9vT+4XHx8PBwdHeXaHB0dER8fX+DyWVlZyMrKEl+npKQoJ2AiJTA1NZWbs2fdunW4e/cutm3bhnHjxqkxMiIiUpawsDBcu3YNUqkUNWrUAADcvXsX+vr6qFmzJtatW4epU6fi7NmzqF27tlzfssy9iIiIqPxQqNATERFRYHvet0ru7u6FTsocGBiI5cuX4+eff4aBgcI1J7n13rhxA2fPnlXaOoG3Ez4vXLhQqeskKiuzZs2Co6MjatasKV6+lZubiy1btmDw4MFyRSEiItIMeaN1tmzZIg77Tk5OxqhRo/DRRx9h9OjRGDhwIKZMmYKjR4/m619WuRcRERGVHwrN0aOnpyf+AZm3mnfnAzE0NES/fv3w008/wcTEJF//nj174sSJE7CwsICPjw/Mzc3l3t+3b1+JYxo/fjwOHjyI0NBQeHl5Fbqsu7s7goKCMHnyZLFt/vz5OHDgAK5fv55v+YJG9Li5ufE6cdIYW7duxfDhw+Hr64uwsDDO30Ok4zjfieapVKkSjh8/nm+0zs2bN9GuXTvExsbi2rVraNeuHV6+fJmvf1nkXqrEzywREWkqjZmjZ//+/Zg+fTqmTZuGRo0aAQAuXbqEVatWYf78+cjNzcWMGTMwZ84cfPPNN/n629jYoHfv3oqEIBIEARMmTMD+/fsREhJSZJEHAAICAnDixAm5Qs/x48cREBBQ4PLGxsYqv208kTKZm5vD3d0dgwYNkivySKVSzuFDRKQBkpOTkZCQkK/Q8+LFC/GSchsbG2RnZxfYX5m5FxEREZVPChV6lixZgh9++AHt27cX23x8fODq6oq5c+fi0qVLMDc3x9SpUwss9GzZskWRzcsJDAzEjh07cPDgQVhaWorz7FhbW4uXqAwZMgSVKlVCcHAwAGDSpElo2bIlVq1ahc6dO2Pnzp24cuUKNm7cqLS4iMqTPn36oHv37pDJZGJbWFgYevfujeXLl6NPnz5qjI6IiIrSvXt3jBgxAqtWrULDhg0BAJcvX8aXX36JHj16AHj7pVv16tUL7K/M3IuIiIjKJ4UKPZGRkfDw8MjX7uHhgcjISABAvXr1EBcXV+h6Xrx4gaioKABAjRo1ULFixRLHsn79egBAq1at5Nq3bNmCYcOGAQBiYmKgp6cnvte0aVPs2LEDc+bMwaxZs1CtWjUcOHCg0AmciTSdkZGR3Ovly5cjOjoa+/fvZ6GHiKic++mnnzBlyhT0798fubm5AAADAwMMHToU3333HQCgZs2a+PnnnwtdjzJyLyIiIiqfFJqjx8/PD76+vti4caP4x2NOTg5Gjx6N69evIywsDOfOncPgwYMRHR2dr39aWhomTJiAbdu2iSMM9PX1MWTIEPz4448wMzMrbWgqwevESRukp6fjm2++wfDhw+Hm5gbg7Wc7PT0dTk5Oao6OiMoSz2OaKzU1FQ8fPgQAVK5cGRYWFsXqx9yLiIhIPVR5DtMrepEPW7t2LQ4dOgRXV1e0bdsWbdu2haurKw4dOiSOsHn48OEHb+0cFBSE06dP46+//kJSUhKSkpJw8OBBnD59GlOnTlUkNCIqJjMzM8ybN08s8gDA4sWLUb16dWzdulV9gRER0QdZWFigbt26qFu3brGLPABzLyIiIl2g0IgeAHjz5g22b9+Ou3fvAng7/HfgwIGwtLQssq+9vT327NmT73KrU6dOoW/fvnjx4oUioZU5fqtE2kgqlaJVq1Y4e/YsDh06hM6dO6s7JCIqIzyPaaYrV65g165diImJyTfpclF3zWLuRUREpB4ac9ctALC0tMSYMWNK1Tc9PR2Ojo752h0cHJCenq5oaERUCvr6+jh9+jSOHTsmN9F6aGgobG1t4ePjo8boiIh0286dOzFkyBC0b98ex44dQ7t27XD37l08f/4cPXv2LLI/cy8iIiLtp/CIHgC4detWgd8qdevWrdB+bdq0gZ2dHbZt2wYTExMAQEZGBoYOHYrXr1/j33//VTS0MsVvlUhXZGZmonbt2nj8+DH2799f5M82EWkGnsc0T926dfHFF18gMDAQlpaWuH79Ory8vPDFF1/A2dkZCxcuLLQ/cy8iIiL10JgRPQ8fPkTPnj0RGRkJiUSCvJqRRCIB8PYSkMJ8//336NChA1xdXeHr6wsAuH79OkxMTHD06FFFQiMiJUpNTUWDBg2QnZ2Njz/+WN3hEBHprAcPHoiX1BoZGSEtLQ0SiQRTpkzBxx9/XGShh7kXERGR9lNoMuZJkybBy8sLCQkJMDMzw82bNxEaGgp/f3+EhIQU2d/Hxwf37t1DcHAw6tWrh3r16mHZsmW4d+8e6tSpo0hoRKRE9vb22L17NyIiIuQm/Zw2bRoOHz6sxsiIiHSLra0t3rx5AwCoVKkSbty4AQBISkoq1qVXzL2IiIi0n0Ijes6fP4+TJ0/C3t4eenp60NPTw0cffYTg4GBMnDgRYWFhH+ybk5ODmjVr4tChQxg9erQiYRCRilSoUEF8HhISgm+++Qbffvst7t+/Dy8vLzVGRkSkG1q0aIHjx4/Dx8cHffr0waRJk3Dy5EkcP34cbdq0KbQvcy8iIiLdoFChRyqVinfXsre3x7Nnz1CjRg14eHggKiqq0L6GhobIzMxUZPNEpEZ+fn748ssvkZubK1fkkUql0NfXV2NkRETaa82aNWL+NHv2bBgaGuK///5D7969MWfOnEL7MvciIiLSDQpduuXt7Y3r168DABo3bowVK1bg3LlzWLRoESpXrlxk/8DAQCxfvhy5ubmKhEFEamBtbY2VK1fiu+++E9ueP3+OatWqYd26dUXO0UVERCWTm5uLQ4cOicV0PT09zJgxA3/++SdWrVoFW1vbItfB3IuIiEj7KTSiZ86cOUhLSwMALFq0CF26dEHz5s1hZ2eHP/74o8j+ly9fxokTJ3Ds2DH4+PjA3Nxc7v19+/YpEh4RqdiaNWsQHR2NLVu2YMyYMeoOh4hIqxgYGGDMmDG4fft2qdfB3IuIiEj7KVToad++vfi8atWquHPnDl6/fg1bW1vxzluFsbGxQe/evRUJgYjKkfnz58PZ2Rl+fn7Q03s7YFAqlSImJoZz+BARKUGjRo0QHh4ODw+PUvVn7kVERKT9Sl3oycnJgampKcLDw+Ht7S22vztZa2Fyc3PRunVrtGvXDk5OTqUNg4jKEQMDA4wbN06ubevWrRg7dizmzp2LuXPnqikyIiLtMG7cOAQFBeHJkydo0KBBvhE5devW/WBf5l5ERES6odSFHkNDQ7i7u5d6Hg5lDD8movIvNDQUOTk5+f4YISKikuvfvz8AYOLEiWKbRCKBIAiQSCSF5mXMvYiIiHSDQpMxz549G7NmzcLr169L1b9Ro0aF3oKdiDTf1q1bcfToUYwfP15si4iIwJ9//glBENQYGRGR5omOjs73ePjwofhvUZSVewUHB6Nhw4awtLSEg4MDevToUeQdVwFg9+7dqFmzJkxMTODj44PDhw8rHAsRERHJU2iOnjVr1uD+/ftwcXGBh4dHvm/sr127Vmj/cePGYerUqXj69GmJhx8TkWaQSCRo166d+FoQBEyePBmnTp3C4sWLMXv2bDVGR0SkWUo7N08eZeVep0+fRmBgIBo2bIjc3FzMmjUL7dq1w61btz44gvO///7DgAEDEBwcjC5dumDHjh3o0aMHrl27JjcNABERESlGIijwlfrChQsLfX/+/PmFvp83WatcQMUcflwepKSkwNraGsnJybCyslJ3OEQaIScnB/Pnz8f69esVmlCUiBTH85hm+vXXX7FhwwZER0fj/Pnz8PDwwPfffw8vLy9079690L5llXu9ePECDg4OOH36NFq0aFHgMv369UNaWhoOHToktjVp0gT16tXDhg0birUdfmaJiEhTqfIcptCInqIKOUWJjo5WqD8RaR5DQ0MsXboUs2bNgoWFhdg+b948ZGVlYebMmbCxsVFfgERE5dj69esxb948TJ48GUuWLBELMzY2Nvj++++LLPSUVe6VnJwMoPCbcpw/fx5BQUFybe3bt8eBAwfKJCYiIiJdpVChBwCSkpKwZ88ePHjwANOmTUOFChVw7do1ODo6olKlSoX25Tf5RLrr3SJPXFwcVqxYgaysLLRo0QKdO3dWY2REROXXjz/+iE2bNqFHjx5YtmyZ2O7v748vv/yyyP5lkXvJZDJMnjwZzZo1K/QSrPj4eDg6Osq1OTo6Ij4+/oN9srKykJWVJb5OSUlRPGAiIiItp9BkzBEREahevTqWL1+Ob775BklJSQCAffv2YebMmcVax6+//opmzZrBxcUFjx8/BgB8//33OHjwoCKhEZEGcXJywt69e/HFF1+gU6dOYvuzZ884YTMR0Tuio6Ph5+eXr93Y2BhpaWnFWoeyc6/AwEDcuHEDO3fuLFX/wgQHB8Pa2lp8uLm5KX0bRERE2kahQk9QUBCGDRuGe/fuwcTERGzv1KkTQkNDi+y/fv16BAUFoVOnTkhKSso3/JiIdINEIkHnzp2xYcMGSCQSAG+/xW3evDmaN2+OR48eqTdAIqJywsvLC+Hh4fna//nnH9SqVavI/srOvcaPH49Dhw7h1KlTcHV1LXRZJycnPH/+XK7t+fPncHJy+mCfmTNnIjk5WXw8efKkxDESERHpGoUKPZcvX8YXX3yRr71SpUqFDsPNkzf8ePbs2dDX1xfb/f39ERkZqUhoRKThrl69iri4ODx8+BAVK1ZUdzhEROVCUFAQAgMD8ccff0AQBFy6dAlLlizBzJkz8dVXXxXZX1m5lyAIGD9+PPbv34+TJ0/Cy8uryD4BAQE4ceKEXNvx48cREBDwwT7GxsawsrKSexAREVHhFJqjx9jYuMBrpe/evVusP8yUMfyYiLRT06ZNcffuXTx8+FDuVr2bN29Gz549YWtrq8boiIjUY9SoUTA1NcWcOXOQnp6OgQMHwsXFBT/88AP69+9fZH9l5V6BgYHYsWMHDh48CEtLS/ELPmtra5iamgIAhgwZgkqVKiE4OBgAMGnSJLRs2RKrVq1C586dsXPnTly5cgUbN24s9naJiIioaAqN6OnWrRsWLVqEnJwcAG8vv4iJicH06dPRu3fvIvsrOvyYiLSbq6ur3G16Q0NDMXLkSNSoUQNv3rxRY2REROozaNAg3Lt3D6mpqYiPj8fTp08xcuTIYvVVVu61fv16JCcno1WrVnB2dhYff/zxh7hMTEwM4uLixNdNmzbFjh07sHHjRvj6+mLPnj04cOBAoRM4ExERUckpNKJn1apV+PTTT+Hg4ICMjAy0bNkS8fHxCAgIwJIlS4rsnzf8ODMzUxx+/PvvvyM4OBg///yzIqERkRbS19eHt7c3mjVrBktLS3WHQ0SkcosXL8agQYPg5eUFMzMzmJmZlai/snKv4kyUHxISkq+tT58+6NOnT0lCJiIiohKSCEq4pc3Zs2cRERGB1NRU1K9fH23bti123+3bt2PBggV48OABAMDFxQULFy4s9jdT6pSSkgJra2skJyfzmnEiFZFKpcjIyBBvz/78+XN89tlnWLBgAZo2barm6Ig0C89jmsfX1xc3btxA48aNMXjwYPTt2xf29vYlWgdzLyIiItVT5TlMoULPkydPlHaby/T0dKSmpsLBwUEp61MFJhtE6jdhwgSsWbMGDRs2xMWLF8W7dhFR0Xge00w3b97E9u3bsXPnTjx9+hSffPIJBg0ahB49epRohA9zLyIiItVR5TlMoTl6PD090bJlS2zatAmJiYkKBWJmZqZRiQYRlQ8zZ87EqFGj8M0334hFnpycnHy38CUi0hZ16tTB0qVL8fDhQ5w6dQqenp6YPHlyobcpLwhzLyIiIu2kUKHnypUraNSoERYtWgRnZ2f06NEDe/bsQVZWlrLiIyIqlIuLCzZt2iQ3afMvv/yCypUrY8WKFWqMjIio7Jmbm8PU1BRGRkbizTGIiIhItylU6PHz88PKlSsRExODI0eOoGLFivj888/h6OiIESNGKCtGIqISOXr0KNLT02FkZKTuUIiIlC46OhpLlixBnTp14O/vj7CwMCxcuFC8xTkRERHpNqVMxvyua9euYeTIkYiIiIBUKlXmqssdXidOVD4JgoDDhw/j448/hqmpKYC3IxBPnDiB8ePHw9zcXM0REpUPPI9pniZNmuDy5cuoW7cuBg0ahAEDBqBSpUrqDktl+JklIiJNpcpzmEK3V8/z9OlT7NixAzt27MCNGzcQEBCAtWvXlmgdmZmZMDExUUY4RKTjJBIJOnfuLNc2e/ZsHDt2DE+fPsWPP/6opsiIiBTTpk0bbN68GbVr11Z4Xcy9iIiItJNCl2799NNPaNmyJTw9PbFt2zb069cPDx48wJkzZzBmzJgi+8tkMnz99deoVKkSLCws8PDhQwDA3Llz8b///U+R0IiIRIIgYPDgwahZsyaCgoLE9tTUVM5pQUQaZcmSJQoVeZh7ERERaT+FCj2LFy9G48aNcfXqVdy4cQMzZ86Eh4dHifpv3boVK1askJtLw9vbGz///LMioRERiSQSCT777DPcunULXl5eYvvChQtRs2ZNHD58WI3RERGVzNOnT7Fu3TrMmDEDQUFBco+iMPciIiLSfgpduhUTEyPezrg0tm3bho0bN6JNmzZyI4B8fX1x584dRUIjIsrn3d9XOTk52Lt3L6KjoxX6PUZEpEonTpxAt27dULlyZdy5cwfe3t549OgRBEFA/fr1i+zP3IuIiEj7KVToyfvjKD09HTExMcjOzpZ7v27duoX2j42NRdWqVfO1y2QyXk5BRGXK0NAQkZGR2LNnDzp06CC2//3338jNzUW3bt1YACKicmfmzJn48ssvsXDhQlhaWmLv3r1wcHDAoEGD5H6XfQhzLyIiIu2n0KVbL168QOfOnWFpaYk6derAz89P7lGU2rVr48yZM/na9+zZU6z+7woNDUXXrl3h4uICiUSCAwcOFLp8SEgIJBJJvgdvTUqkO8zNzTF06FCxoJOTk4OJEyeiR48e2Lx5s5qjIyLK7/bt2xgyZAgAwMDAABkZGbCwsMCiRYuwfPnyIvsrM/ciIiKi8kmhET2TJ09GcnIyLl68iFatWmH//v14/vw5Fi9ejFWrVhXZf968eRg6dChiY2Mhk8mwb98+REVFYdu2bTh06FCJYklLS4Ovry9GjBiBXr16FbtfVFSU3K3NHBwcSrRdItIeOTk56NevH/744w/0799fbM/IyBBv005EpE7m5ubiCGpnZ2c8ePAAderUAQC8fPmyyP7KzL2IiIiofFKo0HPy5EkcPHgQ/v7+0NPTg4eHBz755BNYWVkhODg43+2N39e9e3f89ddfWLRoEczNzTFv3jzUr18ff/31Fz755JMSxdKxY0d07NixxPvg4OAAGxubEvcjIu1jZmaGpUuXYtGiRTAw+L9fj3369EF2djZ++OEH1KpVS40REpGua9KkCc6ePYtatWqhU6dOmDp1KiIjI7Fv3z40adKkyP7KzL2IiIiofFKo0JOWliaOgLG1tcWLFy9QvXp1+Pj44Nq1a8VaR/PmzXH8+HFFwlBIvXr1kJWVBW9vbyxYsADNmjX74LJZWVnIysoSX6ekpKgiRCJSsXeLPI8fP8axY8cgk8mgr6+vxqiIiIBvv/0WqampAN7eOTA1NRV//PEHqlWrhm+//bZY61B37kVERERlS6E5emrUqIGoqCgAb+/W8NNPPyE2NhYbNmyAs7Nzkf0rV66MV69e5WtPSkpC5cqVFQmtSM7OztiwYQP27t2LvXv3ws3NDa1atSq0QBUcHAxra2vx4ebmVqYxEpH6eXh4ICoqCps2bUL16tXF9j/++AO3bt1SY2REpIsqV64s3uzC3NwcGzZsQEREBPbu3QsPD49i9VdX7kVERESqIREEQSht599++w25ubkYNmwYrl69ig4dOuD169cwMjLC1q1b0a9fv0L76+npIT4+Pt+8OM+fP4e7u7vc6JmSkEgk2L9/P3r06FGifi1btoS7uzt+/fXXAt8vaESPm5sbkpOT5eb5ISLtlpCQgMqVKyMjIwMXLlxAw4YN1R0SUamkpKTA2tqa5zENNW7cOCxatAj29vbF7lNWuZeq8DNLRESaSpXnMIUu3Ro8eLD4vEGDBnj8+DHu3LkDd3f3QpOOP//8U3x+9OhRWFtbi6+lUilOnDgBT09PRUIrlUaNGuHs2bMffN/Y2BjGxsYqjIiIyqPMzEy0a9cOz549g7+/v9guCAJvyU5EKvPbb7/hyy+/LFahp7zmXkRERKR8ChV63nXu3Dn4+/ujfv36RS6bN9JGIpFg6NChcu8ZGhrC09OzWHftUrbw8PBiXXJGRLrN3d0d+/btQ0ZGhljYyc3NRatWrdCzZ08EBgbCxMREzVESkbYryaDs8pp7ERERkfIprdDTsWNHhIeHF+v6bplMBgDw8vLC5cuXSzTk+ENSU1Nx//598XV0dDTCw8NRoUIFuLu7Y+bMmYiNjcW2bdsAAN9//z28vLxQp04dZGZm4ueff8bJkydx7NgxhWMhIt3w7i3Xd+/ejXPnzuH27dsYNWoUCz1EVK6URe5FRERE5ZPSCj2lmeonOjpaWZvHlStX0Lp1a/F1UFAQAGDo0KHYunUr4uLiEBMTI76fnZ2NqVOnIjY2FmZmZqhbty7+/fdfuXUQERVX3759kZmZCYlEIndJxPXr11G3bl1e0kVESvfmzZsS91Fm7kVERETlk0KTMb/L0tIS169fL9EdGxYtWlTo+/PmzVM0rDLFCQGJqDBXr16Fv78/2rZti8OHD8PQ0FDdIRHJ4XlMMz148ABbtmzBw4cP8f3338PBwQFHjhyBu7s76tSpU2hf5l5ERETqoTGTMb/rp59+gqOjY4n67N+/X+51Tk4OoqOjYWBggCpVqpT7ZIOIqDBhYWEwMjKCk5MTizxEpBSnT59Gx44d0axZM4SGhmLx4sVwcHDA9evX8b///Q979uwptD9zLyIiIu2nlELP/fv3YWdnBz09PQDFv/NMWFhYvraUlBQMGzYMPXv2VEZoRERqM2rUKHzyyScwMPi/X7WJiYlYsmQJvvrqq3y3NyYiKsqMGTOwePFiBAUFwdLSUmz/+OOPsWbNmiL7M/ciIiLSfnqKdH716hXatm2L6tWro1OnToiLiwMAjBw5ElOnTi3VOq2srLBw4ULMnTtXkdCIiMoFDw8PVKpUSXwdHByMVatWiXfAISIqicjIyAILMg4ODnj58mWp1snci4iISLsoVOiZMmUKDAwMEBMTAzMzM7G9X79++Oeff0q93uTkZCQnJysSGhFRudSxY0c0aNAAs2fPFttkMhlycnLUGBURaQobGxvxi7V3hYWFyRWVS4q5FxERkfZQ6NKtY8eO4ejRo3B1dZVrr1atGh4/flxk/9WrV8u9FgQBcXFx+PXXX9GxY0dFQiMiKpdat26NS5cuyV3eumvXLsyfPx/ffPMNunbtqsboiKi869+/P6ZPn47du3dDIpFAJpPh3Llz+PLLLzFkyJAi+zP3IiIi0n4KFXrS0tLkRvLkef36NYyNjYvs/91338m91tPTQ8WKFTF06FDMnDlTkdCIiMqtvPnM8vzwww+4e/curl+/zkIPERVq6dKlCAwMhJubG6RSKWrXrg2pVIqBAwdizpw5RfZn7kVERKT9FLq9eqdOndCgQQN8/fXXsLS0REREBDw8PNC/f3/IZLIi7/yg6XiLTyJShpSUFKxZswaTJk2Cubk5gLe3T87MzCzyVslEiuB5THM9efIEkZGRSE1NhZ+fH6pVq6bukFSCn1kiItJUqjyHKVTouXHjBtq0aYP69evj5MmT6NatG27evInXr1/j3LlzqFKlijJjLXeYbBBRWenevTsOHTqENWvWYOzYseoOh7QUz2OkafiZJSIiTaXKc5hCl255e3vj7t27WLNmDSwtLZGamopevXohMDAQzs7OBfbp1atXsde/b98+RcIjItJI2dnZMDQ0hEQiQevWrdUdDhGVI71790ajRo0wffp0ufYVK1bg8uXL2L17d74+zL2IiIh0i0KFHgCwtraWu3tMcZYnIqIPMzIywp49e/Do0SN4enqK7d9//z0AYOzYscWaB42ItE9oaCgWLFiQr71jx45YtWpVgX2YexEREekWhQs9mZmZiIiIQEJCAmQymdx73bp1y7f8li1bFN0kEZFOeLfI8/z5c8yZMwdpaWnw8PBAz5491RcYEalNamoqjIyM8rUbGhoiJSWlwD7MvYiIiHSLQoWef/75B0OGDMHLly/zvSeRSCCVSou1nhcvXiAqKgoAUKNGDVSsWFGRsIiItI6dnR2+++47/P333+jRo4fYnpyczG/riXSIj48P/vjjD8ybN0+ufefOnahdu3ax18Pci4iISHspVOiZMGEC+vTpg3nz5sHR0bHE/dPS0jBhwgRs27ZNHA2kr6+PIUOG4Mcffyzw1u1ERLrIwMAAo0ePxujRo8U2qVSKZs2awdPTE+vXr4ebm5saIyQiVZg7dy569eqFBw8e4OOPPwYAnDhxAr///nuB8/O8j7kXERGR9tNTpPPz588RFBRUqiIPAAQFBeH06dP466+/kJSUhKSkJBw8eBCnT5/G1KlTFQmNiEjrXbp0CVFRUfjvv/9gYWGh7nCISAW6du2KAwcO4P79+xg3bhymTp2Kp0+f4t9//5Ub7fchzL2IiIi0n0K3Vx8xYgSaNWuGkSNHlqq/vb099uzZg1atWsm1nzp1Cn379sWLFy9KG5pK8BafRKRud+/exd27d9GlSxexbdeuXWjfvj0v6aIi8Tyme5h7ERERqYfG3F59zZo16NOnD86cOQMfHx8YGhrKvT9x4sRC+6enpxc4GsjBwQHp6emKhEZEpBOqV6+O6tWri6/DwsLQr18/ODg44M6dO7C1tVVjdERU3jD3IiIi0n4KFXp+//13HDt2DCYmJggJCYFEIhHfk0gkRRZ6AgICMH/+fGzbtg0mJiYAgIyMDCxcuBABAQGKhEZEpJPS0tJQo0YN+Pv7s8hDpIWkUim+++477Nq1CzExMcjOzpZ7//Xr14X2Z+5FRESk/RQq9MyePRsLFy7EjBkzoKdX8ul+fvjhB7Rv3x6urq7w9fUFAFy/fh0mJiY4evSoIqEREemkjz76CDdu3EBqaqrYlpiYiAEDBmDWrFlo0aKFGqMjIkUtXLgQP//8M6ZOnYo5c+Zg9uzZePToEQ4cOJDvTlwFYe5FRESk/RSao6dChQq4fPkyqlSpUuoA0tPTsX37dty5cwcAUKtWLQwaNAimpqalXqeq8DpxItIEs2bNQnBwMOrUqYOIiIhSFeZJO/E8pnmqVKmC1atXo3PnzrC0tER4eLjYduHCBezYsaPIdTD3IiIiUj2NmaNn6NCh+OOPPzBr1qxSr8PMzEzudsFERKRcEydORFJSErp06SIWeQRBQEJCQqnvmkhE6hEfHw8fHx8AgIWFBZKTkwEAXbp0wdy5c4u1DuZeRERE2k2hr3WlUilWrFiBli1bYsKECQgKCpJ7FOWXX37B33//Lb7+6quvYGNjg6ZNm+Lx48eKhEZERP+fk5MT1q1bh06dOoltu3fvRuXKlbFs2TI1RkZEJeXq6oq4uDgAb0f3HDt2DABw+fJlGBsbF9mfuRcREZH2U6jQExkZCT8/P+jp6eHGjRsICwsTH+Hh4UX2X7p0qThM+Pz581izZg1WrFgBe3t7TJkyRZHQiIioEH/++SfS09PzTeRKROVbz549ceLECQDAhAkTMHfuXFSrVg1DhgzBiBEjiuyvzNwrNDQUXbt2hYuLCyQSCQ4cOFBkn+3bt8PX1xdmZmZwdnbGiBEj8OrVqxJtl4iIiAqn0Bw9ijIzM8OdO3fg7u6O6dOnIy4uDtu2bcPNmzfRqlUrvHjxQl2hFQuvEyciTSUIAg4ePIi2bdvCwsICABAVFYWIiAj07t2b8/joCJ7HNN/58+dx/vx5VKtWDV27di1yeWXmXkeOHMG5c+fQoEED9OrVC/v370ePHj0+uPy5c+fQokULfPfdd+jatStiY2MxZswYVK9eHfv27SvWNvmZJSIiTaUxc/QoysLCAq9evYK7uzuOHTsmXu5lYmKCjIwMdYZGRKTVJBJJvj/IZsyYgQMHDmDatGlYsWKFegIjohIJCAgo0W3RlZl7dezYER07diz28ufPn4enpycmTpwIAPDy8sIXX3yB5cuXl2i7REREVLgSF3p69eqFrVu3wsrKCr169Sp02aK+nfnkk08watQo+Pn54e7du+L8ETdv3oSnp2dJQyMiolKSyWTw9fXFqVOnMHz4cLl2ju4hKl+ioqLw448/4vbt2wDe3jVrwoQJqFGjRpF91Zl7BQQEYNasWTh8+DA6duyIhIQE7NmzR27+sPdlZWUhKytLfJ2SklKmMRIREWmDEmfv1tbWkEgk4vPCHkVZu3YtAgIC8OLFC+zduxd2dnYAgKtXr2LAgAElDY2IiEpJT08PCxYsQGxsLGrVqiW2L168GN26dcPNmzfVGB0R5dm7dy+8vb1x9epV+Pr6wtfXF9euXYO3tzf27t1bZH915l7NmjXD9u3b0a9fPxgZGcHJyQnW1tZYu3btB/sEBwfL5ZZubm5lGiMREZE2KNUcPYsWLcKXX34JMzOzsohJY/A6cSLSZhkZGahUqRISExOxa9cu9OnTR90hkZLxPKZ5qlSpgkGDBmHRokVy7fPnz8dvv/2GBw8eqCUuiURS5Bw9t27dQtu2bTFlyhS0b98ecXFxmDZtGho2bIj//e9/BfYpaESPm5sbP7NERKRxVJl3larQo6+vj7i4ODg4OCgcQGJiIv73v//JDT8eMWIEKlSooPC6yxoTZCLSdlFRUdi8eTOCg4PFS7jCwsLg5OQEZ2dnNUdHiuJ5TPOYmZkhIiICVatWlWu/d+8efH19kZ6eXuQ6yiL3Kk6h57PPPkNmZiZ2794ttp09exbNmzfHs2fPivU7hZ9ZIiLSVKo8h5Vq4gVl3agrNDQUnp6eWL16NRITE5GYmIgff/wRXl5eCA0NVco2iIio9GrUqIHly5eLRR6pVIrPPvsMVapUwT///KPm6Ih0T6tWrXDmzJl87XkFk6KoM/dKT0/PN+eXvr4+AOXllkRERKTAXbfy5ulRRGBgIPr164f169eLJ3qpVIpx48YhMDAQkZGRCm+DiIiU5+XLl7C2toaxsTEaN26s7nCIdE63bt0wffp0XL16FU2aNAEAXLhwAbt378bChQvx559/yi37PmXmXqmpqbh//774Ojo6GuHh4ahQoQLc3d0xc+ZMxMbGYtu2bQCArl27YvTo0Vi/fr146dbkyZPRqFEjuLi4lOp4EBERUX6lunRLT09PblLmD3n9+nWh75uamiI8PDzfXSKioqJQr169cn+LdQ4fJiJdJAgCHj16BC8vL7Ft0qRJqFq1Kj7//HMYGxurMToqCZ7HNE9x74InkUgglUrztSsz9woJCUHr1q3ztQ8dOhRbt27FsGHD8OjRI4SEhIjv/fjjj9iwYQOio6NhY2ODjz/+GMuXL0elSpWKtU1+ZomISFOp8hxW6hE9CxcuLNadtQpTv3593L59O1+ycfv2bfj6+iq0biIiKhsSiUSuyBMZGYnVq1cDAFq0aMHf30RlSCaTKdRfmblXq1atCr3kauvWrfnaJkyYgAkTJpRoO0RERFQypS709O/fv1STMUdERIjPJ06ciEmTJuH+/ftyw4/Xrl2LZcuWlTY0IiJSoZo1a2LDhg35/lC8f/8+qlSpopRLfYl03fnz5/Hq1St06dJFbNu2bRvmz5+PtLQ09OjRAz/++GOBI+qYexEREekWld91S09PDxKJpMhJ9z405Lg84fBhIqKCJSUloXLlyqhatSoOHDjA+TfKKZ7HNEfHjh3RqlUrTJ8+HcDbkXT169fHsGHDUKtWLaxcuRJffPEFFixYkK8vcy8iIiL1K/eXbilyZ4To6OhS9yUiIs1w5coVZGdnIy0tDY6OjuoOh0jjhYeH4+uvvxZf79y5E40bN8amTZsAAG5ubpg/f36BhR7mXkRERLqlVIUeRa4P9/DwKHXfwoSGhmLlypW4evUq4uLisH//fvTo0aPQPiEhIQgKCsLNmzfh5uaGOXPmYNiwYWUSHxGRLmnbti0ePHiAuLg4udsnf/XVVxg2bBjq1Kmj5giJNEtiYqJc0fT06dPo2LGj+Lphw4Z48uRJgX3LKvciIiKi8qnUc/Qo061btxATE4Ps7Gy59oJuC/ohaWlp8PX1xYgRI9CrV68il4+Ojkbnzp0xZswYbN++HSdOnMCoUaPg7OyM9u3bl3gfiIhInqOjo9wfpnv37sU333yDTZs2ITY2Fubm5mqMjkizODo6Ijo6Gm5ubsjOzsa1a9ewcOFC8f03b97A0NCw2OtTRu5FRERE5ZNaCz0PHz5Ez549ERkZKXfteN7EnSW5Trxjx45y32wVZcOGDfDy8sKqVasAALVq1cLZs2fx3XffsdBDRFQGfHx80Lt3b/j4+MgVecLDw+Hr68tJm4kK0alTJ8yYMQPLly/HgQMHYGZmhubNm4vvR0REoEqVKkWuR5m5FxEREZVPeurc+KRJk+Dl5YWEhASYmZnh5s2bCA0Nhb+/P0JCQsp02+fPn0fbtm3l2tq3b4/z589/sE9WVhZSUlLkHkREVDw1atTAnj17MG/ePLHt9u3b8PPzg7+/PzIzM9UYHVH59vXXX8PAwAAtW7bEpk2bsGnTJhgZGYnvb968Ge3atStyPerMvYiIiEg11Dqi5/z58zh58iTs7e2hp6cHPT09fPTRRwgODsbEiRMRFhZWZtuOj4/PN0Goo6MjUlJSkJGRAVNT03x9goOD5YZJExFRyb07cic8PBxmZmbw8PCAiYmJ2C6VSsW5fYgIsLe3R2hoKJKTk2FhYZHv52P37t2wsLAocj3qzL2IiIhINdQ6okcqlcLS0hLA2wTm2bNnAN5OGhgVFaXO0Ao0c+ZMJCcni48PTXpIRETFM2DAAMTExODbb78V25KTk+Hl5YWvvvoKGRkZaoyOqPyxtrYusAhaoUIFuRE+H6JpuRcRERGVnFpH9Hh7e+P69evw8vJC48aNsWLFChgZGWHjxo2oXLlymW7byckJz58/l2t7/vw5rKysChzNAwDGxsYwNjYu07iIiHSNnZ0d7OzsxNc7d+7EkydP8Pfff2PZsmVqjIxI+6gz9yIiIiLVUGuhZ86cOUhLSwMALFq0CF26dEHz5s1hZ2eHP/74o0y3HRAQgMOHD8u1HT9+HAEBAWW6XSIiKtzo0aNRqVIl6OvrQ0/v7cBTmUyGL774An379kXbtm05cTNRKakz9yIiIiLVkAh5t1soJ16/fg1bW9sSJ/Gpqam4f/8+AMDPzw/ffvstWrdujQoVKsDd3R0zZ85EbGwstm3bBuDt7dW9vb0RGBiIESNG4OTJk5g4cSL+/vvvYt91KyUlBdbW1khOToaVlVXJdpSIiIrt0KFD6Nq1K6ysrPD06VPx0hNSDM9jBJQ+91IHfmaJiEhTqfIcptYRPQWpUKFCqfpduXIFrVu3Fl8HBQUBAIYOHYqtW7ciLi4OMTEx4vteXl74+++/MWXKFPzwww9wdXXFzz//zFurExGVQ3Xr1sWkSZNga2srV+TZu3cv2rZtC2trazVGR6TZSpt7ERERUflU7kb0aBJ+q0REpD5RUVGoWbMmrK2t8fDhQ/6xWgo8j5Gm4WeWiIg0lU6P6CEiIiqOV69eoU6dOqhSpYpckSchIQEODg5qjIyIiIiISH1Y6CEiIo3UtGlTREZGIjk5WWxLSUlB9erV4e/vj99//x0VK1ZUY4RERERERKqnp+4AiIiISksikcDGxkZ8ffbsWaSmpuLp06dyt2y/evUqEhMT1RAhEREREZFqcUQPERFpjU6dOuHhw4eIjY0Vb80uCAK6dOmC+Ph4XLp0CQ0bNgTw9pbtecsQEREREWkLZrhERKRV3N3dERAQIL5+9eoVrK2tYWxsDB8fH7F95cqVqFKlCtasWaOOMImIiIiIygQLPUREpNXs7e1x584dxMfHw8TERGw/e/YsHj58iNzcXLEtNTUVffv2xerVqyGVStURLhERERGRQnjpFhER6YR35/IBgO3bt+O///5D7dq1xbbz589j9+7duHjxIiZOnCi2Hz16FBYWFvD394exsbGqQiYiIiIiKjEWeoiISCdZWVmhQ4cOcm1VqlTB4sWLYWRkJNc+depU3Lx5E/v27UPPnj0BvB39IwgCLC0tVRYzEREREVFReOkWERHR/1e5cmXMnj0b06ZNE9tyc3NRs2ZNODg44KOPPhLbf//9d9ja2mLMmDFy64iLi4NMJlNZzERERERE7+KIHiIiokIYGBhgz549EAQBEolEbL958yakUikqVqwotuXk5MDV1RWGhoaIiYmBg4MDACA8PBxPnz5F3bp14e7urvJ9ICIiIiLdwRE9RERExfBukQcAvv/+e8TExGDcuHFi27Nnz6Cnpwc9PT25AtDmzZvRtWtXrF27VmzLzc3F8OHD8fXXXyMzM7Psd4CIiIiIdAILPURERKXk5uYGZ2dn8bWHhwcyMjJw9+5ducKQs7Mz6tWrhzp16ohtMTEx2Lp1K5YsWSI3J9CcOXPg5+eHbdu2iW1SqRQPHjyQu0MYEREREVFBeOkWERGREhkYGMDV1VWubebMmZg5c6Zcm5mZGRYvXoy0tDTo6f3f9y4REREIDw9Henq62PbkyRNUrVoV5ubmSElJkVueiIiIiOhdLPQQERGpgZOTE2bPnp2v/bvvvsPnn38OHx8fsS0uLg7GxsZwdXVlkYeIiIiICsVCDxERUTlSpUoVVKlSRa4tICAA6enpSExMVFNURERERKQp+LUgERGRBtDT04OdnZ26wyAiIiKico6FHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJXjXLQUIggAASElJUXMkREREJZd3/so7nxGVd8y9iIhIU6ky72KhRwFv3rwBALi5uak5EiIiotJ78+YNrK2t1R0GUZGYexERkaZTRd4lEfg1XqnJZDI8e/YMlpaWkEgkcu+lpKTAzc0NT548gZWVlZoiVC1d3GdAN/eb+6wb+wzo5n7r0j4LgoA3b97AxcUFenq8mpvKv8Jyr9LQpZ/34uDxyI/HJD8eE3k8HvnxmMjLOx4xMTGQSCQqybs4okcBenp6cHV1LXQZKysrnftw6+I+A7q539xn3aGL+60r+8yRPKRJipN7lYau/LwXF49Hfjwm+fGYyOPxyI/HRJ61tbXKjge/viMiIiIiIiIi0hIs9BARERERERERaQkWesqIsbEx5s+fD2NjY3WHojK6uM+Abu4391l36OJ+6+I+E+kq/rzL4/HIj8ckPx4TeTwe+fGYyFPH8eBkzEREREREREREWoIjeoiIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChpwysXbsWnp6eMDExQePGjXHp0iV1h6Q0wcHBaNiwISwtLeHg4IAePXogKipKbpnMzEwEBgbCzs4OFhYW6N27N54/f66miJVv2bJlkEgkmDx5stimrfscGxuLwYMHw87ODqampvDx8cGVK1fE9wVBwLx58+Ds7AxTU1O0bdsW9+7dU2PEipFKpZg7dy68vLxgamqKKlWq4Ouvv8a7c9Zrwz6Hhoaia9eucHFxgUQiwYEDB+TeL84+vn79GoMGDYKVlRVsbGwwcuRIpKamqnAvSqawfc7JycH06dPh4+MDc3NzuLi4YMiQIXj27JncOjRtn4mocNqcr71LWblbTEwMOnfuDDMzMzg4OGDatGnIzc1V5a6UidLmddp2PJSR82nTeVJZOaEmHxNV5YsRERFo3rw5TExM4ObmhhUrVpT1rpWKqnJJpR0PgZRq586dgpGRkbB582bh5s2bwujRowUbGxvh+fPn6g5NKdq3by9s2bJFuHHjhhAeHi506tRJcHd3F1JTU8VlxowZI7i5uQknTpwQrly5IjRp0kRo2rSpGqNWnkuXLgmenp5C3bp1hUmTJont2rjPr1+/Fjw8PIRhw4YJFy9eFB4+fCgcPXpUuH//vrjMsmXLBGtra+HAgQPC9evXhW7dugleXl5CRkaGGiMvvSVLlgh2dnbCoUOHhOjoaGH37t2ChYWF8MMPP4jLaMM+Hz58WJg9e7awb98+AYCwf/9+ufeLs48dOnQQfH19hQsXLghnzpwRqlatKgwYMEDFe1J8he1zUlKS0LZtW+GPP/4Q7ty5I5w/f15o1KiR0KBBA7l1aNo+E9GHaXu+9i5l5G65ubmCt7e30LZtWyEsLEw4fPiwYG9vL8ycOVMdu6Q0pc3rtO14KCvn06bzpLJyQk0+JqrIF5OTkwVHR0dh0KBBwo0bN4Tff/9dMDU1FX766SdV7WaxqSKXVObxYKFHyRo1aiQEBgaKr6VSqeDi4iIEBwerMaqyk5CQIAAQTp8+LQjC2w+5oaGhsHv3bnGZ27dvCwCE8+fPqytMpXjz5o1QrVo14fjx40LLli3FhEBb93n69OnCRx999MH3ZTKZ4OTkJKxcuVJsS0pKEoyNjYXff/9dFSEqXefOnYURI0bItfXq1UsYNGiQIAjauc/vn6iKs4+3bt0SAAiXL18Wlzly5IggkUiE2NhYlcVeWgUlK++7dOmSAEB4/PixIAiav89EJE/X8rV3lSZ3O3z4sKCnpyfEx8eLy6xfv16wsrISsrKyVLsDSqJIXqdtx0MZOZ+2nSeVkRNq0zEpq3xx3bp1gq2trdzPzfTp04UaNWqU8R4ppqxySWUeD166pUTZ2dm4evUq2rZtK7bp6emhbdu2OH/+vBojKzvJyckAgAoVKgAArl69ipycHLljULNmTbi7u2v8MQgMDETnzp3l9g3Q3n3+888/4e/vjz59+sDBwQF+fn7YtGmT+H50dDTi4+Pl9tva2hqNGzfW2P1u2rQpTpw4gbt37wIArl+/jrNnz6Jjx44AtHOf31ecfTx//jxsbGzg7+8vLtO2bVvo6enh4sWLKo+5LCQnJ0MikcDGxgaAbuwzka7QxXztXaXJ3c6fPw8fHx84OjqKy7Rv3x4pKSm4efOmCqNXHkXyOm07HsrI+bTtPKmMnFDbjsm7lLX/58+fR4sWLWBkZCQu0759e0RFRSExMVFFe1M2SpNLKvN4GCi+C5Tn5cuXkEqlcr/0AcDR0RF37txRU1RlRyaTYfLkyWjWrBm8vb0BAPHx8TAyMhI/0HkcHR0RHx+vhiiVY+fOnbh27RouX76c7z1t3eeHDx9i/fr1CAoKwqxZs3D58mVMnDgRRkZGGDp0qLhvBX3eNXW/Z8yYgZSUFNSsWRP6+vqQSqVYsmQJBg0aBABauc/vK84+xsfHw8HBQe59AwMDVKhQQSuOQ2ZmJqZPn44BAwbAysoKgPbvM5Eu0bV87V2lzd3i4+MLPF5572kaRfM6bTseysj5tO08qYycUNuOybuUtf/x8fHw8vLKt46892xtbcsk/rJW2lxSmceDhR4qtcDAQNy4cQNnz55Vdyhl6smTJ5g0aRKOHz8OExMTdYejMjKZDP7+/li6dCkAwM/PDzdu3MCGDRswdOhQNUdXNnbt2oXt27djx44dqFOnDsLDwzF58mS4uLho7T6TvJycHPTt2xeCIGD9+vXqDoeISKl0JXcrjK7mdYXRxZyvKMwJqbTKSy7JS7eUyN7eHvr6+vlm5X/+/DmcnJzUFFXZGD9+PA4dOoRTp07B1dVVbHdyckJ2djaSkpLkltfkY3D16lUkJCSgfv36MDAwgIGBAU6fPo3Vq1fDwMAAjo6OWrfPAODs7IzatWvLtdWqVQsxMTEAIO6bNn3ep02bhhkzZqB///7w8fHBZ599hilTpiA4OBiAdu7z+4qzj05OTkhISJB7Pzc3F69fv9bo45B3Yn78+DGOHz8ufgMDaO8+E+kiXcrX3qVI7ubk5FTg8cp7T5MoI6/TpuMBKCfn07bzpDJyQm07Ju9S1v5r28+SormkMo8HCz1KZGRkhAYNGuDEiRNim0wmw4kTJxAQEKDGyJRHEASMHz8e+/fvx8mTJ/MNLWvQoAEMDQ3ljkFUVBRiYmI09hi0adMGkZGRCA8PFx/+/v4YNGiQ+Fzb9hkAmjVrlu/2q3fv3oWHhwcAwMvLC05OTnL7nZKSgosXL2rsfqenp0NPT/7Xor6+PmQyGQDt3Of3FWcfAwICkJSUhKtXr4rLnDx5EjKZDI0bN1Z5zMqQd2K+d+8e/v33X9jZ2cm9r437TKSrdCFfe5cycreAgABERkbK/ZGS90fM+wWC8k4ZeZ02HQ9AOTmftp0nlZETatsxeZey9j8gIAChoaHIyckRlzl+/Dhq1KihcZdtKSOXVOrxKPH0zVSonTt3CsbGxsLWrVuFW7duCZ9//rlgY2MjNyu/Jhs7dqxgbW0thISECHFxceIjPT1dXGbMmDGCu7u7cPLkSeHKlStCQECAEBAQoMaole/duzMIgnbu86VLlwQDAwNhyZIlwr1794Tt27cLZmZmwm+//SYus2zZMsHGxkY4ePCgEBERIXTv3l3jbjX+rqFDhwqVKlUSb6W5b98+wd7eXvjqq6/EZbRhn9+8eSOEhYUJYWFhAgDh22+/FcLCwsS7AhRnHzt06CD4+fkJFy9eFM6ePStUq1atXN8utLB9zs7OFrp16ya4uroK4eHhcr/b3r3rgabtMxF9mLbna+9SRu6Wdzvxdu3aCeHh4cI///wjVKxYUWNvJ/6+kuZ12nY8lJXzadN5Ulk5oSYfE1Xki0lJSYKjo6Pw2WefCTdu3BB27twpmJmZlcvbq6sil1Tm8WChpwz8+OOPgru7u2BkZCQ0atRIuHDhgrpDUhoABT62bNkiLpORkSGMGzdOsLW1FczMzISePXsKcXFx6gu6DLyfEGjrPv/111+Ct7e3YGxsLNSsWVPYuHGj3PsymUyYO3eu4OjoKBgbGwtt2rQRoqKi1BSt4lJSUoRJkyYJ7u7ugomJiVC5cmVh9uzZcr+gtWGfT506VeDP8dChQwVBKN4+vnr1ShgwYIBgYWEhWFlZCcOHDxfevHmjhr0pnsL2OTo6+oO/206dOiWuQ9P2mYgKp8352ruUlbs9evRI6Nixo2BqairY29sLU6dOFXJyclS8N2WjNHmdth0PZeR82nSeVFZOqMnHRFX54vXr14WPPvpIMDY2FipVqiQsW7ZMVbtYIqrKJZV1PCSCIAglGwNERERERERERETlEefoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQkVIJggAAWLBggdxrIiIiIlIP5mdEukUi8KeciJRo3bp1MDAwwL1796Cvr4+OHTuiZcuW6g6LiIiISGcxPyPSLRzRQ0RKNW7cOCQnJ2P16tXo2rVrsZKIVq1aQSKRQCKRIDw8vOyDfM+wYcPE7R84cEDl2yciIiIqSyXNz0qTmzGfIio/WOghIqXasGEDrK2tMXHiRPz11184c+ZMsfqNHj0acXFx8Pb2LuMI8/vhhx8QFxen8u0SERERKdOUKVPQq1evfO2lyc9KmpsxnyIqPwzUHQARaZcvvvgCEokECxYswIIFC4p9DbiZmRmcnJzKOLqCWVtbw9raWi3bJiIiIlKWS5cuoXPnzvnaS5OflTQ3Yz5FVH5wRA8RlcjSpUvFYbnvPr7//nsAgEQiAfB/k/3lvS6pVq1aYcKECZg8eTJsbW3h6OiITZs2IS0tDcOHD4elpSWqVq2KI0eOKKUfERERkabKzs6GoaEh/vvvP8yePRsSiQRNmjQR31dWfrZnzx74+PjA1NQUdnZ2aNu2LdLS0hSOn4iUi4UeIiqRCRMmIC4uTnyMHj0aHh4e+PTTT5W+rV9++QX29va4dOkSJkyYgLFjx6JPnz5o2rQprl27hnbt2uGzzz5Denq6UvoRERERaSIDAwOcO3cOABAeHo64uDj8888/St1GXFwcBgwYgBEjRuD27dsICQlBr169eAcvonKIhR4iKhFLS0s4OTnByckJa9euxbFjxxASEgJXV1elb8vX1xdz5sxBtWrVMHPmTJiYmMDe3h6jR49GtWrVMG/ePLx69QoRERFK6UdERESkifT09PDs2TPY2dnB19cXTk5OsLGxUeo24uLikJubi169esHT0xM+Pj4YN24cLCwslLodIlIcCz1EVCrz5s3Dr7/+ipCQEHh6epbJNurWrSs+19fXh52dHXx8fMQ2R0dHAEBCQoJS+hERERFpqrCwMPj6+pbZ+n19fdGmTRv4+PigT58+2LRpExITE8tse0RUeiz0EFGJzZ8/H9u2bSvTIg8AGBoayr2WSCRybXnXl8tkMqX0IyIiItJU4eHhZVro0dfXx/Hjx3HkyBHUrl0bP/74I2rUqIHo6Ogy2yYRlQ4LPURUIvPnz8cvv/xS5kUeIiIiIiq+yMhI1KtXr0y3IZFI0KxZMyxcuBBhYWEwMjLC/v37y3SbRFRyvL06ERXb4sWLsX79evz5558wMTFBfHw8AMDW1hbGxsZqjo6IiIhId8lkMkRFReHZs2cwNzdX+q3OL168iBMnTqBdu3ZwcHDAxYsX8eLFC9SqVUup2yEixXFEDxEViyAIWLlyJV68eIGAgAA4OzuLD05qTERERKReixcvxtatW1GpUiUsXrxY6eu3srJCaGgoOnXqhOrVq2POnDlYtWoVOnbsqPRtEZFiOKKHiIpFIpEgOTlZZdsLCQnJ1/bo0aN8be/f0rO0/YiIiIg02eDBgzF48OAyW3+tWrWUfst2IiobHNFDROXCunXrYGFhgcjISJVve8yYMbw1KBEREdE7SpqbMZ8iKj8kAr/WJiI1i42NRUZGBgDA3d0dRkZGKt1+QkICUlJSAADOzs4wNzdX6faJiIiIypPS5GbMp4jKDxZ6iIiIiIiIiIi0BC/dIiIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLTE/wNipJZgUjPSGgAAAABJRU5ErkJggg==",
+                                    "image/png": 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xYkWVy96xYwc452jfvr1rmW3btgGAtNC1SPv27Y0yIjfeeCPeeOMNANraORdddJFtB7Qrr7wS11xzDc4991ykpaXhsssuwwsvvICjR49K8t1k6+lO8oljo7i4GAkJCTX+Ki4urm3Tq5z27dtLP7oRFUcfF3/++aeR9uSTTyIhIcG2HELTpk2RkJCAPXv2GGkvvvgiEhISMHr0aKlsq1atkJCQIK2rWZHH+CqD9bxPnjxZGuszZ8601XnggQewc+dOvPPOO7a8P//8E4cOHYp4nwaA7du3G/JPZCj0hSDqATt27EBRURE6d+4MAFi3bh0AGBE9a9euRcuWLZGUlFRrOrZv3962joAbaWlprnnigsxJSUn4xz/+gb59+2Lw4MFVomdlyM7ORvv27fHuu+/ivvvuw7vvvgvOOe22RRAEcQKyf/9+AMBpp53mmK+n6+WqkspEUVQ24uK6667DPffcg19//RVz5sxxnNx4PB7Mnj0b//znP/HVV19h5cqVeOyxx/DEE0/ghx9+MP7u1rdoD6Jmeeyxx/DYY48ZxyUlJfj+++8lh8GmTZuQmZlZpXI55zW6bTVxYmA973fddRdGjRplHDdu3NhWp0mTJrjzzjsxZcoU248UFb3/1ZX7JDl6CKIeoO+QooeD//zzz2jcuDHS09Px559/YtmyZfj73/9emyoiNTVVuvkeK+IW62vWrMHevXvx/vvvH3e7x8qIESPw4IMPYt26dZg7dy7atGmD7t2715o+BEEQhDO6M2PDhg04++yzbfkbNmyQylUlbdq0AWPMtpmASNu2bQFoP2T06tXLlr9582bjb6BISkoKLr74YowePRqlpaUYNGgQCgoKHGU0b94c119/Pa6//nr84x//QNu2bTFr1iw88sgjaNu2reOulrpsUUfi+ImLi0NhYWGtyK0u/v73v+Oqq64yjkeMGIFhw4bh8ssvN9KcHok5XjZv3lwnHh89EdHHoDgu7rrrLkycONG2HIoeTS9uNjJ27FjcdNNNtkh2PdJGLFsV8wAR63lv3LgxWrduXW69SZMm4aWXXsJLL70kpTdp0gTJyckR79OAeR/csmULunbtegya1wwn/KNbBEGUT/PmzQEAubm5ALSIHj2a5/bbb4eiKJg4cWJtqVeltG7dGj6fD6tWrcK0adNw+eWX1+ojm3r0zpQpU7B27VqK5iEIgjhB6dOnD1q1aoXHHnvMtj6DqqqYNm0asrKy0KdPnyqX3ahRIwwYMAAvvvii8Ti1yOHDh9G/f380atQITz/9tC3/448/xvbt23HNNdc4tn/jjTdi6dKl+Nvf/lbhR4cbNmyItLQ0Q5/hw4dj+/bt+OSTT2xln376aaSkpODCCy+sUNtE+TDGEB8fX+Ov6ox8adSoEVq3bm28YmNj0bRpUymtqtdS/eqrr7B+/XoMGzasSts9WXAaFz6fD/Hx8YiOjnYsKz76GhUVhfj4eMTExFSobFVxPOc9ISEBDz74IKZOnSo5xRVFwfDhw/HOO+9g3759tnqFhYUIBoPo0qULOnbsiKefftpxrZ/Dhw9XWqfqgBw9BFEPyMzMxHnnnYd///vfuPvuu/Hzzz/D7/djyJAhePfdd/Haa68hKysLqqritttuQ+PGjZGcnIzu3btLz+Q6sXPnTgwePBgpKSlIS0uTQsIZY3j++eeRmZmJ1NRUPPnkk9VtKjweD9q2bYs5c+bg0KFDUohwbZCVlYVevXrho48+AgBy9BAEQZygeDwePP3001i4cCGGDh2K3NxcFBQUIDc3F0OHDsXChQvx1FNPVdsaay+++CJCoRDOOussfPDBB9i+fTs2b96MmTNnomfPnoiPj8crr7yCjz76CGPGjMG6deuwa9cuvP766xg1ahSuuOIKKVpCZODAgfjjjz/w6KOPOua/8soruOWWW/DFF1/gl19+wcaNGzF58mRs3LjRWHx5+PDhuOyyyzBy5Ei8/vrr2LVrF9atW4ebb74ZH3/8MV577TVpIelQKIS1a9dKL7eIIKLuUlhYaJxfQPteuHbtWmmdlpqS4ff7kZeXh99//x1r1qzBY489hksvvRQXX3xxjW7IQdQs1XHex4wZg6SkJGOdTZ2pU6ciIyMDPXr0wFtvvYVNmzZh+/bteOONN9C1a1cUFhaCMYbZs2dj27Zt6NOnDz799FP8+uuvWLduHaZOnYpLL720Ksw+fqp9Xy+CIGqE/fv384svvpjHxMRwANzn8/HevXvzJUuWGGU+++wznp2dzY8cOcKDwSBfvXo1LygocG0zEAjwDh068IceeoiXlJTwI0eO8B9//NHIB8D79+/Pjxw5wjdv3sxTU1P5l19+Wa12cs75VVddxQHwMWPGVLsst+3VRV588UUOgJ911lnltkfbqxMEQRw7VbE17QcffMBbtWrFARivrKysat1aXWffvn187NixvGXLltzn8/HmzZvzSy65xNgGnXPOly9fzgcMGMATExO5z+fjnTp14k899RQPBoNSW4iw1fWhQ4ek7dXXrFnDr7vuOp6VlcWjo6N5SkoK79u3r20r9UAgwJ988kneqVMn7vP5eGJiIh8wYAD/5ptvpHKzZ8+W+k9/nXrqqcfdR8SJxddff+14rkeOHFlu3Ypur14RGSNHjjTSvV4vb9KkCc/JyeFvvPGG4xbXRP2goue9IturW/Pnzp3LAUjbq3PO+eHDh/k999zD27Rpw30+H2/WrBnPycnh8+fP56qqGuW2bt3K//a3v/H09HTu8/l4y5Yt+TXXXMPXrFnjqkdNbq/OOK8jqwkRBFEhFi5ciCFDhuCnn35Cly5dpLwlS5bg1ltvxb///W9079693PDdb7/9FsOHD8fu3bsdd7RijOHrr7/GeeedB0Bbyf7gwYN49dVXq8qcWmfUqFH46quvsGbNGni93mPeMrG0tBSFhYWYPn06nnzySfzxxx+Oi8QRBEEQ7pSWlmLnzp3IysqyPSpQGUKhEFasWIH9+/cjLS0Nffr0od0SCYIgiGqlqv6GVQRajJkg6hlbtmwBYwzt2rWz5V1wwQX4+9//jjFjxiAvLw/XXXcdpk2b5vrM7G+//YaWLVtG3LZc3II3IyMDP//88/EbcYKxd+9eNGnSBJ06dTIW66wss2bNwu23317FmhEEQRDHgsfjMX6kIAiCIIj6Bjl6CKKesWXLFmRmZkqr3IvcfvvtuP3227F3715cdNFFOO2001xXwc/IyMDu3bsjblu5d+9enHrqqcbn6titpDa5++67cd111wHQFm87VoYNGyZt6VubW90TBEEQBEEQBFF/IUcPQdQztmzZgvbt2zvm/fjjj+Cco2vXrmjQoAGioqKkUHXd4TNnzhwAwFlnnYUGDRrgH//4B+6++26UlZVh+/btyM7ONuo88cQTOPPMM7F//3688cYbeOutt6rNttqgY8eOjtvZVpaMjAwp+okgCIIgCIIgCKI6oF23CKKe8c0332DRokWOeUeOHMGNN96I5ORktGvXDr1798a1115r5P/222/o3bu3cez1erFw4UJ89913SEtLQ7t27Ywt3HX0qKC+ffvitttuQ05OTvUYRhAEQRAEQRAEQZQLLcZMEAQAIBgM4vTTT8fPP//sumaPFcYY9u7dixYtWlSzdgRBEARRswtZEgRBEERVQosxEwRR43i9XmzatKm21SAIgiCIcqHfKQmCIIi6Rk3+7aJHtwiCIAiCIIg6gR5xWlxcXMuaEARBEETlKCsrAwBpjdTqgiJ6CII4ZugXVYIgCKIm8Xg8SE5OxsGDBwEAcXFxrrtCEgRBEMSJgqqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwpajJkgCIIgCIKoc4RCIQQCgdpWgyAIgiAqhM/ng6LUzOo55OghCIIgCIIgCIIgCIKoJ9BizARBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD21xPLlyzFkyBCkp6eDMYYFCxZUSbtLly7FmWeeiejoaLRu3Rpz5sxxLfv444+DMYaJEydWiezymDZtGrp3744GDRqgadOmGDp0KLZu3Volbb///vto3749YmJi0LlzZ3z66aeuZf/+97+DMYYZM2ZUiexIvPzyyzj99NORmJiIxMRE9OzZE5999tlxt3ui2mulKsfYiWzzww8/DMaY9Grfvv1xt3si2/z777/juuuuQ0pKCmJjY9G5c2f8+OOPx93uiXoPa9Wqle0cM8YwduzY42r3RD7HBEEQBEEQRN2EHD21RFFREc444wy8+OKLVdbmzp07MXjwYPTr1w9r167FxIkT8X//93/4/PPPbWVXrVqFV155BaeffnqVyS+PZcuWYezYsfj++++xePFiBAIB9O/fH0VFRcfV7nfffYdrrrkGo0ePxk8//YShQ4di6NCh2LBhg63s/Pnz8f333yM9Pf24ZFaUFi1a4PHHH8fq1avx448/4vzzz8ell16KjRs3HnObJ7K9IlU5xuqCzZ06dcL+/fuN1zfffHNc7Z3INh86dAi9e/dGVFQUPvvsM2zatAlPP/00GjZseFztnsj3sFWrVknnd/HixQCAK6+88pjbPJHPMUEQBEEQBFGH4UStA4DPnz9fSistLeV33HEHT09P53Fxcfyss87iX3/9dcR27r77bt6pUycp7eqrr+YDBgyQ0goKCnibNm344sWL+bnnnssnTJhQBVZUnoMHD3IAfNmyZUbaoUOH+OjRo3njxo15gwYNeL9+/fjatWsjtnPVVVfxwYMHS2k9evTgN998s5T222+/8ebNm/MNGzbwli1b8meffbbKbKkMDRs25K+99hrnvP7aG2mM1UebH3roIX7GGWe45tc3mydPnszPOeeciGXq+z1swoQJ/NRTT+WqqnLO6985JgiCIAiCIOouFNFzgjJu3Djk5uZi3rx5WLduHa688koMHDgQ27dvd62Tm5uLnJwcKW3AgAHIzc2V0saOHYvBgwfbytY0R44cAQA0atTISLvyyitx8OBBfPbZZ1i9ejXOPPNMXHDBBcjPz3dtpyJ2q6qK66+/HnfddRc6depUxZZUjFAohHnz5qGoqAg9e/YEUH/tjTTG6qvN27dvR3p6Ok455RSMGDECe/bsMfLqm80ff/wxunXrhiuvvBJNmzZF165d8a9//UsqU5/vYWVlZXj77bdx4403gjEGoP6dY4IgCIIgCKLu4q1tBQg7e/bswezZs7Fnzx4jTP/OO+/EokWLMHv2bDz22GOO9fLy8tCsWTMprVmzZjh69ChKSkoQGxuLefPmYc2aNVi1alW12xEJVVUxceJE9O7dG6eddhoA4JtvvsEPP/yAgwcPIjo6GgDw1FNPYcGCBfjvf/+LMWPGOLblZndeXp5x/MQTT8Dr9eK2226rJovcWb9+PXr27InS0lIkJCRg/vz56NixY721N9IYq6829+jRA3PmzEG7du2wf/9+PPLII+jTpw82bNiAn3/+ud7Z/Ouvv+Lll1/GpEmTcN9992HVqlW47bbb4PP5MHLkyHp/D1uwYAEOHz6MUaNGAai/45ogCIIgCIKom5Cj5wRk/fr1CIVCaNu2rZTu9/uRkpICAEhISDDSr7vuOsyaNavcdvfu3YsJEyZg8eLFiImJqVqlK8nYsWOxYcMGaR2Tn3/+GYWFhYaNOiUlJfjll1+wZ88edOzY0Ui/7777cN9995Ura/Xq1XjuueewZs0a49f3mqRdu3ZYu3Ytjhw5gv/+978YOXIkli1bVi/tLW+M1UebAWDQoEHG59NPPx09evRAy5Yt8d5776G0tLTe2ayqKrp162Y4bLp27YoNGzZg1qxZGDlyZL2/h73++usYNGiQ4cSqr+OaIAiCIAiCqJuQo+cEpLCwEB6PB6tXr4bH45Hy9MnR2rVrjbTExEQAQGpqKg4cOCCVP3DgABITExEbG4vVq1fj4MGDOPPMM438UCiE5cuX44UXXoDf77fJqw7GjRuHhQsXYvny5WjRooWRXlhYiLS0NCxdutRWJzk5GcnJyZLd+iNfbnanpqYCAFasWIGDBw8iMzPTyA+FQrjjjjswY8YM7Nq1q+qMc8Dn86F169YAgOzsbKxatQrPPfccTjnllHpnb3ljbOrUqfXOZieSk5PRtm1b7NixA8nJyfXO5rS0NMlxAQAdOnTABx98AKB+38N2796NL7/8Eh9++KGRVl/vXQRBEARBEETdhBw9JyBdu3ZFKBTCwYMH0adPH8cyuuNApGfPnrateRcvXmysB3PBBRdg/fr1Uv4NN9yA9u3bY/LkydU+QeKcY/z48Zg/fz6WLl2KrKwsKf/MM89EXl4evF4vWrVq5diGm91LliyRtlgW7b7++usd18G4/vrrccMNNxyfUceAqqrw+/310t7yxtj+/fvrnc1OFBYW4pdffsH111+PDh061Dube/fuja1bt0pp27ZtQ8uWLQHU33sYAMyePRtNmzbF4MGDjbT6eC0TBEEQBEEQdZjaXg36ZKWgoID/9NNP/KeffuIA+DPPPMN/+uknvnv3bs455yNGjOCtWrXiH3zwAf/111/5ypUr+WOPPcYXLlzo2uavv/7K4+Li+F133cU3b97MX3zxRe7xePiiRYtc69TkjjW33HILT0pK4kuXLuX79+83XsXFxZxzzlVV5eeccw4/44wz+Oeff8537tzJv/32W37ffffxVatWubb77bffcq/Xy5966im+efNm/tBDD/GoqCi+fv161zo1tXPNPffcw5ctW8Z37tzJ161bx++55x7OGONffPFFvbTXCXGM1Veb77jjDr506VLDnpycHN64cWN+8ODBemnzDz/8wL1eL586dSrfvn07f+edd3hcXBx/++23jTL18R4WCoV4ZmYmnzx5spReH88xQRAEQRAEUXchR08t8fXXX3MAttfIkSM555yXlZXxKVOm8FatWvGoqCielpbGL7vsMr5u3bpy2+3SpQv3+Xz8lFNO4bNnz45YviYnSU72ApB0PHr0KB8/fjxPT0/nUVFRPCMjg48YMYLv2bMnYtvvvfceb9u2Lff5fLxTp078f//7X8TyNTVZuvHGG3nLli25z+fjTZo04RdccAH/4osvjPz6Zq8T1jFWH22++uqreVpaGvf5fLx58+b86quv5jt27DDy66PNn3zyCT/ttNN4dHQ0b9++PX/11Vel/Pp4D/v88885AL5161ZbXn08xwRBEARBEETdhHHOea2EEhEEQRAEQRDEMRIKhRAIBGpbDYIgCIKoED6fD4qi1IgsWqOHIAiCIAiCqDNwzpGXl4fDhw/XtioEQRAEUWEURUFWVhZ8Pl+1y6KIHoIgCIIgCKLOsH//fhw+fBhNmzZFXFwcGGO1rRJBEARBRERVVezbtw9RUVHIzMys9r9dFNFDEARBEARB1AlCoZDh5ElJSaltdQiCIAiiwjRp0gT79u1DMBhEVFRUtcqqmQfECIIgCIIgCOI40dfkiYuLq2VNCIIgCKJy6I9shUKhapdFjh6CIAiCIAiiTkGPaxEEQRB1jZr820WOHoIgCIIgCIIgCIIgiHoCOXrqOH6/Hw8//DD8fn9tq1JjnGw2n2z2AmTzyQLZTBDEycK0adPQvXt3NGjQAE2bNsXQoUOxdetWqUxpaSnGjh2LlJQUJCQkYNiwYThw4IBUZs+ePRg8eDDi4uLQtGlT3HXXXQgGgzVpClGP+f3333HdddchJSUFsbGx6Ny5M3788Ucjn3OOKVOmIC0tDbGxscjJycH27dulNvLz8zFixAgkJiYiOTkZo0ePRmFhYU2bQtQzli9fjiFDhiA9PR2MMSxYsMBWpqrG57p169CnTx/ExMQgIyMD06dPr07Tqg1y9NRx/H4/HnnkkZNq0nCy2Xyy2QuQzScLZDNBECcLy5Ytw9ixY/H9999j8eLFCAQC6N+/P4qKiowyt99+Oz755BO8//77WLZsGfbt24fLL7/cyA+FQhg8eDDKysrw3Xff4c0338ScOXMwZcqU2jCJqGccOnQIvXv3RlRUFD777DNs2rQJTz/9NBo2bGiUmT59OmbOnIlZs2Zh5cqViI+Px4ABA1BaWmqUGTFiBDZu3IjFixdj4cKFWL58OcaMGVMbJhH1iKKiIpxxxhl48cUXXctUxfg8evQo+vfvj5YtW2L16tV48skn8fDDD+PVV1+tVvuqBU7UaY4cOcIB8CNHjtS2KjXGyWbzyWYv52TzyQLZTBBEZSkpKeGbNm3iJSUlta3KcXHw4EEOgC9btoxzzvnhw4d5VFQUf//9940ymzdv5gB4bm4u55zzTz/9lCuKwvPy8owyL7/8Mk9MTOR+v99Rjt/v52PHjuWpqak8OjqaZ2Zm8scee6waLSPqKpMnT+bnnHOOa76qqjw1NZU/+eSTRtrhw4d5dHQ0f/fddznnnG/atIkD4KtWrTLKfPbZZ5wxxn///XfXdh966CGekZHBfT4fT0tL4+PHj68iq4j6CAA+f/58Ka2qxudLL73EGzZsKN1TJ0+ezNu1a+eqT35+Pr/22mt548aNeUxMDG/dujV/4403HMvW5N8w2l6dIAiCIAiCqLNwzlFcXFzjcuPi4o55Yc0jR44AABo1agQAWL16NQKBAHJycowy7du3R2ZmJnJzc3H22WcjNzcXnTt3RrNmzYwyAwYMwC233IKNGzeia9euNjkzZ87Exx9/jPfeew+ZmZnYu3cv9u7de0w6E8cG5xzBkrJake2N9VV4jH788ccYMGAArrzySixbtgzNmzfHrbfeiptuugkAsHPnTuTl5UljNCkpCT169EBubi6GDx+O3NxcJCcno1u3bkaZnJwcKIqClStX4rLLLrPJ/eCDD/Dss89i3rx56NSpE/Ly8vDzzz8fp+VEReGcA6Gav38CADzHfg+1UlXjMzc3F3379jV2xwK0++wTTzyBQ4cOSRFuOg8++CA2bdqEzz77DI0bN8aOHTtQUlJSJXYdD+ToOU5KS0tRVlY7N29ACy8T308GTjabTzZ7AbL5ZIFsPvHw+XyIiYmpbTUIolIUFxcjISG5xuUWFh5GfHx8peupqoqJEyeid+/eOO200wAAeXl58Pl8SE5Olso2a9YMeXl5RhnRyaPn63lO7NmzB23atME555wDxhhatmxZaX2J4yNYUoZXuk6oFdk3//QcouKiK1T2119/xcsvv4xJkybhvvvuw6pVq3DbbbfB5/Nh5MiRxhhzGoPiGG3atKmU7/V60ahRo4hjNDU1FTk5OYiKikJmZibOOuusyppKHCuhYqjvNS2/XDWgXHUQ8Fb+HupEVY3PvLw8ZGVl2drQ85wcPXv27EHXrl0NB1KrVq2O36AqgBw9x0FpaSmSYhuiDKXlF65mMjIyaluFGudks/lksxcgm08WyOYTh9TUVOzcuZOcPQRRjYwdOxYbNmzAN998U+2yRo0ahQsvvBDt2rXDwIEDcfHFF6N///7VLpeoe6iqim7duuGxxx4DAHTt2hUbNmzArFmzMHLkyGqTe+WVV2LGjBk45ZRTMHDgQFx00UUYMmQIvF6aphJ1g1tuuQXDhg3DmjVr0L9/fwwdOhS9evWqbbXI0XM8lJWVoQylOAcXwcs0bzlTGMAU8zMA6CFpCjM+M0Ux88TP4XcWbgPWNhzLW2RIeYo9DcyxPDfSYGnDLC+VEeWLeYI+jmlC+5yZn21tKfZ2reU5TJN0W7hgpq08E2VEyFPc2zBgsiz39i39YMlzLu/QLlzSmKUfymvDRaZjmsUmHdc0S1uR9AHjDmkO7YvlYUEqz+1tCXXtesjlmVsbsJYztWAR2mDGu5Msbqoolgu/K7Y2uGueAm6qK+TZyonlrWlCeUXQzS1PYRwKrGmqUNeUped5mD1Nv/14oOuqGm2a5YU04TMAeJhqyPIYddVwm+JnQaa1jfCxh6mGbkZbUI3boSlbFerIOnrE9oW2PJb+8Ah6Mase4IJuXEiD3H+6XgzwhM+WmcagGGnyu5anWNIUKGA4WqCiZfYulJWVkaOHqFPExcWhsPBwrcitLOPGjTMWAG3RooWRnpqairKyMhw+fFiK6jlw4ABSU1ONMj/88IPUnr4rl17GyplnnomdO3fis88+w5dffomrrroKOTk5+O9//1tp3Yljwxvrw80/PVdrsitKWloaOnbsKKV16NABH3zwAQBzjB04cABpaWlGmQMHDqBLly5GmYMHD0ptBINB5Ofnu47RjIwMbN26FV9++SUWL16MW2+9FU8++SSWLVuGqKioCutPHCOeOC2yppZkVxVVNT5TU1Ntux2Wd58dNGgQdu/ejU8//RSLFy/GBRdcgLFjx+Kpp56qEtuOFXL0VAFeRMHLtBsRY8x09IjOFi3BdPRIaRbHjVJZRw+T6sp5Do4el/LH7uhxz6t2R484Ia8hR0+5DhknR0xVOXrcykMuX22OHqc0HJtNx+ToscisVUcPq7ijx17OydHj7swp19FjXM5V5+gRHTmVdfQ45Tk7emSHhnIMjh6bc4aJDhO7o8fWRjmOHo9RjoXrMeOzqaN+zASnCxfSrA4hCDrq7Tul2R09nko6esw8JuQ5OXpoE06i7sIYO6ZHqGoSzjnGjx+P+fPnY+nSpbZHA7KzsxEVFYUlS5Zg2LBhAICtW7diz5496NmzJwCgZ8+emDp1Kg4ePGg8frB48WIkJibaJugiiYmJuPrqq3H11VfjiiuuwMCBA5Gfn2+sD0RUL4yxCj8+VZv07t0bW7duldK2bdtmPO6XlZWF1NRULFmyxJg4Hz16FCtXrsQtt9wCQBujhw8fxurVq5GdnQ0A+Oqrr6CqKnr06OEqOzY2FkOGDMGQIUMwduxYtG/fHuvXr8eZZ55ZDZYSIoyxKnt8qjapqvHZs2dP3H///QgEAoajcfHixWjXrp3jY1s6TZo0wciRIzFy5Ej06dMHd911Fzl6CIIgCIIgCKI+M3bsWMydOxcfffQRGjRoYKwHkZSUhNjYWCQlJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OhGeeeQZpaWno2rUrFEXB+++/j9TUVNtaQARx++23o1evXnjsscdw1VVX4YcffsCrr75qbCvNGMPEiRPxz3/+E23atEFWVhYefPBBpKenY+jQoQC0CKCBAwfipptuwqxZsxAIBDBu3DgMHz4c6enpjnLnzJmDUCiEHj16IC4uDm+//TZiY2NpPSlCorCwEDt27DCOd+7cibVr16JRo0bIzMyssvF57bXX4pFHHsHo0aMxefJkbNiwAc899xyeffZZV92mTJmC7OxsdOrUCX6/HwsXLkSHDh2qtT8qAjl6CIIgCIIgCKIaefnllwEA5513npQ+e/ZsjBo1CgDw7LPPQlEUDBs2DH6/HwMGDMBLL71klPV4PFi4cCFuueUW9OzZE/Hx8Rg5ciQeffRRV7kNGjTA9OnTsX37dng8HnTv3h2ffvopFIWi+AiZ7t27Y/78+bj33nvx6KOPIisrCzNmzMCIESOMMnfffTeKioowZswYHD58GOeccw4WLVokPe77zjvvYNy4cbjggguM8Txz5kxXucnJyXj88ccxadIkhEIhdO7cGZ988glSUlKq1V6ibvHjjz+iX79+xvGkSZMAACNHjsScOXMAVM34TEpKwhdffIGxY8ciOzsbjRs3xpQpUzBmzBhX3Xw+H+69917s2rULsbGx6NOnD+bNm1fFPVB5GOfctuwFUTGOHj2KpKQknIdL4VUirdEjPIZVkTV66NEtenTLqd1I5SGXp0e3ZD3o0S16dMvQ45gf3arYGj2eiGv0qFL78mNaFVujpzof3TpaEELDtr/iyJEjSExMBEGciJSWlmLnzp3IysqitaQIgiCIOkVN/g0jdz5BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BPI0UMQBEEQBEEQBEEQBFFPIEcPQRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQRA3x+OOPgzGGiRMnSumlpaUYO3YsUlJSkJCQgGHDhuHAgQNSmT179mDw4MGIi4tD06ZNcddddyEYDNag9kR9JRQK4cEHH0RWVhZiY2Nx6qmn4h//+Ac450YZzjmmTJmCtLQ0xMbGIicnB9u3b5fayc/Px4gRI5CYmIjk5GSMHj0ahYWFNW0OQZz0kKOHIAiCIAiCIGqAVatW4ZVXXsHpp59uy7v99tvxySef4P3338eyZcuwb98+XH755UZ+KBTC4MGDUVZWhu+++w5vvvkm5syZgylTptSkCUQ95YknnsDLL7+MF154AZs3b8YTTzyB6dOn4/nnnzfKTJ8+HTNnzsSsWbOwcuVKxMfHY8CAASgtLTXKjBgxAhs3bsTixYuxcOFCLF++HGPGjKkNkwjipIYcPQRBEARBEARRzRQWFmLEiBH417/+hYYNG0p5R44cweuvv45nnnkG559/PrKzszF79mx89913+P777wEAX3zxBTZt2oS3334bXbp0waBBg/CPf/wDL774IsrKyhxllpWVYdy4cUhLS0NMTAxatmyJadOmVbutRN3ju+++w6WXXorBgwejVatWuOKKK9C/f3/88MMPALRonhkzZuCBBx7ApZdeitNPPx1vvfUW9u3bhwULFgAANm/ejEWLFuG1115Djx49cM455+D555/HvHnzsG/fPke5nHM8/PDDyMzMRHR0NNLT03HbbbfVlNkEUW8hRw9BEARBEARRZ+Gco6TIX+Mv8ZGWijB27FgMHjwYOTk5trzVq1cjEAhIee3bt0dmZiZyc3MBALm5uejcuTOaNWtmlBkwYACOHj2KjRs3OsqcOXMmPv74Y7z33nvYunUr3nnnHbRq1apSehPHB+ccamlJrbwqM0Z79eqFJUuWYNu2bQCAn3/+Gd988w0GDRoEANi5cyfy8vKkMZqUlIQePXpIYzQ5ORndunUzyuTk5EBRFKxcudJR7gcffIBnn30Wr7zyCrZv344FCxagc+fOle5ngiBkvLWtAEEQBEEQBEEcK6XFZbi46cQal7vw4AzExkdXqOy8efOwZs0arFq1yjE/Ly8PPp8PycnJUnqzZs2Ql5dnlBGdPHq+nufEnj170KZNG5xzzjlgjKFly5YV0peoOri/FLuutTv3aoJWc78Ei4mtUNl77rkHR48eRfv27eHxeBAKhTB16lSMGDECgDnGnMagOEabNm0q5Xu9XjRq1CjiGE1NTUVOTg6ioqKQmZmJs846q1J2EgRhhxw9VUAQAYBrwVGMM+iBUtpnAAi/c2Z8Zlwx88TPAKAyMGZJY+K7YkkTPtvKK/Y0MPMzN/O4kQZLG2Z5qYwoX8xTYS8v6Giay2B2kUNbCmxtWMsbv1MwAIouE+7lzVMQOU9xb8OAybLc27f0gyXPubxDu3BJY5Z+KK8NF5mOaRabdFzTLG1F0geMO6Q5tC+WhwWpPLe3JdS16yGXZ25twFrO1IJFaIMZ706yuKmiWC78zm1tcNc8Dm6oqwp5iqWcfqyA29OE8oqgm1uewjgUWNNUoa4pS8/zMHta+JKFB7quqtGmWV5IEz4DgIephiyPUVcNtyl+FmRa2wgfe5hq6Ga0BdUIezVlq0IdWUeP2L7QlsfSHx5BL2bVA1zQjQtpkPtP14sBnvDZMtMYFCNNftfyYEnT2j1aoIIgiKpn7969mDBhAhYvXoyYmJgalT1q1ChceOGFaNeuHQYOHIiLL74Y/fv3r1EdiLrBe++9h3feeQdz585Fp06dsHbtWkycOBHp6ekYOXJktcm98sorMWPGDJxyyikYOHAgLrroIgwZMgReL01TCeJ4oCvoOPD5fEhNTcU3eZ+aM+1QrapEEARBEMdMamoqfD5fbatBEJUiJs6HhQdn1IrcirB69WocPHgQZ555ppEWCoWwfPlyvPDCC/D7/UhNTUVZWRkOHz4sRfUcOHAAqampALTrU18vRczX85w488wzsXPnTnz22Wf48ssvcdVVVyEnJwf//e9/K2MqcRyw6Bi0mvtlrcmuKHfddRfuueceDB8+HADQuXNn7N69G9OmTcPIkSONMXbgwAGkpaUZ9Q4cOIAuXboA0MbhwYMHpXaDwSDy8/Ndx2hGRga2bt2KL7/8EosXL8att96KJ598EsuWLUNUVFRlzCUIQoAcPcdBTEwMdu7c6boAHkEQBEHUJXw+X41HHBDE8cIYq/AjVLXBBRdcgPXr10tpN9xwA9q3b4/JkyfD4/EgOzsbUVFRWLJkCYYNGwYA2Lp1K/bs2YOePXsCAHr27ImpU6fi4MGDxuMxixcvRmJiIjp27OgqPzExEVdffTWuvvpqXHHFFRg4cCDy8/PRqFGjarKYEGGMVfjxqdqkuLgYiiIv3+rxeKCqWrRnVlYWUlNTsWTJEsOxc/ToUaxcuRK33HILAG2MHj58GKtXr0Z2djYA4KuvvoKqqujRo4er7NjYWAwZMgRDhgzB2LFj0b59e6xfv15yjhIEUTnI0XOcxMTE0JdigiAIgiAIwpEGDRrgtNNOk9Li4+ORkpJipCclJWH06NGYNGkSGjVqhMTERIwfPx49e/bE2WefDQDo378/OnbsiOuvvx7Tp09HXl4eHnjgAYwdOxbR0c6OrmeeeQZpaWno2rUrFEXB+++/j9TUVNtaQAQxZMgQTJ06FZmZmejUqRN++uknPPPMM7jxxhsBaA6riRMn4p///CfatGmDrKwsPPjgg0hPT8fQoUMBAB06dMDAgQNx0003YdasWQgEAhg3bhyGDx+O9PR0R7lz5sxBKBRCjx49EBcXh7fffhuxsbG0nhRBHCfk6CEIgiAIgiCIWubZZ5+FoigYNmwY/H4/BgwYgJdeesnI93g8WLhwIW655Rb07NkT8fHxGDlyJB599FHXNhs0aIDp06dj+/bt8Hg86N69Oz799FNb5AZBPP/883jwwQdx66234uDBg0hPT8fNN9+MKVOmGGXuvvtuFBUVYcyYMTh8+DDOOeccLFq0SPrR+5133sG4ceNwwQUXGON55syZrnKTk5Px+OOPY9KkSQiFQujcuTM++eQTpKSkVKu9BFHfYbyye0MSBEEQBEEQRC1QWlqKnTt3IisriyKqCYIgiDpFTf4NI3c+QRAEQRAEQRAEQRBEPYEcPQRBEARBEARBEARBEPUEcvQQBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQRJ2CNo0lCIIg6ho1+beLHD0EQRAEQRBEnSAqKgoAUFxcXMuaEARBEETlKCsrAwB4PJ5ql+WtdgkEQRAEQRAEUQV4PB4kJyfj4MGDAIC4uDgwxmpZK4IgCIKIjKqq+OOPPxAXFwevt/rdMOToIQiCIAiCIOoMqampAGA4ewiCIAiiLqAoCjIzM2vkBwrG6SFngiAIgiAIoo4RCoUQCARqWw2CIAiCqBA+nw+KUjOr55CjhyAIgiAIgiAIgiAIop5AizETBEEQBEEQBEEQBEHUE8jRQxAEQRAEQRAEQRAEUU8gRw9BEARBEARBEARBEEQ9gRw9BEEQBEEQBEEQBEEQ9QRy9BAEQRAEQRAEQRAEQdQTyNFDEARBEARBEARBEARRTyBHD0EQBEEQBEEQBEEQRD2BHD0EQRAEQRAEQRAEQRD1BHL0EARBEARBEARBEARB1BNOSEfP8uXLMWTIEKSnp4MxhgULFhh5gUAAkydPRufOnREfH4/09HT87W9/w759+6Q28vPzMWLECCQmJiI5ORmjR49GYWGhVGbdunXo06cPYmJikJGRgenTp9eEeQRBEARBEARBEARBENXCCenoKSoqwhlnnIEXX3zRlldcXIw1a9bgwQcfxJo1a/Dhhx9i69atuOSSS6RyI0aMwMaNG7F48WIsXLgQy5cvx5gxY4z8o0ePon///mjZsiVWr16NJ598Eg8//DBeffXVarePIAiCIAiCIAiCIAiiOmCcc17bSkSCMYb58+dj6NChrmVWrVqFs846C7t370ZmZiY2b96Mjh07YtWqVejWrRsAYNGiRbjooovw22+/IT09HS+//DLuv/9+5OXlwefzAQDuueceLFiwAFu2bKkJ0wiCIAiCIAiCIAiCIKqUEzKip7IcOXIEjDEkJycDAHJzc5GcnGw4eQAgJycHiqJg5cqVRpm+ffsaTh4AGDBgALZu3YpDhw7VqP4EQRAEQRAEQRAEQRBVgbe2FTheSktLMXnyZFxzzTVITEwEAOTl5aFp06ZSOa/Xi0aNGiEvL88ok5WVJZVp1qyZkdewYUObLL/fD7/fbxyrqor8/HykpKSAMValdhEEQRBEdcM5R0FBAdLT06Eo9eK3H6Keo6oq9u3bhwYNGtB3L4IgCKJOUZPfu+q0oycQCOCqq64C5xwvv/xytcubNm0aHnnkkWqXQxAEQRA1yd69e9GiRYvaVoMgymXfvn3IyMiobTUIgiAI4pipie9dddbRozt5du/eja+++sqI5gGA1NRUHDx4UCofDAaRn5+P1NRUo8yBAwekMvqxXsbKvffei0mTJhnHR44cQWZmJvbu3SvJJwiCIIi6wNGjR5GRkYEGDRrUtioEUSH0sUrfvQiCIIi6Rk1+76qTjh7dybN9+3Z8/fXXSElJkfJ79uyJw4cPY/Xq1cjOzgYAfPXVV1BVFT169DDK3H///QgEAoiKigIALF68GO3atXN8bAsAoqOjER0dbUtPTEykLxsEQRBEnYUegSHqCvpYpe9eBEEQRF2lJr53nZAP5BcWFmLt2rVYu3YtAGDnzp1Yu3Yt9uzZg0AggCuuuAI//vgj3nnnHYRCIeTl5SEvLw9lZWUAgA4dOmDgwIG46aab8MMPP+Dbb7/FuHHjMHz4cKSnpwMArr32Wvh8PowePRobN27Ef/7zHzz33HNSxA5BEARBEARRPykpLMDS+0fjm4kDsfT+0SgpLKhtlQiCIAiiSjght1dfunQp+vXrZ0sfOXIkHn74Ydsiyjpff/01zjvvPABAfn4+xo0bh08++QSKomDYsGGYOXMmEhISjPLr1q3D2LFjsWrVKjRu3Bjjx4/H5MmTK6zn0aNHkZSUhCNHjtCvSgRBEESdg/6OEXWNqhqzyyddgu7tV8CXUGaklRX6sGpLH/R95uOqUNWgpLAAK6dNhLfodwTjm6PHvTMQm0CPSxIEQZxs1OT3rhPS0VNXoC/IBEEQRF2G/o4RdY2qGLPLJ12CXtlLkL+3ETaXXYY2w27C9g/+hQ6++WiUkY/vVl9QZc6emnQoEQRBECc2Nfm9q06u0XOisXvLPxEfHwXOtS3TwAGVq+FjCMdaAg+/jGfzmPacHgPAFAYwQGFMS1OgpTMFigJA5QghCMZ8UAFwVQXAoKoqODR5WhqHyllYpgqucugePcY4oDBo/2DKYQBDWA60Y0VRwHkIQTAw5oXKObiq28jBodmkckBVOcAAzlVwNfwOLU8xbFEAcDAFUBTdfgUK0/qDMQWMcYRCAYQUHxi8Whth+6CG+xLQZIBD5VofcFUN68IBxsAZC/ejdg6YwrT+DdusKAwcHB5FAQMQ5CGEEAsW7kdV1ezjnCGkqmCcQwWggoOr4fOKEMAZQioHYx6Aca3PAK1/w3ZzCOeUwej/QDAEVYkDY1p/qqqmPwOg6n3IOTgYOFehqoBmNRBSGRgUMEXLZwoD4xxQwicTHJ5wPzAwMA8DwFCmcjAWG+5DDs5VhLjWt1pfayNF1ceyqgJM6/eAqmh2wOxLMLNPGWNAuN85AKYoYApQGgAUJUbrV64CYTtCuj3ha0IfXzwsX0vzaCJYePSwsGyuXRdcv26gjVcwDjAPSgMcXiVaGDuqJjYUPofhi1PvZxYe0yo4oBoXHsC0C0Ix7BQvEC1d5YCieOAPcEQpPnBVG/tc5eF2tfsBVABM62PjfgBADYXvB9y8H4ABgKJdpYrQvwAUpmjXPecIBYFo5g2fT9XsT6NvEb7vqOHrFoZcpobHpnQeFUO+bisL5zNFgRoKwRtQ4PF6jPMWPrHaPSl8rGqDVZOl93F4zClMAWeadZJMaHlgDEw7sYDCEAwEEc098ChKePzoYwaAGgpfH2EdwvcDcBUq51A0pUxbGAO4fj9iYdu0fubQrl9whlAwhFjmCeusX4faOOLhe542bsL3nPD9Amr4vIvnKnxPYtCHlfFJG2KcQVEUBEr9iGFarxh/QxCWrYbv5Vy/L4Wv1bB9ajBg3l8QvgY5g7RzJ9d00C4fBQqAZpnyGncEUd8pKSxA9/YrkL+3EZLHb0Df2HgwxYv0TjNRVvI48p9rh+7tvkFJYcFxR91IDqU82aHUK3sJlk+6pEqdPVwNAX98C16SBxabCjTpDaZ4qqx9giAIou5Ajp4qoODPuVCLtdm84WwxXuHJgSVP5SpKS7WJpjmp0tpj4IbTRZ8ZKOF3Bo6Y2LADSW9Pb5+bx7qDiYcnepwDQZWjtEzVpjZMa0Cfq+vymf5ZTGOANxqGA4TDSQ6MiZeuj8qBQJCjNMCN1aD0uTMc5ADmwlRM4fBEM8lGs0+ZYb/ucIKQ7w8A/iAzJlYQ5BkydVnhY6YA8DDA67XJRFie6XCx9D/nKCrzIqgyzeHCdMeOMW+XOlafiEEBEOWFqnjAuaaVGu5PcLE/mSBPn2wyHC31QOWmQQza5NnsX2aTzxQgFBUNlXnMcyfZKp9D8/xqxhwpUTRdjUYZxEOu97lwchkDyqJiwaGE7TDHpGSr5iWUrxeVo7BY0fKMNctEI+W+1a4QzfaANwacK+H2uClPOMGqIEvvBFXlKCkWZsfm4ISQCG6Ty6B6YrT+EfrSNEYXInzW7wchgJdoDgvJJvHcSf0KQD/fiDbbNE6kaaORyeU0HlDBSkPh+wEAbuljyWZZF6/qC6dwmy0A128vklzOARYIgZWpQteaFwnnppPCcHgJukWFvNpHONvFhLEkdDKYPwSUhaTbgHGRCjYyvUrYM+tRGaJCHtORZTl3XLhQWPheq/c7K/FD82KG2+bGJ3D9LHNzPGlOLQaPn0MJcbN96Pc26YYTVkO6+SJYVBAeSCycznSDDMnMMCHsSAMPOwcJ4uRh5bSJ6NOpDJvzLkP8Uzega5vPoYYYggEvQgEPYpMBXwM/jrzeBn8GGkJlMVA98WC+RChxyYhu2BTxjdMR0ygNLK4RWGxDsKgEIKoB4I0HvA0ATwxKiwpNh9KErTg3Ng4AqsWhBAB870cIrb4HrHiPdgyAx2XCk/04WMalx90+QRAEUbcgR08V0K61D4kNKreudTi24JgI8pDxmUcoZyUcq1BuHaf8AI+ssVubqkM9t7JiekgF/BHKWNuQ6nIGFR5beqQ6ABBQgVJEhfOYQ1nm2l6Z6oUKxdE2a5+Ln/0hL0p5tCVP/7U/Ul2G0lCUoadTGbMtSOVKQz741SihvDkFdpInHieEfNAn4pLfxEE/0YaioA8B1bzdGHNxUSaHzR4VgC/kg4joqzHOCbfrXRyIQTCkOOinOxDlcyza5Al5AS6fb8k+Yd4tJvlLo8FVS7tOWBrWnD0eu0525eUsFWClHoBb7j8R6oidq4SYkcas+WCWY7OepwSVlskAMBVQQoJTxSbT5TioIqrEni+145QGgIVUeAIubYvlrMdlQUSVhGxlmSRPcP4IdRVPKOywsRjleMFwU7ZSBk9J+PEOyYtukQUgHNKIsJcJ3Bsb9hTDfBccQTY9wo6qIHO60xJE/cVb9DsAoM2wm/D7a38HACgeDp8nAMSYN4ukJgVIgsPizEXh127t0Ol7FVcBT0iBJ0FFPC9Cwb9aIxiKQlkoDgFvCrzJLVHETkVKg1VY/cwodL97FhCdYkaMVhK+9yOEll+Lgt+TUbStDQJHYhCVVIr4tn+gQdG18PSdS84egiCIkwxy9NQS+m+tVdGOE85tM/NX53LLusiKUNgpSwEQKqeMW16k6XKk/jN+9RdaqMi8V6sru3R0x4PeJhOcPaYOzFUfLrThrCuDIpwVsx1Tf7G22J7Cwo/nIVJf6blmKYWrUJhqccrIn8z+Eif7ChSmQuWKGQmll+OiJF2eXaYx12W6Q0frU1NDa7+H8xw7V7Zaj3kx5sSca48pOvaJVl53rIR9RlKkhdWpI0qyOgtMZxO3RP64iLcWcXA6lduGeOAm0jowRdmWPN0Ga7SJk1yOCGZGkmnTXTiOdHFa29CDVkR5zPB5SDrYpk3i8HSSBZjnUg/LEy4jW9dwbgswY+LN0jp4bXbKdyApekpy8FiuTik0Mqwgd3HHixeqJNapIwiifhOMbw4A2P7Bv1CaMRrvvrsEPlaCGFaC6KgytGj2B9r3/gW/rW2OksJY+KKDiPJpL29UEJ6okPFSvCqYNwQlSgXzqlC82jXIFMCjaJ9jGvgR00B0qO4B8BOQoR1lt14E9cNWUEMMZaU+lJbEoCQQjwBrBJbQHA1adkLSqWdAaXgKWGwaENMETBF+OFFDOPr5GCiHE/HzF51wpNuF6Hbn5fhxzodI+mIxsgeuh7roZiSOvpge4yIIgjiJoMWYjwN9MaU/t2bVWkSPG85zpvKlug2GYCUiesQJsmqRWZHBxqFF9AQcyrtNS8S0oBDRI+ZFmtcBWkRPGbzgwtRQrCs6bKx1/aoHKjzO/QBAjAYSHSz+kAcB+Gx53FLHWg8ASkNaFJGbTlxwW4ltlgajEIBX94UYZd31NPOLglGAQ1m9vP6omWw3Q3HQhyA8RqCBOUaY5Exysr84aEb0SPlcPi/S+eEMxQEfQlxxbVeasYtlOFAW9CJStJSTM4iDwe/3godlRsTqVOBAKOiBq8fGZeBzlYH5FdiiawC4XrB6OyEzoscpMgZ6jlVuCPD4UWmZDAALAR4nz285FycLqvD64XgyGHc51usGVHiC7m07tQEASlkQXj8XLxLJFtkJI39WSsWIHm7TW4qeCtdlAHhpGTylQS3ReI5TLyvIEqN9wnm8uBiwypQeY+OCPFNuMOTH0rL/0mLMRJ3heBeyLCksAOZmoiA/AckTtsIXfqQKAMpKinH4uXZo0LAIGLHb9kgV5xyFRwpxcM8B/Lk3D4d/O4jC/QdRkn8EgcNHoBYVwBsohI8XoXWTnTgzZxM2Lm+NwsJ4xPgCiI8vRXyCH7HxpYhJKkFMSjHUMgWKr+LfCTkHykqj4C+JQYk/DvBGoVmT37BvfRpSrr4XMekdweIzgLh0BPxlWH9DP3QZsg6B3gsQ0/LCSveXE2qgDGWr/w1+eBdYciv4sq+HEuUrvyJBEMRJDi3GfBJQVRE9ViK3acajuDskKtAut+e5tcEqUMZJlrW8+NkpYMAqkwGuDqZIc0rts+rQpjyxN+1iUr5VD60197grzRZVakvOd44ggi1NliyncUljhBfJdp5j6+PDGi0T1kYPcBAmtpy79Y2gDVc1B4hNT3MCym12hfuW6Wv6mHJER43sfgtHOImLBNvQH5XR9JG/XsvRFLYACD3Tmh7uFGkdICdc85ixXo1N5YgD1qGCfcA412Ww22l1Qri0y93KuVycUkCPHoDiNABdZNr6Vex/vT3hGDDTmGCnm44cchvg4buAtM6RoI9RUb4iDTsNmZbJm1TfLGvGWnL5pQ8uyX6X822ENHEXWy0dIziJCOJkIjahAZZv6aMtkvxcO2z2D0Xry0Zjx/zX0SF6gbnrlsO6OYwxNEhugAbJDXDq6a0jyikpLEDZ3EyktspH8oRcyaFUfPQoCl/sCMUXxPurb0Pozz8QFzyAZN8RJMUVITGhGAnxfsQmlCI63g9vbACemAA8MUEwBkTHBhAdG0Ci8GhZeuf9wKbbwDdpl3coqKD4aBxST48FAPz27j04ZWQ0WMP2QHQTc0OQSlKyZCqUXc8iKjb8PG0+ENg4GWqr2xF7wf3H1CZBEARR9ZCjp5aorq/WkR1IdidP+XXMMkYrzDnPyRlknQNWBN1JY3d7OOvpNLfUHwlyUCniUySGQyNcytEJ4FDP6ZE4ey2Ls8D4pO/SZbXRHlFi7RV3Z43pBBGjdbQ3bccmcaIuR69Ye94e5SPNm3W/h0sXaAspmzspGbZy85EtY91Ya3SO4OAR1u0N1+M2vblw3sydjUTdhHPIrHmS0rJtsI8zs0VmOj9EH5DbYBXzxML6gtT27pcHhpDPFGY+XmSVWd7FIrQpfd+X5OgXMpP0sM0PnAei2f1cEB3pQnS5OJ2cNcztKSUnP0hFbrjh9pmhBzMHG2CseyO5I/U+stzvTEeWw83S6vySyun9rJ9XVfZcOS60bbWDCc4bF6+WkcVtKhLEyUDfZz42tj0/J+ENYMMbSG0DlBVEV9nW6uU6lDIP4bvVF+Bvrz7i2gbnHPt/+R1bvvsZB37eipK9exBTmockzyEkxRUiKaEEzdLzkdLmDxQfSIDiVeGNCcATG4DHq6JBo0I0aFQIAMjK3AL+9SBwAMEyDwqPxqPQn4JQwqlofEY/JLTuBZbcDsyX5KpPyZKpiMp7DIcONsKPy3vgt9+boEXzP9Ct7zo0jHkMJUtAzh6CIIgTBHp06zg4nke3uEPUSEVxe3SrvPbCm/Iax5V5eKysnF+m3WRzqCjnqQnHPP3RrfLq6PlSXc4QgubMcMq3tqnnawtAex3KWFfusabbF2O2y7Q7Szi0R4S0x8WsTh3rY1EWBwiAUjVKWy8njBg5xG31zHZLgh4EYD6CJZW1Oej0xZK1siXCAtAAM9d8dZAp2l4U8CHAHR6nM+adou7mZxUMJUF5gWxzlzBnp5cecVMY8EHlCqzn2njsStJdeASOA2XhxZgleZIwh/OpAv5AFBwfaSpnEHLOoAb1evKuck7ljSQV2qNb8FTsQhHfVUAJMimfRVoriJsvpVRzUFZEpugvYiFhMWaxzfJkhlREWdcMdrqgIPtmwAElqEIRb0KqpZ6op5geCMBX6jDAAWHgcqOuZKc/BCUk3DNV7n4uRdmlfnhKA/oAt8sU30WnDefgxSWAqprp1ke/4KQDRzBQiqWBD+jRLaLOUJVh7yWFBVg5bSK8Rb8jGN8cPe6dUSU7YInoDiVfQpmRVlYQjVVbz6kSh9J/xz+EIafPxB9lLfFbixnYu3ozCrZtRVLpLjSLP4gzem1DdLwf/kNx8CX44YkLRFxKrqwkCoUFCSgMNAGS26JptwsR26oHeGwmyv6dhaK/4vHc6/3R/m/not91Ofj67S+x5a1lmDD6C8SnFCF6VB49xkUQBOFCTT66RY6e4+BEXKMnsiPFedetigyAgMXR4zQXA2B7CkCFCqu2FZEXFBw9bvXcnCqao8d5wcFItgdVoAxRkuNBfvzLKcJGOw6oHoQsa/RY64nHhoMo5IGf+xzzxM9OOviFNXqsTiVhA2uLDgwlQW/Y0WM6TJwdWXrfmvoXhbyGI8M851YHE4PVQVQU9CHEzZ3Q9HmsrpOTnRyaI6NEdVijh5vlRLsNWzhQFPDJzi/rLlqq3VcAaPPjslCU4eiR5tOGfXbbVQB+vxfS8r9uF4olj6sRdt1yasuQD23XLV2m0wVura+/h0xHD3MqZ6tn9oenFJWWycIyvcEI5dx0DaqIKrPkhwe56GCxtRF29HhCcO178bMippcFEeXnZhl50IbrhRdetfSB4g9BCQqJTg4bvZ6YXxqAx19mpumP5nEjQbgpiY4bDl5Yog0k25pC3Cwr6a+lB4N+cvQQdYqa/JJcVVSnQ6mkqBh5d5yNjD6/AGkD4el8N5DcETi8CaH104H9i7B3xako/dvb2PTFtyjYsB4NQ7vRrMFfaJxSgKSGRYhNKkZUgh/eWKcbtMzRgwnIL8uEN7UzUs8aCF/mOQh6UvDhpUNxxd+WoyTjGST0ublKbCMIgqhv0Bo9JwFuj4EcK5Vp61hku82jrD8SO8mqbLt6uqvMCqDvReXmDHJqSysjrpUhP/qk95v4SBiH+ZCU/qu+PWDA+oiRsD4OF48gSGfC/4DpPND10h7xMB+Fsm+NLsvmpn3caqW+FpC1tixTkyI8pmSLOrJaoZ0BfQWiSD5l0069n8I9y8yzyCGfD3OdIP1Y7gEO67pAHLZFlAWZpo2WJ6zCT9OY0Uvc8ckZ7hQpIh67ORoA41ko8VdWwY/gXk8FHJWpCIKhRlcaWQ6PgwnvnNsfkSpPlP6uPwVmtOnUL07v1nLhQaOfI92ZJN1zGKTNqSI5e/QnmaRE65i1F7KMHvHAUtdhvR+935mRrwq2OlzR3GqEcGjcoIwL1O70cYSe3SKI6iY2oQHOm/p69bQdH4fvj/ZDTG4IiZ2/RkzeIiOvtCAaBetb4fuj/XBNr9PRodfptvqhUAibvl2HjQuXwb9jAxpjL5olHULjlAI0aFiE2MQSeBv44fFpP9slNi1EIjYB2ASs+Q/UNUCwJAp9z9ccV/u/eR2ndhkAltDymNcBEuFqCPjjW/CSPLDYVKBJb9o9jCAIogKQo6eWqEonD1Bx5015zo5I7VvrsPB/0g/HFWhf1NXtK4BtwubSrrujSK6tt8ctaWIbWhl93Ri7Q8Bp1y1n54bofpAdFPKcU1vcmBnOBybUcZKv1eRygiCdSbJ1B5HNjcE0K8U1cuQeMZuXnS+AsY6MLlWIQLHKFN1D4PpCzpby3GITLP3M5TQupMlL7DBpDR+uajba1unRy7j4RnQXmHW+L9uH8MLJljFmPnkl1bMdOwrmsI5ZaRA5HQOaL44x5xtAJAeK/lmwzVbZmqh7aTjsa+aUI1MckzYHUaQbgjig7ReU4eyUrwVZB+m+ZL04dVPF9oxGrP3qfEKt17pZQh93QqreuNO5BNMGkW6rKnivXBcWF4UzuYxkt+gVE20nJw9B1Aeumfsc3r12Ajp9shYpzQ/BExNEqNSLv35viI2JXXDN3Odc63o8HnTu2xWd+3a15QXKAljzxffY8cU3yPjrE/Qe/DN2/ZQBn8KRkFyEmOQSRCWWwhcbQNMW+QCAU1puBP+kE8r8XhzOT0Yhy0JK9iVI6nIxWGJrMFbxCHi+9yOEVt8DVrxHOwbA4zLhyX4cLOPSynUSQRDESQY5emqJijpmKkJl2inP2VERGbayESpHcuQ4/F4tNek8Ea+QWGjxKY7TV9e6mkyzhPXRKdEBZJ8vMkebRHlyFI32SeV6zAuTpMu7fCmCTNMdxJm5lotbdI24o5cunYd33QKsTg3R8SPLNDRiTHLQ6OsAy3NQff0pZkgxo2vknndyNBmWhJ1DokXMLAauMqkvRT30fcWkKB/ro1uCTFkf0eFmFjHm20DYMWOZ+Lus/VIhmB5TJehjHVBObekRPRWVZ3Wq2G4AYWeE05Z1ohjRD1SOTCbI1H1FpjPFQY6TTKcLiwtFdIcPLG2LNrrJFP0uoi/E5lyxO0pssrg5imWjIQ8g0XkkneOQy/l0MMKoo8vRPVaCPABGlJD1BAIAVAfPG0EQdZFr5j6HkqJi/O+eJ1H2ex58aakY/J+7cHp8XPmVXYjyRaHHxX3Q4+I+eP3uRHQv3IyGzQNIHr8V/pIAvnt/MfYuXIxmgc3o2+8n+BLKECiMhi+pFFHRQTRJ+xNN8Cfw+yrw3x9EMKDgcH4SjgZbIrFzfzQ+axhYcnswxT4d4Xs/Qmj5tSj4PRlF29ogcCQGUUmliG/7BxoUXQtP37nk7CEIgogArdFzHNTWGj2B41ijJ2RzNkQqL8pUbWliObdRpBquhsrJVB3W6BHz3RwqgH0xZqf6TulBFfALixRbyzsvdqyhr9HjrqvimKev0SM6OwSXhq3vxMiWspDXaJcbL1FHc5Yrtlkc9CLAo2zppmPGap/pICkJhdeg4YLTA8yin11mYdCHoMNizABzWOxY0EMF/GqUKYM76eYQAcT1NXrkRYqNdi2OAmksccsaPTa5LHws2qm9G2v0uDplHNLDdqohr20+LinuksxKPHBdANrpXf+sVnKNHh0V8JSySstkYZmVWqNHJ+C+GDML62TLC+cba/RY2xV0tPo6GAcQCCKqVChk6wfTS2Tdml3xB831ftycO1YbOIAScY0es33pxghrO9rFyItLw4s+c0ueIEhMC3/W1uj5b51a74Q4uamLa/TUB0pL/Mgd1R99LvkRZcWt4DnzAXjbnI/g9q8QWvNP+OJ2YcXH3eC57D7s/HQxGhVtRMuUA2iWdhgJjQvgSy6B4rH/MQsFFRw91ABH/M0R26YfUvtcDSR1RMGbraAcVrB6UWcc6XYhuo26HD/O+RBJPy5G9sD1UJM4Ekf/To9xEQRRp6A1ek4Cjjei51jqcrhH2Di1KeroNFeU6rk0zLjoRrDPsyLNaZ1cYeXbzaBaJv9iPbsTwyQIhBeONnvKXtbZ2ROCYmtPzHfeY40hJEWGAGbUD4O4qK/TEy2ig0VPtT5UYl3zR3NyyD0vr6jj7LA02hWdH0yUqM8bxb3dzHV/OFdhXcTZlG1GD4WbkJ5e4WItUbxlLmufV6uavtzpXIqxWDJ6RI+kp8UTYl1bCWCyI6cSzhqtuiWSx6meq/OIu+dbVBROtfmS6rHIkUThdJWHz1o5MplFpusjX+XJdGpfMNsaNSS1WV4b3HJvNMaVoJBDlA2zCYTQEBPqSKE+MB6xkhwxershSS+b0uIjWEY9y0VhS9PbU2WZ0iLPBEEQkYmJjcaa6EHwfQKcef46eLf+H7BVm0ioajRWftINa6IH4Y7hA9B3+ACjHuccP3+1Cuve/QSJ+WuR2Wg/0lIPoUGTAkQnl8ATpaJhkyNoiCNAySbwL14EV4GEOODwn8loNepUZFzQC0rTFrj08btQVjoe62/ohy5D1sG/9yvEtLyw9jqFIAjiBIYcPbXE8X61rozDRqwTIaggYhvM8i6Ws87RrPlueWJ7TvNRNxvLm7tFcqKJ80xrOS3Pbqmoi+bEkB0HAKCAh6OInO0QV8oRI4MUxqQtrXUnhNPTJtZ5MLf0ku5W4YLTSdOBSS2K68s4uTvc+lWrywHOBN+Cnis7pVjY2Wb0AVOgP15m9TcY9vBwChNs5cLc1U1HxsIORW23MX0uzKDJ1Ktp41/oCQZ57VuhdevuV/pTN6bOzGjPKKMHkTkNXg55bWtbJ9sdCTacLojyZJbn7HByJlhPjvWEqfoYKl+maJbRh5GcQ042MkDauo/Zs402HHwXut/FdrFLioU/q25lmE1HbqyJEx6ZkmfawVDjPHDhRmRxyDAFQEjQSXAScaZdDKpQXoregeWdCyfAYrx4btVIf00IgiBM7njrHjz9N+Db6a3RteNvaJBYjIKjcfhpUwt4erXBHW/dY6vDGEOXC85ClwvOktK3rtqEVXM+RMy+1WiZ/DvSUg8hqUkBohsVGws/J2ceRjLeAr5+C8EgQ/6fDXFUOQ3+Nq0BrMPa2a/h7IfJ0UMQBOEEOXpqiUjOiIpwLHX1OVh5sq3zt/KcNeXpUlmHTaR2nRxCTsdONnLLZ+d5r2a9NVpDd8BAShVtsEuUZXDBKWCmamv02NwMgMO26WJt63nkUilukcQl/bktikj/3x6VZB8vzDY3tfaXGO1i7pClQuVOj6+ZUUlMzjA+cocM0/GieR64oIPZJ+auW5K1Dmv1mDaGLWJ2Z4/ugDJat3gUuO6tchu4rheKPvHmwmehnqmWPcxN99yWd7E45TneDByuVrfHoiooUzx9uo8x4rIwFb3ZcAdtLd0X8SbjtA6R00VlccJJbl6xfHh4Mw7Yt08LZ+h5HDC2aZNsUmV5Vo+ZbTet8Elk3MzT9RLvAvrCSEZUj95GxIFJEARh44637kFpiR9vPzwHf+08iJQOTXHbf0YhJja6Uu20694R7bp3lNJ2b9mJ7177EK3z5+HMnE34Y3sTxMX7EZNSBE90CI1T89EYy3FKU618x8ZfYfvT5yMt5ybEd7wYLKpqtqwnCIKoD5Cjp5Y43q/WkX6DdWvb+sN4Zes6/Xiv51V0PlUZLBtju+pltcnNiWOt5+Qo0uM9ZJvkB8HCQQ3GZ+2dw+2xJx3FKGk6QBQGYdctvS1mlNP1EHVxssnJySWulyO6fxhjULi5Do5ZgtnasK7M5BQgIPd3eLersDwj2IRp8TVidTMQRZsAG9NO0aFiNC73kYwoEzD7zlzMWepHxk2/Svjd7IfwItwOziCrTrI95mLVNkSnio4l+kPyD0VyoFidQHpEj92H6IzYjlN4n9NiW8xyoMuriEzBLsY0P4PNyeN0cTrh0rW2NiwXhqNTyTzhZtvWGwfTKzOpDgdgeh/DI044gVw/dnIGiU4avV1xjXJFMR1BTGhQb8SILBLa5xY5Ul9YB7m1E5nzmCUIgohATGw0/u+Jm6u83Zbts9DyqTvw3/FHcXrRDoSaNUTpRZ/i81feg2fL12jTZDfSM/9EYsYhKB6O+EbFOAUrgY0rEVoPHPkrEfn+1mhy7igkdR0G5kuusOySwgKsnDYR3qLfEYxvjh73zkBsAjmOCIKou5Cjp5YoL6omEsdTz81Zo+M2d3OTWZH6btMI69y1PJm2OYxQvyLynORb2zMjepxaY7Y6Zl153ytn3TR3kOg8UbkHYiSKcx9ID1456mbW1f+XJctzP6eIHga5hbAjymgrLDFcjDOxrl2mHtUjRvRwi0NHdpMJFlkcOyycqM995XNmzm5Vw2FmrkWkrUfk3meQ2pO9KNb1h6Q5eliOavU0qKJ2clYk3HZId0Qs5OisKUeug9PI6d5gKyB8dnXWON0QHHweFbpGrRe1xWPsFhUkrQtUnrBIdlh33RJssJVnlrNn3c480o1L9PKpwgmVdBOcOvr1YdgmyLKOR4XJY1KSF65A+zEQBHGCMfjxydh3x3xk9NkG7BiHKybeDSTfCBzehLKfp4EdWIy8VRn4oygeLTL+RIPUo/DGlyG5yVEkYw3wyxqEdtyGgvwE/FmchYY9rkXK2deCxTR2lLd80iXo3n4F+nQqM9LK5n6I5Vv6oO8zH9eU2QRBEFVK5baKqiGWL1+OIUOGID09HYwxLFiwQMrnnGPKlClIS0tDbGwscnJysH37dqlMfn4+RowYgcTERCQnJ2P06NEoLCyUyqxbtw59+vRBTEwMMjIyMH369Oo2zbThOOoyl1dlZLs5FdwcGW6yIkX66Pnc5eUm02Ee6qiD875aVg3c+9q5H5nFTllrJrxM15DoZtEXgtYjZOTWNL2Z+WKQJIvynXqXO/aivQWxJSb1FoPCnCw2W7I6OLj4zzJHRHixYxZ+wVj8mEFfI0eyijEw4yU6cbilSQ7OdOnMWFSZW/RmegXo7bGwZEVb/0iQo5nNjRcDB2PaS5Osai/OIZko9IXYVQyAh2nrLClMkypF2DChYDkXrDnXd/NeuHx2ugjKuznox9ySZBMt9AATX4D1qbaK3pBsfpdIN0KxLQ7NVqF/9civ8l4VlmG7uRiDBvrYBRPvABbHrySYR2ifWWxnglxFkGVVVK8rCOIqzMe93O7MTK6vhF9MOCYIgjiBiI2Pw/dH++GP3Fbwb/8a6uLzob6fCnXx+Qj8shx/5LbC8gMD0OXV1Ui+Zwe+9r+M/35wCdYu6Yi/tjdFoCAajAGJKYU4JWM9Gu67F+qHLXHkxSbY8Xg28j57DGrxfgCak6dX9hIU5Cfgmx2jceCM7/HNjtEoyE9Ar+wlWD7pklruDYIgiGPjhIzoKSoqwhlnnIEbb7wRl19+uS1/+vTpmDlzJt58801kZWXhwQcfxIABA7Bp0ybExMQAAEaMGIH9+/dj8eLFCAQCuOGGGzBmzBjMnTsXgLa1Wf/+/ZGTk4NZs2Zh/fr1uPHGG5GcnIwxY8ZUu4361/Vj5XgdRU7tWL/uOzlfypPvVK4y0wi9vJvzJ9IP5CbMduSkr7Ut7bd002Fjd7IwqbwsTf/ftheTUIcZravhY32dFzk/Es4zfr1d0ya55xnM6BqVc7jJdHbymXYzZs4pI8lk+rHxiJgWOeAsU94tzLo4NQOXHQtcGB/WyIkwKjhUKaLHei7FtiwjlElvRlkm5IsbXekOMIADqrDNa6STacljYuNOA9ZtALtdKE51rAPeyVlj8+A417duJ16uTBeR5d4IRV2FiB5nx1Q4r7I3RyeddWeNGG0j+G4cnWLSILaMS+t50huy3vBCXBhcLu/SANXHi9NCSmIdLr1JXjCnKDSCIIha5pq5z+Hdayeg0ydrkdL8EDwxQYRKvfjr94bYmNgF18x9DgDgjYrCwHHDgXHDAQChUAgr3v4f9s//L05J2IqWGX8gKe0IfEmlSGhYjISGW4BDU8EXTEXBkVj0PMOPor/ikDR6Mc5t0h4AkN5pJspKHkf+c+3Qvd03KCksoMe4CIKoczDOT+y4bcYY5s+fj6FDhwIAOOdIT0/HHXfcgTvvvBMAcOTIETRr1gxz5szB8OHDsXnzZnTs2BGrVq1Ct27dAACLFi3CRRddhN9++w3p6el4+eWXcf/99yMvLw8+nw8AcM8992DBggXYsmVLhXQ7evQokpKS8OfWLCQ2qFxwlHqMK9dwcARtX+or5vhRETKkVtRxoxMQZEZymliPOecIurQZSQdVBQIR6rg7gRiCHAjBY8svT4eACpTB5yoH4fad5m5B1YugESBndcDI9cV0f9CDMvhsNrg5lUSZpaEocEGm1XkifuZCWmnAizJEWdpkrraJehQHfaZ9FoeMqDM32tLSigJeBC0y7fNga3vafyWhaNs8FVJ9Ib6Km8clgSiEuEeqZ60v5elOKRUoC0XZHUBG+zpMaoNzoLTMC3BP+ReURSFVBdSQB2bfll9H/8hKFEAY75HKa8LMdyUoOAPddHaor5QyMO5wz4sgkwFACPCGHMqXJzOgIspvb9OpDhOPOaAEVHicFvKyYHNelQXhK1XlNGsZLoxywSmklAWhhCwnCmZ5eeALI80fgKckYJaVHh1TTZniBaRHoJWUmE4bo5pQRtyhy6jPEeQBLPW/jyNHjiAxMdHeMQRxgqF/96Ixe3JQUlSM/93zJMr258GXlorBj9+F2Pi4CtfnnGPlgq/x63/eRSvfRrTMPIiGaYfhSy6xLVFWWhCNA/ktEHvm35B6/s1Y/th9OKfNG1ixcTjOm/p6FVtGEMTJSE3+DTshI3oisXPnTuTl5SEnJ8dIS0pKQo8ePZCbm4vhw4cjNzcXycnJhpMHAHJycqAoClauXInLLrsMubm56Nu3r+HkAYABAwbgiSeewKFDh9CwYUObbL/fD7/fnG0cPXoUgOZ8se6cVD7cdU5UHmqla4hSnQVGiuZxqWLLc5ubusUIWH/Ml34Qd2nTyT1mLcMsEt3acnYWcVh3vDLL2Z0vVp25Qw3rkX6sPSjEw+dTdHI495rVGSLrzi1p+rG50qse1WOOV+Zgo2yb6Kyx2cPEh9ZkmfoCywjLVGFG11jlmTbJjhPJNiaWF9/lyAnjUTDOpIgeUY4k22kbdT2Yy+k6YYZZMCOUBH3cPKlWLPnakzqCfpEiXUSVQ2GFreUregGH6+nBIVrZcmLwuOAUqaRMp7WAI+qn5zn0h6OW3CE9kjluOqsQwtfkPCmih0Fy8MhCuUP/MEtZbnaKyoGQsOuWzTEjtCFGAxkZukzxIhKcQkD43HK5P9VjXS6fIAii+omNj8MVzz90zPUZYzj7svNx9mXnG2lrl/yADW+8id4tFiPzjN9Rmh+L6OQSxDTwo2WDX4C/HkJw3sPokJAMAEgIbjteMwiCIGqcE3KNnkjk5eUBAJo1ayalN2vWzMjLy8tD06ZNpXyv14tGjRpJZZzaEGVYmTZtGpKSkoxXRkbGcVjivrBvdWFO0M2XroP1ZYVZXtY8OKTraW4yKiLbKte6BIrTy8npVtF+1trQXDDmu74ihxp+yWv0aC4E62NXeoSM+W5dv0cBgwf6SjbW/tPblXtGli0vMizK0FeqkdsVV+2R1yISY3HsZ8OUabVaXJ9HW6NHsZxIrWUPtPWI5DVxTJug9294HRgl/NL3z+LcXKeHcQaoukxAXCcI4XIMCK/RA6NNxlSAqWBMk8PAoYSPrXbb4hz1Obcx97auvxSWL27zVZlXuHcr7DA2h4DpdXG6UMTT4HQRCz4B0x8g9AWXTqb2URF8QZWU6egTsTpDnLqAa0NL6i8mvPRjxZJeGSePqLMCgKvhtWzCieF3zi1r9IjeK72oo5MnbJw4/pnQzwzmrluwyJVusoIzR0+U2hd1EduxltcPI3USQRBE/aPLBWfhundexG5vHwDANzsvwX+W3IzlC7ORtzENgYJoKB6OlLRDAICuZ6xBwazG2DatBw798DZ4qCxS8wRBECcEdS6ipza59957MWnSJOP46NGjx+HskWMzKuPwEX+MrQymI8QU6vQV363tSOmR8o51GuEy1y63nLzVtp7mXt7MFx0z9rJO0TSmXm77bkWWzxm3bYrj5Hpx08Gcr3Phs5kmz/rCbgSFSUt6iM4im35W7Zk51wTMtXNku/WJpHAWFAY15DYSwv1udToY71yai3JACLZgQim9tfB5YAxclW2SHg9z8AIYa+iGm7X4q0yNwxNzab6thGVX5OJ0KMMYs83fXS8s6WQzezmnAewkW2iH2WQyuw4M2mNYThdjBWQaaxuL5Zjls5OODGBi4AmXi4uDhlnKuOni2L/CMQOTo10c27ddvNpHbnH96p1rkxdWQtQlUkSP3hgDjEe5rJ3FhTaMQWxVXpdNEARx8tLj3hkom/shujZZjOR/boUvVnskbMXcT5H34Wu46Pxl8CX4wRQgLrEEpyZuAHbcjMDmW3Bgf1OUpQ9E1rD7ocSn17IlBEEQduqcoyc1NRUAcODAAaSlpRnpBw4cQJcuXYwyBw8elOoFg0Hk5+cb9VNTU3HgwAGpjH6sl7ESHR2N6OjoKrFDm9wei7vm2Jw8kkxLAxVtjwGOk1inuZmOAu3pEjcq4yAKzzErRIT5W4Sytn10hPmXOXGzPgKkBQCwCHaazharE4VxBkUaCbpE3XXkju7s4IJOdpvkEAsGACo3ZJqOHP2hLtlJpsA2jRQcU0I/cHeZ0GUy1ZiDWiOgFMFppGsDwIieEJ1AopGG3Y7OBT16yFJJ6A3d2WOcSwZoUUGOVYQ0QX8u9gEvP0LCaXBzJj1mVu5Fae0Idx9aZGeR1U+hN2VzIOiHZn+5mlmeJ/pYnFLWPrM64sJpzH57s28qZV5i7nrp51Jh9nTbueO2vmDijc9607TZKSoteMKMftQHv+VOawgNGw4G+6LMkQRzOHcEQRDEyUFsQgMs39IHvbKXIP+5dtjsH4rWl42Guv0znNttFaKTSrHi+3Oxr7gjsoLfofUp+5DU4hC8sUGkZ+YBmAP+0Rwc+rMBDpR0RMaw+xB36vlgzP2BiZLCAqycNhHeot8RjG+OHvfOoIWeCYKoFuqcoycrKwupqalYsmSJ4dg5evQoVq5ciVtuuQUA0LNnTxw+fBirV69GdnY2AOCrr76Cqqro0aOHUeb+++9HIBBAVJS2SOzixYvRrl07x/V5IuH0I3X58OOKzDFbqQxOv+pWrD1dV+uExuoEkNJ4eKmLSulotiX9yG3RIxLWOaHTsSzLqqH5B1qW726JaWfknbPk4AUGNexYkPOZmR+hJd0xUNGxZ9igMIihOFaZen+JfY5wGe4wXphQyPHRHLDwzFd7rEtf50Z26DjUMdo3nSf28x9ed0ia+OuRWYopzNK20Rf6nNqit+jgcgt8MHTRHQxMezxNBSKfDKsRxmSeyWVgLQN7vv64mFN+eV5RQSQTjyVVmH0gqDAiniotE5Z7iKNM2GQakUAWmUwsq9/eKuBXEds26lgGveJ0ZQnjRTKIC4lc87cwSXlzgBlipQGm/z3gZnm9L/VHt4xIH+biPLKcUOeLUXYqiRcvQRDESUjfZz7G8kmXoHv7FTgn4Q1gwxtIbQOUFUTju9UXoN/zHxtlQ6EQPp/5NkJfvIeOLX5Faqs/EZNSjMTGBUjESuCHS1G6LAp5B5rD23E4mg8aDyU62aivy+nTyXz0q2zuh1i+pQ/6PvMxCIIgqpITctetwsJC7NixAwDQtWtXPPPMM+jXrx8aNWqEzMxMPPHEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvfqRI0fQrl079O/fH5MnT8aGDRtw44034tlnn63w9ur6qtl/bG0VYdct5+5VUYF4HpcCgYgxMpGa48ZCzg5rjEYkIC4B7VDB7XdhDm7+sO1Qx42gCmm3Lrcf/eVjbcIS5AwqFDg86RFR56AKYzcquzzTnSfPZbW0gOpFyMHJIzuI7PXLgh74rbtRCRMvDnsUkO6IKlWjjJ2iVMtkTdZbbrckvOuWmxPLKlNL02QWh3ejMh+bEuafEWQWBaIQ4B4pTbbLSX8FnAPFapQ2T+ayDFEHcPs5Lgz4oHK3hbUBqIrDuNXaLQt5NTvdHnETbObCh9KyKMBpNyp7I1Ia5yy86xZcd8p2OuYqwPyWXbfsA8a5HRVQQky+EERHntXDJZRRSgAmLvFWjkzjCggBivXRL7eLW0wLWnbdsjp8HG5Aut9FCapQgi46Wn04YnogAF8pj9CH3AhvM+zT7fWHtF239JNZzg3MuOJKyuApDULe2l3QwRpqxs00XlyiPWomXShCG8Zb+LOq1Q2GSrE08AHtYETUGWjXLaI6OJZIm22rNmH1jGfRNu4nZJ2ShwbpR6BECbvUqgz5B5Lxl6c7CvMOo2vnH5C/txE2l12GNsNuwvYP/oUOvvlolJGP71ZfQM4egjgJqMm/YSeko2fp0qXo16+fLX3kyJGYM2cOOOd46KGH8Oqrr+Lw4cM455xz8NJLL6Ft27ZG2fz8fIwbNw6ffPIJFEXBsGHDMHPmTCQkJBhl1q1bh7Fjx2LVqlVo3Lgxxo8fj8mTJ1dYT/1EHdza8hi2V4/Q7eWckaDg6KnMyePhTd0d67j8+GvKtGsccd5kzCV4xB/53WSGhO3VxTLl77rFEOQMIcs6407zLGua6ehRXMozW12dgOoRZLrtZmV3gPhDCsq4dUt3ezmnvNKQF1zQ1Vmug6MnGGU6tLiTHKeFpbW8olCU4QCwOogcH+kKR9cUB30IwmPOTfV8bpcntquqDCWq1REGZ92NY61Pist8hgPMdEYJ55A7O4k4B8qCXlgf0ZN0E+fNup0c8Pu94LqjJ9IFZTlRnANq0OtexcGRoaUzwK+YziVeTnmxTCjs6IElysR2oQsOHw5ABTx+wIh8q6BMhrCjJ2SJIXGUaakbUuH1O6Q7XNDWiB4WUOEJymmuzigu6FYWRJRfOEHckm8MYLszSCkNO3rsA8UmT8or9cPjDwgOHOGCEQeL6MwJ5/HiYvMilBb+4pa6kPKCoVIsLSNHD1F3IEcPcSJSWlyKhf98AUm7PkX7VnvROPMv+BL9Uhk1qGDnnjZI7nsLmvS+HswTg7KSYhx+rh0aNCwCRuymx7gIop5z0jt66grH4+hx3F+ngmeiKiJ6dHEVPftlVheLZf7iPr/jkrZOjhYnxIieitQRJ/AhzhC0PH7lNI+0thsqN6LH6uwxPwdUrxRVI++ybUb0iHI5tIieMltEj1nWKkuUWaJGGU4FbtNNbkdy9AS8CFjstOomyzJ1KQ75ID5uJS5obJUj1i0O+BAwtk2yy7Gfn/CuRhwoCQn9w4XyovPGYT5dGPBJ/SPlW9bhMXfQghHRYzi0xLm2WN/SJlcBf5nmKLTh6qjR7WJQg4Kj0OnCcqgvRfSUdy1b21QBJSg7cZhbFI9oAweU0nBETwVkipEyNkdPBZw8+sUZ5XcpZ3k3z2RYVz2iR0e11BP1FNP1iB5HeVx6N2TqOvhDUELCPVO1O4McZZf64SkVHD0Wp4xRQdJByzMiesAFeRZBNh04ggGK6CHqFuToIeoCqxYux+63X0aHlE1o1WEfYhoVS/mhgIJ9vzeHt8v/4dcff0Xv1m9ixcbhOG/q67WkMUEQNUFN/g2rc2v01Bds38ErgdNSFhWTad822mmpjfJkApAWzpV0sTRmbZtZPrvJFndq1stFmCdDs04rHUkmF9LEdpmltL6+htyWlmp/ssZcZIRLJUWZzPF/hZmTax6uZ7bvtN6PUNYybxOlc0sNc/FjBsYYpLV4xIWFy3msSlvMW+itcPiE7vAR+1F0/HDGjEd9hLmprX2rnfI+3tqbMaFmWl1zmREWnsvquijG1te6nWLzTHQaSfZaPjPTLuu5lBbMVoxE58Yi5XEutQ2L2dIgFeoxBZC2dBcvlEgXtuWCMvvQ4jGT5Ivn3WJTBJn6fcG8SgTcLk6nY/H0WW8IVluEdJvTzHYzM9OYpKDtxAM8PLrEhZGZRY7eBBdPOHM8l7a1gfR1pQCYq61z84IHIjuNjPYtJ0hc6Ft3+NBvPARBENVG94v7ovvFfQEAK8ZfiF49v8PKxafhlJYH0TAjH97YIDJa7QUOP4Qmmdrj143xE7gaBFNoekYQxPFTuTAUosowJ1aVhwuvyspkzHluVRmZ4rzJmIe4KORkol5Uhb1dPV11+Oxmk/nZ6myw6+4kH1I+D//THzoztdT/SRED0Fwa3Cabh9dhsrYqtsQR4uYxwiWYYD0zdJCtYdDmkeZ0joU1sTouzJa11lVwzrWFnPV3qIJbRjXkOsfqMG0MMUGmfRsiQyoTrRbkahNms4w1nsiId2NcskzawN4YgFpVrurtqwDXzp8uU7cTgt0cVtv1vnTGGONCm+JLVbm9ktvFZi3KmDGupPElD0HbRcPD66w4XiiODVryBHWMuT8PZ3LLS5Ctd2dFZeqLKVt9XeVenE55+hBiQhKD5SzCcKhIPg8nudZ2Dd+K9ebGw75EDs7Mq9a1Wxm39IWlrfAY5MYjWtq4NS9/QxGzdf3cWG/kkhKis5A722ooy+HeEEEQBFEVhBIzAQCBrB5oOuUXrFBfw6JP+mL/xjQES7zw+rTY9w6dtsI/OwXb/3kG/sj9N3jEnRQJgiAiQ46eWkL87n4sHEs1cVJ0LPLsDg5hAufSppuDxtqm9aVY3p3mqk7SxAAD25zG5QXpMwv/U4xXJOm6DGtgA6BfXKYDSGvP/AcweBiEY1Gm9uLGZ9PVors7xLVILPEggraKIUuzSosYYGFvjSlL721RNoRW9celuDn3hRwh5iQT0GWw8Fhh8ktw4+gtWB+j0vNV3XbdByQ1wcAU7aXv8KU5pRgYU7QXFMFup1EluuDCtstzfNNWxkwZ+j99H2/HC0USY4dzacxycWA5DVTpQmHlXyjc8m61TX8x2BOlArr9qJRMUTwTDdVtgvC5/ItUqqP7K/Rsaz9KgStubQmfdR8K0w/KOaHGOLGYZZQzEi0OGpud4bFrXJLMPMdGJzoMHslwwWDRSWTIlW4a0Lb9IwiCIKqTHvfOQFmhDx188xH0lyLnpisx+N3P0GLqDiwtfRGFB+MRCigI+T2Iig3ilFN2oNHOv6PktRRs/Wc35P/0EWilDYIgKgs5emqJ4/16fSy3+/KnLJHlufy4bv5QXUmZTj80WyN5nH7YL0/PSHa5RRGZLzGiR5zyO2sg2uLmCjJtU23thrgpC0a+HlEjR/NY3SB6FIOWJ0s3z5MpU/ukgnPVjOgRZFojiETXiyFfn/uGUxTh2OwdvT2hL7kqROCYLzOaRpweh6N3LI4HI51pL2PchdvlnIOrHEY0D/RoG9Nm027386n3pm6v/hLt5FLfhdvTI3pcL5QICBE9Uue7XSjSBcPt8lzlWD7bLpSwodZoHuMVFuNkYwSZom+D22RWwE63G5D+0c3xI8py09PSrhEkZo3mgfUY9nECcwwZDUr9zIXmhGvBiN4JmWliWScPl6EzN29AoveTKdqzoRDkQKgDANC3QCPqIy+++CJatWqFmJgY9OjRAz/88EPE8u+//z7at2+PmJgYdO7cGZ9++qmUzznHlClTkJaWhtjYWOTk5GD79u1Smfz8fIwYMQKJiYlITk7G6NGjUVhYaGvnqaeeQtu2bREdHY3mzZtj6tSpVWM0QZyAxCY0wKotfdAoIx+Hn2uHZY+Mx+/r1mLZI+PRteBexDcpwvdrz8OS/BlY+ulZ+HN7E6hlCqLjy9D6lM1I2nwtil9NwZZ/9sSRLV+S04cgiApBD4HWEtItmlkTqlemMTGqhExxriQeo5y2Is0FIzmY9B+13VQsT3W3fKc25R/YnV1E1jpWe1jYPWOFCZ/MdXLMuh7GEOSK0K/mWjpi+7IbJDyvEwroi2yLj5Bx6JErZtsAwJgizYyZ0APWdWoFF4+9Ty3ryugy9Ra5YTXAxLVHbO1Y22fG3FaPWtb7xRqdYcq0nDUelq07LcJijHHMpaIW+c4Lpctj3zyfRllFKOB4ocChE0WF5EWmDSJ5EvXIDzd5Tu+iLm6GOsnS00Lh6KxKyNTHq3XcSvUiyRSdXhaZDJCebuSWD0ys43LxM+5wzKyGMYut3DhfUnQdBxjngldL6AQwuzBJNw9gLF/P7TpbQ+iMfhNlhD8Yg0mXKd7Fw2PGci8g6g//+c9/MGnSJMyaNQs9evTAjBkzMGDAAGzduhVNmza1lf/uu+9wzTXXYNq0abj44osxd+5cDB06FGvWrMFpp50GAJg+fTpmzpyJN998E1lZWXjwwQcxYMAAbNq0CTExMQCAESNGYP/+/Vi8eDECgQBuuOEGjBkzBnPnzjVkTZgwAV988QWeeuopdO7cGfn5+cjPz6+ZjiGIWqLvMx9j+aRL0L39CpyT8Aaw4Q2ktgHKCqK1rdWf1bdWvxGhUAj/e2wWkrbPQ8d2u5GccQgxDfxo02AdsOZSFH4di735HdHy2icQn9XTUd6xbBdPEET9gnbdOg7q2q5bWjSHu6hI4vVdtypbj3N5e3WnuaZTe+L26uXJktPM7dXd9HLZdEfYXl1rRyxjOjSYVEd/t+66Jefbd7TS2ykNKgjA51hHm/hb2zSPS0NeqNLuYmaek8NGzy8JRCEo7LolO1h0uVa7NYpDURB3uRL7yTmgRcsvCvgQ4l5JJ7F9x23Pw3aIu25J9TmgclNfQ2Z4jlsciILKPeFEB8cZt59LQGszEPLa1h7iRr7F4aTbYN11y1omgqNH33XL2t+mUs51y911y0kHoU0lyKQ0KTLM7ULh5ey6ZfFFSMcqtK3Oj+HijCqz5LnIsckMqPCEHNq0yGTWvLIAfOL26tZ2I3gLldIQFNsW50IZcVcsLsgu9cPjD4X7WRgwQlnj3RKhw4tLtAGhVzMcPTx8cXJBf24M5KBaRrtu1UN69OiB7t2744UXXgAAqKqKjIwMjB8/Hvfcc4+t/NVXX42ioiIsXLjQSDv77LPRpUsXzJo1C5xzpKen44477sCdd94JADhy5AiaNWuGOXPmYPjw4di8eTM6duyIVatWoVu3bgCARYsW4aKLLsJvv/2G9PR0bN68Gaeffjo2bNiAdu3aHZNttOsWUZeprAMmGAjgk4efRdN9C9Ch7R40aHEYise8/xfmx+O3o6fj1BueQUza6QBgOJR8CWVGubJCH1Zt6YO+z3xsk0EQRM1Rk3/D6NGtWsJxnlMTP6yGAxzEJzEizD8l1Risq7iYaW6q6227PYYlPdUgpFmX/3AqZ9VbdjC447T+j4Lwj95cCb/kjmJcAQtvya1yJr14OJ07pusvBeAegHugci9U7oHKPWDwgFteKjzgxvo8+vo0SvjFEAq/nJw4MD4zcOlMeaCvx8OEdUfMf+Z6M27nkOtHQoL5OJQZu6OvBcTgCb+H18jR18cR1uhhxrFgR7jb9eVDzDVdzPOh7RRmrvtjrD8EYT0ecQ0dFt7hTJSr2O3TbbQvMG0W0FcAUqQrQl8fSCjvNoCdLhZjVyRmV8jpYpEGrks+IMuK4Hgyq4S9WFpYivZSnD5XTKawVrZppptzSOgC+8UplLXIZJY+tTwd6Ixbe9xaRh+EgrDwi0Mxrn0A8g0OHLbFiIxDLtjFLC/Foh8T3hzGF3jYuWNxRBkOIW72q7UDGAOUSHdvoq5SVlaG1atXIycnx0hTFAU5OTnIzc11rJObmyuVB4ABAwYY5Xfu3Im8vDypTFJSEnr06GGUyc3NRXJysuHkAYCcnBwoioKVK1cCAD755BOccsopWLhwIbKystCqVSv83//9X8SIHr/fj6NHj0ovgqirxCY0wHlTX8c5MxbhvKmvlxtl442KwmVT70bv2d8h/vZf8PHau7BqyWko+C0ZXGVIaFSE9q1yEfV1T+Q/l4qdj7RBr+wlKMhPwDc7RuPAGd/jmx2jUZCfgF7ZS7B80iU1ZClBELUNOXpqCenX44p6W6oKYd5WkTlouIq0bo51Z6xIqlfWYWNNd+siq65MqOc4l7a8nNbrgbDminVh3kjOKbdtus2+EVszezDkKs1p9RqrbOsixvpLd5nYt2tiMHf5kf+pjufBOueWJp7G/JQJA0rY3QuquS4Rt+yAZXw2J/+GbcL4BGCu6SJkMCb2uCnTXB9IlwtjCRQ17JhCeHMjcTML2T4Gx+eLTD8TONN7zDyXXJ9wOw1aq6fT2rD4LJTTABMdEeUN4orcU0SnilUX60uVDIfNQ1yOTEMMB4Qn6dxxskvME162hZ1h8ZuI9awyxHrmZSeXge4s4cZnxjkYD6+jxQQXa9jnZyw6zi39aDjCwsdWjxTnMCNyRMUgyZfTmSmUw3LqxE4QxphVnusgIeoqf/75J0KhEJo1ayalN2vWDHl5eY518vLyIpbX38srY30szOv1olGjRkaZX3/9Fbt378b777+Pt956C3PmzMHq1atxxRVXuNozbdo0JCUlGa+MjIzyuoAg6iXRMdEY9vRDOPv1lYi6eSs+XHkbflraAYX7E8FVIKlJATLb7NP8+NFBpMT9jqYZSTh3ykwkT9iK/L2N0L3dNygpLKhtUwiCqAHI0VNLcOO/Gparzw8ivJzQJ/zWaB67I8CO21y0PJxkOb3c5oOIcGzV23w5/ZPdLqJDxr7tt1Nr2idFal+zTBFKQpJone+bskV3jh3rjlJyL/JwRI+smRnRI/YPYLgwzH/WibEwBzain6AYkTxmRA+TXmY0D6TZOgeHyrQX19OF9rkY1SPpK8bviLtuQYjmCc91FQ6mmIs6i+fWcI5x2dEmja+wg0Hcc0uP8RG2bJIvCuupkNC3e7f3Laxp1natG6ZFulCsjiOnC9G6+LBurO1VQZmimLBvw+ZDc7o43frQ8rL5nXT/hX7MHNp3kmONjAKEc6kPHO2d69E8UMwrX3dO6cE1Tv0oGcyN9qTBqSiCY0a0n5k6GQrq14cqyBNEGR0iyHWKIHJbO4sgqgFVVeH3+/HWW2+hT58+OO+88/D666/j66+/xtatWx3r3HvvvThy5Ijx2rt3bw1rTRAnHvFJCbjq+cfQ7dUfwW7Yig9XjMHe9ekAtFt/w2ZH0SF9EfBxB+x7oiX2zH8MW/yXwNfAj5XTJtau8gRB1Ajk6KkljO/ukSZj1SXY+l3fZW4q4jZtsU5hnOpXZM5ZnkynNN2B5CSvIjjZZI/nMV0AsktGkyS6etxaFd1DqvRJFZxgYinZpSP2EXPpRS58kltQ7cdctsxqrbh+lOycYlKAgD7RBuNGlIs1qsewl4f1cIzmsVjHAcYZWDjdnGdbd+nS9dTsUjmHqu+ype/6FZZjbGpkcRRxLkZFiZ4LbutlJnc0AG6X6RbR4zbn19vWbawIYhsVjHxxzIsoz82jor2cImkcZVoui0h+F1c79XYstlr9TqJPxjguz86I58kYoNpL3ymOC1c9D49a603U6uWy3jT1ItaIHlVw2kj1wpUMncLnQnLWMKFzwx2jCCfA8IiJF4Tw2BdRb2jcuDE8Hg8OHDggpR84cACpqamOdVJTUyOW19/LK3Pw4EEpPxgMIj8/3yiTlpYGr9eLtm3bGmU6dOgAANizZ4+jbtHR0UhMTJReBEGYJDZKxFWvPIvfSk4FAHy0aBg2f9caJX/GgylAs4w/kYVncVaLtwEACYEttakuQRA1BDl6agluPYgwCawW+Zb5RXkqVMZJwy35kea6bvNFvW4kPdxXl+ARjpzbMtuUVnYR7HPqGdPxwIzZqP2l/8/A4BE+66vK2Eu59yt3OVPM+N8ej6SE8/TIE8Wmn+i6ktfp0WWKET3czNCcMeGJv8JhOgOgr9Nj6UlLNI+4fbphVdhppEf06A4ZSE6ZcKt6Bejr8IhrBMlyjOgh/fEvCFu1h51FZkiGy/zcclIUxoyXFtFjOf2APc3h5JrrybiMVutFZyjg3F4kWcaxfThbDty9Kq5bpJcj0+Z3iXS/E9visEUQVfgpsorKsEUnGYMG4ti1xvGJvhWbUMdzL3aG2DbCET16mkMHGp5W3RmkP4cYPjeOhjG5vsLkKCKK6Kl3+Hw+ZGdnY8mSJUaaqqpYsmQJevZ03qGnZ8+eUnkAWLx4sVE+KysLqampUpmjR49i5cqVRpmePXvi8OHDWL16tVHmq6++gqqq6NGjBwCgd+/eCAaD+OWXX4wy27ZtAwC0bNnyeMwmiJOeYHxzAEDjNsk47YWfUTB4DRYsugx7fm6BYKkXUbFBAEDXLmvxx9PNsXnWLVADxbWpMkEQ1QjtunUcHPeuW8fY88e66xaHilAlZIpF9V233PLd0lTLrlvlldePVRUoc0i3vtvbYAhxhqDFhxlJV/29LASUCTtgAeK6O8xSRz4us+y6ZdXJqq8+SfSHPJZdt8R2re3J7ZTqO2DBuT+sbej6lQS8CCLKYgszPtvbMmWUBO0y7fK0fJWbOhcFvAhxq0ynz4C4QxY37BTzRQeMOWnmgLGIsgqguEzfdctp9zJI9aR+4kBZ0AvrOZfqq2Y74uZK/tIoaFtku1S0KRA+5EAo6JHsiVTemOdzmLtuWcu5tSG8KwHTRailOUz6rfVUwFMKOO4uFkEmA8xdt5zKRJIZUhFVYmYzMc9Sh1n0MXbdcpMp+GQkZ00gAJ9ftekiybYPJK1fy0JQQpa6NkeaqHO4fkkZPP7wfoPGc7bWdyeZKnhRiXMdbi2rXyxaiFQwRLtu1Uf+85//YOTIkXjllVdw1llnYcaMGXjvvfewZcsWNGvWDH/729/QvHlzTJs2DYC2vfq5556Lxx9/HIMHD8a8efPw2GOPSdurP/HEE3j88cel7dXXrVsnba8+aNAgHDhwALNmzTK2V+/WrZuxvbqqqujevTsSEhIwY8YMqKqKsWPHIjExEV988UWFbKNdtwjCmZLCAmBuJgryE5A8YSt8sXFG3pJZb+OsstsRk1wC5uGGjz/o92D3niw0uuQxNO46uJY0J4iTh5r8G+at1tYJV8JzYEtCzckUf3SuCNbpH4ez+tZgAQZ3MeXNZSPJd2qXO3yK1K7eht62HjBgczxIn/SJMbekcogBclb9mOA0YEKKB0BQaNds0+lXdm5pV27Neg6YYAsXdNAjeuyBHUyoJ8uxzWuB8KNVIqYsLmhhSGPMZpW2ARGX6zChb3k43XISWbiY3lfcksugRdtwi5VSH0mDmIcf4zILOfofpIEdPod6VIk+BCpzLVvm5a6CnQa8NeJFT3ORYTvhjhdQBOWFgcUZMx0qlZBp2wSqvL4S6wt9G1FVi8xy41XE8mJ/OkXW6GPO6HN9BOo3VssJFesyMV9sUyjrsUTYcC4cW502usNGqG+0y6Q3U1fh5OsLOCvl9hBRB7n66qvxxx9/YMqUKcjLy0OXLl2waNEiYzHlPXv2QFHMv1m9evXC3Llz8cADD+C+++5DmzZtsGDBAsPJAwB33303ioqKMGbMGBw+fBjnnHMOFi1aZDh5AOCdd97BuHHjcMEFF0BRFAwbNgwzZ8408hVFwSeffILx48ejb9++iI+Px6BBg/D000/XQK8QRP0mNqEBlm/pg17ZS5D/XDts9g9F68tGY8f819E5egFiM4rxzQ/98GdJBk5PWI7MDvsQFV+GU9vsADZfhUPLG+C34LnoOHYWPDENa9scgiCOE4roOQ6OJ6JHjTSbLIfjiehRgQpHEonFnCJ6rOWcmq1sRI+eJkb0iGWc2rJO8p0iepxkWefWwRBQCh/sThcYabITw8wvUz1QXaKI3KJJVDAhokds35QHy7HY1/5wRE9ku+yRSCUBLwKIsrTJHO2yOlJK9CgirtvgJFfrB3NOy1AQjuhxipIxdbRE54RfpaEoSYahmzHjNusgrK/KgSIhosepL7Q27JNcPaLHet4kO7ksU59vGxE95V1jlnzOATXolZMjnVg9KQSwUg/AlPIHuPUzd4joAWCL6rG2GxIieiohU4/oUYIOzpfy2qloRA+32gKwoBo5ikj0yZgXJxAKwVccjFwOML1XQrriD0FR7dFAthMstqlyoMQPjz8olFUNuyyD3OJU4uDFJebaO1K+IFgVPoefFwyG/BTRQ9QpKKKHICKzfNIl6N5+BXwJZmx8WUE0Vm09B32f+dhIW/3x1zj036no0n4rkjMPgSna3wc1qGDv7haI7jUZ6eeP1DbRIAiiSqCInpOJGozqMeYLlh+MK4JTRI+Yx4V3a71IIioiXpRdXsBEeMpTaTliVI+4xK8Y46IfaxEoslPEOl22z+uY7ZP+wE1QKMml2B97v0b6U2t1EGlH1mNztyh7+069ZMYF6c4M0SbrDkqyc4QZOnnAwuupmJNPbvSa3rfh/5kZWGE8/sXFVhGOBBJkho9VoZQW0aOV4JKccCscYCwcTVTunt+6zk52WiKETCEVRttVuyJn2dTFKKqfhIpE1oioDsVEp4D0UWiAab4gx4WOrTcGS57j/aY8h1a4T7limsqtss2L09HB5IjbcAe0izPItWgX/cap62F11rBwPJ10MXGH9qVBG5bH5fb1NXq4nsfMwWW079BB4nZ4UkSSKF7Ik84TfYEnCIKoT/R95mOUFBZgxbSJ8Bb9jmB8c/S4dwb6JjSQymVf0g+4pB8CZWX4+O6H0Ta4EKee9ht8iX60PHUPcGAsCl++EzsPdUeHcf9CVFILR3klhQVYaZEVa5FFEETNQxE9x0GVRPToVOIsHHdETzkynZLLi+hxU/9YInoAIKQC/grKkdO0iJ4QlHLLWvMDIaAU0a7tRoqeCagehCoV0aOl+UMK/Igx0u3OGqf1fRCu6wXXo2ci2KXroKeXBqPg5z5LeTOiR6yvWvQpDnm1GbfNRottgiOHAygKRiGgWmWaNqpClJBoI4cc0SPJ0p1A4UTRAcUBFPqjoXK5fyQHnct6NCqAspDXMd967Yh3Tw6gtCQajuvcuDZkthMKOvjdyzmxXAVYiQe2dfUj1dOPVUAJmRFRzFaeWY7Nep5iVFqmEdETcoqKcdFRJyhH9Oj5ks5OaQBYSIUn4NK+IMemU1kQvpKQrY5RTnSKWW5OSlkIStDhzud4wXCzzdIyeErCv8Bad98SZQEIeweNMrxEj+jR65p59rpmuaDqx9Ky/1J0BFFnoIgegqg+tn2/DjteuBdnttmIlKy/oHi1v2U8xLBvbzME2t2MUy6/E4xp3wEco4cKfVi1pY8UPUQQhAZF9JwEiD/81qRU7vBLc0VUMH6Idihs/ZFaRAEiuqXcZOs/XlvlRHIaRWo3ko3Sj9tSTIjVsWNdI0d2yDjFDlgdN9a5rRZbo7tT9F189FLW2CG5LcZgibARnTZOOuqFGBSI27frUTuaNPF8WiOomKWHAMhPiEgyBV05g8JUYw5q7VsmeBrMGCKY259bZRjHLs4IAAoTN4o35Zi9K8owVWZcjiBy9pwxSRfjWnbbz9vahi2ST4/KcLfHdqKtnVFRxNMjOqqsjgwpE5IDywg0ORbcLs5INlu7VY+ugZzGxLRwGVcXfKSbhG6/wmzp+uky07gtKMbWN1aPoFWwpLR0IZgVuHhlAsYOXHp4FWMwVgl3xMlg7pBOEARBnKy0Pft0tD37fwiFQvj0H8+ged48tOu0BzEpxWjeKg/wP4KSfz2OX/efhsJCH3pl5yJ/byNszrsMbYbdhO0f/AsdfPPRK3sJlk+6hJw9BFGL1Mnt1UOhEB588EFkZWUhNjYWp556Kv7xj39ADE7inGPKlClIS0tDbGwscnJysH37dqmd/Px8jBgxAomJiUhOTsbo0aNRWFhYIzbYvtdXYtZ0DFUMqQyQdgyuaHvG3JDJL7GyU5pqab+yL1NzZ+ePs5X2Ntza1fLM7b/1Lcitey1zh3cutWx1CYly5dKmVCWsqyyP2bSVZRvHXJQBoX2n2CNTrvUEMqEVJ9ni/xzm/NJpTOi2c2svMEV7QdF+BTK2WHcYNJLNMMasvjM005tj0LZGZ9CCjBjCW24z8LAsa9vm+VOMl66LaJUi2uiinlFFF6MwbV3byl+YmrMAwoN2kQasmA/mvJiuPiTL83C63QOcjBeGuuPTPhWRGS4nbc/udJFa9GAu/cAAfcd38wk2DnEneEe/ilWeYarQsGJTBrau0MamJRG6v8VpsDDzH9ffFSPd6FxRcWNLdIRvE4JOoiOMW2SK+dayhj3HOmAJgiCI+ozH48GQh+/CmbNW48igNVi46CLs35QGtUxBdIIfHdqsRrcuuQiWenGw0RXo+8DTSO/UGedOmYnkCVuRv7cRurf7RtsJjCCIWqFOOnqeeOIJvPzyy3jhhRewefNmPPHEE5g+fTqef/55o8z06dMxc+ZMzJo1CytXrkR8fDwGDBiA0tJSo8yIESOwceNGLF68GAsXLsTy5csxZsyYGrFB+mpdyR9Vj/13WN0NIDdQkfbEMuE1PI0nC/SXUxpzqh9+qS7pYp6seeQf4cWy1jmntX3ZLj3yQ39XoT1cZ9bQHi5ShWPdmSK6e7iUb8qwb++tyVLDepryRF2sPcSMNN1Op52zTJmy5YLFnIOHX5DkmTLlNPvqMbZW9XMPaZopyFTBjZdFB+40EoSHxlhYB86hGvX0dWeZNs7CTWgTfA7GVa1frQNSPJ9WPay9yOTq5mdmH/cAoHJtTaHKXpgcxuReujbdLgZpYHN5f3cxP9L83ZJvPMkjtcHsdbhZvkIynbqVGafULCOWdbDVdnq4kM1kVfVjacxanUfW9i1qg4cfr7V2jFCeM9jHDg83r/ttw9e2+RiWcJUz8x0s/K43Ij22xc1HssTFlq2dYdgm6CPZbXVIMbN9olpYsWIFAODbb7+tZU0IgiCOnfTWGbj0rffR4p87sKzwefzw1ekoPRQLxoCo2CA6JLwK/+wm2PjQ2Sjer23rvtk/FL4GfqycNrG21SeIk5Y6+ejWd999h0svvRSDBw8GALRq1QrvvvsufvjhBwDal+8ZM2bggQcewKWXXgoAeOutt9CsWTMsWLAAw4cPx+bNm7Fo0SKsWrUK3bp1AwA8//zzuOiii/DUU08hPT29Wm2QvlpbJx7lUMnizqWFRhi35TrINCvokypreenX8/DkQXePVFAr49ic6jvO71zb0etZ15Zxk2n9xZ8JThnZWWRzXQgtAFZnjtwmN/43UYRABj2yx9525CAJ+4NdslZ6KUvoBBMWY+ZyDSdb9RRDD64fm1rrj5GZMi21mQIWttlmD3fqW10/cxbvFEVi6BBu2IzYAcxQG1GgOI7D7bpM9A27LH1gRR8tXJHLV+jiFB02kmfCJsC8IKyTd/HxIv2UlHehWPQTd/A2Zeg6MSFNS5bOhSjT6aZgkcWt9cU2rDrqTiHRewvzs3UxcMCexo3/HHDQj4n/GXK5oSOT+toYeEZlruvFAdvDj4INhrnhPjYeKxQ9U/qCzHpB43lNsUMtnS/pJHaa5UZvuS0QVc9nn30Gr9eL//3vf+jdu3dtq0MQBHHc5IwbBWAUVozvj149v8Xe9elIb3MQUbEBtG+3HvzLbOzalY7Gbf4PAOAt+r1W9SWIk5k6GdHTq1cvLFmyBNu2bQMA/Pzzz/jmm28waNAgAMDOnTuRl5eHnJwco05SUhJ69OiB3NxcAEBubi6Sk5MNJw8A5OTkQFEUrFy5stptcPxuXUGvDQcc51QVlWr8oMth/yXfBTHmQlOC2yIi9EgN7Vdr+9zNqjtH5KieSPNGKzxCGXGaU758eynmmma2bi8T2R4ONfzSP1ujedx0EA21O0h0beRjLv8Tomr0tmX95eglvQWjZSFkR4rusvS3lMI5uKpKY4TpY8jFVm0eLc6atXoqFwIb9OYMdbUDxoVICi7bpqczroYrWkNmIA8iLtupRxRJ8jkHV7l7cER5k+nw5Fy8NkV1Ja+n9CrnImYOLwedjKYA+zm1XkAQutR6Y3CQFUGss95c/iydQmtR5tAllr62db37RWnYpvWFw+A2qojXk9107UN47Eo3W3PQcC5f88a1L54AcWgaulp7VOgMsdMgXhiWDjn2XwuICvLII48gGAzi/PPPRygUwqOPPlrbKhEEQVQZocQMAMDu2EFYEz8X3315JooPJoB5ODJO/R3t8QiA/2fv2wO1Ksq9f7PevTcgV0EBUfBKooigiIjXPiWp6GLaOXqOpRlpX2GJaCWaeiwV9aippVF+mdXJNCs9ZmkRBt4QETSv4K0UL4CK3Pd+b2u+P9aamWeembXed29gv8CeH679rjVr5rnMmrV85/c+6xmg9w6rIeOOLSITEBCwadgmI3ouuOACrF27FiNGjEChUEC1WsUVV1yBU089FQCwfPlyAMCgQYOsdoMGDdLnli9fjoEDB1rnm5qa0L9/f12Ho1gsolg0a0GtXbu2wz5I/af9yCIyaut0J6K1fvzP0hkTkoHLsHIl1ZDj+UEdAia3j6WzDjt9mur9wTqZ/9grSknrLK+vKAkVs2PoHx6AIEF9NRE1yd/I0w8k8oSVaUogzesi4R8TdpmJpkmia0y72JoZG5KIlyXl0ppJizTcgo4r6qdUJULovtV9Ik09bjtNxqwmt2qOG7F6amItgSSRspZnInqEkuUYmb2SmuovmjLF0DGmro52i2BfdE8whVdR2hlWP/iiLTwykkUvhF3PFwrnA79mmTeKNB2RHgpuX45O3R3Sc9rnH78ZPH1hvfrlE0ns0txI3s2Zltu5tIVrn6TxfmkhDaBRpI9jn7D9EqSeuTGhB5FA0slxTNqkAmNfR8J0sLpW+lMXGgNSXwK2HC699FLceuut+P73v49+/frhK1/5SqNNCggICNhsGD/jBpTu+AP2a7kH/T5zFVpOnoz331qJeRd9DYcOX4j+e34AIYADDngOG3+6M159bzxGTv8Vmnru1GjTAwK6DLbJiJ7f/va3+PWvf4077rgDixcvxi9+8Qtce+21+MUvfrFF9c6cORN9+/bV29ChQzssK39ilQ/fj9H16hSeuUtHdGoZPJKHTR58LlI5NJ6i1pbxO3bmRJ0eZ2WBcfXY0TW+aB57hmhH99D6/tpIJScRPTGEpdNnHY+4kUoaiZgwvgpHX5z6Fau8QyxPjkitoFfElFFPElJDJ19Or4g7XyQ20t5kY0V4/WNUk7DTJwtIK+BBh6HoQJ3EP6Q5eqxoMxLhQ8t4tJQv+oTrpFE9SlbMJ+B5Nxc/J8xLg3qc0xsk4yZJkv5Ku5wSI5ZArtM2RyKVp5Pc8M3YLbltVKZHp3reOWZk3STcX1ZXSvijebhsqtMhbVzZPHKLXfiUUHTj75QM7TbvB3VBpe2Eui/0xVRLpscwuZe0gvQ6pMSpC/VAoBeWyKD3i7aRDpiAzY1KpYLzzz8f1Wr4NTsgIGD7Qo9evbFwyVHoP3QVVt+4L+Zd9g0UV72D3nsNAZqS/9Gs+ld/VIsFdO9TxAF7P4z47r2w5NIx+HDJEw22PiCga2CbJHq+9a1v4YILLsApp5yCUaNG4Ytf/CLOPfdczJw5EwAwePBgAMCKFSusditWrNDnBg8ejJUrV1rnK5UKVq1apetwzJgxA2vWrNHbsmXLOuwD/e7eEXSk2abo9M0XE+JIzfr96zzxMmd+286Nm+/TmfxAbk/CfLbb62qpja/AZTa+6pUqsykC2o7rpBoSCxLywv0HTWuYjXohifPcT9eaJJomQpInR68ypaNsVE9E1uauLAZo2kemfSxtfQkij68izQUr7M3pW9j7EqSf7TxIiYh0Jh8JiHTTq3shSlf2Slb6UscmRxEfFf4X6KwJvLqSwvZPQOlG9pYHFgUnabfTjS8Ip1ZhqqUr6+Y0fJpeodtetirdIrVv+t65gbJ0SmvXfpNO+Qmyn3fjex4C2mSoe5/xGtR3nyxSbg9Pe4y6Rvpd5s8D64JSpl2YIuh7I0r6tQBTNwLsHszwxXFYkjrCNVkRSgFbDF/72tcAAF/96lcbbElAQEDA5sfR19+Hxxcdh9791+PI4bdh8PNH4Mjht6H3jhvw+KLjsPOFy/B4+WY88/AIlNZ2Q6GliuH7voLeTx2Ht67cE6/e+0PnB+KAgIDNh22S6Nm4cSOiyDa9UCggTsPc99xzTwwePBhz5szR59euXYsFCxZgwoQJAIAJEyZg9erVWLRoka7z0EMPIY5jjB8/3qu3W7du6NOnj7V1FLXmfbXQkceiT2c9c1ClL+PHdc9sKlunO7Vu3wbU9l39Ru2b19TWa+fMoFN+wSSol20EK5Okvt1fXG6MKrOGnjMRL4YOUV4IWIotG2wfk78mnsdEuSQRBWrFLxPJQ1NZCyYTEGR+KfRy53YtE4Kio5dkTCJwzGZHTCm5abkiHtg1ECLZIJH6kchVeXJ0NI/21az2Zfy2ddr9KOzrSifkyitporL0NVMRPXmDl3en1pFxF1LmwhfdE6s+zdCVBy9xosiJdIvJPlnKysqZU0sn4zZyiaH2PAQk2c0ifqiuvOtA/HIieqhyxlI54wRmDGnBFlckiQ9Sj1uddEpWyfOUXFdFGPk6Tl1/gYQo0gNWvbpIbee+S3gzWgdsVnz44YeNNiEgICBgi+Do6+8D/vNNPPLCKZj/5FF45IVTgFPfSMoBfPSsL2LsrEV4d+zDmPvXCVj3Th+ICNhlj5XYc+MFWH3TYDxz/RmIq6UGexIQsP1hm8zR8+lPfxpXXHEFhg0bhpEjR+Lpp5/G9ddfjy9/+csAkl9ip02bhssvvxzDhw/HnnvuiYsvvhhDhgzBCSecAADYb7/98PGPfxxnnnkmZs2ahXK5jLPPPhunnHLKFl9xC2DzIsELtqxOPW9oh046V6LHACCFyGTkfaU2bZCtr1ZgQpb5eTK5/dSvRKe96pbR5YtY4mXCuZTCU5fqLECgQvhWvRoW2PUiMugcjfpldBtvFCLA+CUEmRkL6y9PtSLJWS1bL7tmKC1jh63XxM6QFbA4yCxd6dNjNDZyFE+k6wmqk5AIaSclr5kRX4l635xXkhLnWpP5uSGD2PXQS2rDXAImI3PQSnPlHMMcMoac9+Xo4e0zdfrOkUGlPiXbl8LwB9wurpNcL92kPTcuHdwZOnk+HOf+4PZzfdJD1kiw8ZoaoduQ68W4EimRJBkXgFkRS30I6Fw6VL6uVgCgXvORrs2+ztNkqLphJOuEVAhfWk2mZBDPXh2w2TF8+HBcfvnl+OpXv5pGAwYEBARsP+jRqzc+esXPcuvsNe4A7HX737Bh9Tr86fypOGTXRzFwn5Xos/N6jMJvUfz5PXhl2Wh85JxfoXv/YV4ZcbmE0qJfQa7+F0S/PdAy9ouImlu2hEsBAdsFhNwGY+bWrVuHiy++GPfccw9WrlyJIUOG4D/+4z9wySWXoKUlueGllLj00kvx05/+FKtXr8aRRx6JW265BR/5yEe0nFWrVuHss8/GH//4R0RRhJNOOgk33XQTevXqVZcda9euRd++fbFy6e7o07t9wVEqpoAU1I0yOva+f6xXefLrzDOhRCgEjvz5q0Ql1yY/KjFQzpKZpw9AVQrE6azbV4/rVHUqVaCEJqsdJVm4PPoaUSkuIE6XSedyEwLF/nKvzrVVC6igWct25QurPrWhWG2CJDppW58+ZW9ruSnVyfvSLEvuI3oAYEO1CUBkJq2kHk0pYtondTaUW1CVTY6dhthhZE86+YwBtFWb/f0qVSJpu+9UgMPGcjNiWUgLXQLNJZgSxFKgXG2ykzeTOnEqxB5bAjIGiqVm6EBJn9EZN1AMAVmOrH6w4Fk8CUjm9aIUAcpPn0O8HbElKhNSjl1T782Z+h61peRd3o0IwkEoHTEQVTKCSOyBY5dXYjSX2DkqN09nOUah6pGpoMlEdr5URktRDWrptBOeMrUfFWNEVdKBnFmMiVxJbG4rolCqELuoDmk+6blUjtzYaidvlqROzAggHbYEVOIi5pZ/jzVr1mxSlGpANmbOnImZM2dir732wg9/+EMcddRRjTZpm4b67hXGbEDAto37L70C+6z9DfY8YBmaeiT/74srEd54fSh6Tr4Wu4z7pK7bOucKRP/6AZp6tOqySmsPxHucix7HXdTptgcEdBSd+f+wbZLo2VqwKURP7J211dd2U4geZ3GcunV6ohyIiKxzVSm91tZS6yN6+Pwua64YS4EKCp6z+XrLVaAI+5cBm+gxLzVxn8sW0eMSCJKsOEUn8+VqZOnkxAtvQ8+XGNGjYGykdpj2bZUmlGUzs1Hoeu5825BLrdUmSGkm+DZJJJxjVWdDpQVVWci4doIsJMT8lEBbpdk65xJ1hnjS41sqoididQmJRwgba64ugVLF/YXImqtrIsomZtqKTQDTaTmdMQBlDFQrTXb9Wkjn6qJNJXXx6PPJ04wVEFWEIRpy9FioAlFRQHA/a+gWadsmH/ObdTMrlGM0F/3nte18CfoUUTlGwUeUMRsFlQEA5Qpa2pgwTurE5O4kVaNiBRFNfGyJccu1D20lFNrSJ1/MDNQkj7TlqFfAWtsM0UOJJF1XMvuTg0qliLnl34VJ8xbGu+++iwsvvBC/+tWv8G//9m+49tprseuuuzbarG0SgegJCNi+sPAPD6DywPdw4JhX0H1HQ+S8t2wAVg3+MvbctQnNK2biwzf746mHD8Rbb++M3XZ9D4cc/Sx2HLYK5cEXBrInYJtBZ/4/bJvM0bM9QH+xl3AmJltcr4TJswp7y4LM2GqtmpX3GhV9kYBvvnJuS56tNs2R74+9eJC96pZ3dR24/WWTHElt2lbJTWoajdXUAp8mn+/8WmX3C89/E+sytcqP/S92dFF95q0kYZ2wEiKngyqRZvLkxLBz5NAVv2h6J+1bOjYjNVdlJ1SOHr0qmDQ6Vb+aHD2J/FiqNCipTjbx5y+ceUNO0rfApDAa6OiRWTl6AHOT+CCTjtRjNuuG5INVwn/zwXPsA309y3p3iWx85a04jaDKuuGJTufZIpPxUvNNIZ9f9FyOTgCwEiorQ3g/ZPWXdVOnhXz1LSkhZJxsQtpdp99QVI6SjfexR65+V5ESNOpYbTRkSaby1DLq+pp6OoHn6/HdCAFbDLvssgt+/vOfY8GCBXjrrbew77774vLLL0exyBnMgICAgK6FcSd+AhNunY/1H38as//6f7D6zR0hJbDz0A+wb/N/o/ndq9D64Q64+Zf/B6sOPxmT//dKrDr8ZNx02/FYvaw/8Pp1iMshx09AAEcgehoEqf90sl4yP8iZK3nhI4UUCeBfwao2IaNk1KOPz0vz2rmpk13ChOtJboZkxSj3n+klnnDZjmWxLbbX8KKrPSW9ViA6werYfhrdZt/vvZ1KmK6klSxS7l0xSvtt9w+lTaxVtsigMbljk8ktXeFL76crfakNeh8JeUP8i4XZEgVKl0giadLN5BwC8SDxNRKpP+n8ViWNTjYJEXHCyFzPRKdNk+nxk+6odb3UCmnQK3o5Q8A3LNglS4kvNYrybkguJ+vGy9LHbwqqTx04S4qnG8ynMG5n6tRDQolXXISPdMnz0XezUj1EB3kjSevPRJ4eQDE2ZuDADKIkki6yr1lKwEnnIrKLqVky3+BUkVl8LAnzqQ1VYzVVynO/6A6hBdSPCCZhc0BnYezYsXjkkUfws5/9DD/72c+w33774Z577mm0WQEBAQENx6C9h+Ljt9+P3ue+hj8/cQbeen4I4oqAKEjs0H8jZnzrfzG69CP0ij7EqRefhkte+DHmzD0ALT2L2PjEzxttfkDAVodA9DQI6kfWmpONLaHYmvSSOUSOCVnEUL0kUT1mbapOOm910yR75nFMVvLbNo/oMRSAIW+ULN+y37aFdA0vlSVJ7ct01S13pS+b0qH2+5cFp33BJcTuseT2uR4reREYJaRm6YZvSYgaAUiyHLeKsDEEUWqHL5pHkzapdxIQUkCkERFm+Wl3BEjtfYxYSsR6ha101a9Uj17USFqGQxLdNnPBF7T3jVGJmK7qpWb5HblZUgIqF8oYd9DWr4+TJpl6yMOBXmxF5vlk17o5ZTsfb1QW99sMNTsykT/Paj2YqK3OPlOoo3nIXS/TUes8RMnIyeoTAVikGl11C55xBFJfXRNN2AimI+0YvSyeBE2ijnR1Ok0SBXQ6Tj75ZCxZsgRTpkzB6aefjo997GONNikgICBgq0BLt274zA9/hN2vfAWLFx0EAChvaEZT9wr22+8F9Hh4PF6+ZCTWvLoY4rAkj8+S2X9ppMkBAVsltslVt7YHSGenc5W3Vy2foElPGT1HP/Nk5tVpr04jTzrn6tGhojRcmohSH4LIlFpn0sLlTQXbK4DmdRHWK1HUPzqvVYhzvDHUBO+jhDyJIXQdGqNk6iv9IlenTlAs7Xa2DckZtdKXBJLoHSl8C2DpFdt0Th9B+lvNaz1XXqjzyhIh9NxZ01MsWsGKg5ImmMFEYUBPiH1cBR+0EZEv1cSbN6rjRqGvsGXCvmDQQ66eG8V3U2ZlJXcPGEGUXEfBq2XpVOd8z516b056e0nro+aDJpfs4cQX7RPFGrF+l55+Et78N9LVoQr0kuiCXC8JRIS0EdxRENJGJsZycoc7xgeWIqRouxDR06kolUpYsmQJnn/+eb316NEDDz30UKNNCwgICNjq8H5xFwDAq/JsfDDnERx84EvYYecN2HvEvyAXTcRR3QcAAFasDFPagACODt0V9913X7vbfOxjH0OPHj06om67hJmQdq7OzLmHB7KO/Zr62gE+/6hXv2uPiQ7hc17fj+pqbmfy9JiZnbRq8pbUVpsBkKwuf9Eq8VMihkpQzMgOps1HwlCbpPM3q58kkldOJLMd+iw9ptSRIrQkksmikefqEyA5YwGTn8er02hJ5re2X4ISP2mhHid64pvaopMkq2gbQ8O5/UWa0uvLyCgfv0KNiamwmPV61sWgEHY/ZzKgdAhm5bCpVyeVZ90onruWy5OMPKnn5szhPHLtpJ1PSBiHZKIi2/tc9dksYZMknLfxPUgseYzZ8l0n2u+K+KlSnVmfaWMRETlZrF3GeIy1k53/P6IuiMsuu0yTOq+99hoqlQr69u2LAw44AAceeCA++clP4sADD2y0mQEBAQFbHZb3OQal9bMxZONvsP9Pl2LNytX424Wn47BRi9Fr8FoMHPY+pAT2H7QYbz3+J+x2+ORGmxwQsNWgQ6tuRVH73vgSQuCVV17BXnvt1V5VWzU266pb7bgKm23VrXbozFte3a8r/cxYdYvL4nKrZNWt2sSOPY+qSiBGAYBwghby5CTLqzdbJIQ7fbKnqup8OW5Cla30lUfKKDklveqWIOdcPb6+SpYdV+POv5S8vVpXst9WbkIJZtUttUoXlcHnpWp/Y8XYyiNfuC5KoGwgS7pbbax5NO+j5E9rpZvTTh0oHXRuro5by82opsuOe6e90i6TKi9PDJSq9jjQTaxOYfZKoK3UBL3Ued4AZ47GEogryZjNrcuOJWBW3aqX8NE3ChBVU4rJR0Rk6Y/bseqWQpyOhCpQqOYTNd725XR5dd6nnj4W7HykllfPkw9ikzpXqqClLbbbOPola5scR6Uyoiqrq/c5AURGWrGMQmvZ1LPIIkk2mHMqIo6uuqX1kTrWK2GGPKrIEuYWw6pbWxIHHHAARo0ahQMPPFB/Dhs2rNFmbbMIq24FBHQdtLUWMf9Lx+OozzyF0sY9UDj4u2gafizW/+N+yMUXoddO60x0dgy888/BiI+4Cnsc+2+NNTwgIAOd+f+wDse5LV++HAMHDqyrbu/evTuqZvtHJ/6Yql6qoYuz5ME3T63X3Hrk0x/zabQE15VH9Pjt4REp/vo8QMJ8Sk9LNwLFlZvVO3ZEj6obQ6Q5eoxO33ybEiYys4zLh6d+Eo1j8gRlk0l29ArpT8VDSABCWkQYzewj9ASV6JRGp20jjeixiY6Et7F7RM9xhWmlJ/nkjRUa0UP1uGSRPV7o62X8Wlj+E491II/FpHKjMyCgc71IpdjXhperwRJnGFvrRhEZ+3x8e8gVIT3n6tApsnTWIn3qjejxPdtqPYx8NseAXsmKnbN002gcCxH0q1W0vu8C00FbJTlzLDKH7JsQML8T3gggafch9Y1HoQVsdjz//PONNiEgICBgm0T3Ht2wuNsn0PJH4OBjn0XT0q8AS4FeAErdu2HBHw/Bu6074NjDn0Xfoaux697LId/9Et664tsoHvRf2PuTpzfahYCAhqFDRM/pp5/ertewvvCFL4RfXbJQ72RnM0DPF4Q5rldnPcQN1ZMnOmN+5uiy3ijJscudp7kaas27lY6IyDC6DVHjs8XIcl8N8p2jugoAKqRVkkPHtZATYj74LBTOMfR6WGo8mD70kUWafknq07kjhPOajE2CmaieAoSbe8fpXUO0KFIyTgeslWYktcNMl01USkxqRcKs5UX1aFpJH5hXvmxf3AJzXe36kUDy9kw9gzZDrtULtW4SEB3809cmf+DSrvAogs1+ifQaZfmZpVPavInPDu+x4vIiQzBJqruGj5mrfOWRXxGAikwvLKwbxcnRk+aG0oPSImV4XW4zJXJg5+hRStWF0TZIWwYUaUOYKAm4+aKoE9Tn9gzWgICAgICAzsV5v7wA150GPHbNPjho/7fQu89GrFu7A55+cTcUDh+O8+68AMW2Ih745hQctuc87Lj7Kuyy50pg9dfxzsxLsHbfCzDixK812o2AgE5Hh17dCkiwrb26JdNXt+ohePjpYiaFki8j79UtLo/uV2OAv6lRq41uK0X66lbtOSQtr1SBNrRk1FWkgT8KphwXUGXJmLNIAUqiFKsFFGFeTTKvUdl6aVt1XCSvF+X1Ca/TVmlGUXI/BYyPXJ+xZ2O1Gc4rSx6/1PLkCusqzajE2X2rE1SnJ2hgTFu12aqtdRKyiM9dJQTWF7shTl8v8l0LH7mDlCwqVZqc62bZTQkHamtrN3jX9M67cVLyoFptqm/AUp0xINoKCQuS1S5Ld5y+uiUNl2BAfOCDIgYKbWifzlSHiIGomkO+ZB1XYjS3uvJ8NgpaBkBUYhQqsOEhe5w+KFXQ0lp12lj1PDl8ACAqVhFVPS+Ocr381a+2EgqtJSZbDW5O8EiSaweQG1vT1bRIW3jaUiJKSlTiIuaWwqtbAdsOwqtbAQFdE22tRfzPf92OD/65EgP2HIgv/NeX0L1HN6tOuVTCg9O+hvFDZqP/nh/o3zLeW9Yfq4aeg/3/8/wGWB4QYLBNvLql0NraCikldthhBwDAG2+8gXvuuQf7778/jj/++E02cHsF/eG3M7VKzy/N9ZhQz2++XE4SPQJkparwlflktBdZbbL0WD9u1+gN+yUs/h5KFjHgRtWoc4ZeUcd2jJKdGhmeGr594dS3jRI6csn21rRW5b43RLj/dNJu6zcEUyQFIiGtOSZto/tdJGSMXmBJkTmeOa7yTsnUUUdplUiYBePdWCvTM5KSUsIEWXiakDLh1pGqMzoYIUHDVOq5UWhncJW0M7LMIRfaugd8DyYVapXKk8gJBOE62XEuyZN1TsKs9JVR1bl2qX9RPQ8E2kafk3ZEj69ZGmHD+0I4oW4ZzxVnsPHniLSuk03iII3+YRUsW1QED39qCGS/ZxgQEBAQELD1oXuPbvjK1V/NrdPc0oJP3/IzVKtVPDD9GzhkwJ+x017vYeehq7AzLsXK//4Blu/0VRx4xiWdZHVAQOPQvjAUDz772c/il7/8JQBg9erVGD9+PK677jp89rOfxY9//ONNNnB7hTUZ8XMEmehAE61VIJ2gEQH1yKNT96z6vjL+m7avPS+zaYJ88Drk923nvI+W8etMSgQ5K9mnu++jTLKPjDxoPVSjz0puA0137FIrUvcF3/QSy4K2Ufuu7/QvhOTNk4k/MZVfg6ROZPpLJMue0w1MH/VZjVkhknm3EICI1DhOJrYySnmItLIUEdTa49SXZIsgyQaPDZGw/fNtwikT2aRCLUhjqVcnhy4X2UxGrfk7IWAsVdkHyYeEn+QRGTpZXcn98t2Y7Fhdfy6TDAFNIOkymWGSx0ZBz6XnI+43u/bmsqtCU11KTwM1vmV6r8skobVQg5d2rtVnZOBHwrze5VwDYW4CRcqpCB9I6Ne7aOZtfY0DAgICAgK2HxQKBXzqxluw80Wv4S/PfBXLlw6CjIEBu67GyG5X44PrhuCZWRfA92JL6/p1mHvRFDw67eOYe9EUtK5f1wAPAgI2HZtM9CxevBhHHXUUAOB3v/sdBg0ahDfeeAO//OUvcdNNN22ygdsrrK/WbmhFLqxJewe06h/sya/5teTZPyhLvanVXLLK6Lwti3jw6a/Xv6x5pT/TjSuf607okWShdUqVCGc9a1UvaZlQCrS+scU/kTILmUutT6VJ5vKVTFVmPKV9S8ul0+tUNblWlj6jk9NEUksWfql0HunzW6Y+ymSzbJBZfavmoakN0iyZniRbTifUEhCpiGSCLyFknPar70rHEIgh2Nh1+knYzXQVNYkm5TLt1w7ltZXQE3fr3qzn5lAdweVJ5M/f2Xnlg6Tn6StozA7ne1GeTt6ttF/BPjN8dS4PfXYJf/eYMcvs8hBMzmWT6eu1vGOUPWqXjx3J+DE1ls3gMf+E+dTMlBLCV8hShE0Mt/Np9JDWKR0fLaZMGankB3Q6Xn75ZVQq/J3CgICAgIDNiUKhgMnXXo9dL3sdf3nxHLz94i6QVYF+u6zBqD4/xOobd8Him87RhM/D0z8D3DEMR428ExMOfQRHjbwTuGNYUh4QsI1hk4mejRs36lW1/vrXv+LEE09EFEU47LDD8MYbb2yygdsrrK/Web/ce9DO6n6t9MfmOmRFaZ0I6S/YbNORGenG527cbkHleXzK8pFPSWrVywgQ8NqQnFclEVQkSEKx2JZKpjnxk56nOn3ZXgTUC0pC6zM6/TZwb6TVv7Tcto71akYkDTx+2rSZISI4jWZHeChiiFwFEUGICFH6aY0fb9+muXVMmA6LAkoDWXQ4R7Il1SMT0aN12P0KRJBq7PpGESUuTLd5B1AiJjGIR8vVDbpyU9Yghe982hH85qlLJxGp/LB0sNFFdDh+WgOEgdkjWb/m3vTCuOncyKmJQrqnaHVNnoEWuD5ZRSId9bR/qT26nrqPzHmLrPNeTGH/kwKQUfIJpVzppsewH1baH9b5tZY284ztgM7Hfvvth9dff73RZgQEBAR0GUy+8koMu/xV/O31b2PZ80MQVwX6DFyH0Tv9P6z94WC8cMGBOHzsHKxb1QuPvjoFK0Y/gUdfnYJ1q3rh8LFzAtkTsM1hk4meffbZB/feey+WLVuGv/zlLzovz8qVK0OSvBw43639M3YvfD98t0er/m7v+cE6Cyo2wiRzlnrzRfPYGl27JZPHfcry0ZnjZNRTJVkyqX7bBreWyCijZIehKAzlk2dn8lt+nJaZSJ78aB7+S77wzP3MzM20kPY/GZtrldEjdgSToW74pJtecjPd9HgvY8g4tsdIhg10rSwhZLKp82k0D29qi4kh0ggiV4cp1xE93r4lH44OaZmciJGQsbRV1QNVLw1zofemNUBpOhVLh3TssQZ8rk67SIvhtzG/gZAxdHLgpR6y2jJ9yiafjnryXgv1J4sIY35q/8E7gthj3U/0GUD0RZKQZfag1c9Mcs/rcSjJuI3h9IffS19npLJ8Y9vD/wR0LsI6GAEBAQGNwccvvQR7XPkK5r3zX/jXP3ZDXInQa6f1GHHga4grEf5VPQZHffcHGDJyFI655Cb0O2cpVi3rj3H7Phpe4wrYprDJRM8ll1yC888/H3vssQfGjx+PCRMmAEiiew466KBNNnB7hZqjtWtSmML3Y3h9OqUziatXBg0siADrV+esaJ5EZ7bdVJ7/d29783VVtv02qZW1ubrNP0Pd5EtRNARgR91wu+gZoSN4ABW5Y0eWREw+p3SEvoj2VM5+bcxsRocQEcuPEzG9ae4az0hTr00phUKYRLSS6QXpFxXRY0WDEf/s/oy0F1KKNFlyWi+N5uHRNSYlT3rAInqMn7Sc95LrBwA61FO9wtat+jKyy2veWNaNImHRefwGybhJhPKB1ldoh37tluo+q49TwoL45ARJ1aNKsvs3q5/qKKdmcViXlerkDyNPQ6sfVAdbncGfE7DGv7VAlr4xycUS9n5yP/KLnZ6PIrsflNNZ7Lyqp1lXdWPwJwLsTqk1TgICAgICArZDTPzO+dj76qV4dPU1WPnqzgCAQnOMsXveg42zBmHh909BXK2gpccOeKl4Alp6F7Fg5rTGGh0Q0A5s8qpbn//853HkkUfi3XffxejRo3X5cccdh8997nObKn67RToH6RA62o7PnToSeEBlqV8ks4gdVS9PVl7QQeY8poZtPpk8KbQv6EH9rk6Ps6+TOUMpAlqaFVxBI0hiNCHJJcN7Suoy1+9Ei1pIx6yglU2yKR3JZDTWfkpdStf+ojojq0+s11SkyMxJw0cGjXywvXD9s3pWUBotnT9LRQL5nDV9KxEDMmJXFDryQaYzcjuttX0ljE7bO5lO4KmfMQ+eaM+NKtzV0aRndW4uU8apFZzE4GyVr73wFMcpwcHbcL0ZJItPnyDnvJxC9qDNPKdf/8oielR9Yeu3bkiPbHtMSVaQyPBFYigOxi5glaR0dCpSSGjlMbFN6nrmUzFtVAA3Rhi7JZfBDBCANywqICAgICCgi+D/fPNreHTa/2Ig3sNrTw/FHiPfQY9+rTi43x+x/se74OXiv2OfE74CvHAbmja83WhzAwLqxiYTPQAwePBgDB482Co79NBDN4fo7Rb6O3sHWJsswqNenR2JGPfSEOmk354DS6de3nHWFMM3T9J6weY+3nqUQvCTCX6dyV93HskXXqf0TrbFLplhVpWSQBq74ralpI1N8BDZ0swJpW4lHB9lGnsglX+CHFvLhHMbXCor6VmRzjFlOqc0BInRTfsw7Vmh9lNJWjEnqOw61jHMPFZFsyTjmuQRUgma0zw82jo1L868gSTrT1PPXo5eaL8tUsiXP0UbzYQ6NwanxmCUUnDbVRSTT2etMuKoMBfO31jbn/atQG2fWLGqTl/zs2XD9o+7xc9JUkxJIKY883nH9UrWlSpyS7f3GenXa90Rup+FMZb4p+1XUWcqQipWRiFhGhN23e8HkBA29LzTry7RZBFKAQEBAQEBXRSVnrsCAN7tfTxWNR+J3k9ejuFj/oWe/TfiINyODQ/fDQww9QICtgV06NWtZ599FnHs+7nZjxdeeGGzry7x9ttv4wtf+AIGDBiAHj16YNSoUXjqqaf0eSklLrnkEuyyyy7o0aMHJk6ciFdeecWSsWrVKpx66qno06cP+vXrhylTpmD9+vWb1c4sCGenfjgT0XbqdBZfaYdO30Zz9WTppMc2bdC+DajPd+8P6nXptXNm2Ctv2VZIXQ6rNa0vmT4jN0m8UrXKqM5Y61O+OHl6cq4jJ4BinQco1itXJa8L0RXGVG6emMm0p7DC2hPOq1tIZVm+6le+pN5oTiPv1ZFqru1eA7VykySJZWScbJBqBbOY5I9K/HYT/NhQE3Rr/AhYA0pFJzm5ldTKSLUGrW8Akxw9DpQcmq+HlmXl6OEyfM4Ksq926BLdMftM5UjuWw2dlhrWn46f9T4EJNk1piWiU17ESuJsD2O/LGuI0HHCHZeWKArnGlr+SqKXPDf1VtXjWdeVqRDplW6uv5CEbUzrRcTReh6cAQEBAQEBXRDjZ9yA0voW7NdyDw4+8VMYecOzeDK+Gf/8x1DIqkDPARsAAMN3+BtefeCOBlsbEFAfOkT0HHTQQfjggw/qrj9hwgS8+eabHVHlxYcffogjjjgCzc3NeOCBB/Diiy/iuuuuw4477qjrXHPNNbjpppswa9YsLFiwAD179sSkSZPQ1tam65x66ql44YUXMHv2bNx///14+OGHcdZZZ202O/NgfefuANmzKTrpvKHe7/58XmbNXQSPdnF1ZpWJDmx5soHs7vTZ76Y/oXtuvh4uxX5diufZEblyAYFCaoWtTeXJsekdhxqRtl+UcOI6qYbImgzSHlC5eSJnKktJIzPnlSnZQvVTOYkvEc074ttIv0p6dQSVnfZFSkCoV3dMzpwkR46I0nw56p9IcxIhQkTyEklHJywfnXuDFKi+jXhuJV+OHj5wM28U6Z62mAvY+XrsZfA6dqN4HwASoMt9R+RTJxeGu7BTlk7CV+jiLELK5wPPTeSpS4kdSzzlaVBbBi1LhpVHsR6zxFXmC02RY7VVD159w5J7IFJyC8QQZm/WE1t1gGWMNJ2gZbm2u8uZBWxPuPnmm7HHHnuge/fuGD9+PJ588snc+nfffTdGjBiB7t27Y9SoUfjzn/9snd/cP6C9+uqr6N27N/r167dJfgYEBARsKnr06o2FS45C/6GrsPrGfTHvsm9gz0NG4e0dPoY1y/vo7xSD93wfe3xwJl67dF+88/QjjTY7ICAXQnZg6YcoinDWWWdhhx12qKv+LbfcghdffBF77bVXuw304YILLsBjjz2GRx7x32BSSgwZMgTnnXcezj//fADAmjVrMGjQINx+++045ZRT8NJLL2H//ffHwoULccghhwAAHnzwQXzyk5/EW2+9hSFDhtS0Y+3atejbty9WLt0dfXq3jzNTcRukoG6U0ziQ9iKJ6yCqJD+fjZKZ5nvk5s1fJfJiubLiwioxUM6SmacPQFUKxOkM0VcvK19PpQqU0GS1s3+3z37FqBQXECNi5009HquhzrVVCyijhemC1sVfW6LcbGu1mfhiy4+ZD9SPjeUmVNDs2OLTmZy3derJOusPnTvH0SuwsdyMqmxy/FTnHT9TWXGqE06bZCeW9nXRE3wAG8vNiGU6cSa2mfa8b40f5WqTfiWN14kZeQHlQwwUS83Q18g/GFzIpM/jcuT0vTHK31bGgChFgPLT5xBvR2yJykafUEQE1emxFRKI2hL6MPdGtOSm+zFQqPh9sS4Ot78So7nk2q/lkmPBz1Viv059QUk7eq5URkuRKGMPHsHz35D9qBgjqpIOpP+bVZFfqpyQVGgtolCqELuoDml/WquBSciNrWmiKDjy9epb9s0DSKASFzG3/HusWbMmrJTZiYiiCEuWLMFHPvKRLSL/rrvuwmmnnYZZs2Zh/PjxuOGGG3D33Xdj6dKlGDhwoFP/8ccfx9FHH42ZM2fiU5/6FO644w5cffXVWLx4MQ444AAAwNVXX42ZM2fiF7/4Bfbcc09cfPHFeO655/Diiy+ie/fuAIBPfOITePfdd/GTn/wE5XIZZ5xxBsaNG4c77rB/AS+Xyzj88MOx88474/HHH8fq1avr9k199wpjNiAgYHPj4emfwbgRj6ClV0mXldZ1w8KlR2KdGIbxO/8vdhy2CgAQVwReXrIPdvv679B3t30aZXLANobO/H9Yh4iej370o3ZS1jpwxx13YJdddmmvKi/2339/TJo0CW+99RbmzZuHXXfdFV//+tdx5plnAgBef/117L333nj66acxZswY3e6YY47BmDFjcOONN+K2227Deeedhw8//FCfr1Qq6N69O+6+++66EklvCtETe2dt9bXtKNETp0SPpapunTKzah7xUpXSsbYelT6ih8+nsuaKsRSooOA5m6+7XAWKKeni1jfEgx3hkrbVRI+fHFBkCSdSytUIRbRkEkhmfi+scxJAsVqAWR2Lz5M5AWJkbqw0oSJtoofrtH2mOpuAdCUsu73RyfVJABsrLajKQgbfIcjcl/kigbZKs3XOJeoiTZbQ+W1C9NjLUFl9RAgbq/8lUKo2gyeptefVdt8otBWbAMmeBS7z5UDGQLXSZNevBZnYJNoigI533sk5BEdUETbRkKHHQhWIigKC+1lDt0jbNvmY36ybWaEco7noP69t50vQp4gqMQpVjw5mo6AyAKBcQUsbE8YfQjG5e0jVQrEKQV9vdgaeXa7bF0sotKVPvpgZqMgduu69Jnwk5MY2Q/TErE4GOQQAlUoRc8u/C5PmTsaMGTNw/vnnY8CAAVtE/vjx4zFu3Dj86Ec/AgDEcYyhQ4fiG9/4Bi644AKn/sknn4wNGzbg/vvv12WHHXYYxowZg1mzZm32H9C+853v4J133sFxxx2HadOmBaInICBgq0Hr+nVYMHMamja8jUrPXTF+xg3o0as3AKBcKmHOOV/E4fvORc+BSbRitVjACy8fiJEX/REtvXZspOkB2wA68/9hHUrGPHfu3M1sRvvw+uuv48c//jGmT5+OCy+8EAsXLsQ3v/lNtLS04PTTT8fy5csBAIMGDbLaDRo0SJ9bvny586tWU1MT+vfvr+twFItFFItmtrF27doO+yBgvn93JgTgJkMlaC+Zk0fyKH15ZXnts8prZYeSUFN6N6JH9btvDhynf90oGtXSlApwosNegcmUG63qDM3dU02P7PmjBH1lxFAfvDcFqa9KsuywJ3h8dTGu05wRTkmiiGpRdkol3tKYzC9jSOmLeCIRJUyJQEp0iYwWitzRrz1xn9JrKe0+UcmkKehrP75RK9NizZHQybbai0XHbhQp0jd60jHL1RNixkFGpE/NGxMgrxBRPUx5Rvhb5qpbnNhh54RI3c2zrd6HQkYd4RuwNUgecnMyQoYSK+acXulMsLapb8kl5a9JqTZqnMSur2qpNT6+6DF/iCk9kpyjibO0QcQXQhB1+v+EAgAAM2fO3GKyS6USFi1ahBkzZuiyKIowceJEzJ8/39tm/vz5mD59ulU2adIk3HvvvQCAf/7zn1i+fDkmTpyoz/ft2xfjx4/H/Pnzccopp2D+/Pno16+fJnkAYOLEiYiiCAsWLNA/oD300EO4++678cwzz+APf/hDTX8253evgICAgFro0as3PnrFz7znmlta8PEf34V1q1Zj3ndOxmEHPYVu/dpw4KinUfrNXnjyzaMw9pLfodDc4m0fENCZ6FCOnkYjjmMcfPDBuPLKK3HQQQfhrLPOwplnnolZs2ZtUb0zZ85E37599TZ06NAOy2oEyQOw7/meLQ+CbarMzXNjtlrzOSWjHn3CU57Vji81rvT5iB+1JTcDz5ej/pleon+VJnutK7PZWXvoUdJrBVIurFp2QmAJYaU3NqSFr0doNE+anyb9FIisHDbI0WkiYIhOxULQhL1SJKt3pblzVJ4fQT9JfhyhcuqINJGzygeT6omFhBTJJ9SEWiY6JdGpCDmRTqojmPxGkSDXUZgUKMkmISIJIcz1o9dS+cnvC33PSmjPBLmWIitXDh+4HOkEXI+irJvSJzfrxvPpB5NnMXGkkCYHpvl6YD6FcTtTp1TDRIkXGQRR3k3J7ffpITq06TD6c5GlBzBkCc0lle7LNA+Vdc3SRNk66XjWhdTnUnnW4CQ5eizbiC1In6yKuJFKKYPuEFpg+wERAe2Mzg3Y+vH++++jWq3m/tjFsXz58po/jqmyvDq1fkD74IMP8KUvfQm333573b9kbs7vXgEBAQGbA73798Oxt/4Fq49biCcfHoPyxma09Cxh7H5zsPGnQ/DkVWd6F6kJCOhMbJNEzy677IL999/fKttvv/10wme11PuKFSusOitWrNDnBg8ejJUrV1rnK5UKVq1a5SwVrzBjxgysWbNGb8uWLeuwD0L9yZvUbAnw7/lqq6E+ixgi85tM4iiPkMlDnk4faUPnrd55m2ef+8FXUqJra5kUx3aCZjsHj20hXcMLRLbqtSqp5a7zZYgcM6e3B4tgOl0CKtETg61EZVnm1wlLC+vRlIxJPmNIkRAnyaZW8iLLREmz2pfZ1MQ8na2rnqXkEYSZ21LSQbAF6IVeUyz1Me1rmbzFEus5sTCbNNcw+TRJpBP5nluTkDZS2NdSrS7mvUGyBq1iKhTpZTrdRfagrf9GoTKy9CliQW2M1NMsTt6Nn+rUq18pngjw+8bBZQm/DmtIULNB9NajA3D7iq/SplaLk3GyIYaQ0vgUUU6IMVH6U51T/UAHp0Sy6lZqiLZPkjIJE/XDyRp6fyqbSLn2k+rLIIkCArYQzjzzTPznf/4njj766LrbbM7vXgEBAQGbE0P23QsTZj2Gfw3/M55/4iOoFgvYYcdWjB12Bz78wRAs/slljTYxoAtjmyR6jjjiCCxdutQqe/nll7H77rsDAPbcc08MHjwYc+bM0efXrl2LBQsWYMKECQCSlcBWr16NRYsW6ToPPfQQ4jjG+PHjvXq7deuGPn36WFtHIelOHnuxJeCbv9RQ7+Oi9FyCbbxePS5lzWuzdPqIKbNvyAul3+cbl2PiT3g0TyLFjuqRVpl67YZvPE4nskoj69jVSb0xC5bbxA7XF2mNhoAi0TxkrSjbRv6iiSR9x3vQEDHJZ6T3DWljjwohhLXqlYrmocun654VZhNQy7JnEA5Q54z3EekHHjCBKI3miSgxpa5hury8Wk0M7qYHjiYXzLUUeqbPBqdv0OrOTkkqtcw2VcxBul+390XUZOnkMnw3CCcXeFSPYk7UJc7yjXAb6vKBiHZ84uCyJLwPGCrfMp24UrcOtjK5JxQMOppH0IieVHkMwskwNsoMGOY7lQ0gIqSNtoUYaa2cpYiaGBY5RH3WHSCNTI9PAdsXdtppJxQKhdwfuzgGDx5c88cxVZZXp9YPaA899BCuvfZaNDU1oampCVOmTMGaNWvQ1NSE2267zWvb5vzuFRAQELAlMOKYwzH6pqfxdPf/h9efGYa4KtB38FqM7n0N3r1yd7x0r//51rp+HeZeNAWPTvs45l40Ba3r13Wy5QHbM7ZJoufcc8/FE088gSuvvBKvvvoq7rjjDvz0pz/F1KlTAQBCCEybNg2XX3457rvvPjz33HM47bTTMGTIEJxwwgkAkgigj3/84zjzzDPx5JNP4rHHHsPZZ5+NU045pa4VtzYVgu7ksRebWaeaN3hXus4xwTsXzNg2xT6li+tsX+CCTVzkdauaQyodKjaDR7lkzaSl9ekSQYYw8UXRxBZ5Y5915/Ui9cyduXOZvFdiq07s2GF0+vrHIoUE9ISfR/RIGm0j7CuWTIB90TzQfah7NiWRhHR1Wq94KZt1WRKtFMu0F7Iih8irX1InlibEVDrx5WNGH0vT84BMI5XSiB6VVbqdN0pyD7p0m+di2CRNvTdG3o0pDOdgb4oEoKyKsLgLjVp6pXHB8SkPhKOgEUROJE+NzZHp0+E8eCQhUMwmINNIHpJ+3Nd3vn6hOZXoowTpuKlSncQOUDvSRiqaJ/JE9PCLrn1TpJTtU0BjEccxfvnLX+Kss87CV7/6VfzqV79CtdqxRRcAoKWlBWPHjrV+7IrjGHPmzNE/dnFMmDDBqg8As2fP1vU31w9o8+fPxzPPPKO3733ve+jduzeeeeaZuhbBCAgICNiaMf4//h3Dr3kJj6y4DO8sGQwpgYF7vI/h67+Bf176ESx7wjxDH57+GeCOYThq5J2YcOgjOGrkncAdw5LygIDNgA4lY240xo0bh3vuuQczZszA9773Pey555644YYbcOqpp+o63/72t7FhwwacddZZWL16NY488kg8+OCDeglQAPj1r3+Ns88+G8cddxyiKMJJJ52Em266qVN8cL5ad8J3bWfe1Q6dfApRa35WK2EyV0/3BfusBUqxGGmJlXku8vmWmur7V86ya/tTHhveVID7xL2h+XDoX3vPnaNLtudrRY9oH5jYkyydvv6ykhxLU1fnlnVsNiV0H8LthSR/rImPUoK1RkXQMCjux2pneQlEgi9rb5NZwhrIUi8PbxM5rr3KALUceUGQ+XhEGtZzo1g8Qo0G/CZR+mrdnL52rFzwek5D0CEFKYT9WlSWTmEf1xXN49PJ+paOj9w2viruzemXQckTIlPfAuQhqke2ZI66tz0ha+DWLShyjShTyZsBmLw85LxuT+WaewnUNuumTeVG9T5pA7YUpkyZglKphFNOOQVCCNx1112YM2cObr/99g7LnD59Ok4//XQccsghOPTQQ3HDDTdgw4YNOOOMMwAAp512GnbddVedFPqcc87BMcccg+uuuw6TJ0/GnXfeiaeeego//elPAcD6AW348OF6efWsH9BmzZqFcrns/IC23377WXY+9dRTiKJIL+EeEBAQsD3g2PPPA3Ae/nLheTi492/Rf/dVGLbv24hf/ixevG8vrGodhMMPeRyrlvXHS8s/h+EnnYlXfn8r9mu5B4ePnYOHp38GR19/X6PdCNjG0aHl1QMSbJbl1TvQ+x1dXl1HkNSpk1YrOsRGffAtr56lQx1LJKsEl3LqZLVPdArE6Qw4ry1FDKBaBdqglh3nxIc59q3KVUqXV/frEh6bk+w5xWoBJb2ku/DqA9FJdbdVm8Ay1bC6Rg6Vv7HchJLXT9dn7m+rWl4dNmSW7Wnd9eVmVGWTRwfvT2Hmr+nWVm0GRZwKsPwjc1yZ6txQakYsC6kf7jUz7eyJbiyBUqUJvvFj/LJ1qtWsi23N0Eudt4PUkDEQV5oc27IbQL8qJIrp8urtvVHU8urgRA+b+NMBluotFAGky9rXpVOm3EkMRJU6o2zofjVGc6sp8hJT0uix9FZiFPiS7h47LZnpA6GlteIQY45uStykn1Gpiqgau6SaO/iITAlsLKJQrBi59GagA47+/4Mvr07JJl4/Ju2QkEaVahFzy78PS1U3EPvvvz9efPHFmmXtxY9+9CP893//N5YvX44xY8bgpptu0pE1H/3oR7HHHntYZNLdd9+N7373u/jXv/6F4cOH45prrsEnP/lJfV5KiUsvvRQ//elP9Q9ot9xyCz7ykY/oOqtWrcLZZ5+NP/7xj9YPaL169fLaePvtt4fl1QMCArZrVCoV/G3a6Ziw9xz0GpS8miUl0LamO6qfewZ9dzEJ5kutG7H6xn3Re8cNwKlv6GXdA7YfdOb/w9pN9DzyyCM46qij8Nhjj+GII47YUnZtE9gsRA9FnVeio0RPnBI9lro6dZbaQfTQOU2cQfTUImCqMVDO0FdzTiiBGGqCX58+AKhUgRKaWZtaxESyleMmVNUEH/DodX81lxAoViOU0GKRM5yg8RFGEkBrtYURLD4bk09z3QVay00oMz/r1llpsf23lPt1SghsLDehQgIIzStbrj56HEugtdLNvX6kQMuRts7WcjOqeUu6W3NlY0tC9DQ7BBAnkzhkDLSVmgBZcM7VIm3iGIgrBce2TBlkLi/aUqLHVz9PbwWIqiSmK68+9TcGoiIgpOeZlyVDpvKrQBN/INRzc5ZjNJVgoohqEEqWreUYhaqnPtPDCSKUKmgpsiXQnbbpiNKES1IUlSqIKtYgtRXS9jHR3VZGoa1sETFJNbbPbZExZGtrSuRIT/+k7S2WNCV64hLmln4XJs0NxMknn4wLLrgABx10EADgmWeewdVXX43f/OY3DbZs60QgegICArZFbFy7HvO/9e84/OD5aOmd/Jxd3tCMZ18/HAdfdg+aWroBAOZd9g0cOfw2PPLCKZnLvAdsu+jM/4e1O0fPAw88gPnz5+NPf/rTlrCna6LWhGwzgbyIoCdeAv4ty8R6tnbbsgk6/QsauV749Pn8TeTR1bQMOZCk643YeQFDhhhL7CTN9jm+RHpM9PgIF9sWuvoXMq01epJMNqZOnJbyfuOki19n8h/xUyUzFiBJlFX2HNVaRZKZnDg2iUKWq07Px+kmkbwjZSVOFhKIkk+p3uGCZCt/qdw5AqB6JdURIZYRpCRZeGXWHWAPGJrEWW+RBGTUvpsl3Uhcja3Lp59WlelB1o0BZOv1vJlkfBSwc/QQSEIKUX21dMIvruZxWsbSPtk5emRy3srdowiULPl5Dx0FlcsG5tO6+2WyWRmgfcK1IWyTgvU1mD4Y2Zaw5F5M6sS2Ot968yp3lLKD2hOCehuOJUuWYNy4cRgxYgRGjBiBsWPH4sUXX8S4ceNw6KGHNtq8gICAgIDNgB369MJxP/kznnr+YAAJydPcs4yxo+Zh4093w8IbLgAA7PO5KQCApg1vN8zWgO0D7crRc9lll6FSqeDYY4/FN7/5TXzve9/DJZdcsqVs266hv6N3slZfAFc9ZmRMfXPl+OZ0Pl28jB7Xo5cjq42sY9+N3+GyVZ4XTiT5CQJ/XXNOwLCtZp4odU1qj4QgxBF0mc9KEqdhSZTpWaoz+bR1qvW8HJ3S1in1H1cnlV9ASgRpGaauIqJUU0ntU/psEwxxoFqmE346105y9Egzv7UsIr1LX0VLeCV3BPCJNq+jyRNb2yYh78ZQxyLd8ZEZlNDxQdVhooXkJapueh1SLstarIkO6lpdkOdX1gNCppxIXlV+7VL/MlPQ+HRZBJqElVTZ1yx9kHNCyYmz8z7spXUN/CSRrw5xUihyUtr1dXPaabxzYlddQENw330hD0NAQEBAV0G1zx4AnsCjSz+DAcXnMfLgV9Gz/0YcjB9i2ffvwj/XH4LBo4FKz10bbWrANo52RfRceumlGD58OL7//e9j+PDhgeTZBKRzY3urEx1oorUKj8565NWjyycni/zhwQm+YIV6dXLYZIm/rue3dVKu/pqzkn3aGrIInuwjWx6ldmBpt0u4N3xxdaNB6l7g8UOAEMJaobxdOgX0Cm3acwFAR/MYnTQAAyIy1jpLrIsMnSIlmuCuEBdRvUgCabRPAjJZS117Za9WppbGTjZlD+8Jy3frtEQSZQS7IyLR8by2Hq6mvptSdDyZLiFkrDHkO5CkIuE/OqSW++W7MWGXOcFF1G5pPq0yxZFkEUtEj6Dn0vORUkztMkPYrFTIly0E0lf7WAM1tmQ6vmSkN6tTmCxLYZQOfhH5B4y1slzqPItIst7R09c4oJG4+eab0bdvX+y+++7Yfffd0adPH/z4xz/WxwEBAQEB2w/Gz7gBpfUtOHCnv2PkNY/i6eYf462XdoGUwJC9V+LwUX9GpVjAgV+/tNGmBmzjaPerW5VKBeeff/4mLf0ZwL5a01l4HZBodxNXKxNQS54+Z62LbTbJNlVG5yuyjg2ezzxkBTmIGhKydSuKwn39KLuVoiRofVVu/nILVCSLf1H1Wr1kS7VbitQiU9O6DvzatVen9Oikk0umM5lsp30qkw3WWOEvr7F+FKZuzJdml0K/pqPmsOp1Gko12cvdxxCI9Ss32g7ikSDRR+5GJ9KkVSx1rtt2QSKdyDOBvpvC7Xg4Sun5rDm8tM9Jplb7mSHXS55k6WR1hYDrD5Xh8VVm9Ie+HLwJKXNeF/P0iXPZZJpHzZsPJ/2QYGMn+bDIMiTjm5It+p8wn3aCJEkMIoNdvx7HOkk43pvzlt+c2SJkUEBDMXv2bPTr108f77jjjvjrX//aOIMCAgICArYYevTqjYVLjkL/oauw+sZ90fbaE2j6t99hwWMHo1osQERAU7cqut8/BvMvO937NkZAQD1oN9Hzta99DQDw1a9+dbMb05Vg3bLWL+i10Y6q2VqZzlrydAAFicagIRa8jM/duEq1RRnl9frp6zpDEbgvVOXZAN0iWWhdtU7oGNtSyTRTnTbJwo9MqUCUzrd51IlqxVu6OumZPH2EwrCvF2jsiokwMvK4TqnlWdaKrP8JpVE5IoIQEaL0E3S8IGJ9R/ZTUoWOOzt9jLRMTKqnEQ9ptI4b95Sck2rsekefGu/slGfTUUmR8OegqQdSwhNiZZtsGUYMjEQ2kZH33YA+DpQfqrnqW98zA3D9VPucqOH2qDp5NzyTa10HJpNG7uhTrEwTVx6/sx4ySV+QgUZstLtf3UfmvEUQKhl0sFh3fBLhA6meN2D6iA3pkHb7k3iaOwAZm+fr74CGII5jrFu3Th+vXbsW5XK5gRYFBAQEBGxJHH39fXh80XHo3X89jhx+GwY/fwQOO3IxqqUI/3p2N1Q2NqNb7yIOHf47vPffQ/HC70NS5oD2o105egI2HwTYfKgdZG3Hed1Eq54WyPplmdWMSOM67FHzCN/8L69tVv162hsaxLesefZ+opPOgmx50ilTGXsMJWQ05yzNbekpwKzBZs/QFQVB21C5kUgTwYL2l9Ce0ytkiCiJSFYhHI5XkBrQPimdai8CkqTHgImo0RJsPVZfyZi8zmL3BQcdpUJI2xdJRjARYP/YEZtPGbn3GhJSxc3FkzHjZUEdqr11mkQatesG1SyoAIRMJv3UHy7Lka0iPjx18m4uwHLXIkOsYBDhlScpuVKHv4If0H7K81ES2zx69Btl0i4TRI4mrlQZHxDcDt2d6oJ67NENkgKHLxFIk3MDTlQQXLHqDkvkxaZdpp1ZTz5J/PPY7yMBO/4/lIDNhHPOOQdHHnkkTj75ZADAXXfdhXPPPbfBVgUEBAQEbEkcff19aF2/Do/MnIamDW+j0nNXjJ9xA/bu1Rsv/m0uCn/5OvY+8E0M2O1D9G/9Jl668AcY/NV7sePu+zTa9IBtBO1eXp3j8MMPx4MPPtgll7jc5OXVO9jzFbZgef1kTZz5akktGWVWI/bU0fMGMqRioK7F4Ln+Srq8ei2djm4AsRTWUuc+Hb59tby6mg3FVj3htFOkioRAOY4Qo5CjQ7D95LhUFSiimz5Hl0HPKlNETLFaSFcB43BJJknqtVWaUSIcbyy5bX4/AWBDpQkqvIT2jT0nVTKEtnVjpQUVsuy47lvp2mstNR8LtMbNHpvoPJjUJ0t+byi1IKb9Llm/SE8ZkjehytUmZwl1M8YM+WHpl0BbsQmgy47nTaYpsSCBaqUG7+4jOySAtgKskJ+Ync/SG6fLq0tGUniJF2G1KxTh15lDLgmZ1PMur55lp745YzSXmDz+QLAIKlKvEqNQ8ejwEFtWYFqpguYiKYil3VeUgWKyorYqoioxMOPB4CwX31ZCoY04qkmftJK1zLpth9y4kawSRs+TjvScq1TbMLf0+7BUdYPx/PPP4+9//zsA4Nhjj8XIkSMbbNHWi7C8ekBAQFfBQ1f+Fw6M/h92HPYhAKDS2oRnXj0SY793D5qaWxpsXUBH0Jn/D9vkiJ4nnngCbW1tjqFr167FFVdcgauvvnpTVWyXcKMM6kdH2/FfnbPmV/XoTCI6pHPOVy9PVs68MFNW1nwt77jGHI/U4xRHlo009sVIMMEKNmFi6zGaYjQhySXjvI+hy1y/09//hXlFJZHlJyaS/URHQoYYCkvqUhqPRHVGVp9Yr6lIUYM4lORYgnPKtG/5fqz2BKWLUj951IulKiZFSUSP5JWs9mZ1Mau/pd3vNpEl0//sPottZrB9N6oQzr2Zy6aqwzi9crScd2ZWex7Rk+rkKbh9N5CO6Kl1w1NiRLinHZvcGzLznLXYlM8ETvyoHU5KERlWJBUlVYgM6Rl4DknkPGwZwaJ8SD+FrsOIHcdOItQJSVMRPcJeFl635fWlR0ZAo3DAAQfggAMOaLQZAQEBAQFbEY698L9QKV+EueeciAkHPo5ufdpwyKi5WHfzULzaMhWHfD0sjBSQjXbn6FH4/Oc/j6uuugpCCKxcudI5v2HDBlx77bWbZNz2DP6dvT1wXhNop872EDxcJ9Xty9Xj1dkOubU2oLbv6jUI6ifvs2wdJmcLz93i5npR5a5U21aafUdpiNJ/Ms0GRP+lOWQgyEZzBKWyyRxOUTW8n6hO/Umum7ASfwiYRCBqVSreW5IQH9LKzWP3r9uHfKxk5uUhlpskwaYv9EJIIiWeBJL8OOmm8vMIsqKWEJHx1VrCifYYTZRr5vpqIk56Hiq3j/VP5crJGrQOk0OPDWWkT/kSWPGySHWERydHDgOq89oIeuDbTN/XpZP5RPuUDFwDX5/5+lCwJuQaWeJ9ZBGXQeTSVd2s8Zp7MW0/jQphTqg2fMk6JV4pR5Rc0wIxJkqFCC3MhuW4NCxcRGz3PZ878j+CgICAgICAgE5FU3Mzjrvlj1h11KN4fsG+iMsReg9cj4P6XY1/XTocbz31SKNNDNhK0WGiZ9iwYbj//vshpcTo0aMxcOBAfOxjH8P555+P//mf/8Ett9yCXXbZZXPaul1BODv1o6Pfz635RDvV87mZmUhJPbnwvQXok8/nu+3ZADjzNp+tvvmnL1jA3exVsOyVt2wraEYaXqbqm+m7+atW9pKIUbU00YXJzUpUlO6xvMi4jm6fyXQNsVQ6XSHNWWFMLe0Dyy+qSVh7hniRpL5a8Ur7Q1ZjUxtfqczxQFIOw74GauUmqZkgCRknG2Ss+9esBBcj5qvFeUaPmqDrsUMGknXlpURs9arUr/ZkDlrfANaKBfHP7Qrr8jjHHr0cXp0gDwVSSJfojtlxKsdLoOTotNRkkUPtfQhIskuuj0X8pGY7Ngp47beGCDzjxVn+y89TWd1h+SuJD/S5qfRU0+tKy1Ih6pND1YmQMFScuQK13YcO/E8ooC488kjy5fuxxx5rV7t3330XxWJxS5gUEBAQELCNYreR+2H0jYuxsHwdlr8yEAAwdN93MPD5T2LRt49EaePaBlsYsLWhw69uXX/99QCAlpYWPPbYY3jnnXfw9NNP45lnnsE999yDOI5xzTXXbDZDtzc4E4Gs7+BbQKeeN7RDJ58r6eN0lp+V6qmWiqwpRj2BCfnTFuHUod3M57dS6/StPOWdggNOmcjuJ8eypKwAgQroyldGrs9Pyw7p+qU8EaQ+kMS3qDqRiFCFSkZMbRIZfUv7UpqZs5RWG0VVKP22JSqSwTdR9VFKaf3YbmO9ASOoTkJgpjN/AbOymCKNlHzfK2e0D6XnhPEJ4By5VEW1Bm0WWF86AwieYz25z1BaU6epY/ySLnNhTpq+5c8sPvDZpxIhAfsVJ4osVtjcnN5+4YuD8WsX+W9G65gT74KTKuoVJ81qktEiYUXXaXkChIEihA2XZaEAiCr0A9rq54yO0wSSAMhrmVqvEqLZUadxwBbCAw88gKamJvzpT3/CEUccUXe7L37xi3jttddw0kknhcjogICAgAALh3/lLABn4a/nno4Jez2AHQZswJgxT6P19r2waPW/YcKFP260iQFbCTY5R8+GDRvQ3JwkS/3sZz+7yQZ1FXQSt5Or0/eDehZkxn49OvNsyZKVd64WNCGRoZ3LpXNSe60q3l8u2WNr4Bl+VKmdy4XuVYkWe09Jt8kjW59LZiWl0tEpYVYNozl6bJuVdN9KVS4Z5OSV0RNLO64pKU3y5fA+4ro5GZDMk0ktQfpBCqLT9J+KxkgietTk1tdXvt5MbLY4ATaMEp2xK6ueHD2ZJAdjVPjQ5bKtcj55r8MOnw5emJHsGDAcRM2bVNj19FtQvsGVlURako2XgXAmSgeT643oYXKse0t1py9siRxyPcYYpVgZxBRYtx+7drJq2jnkORkblKnUg5QwTITfMbIk06uO89LcB3QUl112GSqVCo499lh885vfxPe+9z1cckl9uRT+9re/AQCWLFmyJU0MCAgICNiGcfwPfoE1K9/Hwss+i4PGPo8e/VpxaL9f4t0r/4Q1o6/BiMmn6Lqt69dhAVvdq0ev3g20PqAzsMmrbnVlbPKqWxx1XolyXetY+XTGnmXS69XpJzGUiKxzVSkda+tR6Vt1i7bN0imRJOuteFbdqqW7XAWKsDPYcyJCvdTE9ZdjtQIWp37Up1mpitYpVyNLpy2XRtgI53yp2gS6mpaCsZFGypj91koTyrLZe859Qc22o7WS6KSTbGMrTZts27yBrbrFSRL11hO4nzJZJYyei1lbtQqWRSxJYGO5GTFdAYv7Rua+1lxbAqWKWX2Nlmu/yD7tqyJfdYs6m3OjyBioVgoAkV0T6VxdtKmkLkynfRFde2IgqgjDD+TosVAFoqKA4H7Suh7dIm3bVKlDBz8ux2jmb7JwYoc+3Ej7qBKjUM3RQfgY+6auoKWNFXKyKSajjlSNilVEMVt1yxp4drn2oa2EQlvZ1FM3ga4rSXtyXkrIjUUgTp+2sa8O153sVypFzC3/LqxgtAVw6623Ys2aNejXrx++8pWvZNabPXs2JkyYgF69euGWW27BM888g/POOw/77rtvJ1q77SCsuhUQEBBg49n7/4Rej03HsJFvQURAXBVY+vw+2P3c+/H0NV/HuBGPoKWXWdWztL4FC5cchaOvv6+BVndNdOb/wzqcoydg00B/Pc5lSjY31MRCbbC3Gs2cTWV0yTqf9TYG1eeTmaUTyA5uoLZm+ePTGZMNLO8Kz9SjWrs+0AiaZKOra9FjQTRWUwtcTVSb+Wvr5cuS81bcy9Qeaeyj+YKUdt5fAuStJJ0JOdmshMjCyEvy11TNvjR5elSuHpM/x9alxqZ65UayE0JtxB/TdyrnEPEpnRtbuYnYILLHi3AGj0yLZWqHkU5GT705ejgkdHSUfuXHd1PGbLMva/aWBUE+BdhBuvEcPXFS7iRU9uj0uZH1lpkdGpbjQx1+Wjm/lWjeDzLj07IjFezJ0yNknGxC2l0Xkagl3gN6N93hOaOkRDIwU92KoNEf6uHNbVfyYrujBdHLO9jSlzdIAjYFlUoF559/PqrV/B9nzj//fPTq1QtPPPEEfv3rX2PixIn48pe/3ElWBgQEBARs6zjwU5Ox18ylmPvq2Vj7bh9EBYn9Rr+Clj+PxOFj52Ddqp549NUpWDH6CTz66hSsW9ULh4+dg4enf6bRpgdsQQSip0Gg3+Ebpb89c0LAnbhREsC3QBB9cyDLBiWX68nSx8vzZPvmdj57qB/wrIGV/KMEjvmrKB47U4290T0Bu9cKRCeINmWN7adk2v29K4heuopWQgxFOm8NX+lLkMeB0klpE0WaWJ0pQRIi2yuKCRT0vln9iqz4pfclIYkkYiEhRfJpdCVEgySkA83NozxQK2tFglxHOvFXWyQhInP96LVMnPKniVYFal0vc60iQnh5Nvdi0guWEF9qFGXdlD6ZWTdeFlnESROqTx1Q4oGuuAXzKYzbmTr1ZaOqfEPWd2Nm2e/TQ3TQN5K0/lpkV9YDxV7ezVQSAlKoVenINUsJOKkM8V1Iek6vqmUNTGOYdwyRA0HIGimNvVa/StO3OjkS05fJvgVsKr72ta8BAL761a/WVf/ee+/F//2//xf//u//jo0bN25J0wICAgICtkNM/K+r0fPrr+ORR49EeUMLmrpXIATQ1FxFn6F7YMjIUTjmkpvQ75ylWLWsP8bt+yha169rtNkBWwiB6GkQ9I+seZOyLaXYN/Gtod43baFBBVmBBbVMyUMWGZUl3zcn5vronJvrSHzwRfQYOscmY+h6WD6JtH2igUeeVJ1adM/QD5QqUtbTDDn2/J22tENAhFqJKuMfJbMAm9CwUkZ7x64iBUxETZx+Il31SlqbmpgLQhIJCGk2CTW3lc5mvTQlTCyRTCOIFDEVp5vWFQvIdAPMdZXpkvLJDSId96yBIwAp3Cgi7wpY/Obgo1ImN6Feqj6PEFIXW8mpJ6KH3xhKRhYBJWA/HBS5BvqJ/BtfuSLN0IAkz708CI8sXxkfEtRsEL31PNQAt6+8ETdpNE8cQyCGkNL4FBFOiDNR+hOMWLEGpyFtQMeRJGVpHUg9bjRZQ3gcvUXk4mo/ffoCGokhQ4bgi1/8In7zm9/gU5/6FIrFYs0ooICAgICAAB9aevTAR2/5Cx5/6TgAgKwK9N1lLUY1X4xXLtoPq/75Clp67ICXiiegpXcRC2ZOa6zBAVsMgehpECTdaQ87spmUO/OXGuq9Py5Dz28yf9yvF1nz2iydPh2U9qCvIeXNea05EZDGtfBol0SKHdUjrTKhZ6P2ZqgZ9x+SWBdN28A6Q30mrykRfW4aZZeIsq9QGtFDfLTr015JYBZft/vUdEVCyNBjSNOLkepRIRCxqB4zKTfkkoSJ5pEpmSO1XLbpCaxISQQVl0SuorADJtQ7YSIir4CpKBUkk3d1Q+TemppcMFdM6Jk+60bfoBW2IPoKW+aNyCfyQPZN59MnmQyfPk4u0Kge5bRqX0c0ER0WIKIdnzh8tnseMnroCWY61VuvDrYyuRttk2wSkYnoESZHlB460upIu++UbN3ndHACiMi7X9oWYiStC0XU+Mgh2r+qQMl0fQpoLH73u9/hc5/7HP72t79hxx13xKpVq8JqWwEBAQEBm4SWeD0AYN4b38T7/xwAEQF7jXwTO/ztUDx2yRewz+emAACaNrzdSDMDtiC2C6LnqquughAC06ZN02VtbW2YOnUqBgwYgF69euGkk07CihUrrHZvvvkmJk+ejB122AEDBw7Et771LVQqvkyhmx+C7uSxF5tZp5o3eF9nyTHBN+nd0hxVe3Ta+k28DXJ84npM7IsvW44tRf010SB0Ju1Gx9BIHpobx2TIoHWz8hBxAsd/ldwonZjY5Hpn96P9QphNBQnrDZZkoi3JBtAQi8RD9cqXZLl5CJegtaRiSUQPtE6bcLCIJ12WRCvFUuUF8kcO0Ve/pHodjNKV6c3Ae5gfa61SphFEOXl6atwsKtdR7q3PiQ+g/ogeCXe4ELmS7JtNkQCUVREWd8E6I1sn4Yccn7LA5ZAIIieSh5ut9n0PJV6W2WeKtaGDlcbwScPdOA9Q4ZevVkmzBpPSA6BKdZJ2oHakHaeieSLf61dpfdb/mnGLbZ8COg/PPvssbr75Ztx666144YUXAAA9e/bEiSeeiOHDhwMAdtllFxx//PGNNDMgICAgYBtHpeeuAICmeD12uuCfmDv/4yitb0FzzxIOG3EPuv/1k1a9gO0P2zzRs3DhQvzkJz/BgQceaJWfe+65+OMf/4i7774b8+bNwzvvvIMTTzxRn69Wq5g8eTJKpRIef/xx/OIXv8Dtt99e9/KnmwrJD3ImgZtTp/6+Lz1bjgmcWsjip+g8tJYbtc63R6c9kBV1InJ9UsdURvKjvh0TY2rbEtzcLsYiOzMPLIlKR6T3XZ18yq88kpkeCUcSf+WKltt1lQbh6DVaFWFD1atoHkLMpESAlB4LrNw8dG7MSDVCHgHSIWgs6im1g7/eJkAjiJRqmyEQoFE9huLjET2+MQOhxkyiJxKpj05USI0tFZrcg9LR5yjnBAqPqsnSSex2nUmL84gOxq5IKsenk59TKn3kUBb4A4X4SoaafxM54vNsj+gnOUFIL5vqoTqoUpkvnxugzheILiubtDD1LNInBmKVo8fnFPWD6I6UbJG+3hXQGbjxxhsxZswYXHTRRbjgggswatQojB49Gs8880yjTQsICAgI2M4wfsYNKK1vwX4t96BaKuK4H/4eyw/6G177xzDIGOg3ZA2kBHq0/ROVcqm2wIBtDts00bN+/XqceuqpuPXWW7Hjjjvq8jVr1uBnP/sZrr/+ehx77LEYO3Ysfv7zn+Pxxx/HE088AQD461//ihdffBH/8z//gzFjxuATn/gEvv/97+Pmm29GqdSJg70Tf0zVX/elPffIm4NSM/UP0nVsPhk+e3zHknzW0mmvmGVLquUTn0eagAEezQNLgp2XR03+qMUuGePm4VFZbEBKqb9Gs/R4Yy+JDlCLs3LvUL32GcDQLT5ugJAoFplgXrFKthgQcTovlsl+qiGGiepxyUVKqgmLQJIQNjmjdCmZqR36FTqq08oHZBI5q30T0RNBSpONSDnpG796zi1V3xNdkO2LsJFGqND9nKGYDwF14am+jubr4bKd13rIpok9YkOWDo9OkaUzD+qBQvzLjObxnPOCnvDZriJvQAerGq0yzc0jrVcP9YMVgP2uGtlidY4YQK9nlbz/JWNLrxXNA8DKz2P5o+qT9qpDFNNm2UQHRcDmxm233YbFixejWCziiiuuwFVXXYUPP/wQH3zwAV5//XV84hOfwFFHHYXHH3+80aYGBAQEBGxH6NGrNxYuOQr9h67C6hv3xbzLvoGWbgW8u8NErFvRW6/jcMjYBVj7w93xjzt+0miTAzYztmmiZ+rUqZg8eTImTpxolS9atAjlctkqHzFiBIYNG4b58+cDAObPn49Ro0Zh0KBBus6kSZOwdu1aHU7NUSwWsXbtWmvbZNQ72dkM0PMYOmdD9pZlZr0EUd70getg817nM2vzBTT4NGfpU5uSZeSpSZ2hd/QkD+5v+pxaoq90caImiXVBui6V3cqV4q4KZcrsQcPJJ6XLH0lkonfs/jakEJ2xm6XMaf/ZkTzJfkQ61kiOYEfW6MTDpK+1Hr6EOiVnHH0wr3jp17BSncK8aiZEnJBAIrZkm2ge129nFNE5vwAkIUEikdpFB2K9N4w02nPhIYhq6kLGeeagpDs81I9WIM+OTL05Op3AE2aH9zgtkzSiB3CjeoRn84iq60GnInoo2ZUV0SO440CSsMejmOY7AqBXzlK6rRw97JN3kJbPHNXXRjAf6EmYshDRs0Vx7bXXYvz48ejVqxc++OADLFy4EDfeeCPmzZuHHXfcEVdddRWuuuoqnH/++Y02NSAgICBgO8PR19+Hxxcdh9791+PI4bdh8PNH4Mjht6F7rxIee/JoPLtgBOJKhL6D12Jk9Tw8+62xaF27utFmB2wmNDXagI7izjvvxOLFi7Fw4ULn3PLly9HS0oJ+/fpZ5YMGDcLy5ct1HUryqPPqnA8zZ87EZZddthmsZ+ikH1Str/O+CZd92nu8uUzlc02RoTNjzufdt6ULp4Tr88mMyVlbtk3p8HacGlD+CN1GOm3UPo/O8RE4dn16ZM4Jx4rkSFr+mEgi1x7/iKD6hZDpa1mpbOHrA8UEmJZJRA/3k1ourBMxrSekO83VBIlhShTPok7G0uiklJWW47x/lM67vb0A3T1CC2TUTISEjag1aLNFuwW+CbwzgDyMRu2bxCKaXEOIRZLVY1xFXToVl5EODcF11joWSALEiDz+PNM6uA28c+2bM9sHwI524fIBOBEyWkcEK85QAFaUD5XpPIyk+2nZpQaiNPZZN4gwbRWLqD6VHUKm5okQ0bOF8eKLL6KtrQ3PPfccjjrqKERRhDvvvBMXXXQR2trasPvuu2OvvfbCokWL8Kc//QkjR47EHnvs0WizAwICAgK2Exx9/X1oXb8Oj8ychqYNb6PSc1eMn3EDjunVGwDw2M0/wIgN16Lfbqsx8qAlaP2ffTB/45cx4fxrGmx5wKZimyR6li1bhnPOOQezZ89G9+7dO03vjBkzMH36dH28du1aDB06tEOyBMh38U6D8Cqtxwyh4vtqwDM/q6krb67Vkd+as9rUMQ/1Ujy2bPqCE9fqavaRKHS+rmIEzLFLCpnz5hWnLAs5EaUkUkSkzO4Tn1Sh9QNIX3myPaGTdrtvTG8WINJXrmDmnmmdhIoyE1YJoUMNdbJmrkNXF/pY9YuqGgmzNpmPYtJ0FfVJQC/rbsEz0fbmm8ldz7sWWLtaJIQeQJ7rRjsjyxw2kPQ9QDuYN5AAUi7LeRzUo9Nnbi1SKpVpiLyMqoIRdak9mQErGS6ac+SZ5+kno4Mssa6K+XOWHut+9wxaywhWh9qlLxYn+TgDRuynnUOJrhDQs8XRvXt3jBs3DkcccQRGjx6Nu+66C3EcY8mSJXjmmWfw8MMP46GHHsJpp52GDz/8EL169do8EcMBAQEBAQFIXuP66BU/8547Yuq5KLV9DY+fNxmHjn0KPfq1Ylzfm/Hqd/8Xfb74Bwzcd2QnWxuwubBNvrq1aNEirFy5EgcffDCamprQ1NSEefPm4aabbkJTUxMGDRqEUqmE1atXW+1WrFiBwYMHAwAGDx7srMKljlUdjm7duqFPnz7W1lFIwHADfo4gEx1oYrR6dNYjT9S5BC+Xw+dSPpd9x/XCV1eSv1l1s3QmZeqvqSXZp9Hgp17yyDMzr7Nf1uJranEbTHv72PZLSaeZeWBtfIUg+5rwf3af8PpOZwrPvBUARGTsdpZY5xZQb1LNAtabLEK/xiM9q66L9HUaX2rtpFySTdvj6Xmrk4XRCUj7jZpUXYffgpGwXmezdNJjfh7ouFIyybeuqT6gF5VUrI/zzVab55fvQaSuecZDQufjMVyhlacnn7Rj4sjgjSwlZqPjMdmnB0oMuylIXwqpEolHerMa+24wpSNKB38Uuf2krhN9b02trgXySZd51xc8oDNw3XXX4ZprrsFXvvIVLF68GMOHD8enP/1p9OrVC0OGDMEHH3yAN998E7/97W8bbWpAQEBAQBdCS/fuOOrmOXh54K/w7suDIASw5/5voc8jR+Cxi05utHkBHcQ2SfQcd9xxeO655/DMM8/o7ZBDDsGpp56q95ubmzFnzhzdZunSpXjzzTcxYcIEAMCECRPw3HPPYeXKlbrO7Nmz0adPH+y///5b3Afrq7V3ZpwNa9LeTq2+CU09Juhz3uW6aNJbs4y2+qVbEBm1NktXHcgMcqijnV93kuZXr8CUbsKT6ZauuZNM1eg5CkrSUBtUPXtRd55AmeqjOrN9FI4FtvPs2gFEj5uq2fw1E2c+XmjOWG//y9RHmWywxoovizBg+pGPL2q+i4R+bAAA0iBJREFUsNKeJPtJslx/quvER5Fu1tilfUuij9yNTKQlsTjehLdgBLlLsm4Ke/CYT66U1s27Ecg5vuqe94FAypzAwDydrK7gZdxHfo6MLd9Dgr+5JlmZw2V4yCLnssnkTrAeXumnNkXCjGHdMYwsQzq+Cdmi/wn1GcN5F06SHTXYdbZ41SGuH04nWn4z4kqzsh0dtAHtxZgxY7Bo0SK88cYbOOyww9C9e3f069cPP/zhD3H11VcDAHbbbTd8/OMfb7ClAQEBAQFdEaM+9Rns9l+vY+6Tn0ZpXTc071DGYSPvx7tX7o5X5vyp0eYFtBPb5KtbvXv3xgEHHGCV9ezZEwMGDNDlU6ZMwfTp09G/f3/06dMH3/jGNzBhwgQcdthhAIDjjz8e+++/P774xS/immuuwfLly/Hd734XU6dORbdu3ba4D9ZXa5F1wg8+92iPVt2G6fT+8u3RSScL0nPenvzbKX3b+7txPX76uo5SLVyG8jPLFhXVIUldTqtI1oLr5PqURipTxYwkZb7F4bN01tOLxnvfq1zJf+aVJyPX88qStpl6YvRoTSqSwcx307+pb0KlgDZ9qvuC5cqhvkqVaDnDbf+LWVHaOonWceexJJIqDcTwzXXpXDgPIvVBpnl07X6tA+lkO7FBZF9ifsOpd5kiQzoZg8CXovPoJOK8NwoTSOr468Pvt+/h4iODfDezgHkbTrC6Ev631jy8l5dYInY4wVSC0OKasCH20IqANYCkkkevJ+snm5sxd6vUMpU+Ss6Qm8vrj8wZgAJ6QPBxsinhWQHtxt57743Zs2djxYoVeOKJJ1AqlTBhwgTstttujTYtICAgICAAAHDcDXfireeew/pfnoJ9Rv8LA/d4HwOWnYInzx+Lg2f+FU3NLY02MaAObJNETz34wQ9+gCiKcNJJJ6FYLGLSpEm45ZZb9PlCoYD7778fX/va1zBhwgT07NkTp59+Or73ve91in3O3KcdE8OO//5q8q2ouUC9siT75BObrPpqfpY1B8zSn1W/nvaKsiCZX5y62fuUhjDyLFLDaUfpIdXar9t8Kj0FqNTI3N6khBNElDyxJ51+Qsr8VYmLTfSK8U/VEh45po7Q/AKghoAv4bHtQ/IRa9KG9wkHJZbU6lhS6yOEmppIk3MJVH/GgIzg3GuQ6cpdfJy5E17p9K85sPwlkUbtJnkAQK0QJrkvTJ7voeFJGFyfTlLk0alHu+fCWqtn1aFT8APaTzX81byFR49+o0xmlwn9BxZJxHXQJeMTdZ6LYT0zzXmLM1E6IiWIEDSqJRMrlD5IJBFu8Edq6c+sJ5+0/ctkMGvUCdjiGDRoED772c822oyAgICAgAAvdhs1CvjvF/DQZd/CITv+Aj132oCxBy/E2h8Ow+v9L8TYL32z0SYG1ICQMnzL6yjWrl2Lvn37YuXS3dGnd/vegovbw7IwVFDV++0RESPOfLWklpyyPb3JJW3okIoBYm02uLxKDJR9smu0lwCqUiBGIVeHb79SBYpohh2lo5BdBgCluAkxohwdgu0nx6WqQBEmgswOwhDeMiW3WE105ukyZca2tkozSoTj1QmPQT/9OjdUmgDmZxJ54+srEzmzsdKCijTXRL1Upcgan/0SgIwFWuNmj00gOgU5NvfhhlILYtrvPMmy9JQhmV+XK01OP5prb8gPrr+t2IRkHXDQCjVumKRttVKDd/fdeBJAWwGa/ON18vTGQFQVhNTJaJNeI9quUIRfZ84NKmTStok/EPIeKPrmjNFc9Mjz2O1E55RjFKrI7hdKCNG+KFXQXCQVY2mfpwwU8z9qqyKqkjs3o38EL28rodBWIu1i0g/pQOHEUXpObtxocvJY/pGOtNom9SrVNswt/R5r1qzZpLxzAQGdBfXdK4zZgICAgM7B+vdXYcn3JmH02CWImmPIqsBLz+2Dvb49Gz132lnXa12/DgvY6l490tW9AhJ05v/DtskcPdsDNiVYXpKtvTrpRmW1V6cAkpVlPHl5uM48WTrtRI0tT149tmfp4OX0yM6Jw8sBYVlnzgGSlXK/k/wxyT8BSfLiZOvzzBSFfT2NZVwfoPLVSEidI0ddO1h6kytichKxPkmTzqpXW6R0X43y9WWi1x4rQrq+Klt0Dwpjm+5baeeYhQRJ8xPrTbIcPHq0Wb5zX0l0Btm3onV0e7WbyIpjPsFG/RCetNsx2zzdlQR/SPfmpM44gt16WmQMONmtpbD1g/SF3XVefXpXZNy/vJ/yHgBED0mTZFdlvgp+QfNvTqi0UdYzTQ1yAZNbR/9zXXf7wXfxzH1hLmZMbkPfeCJCnc5UHZ6eM46Qccv6UzAdAQEBAQEBAQEMvXbqj0NuWohFpavw4bIdIQoS+495Bbh7BB678hwAwMPTPwPcMQxHjbwTEw59BEeNvBO4Y1hSHtAQBKKnQdDf2TuArHlbvTqz5k716tR6yepJalUY35ytPXJrbYDrN9ehXoPgc9+8+a/Z+ApNZnNW0iF0D5dq22r/Tf5F6T+pS8xqXxFMeme1sZV5iOPKT3tNMFqbraNFV71KdSUtI7YZisWaJat5KaS1UpStl/qS7pNxkoyVWv2s5qbsCqXzWKH+CEBEgIgEhFqVCElOIONrZPrVWsKJ9hghpYSZG2vOiXoq3PXJEt0el8D2ybUzxzY1BuhLYG+8LDJ94OjkyCFU9CpVAuSAb0a28NmTo9biUzjnwdvyPss6psXEPEs84Wi0Ab4HCfFLrepmjVeq2Fk2LC2mPIrzsE3bqGvFirRyREmfFqgx1EbPE9VynDgc2febk4unI/8jCAgICAgICOiyOOzMqeg3/Z947PHDUWlrQve+bThsj/+H96/ZFYePnYN1q3rh0VenYMXoJ/Doq1OwblUvHD52TiB7GoTtNkfP1g71vdzs1I+OfjdXqqjKetVnEjjSjl7x6cyyI6uNKs87l2WXKvPNqX3teBnNXeP8+K2PhVXT0DqS2WOy9kinTvK3iiarHT2nvHB9Eea0tHf9ftl2xZJGImSNBJMsms+OhUCaoyUrBxL1R61epSIXqAbXVssOqUgd2wrdB5KWQif/VvolYkj9GpWR6cJlGrRtpKvVa48yJbucRNA8uqXWQLVUJp7RRd0NYZAjR6/CVEN+1s0pyGkyeKzF5T03o5WjJ0s+KbO4iizCy70Z6zqmz1HtEh3WvoEmYN6JJOX2EOUskVNBi3L2fWSSai/JPkhfSsC8qiWtOrYGbhM5JaLUbnpC2vVyPQgICAgICAgIyEZTczOO/tFsvPjXv6DXY1/Hrvsux467rUZcFVi6aiyOvvpGCCEwZORNKLVehVU37otx+z6K1vXrwmtcnYwQ0dMgNOKHVDU3ksI+rgeZAQpZvxYTnfXKrXerJZvSI3xux/W6QRN0z460kY4F9ktVYPVdu4VTp8DaqWgfE2XDXyAjfhGSh1pE/YOWGCFS+yKNdNE9EFl6aTQP1aDpFsPyeSbzQstDKlNH9Qi4EQYsssay3mJD0/PqVaK0WBCZKqJHRSoJ2NE8ApGOZLKjhLQ3jCiyT9hjPzI6lK9ZET3OZB8upPviHu0Ka8CaS5Qd0VPP3J0ospoKwInmici+qkORpTPVQSkH4ThZw1efT6RMm8vc0nyJj/TxyFBlJpCGjk913n3WsdXV2WJl6XjVdYgy770QkYvA7M3K9K2vi4D1jqE1iDz+OwoCAgICAgICAurD/sdPwrDLXsMzT+wPAIgKEoePmY0VV+2Ol/78ewBAS48d8FLxBLT0LmLBzGkNtLZrIhA9DYLIPNiyOq0JDS/LgczY9PmMnN5Zsr2Tsjq3emzNq5kvn+7RLBzumljKD0oV0PpcIphsQKLq1UeTstg6rE9B/aU20R5QsmOTCUjl7YBEkhbcbP500Pa+RWzROao+a+TFVLaaBZNNssmrprbI3JX2l0hPJKtxwZKpc+VIafTKxF+aH4jrtHWnfsG+V+iWtOb9Jk1OldwbxVGb6uDxUZ5O9+XsoRE97dbpq6KcFECcbjxfDwiZUUunJk20m/6HgvDIquUT4TNooJZLWrE2FJwTAR2ehDRRldm7YFq8c5MK0gfSNor6E0uzSQmganQ6kVqKlWI3vu4AaTtNSVR9nzAn43qfqgHbIm6++Wbsscce6N69O8aPH48nn3wyt/7dd9+NESNGoHv37hg1ahT+/Oc/W+ellLjkkkuwyy67oEePHpg4cSJeeeUVq86qVatw6qmnok+fPujXrx+mTJmC9evX6/Nz587FZz/7Weyyyy7o2bMnxowZg1//+tebz+mAgICAgE5FazQAAPDqM8MgY2Dn3T/A3u99CfPP+xjiOMY+n5sCAGja8HYjzeySCERPg+B8te4EsodNWewfmmsg44dwPW/x/jrtydnD7eCyam2qTZ7NgvzNOk83GjhAQwj4P75ylm/+6cvvQ6VR2YBAQVtgR/OYqBiqg+lknWATMa6X2hNh22GiU1T0jT1OLBvUXDKdOMp08pjMfamuCAIFnYlIiMjJ0WPleNI60ggmxTNIcs0Y4eAbd0nEEvFLmEgepQ9EJ/XN9LN0rqsqSObsptdoPiWoiB56QazJfw6ktMaXM7joQCUqRYGc990seTcKOef0horioZE81nGGXq6T8hU2R+LaknXTm5vTNpjwGdby6tLeNHfpuwbcdlJPOKwUG7+Qlm/2Ju2m9JzlXzpuVD6dJDmP0aWNgdZJE0PrG5IOFE3q+AYR7bRUb83BGbAt4q677sL06dNx6aWXYvHixRg9ejQmTZqElStXeus//vjj+I//+A9MmTIFTz/9NE444QSccMIJeP7553Wda665BjfddBNmzZqFBQsWoGfPnpg0aRLa2tp0nVNPPRUvvPACZs+ejfvvvx8PP/wwzjrrLEvPgQceiN///vd49tlnccYZZ+C0007D/fffv+U6IyAgICBgi6HSc1cAwPJeH8Ojy6Zhw3u9UGiOcejYx/He1XvglV9dadUL6DyE5dU3AZuyvLpMJwkdQbmuBctd5C2vrpB1uqSnwD65OfKkRKWGPi5TAohjoOSpn9WGnqPLq9cimmidchUow15W29Tjy5jb62Aly6v72hnygtsAAG3VAipohvVKlv6MWH27Dl1e3dUJjx+Jza3lplQnb6eSPnM7jJyN1ebk9SnSxugzk3oqMwawsdyCqmzy9jtdrj2ZNBv5MYDWajMoJNmREIil3S9AQhBtLDcjlgUtz9It/dcjaStQrjZZdXhfxZ5BL2OBYqkZQOQf1DmDVkIgLvNX5rhSn05AFFX2Xk+brBsg/YzKhBbVZB0B16l4oDYgkhnPPEogMf0iBgplfzOXVSTH1XR5dc8AEr5jIktUYhT4Q4j3j3TloFRGSzE1hulNdEivLAFAtFUR0YctfdhLmEga1Z+qvFhEoVg1NxOoDml/Svuc3NgKezl2Wkeac7S9lKjEJcwth+XVtzeMHz8e48aNw49+9CMAQBzHGDp0KL7xjW/gggsucOqffPLJ2LBhg0W4HHbYYRgzZgxmzZoFKSWGDBmC8847D+effz4AYM2aNRg0aBBuv/12nHLKKXjppZew//77Y+HChTjkkEMAAA8++CA++clP4q233sKQIUO8tk6ePBmDBg3CbbfdVpdvYXn1gICAgK0HrevXAXcMw7pVvdDvnKUobSzi1cuPwwFjX4aIJGRVoFIqQJ78VsjRg7C8epeAnlP5fhHfkkj1WMERyJ+Heppb5vpSaagtT6bnd2b9Az4vz7Ijy0aegDhPJw2SsCJfrH80YMGN5rGn526uHhqzI4nGAtEJS19EdAqtgW/+PjF6bQ+TfRWpwPMCCfI4oMEZrq+mQJKoHrUakcoJlMQrqYgeYW32im1JcIEmo9JxGadl+jUuJ6In9VBAe6AibCJBI5hIEIbO5wPNx7g+uiSs6g9lS4GMFB2BRSN6fAM0bzCTHD2ZF9kT0eO98YBsO3xMm6WHKacRPSqqB9JdtIzrFq40APkRPbQ/fA8E3zmuW8KO6MlQ59jqe2AJUok6LASkSO5j3zWTeRdRESxkLOpoHr1iHLHPZ686SMeNXkKdg5A5tkyiL4qInwHbC0qlEhYtWoSJEyfqsiiKMHHiRMyfP9/bZv78+VZ9AJg0aZKu/89//hPLly+36vTt2xfjx4/XdebPn49+/fppkgcAJk6ciCiKsGDBgkx716xZg/79+7ff0YCAgICAhqNHr95YuOQo9B+6Cqtv3BeLfnQJdj7jNsxfMA7VcgRRkGjuUcH71x+IZU/nv0IcsHkRiJ4Gwfpq3R6mZXPpp/M31OaafBNimiYkzqiTa0OG/NhTlmUHt1F9qil4HqjtxgedyYb8s+3y9ZX0eGO3TyRIprVKaiJDr4+cArL7j7YU7AoJlbfG+Rdb7ZROTuJBTZ7NnJcYZgaVk6NHxslqX9ZmT8o9YvS8VqYnJDlJY6psPxJ9KmrOSoGSdodMu8TtS+UYvKBckxopqn917iOH3YBbxoUKYfEKmYPXm6PHIz9LJ2XwcvWRC2s5rco8OjJ0qmsKcj1t/z1+clnCUyaNXHpzWm/z+VzL6h/ngSK9m5AxRBxDIIaQ0vgUwSYUaf/pT3WOGBJT+TEsckbVsfL2pBdd3YhpcnXvgyKiugQhh5TeDJIoYJvG+++/j2q1ikGDBlnlgwYNwvLly71tli9fnltffdaqM3DgQOt8U1MT+vfvn6n3t7/9LRYuXIgzzjgj059isYi1a9daW0BAQEDA1oOjr78Pjy86Dr37r8eRw2/D4OePwOETnkS1rYD3/jkAUgJD9lmJnRcdj4cvOr3R5nYZBKKnQZDOTucqr2N+ZiHvh3seZFCLNMqSz/fzdPp02NMne8lyn29cjsqM48b0JFJoTA1N0GzWxXKto3E9rtSIpB+xa9G0JHTdLarXnb/bMtwrlEQhmOgXWp/HUSVIKQzyj3eqSHPnmGNI04uR6lWR5s6xNjUxpvRUQubEitSBNCttOUmBUzsl94SsJCZMwIReTSqSEFGa0DndzLVUE+2cCKr0gEZpqZGTuQJW3o0haJJorozAN8TyInr4RuUKsu8liAj5YEKqoN/FytLtsUUNC7Vv+VbvA0fC+5DRQ08w04neTB4jry8BOINH5x2LIEWUfqaRZSnZZFLjEAaO9p2SrfucDk6QCBsyjqiRVuZzpZSQNZnklTT73tW+AgI6H3//+99xxhln4NZbb8XIkSMz682cORN9+/bV29ChQzvRyoCAgICAenD09fcB//kmHnnhFMx/8ig88sIpwKnLMPiiN/Hwi19EcW03NO9QxhEjf4fXv7sv3nvt5UabvN0jED0NgqA7tSaCm1Gnmjd4cyfnmJBHDNUiibJQq357dHL9gu1ndSuVo0gNQ2xQubYU9ZdSPVQaj44xEiltEpO1tWgNuuaWDVHHVXLjgmJik+ud3XcugWTRU5owQTrRlmQD7KiepC8Tska94uWL5lFaUrEpeSRUOY3iEXYfJ3NlciwlYklX3JK2Hvbql5Tpku2Urkyz+/Ie5sdKa6xW9kLsrrxVZ6ibev0s99b3kTZZ8n2bQzCZT0nL9OZcbM2oCO5HLZ2EH7Lcz3OYyyE3Rl5EovUs891E9dgtQVkbOljtu15xN/wBqv7XymWqVdKswaT0AKhSnaSdMlq/j6aIn4iQQ/xpSByV7DO2fQrYvrDTTjuhUChgxYoVVvmKFSswePBgb5vBgwfn1lefterwZM+VSgWrVq1y9M6bNw+f/vSn8YMf/ACnnXZarj8zZszAmjVr9LZs2bLc+gEBAQEBjUGPXr3x0St+hiNveBAfveJnOifPsVfMwodHzMW/XtgNALD7/m+h598Ow6NXnNNIc7d7BKKnQZD8oMZEcHPp1N/3pWfLMYFTC1n8lPOD9SagPTrt+aKZYdbqVtqexIHoSbeR65NgJPOIHmHt22SJigSJIEgQgYqBMZEiXFPymeWRrZtG7bixO7wul8G9JP/IPNRE8xBiRpEo0mOBlZuHzo0ZqUbII8CO6JGS2Z7aYV6dI/77IofYq18mqsfELtHkLr4rrroquVaJHrXSlxsVUmNLhSYqrZgpFz4ChUfVZOkkdmvErDiP6GDsCstlXeuGNC5IcqrWQ4ITW8RXX36xrM0rN8tuK/cROUFILx7Hp3X4iJks+dwgdb5AdNHrqQ74hZJxkhnc6lhqNz2GkavySemVtwK2J7S0tGDs2LGYM2eOLovjGHPmzMGECRO8bSZMmGDVB4DZs2fr+nvuuScGDx5s1Vm7di0WLFig60yYMAGrV6/GokWLdJ2HHnoIcRxj/Pjxumzu3LmYPHkyrr76amtFrix069YNffr0sbaAgICAgG0Lu446EHtfsRTzFn8W5Q0t6Na7iMP2+H9YcsFIrFnxTqPN2y7R1GgDujw68cdUPWeSnrlGBvgPwXw/T1c9dThVYZFRGTpr25JI8sVH+Ags+mN7rGsJVp9SPllys3rGftGKzgdpRA+siSOdzQP+HqU22vJ91ugf81MtUtumzic6BasvSB0h0rl/OuGkizJJYrNQx1JlCUpIA64zaUd1JrN3ayyQsAyB9BopLiaNZBDpsRBSr5alcgIl1W1Om07O6fUUgHkrzHM51bxZxtRnsww86xD/vkeokKpvRaZuawhQffXeHL7B79vnY02SYhJR0xGdKpUM5S5qPlBUg5hZ52vXjmeb10arXxWZYp8TtJ56oFLDkoFoKjkPNeY4vd5V+hpWxqdqYC3/Hptypz6gbxDf64G1lmIM2CYxffp0nH766TjkkENw6KGH4oYbbsCGDRt0LpzTTjsNu+66K2bOnAkAOOecc3DMMcfguuuuw+TJk3HnnXfiqaeewk9/+lMAgBAC06ZNw+WXX47hw4djzz33xMUXX4whQ4bghBNOAADst99++PjHP44zzzwTs2bNQrlcxtlnn41TTjlFr7j197//HZ/61Kdwzjnn4KSTTtK5e1paWkJC5oCAgIAugGOvvQOvPvYous0+DUM+sgLDD/wXWn93IB4vT8Xh0y5rtHnbFQLR02j4Jm9bCGbiTNTVqdOaw9Shp5bojDmapUt4yvLsMp+29DxdVI5ZectM4A2ZIslRlizhlPnOGR5BoACQ5ed577re5M2NOQElyF+JxDcJE9Gj+tf+tHXyOvaKzpxK85NjKrYnFnYNRcgI0ruCz9QlwJdHd8nKNJ5KUp4libJRY14QCk9pMt1t7LEJIAY1V9auCV0psggwENl1ILb7NhN55A+9UFltnH61DwV1niuhnS+SSyJoWZ06HZK5nptTtY1sXsWpn3FzOsV5DyhVmUb0cB8cBWlkGWeMvPLpDaSqkgP1Gpa+6RShQ2X6ZJACPRZ8F0YYfYq1DRE92yVOPvlkvPfee7jkkkuwfPlyjBkzBg8++KBOpvzmm28iigwBfvjhh+OOO+7Ad7/7XVx44YUYPnw47r33XhxwwAG6zre//W1s2LABZ511FlavXo0jjzwSDz74ILp3767r/PrXv8bZZ5+N4447DlEU4aSTTsJNN92kz//iF7/Axo0bMXPmTE0yAcAxxxyDuXPnbsEeCQgICAjYWrDPEUcCR7yOeed8GoeNfhg9dmzFofG1+Mf5f8RHLp2LHr1D5ObmgJCy5vQiIANr165F3759sXLp7ujTu31vwcV8ltUOlFFtXwOtJjYta+jkp4teCqW2jFjKXGuduUqKagyUMvRltdFtpUCskw9nt+XllSrQhma4hAW0LE6mqDqluJDqdM9ltQEEWqsFlNGidcZpuV3fJpBU+2K1CckSz64vvD0t21huRtnxU4D2F/2k+lurzZqg4fZYBI8Ulv/ryk2oSn/fSl1mCAW68lpbtdnxTc1rtW3S1h9LYEOpGbFsYu2I7c77R6luCZQqTc51s+xWc23iSCyBYlszIAuu0LwbJyVAqpUCMqNpfGUSkDEg2pLF3jPbZemOgahqIqjsesQOfoGrQKGIhHmpV2eqQ8RAVIWbO6fWcSVGc6srz6pPeA4qQ1RiFCqw4SF7nD4oVdDSWnXaWPUyEmhHpSqiCg3v8utU7bXMjW0oFCtMdhbBI0k0joTc2JY8OGlbSg5xm9O2lWob5pZ/jzVr1oRXYgK2CajvXmHMBgQEBGz7eO7+ezHw2W9ipz0+AACsf68XXu55IcZ95ZwGW7Zl0Jn/Dws5ehoE/cW+U2k24eiUGZvTUprktHkb0+bAp4frpPv16PTbkDNJ99ihzim6I2uLkC6r7JzzW0Nzx6iNZsYpkJqKhuAZQESqE9oGTt1Qv7gN9ipb9j5v6+8Vao+dyyn1x5Ojx/YZKECkCxjZCZXp1RCIk1w5QiJSG6ROmCxpnp442ac6hT6fei5MImeaiyfZJCIRIxIxhMdnp3etKulVIvmCtP56V93yDtwaJI/vRhGANyOxvWxb9ubjKSRgJ+qVdsUoPeqATrWIV6afvgdQWia5LL5F6UaPhf855OhVsB4iEk5Can7nC3InkmrO/clzHUECiM2+Oh95ki5RBzRpk96Ilm2pbDoeQOzXcohcuhJYQEBAQEBAQEAnY9SnTsCAb72G+fMPQ7VUQK+d12NMy0V4avrhqJSKjTZvm0YgehoEZ6LUDviJjTq1euYQ9cgTdS7BW8smny7fcb3IruvO4gTb9/vtn6FK9mlrEOD68vg7TqHkzcSFx0JNsHj98pEo9uJMnIygyZqpXpcKEk4PqQLpmmm3jmxf7SXW+WTaUD8SNJky2fR82IxpZYORZxZAt61WVFmyRLZOEG31AvPeEpFaxwdtlJBLHYJESoB5dHo7lBz7Xr2ph0BWRBFXpf0k10WSiin/0RGdgo4Vqo/rhl1GcxODV5fk07PvBG7Vd3OmrzgyuwTpGlXkWaJcclaKjG/9T0bJZp23m9kdINIkymqlrdRg6x06cr0oKQRps9nWwO3gmA0ICAgICAgI2Axoam7GkT+cg+eab8Tqt/siapI46JB/YO0P98Szv/tlo83bZrFNEj0zZ87EuHHj0Lt3bwwcOBAnnHACli5datVpa2vD1KlTMWDAAPTq1QsnnXSSsyzom2++icmTJ2OHHXbAwIED8a1vfQuVCo/x3zKwvlrXMylj1fOib/K0qrmB84NzDXnm137p3STbzFLExldfvIjPl/b45Pvxv56pC9Vp57ONyWZqCXasaAiFRKf/nE3SUBtUPSrb1i+9Mq0Zm8c3e0UgyWvya6f1mMgh9Y9HJqj0Ic5YIfNJn6/JayIxIGNIGVtjBlJCSNtP1Z+K9lF1Y9/S7M5AUn5lrT2uorJie/xSCk6Q9NmOCDKRlqQv4uRVxQ6B52Hx3RT24DGfPJmuf+h5dJImTK2jX9jHmXlyfDpZXcHLuI/8nPToo6eFp4mwTfcSS3l9I9PXa+nDK/20h5pK+G3qan3KUakSLNtjTQp672XdPGSw68cC7RD2tLOisBishz91pINjNiAgICAgICBgM2LsqWeg99mvYdGC0YgrEfrusgb7bZiK+eceh0q5rOu1rl+HuRdNwaPTPo65F01B6/p1DbR668U2SfTMmzcPU6dOxRNPPIHZs2ejXC7j+OOPx4YNG3Sdc889F3/84x9x9913Y968eXjnnXdw4okn6vPVahWTJ09GqVTC448/jl/84he4/fbbcckll3SKD9ZXa+cX3Hx0/DfYdPLKAxzqkEd/vVYbDbHwlfnmivVu9frpa+fTSetn2ZKUqwXQ1WLn5qUtWpvSEUanfR7OEbUl0ZOQMCbyJOsVMIBH/yitvj6RzALWYyK5jtITksL91OQg8dM3XHlQg7TsS6IQhIjIJxkvzDfaWyoyQo2viCyXrl4FowYlHIyAFOni9cIXNZX4qaJ5IHi/Q8/brUm73mydOtokEnaUT71IJ9tS3Zx5N4XucFVXdQQ7J2DfCF6dpAnxQ/tsMTu2HY6feTqZ7VnPIP/ASot8dQErckc3YWWauLIMsHU63StURA+9wMQetalxzMVr9ljYxgt1T9mRPVCRPUq5itKhz1SVLZ4ba/mTNQAFrGsJ2EusBwQEBAQEBARsBWjp0QOH3vg4Fq6/DOtX9kbUHOPQcU/gg2v3wtLZf8bD0z8D3DEMR428ExMOfQRHjbwTuGNYUh5gYbtIxvzee+9h4MCBmDdvHo4++misWbMGO++8M+644w58/vOfBwAsWbIE++23H+bPn4/DDjsMDzzwAD71qU/hnXfe0atQzJo1C9/5znfw3nvvoaWlpabeTUnGrH7HJQV1o+PJmKVOAq0mP/WqLVnt7FY+GfRH+iqy55xZ+isxUM44lzVnU8exFIhR8OqU7JPuV6tAUS9EJzz1fMmdk4lVOS6gmvKm0jpn73P9pWoBJTRnygfcOB6kBFExNomRqa9+O42stnJTmow5y1b72NghsLHabHzR0R3cN8HapQmgdZJiAX5t3ITVKekj0wTQVB8hYxxbiIwN5RbEkl8TgjhidhhZpWpTmheI2kTqSSYzDaQolprhJCl2FDPEiR9xJUkqkxstw85JCYi2CEm2J4+eGkRPVBH+m1MTJcLtPAlEbUiItFo6FV+RHooqULDeI/TXd44rMZr5a9qUqPL4oLiSqBIjqrA2Phv5uXIZLW1sAFjtpB54Fv8kAVGsIqqmDXQy5Wz9+o5tK6HQlj75qp6LrRrQB3i6Lze2AnGajFlFeUmaEFq6fkuJSiUkYw7YthCSMQcEBAR0DWxcvRovX3YcDjh4KURBIi5HEE0xVi3rj5dKn8Pwk87EK7+/Ffu13IP+Q1fh8UXH4ejr72u02bkIyZjbiTVr1gAA+vfvDwBYtGgRyuUyJk6cqOuMGDECw4YNw/z58wEA8+fPx6hRozTJAwCTJk3C2rVr8cILL2xxm535V9Yv9x60oyrTyWdC5sfiWlA/JCerDpOGGdE8Rqfx1Rek4MaP5Acz8H7L6ge6HHpW0IA/qMBYxdMw09p2mSFH3Kgbo9f+jCB0FhA7goj3ji2XrLIlvS99ePxWUUppBIEgkSxOolkaNkB1MjKHTrLZBaB6dUkayZNsdJxkj4JkHirMldQ2k6Zp6IbJ2ZMeiNQPx8/IbDqawTOK1OSfD2uy6Ry3QkBEyZY5cLNAOzTrRvFdEqIbXG/dOkkZ9dPqFgkn2bN1nunMGIhUpfPWkXDr8xtGAI4eQTZLJStTnEgm6cV16X7gF5+baPJMaV2EgzHKvYNG54Uyzxx2kQuRm+CaCnb80cwr8c0R4PZFPWMmICAgICAgIKCTsUO/fhjzg0V4/J1paF21A6LmGEIApVITdp90CoaMHIVjLrkJ/c5ZilXL+mPcvo+G17gItnmiJ45jTJs2DUcccQQOOOAAAMDy5cvR0tKCfv36WXUHDRqE5cuX6zqU5FHn1TkfisUi1q5da20dhf7O7ttqoB1VM3UK9UOzRF0pGviP8MkcMGnM8/LQIDE+h+CuZmVRyeqS+uexwjrOztbCy8weX/0KnnPUOmt1qgx/zRYjTuOrkjgrnqvD1uWbnUs2+aQvbFHdiVcm904sY5P2Q9I6UtsCyyL3Sqi5r5SCyHD7krblOZxoXh73aiumxRzTlb9iNW7VsNMXMj2QceIz1amuOB2nUuUlckH5BnsBKtVe6U/zB9GOsC+VH9bAZvSYGpx8Y10lVUfwm5M74SOBOLcloVcz05s6Zg8A2p3ODcpJIdhUg6XW9yDzDAkJOHpYqiSzefRbNtW4OfUQ0gfq4kOTVtL657quL6Ig+2wVM52rhypWzsYAvONJmE/G39hMFYgjanwweZDp/wgQEBAQEBAQELDV4ujvXI6Fb04CkHyd2WWflRj83Ccxb8YXAAAtPXbAS8UT0NK7iAUzpzXQ0q0L2zzRM3XqVDz//PO48847t7iumTNnom/fvnobOnRoh2Wp7+odQXt+vPfpVFON9sjwzhc90Ty+ORuXkyc3b8uS4dNhp9bNDxywNztXjpsa2N0kk6oWWze22n9VHqBIZwFR0TzJr/pSb2Yhd/XTfqxXjBIJuQABs+C7uramXazbRPpTiijNY2NWnorJeSNP2RChmm4AoJY6j9O1q1W/2am31b6JSnJzOGVfBfuVLLvcjjoRJIhHRdWoCCmyopbK2ZOZl4fRU8LMjTXnRK+zMLmL9D9fRE8Ww+HcKFIXM5ftrnSDrUAuQv5NnUOo6Jw2NExGSCCix0a2NxgrR63FrXBOz/dQyPqkupTtcMyjfJxNZEu4csm+Hp5RcmWdUC4JT+NUNOXbpDT1NTGjNtsfYdmTRvEUiDGWygxWRg9h4nBEFPhCN30kW0BAQEBAQEDAVojm0vsAgHn/OAnFtd3RvEMZR466B69cOAJrVryDfT43BQDQtOHtRpq5VWGbJnrOPvts3H///fj73/+O3XbbTZcPHjwYpVIJq1evtuqvWLECgwcP1nX4KlzqWNXhmDFjBtasWaO3ZcuWddj2nHlRTfB5Unt1dkR3xo/fTjRPls6ssky5no228e3TMt+cl7fzb/y3ekqf2JZIXU5LbIulVRNEQxJxUoUgupKwDUPT2DoTv0jcjjD0Cu9X218jX4JF1qR2IEOnak+TO6tlwNl8m/Wt8tLELVnjRGZFS9m9lcyLTT2rP/TENimSsUyjIGLtl/HVRPiYVbn8dxGlgOhk3JAVaQQPHy2xhOOOxXA4qohSATWWLN1cFo/qiVPhWTdLHih/QTiJpF+FZzOyHbKmhk5rjGYRUrV8zfBRm+u6ZfMb3EbPtbGGKOhzzbMpcoj5qe5N15hUmdabjkMemiaR5tjxXUzPE1VVUw5bjJzSKV1ReTIDAgICAgICArYiVHruCgBo6tUPa495FP96Ppn773XAMkS/HY3Xfj7DqhewjRI9UkqcffbZuOeee/DQQw9hzz33tM6PHTsWzc3NmDNnji5bunQp3nzzTUyYMAEAMGHCBDz33HNYuXKlrjN79mz06dMH+++/v1dvt27d0KdPH2vrsA9qp4PfsTeJrBH2cb366NzMmtBkRPMoHfXKrXerBz66wjch868xpfbc6B4uxSQ+ttvYklQrXidKfri3rDFhG4ZeMXl5KD2S8C2UZrJh64z0xnPW6HKm08ikfkpACs1BkPmup+ci/Q9p9A9PesOjeqQlSSkhUVWKdCCidIROJNKVr5RfdjSPgMkP5MulZKbWjAKy5/VpKyWTXO+siB4+bLw3ilnMXuuWcGXwqJ5I+Z+jL1Mn8U3a5EhuZI+uQ2Tl6ZTWrkmc7Buweb769CnXyfVRZJX+5AyQR5cgck13CrjEiSoz9tOoL8stkf6xSDKijCeASsetuQi8PzOe2NQvPXCkbUjmuPDoCQgICAgICAjYyjB+xg0orW9JEi/vsTv2vnIpHnnq46gWC9hhwEYcPvZhVNoKGD/jhkabutWgqXaVrQ9Tp07FHXfcgf/93/9F7969dU6dvn37okePHujbty+mTJmC6dOno3///ujTpw++8Y1vYMKECTjssMMAAMcffzz2339/fPGLX8Q111yD5cuX47vf/S6mTp2Kbt26bXEf9ETZOagP7ayu1WhVAm7C0jr1+eepfglZ0whlR5beekiovPMip07+D/vSKudEEV8ditMEfn2EJLF0yjSiJ4Y7CLL0CKtQklkstYXqNJYlOpIEx5LU9esEjM9Uv0l0LfQ8UcDO9wMgjeRJdSJGshpV1lUT5K/UE9Dkg1goDP8hY/sqWxN89U9HSwjYklx/jTRhjx9SzQzz2O03nq/Gt5/pvkh71r6+xrGMtjH80Rq1blgqX/UpddZnL31eKGIl7wZW4snQ1ryJj+RhC0Q5n9wWQq7Q55l1VR0SxGOnNGMKINfY994XkyH4Dn2o67w+rBH1xbl2VVundY7cbNZKXpJVoTcJzM0JT5ss8iggICAgICAgYCtCj1698fCSo3D42DlYdeO+eKl4AoZ/6WI89vMqDj/wITR1q6KpexXvXDcahc/fiyEjD2y0yQ3HNrm8ushYJurnP/85vvSlLwEA2tracN555+E3v/kNisUiJk2ahFtuucV6LeuNN97A1772NcydOxc9e/bE6aefjquuugpNTfXxX5uyvHrs+3Jd55Xo6PLqyWs0TFXdOjlVYZBnTSylc74elVnLq6u2/vS5yflqury6T1fevLRSBYpoyagv2DzUjmAqx02I01mlOzcmOWfYuXK1gCJayHzWHtuxI88cl6oFxGRZHtu+iOwDdPq7odKEimy2fKM6YmYr1VmsNCX1Pcu6WyuCMV82VFpQlYUMvoMmdLblyjhZRj4p9KWlZn2bnowlsLHckowDSgjQz9gcW3ZLoFRthhXtIO3+UOwCt6dYbAIQZZM8GWSDlEC10uS2yYMiKFrJ8uq0fY4+AECcLq+O+kkb1a7QJpC5jHwGOSRk0rapAhe1btRy9vLqIrXJaZt+RpUYhSo756knuK3lCprbOKNEEMduW6WzWEVEl+CyHljS1a32W8soFEvpOdJefciY2EGIKCkhN7aZLOWSKJAg9dy2lUoRc8u/C0tVB2wzCMurBwQEBHRdPDz9Mxg34hG09CrpstK6Zqxa2Q+D9noPQgDFtd2weN3/xZHfurKBlvrRmf8P2yaJnq0Fm0L0JNEGHdO7SURPDZ1Zp0s5RE8W6QIAUkr45nW12lZjoJRxLu836FpEj0+nqlOuAmU0WQQFJTHsY2HN1Uqa6HHl8tWw6Lm2agHllFxySRKX5KGETWuV6zT7sdbLbQHays0oo9lrJ9cJZntrtUmTPKaNkWOWQ7d1biy3oCqbMubzkd1G2n60Vps9bZDOYYXWafEpEthYbkYsC+DRPFyHJRNALAXKjp923dgzcGUsUCw1wyF6tFFuG+pHXC74qxBixtUJiGIG0cMN95yLypzoYSS672aRgGgDIh/Rw3Q6S5/HQMHH4BLZjt0SQDUlejxEjfAdEzmiEqPAH0Ie8o3LQamMlmJagelNdEirjH5GbVVE9GHL66pzKZmjbS4WUShWCTlDCRppf0r7nNzYaoggSdpoPbHbXkpU4hLmln8fJs0B2wwC0RMQEBDQtdG6fh0WzJyGpg1vo9JzV4yfcQN69OqNv3/3LEzY/bdo7lmGrAo8s3gUxlzzMJqam2sL7SR05v/DtslXt7YH+OZU5sSWg3r1xVLTDp1OigeYRE/tIYFU3bwMEfQcl533tpt6Wcgn3VeqjqP0b55NSd+50TnCkmwmz6qUvvZFyZkCBCqktekXSnbwF8S47Ty6RrC/wrIFQkB4SAz75S8WWeN0hO0jJYhUimhHp4dAs95OUjcFHaPSkAV+Ak75o97+oSuepTrp62XqvArKiX3+uSSs9jPVE1n9k/ZFBPuic0OzXiGir9ZowzzgvIq6+Xw3ZZ4dYGV5DwP+tp0AUM2OquQ6edNMcjvvgZDqzDxPZRAuhDb3yrQMS/fpA0uQTrRvzlSHej1LmrxD3BivjZLoF/a1EhGSDlbN0x2R6hPS2Kg7VLrXUhM6pIDK1A+ljMisgICAgICAgICtFD169cZHr/iZU/5/Lv8pXv77yej7xJew0+6rcNC4Z/HBdXth7YTbsc8xxzXA0sYifMNrEKzJR+ZsegshnTzzVZPteBGnSeYWI3uBnCxwXVweL/PV89moPm1aIdunmG2etZQcnb6+kh5v7PamjGqsejRxnVkLkdMZtb0CmHuFYrUKVbryVC2dlDKim9STRAmpN0AKCZFuSqekPSslYrLal9kMx6H1kLEJQC/6pHTC0gOo1cRipldFzSkdcTrJljHUwlwawtoTmTcCtSXWvZbqk2pVL6sj6ZDIGLgCVnbjvJvRHqz+G4/ry7pZKC9JiQe+0ZW3YlXm0ZGhU11TyMTNuvL/clnCUybhJnZOu5KvZO4F7TvA7SdrNSyzL2ScbHqlOij2j+RqZv1HO9kiyaT9upUeQ8yodIU8JzmSJIrVOb2JdIl1ag/RpVaok1l0fEBAQEBAQEDAtoWP/J/jMOD817FowWjEVYEBw1Zht1dOwryLz2y0aZ2OQPQ0CNLZ6Vy9dczPLGSRQek0w7tATj3zOSqH7/tk+QkPV4aagptj1zcuJ/FBeP5RCZSwMWtg0XgWutE1uOySRFtEykwNN6rIzOm5HYresXXa9BBdWSvSPtpt/JFMkuvUu2lUUPqp9qW0e1P3qhCIhFoFi27QpI1ekl0kmxRUp3A2TeVJob1Ua32ZVbfsiT8EICKZbIycSnpMTbTdhd+l3SnWtVN97F0ByzfYKERKeunJO1dI9PoGbb03CiNEnIcBQAgHsmnWTdryfbo9tqjLB2HEOX5x+Gz36LOWhVebPUz98n196fShb/AISERmUyu4ER4m4Wgy2CjBbACVDyCKiC5iC4x+014pjQkxxPylg1Z6fBJMZkBAQEBAQEDANo6m5mYceuPjeOLdc9C2pjuadyjjiBF34PnzR6N13dpGm9dpCERPg8C/v+dOBDejTpHueL/v56jPI4ZqkURZyKpfDxnFdXL9zlzaU8Z1kDgQ8k/JtXtH/aVUD5VIyRFKYygNKvIkdmqYmtx+RSy4dtD2cRrZwj0zNsVk395zoWggM9FMa1kRPbHeh464MeESavUrmc6CVSSPCZRQWlJNEoY8SskaLdfq19RmYeuJZRq95OiyiSKd34eTU0KkuvzcCR07sY5USiOJVFZpu+tr3ij0/ssErVBLvm/z3RTpp6RleiMEh+432NwF7Yw8nYTnqPs5weUwX7MiEq1nWRbJ49Ph+KEHKOiAte56xd04D1Dhkad8UPcQMUKVValOalt6XidoVvdjZJNDui7pcOqz+oxtnwICAgICAgICtjcc9e0rsObouXhr6WAIAex38Ktou3VfPPu7XzfatE5BIHoaBMkPakwEN5dO/X1ferYc9VmT3jyOqqNu+HTU0mnPYc2spla30vb+iB4qk0uwiQcqTVj7sCQqwibS8nkN+7Uz2we+J5z2kSWFkkLmvN0Gjk7bS2KBDs0wkTx8I+83WToUcWAieQxRZV0jAUMcQSY+EnLGkitBdCGRnfoYcV3W+4rpRN2K6klJOLJ0tY/PUBAACmmkUqRyENGoE5D9vEFr3X85dw01RrXlUS5ZOn0g70gKKt9HdLC+c16/qlMn5X5qPuuoLOn66o3m8WxeuVl2R/STnCCkl6Z5WBJwLzGTJZ8bpM4XiC4VaSOI0fxCyThd/YsPDjYgtKxUV6R0CvJ6V0BAQEBAQEDA9oUhI0dh98tew/wFRyAuR+gzeC1GrPsaHj7/xEabtsURiJ5GoxN/TKVzhFrzTz5Xq2dCxSdWtaYPvrmi0tUenTF4N1JiI3/uSeeRJvWJm5eHSjARPDx7TnaP8HgdlVPGPmsm+9n9yAkUu1UMO8uQOkfrxaS+0ZNHMdAJb1qXRPBItvmiemIAvmgeZR2haiwSSfcrJxqUnwKwE7WYTEt2TiBhRfRIK6IngpSG5stL7qLmxnavpbog2xdhQ+blVm6XrBvHN5Dr1QfySeVxud7XegjZoV/XI3LawbQIWpRHQlEodij1NS+/mBoONfgmVz63V0fe0MFq7iUhFUEIc29Z3Irwy44BhyWjh1Xy/pfKnWPZQRoIARPRwwWRNtQxzuhLoOZSjAEBAQEBAQEB2ziOvPGvWFy8Ahve74lCtyqOOPgveO2ifbH6rWWNNm2LIRA9jUbds5FNh54jpFu980NuZnvIoTxbfHNFJb89OnkQBdecMee0ypUck2uI0jmmtmCbTaQYqygRJJkHKqpHZc3xx9/wvnB1Jv9sS01kji1VEkk8W1AW4eYjiZL/1ACyyRNfVI+yJUoFq8gaIahF5urQKBtN4EhXl9EHQwiR17AEVESP0hsnxFO6CSuiJ9aklLT8dAeMzmErSD/AE9FjX/L8jfIIeci6MflnFsvBy4kcqeVLd6O6TVf75dYotxacYjbkQgIpF+ema1LmCfJJzvlkWT5RWA8VPXgAX0SPGrk0kbaW7RtAMAyVZQ85Vq9hWRE96pg5oEmpVJeQ9nnaFgLueEgLQkRPQEBAQEBAQBfA+LO+ifjzz+C1Z3cHAOwx8i0U7j0YT/z42gZbtmUQiJ6tAZ30gyqfYNVD1pDqdW+5ekmZj9DgOmsFLPAVv2xpnGLx20DLE3kqLa9NQQACMSKHXFFUBbVMWJ/2OZoXx+QF8vchoT8cnSJlCVwSCERP7NTh8T48IipXpyDEDHntSbCcPXaUjdQBEiqyRr32AhjChObMiUnUjYkQkmaiHCWfasUvbg+gInqgCSAVuZNs6TUmZZQEyxq0dj4r28cORfRILdoei3kDlsJiOMjmu6h8gBF5ghuRl6iXchVZOrP8zCKJ8qAuCVttzInqoWXknLcffeXcbrWSlS+iRxGFMtlcYocJpxfWtwqXImPi2NFn2EXVjx7mjD7yrI3KASGPYHSHiJ6AgICAgICALoJ+Q4bgI1e9iEcWT0alrQk79N+IsT0vw/xzj0Ucb18rkQaip0EQQDYzsiW1Mp05c0/esq6Nt1E6KLIJDXe/Xr2uDfaRT5/PZzuax35JC5CIECPSVBDd/NbQuBVK0KijAvQLQzl9SOOC7FW+fJbaeox0E3ckOqwzmc/a/ilyxkT0ROS1K2H8FIC9BLuRn0hVUTcSkdogCVGTTJJlLJJlvtUrRCTCJ6mbaDYRPXbkjiARPZGgdB4hbTIGrZkze/pbL5PuHQo1N0oLap0eG6z91D9HJ88Fk3WjSFe0RRDoHD3kXETqZunM8pGSRD4/fQ8g1ee+HD1qQ3JeeiJ++Pj26vX5I2M1aJMCFdFDX/0TIo3osdsKJ2qHOKLHPyFEVT/rxMpgepkwndCZXVDnfTZqP3WSyAqrbgUEBAQEBAR0MXz02t9iyY63Ys07fRE1xTh03AK8c/neePvZpxtt2mZDU6MN6KrQP8xaBfVBtK+6rYR/x/fXympdt968ej63BduvZUdtXWoW7JeZNbWJyRl7/ik8ZVS63Tu2X8J7TlEclDuWzDIeEwTnrAu7vrFEWSGEsH7Ez9Pp6pVWXwrAitBI5rN230sAiCLEVX+va30O6ZCWC2ZheiD1TeR6muxH6atcvE/SMp4rReaMQVZVpGwStRtRqts3IPNuCM2lSPta+Aatz0Ah7EFE29S6YYXSzcroDh3aAgk/4ZOdp5PwEpp8qffmVzyFfaOY6jKnDPb4tGT7HqTkWEAkETY+nfpYOueBZGx5+1SyQiWD2qJflyOEDpWjOlDl8KED1745zDkqR3jqBwQEBAQEBAR0MYz+3L+jdf0n8PwlR2PkwS9jl31Wojj/ODzyp6/gqBnXAABa16/DgpnT0LThbVR67orxM25Aj169G2x5fQhET4Ogv9d3gLHpGMmTaFVRGR2RmUygsmvyuRn9zNKRV1bPFMSmFPLbZZEXtt2xdWT22CQclMTx22Dn5/HrTQIhzGtUeT5TfTw0w0dK+fwAACml1lkfjE4hhEOeJAcsGoWRbDKOIYTQ805DxpAAChIYocgxrYuQQJSAkGSfQyKGEL6gRSWF+aIJCGG/0eLIFk6ZifSRHYyQUJN3vy9eQkJvnKmxxWZeaErQcT7BlzCYcR6ZbnKdbGBnRvTk+Z1xc1CuT2SUOc2y+pnZLCGTiB56c0pGHKUdpwNrlB0RkqXSSR2vHl9Bep9oxkr3AWOLdD1N4RLyx+Mzt4UTSwEBAQEBAQEBXQw9evXG6OufxtxLvo7xQ3+Dbr2LmLDDLVh07lyslzvj0P0fx1EjS7p+6Y4/4OElR+Ho6+9roNX1Iby61SBYP6jac/aaaEdVR6ueBDGdtWTq6nrJagG+Og8v43MprjZvq9dP3s7odAkYn2xXJ02VnJxJX/KxarupmoVzHs6RTX+INN+PqmXHACgKKKtnANq7dkve89Lql2RSKvTs1KZk8nVagQFUr5NkVuq/EgJCRAnpoj/JeIH7GpT2RCVXTuur17HsJdOhX+tJXtcRiR5EMDNwukWQ6abHLtdNSQJHhHTKhAAQmeTP7UJK1ui+zbsprM5PFaulsvn5LNII7jk795DykVRSxIMw9evW6bXd45vPV3KtfbeAlYsH/rJMXoVeP2anEMkTwBhA7KFN1Tgm9kogJcXo4LA361/6vpmwlCiCh3SAzhTPOlSSncwBKGBdS+WkGkcBAQEBAQEBAV0YH/3eLXhnv//Fe/8cAFGQGDPuBRwxbi42rO6BR1+dghWjn8Cjr07BulW9cPjYOXh4+mcabXJNhIieBoF87U7Qjh9VNzWiR8tg3/vr0Zm0k95zvjI17ag3UIGWt8cuijydPHjAPW9+vqc+JGdsSsS0F7Dz5ij9pr6rT6VDFnrP9kBRJLYc01pNGG1bkVFfEKuktDUaYqy2zmROmOpUc1HNHrrQfSdjkoBZlVIL/JNTlUsn5UJs/6TZV/NiIzlNRC39ET2JG8k5EwNhT9gh/QEQZjZPyilZ05EbVOUTYjqZYnac7sReIz0PGa6TNGMEnmlHK9n1hafc0SlNsVPHdxN69k3/es4RwkefYmUWiWNuA0uW5DJk+izgSZatQ3OxGP9ESClyQTMuU0IeqTuSdYw98H2tbUGQGded3GOCyQsRPQEBAQEBAQEB2PvIo1EZ/xoWnf9RHHzoMxAC6NV/I+Tr6zBk5CgMGXkTSq1XYdWN+2Lcvo+idf26rfo1rhDR0yA4X62zfrn3oB1VvVqtOZz6wbkGVDxL8oMy+ZU5K8KHaOSkD92ijHK+2R4YZPWDmjb5AgGy9Cfn7VKThtgX0WOyzyqKRHqt9kwE9eLqic4o3bhe13Ka8VYtr877xFdCZIrIjsxqh061pLviFnRAgPM+ji1DRfREqW47TMM3CqJ0jmvIIaHHG2mqQjd0RI+AFBGkSMMfnHCQSG8mka5nFCn/SHNvcJBI7gcRCRNZ056bk062s24UHcnhnheiA3o9BItz69L+VeQBke/0BZetBScbVemNXIKnTNjF3De6upav3DIpj4BjN6ca00JddHVtHRPVPWTrMuSn+iQDSFitk39SQGeSpsZEUXrtlR3KuVSwl8zJco7UUUYqOfWO1YCAgICAgICA7RxNzc3Y2GsEhABK61vQvEMZR478LV789ihUymW09NgBLxVPQEvvIhbMnNZoc3MRiJ4GQX3H9m410I6q3sZqImQtrFMDfAnzZA4o0yiGZAM5BqmXZbtE/atRZ8nLcNGqmafHLTd77rLl7jlqnb2Eud9fs8WI01gSmS6wThc89+mkEgVkQnowaoacJf7b3koZ29fNo1Nk6YRI9tO5p1mYye5vs4R76ptUUT1mnAjp71t6Ffk63gIwq02n1aVEkppEsQgyBmSMJKKH+pj2gZRWPeFkMlbXk4wiok+RMkZ/oiNWS2DXC2tgG23WsyFmG+sqqTqC35zcCR8JxAkKdR3pFqtPWDoktY/foJwUIqocM3wPMs9QkICjx1pxi24e/ZZNNW5OPTT0gbrY0KSVpP/MKbakuyR2UDmpBHUPUsXK2RhmSXXaL5o1Ew5BZT0F1I0i1XiH+aQCaX8EBAQEBAQEBASgacPbAIDXd7sdby3ZBUIAPVqKaGpuBgDs87kpVr2tFYHoaRDUd/WOoL1BA1ynmmq0R4Z3vuiJ5vHN2bicPLl5G2+Tp4PSJr422Xoi/Us7pW4AHq1jNkkk0sXWjb+C1DDLnkc6hocudq7yx/h0wtYhTUQP99NuYfIOAcLKkSOEOUd18sge45fUs2spYed1sWygcUoi1RexsZLXp4JcXLuvaWoRFVGRpP/RO9ARU9rPNDdQZl4eMgGHNAQC6VzjY2KAEKZ3BdKonqyBW+tmk0q34RScC0oje2gZPHrrASE3TE4bdZBuET02sp1grDrU6H1O5PgeClmf1D9lOxzzDK9BOBrrwQcmK93XwzNdQc2JCNPXhnZGKp7xbRYLZckyIpwoKqRRPAWQpd099nLoIUzInIg65GnsI9kCAgICAgICAro4Kj13BQC8/9Qc7P69V/HIoknodcYD+vyr9/zMqre1IhA9DUK9czEf+DypvTo7opv/8K3LWDRPls565eZtvE2ezIw5oVXHH+VjomskO+NLcgxNSfA6lGiSpAaNdInTOBoezRMTasXVqWiQZJVlX6JnY53ak2lISBKBEJtoAmnO1aMzIYoMteXrW6UR2sfUczZW/NFSds9B++heg0SMmV3LWEKkUTpKt/Ez1pFMOvKMyLKJKkMBUQLA6k8pEUsrriONroG7+TqJd5hQvcvm5VwWH7RxKizrZrEvCtNpNisyRgLeqB4i2yFrasAao76bk9qY5SvfCBcoyfWxOC+HSPHoIcf0UWZF9KgKgu/zsUOOHfKNdViqQ0ecmVCi1HdPB1uHtAO4w5zUybtQm/J/o4CAgICAgICA7QvjZ9yA0voW7NdyD0qtG/HR6/6AnffeGwBQat2I/brdi9K6bhg/44bGGloDgegBcPPNN2OPPfZA9+7dMX78eDz55JNbXKf+2t3B79ibRNYIu6xefXz6kMwrzC/GPlm+Mt/EqD1bPfKzIl1oHV+AhLD2zD9DqZhZI6UqqBZVn+f/4XKBKI2jodaY6BpDr0TaV0unlJZ03zze1mlHuShdqjxLJ7hOKTQHAWlP4jUZpPUmkUtqyXIrwkC4ET2SSdLEArkGakl0O9jC5MkRkYBaNU1H8yg/nZw9lKZTmx0Npuf1Vt+q/qwjoocPPu+NYkdnqb51ZPBBq1Zh6siNwjgLSo44UT00skfXsTokW6e0+RRhOZkhJ+sGBWujXCfXR5FV+lN62rGbk5JBpjv5WCEVKOkFe1+qPZHWtx72gt0wgm0RUSdMPUUE+dhCWp8SU9xv7zXiBQEBAQEBAQEBXRs9evXGwiVHof/QVVh9476Yd9k38Pazz2DeZd/A6hv3Rf+hq7Bw6ZFbdSJmIBA9uOuuuzB9+nRceumlWLx4MUaPHo1JkyZh5cqVW1SvyDyoD/USNLXU+L77+/Vl/+N5eWrppOV5P9hnbSBt/bbm+5Uv357u02gbnvnG0A92mWTWunuqTowqAOkkYvHn6HFeqhI0GsidyymdJn4o3VO5ckAiX3J0gusUKiWzUajmvjHpOSNZRdcAdsiEL89RSucoTkF3LukDkWy2qWnkTpzkyol5biAS3WOieWzYRBVRzXw0V9D0qoQ0OVVqDVwfhIBDlSojVHtfrh4V+dFefUp++im5s040D9nAOIcaN6ciTrRK381J/aQbj+qh8gmfQfkKi++qRbIpGaTckESUXCEVpbQ4GD1OLZ8io8sidrh/qTyda6lq69R+e5ga3f9pPUhY7KcgRkpaD8ZhyZMsBQQEBAQEBAQEHH39fXh80XHo3X89jhx+GwY/fwSOHH4beu+4AY8vOg5HX39fo02siS6/vPr111+PM888E2eccQYAYNasWfjTn/6E2267DRdccMEW0+t8tRa+ws0gl51TE1xZhz7fvM3XhL42weGu/CzZsR/6F3WPTQJ0SWx/2/acU12RnIsy6vKUvULbU0uDIH/p3E8CKECgkq4wxdsndfj039iRJDM2kz9jnwTS/D+mFZErIggpiC2C2cV9E5ZsARNVY+pJCMYbR0xnVu8L2HNQQJg3hWJiF48+8LAG6iiWiWQh3Am3utZ6rgt+HT00kFS9avrHGQP2gmjufh4Bk0Zn6duS1hPsk9iEQnrC17V5Ny0r51fT0skJixhJZBQv9+ki11Vxc/6HCLKGh6uDHYvUJscE5UqtBw1vKBR5IzwnaP3YJGJ2SCjpPtN9/UUZIwkkF7RKGqQn1FiXsd1U6yFsEw9jkrRBWqAyqUdRQuYFBAQEBAQEBARYOPr6+9C6fh0emTkNTRveRqXnrhg/4wYcvZVH8ih0aaKnVCph0aJFmDFjhi6LoggTJ07E/Pnzt6huOtHsCDrSVCJ/0sNP0XlKLtGTo5NOhT3Tpg7JreV7e8+r41j/FV67PDEXZP6uzrlElr0Ol32uChNdYv81P/8LIgewqSjahpI5SaQJ9YPYxSJapEen8k3p0DoVQSTMVTT2xea8JRdpfpwIbt8b6ZTk0IQNIWf44KXEAeVQpN7ihBySdBSqNjY5ZNskrLHJqidWS04HCXsFKt8gy71RGGnECYis9jqXi0efzw5LZ41Cn860LJbJ21xeeAkkWEEmXrX8InrkWcfSfAhywbSKvAcMHyzsXDJGyEkPcaJ5Ri6XP+gssgbJYKJROJaNVWYXcVIJp9FsoCy7tO5L60ELEJJItZe2joCAgICAgICAAAs9evXGR6/4WaPN6BC6NNHz/vvvo1qtYtCgQVb5oEGDsGTJEqd+sVhEsVjUx2vWrAEArFufF1/iR5z15bqO79zl3HiWbDHmhZbalXlR2deOwWdVLM3v01xDnrw4BspsFlUv6RRLgQoA30w2T2elChRZLU7PZM3lKzFQzViCSNEmiqiJSZ1yHKNERoNvPkrrU/KmWAVoRIuZlgrnk8ouVqqoEubCXDcqx943OqVFntj+URn2+VK1kiYv9kGk143bkPwpVygR5c7NJXlNRteRQLUMxNIefTGzi86zdbkEKpUWx0pr3i3dawkAcTGGNwlWHnGR6owrhcT+eufdMmkn2tQyTdnyvfpjABVhcRBZeixUgagkUJWeN3+5Hk76VIH05szXwY8rMaKSv462PePmjCqxuww816P6gD7EKmVUKi7Bo0ESJgvma1SpIopZA63LQxrpa1KGjMvpvofRgiQ2pgMgJWykLANx1dimzuvGnsEugIqspGYFwidg24Aaq2vXrm2wJQEBAQEBAe2D+n9XZ3zv6tJET3sxc+ZMXHbZZU753mOXNcCagICAgICAzYN169ahb9++jTYjIKAm1q1bBwAYOnRogy0JCAgICAjoGDrje1eXJnp22mknFAoFrFixwipfsWIFBg8e7NSfMWMGpk+fro/jOMaqVaswYMCAdBWjbR9r167F0KFDsWzZMvTp06fR5jQcoT8MQl/YCP1hEPrCxrbUH1JKrFu3DkOGDGm0KQEBdWHIkCFYtmwZevfu7Xz32pbuvc2F4HPX8Bnomn4Hn7uGz0DX8bszv3d1aaKnpaUFY8eOxZw5c3DCCScASMibOXPm4Oyzz3bqd+vWDd26dbPK+vXr1wmWdj769OmzXd9k7UXoD4PQFzZCfxiEvrCxrfRHiOQJ2JYQRRF222233Drbyr23ORF87jroin4Hn7sOuoLfnfW9q0sTPQAwffp0nH766TjkkENw6KGH4oYbbsCGDRv0KlwBAQEBAQEBAQEBAQEBAQEB2wq6PNFz8skn47333sMll1yC5cuXY8yYMXjwwQedBM0BAQEBAQEBAQEBAQEBAQEBWzu6PNEDAGeffbb3Va2uiG7duuHSSy91XlHrqgj9YRD6wkboD4PQFzZCfwQENAZd8d4LPncddEW/g89dB13V7y0JIcOaqgEBAQEBAQEBAQEBAQEBAQHbBaJGGxAQEBAQEBAQEBAQEBAQEBAQsHkQiJ6AgICAgICAgICAgICAgICA7QSB6AkICAgICAgICAgICAgICAjYThCInoCAgICAgICAgICAgICAgIDtBIHo6aKYOXMmxo0bh969e2PgwIE44YQTsHTpUqtOW1sbpk6digEDBqBXr1446aSTsGLFigZZ3Hm46qqrIITAtGnTdFlX6ou3334bX/jCFzBgwAD06NEDo0aNwlNPPaXPSylxySWXYJdddkGPHj0wceJEvPLKKw20eMuhWq3i4osvxp577okePXpg7733xve//33QHPbbc388/PDD+PSnP40hQ4ZACIF7773XOl+P76tWrcKpp56KPn36oF+/fpgyZQrWr1/fiV5sHuT1Rblcxne+8x2MGjUKPXv2xJAhQ3DaaafhnXfesWRsL30RELA14uabb8Yee+yB7t27Y/z48XjyyScbbdJmQ/jO1rW+m3W172Fd5btWV/xOFb47NRaB6OmimDdvHqZOnYonnngCs2fPRrlcxvHHH48NGzboOueeey7++Mc/4u6778a8efPwzjvv4MQTT2yg1VseCxcuxE9+8hMceOCBVnlX6YsPP/wQRxxxBJqbm/HAAw/gxRdfxHXXXYcdd9xR17nmmmtw0003YdasWViwYAF69uyJSZMmoa2trYGWbxlcffXV+PGPf4wf/ehHeOmll3D11VfjmmuuwQ9/+ENdZ3vujw0bNmD06NG4+eabvefr8f3UU0/FCy+8gNmzZ+P+++/Hww8/jLPOOquzXNhsyOuLjRs3YvHixbj44ouxePFi/OEPf8DSpUvxmc98xqq3vfRFQMDWhrvuugvTp0/HpZdeisWLF2P06NGYNGkSVq5c2WjTNgu6+ne2rvTdrCt+D+sq37W64neq8N2pwZABAVLKlStXSgBy3rx5UkopV69eLZubm+Xdd9+t67z00ksSgJw/f36jzNyiWLdunRw+fLicPXu2POaYY+Q555wjpexaffGd73xHHnnkkZnn4ziWgwcPlv/93/+ty1avXi27desmf/Ob33SGiZ2KyZMnyy9/+ctW2YknnihPPfVUKWXX6g8A8p577tHH9fj+4osvSgBy4cKFus4DDzwghRDy7bff7jTbNzd4X/jw5JNPSgDyjTfekFJuv30RELA14NBDD5VTp07Vx9VqVQ4ZMkTOnDmzgVZtOXSl72xd7btZV/we1hW/a3XF71Thu1PnI0T0BAAA1qxZAwDo378/AGDRokUol8uYOHGirjNixAgMGzYM8+fPb4iNWxpTp07F5MmTLZ+BrtUX9913Hw455BD827/9GwYOHIiDDjoIt956qz7/z3/+E8uXL7f6om/fvhg/fvx21xcAcPjhh2POnDl4+eWXAQD/+Mc/8Oijj+ITn/gEgK7XHxT1+D5//nz069cPhxxyiK4zceJERFGEBQsWdLrNnYk1a9ZACIF+/foB6Np9ERCwJVEqlbBo0SLrWRRFESZOnLjdPoe70ne2rvbdrCt+DwvftcJ3KoXw3WnzoqnRBgQ0HnEcY9q0aTjiiCNwwAEHAACWL1+OlpYWfaMpDBo0CMuXL2+AlVsWd955JxYvXoyFCxc657pSX7z++uv48Y9/jOnTp+PCCy/EwoUL8c1vfhMtLS04/fTTtb+DBg2y2m2PfQEAF1xwAdauXYsRI0agUCigWq3iiiuuwKmnngoAXa4/KOrxffny5Rg4cKB1vqmpCf3799+u+6etrQ3f+c538B//8R/o06cPgK7bFwEBWxrvv/8+qtWq91m0ZMmSBlm15dCVvrN1xe9mXfF7WPiuFb5TAeG705ZAIHoCMHXqVDz//PN49NFHG21KQ7Bs2TKcc845mD17Nrp3795ocxqKOI5xyCGH4MorrwQAHHTQQXj++ecxa9as/9/efYdFcb1vA7+X3osgTaq9gIhiQWOLxt6N3dhNVKwYY69RscQUY4vmq8ZEY+wmRqNGRdTYBcGGDUURxEKRDrvz/uHL/FxB2i677O79ua693D07Z+aZcWEenj1zBkOHDlVzdKq3a9cubN++HTt27ECdOnUQHh6OyZMnw8XFRSePBxUtJycHffv2hSAIWL9+vbrDISItoys5m67mZrqYhzHXIuZOZYOXbum48ePH49ChQzh16hRcXV3FdicnJ2RnZyMpKUlu+efPn8PJyUnFUZatq1evIiEhAfXr14eBgQEMDAxw+vRprF69GgYGBnB0dNSZY+Hs7IzatWvLtdWqVQsxMTEAIO7v+3e10MZjAQDTpk3DjBkz0L9/f/j4+OCzzz7DlClTEBwcDED3jse7irPvTk5O+SZDzc3NxevXr7Xy+OQlKo8fP8bx48fFb6QA3TsWRKpib28PfX19nfg9rEs5m67mZrqYhzHX0u2cirlT2WGhR0cJgoDx48dj//79OHnyJLy8vOTeb9CgAQwNDXHixAmxLSoqCjExMQgICFB1uGWqTZs2iIyMRHh4uPjw9/fHoEGDxOe6ciyaNWuW75atd+/ehYeHBwDAy8sLTk5OcsciJSUFFy9e1LpjAby9I4CenvyvSX19fchkMgC6dzzeVZx9DwgIQFJSEq5evSouc/LkSchkMjRu3FjlMZelvETl3r17+Pfff2FnZyf3vi4dCyJVMjIyQoMGDeR+F8lkMpw4cUJrfg/rYs6mq7mZLuZhzLV0N6di7lTG1DsXNKnL2LFjBWtrayEkJESIi4sTH+np6eIyY8aMEdzd3YWTJ08KV65cEQICAoSAgAA1Rq06797ZQRB051hcunRJMDAwEJYsWSLcu3dP2L59u2BmZib89ttv4jLLli0TbGxshIMHDwoRERFC9+7dBS8vLyEjI0ONkZeNoUOHCpUqVRIOHTokREdHC/v27RPs7e2Fr776SlxGm4/HmzdvhLCwMCEsLEwAIHz77bdCWFiYeDeE4ux7hw4dBD8/P+HixYvC2bNnhWrVqgkDBgxQ1y6VWmHHIjs7W+jWrZvg6uoqhIeHy/1OzcrKEtehLceCqLzZuXOnYGxsLGzdulW4deuW8Pnnnws2NjZCfHy8ukNTCuZsb+lCbqaLeZiu5Fq6mFMxd1IvFnp0FIACH1u2bBGXycjIEMaNGyfY2toKZmZmQs+ePYW4uDj1Ba1C7ycTunQs/vrrL8Hb21swNjYWatasKWzcuFHufZlMJsydO1dwdHQUjI2NhTZt2ghRUVFqirZspaSkCJMmTRLc3d0FExMToXLlysLs2bPlTkDafDxOnTpV4O+JoUOHCoJQvH1/9eqVMGDAAMHCwkKwsrIShg8fLrx580YNe6OYwo5FdHT0B3+nnjp1SlyHthwLovLoxx9/FNzd3QUjIyOhUaNGwoULF9QdktIwZ3tLV3IzXcvDdCXX0sWcirmTekkEQRCUP06IiIiIiIiIiIhUjXP0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqISKkEQQAALFiwQO41ERERESkfcy8iep9E4G8CIlKidevWwcDAAPfu3YO+vj46duyIli1bqjssIiIiIq3E3IuI3scRPUSkVOPGjUNycjJWr16Nrl27FivRaNWqFSQSCSQSCcLDw8s+yPcMGzZM3P6BAwdUvn0iIiKi0mLuRUTvY6GHiJRqw4YNsLa2xsSJE/HXX3/hzJkzxeo3evRoxMXFwdvbu4wjzO+HH35AXFycyrdLREREpCjmXkT0PgN1B0BE2uWLL76ARCLBggULsGDBgmJfJ25mZgYnJ6cyjq5g1tbWsLa2Vsu2iYiIiBTB3IuI3scRPURUIkuXLhWH2r77+P777wEAEokEwP9NCJj3uqRatWqFCRMmYPLkybC1tYWjoyM2bdqEtLQ0DB8+HJaWlqhatSqOHDmilH5ERERE5RFzLyIqKRZ6iKhEJkyYgLi4OPExevRoeHh44NNPP1X6tn755RfY29vj0qVLmDBhAsaOHYs+ffqgadOmuHbtGtq1a4fPPvsM6enpSulHREREVN4w9yKikuJdt4io1ObOnYtff/0VISEh8PT0LPV6WrVqhXr16onfTOW1SaVS8TpzqVQKa2tr9OrVC9u2bQMAxMfHw9nZGefPn0eTJk0U6ge8/QZs//796NGjR6n3hYiIiKisMPciouLgiB4iKpV58+YpJdEoTN26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIqLyirkXERUXCz1EVGLz58/Htm3byjTRAABDQ0O51xKJRK4t7xp0mUymlH5ERERE5RFzLyIqCRZ6iKhE5s+fj19++aXMEw0iIiIiYu5FRCXH26sTUbEtXrwY69evx59//gkTExPEx8cDAGxtbWFsbKzm6IiIiIi0C3MvIioNFnqIqFgEQcDKlSuRkpKCgIAAufcuXbqEhg0bqikyIiIiIu3D3IuISouFHiIqFolEguTkZJVtLyQkJF/bo0eP8rW9f+PA0vYjIiIiKk+YexFRaXGOHiIqF9atWwcLCwtERkaqfNtjxoyBhYWFyrdLREREpC7MvYi0l0RgaZWI1Cw2NhYZGRkAAHd3dxgZGal0+wkJCUhJSQEAODs7w9zcXKXbJyIiIlIl5l5E2o2FHiIiIiIiIiIiLcFLt4iIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZZgoYeIiIiIiIiISEuw0ENEREREREREpCVY6CEiIiIiIiIi0hIs9BARERERERERaQkWeoiIiIiIiIiItAQLPUREREREREREWoKFHiIiIiIiIiIiLcFCDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BAs9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi1Rrgs9r169goODAx49elTksjNmzMCECRPKPigiIiIiLVVU7hUSEgKJRIKkpCQAwD///IN69epBJpOpLkgiIiIqVLku9CxZsgTdu3eHp6dnkct++eWX+OWXX/Dw4cOyD4yIiIhIC5Uk9wKADh06wNDQENu3by/bwIiIiKjYDNQdwIekp6fjf//7H44ePVqs5e3t7dG+fXusX78eK1euLOPoiKg8kEqlyMnJUXcYRBrJ0NAQ+vr66g6DypGS5l55hg0bhtWrV+Ozzz4ro8iIqDxg3kWkGCMjI+jpqWasTbkt9Bw+fBjGxsZo0qSJ2Hbz5k1Mnz4doaGhEAQB9erVw9atW1GlShUAQNeuXTF79mwWeoi0nCAIiI+PFy8dIKLSsbGxgZOTEyQSibpDoXKgoNzr8OHDmDx5Mp48eYImTZpg6NCh+fp17doV48ePx4MHD8ScjIi0B/MuIuXQ09ODl5cXjIyMynxb5bbQc+bMGTRo0EB8HRsbixYtWqBVq1Y4efIkrKyscO7cOeTm5orLNGrUCE+fPsWjR4+KPeSYiDRPXrLh4OAAMzMz/pFKVEKCICA9PR0JCQkAAGdnZzVHROXB+7nXkydP0KtXLwQGBuLzzz/HlStXMHXq1Hz93N3d4ejoiDNnzrDQQ6SFmHcRKU4mk+HZs2eIi4uDu7t7mf8cldtCz+PHj+Hi4iK+Xrt2LaytrbFz504YGhoCAKpXry7XJ2/5x48fs9BDpKWkUqmYbNjZ2ak7HCKNZWpqCgBISEiAg4MDL+OifLnX+vXrUaVKFaxatQoAUKNGDURGRmL58uX5+rq4uODx48cqi5WIVIN5F5HyVKxYEc+ePUNubq5Y0ygr5XYy5oyMDJiYmIivw8PD0bx580IPSF7Smp6eXubxEZF65F0bbmZmpuZIiDRf3s8R51wgIH/udfv2bTRu3FhumYCAgAL7mpqaMv8i0kLMu4iUJ++SLalUWubbKreFHnt7eyQmJoqv84o4hXn9+jWAt5UyItJuHDZMpDj+HNG73s+9SuL169fMv4i0GM8XRIpT5c9RuS30+Pn54datW+LrunXr4syZM4V+63jjxg0YGhqiTp06qgiRiIiISGu8n3vVqlULly5dklvmwoUL+fplZmbiwYMH8PPzK/MYiYiIqGjlttDTvn173Lx5U/xmafz48UhJSUH//v1x5coV3Lt3D7/++iuioqLEPmfOnEHz5s2LNfqHiEjVQkND0bVrV7i4uEAikeDAgQNq2cawYcMgkUggkUhgaGgIR0dHfPLJJ9i8eTNkMpnSY9ImxT12np6e4nJ5D1dX13zvv/9H8+TJk9GqVSu5tpSUFMyePRs1a9aEiYkJnJyc0LZtW+zbtw+CIIjL3b9/H8OHD4erqyuMjY3h5eWFAQMG4MqVK2VzMEjrvJ97jRkzBvfu3cO0adMQFRWFHTt2YOvWrfn6XbhwAcbGxh+8rIuISF2Ye2k25l2lV24LPT4+Pqhfvz527doFALCzs8PJkyeRmpqKli1bokGDBti0aZPcnD07d+7E6NGj1RUyEVGh0tLS4Ovri7Vr15a4b6tWrQr8A6u02+jQoQPi4uLw6NEjHDlyBK1bt8akSZPQpUsXubsZUn7FPXaLFi1CXFyc+AgLC5Nbj4mJCaZPn17otpKSktC0aVNs27YNM2fOxLVr1xAaGop+/frhq6++QnJyMgDgypUraNCgAe7evYuffvoJt27dwv79+1GzZs0C75JEVJD3cy93d3fs3bsXBw4cgK+vLzZs2IClS5fm6/f7779j0KBBnMODiMod5l6aj3lXKQnl2KFDh4RatWoJUqm0yGUPHz4s1KpVS8jJyVFBZESkLhkZGcKtW7eEjIwMdYeiEADC/v37i718y5YthS1btihlG0OHDhW6d++er/3EiRMCAGHTpk0l2o4uKe6x8/DwEL777rsPrsfDw0OYOHGiYGRkJPz9999i+6RJk4SWLVuKr8eOHSuYm5sLsbGx+dbx5s0bIScnR5DJZEKdOnWEBg0aFHi+TExM/GAc2vLzRMpTktxLEAThxYsXQoUKFYSHDx+WcWREpA7adJ5g7qV5mHeVXrm9vToAdO7cGffu3UNsbCzc3NwKXTYtLQ1btmyBgUG53iUiUjJBENR2pxczMzOtmpzw448/hq+vL/bt24dRo0apJYa0tDQA8sc2OzsbOTk5MDAwgLGxcb5lTU1Noaf3doBqTk4OsrOzoa+vL3f3oIKWVabSHDsvLy+MGTMGM2fORIcOHfLFJZPJsHPnTgwaNEjultd5LCwsAABhYWG4efMmduzYUeC+2djYlHyHSGeVJPcCgEePHmHdunXw8vJSQXREVB4w91Iededeqsy7cnJylHZLceZdRSu3l27lmTx5crESjU8//TTfLUCJSPulp6fDwsJCLQ9tvJVwzZo18ejRI7VtP+/Yvnz5UmxbuXIlLCwsMH78eLllHRwcYGFhgZiYGLFt7dq1sLCwwMiRI+WW9fT0hIWFBW7fvl1msb9/7KZPny73eVm9enW+PnPmzEF0dDS2b9+e772XL18iMTERNWvWLHS79+7dE7dPpAzFzb0AwN/fH/369SvjiIioPGHupVzqzL1UmXcV5zK4kmDeVbhyX+ghItJFS5culTtZnTlzBmPGjJFre/dEqyyCIGjVN2Wq9P6xmzZtGsLDw8XHkCFD8vWpWLEivvzyS8ybNw/Z2dn51lfc7RIREZFimHtpFuZdheN1TkSk0czMzJCamqq2bZeVMWPGoG/fvuLrQYMGoXfv3ujVq5fYVtCwUkXdvn1brZdg5P1fvntsp02bhsmTJ+e7NDchIQEA5O60GBgYiNGjR0NfX19u2bxvfMryrozvHzt7e3tUrVq1yH5BQUFYt24d1q1bJ9desWJF2NjY4M6dO4X2r169OgDgzp07vL01ERGVOeZeyqXO3EuVedewYcOUGTrzriKw0ENEGk0ikcDc3FzdYShdhQoVUKFCBfG1qakpHBwcinUCK62TJ08iMjISU6ZMKbNtFKWg/0sjIyMYGRkVa1lDQ8MCr/8u68+IIsfOwsICc+fOxYIFC9CtWzexXU9PD/3798evv/6K+fPn50suU1NTYWJignr16qF27dpYtWoV+vXrl+968aSkpHJzvTgREWk+5l7Ko+7cS5V5l7Lm5wGYdxUHL90iIlKR1NRUcTgpAERHRyM8PFypw4CLu42srCzEx8cjNjYW165dw9KlS9G9e3d06dKlwKGu9H/K4th9/vnnsLa2xo4dO+TalyxZAjc3NzRu3Bjbtm3DrVu3cO/ePWzevBl+fn5ITU2FRCLBli1bcPfuXTRv3hyHDx/Gw4cPERERgSVLlqB79+7K2G0iIiKNw9xL8zHvKh2O6CEiUpErV66gdevW4uugoCAAwNChQ5U2QV1xt/HPP//A2dkZBgYGsLW1ha+vL1avXo2hQ4eWyV2ptElZHDtDQ0N8/fXXGDhwoFx7hQoVcOHCBSxbtgyLFy/G48ePYWtrCx8fH6xcuRLW1tYAgEaNGuHKlStYsmQJRo8ejZcvX8LZ2RlNmzbF999/r+guExERaSTmXpqPeVfpSARNmU2IiAhAZmYmoqOj4eXlJXcbRyIqOf48ERFRYXieIFIeVf48sXRIRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4i0ki8YSCR4vhzRERExcHzBZHiVPlzxEIPEWkUQ0NDAEB6erqaIyHSfHk/R3k/V0RERO9i3kWkPNnZ2QAAfX39Mt+WQZlvgYhIifT19WFjY4OEhAQAgJmZGSQSiZqjItIsgiAgPT0dCQkJsLGxUUnCQUREmod5F5FyyGQyvHjxAmZmZjAwKPsyDAs9RKRxnJycAEBMOoiodGxsbMSfJyIiooIw7yJSDj09Pbi7u6ukWCoReMElEWkoqVSKnJwcdYdBpJEMDQ05koeIiIqNeReRYoyMjKCnp5rZc1joISIiIiIiIiLSEpyMWUlCQ0PRtWtXuLi4QCKR4MCBA2W+zdjYWAwePBh2dnYwNTWFj48Prly5UubbJSIiIlI35l5EREQFY6FHSdLS0uDr64u1a9eqZHuJiYlo1qwZDA0NceTIEdy6dQurVq2Cra2tSrZPREREpE7MvYiIiArGS7fKgEQiwf79+9GjRw+xLSsrC7Nnz8bvv/+OpKQkeHt7Y/ny5WjVqlWptjFjxgycO3cOZ86cUU7QRERERBqKuRcREdH/4YgeFRk/fjzOnz+PnTt3IiIiAn369EGHDh1w7969Uq3vzz//hL+/P/r06QMHBwf4+flh06ZNSo6aiIiISDMx9yIiIl3FET1l4P1vlWJiYlC5cmXExMTAxcVFXK5t27Zo1KgRli5dWuJtmJiYAACCgoLQp08fXL58GZMmTcKGDRswdOhQpewHERERkSZg7kVERPR/DNQdgC6IjIyEVCpF9erV5dqzsrJgZ2cHALhz5w5q1apV6HqmT5+OZcuWAQBkMhn8/f3FRMXPzw83btxgskFEREQ6j7kXERHpMhZ6VCA1NRX6+vq4evUq9PX15d6zsLAAAFSuXBm3b98udD15iQkAODs7o3bt2nLv16pVC3v37lVS1ERERESaibkXERHpMhZ6VMDPzw9SqRQJCQlo3rx5gcsYGRmhZs2axV5ns2bNEBUVJdd29+5deHh4KBQrERERkaZj7kVERLqMhR4lSU1Nxf3798XX0dHRCA8PR4UKFVC9enUMGjQIQ4YMwapVq+Dn54cXL17gxIkTqFu3Ljp37lzi7U2ZMgVNmzbF0qVL0bdvX1y6dAkbN27Exo0blblbREREROUScy8iIqKCcTJmJQkJCUHr1q3ztQ8dOhRbt25FTk4OFi9ejG3btiE2Nhb29vZo0qQJFi5cCB8fn1Jt89ChQ5g5cybu3bsHLy8vBAUFYfTo0YruChEREVG5x9yLiIioYCz0EBERERERERFpCT11B0BERERERERERMrBQg8RERERERERkZbgZMwKkMlkePbsGSwtLSGRSNQdDhERUYkIgoA3b97AxcUFenr87ofKP+ZeRESkqVSZd7HQo4Bnz57Bzc1N3WEQEREp5MmTJ3B1dVV3GERFYu5FRESaThV5l9YUeoKDg7Fv3z7cuXMHpqamaNq0KZYvX44aNWp8sM/WrVsxfPhwuTZjY2NkZmYWa5uWlpYA3v5HWVlZlT54IiIiNUhJSYGbm5t4PiMq75h7ERGRplJl3qU1hZ7Tp08jMDAQDRs2RG5uLmbNmoV27drh1q1bMDc3/2A/KysrREVFia9LMgw4b1krKysmG0REpLF4CQxpCuZeRESk6VSRd2lNoeeff/6Re71161Y4ODjg6tWraNGixQf7SSQSODk5lXV4RERERERERERlTmtnXkxOTgYAVKhQodDlUlNT4eHhATc3N3Tv3h03b9784LJZWVlISUmRexARERERERERlRdaWeiRyWSYPHkymjVrBm9v7w8uV6NGDWzevBkHDx7Eb7/9BplMhqZNm+Lp06cFLh8cHAxra2vxwckAiYiIiIiIiKg8kQiCIKg7CGUbO3Ysjhw5grNnz5ZoNuucnBzUqlULAwYMwNdff53v/aysLGRlZYmv8yZTSk5OVtp14ps3b4aDgwM+/vhjmJmZKWWdREREBUlJSYG1tbVSz2NEZUnZn9nY2FicPn0a/fv3L/Nb3RIRkW5TZd6lNXP05Bk/fjwOHTqE0NDQEt+yzNDQEH5+frh//36B7xsbG8PY2FgZYRYoOzsbo0aNgiAIeP78uVjo2b59O/bu3YuePXvis88+E5fP+4BwEk0iIiKikhs8eDBCQkIQExODGTNmqDscIiIipdCary4EQcD48eOxf/9+nDx5El5eXiVeh1QqRWRkJJydncsgwqKlpqaiR48eaNy4MSpWrCi2X7hwAfv378etW7fEtuzsbNja2sLGxgavXr0S269cuYK9e/fiwYMHKo2diIiISJNkZ2cjJCQEADBr1ixo4SB3IiLSUVpT6AkMDMRvv/2GHTt2wNLSEvHx8YiPj0dGRoa4zJAhQzBz5kzx9aJFi3Ds2DE8fPgQ165dw+DBg/H48WOMGjVKHbuAChUqYN++fbhw4YLcKJ0hQ4ZgzZo16Natm9gWGxsLQRCQnZ0tN+H0tm3b8Omnn+Lnn38W23JyctC5c2eMGzdO7ngQERER6apLly6JzwVBwK+//qrGaIiIiJRHay7dWr9+PQCgVatWcu1btmzBsGHDAAAxMTFy118nJiZi9OjRiI+Ph62tLRo0aID//vsPtWvXVlXYxdKwYUM0bNhQrs3LywupqamIj4+XKwq5u7ujSZMmcpNQP3nyBIcPH4aJiQnWrl0rti9fvhynTp1CYGAgunbtWvY7QkRERFROnDp1Su71mDFjYG9vj06dOqkpIiIiIuXQysmYVUVTJrF8/fo19u3bh5SUFAQFBYntLVu2RGhoKDZu3IjRo0cDAF69eoU1a9agZcuW+YpmRESkXTTlPEaUR5mf2aSkJISGhsLa2horVqzA4cOHAQBffPEFZs+ezburEhGRUqky72KhRwGaniBHRkYiJCQE3bt3h7u7OwDgwIED6NmzJ2rXro2bN2+Ky964cQMeHh6wtLRUV7hERKRkmn4eI91TVp/ZrKwsTJs2DT/++CMAQE9PD61bt0bv3r3RokUL1KpVi3flIiIihfCuW6QSPj4+8PHxkWtzcHDAgAEDULVqVbn2Ll264OnTpzhz5gwCAgIAvL2enXf8IiIiIk1nbGyM1atXo3fv3li4cCFOnTqFEydO4MSJEwAACwsLVK9eHdWqVYOnpyccHBxQsWJFODg4oEKFCjA3N4eZmZn4r6mpKQtDVC5kZ2fj5s2buHbtGq5du4br16/j5cuXePPmDYyNjeHo6Ahvb2/Uq1cPzZs3h7e3Nz+7RFqAI3oUoCvfhCYlJcHPzw9Pnz5FYmIiLCwsAACrV6/G5s2bMW7cOHz++edqjpKIiEpKV85jpD1U9Zl9+PAhdu3ahWPHjuHSpUtIS0sr8TqMjY2hp6cHiUSS71+JRAKZTAZBEMR/330ukUhQsWJFODs7w9nZGS4uLqhUqZL4cHFxgZWVFUxMTMSHgYEBv4DTccnJybh+/br4CAsLQ2RkJHJycoq9DltbW3z00Udo0aIFWrRoAT8/PxgaGha4rCAIyMjIQEZGBmQyGYC3n3tLS0t+FokKwEu3NISuJcjPnz+Ho6Oj+LpXr17Yv38/li1bhunTpwMAMjMzERQUhObNm6Nv377Q19dXV7hERFQEXTuPkeZTx2c2NzcX9+7dw71793D37l08ffoUL168QEJCAhISEvD69WtkZGQgPT1drXc31dPTE4s+xsbGMDY2hkwmQ+3atVGzZk1kZmYiLi5OXFZPTw/6+vowMDCAoaEhDAwM8j3PK0ABEJ+/+6dDXvHqQwWtov7NKwbkPTcwMICNjQ0yMzORlJSEnJycfDG8G0tRzxV9v7iPd5c3MDCAlZWV+LC2toaDgwMcHR3h6OgIBwcHGBsbl/j/NzMzU7yrcN7jyZMnePTokfh49uxZgX1tbGxQv3591K9fH35+fnB1dYWFhQWys7Px+PFjRERE4PLly/jvv//yFTWNjY1hZ2cHKysryGQy5OTkICsrC6mpqUhNTRULPO/S19dHhQoVUKFCBVhYWMDMzEx85I16MzMzEwuU7z/e/QzmPYyMjOT65eTkIDMzE1lZWcjMzERKSgqSk5PFf/OeC4IAQ0NDGBkZwdjYWPw/eff/J++5jY0NXFxcYG1tzUIVlQkWejSErifIcXFxOHPmDOrXry9e6hUaGoqWLVvC0dERcXFx4i/JM2fOwNLSEt7e3jAw4BWDRETlga6fx0jzlPfPrEwmkyv6vDtK5/1/Cyp65D3Pzc1FQkIC4uLiEBcXh2fPniE2NhaxsbF49uwZnj17hrS0NGRnZ6t7l6kUbGxs5Io/jo6OsLe3h1QqFT8/iYmJckWdpKSkYq3b3d0dvr6+8PX1Rb169VC/fn14enoWq3CRk5ODsLAwhIaG4syZMzhz5gwSExMV3FvNY2lpCTc3N3h4eKBq1aqoUqWK+PDy8oKJiYm6QyQNxUKPhijvyYY63LlzBxs3boSpqSmWLFkittevXx9hYWHYtWsX+vTpAwB48+YNsrKyYG9vr65wiYh0Gs9jVFrBwcHYt28f7ty5A1NTUzRt2hTLly9HjRo1Pthn06ZN2LZtG27cuAEAaNCgAZYuXYpGjRoVe7v8zMqTyWTiiIb3H1lZWRAEARcuXEBCQgIMDAxQqVIl6OvrQyaTQSqVQiqVIjc3F7m5ucjJycn3b14BqqDHuyNaZDLZBwtaRf377oiY7OxsJCcnw8TEBDY2NuLIl/dH/xTUVpL3S7rsu48Ptec9srOz8ebNG7x58wYpKSlITEzE8+fPkZCQgOfPnyM3N7fU/9/GxsZwcnKCk5OTeEmfl5cXPD094enpiSpVqsDW1rbU63+fTCZDTEwMEhMTkZKSIo4AMzIygqWlJSwsLGBpaSk3J1VGRgYSExPx6tUrvH79Gunp6XKPtLQ08XlmZiakUqn4mSvskZmZKRbBMjMzYWhoKI5eMzExgaWlJaytrcUROnn/6uvrIzs7G9nZ2eLIn3dH/7z7/PXr10UW1CQSCSpVqiRX/KlatSqcnJzkRiyZm5vD3NwcJiYmHB1EIhZ6NASTjeKRyWTo3Lkzzp07h1u3bsHV1RUA8Msvv2DYsGHo168fdu7cKS7PSZ6JiFSD5zEqrQ4dOqB///5o2LAhcnNzMWvWLNy4cQO3bt2Cubl5gX0GDRqEZs2aoWnTpjAxMcHy5cuxf/9+3Lx5E5UqVSrWdvmZJU0mCIJY+Ml75BWAXr58CUNDQ5iamsLMzAzW1tZiQSevuMNLispeWloanj59ipiYGDx69Aj379/HgwcPxMebN29KtD4DA4N8BagPPWxtbcVHhQoVYGtrCysrK06OrUV41y3SKnp6ejhy5AikUqncnD0PHjwAALi5uYltMplMHCb5+++/w8nJSeXxEhERUeH++ecfuddbt26Fg4MDrl69ihYtWhTYZ/v27XKvf/75Z+zduxcnTpzAkCFDyixWovJCIpGIc9fUqlVL3eFQAczNzVGjRo0CRycKgoCXL1/KFX7yCkGvXr0SRyylpaUhKysLwNs5vl69eoVXr16VKh49PT2xCJQ3iipvtFBRz/NGO5mYmIgFRFNTU5iamopthoaGLB5qKRZ6SGXen5h50aJFmDJlitz15VFRUXj69ClevXoFOzs7sX3t2rW4cOEChg0bhjZt2qgsZiIiIipacnIyAKBChQrF7pOeno6cnJxC+2RlZYl/MAFvvw0lIlKHvLvhVaxYEU2aNCl0WalUirS0NLnJoYt6JCYmyj3y7maW97qs9imvQFSc4pGVlRVcXFzEOYxcXV054qicYqGH1Or964irV6+OGzdu4OHDh3K3cjx48CCOHz+Opk2bioWeV69eYdWqVQgICEDXrl1VGjcRERG9JZPJMHnyZDRr1gze3t7F7jd9+nS4uLigbdu2H1wmODgYCxcuVEaYREQqo6+vL97NK2/aipLKysqSK/zk3eksLS1N/Lew53nzd2VkZIiPvDmO8giCIK63NExMTFCtWjXUrVtXnATc19dX7k7NpB6co0cBvE5cdU6dOoWQkBAMHjwY1apVAwD8/fff6NKlC2rUqIE7d+6Iy8bGxsLZ2ZnVZSKiIvA8RsowduxYHDlyBGfPni32HzTLli3DihUrEBISgrp1635wuYJG9Li5ufEzS0RUSnkTn79b/ClO4Sg1NRXJycl4+vSpOI9RTk5OgdtwdHQUiz55RaCaNWvKfZGvizhHD9F7WrdujdatW8u1OTo6YuTIkfkmcPz444+RkpKCv/76C/7+/qoMk4iISKeMHz8ehw4dQmhoaLGLPN988w2WLVuGf//9t9AiD/D2LkN5d14iIiLFSSQS8XerjY1NqdeTm5uLmJgY3Lx5ExEREbh+/TquX7+Oe/fu4fnz5zh27BiOHTsmLm9kZITatWujVq1a8PT0hJeXl/hwd3cvl0Wg2NhYzJ49G2PHjkXjxo3VHU6JcESPAvhNaPnz/PlzVKtWDZmZmXj58qX4/3LixAk8evQIXbp04VBCIqL/j+cxKi1BEDBhwgTs378fISEh4mjboqxYsQJLlizB0aNHi5zjoiD8zBIRlW9paWm4ceMGrl+/LhaAIiIiCp1jTU9PD66urvkKQHkPFxcXlV6tkZWVJY48TU9PR7NmzXDmzBmFJ67W6hE9QUFBJe4zZ86cEk3uR7rL0dERL1++REREhNwPz5o1a3DgwAEsXLgQ8+bNA/A2SQXAmeaJiEirlUXuFRgYiB07duDgwYOwtLREfHw8AMDa2hqmpqYAgCFDhqBSpUoIDg4GACxfvhzz5s3Djh074OnpKfaxsLCAhYVFiWMkIqLyx9zcHI0bN5YbASMIAh49eoTr16/j/v37iI6OFh+PHj1CZmYmYmJiEBMTg9DQ0HzrtLa2RrNmzfDRRx+hffv28PPzK7O/4WQyGUaMGIEdO3YAAJo1a4Zvv/1W4/5mVPmIHj09PQQEBMDIyKhYy589exZRUVGoXLlyGUdWcvxWSXN88803+OOPP7Bp0ybUq1cPAHDhwgUMGjQIgwYNwqJFi9QbIBGRGvA8phvKIvf6UMK7ZcsWDBs2DADQqlUreHp6YuvWrQAAT09PPH78OF+f+fPnY8GCBcWKjZ9ZIiLtIggC4uPjxaLPu0Wg6OhoxMTEQCqVyvWpXLkyPv30U4wePRpVq1ZVWiwxMTEYOXIk/v33X+jr6+OXX37BwIEDlVbkUeU5TC2Fnvj4eDg4OBRreUtLS1y/fp2FHlK62bNnY+nSpejXrx927twptv/7779o2LAhrK2t1RgdEVHZ43lMNzD3IiIiTZWTk4OIiAicO3cOp06dwtGjR5GRkQHg7ZcO3bt3x8KFC4uc860oL168QNOmTXH//n0YGxtj48aNGDJkiDJ2QaTVl25t2bKlRH9A//TTT5xTRYPIZDKkpqYiKSkJSUlJePPmDYyNjcVh2ebm5rCwsCgXk23NmjUL/v7+cHJyEttevXqFDh06QE9PD0+fPi12UkxE6pN394h37w6Rd6vQrKwsWFlZwcbGRnyYmppq3PBbIkUw9yIiIk1laGiIBg0aoEGDBpg4cSLS0tJw+PBhbN68Gf/88w8OHDiAP//8E2PHjsXixYtLNcF0WloaunTpgvv378PT0xNHjx5F9erVlb8zKqSWyZilUin09fVVvVml08ZvlQRBkCvUvPtITEwssP3dR3JyMmQyWZHbMTQ0zFf8yfv3Q8+L02ZoaKjQH3BhYWEYNGgQDA0Ncf36dbF92bJlePPmDYYPH67U4YGaTBAEZGVl4c2bN0hJScGbN2/knhf0b25urkpiMzAwgJWVFSwtLWFpaSk+L6jN0tISJiYm/MNfBfI+M3m36SyoMFPa5yX5bBkZGckVforzsLW1FZ+bmJiU4VFSLW08j1HBmHsREZG2uXXrFubNm4e9e/cCADw8PLBr1y40atSo2OuQSqXo1asX/vzzT1SoUAH//fcfatSoUSbxavWlWwDg5OSEYcOGYcSIERpdKVN1siGTyZCVlYXMzMxiPTIyMj74Xlpa2geLNcUp1BTFyMgItra2sLCwQFZWlvhHWVn/oa+vrw9zc3OYm5vDzMys1M9lMhkcHR1hbm4OU1NTNG7cGHFxcfj777/RqVOnMt0HZZDJZMjOzkZOTg5ycnLE5wW15f3/FFWkKejfnJwcde+qUhgYGBS7KPTucwsLCxgZGcHQ0FD89/3n779W5R0DSiMnJ0cswqSnp8sVZd5/Xdy2d18r4/dLYfJGEOYVgY2MjPDmzRuxUK2M7efdjvT9h5mZGUxMTJT2ULRwXRz8o1l3MPciIiJtderUKYwaNQoPHz6EoaEh1qxZg88//7xYfcePH4+1a9fCxMQEJ06cQNOmTcssTq0v9Hz99df45ZdfEB0djaZNm2LkyJHo27cvzMzMVB2KQpT5H5WUlIQePXoUWqTJzs5WUuRFMzQ0lPsGu7jfdBf1jfeHLq94v6047727jKqOjZ6eHkxMTKCvr4/c3Fzk5ubC3NwcJiYm0NPTg76+PvT19ZXyXCKRFFmk+VBbWf8x/T5zc/N8BZKCCibFnQhUUdnZ2R8cafTu89TUVJXE8y59ff1iFYTef08QBEilUkilUshksgKfK+M9VX12jIyMCh3JV5xRfgU9NzD48BXJhY1YLO7IRVWeMiUSSYEFIAsLC1y6dEkp2+AfzbqDuRcREWmz5ORkjBo1Cnv27AHw9rw3e/bsQr8027hxI7744gtIJBLs3r0bvXv3LtMYtb7QkyckJARbtmzB3r17oa+vj759+2LUqFFyt2Irz5T5H5WcnFyi6wklEglMTU3FxP/d58V5mJqaFlrI0bQ5LHJycuRGDZT0eVHLafrIFYlE8sFCQt4f3EUVaj70r4WFhcZeDpA3p1RxikIfKhS9X3B7/7Wm+tDouA+1FfX63TYzM7NyMU9XSb0/B9n7RaHCRlEW95GVlVVkHObm5korUvKPZs1ga2tb7HPy69evC32fuRcREWkrQRCwYMEC8Y7KEydOxHfffVfgiPoLFy6gRYsWyMnJwdKlSzFz5swyj09nCj15UlNTsXPnTmzduhX//fcfatWqhZEjRyIoKEjdoRVKmf9RUqkU+/btK3ahxsDAQKMKMZou73KW9PR0ZGVlQSqVIioqCr///js+//xz2NjYQCqV4tq1a9i1axd69eqFOnXqfHAERXGeC4LwwcJMSdqMjIw0thCj6QRBQG5uboEFoJI+19PTkxv5VdioMEVem5iYwMzMDEZGRvwdowZ5l10WVgzKzc1F27ZtlbI9/tGsGX755Rfx+atXr7B48WK0b98eAQEBAIDz58/j6NGjmDt3LqZMmVKsdTL3IiIibbV69WpMmjQJANC/f39s3boVxsbG4vvPnj2Dv78/4uLi0Lt3b+zevVsleW+5LvSUJAH49ttvSxzQ33//jSFDhiApKQlSqbTE/VWJyQa9b+DAgfj9998xevRobNy4Ud3hEBEViucxzdO7d2+0bt0a48ePl2tfs2YN/v33Xxw4cKDE62TuRURE2mbHjh0YNmwYcnJy0KZNG+zZsweWlpY4ePAgpkyZgpiYGHh7e+P8+fOwsLBQSUzl+vbqYWFhcq+vXbuG3NxccWbqu3fvQl9fHw0aNCj2OtPT07Fr1y5s2bIFZ8+eRZUqVTBt2rSShkakdl9++SUsLCwwduxYse3p06eYN28exowZU6IZ4ImIiN539OhRLF++PF97hw4dMGPGjGKvh7kXERFps4EDB8Le3h69evXCiRMnULlyZZiamuLZs2cAAC8vLxw4cEBlRR5VK3Gh59SpU+Lzb7/9FpaWlvjll19ga2sLAEhMTMTw4cPRvHnzItf133//YfPmzdi9ezdyc3Px6aef4uuvv0aLFi1KGhZRuVC/fv18I3l+/vlnbNmyBQ8fPkRISIh6AiMiIq1gZ2eHgwcPYurUqXLtBw8ehJ2dXZH9mXsREZGuaNeuHU6fPo2BAwfi7t27SExMhK2tLcaOHYvZs2dr3A0JSqLEhZ53rVq1CseOHROLPMDbCQMXL16Mdu3a5UtC8qxYsQJbtmzB3bt34e/vj5UrV2LAgAGwtLRUJByicqlz5854+PAhunfvLrZlZWVhxowZGDZsGHx9fdUYHRERaZKFCxdi1KhRCAkJESdQvnjxIv755x9s2rTpg/2YexERkS5q0KABbty4gdDQUMhkMjRv3vyDd4jWJgpNxmxpaYm//voLrVq1kms/deoUunXrhjdv3hTYr2LFihg8eDBGjhwJb2/v0m5e7XidOJXWjh07MGjQILi5uSE6OpqTJRORWvA8ppkuXryI1atX4/bt2wCAWrVqYeLEiYXeOYu5FxERkXqV6zl63tWzZ08MHz4cq1atEuceuXjxIqZNm4ZevXp9sN+zZ8808ta6RMpSvXp19O3bF35+fmKRRxAErFy5Et26dUPNmjXVHCEREZVXjRs3xvbt20vUh7kXERGR7sh/Q/kS2LBhAzp27IiBAwfCw8MDHh4eGDhwIDp06IB169YV2Gf16tUluqPDhg0bPjgyiEhT+fv7448//pCbOPPKlSuYPn066tWrh5SUFDVGR0RE5dmDBw8wZ84cDBw4EAkJCQCAI0eO4ObNmwUuz9yLiIhItyhU6DEzM8O6devw6tUrhIWFISwsDK9fv8a6detgbm5eYJ8pU6aUKHn46quv8OLFC0XCJNIIhoaG6NatG/r37y83lO/y5cvIzc1VY2RERFRenD59Gj4+Prh48SL27t2L1NRUAMD169cxf/78Avsw9yIiItItCl26lScuLg5xcXFo0aIFTE1NIQgCJBJJgcsKgoA2bdrAwKB4m87IyFBGiETlXr169XDw4EHIZDKx7dmzZ2jevDnc3Nxw7tw5ODg4qDFCIiJStxkzZmDx4sUICgqSm0j5448/xpo1awrsw9yLiIhItyhU6Hn16hX69u2LU6dOQSKR4N69e6hcuTJGjhwJW1tbrFq1Kl+fD33b9CHdu3dHhQoVFAmTSKPo6f3fQLvbt2/DwsICjo6OqFixohqjIiKi8iAyMhI7duzI1+7g4ICXL18W2Ie5FxERkW5RqNAzZcoUGBoaIiYmBrVq1RLb+/Xrh6CgIKUUeoh0WZs2bfDo0SMkJCSIo+Sys7PRoUMHDBo0CEOGDOHkmkREOsTGxgZxcXHw8vKSaw8LC0OlSpUK7MPci4iISLcoVOg5duwYjh49CldXV7n2atWq4fHjxwoFRkRvWVhYwMLCQny9fft2nDp1Crdv38bAgQNZ6CEi0iH9+/fH9OnTsXv3bkgkEshkMpw7dw5ffvklhgwZou7wiIiIqBxQaDLmtLQ0mJmZ5Wt//fo1jI2NFVl1iQUHB6Nhw4awtLSEg4MDevTogaioqCL77d69GzVr1oSJiQl8fHxw+PBhFURLVHp9+/bFqlWrsGTJEpiamortf/31F7Kzs9UYGRERlbWlS5eiZs2acHNzQ2pqKmrXro0WLVqgadOmmDNnjrrDIyIionJAoUJP8+bNsW3bNvF13jdLK1asQOvWrRUOriROnz6NwMBAXLhwAcePH0dOTg7atWuHtLS0D/b577//MGDAAIwcORJhYWHo0aMHevTogRs3bqgwcqKSMTc3R1BQEEaMGCG2nT9/Ht26dUPt2rWRmZmpxuiIiKgsGRkZYdOmTXjw4AEOHTqE3377DXfu3MGvv/4KfX19dYdHRERE5YBEEAShtJ1v3LiBNm3aoH79+jh58iS6deuGmzdv4vXr1zh37hyqVKmizFhL5MWLF3BwcMDp06fRokWLApfp168f0tLScOjQIbGtSZMmqFevHjZs2FDkNlJSUmBtbY3k5GS522ETqdqff/6JMWPGoGPHjvjf//4nthd2BzwiIp7HSNPwM0tERJpKlecwhebo8fb2xt27d7FmzRpYWloiNTUVvXr1QmBgIJydnQvtm5OTg5o1a+LQoUNyEzkrS3JyMgAUeteI8+fPIygoSK6tffv2OHDggNLjISpL3bp1wyeffIL09HSx7fnz52jRogUmTpyIMWPG8JteIiIt8H7ekkcikcDExARVq1b94F2zyjr3IiIiovJBoUIPAFhbW2P27Nkl7mdoaFhml5jIZDJMnjwZzZo1g7e39weXi4+Ph6Ojo1ybo6Mj4uPjC1w+KysLWVlZ4uuUlBTlBEykBKampnJz9qxbtw53797Ftm3bMG7cODVGRkREyhIWFoZr165BKpWiRo0aAIC7d+9CX18fNWvWxLp16zB16lScPXsWtWvXlutblrkXERERlR8KFXoiIiIKbM/7Vsnd3b3QSZkDAwOxfPly/PzzzzAwULjmJLfeGzdu4OzZs0pbJ/B2wueFCxcqdZ1EZWXWrFlwdHREzZo1xcu3cnNzsWXLFgwePFiuKERERJohb7TOli1bxGHfycnJGDVqFD766COMHj0aAwcOxJQpU3D06NF8/csq9yIiIqLyQ6E5evT09MQ/IPNW8+58IIaGhujXrx9++uknmJiY5Ovfs2dPnDhxAhYWFvDx8YG5ubnc+/v27StxTOPHj8fBgwcRGhoKLy+vQpd1d3dHUFAQJk+eLLbNnz8fBw4cwPXr1/MtX9CIHjc3N14nThpj69atGD58OHx9fREWFsb5e4h0HOc70TyVKlXC8ePH843WuXnzJtq1a4fY2Fhcu3YN7dq1w8uXL/P1L4vcS5X4mSUiIk2lMXP07N+/H9OnT8e0adPQqFEjAMClS5ewatUqzJ8/H7m5uZgxYwbmzJmDb775Jl9/Gxsb9O7dW5EQRIIgYMKECdi/fz9CQkKKLPIAQEBAAE6cOCFX6Dl+/DgCAgIKXN7Y2Fjlt40nUiZzc3O4u7tj0KBBckUeqVTKOXyIiDRAcnIyEhIS8hV6Xrx4IV5SbmNjg+zs7AL7KzP3IiIiovJJoULPkiVL8MMPP6B9+/Zim4+PD1xdXTF37lxcunQJ5ubmmDp1aoGFni1btiiyeTmBgYHYsWMHDh48CEtLS3GeHWtra/ESlSFDhqBSpUoIDg4GAEyaNAktW7bEqlWr0LlzZ+zcuRNXrlzBxo0blRYXUXnSp08fdO/eHTKZTGwLCwtD7969sXz5cvTp00eN0RERUVG6d++OESNGYNWqVWjYsCEA4PLly/jyyy/Ro0cPAG+/dKtevXqB/ZWZexEREVH5pFChJzIyEh4eHvnaPTw8EBkZCQCoV68e4uLiCl3PixcvEBUVBQCoUaMGKlasWOJY1q9fDwBo1aqVXPuWLVswbNgwAEBMTAz09PTE95o2bYodO3Zgzpw5mDVrFqpVq4YDBw4UOoEzkaYzMjKSe718+XJER0dj//79LPQQEZVzP/30E6ZMmYL+/fsjNzcXAGBgYIChQ4fiu+++AwDUrFkTP//8c6HrUUbuRUREROWTQnP0+Pn5wdfXFxs3bhT/eMzJycHo0aNx/fp1hIWF4dy5cxg8eDCio6Pz9U9LS8OECROwbds2cYSBvr4+hgwZgh9//BFmZmalDU0leJ04aYP09HR88803GD58ONzc3AC8/Wynp6fDyclJzdERUVnieUxzpaam4uHDhwCAypUrw8LColj9mHsRERGphyrPYXpFL/Jha9euxaFDh+Dq6oq2bduibdu2cHV1xaFDh8QRNg8fPvzgrZ2DgoJw+vRp/PXXX0hKSkJSUhIOHjyI06dPY+rUqYqERkTFZGZmhnnz5olFHgBYvHgxqlevjq1bt6ovMCIi+iALCwvUrVsXdevWLXaRB2DuRUREpAsUGtEDAG/evMH27dtx9+5dAG+H/w4cOBCWlpZF9rW3t8eePXvyXW516tQp9O3bFy9evFAktDLHb5VIG0mlUrRq1Qpnz57FoUOH0LlzZ3WHRERlhOcxzXTlyhXs2rULMTEx+SZdLuquWcy9iIiI1ENj7roFAJaWlhgzZkyp+qanp8PR0TFfu4ODA9LT0xUNjYhKQV9fH6dPn8axY8fkJloPDQ2Fra0tfHx81BgdEZFu27lzJ4YMGYL27dvj2LFjaNeuHe7evYvnz5+jZ8+eRfZn7kVERKT9FB7RAwC3bt0q8Fulbt26FdqvTZs2sLOzw7Zt22BiYgIAyMjIwNChQ/H69Wv8+++/ioZWpvitEumKzMxM1K5dG48fP8b+/fuL/NkmIs3A85jmqVu3Lr744gsEBgbC0tIS169fh5eXF7744gs4Oztj4cKFhfZn7kVERKQeGjOi5+HDh+jZsyciIyMhkUiQVzOSSCQA3l4CUpjvv/8eHTp0gKurK3x9fQEA169fh4mJCY4ePapIaESkRKmpqWjQoAGys7Px8ccfqzscIiKd9eDBA/GSWiMjI6SlpUEikWDKlCn4+OOPiyz0MPciIiLSfgpNxjxp0iR4eXkhISEBZmZmuHnzJkJDQ+Hv74+QkJAi+/v4+ODevXsIDg5GvXr1UK9ePSxbtgz37t1DnTp1FAmNiJTI3t4eu3fvRkREhNykn9OmTcPhw4fVGBkRkW6xtbXFmzdvAACVKlXCjRs3AABJSUnFuvSKuRcREZH2U2hEz/nz53Hy5EnY29tDT08Penp6+OijjxAcHIyJEyciLCzsg31zcnJQs2ZNHDp0CKNHj1YkDCJSkQoVKojPQ0JC8M033+Dbb7/F/fv34eXlpcbIiIh0Q4sWLXD8+HH4+PigT58+mDRpEk6ePInjx4+jTZs2hfZl7kVERKQbFCr0SKVS8e5a9vb2ePbsGWrUqAEPDw9ERUUV2tfQ0BCZmZmKbJ6I1MjPzw9ffvklcnNz5Yo8UqkU+vr6aoyMiEh7rVmzRsyfZs+eDUNDQ/z333/o3bs35syZU2hf5l5ERES6QaFLt7y9vXH9+nUAQOPGjbFixQqcO3cOixYtQuXKlYvsHxgYiOXLlyM3N1eRMIhIDaytrbFy5Up89913Ytvz589RrVo1rFu3rsg5uoiIqGRyc3Nx6NAhsZiup6eHGTNm4M8//8SqVatga2tb5DqYexEREWk/hUb0zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/FNn/8uXLOHHiBI4dOwYfHx+Ym5vLvb9v3z5FwiMiFVuzZg2io6OxZcsWjBkzRt3hEBFpFQMDA4wZMwa3b98u9TqYexEREWk/hQo97du3F59XrVoVd+7cwevXr2FrayveeaswNjY26N27tyIhEFE5Mn/+fDg7O8PPzw96em8HDEqlUsTExHAOHyIiJWjUqBHCw8Ph4eFRqv7MvYiIiLRfqQs9OTk5MDU1RXh4OLy9vcX2dydrLUxubi5at26Ndu3awcnJqbRhEFE5YmBggHHjxsm1bd26FWPHjsXcuXMxd+5cNUVGRKQdxo0bh6CgIDx58gQNGjTINyKnbt26H+zL3IuIiEg3lLrQY2hoCHd391LPw6GM4cdEVP6FhoYiJycn3x8jRERUcv379wcATJw4UWyTSCQQBAESiaTQvIy5FxERkW5QaDLm2bNnY9asWXj9+nWp+jdq1KjQW7ATkebbunUrjh49ivHjx4ttERER+PPPPyEIghojIyLSPNHR0fkeDx8+FP8tirJyr+DgYDRs2BCWlpZwcHBAjx49irzjKgDs3r0bNWvWhImJCXx8fHD48GGFYyEiIiJ5Cs3Rs2bNGty/fx8uLi7w8PDI9439tWvXCu0/btw4TJ06FU+fPi3x8GMi0gwSiQTt2rUTXwuCgMmTJ+PUqVNYvHgxZs+ercboiIg0S2nn5smjrNzr9OnTCAwMRMOGDZGbm4tZs2ahXbt2uHXr1gdHcP73338YMGAAgoOD0aVLF+zYsQM9evTAtWvX5KYBICIiIsVIBAW+Ul+4cGGh78+fP7/Q9/Mma5ULqJjDj8uDlJQUWFtbIzk5GVZWVuoOh0gj5OTkYP78+Vi/fr1CE4oSkeJ4HtNMv/76KzZs2IDo6GicP38eHh4e+P777+Hl5YXu3bsX2rescq8XL17AwcEBp0+fRosWLQpcpl+/fkhLS8OhQ4fEtiZNmqBevXrYsGFDsbbDzywREWkqVZ7DFBrRU1QhpyjR0dEK9ScizWNoaIilS5di1qxZsLCwENvnzZuHrKwszJw5EzY2NuoLkIioHFu/fj3mzZuHyZMnY8mSJWJhxsbGBt9//32RhZ6yyr2Sk5MBFH5TjvPnzyMoKEiurX379jhw4ECZxERERKSrFCr0AEBSUhL27NmDBw8eYNq0aahQoQKuXbsGR0dHVKpUqdC+/CafSHe9W+SJi4vDihUrkJWVhRYtWqBz585qjIyIqPz68ccfsWnTJvTo0QPLli0T2/39/fHll18W2b8sci+ZTIbJkyejWbNmhV6CFR8fD0dHR7k2R0dHxMfHf7BPVlYWsrKyxNcpKSmKB0xERKTlFJqMOSIiAtWrV8fy5cvxzTffICkpCQCwb98+zJw5s1jr+PXXX9GsWTO4uLjg8ePHAIDvv/8eBw8eVCQ0ItIgTk5O2Lt3L7744gt06tRJbH/27BknbCYiekd0dDT8/PzytRsbGyMtLa1Y61B27hUYGIgbN25g586dpepfmODgYFhbW4sPNzc3pW+DiIhI2yhU6AkKCsKwYcNw7949mJiYiO2dOnVCaGhokf3Xr1+PoKAgdOrUCUlJSfmGHxORbpBIJOjcuTM2bNgAiUQC4O23uM2bN0fz5s3x6NEj9QZIRFROeHl5ITw8PF/7P//8g1q1ahXZX9m51/jx43Ho0CGcOnUKrq6uhS7r5OSE58+fy7U9f/4cTk5OH+wzc+ZMJCcni48nT56UOEYiIiJdo1Ch5/Lly/jiiy/ytVeqVKnQYbh58oYfz549G/r6+mK7v78/IiMjFQmNiDTc1atXERcXh4cPH6JixYrqDoeIqFwICgpCYGAg/vjjDwiCgEuXLmHJkiWYOXMmvvrqqyL7Kyv3EgQB48ePx/79+3Hy5El4eXkV2ScgIAAnTpyQazt+/DgCAgI+2MfY2BhWVlZyDyIiIiqcQnP0GBsbF3it9N27d4v1h5kyhh8TkXZq2rQp7t69i4cPH8rdqnfz5s3o2bMnbG1t1RgdEZF6jBo1CqamppgzZw7S09MxcOBAuLi44IcffkD//v2L7K+s3CswMBA7duzAwYMHYWlpKX7BZ21tDVNTUwDAkCFDUKlSJQQHBwMAJk2ahJYtW2LVqlXo3Lkzdu7ciStXrmDjxo3F3i4REREVTaERPd26dcOiRYuQk5MD4O3lFzExMZg+fTp69+5dZH9Fhx8TkXZzdXWVu01vaGgoRo4ciRo1auDNmzdqjIyISH0GDRqEe/fuITU1FfHx8Xj69ClGjhxZrL7Kyr3Wr1+P5ORktGrVCs7OzuLjjz/+EJeJiYlBXFyc+Lpp06bYsWMHNm7cCF9fX+zZswcHDhwodAJnIiIiKjmFRvSsWrUKn376KRwcHJCRkYGWLVsiPj4eAQEBWLJkSZH984YfZ2ZmisOPf//9dwQHB+Pnn39WJDQi0kL6+vrw9vZGs2bNYGlpqe5wiIhUbvHixRg0aBC8vLxgZmYGMzOzEvVXVu5VnInyQ0JC8rX16dMHffr0KUnIREREVEISQQm3tDl79iwiIiKQmpqK+vXro23btsXuu337dixYsAAPHjwAALi4uGDhwoXF/mZKnVJSUmBtbY3k5GReM06kIlKpFBkZGeLt2Z8/f47PPvsMCxYsQNOmTdUcHZFm4XlM8/j6+uLGjRto3LgxBg8ejL59+8Le3r5E62DuRUREpHqqPIcpVOh58uSJ0m5zmZ6ejtTUVDg4OChlfarAZINI/SZMmIA1a9agYcOGuHjxonjXLiIqGs9jmunmzZvYvn07du7ciadPn+KTTz7BoEGD0KNHjxKN8GHuRUREpDqqPIcpNEePp6cnWrZsiU2bNiExMVGhQMzMzDQq0SCi8mHmzJkYNWoUvvnmG7HIk5OTk+8WvkRE2qJOnTpYunQpHj58iFOnTsHT0xOTJ08u9DblBWHuRUREpJ0UKvRcuXIFjRo1wqJFi+Ds7IwePXpgz549yMrKUlZ8RESFcnFxwaZNm+Qmbf7ll19QuXJlrFixQo2RERGVPXNzc5iamsLIyEi8OQYRERHpNoUKPX5+fli5ciViYmJw5MgRVKxYEZ9//jkcHR0xYsQIZcVIRFQiR48eRXp6OoyMjNQdChGR0kVHR2PJkiWoU6cO/P39ERYWhoULF4q3OCciIiLdppTJmN917do1jBw5EhEREZBKpcpcdbnD68SJyidBEHD48GF8/PHHMDU1BfB2BOKJEycwfvx4mJubqzlCovKB5zHN06RJE1y+fBl169bFoEGDMGDAAFSqVEndYakMP7NERKSpVHkOU+j26nmePn2KHTt2YMeOHbhx4wYCAgKwdu3aEq0jMzMTJiYmygiHiHScRCJB586d5dpmz56NY8eO4enTp/jxxx/VFBkRkWLatGmDzZs3o3bt2gqvi7kXERGRdlLo0q2ffvoJLVu2hKenJ7Zt24Z+/frhwYMHOHPmDMaMGVNkf5lMhq+//hqVKlWChYUFHj58CACYO3cu/ve//ykSGhGRSBAEDB48GDVr1kRQUJDYnpqayjktiEijLFmyRKEiD3MvIiIi7adQoWfx4sVo3Lgxrl69ihs3bmDmzJnw8PAoUf+tW7dixYoVcnNpeHt74+eff1YkNCIikUQiwWeffYZbt27By8tLbF+4cCFq1qyJw4cPqzE6IqKSefr0KdatW4cZM2YgKChI7lEU5l5ERETaT6FLt2JiYsTbGZfGtm3bsHHjRrRp00ZuBJCvry/u3LmjSGhERPm8+/sqJycHe/fuRXR0tEK/x4iIVOnEiRPo1q0bKleujDt37sDb2xuPHj2CIAioX79+kf2ZexEREWk/hQo9eX8cpaenIyYmBtnZ2XLv161bt9D+sbGxqFq1ar52mUzGyymIqEwZGhoiMjISe/bsQYcOHcT2v//+G7m5uejWrRsLQERU7sycORNffvklFi5cCEtLS+zduxcODg4YNGiQ3O+yD2HuRUREpP0UunTrxYsX6Ny5MywtLVGnTh34+fnJPYpSu3ZtnDlzJl/7nj17itX/XaGhoejatStcXFwgkUhw4MCBQpcPCQmBRCLJ9+CtSYl0h7m5OYYOHSoWdHJycjBx4kT06NEDmzdvVnN0RET53b59G0OGDAEAGBgYICMjAxYWFli0aBGWL19eZH9l5l5ERERUPik0omfy5MlITk7GxYsX0apVK+zfvx/Pnz/H4sWLsWrVqiL7z5s3D0OHDkVsbCxkMhn27duHqKgobNu2DYcOHSpRLGlpafD19cWIESPQq1evYveLioqSu7WZg4NDibZLRNojJycH/fr1wx9//IH+/fuL7RkZGeJt2omI1Mnc3FwcQe3s7IwHDx6gTp06AICXL18W2V+ZuRcRERGVTwoVek6ePImDBw/C398fenp68PDwwCeffAIrKysEBwfnu73x+7p3746//voLixYtgrm5OebNm4f69evjr7/+wieffFKiWDp27IiOHTuWeB8cHBxgY2NT4n5EpH3MzMywdOlSLFq0CAYG//frsU+fPsjOzsYPP/yAWrVqqTFCItJ1TZo0wdmzZ1GrVi106tQJU6dORWRkJPbt24cmTZoU2V+ZuRcRERGVTwoVetLS0sQRMLa2tnjx4gWqV68OHx8fXLt2rVjraN68OY4fP65IGAqpV68esrKy4O3tjQULFqBZs2YfXDYrKwtZWVni65SUFFWESEQq9m6R5/Hjxzh27BhkMhn09fXVGBUREfDtt98iNTUVwNs7B6ampuKPP/5AtWrV8O233xZrHerOvYiIiKhsKTRHT40aNRAVFQXg7d0afvrpJ8TGxmLDhg1wdnYusn/lypXx6tWrfO1JSUmoXLmyIqEVydnZGRs2bMDevXuxd+9euLm5oVWrVoUWqIKDg2FtbS0+3NzcyjRGIlI/Dw8PREVFYdOmTahevbrY/scff+DWrVtqjIyIdFHlypXFm12Ym5tjw4YNiIiIwN69e+Hh4VGs/urKvYiIiEg1JIIgCKXt/NtvvyE3NxfDhg3D1atX0aFDB7x+/RpGRkbYunUr+vXrV2h/PT09xMfH55sX5/nz53B3d5cbPVMSEokE+/fvR48ePUrUr2XLlnB3d8evv/5a4PsFjehxc3NDcnKy3Dw/RKTdEhISULlyZWRkZODChQto2LChukMiKpWUlBRYW1vzPKahxo0bh0WLFsHe3r7Yfcoq91IVfmaJiEhTqfIcptClW4MHDxafN2jQAI8fP8adO3fg7u5eaNLx559/is+PHj0Ka2tr8bVUKsWJEyfg6empSGil0qhRI5w9e/aD7xsbG8PY2FiFERFReZSZmYl27drh2bNn8Pf3F9sFQeAt2YlIZX777Td8+eWXxSr0lNfci4iIiJRPoULPu86dOwd/f3/Ur1+/yGXzRtpIJBIMHTpU7j1DQ0N4enoW665dyhYeHl6sS86ISLe5u7tj3759yMjIEAs7ubm5aNWqFXr27InAwECYmJioOUoi0nYlGZRdXnMvIiIiUj6lFXo6duyI8PDwYl3fLZPJAABeXl64fPlyiYYcf0hqairu378vvo6OjkZ4eDgqVKgAd3d3zJw5E7Gxsdi2bRsA4Pvvv4eXlxfq1KmDzMxM/Pzzzzh58iSOHTumcCxEpBveveX67t27ce7cOdy+fRujRo1ioYeIypWyyL2IiIiofFJaoac0U/1ER0cra/O4cuUKWrduLb4OCgoCAAwdOhRbt25FXFwcYmJixPezs7MxdepUxMbGwszMDHXr1sW///4rtw4iouLq27cvMjMzIZFI5C6JuH79OurWrctLuohI6d68eVPiPsrMvYiIiKh8Umgy5ndZWlri+vXrJbpjw6JFiwp9f968eYqGVaY4ISARFebq1avw9/dH27ZtcfjwYRgaGqo7JCI5PI9ppgcPHmDLli14+PAhvv/+ezg4OODIkSNwd3dHnTp1Cu3L3IuIiEg9NGYy5nf99NNPcHR0LFGf/fv3y73OyclBdHQ0DAwMUKVKlXKfbBARFSYsLAxGRkZwcnJikYeIlOL06dPo2LEjmjVrhtDQUCxevBgODg64fv06/ve//2HPnj2F9mfuRUREpP2UUui5f/8+7OzsoKenB6D4d54JCwvL15aSkoJhw4ahZ8+eygiNiEhtRo0ahU8++QQGBv/3qzYxMRFLlizBV199le/2xkRERZkxYwYWL16MoKAgWFpaiu0ff/wx1qxZU2R/5l5ERETaT0+Rzq9evULbtm1RvXp1dOrUCXFxcQCAkSNHYurUqaVap5WVFRYuXIi5c+cqEhoRUbng4eGBSpUqia+Dg4OxatUq8Q44REQlERkZWWBBxsHBAS9fvizVOpl7ERERaReFCj1TpkyBgYEBYmJiYGZmJrb369cP//zzT6nXm5ycjOTkZEVCIyIqlzp27IgGDRpg9uzZYptMJkNOTo4aoyIiTWFjYyN+sfausLAwuaJySTH3IiIi0h4KXbp17NgxHD16FK6urnLt1apVw+PHj4vsv3r1arnXgiAgLi4Ov/76Kzp27KhIaERE5VLr1q1x6dIluctbd+3ahfnz5+Obb75B165d1RgdEZV3/fv3x/Tp07F7925IJBLIZDKcO3cOX375JYYMGVJkf+ZeRERE2k+hQk9aWprcSJ48r1+/hrGxcZH9v/vuO7nXenp6qFixIoYOHYqZM2cqEhoRUbmVN59Znh9++AF3797F9evXWeghokItXboUgYGBcHNzg1QqRe3atSGVSjFw4EDMmTOnyP7MvYiIiLSfQrdX79SpExo0aICvv/4alpaWiIiIgIeHB/r37w+ZTFbknR80HW/xSUTKkJKSgjVr1mDSpEkwNzcH8Pb2yZmZmUXeKplIETyPaa4nT54gMjISqamp8PPzQ7Vq1dQdkkrwM0tERJpKlecwhQo9N27cQJs2bVC/fn2cPHkS3bp1w82bN/H69WucO3cOVapUUWas5Q6TDSIqK927d8ehQ4ewZs0ajB07Vt3hkJbieYw0DT+zRESkqVR5DlPo0i1vb2/cvXsXa9asgaWlJVJTU9GrVy8EBgbC2dm5wD69evUq9vr37dunSHhERBopOzsbhoaGkEgkaN26tbrDIaJypHfv3mjUqBGmT58u175ixQpcvnwZu3fvzteHuRcREZFuUajQAwDW1tZyd48pzvJERPRhRkZG2LNnDx49egRPT0+x/fvvvwcAjB07tljzoBGR9gkNDcWCBQvytXfs2BGrVq0qsA9zLyIiIt2icKEnMzMTERERSEhIgEwmk3uvW7du+ZbfsmWLopskItIJ7xZ5nj9/jjlz5iAtLQ0eHh7o2bOn+gIjIrVJTU2FkZFRvnZDQ0OkpKQU2Ie5FxERkW5RqNDzzz//YMiQIXj58mW+9yQSCaRSabHW8+LFC0RFRQEAatSogYoVKyoSFhGR1rGzs8N3332Hv//+Gz169BDbk5OT+W09kQ7x8fHBH3/8gXnz5sm179y5E7Vr1y72eph7ERERaS+FCj0TJkxAnz59MG/ePDg6Opa4f1paGiZMmIBt27aJo4H09fUxZMgQ/PjjjwXeup2ISBcZGBhg9OjRGD16tNgmlUrRrFkzeHp6Yv369XBzc1NjhESkCnPnzkWvXr3w4MEDfPzxxwCAEydO4Pfffy9wfp73MfciIiLSfnqKdH7+/DmCgoJKVeQBgKCgIJw+fRp//fUXkpKSkJSUhIMHD+L06dOYOnWqIqEREWm9S5cuISoqCv/99x8sLCzUHQ4RqUDXrl1x4MAB3L9/H+PGjcPUqVPx9OlT/Pvvv3Kj/T6EuRcREZH2U+j26iNGjECzZs0wcuTIUvW3t7fHnj170KpVK7n2U6dOoW/fvnjx4kVpQ1MJ3uKTiNTt7t27uHv3Lrp06SK27dq1C+3bt+clXVQknsd0D3MvIiIi9dCY26uvWbMGffr0wZkzZ+Dj4wNDQ0O59ydOnFho//T09AJHAzk4OCA9PV2R0IiIdEL16tVRvXp18XVYWBj69esHBwcH3LlzB7a2tmqMjojKG+ZeRERE2k+hQs/vv/+OY8eOwcTEBCEhIZBIJOJ7EomkyEJPQEAA5s+fj23btsHExAQAkJGRgYULFyIgIECR0IiIdFJaWhpq1KgBf39/FnmItJBUKsV3332HXbt2ISYmBtnZ2XLvv379utD+zL2IiIi0n0KFntmzZ2PhwoWYMWMG9PRKPt3PDz/8gPbt28PV1RW+vr4AgOvXr8PExARHjx5VJDQiIp300Ucf4caNG0hNTRXbEhMTMWDAAMyaNQstWrRQY3REpKiFCxfi559/xtSpUzFnzhzMnj0bjx49woEDB/LdiasgzL2IiIi0n0Jz9FSoUAGXL19GlSpVSh1Aeno6tm/fjjt37gAAatWqhUGDBsHU1LTU61QVXidORJpg1qxZCA4ORp06dRAREVGqwjxpJ57HNE+VKlWwevVqdO7cGZaWlggPDxfbLly4gB07dhS5DuZeREREqqcxc/QMHToUf/zxB2bNmlXqdZiZmcndLpiIiJRr4sSJSEpKQpcuXcQijyAISEhIKPVdE4lIPeLj4+Hj4wMAsLCwQHJyMgCgS5cumDt3brHWwdyLiIhIuyn0ta5UKsWKFSvQsmVLTJgwAUFBQXKPovzyyy/4+++/xddfffUVbGxs0LRpUzx+/FiR0IiI6P9zcnLCunXr0KlTJ7Ft9+7dqFy5MpYtW6bGyIiopFxdXREXFwfg7eieY8eOAQAuX74MY2PjIvsz9yIiItJ+ChV6IiMj4efnBz09Pdy4cQNhYWHiIzw8vMj+S5cuFYcJnz9/HmvWrMGKFStgb2+PKVOmKBIaEREV4s8//0R6enq+iVyJqHzr2bMnTpw4AQCYMGEC5s6di2rVqmHIkCEYMWJEkf2VmXuFhoaia9eucHFxgUQiwYEDB4rss337dvj6+sLMzAzOzs4YMWIEXr16VaLtEhERUeEUmqNHUWZmZrhz5w7c3d0xffp0xMXFYdu2bbh58yZatWqFFy9eqCu0YuF14kSkqQRBwMGDB9G2bVtYWFgAAKKiohAREYHevXtzHh8dwfOY5jt//jzOnz+PatWqoWvXrkUur8zc68iRIzh37hwaNGiAXr16Yf/+/ejRo8cHlz937hxatGiB7777Dl27dkVsbCzGjBmD6tWrY9++fcXaJj+zRESkqTRmjh5FWVhY4NWrV3B3d8exY8fEy71MTEyQkZGhztCIiLSaRCLJ9wfZjBkzcODAAUybNg0rVqxQT2BEVCIBAQElui26MnOvjh07omPHjsVe/vz58/D09MTEiRMBAF5eXvjiiy+wfPnyEm2XiIiIClfiQk+vXr2wdetWWFlZoVevXoUuW9S3M5988glGjRoFPz8/3L17V5w/4ubNm/D09CxpaEREVEoymQy+vr44deoUhg8fLtfO0T1E5UtUVBR+/PFH3L59G8Dbu2ZNmDABNWrUKLKvOnOvgIAAzJo1C4cPH0bHjh2RkJCAPXv2yM0f9r6srCxkZWWJr1NSUso0RiIiIm1Q4uzd2toaEolEfF7Yoyhr165FQEAAXrx4gb1798LOzg4AcPXqVQwYMKCkoRERUSnp6elhwYIFiI2NRa1atcT2xYsXo1u3brh586YaoyOiPHv37oW3tzeuXr0KX19f+Pr64tq1a/D29sbevXuL7K/O3KtZs2bYvn07+vXrByMjIzg5OcHa2hpr1679YJ/g4GC53NLNza1MYyQiItIGpZqjZ9GiRfjyyy9hZmZWFjFpDF4nTkTaLCMjA5UqVUJiYiJ27dqFPn36qDskUjKexzRPlSpVMGjQICxatEiuff78+fjtt9/w4MEDtcQlkUiKnKPn1q1baNu2LaZMmYL27dsjLi4O06ZNQ8OGDfG///2vwD4Fjehxc3PjZ5aIiDSOKvOuUhV69PX1ERcXBwcHB4UDSExMxP/+9z+54ccjRoxAhQoVFF53WWOCTETaLioqCps3b0ZwcLB4CVdYWBicnJzg7Oys5uhIUTyPaR4zMzNERESgatWqcu337t2Dr68v0tPTi1xHWeRexSn0fPbZZ8jMzMTu3bvFtrNnz6J58+Z49uxZsX6n8DNLRESaSpXnsFJNvKCsG3WFhobC09MTq1evRmJiIhITE/Hjjz/Cy8sLoaGhStkGERGVXo0aNbB8+XKxyCOVSvHZZ5+hSpUq+Oeff9QcHZHuadWqFc6cOZOvPa9gUhR15l7p6en55vzS19cHoLzckoiIiBS461bePD2KCAwMRL9+/bB+/XrxRC+VSjFu3DgEBgYiMjJS4W0QEZHyvHz5EtbW1jA2Nkbjxo3VHQ6RzunWrRumT5+Oq1evokmTJgCACxcuYPfu3Vi4cCH+/PNPuWXfp8zcKzU1Fffv3xdfR0dHIzw8HBUqVIC7uztmzpyJ2NhYbNu2DQDQtWtXjB49GuvXrxcv3Zo8eTIaNWoEFxeXUh0PIiIiyq9Ul27p6enJTcr8Ia9fvy70fVNTU4SHh+e7S0RUVBTq1atX7m+xzuHDRKSLBEHAo0eP4OXlJbZNmjQJVatWxeeffw5jY2M1RkclwfOY5inuXfAkEgmkUmm+dmXmXiEhIWjdunW+9qFDh2Lr1q0YNmwYHj16hJCQEPG9H3/8ERs2bEB0dDRsbGzw8ccfY/ny5ahUqVKxtsnPLBERaSpVnsNKPaJn4cKFxbqzVmHq16+P27dv50s2bt++DV9fX4XWTUREZUMikcgVeSIjI7F69WoAQIsWLfj7m6gMyWQyhforM/dq1apVoZdcbd26NV/bhAkTMGHChBJth4iIiEqm1IWe/v37l2oy5oiICPH5xIkTMWnSJNy/f19u+PHatWuxbNmy0oZGREQqVLNmTWzYsCHfH4r3799HlSpVlHKpL5GuO3/+PF69eoUuXbqIbdu2bcP8+fORlpaGHj164McffyxwRB1zLyIiIt2i8rtu6enpQSKRFDnp3oeGHJcnHD5MRFSwpKQkVK5cGVWrVsWBAwc4/0Y5xfOY5ujYsSNatWqF6dOnA3g7kq5+/foYNmwYatWqhZUrV+KLL77AggUL8vVl7kVERKR+5f7SLUXujBAdHV3qvkREpBmuXLmC7OxspKWlwdHRUd3hEGm88PBwfP311+LrnTt3onHjxti0aRMAwM3NDfPnzy+w0MPci4iISLeUqtCjyPXhHh4epe5bmNDQUKxcuRJXr15FXFwc9u/fjx49ehTaJyQkBEFBQbh58ybc3NwwZ84cDBs2rEziIyLSJW3btsWDBw8QFxcnd/vkr776CsOGDUOdOnXUHCGRZklMTJQrmp4+fRodO3YUXzds2BBPnjwpsG9Z5V5ERERUPpV6jh5lunXrFmJiYpCdnS3XXtBtQT8kLS0Nvr6+GDFiBHr16lXk8tHR0ejcuTPGjBmD7du348SJExg1ahScnZ3Rvn37Eu8DERHJc3R0lPvDdO/evfjmm2+wadMmxMbGwtzcXI3REWkWR0dHREdHw83NDdnZ2bh27RoWLlwovv/mzRsYGhoWe33KyL2IiIiofFJroefhw4fo2bMnIiMj5a4dz5u4syTXiXfs2FHum62ibNiwAV5eXli1ahUAoFatWjh79iy+++47FnqIiMqAj48PevfuDR8fH7kiT3h4OHx9fTlpM1EhOnXqhBkzZmD58uU4cOAAzMzM0Lx5c/H9iIgIVKlSpcj1KDP3IiIiovJJT50bnzRpEry8vJCQkAAzMzPcvHkToaGh8Pf3R0hISJlu+/z582jbtq1cW/v27XH+/PkP9snKykJKSorcg4iIiqdGjRrYs2cP5s2bJ7bdvn0bfn5+8Pf3R2ZmphqjIyrfvv76axgYGKBly5bYtGkTNm3aBCMjI/H9zZs3o127dkWuR525FxEREamGWkf0nD9/HidPnoS9vT309PSgp6eHjz76CMHBwZg4cSLCwsLKbNvx8fH5Jgh1dHRESkoKMjIyYGpqmq9PcHCw3DBpIiIquXdH7oSHh8PMzAweHh4wMTER26VSqTi3DxEB9vb2CA0NRXJyMiwsLPL9fOzevRsWFhZFrkeduRcRERGphlpH9EilUlhaWgJ4m8A8e/YMwNtJA6OiotQZWoFmzpyJ5ORk8fGhSQ+JiKh4BgwYgJiYGHz77bdiW3JyMry8vPDVV18hIyNDjdERlT/W1tYFFkErVKggN8LnQzQt9yIiIqKSU+uIHm9vb1y/fh1eXl5o3LgxVqxYASMjI2zcuBGVK1cu0207OTnh+fPncm3Pnz+HlZVVgaN5AMDY2BjGxsZlGhcRka6xs7ODnZ2d+Hrnzp148uQJ/v77byxbtkyNkRFpH3XmXkRERKQaai30zJkzB2lpaQCARYsWoUuXLmjevDns7Ozwxx9/lOm2AwICcPjwYbm248ePIyAgoEy3S0REhRs9ejQqVaoEfX196Om9HXgqk8nwxRdfoG/fvmjbti0nbiYqJXXmXkRERKQaEiHvdgvlxOvXr2Fra1viJD41NRX3798HAPj5+eHbb79F69atUaFCBbi7u2PmzJmIjY3Ftm3bALy9vbq3tzcCAwMxYsQInDx5EhMnTsTff/9d7LtupaSkwNraGsnJybCysirZjhIRUbEdOnQIXbt2hZWVFZ4+fSpeekKK4XmMgNLnXurAzywREWkqVZ7D1DqipyAVKlQoVb8rV66gdevW4uugoCAAwNChQ7F161bExcUhJiZGfN/Lywt///03pkyZgh9++AGurq74+eefeWt1IqJyqG7dupg0aRJsbW3lijx79+5F27ZtYW1trcboiDRbaXMvIiIiKp/K3YgeTcJvlYiI1CcqKgo1a9aEtbU1Hj58yD9WS4HnMdI0/MwSEZGm0ukRPURERMXx6tUr1KlTB1WqVJEr8iQkJMDBwUGNkRERERERqQ8LPUREpJGaNm2KyMhIJCcni20pKSmoXr06/P398fvvv6NixYpqjJCIiIiISPX01B0AERFRaUkkEtjY2Iivz549i9TUVDx9+lTulu1Xr15FYmKiGiIkIiIiIlItjughIiKt0alTJzx8+BCxsbHirdkFQUCXLl0QHx+PS5cuoWHDhgDe3rI9bxkiIiIiIm3BDJeIiLSKu7s7AgICxNevXr2CtbU1jI2N4ePjI7avXLkSVapUwZo1a9QRJhERERFRmWChh4iItJq9vT3u3LmD+Ph4mJiYiO1nz57Fw4cPkZubK7alpqaib9++WL16NaRSqTrCJSIiIiJSCC/dIiIinfDuXD4AsH37dvz333+oXbu22Hb+/Hns3r0bFy9exMSJE8X2o0ePwsLCAv7+/jA2NlZVyEREREREJcZCDxER6SQrKyt06NBBrq1KlSpYvHgxjIyM5NqnTp2KmzdvYt++fejZsyeAt6N/BEGApaWlymImIiIiIioKL90iIiL6/ypXrozZs2dj2rRpYltubi5q1qwJBwcHfPTRR2L777//DltbW4wZM0ZuHXFxcZDJZCqLmYiIiIjoXRzRQ0REVAgDAwPs2bMHgiBAIpGI7Tdv3oRUKkXFihXFtpycHLi6usLQ0BAxMTFwcHAAAISHh+Pp06eoW7cu3N3dVb4PRERERKQ7OKKHiIioGN4t8gDA999/j5iYGIwbN05se/bsGfT09KCnpydXANq8eTO6du2KtWvXim25ubkYPnw4vv76a2RmZpb9DhARERGRTmChh4iIqJTc3Nzg7Owsvvbw8EBGRgbu3r0rVxhydnZGvXr1UKdOHbEtJiYGW7duxZIlS+TmBJozZw78/Pywbds2sU0qleLBgwdydwgjIiIiIioIL90iIiJSIgMDA7i6usq1zZw5EzNnzpRrMzMzw+LFi5GWlgY9vf/73iUiIgLh4eFIT08X2548eYKqVavC3NwcKSkpcssTEREREb2LhR4iIiI1cHJywuzZs/O1f/fdd/j888/h4+MjtsXFxcHY2Biurq4s8hARERFRoVjoISIiKkeqVKmCKlWqyLUFBAQgPT0diYmJaoqKiIiIiDQFvxYkIiLSAHp6erCzs1N3GERERERUzrHQQ0RERERERESkJVjoISIiIiIiIiLSEiz0EBERERERERFpCRZ6iIiIiIiIiIi0BO+6pQBBEAAAKSkpao6EiIio5PLOX3nnM6LyjrkXERFpKlXmXSz0KODNmzcAADc3NzVHQkREVHpv3ryBtbW1usMgKhJzLyIi0nSqyLskAr/GKzWZTIZnz57B0tISEolE7r2UlBS4ubnhyZMnsLKyUlOEqqWL+wzo5n5zn3VjnwHd3G9d2mdBEPDmzRu4uLhAT49Xc1P5V1juVRq69PNeHDwe+fGY5MdjIo/HIz8eE3l5xyMmJgYSiUQleRdH9ChAT08Prq6uhS5jZWWlcx9uXdxnQDf3m/usO3Rxv3VlnzmShzRJcXKv0tCVn/fi4vHIj8ckPx4TeTwe+fGYyLO2tlbZ8eDXd0REREREREREWoKFHiIiIiIiIiIiLcFCTxkxNjbG/PnzYWxsrO5QVEYX9xnQzf3mPusOXdxvXdxnIl3Fn3d5PB758Zjkx2Mij8cjPx4Teeo4HpyMmYiIiIiIiIhIS3BEDxERERERERGRlmChh4iIiIiIiIhIS7DQQ0RERERERESkJVjoISIiIiIiIiLSEiz0lIG1a9fC09MTJiYmaNy4MS5duqTukJQmODgYDRs2hKWlJRwcHNCjRw9ERUXJLZOZmYnAwEDY2dnBwsICvXv3xvPnz9UUsfItW7YMEokEkydPFtu0dZ9jY2MxePBg2NnZwdTUFD4+Prhy5Yr4viAImDdvHpydnWFqaoq2bdvi3r17aoxYMVKpFHPnzoWXlxdMTU1RpUoVfP3113h3znpt2OfQ0FB07doVLi4ukEgkOHDggNz7xdnH169fY9CgQbCysoKNjQ1GjhyJ1NRUFe5FyRS2zzk5OZg+fTp8fHxgbm4OFxcXDBkyBM+ePZNbh6btMxEVTpvztXcpK3eLiYlB586dYWZmBgcHB0ybNg25ubmq3JUyUdq8TtuOhzJyPm06TyorJ9TkY6KqfDEiIgLNmzeHiYkJ3NzcsGLFirLetVJRVS6ptOMhkFLt3LlTMDIyEjZv3izcvHlTGD16tGBjYyM8f/5c3aEpRfv27YUtW7YIN27cEMLDw4VOnToJ7u7uQmpqqrjMmDFjBDc3N+HEiRPClStXhCZNmghNmzZVY9TKc+nSJcHT01OoW7euMGnSJLFdG/f59evXgoeHhzBs2DDh4sWLwsOHD4WjR48K9+/fF5dZtmyZYG1tLRw4cEC4fv260K1bN8HLy0vIyMhQY+Slt2TJEsHOzk44dOiQEB0dLezevVuwsLAQfvjhB3EZbdjnw4cPC7Nnzxb27dsnABD2798v935x9rFDhw6Cr6+vcOHCBeHMmTNC1apVhQEDBqh4T4qvsH1OSkoS2rZtK/zxxx/CnTt3hPPnzwuNGjUSGjRoILcOTdtnIvowbc/X3qWM3C03N1fw9vYW2rZtK4SFhQmHDx8W7O3thZkzZ6pjl5SmtHmdth0PZeV82nSeVFZOqMnHRBX5YnJysuDo6CgMGjRIuHHjhvD7778Lpqamwk8//aSq3Sw2VeSSyjweLPQoWaNGjYTAwEDxtVQqFVxcXITg4GA1RlV2EhISBADC6dOnBUF4+yE3NDQUdu/eLS5z+/ZtAYBw/vx5dYWpFG/evBGqVasmHD9+XGjZsqWYEGjrPk+fPl346KOPPvi+TCYTnJychJUrV4ptSUlJgrGxsfD777+rIkSl69y5szBixAi5tl69egmDBg0SBEE79/n9E1Vx9vHWrVsCAOHy5cviMkeOHBEkEokQGxursthLq6Bk5X2XLl0SAAiPHz8WBEHz95mI5Olavvau0uRuhw8fFvT09IT4+HhxmfXr1wtWVlZCVlaWandASRTJ67TteCgj59O286QyckJtOiZllS+uW7dOsLW1lfu5mT59ulCjRo0y3iPFlFUuqczjwUu3lCg7OxtXr15F27ZtxTY9PT20bdsW58+fV2NkZSc5ORkAUKFCBQDA1atXkZOTI3cMatasCXd3d40/BoGBgejcubPcvgHau89//vkn/P390adPHzg4OMDPzw+bNm0S34+OjkZ8fLzcfltbW6Nx48Yau99NmzbFiRMncPfuXQDA9evXcfbsWXTs2BGAdu7z+4qzj+fPn4eNjQ38/f3FZdq2bQs9PT1cvHhR5TGXheTkZEgkEtjY2ADQjX0m0hW6mK+9qzS52/nz5+Hj4wNHR0dxmfbt2yMlJQU3b95UYfTKo0hep23HQxk5n7adJ5WRE2rbMXmXsvb//PnzaNGiBYyMjMRl2rdvj6ioKCQmJqpob8pGaXJJZR4PA8V3gfK8fPkSUqlU7pc+ADg6OuLOnTtqiqrsyGQyTJ48Gc2aNYO3tzcAID4+HkZGRuIHOo+joyPi4+PVEKVy7Ny5E9euXcPly5fzvaet+/zw4UOsX78eQUFBmDVrFi5fvoyJEyfCyMgIQ4cOFfetoM+7pu73jBkzkJKSgpo1a0JfXx9SqRRLlizBoEGDAEAr9/l9xdnH+Ph4ODg4yL1vYGCAChUqaMVxyMzMxPTp0zFgwABYWVkB0P59JtIlupavvau0uVt8fHyBxyvvPU2jaF6nbcdDGTmftp0nlZETatsxeZey9j8+Ph5eXl751pH3nq2tbZnEX9ZKm0sq83iw0EOlFhgYiBs3buDs2bPqDqVMPXnyBJMmTcLx48dhYmKi7nBURiaTwd/fH0uXLgUA+Pn54caNG9iwYQOGDh2q5ujKxq5du7B9+3bs2LEDderUQXh4OCZPngwXFxet3WeSl5OTg759+0IQBKxfv17d4RARKZWu5G6F0dW8rjC6mPMVhTkhlVZ5ySV56ZYS2dvbQ19fP9+s/M+fP4eTk5Oaoiob48ePx6FDh3Dq1Cm4urqK7U5OTsjOzkZSUpLc8pp8DK5evYqEhATUr18fBgYGMDAwwOnTp7F69WoYGBjA0dFR6/YZAJydnVG7dm25tlq1aiEmJgYAxH3Tps/7tGnTMGPGDPTv3x8+Pj747LPPMGXKFAQHBwPQzn1+X3H20cnJCQkJCXLv5+bm4vXr1xp9HPJOzI8fP8bx48fFb2AA7d1nIl2kS/nauxTJ3ZycnAo8XnnvaRJl5HXadDwA5eR82naeVEZOqG3H5F3K2n9t+1lSNJdU5vFgoUeJjIyM0KBBA5w4cUJsk8lkOHHiBAICAtQYmfIIgoDx48dj//79OHnyZL6hZQ0aNIChoaHcMYiKikJMTIzGHoM2bdogMjIS4eHh4sPf3x+DBg0Sn2vbPgNAs2bN8t1+9e7du/Dw8AAAeHl5wcnJSW6/U1JScPHiRY3d7/T0dOjpyf9a1NfXh0wmA6Cd+/y+4uxjQEAAkpKScPXqVXGZkydPQiaToXHjxiqPWRnyTsz37t3Dv//+Czs7O7n3tXGfiXSVLuRr71JG7hYQEIDIyEi5P1Ly/oh5v0BQ3ikjr9Om4wEoJ+fTtvOkMnJCbTsm71LW/gcEBCA0NBQ5OTniMsePH0eNGjU07rItZeSSSj0eJZ6+mQq1c+dOwdjYWNi6datw69Yt4fPPPxdsbGzkZuXXZGPHjhWsra2FkJAQIS4uTnykp6eLy4wZM0Zwd3cXTp48KVy5ckUICAgQAgIC1Bi18r17dwZB0M59vnTpkmBgYCAsWbJEuHfvnrB9+3bBzMxM+O2338Rlli1bJtjY2AgHDx4UIiIihO7du2vcrcbfNXToUKFSpUrirTT37dsn2NvbC1999ZW4jDbs85s3b4SwsDAhLCxMACB8++23QlhYmHhXgOLsY4cOHQQ/Pz/h4sWLwtmzZ4Vq1aqV69uFFrbP2dnZQrdu3QRXV1chPDxc7nfbu3c90LR9JqIP0/Z87V3KyN3ybiferl07ITw8XPjnn3+EihUrauztxN9X0rxO246HsnI+bTpPKisn1ORjoop8MSkpSXB0dBQ+++wz4caNG8LOnTsFMzOzcnl7dVXkkso8Hiz0lIEff/xRcHd3F4yMjIRGjRoJFy5cUHdISgOgwMeWLVvEZTIyMoRx48YJtra2gpmZmdCzZ08hLi5OfUGXgfcTAm3d57/++kvw9vYWjI2NhZo1awobN26Ue18mkwlz584VHB0dBWNjY6FNmzZCVFSUmqJVXEpKijBp0iTB3d1dMDExESpXrizMnj1b7he0NuzzqVOnCvw5Hjp0qCAIxdvHV69eCQMGDBAsLCwEKysrYfjw4cKbN2/UsDfFU9g+R0dHf/B326lTp8R1aNo+E1HhtDlfe5eycrdHjx4JHTt2FExNTQV7e3th6tSpQk5Ojor3pmyUJq/TtuOhjJxPm86TysoJNfmYqCpfvH79uvDRRx8JxsbGQqVKlYRly5apahdLRFW5pLKOh0QQBKFkY4CIiIiIiIiIiKg84hw9RERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iUipBEAAACxYskHtNREREROrB/IxIt0gE/pQTkRKtW7cOBgYGuHfvHvT19dGxY0e0bNlS3WERERER6SzmZ0S6hSN6iEipxo0bh+TkZKxevRpdu3YtVhLRqlUrSCQSSCQShIeHl32Q7xk2bJi4/QMHDqh8+0RERERlqaT5WWlyM+ZTROUHCz1EpFQbNmyAtbU1Jk6ciL/++gtnzpwpVr/Ro0cjLi4O3t7eZRxhfj/88APi4uJUvl0iIiIiZZoyZQp69eqVr700+VlJczPmU0Tlh4G6AyAi7fLFF19AIpFgwYIFWLBgQbGvATczM4OTk1MZR1cwa2trWFtbq2XbRERERMpy6dIldO7cOV97afKzkuZmzKeIyg+O6CGiElm6dKk4LPfdx/fffw8AkEgkAP5vsr+81yXVqlUrTJgwAZMnT4atrS0cHR2xadMmpKWlYfjw4bC0tETVqlVx5MgRpfQjIiIi0lTZ2dkwNDTEf//9h9mzZ0MikaBJkybi+8rKz/bs2QMfHx+YmprCzs4Obdu2RVpamsLxE5FysdBDRCUyYcIExMXFiY/Ro0fDw8MDn376qdK39csvv8De3h6XLl3ChAkTMHbsWPTp0wdNmzbFtWvX0K5dO3z22WdIT09XSj8iIiIiTWRgYIBz584BAMLDwxEXF4d//vlHqduIi4vDgAEDMGLECNy+fRshISHo1asX7+BFVA6x0ENEJWJpaQknJyc4OTlh7dq1OHbsGEJCQuDq6qr0bfn6+mLOnDmoVq0aZs6cCRMTE9jb22P06NGoVq0a5s2bh1evXiEiIkIp/YiIiIg0kZ6eHp49ewY7Ozv4+vrCyckJNjY2St1GXFwccnNz0atXL3h6esLHxwfjxo2DhYWFUrdDRIpjoYeISmXevHn49ddfERISAk9PzzLZRt26dcXn+vr6sLOzg4+Pj9jm6OgIAEhISFBKPyIiIiJNFRYWBl9f3zJbv6+vL9q0aQMfHx/06dMHmzZtQmJiYpltj4hKj4UeIiqx+fPnY9u2bWVa5AEAQ0NDudcSiUSuLe/6cplMppR+RERERJoqPDy8TAs9+vr6OH78OI4cOYLatWvjxx9/RI0aNRAdHV1m2ySi0mGhh4hKZP78+fjll1/KvMhDRERERMUXGRmJevXqlek2JBIJmjVrhoULFyIsLAxGRkbYv39/mW6TiEqOt1cnomJbvHgx1q9fjz///BMmJiaIj48HANja2sLY2FjN0RERERHpLplMhqioKDx79gzm5uZKv9X5xYsXceLECbRr1w4ODg64ePEiXrx4gVq1ail1O0SkOI7oIaJiEQQBK1euxIsXLxAQEABnZ2fxwUmNiYiIiNRr8eLF2Lp1KypVqoTFixcrff1WVlYIDQ1Fp06dUL16dcyZMwerVq1Cx44dlb4tIlIMR/QQUbFIJBIkJyerbHshISH52h49epSv7f1bepa2HxEREZEmGzx4MAYPHlxm669Vq5bSb9lORGWDI3qIqFxYt24dLCwsEBkZqfJtjxkzhrcGJSIiInpHSXMz5lNE5YdE4NfaRKRmsbGxyMjIAAC4u7vDyMhIpdtPSEhASkoKAMDZ2Rnm5uYq3T4RERFReVKa3Iz5FFH5wUIPEREREREREZGW4KVbRERERERERERagoUeIiIiIiIiIiItwUIPEREREREREZGWYKGHiIiIiIiIiEhLsNBDRERERERERKQlWOghIiIiIiIiItISLPQQEREREREREWkJFnqIiIiIiIiIiLQECz1ERERERERERFqChR4iIiIiIiIiIi3BQg8RERERERERkZb4fy14mWAONTr2AAAAAElFTkSuQmCC",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
@@ -683,7 +687,7 @@
                   "outputs": [
                         {
                               "data": {
-                                    "image/png": 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",
                                     "text/plain": [
                                           "<Figure size 1300x700 with 5 Axes>"
                                     ]
diff --git a/pybamm/models/submodels/thermal/lumped.py b/pybamm/models/submodels/thermal/lumped.py
index 62c147755b..0f396a3f77 100644
--- a/pybamm/models/submodels/thermal/lumped.py
+++ b/pybamm/models/submodels/thermal/lumped.py
@@ -56,10 +56,9 @@ def set_rhs(self, variables):
         # Newton cooling, accounting for surface area to volume ratio
         cell_surface_area = self.param.A_cooling
         cell_volume = self.param.V_cell
-        total_cooling_coefficient = (
-            -self.param.h_total * cell_surface_area / cell_volume
+        Q_cool_vol_av = (
+            -self.param.h_total * (T_vol_av - T_amb) * cell_surface_area / cell_volume
         )
-        Q_cool_vol_av = total_cooling_coefficient * (T_vol_av - T_amb)
 
         self.rhs = {
             T_vol_av: (Q_vol_av + Q_cool_vol_av) / self.param.rho_c_p_eff(T_vol_av)
diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
index a6555170fc..2611dbafdc 100644
--- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
+++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_1D_current_collectors.py
@@ -58,33 +58,29 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
-        # Account for surface area to volume ratio of pouch cell in surface and side
-        # cooling terms
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
+        # Calculate cooling, accounting for surface area to volume ratio of pouch cell
+        edge_area = self.param.L_z * self.param.L
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = (
             -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
+            * (T_av - T_amb)
             * yz_surface_area
             / cell_volume
         )
-
-        side_edge_area = self.param.L_z * self.param.L
-        side_edge_cooling_coefficient = (
+        Q_edge = (
             -(self.param.h_edge(0, z) + self.param.h_edge(self.param.L_y, z))
-            * side_edge_area
+            * (T_av - T_amb)
+            * edge_area
             / cell_volume
         )
-
-        total_cooling_coefficient = (
-            yz_surface_cooling_coefficient + side_edge_cooling_coefficient
-        )
+        Q_cool_total = Q_yz_surface + Q_edge
 
         self.rhs = {
             T_av: (
                 pybamm.div(self.param.lambda_eff(T_av) * pybamm.grad(T_av))
                 + Q_av
-                + total_cooling_coefficient * (T_av - T_amb)
+                + Q_cool_total
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -94,7 +90,7 @@ def set_boundary_conditions(self, variables):
         T_amb = variables["Ambient temperature [K]"]
         T_av = variables["X-averaged cell temperature [K]"]
 
-        # find tab locations (top vs bottom)
+        # Find tab locations (top vs bottom)
         L_y = param.L_y
         L_z = param.L_z
         neg_tab_z = param.n.centre_z_tab
@@ -104,11 +100,10 @@ def set_boundary_conditions(self, variables):
         pos_tab_top_bool = pybamm.Equality(pos_tab_z, L_z)
         pos_tab_bottom_bool = pybamm.Equality(pos_tab_z, 0)
 
-        # calculate tab vs non-tab area on top and bottom
+        # Calculate tab vs non-tab area on top and bottom
         neg_tab_area = param.n.L_tab * param.n.L_cc
         pos_tab_area = param.p.L_tab * param.p.L_cc
         total_area = param.L * param.L_y
-
         non_tab_top_area = (
             total_area
             - neg_tab_area * neg_tab_top_bool
@@ -120,18 +115,22 @@ def set_boundary_conditions(self, variables):
             - pos_tab_area * pos_tab_bottom_bool
         )
 
-        # calculate effective cooling coefficients
+        # Calculate heat fluxes weighted by area
         # Note: can't do y-average of h_edge here since y isn't meshed. Evaluate at
         # midpoint.
-        top_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_top_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_top_bool
-            + param.h_edge(L_y / 2, L_z) * non_tab_top_area
+        q_tab_n = -param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -param.p.h_tab * (T_av - T_amb)
+        q_edge_top = -param.h_edge(L_y / 2, L_z) * (T_av - T_amb)
+        q_edge_bottom = -param.h_edge(L_y / 2, 0) * (T_av - T_amb)
+        q_top = (
+            q_tab_n * neg_tab_area * neg_tab_top_bool
+            + q_tab_p * pos_tab_area * pos_tab_top_bool
+            + q_edge_top * non_tab_top_area
         ) / total_area
-        bottom_cooling_coefficient = (
-            param.n.h_tab * neg_tab_area * neg_tab_bottom_bool
-            + param.p.h_tab * pos_tab_area * pos_tab_bottom_bool
-            + param.h_edge(L_y / 2, 0) * non_tab_bottom_area
+        q_bottom = (
+            q_tab_n * neg_tab_area * neg_tab_bottom_bool
+            + q_tab_p * pos_tab_area * pos_tab_bottom_bool
+            + q_edge_bottom * non_tab_bottom_area
         ) / total_area
 
         # just use left and right for clarity
@@ -141,21 +140,14 @@ def set_boundary_conditions(self, variables):
         self.boundary_conditions = {
             T_av: {
                 "left": (
-                    pybamm.boundary_value(
-                        bottom_cooling_coefficient * (T_av - T_amb),
-                        "left",
-                    )
-                    / pybamm.boundary_value(lambda_eff, "left"),
+                    pybamm.boundary_value(-q_bottom / lambda_eff, "left"),
                     "Neumann",
                 ),
                 "right": (
-                    pybamm.boundary_value(
-                        -top_cooling_coefficient * (T_av - T_amb), "right"
-                    )
-                    / pybamm.boundary_value(lambda_eff, "right"),
+                    pybamm.boundary_value(q_top / lambda_eff, "right"),
                     "Neumann",
                 ),
-            }
+            },
         }
 
     def set_initial_conditions(self, variables):
diff --git a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
index eb8e1b7e49..a5c7c42b17 100644
--- a/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
+++ b/pybamm/models/submodels/thermal/pouch_cell/pouch_cell_2D_current_collectors.py
@@ -58,20 +58,22 @@ def set_rhs(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate cooling
+        Q_yz_surface_W_per_m2 = -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z)) * (
+            T_av - T_amb
+        )
+        Q_edge_W_per_m2 = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Account for surface area to volume ratio of pouch cell in surface cooling
         # term
-        cell_volume = self.param.L * self.param.L_y * self.param.L_z
-
         yz_surface_area = self.param.L_y * self.param.L_z
-        yz_surface_cooling_coefficient = (
-            -(self.param.n.h_cc(y, z) + self.param.p.h_cc(y, z))
-            * yz_surface_area
-            / cell_volume
+        cell_volume = self.param.L * self.param.L_y * self.param.L_z
+        Q_yz_surface = pybamm.source(
+            Q_yz_surface_W_per_m2 * yz_surface_area / cell_volume, T_av
         )
-
         # Edge cooling appears as a boundary term, so no need to account for surface
         # area to volume ratio
-        edge_cooling_coefficient = -self.param.h_edge(y, z)
+        Q_edge = pybamm.source(Q_edge_W_per_m2, T_av, boundary=True)
 
         # Governing equations contain:
         #   - source term for y-z surface cooling
@@ -88,10 +90,8 @@ def set_rhs(self, variables):
             T_av: (
                 self.param.lambda_eff(T_av) * pybamm.laplacian(T_av)
                 + pybamm.source(Q_av, T_av)
-                + pybamm.source(yz_surface_cooling_coefficient * (T_av - T_amb), T_av)
-                + pybamm.source(
-                    edge_cooling_coefficient * (T_av - T_amb), T_av, boundary=True
-                )
+                + Q_yz_surface
+                + Q_edge
             )
             / self.param.rho_c_p_eff(T_av)
         }
@@ -102,24 +102,21 @@ def set_boundary_conditions(self, variables):
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
 
+        # Calculate heat fluxes
+        q_tab_n = -self.param.n.h_tab * (T_av - T_amb)
+        q_tab_p = -self.param.p.h_tab * (T_av - T_amb)
+        q_edge = -self.param.h_edge(y, z) * (T_av - T_amb)
+
         # Subtract the edge cooling from the tab portion so as to not double count
         # Note: tab cooling is also only applied on the current collector hence
-        # the (l_cn / l) and (l_cp / l) prefactors. We also still have edge cooling
+        # the (l_cn / l) and (l_cp / l) prefactors. We still have edge cooling
         # in the region: x in (0, 1)
-        h_tab_n_corrected = (self.param.n.L_cc / self.param.L) * (
-            self.param.n.h_tab - self.param.h_edge(y, z)
-        )
-        h_tab_p_corrected = (self.param.p.L_cc / self.param.L) * (
-            self.param.p.h_tab - self.param.h_edge(y, z)
-        )
-
-        negative_tab_bc = pybamm.boundary_value(
-            -h_tab_n_corrected * (T_av - T_amb) / self.param.n.lambda_cc(T_av),
+        negative_tab_bc = (self.param.n.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_n - q_edge) / self.param.n.lambda_cc(T_av),
             "negative tab",
         )
-        positive_tab_bc = pybamm.boundary_value(
-            -h_tab_p_corrected * (T_av - T_amb) / self.param.p.lambda_cc(T_av),
-            "positive tab",
+        positive_tab_bc = (self.param.p.L_cc / self.param.L) * pybamm.boundary_value(
+            (q_tab_p - q_edge) / self.param.p.lambda_cc(T_av), "positive tab"
         )
 
         self.boundary_conditions = {

From e8610dd3acabaed3185643a9424be9181c647146 Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Thu, 12 Oct 2023 00:30:57 +0530
Subject: [PATCH 187/615] Update noxfile.py

- Altered and removed the darwin platform
---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 262af5892a..3f14381886 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -121,7 +121,7 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    if sys.platform == "linux" or sys.platform == "darwin":
+    if sys.platform == "linux":
         session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True)
     else:
         session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)

From 7e1930cdd129e5cb36e668c18bd93591f01da018 Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Thu, 12 Oct 2023 15:42:30 +0000
Subject: [PATCH 188/615] #3431 fix some vstudio compile errors

---
 pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp        | 2 +-
 pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
index c1c71a967d..ad51eda4e1 100644
--- a/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/CasadiSolverOpenMP.cpp
@@ -275,7 +275,7 @@ Solution CasadiSolverOpenMP::solve(
 
   // set inputs
   auto p_inputs = inputs.unchecked<2>();
-  for (uint i = 0; i < functions->inputs.size(); i++)
+  for (int i = 0; i < functions->inputs.size(); i++)
     functions->inputs[i] = p_inputs(i, 0);
 
   // set initial conditions
diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
index 4f31dbe57d..0c188f6304 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
@@ -219,7 +219,8 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
         jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
       }
     } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) {
-      realtype newjac[SUNSparseMatrix_NNZ(JJ)];
+      const int JJ_nnz = SUNSparseMatrix_NNZ(JJ);
+      realtype newjac[JJ_nnz];
       sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ);
       sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ);
 

From 75f87071b42b5430f943c671b511473ac755aa23 Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Thu, 12 Oct 2023 16:29:00 +0000
Subject: [PATCH 189/615] #3431 make nnz a constant

---
 pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
index 0c188f6304..b3eb5d9f66 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
@@ -219,8 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
         jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
       }
     } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) {
-      const int JJ_nnz = SUNSparseMatrix_NNZ(JJ);
-      realtype newjac[JJ_nnz];
+      realtype newjac[SM_NNZ_S(JJ)];
       sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ);
       sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ);
 

From 10e94f7c905bc07f8d83b82b5601a7e2eef35dcd Mon Sep 17 00:00:00 2001
From: martinjrobins <martinjrobins@gmail.com>
Date: Thu, 12 Oct 2023 16:31:43 +0000
Subject: [PATCH 190/615] #3431 revert to SUNSparseMatrix_NNZ

---
 pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
index b3eb5d9f66..4f31dbe57d 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
@@ -219,7 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
         jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
       }
     } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) {
-      realtype newjac[SM_NNZ_S(JJ)];
+      realtype newjac[SUNSparseMatrix_NNZ(JJ)];
       sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ);
       sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ);
 

From a064a8a2835acb7cf11e316595799bb9c7df089c Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 12 Oct 2023 14:17:13 -0400
Subject: [PATCH 191/615] #3428 exchange-current density error

---
 pybamm/parameters/parameter_values.py         | 20 ++++++++++++++++++-
 .../test_parameters/test_parameter_values.py  | 13 ++++++++++++
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 136d9737aa..e69291035d 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -119,7 +119,25 @@ def create_from_bpx(filename, target_soc=1):
         return pybamm.ParameterValues(pybamm_dict)
 
     def __getitem__(self, key):
-        return self._dict_items[key]
+        try:
+            return self._dict_items[key]
+        except KeyError as err:
+            if (
+                "Exchange-current density for lithium metal electrode [A.m-2]"
+                in err.args[0]
+                and "Exchange-current density for plating [A.m-2]" in self._dict_items
+            ):
+                raise KeyError(
+                    "'Exchange-current density for plating [A.m-2]' has been renamed "
+                    "to 'Exchange-current density for lithium metal electrode [A.m-2]' "
+                    "when referring to the reaction at the surface of a lithium metal "
+                    "electrode. This is to avoid confusion with the exchange-current "
+                    "density for the lithium plating reaction in a porous negative "
+                    "electrode. To avoid this error, change your parameter file to use "
+                    "the new name."
+                )
+            else:
+                raise err
 
     def get(self, key, default=None):
         """Return item corresponding to key if it exists, otherwise return default"""
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index c6a4831e86..cc1f954686 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -977,6 +977,19 @@ def test_evaluate(self):
         with self.assertRaises(ValueError):
             parameter_values.evaluate(y)
 
+    def test_exchange_current_density_plating(self):
+        parameter_values = pybamm.ParameterValues(
+            {"Exchange-current density for plating [A.m-2]": 1}
+        )
+        param = pybamm.Parameter(
+            "Exchange-current density for lithium metal electrode [A.m-2]"
+        )
+        with self.assertRaisesRegex(
+            KeyError,
+            "referring to the reaction at the surface of a lithium metal electrode",
+        ):
+            parameter_values.evaluate(param)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From f7e59ad0bcc8c2feebd409eb88d7f92f5746576d Mon Sep 17 00:00:00 2001
From: Akhilender Bongirwar
 <112749383+akhilender-bongirwar@users.noreply.github.com>
Date: Fri, 13 Oct 2023 12:00:29 +0530
Subject: [PATCH 192/615] Updated styles

- Made a single line of 89 characters into few smaller lines
---
 noxfile.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 3f14381886..615da67ef4 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -122,7 +122,13 @@ def set_dev(session):
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     if sys.platform == "linux":
-        session.run(python, "-m", "pip", "install", ".[all,dev,jax,odes]", external=True)
+        session.run(python, 
+                    "-m", 
+                    "pip", 
+                    "install", 
+                    ".[all,dev,jax,odes]", 
+                    external=True,
+        )
     else:
         session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
 

From 762941481d96a74bd0807633dc3fafb10ee673ff Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 13 Oct 2023 06:30:42 +0000
Subject: [PATCH 193/615] style: pre-commit fixes

---
 noxfile.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 615da67ef4..430ad59659 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -122,11 +122,11 @@ def set_dev(session):
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
     if sys.platform == "linux":
-        session.run(python, 
-                    "-m", 
-                    "pip", 
-                    "install", 
-                    ".[all,dev,jax,odes]", 
+        session.run(python,
+                    "-m",
+                    "pip",
+                    "install",
+                    ".[all,dev,jax,odes]",
                     external=True,
         )
     else:

From 9c474fb241d924e8a29b172b74ad1d5521589e65 Mon Sep 17 00:00:00 2001
From: John Brittain <jsb1982@gmail.com>
Date: Fri, 13 Oct 2023 09:31:03 +0100
Subject: [PATCH 194/615] #3431 refactor variable length array to std::vector

---
 .../solvers/c_solvers/idaklu/casadi_sundials_functions.cpp  | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
index 4f31dbe57d..b0ea180641 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_sundials_functions.cpp
@@ -219,7 +219,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
         jac_colptrs[i] = p_jac_times_cjmass_colptrs[i];
       }
     } else if (SUNSparseMatrix_SparseType(JJ) == CSR_MAT) {
-      realtype newjac[SUNSparseMatrix_NNZ(JJ)];
+      std::vector<realtype> newjac(SUNSparseMatrix_NNZ(JJ));
       sunindextype *jac_ptrs = SUNSparseMatrix_IndexPointers(JJ);
       sunindextype *jac_vals = SUNSparseMatrix_IndexValues(JJ);
 
@@ -229,7 +229,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
       p_python_functions->jac_times_cjmass.m_arg[2] =
         p_python_functions->inputs.data();
       p_python_functions->jac_times_cjmass.m_arg[3] = &cj;
-      p_python_functions->jac_times_cjmass.m_res[0] = newjac;
+      p_python_functions->jac_times_cjmass.m_res[0] = newjac.data();
       p_python_functions->jac_times_cjmass();
 
       // convert (casadi's) CSC format to CSR
@@ -237,7 +237,7 @@ int jacobian_casadi(realtype tt, realtype cj, N_Vector yy, N_Vector yp,
           std::remove_pointer_t<decltype(p_python_functions->jac_times_cjmass_rowvals.data())>,
           std::remove_pointer_t<decltype(jac_vals)>
       >(
-        newjac,
+        newjac.data(),
         p_python_functions->jac_times_cjmass_rowvals.data(),
         p_python_functions->jac_times_cjmass_colptrs.data(),
         jac_data,

From dc41fbbc417e19f39b3ebc17efcf12bad34b19b5 Mon Sep 17 00:00:00 2001
From: John Brittain <jsb1982@gmail.com>
Date: Fri, 13 Oct 2023 10:30:12 +0100
Subject: [PATCH 195/615] another variable length array to std::vector

---
 pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
index d29cb7c961..1a33b957f8 100644
--- a/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
+++ b/pybamm/solvers/c_solvers/idaklu/casadi_functions.hpp
@@ -21,7 +21,7 @@
  */
 template<typename T1, typename T2>
 void csc_csr(const realtype f[], const T1 c[], const T1 r[], realtype nf[], T2 nc[], T2 nr[], int N, int cols) {
-  int nn[cols+1];
+  std::vector<int> nn(cols+1);
   std::vector<int> rr(N);
   for (int i=0; i<cols+1; i++)
     nc[i] = 0;

From 2af75af4ce7a8a37442cdbbbf6634b249733a9ff Mon Sep 17 00:00:00 2001
From: John Brittain <jsb1982@gmail.com>
Date: Fri, 13 Oct 2023 11:17:40 +0100
Subject: [PATCH 196/615] Make variable length arrays a compile error in cmake

---
 CMakeLists.txt | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 38e3d4976c..2a78ee9d62 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,7 +24,10 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
 set(CMAKE_EXPORT_COMPILE_COMMANDS 1)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
-
+if(NOT MSVC)
+    # MSVC does not support variable length arrays (vla)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror=vla")
+endif()
 
 # casadi seems to compile without the newer versions of std::string
 add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)

From dda612a03501f69dd5189b8d660c370cd72ae1fd Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 13 Oct 2023 18:39:41 +0530
Subject: [PATCH 197/615] Add parallel job to test `install_odes`

---
 .github/workflows/test_on_push.yml | 50 ++++++++++++++++++++++++++----
 1 file changed, 44 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index aff1cfbe49..e8c82f5200 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -95,12 +95,6 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all,docs]
 
-      - name: Test pybamm_install_odes on MacOS (for only this PR)
-        if: matrix.os == 'macos-latest'
-        run: |
-          pip install wget
-          pybamm_install_odes
-
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         if: matrix.os == 'ubuntu-latest'
@@ -183,6 +177,50 @@ jobs:
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
 
+  test_install_odes:
+    needs: style
+    runs-on: macos-latest
+    strategy:
+      fail-fast: false
+    name: Test pybamm_install_odes on MacOS
+
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      - name: Install macOS system dependencies
+        if: matrix.os == 'macos-latest'
+        env:
+          # Homebrew environment variables
+          HOMEBREW_NO_INSTALL_CLEANUP: 1
+          HOMEBREW_NO_AUTO_UPDATE: 1
+          HOMEBREW_NO_COLOR: 1
+          # Speed up CI
+          NONINTERACTIVE: 1
+        run: |
+          brew analytics off
+          brew update
+          brew install graphviz openblas
+
+      - name: Set up Python ${{ matrix.python-version }}
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
+
+      - name: Install PyBaMM dependencies
+        run: |
+          pip install --upgrade pip wheel setuptools nox
+          pip install -e .[all,docs]
+
+      - name: Test pybamm_install_odes on MacOS
+        if: matrix.os == 'macos-latest'
+        run: |
+          pip install wget
+          pybamm_install_odes
+
   run_integration_tests:
     needs: style
     runs-on: ${{ matrix.os }}

From 9b0df12acc8351f91f8c7d9f68e9443b6ae23199 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 3 Oct 2023 13:41:14 -0400
Subject: [PATCH 198/615] Minor cleanup before starting

---
 benchmarks/different_model_options.py | 1 +
 benchmarks/memory_unit_benchmarks.py  | 6 +++---
 benchmarks/time_solve_models.py       | 6 ------
 benchmarks/unit_benchmarks.py         | 6 +++---
 tests/testcase.py                     | 2 +-
 5 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 0e9e7bc23b..406ce3b105 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,5 +1,6 @@
 import pybamm
 import numpy as np
+import importlib.util
 
 
 def compute_discretisation(model, param):
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 4b20996b75..867c089236 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -31,7 +31,7 @@ def mem_create_expression(self):
         return self.model
 
 
-class MemParameteriseModel:
+class MemParameteriseModel(MemCreateExpression):
     def setup(self):
         MemCreateExpression.mem_create_expression(self)
 
@@ -58,7 +58,7 @@ def mem_parameterise(self):
         return param
 
 
-class MemDiscretiseModel:
+class MemDiscretiseModel(MemParameteriseModel):
     def setup(self):
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
@@ -76,7 +76,7 @@ def mem_discretise(self):
         return disc
 
 
-class MemSolveModel:
+class MemSolveModel(MemDiscretiseModel):
     def setup(self):
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index f277769497..e17aed824e 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -18,9 +18,7 @@ class TimeSolveSPM:
             "ORegan2022",
             "NCA_Kim2011",
             "Prada2013",
-            # "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
@@ -75,9 +73,7 @@ class TimeSolveSPMe:
             "ORegan2022",
             "NCA_Kim2011",
             "Prada2013",
-            # "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
@@ -130,11 +126,9 @@ class TimeSolveDFN:
         [
             "Marquis2019",
             "ORegan2022",
-            # "NCA_Kim2011",
             "Prada2013",
             "Ai2020",
             "Ramadass2004",
-            # "Mohtat2020",
             "Chen2020",
             "Ecker2015",
         ],
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index acee9c210a..9e743e0577 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -30,7 +30,7 @@ def time_create_expression(self):
         }
 
 
-class TimeParameteriseModel:
+class TimeParameteriseModel(TimeCreateExpression):
     def setup(self):
         TimeCreateExpression.time_create_expression(self)
 
@@ -56,7 +56,7 @@ def time_parameterise(self):
         param.process_geometry(self.geometry)
 
 
-class TimeDiscretiseModel:
+class TimeDiscretiseModel(TimeParameteriseModel):
     def setup(self):
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
@@ -73,7 +73,7 @@ def time_discretise(self):
         disc.process_model(self.model)
 
 
-class TimeSolveModel:
+class TimeSolveModel(TimeDiscretiseModel):
     def setup(self):
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
diff --git a/tests/testcase.py b/tests/testcase.py
index f2daa7ba9a..7438cec241 100644
--- a/tests/testcase.py
+++ b/tests/testcase.py
@@ -13,7 +13,7 @@ def FixRandomSeed(method):
     Wraps a method so that the random seed is set to a hash of the method name
 
     As the wrapper fixes the random seed before calling the method, tests can
-    explicitely reinstate the random seed within their method bodies as desired,
+    explicitly reinstate the random seed within their method bodies as desired,
     e.g. by calling np.random.seed(None) to restore normal behaviour.
 
     Generating a random seed from the method name allows particularly awkward

From 64af493ecf710ef534d9d2f1bfc9744e338f03f8 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 3 Oct 2023 16:01:31 -0400
Subject: [PATCH 199/615] Adding random seed to the setup functions

---
 benchmarks/different_model_options.py    | 36 ++++++++++++++++++++----
 benchmarks/memory_sims.py                |  9 ++++++
 benchmarks/memory_unit_benchmarks.py     |  6 ++++
 benchmarks/time_setup_models_and_sims.py | 18 ++++++++++++
 benchmarks/time_sims_experiments.py      |  1 +
 benchmarks/time_solve_models.py          |  3 ++
 benchmarks/unit_benchmarks.py            |  6 ++++
 pybamm/util.py                           |  4 +++
 8 files changed, 77 insertions(+), 6 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 406ce3b105..ed21d9728b 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -83,11 +83,14 @@ class TimeBuildModelLossActiveMaterial:
         ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Ai2020", model, "loss of active material", params)
 
 
-class TimeSolveLossActiveMaterial:
+class TimeSolveLossActiveMaterial(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -96,6 +99,7 @@ class TimeSolveLossActiveMaterial:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         SolveModel.solve_setup(
             self, "Ai2020", model, "loss of active material", params, solver_class
         )
@@ -111,11 +115,14 @@ class TimeBuildModelLithiumPlating:
         ["none", "irreversible", "reversible", "partially reversible"],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("OKane2022", model, "lithium plating", params)
 
 
-class TimeSolveLithiumPlating:
+class TimeSolveLithiumPlating(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -124,6 +131,7 @@ class TimeSolveLithiumPlating:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         SolveModel.solve_setup(
             self, "OKane2022", model, "lithium plating", params, solver_class
         )
@@ -147,11 +155,14 @@ class TimeBuildModelSEI:
         ],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "SEI", params)
 
 
-class TimeSolveSEI:
+class TimeSolveSEI(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -168,6 +179,7 @@ class TimeSolveSEI:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class)
 
     def time_solve_model(self, model, params, solver_class):
@@ -186,11 +198,14 @@ class TimeBuildModelParticle:
         ],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "particle", params)
 
 
-class TimeSolveParticle:
+class TimeSolveParticle(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -204,6 +219,7 @@ class TimeSolveParticle:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "particle", params, solver_class
         )
@@ -219,11 +235,14 @@ class TimeBuildModelThermal:
         ["isothermal", "lumped", "x-full"],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "thermal", params)
 
 
-class TimeSolveThermal:
+class TimeSolveThermal(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -232,6 +251,7 @@ class TimeSolveThermal:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "thermal", params, solver_class
         )
@@ -247,11 +267,14 @@ class TimeBuildModelSurfaceForm:
         ["false", "differential", "algebraic"],
     )
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "surface form", params)
 
 
-class TimeSolveSurfaceForm:
+class TimeSolveSurfaceForm(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -260,6 +283,7 @@ class TimeSolveSurfaceForm:
     )
 
     def setup(self, model, params, solver_class):
+        pybamm.util.set_random_seed()
         if (model, params, solver_class) == (
             pybamm.lithium_ion.SPM,
             "differential",
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index 1857873476..c58c005733 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -7,6 +7,9 @@ class MemSPMSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def mem_setup_SPM_simulationCCCV(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPM()
@@ -51,6 +54,9 @@ class MemSPMSimulationGITT:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def mem_setup_SPM_simulationGITT(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPM()
@@ -67,6 +73,9 @@ class MemDFNSimulationGITT:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def mem_setup_DFN_simulationGITT(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPM()
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 867c089236..55b7715c18 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -3,6 +3,9 @@
 
 
 class MemCreateExpression:
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def mem_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
         D = pybamm.Parameter("Diffusion coefficient [m2.s-1]")
@@ -33,6 +36,7 @@ def mem_create_expression(self):
 
 class MemParameteriseModel(MemCreateExpression):
     def setup(self):
+        pybamm.util.set_random_seed()
         MemCreateExpression.mem_create_expression(self)
 
     def mem_parameterise(self):
@@ -60,6 +64,7 @@ def mem_parameterise(self):
 
 class MemDiscretiseModel(MemParameteriseModel):
     def setup(self):
+        pybamm.util.set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
 
@@ -78,6 +83,7 @@ def mem_discretise(self):
 
 class MemSolveModel(MemDiscretiseModel):
     def setup(self):
+        pybamm.util.set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
         MemDiscretiseModel.mem_discretise(self)
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 4e9b2423a1..4f77378214 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -34,6 +34,9 @@ class TimeBuildSPM:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_SPM(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPM()
@@ -45,6 +48,9 @@ class TimeBuildSPMe:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_SPMe(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPMe()
@@ -56,6 +62,9 @@ class TimeBuildDFN:
     param_names = ["parameter"]
     params = parameters
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_DFN(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.DFN()
@@ -67,6 +76,9 @@ class TimeBuildSPMSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_SPM_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPM()
@@ -85,6 +97,9 @@ class TimeBuildSPMeSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_SPMe_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.SPMe()
@@ -103,6 +118,9 @@ class TimeBuildDFNSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_setup_DFN_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
         self.model = pybamm.lithium_ion.DFN()
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index 5e05470734..c82f4eabca 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -21,6 +21,7 @@ class TimeSimulation:
     }
 
     def setup(self, experiment, parameters, model_class, solver_class):
+        pybamm.util.set_random_seed()
         if (experiment, parameters, model_class, solver_class) == (
             "GITT",
             "Marquis2019",
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index e17aed824e..7f64e92553 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -29,6 +29,7 @@ class TimeSolveSPM:
     )
 
     def setup(self, solve_first, parameters, solver_class):
+        pybamm.util.set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPM()
         c_rate = 1
@@ -84,6 +85,7 @@ class TimeSolveSPMe:
     )
 
     def setup(self, solve_first, parameters, solver_class):
+        pybamm.util.set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPMe()
         c_rate = 1
@@ -139,6 +141,7 @@ class TimeSolveDFN:
     )
 
     def setup(self, solve_first, parameters, solver_class):
+        pybamm.util.set_random_seed()
         if (parameters, solver_class) == (
             "ORegan2022",
             pybamm.CasadiSolver,
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 9e743e0577..0af55a1bd8 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -3,6 +3,9 @@
 
 
 class TimeCreateExpression:
+    def setup(self):
+        pybamm.util.set_random_seed()
+
     def time_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
         D = pybamm.Parameter("Diffusion coefficient [m2.s-1]")
@@ -32,6 +35,7 @@ def time_create_expression(self):
 
 class TimeParameteriseModel(TimeCreateExpression):
     def setup(self):
+        pybamm.util.set_random_seed()
         TimeCreateExpression.time_create_expression(self)
 
     def time_parameterise(self):
@@ -58,6 +62,7 @@ def time_parameterise(self):
 
 class TimeDiscretiseModel(TimeParameteriseModel):
     def setup(self):
+        pybamm.util.set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
 
@@ -75,6 +80,7 @@ def time_discretise(self):
 
 class TimeSolveModel(TimeDiscretiseModel):
     def setup(self):
+        pybamm.util.set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
         TimeDiscretiseModel.time_discretise(self)
diff --git a/pybamm/util.py b/pybamm/util.py
index 562352bfac..48819f118d 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -27,6 +27,10 @@
 JAXLIB_VERSION = "0.4"
 
 
+def set_random_seed(seed_value=42):
+    np.random.seed(seed_value)
+
+
 def root_dir():
     """return the root directory of the PyBaMM install directory"""
     return str(pathlib.Path(pybamm.__path__[0]).parent)

From 6adee6bbf1d306cb6dcf24cbb9a5f71745b4dc88 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 3 Oct 2023 16:03:13 -0400
Subject: [PATCH 200/615] Pre-commit changes

---
 benchmarks/different_model_options.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index ed21d9728b..b3d5a19488 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,6 +1,5 @@
 import pybamm
 import numpy as np
-import importlib.util
 
 
 def compute_discretisation(model, param):

From fb44d43af2f73102c2a86625f4ce55b836388379 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 3 Oct 2023 16:29:30 -0400
Subject: [PATCH 201/615] Make unit tests use a common seed function

---
 tests/testcase.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/testcase.py b/tests/testcase.py
index 7438cec241..3d931659bd 100644
--- a/tests/testcase.py
+++ b/tests/testcase.py
@@ -3,10 +3,11 @@
 #
 import unittest
 import hashlib
-import numpy as np
 from functools import wraps
 from types import FunctionType
 
+import pybamm
+
 
 def FixRandomSeed(method):
     """
@@ -23,7 +24,7 @@ def FixRandomSeed(method):
 
     @wraps(method)
     def wrapped(*args, **kwargs):
-        np.random.seed(
+        pybamm.util.set_random_seed(
             int(hashlib.sha256(method.__name__.encode()).hexdigest(), 16) % (2**32)
         )
         return method(*args, **kwargs)

From cbb0d209f40f7b43e04bf06762eac98be124921f Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Wed, 4 Oct 2023 13:37:26 -0400
Subject: [PATCH 202/615] Change imports

---
 benchmarks/different_model_options.py    | 1 +
 benchmarks/memory_sims.py                | 1 +
 benchmarks/memory_unit_benchmarks.py     | 1 +
 benchmarks/time_setup_models_and_sims.py | 1 +
 benchmarks/time_sims_experiments.py      | 1 +
 benchmarks/time_solve_models.py          | 1 +
 benchmarks/unit_benchmarks.py            | 1 +
 7 files changed, 7 insertions(+)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index b3d5a19488..218dfaa4fb 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 import numpy as np
 
 
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index c58c005733..c0873ed2b9 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 
 parameters = ["Marquis2019", "Chen2020"]
 
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 55b7715c18..bbef0dbf75 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 import numpy as np
 
 
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 4f77378214..9ff3a2c50d 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 
 parameters = [
     "Marquis2019",
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index c82f4eabca..16dfb4d488 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 
 
 class TimeSimulation:
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index 7f64e92553..7472100b07 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -2,6 +2,7 @@
 # See "Writing benchmarks" in the asv docs for more information.
 
 import pybamm
+import pybamm.util
 import numpy as np
 
 
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 0af55a1bd8..8342b02a18 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -1,4 +1,5 @@
 import pybamm
+import pybamm.util
 import numpy as np
 
 

From a18cf42d782450986416b94ad912e4a351e59741 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Wed, 4 Oct 2023 13:56:56 -0400
Subject: [PATCH 203/615] Changing imports again

---
 benchmarks/different_model_options.py    | 26 ++++++++++++------------
 benchmarks/memory_sims.py                |  8 ++++----
 benchmarks/memory_unit_benchmarks.py     | 10 ++++-----
 benchmarks/time_setup_models_and_sims.py | 14 ++++++-------
 benchmarks/time_sims_experiments.py      |  4 ++--
 benchmarks/time_solve_models.py          |  8 ++++----
 benchmarks/unit_benchmarks.py            | 10 ++++-----
 7 files changed, 40 insertions(+), 40 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 218dfaa4fb..cc1b2e0191 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,5 +1,5 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 import numpy as np
 
 
@@ -84,7 +84,7 @@ class TimeBuildModelLossActiveMaterial:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Ai2020", model, "loss of active material", params)
@@ -99,7 +99,7 @@ class TimeSolveLossActiveMaterial(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Ai2020", model, "loss of active material", params, solver_class
         )
@@ -116,7 +116,7 @@ class TimeBuildModelLithiumPlating:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("OKane2022", model, "lithium plating", params)
@@ -131,7 +131,7 @@ class TimeSolveLithiumPlating(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         SolveModel.solve_setup(
             self, "OKane2022", model, "lithium plating", params, solver_class
         )
@@ -156,7 +156,7 @@ class TimeBuildModelSEI:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "SEI", params)
@@ -179,7 +179,7 @@ class TimeSolveSEI(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class)
 
     def time_solve_model(self, model, params, solver_class):
@@ -199,7 +199,7 @@ class TimeBuildModelParticle:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "particle", params)
@@ -219,7 +219,7 @@ class TimeSolveParticle(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "particle", params, solver_class
         )
@@ -236,7 +236,7 @@ class TimeBuildModelThermal:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "thermal", params)
@@ -251,7 +251,7 @@ class TimeSolveThermal(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         SolveModel.solve_setup(
             self, "Marquis2019", model, "thermal", params, solver_class
         )
@@ -268,7 +268,7 @@ class TimeBuildModelSurfaceForm:
     )
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "surface form", params)
@@ -283,7 +283,7 @@ class TimeSolveSurfaceForm(SolveModel):
     )
 
     def setup(self, model, params, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         if (model, params, solver_class) == (
             pybamm.lithium_ion.SPM,
             "differential",
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index c0873ed2b9..edee2c9f11 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 
 parameters = ["Marquis2019", "Chen2020"]
 
@@ -9,7 +9,7 @@ class MemSPMSimulationCCCV:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def mem_setup_SPM_simulationCCCV(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -56,7 +56,7 @@ class MemSPMSimulationGITT:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def mem_setup_SPM_simulationGITT(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -75,7 +75,7 @@ class MemDFNSimulationGITT:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def mem_setup_DFN_simulationGITT(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index bbef0dbf75..218ff8ea0a 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -1,11 +1,11 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 import numpy as np
 
 
 class MemCreateExpression:
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def mem_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
@@ -37,7 +37,7 @@ def mem_create_expression(self):
 
 class MemParameteriseModel(MemCreateExpression):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
 
     def mem_parameterise(self):
@@ -65,7 +65,7 @@ def mem_parameterise(self):
 
 class MemDiscretiseModel(MemParameteriseModel):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
 
@@ -84,7 +84,7 @@ def mem_discretise(self):
 
 class MemSolveModel(MemDiscretiseModel):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         MemCreateExpression.mem_create_expression(self)
         MemParameteriseModel.mem_parameterise(self)
         MemDiscretiseModel.mem_discretise(self)
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 9ff3a2c50d..96c61da436 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 
 parameters = [
     "Marquis2019",
@@ -36,7 +36,7 @@ class TimeBuildSPM:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_SPM(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -50,7 +50,7 @@ class TimeBuildSPMe:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_SPMe(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -64,7 +64,7 @@ class TimeBuildDFN:
     params = parameters
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_DFN(self, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -78,7 +78,7 @@ class TimeBuildSPMSimulation:
     params = ([False, True], parameters)
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_SPM_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -99,7 +99,7 @@ class TimeBuildSPMeSimulation:
     params = ([False, True], parameters)
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_SPMe_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
@@ -120,7 +120,7 @@ class TimeBuildDFNSimulation:
     params = ([False, True], parameters)
 
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_setup_DFN_simulation(self, with_experiment, parameters):
         self.param = pybamm.ParameterValues(parameters)
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index 16dfb4d488..a8a8476939 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -1,5 +1,5 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 
 
 class TimeSimulation:
@@ -22,7 +22,7 @@ class TimeSimulation:
     }
 
     def setup(self, experiment, parameters, model_class, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         if (experiment, parameters, model_class, solver_class) == (
             "GITT",
             "Marquis2019",
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index 7472100b07..79706477d0 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -2,7 +2,7 @@
 # See "Writing benchmarks" in the asv docs for more information.
 
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 import numpy as np
 
 
@@ -30,7 +30,7 @@ class TimeSolveSPM:
     )
 
     def setup(self, solve_first, parameters, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPM()
         c_rate = 1
@@ -86,7 +86,7 @@ class TimeSolveSPMe:
     )
 
     def setup(self, solve_first, parameters, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         self.solver = solver_class()
         self.model = pybamm.lithium_ion.SPMe()
         c_rate = 1
@@ -142,7 +142,7 @@ class TimeSolveDFN:
     )
 
     def setup(self, solve_first, parameters, solver_class):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         if (parameters, solver_class) == (
             "ORegan2022",
             pybamm.CasadiSolver,
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 8342b02a18..8995f9a7b8 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -1,11 +1,11 @@
 import pybamm
-import pybamm.util
+from pybamm.util import set_random_seed
 import numpy as np
 
 
 class TimeCreateExpression:
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
 
     def time_create_expression(self):
         self.R = pybamm.Parameter("Particle radius [m]")
@@ -36,7 +36,7 @@ def time_create_expression(self):
 
 class TimeParameteriseModel(TimeCreateExpression):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
 
     def time_parameterise(self):
@@ -63,7 +63,7 @@ def time_parameterise(self):
 
 class TimeDiscretiseModel(TimeParameteriseModel):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
 
@@ -81,7 +81,7 @@ def time_discretise(self):
 
 class TimeSolveModel(TimeDiscretiseModel):
     def setup(self):
-        pybamm.util.set_random_seed()
+        set_random_seed()
         TimeCreateExpression.time_create_expression(self)
         TimeParameteriseModel.time_parameterise(self)
         TimeDiscretiseModel.time_discretise(self)

From 18699c00595b0ae3cbb392bbc567051cee5813ef Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Wed, 4 Oct 2023 14:02:54 -0400
Subject: [PATCH 204/615] Moving seed function

---
 benchmarks/benchmark_utils.py            | 5 +++++
 benchmarks/different_model_options.py    | 2 +-
 benchmarks/memory_sims.py                | 2 +-
 benchmarks/memory_unit_benchmarks.py     | 2 +-
 benchmarks/time_setup_models_and_sims.py | 2 +-
 benchmarks/time_sims_experiments.py      | 2 +-
 benchmarks/time_solve_models.py          | 2 +-
 benchmarks/unit_benchmarks.py            | 2 +-
 pybamm/util.py                           | 4 ----
 9 files changed, 12 insertions(+), 11 deletions(-)
 create mode 100644 benchmarks/benchmark_utils.py

diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py
new file mode 100644
index 0000000000..e5431ff4ea
--- /dev/null
+++ b/benchmarks/benchmark_utils.py
@@ -0,0 +1,5 @@
+import numpy as np
+
+
+def set_random_seed(seed_value=42):
+    np.random.seed(seed_value)
diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index cc1b2e0191..50755d67c0 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,5 +1,5 @@
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index edee2c9f11..d2e06f545e 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 
 parameters = ["Marquis2019", "Chen2020"]
 
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 218ff8ea0a..6c2014d6a3 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -1,5 +1,5 @@
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 96c61da436..d7d5e6c6eb 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 
 parameters = [
     "Marquis2019",
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index a8a8476939..8c0f4348ea 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -1,5 +1,5 @@
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 
 
 class TimeSimulation:
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index 79706477d0..feebf6ac09 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -2,7 +2,7 @@
 # See "Writing benchmarks" in the asv docs for more information.
 
 import pybamm
-from pybamm.util import set_random_seed
+from benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 8995f9a7b8..318c215b73 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -1,6 +1,6 @@
 import pybamm
-from pybamm.util import set_random_seed
 import numpy as np
+from benchmark_utils import set_random_seed
 
 
 class TimeCreateExpression:
diff --git a/pybamm/util.py b/pybamm/util.py
index 48819f118d..562352bfac 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -27,10 +27,6 @@
 JAXLIB_VERSION = "0.4"
 
 
-def set_random_seed(seed_value=42):
-    np.random.seed(seed_value)
-
-
 def root_dir():
     """return the root directory of the PyBaMM install directory"""
     return str(pathlib.Path(pybamm.__path__[0]).parent)

From 64602dbc8bfdb125a9fc3b7aac39a10fc7060277 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Wed, 4 Oct 2023 14:09:41 -0400
Subject: [PATCH 205/615] Moving function again

---
 benchmarks/different_model_options.py    | 2 +-
 benchmarks/memory_sims.py                | 2 +-
 benchmarks/memory_unit_benchmarks.py     | 2 +-
 benchmarks/time_setup_models_and_sims.py | 2 +-
 benchmarks/time_sims_experiments.py      | 2 +-
 benchmarks/time_solve_models.py          | 2 +-
 benchmarks/unit_benchmarks.py            | 2 +-
 tests/testcase.py                        | 5 ++---
 8 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 50755d67c0..81bf2930f8 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -1,5 +1,5 @@
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index d2e06f545e..0c7a030fde 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 parameters = ["Marquis2019", "Chen2020"]
 
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 6c2014d6a3..5f2c360280 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -1,5 +1,5 @@
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index d7d5e6c6eb..498086f7f0 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -1,5 +1,5 @@
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 parameters = [
     "Marquis2019",
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index 8c0f4348ea..13bdc3432e 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -1,5 +1,5 @@
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class TimeSimulation:
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index feebf6ac09..15a78dde64 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -2,7 +2,7 @@
 # See "Writing benchmarks" in the asv docs for more information.
 
 import pybamm
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 import numpy as np
 
 
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 318c215b73..432fb849b7 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -1,6 +1,6 @@
 import pybamm
 import numpy as np
-from benchmark_utils import set_random_seed
+from benchmarks.benchmark_utils import set_random_seed
 
 
 class TimeCreateExpression:
diff --git a/tests/testcase.py b/tests/testcase.py
index 3d931659bd..ae4019bcb3 100644
--- a/tests/testcase.py
+++ b/tests/testcase.py
@@ -5,8 +5,7 @@
 import hashlib
 from functools import wraps
 from types import FunctionType
-
-import pybamm
+import numpy as np
 
 
 def FixRandomSeed(method):
@@ -24,7 +23,7 @@ def FixRandomSeed(method):
 
     @wraps(method)
     def wrapped(*args, **kwargs):
-        pybamm.util.set_random_seed(
+        np.random.seed(
             int(hashlib.sha256(method.__name__.encode()).hexdigest(), 16) % (2**32)
         )
         return method(*args, **kwargs)

From af4399657e26150d53bc39187c3bb391e63e39c3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 01:42:58 +0530
Subject: [PATCH 206/615] Install `pathlib` as required

---
 .github/workflows/test_on_push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index e8c82f5200..e12722aff2 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -218,7 +218,7 @@ jobs:
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget
+          pip install wget pathlib
           pybamm_install_odes
 
   run_integration_tests:

From 7018c19a00b563b0eaa55987d67dc536b0a11185 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 02:19:20 +0530
Subject: [PATCH 207/615] Remove condition

---
 .github/workflows/test_on_push.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index e12722aff2..3811dfddfd 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -189,7 +189,6 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install macOS system dependencies
-        if: matrix.os == 'macos-latest'
         env:
           # Homebrew environment variables
           HOMEBREW_NO_INSTALL_CLEANUP: 1
@@ -218,7 +217,7 @@ jobs:
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget pathlib
+          pip install wget
           pybamm_install_odes
 
   run_integration_tests:

From 0767211a5a932e53ae78411b5cdbe92c7cf8dc7a Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Sat, 14 Oct 2023 21:04:10 -0400
Subject: [PATCH 208/615] Change seed

---
 benchmarks/benchmark_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py
index e5431ff4ea..1ccc98280e 100644
--- a/benchmarks/benchmark_utils.py
+++ b/benchmarks/benchmark_utils.py
@@ -1,5 +1,5 @@
 import numpy as np
 
 
-def set_random_seed(seed_value=42):
+def set_random_seed(seed_value=142):
     np.random.seed(seed_value)

From 4e622493a90f5fa7691adbe326246abb23516c6f Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Sat, 14 Oct 2023 21:43:16 -0400
Subject: [PATCH 209/615] Revert some changes

---
 benchmarks/benchmark_utils.py         |  2 +-
 benchmarks/different_model_options.py | 12 ++++++------
 benchmarks/memory_unit_benchmarks.py  |  6 +++---
 benchmarks/unit_benchmarks.py         |  6 +++---
 pybamm/solvers/casadi_solver.py       |  2 +-
 5 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/benchmarks/benchmark_utils.py b/benchmarks/benchmark_utils.py
index 1ccc98280e..e5431ff4ea 100644
--- a/benchmarks/benchmark_utils.py
+++ b/benchmarks/benchmark_utils.py
@@ -1,5 +1,5 @@
 import numpy as np
 
 
-def set_random_seed(seed_value=142):
+def set_random_seed(seed_value=42):
     np.random.seed(seed_value)
diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 81bf2930f8..4a17a4fcef 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -90,7 +90,7 @@ def time_setup_model(self, model, params):
         build_model("Ai2020", model, "loss of active material", params)
 
 
-class TimeSolveLossActiveMaterial(SolveModel):
+class TimeSolveLossActiveMaterial:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -122,7 +122,7 @@ def time_setup_model(self, model, params):
         build_model("OKane2022", model, "lithium plating", params)
 
 
-class TimeSolveLithiumPlating(SolveModel):
+class TimeSolveLithiumPlating:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -162,7 +162,7 @@ def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "SEI", params)
 
 
-class TimeSolveSEI(SolveModel):
+class TimeSolveSEI:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -205,7 +205,7 @@ def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "particle", params)
 
 
-class TimeSolveParticle(SolveModel):
+class TimeSolveParticle:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -242,7 +242,7 @@ def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "thermal", params)
 
 
-class TimeSolveThermal(SolveModel):
+class TimeSolveThermal:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -274,7 +274,7 @@ def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "surface form", params)
 
 
-class TimeSolveSurfaceForm(SolveModel):
+class TimeSolveSurfaceForm:
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 5f2c360280..a355442278 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -35,7 +35,7 @@ def mem_create_expression(self):
         return self.model
 
 
-class MemParameteriseModel(MemCreateExpression):
+class MemParameteriseModel:
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
@@ -63,7 +63,7 @@ def mem_parameterise(self):
         return param
 
 
-class MemDiscretiseModel(MemParameteriseModel):
+class MemDiscretiseModel:
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
@@ -82,7 +82,7 @@ def mem_discretise(self):
         return disc
 
 
-class MemSolveModel(MemDiscretiseModel):
+class MemSolveModel:
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 432fb849b7..dd5c55a5d4 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -34,7 +34,7 @@ def time_create_expression(self):
         }
 
 
-class TimeParameteriseModel(TimeCreateExpression):
+class TimeParameteriseModel:
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
@@ -61,7 +61,7 @@ def time_parameterise(self):
         param.process_geometry(self.geometry)
 
 
-class TimeDiscretiseModel(TimeParameteriseModel):
+class TimeDiscretiseModel:
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
@@ -79,7 +79,7 @@ def time_discretise(self):
         disc.process_model(self.model)
 
 
-class TimeSolveModel(TimeDiscretiseModel):
+class TimeSolveModel:
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py
index 86246588e9..4cf863ede1 100644
--- a/pybamm/solvers/casadi_solver.py
+++ b/pybamm/solvers/casadi_solver.py
@@ -41,7 +41,7 @@ class CasadiSolver(pybamm.BaseSolver):
         specified by 'root_method' (e.g. "lm", "hybr", ...)
     root_tol : float, optional
         The tolerance for root-finding. Default is 1e-6.
-    max_step_decrease_counts : float, optional
+    max_step_decrease_count : float, optional
         The maximum number of times step size can be decreased before an error is
         raised. Default is 5.
     dt_max : float, optional

From a8ce937f9d9f9ee490810470604c8d541e6fa9df Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Sat, 14 Oct 2023 22:44:59 -0400
Subject: [PATCH 210/615] Trying to adjust the setup method

---
 benchmarks/different_model_options.py | 24 ++++++++++++------------
 benchmarks/memory_unit_benchmarks.py  |  6 +++---
 benchmarks/unit_benchmarks.py         |  6 +++---
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 4a17a4fcef..6a1ca22448 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -83,14 +83,14 @@ class TimeBuildModelLossActiveMaterial:
         ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Ai2020", model, "loss of active material", params)
 
 
-class TimeSolveLossActiveMaterial:
+class TimeSolveLossActiveMaterial(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -115,14 +115,14 @@ class TimeBuildModelLithiumPlating:
         ["none", "irreversible", "reversible", "partially reversible"],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("OKane2022", model, "lithium plating", params)
 
 
-class TimeSolveLithiumPlating:
+class TimeSolveLithiumPlating(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -155,14 +155,14 @@ class TimeBuildModelSEI:
         ],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "SEI", params)
 
 
-class TimeSolveSEI:
+class TimeSolveSEI(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -198,14 +198,14 @@ class TimeBuildModelParticle:
         ],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "particle", params)
 
 
-class TimeSolveParticle:
+class TimeSolveParticle(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -235,14 +235,14 @@ class TimeBuildModelThermal:
         ["isothermal", "lumped", "x-full"],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "thermal", params)
 
 
-class TimeSolveThermal:
+class TimeSolveThermal(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
@@ -267,14 +267,14 @@ class TimeBuildModelSurfaceForm:
         ["false", "differential", "algebraic"],
     )
 
-    def setup(self):
+    def setup(self, model, params, solver_class):
         set_random_seed()
 
     def time_setup_model(self, model, params):
         build_model("Marquis2019", model, "surface form", params)
 
 
-class TimeSolveSurfaceForm:
+class TimeSolveSurfaceForm(SolveModel):
     param_names = ["model", "model option", "solver class"]
     params = (
         [pybamm.lithium_ion.SPM, pybamm.lithium_ion.DFN],
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index a355442278..5f2c360280 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -35,7 +35,7 @@ def mem_create_expression(self):
         return self.model
 
 
-class MemParameteriseModel:
+class MemParameteriseModel(MemCreateExpression):
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
@@ -63,7 +63,7 @@ def mem_parameterise(self):
         return param
 
 
-class MemDiscretiseModel:
+class MemDiscretiseModel(MemParameteriseModel):
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
@@ -82,7 +82,7 @@ def mem_discretise(self):
         return disc
 
 
-class MemSolveModel:
+class MemSolveModel(MemDiscretiseModel):
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index dd5c55a5d4..432fb849b7 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -34,7 +34,7 @@ def time_create_expression(self):
         }
 
 
-class TimeParameteriseModel:
+class TimeParameteriseModel(TimeCreateExpression):
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
@@ -61,7 +61,7 @@ def time_parameterise(self):
         param.process_geometry(self.geometry)
 
 
-class TimeDiscretiseModel:
+class TimeDiscretiseModel(TimeParameteriseModel):
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
@@ -79,7 +79,7 @@ def time_discretise(self):
         disc.process_model(self.model)
 
 
-class TimeSolveModel:
+class TimeSolveModel(TimeDiscretiseModel):
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)

From 7f0bea9e0d5832659fd8072b5d0bb523c54d3ed5 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 15 Oct 2023 16:07:42 +0530
Subject: [PATCH 211/615] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3811dfddfd..dca8e3c9b1 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -181,6 +181,9 @@ jobs:
     needs: style
     runs-on: macos-latest
     strategy:
+        matrix:
+            os: [ubuntu-latest, macos-latest]
+            python-version: ["3.8", "3.9", "3.10", "3.11"]
       fail-fast: false
     name: Test pybamm_install_odes on MacOS
 
@@ -199,7 +202,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install openblas
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
@@ -212,12 +215,12 @@ jobs:
       - name: Install PyBaMM dependencies
         run: |
           pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+          pip install -e .[all]
 
       - name: Test pybamm_install_odes on MacOS
         if: matrix.os == 'macos-latest'
         run: |
-          pip install wget
+          pip install wget cmake
           pybamm_install_odes
 
   run_integration_tests:

From 7ee2a9a522d0ddf8652b04857267fdbd67501e78 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 15 Oct 2023 17:57:13 +0530
Subject: [PATCH 212/615] Correctly indent key

---
 .github/workflows/test_on_push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index dca8e3c9b1..65afcffe6c 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -184,7 +184,7 @@ jobs:
         matrix:
             os: [ubuntu-latest, macos-latest]
             python-version: ["3.8", "3.9", "3.10", "3.11"]
-      fail-fast: false
+        fail-fast: false
     name: Test pybamm_install_odes on MacOS
 
     steps:

From 9728d171fc8c7e1de8e632b97b5726f8499cfee6 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 15 Oct 2023 18:51:09 +0530
Subject: [PATCH 213/615] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 65afcffe6c..ef6391d4ad 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -185,7 +185,7 @@ jobs:
             os: [ubuntu-latest, macos-latest]
             python-version: ["3.8", "3.9", "3.10", "3.11"]
         fail-fast: false
-    name: Test pybamm_install_odes on MacOS
+    name: Test pybamm_install_odes on ${{ matrix.os }}
 
     steps:
       - name: Check out PyBaMM repository
@@ -203,6 +203,7 @@ jobs:
           brew analytics off
           brew update
           brew install openblas
+          brew reinstall gcc gfortran
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
@@ -217,8 +218,7 @@ jobs:
           pip install --upgrade pip wheel setuptools nox
           pip install -e .[all]
 
-      - name: Test pybamm_install_odes on MacOS
-        if: matrix.os == 'macos-latest'
+      - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
           pip install wget cmake
           pybamm_install_odes

From 5ad9cea749d771240fc9bf5770bc9a21e21c143d Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Sun, 15 Oct 2023 10:00:06 -0400
Subject: [PATCH 214/615] Adding unused parameters to setup functions

---
 benchmarks/different_model_options.py    | 26 ++++++++---------
 benchmarks/memory_sims.py                | 25 ++++++++--------
 benchmarks/time_setup_models_and_sims.py | 36 ++++++++++++------------
 benchmarks/time_sims_experiments.py      |  2 +-
 benchmarks/time_solve_models.py          |  6 ++--
 5 files changed, 49 insertions(+), 46 deletions(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 6a1ca22448..3010604072 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -72,7 +72,7 @@ def solve_setup(self, parameter, model_, option, value, solver_class):
         disc = pybamm.Discretisation(mesh, self.model.default_spatial_methods)
         disc.process_model(self.model)
 
-    def solve_model(self, model, params):
+    def solve_model(self, _model, _params):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -83,7 +83,7 @@ class TimeBuildModelLossActiveMaterial:
         ["none", "stress-driven", "reaction-driven", "stress and reaction-driven"],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -104,7 +104,7 @@ def setup(self, model, params, solver_class):
             self, "Ai2020", model, "loss of active material", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -115,7 +115,7 @@ class TimeBuildModelLithiumPlating:
         ["none", "irreversible", "reversible", "partially reversible"],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -136,7 +136,7 @@ def setup(self, model, params, solver_class):
             self, "OKane2022", model, "lithium plating", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -155,7 +155,7 @@ class TimeBuildModelSEI:
         ],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -182,7 +182,7 @@ def setup(self, model, params, solver_class):
         set_random_seed()
         SolveModel.solve_setup(self, "Marquis2019", model, "SEI", params, solver_class)
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -198,7 +198,7 @@ class TimeBuildModelParticle:
         ],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -224,7 +224,7 @@ def setup(self, model, params, solver_class):
             self, "Marquis2019", model, "particle", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -235,7 +235,7 @@ class TimeBuildModelThermal:
         ["isothermal", "lumped", "x-full"],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -256,7 +256,7 @@ def setup(self, model, params, solver_class):
             self, "Marquis2019", model, "thermal", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -267,7 +267,7 @@ class TimeBuildModelSurfaceForm:
         ["false", "differential", "algebraic"],
     )
 
-    def setup(self, model, params, solver_class):
+    def setup(self, _model, _params):
         set_random_seed()
 
     def time_setup_model(self, model, params):
@@ -294,5 +294,5 @@ def setup(self, model, params, solver_class):
             self, "Marquis2019", model, "surface form", params, solver_class
         )
 
-    def time_solve_model(self, model, params, solver_class):
+    def time_solve_model(self, _model, _params, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index 0c7a030fde..4d01246a7b 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -8,11 +8,11 @@ class MemSPMSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def mem_setup_SPM_simulationCCCV(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def mem_setup_SPM_simulationCCCV(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [
@@ -33,8 +33,11 @@ class MemDFNSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
 
-    def mem_setup_DFN_simulationCCCV(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def setup(self, _params):
+        set_random_seed()
+
+    def mem_setup_DFN_simulationCCCV(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         exp = pybamm.Experiment(
             [
@@ -55,11 +58,11 @@ class MemSPMSimulationGITT:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def mem_setup_SPM_simulationGITT(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def mem_setup_SPM_simulationGITT(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20
@@ -74,11 +77,11 @@ class MemDFNSimulationGITT:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def mem_setup_DFN_simulationGITT(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def mem_setup_DFN_simulationGITT(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         exp = pybamm.Experiment(
             [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 20
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index 498086f7f0..b6a1baca1c 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -35,11 +35,11 @@ class TimeBuildSPM:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def time_setup_SPM(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_SPM(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -49,11 +49,11 @@ class TimeBuildSPMe:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def time_setup_SPMe(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_SPMe(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPMe()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -63,11 +63,11 @@ class TimeBuildDFN:
     param_names = ["parameter"]
     params = parameters
 
-    def setup(self):
+    def setup(self, _params):
         set_random_seed()
 
-    def time_setup_DFN(self, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_DFN(self, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         self.param.process_model(self.model)
         compute_discretisation(self.model, self.param).process_model(self.model)
@@ -77,11 +77,11 @@ class TimeBuildSPMSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
-    def setup(self):
+    def setup(self, _with_experiment, _params):
         set_random_seed()
 
-    def time_setup_SPM_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_SPM_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPM()
         if with_experiment:
             exp = pybamm.Experiment(
@@ -98,11 +98,11 @@ class TimeBuildSPMeSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
-    def setup(self):
+    def setup(self, _with_experiment, _params):
         set_random_seed()
 
-    def time_setup_SPMe_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_SPMe_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.SPMe()
         if with_experiment:
             exp = pybamm.Experiment(
@@ -119,11 +119,11 @@ class TimeBuildDFNSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
 
-    def setup(self):
+    def setup(self, _with_experiment, _params):
         set_random_seed()
 
-    def time_setup_DFN_simulation(self, with_experiment, parameters):
-        self.param = pybamm.ParameterValues(parameters)
+    def time_setup_DFN_simulation(self, with_experiment, params):
+        self.param = pybamm.ParameterValues(params)
         self.model = pybamm.lithium_ion.DFN()
         if with_experiment:
             exp = pybamm.Experiment(
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index 13bdc3432e..d380999c99 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -48,5 +48,5 @@ def time_setup(self, experiment, parameters, model_class, solver_class):
         exp = pybamm.Experiment(self.experiment_descriptions[experiment])
         pybamm.Simulation(model, parameter_values=param, experiment=exp, solver=solver)
 
-    def time_solve(self, experiment, parameters, model_class, solver_class):
+    def time_solve(self, _experiment, _parameters, _model_class, _solver_class):
         self.sim.solve()
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index 15a78dde64..efcf97b450 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -62,7 +62,7 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -118,7 +118,7 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)
 
 
@@ -179,5 +179,5 @@ def setup(self, solve_first, parameters, solver_class):
         if solve_first:
             solve_model_once(self.model, self.solver, self.t_eval)
 
-    def time_solve_model(self, solve_first, parameters, solver_class):
+    def time_solve_model(self, _solve_first, _parameters, _solver_class):
         self.solver.solve(self.model, t_eval=self.t_eval)

From 061f35d2ebefbde7210e884433a8f0611dd1199b Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Sun, 15 Oct 2023 13:07:59 -0400
Subject: [PATCH 215/615] Cleaning up member variables to reduce static
 analysis warnings

---
 benchmarks/different_model_options.py            |  4 ++++
 benchmarks/memory_sims.py                        | 12 ++++++++++++
 benchmarks/memory_unit_benchmarks.py             |  6 ++++++
 benchmarks/time_setup_models_and_sims.py         | 10 ++++++++++
 benchmarks/time_sims_experiments.py              |  5 +++++
 benchmarks/time_solve_models.py                  |  9 +++++++++
 benchmarks/unit_benchmarks.py                    |  6 ++++++
 benchmarks/work_precision_sets/time_vs_dt_max.py |  1 -
 8 files changed, 52 insertions(+), 1 deletion(-)

diff --git a/benchmarks/different_model_options.py b/benchmarks/different_model_options.py
index 3010604072..a4cf787ad9 100644
--- a/benchmarks/different_model_options.py
+++ b/benchmarks/different_model_options.py
@@ -31,6 +31,10 @@ def build_model(parameter, model_, option, value):
 
 
 class SolveModel:
+    solver: pybamm.BaseSolver
+    model: pybamm.BaseModel
+    t_eval: np.ndarray
+
     def solve_setup(self, parameter, model_, option, value, solver_class):
         import importlib
 
diff --git a/benchmarks/memory_sims.py b/benchmarks/memory_sims.py
index 4d01246a7b..45d3e41834 100644
--- a/benchmarks/memory_sims.py
+++ b/benchmarks/memory_sims.py
@@ -7,6 +7,9 @@
 class MemSPMSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
     def setup(self, _params):
         set_random_seed()
@@ -32,6 +35,9 @@ def mem_setup_SPM_simulationCCCV(self, params):
 class MemDFNSimulationCCCV:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
     def setup(self, _params):
         set_random_seed()
@@ -57,6 +63,9 @@ def mem_setup_DFN_simulationCCCV(self, params):
 class MemSPMSimulationGITT:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
     def setup(self, _params):
         set_random_seed()
@@ -76,6 +85,9 @@ def mem_setup_SPM_simulationGITT(self, params):
 class MemDFNSimulationGITT:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    sim: pybamm.Simulation
 
     def setup(self, _params):
         set_random_seed()
diff --git a/benchmarks/memory_unit_benchmarks.py b/benchmarks/memory_unit_benchmarks.py
index 5f2c360280..79970c70ef 100644
--- a/benchmarks/memory_unit_benchmarks.py
+++ b/benchmarks/memory_unit_benchmarks.py
@@ -4,6 +4,9 @@
 
 
 class MemCreateExpression:
+    R: pybamm.Parameter
+    model: pybamm.BaseModel
+
     def setup(self):
         set_random_seed()
 
@@ -36,6 +39,9 @@ def mem_create_expression(self):
 
 
 class MemParameteriseModel(MemCreateExpression):
+    r: pybamm.SpatialVariable
+    geometry: dict
+
     def setup(self):
         set_random_seed()
         MemCreateExpression.mem_create_expression(self)
diff --git a/benchmarks/time_setup_models_and_sims.py b/benchmarks/time_setup_models_and_sims.py
index b6a1baca1c..2677c9936c 100644
--- a/benchmarks/time_setup_models_and_sims.py
+++ b/benchmarks/time_setup_models_and_sims.py
@@ -34,6 +34,8 @@ def compute_discretisation(model, param):
 class TimeBuildSPM:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
     def setup(self, _params):
         set_random_seed()
@@ -62,6 +64,8 @@ def time_setup_SPMe(self, params):
 class TimeBuildDFN:
     param_names = ["parameter"]
     params = parameters
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
     def setup(self, _params):
         set_random_seed()
@@ -76,6 +80,8 @@ def time_setup_DFN(self, params):
 class TimeBuildSPMSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
     def setup(self, _with_experiment, _params):
         set_random_seed()
@@ -97,6 +103,8 @@ def time_setup_SPM_simulation(self, with_experiment, params):
 class TimeBuildSPMeSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
     def setup(self, _with_experiment, _params):
         set_random_seed()
@@ -118,6 +126,8 @@ def time_setup_SPMe_simulation(self, with_experiment, params):
 class TimeBuildDFNSimulation:
     param_names = ["with experiment", "parameter"]
     params = ([False, True], parameters)
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
 
     def setup(self, _with_experiment, _params):
         set_random_seed()
diff --git a/benchmarks/time_sims_experiments.py b/benchmarks/time_sims_experiments.py
index d380999c99..bcd3e71f2f 100644
--- a/benchmarks/time_sims_experiments.py
+++ b/benchmarks/time_sims_experiments.py
@@ -20,6 +20,11 @@ class TimeSimulation:
         ],
         "GITT": [("Discharge at C/20 for 1 hour", "Rest for 1 hour")] * 10,
     }
+    param: pybamm.ParameterValues
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    exp: pybamm.Experiment
+    sim: pybamm.Simulation
 
     def setup(self, experiment, parameters, model_class, solver_class):
         set_random_seed()
diff --git a/benchmarks/time_solve_models.py b/benchmarks/time_solve_models.py
index efcf97b450..e41a7ccd16 100644
--- a/benchmarks/time_solve_models.py
+++ b/benchmarks/time_solve_models.py
@@ -28,6 +28,9 @@ class TimeSolveSPM:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
         set_random_seed()
@@ -84,6 +87,9 @@ class TimeSolveSPMe:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
         set_random_seed()
@@ -140,6 +146,9 @@ class TimeSolveDFN:
             pybamm.IDAKLUSolver,
         ],
     )
+    model: pybamm.BaseModel
+    solver: pybamm.BaseSolver
+    t_eval: np.ndarray
 
     def setup(self, solve_first, parameters, solver_class):
         set_random_seed()
diff --git a/benchmarks/unit_benchmarks.py b/benchmarks/unit_benchmarks.py
index 432fb849b7..73af4dda26 100644
--- a/benchmarks/unit_benchmarks.py
+++ b/benchmarks/unit_benchmarks.py
@@ -4,6 +4,9 @@
 
 
 class TimeCreateExpression:
+    R: pybamm.Parameter
+    model: pybamm.BaseModel
+
     def setup(self):
         set_random_seed()
 
@@ -35,6 +38,9 @@ def time_create_expression(self):
 
 
 class TimeParameteriseModel(TimeCreateExpression):
+    r: pybamm.SpatialVariable
+    geometry: dict
+
     def setup(self):
         set_random_seed()
         TimeCreateExpression.time_create_expression(self)
diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py
index 1368dce350..3926a4bcd6 100644
--- a/benchmarks/work_precision_sets/time_vs_dt_max.py
+++ b/benchmarks/work_precision_sets/time_vs_dt_max.py
@@ -41,7 +41,6 @@
 ):
     for params in parameters:
         time_points = []
-        # solver = pybamm.CasadiSolver()
 
         model = model_.new_copy()
         c_rate = 10

From 242b672466173539862147fe37da3daebe5be19d Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Mon, 16 Oct 2023 15:41:55 -0400
Subject: [PATCH 216/615] changelog

---
 CHANGELOG.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 421d3bfa29..d9583ee31c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,7 @@
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features
+
 - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237))
 - Processed variables now get the spatial variables automatically, allowing plotting of more generic models ([#3234](https://github.com/pybamm-team/PyBaMM/pull/3234))
@@ -44,6 +45,7 @@
 
 ## Breaking changes
 
+- The parameter "Exchange-current density for lithium plating [A.m-2]" has been renamed to "Exchange-current density for lithium metal electrode [A.m-2]" when referring to the lithium plating reaction on the surface of a lithium metal electrode ([#3445](https://github.com/pybamm-team/PyBaMM/pull/3445))
 - Dropped support for i686 (32-bit) architectures on GNU/Linux distributions ([#3412](https://github.com/pybamm-team/PyBaMM/pull/3412))
 - The class `pybamm.thermal.OneDimensionalX` has been moved to `pybamm.thermal.pouch_cell.OneDimensionalX` to reflect the fact that the model formulation implicitly assumes a pouch cell geometry ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - The "lumped" thermal option now always used the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" to compute the cell cooling regardless of the chosen "cell geometry" option. The user must now specify the correct values for these parameters instead of them being calculated based on e.g. a pouch cell. An `OptionWarning` is raised to let users know to update their parameters ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))

From 45b85a7652aef8aac71c153eb0e9a2c027907754 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:35:40 +0530
Subject: [PATCH 217/615] #3049 add `pyproject.toml` to release workflows

---
 .github/release_workflow.md | 5 ++++-
 scripts/update_version.py   | 2 +-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 1af23fca25..280a1c160f 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -9,6 +9,7 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub an
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -32,6 +33,7 @@ If a new release candidate is required after the release of `rc0` -
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -53,6 +55,7 @@ Once satisfied with the release candidates -
    - `pybamm/version.py`
    - `docs/conf.py`
    - `CITATION.cff`
+   - `pyproject.toml`
    - `vcpkg.json`
    - `docs/_static/versions.json`
    - `CHANGELOG.md`
@@ -70,7 +73,7 @@ Once satisfied with the release candidates -
 Some other essential things to check throughout the release process -
 
 - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id.
-- Update jax and jaxlib to the latest version in `pybamm.util` and `setup.py`, fixing any bugs that arise
+- Update jax and jaxlib to the latest version in `pybamm.util` and `pyproject.toml`, fixing any bugs that arise
 - Make sure the URLs in `docs/_static/versions.json` are valid
 - As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
   ```
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 003edee274..8a2d832e59 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -48,7 +48,7 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
-    # docs/source/_static/versions.json for readthedocs build
+    # docs/_static/versions.json for readthedocs build
     if "rc" not in release_version:
         with open(
             os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"),

From 37c991d5907c22e7436c3d0299b463e3bb3b3ee0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:36:16 +0530
Subject: [PATCH 218/615] #3049 remove `setup.py` as CI cache dependency path

---
 .github/workflows/test_on_push.yml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 6821016e45..88ada069b4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,7 +90,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
@@ -145,7 +144,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -225,7 +223,6 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
@@ -281,7 +278,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: pipx run nox -s doctests
@@ -321,7 +317,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -374,7 +369,6 @@ jobs:
         with:
           python-version: 3.11
           cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 925d39005b81918bc0984f7b572232278da043b0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:36:36 +0530
Subject: [PATCH 219/615] #3049 add note about new file to manage deps

---
 docs/source/user_guide/installation/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 6338323e79..93e54c51fe 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -76,7 +76,7 @@ Optional Dependencies
 PyBaMM has a number of optional dependencies for different functionalities.
 If the optional dependency is not installed, PyBaMM will raise an ImportError when the method requiring that dependency is called.
 
-If using pip, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt or setup.py)
+If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt, setup.py, or pyproject.toml)
 as optional extras (e.g.,``pybamm[dev,plot]``). All optional dependencies can be installed with ``pybamm[all]``,
 and specific sets of dependencies are listed in the sections below.
 

From 1d16f5d6ee3feecb16abb191d1f621b312c22932 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:45:39 +0530
Subject: [PATCH 220/615] #3049 clarify usage of `cmake` and `casadi`

In the build-time requirements for Windows, we don't use cmake and casadi from pip, but from other sources. This is because we use Visual Studio to compile.
---
 pyproject.toml |  4 ++--
 setup.py       | 23 +++++++++++++----------
 2 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c91d275789..002c29d76b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,8 +2,8 @@
 requires = [
     "setuptools",
     "wheel",
+    # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.0; platform_system!='Windows'",
-    # use CMake bundled from MSVC on Windows
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
@@ -72,7 +72,7 @@ examples = [
 # Plotting functionality
 plot = [
     "imageio>=2.9.0",
-    # Note: matplotlib is loaded for debug plots, but to ensure pybamm runs
+    # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
     "matplotlib>=2.0",
diff --git a/setup.py b/setup.py
index a0180cb3e8..9cfc4df4ff 100644
--- a/setup.py
+++ b/setup.py
@@ -16,13 +16,11 @@
     from distutils.command.build_ext import build_ext
 
 
-# ---------- CMake steps for IDAKLU target (non-Windows) -------------------------------
-
-
 default_lib_dir = (
     "" if system() == "Windows" else os.path.join(os.getenv("HOME"), ".local")
 )
 
+# ---------- set environment variables for vcpkg on Windows ----------------------------
 
 def set_vcpkg_environment_variables():
     if not os.getenv("VCPKG_ROOT_DIR"):
@@ -41,6 +39,7 @@ def set_vcpkg_environment_variables():
         os.getenv("VCPKG_FEATURE_FLAGS"),
     )
 
+# ---------- CMakeBuild class (custom build_ext for IDAKLU target) ---------------------
 
 class CMakeBuild(build_ext):
     user_options = build_ext.user_options + [
@@ -119,6 +118,8 @@ def run(self):
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             os.remove(os.path.join(build_dir, "CMakeError.log"))
 
+# ---------- configuration for vcpkg on Windows ----------------------------------------
+
         build_env = os.environ
         if os.getenv("PYBAMM_USE_VCPKG"):
             (
@@ -130,26 +131,29 @@ def run(self):
             build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
             build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
 
+# ---------- Run CMake and build IDAKLU module -----------------------------------------
+
         cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
-        print("-" * 10, "Running CMake for idaklu solver", "-" * 40)
+        print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40)
         subprocess.run(
             ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
-        )
+        , check=True)
 
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             msg = (
-                "cmake configuration steps encountered errors, and the idaklu module"
+                "cmake configuration steps encountered errors, and the IDAKLU module"
                 " could not be built. Make sure dependencies are correctly "
                 "installed. See "
                 "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
             )
             raise RuntimeError(msg)
         else:
-            print("-" * 10, "Building idaklu module", "-" * 40)
+            print("-" * 10, "Building IDAKLU module", "-" * 40)
             subprocess.run(
                 ["cmake", "--build", ".", "--config", "Release"],
                 cwd=build_dir,
                 env=build_env,
+                check=True,
             )
 
             # Move from build temp to final position
@@ -218,7 +222,7 @@ def run(self):
         install.run(self)
 
 
-# ---------- custom wheel build (non-Windows) ------------------------------------------
+# ---------- Custom class for building wheels ------------------------------------------
 
 
 class bdist_wheel(orig.bdist_wheel):
@@ -250,8 +254,7 @@ def compile_KLU():
     # Return True if:
     # - Not running on Windows AND
     # - CMake is found AND
-    # - The pybind11 and casadi-headers directories are found
-    #   in the PyBaMM project directory
+    # - The pybind11/ directory is found in the PyBaMM project directory
     CMakeFound = True
     PyBind11Found = True
     windows = (not system()) or system() == "Windows"

From 15c4a8b086acc70f838c162797bad9f9d1ae4e0d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 16:51:48 +0530
Subject: [PATCH 221/615] #3049 update version to 23.9rc0

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 002c29d76b..5f26d260de 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.5"
+version = "23.9rc0"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]

From a4cc36191f4c581c39bf40deb934cc5543b26aac Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 17 Oct 2023 18:00:38 +0530
Subject: [PATCH 222/615] Make testpypi the default option + update tag
 instructions

---
 .github/release_workflow.md        | 2 +-
 .github/workflows/publish_pypi.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 1af23fca25..7afa24a6d6 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -75,5 +75,5 @@ Some other essential things to check throughout the release process -
 - As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
   ```
   git tag -f <tag_name> <commit_hash>
-  git push origin <tag_name>  # can only be carried out by the maintainers
+  git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
   ```
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 25fbafc0af..7d01fe0bee 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -10,7 +10,7 @@ on:
     inputs:
       target:
         description: 'Deployment target. Can be "pypi" or "testpypi"'
-        default: "pypi"
+        default: "testpypi"
       debug_enabled:
         type: boolean
         description: 'Run the build with tmate debugging enabled (https://github.com/marketplace/actions/debugging-with-tmate)'

From 9aeb6370bfb35d0eea4aec29994ff3747ac124e9 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 19:18:06 +0530
Subject: [PATCH 223/615] Use `pipx` to run `nox` in workflows

---
 .github/workflows/run_periodic_tests.yml | 20 +++++++-------
 .github/workflows/test_on_push.yml       | 34 ++++++++++++------------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 2f9b5d89a8..f6e51bc11b 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -84,44 +84,44 @@ jobs:
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
-      - name: Install standard python dependencies
+      - name: Install standard Python dependencies
         run: |
-          python -m pip install --upgrade pip wheel setuptools nox
+          python -m pip install --upgrade pip wheel setuptools
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: nox -s unit
+        run: pipx run nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: nox -s integration
+        run: pipx run nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Check if the documentation can be built
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s docs
+        run: pipx run nox -s docs
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s examples
+        run: pipx run nox -s examples
 
       - name: Run example scripts tests
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s scripts
+        run: pipx run nox -s scripts
 
   #M-series Mac Mini
   build-apple-mseries:
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b740da2e1b..cb22fb87f7 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,7 +92,7 @@ jobs:
 
       - name: Install PyBaMM dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
+          pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -111,10 +111,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s unit
+        run: pipx run nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -169,10 +169,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: nox -s coverage
+        run: pipx run nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -235,7 +235,7 @@ jobs:
 
       - name: Install PyBaMM dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
+          pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -254,10 +254,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: nox -s integration
+        run: pipx run nox -s integration
 
 # Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
@@ -296,14 +296,14 @@ jobs:
 
       - name: Install PyBaMM dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
+          pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: nox -s doctests
+        run: pipx run nox -s doctests
 
       - name: Check if the documentation can be built for GNU/Linux with Python 3.11
-        run: nox -s docs
+        run: pipx run nox -s docs
 
   # Runs only on Ubuntu with Python 3.11
   run_example_tests:
@@ -341,7 +341,7 @@ jobs:
 
       - name: Install PyBaMM dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
+          pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -358,10 +358,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: nox -s examples
+        run: pipx run nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -399,7 +399,7 @@ jobs:
 
       - name: Install PyBaMM dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
+          pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
@@ -416,7 +416,7 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: nox -s pybamm-requires
+        run: pipx run nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: nox -s scripts
+        run: pipx run nox -s scripts

From 39ee77b465bffbeedc1de5bf576e201bb4f3aa44 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 17 Oct 2023 20:00:59 +0530
Subject: [PATCH 224/615] #3049 make cmake a bit verbose about sundials and
 suitesparse

---
 CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index bea0b0e5a4..182fd489f3 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -87,7 +87,7 @@ endif()
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR})
 # Sundials
 find_package(SUNDIALS REQUIRED)
-message("sundials ${SUNDIALS_INCLUDE_DIR} ${SUNDIALS_LIBRARIES}")
+message("SUNDIALS found in ${SUNDIALS_INCLUDE_DIR}: ${SUNDIALS_LIBRARIES}")
 target_include_directories(idaklu PRIVATE ${SUNDIALS_INCLUDE_DIR})
 target_link_libraries(idaklu PRIVATE ${SUNDIALS_LIBRARIES} casadi)
 
@@ -98,6 +98,7 @@ if(DEFINED VCPKG_ROOT_DIR)
   find_package(SuiteSparse CONFIG REQUIRED)
 else()
   find_package(SuiteSparse REQUIRED)
+  message("SuiteSparse found in ${SuiteSparse_INCLUDE_DIRS}: ${SuiteSparse_LIBRARIES}")
 endif()
 include_directories(${SuiteSparse_INCLUDE_DIRS})
 target_link_libraries(idaklu PRIVATE ${SuiteSparse_LIBRARIES})

From 35bafb775188ca9e07b841a1cae6139d3cdbd47c Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Tue, 17 Oct 2023 11:51:30 -0700
Subject: [PATCH 225/615] hotfix make initial soc work with halfcell

---
 .../full_battery_models/base_battery_model.py |  2 +-
 .../lithium_ion/__init__.py                   |  5 +-
 .../lithium_ion/electrode_soh_half_cell.py    | 91 +++++++++++++++++++
 pybamm/parameters/parameter_values.py         | 36 ++++++++
 pybamm/simulation.py                          | 19 +++-
 tests/unit/test_simulation.py                 | 27 ++++++
 6 files changed, 173 insertions(+), 7 deletions(-)

diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 79e135123f..971bd1a880 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -551,7 +551,7 @@ def __init__(self, extra_options):
             )
         if options["working electrode"] == "negative":
             raise pybamm.OptionError(
-                "The 'negative' working elecrtrode option has been removed because "
+                "The 'negative' working electrode option has been removed because "
                 "the voltage - and therefore the energy stored - would be negative."
                 "Use the 'positive' working electrode option instead and set whatever "
                 "would normally be the negative electrode as the positive electrode."
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 95a5059f5a..4afb23f493 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -9,7 +9,10 @@
     get_initial_ocps,
     get_min_max_ocps,
 )
-from .electrode_soh_half_cell import ElectrodeSOHHalfCell
+from .electrode_soh_half_cell import (
+    ElectrodeSOHHalfCell,
+    get_initial_stoichiometry_half_cell
+)
 from .spm import SPM
 from .spme import SPMe
 from .dfn import DFN
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
index d938fdc769..1e237e73c8 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
@@ -56,3 +56,94 @@ def __init__(self, name="Electrode-specific SOH model"):
     def default_solver(self):
         # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings
         return pybamm.AlgebraicSolver()
+
+
+def get_initial_stoichiometry_half_cell(
+    initial_value,
+    parameter_values,
+    param=None,
+    known_value="cyclable lithium capacity",
+    options=None,
+):
+    """
+    Calculate initial stoichiometry to start off the simulation at a particular
+    state of charge, given voltage limits, open-circuit potential, etc defined by
+    parameter_values
+
+    Parameters
+    ----------
+    initial_value : float
+        Target initial value.
+        If integer, interpreted as SOC, must be between 0 and 1.
+        If string e.g. "4 V", interpreted as voltage,
+        must be between V_min and V_max.
+    parameter_values : pybamm.ParameterValues
+        The parameter values to use in the calculation
+
+    Returns
+    -------
+    x
+        The initial stoichiometry that give the desired initial state of charge
+    """
+    param = pybamm.LithiumIonParameters(options)
+    x_0, x_100 = get_min_max_stoichiometries(parameter_values)
+
+    if isinstance(initial_value, str) and initial_value.endswith("V"):
+        V_init = float(initial_value[:-1])
+        V_min = parameter_values.evaluate(param.voltage_low_cut)
+        V_max = parameter_values.evaluate(param.voltage_high_cut)
+
+        if not V_min < V_init < V_max:
+            raise ValueError(
+                f"Initial voltage {V_init}V is outside the voltage limits "
+                f"({V_min}, {V_max})"
+            )
+
+        # Solve simple model for initial soc based on target voltage
+        soc_model = pybamm.BaseModel()
+        soc = pybamm.Variable("soc")
+        Up = param.p.prim.U
+        T_ref = parameter_values["Reference temperature [K]"]
+        x = x_0 + soc * (x_100 - x_0)
+
+        soc_model.algebraic[soc] = Up(x, T_ref) - V_init
+        # initial guess for soc linearly interpolates between 0 and 1
+        # based on V linearly interpolating between V_max and V_min
+        soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min)
+        soc_model.variables["soc"] = soc
+        parameter_values.process_model(soc_model)
+        initial_soc = pybamm.AlgebraicSolver().solve(soc_model, [0])["soc"].data[0]
+    elif isinstance(initial_value, (int, float)):
+        initial_soc = initial_value
+        if not 0 <= initial_soc <= 1:
+            raise ValueError("Initial SOC should be between 0 and 1")
+
+    else:
+        raise ValueError(
+            "Initial value must be a float between 0 and 1, "
+            "or a string ending in 'V'"
+        )
+
+    x = x_0 + initial_soc * (x_100 - x_0)
+
+    return x
+
+
+def get_min_max_stoichiometries(
+    parameter_values, options={"working electrode": "positive"}
+):
+    """
+    Get the minimum and maximum stoichiometries from the parameter values
+
+    Parameters
+    ----------
+    parameter_values : pybamm.ParameterValues
+        The parameter values to use in the calculation
+    """
+    esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell(options)
+    param = pybamm.LithiumIonParameters(options)
+    esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values)
+    Q_w = parameter_values.evaluate(param.p.Q_init)
+    esoh_sol = esoh_sim.solve([0], inputs={"Q_w": Q_w})
+    x_0, x_100 = esoh_sol["x_0"].data[0], esoh_sol["x_100"].data[0]
+    return x_0, x_100
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index e69291035d..5e3dccfdef 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -271,6 +271,42 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
         # reset processed symbols
         self._processed_symbols = {}
 
+    def set_initial_stoichiometry_half_cell(
+        self,
+        intial_value,
+        param=None,
+        known_value="cyclable lithium capacity",
+        inplace=True,
+        options=None,
+    ):
+        """
+        Set the initial stoichiometry of the working electrode, based on the initial
+        SOC or voltage
+        """
+        param = param or pybamm.LithiumIonParameters(options)
+        x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+            intial_value, self, param=param, known_value=known_value, options=options
+        )
+        if inplace:
+            parameter_values = self
+        else:
+            parameter_values = self.copy()
+
+        if options["working electrode"] == "positive":
+            c_max = self.evaluate(param.p.prim.c_max)
+        else:
+            c_max = self.evaluate(param.n.prim.c_max)
+
+        parameter_values.update(
+            {
+                "Initial concentration in {} electrode [mol.m-3]".format(
+                    options["working electrode"]
+                ): x
+                * c_max
+            }
+        )
+        return parameter_values
+
     def set_initial_stoichiometries(
         self,
         initial_value,
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 04a373b436..8805e925d0 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -290,9 +290,10 @@ def update_new_model_events(self, new_model, op):
                 # figure out whether the voltage event is greater than the starting
                 # voltage (charge) or less (discharge) and set the sign of the
                 # event accordingly
-                if (isinstance(op.value, pybamm.Interpolant) or
-                    isinstance(op.value, pybamm.Multiplication)):
-                    inpt = {"start time":0}
+                if isinstance(op.value, pybamm.Interpolant) or isinstance(
+                    op.value, pybamm.Multiplication
+                ):
+                    inpt = {"start time": 0}
                     init_curr = op.value.evaluate(t=0, inputs=inpt).flatten()[0]
                     sign = np.sign(init_curr)
                 else:
@@ -373,8 +374,16 @@ def set_initial_soc(self, initial_soc):
         options = self.model.options
         param = self._model.param
         if options["open-circuit potential"] == "MSMR":
-            self._parameter_values = self._unprocessed_parameter_values.set_initial_ocps(  # noqa: E501
-                initial_soc, param=param, inplace=False, options=options
+            self._parameter_values = (
+                self._unprocessed_parameter_values.set_initial_ocps(  # noqa: E501
+                    initial_soc, param=param, inplace=False, options=options
+                )
+            )
+        elif options["working electrode"] == "positive":
+            self._parameter_values = (
+                self._unprocessed_parameter_values.set_initial_stoichiometry_half_cell(
+                    initial_soc, param=param, inplace=False, options=options
+                )
             )
         else:
             self._parameter_values = (
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index c98586ee59..dac94a2538 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -204,6 +204,33 @@ def test_solve_with_initial_soc(self):
         sim.build(initial_soc=0.5)
         self.assertEqual(sim._built_initial_soc, 0.5)
 
+        # Test whether initial_soc works with half cell (solve)
+        options = {"working electrode": "positive"}
+        model = pybamm.lithium_ion.DFN(options)
+        sim = pybamm.Simulation(model)
+        sim.solve([0,1], initial_soc = 0.9)
+        self.assertEqual(sim._built_initial_soc, 0.9)
+
+        # Test whether initial_soc works with half cell (build)
+        options = {"working electrode": "positive"}
+        model = pybamm.lithium_ion.DFN(options)
+        sim = pybamm.Simulation(model)
+        sim.build(initial_soc = 0.9)
+        self.assertEqual(sim._built_initial_soc, 0.9)
+
+        # Test whether initial_soc works with half cell when it is a voltage
+        model = pybamm.lithium_ion.SPM({"working electrode": "positive"})
+        parameter_values = model.default_parameter_values
+        ucv = parameter_values["Open-circuit voltage at 100% SOC [V]"]
+        parameter_values["Open-circuit voltage at 100% SOC [V]"] = ucv + 1e-12
+        parameter_values["Upper voltage cut-off [V]"] = ucv + 1e-12
+        options = {"working electrode": "positive"}
+        parameter_values["Current function [A]"] = 0.0
+        sim = pybamm.Simulation(model, parameter_values=parameter_values)
+        sol = sim.solve([0,1], initial_soc = "{} V".format(ucv))
+        voltage = sol["Terminal voltage [V]"].entries
+        self.assertAlmostEqual(voltage[0], ucv, places=5)
+
         # test with MSMR
         model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
         param = pybamm.ParameterValues("MSMR_Example")

From d66e755728a70195544794fcc1b6084ca17e0dbe Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Tue, 17 Oct 2023 14:47:28 -0700
Subject: [PATCH 226/615] log change in change log

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d9583ee31c..72eda6ca97 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -37,6 +37,7 @@
 - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132))
 - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096))
 - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456]https://github.com/pybamm-team/PyBaMM/pull/3456)
 
 ## Optimizations
 

From 098f1e8016f04077764784caba433c298da45571 Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Tue, 17 Oct 2023 14:47:57 -0700
Subject: [PATCH 227/615] log change in change log

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 72eda6ca97..008cad125f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -37,7 +37,7 @@
 - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132))
 - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096))
 - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042))
-- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456]https://github.com/pybamm-team/PyBaMM/pull/3456)
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 
 ## Optimizations
 

From 3966a6198809f67ce47fe5e3556e9cdae22b5f5c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 18 Oct 2023 20:28:28 +0530
Subject: [PATCH 228/615] Do not run image build workflow on forks

---
 .github/workflows/docker.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index 9c16f95705..b6994795d6 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -9,7 +9,7 @@ on:
 jobs:
   build_docker_images:
     # This workflow is only of value to PyBaMM and would always be skipped in forks
-    # if: github.repository_owner == 'pybamm-team'
+    if: github.repository_owner == 'pybamm-team'
     name: Image (${{ matrix.build-args }})
     runs-on: ubuntu-latest
     strategy:

From e2e33c2d423859d850d755e37853fa15c6ad7a1b Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Wed, 18 Oct 2023 11:14:21 -0700
Subject: [PATCH 229/615] add tests for inplace and errors

---
 pybamm/parameters/parameter_values.py         |  4 ++--
 .../test_lithium_ion/test_electrode_soh.py    | 11 +++++++++
 .../test_parameters/test_parameter_values.py  | 23 +++++++++++++++++++
 3 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 5e3dccfdef..254a0f6806 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -273,7 +273,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
 
     def set_initial_stoichiometry_half_cell(
         self,
-        intial_value,
+        initial_value,
         param=None,
         known_value="cyclable lithium capacity",
         inplace=True,
@@ -285,7 +285,7 @@ def set_initial_stoichiometry_half_cell(
         """
         param = param or pybamm.LithiumIonParameters(options)
         x = pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
-            intial_value, self, param=param, known_value=known_value, options=options
+            initial_value, self, param=param, known_value=known_value, options=options
         )
         if inplace:
             parameter_values = self
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
index 49f7a5d855..7bd156d0bc 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -358,6 +358,17 @@ def test_error(self):
         with self.assertRaisesRegex(ValueError, "must be a float"):
             pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values)
 
+        with self.assertRaisesRegex(ValueError, "outside the voltage limits"):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell("1 V", parameter_values)
+
+        with self.assertRaisesRegex(ValueError, "must be a float"):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell("5 A", parameter_values)
+
+        with self.assertRaisesRegex(
+            ValueError, "Initial SOC should be between 0 and 1"
+        ):
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(2, parameter_values)
+
 
 class TestGetInitialOCP(TestCase):
     def test_get_initial_ocp(self):
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index cc1f954686..ab699f0365 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -119,6 +119,29 @@ def test_set_initial_stoichiometries(self):
         y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
+    def test_set_initial_stoichiometry_half_cell(self):
+        param = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
+        param = param.set_initial_stoichiometry_half_cell(0.4, inplace=False, options={"working electrode": "positive"})
+        param_0 = param.set_initial_stoichiometry_half_cell(0, inplace=False, options={"working electrode": "positive"})
+        param_100 = param.set_initial_stoichiometry_half_cell(1, inplace=False, options={"working electrode": "positive"})
+
+        y = param["Initial concentration in positive electrode [mol.m-3]"]
+        y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
+        y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
+        self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
+
+        #inplace for 100% coverage
+        param_t = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
+        param_t.set_initial_stoichiometry_half_cell(0.4, inplace=True, options={"working electrode": "positive"})
+        y = param_t["Initial concentration in positive electrode [mol.m-3]"]
+        param_0 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
+        param_0.set_initial_stoichiometry_half_cell(0, inplace=True, options={"working electrode": "positive"})
+        y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
+        param_100 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
+        param_100.set_initial_stoichiometry_half_cell(1, inplace=True, options={"working electrode": "positive"})
+        y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
+        self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
+
     def test_set_initial_ocps(self):
         options = {
             "open-circuit potential": "MSMR",

From 7f6d55ae054626571924b4e8395a0d1f08c19d6d Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Wed, 18 Oct 2023 11:28:59 -0700
Subject: [PATCH 230/615] better handling of negative electrode case

---
 pybamm/parameters/parameter_values.py         |  5 +-
 .../test_lithium_ion/test_electrode_soh.py    | 15 ++++--
 .../test_parameters/test_parameter_values.py  | 50 +++++++++++++++----
 3 files changed, 52 insertions(+), 18 deletions(-)

diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 254a0f6806..d5f12f362f 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -292,10 +292,7 @@ def set_initial_stoichiometry_half_cell(
         else:
             parameter_values = self.copy()
 
-        if options["working electrode"] == "positive":
-            c_max = self.evaluate(param.p.prim.c_max)
-        else:
-            c_max = self.evaluate(param.n.prim.c_max)
+        c_max = self.evaluate(param.p.prim.c_max)
 
         parameter_values.update(
             {
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
index 7bd156d0bc..628017d5d8 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -346,6 +346,9 @@ def test_initial_soc_cell_capacity(self):
 
     def test_error(self):
         parameter_values = pybamm.ParameterValues("Chen2020")
+        parameter_values_half_cell = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
 
         with self.assertRaisesRegex(
             ValueError, "Initial SOC should be between 0 and 1"
@@ -359,15 +362,21 @@ def test_error(self):
             pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values)
 
         with self.assertRaisesRegex(ValueError, "outside the voltage limits"):
-            pybamm.lithium_ion.get_initial_stoichiometry_half_cell("1 V", parameter_values)
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                "1 V", parameter_values_half_cell
+            )
 
         with self.assertRaisesRegex(ValueError, "must be a float"):
-            pybamm.lithium_ion.get_initial_stoichiometry_half_cell("5 A", parameter_values)
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                "5 A", parameter_values_half_cell
+            )
 
         with self.assertRaisesRegex(
             ValueError, "Initial SOC should be between 0 and 1"
         ):
-            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(2, parameter_values)
+            pybamm.lithium_ion.get_initial_stoichiometry_half_cell(
+                2, parameter_values_half_cell
+            )
 
 
 class TestGetInitialOCP(TestCase):
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index ab699f0365..fa6e2398ee 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -15,6 +15,7 @@
     lico2_ocp_Dualfoil1998,
     lico2_diffusivity_Dualfoil1998,
 )
+from pybamm.expression_tree.exceptions import OptionError
 import casadi
 
 
@@ -120,28 +121,55 @@ def test_set_initial_stoichiometries(self):
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
     def test_set_initial_stoichiometry_half_cell(self):
-        param = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
-        param = param.set_initial_stoichiometry_half_cell(0.4, inplace=False, options={"working electrode": "positive"})
-        param_0 = param.set_initial_stoichiometry_half_cell(0, inplace=False, options={"working electrode": "positive"})
-        param_100 = param.set_initial_stoichiometry_half_cell(1, inplace=False, options={"working electrode": "positive"})
+        param = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param = param.set_initial_stoichiometry_half_cell(
+            0.4, inplace=False, options={"working electrode": "positive"}
+        )
+        param_0 = param.set_initial_stoichiometry_half_cell(
+            0, inplace=False, options={"working electrode": "positive"}
+        )
+        param_100 = param.set_initial_stoichiometry_half_cell(
+            1, inplace=False, options={"working electrode": "positive"}
+        )
 
         y = param["Initial concentration in positive electrode [mol.m-3]"]
         y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
         y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
-        #inplace for 100% coverage
-        param_t = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
-        param_t.set_initial_stoichiometry_half_cell(0.4, inplace=True, options={"working electrode": "positive"})
+        # inplace for 100% coverage
+        param_t = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_t.set_initial_stoichiometry_half_cell(
+            0.4, inplace=True, options={"working electrode": "positive"}
+        )
         y = param_t["Initial concentration in positive electrode [mol.m-3]"]
-        param_0 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
-        param_0.set_initial_stoichiometry_half_cell(0, inplace=True, options={"working electrode": "positive"})
+        param_0 = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_0.set_initial_stoichiometry_half_cell(
+            0, inplace=True, options={"working electrode": "positive"}
+        )
         y_0 = param_0["Initial concentration in positive electrode [mol.m-3]"]
-        param_100 = pybamm.lithium_ion.DFN({"working electrode": "positive"}).default_parameter_values
-        param_100.set_initial_stoichiometry_half_cell(1, inplace=True, options={"working electrode": "positive"})
+        param_100 = pybamm.lithium_ion.DFN(
+            {"working electrode": "positive"}
+        ).default_parameter_values
+        param_100.set_initial_stoichiometry_half_cell(
+            1, inplace=True, options={"working electrode": "positive"}
+        )
         y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
+        # test error
+        param = pybamm.ParameterValues("Chen2020")
+        with self.assertRaisesRegex(OptionError, "working electrode"):
+            param.set_initial_stoichiometry_half_cell(
+                0.1, options={"working electrode": "negative"}
+            )
+
     def test_set_initial_ocps(self):
         options = {
             "open-circuit potential": "MSMR",

From 761ad1b5b6da0c8f643edc47bc55537cc6aaf33f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 19 Oct 2023 06:39:03 +0530
Subject: [PATCH 231/615] Do not perform user installation for CMake

---
 scripts/Dockerfile | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index c3d12bb7fe..429ed64eed 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -33,7 +33,8 @@ ARG ODES
 ARG JAX
 ARG ALL
 
-RUN pip install --upgrade --user pip setuptools wheel wget cmake
+RUN pip install --upgrade --user pip setuptools wheel wget
+RUN pip install cmake
 
 RUN if [ "$IDAKLU" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \

From c976eeae87d4e389e6bcb9c884b76398cd45cd8b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 19 Oct 2023 06:51:11 +0530
Subject: [PATCH 232/615] The `scikits.odes` solver does not need `pybind11`

---
 scripts/Dockerfile | 2 --
 1 file changed, 2 deletions(-)

diff --git a/scripts/Dockerfile b/scripts/Dockerfile
index 429ed64eed..8def7ced9e 100644
--- a/scripts/Dockerfile
+++ b/scripts/Dockerfile
@@ -45,8 +45,6 @@ RUN if [ "$IDAKLU" = "true" ]; then \
 
 RUN if [ "$ODES" = "true" ]; then \
     python scripts/install_KLU_Sundials.py && \
-    rm -rf pybind11 && \
-    git clone https://github.com/pybind/pybind11.git && \
     pip install --user -e ".[all,dev,docs,odes]"; \
     fi
 

From 0cc0aeeb323d713e302904b4572af745f3f18425 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 19 Oct 2023 16:24:42 -0700
Subject: [PATCH 233/615] Edits after review * Add pybamm version to JSON file
 * Re-word missing variable message * Refactor unary_operator _from_json()

---
 .../expression_tree/operations/serialise.py   |  1 +
 pybamm/expression_tree/unary_operators.py     | 28 ++-----------------
 pybamm/plotting/quick_plot.py                 |  2 +-
 .../test_expression_tree/test_interpolant.py  |  3 +-
 .../test_unary_operators.py                   |  4 +--
 .../test_serialisation/test_serialisation.py  |  2 +-
 6 files changed, 8 insertions(+), 32 deletions(-)

diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index e3b3d38472..14ff251b6a 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -109,6 +109,7 @@ def save_model(
         model_json = {
             "py/object": str(type(model))[8:-2],
             "py/id": id(model),
+            "pybamm_version": pybamm.__version__,
             "name": model.name,
             "options": model.options,
             "bounds": [bound.tolist() for bound in model.bounds],
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 8745e5f33c..4c047cf0e6 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -35,13 +35,13 @@ def __init__(self, name, child, domains=None):
         self.child = self.children[0]
 
     @classmethod
-    def _from_json(cls, name, snippet: dict):
+    def _from_json(cls, snippet: dict):
         """Use to instantiate when deserialising"""
 
         instance = cls.__new__(cls)
 
         super(UnaryOperator, instance).__init__(
-            name,
+            snippet["name"],
             snippet["children"],
             domains=snippet["domains"],
         )
@@ -114,12 +114,6 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("-", child)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.UnaryOperator._from_json()`."""
-        instance = super()._from_json("-", snippet)
-        return instance
-
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         return "{}{!s}".format(self.name, self.child)
@@ -150,12 +144,6 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("abs", child)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.UnaryOperator._from_json()`."""
-        instance = super()._from_json("abs", snippet)
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return sign(self.child) * self.child.diff(variable)
@@ -216,12 +204,6 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("floor", child)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.UnaryOperator._from_json()`."""
-        instance = super()._from_json("floor", snippet)
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return pybamm.Scalar(0)
@@ -244,12 +226,6 @@ def __init__(self, child):
         """See :meth:`pybamm.UnaryOperator.__init__()`."""
         super().__init__("ceil", child)
 
-    @classmethod
-    def _from_json(cls, snippet: dict):
-        """See :meth:`pybamm.UnaryOperator._from_json()`."""
-        instance = super()._from_json("ceil", snippet)
-        return instance
-
     def diff(self, variable):
         """See :meth:`pybamm.Symbol.diff()`."""
         return pybamm.Scalar(0)
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index bfe46b8ed0..584f9ef1be 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -156,7 +156,7 @@ def __init__(
 
         # check variables have been provided after any serialisation
         if any(len(m.variables) == 0 for m in models):
-            raise AttributeError("Variables not provided by the serialised model")
+            raise AttributeError("No variables to plot")
 
         self.n_rows = n_rows or int(
             len(output_variables) // np.sqrt(len(output_variables))
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 0b5ca5f64a..92e9ef86c2 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -326,12 +326,11 @@ def test_processing(self):
 
         self.assertEqual(interp, interp.new_copy())
 
-    def test_to_json_error(self):
+    def test_to_json(self):
         x = np.linspace(0, 1, 10)
         y = pybamm.StateVector(slice(0, 2))
         interp = pybamm.Interpolant(x, 2 * x, y)
 
-        print(interp.children)
         expected_json = {
             "name": "interpolating_function",
             "id": mock.ANY,
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index f11c5d5d10..7e6c71e1dc 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -56,7 +56,7 @@ def test_negation(self):
         # Test from_json
         input_json = {
             "name": "-",
-            "id": -2659857727954094888,
+            "id": mock.ANY,
             "domains": {
                 "primary": [],
                 "secondary": [],
@@ -749,7 +749,7 @@ def test_to_from_json(self):
         self.assertEqual(un.to_json(), un_json)
 
         un_json["children"] = [a]
-        self.assertEqual(pybamm.UnaryOperator._from_json("unary test", un_json), un)
+        self.assertEqual(pybamm.UnaryOperator._from_json(un_json), un)
 
         # Index
         vec = pybamm.StateVector(slice(0, 5))
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 6ae39c05cc..533baa718f 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -481,7 +481,7 @@ def test_save_load_model(self):
         # check an error is raised when plotting the solution
         with self.assertRaisesRegex(
             AttributeError,
-            "Variables not provided by the serialised model",
+            "No variables to plot",
         ):
             new_solution.plot()
 

From 2713ce5b946d96cf962734e95d75ee77d9a1a6a3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 01:46:28 +0530
Subject: [PATCH 234/615] Add `.zshrc` for macOS

---
 pybamm/install_odes.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 4bf310a0f2..27ff19f356 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -89,13 +89,19 @@ def update_LD_LIBRARY_PATH(install_dir):
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        if sys.platform == "linux":
+            script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        if sys.platform == "darwin":
+            script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
     if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
         "LD_LIBRARY_PATH"
     ):
         print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
-        print("--> Not updating venv activate or .bashrc scripts")
+        if sys.platform == "linux":
+            print("--> Not updating venv activate or .bashrc scripts")
+        if sys.platform == "darwin":
+            print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:
             # Just check that export statement is not already there.

From 8db4f7ced9a90e37ad3910bd88d43d70857a354c Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:05:59 +0530
Subject: [PATCH 235/615] Install required modules before initializing

---
 pybamm/install_odes.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 27ff19f356..fe5eae1314 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,23 +8,22 @@
 
 from pybamm.util import root_dir
 
-try:
-    # wget module is required to download SUNDIALS or SuiteSparse.
-    import wget
+def install_required_module(module):
+    try:
+        __import__(module)
+    except ModuleNotFoundError:
+        print(f"{module} module not found. Installing {module}...")
+        subprocess.run(["pip", "install", module], check=True)
+
+required_modules = ["wget", "cmake"]
 
-    NO_WGET = False
-except ModuleNotFoundError:
-    NO_WGET = True
+for module in required_modules:
+    install_required_module(module)
 
+import wget # noqa: E402
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
-    if NO_WGET:
-        error_msg = (
-            "Could not find wget module."
-            " Please install wget module (pip install wget)."
-        )
-        raise ModuleNotFoundError(error_msg)
     archive = wget.download(url, out=directory)
     tar = tarfile.open(archive)
     tar.extractall(directory)

From 45de35620ced05e73e450be3b0421e004171625e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:21:09 +0530
Subject: [PATCH 236/615] Using f-strings instead of `format()`

---
 pybamm/install_odes.py | 45 ++++++++++++++----------------------------
 1 file changed, 15 insertions(+), 30 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index fe5eae1314..528be140aa 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,7 +20,7 @@ def install_required_module(module):
 for module in required_modules:
     install_required_module(module)
 
-import wget # noqa: E402
+import wget  # noqa: E402
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
@@ -28,7 +28,6 @@ def download_extract_library(url, directory):
     tar = tarfile.open(archive)
     tar.extractall(directory)
 
-
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
@@ -40,10 +39,7 @@ def install_sundials(download_dir, install_dir):
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
     url = (
-        "https://github.com/LLNL/"
-        + "sundials/releases/download/v{}/sundials-{}.tar.gz".format(
-            sundials_version, sundials_version
-        )
+        f"https://github.com/LLNL/sundials/releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
     )
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
@@ -53,7 +49,7 @@ def install_sundials(download_dir, install_dir):
         "-DSUNDIALS_INDEX_SIZE=32",
         "-DBUILD_ARKODE:BOOL=OFF",
         "-DEXAMPLES_ENABLE:BOOL=OFF",
-        "-DCMAKE_INSTALL_PREFIX=" + install_dir,
+        f"-DCMAKE_INSTALL_PREFIX={install_dir}",
     ]
 
     # SUNDIALS are built within directory 'build_sundials' in the PyBaMM root
@@ -65,7 +61,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", "../sundials-{}".format(sundials_version)] + cmake_args,
+        ["cmake", f"../sundials-{sundials_version}"] + cmake_args,
         cwd=build_directory,
         check=True,
     )
@@ -74,15 +70,12 @@ def install_sundials(download_dir, install_dir):
     make_cmd = ["make", "install"]
     subprocess.run(make_cmd, cwd=build_directory, check=True)
 
-
 def update_LD_LIBRARY_PATH(install_dir):
-    # Look for current python virtual env and add export statement
-    # for LD_LIBRARY_PATH in activate script.  If no virtual env found,
-    # then the current user's .bashrc file is modified instead.
+    # Look for the current python virtual env and add an export statement
+    # for LD_LIBRARY_PATH in the activate script. If no virtual env is found,
+    # the current user's .bashrc file is modified instead.
 
-    export_statement = "export LD_LIBRARY_PATH={}/lib:$LD_LIBRARY_PATH".format(
-        install_dir
-    )
+    export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
     venv_path = os.environ.get("VIRTUAL_ENV")
     if venv_path:
@@ -93,10 +86,8 @@ def update_LD_LIBRARY_PATH(install_dir):
         if sys.platform == "darwin":
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
-    if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
-        "LD_LIBRARY_PATH"
-    ):
-        print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):  # noqa: E501
+        print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         if sys.platform == "linux":
             print("--> Not updating venv activate or .bashrc scripts")
         if sys.platform == "darwin":
@@ -106,14 +97,9 @@ def update_LD_LIBRARY_PATH(install_dir):
             # Just check that export statement is not already there.
             if export_statement not in fh.read():
                 fh.write(export_statement)
-                print(
-                    "Adding {}/lib to LD_LIBRARY_PATH"
-                    " in {}".format(install_dir, script_path)
-                )
-
+                print(f"Adding {install_dir}/lib to LD_LIBRARY_PATH in {script_path}")
 
 def main(arguments=None):
-
     log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
     logger = logging.getLogger("scikits.odes setup")
 
@@ -145,24 +131,24 @@ def main(arguments=None):
         else os.path.join(pybamm_dir, args.install_dir)
     )
 
-    # Check is sundials is already installed
+    # Check if sundials is already installed
     SUNDIALS_LIB_DIRS = [join(os.getenv("HOME"), ".local"), "/usr/local", "/usr"]
 
     if args.sundials_libs:
         SUNDIALS_LIB_DIRS.insert(0, args.sundials_libs)
     for DIR in SUNDIALS_LIB_DIRS:
-        logger.info("Looking for sundials at {}".format(DIR))
+        logger.info(f"Looking for sundials at {DIR}")
         SUNDIALS_FOUND = isfile(join(DIR, "lib", "libsundials_ida.so")) or isfile(
             join(DIR, "lib", "libsundials_ida.dylib")
         )
         if SUNDIALS_FOUND:
             SUNDIALS_LIB_DIR = DIR
-            logger.info("Found sundials at {}".format(SUNDIALS_LIB_DIR))
+            logger.info(f"Found sundials at {SUNDIALS_LIB_DIR}")
             break
 
     if not SUNDIALS_FOUND:
         logger.info("Could not find sundials libraries.")
-        logger.info("Installing sundials in {}".format(install_dir))
+        logger.info(f"Installing sundials in {install_dir}")
         download_dir = os.path.join(pybamm_dir, "sundials")
         if not os.path.exists(download_dir):
             os.makedirs(download_dir)
@@ -178,6 +164,5 @@ def main(arguments=None):
     env = os.environ.copy()
     subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
 
-
 if __name__ == "__main__":
     main(sys.argv[1:])

From 4a8f13ca5e1d6cf5c361a275beec37748b869ff1 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 02:23:53 +0530
Subject: [PATCH 237/615] gitignore `scikits_odes_setup.log`

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 3e01fcac83..374e52cb45 100644
--- a/.gitignore
+++ b/.gitignore
@@ -107,6 +107,9 @@ KLU_module_deps
 # setup
 setup.log
 
+# odes setup
+scikits_odes_setup.log
+
 # test
 test.c
 test.json

From edcccf5029cb92ac97032da49ae6bb72009b3b96 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:05:03 +0530
Subject: [PATCH 238/615] Update doc in solver section for `install_odes`

---
 docs/source/user_guide/installation/GNU-linux.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..5abb373404 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -125,9 +125,11 @@ Currently, only GNU/Linux and macOS are supported.
 
    .. code:: bash
 
-	  pip install scikits.odes
+      brew install openblas
+      pybamm_install_odes
 
-   Assuming that SUNDIALS was installed as described :ref:`above<user-install-label>`.
+   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~

From 965555004aa5bf30ba574cfd128ffa220bd715d8 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:12:23 +0530
Subject: [PATCH 239/615] Exit early on windows

---
 pybamm/install_odes.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 528be140aa..639af99473 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,6 +8,12 @@
 
 from pybamm.util import root_dir
 
+def check_platform():
+    if sys.platform == "win32":
+        raise Exception("pybamm_install_odes is not supported on Windows.")
+
+check_platform()
+
 def install_required_module(module):
     try:
         __import__(module)

From 8b33770fd014172b5b97db4b7786cd15651c1d2b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:17:26 +0530
Subject: [PATCH 240/615] Changelog

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 008cad125f..00a6e974d7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Features
+
+- Extend `pybamm_install_odes` to include support for macOS systems ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features

From ac803c5009db5866ab6a2eb681cb7ef9af13d764 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 21 Oct 2023 03:20:13 +0530
Subject: [PATCH 241/615] Remove cache before installation

---
 .github/workflows/test_on_push.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 3bd78cbcd2..ead0bb4b3d 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -220,6 +220,7 @@ jobs:
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
+          pip cache purge
           pip install wget cmake
           pybamm_install_odes
 

From 616c0d8acd9c4521eef41170228e14a00242c287 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 20 Oct 2023 14:52:19 -0700
Subject: [PATCH 242/615] Serialisation: fix integration tests

---
 .../examples/notebooks/models/saving_models.ipynb  |  8 ++++++--
 pybamm/expression_tree/operations/serialise.py     | 14 +++++++++++++-
 .../full_battery_models/base_battery_model.py      |  4 ----
 3 files changed, 19 insertions(+), 7 deletions(-)

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index 85ca516a59..c3f72bc4e4 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -65,7 +65,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "raises-exception"
+    ]
+   },
    "outputs": [],
    "source": [
     "dfn_models = [dfn_model, new_dfn_model]\n",
@@ -261,7 +265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "orig_nbformat": 4
  },
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index 14ff251b6a..b54f7b1078 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -178,7 +178,7 @@ def load_model(
 
         recon_model_dict = {
             "name": model_data["name"],
-            "options": model_data["options"],
+            "options": self._convert_options(model_data["options"]),
             "bounds": tuple(np.array(bound) for bound in model_data["bounds"]),
             "concatenated_rhs": self._reconstruct_expression_tree(
                 model_data["concatenated_rhs"]
@@ -383,3 +383,15 @@ def recurse(obj):
             return obj
 
         return recurse(obj)
+
+    def _convert_options(self, d):
+        """
+        Converts a dictionary with nested lists to nested tuples,
+        used to convert model options back into correct format
+        """
+        if isinstance(d, dict):
+            return {k: self._convert_options(v) for k, v in d.items()}
+        elif isinstance(d, list):
+            return tuple(self._convert_options(item) for item in d)
+        else:
+            return d
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index cd0b256113..d5593fa55e 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -630,10 +630,6 @@ def __init__(self, extra_options):
             ]:  # some options accept non-strings
                 value = (value,)
             else:
-                # serialised options save tuples as lists which need to be converted
-                if isinstance(value, list) and len(value) == 2:
-                    value = tuple(tuple(v) if len(v) == 2 else v for v in value)
-
                 if not (
                     (
                         option

From 8e3271826895a0d4018c73906e3ae4e284ff3cfa Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 20 Oct 2023 16:53:44 -0700
Subject: [PATCH 243/615] Reduce test tolerance of
 sei_asymmetric_ec_reaction_limited

---
 tests/integration/test_models/standard_model_tests.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index d0e38501c9..ccf12c6143 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -152,7 +152,10 @@ def test_serialisation(self, solver=None, t_eval=None):
         else:
             new_solver = new_model.default_solver
 
-        if isinstance(new_model, pybamm.lithium_ion.BaseModel):
+        if (
+            isinstance(new_model, pybamm.lithium_ion.BaseModel)
+            and new_model.options["SEI"] != "ec reaction limited (asymmetric)"
+        ):
             new_solver.rtol = 1e-8
             new_solver.atol = 1e-8
             accuracy = 6

From 1e16b92de780ba42db4909bc87eb7b7dcf059949 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 20 Oct 2023 17:56:36 -0700
Subject: [PATCH 244/615] fix: change serialisation test accuracy Required for
 macOS python<3.11

---
 tests/integration/test_models/standard_model_tests.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index ccf12c6143..d4074e15ef 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -152,15 +152,11 @@ def test_serialisation(self, solver=None, t_eval=None):
         else:
             new_solver = new_model.default_solver
 
-        if (
-            isinstance(new_model, pybamm.lithium_ion.BaseModel)
-            and new_model.options["SEI"] != "ec reaction limited (asymmetric)"
-        ):
+        if isinstance(new_model, pybamm.lithium_ion.BaseModel):
             new_solver.rtol = 1e-8
             new_solver.atol = 1e-8
-            accuracy = 6
-        else:
-            accuracy = 5
+
+        accuracy = 5
 
         Crate = abs(
             self.parameter_values["Current function [A]"]

From 62ed4c4e607f6f95c98b4193705ab2b0cfc6b2f9 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 15:59:32 +0530
Subject: [PATCH 245/615] #3049 add suggestions from code review

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/publish_pypi.yml | 105 ++++++++++++++---------------
 1 file changed, 50 insertions(+), 55 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 29c352cf6c..8c07858825 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -1,7 +1,4 @@
-# name: Build and publish package to PyPI
-name: Test building wheels
-# Temporarily disable publishing to PyPI and enable
-# building wheels on pull requests
+name: Build and publish package to PyPI
 on:
   release:
     types: [published]
@@ -33,15 +30,15 @@ jobs:
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
-      - name: Install vcpkg on windows
+      - name: Install vcpkg on Windows
         run: |
           cd C:\
           rm -r -fo 'C:\vcpkg'
-          git clone https://github.com/microsoft/vcpkg --branch 2023.08.09
+          git clone https://github.com/microsoft/vcpkg
           cd vcpkg
           .\bootstrap-vcpkg.bat
 
-      - name: Cache packages installed through vcpkg on windows
+      - name: Cache packages installed through vcpkg on Windows
         uses: actions/cache@v3
         env:
           cache-name: vckpg_binary_cache
@@ -54,13 +51,13 @@ jobs:
         uses: mxschmitt/action-tmate@v3
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_enabled }}
 
-      - name: Build 64 bits wheels on Windows
+      - name: Build 64-bit wheels on Windows
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
 
-      - name: Upload windows wheels
+      - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
         with:
           name: windows_wheels
@@ -109,8 +106,6 @@ jobs:
             python -m pip
             install cmake casadi numpy &&
             python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
-          # got error "re.error: multiple repeat at position 104" on python 3.7 when --require-archs added, so remove
-          # it for mac
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
             delocate-listdeps {wheel} &&
             delocate-wheel -v -w {dest_dir} {wheel}
@@ -146,47 +141,47 @@ jobs:
           path: ./dist/*.tar.gz
           if-no-files-found: error
 
-  # publish_pypi:
-  #   if: github.event_name != 'schedule'
-  #   name: Upload package to PyPI
-  #   needs: [build_wheels, build_windows_wheels, build_sdist]
-  #   runs-on: ubuntu-latest
-  #   steps:
-  #     - name: Download all artifacts
-  #       uses: actions/download-artifact@v3
-
-  #     - name: Move all package files to files/
-  #       run: |
-  #         mkdir files
-  #         mv windows_wheels/* wheels/* sdist/* files/
-
-  #     - name: Publish on PyPI
-  #       if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
-  #       uses: pypa/gh-action-pypi-publish@release/v1
-  #       with:
-  #         user: __token__
-  #         password: ${{ secrets.PYPI_TOKEN }}
-  #         packages-dir: files/
-
-  #     - name: Publish on TestPyPI
-  #       if: github.event.inputs.target == 'testpypi'
-  #       uses: pypa/gh-action-pypi-publish@release/v1
-  #       with:
-  #         user: __token__
-  #         password: ${{ secrets.TESTPYPI_TOKEN }}
-  #         packages-dir: files/
-  #         repository-url: https://test.pypi.org/legacy/
-
-  # open_failure_issue:
-  #   needs: [build_windows_wheels, build_wheels, build_sdist]
-  #   name: Open an issue if build fails
-  #   if: ${{ always() && contains(needs.*.result, 'failure') }}
-  #   runs-on: ubuntu-latest
-  #   steps:
-  #   - uses: actions/checkout@v4
-  #   - uses: JasonEtco/create-an-issue@v2
-  #     env:
-  #       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  #       LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
-  #     with:
-  #       filename: .github/wheel_failure.md
+  publish_pypi:
+    if: github.event_name != 'schedule'
+    name: Upload package to PyPI
+    needs: [build_wheels, build_windows_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Download all artifacts
+        uses: actions/download-artifact@v3
+
+      - name: Move all package files to files/
+        run: |
+          mkdir files
+          mv windows_wheels/* wheels/* sdist/* files/
+
+      - name: Publish on PyPI
+        if: github.event.inputs.target == 'pypi' || github.event_name == 'release'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_TOKEN }}
+          packages-dir: files/
+
+      - name: Publish on TestPyPI
+        if: github.event.inputs.target == 'testpypi'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.TESTPYPI_TOKEN }}
+          packages-dir: files/
+          repository-url: https://test.pypi.org/legacy/
+
+  open_failure_issue:
+    needs: [build_windows_wheels, build_wheels, build_sdist]
+    name: Open an issue if build fails
+    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - uses: JasonEtco/create-an-issue@v2
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        LOGS: ${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}
+      with:
+        filename: .github/wheel_failure.md

From db91a889efd01ff8ce904298b088b1172cc988ab Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:07:08 +0530
Subject: [PATCH 246/615] #3049 clarify extension language

---
 CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7e9a0e94af..9f583e324d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -387,7 +387,7 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ### Installation
 
-Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 

From 076860934c089d8fec07232593be7576c26223bf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:07:35 +0530
Subject: [PATCH 247/615] #3049 include citation file in source distribution

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 MANIFEST.in | 1 +
 1 file changed, 1 insertion(+)

diff --git a/MANIFEST.in b/MANIFEST.in
index 24ae488d04..bfc9d0e718 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,4 +1,5 @@
 graft pybamm
+include CITATION.cff
 prune tests
 
 exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md GOVERNANCE.md CMakeLists.txt

From f56e3664c3b7fcf9f3cf8434c2eaf1e6bf216875 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:08:32 +0530
Subject: [PATCH 248/615] #3049 remove note about requirements.txt

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 docs/source/user_guide/installation/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 93e54c51fe..0e9b02de01 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -76,7 +76,7 @@ Optional Dependencies
 PyBaMM has a number of optional dependencies for different functionalities.
 If the optional dependency is not installed, PyBaMM will raise an ImportError when the method requiring that dependency is called.
 
-If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g. requirements.txt, setup.py, or pyproject.toml)
+If you are using ``pip``, optional PyBaMM dependencies can be installed or managed in a file (e.g., setup.py, or pyproject.toml)
 as optional extras (e.g.,``pybamm[dev,plot]``). All optional dependencies can be installed with ``pybamm[all]``,
 and specific sets of dependencies are listed in the sections below.
 

From e311029ab5631bdae38b67e4011a80ed58fe8d0e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:13:21 +0530
Subject: [PATCH 249/615] #3049 fix docs indentation

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 docs/source/user_guide/installation/install-from-source.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index d4de957b16..003c7f143a 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -106,7 +106,7 @@ Installing PyBaMM
 You should now have everything ready to build and install PyBaMM successfully.
 
 Using ``Nox`` (recommended)
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. code:: bash
 

From 052a1637eb910d2ce87323cc1d56ebfd0cfade62 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:24:11 +0530
Subject: [PATCH 250/615] #3049 specify lower bounds for `setuptools`

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5f26d260de..32912383f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [build-system]
 requires = [
-    "setuptools",
+    "setuptools>=64",
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.0; platform_system!='Windows'",

From 5e587c0df28cd4c609f5b6c3fb3ead545bcdd415 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 16:37:19 +0530
Subject: [PATCH 251/615] #3049 cleanup and clarify user installation page

---
 docs/source/user_guide/installation/GNU-linux.rst | 2 +-
 docs/source/user_guide/installation/windows.rst   | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..ca95bbe1b5 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -6,7 +6,7 @@ GNU-Linux & MacOS
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6ff48293bd..5b104e91bd 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -6,7 +6,7 @@ Windows
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to
@@ -27,7 +27,7 @@ install PyBaMM. You can find a reminder of how to navigate the terminal
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution python files.
 
-To install virtualenv type:
+To install ``virtualenv``, type:
 
 .. code:: bash
 

From 3b00b181245827b1420b0fa4162b2cd401990635 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 22:57:33 +0530
Subject: [PATCH 252/615] #3049 fix whitespace in `pybamm-requires` session

Co-Authored-By: Eric G. Kratz <kratman@users.noreply.github.com>
---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 4af892565f..45787b6f4e 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -41,7 +41,7 @@ def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("wget", "cmake" , silent=False)
+        session.install("wget", "cmake", silent=False)
         session.run("python", "scripts/install_KLU_Sundials.py")
         if not os.path.exists("./pybind11"):
             session.run(

From b49b6f331625587665e45f013459dee8c336d5be Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 21 Oct 2023 23:00:28 +0530
Subject: [PATCH 253/615] #3049 code review suggestions from Eric

do not install gcc or gfortran if they are already installed, just reinstall them

Co-Authored-By: Eric G. Kratz <kratman@users.noreply.github.com>
---
 .github/workflows/publish_pypi.yml | 2 +-
 .github/workflows/test_on_push.yml | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 8c07858825..fda75d4489 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -85,7 +85,7 @@ jobs:
         if: matrix.os == 'macos-latest'
         run: |
           brew update
-          brew install gcc gfortran libomp graphviz openblas
+          brew install graphviz openblas libomp
           brew reinstall gcc
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 88ada069b4..5aac923da2 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -77,7 +77,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas gcc gfortran libomp
+          brew install graphviz openblas libomp
           brew reinstall gcc
 
       - name: Install Windows system dependencies
@@ -210,7 +210,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas gcc gfortran libomp
+          brew install graphviz openblas libomp
           brew reinstall gcc
 
       - name: Install Windows system dependencies

From 0ed80bbbcff95b5bfb69f7e711aa596c442f5621 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Mon, 23 Oct 2023 23:22:42 +0530
Subject: [PATCH 254/615] Applied suggestions

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/test_on_push.yml | 2 --
 CHANGELOG.md                       | 2 +-
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index ead0bb4b3d..68bdc187b4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -210,8 +210,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install PyBaMM dependencies
         run: |
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 00a6e974d7..b6148896df 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,7 @@
 
 ## Features
 
-- Extend `pybamm_install_odes` to include support for macOS systems ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 

From 2698a875d99cda32736724e8bb4f93d988fc8bf9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 23 Oct 2023 23:59:40 +0530
Subject: [PATCH 255/615] Move `test_install_odes` to scheduled

---
 .github/workflows/run_periodic_tests.yml | 45 ++++++++++++++++++++++++
 .github/workflows/test_on_push.yml       | 45 ------------------------
 2 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f6e51bc11b..197c8e5872 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -164,3 +164,48 @@ jobs:
           eval "$(pyenv init -)"
           pyenv activate pybamm-${{ matrix.python-version }}
           pyenv uninstall -f $( python --version )
+
+  test_install_odes:
+    needs: style
+    runs-on: macos-latest
+    strategy:
+        matrix:
+            os: [ubuntu-latest, macos-latest]
+            python-version: ["3.8", "3.9", "3.10", "3.11"]
+        fail-fast: false
+    name: Test pybamm_install_odes on ${{ matrix.os }}
+
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      - name: Install macOS system dependencies
+        env:
+          # Homebrew environment variables
+          HOMEBREW_NO_INSTALL_CLEANUP: 1
+          HOMEBREW_NO_AUTO_UPDATE: 1
+          HOMEBREW_NO_COLOR: 1
+          # Speed up CI
+          NONINTERACTIVE: 1
+        run: |
+          brew analytics off
+          brew update
+          brew install openblas
+          brew reinstall gcc gfortran
+
+      - name: Set up Python ${{ matrix.python-version }}
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install PyBaMM dependencies
+        run: |
+          pip install --upgrade pip wheel setuptools nox
+          pip install -e .[all]
+
+      - name: Test pybamm_install_odes on ${{ matrix.os }}
+        run: |
+          pip cache purge
+          pip install wget cmake
+          pybamm_install_odes
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 68bdc187b4..cb22fb87f7 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -177,51 +177,6 @@ jobs:
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
 
-  test_install_odes:
-    needs: style
-    runs-on: macos-latest
-    strategy:
-        matrix:
-            os: [ubuntu-latest, macos-latest]
-            python-version: ["3.8", "3.9", "3.10", "3.11"]
-        fail-fast: false
-    name: Test pybamm_install_odes on ${{ matrix.os }}
-
-    steps:
-      - name: Check out PyBaMM repository
-        uses: actions/checkout@v4
-
-      - name: Install macOS system dependencies
-        env:
-          # Homebrew environment variables
-          HOMEBREW_NO_INSTALL_CLEANUP: 1
-          HOMEBREW_NO_AUTO_UPDATE: 1
-          HOMEBREW_NO_COLOR: 1
-          # Speed up CI
-          NONINTERACTIVE: 1
-        run: |
-          brew analytics off
-          brew update
-          brew install openblas
-          brew reinstall gcc gfortran
-
-      - name: Set up Python ${{ matrix.python-version }}
-        id: setup-python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all]
-
-      - name: Test pybamm_install_odes on ${{ matrix.os }}
-        run: |
-          pip cache purge
-          pip install wget cmake
-          pybamm_install_odes
-
   run_integration_tests:
     needs: style
     runs-on: ${{ matrix.os }}

From ac5cabdfe5c85d7e3a291ef09af5944e5332a5c7 Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Mon, 23 Oct 2023 12:13:21 -0700
Subject: [PATCH 256/615] fix esoh bug

---
 .../lithium_ion/electrode_soh_half_cell.py                    | 4 ++--
 pybamm/simulation.py                                          | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
index 1e237e73c8..8c22cf2ada 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
@@ -21,7 +21,7 @@ class ElectrodeSOHHalfCell(pybamm.BaseModel):
 
     """
 
-    def __init__(self, name="Electrode-specific SOH model"):
+    def __init__(self, name="ElectrodeSOH model"):
         pybamm.citations.register("Mohtat2019")
         super().__init__(name)
         param = pybamm.LithiumIonParameters({"working electrode": "positive"})
@@ -140,7 +140,7 @@ def get_min_max_stoichiometries(
     parameter_values : pybamm.ParameterValues
         The parameter values to use in the calculation
     """
-    esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell(options)
+    esoh_model = pybamm.lithium_ion.ElectrodeSOHHalfCell("ElectrodeSOH")
     param = pybamm.LithiumIonParameters(options)
     esoh_sim = pybamm.Simulation(esoh_model, parameter_values=parameter_values)
     Q_w = parameter_values.evaluate(param.p.Q_init)
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 8805e925d0..380105d215 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -552,7 +552,7 @@ def solve(
                 )
             if (
                 self.operating_mode == "without experiment"
-                or self._model.name == "ElectrodeSOH model"
+                or "ElectrodeSOH" in self._model.name
             ):
                 if t_eval is None:
                     raise pybamm.SolverError(

From 4535854b795468def4b9a5c108a83cfd20fef228 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 23 Oct 2023 19:39:25 +0000
Subject: [PATCH 257/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.0.292 → v0.1.1](https://github.com/astral-sh/ruff-pre-commit/compare/v0.0.292...v0.1.1)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8d253a49ee..12e48d913b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.0.292"
+    rev: "v0.1.1"
     hooks:
       - id: ruff
         args: [--fix, --ignore=E741, --exclude=__init__.py]

From c062b17b2a54f4a3a2e1440c1e575cde9eb544f3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 11:14:16 +0530
Subject: [PATCH 258/615] Check platform without function

---
 pybamm/install_odes.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 639af99473..90df811219 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -8,11 +8,8 @@
 
 from pybamm.util import root_dir
 
-def check_platform():
-    if sys.platform == "win32":
-        raise Exception("pybamm_install_odes is not supported on Windows.")
-
-check_platform()
+if sys.platform == "win32":
+    raise Exception("pybamm_install_odes is not supported on Windows.")
 
 def install_required_module(module):
     try:

From e3c62b59a3ee3a7eda7537d89c0920bf4836794d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 11:30:50 +0530
Subject: [PATCH 259/615] Import module with importlib

---
 pybamm/install_odes.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 90df811219..20c84c0fbd 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from importlib import import_module
 
 from pybamm.util import root_dir
 
@@ -13,7 +14,7 @@
 
 def install_required_module(module):
     try:
-        __import__(module)
+        import_module(module)
     except ModuleNotFoundError:
         print(f"{module} module not found. Installing {module}...")
         subprocess.run(["pip", "install", module], check=True)

From 79539f46e625c13d3b4c473a028038fc33c18de3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 12:10:47 +0530
Subject: [PATCH 260/615] Define sundials version on top

---
 pybamm/install_odes.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 20c84c0fbd..a3050a8b27 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -12,6 +12,8 @@
 if sys.platform == "win32":
     raise Exception("pybamm_install_odes is not supported on Windows.")
 
+SUNDIALS_VERSION = "6.5.0"
+
 def install_required_module(module):
     try:
         import_module(module)
@@ -35,7 +37,6 @@ def download_extract_library(url, directory):
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
-    sundials_version = "6.5.0"
 
     try:
         subprocess.run(["cmake", "--version"])
@@ -43,7 +44,7 @@ def install_sundials(download_dir, install_dir):
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
     url = (
-        f"https://github.com/LLNL/sundials/releases/download/v{sundials_version}/sundials-{sundials_version}.tar.gz"
+        f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
     )
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
@@ -65,7 +66,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", f"../sundials-{sundials_version}"] + cmake_args,
+        ["cmake", f"../sundials-{SUNDIALS_VERSION}"] + cmake_args,
         cwd=build_directory,
         check=True,
     )

From 6292cbfba65562ffe37e355d900b064fbb073806 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 24 Oct 2023 14:06:13 +0530
Subject: [PATCH 261/615] Detect terminal with `os.environ`

---
 pybamm/install_odes.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index a3050a8b27..8cec6fc68b 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -86,16 +86,16 @@ def update_LD_LIBRARY_PATH(install_dir):
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        if sys.platform == "linux":
+        if 'BASH' in os.environ:
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
-        if sys.platform == "darwin":
+        if 'ZSH' in os.environ:
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
 
     if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):  # noqa: E501
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
-        if sys.platform == "linux":
+        if 'BASH' in os.environ:
             print("--> Not updating venv activate or .bashrc scripts")
-        if sys.platform == "darwin":
+        if 'ZSH' in os.environ:
             print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:

From 1bca02ac2bcd9d2692ea789d8fef43ef09803e87 Mon Sep 17 00:00:00 2001
From: John Brittain <john.brittain@dtc.ox.ac.uk>
Date: Wed, 25 Oct 2023 10:12:18 +0100
Subject: [PATCH 262/615] Amend changelog

---
 CHANGELOG.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4d2034e945..42e1d90f91 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,7 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features
@@ -19,7 +21,6 @@
 
 ## Bug fixes
 
-- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).

From 09e8cc3a65215d04fc416c1cfd578b4c845e7226 Mon Sep 17 00:00:00 2001
From: John Brittain <john.brittain@dtc.ox.ac.uk>
Date: Wed, 25 Oct 2023 14:51:47 +0100
Subject: [PATCH 263/615] Disable MyST navigation by keys

---
 docs/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/conf.py b/docs/conf.py
index 74d1f76488..49e5cf3dc9 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -154,6 +154,7 @@
     "navbar_end": ["theme-switcher", "navbar-icon-links"],
     # add Algolia to the persistent navbar, this removes the default search icon
     "navbar_persistent": "algolia-searchbox",
+    "navigation_with_keys": False,
     "use_edit_page_button": True,
     "analytics": {
         "plausible_analytics_domain": "docs.pybamm.org",

From 4c237c12fb973827eb167f1390e285f3b3229e0a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 12:12:50 +0530
Subject: [PATCH 264/615] prevent `pybtex` default installation

---
 pybamm/__init__.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 9aa1ca79a0..a8ffbcf83b 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -52,7 +52,13 @@
 )
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
-from .citations import Citations, citations, print_citations
+try:
+    import pybtex
+
+    if pybtex is not None:
+        from .citations import Citations, citations, print_citations
+except ImportError:
+    pass
 
 #
 # Classes for the Expression Tree

From 7aee06e816ed9b623274916c3dd1d9f3135b513d Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Thu, 26 Oct 2023 18:56:17 +0530
Subject: [PATCH 265/615] Added docstring for print_parameter_info method

---
 pybamm/models/base_model.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..985d0b4568 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -365,6 +365,9 @@ def input_parameters(self):
         return self._input_parameters
 
     def print_parameter_info(self):
+        """
+        Returns parameters used in the model
+        """
         self._parameter_info = ""
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:

From be87f8184555b09d87147493323d886423e357d3 Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Thu, 26 Oct 2023 19:10:49 +0530
Subject: [PATCH 266/615] PEP8 adherence for One-line docstring

---
 pybamm/models/base_model.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 985d0b4568..48b8097dc7 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -365,9 +365,7 @@ def input_parameters(self):
         return self._input_parameters
 
     def print_parameter_info(self):
-        """
-        Returns parameters used in the model
-        """
+        """Returns parameters used in the model"""
         self._parameter_info = ""
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:

From 6d30b3adea028d33ab3a377fe1fc870cf3f53abc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 20:42:17 +0530
Subject: [PATCH 267/615] resolve `anytree` default installation

---
 pybamm/expression_tree/symbol.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 5d28884ed5..88c4d02ab8 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -3,10 +3,14 @@
 #
 import numbers
 
-import anytree
+
+try:
+    import anytree
+    from anytree.exporter import DotExporter
+except ImportError:
+    pass
 import numpy as np
 import sympy
-from anytree.exporter import DotExporter
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 

From e3b3b35aa48fe60d9c08454574e2df8aa150b590 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:02:00 +0530
Subject: [PATCH 268/615] resolve `autograd` default imports

---
 pybamm/expression_tree/functions.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 80c2848ad9..788af40d50 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -3,7 +3,10 @@
 #
 import numbers
 
-import autograd
+try:
+    import autograd
+except ImportError:
+    pass
 import numpy as np
 import sympy
 from scipy import special

From 4dd231799f25f45238b58458a386c882fa64fc5e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:06:51 +0530
Subject: [PATCH 269/615] resolve `skfem` default imports

---
 pybamm/meshes/scikit_fem_submeshes.py           | 5 ++++-
 pybamm/spatial_methods/scikit_finite_element.py | 5 ++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index f25dce80b1..c067c43a8a 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -4,7 +4,10 @@
 import pybamm
 from .meshes import SubMesh
 
-import skfem
+try:
+    import skfem
+except ImportError:
+    pass
 import numpy as np
 
 
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 0f0a42bbcb..7556645028 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -6,7 +6,10 @@
 from scipy.sparse import csr_matrix, csc_matrix
 from scipy.sparse.linalg import inv
 import numpy as np
-import skfem
+try:
+    import skfem
+except ImportError:
+    pass
 
 
 class ScikitFiniteElement(pybamm.SpatialMethod):

From 9e24562b7bf00a7e1149b7910f3f2e2f6b3a0107 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 26 Oct 2023 21:20:40 +0530
Subject: [PATCH 270/615] resolve `tqdm` default imports

---
 pybamm/simulation.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 380105d215..dfca7e0583 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -8,7 +8,10 @@
 import sys
 from functools import lru_cache
 from datetime import timedelta
-import tqdm
+try:
+    import tqdm
+except ImportError:
+    pass
 
 
 def is_notebook():

From 3c59897a3ef85e0753997dbb7cc9d3e1c1814835 Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Fri, 27 Oct 2023 13:07:32 +0530
Subject: [PATCH 271/615] Mentioned that arrays can be passed as values for
 drive cycles.

---
 pybamm/step/_steps_util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/step/_steps_util.py b/pybamm/step/_steps_util.py
index e524bc6064..eaa9c64636 100644
--- a/pybamm/step/_steps_util.py
+++ b/pybamm/step/_steps_util.py
@@ -37,7 +37,7 @@ class _Step:
         or "resistance".
     value : float
         The value of the step, corresponding to the type of step. Can be a number, a
-        2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        2-tuple (for cccv_ode), or a 2-column array. Can pass list as argument (for drive cycles)
     duration : float, optional
         The duration of the step in seconds.
     termination : str or list, optional

From 6cc394076b4d63f19235f1c0c5471bb5b2837599 Mon Sep 17 00:00:00 2001
From: jsbrittain <98161205+jsbrittain@users.noreply.github.com>
Date: Fri, 27 Oct 2023 10:20:04 +0100
Subject: [PATCH 272/615] Update CHANGELOG.md

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 CHANGELOG.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 42e1d90f91..b02df8ed4c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,7 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Bug fixes
+
 - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31

From aeda214110049e0ff5af6a88e49a9a01f9182241 Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Sat, 28 Oct 2023 17:57:09 +0530
Subject: [PATCH 273/615] fix/Replace nbqa-ruff with ruff

---
 .pre-commit-config.yaml | 10 ++--------
 ruff.toml               |  6 ++++++
 2 files changed, 8 insertions(+), 8 deletions(-)
 create mode 100644 ruff.toml

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 12e48d913b..01eb62bcd0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,18 +4,12 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.1"
+    rev: "v0.1.3"
     hooks:
       - id: ruff
+        types_or: [python, pyi, jupyter]
         args: [--fix, --ignore=E741, --exclude=__init__.py]
 
-  - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.7.0
-    hooks:
-      - id: nbqa-ruff
-        additional_dependencies: [ruff==0.0.284]
-        args: ["--fix","--ignore=E501,E402"]
-
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.16.0"
     hooks:
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000000..ea62900699
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,6 @@
+[tool.ruff]
+extend-include = ["*.ipynb"]
+
+
+[tool.ruff.lint]
+ignore = ["E402","E703"]

From 641075298495c78cf5c18c02df60c9e6da95087f Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Sat, 28 Oct 2023 18:25:02 +0530
Subject: [PATCH 274/615] added pyproject.toml and removed ruff.toml because of
 failing pre-commit

---
 ruff.toml => pyproject.toml | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename ruff.toml => pyproject.toml (100%)

diff --git a/ruff.toml b/pyproject.toml
similarity index 100%
rename from ruff.toml
rename to pyproject.toml

From 6cb6347b3adec8ddf5848bf0606586de12d6fe14 Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Sat, 28 Oct 2023 19:53:18 +0530
Subject: [PATCH 275/615] Added ruff.toml again and deleted pyproject.toml

---
 pyproject.toml | 6 ------
 ruff.toml      | 2 ++
 2 files changed, 2 insertions(+), 6 deletions(-)
 delete mode 100644 pyproject.toml
 create mode 100644 ruff.toml

diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index ea62900699..0000000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,6 +0,0 @@
-[tool.ruff]
-extend-include = ["*.ipynb"]
-
-
-[tool.ruff.lint]
-ignore = ["E402","E703"]
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000000..a20f5ea464
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,2 @@
+[lint]
+ignore = ["E402","E703"]

From 50315e7f838ecbf2b3cf73e2d02239124aecb286 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 28 Oct 2023 21:17:12 +0530
Subject: [PATCH 276/615] Raise import error for `anytree` requiring functions

---
 pybamm/expression_tree/symbol.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 88c4d02ab8..6904854050 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -8,7 +8,9 @@
     import anytree
     from anytree.exporter import DotExporter
 except ImportError:
-    pass
+    _has_anytree = False
+else:
+    _has_anytree = True
 import numpy as np
 import sympy
 from scipy.sparse import csr_matrix, issparse
@@ -446,6 +448,8 @@ def render(self):  # pragma: no cover
         """
         Print out a visual representation of the tree (this node and its children)
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
                 print("{}{} = {}".format(pre, node.name, node.value))
@@ -463,6 +467,8 @@ def visualise(self, filename):
         filename : str
             filename to output, must end in ".png"
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
 
         # check that filename ends in .png.
         if filename[-4:] != ".png":
@@ -483,6 +489,8 @@ def relabel_tree(self, symbol, counter):
         Finds all children of a symbol and assigns them a new id so that they can be
         visualised properly using the graphviz output
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         name = symbol.name
         if name == "div":
             name = "&nabla;&sdot;"
@@ -526,6 +534,8 @@ def pre_order(self):
         a
         b
         """
+        if not _has_anytree:
+            raise ImportError("Module 'anytree' is required to do this")
         return anytree.PreOrderIter(self)
 
     def __str__(self):

From 5431decb218cfe70b612e392d3c30cd117558d89 Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Sat, 28 Oct 2023 21:55:36 +0530
Subject: [PATCH 277/615]  Added [lint.per-file-ignores] in ruff.toml

---
 ruff.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ruff.toml b/ruff.toml
index a20f5ea464..56d383806f 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,2 +1,2 @@
-[lint]
-ignore = ["E402","E703"]
+[lint.per-file-ignores]
+"**.ipynb" = ["E402", "E703"]

From 1bc0ba788e3eef37138a8309b787bb3c173c83c6 Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Sun, 29 Oct 2023 19:07:51 +0530
Subject: [PATCH 278/615] trying to move jupyter nootebook configuration of
 pro-commit-config.yaml to ruff.toml

---
 .pre-commit-config.yaml | 1 -
 ruff.toml               | 2 ++
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 01eb62bcd0..46d6918d95 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,6 @@ repos:
     rev: "v0.1.3"
     hooks:
       - id: ruff
-        types_or: [python, pyi, jupyter]
         args: [--fix, --ignore=E741, --exclude=__init__.py]
 
   - repo: https://github.com/adamchainz/blacken-docs
diff --git a/ruff.toml b/ruff.toml
index 56d383806f..7c1040e9d8 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,2 +1,4 @@
+extend-include = ["*.ipynb"]
+
 [lint.per-file-ignores]
 "**.ipynb" = ["E402", "E703"]

From e09fcea3888ce5571812f1a5a54b44d2176554db Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 05:04:02 +0530
Subject: [PATCH 279/615] Make simple function to check optional dependency

---
 pybamm/__init__.py | 1 +
 pybamm/util.py     | 9 +++++++++
 2 files changed, 10 insertions(+)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index a8ffbcf83b..8f92c71e18 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -47,6 +47,7 @@
     get_parameters_filepath,
     have_jax,
     install_jax,
+    have_optional_dependency,
     is_jax_compatible,
     get_git_commit_info,
 )
diff --git a/pybamm/util.py b/pybamm/util.py
index 562352bfac..c98ee6beda 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -345,3 +345,12 @@ def install_jax(arguments=None):  # pragma: no cover
             f"jaxlib>={JAXLIB_VERSION}",
         ]
     )
+
+
+def have_optional_dependency(module):
+    try:
+        importlib.import_module(module)
+        _has_module = True
+    except ImportError:
+        _has_module = False
+    return _has_module

From a07b34251586319c4d98cd6a9d0c9ac4b49bab44 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 07:57:22 +0530
Subject: [PATCH 280/615] Make decorater function

---
 pybamm/__init__.py  |  9 +--------
 pybamm/citations.py |  1 +
 pybamm/util.py      | 29 ++++++++++++++++++++++-------
 3 files changed, 24 insertions(+), 15 deletions(-)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 8f92c71e18..07d8a1c0ea 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -53,14 +53,7 @@
 )
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
-try:
-    import pybtex
-
-    if pybtex is not None:
-        from .citations import Citations, citations, print_citations
-except ImportError:
-    pass
-
+from .citations import Citations, citations, print_citations
 #
 # Classes for the Expression Tree
 #
diff --git a/pybamm/citations.py b/pybamm/citations.py
index da619062e0..87f8271dde 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -177,6 +177,7 @@ def _tag_citations(self):
             for key, entry in self._citation_tags.items():
                 print(f"{key} was cited due to the use of {entry}")
 
+    @pybamm.util.have_optional_dependency("pybtex")
     def print(self, filename=None, output_format="text", verbose=False):
         """Print all citations that were used for running simulations. The verbose
         option is provided to print tags for citations in the output such that it can
diff --git a/pybamm/util.py b/pybamm/util.py
index c98ee6beda..0b68173504 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -347,10 +347,25 @@ def install_jax(arguments=None):  # pragma: no cover
     )
 
 
-def have_optional_dependency(module):
-    try:
-        importlib.import_module(module)
-        _has_module = True
-    except ImportError:
-        _has_module = False
-    return _has_module
+def have_optional_dependency(module_name, attribute=None):
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            try:
+                module = importlib.import_module(module_name)
+                if attribute:
+                    if hasattr(module, attribute):
+                        imported_attribute = getattr(module, attribute)
+                        print(f"The {module_name}.{attribute} is available.")
+                        kwargs[attribute] = imported_attribute
+                    else:
+                        print(f"The {module_name}.{attribute} is not available.")
+                else:
+                    print(f"The {module_name} module is available.")
+                return func(*args, **kwargs)
+            except ImportError:
+                if attribute:
+                    print(f"The {module_name}.{attribute} is not available.")
+                else:
+                    print(f"The {module_name} module is not available.")
+        return wrapper
+    return decorator

From 9d9db2bf2dc089f17adabf7014ea1d63109c6883 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:31:25 +0530
Subject: [PATCH 281/615] Make normal reusable function for optional deps

---
 pybamm/util.py | 40 +++++++++++++++++++---------------------
 1 file changed, 19 insertions(+), 21 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 0b68173504..f481480635 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -348,24 +348,22 @@ def install_jax(arguments=None):  # pragma: no cover
 
 
 def have_optional_dependency(module_name, attribute=None):
-    def decorator(func):
-        def wrapper(*args, **kwargs):
-            try:
-                module = importlib.import_module(module_name)
-                if attribute:
-                    if hasattr(module, attribute):
-                        imported_attribute = getattr(module, attribute)
-                        print(f"The {module_name}.{attribute} is available.")
-                        kwargs[attribute] = imported_attribute
-                    else:
-                        print(f"The {module_name}.{attribute} is not available.")
-                else:
-                    print(f"The {module_name} module is available.")
-                return func(*args, **kwargs)
-            except ImportError:
-                if attribute:
-                    print(f"The {module_name}.{attribute} is not available.")
-                else:
-                    print(f"The {module_name} module is not available.")
-        return wrapper
-    return decorator
+    try:
+        module = importlib.import_module(module_name)
+        if attribute:
+            if hasattr(module, attribute):
+                imported_attribute = getattr(module, attribute)
+                print(f"The {module_name}.{attribute} is available.")
+                return imported_attribute
+            else:
+                print(f"The {module_name}.{attribute} is not available.")
+                return None
+        else:
+            print(f"The {module_name} module is available.")
+            return module
+    except ImportError:
+        if attribute:
+            print(f"The {module_name}.{attribute} is not available.")
+        else:
+            print(f"The {module_name} module is not available.")
+        return None

From 34311ee63326d936bffa473acebdfc1462e6eb14 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:32:25 +0530
Subject: [PATCH 282/615] Update `citations.py` for `pybtex` as optional
 dependency

---
 pybamm/citations.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index 87f8271dde..fa3c4651b7 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -6,10 +6,10 @@
 import pybamm
 import os
 import warnings
-import pybtex
+# import pybtex
 from sys import _getframe
-from pybtex.database import parse_file, parse_string, Entry
-from pybtex.scanner import PybtexError
+# from pybtex.database import parse_file, parse_string, Entry
+# from pybtex.scanner import PybtexError
 
 
 class Citations:
@@ -76,6 +76,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
+        parse_file = pybamm.util.have_optional_dependency("pybtex.database","parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -86,6 +87,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
+        Entry = pybamm.util.have_optional_dependency("pybtex.database","Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -151,6 +153,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
+        PybtexError = pybamm.util.have_optional_dependency("pybtex.scanner","PybtexError")
+        parse_string = pybamm.util.have_optional_dependency("pybtex.database","parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
@@ -177,7 +181,6 @@ def _tag_citations(self):
             for key, entry in self._citation_tags.items():
                 print(f"{key} was cited due to the use of {entry}")
 
-    @pybamm.util.have_optional_dependency("pybtex")
     def print(self, filename=None, output_format="text", verbose=False):
         """Print all citations that were used for running simulations. The verbose
         option is provided to print tags for citations in the output such that it can
@@ -218,6 +221,7 @@ def print(self, filename=None, output_format="text", verbose=False):
         """
         # Parse citations that were not known keys at registration, but do not
         # fail if they cannot be parsed
+        pybtex = pybamm.util.have_optional_dependency("pybtex")
         try:
             for key in self._unknown_citations:
                 self._parse_citation(key)

From 12180658beaf910c513756269b0f3c6df9a16941 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:42:31 +0530
Subject: [PATCH 283/615] Execute silently, raise ImportError & import function
 correctly

---
 pybamm/citations.py | 14 ++++++--------
 pybamm/util.py      | 10 +++-------
 2 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index fa3c4651b7..7d0959d89c 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -6,10 +6,8 @@
 import pybamm
 import os
 import warnings
-# import pybtex
 from sys import _getframe
-# from pybtex.database import parse_file, parse_string, Entry
-# from pybtex.scanner import PybtexError
+from pybamm.util import have_optional_dependency
 
 
 class Citations:
@@ -76,7 +74,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
-        parse_file = pybamm.util.have_optional_dependency("pybtex.database","parse_file")
+        parse_file = have_optional_dependency("pybtex.database","parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -87,7 +85,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
-        Entry = pybamm.util.have_optional_dependency("pybtex.database","Entry")
+        Entry = have_optional_dependency("pybtex.database","Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -153,8 +151,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
-        PybtexError = pybamm.util.have_optional_dependency("pybtex.scanner","PybtexError")
-        parse_string = pybamm.util.have_optional_dependency("pybtex.database","parse_string")
+        PybtexError = have_optional_dependency("pybtex.scanner","PybtexError")
+        parse_string = have_optional_dependency("pybtex.database","parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
@@ -221,7 +219,7 @@ def print(self, filename=None, output_format="text", verbose=False):
         """
         # Parse citations that were not known keys at registration, but do not
         # fail if they cannot be parsed
-        pybtex = pybamm.util.have_optional_dependency("pybtex")
+        pybtex = have_optional_dependency("pybtex")
         try:
             for key in self._unknown_citations:
                 self._parse_citation(key)
diff --git a/pybamm/util.py b/pybamm/util.py
index f481480635..a2625e5405 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -353,17 +353,13 @@ def have_optional_dependency(module_name, attribute=None):
         if attribute:
             if hasattr(module, attribute):
                 imported_attribute = getattr(module, attribute)
-                print(f"The {module_name}.{attribute} is available.")
                 return imported_attribute
             else:
-                print(f"The {module_name}.{attribute} is not available.")
-                return None
+                raise ImportError(f"{module_name}.{attribute} is not available.")
         else:
-            print(f"The {module_name} module is available.")
             return module
     except ImportError:
         if attribute:
-            print(f"The {module_name}.{attribute} is not available.")
+            raise ImportError(f"{module_name}.{attribute} is not available.")
         else:
-            print(f"The {module_name} module is not available.")
-        return None
+            raise ImportError(f"{module_name} module is not available.")

From 90ac2ee2f812c34dfaec49434c128c7f789937c6 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 30 Oct 2023 16:56:59 +0530
Subject: [PATCH 284/615] Update `Symbol` for `anytree` as optional dependency

---
 pybamm/expression_tree/symbol.py | 21 +++++----------------
 1 file changed, 5 insertions(+), 16 deletions(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 6904854050..8ad717f7ff 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -3,20 +3,13 @@
 #
 import numbers
 
-
-try:
-    import anytree
-    from anytree.exporter import DotExporter
-except ImportError:
-    _has_anytree = False
-else:
-    _has_anytree = True
 import numpy as np
 import sympy
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from pybamm.expression_tree.printing.print_name import prettify_print_name
 
 DOMAIN_LEVELS = ["primary", "secondary", "tertiary", "quaternary"]
@@ -448,8 +441,7 @@ def render(self):  # pragma: no cover
         """
         Print out a visual representation of the tree (this node and its children)
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
                 print("{}{} = {}".format(pre, node.name, node.value))
@@ -467,9 +459,8 @@ def visualise(self, filename):
         filename : str
             filename to output, must end in ".png"
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
 
+        DotExporter = have_optional_dependency("anytree.exporter","DotExporter")
         # check that filename ends in .png.
         if filename[-4:] != ".png":
             raise ValueError("filename should end in .png")
@@ -489,8 +480,7 @@ def relabel_tree(self, symbol, counter):
         Finds all children of a symbol and assigns them a new id so that they can be
         visualised properly using the graphviz output
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         name = symbol.name
         if name == "div":
             name = "&nabla;&sdot;"
@@ -534,8 +524,7 @@ def pre_order(self):
         a
         b
         """
-        if not _has_anytree:
-            raise ImportError("Module 'anytree' is required to do this")
+        anytree = have_optional_dependency("anytree")
         return anytree.PreOrderIter(self)
 
     def __str__(self):

From aca3a86b66344fafb64203be83e355d354cb0325 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 30 Oct 2023 19:19:49 +0000
Subject: [PATCH 285/615] Bump awalsh128/cache-apt-pkgs-action from 1.3.0 to
 1.3.1

Bumps [awalsh128/cache-apt-pkgs-action](https://github.com/awalsh128/cache-apt-pkgs-action) from 1.3.0 to 1.3.1.
- [Release notes](https://github.com/awalsh128/cache-apt-pkgs-action/releases)
- [Commits](https://github.com/awalsh128/cache-apt-pkgs-action/compare/v1.3.0...v1.3.1)

---
updated-dependencies:
- dependency-name: awalsh128/cache-apt-pkgs-action
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/test_on_push.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index cb22fb87f7..db71c32586 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -50,7 +50,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -130,7 +130,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -193,7 +193,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         if: matrix.os == 'ubuntu-latest'
         with:
           packages: gfortran gcc graphviz pandoc
@@ -274,7 +274,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -319,7 +319,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz pandoc
           execute_install_scripts: true
@@ -377,7 +377,7 @@ jobs:
 
       # Install and cache apt packages
       - name: Install Linux system dependencies
-        uses: awalsh128/cache-apt-pkgs-action@v1.3.0
+        uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
           packages: gfortran gcc graphviz
           execute_install_scripts: true

From e6eec081ea20cafbdf68cba46e27d83360f037bc Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 30 Oct 2023 19:28:27 +0000
Subject: [PATCH 286/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.1 → v0.1.3](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.1...v0.1.3)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 12e48d913b..88d0f264ee 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.1"
+    rev: "v0.1.3"
     hooks:
       - id: ruff
         args: [--fix, --ignore=E741, --exclude=__init__.py]

From c0f7ead225d2b81ac394c63043a4473de2b4f10c Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Tue, 31 Oct 2023 15:57:41 +0530
Subject: [PATCH 287/615] fix changes as per review added to
 pre-commit-config.yaml "--show-fixes" and some ruff.toml changes

---
 .pre-commit-config.yaml | 2 +-
 ruff.toml               | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 46d6918d95..76ff077177 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ repos:
     rev: "v0.1.3"
     hooks:
       - id: ruff
-        args: [--fix, --ignore=E741, --exclude=__init__.py]
+        args: [--fix, --show-fixes]
 
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.16.0"
diff --git a/ruff.toml b/ruff.toml
index 7c1040e9d8..7304d64570 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,4 +1,8 @@
 extend-include = ["*.ipynb"]
+extend-exclude = ["__init__.py"]
+
+[lint]
+ignore = ["E741"]
 
 [lint.per-file-ignores]
 "**.ipynb" = ["E402", "E703"]

From f91fed90a16b59f7c1e3162f9fed4d904a813890 Mon Sep 17 00:00:00 2001
From: Rjchauhan18 <rjchauhan5000000@gmail.com>
Date: Tue, 31 Oct 2023 18:18:16 +0530
Subject: [PATCH 288/615] added types_or: [python, pyi, jupyter] in
 pre-commit-config.yaml

---
 .pre-commit-config.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 76ff077177..5d7c85492f 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -8,6 +8,7 @@ repos:
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]
+        types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.16.0"

From 4d9d6e0586f8a5c6bfb6d1cb5feb1b8cd8bec3ed Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 31 Oct 2023 15:17:06 +0530
Subject: [PATCH 289/615] Execute notebook (a trial of a thousand times)

---
 .../test_simulation_stochasticity.yml         | 51 +++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 .github/workflows/test_simulation_stochasticity.yml

diff --git a/.github/workflows/test_simulation_stochasticity.yml b/.github/workflows/test_simulation_stochasticity.yml
new file mode 100644
index 0000000000..2df95a60c9
--- /dev/null
+++ b/.github/workflows/test_simulation_stochasticity.yml
@@ -0,0 +1,51 @@
+name: Test random failures for half-cell models example notebook (no fixed seed)
+
+on:
+  push:
+  workflow_dispatch:
+
+jobs:
+  test_half_cell_example_notebook:
+    name: Execute half-cell models example notebook / Python ${{ matrix.python-version }}
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    steps:
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+
+      - name: Install Linux system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng
+
+      - name: Set up Python ${{ matrix.python-version }}
+        id: setup-python
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install SuiteSparse and SUNDIALS
+        run: pipx run nox -s pybamm-requires
+
+      - name: Install Python dependencies and PyBaMM
+        run: |
+          pip install --upgrade pip setuptools wheel cmake
+          pip install -e .[all,dev]
+
+
+      - name: Run half-cell models example notebook
+        run: |
+          for i in {1..500}
+          do
+            pytest --nbmake docs/source/examples/notebooks/models/half-cell.ipynb >> logs-${{ matrix.python-version }}.txt || true
+          done
+
+      - name: Upload results
+        uses: actions/upload-artifact@v3
+        with:
+          name: Results
+          path: logs-${{ matrix.python-version }}.txt
+          if-no-files-found: error

From 4b94b297fc30735cb28061703b0ccbf8b72f5301 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 31 Oct 2023 21:47:25 +0530
Subject: [PATCH 290/615] Execute notebook, this time with a random seed

---
 pybamm/simulation.py | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 380105d215..3ecc838c21 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -4,6 +4,7 @@
 import pickle
 import pybamm
 import numpy as np
+import hashlib
 import warnings
 import sys
 from functools import lru_cache
@@ -29,6 +30,39 @@ def is_notebook():
         return False  # Probably standard Python interpreter
 
 
+def fix_random_seed_for_class(cls):
+    """
+    Wraps a class so that a random seed is set to a SHA-256 hash of the class name.
+
+    As the wrapper fixes the random seed during class initialization, instances of
+    the class will be initialized with the same random seed for reproducibility.
+
+    Generating a random seed from the class name allows one to alter the seed by
+    changing the class name if needed.
+
+    Usage: as a decorator on class definition.
+
+    ```
+    @FixRandomSeedClass
+    class Simulation:
+        def __init__(self, model, solver, other_args):
+            # Your class initialization code here
+    ```
+    """
+
+    original_init = cls.__init__
+
+    def new_init(self, *args, **kwargs):
+        np.random.seed(
+            int(hashlib.sha256(cls.__name__.encode()).hexdigest(), 16) % (2**32)
+        )
+        original_init(self, *args, **kwargs)
+
+    cls.__init__ = new_init
+    return cls
+
+
+@fix_random_seed_for_class
 class Simulation:
     """A Simulation class for easy building and running of PyBaMM simulations.
 

From 3e686173bc57e434209a08346214c382a2519b41 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 13:38:54 +0530
Subject: [PATCH 291/615] Update `simulation` for `tqdm` as optional dependency

---
 pybamm/simulation.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index dfca7e0583..0b1a6b2525 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -8,10 +8,7 @@
 import sys
 from functools import lru_cache
 from datetime import timedelta
-try:
-    import tqdm
-except ImportError:
-    pass
+from pybamm.util import have_optional_dependency
 
 
 def is_notebook():
@@ -535,6 +532,7 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
+        tqdm = have_optional_dependency("tqdm")
         # Setup
         if solver is None:
             solver = self._solver

From 5551dac392adfb0111b5b208a46d22c6bec747f2 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 13:47:08 +0530
Subject: [PATCH 292/615] Update `Function` class for `autograd` as optional
 dependency

---
 pybamm/expression_tree/functions.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index 788af40d50..ebfb313199 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -3,16 +3,12 @@
 #
 import numbers
 
-try:
-    import autograd
-except ImportError:
-    pass
 import numpy as np
 import sympy
 from scipy import special
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Function(pybamm.Symbol):
     """
@@ -99,6 +95,7 @@ def _function_diff(self, children, idx):
         Derivative with respect to child number 'idx'.
         See :meth:`pybamm.Symbol._diff()`.
         """
+        autograd = have_optional_dependency("autograd")
         # Store differentiated function, needed in case we want to convert to CasADi
         if self.derivative == "autograd":
             return Function(

From 64d9037299a1b48c0e2801919699a3465df935d4 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 14:03:03 +0530
Subject: [PATCH 293/615] Resolve `scikit-fem` based methods

---
 pybamm/meshes/scikit_fem_submeshes.py           |  8 +++-----
 pybamm/spatial_methods/scikit_finite_element.py | 13 +++++++++----
 2 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index c067c43a8a..23c024dbbb 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -3,13 +3,10 @@
 #
 import pybamm
 from .meshes import SubMesh
-
-try:
-    import skfem
-except ImportError:
-    pass
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 class ScikitSubMesh2D(SubMesh):
     """
@@ -30,6 +27,7 @@ class ScikitSubMesh2D(SubMesh):
     """
 
     def __init__(self, edges, coord_sys, tabs):
+        skfem = have_optional_dependency("skfem")
         self.edges = edges
         self.nodes = dict.fromkeys(["y", "z"])
         for var in self.nodes.keys():
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 7556645028..2d51e16c32 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -6,10 +6,8 @@
 from scipy.sparse import csr_matrix, csc_matrix
 from scipy.sparse.linalg import inv
 import numpy as np
-try:
-    import skfem
-except ImportError:
-    pass
+
+from pybamm.util import have_optional_dependency
 
 
 class ScikitFiniteElement(pybamm.SpatialMethod):
@@ -90,6 +88,7 @@ def gradient(self, symbol, discretised_symbol, boundary_conditions):
             to the y-component of the gradient and the second column corresponds
             to the z component of the gradient.
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -145,6 +144,7 @@ def gradient_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element gradient matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -190,6 +190,7 @@ def laplacian(self, symbol, discretised_symbol, boundary_conditions):
             Contains the result of acting the discretised gradient on
             the child discretised_symbol
         """
+        skfem = have_optional_dependency("skfem")
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
 
@@ -261,6 +262,7 @@ def stiffness_matrix(self, symbol, boundary_conditions):
         :class:`pybamm.Matrix`
             The (sparse) finite element stiffness matrix for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]
@@ -323,6 +325,7 @@ def definite_integral_matrix(self, child, vector_type="row"):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = child.domain[0]
         mesh = self.mesh[domain]
@@ -384,6 +387,7 @@ def boundary_integral_vector(self, domain, region):
         :class:`pybamm.Matrix`
             The finite element integral vector for the domain
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         mesh = self.mesh[domain[0]]
 
@@ -501,6 +505,7 @@ def assemble_mass_form(self, symbol, boundary_conditions, region="interior"):
         :class:`pybamm.Matrix`
             The (sparse) mass matrix for the spatial method.
         """
+        skfem = have_optional_dependency("skfem")
         # get primary domain mesh
         domain = symbol.domain[0]
         mesh = self.mesh[domain]

From 9ee911bd3cb3a16b869e17a62c0dcc26c16aca11 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:26:41 +0530
Subject: [PATCH 294/615] Resolve `sympy` based methods

---
 pybamm/expression_tree/array.py                          | 3 ++-
 pybamm/expression_tree/binary_operators.py               | 6 +++++-
 pybamm/expression_tree/concatenations.py                 | 3 ++-
 pybamm/expression_tree/functions.py                      | 5 ++++-
 pybamm/expression_tree/independent_variable.py           | 5 +++--
 pybamm/expression_tree/operations/latexify.py            | 6 ++++--
 pybamm/expression_tree/parameter.py                      | 4 +++-
 pybamm/expression_tree/printing/sympy_overrides.py       | 4 +++-
 pybamm/expression_tree/scalar.py                         | 4 ++--
 pybamm/expression_tree/symbol.py                         | 2 +-
 pybamm/expression_tree/unary_operators.py                | 9 ++++++---
 pybamm/expression_tree/variable.py                       | 3 ++-
 tests/unit/test_expression_tree/test_binary_operators.py | 3 ++-
 tests/unit/test_expression_tree/test_concatenations.py   | 3 ++-
 tests/unit/test_expression_tree/test_functions.py        | 3 ++-
 .../test_expression_tree/test_independent_variable.py    | 3 ++-
 tests/unit/test_expression_tree/test_parameter.py        | 5 +++--
 .../test_printing/test_sympy_overrides.py                | 4 ++--
 tests/unit/test_expression_tree/test_symbol.py           | 3 ++-
 tests/unit/test_expression_tree/test_unary_operators.py  | 9 ++++++---
 tests/unit/test_expression_tree/test_variable.py         | 3 ++-
 21 files changed, 60 insertions(+), 30 deletions(-)

diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index a9141041b3..2736886d95 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -2,10 +2,10 @@
 # NumpyArray class
 #
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Array(pybamm.Symbol):
@@ -125,6 +125,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         entries_list = self.entries.tolist()
         return sympy.Array(entries_list)
 
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..9fc6d2642e 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -4,11 +4,11 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 import functools
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def _preprocess_binary(left, right):
@@ -147,6 +147,7 @@ def _sympy_operator(self, left, right):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -323,6 +324,7 @@ def _binary_evaluate(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         left = sympy.Matrix(left)
         right = sympy.Matrix(right)
         return left * right
@@ -626,6 +628,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Min(left, right)
 
 
@@ -662,6 +665,7 @@ def _binary_new_copy(self, left, right):
 
     def _sympy_operator(self, left, right):
         """Override :meth:`pybamm.BinaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Max(left, right)
 
 
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 2185a0fad6..1c82aff122 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -5,10 +5,10 @@
 from collections import defaultdict
 
 import numpy as np
-import sympy
 from scipy.sparse import issparse, vstack
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Concatenation(pybamm.Symbol):
@@ -135,6 +135,7 @@ def is_constant(self):
 
     def _sympy_operator(self, *children):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         self.concat_latex = tuple(map(sympy.latex, children))
 
         if self.print_name is not None:
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index ebfb313199..0c7e98b508 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -4,7 +4,6 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
@@ -202,6 +201,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -250,6 +250,7 @@ def _function_new_copy(self, children):
 
     def _sympy_operator(self, child):
         """Apply appropriate SymPy operators."""
+        sympy = have_optional_dependency("sympy")
         class_name = self.__class__.__name__.lower()
         sympy_function = getattr(sympy, class_name)
         return sympy_function(child)
@@ -267,6 +268,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.asinh(child)
 
 
@@ -287,6 +289,7 @@ def _function_diff(self, children, idx):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.Function._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.atan(child)
 
 
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index efeb73f8bc..4c139c30a8 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -1,9 +1,8 @@
 #
 # IndependentVariable class
 #
-import sympy
-
 import pybamm
+from pybamm.utili import have_optional_dependency
 
 KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"]
 
@@ -44,6 +43,7 @@ def _jac(self, variable):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -77,6 +77,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol("t")
 
 
diff --git a/pybamm/expression_tree/operations/latexify.py b/pybamm/expression_tree/operations/latexify.py
index 67e0199656..9f2949069e 100644
--- a/pybamm/expression_tree/operations/latexify.py
+++ b/pybamm/expression_tree/operations/latexify.py
@@ -5,10 +5,9 @@
 import re
 import warnings
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 def get_rng_min_max_name(rng, min_or_max):
@@ -88,6 +87,7 @@ def _get_bcs_displays(self, var):
         Returns a list of boundary condition equations with ranges in front of
         the equations.
         """
+        sympy = have_optional_dependency("sympy")
         bcs_eqn_list = []
         bcs = self.model.boundary_conditions.get(var, None)
 
@@ -118,6 +118,7 @@ def _get_bcs_displays(self, var):
 
     def _get_param_var(self, node):
         """Returns a list of parameters and a list of variables."""
+        sympy = have_optional_dependency("sympy")
         param_list = []
         var_list = []
         dfs_nodes = [node]
@@ -160,6 +161,7 @@ def _get_param_var(self, node):
         return param_list, var_list
 
     def latexify(self, output_variables=None):
+        sympy = have_optional_dependency("sympy")
         # Voltage is the default output variable if it exists
         if output_variables is None:
             if "Voltage [V]" in self.model.variables:
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index 10addae464..eebe77ad2f 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -5,9 +5,9 @@
 import sys
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class Parameter(pybamm.Symbol):
@@ -44,6 +44,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -217,6 +218,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index a96aa19729..59f9567c5d 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -3,9 +3,10 @@
 #
 import re
 
-from sympy.printing.latex import LatexPrinter
+from pybamm.util import have_optional_dependency
 
 
+LatexPrinter = have_optional_dependency("sympy.printing.latex","LatexPrinter")
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
 
@@ -21,4 +22,5 @@ def _print_Derivative(self, expr):
 
 
 def custom_print_func(expr, **settings):
+    have_optional_dependency("sympy.printing.latex","LatexPrinter")
     return CustomPrint().doprint(expr)
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index 3149bf7bee..0209c02a8e 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -2,10 +2,9 @@
 # Scalar class
 #
 import numpy as np
-import sympy
 
 import pybamm
-
+from pybamm.util import have_optional_dependency
 
 class Scalar(pybamm.Symbol):
     """
@@ -70,6 +69,7 @@ def is_constant(self):
 
     def to_equation(self):
         """Returns the value returned by the node when evaluated."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 8ad717f7ff..85c392e590 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -4,7 +4,6 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
 from functools import lru_cache, cached_property
 
@@ -987,4 +986,5 @@ def print_name(self, name):
         self._print_name = prettify_print_name(name)
 
     def to_equation(self):
+        sympy = have_optional_dependency("sympy")
         return sympy.Symbol(str(self.name))
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 7f9c45775c..e555f48455 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -4,11 +4,9 @@
 import numbers
 
 import numpy as np
-import sympy
 from scipy.sparse import csr_matrix, issparse
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class UnaryOperator(pybamm.Symbol):
@@ -83,6 +81,7 @@ def _sympy_operator(self, child):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
@@ -368,6 +367,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
         return sympy_Gradient(child)
 
 
@@ -403,6 +403,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
         return sympy_Divergence(child)
 
 
@@ -579,6 +580,7 @@ def _evaluates_on_edges(self, dimension):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         return sympy.Integral(child, sympy.Symbol("xn"))
 
 
@@ -889,6 +891,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
+        sympy = have_optional_dependency("sympy")
         if (
             self.child.domain[0] in ["negative particle", "positive particle"]
             and self.side == "right"
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index f9f7d94efc..0d1e1fd424 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -3,9 +3,9 @@
 #
 
 import numpy as np
-import sympy
 import numbers
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class VariableBase(pybamm.Symbol):
@@ -124,6 +124,7 @@ def _evaluate_for_shape(self):
 
     def to_equation(self):
         """Convert the node and its subtree into a SymPy equation."""
+        sympy = have_optional_dependency("sympy")
         if self.print_name is not None:
             return sympy.Symbol(self.print_name)
         else:
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 6acd7c41b0..225f8e93c9 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy.sparse import coo_matrix
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestBinaryOperators(TestCase):
@@ -746,6 +746,7 @@ def test_inner_simplifications(self):
         self.assertEqual(pybamm.inner(a3, a3).evaluate(), 9)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         pybamm.Addition.print_name = "test"
         self.assertEqual(pybamm.Addition(1, 2).to_equation(), sympy.Symbol("test"))
diff --git a/tests/unit/test_expression_tree/test_concatenations.py b/tests/unit/test_expression_tree/test_concatenations.py
index df5add0f98..4b07b09fea 100644
--- a/tests/unit/test_expression_tree/test_concatenations.py
+++ b/tests/unit/test_expression_tree/test_concatenations.py
@@ -5,9 +5,9 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 from tests import get_discretisation_for_testing, get_mesh_for_testing
 
 
@@ -370,6 +370,7 @@ def test_numpy_concatenation(self):
         )
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test a")
         b = pybamm.Symbol("b", domain="test b")
         func_symbol = sympy.Symbol(r"\begin{cases}a\\b\end{cases}")
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index ac5410d9e1..6d22571a01 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -5,10 +5,10 @@
 import unittest
 
 import numpy as np
-import sympy
 from scipy import special
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def test_function(arg):
@@ -120,6 +120,7 @@ def test_function_unnamed(self):
         self.assertEqual(fun.name, "function (cos)")
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         a = pybamm.Symbol("a", domain="test")
 
         # Test print_name
diff --git a/tests/unit/test_expression_tree/test_independent_variable.py b/tests/unit/test_expression_tree/test_independent_variable.py
index 95141f0f03..b748a6fbe9 100644
--- a/tests/unit/test_expression_tree/test_independent_variable.py
+++ b/tests/unit/test_expression_tree/test_independent_variable.py
@@ -4,9 +4,9 @@
 from tests import TestCase
 import unittest
 
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestIndependentVariable(TestCase):
@@ -64,6 +64,7 @@ def test_spatial_variable_edge(self):
         self.assertTrue(x.evaluates_on_edges("primary"))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.IndependentVariable("a")
         func.print_name = "test"
diff --git a/tests/unit/test_expression_tree/test_parameter.py b/tests/unit/test_expression_tree/test_parameter.py
index f67ee2dd62..d9a756b45d 100644
--- a/tests/unit/test_expression_tree/test_parameter.py
+++ b/tests/unit/test_expression_tree/test_parameter.py
@@ -5,9 +5,8 @@
 import numbers
 import unittest
 
-import sympy
-
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestParameter(TestCase):
@@ -21,6 +20,7 @@ def test_evaluate_for_shape(self):
         self.assertIsInstance(a.evaluate_for_shape(), numbers.Number)
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.Parameter("test_string")
         func1 = pybamm.Parameter("test_name")
 
@@ -98,6 +98,7 @@ def _myfun(x):
         self.assertEqual(_myfun(x).print_name, None)
 
     def test_function_parameter_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         func = pybamm.FunctionParameter("test", {"x": pybamm.Scalar(1)})
         func1 = pybamm.FunctionParameter("func", {"var": pybamm.Variable("var")})
 
diff --git a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
index b5ae229ae5..de3ff08c43 100644
--- a/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
+++ b/tests/unit/test_expression_tree/test_printing/test_sympy_overrides.py
@@ -4,14 +4,14 @@
 from tests import TestCase
 import unittest
 
-import sympy
-
 import pybamm
 from pybamm.expression_tree.printing.sympy_overrides import custom_print_func
+from pybamm.util import have_optional_dependency
 
 
 class TestCustomPrint(TestCase):
     def test_print_Derivative(self):
+        sympy = have_optional_dependency("sympy")
         # Test force_partial
         der1 = sympy.Derivative("y", "x")
         der1.force_partial = True
diff --git a/tests/unit/test_expression_tree/test_symbol.py b/tests/unit/test_expression_tree/test_symbol.py
index 3f91633fbe..3eb7adae47 100644
--- a/tests/unit/test_expression_tree/test_symbol.py
+++ b/tests/unit/test_expression_tree/test_symbol.py
@@ -8,10 +8,10 @@
 
 import numpy as np
 from scipy.sparse import csr_matrix, coo_matrix
-import sympy
 
 import pybamm
 from pybamm.expression_tree.binary_operators import _Heaviside
+from pybamm.util import have_optional_dependency
 
 
 class TestSymbol(TestCase):
@@ -484,6 +484,7 @@ def test_test_shape(self):
             (y1 + y2).test_shape()
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         self.assertEqual(pybamm.Symbol("test").to_equation(), sympy.Symbol("test"))
 
     def test_numpy_array_ufunc(self):
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index b0513c974b..d8bf30d79f 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -5,12 +5,10 @@
 from tests import TestCase
 
 import numpy as np
-import sympy
 from scipy.sparse import diags
-from sympy.vector.operators import Divergence as sympy_Divergence
-from sympy.vector.operators import Gradient as sympy_Gradient
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestUnaryOperators(TestCase):
@@ -613,6 +611,11 @@ def test_not_constant(self):
         self.assertFalse((2 * a).is_constant())
 
     def test_to_equation(self):
+
+        sympy = have_optional_dependency("sympy")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+
         a = pybamm.Symbol("a", domain="negative particle")
         b = pybamm.Symbol("b", domain="current collector")
         c = pybamm.Symbol("c", domain="test")
diff --git a/tests/unit/test_expression_tree/test_variable.py b/tests/unit/test_expression_tree/test_variable.py
index be791903e2..583008f882 100644
--- a/tests/unit/test_expression_tree/test_variable.py
+++ b/tests/unit/test_expression_tree/test_variable.py
@@ -5,9 +5,9 @@
 import unittest
 
 import numpy as np
-import sympy
 
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 class TestVariable(TestCase):
@@ -55,6 +55,7 @@ def test_variable_bounds(self):
             pybamm.Variable("var", bounds=(1, 1))
 
     def test_to_equation(self):
+        sympy = have_optional_dependency("sympy")
         # Test print_name
         func = pybamm.Variable("test_string")
         func.print_name = "test"

From efe887747422cf379b706af19e2dfd1116c5a5b7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:43:29 +0530
Subject: [PATCH 295/615] Fix Typo

---
 pybamm/expression_tree/independent_variable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 4c139c30a8..2f30da9a5e 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -2,7 +2,7 @@
 # IndependentVariable class
 #
 import pybamm
-from pybamm.utili import have_optional_dependency
+from pybamm.util import have_optional_dependency
 
 KNOWN_COORD_SYS = ["cartesian", "cylindrical polar", "spherical polar"]
 

From 911105521377a8fa8e014a31f5c3e5a2a4b1fe7e Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 1 Nov 2023 15:55:42 +0530
Subject: [PATCH 296/615] Return more helpful message

---
 pybamm/util.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index a2625e5405..9af22a8ab3 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -355,11 +355,11 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute
             else:
-                raise ImportError(f"{module_name}.{attribute} is not available.")
+                raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             return module
     except ImportError:
         if attribute:
-            raise ImportError(f"{module_name}.{attribute} is not available.")
+            raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
-            raise ImportError(f"{module_name} module is not available.")
+            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From c14adaf6b8a686a821a227c5f19ce5ae819ec780 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 1 Nov 2023 22:02:09 +0530
Subject: [PATCH 297/615] Set hash inside `__init__` instead of a decorator

and trigger another experiment with 500 runs on each Python version, amounting to a total of 2000 executions of the same notebook
---
 pybamm/simulation.py | 44 +++++++++++---------------------------------
 1 file changed, 11 insertions(+), 33 deletions(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 3ecc838c21..b50337eff4 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -30,39 +30,6 @@ def is_notebook():
         return False  # Probably standard Python interpreter
 
 
-def fix_random_seed_for_class(cls):
-    """
-    Wraps a class so that a random seed is set to a SHA-256 hash of the class name.
-
-    As the wrapper fixes the random seed during class initialization, instances of
-    the class will be initialized with the same random seed for reproducibility.
-
-    Generating a random seed from the class name allows one to alter the seed by
-    changing the class name if needed.
-
-    Usage: as a decorator on class definition.
-
-    ```
-    @FixRandomSeedClass
-    class Simulation:
-        def __init__(self, model, solver, other_args):
-            # Your class initialization code here
-    ```
-    """
-
-    original_init = cls.__init__
-
-    def new_init(self, *args, **kwargs):
-        np.random.seed(
-            int(hashlib.sha256(cls.__name__.encode()).hexdigest(), 16) % (2**32)
-        )
-        original_init(self, *args, **kwargs)
-
-    cls.__init__ = new_init
-    return cls
-
-
-@fix_random_seed_for_class
 class Simulation:
     """A Simulation class for easy building and running of PyBaMM simulations.
 
@@ -156,6 +123,17 @@ def __init__(
         self._solver = solver or self._model.default_solver
         self._output_variables = output_variables
 
+        # If the solver being used is CasadiSolver or its variant, set a fixed
+        # random seed during class initialization to the SHA-256 hash of the class
+        # name for purposes of reproducibility.
+        if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
+            self._solver, pybamm.CasadiAlgebraicSolver
+        ):
+            np.random.seed(
+                int(hashlib.sha256(self.__class__.__name__.encode()).hexdigest(), 16)
+                % (2**32)
+            )
+
         # Initialize empty built states
         self._model_with_set_params = None
         self._built_model = None

From f6a2f8353bd2b9aeaf593425361815c20a42ce02 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 2 Nov 2023 02:39:12 +0530
Subject: [PATCH 298/615] Use a member function, then call it in `__init__`

---
 pybamm/simulation.py | 26 +++++++++++++++-----------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index b50337eff4..39c182acc0 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -123,17 +123,6 @@ def __init__(
         self._solver = solver or self._model.default_solver
         self._output_variables = output_variables
 
-        # If the solver being used is CasadiSolver or its variant, set a fixed
-        # random seed during class initialization to the SHA-256 hash of the class
-        # name for purposes of reproducibility.
-        if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
-            self._solver, pybamm.CasadiAlgebraicSolver
-        ):
-            np.random.seed(
-                int(hashlib.sha256(self.__class__.__name__.encode()).hexdigest(), 16)
-                % (2**32)
-            )
-
         # Initialize empty built states
         self._model_with_set_params = None
         self._built_model = None
@@ -145,6 +134,9 @@ def __init__(
         self._solution = None
         self.quick_plot = None
 
+        # Initialise instances of Simulation class with the same random seed
+        self.set_random_seed()
+
         # ignore runtime warnings in notebooks
         if is_notebook():  # pragma: no cover
             import warnings
@@ -168,6 +160,18 @@ def __setstate__(self, state):
         self.__dict__ = state
         self.get_esoh_solver = lru_cache()(self._get_esoh_solver)
 
+    # If the solver being used is CasadiSolver or its variant, set a fixed
+    # random seed during class initialization to the SHA-256 hash of the class
+    # name for purposes of reproducibility.
+    def set_random_seed(self):
+        if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
+            self._solver, pybamm.CasadiAlgebraicSolver
+        ):
+            np.random.seed(
+                int(hashlib.sha256(self.__class__.__name__.encode()).hexdigest(), 16)
+                % (2**32)
+            )
+
     def set_up_and_parameterise_experiment(self):
         """
         Set up a simulation to run with an experiment. This creates a dictionary of

From 9438b2b8d5746c8162c116633369adf16c83b4c2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 2 Nov 2023 02:43:23 +0530
Subject: [PATCH 299/615] Update workflow to rename the log files

This workflow will subsequently be removed in the next commit, after a final run on a push of this commit.
---
 .github/workflows/test_simulation_stochasticity.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test_simulation_stochasticity.yml b/.github/workflows/test_simulation_stochasticity.yml
index 2df95a60c9..fa32f3b5ef 100644
--- a/.github/workflows/test_simulation_stochasticity.yml
+++ b/.github/workflows/test_simulation_stochasticity.yml
@@ -40,12 +40,12 @@ jobs:
         run: |
           for i in {1..500}
           do
-            pytest --nbmake docs/source/examples/notebooks/models/half-cell.ipynb >> logs-${{ matrix.python-version }}.txt || true
+            pytest --nbmake docs/source/examples/notebooks/models/half-cell.ipynb >> new-logs-${{ matrix.python-version }}.txt || true
           done
 
       - name: Upload results
         uses: actions/upload-artifact@v3
         with:
           name: Results
-          path: logs-${{ matrix.python-version }}.txt
+          path: new-logs-${{ matrix.python-version }}.txt
           if-no-files-found: error

From 495d6d709b7ffe867221a11d306fd72624a7ddb3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 2 Nov 2023 15:58:07 +0530
Subject: [PATCH 300/615] Delete stress-testing workflow

---
 .../test_simulation_stochasticity.yml         | 51 -------------------
 1 file changed, 51 deletions(-)
 delete mode 100644 .github/workflows/test_simulation_stochasticity.yml

diff --git a/.github/workflows/test_simulation_stochasticity.yml b/.github/workflows/test_simulation_stochasticity.yml
deleted file mode 100644
index fa32f3b5ef..0000000000
--- a/.github/workflows/test_simulation_stochasticity.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-name: Test random failures for half-cell models example notebook (no fixed seed)
-
-on:
-  push:
-  workflow_dispatch:
-
-jobs:
-  test_half_cell_example_notebook:
-    name: Execute half-cell models example notebook / Python ${{ matrix.python-version }}
-    runs-on: ubuntu-latest
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
-    steps:
-      - name: Check out PyBaMM repository
-        uses: actions/checkout@v4
-
-      - name: Install Linux system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install gfortran gcc graphviz pandoc libopenblas-dev texlive-latex-extra dvipng
-
-      - name: Set up Python ${{ matrix.python-version }}
-        id: setup-python
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install SuiteSparse and SUNDIALS
-        run: pipx run nox -s pybamm-requires
-
-      - name: Install Python dependencies and PyBaMM
-        run: |
-          pip install --upgrade pip setuptools wheel cmake
-          pip install -e .[all,dev]
-
-
-      - name: Run half-cell models example notebook
-        run: |
-          for i in {1..500}
-          do
-            pytest --nbmake docs/source/examples/notebooks/models/half-cell.ipynb >> new-logs-${{ matrix.python-version }}.txt || true
-          done
-
-      - name: Upload results
-        uses: actions/upload-artifact@v3
-        with:
-          name: Results
-          path: new-logs-${{ matrix.python-version }}.txt
-          if-no-files-found: error

From 02b67aba407237f954a1c2668a11bbbf9facf370 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 2 Nov 2023 16:00:03 +0530
Subject: [PATCH 301/615] Mark seed fixer as semiprivate, do not expose it

---
 pybamm/simulation.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 39c182acc0..8fbcb387f1 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -135,7 +135,7 @@ def __init__(
         self.quick_plot = None
 
         # Initialise instances of Simulation class with the same random seed
-        self.set_random_seed()
+        self._set_random_seed()
 
         # ignore runtime warnings in notebooks
         if is_notebook():  # pragma: no cover
@@ -160,10 +160,10 @@ def __setstate__(self, state):
         self.__dict__ = state
         self.get_esoh_solver = lru_cache()(self._get_esoh_solver)
 
-    # If the solver being used is CasadiSolver or its variant, set a fixed
+    # If the solver being used is CasadiSolver or its variants, set a fixed
     # random seed during class initialization to the SHA-256 hash of the class
     # name for purposes of reproducibility.
-    def set_random_seed(self):
+    def _set_random_seed(self):
         if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
             self._solver, pybamm.CasadiAlgebraicSolver
         ):

From e8cf8dc6d9d3e2b3569fd6c3b11b96938b45ca6d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 2 Nov 2023 16:23:26 +0530
Subject: [PATCH 302/615] Add a CHANGELOG entry

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 008cad125f..70e8622a75 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Bug fixes
+
+- Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features

From c65a2a29e785f71bfeb4905f45761a7c394000a9 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Fri, 3 Nov 2023 14:12:08 +0530
Subject: [PATCH 303/615] Abstraction to only show module name if not available

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 pybamm/util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 9af22a8ab3..8656f00701 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -355,11 +355,11 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute
             else:
-                raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             return module
     except ImportError:
         if attribute:
-            raise ImportError(f"Optional dependency {module_name}.{attribute} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From 4d32e32c7ac0a195d532f266de9dfaab26e11df7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 3 Nov 2023 23:48:03 +0530
Subject: [PATCH 304/615] Update docs for have_optional_deps

---
 CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index bec0fee02a..8eceda7972 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -100,9 +100,9 @@ On the other hand... We _do_ want to compare several tools, to generate document
 
 Only 'core pybamm' is installed by default. The others have to be specified explicitly when running the installation command.
 
-### Matplotlib
+### Managing Optional Dependencies and Their Imports
 
-We use Matplotlib in PyBaMM, but with two caveats:
+PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
 
 First, Matplotlib should only be used in plotting methods, and these should _never_ be called by other PyBaMM methods. So users who don't like Matplotlib will not be forced to use it in any way. Use in notebooks is OK and encouraged.
 

From fd0916322fd390fee7502d9022d8e5536c59124a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 6 Nov 2023 15:50:06 +0530
Subject: [PATCH 305/615] Update for `have_optional_dependency`

---
 CONTRIBUTING.md | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 8eceda7972..648996a024 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -54,7 +54,7 @@ You now have everything you need to start making changes!
 10. [Test your code!](#testing)
 11. PyBaMM has online documentation at http://docs.pybamm.org/. To make sure any new methods or classes you added show up there, please read the [documentation](#documentation) section.
 12. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks).
-13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM's GitHub page](https://github.com/pybamm-team/PyBaMM).
+13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM&#39;s GitHub page](https://github.com/pybamm-team/PyBaMM).
 14. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into PyBaMM main repository.
 
 Finally, if you really, really, _really_ love developing PyBaMM, have a look at the current [project infrastructure](#infrastructure).
@@ -104,17 +104,25 @@ Only 'core pybamm' is installed by default. The others have to be specified expl
 
 PyBaMM utilizes optional dependencies to allow users to choose which additional libraries they want to use. Managing these optional dependencies and their imports is essential to provide flexibility to PyBaMM users.
 
-First, Matplotlib should only be used in plotting methods, and these should _never_ be called by other PyBaMM methods. So users who don't like Matplotlib will not be forced to use it in any way. Use in notebooks is OK and encouraged.
+PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it:
 
-Second, Matplotlib should never be imported at the module level, but always inside methods. For example:
+Optional Dependencies should never be imported at the module level, but always inside methods. For example:
 
 ```
-def plot_great_things(self, x, y, z):
-    import matplotlib.pyplot as pl
+def use_pybtex(x,y,z):
+    pybtex = have_optional_dependency("pybtex")
     ...
 ```
 
-This allows people to (1) use PyBaMM without ever importing Matplotlib and (2) configure Matplotlib's back-end in their scripts, which _must_ be done before e.g. `pyplot` is first imported.
+While importing a specific attribute instead of whole module:
+
+```
+def use_parse_file(x,y,z):
+    parse_file = have_optional_dependency("pybtex.database","parse_file")
+    ...
+```
+
+This allows people to (1) use PyBaMM without importing Optional dependency by default and (2) configure module dependent functionality in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
 ## Testing
 
@@ -266,7 +274,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       This will start the debugger at the point where the `ValueError` was raised, and allow you to investigate further. Sometimes, it is more informative to put the try-except block further up the call stack than exactly where the error is raised.
-
    2. Warnings. If functions are raising warnings instead of errors, it can be hard to pinpoint where this is coming from. Here, you can use the `warnings` module to convert warnings to errors:
 
       ```python
@@ -276,7 +283,6 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       Then you can use a try-except block, as in a., but with, for example, `RuntimeWarning` instead of `ValueError`.
-
    3. Stepping through the expression tree. Most calls in PyBaMM are operations on [expression trees](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb). To view an expression tree in ipython, you can use the `render` command:
 
       ```python
@@ -284,11 +290,8 @@ This also means that, if you can't fix the bug yourself, it will be much easier
       ```
 
       You can then step through the expression tree, using the `children` attribute, to pinpoint exactly where a bug is coming from. For example, if `expression_tree.jac(y)` is failing, you can check `expression_tree.children[0].jac(y)`, then `expression_tree.children[0].children[0].jac(y)`, etc.
-
 3. To isolate whether a bug is in a model, its Jacobian or its simplified version, you can set the `use_jacobian` and/or `use_simplify` attributes of the model to `False` (they are both `True` by default for most models).
-
 4. If a model isn't giving the answer you expect, you can try comparing it to other models. For example, you can investigate parameter limits in which two models should give the same answer by setting some parameters to be small or zero. The `StandardOutputComparison` class can be used to compare some standard outputs from battery models.
-
 5. To get more information about what is going on under the hood, and hence understand what is causing the bug, you can set the [logging](https://realpython.com/python-logging/) level to `DEBUG` by adding the following line to your test or script:
 
    ```python3

From 926f8d74b6a40c98a3c9a40a40af5ffb4fad154b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 6 Nov 2023 16:00:02 +0530
Subject: [PATCH 306/615] Add comments to `have_optional_dependency`

---
 pybamm/util.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index 8656f00701..78a5cff27d 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -346,19 +346,26 @@ def install_jax(arguments=None):  # pragma: no cover
         ]
     )
 
-
+# https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
 def have_optional_dependency(module_name, attribute=None):
     try:
+        # Attempt to import the specified module
         module = importlib.import_module(module_name)
+
         if attribute:
+            # If an attribute is specified, check if it's available
             if hasattr(module, attribute):
                 imported_attribute = getattr(module, attribute)
-                return imported_attribute
+                return imported_attribute  # Return the imported attribute
             else:
+                # Raise an ImportError if the attribute is not available
                 raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
+            # Return the entire module if no attribute is specified
             return module
+
     except ImportError:
+        # Raise an ImportError if the module or attribute is not available
         if attribute:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:

From 29e70898778934b58549cd3dd0c9902a7e52be43 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 6 Nov 2023 19:21:44 +0000
Subject: [PATCH 307/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.3 → v0.1.4](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.3...v0.1.4)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5d7c85492f..fa0de6f56c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.3"
+    rev: "v0.1.4"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 0f739bf202ab752ae7e6aea5e56341b0062f7cf3 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 6 Nov 2023 19:21:58 +0000
Subject: [PATCH 308/615] style: pre-commit fixes

---
 docs/source/examples/notebooks/models/lithium-plating.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index a7329b0b70..57049a0ea7 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -29,7 +29,6 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "import matplotlib.pyplot as plt\n",
     "os.chdir(pybamm.__path__[0]+'/..')"
    ]
   },

From f25b957f5b9cc2ed61be7884106cc9ff80fa5d46 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 7 Nov 2023 21:43:37 +0530
Subject: [PATCH 309/615] Fix `gfortran` installation

for #3475
---
 .github/workflows/run_periodic_tests.yml | 11 +++--------
 .github/workflows/test_on_push.yml       |  4 ++++
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f6e51bc11b..2fdf19f56d 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -66,19 +66,14 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # Added fixes to homebrew installs:
-      # rm -f /usr/local/bin/2to3
-      # (see https://github.com/actions/virtual-environments/issues/2322)
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install MacOS system dependencies
         if: matrix.os == 'macos-latest'
-        run: |
-          rm -f /usr/local/bin/2to3*
-          rm -f /usr/local/bin/idle3*
-          rm -f /usr/local/bin/pydoc3*
-          rm -f /usr/local/bin/python3*
+        run:
           brew update
           brew install graphviz
           brew install openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index cb22fb87f7..1ccdb48213 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -64,6 +64,7 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -77,6 +78,7 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -207,6 +209,7 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
+      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -220,6 +223,7 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
+          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 65a81e37175849fae9cba876799802e6c62fe556 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 7 Nov 2023 22:45:33 +0530
Subject: [PATCH 310/615] Re-install OpenBLAS `scipy` meson linkage errors

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 .github/workflows/test_on_push.yml       | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 2fdf19f56d..2322adf993 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,7 +72,7 @@ jobs:
         run:
           brew update
           brew install graphviz
-          brew install openblas
+          brew reinstall openblas
           brew reinstall gcc
 
       - name: Install Windows system dependencies
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 1ccdb48213..df114224b3 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -222,8 +222,9 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz openblas
+          brew install graphviz
           brew reinstall gcc
+          brew reinstall openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'

From 62a46ef683504c117668abad0a611a12a79249fb Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Tue, 7 Nov 2023 18:31:37 -0800
Subject: [PATCH 311/615] Additional tests for codecov

---
 .../test_expression_tree/test_interpolant.py  | 16 +++-
 tests/unit/test_meshes/test_meshes.py         |  2 +-
 .../test_meshes/test_scikit_fem_submesh.py    | 65 ++++++++++++++++
 tests/unit/test_models/test_base_model.py     | 77 +++++++++++++++----
 .../test_base_battery_model.py                | 24 ++++++
 5 files changed, 169 insertions(+), 15 deletions(-)

diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 92e9ef86c2..5fa078cffc 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -326,7 +326,7 @@ def test_processing(self):
 
         self.assertEqual(interp, interp.new_copy())
 
-    def test_to_json(self):
+    def test_to_from_json(self):
         x = np.linspace(0, 1, 10)
         y = pybamm.StateVector(slice(0, 2))
         interp = pybamm.Interpolant(x, 2 * x, y)
@@ -371,6 +371,20 @@ def test_to_json(self):
         # check correct re-creation
         self.assertEqual(pybamm.Interpolant._from_json(expected_json), interp)
 
+        # test to_from_json for 2d x & y
+        x = (np.arange(-5.01, 5.01, 0.05), np.arange(-5.01, 5.01, 0.01))
+        xx, yy = np.meshgrid(x[0], x[1], indexing="ij")
+        z = np.sin(xx**2 + yy**2)
+        var1 = pybamm.StateVector(slice(0, 1))
+        var2 = pybamm.StateVector(slice(1, 2))
+        # linear
+        interp = pybamm.Interpolant(x, z, (var1, var2), interpolator="linear")
+
+        interp2d_json = interp.to_json()
+        interp2d_json["children"] = (var1, var2)
+
+        self.assertEqual(pybamm.Interpolant._from_json(interp2d_json), interp)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_meshes/test_meshes.py b/tests/unit/test_meshes/test_meshes.py
index 000ec729a5..3066d14534 100644
--- a/tests/unit/test_meshes/test_meshes.py
+++ b/tests/unit/test_meshes/test_meshes.py
@@ -390,7 +390,7 @@ def test_1plus1D_tabs_right_left(self):
         # positive tab should be "left"
         self.assertEqual(mesh["current collector"].tabs["positive tab"], "left")
 
-    def test_to_from_json(self):
+    def test_to_json(self):
         r = pybamm.SpatialVariable(
             "r", domain=["negative particle"], coord_sys="spherical polar"
         )
diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py
index 88bde7941f..1e0839250e 100644
--- a/tests/unit/test_meshes/test_scikit_fem_submesh.py
+++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py
@@ -218,6 +218,71 @@ def test_to_json(self):
 
         self.assertEqual(mesh_json, expected_json)
 
+        # test Uniform2DSubMesh serialisation
+
+        submesh = mesh["current collector"].to_json()
+
+        expected_submesh = {
+            "edges": {
+                "y": [
+                    0.0,
+                    0.02666666666666667,
+                    0.05333333333333334,
+                    0.08,
+                    0.10666666666666667,
+                    0.13333333333333333,
+                    0.16,
+                    0.18666666666666668,
+                    0.21333333333333335,
+                    0.24000000000000002,
+                    0.26666666666666666,
+                    0.29333333333333333,
+                    0.32,
+                    0.3466666666666667,
+                    0.37333333333333335,
+                    0.4,
+                ],
+                "z": [
+                    0.0,
+                    0.021739130434782608,
+                    0.043478260869565216,
+                    0.06521739130434782,
+                    0.08695652173913043,
+                    0.10869565217391304,
+                    0.13043478260869565,
+                    0.15217391304347827,
+                    0.17391304347826086,
+                    0.19565217391304346,
+                    0.21739130434782608,
+                    0.2391304347826087,
+                    0.2608695652173913,
+                    0.2826086956521739,
+                    0.30434782608695654,
+                    0.32608695652173914,
+                    0.34782608695652173,
+                    0.3695652173913043,
+                    0.3913043478260869,
+                    0.41304347826086957,
+                    0.43478260869565216,
+                    0.45652173913043476,
+                    0.4782608695652174,
+                    0.5,
+                ],
+            },
+            "coord_sys": "cartesian",
+            "tabs": {
+                "negative": {"y_centre": 0.1, "z_centre": 0.5, "width": 0.1},
+                "positive": {"y_centre": 0.3, "z_centre": 0.5, "width": 0.1},
+            },
+        }
+
+        self.assertEqual(submesh, expected_submesh)
+
+        new_submesh = pybamm.ScikitUniform2DSubMesh._from_json(submesh)
+
+        for x, y in zip(mesh['current collector'].edges, new_submesh.edges):
+            np.testing.assert_array_equal(x, y)
+
 
 class TestScikitFiniteElementChebyshev2DSubMesh(TestCase):
     def test_mesh_creation(self):
diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py
index 1274d1a7bf..438b7391a7 100644
--- a/tests/unit/test_models/test_base_model.py
+++ b/tests/unit/test_models/test_base_model.py
@@ -984,29 +984,80 @@ def test_timescale_lengthscale_get_set_not_implemented(self):
             model.length_scales = 1
 
     def test_save_load_model(self):
+        # Set up model
         model = pybamm.BaseModel()
-        c = pybamm.Variable("c")
-        model.rhs = {c: -c}
-        model.initial_conditions = {c: 1}
-        model.variables["c"] = c
-        model.variables["2c"] = 2 * c
+        var_scalar = pybamm.Variable("var_scalar")
+        var_1D = pybamm.Variable("var_1D", domain="negative electrode")
+        var_2D = pybamm.Variable(
+            "var_2D",
+            domain="negative particle",
+            auxiliary_domains={"secondary": "negative electrode"},
+        )
+        var_concat_neg = pybamm.Variable("var_concat_neg", domain="negative electrode")
+        var_concat_sep = pybamm.Variable("var_concat_sep", domain="separator")
+        var_concat = pybamm.concatenation(var_concat_neg, var_concat_sep)
+        model.rhs = {var_scalar: -var_scalar, var_1D: -var_1D}
+        model.algebraic = {var_2D: -var_2D, var_concat: -var_concat}
+        model.initial_conditions = {var_scalar: 1, var_1D: 1, var_2D: 1, var_concat: 1}
+        model.variables = {
+            "var_scalar": var_scalar,
+            "var_1D": var_1D,
+            "var_2D": var_2D,
+            "var_concat_neg": var_concat_neg,
+            "var_concat_sep": var_concat_sep,
+            "var_concat": var_concat,
+        }
 
-        # setup and discretise
-        solution = pybamm.ScipySolver().solve(model, np.linspace(0, 1))
+        # Discretise
+        geometry = {
+            "negative electrode": {"x_n": {"min": 0, "max": 1}},
+            "separator": {"x_s": {"min": 1, "max": 2}},
+            "negative particle": {"r_n": {"min": 0, "max": 1}},
+        }
+        submeshes = {
+            "negative electrode": pybamm.Uniform1DSubMesh,
+            "separator": pybamm.Uniform1DSubMesh,
+            "negative particle": pybamm.Uniform1DSubMesh,
+        }
+        var_pts = {"x_n": 10, "x_s": 10, "r_n": 5}
+        mesh = pybamm.Mesh(geometry, submeshes, var_pts)
+        spatial_methods = {
+            "negative electrode": pybamm.FiniteVolume(),
+            "separator": pybamm.FiniteVolume(),
+            "negative particle": pybamm.FiniteVolume(),
+        }
+        disc = pybamm.Discretisation(mesh, spatial_methods)
+        model_disc = disc.process_model(model, inplace=False)
+        t = np.linspace(0, 1)
+        y = np.tile(3 * t, (1 + 30 + 50, 1))
 
-        # save model
-        model.save_model(filename="test_base_model")
+        # Find baseline solution
+        solution = pybamm.Solution(t, y, model_disc, {})
 
-        # raises warning if variables are saved
-        with self.assertWarns(pybamm.ModelWarning):
-            model.save_model(filename="test_base_model", variables=model.variables)
+        # save model
+        model_disc.save_model(filename="test_base_model")
 
+        # load without variables
         new_model = pybamm.load_model("test_base_model.json")
 
-        new_solution = pybamm.ScipySolver().solve(new_model, np.linspace(0, 1))
+        new_solution = pybamm.Solution(t, y, new_model, {})
 
         # model solutions match
         testing.assert_array_equal(solution.all_ys, new_solution.all_ys)
+
+        # raises warning if variables are saved without mesh
+        with self.assertWarns(pybamm.ModelWarning):
+            model_disc.save_model(
+                filename="test_base_model", variables=model_disc.variables
+            )
+
+        model_disc.save_model(
+            filename="test_base_model", variables=model_disc.variables, mesh=mesh
+        )
+
+        # load with variables & mesh
+        new_model = pybamm.load_model("test_base_model.json")
+
         os.remove("test_base_model.json")
 
 
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 79c6d8a720..91bcfc28cc 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -7,6 +7,7 @@
 import unittest
 import io
 from contextlib import redirect_stdout
+import os
 
 OPTIONS_DICT = {
     "surface form": "differential",
@@ -449,6 +450,29 @@ def test_option_type(self):
         model = pybamm.BaseBatteryModel(options)
         self.assertEqual(model.options, options)
 
+    def test_save_load_model(self):
+        model = (
+            pybamm.lithium_ion.SPM()
+        )
+        geometry = model.default_geometry
+        param = model.default_parameter_values
+        param.process_model(model)
+        param.process_geometry(geometry)
+        mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
+        disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
+        disc.process_model(model)
+
+        # save model
+        model.save_model(filename="test_base_battery_model", mesh=mesh,
+                         variables=model.variables)
+
+        # raises error if variables are saved without mesh
+        with self.assertRaises(ValueError):
+            model.save_model(filename="test_base_battery_model",
+                             variables=model.variables)
+
+        os.remove("test_base_battery_model.json")
+
 
 class TestOptions(TestCase):
     def test_print_options(self):

From ec963d1ae3dbaf673e6f512156f6cddc36f4e52b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 8 Nov 2023 23:19:19 +0530
Subject: [PATCH 312/615] Add `test_have_optional_dependency`

---
 tests/unit/test_util.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index c5060e65a6..8f706d8149 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -88,6 +88,10 @@ def test_git_commit_info(self):
         self.assertIsInstance(git_commit_info, str)
         self.assertEqual(git_commit_info[:2], "v2")
 
+    def test_have_optional_dependency(self):
+        with self.assertRaisesRegex(ImportError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            pybamm.print_citations()
+
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 3aa79ca2d44f2f7298504a5a20e96d72558e27cc Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:28:27 +0530
Subject: [PATCH 313/615] Temporarily remove lower bounds: `numpy`, `scipy`

---
 setup.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index f6fd37f75c..70bb69a56e 100644
--- a/setup.py
+++ b/setup.py
@@ -203,9 +203,9 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.16",
-        "scipy>=1.3",
-        "casadi>=3.6.0",
+        "numpy",
+        "scipy",
+        "casadi",
         "xarray",
     ],
     extras_require={

From dd8a6f21d573f397767b97b662cd26c591d06e0d Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Thu, 9 Nov 2023 18:52:12 +0530
Subject: [PATCH 314/615] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 CONTRIBUTING.md                                         | 8 ++++----
 pybamm/expression_tree/unary_operators.py               | 4 ++--
 pybamm/util.py                                          | 4 ++--
 tests/unit/test_expression_tree/test_unary_operators.py | 4 ++--
 4 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 648996a024..78fbb0fdec 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -106,7 +106,7 @@ PyBaMM utilizes optional dependencies to allow users to choose which additional
 
 PyBaMM provides a utility function `have_optional_dependency`, to check for the availability of optional dependencies within methods. This function can be used to conditionally import optional dependencies only if they are available. Here's how to use it:
 
-Optional Dependencies should never be imported at the module level, but always inside methods. For example:
+Optional dependencies should never be imported at the module level, but always inside methods. For example:
 
 ```
 def use_pybtex(x,y,z):
@@ -114,15 +114,15 @@ def use_pybtex(x,y,z):
     ...
 ```
 
-While importing a specific attribute instead of whole module:
+While importing a specific module instead of an entire package/library:
 
-```
+```python
 def use_parse_file(x,y,z):
     parse_file = have_optional_dependency("pybtex.database","parse_file")
     ...
 ```
 
-This allows people to (1) use PyBaMM without importing Optional dependency by default and (2) configure module dependent functionality in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
+This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
 ## Testing
 
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index e555f48455..81c3dc28c2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -367,7 +367,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
-        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
         return sympy_Gradient(child)
 
 
@@ -403,7 +403,7 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
         return sympy_Divergence(child)
 
 
diff --git a/pybamm/util.py b/pybamm/util.py
index 78a5cff27d..b6825f7eda 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -359,12 +359,12 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ImportError if the attribute is not available
-                raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             # Return the entire module if no attribute is specified
             return module
 
-    except ImportError:
+    except ModuleNotFoundError:
         # Raise an ImportError if the module or attribute is not available
         if attribute:
             raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index d8bf30d79f..fc845cb574 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -613,8 +613,8 @@ def test_not_constant(self):
     def test_to_equation(self):
 
         sympy = have_optional_dependency("sympy")
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators","Divergence")
-        sympy_Gradient = have_optional_dependency("sympy.vector.operators","Gradient")
+        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
+        sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
 
         a = pybamm.Symbol("a", domain="negative particle")
         b = pybamm.Symbol("b", domain="current collector")

From aa2327edd7dfd25298e7b4076902bca74814b880 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 9 Nov 2023 13:22:22 +0000
Subject: [PATCH 315/615] style: pre-commit fixes

---
 CONTRIBUTING.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 78fbb0fdec..de0d626940 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -117,8 +117,8 @@ def use_pybtex(x,y,z):
 While importing a specific module instead of an entire package/library:
 
 ```python
-def use_parse_file(x,y,z):
-    parse_file = have_optional_dependency("pybtex.database","parse_file")
+def use_parse_file(x, y, z):
+    parse_file = have_optional_dependency("pybtex.database", "parse_file")
     ...
 ```
 

From 8782a7af8f713e7802f07d3e68b2562469036075 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:55:52 +0530
Subject: [PATCH 316/615] Exercise minimum version bounds: `numpy`, `scipy`,
 and `casadi`

---
 setup.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index 70bb69a56e..87dd89ec6b 100644
--- a/setup.py
+++ b/setup.py
@@ -203,9 +203,9 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy",
-        "scipy",
-        "casadi",
+        "numpy>=1.24.4",
+        "scipy>=1.10.1",
+        "casadi>=3.6.3",
         "xarray",
     ],
     extras_require={

From dc1f6eddd4e8e838628ef496ce3d4d9a2a30ac79 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 18:56:10 +0530
Subject: [PATCH 317/615] Remove redundant PyBaMM dependencies caching step

---
 .github/workflows/test_on_push.yml | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index df114224b3..2def84a60c 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -92,10 +92,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -152,10 +151,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all,docs]
+          pip install --upgrade pip wheel setuptools
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -238,10 +236,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -299,10 +296,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: pipx run nox -s doctests
@@ -344,10 +340,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -402,10 +397,9 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install PyBaMM dependencies
+      - name: Install Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
-          pip install -e .[all,docs]
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3

From 5a6f03cf8a308e6598b428f8631c647db11e1711 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 19:32:12 +0530
Subject: [PATCH 318/615] Add a lower bound for `sympy`

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 87dd89ec6b..0335c89f77 100644
--- a/setup.py
+++ b/setup.py
@@ -241,7 +241,7 @@ def compile_KLU():
             "pybtex>=0.24.0",
         ],
         "latexify": [
-            "sympy>=1.8",
+            "sympy>=1.12",
         ],
         "bpx": [
             "bpx",

From 844fcec79a23248b3aa74adde3f9f61484da997b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 20:32:31 +0530
Subject: [PATCH 319/615] Clean up `brew` changes and re-trigger build

---
 .github/workflows/test_on_push.yml | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2def84a60c..b660f0a7c9 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -64,7 +64,6 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -78,7 +77,6 @@ jobs:
           brew analytics off
           brew update
           brew install graphviz openblas
-          brew reinstall gcc
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -92,7 +90,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -151,7 +149,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -207,7 +205,6 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         env:
@@ -220,9 +217,7 @@ jobs:
         run: |
           brew analytics off
           brew update
-          brew install graphviz
-          brew reinstall gcc
-          brew reinstall openblas
+          brew install graphviz openblas
 
       - name: Install Windows system dependencies
         if: matrix.os == 'windows-latest'
@@ -236,7 +231,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -296,7 +291,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -340,7 +335,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 
@@ -397,7 +392,7 @@ jobs:
           cache: 'pip'
           cache-dependency-path: setup.py
 
-      - name: Install Python dependencies
+      - name: Install standard Python dependencies
         run: |
           pip install --upgrade pip wheel setuptools
 

From c7aa1172dc8f3b3d9b7846fb0eabce5cc7f299ee Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 9 Nov 2023 20:39:08 +0530
Subject: [PATCH 320/615] Update `sympy>=1.12` for the `[all]` extra as well

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 0335c89f77..8bc6437945 100644
--- a/setup.py
+++ b/setup.py
@@ -276,7 +276,7 @@ def compile_KLU():
             "scikit-fem>=0.2.0",
             "imageio>=2.9.0",
             "pybtex>=0.24.0",
-            "sympy>=1.8",
+            "sympy>=1.12",
             "bpx",
             "tqdm",
             "matplotlib>=2.0",

From c28c7fbfe0f8a3405b2251cc84aa6b159a2891bb Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 21:24:34 +0530
Subject: [PATCH 321/615] Raise simple ModuleNotFoundError even if attribute
 not found

---
 pybamm/util.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index b6825f7eda..dee77b8841 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -358,15 +358,12 @@ def have_optional_dependency(module_name, attribute=None):
                 imported_attribute = getattr(module, attribute)
                 return imported_attribute  # Return the imported attribute
             else:
-                # Raise an ImportError if the attribute is not available
+                # Raise an ModuleNotFoundError if the attribute is not available
                 raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
         else:
             # Return the entire module if no attribute is specified
             return module
 
     except ModuleNotFoundError:
-        # Raise an ImportError if the module or attribute is not available
-        if attribute:
-            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
-        else:
-            raise ImportError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+        # Raise an ModuleNotFoundError if the module or attribute is not available
+        raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")

From fd9ae61636bc92cc31e3efab7765f601218281d7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 21:44:01 +0530
Subject: [PATCH 322/615] Set pybtex to None to avoid import

---
 tests/unit/test_util.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 8f706d8149..bfcac5fa5f 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -89,7 +89,8 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ImportError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex.database is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['pybtex'] = None
             pybamm.print_citations()
 
 

From 7cb2ef69250dba93955ca46468c17ee35ad581ee Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 22:20:28 +0530
Subject: [PATCH 323/615] Add more testcases for optional dependencies

---
 tests/unit/test_util.py | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index bfcac5fa5f..8edf4ad6ec 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -11,7 +11,8 @@
 from unittest.mock import patch
 from io import StringIO
 
-
+def test_function(arg):
+    return arg + arg
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -89,9 +90,21 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex.database is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
             sys.modules['pybtex'] = None
             pybamm.print_citations()
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['tqdm'] = None
+            model = pybamm.BaseModel()
+            v = pybamm.Variable("v")
+            model.rhs = {v: -v}
+            model.initial_conditions = {v: 1}
+            sim = pybamm.Simulation(model)
+            sim.solve([0, 1])
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency autograd is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            sys.modules['autograd'] = None
+            a = pybamm.StateVector(slice(0, 1))
+            pybamm.Function(test_function, a)
 
 
 class TestSearch(TestCase):

From b681bbc69e15171429ee5ea48a1282ba83fdc30c Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Thu, 9 Nov 2023 23:54:54 +0530
Subject: [PATCH 324/615] Add test for case if dependency is available

---
 tests/unit/test_util.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 8edf4ad6ec..7b6864f443 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -101,10 +101,9 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency autograd is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
-            sys.modules['autograd'] = None
-            a = pybamm.StateVector(slice(0, 1))
-            pybamm.Function(test_function, a)
+
+        sys.modules['pybtex'] = pybamm.util.have_optional_dependency("pybtex")
+        pybamm.print_citations()
 
 
 class TestSearch(TestCase):

From f2e37cf0439ec06a994b13c957894c2f194d1dc9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 00:33:43 +0530
Subject: [PATCH 325/615] Reset pybtex to run dependent function

---
 tests/unit/test_util.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 7b6864f443..3ac78986bb 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -91,6 +91,7 @@ def test_git_commit_info(self):
 
     def test_have_optional_dependency(self):
         with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
         with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
@@ -102,7 +103,8 @@ def test_have_optional_dependency(self):
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
 
-        sys.modules['pybtex'] = pybamm.util.have_optional_dependency("pybtex")
+        sys.modules['pybtex'] = pybtex
+        pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
 

From 8d6db99511e3bdbe6f20bacac6714c85fc4a0b11 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 01:46:00 +0530
Subject: [PATCH 326/615] Add test for full coverage

---
 tests/unit/test_util.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 3ac78986bb..a9f70bbcc7 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -10,6 +10,7 @@
 import unittest
 from unittest.mock import patch
 from io import StringIO
+from tempfile import TemporaryDirectory
 
 def test_function(arg):
     return arg + arg
@@ -102,6 +103,15 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+            with TemporaryDirectory() as dir_name:
+                sys.modules['anytree'] = None
+                test_stub = os.path.join(dir_name, "test_visualize")
+                test_name = f"{test_stub}.png"
+                c = pybamm.Variable("c", "negative electrode")
+                d = pybamm.Variable("d", "negative electrode")
+                sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
+                sym.visualise(test_name)
 
         sys.modules['pybtex'] = pybtex
         pybamm.util.have_optional_dependency("pybtex")

From 700ab5af6b0805dc503ded489bbacf23a38cd2d7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 02:07:17 +0530
Subject: [PATCH 327/615] Declare `anytree` onn top to pass
 `test_is_constant_and_can_evaluate`

---
 tests/unit/test_util.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index a9f70bbcc7..b9dd428a4b 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -14,6 +14,8 @@
 
 def test_function(arg):
     return arg + arg
+
+anytree = sys.modules['anytree']
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -31,6 +33,7 @@ def test_rmse(self):
             pybamm.rmse(np.ones(5), np.zeros(3))
 
     def test_is_constant_and_can_evaluate(self):
+        sys.modules['anytree'] = anytree
         symbol = pybamm.PrimaryBroadcast(0, "negative electrode")
         self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol))
         symbol = pybamm.StateVector(slice(0, 1))

From 52112332b9bee283c777a54418494c3bd438e925 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 9 Nov 2023 16:59:51 -0800
Subject: [PATCH 328/615] More coverage updates to serialise and 1D meshes

---
 .../expression_tree/operations/serialise.py   |  5 +---
 .../test_one_dimensional_submesh.py           |  4 ++++
 .../test_serialisation/test_serialisation.py  | 23 ++++++++++++++++++-
 3 files changed, 27 insertions(+), 5 deletions(-)

diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index b54f7b1078..cd2ff15c3d 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -238,10 +238,7 @@ def load_model(
     def _get_pybamm_class(self, snippet: dict):
         """Find a pybamm class to initialise from object path"""
         parts = snippet["py/object"].split(".")
-        try:
-            module = importlib.import_module(".".join(parts[:-1]))
-        except Exception as ex:
-            print(ex)
+        module = importlib.import_module(".".join(parts[:-1]))
 
         class_ = getattr(module, parts[-1])
 
diff --git a/tests/unit/test_meshes/test_one_dimensional_submesh.py b/tests/unit/test_meshes/test_one_dimensional_submesh.py
index a7cafb5e25..514de4248b 100644
--- a/tests/unit/test_meshes/test_one_dimensional_submesh.py
+++ b/tests/unit/test_meshes/test_one_dimensional_submesh.py
@@ -44,6 +44,10 @@ def test_to_json(self):
 
         self.assertEqual(mesh_json, expected_json)
 
+        # check tabs work
+        new_mesh = pybamm.Uniform1DSubMesh._from_json(mesh_json)
+        self.assertEqual(mesh.tabs, new_mesh.tabs)
+
 
 class TestUniform1DSubMesh(TestCase):
     def test_exceptions(self):
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 533baa718f..97299e669d 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -305,7 +305,7 @@ def test_get_pybamm_class(self):
 
         self.assertIsInstance(mesh_class, pybamm.Mesh)
 
-        with self.assertRaises(Exception):
+        with self.assertRaises(AttributeError):
             unrecognised_symbol = {
                 "py/id": mock.ANY,
                 "py/object": "pybamm.expression_tree.scalar.Scale",
@@ -443,6 +443,27 @@ def test_reconstruct_pybamm_dict(self):
 
         self.assertEqual(new_dict, test_dict)
 
+        # test recreation if not passed a dict
+        test_list = ["left", "right"]
+        new_list = Serialise()._reconstruct_pybamm_dict(test_list)
+
+        self.assertEqual(test_list, new_list)
+
+    def test_convert_options(self):
+        options_dict = {
+            "current collector": "uniform",
+            "particle phases": ["2", "1"],
+            "open-circuit potential": [["single", "current sigmoid"], "single"],
+        }
+
+        options_result = {
+            "current collector": "uniform",
+            "particle phases": ("2", "1"),
+            "open-circuit potential": (("single", "current sigmoid"), "single"),
+        }
+
+        self.assertEqual(Serialise()._convert_options(options_dict), options_result)
+
     def test_save_load_model(self):
         model = pybamm.lithium_ion.SPM(name="test_spm")
         geometry = model.default_geometry

From c83711b11f44dfdce2666e7b518de0a773ec32b2 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 9 Nov 2023 21:18:06 -0500
Subject: [PATCH 329/615] #3506 vectorize theoretical energy

---
 pybamm/expression_tree/binary_operators.py    |  4 +-
 pybamm/expression_tree/broadcasts.py          |  6 +-
 .../lithium_ion/electrode_soh.py              | 55 +++++++++----------
 pybamm/parameters/lithium_ion_parameters.py   |  1 +
 pybamm/parameters/thermal_parameters.py       |  6 ++
 .../test_lithium_ion/test_electrode_soh.py    |  4 +-
 6 files changed, 40 insertions(+), 36 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 749384e9bc..bde9a17271 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -14,12 +14,12 @@
 def _preprocess_binary(left, right):
     if isinstance(left, numbers.Number):
         left = pybamm.Scalar(left)
-    if isinstance(right, numbers.Number):
-        right = pybamm.Scalar(right)
     elif isinstance(left, np.ndarray):
         if left.ndim > 1:
             raise ValueError("left must be a 1D array")
         left = pybamm.Vector(left)
+    if isinstance(right, numbers.Number):
+        right = pybamm.Scalar(right)
     elif isinstance(right, np.ndarray):
         if right.ndim > 1:
             raise ValueError("right must be a 1D array")
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 32cf2c002b..d30762ad70 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -546,8 +546,10 @@ def full_like(symbols, fill_value):
         return array_type(entries, domains=sum_symbol.domains)
 
     except NotImplementedError:
-        if sum_symbol.shape_for_testing == (1, 1) or sum_symbol.shape_for_testing == (
-            1,
+        if (
+            sum_symbol.shape_for_testing == (1, 1)
+            or sum_symbol.shape_for_testing == (1,)
+            or sum_symbol.domain == []
         ):
             return pybamm.Scalar(fill_value)
         if sum_symbol.evaluates_on_edges("primary"):
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
index c6a445f316..d975de859c 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
@@ -410,10 +410,7 @@ def solve(self, inputs):
         # Calculate theoretical energy
         # TODO: energy calc for MSMR
         if self.options["open-circuit potential"] != "MSMR":
-            energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                self.parameter_values,
-                sol_dict,
-            )
+            energy = self.theoretical_energy_integral(sol_dict)
             sol_dict.update({"Maximum theoretical energy [W.h]": energy})
         return sol_dict
 
@@ -829,6 +826,27 @@ def get_min_max_ocps(self):
         sol = self.solve(inputs)
         return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]]
 
+    def theoretical_energy_integral(self, inputs, points=1000):
+        x_0 = inputs["x_0"]
+        y_0 = inputs["y_0"]
+        x_100 = inputs["x_100"]
+        y_100 = inputs["y_100"]
+        Q_p = inputs["Q_p"]
+        x_vals = np.linspace(x_100, x_0, num=points)
+        y_vals = np.linspace(y_100, y_0, num=points)
+        # Calculate OCV at each stoichiometry
+        param = self.param
+        T = param.T_amb_av(0)
+        Vs = self.parameter_values.evaluate(
+            param.p.prim.U(y_vals, T) - param.n.prim.U(x_vals, T)
+        ).flatten()
+        # Calculate dQ
+        Q = Q_p * (y_0 - y_100)
+        dQ = Q / (points - 1)
+        # Integrate and convert to W-h
+        E = np.trapz(Vs, dx=dQ)
+        return E
+
 
 def get_initial_stoichiometries(
     initial_value,
@@ -972,7 +990,7 @@ def get_min_max_ocps(
     return esoh_solver.get_min_max_ocps()
 
 
-def theoretical_energy_integral(parameter_values, inputs, points=100):
+def theoretical_energy_integral(parameter_values, param, inputs, points=100):
     """
     Calculate maximum energy possible from a cell given OCV, initial soc, and final soc
     given voltage limits, open-circuit potentials, etc defined by parameter_values
@@ -991,30 +1009,8 @@ def theoretical_energy_integral(parameter_values, inputs, points=100):
     E
         The total energy of the cell in Wh
     """
-    x_0 = inputs["x_0"]
-    y_0 = inputs["y_0"]
-    x_100 = inputs["x_100"]
-    y_100 = inputs["y_100"]
-    Q_p = inputs["Q_p"]
-    x_vals = np.linspace(x_100, x_0, num=points)
-    y_vals = np.linspace(y_100, y_0, num=points)
-    # Calculate OCV at each stoichiometry
-    param = pybamm.LithiumIonParameters()
-    y = pybamm.standard_spatial_vars.y
-    z = pybamm.standard_spatial_vars.z
-    T = pybamm.yz_average(param.T_amb(y, z, 0))
-    Vs = np.empty(x_vals.shape)
-    for i in range(x_vals.size):
-        Vs[i] = (
-            parameter_values.evaluate(param.p.prim.U(y_vals[i], T)).item()
-            - parameter_values.evaluate(param.n.prim.U(x_vals[i], T)).item()
-        )
-    # Calculate dQ
-    Q = Q_p * (y_0 - y_100)
-    dQ = Q / (points - 1)
-    # Integrate and convert to W-h
-    E = np.trapz(Vs, dx=dQ)
-    return E
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param)
+    return esoh_solver.theoretical_energy_integral(inputs, points=points)
 
 
 def calculate_theoretical_energy(
@@ -1045,6 +1041,7 @@ def calculate_theoretical_energy(
     Q_p = parameter_values.evaluate(pybamm.LithiumIonParameters().p.prim.Q_init)
     E = theoretical_energy_integral(
         parameter_values,
+        pybamm.LithiumIonParameters(),
         {"x_100": x_100, "x_0": x_0, "y_100": y_100, "y_0": y_0, "Q_p": Q_p},
         points=points,
     )
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 726e876aa0..c459a4ef1e 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -50,6 +50,7 @@ def _set_parameters(self):
         self.T_ref = self.therm.T_ref
         self.T_init = self.therm.T_init
         self.T_amb = self.therm.T_amb
+        self.T_amb_av = self.therm.T_amb_av
         self.h_edge = self.therm.h_edge
         self.h_total = self.therm.h_total
         self.rho_c_p_eff = self.therm.rho_c_p_eff
diff --git a/pybamm/parameters/thermal_parameters.py b/pybamm/parameters/thermal_parameters.py
index ea1dd12065..8e92ff8d34 100644
--- a/pybamm/parameters/thermal_parameters.py
+++ b/pybamm/parameters/thermal_parameters.py
@@ -51,6 +51,12 @@ def T_amb(self, y, z, t):
             },
         )
 
+    def T_amb_av(self, t):
+        """YZ-averaged ambient temperature [K]"""
+        y = pybamm.standard_spatial_vars.y
+        z = pybamm.standard_spatial_vars.z
+        return pybamm.yz_average(self.T_amb(y, z, t))
+
     def h_edge(self, y, z):
         """Cell edge heat transfer coefficient [W.m-2.K-1]"""
         inputs = {
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
index 628017d5d8..e5e79a6ae4 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -40,9 +40,7 @@ def test_known_solution(self):
                     k: sol_split[k].data[0]
                     for k in ["x_0", "y_0", "x_100", "y_100", "Q_p"]
                 }
-                energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-                    parameter_values, inputs
-                )
+                energy = esoh_solver.theoretical_energy_integral(inputs)
                 self.assertAlmostEqual(sol[key], energy, places=5)
 
         # should still work with old inputs

From bfbe41e36d32b9afc7d8643fd078f9fe70333bff Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Fri, 10 Nov 2023 18:20:35 +0530
Subject: [PATCH 330/615] Apply suggestions from code review

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 pybamm/citations.py                                | 8 ++++----
 pybamm/expression_tree/printing/sympy_overrides.py | 4 ++--
 pybamm/expression_tree/symbol.py                   | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/pybamm/citations.py b/pybamm/citations.py
index 7d0959d89c..b72262989b 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -74,7 +74,7 @@ def read_citations(self):
         """Reads the citations in `pybamm.CITATIONS.bib`. Other works can be cited
         by passing a BibTeX citation to :meth:`register`.
         """
-        parse_file = have_optional_dependency("pybtex.database","parse_file")
+        parse_file = have_optional_dependency("pybtex.database", "parse_file")
         citations_file = os.path.join(pybamm.root_dir(), "pybamm", "CITATIONS.bib")
         bib_data = parse_file(citations_file, bib_format="bibtex")
         for key, entry in bib_data.entries.items():
@@ -85,7 +85,7 @@ def _add_citation(self, key, entry):
         previous entry is overwritten
         """
 
-        Entry = have_optional_dependency("pybtex.database","Entry")
+        Entry = have_optional_dependency("pybtex.database", "Entry")
         # Check input types are correct
         if not isinstance(key, str) or not isinstance(entry, Entry):
             raise TypeError()
@@ -151,8 +151,8 @@ def _parse_citation(self, key):
         key: str
             A BibTeX formatted citation
         """
-        PybtexError = have_optional_dependency("pybtex.scanner","PybtexError")
-        parse_string = have_optional_dependency("pybtex.database","parse_string")
+        PybtexError = have_optional_dependency("pybtex.scanner", "PybtexError")
+        parse_string = have_optional_dependency("pybtex.database", "parse_string")
         try:
             # Parse string as a bibtex citation, and check that a citation was found
             bib_data = parse_string(key, bib_format="bibtex")
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 59f9567c5d..64743f557d 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -6,7 +6,7 @@
 from pybamm.util import have_optional_dependency
 
 
-LatexPrinter = have_optional_dependency("sympy.printing.latex","LatexPrinter")
+LatexPrinter = have_optional_dependency("sympy.printing.latex", "LatexPrinter")
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
 
@@ -22,5 +22,5 @@ def _print_Derivative(self, expr):
 
 
 def custom_print_func(expr, **settings):
-    have_optional_dependency("sympy.printing.latex","LatexPrinter")
+    have_optional_dependency("sympy.printing.latex", "LatexPrinter")
     return CustomPrint().doprint(expr)
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 85c392e590..8f1608e7ba 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -459,7 +459,7 @@ def visualise(self, filename):
             filename to output, must end in ".png"
         """
 
-        DotExporter = have_optional_dependency("anytree.exporter","DotExporter")
+        DotExporter = have_optional_dependency("anytree.exporter", "DotExporter")
         # check that filename ends in .png.
         if filename[-4:] != ".png":
             raise ValueError("filename should end in .png")

From 2f1d3ceea469a4de03c9143706cd9f2e23bdd14d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 10 Nov 2023 23:40:05 +0530
Subject: [PATCH 331/615] Shorten assert string

---
 tests/unit/test_util.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index b9dd428a4b..ea087ad4c4 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -12,10 +12,8 @@
 from io import StringIO
 from tempfile import TemporaryDirectory
 
-def test_function(arg):
-    return arg + arg
-
 anytree = sys.modules['anytree']
+
 class TestUtil(TestCase):
     """
     Test the functionality in util.py
@@ -94,11 +92,11 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available."):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available."):
             sys.modules['tqdm'] = None
             model = pybamm.BaseModel()
             v = pybamm.Variable("v")
@@ -106,7 +104,7 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."):
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None
                 test_stub = os.path.join(dir_name, "test_visualize")

From fe6b9105f0ed0abad3e7284cb94edaea46056ed9 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 00:19:27 +0530
Subject: [PATCH 332/615] Improve readibility & add case to fix coverage

---
 pybamm/util.py          | 5 +++--
 tests/unit/test_util.py | 3 +++
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/pybamm/util.py b/pybamm/util.py
index dee77b8841..6c91948394 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -348,6 +348,7 @@ def install_jax(arguments=None):  # pragma: no cover
 
 # https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
 def have_optional_dependency(module_name, attribute=None):
+    err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."
     try:
         # Attempt to import the specified module
         module = importlib.import_module(module_name)
@@ -359,11 +360,11 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ModuleNotFoundError if the attribute is not available
-                raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+                raise ModuleNotFoundError(err_msg)
         else:
             # Return the entire module if no attribute is specified
             return module
 
     except ModuleNotFoundError:
         # Raise an ModuleNotFoundError if the module or attribute is not available
-        raise ModuleNotFoundError(f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details.")
+        raise ModuleNotFoundError(err_msg)
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index ea087ad4c4..b2ef72fcbc 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -118,6 +118,9 @@ def test_have_optional_dependency(self):
         pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
+        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency flask is not available."):
+            pybamm.util.have_optional_dependency("flask","Flask")
+
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 6239653a57fff5a4f33c35b8919ab8f4814de6a7 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 00:58:15 +0530
Subject: [PATCH 333/615] Modify CONTRIBUTING.md for optional dependency tests

---
 CONTRIBUTING.md | 23 +++++++++++++++++++++++
 pybamm/util.py  |  2 +-
 2 files changed, 24 insertions(+), 1 deletion(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index de0d626940..9a7e3d779d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -124,6 +124,29 @@ def use_parse_file(x, y, z):
 
 This allows people to (1) use PyBaMM without importing optional dependencies by default and (2) configure module-dependent functionalities in their scripts, which _must_ be done before e.g. `print_citations` method is first imported.
 
+**Writing Tests for Optional Dependencies**
+
+Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in _test_util.py_. This ensures that an error is raised upon the absence of said dependency. Here's an example:
+
+```python
+from tests import TestCase
+import pybamm
+
+
+class TestUtil(TestCase):
+    def test_optional_dependency(self):
+        # Test that an error is raised when pybtex is not available
+        with self.assertRaisesRegex(
+            ModuleNotFoundError, "Optional dependency pybtex is not available"
+        ):
+            sys.modules["pybtex"] = None
+            pybamm.function_using_pybtex(x, y, z)
+
+        # Test that the function works when pybtex is available
+        sys.modules["pybtex"] = pybamm.util.have_optional_dependency("pybtex")
+        pybamm.function_using_pybtex(x, y, z)
+```
+
 ## Testing
 
 All code requires testing. We use the [unittest](https://docs.python.org/3.3/library/unittest.html) package for our tests. (These tests typically just check that the code runs without error, and so, are more _debugging_ than _testing_ in a strict sense. Nevertheless, they are very useful to have!)
diff --git a/pybamm/util.py b/pybamm/util.py
index 6c91948394..90cb290c6e 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -360,7 +360,7 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ModuleNotFoundError if the attribute is not available
-                raise ModuleNotFoundError(err_msg)
+                raise ModuleNotFoundError(err_msg)      # pragma: no cover
         else:
             # Return the entire module if no attribute is specified
             return module

From 9f7121b984c4ba176aa9c6b0e3b88313c7d75232 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 11 Nov 2023 17:13:14 +0530
Subject: [PATCH 334/615] #3442 Update release workflow instructions to add
 details about `conda-forge`

---
 .github/release_workflow.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 7afa24a6d6..8334a1d5dc 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -1,6 +1,6 @@
 # Release workflow
 
-This file contains the workflow required to make a `PyBaMM` release on GitHub and PyPI by the maintainers.
+This file contains the workflow required to make a `PyBaMM` release on GitHub, PyPI, and conda-forge by the maintainers.
 
 ## rc0 releases (automated)
 
@@ -77,3 +77,5 @@ Some other essential things to check throughout the release process -
   git tag -f <tag_name> <commit_hash>
   git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
   ```
+- If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe
+- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot.

From 6f5823fed4825ab44da90a79f65a94246ffec0bb Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 18:55:49 +0530
Subject: [PATCH 335/615] Prevent inheriting LatexPrinter instead use a
 function

---
 .../printing/sympy_overrides.py               | 25 +++++++++++--------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 64743f557d..3e89542d10 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -6,21 +6,24 @@
 from pybamm.util import have_optional_dependency
 
 
-LatexPrinter = have_optional_dependency("sympy.printing.latex", "LatexPrinter")
-class CustomPrint(LatexPrinter):
+def custom_latex_printer(expr, **settings):
+    latex = have_optional_dependency("sympy","latex")
+    Derivative = have_optional_dependency("sympy","Derivative")
+    if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
+        latex_str = latex(expr, **settings)
+        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
+        latex_str = latex_str.replace(var1, "\partial").replace(var2, "\partial")
+        return latex_str
+    else:
+        return latex(expr, **settings)
+
+class CustomPrint:
     """Override SymPy methods to match PyBaMM's requirements"""
 
     def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-        eqn = super()._print_Derivative(expr)
-
-        if getattr(expr, "force_partial", False) and "partial" not in eqn:
-            var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-            eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
-
-        return eqn
-
+        return custom_latex_printer(expr)
 
 def custom_print_func(expr, **settings):
     have_optional_dependency("sympy.printing.latex", "LatexPrinter")
-    return CustomPrint().doprint(expr)
+    return CustomPrint()._print_Derivative(expr)

From c093d44614e2dfdb3f24f96357169a9cfb3d6ca3 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 11 Nov 2023 18:59:32 +0530
Subject: [PATCH 336/615] Remove redundant testcase

---
 tests/unit/test_util.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index b2ef72fcbc..ea087ad4c4 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -118,9 +118,6 @@ def test_have_optional_dependency(self):
         pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency flask is not available."):
-            pybamm.util.have_optional_dependency("flask","Flask")
-
 
 class TestSearch(TestCase):
     def test_url_gets_to_stdout(self):

From 2324af9254e4e567264c073f3d0da4e19fc4f230 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sun, 12 Nov 2023 01:26:33 +0530
Subject: [PATCH 337/615] Add a sentence about manual PRs in the feedstock

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 .github/release_workflow.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 8334a1d5dc..ed94962b92 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -78,4 +78,4 @@ Some other essential things to check throughout the release process -
   git push -f <pybamm-team/PyBaMM_remote_name> <tag_name>  # can only be carried out by the maintainers
   ```
 - If changes are made to the API, console scripts, entry points, new optional dependencies are added, support for major Python versions is dropped or added, or core project information and metadata are modified at the time of the release, make sure to update the `meta.yaml` file in the `recipe/` folder of the [conda-forge/pybamm-feedstock](https://github.com/conda-forge/pybamm-feedstock) repository accordingly by following the instructions in the [conda-forge documentation](https://conda-forge.org/docs/maintainer/updating_pkgs.html#updating-the-feedstock-repository) and re-rendering the recipe
-- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates can be carried out either in a personal fork of the feedstock repository, or directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot.
+- The conda-forge release workflow will automatically be triggered following a stable PyPI release, and the aforementioned updates should be carried out directly in the main repository by pushing changes to the automated PR created by the conda-forge-bot. A manual PR can also be created if the updates are not included in the automated PR for some reason. This manual PR **must** bump the build number in `meta.yaml` and **must** be from a personal fork of the repository.

From a5d25736d79c0571eccada2ddee7a330fbb7b2dc Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 17:22:05 +0530
Subject: [PATCH 338/615] Add `anytree` to required & install `[plot,cite]` in
 `examples` session

---
 noxfile.py | 2 +-
 setup.py   | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..83f4c3d717 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all,dev]", silent=False)
+    session.install("-e", ".[plot,cite,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/setup.py b/setup.py
index f6fd37f75c..fca5b83de8 100644
--- a/setup.py
+++ b/setup.py
@@ -207,6 +207,7 @@ def compile_KLU():
         "scipy>=1.3",
         "casadi>=3.6.0",
         "xarray",
+        "anytree>=2.4.3",
     ],
     extras_require={
         "docs": [

From a3952ddfef3361d4411a045228abbda98ef8a840 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 20:32:26 +0530
Subject: [PATCH 339/615] Set iterator based upon `tqdm`

---
 noxfile.py           |  2 +-
 pybamm/simulation.py | 18 +++++++++++++-----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 83f4c3d717..0d5d6e0d20 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[plot,cite,dev]", silent=False)
+    session.install("-e", ".[plot,cite,examples,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 0b1a6b2525..49b46f1dac 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -532,7 +532,10 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
-        tqdm = have_optional_dependency("tqdm")
+        try:
+            tqdm = have_optional_dependency("tqdm")
+        except ModuleNotFoundError:
+            tqdm = False
         # Setup
         if solver is None:
             solver = self._solver
@@ -727,13 +730,18 @@ def solve(
                         # Update _solution
                         self._solution = current_solution
 
-            for cycle_num, cycle_length in enumerate(
-                # tqdm is the progress bar.
-                tqdm.tqdm(
+            if tqdm:
+                iterator = tqdm.tqdm(
                     self.experiment.cycle_lengths,
                     disable=(not showprogress),
                     desc="Cycling",
-                ),
+                )
+            else:
+                iterator = self.experiment.cycle_lengths
+
+            for cycle_num, cycle_length in enumerate(
+                # tqdm is the progress bar.
+                iterator,
                 start=1,
             ):
                 logs["cycle number"] = (

From ae22805107b98aae6867c0cf91c4d8fcd6ba8ba6 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 20:47:10 +0530
Subject: [PATCH 340/615] Clean up tqdm mess

---
 noxfile.py              |  2 +-
 pybamm/simulation.py    | 16 ++++++----------
 tests/unit/test_util.py |  6 +++---
 3 files changed, 10 insertions(+), 14 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 0d5d6e0d20..430ad59659 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -101,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[plot,cite,examples,dev]", silent=False)
+    session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 49b46f1dac..42bda08e31 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -532,10 +532,6 @@ def solve(
             Additional key-word arguments passed to `solver.solve`.
             See :meth:`pybamm.BaseSolver.solve`.
         """
-        try:
-            tqdm = have_optional_dependency("tqdm")
-        except ModuleNotFoundError:
-            tqdm = False
         # Setup
         if solver is None:
             solver = self._solver
@@ -730,18 +726,18 @@ def solve(
                         # Update _solution
                         self._solution = current_solution
 
-            if tqdm:
-                iterator = tqdm.tqdm(
+            # check if a user has tqdm installed
+            if showprogress:
+                tqdm = have_optional_dependency("tqdm")
+                cycle_lengths = tqdm.tqdm(
                     self.experiment.cycle_lengths,
-                    disable=(not showprogress),
                     desc="Cycling",
                 )
             else:
-                iterator = self.experiment.cycle_lengths
+                cycle_lengths = self.experiment.cycle_lengths
 
             for cycle_num, cycle_length in enumerate(
-                # tqdm is the progress bar.
-                iterator,
+                cycle_lengths,
                 start=1,
             ):
                 logs["cycle number"] = (
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index ea087ad4c4..5079842003 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -92,11 +92,11 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency pybtex is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency pybtex is not available."):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency tqdm is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency tqdm is not available."):
             sys.modules['tqdm'] = None
             model = pybamm.BaseModel()
             v = pybamm.Variable("v")
@@ -104,7 +104,7 @@ def test_have_optional_dependency(self):
             model.initial_conditions = {v: 1}
             sim = pybamm.Simulation(model)
             sim.solve([0, 1])
-        with self.assertRaisesRegex(ModuleNotFoundError,"Optional dependency anytree is not available."):
+        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None
                 test_stub = os.path.join(dir_name, "test_visualize")

From b5f74ad7b76fef4adb6c6496f0456dba8f182d24 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 21:03:17 +0530
Subject: [PATCH 341/615] Fix matplotlib errors

---
 pybamm/plotting/plot.py                    |  3 ++-
 pybamm/plotting/plot2D.py                  |  3 ++-
 pybamm/plotting/plot_summary_variables.py  |  3 ++-
 pybamm/plotting/plot_voltage_components.py |  4 +++-
 pybamm/plotting/quick_plot.py              | 18 ++++++++++--------
 5 files changed, 19 insertions(+), 12 deletions(-)

diff --git a/pybamm/plotting/plot.py b/pybamm/plotting/plot.py
index 19aa9dc5e0..88c8dfe442 100644
--- a/pybamm/plotting/plot.py
+++ b/pybamm/plotting/plot.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot(x, y, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot(x, y, ax=None, testing=False, **kwargs):
         Keyword arguments, passed to plt.plot
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py
index 80bb5d0ee2..d4f6d31e3a 100644
--- a/pybamm/plotting/plot2D.py
+++ b/pybamm/plotting/plot2D.py
@@ -3,6 +3,7 @@
 #
 import pybamm
 from .quick_plot import ax_min, ax_max
+from pybamm.util import have_optional_dependency
 
 
 def plot2D(x, y, z, ax=None, testing=False, **kwargs):
@@ -25,7 +26,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs):
         Whether to actually make the plot (turned off for unit tests)
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if not isinstance(x, pybamm.Array):
         raise TypeError("x must be 'pybamm.Array'")
diff --git a/pybamm/plotting/plot_summary_variables.py b/pybamm/plotting/plot_summary_variables.py
index 6fe71518db..e50f38fddf 100644
--- a/pybamm/plotting/plot_summary_variables.py
+++ b/pybamm/plotting/plot_summary_variables.py
@@ -3,6 +3,7 @@
 #
 import numpy as np
 import pybamm
+from pybamm.util import have_optional_dependency
 
 
 def plot_summary_variables(
@@ -25,7 +26,7 @@ def plot_summary_variables(
         Keyword arguments, passed to plt.subplots.
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     if isinstance(solutions, pybamm.Solution):
         solutions = [solutions]
diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py
index ad0e9a8b71..a681094bea 100644
--- a/pybamm/plotting/plot_voltage_components.py
+++ b/pybamm/plotting/plot_voltage_components.py
@@ -3,6 +3,8 @@
 #
 import numpy as np
 
+from pybamm.util import have_optional_dependency
+
 
 def plot_voltage_components(
     solution,
@@ -32,7 +34,7 @@ def plot_voltage_components(
         Keyword arguments, passed to ax.fill_between
 
     """
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     # Set a default value for alpha, the opacity
     kwargs_fill = {"alpha": 0.6, **kwargs_fill}
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 5e9c9ef941..00a07d16a1 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -5,6 +5,7 @@
 import numpy as np
 import pybamm
 from collections import defaultdict
+from pybamm.util import have_optional_dependency
 
 
 class LoopList(list):
@@ -46,7 +47,7 @@ def split_long_string(title, max_words=None):
 
 def close_plots():
     """Close all open figures"""
-    import matplotlib.pyplot as plt
+    plt = have_optional_dependency("matplotlib", "pyplot")
 
     plt.close("all")
 
@@ -469,9 +470,10 @@ def plot(self, t, dynamic=False):
             Dimensional time (in 'time_units') at which to plot.
         """
 
-        import matplotlib.pyplot as plt
-        import matplotlib.gridspec as gridspec
-        from matplotlib import cm, colors
+        plt = have_optional_dependency("matplotlib.pyplot")
+        gridspec = have_optional_dependency("matplotlib.gridspec")
+        cm = have_optional_dependency("matplotlib", "cm")
+        colors = have_optional_dependency("matplotlib", "colors")
 
         t_in_seconds = t * self.time_scaling_factor
         self.fig = plt.figure(figsize=self.figsize)
@@ -668,8 +670,8 @@ def dynamic_plot(self, testing=False, step=None):
                 continuous_update=False,
             )
         else:
-            import matplotlib.pyplot as plt
-            from matplotlib.widgets import Slider
+            plt = have_optional_dependency("matplotlib.pyplot")
+            Slider = have_optional_dependency("matplotlib.widgets", "Slider")
 
             # create an initial plot at time self.min_t
             self.plot(self.min_t, dynamic=True)
@@ -773,8 +775,8 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi
             Name of the generated GIF file.
 
         """
-        import imageio.v2 as imageio
-        import matplotlib.pyplot as plt
+        imageio = have_optional_dependency("imageio.v2")
+        plt = have_optional_dependency("matplotlib.pyplot")
 
         # time stamps at which the images/plots will be created
         time_array = np.linspace(self.min_t, self.max_t, num=number_of_images)

From 78792bcb3aa73840e2db9378792a67a5ae91739f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 21:13:14 +0530
Subject: [PATCH 342/615] Apply suggestions from code review

---
 CONTRIBUTING.md                                    | 4 ++--
 pybamm/expression_tree/printing/sympy_overrides.py | 4 ++--
 pybamm/plotting/quick_plot.py                      | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9a7e3d779d..0a5b17bcb0 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -54,7 +54,7 @@ You now have everything you need to start making changes!
 10. [Test your code!](#testing)
 11. PyBaMM has online documentation at http://docs.pybamm.org/. To make sure any new methods or classes you added show up there, please read the [documentation](#documentation) section.
 12. If you added a major new feature, perhaps it should be showcased in an [example notebook](#example-notebooks).
-13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM&#39;s GitHub page](https://github.com/pybamm-team/PyBaMM).
+13. When you feel your code is finished, or at least warrants serious discussion, run the [pre-commit checks](#pre-commit-checks) and then create a [pull request](https://help.github.com/articles/about-pull-requests/) (PR) on [PyBaMM's GitHub page](https://github.com/pybamm-team/PyBaMM).
 14. Once a PR has been created, it will be reviewed by any member of the community. Changes might be suggested which you can make by simply adding new commits to the branch. When everything's finished, someone with the right GitHub permissions will merge your changes into PyBaMM main repository.
 
 Finally, if you really, really, _really_ love developing PyBaMM, have a look at the current [project infrastructure](#infrastructure).
@@ -126,7 +126,7 @@ This allows people to (1) use PyBaMM without importing optional dependencies by
 
 **Writing Tests for Optional Dependencies**
 
-Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in _test_util.py_. This ensures that an error is raised upon the absence of said dependency. Here's an example:
+Whenever a new optional dependency is added for optional functionality, it is recommended to write a corresponding unit test in `test_util.py`. This ensures that an error is raised upon the absence of said dependency. Here's an example:
 
 ```python
 from tests import TestCase
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 3e89542d10..ec70de22b2 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -7,8 +7,8 @@
 
 
 def custom_latex_printer(expr, **settings):
-    latex = have_optional_dependency("sympy","latex")
-    Derivative = have_optional_dependency("sympy","Derivative")
+    latex = have_optional_dependency("sympy", "latex")
+    Derivative = have_optional_dependency("sympy", "Derivative")
     if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
         latex_str = latex(expr, **settings)
         var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 00a07d16a1..ff657ee375 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -47,7 +47,7 @@ def split_long_string(title, max_words=None):
 
 def close_plots():
     """Close all open figures"""
-    plt = have_optional_dependency("matplotlib", "pyplot")
+    plt = have_optional_dependency("matplotlib.pyplot")
 
     plt.close("all")
 

From a8ac4c784d4b9299d1c1f8beb7e656b1be7b1cc1 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 13 Nov 2023 21:34:56 +0530
Subject: [PATCH 343/615] Remove test for tqdm as ModuleNotFoundError no longer
 being raised for `Simulation.solve()`

---
 tests/unit/test_util.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 5079842003..730e4cc08d 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -96,14 +96,6 @@ def test_have_optional_dependency(self):
             pybtex = sys.modules['pybtex']
             sys.modules['pybtex'] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency tqdm is not available."):
-            sys.modules['tqdm'] = None
-            model = pybamm.BaseModel()
-            v = pybamm.Variable("v")
-            model.rhs = {v: -v}
-            model.initial_conditions = {v: 1}
-            sim = pybamm.Simulation(model)
-            sim.solve([0, 1])
         with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
             with TemporaryDirectory() as dir_name:
                 sys.modules['anytree'] = None

From 36ec186d828919d7835bdc76275a0d8a82ac9ea9 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Mon, 13 Nov 2023 11:52:29 -0500
Subject: [PATCH 344/615] #3506 changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b02df8ed4c..c1cf4b91ed 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Bug fixes
 
+- Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31

From bd2d009c74d29a326857277129f2c713f8a1020e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 22:55:28 +0530
Subject: [PATCH 345/615] Fix sympy overrides

---
 .../printing/sympy_overrides.py               | 28 +++++--------
 pybamm/models/base_model.py                   | 39 ++++++++++++++++---
 2 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 3e89542d10..d127534a0e 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -3,27 +3,19 @@
 #
 import re
 
-from pybamm.util import have_optional_dependency
+from sympy.printing.latex import LatexPrinter
 
 
-def custom_latex_printer(expr, **settings):
-    latex = have_optional_dependency("sympy","latex")
-    Derivative = have_optional_dependency("sympy","Derivative")
-    if isinstance(expr, Derivative) and getattr(expr, "force_partial", False):
-        latex_str = latex(expr, **settings)
-        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", latex_str)[0]
-        latex_str = latex_str.replace(var1, "\partial").replace(var2, "\partial")
-        return latex_str
-    else:
-        return latex(expr, **settings)
+class CustomPrint(LatexPrinter):
+        """Override SymPy methods to match PyBaMM's requirements"""
+        def _print_Derivative(self, expr):
+                """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
+                eqn = super()._print_Derivative(expr)
+                if getattr(expr, "force_partial", False) and "partial" not in eqn:
+                        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+                        eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
-class CustomPrint:
-    """Override SymPy methods to match PyBaMM's requirements"""
-
-    def _print_Derivative(self, expr):
-        """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-        return custom_latex_printer(expr)
+                return eqn
 
 def custom_print_func(expr, **settings):
-    have_optional_dependency("sympy.printing.latex", "LatexPrinter")
     return CustomPrint()._print_Derivative(expr)
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 41192dbe1f..08890757b7 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -9,7 +9,7 @@
 import numpy as np
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
+from pybamm.util import have_optional_dependency
 
 
 class BaseModel:
@@ -1055,14 +1055,43 @@ def generate(
         C.generate()
 
     def latexify(self, filename=None, newline=True, output_variables=None):
-        # For docstring, see pybamm.expression_tree.operations.latexify.Latexify
+        """
+        Converts all model equations in latex.
+
+        Parameters
+        ----------
+        filename: str (optional)
+            Accepted file formats - any image format, pdf and tex
+            Default is None, When None returns all model equations in latex
+            If not None, returns all model equations in given file format.
+
+        newline: bool (optional)
+            Default is True, If True, returns every equation in a new line.
+            If False, returns the list of all the equations.
+
+        Load model
+        >>> model = pybamm.lithium_ion.SPM()
+
+        This will returns all model equations in png
+        >>> model.latexify("equations.png")
+
+        This will return all the model equations in latex
+        >>> model.latexify()
+
+        This will return the list of all the model equations
+        >>> model.latexify(newline=False)
+
+        This will return first five model equations
+        >>> model.latexify(newline=False)[1:5]
+        """
+        sympy = have_optional_dependency("sympy")
+        if sympy:
+            from pybamm.expression_tree.operations.latexify import Latexify
+
         return Latexify(self, filename, newline).latexify(
             output_variables=output_variables
         )
 
-    # Set :meth:`latexify` docstring from :class:`Latexify`
-    latexify.__doc__ = Latexify.__doc__
-
     def process_parameters_and_discretise(self, symbol, parameter_values, disc):
         """
         Process parameters and discretise a symbol using supplied parameter values

From 9d342c0f34ef4b7a579a011526d5441c0069bfc9 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:00:40 +0530
Subject: [PATCH 346/615] fix tabs

---
 .../expression_tree/printing/sympy_overrides.py  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index d127534a0e..e189e536d7 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -7,15 +7,15 @@
 
 
 class CustomPrint(LatexPrinter):
-        """Override SymPy methods to match PyBaMM's requirements"""
-        def _print_Derivative(self, expr):
-                """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
-                eqn = super()._print_Derivative(expr)
-                if getattr(expr, "force_partial", False) and "partial" not in eqn:
-                        var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-                        eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
+    """Override SymPy methods to match PyBaMM's requirements"""
+    def _print_Derivative(self, expr):
+        """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
+        eqn = super()._print_Derivative(expr)
+        if getattr(expr, "force_partial", False) and "partial" not in eqn:
+                var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+                eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
-                return eqn
+        return eqn
 
 def custom_print_func(expr, **settings):
     return CustomPrint()._print_Derivative(expr)

From 2e30131d7ef35c19f538065fc894dc4bf32236f8 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:05:22 +0530
Subject: [PATCH 347/615] Fix CustomPrinter

---
 pybamm/expression_tree/printing/sympy_overrides.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index e189e536d7..678d4f5a37 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -12,10 +12,10 @@ def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
         eqn = super()._print_Derivative(expr)
         if getattr(expr, "force_partial", False) and "partial" not in eqn:
-                var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
-                eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
+            var1, var2 = re.findall(r"^\\frac{(\w+)}{(\w+) .+", eqn)[0]
+            eqn = eqn.replace(var1, "\partial").replace(var2, "\partial")
 
         return eqn
 
 def custom_print_func(expr, **settings):
-    return CustomPrint()._print_Derivative(expr)
+    return CustomPrint().doprint(expr)

From 47113627907f9f2baf7936b82a202ab1fd1bbaf3 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 13 Nov 2023 23:30:31 +0530
Subject: [PATCH 348/615] Fix test

---
 pybamm/expression_tree/printing/sympy_overrides.py            | 1 +
 .../test_expression_tree/test_operations/test_latexify.py     | 4 ----
 2 files changed, 1 insertion(+), 4 deletions(-)

diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 678d4f5a37..1898822ea8 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -17,5 +17,6 @@ def _print_Derivative(self, expr):
 
         return eqn
 
+
 def custom_print_func(expr, **settings):
     return CustomPrint().doprint(expr)
diff --git a/tests/unit/test_expression_tree/test_operations/test_latexify.py b/tests/unit/test_expression_tree/test_operations/test_latexify.py
index be7cc21115..7e0703534e 100644
--- a/tests/unit/test_expression_tree/test_operations/test_latexify.py
+++ b/tests/unit/test_expression_tree/test_operations/test_latexify.py
@@ -8,7 +8,6 @@
 import uuid
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
 
 
 class TestLatexify(TestCase):
@@ -19,9 +18,6 @@ def test_latexify(self):
         model_spme = pybamm.lithium_ion.SPMe()
         func_spme = str(model_spme.latexify())
 
-        # Test docstring
-        self.assertEqual(pybamm.BaseModel.latexify.__doc__, Latexify.__doc__)
-
         # Test model name
         self.assertIn("Single Particle Model with electrolyte Equations", func_spme)
 

From 9970d9736569056393f608305a64485baba1203e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 14 Nov 2023 00:08:04 +0530
Subject: [PATCH 349/615] Fix failing notebook tests

---
 .../notebooks/models/lithium-plating.ipynb    | 72 ++++++-------------
 1 file changed, 23 insertions(+), 49 deletions(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 57049a0ea7..d5fa0e6123 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -13,18 +13,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -70,17 +59,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The linesearch algorithm failed with too small a step.\n",
-      "The linesearch algorithm failed with too small a step.\n",
-      "The linesearch algorithm failed with too small a step.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# specify experiments\n",
     "pybamm.citations.register(\"Ren2018\")\n",
@@ -159,14 +138,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -187,6 +164,7 @@
     "\n",
     "\n",
     "def plot(sims):\n",
+    "    import matplotlib.pyplot as plt\n",
     "    fig, axs = plt.subplots(2, 2, figsize=(13,9))\n",
     "    for (C_rate,sim), color in zip(sims.items(),colors):\n",
     "        # Isolate final equilibration phase\n",
@@ -260,11 +238,11 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 936x648 with 4 Axes>,\n",
-       " array([[<AxesSubplot:xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<AxesSubplot:xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
        "       dtype=object))"
       ]
      },
@@ -274,14 +252,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -313,11 +289,11 @@
     {
      "data": {
       "text/plain": [
-       "(<Figure size 936x648 with 4 Axes>,\n",
-       " array([[<AxesSubplot:xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<AxesSubplot:xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <AxesSubplot:xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
+       "(<Figure size 1300x900 with 4 Axes>,\n",
+       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
+       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
+       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
        "       dtype=object))"
       ]
      },
@@ -327,14 +303,12 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 936x648 with 4 Axes>"
+       "<Figure size 1300x900 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -398,7 +372,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.5"
   },
   "toc": {
    "base_numbering": 1,

From 05c74e41e54a88bea8d0cfaaf24ee93b3f3457b4 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Tue, 14 Nov 2023 01:07:30 +0530
Subject: [PATCH 350/615] Add `anytree` to required deps in docs

---
 docs/source/user_guide/installation/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 6338323e79..2cf61093be 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -66,6 +66,7 @@ Package                                                          Minimum support
 `SciPy <https://docs.scipy.org/doc/scipy/>`__                    2.8.2
 `CasADi <https://web.casadi.org/docs/>`__                        3.6.0
 `Xarray <https://docs.xarray.dev/en/stable/>`__                  2023.04.0
+`Anytree <https://anytree.readthedocs.io/en/stable/>`__          2.4.3
 ================================================================ ==========================
 
 .. _install.optional_dependencies:

From b9b3cb2bd8d7df83f141809209419107ccb3d5a7 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 13 Nov 2023 19:42:56 +0000
Subject: [PATCH 351/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.4 → v0.1.5](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.4...v0.1.5)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index fa0de6f56c..5871b334bf 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.4"
+    rev: "v0.1.5"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 4d118abc78115b9f27ffd970d64d52ca697294a9 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 14 Nov 2023 14:42:02 +0530
Subject: [PATCH 352/615] Fix CHANGELOG

---
 CHANGELOG.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index b02df8ed4c..afbc5073b0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,7 +2,7 @@
 
 ## Bug fixes
 
-- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
@@ -23,6 +23,7 @@
 
 ## Bug fixes
 
+- Fixed a bug where the JaxSolver would fail when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
@@ -61,7 +62,7 @@
 - Added option to use an empirical hysteresis model for the diffusivity and exchange-current density ([#3194](https://github.com/pybamm-team/PyBaMM/pull/3194))
 - Double-layer capacity can now be provided as a function of temperature ([#3174](https://github.com/pybamm-team/PyBaMM/pull/3174))
 - `pybamm_install_jax` is deprecated. It is now replaced with `pip install pybamm[jax]` ([#3163](https://github.com/pybamm-team/PyBaMM/pull/3163))
-- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044))
+- PyBaMM now has optional dependencies that can be installed with the pattern `pip install pybamm[option]` e.g. `pybamm[plot]` ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
 
 # [v23.5](https://github.com/pybamm-team/PyBaMM/tree/v23.5) - 2023-06-18
 

From 72f8ff9991413d84921ae8409d223b91debd67f2 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 15 Nov 2023 02:20:42 +0530
Subject: [PATCH 353/615] Bump to v23.9rc1 manually

---
 CHANGELOG.md      | 8 ++++++--
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 vcpkg.json        | 2 +-
 4 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index afbc5073b0..82b3824272 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,12 @@
 
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
+# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-11-15
+
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
+
 # [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
 
 ## Features
@@ -23,7 +29,6 @@
 
 ## Bug fixes
 
-- Fixed a bug where the JaxSolver would fail when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
@@ -42,7 +47,6 @@
 - Error generated when invalid parameter values are passed ([#3132](https://github.com/pybamm-team/PyBaMM/pull/3132))
 - Parameters in `Prada2013` have been updated to better match those given in the paper, which is a 2.3 Ah cell, instead of the mix-and-match with the 1.1 Ah cell from Lain2019 ([#3096](https://github.com/pybamm-team/PyBaMM/pull/3096))
 - The `OneDimensionalX` thermal model has been updated to account for edge/tab cooling and account for the current collector volumetric heat capacity. It now gives the correct behaviour compared with a lumped model with the correct total heat transfer coefficient and surface area for cooling. ([#3042](https://github.com/pybamm-team/PyBaMM/pull/3042))
-- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 
 ## Optimizations
 
diff --git a/CITATION.cff b/CITATION.cff
index 5a9e1e2ddc..b7f68164fc 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9rc0"
+version: "23.9rc1"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index c8d63f83e1..e5cfaa0882 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9rc0"
+__version__ = "23.9rc1"
diff --git a/vcpkg.json b/vcpkg.json
index 6877dfa094..de71a5a87d 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9rc0",
+  "version-string": "23.9rc1",
   "dependencies": [
     "casadi",
     {

From 88c5d8daa51f0d92b961a05e88b7ce546f5336cf Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 15 Nov 2023 13:48:29 +0530
Subject: [PATCH 354/615] Update CHANGELOG.md

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 82b3824272..d615b3d714 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,7 +4,7 @@
 
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
-# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-11-15
+# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
 
 - Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
 - Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))

From 9e69391d9ea2476f2345c9425bb1f44113af5ecd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:07:45 +0530
Subject: [PATCH 355/615] Remove reinstall of OpenBLAS for scheduled tests

---
 .github/workflows/run_periodic_tests.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 2322adf993..06bd358f71 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -66,13 +66,11 @@ jobs:
           sudo apt install gfortran gcc libopenblas-dev graphviz pandoc
           sudo apt install texlive-full
 
-      # sometimes gfortran cannot be found, so reinstall gcc just to be sure
-      - name: Install MacOS system dependencies
+      - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         run:
           brew update
-          brew install graphviz
-          brew reinstall openblas
+          brew install graphviz openblas
           brew reinstall gcc
 
       - name: Install Windows system dependencies

From f87b6bf5c8306581cdc4ed1e2f4fda3d61f077e4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:09:54 +0530
Subject: [PATCH 356/615] Temporarily remove `pip` wheel caches and test

---
 .github/workflows/test_on_push.yml | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b660f0a7c9..8a97bdffc8 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -87,8 +87,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -146,8 +144,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -228,8 +224,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -288,8 +282,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -332,8 +324,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -389,8 +379,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
-          cache: 'pip'
-          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |

From 778d2a4b756b83afce0d5709c6956678e8f48263 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:21:58 +0530
Subject: [PATCH 357/615] Don't run "brew update" in CI

---
 .github/workflows/run_periodic_tests.yml | 1 -
 .github/workflows/test_on_push.yml       | 2 --
 2 files changed, 3 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 06bd358f71..8e3a8b2644 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -69,7 +69,6 @@ jobs:
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
         run:
-          brew update
           brew install graphviz openblas
           brew reinstall gcc
 
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 8a97bdffc8..e948337b3b 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -75,7 +75,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install graphviz openblas
 
       - name: Install Windows system dependencies
@@ -212,7 +211,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install graphviz openblas
 
       - name: Install Windows system dependencies

From 9098bf0f7290087724ce7c4197be4f62103ceb8c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 01:45:28 +0530
Subject: [PATCH 358/615] Even lower bounds for NumPy and SciPy

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 8bc6437945..bf69298c08 100644
--- a/setup.py
+++ b/setup.py
@@ -203,8 +203,8 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.24.4",
-        "scipy>=1.10.1",
+        "numpy>=1.18.5",
+        "scipy>=1.9.3",
         "casadi>=3.6.3",
         "xarray",
     ],

From b0dc5f9803ad4febc944a190b54b8fb86d8b64b5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:16:01 +0530
Subject: [PATCH 359/615] Exercise tighter lower bounds for dependencies

---
 setup.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/setup.py b/setup.py
index 7dfd713a34..1ec40d9637 100644
--- a/setup.py
+++ b/setup.py
@@ -203,11 +203,11 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.18.5",
-        "scipy>=1.9.3",
+        "numpy>=1.24.4",
+        "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray",
-        "anytree>=2.4.3",
+        "xarray>=2023.1.0",
+        "anytree>=2.12.0",
     ],
     extras_require={
         "docs": [
@@ -231,12 +231,12 @@ def compile_KLU():
             "jupyter",  # For example notebooks
         ],
         "plot": [
-            "imageio>=2.9.0",
+            "imageio>=2.32.0",
             # Note: Matplotlib is loaded for debug plots, but to ensure pybamm runs
             # on systems without an attached display, it should never be imported
             # outside of plot() methods.
             # Should not be imported
-            "matplotlib>=2.0",
+            "matplotlib>=3.7.3",
         ],
         "cite": [
             "pybtex>=0.24.0",
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=0.24",
+            "pandas>=2.0.3",
         ],
         "jax": [
             "jax==0.4.8",
@@ -271,16 +271,16 @@ def compile_KLU():
         ],
         "odes": ["scikits.odes"],
         "all": [
-            "anytree>=2.4.3",
-            "autograd>=1.2",
-            "pandas>=0.24",
-            "scikit-fem>=0.2.0",
-            "imageio>=2.9.0",
+            "anytree>=2.12.0",
+            "autograd>=1.6.2",
+            "pandas>=2.0.3",
+            "scikit-fem>=8.1.0",
+            "imageio>=2.32.0",
             "pybtex>=0.24.0",
             "sympy>=1.12",
             "bpx",
             "tqdm",
-            "matplotlib>=2.0",
+            "matplotlib>=3.7.3",
             "jupyter",
         ],
     },

From 2b8e225e6813ee44f12111a8f92889eeade56392 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:23:19 +0530
Subject: [PATCH 360/615] Add recursive optional dependencies

---
 setup.py | 10 +---------
 1 file changed, 1 insertion(+), 9 deletions(-)

diff --git a/setup.py b/setup.py
index 1ec40d9637..9ff587f5ea 100644
--- a/setup.py
+++ b/setup.py
@@ -271,17 +271,9 @@ def compile_KLU():
         ],
         "odes": ["scikits.odes"],
         "all": [
-            "anytree>=2.12.0",
             "autograd>=1.6.2",
-            "pandas>=2.0.3",
             "scikit-fem>=8.1.0",
-            "imageio>=2.32.0",
-            "pybtex>=0.24.0",
-            "sympy>=1.12",
-            "bpx",
-            "tqdm",
-            "matplotlib>=3.7.3",
-            "jupyter",
+            "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]"
         ],
     },
     entry_points={

From 66037ab035beb3070d82b8ff37ced1ccb61e743f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 02:54:56 +0530
Subject: [PATCH 361/615] Remove bounds for `xarray` and `pandas`

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 9ff587f5ea..26396805c8 100644
--- a/setup.py
+++ b/setup.py
@@ -206,7 +206,7 @@ def compile_KLU():
         "numpy>=1.24.4",
         "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray>=2023.1.0",
+        "xarray",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=2.0.3",
+            "pandas",
         ],
         "jax": [
             "jax==0.4.8",

From f7266365d5b183955421a67c54d537aced97008c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:16:24 +0530
Subject: [PATCH 362/615] Use `python -m nox` instead of `pipx run nox`

---
 .github/workflows/run_periodic_tests.yml | 18 ++++++------
 .github/workflows/test_on_push.yml       | 36 ++++++++++++------------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 8e3a8b2644..06c0f0fb68 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -78,42 +78,42 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          python -m pip install --upgrade pip wheel setuptools
+          python -m pip install --upgrade pip wheel setuptools nox
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
-        run: pipx run nox -s unit
+        run: python -m nox -s unit
 
       - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
-        run: pipx run nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
-        run: pipx run nox -s integration
+        run: python -m nox -s integration
 
       - name: Install docs dependencies and run doctests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s doctests
+        run: python -m nox -s doctests
 
       - name: Check if the documentation can be built
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s docs
+        run: python -m nox -s docs
 
       - name: Install dev dependencies and run example tests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
       - name: Run example scripts tests
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts
 
   #M-series Mac Mini
   build-apple-mseries:
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 38735a15f5..09a98b1131 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -89,7 +89,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -107,10 +107,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: pipx run nox -s unit
+        run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
   check_coverage:
@@ -146,7 +146,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -162,10 +162,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
-        run: pipx run nox -s coverage
+        run: python -m nox -s coverage
 
       - name: Upload coverage report
         uses: codecov/codecov-action@v3.1.4
@@ -225,7 +225,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -243,10 +243,10 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
-        run: pipx run nox -s integration
+        run: python -m nox -s integration
 
 # Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
@@ -283,13 +283,13 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
-        run: pipx run nox -s doctests
+        run: python -m nox -s doctests
 
       - name: Check if the documentation can be built for GNU/Linux with Python 3.11
-        run: pipx run nox -s docs
+        run: python -m nox -s docs
 
   # Runs only on Ubuntu with Python 3.11
   run_example_tests:
@@ -325,7 +325,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -341,10 +341,10 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -380,7 +380,7 @@ jobs:
 
       - name: Install standard Python dependencies
         run: |
-          pip install --upgrade pip wheel setuptools
+          pip install --upgrade pip wheel setuptools nox
 
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
@@ -396,7 +396,7 @@ jobs:
           key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts

From 363c6f11d0cf01b503c92441e8e6dfc7eeb029e5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:24:28 +0530
Subject: [PATCH 363/615] Temporarily remove cache, let Linux tests run

---
 .github/workflows/test_on_push.yml | 124 ++++++++++++++---------------
 1 file changed, 62 insertions(+), 62 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 09a98b1131..eb366a024a 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -91,19 +91,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   if: matrix.os == 'ubuntu-latest'
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -148,18 +148,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -227,19 +227,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        if: matrix.os == 'ubuntu-latest'
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   if: matrix.os == 'ubuntu-latest'
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -327,18 +327,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -382,18 +382,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      - name: Cache pybamm-requires nox environment for GNU/Linux
-        uses: actions/cache@v3
-        with:
-          path: |
-            # Repository files
-            ${{ github.workspace }}/pybind11/
-            ${{ github.workspace }}/install_KLU_Sundials/
-            # Headers and dynamic library files for SuiteSparse and SUNDIALS
-            ${{ env.HOME }}/.local/lib/
-            ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      # - name: Cache pybamm-requires nox environment for GNU/Linux
+      #   uses: actions/cache@v3
+      #   with:
+      #     path: |
+      #       # Repository files
+      #       ${{ github.workspace }}/pybind11/
+      #       ${{ github.workspace }}/install_KLU_Sundials/
+      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
+      #       ${{ env.HOME }}/.local/lib/
+      #       ${{ env.HOME }}/.local/include/
+      #       ${{ env.HOME }}/.local/examples/
+      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 7c15368aa80d3ed3aa08ae3e0c41682363e06dd3 Mon Sep 17 00:00:00 2001
From: Ferran Brosa Planella <Ferran.Brosa-Planella@warwick.ac.uk>
Date: Thu, 16 Nov 2023 11:07:58 +0000
Subject: [PATCH 364/615] Fix bug with identical steps with different end times
 (#3516)

* fix bug with identical steps with different end times

* add copy method for steps

* undo testing changes

* fix failing tests

* update CHANGELOG

* remove copy method as it is unused

* remove raw termination as unused
---
 CHANGELOG.md                                   |  1 +
 pybamm/experiment/experiment.py                |  6 +++---
 pybamm/simulation.py                           | 13 +++++++++----
 tests/unit/test_experiments/test_experiment.py | 12 ++++++++++--
 4 files changed, 23 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index d615b3d714..483ca91a5e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Bug fixes
 
+- Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
 # [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index d1c45015b6..9b02e3a20f 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -78,7 +78,7 @@ def __init__(
         self.operating_conditions_cycles = operating_conditions_cycles
         self.cycle_lengths = [len(cycle) for cycle in operating_conditions_cycles]
 
-        operating_conditions_steps_unprocessed = self._set_next_start_time(
+        self.operating_conditions_steps_unprocessed = self._set_next_start_time(
             [cond for cycle in operating_conditions_cycles for cond in cycle]
         )
 
@@ -89,7 +89,7 @@ def __init__(
         self.temperature = _convert_temperature_to_kelvin(temperature)
 
         processed_steps = {}
-        for step in operating_conditions_steps_unprocessed:
+        for step in self.operating_conditions_steps_unprocessed:
             if repr(step) in processed_steps:
                 continue
             elif isinstance(step, str):
@@ -106,7 +106,7 @@ def __init__(
 
         self.operating_conditions_steps = [
             processed_steps[repr(step)]
-            for step in operating_conditions_steps_unprocessed
+            for step in self.operating_conditions_steps_unprocessed
         ]
 
         # Save the processed unique steps and the processed operating conditions
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 42bda08e31..f9aebb1c54 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -774,14 +774,19 @@ def solve(
                     # human-intuitive
                     op_conds = self.experiment.operating_conditions_steps[idx]
 
+                    # Hacky patch to allow correct processing of end_time and next_starting time
+                    # For efficiency purposes, op_conds treats identical steps as the same object
+                    # regardless of the initial time. Should be refactored as part of #3176
+                    op_conds_unproc = self.experiment.operating_conditions_steps_unprocessed[idx]
+
                     start_time = current_solution.t[-1]
 
                     # If step has an end time, dt must take that into account
-                    if op_conds.end_time:
+                    if getattr(op_conds_unproc, "end_time", None):
                         dt = min(
                             op_conds.duration,
                             (
-                                op_conds.end_time
+                                op_conds_unproc.end_time
                                 - (
                                     initial_start_time
                                     + timedelta(seconds=float(start_time))
@@ -834,9 +839,9 @@ def solve(
                     step_termination = step_solution.termination
 
                     # Add a padding rest step if necessary
-                    if op_conds.next_start_time is not None:
+                    if getattr(op_conds_unproc, "next_start_time", None) is not None:
                         rest_time = (
-                            op_conds.next_start_time
+                            op_conds_unproc.next_start_time
                             - (
                                 initial_start_time
                                 + timedelta(seconds=float(step_solution.t[-1]))
diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py
index 23548be433..ec1a1cbeae 100644
--- a/tests/unit/test_experiments/test_experiment.py
+++ b/tests/unit/test_experiments/test_experiment.py
@@ -183,41 +183,49 @@ def test_no_initial_start_time(self):
             )
 
     def test_set_next_start_time(self):
-        # Defined dummy experiment to access _set_next_start_time
-        experiment = pybamm.Experiment(["Rest for 1 hour"])
         raw_op = [
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 8, 0)
             ),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 12, 0)
             ),
             pybamm.step._Step("current", 1, duration=3600, start_time=None),
+            pybamm.step._Step("voltage", 2.5, duration=3600, start_time=None),
             pybamm.step._Step(
                 "current", 1, duration=3600, start_time=datetime(2023, 1, 1, 15, 0)
             ),
         ]
+        experiment = pybamm.Experiment(raw_op)
         processed_op = experiment._set_next_start_time(raw_op)
 
         expected_next = [
+            None,
             datetime(2023, 1, 1, 12, 0),
             None,
+            None,
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
         expected_end = [
             datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 12, 0),
+            datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             datetime(2023, 1, 1, 15, 0),
             None,
         ]
 
+        # Test method directly
         for next, end, op in zip(expected_next, expected_end, processed_op):
             # useful form for debugging
             self.assertEqual(op.next_start_time, next)
             self.assertEqual(op.end_time, end)
 
+        # TODO: once #3176 is completed, the test should pass for
+        # operating_conditions_steps (or equivalent) as well
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From b25e3ee21533605bb76d01428b5599be2e373d20 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:26:09 +0530
Subject: [PATCH 365/615] Undo cache changes

This reverts commit 363c6f11d0cf01b503c92441e8e6dfc7eeb029e5.
---
 .github/workflows/test_on_push.yml | 124 ++++++++++++++---------------
 1 file changed, 62 insertions(+), 62 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index eb366a024a..09a98b1131 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -91,19 +91,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   if: matrix.os == 'ubuntu-latest'
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        if: matrix.os == 'ubuntu-latest'
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -148,18 +148,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -227,19 +227,19 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   if: matrix.os == 'ubuntu-latest'
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        if: matrix.os == 'ubuntu-latest'
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         if: matrix.os == 'ubuntu-latest'
@@ -327,18 +327,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -382,18 +382,18 @@ jobs:
         run: |
           pip install --upgrade pip wheel setuptools nox
 
-      # - name: Cache pybamm-requires nox environment for GNU/Linux
-      #   uses: actions/cache@v3
-      #   with:
-      #     path: |
-      #       # Repository files
-      #       ${{ github.workspace }}/pybind11/
-      #       ${{ github.workspace }}/install_KLU_Sundials/
-      #       # Headers and dynamic library files for SuiteSparse and SUNDIALS
-      #       ${{ env.HOME }}/.local/lib/
-      #       ${{ env.HOME }}/.local/include/
-      #       ${{ env.HOME }}/.local/examples/
-      #     key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
+      - name: Cache pybamm-requires nox environment for GNU/Linux
+        uses: actions/cache@v3
+        with:
+          path: |
+            # Repository files
+            ${{ github.workspace }}/pybind11/
+            ${{ github.workspace }}/install_KLU_Sundials/
+            # Headers and dynamic library files for SuiteSparse and SUNDIALS
+            ${{ env.HOME }}/.local/lib/
+            ${{ env.HOME }}/.local/include/
+            ${{ env.HOME }}/.local/examples/
+          key: nox-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 72773c43b60b27646db40469a4bca41eebc31a2b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:26:44 +0530
Subject: [PATCH 366/615] Undo `pip` cache changes

This reverts commit f87b6bf5c8306581cdc4ed1e2f4fda3d61f077e4.
---
 .github/workflows/test_on_push.yml | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 09a98b1131..f007b38e33 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -86,6 +86,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -143,6 +145,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -222,6 +226,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -280,6 +286,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -322,6 +330,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |
@@ -377,6 +387,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.11
+          cache: 'pip'
+          cache-dependency-path: setup.py
 
       - name: Install standard Python dependencies
         run: |

From e63346983e4fbdf396c5f09b7ab0ac9cf1abe61a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 16 Nov 2023 17:32:00 +0530
Subject: [PATCH 367/615] Re-introduce `xarray` and `pandas` lower bounds

---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 26396805c8..f6dd93960f 100644
--- a/setup.py
+++ b/setup.py
@@ -206,7 +206,7 @@ def compile_KLU():
         "numpy>=1.24.4",
         "scipy>=1.10.1",
         "casadi>=3.6.3",
-        "xarray",
+        "xarray>=23.1.0",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas",
+            "pandas>=2.0.3",
         ],
         "jax": [
             "jax==0.4.8",

From afa187e6bb0e3f33e62ea40ff6f29a05e3c99779 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Thu, 16 Nov 2023 16:48:05 +0000
Subject: [PATCH 368/615] Update CHANGELOG

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 483ca91a5e..259980804a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Features
+
+- Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
+
 ## Bug fixes
 
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))

From b7453175ef944ff0063e17998f53ef0a846c32ec Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 16 Nov 2023 16:48:46 +0000
Subject: [PATCH 369/615] style: pre-commit fixes

---
 pybamm/models/base_model.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index fa87509f03..ed26a9062a 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -10,7 +10,6 @@
 import numpy as np
 
 import pybamm
-from pybamm.expression_tree.operations.latexify import Latexify
 from pybamm.expression_tree.operations.serialise import Serialise
 from pybamm.util import have_optional_dependency
 

From 73a5132e80ffcdd992dceda355bb96212d3d12c8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:12:29 +0530
Subject: [PATCH 370/615] Update python version bounds and add classifier

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index fca5b83de8..c5dad3fae1 100644
--- a/setup.py
+++ b/setup.py
@@ -186,7 +186,7 @@ def compile_KLU():
     },
     package_data={"pybamm": pybamm_data},
     # Python version
-    python_requires=">=3.8,<3.12",
+    python_requires=">=3.8,<3.13",
     classifiers=[
         "Development Status :: 5 - Production/Stable",
         "Intended Audience :: Developers",
@@ -199,6 +199,7 @@ def compile_KLU():
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
         "Topic :: Scientific/Engineering",
     ],
     # List of dependencies

From 93915b3df43a9c9170798946470f9f1d8cd1d74f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:12:58 +0530
Subject: [PATCH 371/615] Update installation instructions to add 3.12

---
 docs/source/user_guide/installation/GNU-linux.rst           | 4 ++--
 docs/source/user_guide/installation/install-from-source.rst | 2 +-
 docs/source/user_guide/installation/windows.rst             | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index e66c3c2291..66df93d939 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -6,7 +6,7 @@ GNU-Linux & MacOS
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
@@ -50,7 +50,7 @@ User install
 
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution Python files.
-First, make sure you are using Python 3.8, 3.9, 3.10, or 3.11.
+First, make sure you are using Python 3.8, 3.9, 3.10, 3.11, or 3.12.
 To create a virtual environment ``env`` within your current directory type:
 
 .. code:: bash
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index fb448950bf..41e89a959b 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -25,7 +25,7 @@ or download the source archive on the repository's homepage.
 
 To install PyBaMM, you will need:
 
-- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, and 3.11)
+- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, 3.11, and 3.12)
 - The Python headers file for your current Python version.
 - A BLAS library (for instance `openblas <https://www.openblas.net/>`_).
 - A C compiler (ex: ``gcc``).
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6ff48293bd..08f261f72f 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -6,7 +6,7 @@ Windows
 Prerequisites
 -------------
 
-To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use and/or contribute to PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to

From 92a298be0b91ae20d7615fe4d4f7712792ba5e26 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:13:07 +0530
Subject: [PATCH 372/615] Bump RTD docs to 3.12

---
 .readthedocs.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f907ac23d5..fb84bce9cb 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -24,7 +24,7 @@ build:
     - "graphviz"
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
     # You can also specify other tool versions:
     # nodejs: "19"
     # rust: "1.64"

From 7e451a36a9bda2becfc926346c7cdfb7c3ae2acd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:14:28 +0530
Subject: [PATCH 373/615] Bump Python version in workflows

for PyPI pushes, scheduled tests, and tests on PRs
---
 .github/workflows/publish_pypi.yml       |  2 +-
 .github/workflows/run_periodic_tests.yml | 16 ++++----
 .github/workflows/test_on_push.yml       | 52 ++++++++++++------------
 3 files changed, 35 insertions(+), 35 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 7d01fe0bee..be80007c67 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -138,7 +138,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Install dependencies
         run: pip install --upgrade pip setuptools wheel build
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f6e51bc11b..b3c265c829 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -36,7 +36,7 @@ jobs:
       - name: Setup python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -50,7 +50,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
@@ -92,16 +92,16 @@ jobs:
         if: matrix.os == 'ubuntu-latest'
         run: pipx run nox -s pybamm-requires
 
-      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
-        if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
+      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, 3.10, and 3.11 and for macOS and Windows with all Python versions
+        if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.12) || (matrix.os != 'ubuntu-latest')
         run: pipx run nox -s unit
 
-      - name: Run unit tests for GNU/Linux with Python 3.11 and generate coverage report
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
+      - name: Run unit tests for GNU/Linux with Python 3.12 and generate coverage report
+        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.12
         run: pipx run nox -s coverage
 
       - name: Upload coverage report
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
+        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.12
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
@@ -132,7 +132,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index db71c32586..89f44ef3a4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -23,7 +23,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -37,11 +37,11 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
-        # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        # We check coverage on Ubuntu with Python 3.12, so we skip unit tests for it here
         exclude:
           - os: ubuntu-latest
-            python-version: "3.11"
+            python-version: "3.12"
     name: Unit tests (${{ matrix.os }} / Python ${{ matrix.python-version }})
 
     steps:
@@ -116,13 +116,13 @@ jobs:
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: pipx run nox -s unit
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   check_coverage:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Coverage tests (ubuntu-latest / Python 3.11)
+    name: Coverage tests (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -142,11 +142,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
           cache-dependency-path: setup.py
 
@@ -171,7 +171,7 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
 
-      - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
+      - name: Run unit tests for Ubuntu with Python 3.12 and generate coverage report
         run: pipx run nox -s coverage
 
       - name: Upload coverage report
@@ -184,7 +184,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     name: Integration tests (${{ matrix.os }} / Python ${{ matrix.python-version }})
 
     steps:
@@ -259,14 +259,14 @@ jobs:
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: pipx run nox -s integration
 
-# Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
+# Runs only on Ubuntu with Python 3.12. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
   run_doctests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Doctests (ubuntu-latest / Python 3.11)
+    name: Doctests (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -286,11 +286,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
           cache-dependency-path: setup.py
 
@@ -299,19 +299,19 @@ jobs:
           pip install --upgrade pip wheel setuptools
           pip install -e .[all,docs]
 
-      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
+      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.12
         run: pipx run nox -s doctests
 
-      - name: Check if the documentation can be built for GNU/Linux with Python 3.11
+      - name: Check if the documentation can be built for GNU/Linux with Python 3.12
         run: pipx run nox -s docs
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_example_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example notebooks (ubuntu-latest / Python 3.11)
+    name: Example notebooks (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -331,11 +331,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
           cache-dependency-path: setup.py
 
@@ -360,16 +360,16 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
 
-      - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.11
+      - name: Install dev dependencies and run example tests for GNU/Linux with Python 3.12
         run: pipx run nox -s examples
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_scripts_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example scripts (ubuntu-latest / Python 3.11)
+    name: Example scripts (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -389,11 +389,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
           cache-dependency-path: setup.py
 
@@ -418,5 +418,5 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
 
-      - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
+      - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.12
         run: pipx run nox -s scripts

From 0db07c70f0347f452fd6faef2e9bf8f61357d30d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:15:05 +0530
Subject: [PATCH 374/615] Add a custom `install_PyBaMM` callable

---
 noxfile.py | 65 ++++++++++++++++++++++++++++++++++++++++--------------
 1 file changed, 49 insertions(+), 16 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..7c77680800 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -22,7 +22,7 @@
 
 def set_environment_variables(env_dict, session):
     """
-    Sets environment variables for a nox session object.
+    Sets environment variables for a nox Session object.
 
     Parameters
     -----------
@@ -36,6 +36,38 @@ def set_environment_variables(env_dict, session):
         session.env[key] = value
 
 
+def install_PyBaMM(session, extras):
+    """
+    Installs PyBaMM in editable mode along with its dependencies for
+    a nox Session object.
+
+    Parameters
+    -----------
+        session : nox.Session
+            The session to install dependencies for.
+        extras : list[str]
+            A list of dependencies to install. The current
+            options are:
+            [all,dev,jax,odes,docs]
+
+    """
+    # TODO: Remove this mess once [odes] brings support for Python 3.12
+    # See https://github.com/bmcage/odes/issues/162 for details.
+
+    # FIXME: Bump [jax] to get compatibility with Python 3.12 and Windows in this PR
+
+    # For now: silently remove [odes] and [jax] if specified, when running on
+    # 1. Linux and macOS on Python 3.12
+    # 2. Windows on any Python version
+
+    if sys.platform == "win32" or sys.version_info >= (3, 12):
+        if "odes" in extras or "jax" in extras:
+            extras.remove("odes")
+            extras.remove("jax")
+
+    session.install("-e", f".[{','.join(extras)}]", silent=False)
+
+
 @nox.session(name="pybamm-requires")
 def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
@@ -60,10 +92,10 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        install_PyBaMM(session=session, extras=["all","odes","jax"])
+    else:
+        install_PyBaMM(session=session, extras=["all"])
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -73,16 +105,17 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux":
-        session.install("-e", ".[odes]", silent=False)
+        install_PyBaMM(session=session, extras=["all","odes","jax"])
+    else:
+        install_PyBaMM(session=session, extras=["all"])
     session.run("python", "run-tests.py", "--integration")
 
 
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
-    session.install("-e", ".[all,docs]", silent=False)
+    install_PyBaMM(session=session, extras=["all","docs"])
     session.run("python", "run-tests.py", "--doctest")
 
 
@@ -90,10 +123,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        install_PyBaMM(session=session, extras=["all","odes","jax"])
+    else:
+        install_PyBaMM(session=session, extras=["all"])
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -101,7 +134,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all,dev]", silent=False)
+    install_PyBaMM(session=session, extras=["all","dev"])
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
@@ -110,7 +143,7 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
+    install_PyBaMM(session=session, extras=["all"])
     session.run("python", "run-tests.py", "--scripts")
 
 
@@ -137,10 +170,10 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        install_PyBaMM(session=session, extras=["all","odes","jax"])
+    else:
+        install_PyBaMM(session=session, extras=["all"])
     session.run("python", "run-tests.py", "--all")
 
 
@@ -148,7 +181,7 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    session.install("-e", ".[all,docs]", silent=False)
+    install_PyBaMM(session=session, extras=["all","docs"])
     session.chdir("docs")
     # Local development
     if session.interactive:

From 8d6a16c21cc6d412e0cb05c936b4396bd1838394 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 03:17:34 +0530
Subject: [PATCH 375/615] Check coverage on Python 3.11 for now, run unit tests
 on 3.12

---
 .github/workflows/test_on_push.yml | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 89f44ef3a4..59d1805cdc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -38,10 +38,11 @@ jobs:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
-        # We check coverage on Ubuntu with Python 3.12, so we skip unit tests for it here
+        # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here
+        # TODO: check coverage with Python 3.12 when [odes] and [jax] support it
         exclude:
           - os: ubuntu-latest
-            python-version: "3.12"
+            python-version: "3.11"
     name: Unit tests (${{ matrix.os }} / Python ${{ matrix.python-version }})
 
     steps:
@@ -116,13 +117,14 @@ jobs:
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: pipx run nox -s unit
 
-  # Runs only on Ubuntu with Python 3.12
+  # Runs only on Ubuntu with Python 3.11
+  # TODO: check coverage with Python 3.12 when [odes] and [jax] support it
   check_coverage:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Coverage tests (ubuntu-latest / Python 3.12)
+    name: Coverage tests (ubuntu-latest / Python 3.11)
 
     steps:
       - name: Check out PyBaMM repository
@@ -142,11 +144,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.12
+      - name: Set up Python 3.11
         id: setup-python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.12
+          python-version: 3.11
           cache: 'pip'
           cache-dependency-path: setup.py
 
@@ -171,7 +173,7 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: pipx run nox -s pybamm-requires
 
-      - name: Run unit tests for Ubuntu with Python 3.12 and generate coverage report
+      - name: Run unit tests for Ubuntu with Python 3.11 and generate coverage report
         run: pipx run nox -s coverage
 
       - name: Upload coverage report

From 0755c35b4a00975793b50aca5b76834604067ef5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 17 Nov 2023 14:10:12 +0530
Subject: [PATCH 376/615] Bump versions according to SPEC 0000

Co-Authored-By: Saransh Chopra <saransh0701@gmail.com>
---
 setup.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/setup.py b/setup.py
index f6dd93960f..886378f44f 100644
--- a/setup.py
+++ b/setup.py
@@ -203,10 +203,10 @@ def compile_KLU():
     ],
     # List of dependencies
     install_requires=[
-        "numpy>=1.24.4",
-        "scipy>=1.10.1",
+        "numpy>=1.23.5",
+        "scipy>=1.9.3",
         "casadi>=3.6.3",
-        "xarray>=23.1.0",
+        "xarray>=2022.6.0",
         "anytree>=2.12.0",
     ],
     extras_require={
@@ -236,7 +236,7 @@ def compile_KLU():
             # on systems without an attached display, it should never be imported
             # outside of plot() methods.
             # Should not be imported
-            "matplotlib>=3.7.3",
+            "matplotlib>=3.6.0",
         ],
         "cite": [
             "pybtex>=0.24.0",
@@ -263,7 +263,7 @@ def compile_KLU():
             "nbmake",
         ],
         "pandas": [
-            "pandas>=2.0.3",
+            "pandas>=1.5.0",
         ],
         "jax": [
             "jax==0.4.8",

From 553c5621e3bb9be7e9d2b29ce5e5076babee4318 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 17 Nov 2023 09:33:05 -0500
Subject: [PATCH 377/615] #3532 fix bug

---
 pybamm/models/full_battery_models/base_battery_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 971bd1a880..ee3e0b5c6f 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -603,7 +603,7 @@ def __init__(self, extra_options):
                     "current collectors in a half-cell configuration"
                 )
 
-        if options["particle phases"] != "1":
+        if options["particle phases"] not in ["1", ("1", "1")]:
             if not (
                 options["surface form"] != "false"
                 and options["particle size"] == "single"

From 0834d795a9e599617056e6e2da52b10f7e08530d Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Fri, 17 Nov 2023 14:36:51 +0000
Subject: [PATCH 378/615] fix hysteresis option bug

---
 .../submodels/interface/base_interface.py     |  5 +++--
 .../submodels/particle/base_particle.py       |  5 +++--
 .../base_lithium_ion_tests.py                 | 19 +++++++++++++++++++
 3 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py
index 190130064f..b7e160ee2f 100644
--- a/pybamm/models/submodels/interface/base_interface.py
+++ b/pybamm/models/submodels/interface/base_interface.py
@@ -110,9 +110,10 @@ def _get_exchange_current_density(self, variables):
                     c_e = c_e.orphans[0]
                     T = T.orphans[0]
             # Get main reaction exchange-current density (may have empirical hysteresis)
-            if domain_options["exchange-current density"] == "single":
+            j0_option = getattr(domain_options, self.phase)["exchange-current density"]
+            if j0_option == "single":
                 j0 = phase_param.j0(c_e, c_s_surf, T)
-            elif domain_options["exchange-current density"] == "current sigmoid":
+            elif j0_option == "current sigmoid":
                 current = variables["Total current density [A.m-2]"]
                 k = 100
                 if Domain == "Positive":
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index ad751c3911..dd5a94afc6 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -35,9 +35,10 @@ def _get_effective_diffusivity(self, c, T, current):
         domain_options = getattr(self.options, domain)
 
         # Get diffusivity (may have empirical hysteresis)
-        if domain_options["diffusivity"] == "single":
+        diffusivity_option = getattr(domain_options, self.phase)["diffusivity"]
+        if diffusivity_option == "single":
             D = phase_param.D(c, T)
-        elif domain_options["diffusivity"] == "current sigmoid":
+        elif diffusivity_option == "current sigmoid":
             k = 100
             if Domain == "Positive":
                 lithiation_current = current
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6815698588..48832c4726 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -389,3 +389,22 @@ def test_well_posed_current_sigmoid_diffusivity(self):
     def test_well_posed_psd(self):
         options = {"particle size": "distribution", "surface form": "algebraic"}
         self.check_well_posedness(options)
+
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "exchange current density": (
+                ("current sigmoid", "single"),
+                "current sigmoid",
+            ),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        options = {
+            "particle phases": ("2", "1"),
+            "diffusivity": (("current sigmoid", "current sigmoid"), "current sigmoid"),
+            "open-circuit potential": (("current sigmoid", "single"), "single"),
+        }
+        self.check_well_posedness(options)

From 797eb9c158a6164da779d89741eb69ccaf6617bd Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Fri, 17 Nov 2023 15:09:26 +0000
Subject: [PATCH 379/615] fix tests

---
 .../test_lithium_ion/base_lithium_ion_tests.py              | 2 +-
 .../test_lithium_ion/test_newman_tobias.py                  | 6 ++++++
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 48832c4726..f4e3c3cceb 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -393,7 +393,7 @@ def test_well_posed_psd(self):
     def test_well_posed_composite_kinetic_hysteresis(self):
         options = {
             "particle phases": ("2", "1"),
-            "exchange current density": (
+            "exchange-current density": (
                 ("current sigmoid", "single"),
                 "current sigmoid",
             ),
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
index be7d2499c6..4d65804156 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_newman_tobias.py
@@ -22,6 +22,12 @@ def test_well_posed_particle_phases(self):
     def test_well_posed_particle_phases_sei(self):
         pass  # skip this test
 
+    def test_well_posed_composite_kinetic_hysteresis(self):
+        pass  # skip this test
+
+    def test_well_posed_composite_diffusion_hysteresis(self):
+        pass  # skip this test
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 74e924a7eef9ff3d8376567ad4c14f6d1f6c5d48 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 17 Nov 2023 10:15:52 -0500
Subject: [PATCH 380/615] #3532 fix example notebook

---
 .../examples/notebooks/models/lithium-plating.ipynb       | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 57049a0ea7..c2e8b198c6 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -159,7 +159,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -171,6 +171,8 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",
     "\n",
@@ -274,7 +276,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]
@@ -327,7 +329,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 936x648 with 4 Axes>"
       ]

From 9b73f8eacc76b170842eeb2e15021b4a0c886ebf Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 18 Nov 2023 03:33:06 +0530
Subject: [PATCH 381/615] #3443 Add various versions for `jax` and `jaxlib`
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

1. Add support for Python 3.11 on aarch64 containers
2. Keep Python 3.8 support on older version
3. Add Python 3.9–3.11 support on newer version (same as the one for point 1)
4. Add support for CPU-only Windows installation
5. Pin all versions so as to not break anything.
---
 setup.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index 886378f44f..c6ddb9d83f 100644
--- a/setup.py
+++ b/setup.py
@@ -265,15 +265,26 @@ def compile_KLU():
         "pandas": [
             "pandas>=1.5.0",
         ],
+        # Note: jax and jaxlib must be pinned to a specific version
+        # to avoid upstream breaking changes.
         "jax": [
-            "jax==0.4.8",
-            "jaxlib==0.4.7",
+            # 0.4.18 provides support for Jax on aarch64 containers
+            # via the PyBaMM images on Docker Hub which come with
+            # Python 3.11 installed.
+            # It also provides support for CPU-only Jax on Windows.
+            "jax==0.4.18; python_version >= '3.9'",
+            "jaxlib==0.4.18; python_version >= '3.9'",
+            # Jax 0.4.13 was the last version to support Python 3.8.
+            # Support for CPU-only Windows was added in 0.4.13, so
+            # this version supports Windows too.
+            "jax==0.4.13; python_version < '3.9'",
+            "jaxlib==0.4.13; python_version < '3.9'",
         ],
         "odes": ["scikits.odes"],
         "all": [
             "autograd>=1.6.2",
             "scikit-fem>=8.1.0",
-            "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]"
+            "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]",
         ],
     },
     entry_points={

From 8a049d9864e7ef1eaaa0fe93b4a0217ff087e10a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 18 Nov 2023 03:34:33 +0530
Subject: [PATCH 382/615] #3312 #3443 Build both arm64 + amd64 images for all
 solvers

---
 .github/workflows/docker.yml | 27 +--------------------------
 1 file changed, 1 insertion(+), 26 deletions(-)

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index b6994795d6..f92ee76c9e 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -48,8 +48,7 @@ jobs:
             echo "tag=all" >> "$GITHUB_OUTPUT"
           fi
 
-      - name: Build and push Docker image to Docker Hub (no solvers)
-        if: matrix.build-args == 'No solvers'
+      - name: Build and push Docker image to Docker Hub (${{ matrix.build-args }})
         uses: docker/build-push-action@v5
         with:
           context: .
@@ -58,29 +57,5 @@ jobs:
           push: true
           platforms: linux/amd64, linux/arm64
 
-      - name: Build and push Docker image to Docker Hub (with ODES and IDAKLU solvers)
-        if: matrix.build-args == 'ODES' || matrix.build-args == 'IDAKLU'
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
-          push: true
-          build-args: ${{ matrix.build-args }}=true
-          platforms: linux/amd64, linux/arm64
-
-      - name: Build and push Docker image to Docker Hub (with ALL and JAX solvers)
-        if: matrix.build-args == 'ALL' || matrix.build-args == 'JAX'
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          file: scripts/Dockerfile
-          tags: pybamm/pybamm:${{ steps.tags.outputs.tag }}
-          push: true
-          build-args: ${{ matrix.build-args }}=true
-          # exclude arm64 for JAX and ALL builds for now, see
-          # https://github.com/google/jax/issues/13608
-          platforms: linux/amd64
-
       - name: List built image(s)
         run: docker images

From 74ced7e92af78839266d3dfe7176faad87287dc5 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Nov 2023 13:53:22 +0000
Subject: [PATCH 383/615] style: pre-commit fixes

---
 docs/source/examples/notebooks/models/lithium-plating.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 63ba992818..8969f237ef 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -148,7 +148,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",

From c3af572136b996a6c1dc5cae520309a82721219d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 20:22:09 +0530
Subject: [PATCH 384/615] Lint changes, pre-install wheel plus prerequisites

---
 noxfile.py | 58 ++++++++++++++++++++++++++++++++----------------------
 1 file changed, 35 insertions(+), 23 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..a908ce4aca 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -17,7 +17,7 @@
     "SUNDIALS_INST": f"{homedir}/.local",
     "LD_LIBRARY_PATH": f"{homedir}/.local/lib:",
 }
-VENV_DIR = Path('./venv').resolve()
+VENV_DIR = Path("./venv").resolve()
 
 
 def set_environment_variables(env_dict, session):
@@ -121,13 +121,25 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
+    session.run(
+        python,
+        "-m",
+        "pip",
+        "install",
+        "--upgrade",
+        "pip",
+        "setuptools",
+        "wheel",
+        external=True,
+    )
     if sys.platform == "linux":
-        session.run(python,
-                    "-m",
-                    "pip",
-                    "install",
-                    ".[all,dev,jax,odes]",
-                    external=True,
+        session.run(
+            python,
+            "-m",
+            "pip",
+            "install",
+            ".[all,dev,jax,odes]",
+            external=True,
         )
     else:
         session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
@@ -153,26 +165,26 @@ def build_docs(session):
     # Local development
     if session.interactive:
         session.run(
-        "sphinx-autobuild",
-        "-j",
-        "auto",
-        "--open-browser",
-        "-qT",
-        ".",
-        f"{envbindir}/../tmp/html",
+            "sphinx-autobuild",
+            "-j",
+            "auto",
+            "--open-browser",
+            "-qT",
+            ".",
+            f"{envbindir}/../tmp/html",
         )
     # Runs in CI only, treating warnings as errors
     else:
         session.run(
-        "sphinx-build",
-        "-j",
-        "auto",
-        "-b",
-        "html",
-        "-W",
-        "--keep-going",
-        ".",
-        f"{envbindir}/../tmp/html",
+            "sphinx-build",
+            "-j",
+            "auto",
+            "-b",
+            "html",
+            "-W",
+            "--keep-going",
+            ".",
+            f"{envbindir}/../tmp/html",
         )
 
 

From 09c4942a7627e186762478d9161e84f38d0b71e0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 21:08:17 +0530
Subject: [PATCH 385/615] Bump new minimum versions of dependencies

---
 pyproject.toml | 31 ++++++++++++-------------------
 1 file changed, 12 insertions(+), 19 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 32912383f7..675a7de335 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,10 +32,11 @@ classifiers = [
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
-    "numpy>=1.16",
-    "scipy>=1.3",
-    "casadi>=3.6.0",
-    "xarray",
+    "numpy>=1.23.5",
+    "scipy>=1.9.3",
+    "casadi>=3.6.3",
+    "xarray>=2022.6.0",
+    "anytree>=2.12.0",
 ]
 
 [project.urls]
@@ -71,11 +72,11 @@ examples = [
 ]
 # Plotting functionality
 plot = [
-    "imageio>=2.9.0",
+    "imageio>=2.32.0",
     # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.
-    "matplotlib>=2.0",
+    "matplotlib>=3.6.0",
 ]
 # For the Citations class
 cite = [
@@ -83,7 +84,7 @@ cite = [
 ]
 # To generate LaTeX strings
 latexify = [
-    "sympy>=1.8",
+    "sympy>=1.12",
 ]
 # Battery Parameter eXchange format
 bpx = [
@@ -108,7 +109,7 @@ dev = [
 ]
 # Reading CSV files
 pandas =  [
-    "pandas>=0.24",
+    "pandas>=1.5.0",
 ]
 # For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
 jax = [
@@ -121,17 +122,9 @@ odes = [
 ]
 # Contains all optional dependencies, except for odes, jax, and dev dependencies
 all = [
-    "anytree>=2.4.3",
-    "autograd>=1.2",
-    "pandas>=0.24",
-    "scikit-fem>=0.2.0",
-    "imageio>=2.9.0",
-    "matplotlib>=2.0",
-    "pybtex>=0.24.0",
-    "sympy>=1.8",
-    "bpx",
-    "tqdm",
-    "jupyter",
+    "autograd>=1.6.2",
+    "scikit-fem>=8.1.0",
+    "pybamm[examples,plot,cite,latexify,bpx,tqdm,pandas]",
 ]
 
 [project.scripts]

From e51d73fc93814aa3396037aa3610447aee56e23e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 17:37:01 +0530
Subject: [PATCH 386/615] Bump version to v23.9

---
 CHANGELOG.md               | 11 ++++-------
 CITATION.cff               |  2 +-
 docs/_static/versions.json |  5 +++++
 pybamm/version.py          |  2 +-
 vcpkg.json                 |  2 +-
 5 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 483ca91a5e..4403405f90 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,13 +5,7 @@
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 
-# [v23.9rc1](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc1) - 2023-11-15
-
-- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
-- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
-- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
-
-# [v23.9rc0](https://github.com/pybamm-team/PyBaMM/tree/v23.9rc0) - 2023-10-31
+# [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
 ## Features
 
@@ -30,6 +24,9 @@
 
 ## Bug fixes
 
+- Fixed a bug where the JaxSolver would fails when using GPU support with no input parameters ([#3423](https://github.com/pybamm-team/PyBaMM/pull/3423))
+- Make pybamm importable with minimal dependencies ([#3044](https://github.com/pybamm-team/PyBaMM/pull/3044), [#3475](https://github.com/pybamm-team/PyBaMM/pull/3475))
+- Fixed a bug where supplying an initial soc did not work with half cell models ([#3456](https://github.com/pybamm-team/PyBaMM/pull/3456))
 - Fixed a bug where empty lists passed to QuickPlot resulted in an IndexError and did not return a meaningful error message ([#3359](https://github.com/pybamm-team/PyBaMM/pull/3359))
 - Fixed a bug where there was a missing thermal conductivity in the thermal pouch cell models ([#3330](https://github.com/pybamm-team/PyBaMM/pull/3330))
 - Fixed a bug that caused incorrect results of “{Domain} electrode thickness change [m]” due to the absence of dimension for the variable `electrode_thickness_change`([#3329](https://github.com/pybamm-team/PyBaMM/pull/3329)).
diff --git a/CITATION.cff b/CITATION.cff
index b7f68164fc..44f1c5d407 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9rc1"
+version: "23.9"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/docs/_static/versions.json b/docs/_static/versions.json
index 5c9bba7c17..675ecbcf88 100644
--- a/docs/_static/versions.json
+++ b/docs/_static/versions.json
@@ -9,6 +9,11 @@
         "version": "stable",
         "url": "https://docs.pybamm.org/en/stable/"
     },
+    {
+        "name": "v23.9",
+        "version": "23.9",
+        "url": "https://docs.pybamm.org/en/v23.9_a/"
+    },
     {
         "name": "v23.5",
         "version": "23.5",
diff --git a/pybamm/version.py b/pybamm/version.py
index e5cfaa0882..970be77f66 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9rc1"
+__version__ = "23.9"
diff --git a/vcpkg.json b/vcpkg.json
index de71a5a87d..f62c18ddd2 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9rc1",
+  "version-string": "23.9",
   "dependencies": [
     "casadi",
     {

From 9e94bdd45adc0f5477438a83eb495050ed7beac8 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 21:37:23 +0530
Subject: [PATCH 387/615] Fix schedule tests

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 06c0f0fb68..c58d5ca215 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -68,7 +68,7 @@ jobs:
 
       - name: Install macOS system dependencies
         if: matrix.os == 'macos-latest'
-        run:
+        run: |
           brew install graphviz openblas
           brew reinstall gcc
 

From cfc550b14d46621e01999a6209fc76200648921e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 20 Nov 2023 21:38:47 +0530
Subject: [PATCH 388/615] A sentence about package data and extra files

---
 CONTRIBUTING.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 6944cce074..b9800dcd61 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -413,16 +413,17 @@ wherever code is called that uses that citation (for example, in functions or in
 
 ### Installation
 
-Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/) (non-Windows). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
+Installation of PyBaMM and its dependencies is handled via [pip](https://pip.pypa.io/en/stable/) and [setuptools](http://setuptools.readthedocs.io/). It uses `CMake` to compile C++ extensions using [`pybind11`](https://pybind11.readthedocs.io/en/stable/) and [`casadi`](https://web.casadi.org/). The installation process is described in detail in the [source installation](https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html) page and is configured through the `CMakeLists.txt` file.
 
 Configuration files:
 
 ```
 setup.py
 pyproject.toml
+MANIFEST.in
 ```
 
-Note that this file must be kept in sync with the version number in [`pybamm/__init__.py`](https://github.com/pybamm-team/PyBaMM/blob/develop/pybamm/__init__.py).
+Note: `MANIFEST.in` is used to include and exclude non-Python files and auxiliary package data for PyBaMM when distributing it. If a file is not included in `MANIFEST.in`, it will not be included in the source distribution (SDist) and subsequently not be included in the binary distribution (wheel).
 
 ### Continuous Integration using GitHub Actions
 

From b04dcb5fec54042641a4c0761d6f5b1fdfef777e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 22:22:12 +0530
Subject: [PATCH 389/615] Fix governance link

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c7ca111873..474b528bb6 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ explore the effect of different battery designs and modeling assumptions under a
 
 [//]: # "numfocus-fiscal-sponsor-attribution"
 
-PyBaMM uses an [open governance model](./GOVERNANCE.md)
+PyBaMM uses an [open governance model](https://pybamm.org/governance/)
 and is fiscally sponsored by [NumFOCUS](https://numfocus.org/). Consider making
 a [tax-deductible donation](https://numfocus.org/donate-for-pybamm) to help the project
 pay for developer time, professional services, travel, workshops, and a variety of other needs.

From 7850587187a9719d8ccbb856f4073b8cf2e7bf82 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 20 Nov 2023 22:23:28 +0530
Subject: [PATCH 390/615] Add Martin to GOVERNANCE.md

---
 GOVERNANCE.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/GOVERNANCE.md b/GOVERNANCE.md
index f11b785106..aa97669187 100644
--- a/GOVERNANCE.md
+++ b/GOVERNANCE.md
@@ -23,6 +23,7 @@ handled on a case-by-case basis.
 - Scott Marquis
 - [Gregory Offer](https://www.imperial.ac.uk/people/gregory.offer)
 - [Valentin Sulzer](https://sites.google.com/view/valentinsulzer)
+- [Martin Robinson](https://www.sabsr3.ox.ac.uk/people/dr-martin-robinson)
 
 ## Advisory Committee
 

From d5dae10a0f95beef7c8cf02268df13027e3b389e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 20 Nov 2023 19:17:48 +0000
Subject: [PATCH 391/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.5 → v0.1.6](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.5...v0.1.6)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5871b334bf..ed837e6fdb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.5"
+    rev: "v0.1.6"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 0aa469b6f94cc69d57d9e47fd78187edf714233d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 02:00:52 +0530
Subject: [PATCH 392/615] Install `nox` with `pip` instead of `pipx`

---
 .github/workflows/run_periodic_tests.yml |  5 ++++-
 .github/workflows/test_on_push.yml       | 26 ++++++++++++++++++++----
 2 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f182ffaf99..4545dc26df 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -72,9 +72,12 @@ jobs:
         if: matrix.os == 'windows-latest'
         run: choco install graphviz --version=2.38.0.20190211
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index a0e3ffe4b2..2f7f94c9bc 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -90,6 +90,9 @@ jobs:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -106,7 +109,7 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run unit tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s unit
@@ -144,6 +147,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -222,6 +228,9 @@ jobs:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux and macOS
         uses: actions/cache@v3
         if: matrix.os != 'windows-latest'
@@ -238,7 +247,7 @@ jobs:
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
-        run: pipx run nox -s pybamm-requires
+        run: python -m nox -s pybamm-requires
 
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s integration
@@ -277,6 +286,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
         run: python -m nox -s doctests
 
@@ -316,6 +328,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -333,7 +348,7 @@ jobs:
         run: python -m nox -s pybamm-requires
 
       - name: Run example notebooks tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s examples
+        run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.11
   run_scripts_tests:
@@ -368,6 +383,9 @@ jobs:
           python-version: 3.11
           cache: 'pip'
 
+      - name: Install nox
+        run: python -m pip install nox
+
       - name: Cache pybamm-requires nox environment for GNU/Linux
         uses: actions/cache@v3
         with:
@@ -385,4 +403,4 @@ jobs:
         run: python -m nox -s pybamm-requires
 
       - name: Run example scripts tests for GNU/Linux with Python 3.11
-        run: pipx run nox -s scripts
+        run: python -m nox -s scripts

From 8e91218d3a7ac3f59267dfcda602efd06c8b3d2c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 02:16:45 +0530
Subject: [PATCH 393/615] Remove `brew update` from wheels and disable wheels
 on PRs

---
 .github/workflows/publish_pypi.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index fda75d4489..3073c95f09 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -2,7 +2,6 @@ name: Build and publish package to PyPI
 on:
   release:
     types: [published]
-  pull_request:
   schedule:
     # Run at 10 am UTC on day-of-month 1 and 15.
     - cron: "0 10 1,15 * *"
@@ -84,7 +83,6 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on macOS
         if: matrix.os == 'macos-latest'
         run: |
-          brew update
           brew install graphviz openblas libomp
           brew reinstall gcc
           python -m pip install cmake wget

From f1fd05f58b6f2e2918151429988cebb46059f5b0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 21 Nov 2023 04:34:29 +0530
Subject: [PATCH 394/615] Update version to 23.9

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 675a7de335..4569c7c6c3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.9rc0"
+version = "23.9"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]

From 2ee8da23ee24e9e946a60b0dd9e64070b1244552 Mon Sep 17 00:00:00 2001
From: Simon O'Kane <42972513+DrSOKane@users.noreply.github.com>
Date: Wed, 22 Nov 2023 15:48:10 +0000
Subject: [PATCH 395/615] Issue 3339 dead lithium (#3485)

* fixed tests

* Added graphite half-cell parameter files

* Revert "Added graphite half-cell parameter files"

This reverts commit 78001e81eecc38919364190940e095e0e51fab76.

* Revert "fixed tests"

This reverts commit cf53ff1d9e74eda7e68bc65b5dea5c18f7fcf872.

* ruff

* changelog

* coverage

* Fixed minor error in example notebook

* Removed duplicate entry from changelog
---
 CHANGELOG.md                                  |  1 +
 .../notebooks/models/lithium-plating.ipynb    |  4 ++--
 .../interface/lithium_plating/plating.py      | 22 +++++++++++++------
 3 files changed, 18 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4403405f90..2eea9ed7c0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,7 @@
 
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
+- The irreversible plating model now increments `f"{Domain} dead lithium concentration [mol.m-3]"`, not `f"{Domain} lithium plating concentration [mol.m-3]"` as it did previously. ([#3485](https://github.com/pybamm-team/PyBaMM/pull/3485))
 
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 8969f237ef..1e14513620 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -194,8 +194,8 @@
     "    axs[0,0].set_ylabel(\"Voltage [V]\")\n",
     "    axs[0,1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n",
     "    axs[0,1].legend(('Deintercalation current','Stripping current','Total current'))\n",
-    "    axs[1,0].set_ylabel(\"Plated lithium capacity [Ah]\")\n",
-    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [Ah]\")\n",
+    "    axs[1,0].set_ylabel(\"Plated lithium capacity [A.h]\")\n",
+    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n",
     "\n",
     "    for ax in axs.flat:\n",
     "        ax.set_xlabel(\"Time [minutes]\")\n",
diff --git a/pybamm/models/submodels/interface/lithium_plating/plating.py b/pybamm/models/submodels/interface/lithium_plating/plating.py
index a1828dcaa2..9f4de08d2f 100644
--- a/pybamm/models/submodels/interface/lithium_plating/plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/plating.py
@@ -115,18 +115,26 @@ def set_rhs(self, variables):
             ]
             L_sei = variables[f"{Domain} total SEI thickness [m]"]
 
-        # In the partially reversible plating model, coupling term turns reversible
-        # lithium into dead lithium. In other plating models, it is zero.
         lithium_plating_option = getattr(self.options, domain)["lithium plating"]
-        if lithium_plating_option == "partially reversible":
+        if lithium_plating_option == "reversible":
+            # In the reversible plating model, there is no dead lithium
+            dc_plated_Li = -a_j_stripping / self.param.F
+            dc_dead_Li = pybamm.Scalar(0)
+        elif lithium_plating_option == "irreversible":
+            # In the irreversible plating model, all plated lithium is dead lithium
+            dc_plated_Li = pybamm.Scalar(0)
+            dc_dead_Li = -a_j_stripping / self.param.F
+        elif lithium_plating_option == "partially reversible":
+            # In the partially reversible plating model, the coupling term turns
+            # reversible lithium into dead lithium over time.
             dead_lithium_decay_rate = self.param.dead_lithium_decay_rate(L_sei)
             coupling_term = dead_lithium_decay_rate * c_plated_Li
-        else:
-            coupling_term = pybamm.Scalar(0)
+            dc_plated_Li = -a_j_stripping / self.param.F - coupling_term
+            dc_dead_Li = coupling_term
 
         self.rhs = {
-            c_plated_Li: -a_j_stripping / self.param.F - coupling_term,
-            c_dead_Li: coupling_term,
+            c_plated_Li: dc_plated_Li,
+            c_dead_Li: dc_dead_Li,
         }
 
     def set_initial_conditions(self, variables):

From e2d849d80e6dc903428c322ab4d3037b9805bb5a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 00:16:51 +0530
Subject: [PATCH 396/615] #3443 Install `jax` extras in the same command

---
 noxfile.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 430ad59659..d0ca7fc6f6 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -62,8 +62,7 @@ def run_coverage(session):
     session.install("coverage", silent=False)
     session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        session.install("-e", ".[odes,jax]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -92,8 +91,7 @@ def run_unit(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        session.install("-e", ".[odes,jax]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -139,8 +137,7 @@ def run_tests(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+        session.install("-e", ".[odes, jax]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 

From e17163ffbce029bc15e2a8c3548b4c2ec287c3f4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 00:17:44 +0530
Subject: [PATCH 397/615] #3443 Bump to latest version of `jax` and `jaxlib`

tested with `--upgrade` and `--upgrade-strategy eager` plus `--no-cache-dir`
---
 setup.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index c6ddb9d83f..c6e7414829 100644
--- a/setup.py
+++ b/setup.py
@@ -272,8 +272,8 @@ def compile_KLU():
             # via the PyBaMM images on Docker Hub which come with
             # Python 3.11 installed.
             # It also provides support for CPU-only Jax on Windows.
-            "jax==0.4.18; python_version >= '3.9'",
-            "jaxlib==0.4.18; python_version >= '3.9'",
+            "jax==0.4.20; python_version >= '3.9'",
+            "jaxlib==0.4.20; python_version >= '3.9'",
             # Jax 0.4.13 was the last version to support Python 3.8.
             # Support for CPU-only Windows was added in 0.4.13, so
             # this version supports Windows too.

From 05d106158e987f038f7d0c21ac783e4166239004 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 00:20:10 +0530
Subject: [PATCH 398/615] #3443 Add Windows support via nox

---
 noxfile.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index d0ca7fc6f6..4444778162 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -60,9 +60,9 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
-    session.install("-e", ".[all]", silent=False)
+    session.install("-e", ".[all,jax]", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[odes,jax]", silent=False)
+        session.install("-e", ".[odes]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -89,9 +89,9 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
+    session.install("-e", ".[all,jax]", silent=False)
     if sys.platform == "linux":
-        session.install("-e", ".[odes,jax]", silent=False)
+        session.install("-e", ".[odes]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -128,16 +128,16 @@ def set_dev(session):
                     external=True,
         )
     else:
-        session.run(python, "-m", "pip", "install", "-e", ".[all,dev]", external=True)
+        session.run(python, "-m", "pip", "install", "-e", ".[all,dev,jax]", external=True)
 
 
 @nox.session(name="tests")
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
+    session.install("-e", ".[all,jax]", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
-        session.install("-e", ".[odes, jax]", silent=False)
+        session.install("-e", ".[odes]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 

From 30db13bc547dcd93e752950344d1961162033005 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 00:41:32 +0530
Subject: [PATCH 399/615] #3443 Install `[jax]` for the integration tests

---
 noxfile.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 4444778162..156378e2cd 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -72,7 +72,7 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    session.install("-e", ".[all]", silent=False)
+    session.install("-e", ".[all,jax]", silent=False)
     if sys.platform == "linux":
         session.install("-e", ".[odes]", silent=False)
     session.run("python", "run-tests.py", "--integration")

From 5fd45c670704d8b04029f83b284fc0fa27255e51 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 00:52:08 +0530
Subject: [PATCH 400/615] #3443 Fix expression tree Jax evaluator test

---
 .../test_operations/test_evaluate_python.py                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
index 50c9dbb744..0484af9451 100644
--- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
+++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
@@ -503,7 +503,7 @@ def test_evaluator_jax(self):
         expr = pybamm.exp(a * b)
         evaluator = pybamm.EvaluatorJax(expr)
         result = evaluator(t=None, y=np.array([[2], [3]]))
-        self.assertEqual(result, np.exp(6))
+        np.testing.assert_array_almost_equal(result, np.exp(6), decimal=15)
 
         # test a constant expression
         expr = pybamm.Scalar(2) * pybamm.Scalar(3)

From 9103a106668ed3e7c85ee3783ee8c454823d2041 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 02:11:39 +0530
Subject: [PATCH 401/615] Remove explainer comments about version constraints

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 setup.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/setup.py b/setup.py
index c6e7414829..b02293a212 100644
--- a/setup.py
+++ b/setup.py
@@ -268,15 +268,9 @@ def compile_KLU():
         # Note: jax and jaxlib must be pinned to a specific version
         # to avoid upstream breaking changes.
         "jax": [
-            # 0.4.18 provides support for Jax on aarch64 containers
-            # via the PyBaMM images on Docker Hub which come with
-            # Python 3.11 installed.
-            # It also provides support for CPU-only Jax on Windows.
             "jax==0.4.20; python_version >= '3.9'",
             "jaxlib==0.4.20; python_version >= '3.9'",
-            # Jax 0.4.13 was the last version to support Python 3.8.
-            # Support for CPU-only Windows was added in 0.4.13, so
-            # this version supports Windows too.
+            # The versions below can be removed once PyBaMM no longer supports python 3.8
             "jax==0.4.13; python_version < '3.9'",
             "jaxlib==0.4.13; python_version < '3.9'",
         ],

From d99acee69d42c09f738225bf6d8aab77930c02aa Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 02:12:54 +0530
Subject: [PATCH 402/615] Remove explainer comment about pinning

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 setup.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/setup.py b/setup.py
index b02293a212..0fca17f820 100644
--- a/setup.py
+++ b/setup.py
@@ -265,8 +265,6 @@ def compile_KLU():
         "pandas": [
             "pandas>=1.5.0",
         ],
-        # Note: jax and jaxlib must be pinned to a specific version
-        # to avoid upstream breaking changes.
         "jax": [
             "jax==0.4.20; python_version >= '3.9'",
             "jaxlib==0.4.20; python_version >= '3.9'",

From e38e0be445ab0dc9f8775e4e4cfcd9ad4fd6a397 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 15:39:16 +0530
Subject: [PATCH 403/615] Undo custom install command function

This reverts commit 0db07c70f0347f452fd6faef2e9bf8f61357d30d.
---
 noxfile.py | 63 +++++++++++++-----------------------------------------
 1 file changed, 15 insertions(+), 48 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 7c77680800..d485d4c3a1 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -36,38 +36,6 @@ def set_environment_variables(env_dict, session):
         session.env[key] = value
 
 
-def install_PyBaMM(session, extras):
-    """
-    Installs PyBaMM in editable mode along with its dependencies for
-    a nox Session object.
-
-    Parameters
-    -----------
-        session : nox.Session
-            The session to install dependencies for.
-        extras : list[str]
-            A list of dependencies to install. The current
-            options are:
-            [all,dev,jax,odes,docs]
-
-    """
-    # TODO: Remove this mess once [odes] brings support for Python 3.12
-    # See https://github.com/bmcage/odes/issues/162 for details.
-
-    # FIXME: Bump [jax] to get compatibility with Python 3.12 and Windows in this PR
-
-    # For now: silently remove [odes] and [jax] if specified, when running on
-    # 1. Linux and macOS on Python 3.12
-    # 2. Windows on any Python version
-
-    if sys.platform == "win32" or sys.version_info >= (3, 12):
-        if "odes" in extras or "jax" in extras:
-            extras.remove("odes")
-            extras.remove("jax")
-
-    session.install("-e", f".[{','.join(extras)}]", silent=False)
-
-
 @nox.session(name="pybamm-requires")
 def run_pybamm_requires(session):
     """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
@@ -92,10 +60,10 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32":
-        install_PyBaMM(session=session, extras=["all","odes","jax"])
-    else:
-        install_PyBaMM(session=session, extras=["all"])
+        session.install("-e", ".[odes]", silent=False)
+        session.install("-e", ".[jax]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -105,17 +73,16 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux":
-        install_PyBaMM(session=session, extras=["all","odes","jax"])
-    else:
-        install_PyBaMM(session=session, extras=["all"])
+        session.install("-e", ".[odes]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
-    install_PyBaMM(session=session, extras=["all","docs"])
+    session.install("-e", ".[all,docs]", silent=False)
     session.run("python", "run-tests.py", "--doctest")
 
 
@@ -123,10 +90,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux":
-        install_PyBaMM(session=session, extras=["all","odes","jax"])
-    else:
-        install_PyBaMM(session=session, extras=["all"])
+        session.install("-e", ".[odes]", silent=False)
+        session.install("-e", ".[jax]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -134,7 +101,7 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    install_PyBaMM(session=session, extras=["all","dev"])
+    session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
 
@@ -143,7 +110,7 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    install_PyBaMM(session=session, extras=["all"])
+    session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
 
 
@@ -170,10 +137,10 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux" or sys.platform == "darwin":
-        install_PyBaMM(session=session, extras=["all","odes","jax"])
-    else:
-        install_PyBaMM(session=session, extras=["all"])
+        session.install("-e", ".[odes]", silent=False)
+        session.install("-e", ".[jax]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 
@@ -181,7 +148,7 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    install_PyBaMM(session=session, extras=["all","docs"])
+    session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
     # Local development
     if session.interactive:

From a59378bd9cbcf9a489e159d3d064ea73af747967 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 16:07:47 +0530
Subject: [PATCH 404/615] Add a `sys.version_info()` condition instead

---
 noxfile.py | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index d485d4c3a1..75756017e4 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -61,9 +61,9 @@ def run_coverage(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
     session.install("-e", ".[all]", silent=False)
-    if sys.platform != "win32":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+    if sys.platform != "win32" and sys.version_info < (3, 12):
+        # TODO: update this when JAX is bumped to support Python 3.12 and Windows
+        session.install("-e", ".[odes,jax]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -74,7 +74,7 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
-    if sys.platform == "linux":
+    if sys.platform == "linux" and sys.version_info < (3, 12):
         session.install("-e", ".[odes]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
@@ -91,9 +91,9 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
-    if sys.platform == "linux":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+    if sys.platform == "linux" and sys.version_info < (3, 12):
+        # TODO: update this when JAX is bumped to support Python 3.12 and Windows
+        session.install("-e", ".[odes,jax]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -121,7 +121,8 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    if sys.platform == "linux":
+    if sys.platform == "linux" and sys.version_info < (3, 12):
+    # TODO: update this when JAX is bumped to support Python 3.12 and Windows
         session.run(python,
                     "-m",
                     "pip",
@@ -138,9 +139,9 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
-    if sys.platform == "linux" or sys.platform == "darwin":
-        session.install("-e", ".[odes]", silent=False)
-        session.install("-e", ".[jax]", silent=False)
+    if (sys.platform == "linux" or sys.platform == "darwin") and sys.version_info < (3, 12):
+        # TODO: update this when JAX is bumped to support Python 3.12 and Windows
+        session.install("-e", ".[odes,jax]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 

From 84f4007db2dc74f9099f3aed70157fe02286791b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 16:13:33 +0530
Subject: [PATCH 405/615] Add EOL to workflow file, fix pre-commit

---
 .github/workflows/test_on_push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index bfc2652368..0d3154fef4 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -413,4 +413,4 @@ jobs:
         run: python -m nox -s pybamm-requires
 
       - name: Install dev dependencies and run example scripts tests for GNU/Linux with Python 3.11
-        run: python -m nox -s scripts
\ No newline at end of file
+        run: python -m nox -s scripts

From 49622d8fe1b1e739fb5f0df4459e79c3d4f2addd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 23 Nov 2023 16:25:43 +0530
Subject: [PATCH 406/615] Install prerequisites not present in Python 3.12

---
 noxfile.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/noxfile.py b/noxfile.py
index 75756017e4..f18ce6dce0 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -60,6 +60,8 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all]", silent=False)
     if sys.platform != "win32" and sys.version_info < (3, 12):
         # TODO: update this when JAX is bumped to support Python 3.12 and Windows
@@ -73,6 +75,8 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux" and sys.version_info < (3, 12):
         session.install("-e", ".[odes]", silent=False)
@@ -90,6 +94,8 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all]", silent=False)
     if sys.platform == "linux" and sys.version_info < (3, 12):
         # TODO: update this when JAX is bumped to support Python 3.12 and Windows
@@ -101,6 +107,8 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
@@ -109,6 +117,8 @@ def run_examples(session):
 @nox.session(name="scripts")
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
@@ -118,6 +128,8 @@ def run_scripts(session):
 def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
@@ -138,6 +150,8 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all]", silent=False)
     if (sys.platform == "linux" or sys.platform == "darwin") and sys.version_info < (3, 12):
         # TODO: update this when JAX is bumped to support Python 3.12 and Windows
@@ -149,6 +163,8 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
+    # TODO: remove this when PyBaMM moves to pyproject.toml
+    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
     # Local development

From 3fec37ea72b1ca8d65345ba1242b936fcc8e83a2 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 24 Nov 2023 10:14:38 +0000
Subject: [PATCH 407/615] Add error message for experiment

---
 pybamm/simulation.py                                |  7 +++++++
 tests/unit/test_serialisation/test_serialisation.py | 12 ++++++++++++
 2 files changed, 19 insertions(+)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 1ad8f0c682..6da17a61b2 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -1199,6 +1199,13 @@ def save_model(
         mesh = self.mesh if (mesh or variables) else None
         variables = self.built_model.variables if variables else None
 
+        if self.operating_mode == "with experiment":
+            raise NotImplementedError(
+                """
+                Serialising models coupled to experiments is not yet supported.
+                """
+            )
+
         if self.built_model:
             Serialise().save_model(
                 self.built_model, filename=filename, mesh=mesh, variables=variables
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 97299e669d..e304091b22 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -528,6 +528,18 @@ def test_save_load_model(self):
         newest_solver = newest_model.default_solver
         newest_solver.solve(newest_model, [0, 3600])
 
+    def test_save_experiment_model_error(self):
+        model = pybamm.lithium_ion.SPM()
+        experiment = pybamm.Experiment(["Discharge at 1C for 1 hour"])
+        sim = pybamm.Simulation(model, experiment=experiment)
+        sim.solve()
+
+        with self.assertRaisesRegex(
+            NotImplementedError,
+            "Serialising models coupled to experiments is not yet supported.",
+        ):
+            sim.save_model("spm_experiment", mesh=False, variables=False)
+
     def test_serialised_model_plotting(self):
         # models without a mesh
         model = pybamm.BaseModel()

From 92e7c9042cc35b9a5f591ec8b5073a08275da8cf Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Fri, 24 Nov 2023 11:57:15 +0000
Subject: [PATCH 408/615] Update notebook to suggest build() not solve()

---
 .../notebooks/models/saving_models.ipynb      | 26 +++++--------------
 pybamm/simulation.py                          |  2 +-
 2 files changed, 8 insertions(+), 20 deletions(-)

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index c3f72bc4e4..9ac76a611e 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -13,7 +13,7 @@
    "source": [
     "Models which are discretised (i.e. ready to solve/ previously solved, see [this notebook](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb) for more information on the pybamm.Discretisation class) can be serialised and saved to a JSON file, ready to be read in again either in PyBaMM, or a different modelling library. \n",
     "\n",
-    "In the example below, we build and solve a basic DFN model, and then save the model out to `sim_model_example.json`, which should have appear in the 'models' directory."
+    "In the example below, we build a basic DFN model, and then save the model out to `sim_model_example.json`, which should have appear in the 'models' directory."
    ]
   },
   {
@@ -28,7 +28,8 @@
     "# do the example\n",
     "dfn_model = pybamm.lithium_ion.DFN()\n",
     "dfn_sim = pybamm.Simulation(dfn_model)\n",
-    "dfn_sim.solve([0, 3600])\n",
+    "# discretise and build the model\n",
+    "dfn_sim.build()\n",
     "\n",
     "dfn_sim.save_model(\"sim_model_example\")"
    ]
@@ -155,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,22 +229,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pybamm.print_citations()"
    ]
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 6da17a61b2..a25653b507 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -1214,7 +1214,7 @@ def save_model(
             raise NotImplementedError(
                 """
                 PyBaMM can only serialise a discretised model.
-                Ensure the model has been built (e.g. run `solve()`) before saving.
+                Ensure the model has been built (e.g. run `build()`) before saving.
                 """
             )
 

From c066c81abafa932c2aa7e2548d1f7ce8082985dd Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 19:59:41 +0530
Subject: [PATCH 409/615] Bump `jax` and `jaxlib` versions again

and also bump `casadi` build-time dependency minimum version
---
 pyproject.toml | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4569c7c6c3..15f8582537 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ requires = [
     "setuptools>=64",
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
-    "casadi>=3.6.0; platform_system!='Windows'",
+    "casadi>=3.6.3; platform_system!='Windows'",
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"
@@ -108,13 +108,16 @@ dev = [
     "nbmake",
 ]
 # Reading CSV files
-pandas =  [
+pandas = [
     "pandas>=1.5.0",
 ]
 # For the Jax solver. Note: these must be kept in sync with the versions defined in pybamm/util.py.
 jax = [
-    "jax>=0.4,<=0.5",
-    "jaxlib>=0.4,<=0.5",
+    "jax==0.4.20; python_version >= '3.9'",
+    "jaxlib==0.4.20; python_version >= '3.9'",
+    # The versions below can be removed once PyBaMM no longer supports Python 3.8
+    "jax==0.4.13; python_version < '3.9'",
+    "jaxlib==0.4.13; python_version < '3.9'",
 ]
 # For the scikits.odes solver
 odes = [

From f229ab8fa9955747736f577bec58b405f6c25b53 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:15:49 +0530
Subject: [PATCH 410/615] Add a condition to not install `jax` if < py3.9

---
 noxfile.py | 39 ++++++++++++++++++++++++++++++++++-----
 1 file changed, 34 insertions(+), 5 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 55ba5811cb..daa95e9833 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -63,7 +63,10 @@ def run_coverage(session):
     if sys.platform != "win32":
         session.install("-e", ".[all,jax,odes]", silent=False)
     else:
-        session.install("-e", ".[all,jax]", silent=False)
+        if sys.version_info < (3, 9):
+            session.install("-e", ".[all]", silent=False)
+        else:
+            session.install("-e", ".[all,jax]", silent=False)
     session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
@@ -76,7 +79,10 @@ def run_integration(session):
     if sys.platform != "win32":
         session.install("-e", ".[all,jax,odes]", silent=False)
     else:
-        session.install("-e", ".[all,jax]", silent=False)
+        if sys.version_info < (3, 9):
+            session.install("-e", ".[all]", silent=False)
+        else:
+            session.install("-e", ".[all,jax]", silent=False)
     session.run("python", "run-tests.py", "--integration")
 
 
@@ -94,7 +100,10 @@ def run_unit(session):
     if sys.platform != "win32":
         session.install("-e", ".[all,jax,odes]", silent=False)
     else:
-        session.install("-e", ".[all,jax]", silent=False)
+        if sys.version_info < (3, 9):
+            session.install("-e", ".[all]", silent=False)
+        else:
+            session.install("-e", ".[all,jax]", silent=False)
     session.run("python", "run-tests.py", "--unit")
 
 
@@ -143,7 +152,24 @@ def set_dev(session):
             external=True,
         )
     else:
-        session.run(python, "-m", "pip", "install", "-e", ".[all,dev,jax]", external=True)
+        if sys.version_info < (3, 9):
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                ".[all,dev]",
+                external=True,
+            )
+        else:
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                ".[all,dev,jax]",
+                external=True,
+            )
 
 
 @nox.session(name="tests")
@@ -153,7 +179,10 @@ def run_tests(session):
     if sys.platform != "win32":
             session.install("-e", ".[all,jax,odes]", silent=False)
     else:
-        session.install("-e", ".[all,jax]", silent=False)
+        if sys.version_info < (3, 9):
+            session.install("-e", ".[all]", silent=False)
+        else:
+            session.install("-e", ".[all,jax]", silent=False)
     session.run("python", "run-tests.py", "--all")
 
 

From a47e78d1f17893049d6366da4003f118ee73b680 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:19:35 +0530
Subject: [PATCH 411/615] Add a CHANGELOG entry for `jax` and `jax` versions

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2eea9ed7c0..6b95d66bd3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,10 @@
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
 - The irreversible plating model now increments `f"{Domain} dead lithium concentration [mol.m-3]"`, not `f"{Domain} lithium plating concentration [mol.m-3]"` as it did previously. ([#3485](https://github.com/pybamm-team/PyBaMM/pull/3485))
 
+## Optimizations
+
+- Updated `jax` and `jaxlib` to the latest available versions and added Windows (Python 3.9+) support for the Jax solver ([#3550](https://github.com/pybamm-team/PyBaMM/pull/3550))
+
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
 ## Features

From 8301d26732c6fb73374a9af8c007021ea0a88b78 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:20:32 +0530
Subject: [PATCH 412/615] Remove incorrect `jax` and `jaxlib` version pins

---
 pyproject.toml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 15f8582537..966a40bac6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -115,9 +115,6 @@ pandas = [
 jax = [
     "jax==0.4.20; python_version >= '3.9'",
     "jaxlib==0.4.20; python_version >= '3.9'",
-    # The versions below can be removed once PyBaMM no longer supports Python 3.8
-    "jax==0.4.13; python_version < '3.9'",
-    "jaxlib==0.4.13; python_version < '3.9'",
 ]
 # For the scikits.odes solver
 odes = [

From fc8bdf88a4057c86b1394e544329f22625d37d9b Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:30:58 +0530
Subject: [PATCH 413/615] Delete versions.json configuration file

---
 docs/_static/versions.json | 172 -------------------------------------
 1 file changed, 172 deletions(-)
 delete mode 100644 docs/_static/versions.json

diff --git a/docs/_static/versions.json b/docs/_static/versions.json
deleted file mode 100644
index 675ecbcf88..0000000000
--- a/docs/_static/versions.json
+++ /dev/null
@@ -1,172 +0,0 @@
-[
-    {
-        "name": "latest",
-        "version": "latest",
-        "url": "https://docs.pybamm.org/en/latest/"
-    },
-    {
-        "name": "stable",
-        "version": "stable",
-        "url": "https://docs.pybamm.org/en/stable/"
-    },
-    {
-        "name": "v23.9",
-        "version": "23.9",
-        "url": "https://docs.pybamm.org/en/v23.9_a/"
-    },
-    {
-        "name": "v23.5",
-        "version": "23.5",
-        "url": "https://docs.pybamm.org/en/v23.5_a/"
-    },
-    {
-        "name": "v23.4.1",
-        "version": "23.4.1",
-        "url": "https://docs.pybamm.org/en/v23.4.1/"
-    },
-    {
-        "name": "v23.4",
-        "version": "23.4",
-        "url": "https://docs.pybamm.org/en/v23.4/"
-    },
-    {
-        "name": "v23.3",
-        "version": "23.3",
-        "url": "https://docs.pybamm.org/en/v23.3/"
-    },
-    {
-        "name": "v23.2",
-        "version": "23.2",
-        "url": "https://docs.pybamm.org/en/v23.2/"
-    },
-    {
-        "name": "v23.1",
-        "version": "23.1",
-        "url": "https://docs.pybamm.org/en/v23.1/"
-    },
-    {
-        "name": "v22.12",
-        "version": "22.12",
-        "url": "https://docs.pybamm.org/en/v22.12/"
-    },
-    {
-        "name": "v22.11.1",
-        "version": "22.11.1",
-        "url": "https://docs.pybamm.org/en/v22.11.1/"
-    },
-    {
-        "name": "v22.11",
-        "version": "22.11",
-        "url": "https://docs.pybamm.org/en/v22.11/"
-    },
-    {
-        "name": "v22.10",
-        "version": "22.10",
-        "url": "https://docs.pybamm.org/en/v22.10/"
-    },
-    {
-        "name": "v22.9",
-        "version": "22.9",
-        "url": "https://docs.pybamm.org/en/v22.9/"
-    },
-    {
-        "name": "v22.8",
-        "version": "22.8",
-        "url": "https://docs.pybamm.org/en/v22.8/"
-    },
-    {
-        "name": "v22.7",
-        "version": "22.7",
-        "url": "https://docs.pybamm.org/en/v22.7/"
-    },
-    {
-        "name": "v22.6",
-        "version": "22.6",
-        "url": "https://docs.pybamm.org/en/v22.6/"
-    },
-    {
-        "name": "v22.5",
-        "version": "22.5",
-        "url": "https://docs.pybamm.org/en/v22.5/"
-    },
-    {
-        "name": "v22.4",
-        "version": "22.4",
-        "url": "https://docs.pybamm.org/en/v22.4/"
-    },
-    {
-        "name": "v22.3",
-        "version": "22.3",
-        "url": "https://docs.pybamm.org/en/v22.3/"
-    },
-    {
-        "name": "v22.2",
-        "version": "22.2",
-        "url": "https://docs.pybamm.org/en/v22.3/"
-    },
-    {
-        "name": "v22.1",
-        "version": "22.1",
-        "url": "https://docs.pybamm.org/en/v22.1/"
-    },
-    {
-        "name": "v21.12",
-        "version": "21.12",
-        "url": "https://docs.pybamm.org/en/v21.12/"
-    },
-    {
-        "name": "v21.11",
-        "version": "21.11",
-        "url": "https://docs.pybamm.org/en/v21.11/"
-    },
-    {
-        "name": "v21.10",
-        "version": "21.10",
-        "url": "https://docs.pybamm.org/en/v21.10/"
-    },
-    {
-        "name": "v21.9",
-        "version": "21.9",
-        "url": "https://docs.pybamm.org/en/v21.9/"
-    },
-    {
-        "name": "v21.08",
-        "version": "21.08",
-        "url": "https://docs.pybamm.org/en/v21.08/"
-    },
-    {
-        "name": "v0.4.0",
-        "version": "0.4.0",
-        "url": "https://docs.pybamm.org/en/v0.4.0/"
-    },
-    {
-        "name": "v0.3.0",
-        "version": "0.3.0",
-        "url": "https://docs.pybamm.org/en/v0.3.0/"
-    },
-    {
-        "name": "v0.2.3",
-        "version": "0.2.3",
-        "url": "https://docs.pybamm.org/en/v0.2.3/"
-    },
-    {
-        "name": "v0.2.2",
-        "version": "0.2.2",
-        "url": "https://docs.pybamm.org/en/v0.2.2/"
-    },
-    {
-        "name": "v0.2.1",
-        "version": "0.2.1",
-        "url": "https://docs.pybamm.org/en/v0.2.1/"
-    },
-    {
-        "name": "v0.2.0",
-        "version": "0.2.0",
-        "url": "https://docs.pybamm.org/en/v0.2.0/"
-    },
-    {
-        "name": "v0.1.0",
-        "version": "0.1.0",
-        "url": "https://docs.pybamm.org/en/v0.1.0/"
-    }
-]

From 07c2c66c328d3f8a365565709cdb13a30bfc15b8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:31:31 +0530
Subject: [PATCH 414/615] Remove mentions from release workflows+  scripts

---
 .github/release_workflow.md |  4 ----
 scripts/update_version.py   | 20 --------------------
 2 files changed, 24 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 8a032b9f9a..690f7fa407 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -11,7 +11,6 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub, P
    - `CITATION.cff`
    - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
       These changes will be automatically pushed to a new branch `vYY.MM` and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
@@ -35,7 +34,6 @@ If a new release candidate is required after the release of `rc0` -
    - `CITATION.cff`
    - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
       These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
@@ -57,7 +55,6 @@ Once satisfied with the release candidates -
    - `CITATION.cff`
    - `pyproject.toml`
    - `vcpkg.json`
-   - `docs/_static/versions.json`
    - `CHANGELOG.md`
 
       These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
@@ -74,7 +71,6 @@ Some other essential things to check throughout the release process -
 
 - If updating our custom vcpkg registory entries [pybamm-team/sundials-vcpkg-registry](https://github.com/pybamm-team/sundials-vcpkg-registry) or [pybamm-team/casadi-vcpkg-registry](https://github.com/pybamm-team/casadi-vcpkg-registry) (used to build Windows wheels), make sure to update the baseline of the registories in vcpkg-configuration.json to the latest commit id.
 - Update jax and jaxlib to the latest version in `pybamm.util` and `pyproject.toml`, fixing any bugs that arise
-- Make sure the URLs in `docs/_static/versions.json` are valid
 - As the release workflow is initiated by the `release` event, it's important to note that the default `GITHUB_REF` used by `actions/checkout` during the checkout process will correspond to the tag created during the release process. Consequently, the workflows will consistently build PyBaMM based on the commit associated with this tag. Should new commits be introduced to the `vYY.MM` branch, such as those addressing build issues, it becomes necessary to manually update this tag to point to the most recent commit -
   ```
   git tag -f <tag_name> <commit_hash>
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 8a2d832e59..ab8a9345ba 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -48,26 +48,6 @@ def update_version():
         file.seek(0)
         file.write(replace_version)
 
-    # docs/_static/versions.json for readthedocs build
-    if "rc" not in release_version:
-        with open(
-            os.path.join(pybamm.root_dir(), "docs", "_static", "versions.json"),
-            "r+",
-        ) as file:
-            output = file.read()
-            json_data = json.loads(output)
-            json_data.insert(
-                2,
-                {
-                    "name": f"v{release_version}",
-                    "version": f"{release_version}",
-                    "url": f"https://docs.pybamm.org/en/v{release_version}/",
-                },
-            )
-            file.truncate(0)
-            file.seek(0)
-            file.write(json.dumps(json_data, indent=4))
-
     # vcpkg.json
     with open(os.path.join(pybamm.root_dir(), "vcpkg.json"), "r+") as file:
         output = file.read()

From 5b072bec1a401e62ae89745fa4f39f06b4a49615 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:31:47 +0530
Subject: [PATCH 415/615] Remove version switcher Sphinx configuration

---
 docs/conf.py | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 49e5cf3dc9..102d8ada81 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -142,11 +142,6 @@
         },
     ],
     "collapse_navigation": True,
-    # should be kept versioned to use for the version warning bar
-    "switcher": {
-        "version_match": version,
-        "json_url": "https://docs.pybamm.org/en/latest/_static/versions.json",
-    },
     # turn to False to not fail build if json_url is not found
     "check_switcher": True,
     # for dark mode toggle and social media links

From 6ed478bae518079685643a60d3c54927abb75689 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 20:38:30 +0530
Subject: [PATCH 416/615] Add a whitetext logo for dark mode

---
 .gitignore                             |   1 +
 docs/_static/pybamm_logo_whitetext.png | Bin 0 -> 89994 bytes
 docs/conf.py                           |   2 +-
 3 files changed, 2 insertions(+), 1 deletion(-)
 create mode 100644 docs/_static/pybamm_logo_whitetext.png

diff --git a/.gitignore b/.gitignore
index 3e01fcac83..612dc777b1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -138,6 +138,7 @@ results/
 # do not ignore images in _static folder in docs
 !docs/_static/favicon/favicon.png
 !docs/_static/pybamm_logo.png
+!docs/_static/pybamm_logo_whitetext.png
 
 # tests
 test_callback.log
diff --git a/docs/_static/pybamm_logo_whitetext.png b/docs/_static/pybamm_logo_whitetext.png
new file mode 100644
index 0000000000000000000000000000000000000000..3ee7159ed439de4430a823b9f799ac5e83f141bf
GIT binary patch
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Ry^DQ*&kif|``Zrw`G1bls44&e

literal 0
HcmV?d00001

diff --git a/docs/conf.py b/docs/conf.py
index 102d8ada81..40074848a1 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -122,7 +122,7 @@
 html_theme_options = {
     "logo": {
         "image_light": "pybamm_logo.png",
-        "image_dark": "pybamm_logo.png",
+        "image_dark": "pybamm_log_whitetext.png",
     },
     "icon_links": [
         {

From fb4f944c05808f733e53ba0598b3bf3320a6ea58 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 20:41:22 +0530
Subject: [PATCH 417/615] Move ruff, coverage, and pytest configs to
 pyproject.toml

---
 .coveragerc                                   |  3 -
 docs/source/user_guide/installation/index.rst |  1 +
 pyproject.toml                                | 75 +++++++++++++++++++
 pytest.ini                                    | 15 ----
 ruff.toml                                     |  8 --
 5 files changed, 76 insertions(+), 26 deletions(-)
 delete mode 100644 .coveragerc
 delete mode 100644 pytest.ini
 delete mode 100644 ruff.toml

diff --git a/.coveragerc b/.coveragerc
deleted file mode 100644
index a174f5aced..0000000000
--- a/.coveragerc
+++ /dev/null
@@ -1,3 +0,0 @@
-[run]
-source = pybamm
-concurrency = multiprocessing
diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 983f66842e..e771611a37 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -154,6 +154,7 @@ Dependency
 `pre-commit <https://pre-commit.com/index.html>`__                               \-                 dev                For managing and maintaining multi-language pre-commit hooks.
 `ruff <https://beta.ruff.rs/docs/>`__                                            \-                 dev                For code formatting.
 `nox <https://nox.thea.codes/en/stable/>`__                                      \-                 dev                For running testing sessions in multiple environments.
+`coverage <https://coverage.readthedocs.io/en/>`__                               \-                 dev                For calculating coverage of tests.
 `pytest <https://docs.pytest.org/en/stable/>`__                                  6.0.0              dev                For running Jupyter notebooks tests.
 `pytest-xdist <https://pytest-xdist.readthedocs.io/en/latest/>`__                \-                 dev                For running tests in parallel across distributed workers.
 `nbmake <https://github.com/treebeardtech/nbmake/>`__                            \-                 dev                A ``pytest`` plugin for executing Jupyter notebooks.
diff --git a/pyproject.toml b/pyproject.toml
index 4569c7c6c3..40455454b1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -102,6 +102,8 @@ dev = [
     "ruff",
     # For running testing sessions
     "nox",
+    # For coverage
+    coverage[toml],
     # For testing Jupyter notebooks
     "pytest>=6",
     "pytest-xdist",
@@ -172,3 +174,76 @@ pybamm = [
 
 [tool.setuptools.packages.find]
 include = ["pybamm", "pybamm.*"]
+
+[tool.ruff]
+extend-include = ["*.ipynb"]
+extend-exclude = ["__init__.py"]
+
+[tool.ruff.lint]
+extend-select = [
+  # "B",           # flake8-bugbear
+  # "I",           # isort
+  # "ARG",         # flake8-unused-arguments
+  # "C4",          # flake8-comprehensions
+  # "ICN",         # flake8-import-conventions
+  # "ISC",         # flake8-implicit-str-concat
+  # "PGH",         # pygrep-hooks
+  # "PIE",         # flake8-pie
+  # "PL",          # pylint
+  # "PT",          # flake8-pytest-style
+  # "PTH",         # flake8-use-pathlib
+  # "RET",         # flake8-return
+  "RUF",         # Ruff-specific
+  # "SIM",         # flake8-simplify
+  # "T20",         # flake8-print
+  # "UP",          # pyupgrade
+  "YTT",         # flake8-2020
+]
+ignore = [
+  "E741",        # Ambiguous variable name
+  "RUF012",      # Mutable class attributes should be annotated with `typing.ClassVar`
+  "SIM108",      # Use ternary operator
+  "ARG001",      # Unused function argument:
+  "ARG002",      # Unused method arguments
+  "PLR2004",     # Magic value used in comparison
+  "PLR0915",     # Too many statements
+  "PLR0913",     # Too many arguments
+  "PLR0912",     # Too many branches
+  "RET504",      # Unnecessary assignment
+  "RET505",      # Unnecessary `else`
+  "RET506",      # Unnecessary `elif`
+  "B018",        # Found useless expression
+  "RUF002",      # Docstring contains ambiguous
+]
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["T20"]
+"docs/*" = ["T20"]
+"examples/*" = ["T20"]
+"**.ipynb" = ["E402", "E703"]
+
+# NOTE: currently used only for notebook tests with the nbmake plugin.
+[tool.pytest]
+# Use pytest-xdist to run tests in parallel by default, exit with
+# error if not installed
+required_plugins = [
+  "pytest-xdist",
+]
+addopts = [
+  "-nauto",
+  "-v",
+]
+testpaths = [
+  "docs/source/examples/",
+]
+console_output_style = "progress"
+
+# Logging configuration
+log_cli = "true"
+log_cli_level = "INFO"
+log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+log_date_format = "%Y-%m-%d %H:%M:%S"
+
+[tool.coverage.run]
+source = pybamm
+concurrency = multiprocessing
diff --git a/pytest.ini b/pytest.ini
deleted file mode 100644
index ac90f5d695..0000000000
--- a/pytest.ini
+++ /dev/null
@@ -1,15 +0,0 @@
-; NOTE: currently used only for notebook tests with the nbmake plugin.
-[pytest]
-; Use pytest-xdist to run tests in parallel by default, exit with
-; error if not installed
-required_plugins = pytest-xdist
-addopts = -nauto -v
-testpaths =
-    docs/source/examples
-console_output_style = progress
-
-; Logging configuration
-log_cli = true
-log_cli_level = INFO
-log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-log_date_format = %Y-%m-%d %H:%M:%S
diff --git a/ruff.toml b/ruff.toml
deleted file mode 100644
index 7304d64570..0000000000
--- a/ruff.toml
+++ /dev/null
@@ -1,8 +0,0 @@
-extend-include = ["*.ipynb"]
-extend-exclude = ["__init__.py"]
-
-[lint]
-ignore = ["E741"]
-
-[lint.per-file-ignores]
-"**.ipynb" = ["E402", "E703"]

From b8b2f4c561de4010eb28b553b6749b075e1030cd Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 20:41:59 +0530
Subject: [PATCH 418/615] Add coverage as a dev dep

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 40455454b1..ae84321206 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -103,7 +103,7 @@ dev = [
     # For running testing sessions
     "nox",
     # For coverage
-    coverage[toml],
+    "coverage[toml]",
     # For testing Jupyter notebooks
     "pytest>=6",
     "pytest-xdist",

From 2f7908b8df0236317ef0743c4b14c8a874b1d9fe Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 20:42:25 +0530
Subject: [PATCH 419/615] Fix coverage config

---
 pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ae84321206..2ee63d7eb2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -245,5 +245,5 @@ log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 log_date_format = "%Y-%m-%d %H:%M:%S"
 
 [tool.coverage.run]
-source = pybamm
-concurrency = multiprocessing
+source = "pybamm"
+concurrency = "multiprocessing"

From 12c5d77203bd93542785d237bac00bad5ed5469a Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 20:43:14 +0530
Subject: [PATCH 420/615] New ruff and flake8 fixes

---
 .../work_precision_sets/time_vs_abstols.py    |  2 +-
 .../work_precision_sets/time_vs_dt_max.py     |  2 +-
 .../work_precision_sets/time_vs_mesh_size.py  |  2 +-
 .../time_vs_no_of_states.py                   |  2 +-
 .../work_precision_sets/time_vs_reltols.py    |  2 +-
 .../notebooks/models/jelly-roll-model.ipynb   |  2 +-
 .../examples/notebooks/models/latexify.ipynb  |  2 +-
 .../print_model_parameter_combinations.py     |  6 +--
 noxfile.py                                    |  2 +-
 pybamm/citations.py                           |  2 +-
 pybamm/discretisations/discretisation.py      |  4 +-
 pybamm/expression_tree/array.py               |  2 +-
 pybamm/expression_tree/interpolant.py         |  4 +-
 .../operations/evaluate_python.py             |  4 +-
 pybamm/expression_tree/operations/latexify.py |  6 +--
 pybamm/expression_tree/parameter.py           |  4 +-
 pybamm/expression_tree/state_vector.py        |  3 +-
 pybamm/expression_tree/symbol.py              |  4 +-
 pybamm/expression_tree/unary_operators.py     | 31 +++------------
 pybamm/expression_tree/variable.py            |  3 +-
 pybamm/install_odes.py                        |  2 +-
 pybamm/meshes/zero_dimensional_submesh.py     |  2 +-
 pybamm/models/base_model.py                   |  2 +-
 .../full_surface_form_conductivity.py         |  4 +-
 pybamm/parameters_cli.py                      |  2 +-
 pybamm/plotting/quick_plot.py                 |  6 +--
 pybamm/simulation.py                          |  2 +-
 pybamm/solvers/jax_bdf_solver.py              | 10 ++---
 pybamm/solvers/jax_solver.py                  |  2 +-
 pybamm/spatial_methods/finite_volume.py       |  4 +-
 run-tests.py                                  |  4 +-
 scripts/fix_casadi_rpath_mac.py               | 20 +++++-----
 scripts/install_KLU_Sundials.py               |  2 +-
 scripts/update_version.py                     |  4 +-
 setup.py                                      | 19 +++-------
 tests/unit/test_citations.py                  |  2 +-
 .../test_operations/test_evaluate_python.py   | 38 +++++++++----------
 tests/unit/test_models/test_base_model.py     |  4 +-
 .../test_base_battery_model.py                |  2 +-
 .../test_parameters/test_parameter_values.py  |  4 +-
 40 files changed, 93 insertions(+), 131 deletions(-)

diff --git a/benchmarks/work_precision_sets/time_vs_abstols.py b/benchmarks/work_precision_sets/time_vs_abstols.py
index 6447884083..9a96f07514 100644
--- a/benchmarks/work_precision_sets/time_vs_abstols.py
+++ b/benchmarks/work_precision_sets/time_vs_abstols.py
@@ -96,7 +96,7 @@
 plt.savefig(f"benchmarks/benchmark_images/time_vs_abstols_{pybamm.__version__}.png")
 
 
-content = f"# PyBaMM {pybamm.__version__}\n## Solve Time vs Abstols\n<img src='./benchmark_images/time_vs_abstols_{pybamm.__version__}.png'>\n"  # noqa
+content = f"# PyBaMM {pybamm.__version__}\n## Solve Time vs Abstols\n<img src='./benchmark_images/time_vs_abstols_{pybamm.__version__}.png'>\n"
 
 with open("./benchmarks/release_work_precision_sets.md", "r") as original:
     data = original.read()
diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py
index 3926a4bcd6..3e428b702c 100644
--- a/benchmarks/work_precision_sets/time_vs_dt_max.py
+++ b/benchmarks/work_precision_sets/time_vs_dt_max.py
@@ -98,7 +98,7 @@
 plt.savefig(f"benchmarks/benchmark_images/time_vs_dt_max_{pybamm.__version__}.png")
 
 
-content = f"## Solve Time vs dt_max\n<img src='./benchmark_images/time_vs_dt_max_{pybamm.__version__}.png'>\n"  # noqa
+content = f"## Solve Time vs dt_max\n<img src='./benchmark_images/time_vs_dt_max_{pybamm.__version__}.png'>\n"
 
 with open("./benchmarks/release_work_precision_sets.md", "r") as original:
     data = original.read()
diff --git a/benchmarks/work_precision_sets/time_vs_mesh_size.py b/benchmarks/work_precision_sets/time_vs_mesh_size.py
index 7b4d4145d4..f0f13f706b 100644
--- a/benchmarks/work_precision_sets/time_vs_mesh_size.py
+++ b/benchmarks/work_precision_sets/time_vs_mesh_size.py
@@ -78,7 +78,7 @@
 plt.savefig(f"benchmarks/benchmark_images/time_vs_mesh_size_{pybamm.__version__}.png")
 
 
-content = f"## Solve Time vs Mesh size\n<img src='./benchmark_images/time_vs_mesh_size_{pybamm.__version__}.png'>\n"  # noqa
+content = f"## Solve Time vs Mesh size\n<img src='./benchmark_images/time_vs_mesh_size_{pybamm.__version__}.png'>\n"
 
 with open("./benchmarks/release_work_precision_sets.md", "r") as original:
     data = original.read()
diff --git a/benchmarks/work_precision_sets/time_vs_no_of_states.py b/benchmarks/work_precision_sets/time_vs_no_of_states.py
index 0a88ac8b52..eb27aba322 100644
--- a/benchmarks/work_precision_sets/time_vs_no_of_states.py
+++ b/benchmarks/work_precision_sets/time_vs_no_of_states.py
@@ -82,7 +82,7 @@
 )
 
 
-content = f"## Solve Time vs Number of states\n<img src='./benchmark_images/time_vs_no_of_states_{pybamm.__version__}.png'>\n"  # noqa
+content = f"## Solve Time vs Number of states\n<img src='./benchmark_images/time_vs_no_of_states_{pybamm.__version__}.png'>\n"
 
 with open("./benchmarks/release_work_precision_sets.md", "r") as original:
     data = original.read()
diff --git a/benchmarks/work_precision_sets/time_vs_reltols.py b/benchmarks/work_precision_sets/time_vs_reltols.py
index 12e41b526f..93964910a8 100644
--- a/benchmarks/work_precision_sets/time_vs_reltols.py
+++ b/benchmarks/work_precision_sets/time_vs_reltols.py
@@ -102,7 +102,7 @@
 plt.savefig(f"benchmarks/benchmark_images/time_vs_reltols_{pybamm.__version__}.png")
 
 
-content = f"## Solve Time vs Reltols\n<img src='./benchmark_images/time_vs_reltols_{pybamm.__version__}.png'>\n"  # noqa
+content = f"## Solve Time vs Reltols\n<img src='./benchmark_images/time_vs_reltols_{pybamm.__version__}.png'>\n"
 
 with open("./benchmarks/release_work_precision_sets.md", "r") as original:
     data = original.read()
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index fe6173f1ce..557366099a 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -84,7 +84,7 @@
     "delta = pybamm.Parameter(\"Current collector thickness\")\n",
     "delta_p = delta  # assume same thickness\n",
     "delta_n = delta  # assume same thickness\n",
-    "l = 1/2 - delta_p - delta_n  # active material thickness  # noqa: E741\n",
+    "l = 1/2 - delta_p - delta_n  # active material thickness\n",
     "sigma_p = pybamm.Parameter(\"Positive current collector conductivity\")\n",
     "sigma_n = pybamm.Parameter(\"Negative current collector conductivity\")\n",
     "sigma_a = pybamm.Parameter(\"Active material conductivity\")"
diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb
index 63e7c0d519..c2a45ff2c8 100644
--- a/docs/source/examples/notebooks/models/latexify.ipynb
+++ b/docs/source/examples/notebooks/models/latexify.ipynb
@@ -1252,7 +1252,7 @@
    "source": [
     "spme_latex = model_spme.latexify(newline=False)\n",
     "for line in spme_latex:\n",
-    "    display(line)       # noqa: F821"
+    "    display(line)"
    ]
   },
   {
diff --git a/examples/scripts/print_model_parameter_combinations.py b/examples/scripts/print_model_parameter_combinations.py
index 8d24f919c3..f7f00714fb 100644
--- a/examples/scripts/print_model_parameter_combinations.py
+++ b/examples/scripts/print_model_parameter_combinations.py
@@ -19,13 +19,13 @@
             try:
                 model = pybamm.lithium_ion.SPM(options.copy())
             except pybamm.OptionError as e:
-                print(f"Cannot create model with {options}. (OptionError: {str(e)})")
+                print(f"Cannot create model with {options}. (OptionError: {e!s})")
             except pybamm.ModelError as e:
                 # todo: properly resolve the cases that raise these errors
-                print(f"Cannot create model with {options}. (ModelError: {str(e)})")
+                print(f"Cannot create model with {options}. (ModelError: {e!s})")
             except AttributeError as e:
                 # todo: properly resolve the cases that raise these errors
-                print(f"Cannot create model with {options}. (AttributeError: {str(e)})")
+                print(f"Cannot create model with {options}. (AttributeError: {e!s})")
             else:
                 output = f"{options} with {parameter_set} parameters: "
                 try:
diff --git a/noxfile.py b/noxfile.py
index 7a57ad5820..1ab383bddc 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -38,7 +38,7 @@ def set_environment_variables(env_dict, session):
 
 @nox.session(name="pybamm-requires")
 def run_pybamm_requires(session):
-    """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""  # noqa: E501
+    """Download, compile, and install the build-time requirements for Linux and macOS: the SuiteSparse and SUNDIALS libraries."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
         session.install("wget", "cmake", silent=False)
diff --git a/pybamm/citations.py b/pybamm/citations.py
index b72262989b..e73351a4c6 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -267,7 +267,7 @@ def print_citations(filename=None, output_format="text", verbose=False):
         if verbose:  # pragma: no cover
             if filename is not None:  # pragma: no cover
                 raise Exception(
-                    "Verbose output is available only for the terminal and not for printing to files",  # noqa: E501
+                    "Verbose output is available only for the terminal and not for printing to files",
                 )
             else:
                 citations.print(filename, output_format, verbose=True)
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index a120adecc0..bb6e678f4c 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -461,7 +461,7 @@ def process_boundary_conditions(self, model):
                 if (
                     self.mesh[subdomain].coord_sys
                     in ["spherical polar", "cylindrical polar"]
-                    and list(self.mesh.geometry[subdomain].values())[0]["min"] == 0
+                    and next(iter(self.mesh.geometry[subdomain].values()))["min"] == 0
                 ):
                     if bcs["left"][0].value != 0 or bcs["left"][1] != "Neumann":
                         raise pybamm.ModelError(
@@ -753,7 +753,7 @@ def _process_symbol(self, symbol):
             spatial_method = self.spatial_methods[symbol.domain[0]]
             # If boundary conditions are provided, need to check for BCs on tabs
             if self.bcs:
-                key_id = list(self.bcs.keys())[0]
+                key_id = next(iter(self.bcs.keys()))
                 if any("tab" in side for side in list(self.bcs[key_id].keys())):
                     self.bcs[key_id] = self.check_tab_conditions(
                         symbol, self.bcs[key_id]
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index 2736886d95..adbdc88dc2 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -97,7 +97,7 @@ def entries_string(self, value):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`."""
         self._id = hash(
-            (self.__class__, self.name) + self.entries_string + tuple(self.domain)
+            (self.__class__, self.name, *self.entries_string, *tuple(self.domain))
         )
 
     def _jac(self, variable):
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index cd0df4d077..28188ce68f 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -222,9 +222,7 @@ def entries_string(self, value):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`."""
         self._id = hash(
-            (self.__class__, self.name, self.entries_string)
-            + tuple([child.id for child in self.children])
-            + tuple(self.domain)
+            (self.__class__, self.name, self.entries_string, *tuple([child.id for child in self.children]), *tuple(self.domain))
         )
 
     def _function_new_copy(self, children):
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index 1f44a69784..d0cd4c776d 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -42,7 +42,7 @@ class JaxCooMatrix:
     def __init__(self, row, col, data, shape):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"  # noqa: E501
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
             )
 
         self.row = jax.numpy.array(row)
@@ -537,7 +537,7 @@ class EvaluatorJax:
     def __init__(self, symbol):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"  # noqa: E501
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
             )
 
         constants, python_str = pybamm.to_python(symbol, debug=False, output_jax=True)
diff --git a/pybamm/expression_tree/operations/latexify.py b/pybamm/expression_tree/operations/latexify.py
index 9f2949069e..572f01a560 100644
--- a/pybamm/expression_tree/operations/latexify.py
+++ b/pybamm/expression_tree/operations/latexify.py
@@ -93,9 +93,9 @@ def _get_bcs_displays(self, var):
 
         if bcs:
             # Take range minimum from the first domain
-            var_name = list(self.model.default_geometry[var.domain[0]].keys())[0]
-            rng_left = list(self.model.default_geometry[var.domain[0]].values())[0]
-            rng_right = list(self.model.default_geometry[var.domain[-1]].values())[0]
+            var_name = next(iter(self.model.default_geometry[var.domain[0]].keys()))
+            rng_left = next(iter(self.model.default_geometry[var.domain[0]].values()))
+            rng_right = next(iter(self.model.default_geometry[var.domain[-1]].values()))
 
             # Trim name (r_n --> r)
             var_name = re.findall(r"(.)_*.*", str(var_name))[0]
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index eebe77ad2f..00a28017b8 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -152,9 +152,7 @@ def input_names(self, inp=None):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id`"""
         self._id = hash(
-            (self.__class__, self.name, self.diff_variable)
-            + tuple([child.id for child in self.children])
-            + tuple(self.domain)
+            (self.__class__, self.name, self.diff_variable, *tuple([child.id for child in self.children]), *tuple(self.domain))
         )
 
     def diff(self, variable):
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 6ef8bee904..437ba752ed 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -107,8 +107,7 @@ def set_evaluation_array(self, y_slices, evaluation_array):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, tuple(self.evaluation_array))
-            + tuple(self.domain)
+            (self.__class__, self.name, tuple(self.evaluation_array), *tuple(self.domain))
         )
 
     def _jac_diff_vector(self, variable):
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 8f1608e7ba..5fe765af33 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -403,9 +403,7 @@ def set_id(self):
         need to hash once.
         """
         self._id = hash(
-            (self.__class__, self.name)
-            + tuple([child.id for child in self.children])
-            + tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []])
+            (self.__class__, self.name, *tuple([child.id for child in self.children]), *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]))
         )
 
     @property
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 81c3dc28c2..95306ebad5 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -287,14 +287,7 @@ def _unary_jac(self, child_jac):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (
-                self.__class__,
-                self.name,
-                self.slice.start,
-                self.slice.stop,
-                self.children[0].id,
-            )
-            + tuple(self.domain)
+            (self.__class__, self.name, self.slice.start, self.slice.stop, self.children[0].id, *tuple(self.domain))
         )
 
     def _unary_evaluate(self, child):
@@ -554,15 +547,7 @@ def integration_variable(self):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name)
-            + tuple(
-                [
-                    integration_variable.id
-                    for integration_variable in self.integration_variable
-                ]
-            )
-            + (self.children[0].id,)
-            + tuple(self.domain)
+            (self.__class__, self.name, *tuple([integration_variable.id for integration_variable in self.integration_variable]), self.children[0].id, *tuple(self.domain))
         )
 
     def _unary_new_copy(self, child):
@@ -702,9 +687,7 @@ def __init__(self, child, vector_type="row"):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.vector_type)
-            + (self.children[0].id,)
-            + tuple(self.domain)
+            (self.__class__, self.name, self.vector_type, self.children[0].id, *tuple(self.domain))
         )
 
     def _unary_new_copy(self, child):
@@ -757,7 +740,7 @@ def __init__(self, child, region="entire"):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name) + (self.children[0].id,) + tuple(self.domain)
+            (self.__class__, self.name, self.children[0].id, *tuple(self.domain))
         )
 
     def _unary_new_copy(self, child):
@@ -798,8 +781,7 @@ def __init__(self, child, side, domain):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.side, self.children[0].id)
-            + tuple([(k, tuple(v)) for k, v in self.domains.items()])
+            (self.__class__, self.name, self.side, self.children[0].id, *tuple([(k, tuple(v)) for k, v in self.domains.items()]))
         )
 
     def _evaluates_on_edges(self, dimension):
@@ -857,8 +839,7 @@ def __init__(self, name, child, side):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.side, self.children[0].id)
-            + tuple([(k, tuple(v)) for k, v in self.domains.items()])
+            (self.__class__, self.name, self.side, self.children[0].id, *tuple([(k, tuple(v)) for k, v in self.domains.items()]))
         )
 
     def _unary_new_copy(self, child):
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index 0d1e1fd424..22b176b6fc 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -103,8 +103,7 @@ def bounds(self, values):
 
     def set_id(self):
         self._id = hash(
-            (self.__class__, self.name, self.scale, self.reference)
-            + tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []])
+            (self.__class__, self.name, self.scale, self.reference, *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]))
         )
 
     def create_copy(self):
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 4bf310a0f2..0fbbcdc637 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -66,7 +66,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", "../sundials-{}".format(sundials_version)] + cmake_args,
+        ["cmake", "../sundials-{}".format(sundials_version), *cmake_args],
         cwd=build_directory,
         check=True,
     )
diff --git a/pybamm/meshes/zero_dimensional_submesh.py b/pybamm/meshes/zero_dimensional_submesh.py
index 5b2f38e29f..82e8cb6524 100644
--- a/pybamm/meshes/zero_dimensional_submesh.py
+++ b/pybamm/meshes/zero_dimensional_submesh.py
@@ -31,7 +31,7 @@ def __init__(self, position, npts=None):
             raise pybamm.GeometryError("position should only contain a single variable")
 
         # extract the position
-        position = list(position.values())[0]
+        position = next(iter(position.values()))
         spatial_position = position["position"]
         self.nodes = np.array([spatial_position])
         self.edges = np.array([spatial_position])
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 08890757b7..a88f6f4255 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -1205,7 +1205,7 @@ def check_and_convert_equations(self, equations):
             for var, eqn in equations.items():
                 if eqn.has_symbol_of_classes(pybamm.Variable):
                     unpacker = pybamm.SymbolUnpacker(pybamm.Variable)
-                    variable_in_equation = list(unpacker.unpack_symbol(eqn))[0]
+                    variable_in_equation = next(iter(unpacker.unpack_symbol(eqn)))
                     raise TypeError(
                         "Initial conditions cannot contain 'Variable' objects, "
                         "but '{!r}' found in initial conditions for '{}'".format(
diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
index 7afeeac47e..83bcfb8027 100644
--- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
+++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
@@ -224,7 +224,7 @@ class FullAlgebraic(BaseModel):
         The parameters to use for this submodel
     options : dict, optional
         A dictionary of options to be passed to the model.
-    """  # noqa: E501
+    """
 
     def __init__(self, param, domain, options=None):
         super().__init__(param, domain, options)
@@ -258,7 +258,7 @@ class FullDifferential(BaseModel):
         The parameters to use for this submodel
     options : dict, optional
         A dictionary of options to be passed to the model.
-    """  # noqa: E501
+    """
 
     def __init__(self, param, domain, options=None):
         super().__init__(param, domain, options)
diff --git a/pybamm/parameters_cli.py b/pybamm/parameters_cli.py
index 90950b23ee..e3d4a273b8 100644
--- a/pybamm/parameters_cli.py
+++ b/pybamm/parameters_cli.py
@@ -2,7 +2,7 @@ def raise_error():
     raise NotImplementedError(
         "parameters cli has been deprecated. "
         "Parameters should now be defined via python files (see "
-        "https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/parameters/lithium_ion/Ai2020.py"  # noqa: E501
+        "https://github.com/pybamm-team/PyBaMM/tree/develop/pybamm/input/parameters/lithium_ion/Ai2020.py"
         " for example)"
     )
 
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index ff657ee375..0e56c17c75 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -534,7 +534,7 @@ def plot(self, t, dynamic=False):
                 # 1D plot: plot as a function of x at time t
                 # Read dictionary of spatial variables
                 spatial_vars = self.spatial_variable_dict[key]
-                spatial_var_name = list(spatial_vars.keys())[0]
+                spatial_var_name = next(iter(spatial_vars.keys()))
                 ax.set_xlabel(
                     "{} [{}]".format(spatial_var_name, self.spatial_unit),
                 )
@@ -568,12 +568,12 @@ def plot(self, t, dynamic=False):
                 # different order based on whether the domains are x-r, x-z or y-z, etc
                 if self.x_first_and_y_second[key] is False:
                     x_name = list(spatial_vars.keys())[1][0]
-                    y_name = list(spatial_vars.keys())[0][0]
+                    y_name = next(iter(spatial_vars.keys()))[0]
                     x = self.second_spatial_variable[key]
                     y = self.first_spatial_variable[key]
                     var = variable(t_in_seconds, **spatial_vars, warn=False)
                 else:
-                    x_name = list(spatial_vars.keys())[0][0]
+                    x_name = next(iter(spatial_vars.keys()))[0]
                     y_name = list(spatial_vars.keys())[1][0]
                     x = self.first_spatial_variable[key]
                     y = self.second_spatial_variable[key]
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index f9aebb1c54..bdd97f6894 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -375,7 +375,7 @@ def set_initial_soc(self, initial_soc):
         param = self._model.param
         if options["open-circuit potential"] == "MSMR":
             self._parameter_values = (
-                self._unprocessed_parameter_values.set_initial_ocps(  # noqa: E501
+                self._unprocessed_parameter_values.set_initial_ocps(
                     initial_soc, param=param, inplace=False, options=options
                 )
             )
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 2f334ed8ec..8f5b8ed817 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -676,7 +676,7 @@ def while_body(while_state):
             #     )
 
             (state, step_accepted) = tree_map(
-                partial(jnp.where, converged * (error_norm > 1)),  # noqa: E712
+                partial(jnp.where, converged * (error_norm > 1)),
                 (_update_step_size_and_lu(state, factor), False),
                 (state, converged),
             )
@@ -883,9 +883,7 @@ def arg_dicts_to_values(args):
             """
             return sum((tuple(b.values()) for b in args if isinstance(b, dict)), ())
 
-        aug_mass = (mass, mass, onp.array(1.0)) + arg_dicts_to_values(
-            tree_map(arg_to_identity, args)
-        )
+        aug_mass = (mass, mass, onp.array(1.0), *arg_dicts_to_values(tree_map(arg_to_identity, args)))
 
         def scan_fun(carry, i):
             y_bar, t0_bar, args_bar = carry
@@ -961,7 +959,7 @@ def ravel_first_arg(f, unravel):
     @lu.transformation
     def ravel_first_arg_(unravel, y_flat, *args):
         y = unravel(y_flat)
-        ans = yield (y,) + args, {}
+        ans = yield (y, *args), {}
         ans_flat, _ = ravel_pytree(ans)
         yield ans_flat
 
@@ -1007,7 +1005,7 @@ def jax_bdf_integrate(func, y0, t_eval, *args, rtol=1e-6, atol=1e-6, mass=None):
     """
     if not pybamm.have_jax():
         raise ModuleNotFoundError(
-            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"  # noqa: E501
+            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
         )
 
     def _check_arg(arg):
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 4c9759008a..313fddc208 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -61,7 +61,7 @@ def __init__(
     ):
         if not pybamm.have_jax():
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"  # noqa: E501
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
             )
 
         # note: bdf solver itself calculates consistent initial conditions so can set
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index ecbae69796..5c32e5a2c0 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -620,10 +620,10 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs):
         # Dirichlet boundary conditions
         n_bcs = 0
         if lbc_type == "Dirichlet":
-            domain = [domain[0] + "_left ghost cell"] + domain
+            domain = [domain[0] + "_left ghost cell", *domain]
             n_bcs += 1
         if rbc_type == "Dirichlet":
-            domain = domain + [domain[-1] + "_right ghost cell"]
+            domain = [*domain, domain[-1] + "_right ghost cell"]
             n_bcs += 1
 
         # Calculate values for ghost nodes for any Dirichlet boundary conditions
diff --git a/run-tests.py b/run-tests.py
index b9d421daa2..25b1731b18 100755
--- a/run-tests.py
+++ b/run-tests.py
@@ -156,7 +156,7 @@ def test_script(path, executable="python"):
     env["MPLBACKEND"] = "Template"
 
     # Run in subprocess
-    cmd = [executable] + [path]
+    cmd = [executable, path]
     try:
         p = subprocess.Popen(
             cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env
@@ -214,7 +214,7 @@ def test_script(path, executable="python"):
     parser.add_argument(
         "--examples",
         action="store_true",
-        help="Test all Jupyter notebooks in `docs/source/examples/` (deprecated, use nox or pytest instead).",  # noqa: E501
+        help="Test all Jupyter notebooks in `docs/source/examples/` (deprecated, use nox or pytest instead).",
     )
     parser.add_argument(
         "--debook",
diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 23c8a32d59..3f7f71e834 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -30,15 +30,15 @@
     os.path.join(casadi_dir, libcasadi_37_name),
 ]
 
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
+subprocess.run(["otool", "-L", os.path.join(casadi_dir, libcasadi_name)])
 
-print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
-subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_name)
+print(" ".join(["install_name_tool", *install_name_tool_args_for_libcasadi_name]))
+subprocess.run(["install_name_tool", *install_name_tool_args_for_libcasadi_name])
 
-print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name))
-subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name)
+print(" ".join(["install_name_tool", *install_name_tool_args_for_libcasadi_37_name]))
+subprocess.run(["install_name_tool", *install_name_tool_args_for_libcasadi_37_name])
 
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
+subprocess.run(["otool", "-L", os.path.join(casadi_dir, libcasadi_name)])
 
 install_name_tool_args = [
     "-change",
@@ -46,12 +46,12 @@
     os.path.join(casadi_dir, libcppabi_name),
     os.path.join(casadi_dir, libcpp_name),
 ]
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+subprocess.run(["otool", "-L", os.path.join(casadi_dir, libcpp_name)])
 
-print(" ".join(["install_name_tool"] + install_name_tool_args))
-subprocess.run(["install_name_tool"] + install_name_tool_args)
+print(" ".join(["install_name_tool", *install_name_tool_args]))
+subprocess.run(["install_name_tool", *install_name_tool_args])
 
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
+subprocess.run(["otool", "-L", os.path.join(casadi_dir, libcpp_name)])
 
 # Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH
 # This is needed for the casadi python bindings to work while repairing the wheel
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 96e60aeb0e..8f41f5969a 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -156,7 +156,7 @@ def download_extract_library(url, download_dir):
 
 sundials_src = "../sundials-{}".format(sundials_version)
 print("-" * 10, "Running CMake prepare", "-" * 40)
-subprocess.run(["cmake", sundials_src] + cmake_args, cwd=build_dir, check=True)
+subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
 print("-" * 10, "Building the sundials", "-" * 40)
 make_cmd = ["make", "install"]
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 8a2d832e59..ccdec661e2 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -78,7 +78,7 @@ def update_version():
         file.write(replace_version)
 
     # Get latest commit id from pybamm-team/sundials-vcpkg-registry
-    cmd = "git ls-remote https://github.com/pybamm-team/sundials-vcpkg-registry | grep refs/heads/main | cut -f 1 | tr -d '\n'"  # noqa: E501
+    cmd = "git ls-remote https://github.com/pybamm-team/sundials-vcpkg-registry | grep refs/heads/main | cut -f 1 | tr -d '\n'"
     latest_commit_id = os.popen(cmd).read()
 
     # vcpkg-configuration.json
@@ -93,7 +93,7 @@ def update_version():
         file.write(replace_commit_id)
 
     changelog_line1 = "# [Unreleased](https://github.com/pybamm-team/PyBaMM/)\n"
-    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n"  # noqa: E501
+    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n"
 
     # CHANGELOG.md
     with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file:
diff --git a/setup.py b/setup.py
index 9cfc4df4ff..ef82e65e70 100644
--- a/setup.py
+++ b/setup.py
@@ -42,10 +42,7 @@ def set_vcpkg_environment_variables():
 # ---------- CMakeBuild class (custom build_ext for IDAKLU target) ---------------------
 
 class CMakeBuild(build_ext):
-    user_options = build_ext.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
+    user_options = [*build_ext.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
 
     def initialize_options(self):
         build_ext.initialize_options(self)
@@ -136,7 +133,7 @@ def run(self):
         cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
         print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40)
         subprocess.run(
-            ["cmake", cmake_list_dir] + cmake_args, cwd=build_dir, env=build_env
+            ["cmake", cmake_list_dir, *cmake_args], cwd=build_dir, env=build_env
         , check=True)
 
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
@@ -144,7 +141,7 @@ def run(self):
                 "cmake configuration steps encountered errors, and the IDAKLU module"
                 " could not be built. Make sure dependencies are correctly "
                 "installed. See "
-                "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html" # noqa: E501
+                "https://docs.pybamm.org/en/latest/source/user_guide/installation/install-from-source.html"
             )
             raise RuntimeError(msg)
         else:
@@ -201,10 +198,7 @@ def move_output(self, ext):
 class CustomInstall(install):
     """A custom install command to add 2 build options"""
 
-    user_options = install.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
+    user_options = [*install.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
 
     def initialize_options(self):
         install.initialize_options(self)
@@ -228,10 +222,7 @@ def run(self):
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
 
-    user_options = orig.bdist_wheel.user_options + [
-        ("suitesparse-root=", None, "suitesparse source location"),
-        ("sundials-root=", None, "sundials source location"),
-    ]
+    user_options = [*orig.bdist_wheel.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
 
     def initialize_options(self):
         orig.bdist_wheel.initialize_options(self)
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index 5fde193af3..b3e2c88422 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -101,7 +101,7 @@ def test_overwrite_citation(self):
             pybamm.citations.register(r"@article{NotACitation, title = {A New Title}}")
             pybamm.citations._parse_citation(
                 r"@article{NotACitation, title = {A New Title}}"
-            )  # noqa: E501
+            )
         self.assertIn("NotACitation", pybamm.citations._papers_to_cite)
         self.assertNotEqual(
             pybamm.citations._all_citations["NotACitation"], old_citation
diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
index 50c9dbb744..ca36804ba0 100644
--- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
+++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
@@ -35,12 +35,12 @@ def test_find_symbols(self):
         self.assertEqual(len(constant_symbols), 0)
 
         # test keys of known_symbols
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
 
         # test values of variable_symbols
-        self.assertEqual(list(variable_symbols.values())[0], "y[0:1]")
+        self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
 
         var_a = pybamm.id_to_python_variable(a.id)
@@ -57,13 +57,13 @@ def test_find_symbols(self):
         self.assertEqual(len(constant_symbols), 0)
 
         # test keys of variable_symbols
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.children[0].id)
         self.assertEqual(list(variable_symbols.keys())[3], expr.id)
 
         # test values of variable_symbols
-        self.assertEqual(list(variable_symbols.values())[0], "y[0:1]")
+        self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
         self.assertEqual(
             list(variable_symbols.values())[2], "{} + {}".format(var_a, var_b)
@@ -82,13 +82,13 @@ def test_find_symbols(self):
         self.assertEqual(len(constant_symbols), 0)
 
         # test keys of variable_symbols
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.children[1].id)
         self.assertEqual(list(variable_symbols.keys())[3], expr.id)
 
         # test values of variable_symbols
-        self.assertEqual(list(variable_symbols.values())[0], "y[0:1]")
+        self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
         self.assertEqual(list(variable_symbols.values())[2], "-{}".format(var_b))
         var_child = pybamm.id_to_python_variable(expr.children[1].id)
@@ -101,11 +101,11 @@ def test_find_symbols(self):
         variable_symbols = OrderedDict()
         expr = pybamm.Function(test_function, a)
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
-        self.assertEqual(list(constant_symbols.keys())[0], expr.id)
-        self.assertEqual(list(constant_symbols.values())[0], test_function)
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(constant_symbols.keys())), expr.id)
+        self.assertEqual(next(iter(constant_symbols.values())), test_function)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], expr.id)
-        self.assertEqual(list(variable_symbols.values())[0], "y[0:1]")
+        self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         var_funct = pybamm.id_to_python_variable(expr.id, True)
         self.assertEqual(
             list(variable_symbols.values())[1], "{}({})".format(var_funct, var_a)
@@ -117,9 +117,9 @@ def test_find_symbols(self):
         A = pybamm.Matrix([[1, 2], [3, 4]])
         pybamm.find_symbols(A, constant_symbols, variable_symbols)
         self.assertEqual(len(variable_symbols), 0)
-        self.assertEqual(list(constant_symbols.keys())[0], A.id)
+        self.assertEqual(next(iter(constant_symbols.keys())), A.id)
         np.testing.assert_allclose(
-            list(constant_symbols.values())[0], np.array([[1, 2], [3, 4]])
+            next(iter(constant_symbols.values())), np.array([[1, 2], [3, 4]])
         )
 
         # test sparse matrix
@@ -128,9 +128,9 @@ def test_find_symbols(self):
         A = pybamm.Matrix(scipy.sparse.csr_matrix(np.array([[0, 2], [0, 4]])))
         pybamm.find_symbols(A, constant_symbols, variable_symbols)
         self.assertEqual(len(variable_symbols), 0)
-        self.assertEqual(list(constant_symbols.keys())[0], A.id)
+        self.assertEqual(next(iter(constant_symbols.keys())), A.id)
         np.testing.assert_allclose(
-            list(constant_symbols.values())[0].toarray(), A.entries.toarray()
+            next(iter(constant_symbols.values())).toarray(), A.entries.toarray()
         )
 
         # test numpy concatentate
@@ -139,7 +139,7 @@ def test_find_symbols(self):
         expr = pybamm.NumpyConcatenation(a, b)
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
         self.assertEqual(len(constant_symbols), 0)
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
@@ -153,7 +153,7 @@ def test_find_symbols(self):
         expr = pybamm.NumpyConcatenation(a, b)
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
         self.assertEqual(len(constant_symbols), 0)
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
@@ -194,7 +194,7 @@ def test_domain_concatenation(self):
         constant_symbols = OrderedDict()
         variable_symbols = OrderedDict()
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
-        self.assertEqual(list(variable_symbols.keys())[0], a.id)
+        self.assertEqual(next(iter(variable_symbols.keys())), a.id)
         self.assertEqual(list(variable_symbols.keys())[1], b.id)
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
 
@@ -468,9 +468,9 @@ def test_find_symbols_jax(self):
         A = pybamm.Matrix(scipy.sparse.csr_matrix(np.array([[0, 2], [0, 4]])))
         pybamm.find_symbols(A, constant_symbols, variable_symbols, output_jax=True)
         self.assertEqual(len(variable_symbols), 0)
-        self.assertEqual(list(constant_symbols.keys())[0], A.id)
+        self.assertEqual(next(iter(constant_symbols.keys())), A.id)
         np.testing.assert_allclose(
-            list(constant_symbols.values())[0].toarray(), A.entries.toarray()
+            next(iter(constant_symbols.values())).toarray(), A.entries.toarray()
         )
 
     @unittest.skipIf(not pybamm.have_jax(), "jax or jaxlib is not installed")
diff --git a/tests/unit/test_models/test_base_model.py b/tests/unit/test_models/test_base_model.py
index 4167d5fff5..1e90e28f81 100644
--- a/tests/unit/test_models/test_base_model.py
+++ b/tests/unit/test_models/test_base_model.py
@@ -694,7 +694,7 @@ def test_set_initial_conditions(self):
         new_model_disc = model_disc.set_initial_conditions_from(sol, inplace=False)
 
         # Test new initial conditions
-        var_scalar = list(new_model_disc.initial_conditions.keys())[0]
+        var_scalar = next(iter(new_model_disc.initial_conditions.keys()))
         self.assertIsInstance(
             new_model_disc.initial_conditions[var_scalar], pybamm.Vector
         )
@@ -826,7 +826,7 @@ def test_set_initial_conditions(self):
         new_model_disc = model_disc.set_initial_conditions_from(sol_dict, inplace=False)
 
         # Test new initial conditions
-        var_scalar = list(new_model_disc.initial_conditions.keys())[0]
+        var_scalar = next(iter(new_model_disc.initial_conditions.keys()))
         self.assertIsInstance(
             new_model_disc.initial_conditions[var_scalar], pybamm.Vector
         )
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 79c6d8a720..e2c408bb9a 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -49,7 +49,7 @@
 'total interfacial current density as a state': 'false' (possible: ['false', 'true'])
 'working electrode': 'both' (possible: ['both', 'positive'])
 'x-average side reactions': 'false' (possible: ['false', 'true'])
-"""  # noqa: E501
+"""
 
 
 class TestBaseBatteryModel(TestCase):
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index fa6e2398ee..37ec89068f 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -968,9 +968,9 @@ def test_process_model(self):
         self.assertIsInstance(model.initial_conditions[var1], pybamm.Scalar)
         self.assertEqual(model.initial_conditions[var1].value, 2)
         # boundary conditions
-        bc_key = list(model.boundary_conditions.keys())[0]
+        bc_key = next(iter(model.boundary_conditions.keys()))
         self.assertIsInstance(bc_key, pybamm.Variable)
-        bc_value = list(model.boundary_conditions.values())[0]
+        bc_value = next(iter(model.boundary_conditions.values()))
         self.assertIsInstance(bc_value["left"][0], pybamm.Scalar)
         self.assertEqual(bc_value["left"][0].value, 3)
         self.assertIsInstance(bc_value["right"][0], pybamm.Scalar)

From 7208df551d9e50727ae042260ab7aad6aee48075 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 20:44:26 +0530
Subject: [PATCH 421/615] Preserve git blame

---
 .git-blame-ignore-revs | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 228a76373e..9e59bd7f07 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -4,3 +4,5 @@
 a63e49ece0f9336d1f5c2562f7459e555c6e6693
 # activated standard pre-commits - https://github.com/pybamm-team/PyBaMM/pull/3192
 5273214b585c5a4286609aed40e0b092d0e05f42
+# migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
+12c5d77203bd93542785d237bac00bad5ed5469a

From ac76fcc314b0a585cbac370681027056ebfa0e27 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 25 Nov 2023 21:36:38 +0530
Subject: [PATCH 422/615] Fix `nox -s dev` bug

---
 noxfile.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/noxfile.py b/noxfile.py
index 7a57ad5820..4019935ac1 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -139,6 +139,7 @@ def set_dev(session):
             "-m",
             "pip",
             "install",
+            "-e",
             ".[all,dev,jax,odes]",
             external=True,
         )

From 909f6ce5d89bb6f194b611aa5b7551919ebdef0b Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 25 Nov 2023 21:55:01 +0530
Subject: [PATCH 423/615] Fix coverage and notebook sessions

---
 noxfile.py     | 2 +-
 pyproject.toml | 2 ++
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/noxfile.py b/noxfile.py
index 1ab383bddc..5db08ee92f 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -64,7 +64,7 @@ def run_coverage(session):
         session.install("-e", ".[all,odes,jax]", silent=False)
     else:
         session.install("-e", ".[all]", silent=False)
-    session.run("coverage", "run", "--rcfile=.coveragerc", "run-tests.py", "--nosub")
+    session.run("coverage", "run", "run-tests.py", "--nosub")
     session.run("coverage", "combine")
     session.run("coverage", "xml")
 
diff --git a/pyproject.toml b/pyproject.toml
index 2ee63d7eb2..74d60de081 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -174,6 +174,8 @@ pybamm = [
 
 [tool.setuptools.packages.find]
 include = ["pybamm", "pybamm.*"]
+# TODO: remove once https://github.com/pybamm-team/PyBaMM/issues/3480 is resolved
+exclude = ["pybind11*"]
 
 [tool.ruff]
 extend-include = ["*.ipynb"]

From 93aee4f74897dc04c1b6ea4d427fb5b6389e668e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sun, 26 Nov 2023 00:21:42 +0530
Subject: [PATCH 424/615] concurrency should be a list

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 74d60de081..293ac3728c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -248,4 +248,4 @@ log_date_format = "%Y-%m-%d %H:%M:%S"
 
 [tool.coverage.run]
 source = "pybamm"
-concurrency = "multiprocessing"
+concurrency = ["multiprocessing"]

From a02f685f45ec161b8c408bd2d83193b53d930422 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sun, 26 Nov 2023 00:33:46 +0530
Subject: [PATCH 425/615] Ignore pybind11

---
 pyproject.toml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 74d60de081..c5e674d14f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -174,8 +174,6 @@ pybamm = [
 
 [tool.setuptools.packages.find]
 include = ["pybamm", "pybamm.*"]
-# TODO: remove once https://github.com/pybamm-team/PyBaMM/issues/3480 is resolved
-exclude = ["pybind11*"]
 
 [tool.ruff]
 extend-include = ["*.ipynb"]
@@ -234,6 +232,7 @@ required_plugins = [
 addopts = [
   "-nauto",
   "-v",
+  "--ignore=pybind11",
 ]
 testpaths = [
   "docs/source/examples/",

From 51fe23c31b9b12030bb5143b89710aaab326e899 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sun, 26 Nov 2023 01:00:27 +0530
Subject: [PATCH 426/615] Use tool.pytest.ini_options

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d2fc3f3435..b94a4fa30e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -223,7 +223,7 @@ ignore = [
 "**.ipynb" = ["E402", "E703"]
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
-[tool.pytest]
+[tool.pytest.ini_options]
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [

From 352555d1e9affea2a2f576a242264f4c9c77304f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sun, 26 Nov 2023 01:13:44 +0530
Subject: [PATCH 427/615] Remove --ignore

---
 pyproject.toml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index b94a4fa30e..25be1e518e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -232,7 +232,6 @@ required_plugins = [
 addopts = [
   "-nauto",
   "-v",
-  "--ignore=pybind11",
 ]
 testpaths = [
   "docs/source/examples/",

From 5322895e74c7d549706339bdc0af269c7997f95a Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sun, 26 Nov 2023 01:27:23 +0530
Subject: [PATCH 428/615] tool.coverage.run.source should be a list

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 25be1e518e..eae0575117 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -245,5 +245,5 @@ log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
 log_date_format = "%Y-%m-%d %H:%M:%S"
 
 [tool.coverage.run]
-source = "pybamm"
+source = ["pybamm"]
 concurrency = ["multiprocessing"]

From 22e35bb7d2657bbce59c2c7a5779fdbd20dfd87f Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:08:33 +0530
Subject: [PATCH 429/615] Fix missing dark mode logo (#3563)

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index 40074848a1..8e86dcc48d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -122,7 +122,7 @@
 html_theme_options = {
     "logo": {
         "image_light": "pybamm_logo.png",
-        "image_dark": "pybamm_log_whitetext.png",
+        "image_dark": "pybamm_logo_whitetext.png",
     },
     "icon_links": [
         {

From 71e624589d7ac83fd568d68c13e621bcdb5f3080 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:19:05 +0530
Subject: [PATCH 430/615] #3558 Add CasADi to RPATH when linking `idaklu`
 target

---
 CMakeLists.txt | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 182fd489f3..61abf440d4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -72,18 +72,24 @@ execute_process(
 
 if (CASADI_DIR)
   file(TO_CMAKE_PATH ${CASADI_DIR} CASADI_DIR)
-  message("Found python casadi path: ${CASADI_DIR}")
+  message("Found Python casadi path: ${CASADI_DIR}")
 endif()
 
 if(${USE_PYTHON_CASADI})
-  message("Trying to link against python casadi package")
+  message("Trying to link against Python casadi package")
   find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED)
 else()
-  message("Trying to link against any casadi package apart from the python one")
+  message("Trying to link against any casadi package apart from the Python one")
   set(CMAKE_IGNORE_PATH "${CASADI_DIR}/cmake")
   find_package(casadi CONFIG REQUIRED)
 endif()
 
+set_target_properties(
+  idaklu PROPERTIES
+  INSTALL_RPATH "${CASADI_DIR}"
+  INSTALL_RPATH_USE_LINK_PATH TRUE
+)
+
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR})
 # Sundials
 find_package(SUNDIALS REQUIRED)

From 1d08d0fd2c56dc96ee545b8a416dd133d6c947db Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:19:26 +0530
Subject: [PATCH 431/615] #3558 Import `casadi` using `importlib` instead

---
 CMakeLists.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 61abf440d4..cd10b0cf9d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -64,9 +64,11 @@ if (NOT DEFINED USE_PYTHON_CASADI)
   set(USE_PYTHON_CASADI TRUE)
 endif()
 
+# Use importlib to find the casadi path without importing it. This is useful
+# to find the path for the build-time dependency, not the run-time dependency.
 execute_process(
     COMMAND "${PYTHON_EXECUTABLE}" -c
-    "import casadi as _; print(_.__path__[0])"
+    "import importlib.util; print(importlib.util.find_spec('casadi').submodule_search_locations[0])"
     OUTPUT_VARIABLE CASADI_DIR
     OUTPUT_STRIP_TRAILING_WHITESPACE)
 

From dfc0901f4653a33745ada73d52dca8c82b3b57d6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:22:31 +0530
Subject: [PATCH 432/615] #3558 add minimal test command and remove
 LD_LIBRARY_PATH override

---
 .github/workflows/publish_pypi.yml | 29 ++++++++++++++++++-----------
 1 file changed, 18 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 3073c95f09..d0cc3ceb81 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -16,6 +16,13 @@ on:
         required: false
         default: false
 
+# Set options available for all jobs that use cibuildwheel
+env:
+  # Increase pip debugging output, equivalent to `pip -vv`
+  CIBW_BUILD_VERBOSITY: 2
+  # Disable build isolation to allow pre-installing build-time dependencies
+  # CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+
 jobs:
   build_windows_wheels:
     name: Build wheels on windows-latest
@@ -55,6 +62,9 @@ jobs:
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
+          CIBW_BEFORE_BUILD: >
+            python -m pip install --upgrade setuptools wheel
+          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -63,7 +73,7 @@ jobs:
           path: ./wheelhouse/*.whl
           if-no-files-found: error
 
-  build_wheels:
+  build_macos_and_linux_wheels:
     name: Build wheels on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
@@ -96,17 +106,14 @@ jobs:
             yum -y install openblas-devel lapack-devel &&
             bash scripts/install_sundials.sh 6.0.3 6.5.0
           CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi numpy
-          # override; point to casadi install path so that it can be found by the repair command
+            python -m pip install cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
-            LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:$(python -c 'import casadi; print(casadi.__path__[0])')" auditwheel repair -w {dest_dir} {wheel}
+            auditwheel repair -w {dest_dir} {wheel}
           CIBW_BEFORE_BUILD_MACOS: >
-            python -m pip
-            install cmake casadi numpy &&
-            python scripts/fix_casadi_rpath_mac.py && scripts/fix_suitesparse_rpath_mac.sh
+            python -m pip install --upgrade cmake casadi setuptools wheel && scripts/fix_suitesparse_rpath_mac.sh
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
-            delocate-listdeps {wheel} &&
-            delocate-wheel -v -w {dest_dir} {wheel}
+            delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
           CIBW_SKIP: "pp* *musllinux*"
 
       - name: Upload wheels
@@ -142,7 +149,7 @@ jobs:
   publish_pypi:
     if: github.event_name != 'schedule'
     name: Upload package to PyPI
-    needs: [build_wheels, build_windows_wheels, build_sdist]
+    needs: [build_macos_and_linux_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
     steps:
       - name: Download all artifacts
@@ -171,7 +178,7 @@ jobs:
           repository-url: https://test.pypi.org/legacy/
 
   open_failure_issue:
-    needs: [build_windows_wheels, build_wheels, build_sdist]
+    needs: [build_windows_wheels, build_macos_and_linux_wheels, build_sdist]
     name: Open an issue if build fails
     if: ${{ always() && contains(needs.*.result, 'failure') }}
     runs-on: ubuntu-latest

From d0be7ba47c06023985a2569e9239ee54527838c0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:22:58 +0530
Subject: [PATCH 433/615] #3558 remove script for RPATH adjustment

---
 scripts/fix_casadi_rpath_mac.py | 71 ---------------------------------
 1 file changed, 71 deletions(-)
 delete mode 100644 scripts/fix_casadi_rpath_mac.py

diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
deleted file mode 100644
index 23c8a32d59..0000000000
--- a/scripts/fix_casadi_rpath_mac.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-Removes the rpath from libcasadi.dylib and libcasadi.3.7.dylib in the casadi python
-install and uses a fixed path
-
-Used when building the wheels for macOS
-"""
-import casadi
-import os
-import subprocess
-
-casadi_dir = casadi.__path__[0]
-print("Removing rpath references in python casadi install at", casadi_dir)
-
-libcpp_name = "libc++.1.0.dylib"
-libcppabi_name = "libc++abi.dylib"
-libcasadi_name = "libcasadi.dylib"
-libcasadi_37_name = "libcasadi.3.7.dylib"
-
-install_name_tool_args_for_libcasadi_name = [
-    "-change",
-    os.path.join("@rpath", libcpp_name),
-    os.path.join(casadi_dir, libcpp_name),
-    os.path.join(casadi_dir, libcasadi_name),
-]
-
-install_name_tool_args_for_libcasadi_37_name = [
-    "-change",
-    os.path.join("@rpath", libcpp_name),
-    os.path.join(casadi_dir, libcpp_name),
-    os.path.join(casadi_dir, libcasadi_37_name),
-]
-
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
-
-print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_name))
-subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_name)
-
-print(" ".join(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name))
-subprocess.run(["install_name_tool"] + install_name_tool_args_for_libcasadi_37_name)
-
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcasadi_name)])
-
-install_name_tool_args = [
-    "-change",
-    os.path.join("@rpath", libcppabi_name),
-    os.path.join(casadi_dir, libcppabi_name),
-    os.path.join(casadi_dir, libcpp_name),
-]
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
-
-print(" ".join(["install_name_tool"] + install_name_tool_args))
-subprocess.run(["install_name_tool"] + install_name_tool_args)
-
-subprocess.run(["otool"] + ["-L", os.path.join(casadi_dir, libcpp_name)])
-
-# Copy libcasadi.3.7.dylib and libc++.1.0.dylib to LD_LIBRARY_PATH
-# This is needed for the casadi python bindings to work while repairing the wheel
-
-subprocess.run(
-    ["cp",
-    os.path.join(casadi_dir, libcasadi_37_name),
-    os.path.join(os.getenv("HOME"),".local/lib")
-    ]
-)
-
-subprocess.run(
-    ["cp",
-    os.path.join(casadi_dir, libcpp_name),
-    os.path.join(os.getenv("HOME"),".local/lib")
-    ]
-)

From b9edb5ca35f3e8b804a34a09212413449595b45e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 14:28:03 +0530
Subject: [PATCH 434/615] #3558 enable comment to disable build isolation

---
 .github/workflows/publish_pypi.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index d0cc3ceb81..969d79317f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -21,7 +21,7 @@ env:
   # Increase pip debugging output, equivalent to `pip -vv`
   CIBW_BUILD_VERBOSITY: 2
   # Disable build isolation to allow pre-installing build-time dependencies
-  # CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+  CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
 
 jobs:
   build_windows_wheels:

From 6ece7a12f2c914fae1275f219d9a28157d03352c Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Mon, 27 Nov 2023 18:46:50 +0530
Subject: [PATCH 435/615] Remove GOVERNANCE.md (#3565)

---
 GOVERNANCE.md | 140 --------------------------------------------------
 MANIFEST.in   |   2 +-
 2 files changed, 1 insertion(+), 141 deletions(-)
 delete mode 100644 GOVERNANCE.md

diff --git a/GOVERNANCE.md b/GOVERNANCE.md
deleted file mode 100644
index aa97669187..0000000000
--- a/GOVERNANCE.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# PyBaMM Governance
-
-The following contains the formal governance structure of the PyBaMM
-project. This document clarifies how decisions are made with respect
-to community interactions, including the relationship between
-open source development and work that may be funded by for-profit
-and non-profit entities.
-
-## Code of Conduct
-
-The PyBaMM community strongly values inclusivity and diversity. Everyone
-should treat others with the utmost respect. Everyone in the community
-must adhere to the
-[Code of Conduct](https://github.com/pybamm-team/PyBaMM/blob/develop/CODE-OF-CONDUCT.md) which
-reflects the values of our community. Violations of the code should be
-reported to members of the steering council, where the offenses will be
-handled on a case-by-case basis.
-
-## Current Steering Council
-
-- [Ferran Brosa Planella](https://www.brosaplanella.xyz)
-- [Saransh Chopra](https://saransh-cpp.github.io)
-- Scott Marquis
-- [Gregory Offer](https://www.imperial.ac.uk/people/gregory.offer)
-- [Valentin Sulzer](https://sites.google.com/view/valentinsulzer)
-- [Martin Robinson](https://www.sabsr3.ox.ac.uk/people/dr-martin-robinson)
-
-## Advisory Committee
-
-TBA
-
-# Governing Rules and Duties
-
-## Steering Council
-
-The Project has a Steering Council that consists of Project
-Contributors who have produced contributions that are substantial in
-quality and quantity, and sustained over at least one year. The role
-of the Council is to provide active leadership for the Project in
-making everyday decisions on technical and administrative issues,
-through working with and taking input from the Community.
-
-During the everyday project activities, Council Members participate in
-all discussions, code review and other project activities as peers
-with all other Contributors and the Community. In these everyday
-activities, Council Members do not have any special power or privilege
-through their membership on the Council. However, it is expected that
-because of the quality and quantity of their contributions and their
-expert knowledge of the Project Software and Services that Council
-Members will provide useful guidance, both technical and in terms of
-project direction, to potentially less experienced Contributors.
-
-The Steering Council and its Members play a special role in certain
-situations. In particular, the Council may:
-
-- Make decisions about the overall scope, vision and direction of
-  the project.
-- Make decisions about strategic collaborations with other
-  organizations or individuals.
-- Make decisions about specific technical issues, features, bugs and
-  pull requests. They are the primary mechanism of guiding the code
-  review process and merging pull requests.
-- Make decisions about the Services that are run by the Project and
-  manage those Services for the benefit of the Project and Community.
-- Make decisions when regular community discussion does not produce
-  consensus on an issue in a reasonable time frame.
-
-Steering Council decisions are taken by simple majority, with the
-exception of changes to the Governance Documents which follow the
-procedure in the section 'Changing the Governance Documents'.
-
-### Steering Council membership
-
-To become eligible for being a Steering Council Member, an individual
-must be a Project Contributor who has produced contributions that are
-substantial in quality and quantity, and sustained over at least one
-year. Potential Council Members are nominated by existing Council
-Members or by the Community and voted upon by the existing Council
-after asking if the potential Member is interested and willing to
-serve in that capacity.
-
-When considering potential Members, the Council will look at
-candidates with a comprehensive view of their contributions. This will
-include but is not limited to code, code review, infrastructure work,
-mailing list and chat participation, community help/building,
-education and outreach, design work, etc. We deliberately do not
-set arbitrary quantitative metrics to avoid encouraging behavior
-that plays to the metrics rather than the project's overall well-being.
-We want to encourage a diverse array of backgrounds, viewpoints and
-talents in our team, which is why we explicitly do not define code as
-the sole metric on which Council membership will be evaluated.
-
-If a Council Member becomes inactive in the project for a period of
-one year, they will be considered for removal from the Council. Before
-removal, the inactive Member will be approached by another Council
-member to ask if they plan on returning to active participation. If
-not they will be removed immediately upon a Council vote. If they plan
-on returning to active participation soon, they will be given a grace
-period of one year. If they do not return to active participation
-within that time period they will be removed by vote of the Council
-without further grace period. All former Council members can be
-considered for membership again at any time in the future, like any
-other Project Contributor. Retired Council members will be listed on
-the project website, acknowledging the period during which they were
-active in the Council.
-
-The Council reserves the right to eject current Members if they are
-deemed to be actively harmful to the Project's well-being, and
-attempts at communication and conflict resolution have failed.
-
-## Fiscal Decisions
-
-All fiscal decisions are made by the steering council to ensure any
-funds are spent in a manner that furthers the mission of the Project.
-Fiscal decisions require majority approval by acting steering council
-members.
-
-## Advisory Committee
-
-The Project will consider setting up an Advisory Committee that works to ensure the long-term
-well-being of the Project. The role of the Committee will be to advise the Steering Council.
-
-## Conflict of interest
-
-It is expected that Steering Council and Advisory Committee Members
-will be employed at a wide range of companies, universities and non-profit
-organizations. Because of this, it is possible that Members will have
-conflicts of interest. Such conflicts of interest include, but are not
-limited to:
-
-- Financial interests, such as investments, employment or contracting
-  work, outside of the Project that may influence their work on the
-  Project.
-- Access to proprietary information of their employer that could
-  potentially leak into their work with the Project.
-
-All members of the Council and Committee shall disclose any conflict of
-interest they may have. Members with a conflict of interest in a
-particular issue may participate in Council discussions on that issue,
-but must recuse themselves from voting on the issue.
diff --git a/MANIFEST.in b/MANIFEST.in
index bfc9d0e718..0d05e9f158 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -2,6 +2,6 @@ graft pybamm
 include CITATION.cff
 prune tests
 
-exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md GOVERNANCE.md CMakeLists.txt
+exclude CHANGELOG.md CODE-OF-CONDUCT.md CONTRIBUTING.md CMakeLists.txt
 
 global-exclude __pycache__ *.py[cod] .venv

From 8b2cb45c20275f8927cf2d66a6a14bf473e7e46d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 23:27:46 +0530
Subject: [PATCH 436/615] #3558 cleanup jobs, skip PyPI deployment on forks

---
 .github/workflows/publish_pypi.yml | 51 +++++++++++++++++-------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 969d79317f..6b34a69907 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -20,12 +20,16 @@ on:
 env:
   # Increase pip debugging output, equivalent to `pip -vv`
   CIBW_BUILD_VERBOSITY: 2
-  # Disable build isolation to allow pre-installing build-time dependencies
+  # Disable build isolation to allow pre-installing build-time dependencies.
+  # Note: CIBW_BEFORE_BUILD must be present in all jobs using cibuildwheel.
   CIBW_BUILD_FRONTEND: "pip; args: --no-build-isolation"
+  # Skip PyPy and MUSL builds in any and all jobs
+  CIBW_SKIP: "pp* *musllinux*"
+  FORCE_COLOR: 3
 
 jobs:
   build_windows_wheels:
-    name: Build wheels on windows-latest
+    name: Wheels (windows-latest)
     runs-on: windows-latest
     steps:
       - uses: actions/checkout@v4
@@ -62,9 +66,7 @@ jobs:
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
-          CIBW_BEFORE_BUILD: >
-            python -m pip install --upgrade setuptools wheel
-          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
+          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -74,7 +76,7 @@ jobs:
           if-no-files-found: error
 
   build_macos_and_linux_wheels:
-    name: Build wheels on ${{ matrix.os }}
+    name: Wheels ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -82,7 +84,10 @@ jobs:
         os: [ubuntu-latest, macos-latest]
     steps:
       - uses: actions/checkout@v4
+        name: Check out PyBaMM repository
+
       - uses: actions/setup-python@v4
+        name: Set up Python
         with:
           python-version: 3.8
 
@@ -98,28 +103,32 @@ jobs:
           python -m pip install cmake wget
           python scripts/install_KLU_Sundials.py
 
-      - name: Build wheels on ${{ matrix.os }}
+      - name: Build wheels on Linux
         run: pipx run cibuildwheel --output-dir wheelhouse
+        if: matrix.os == 'ubuntu-latest'
         env:
           CIBW_ARCHS_LINUX: x86_64
           CIBW_BEFORE_ALL_LINUX: >
             yum -y install openblas-devel lapack-devel &&
             bash scripts/install_sundials.sh 6.0.3 6.5.0
-          CIBW_BEFORE_BUILD_LINUX: >
-            python -m pip install cmake casadi setuptools wheel
-          CIBW_REPAIR_WHEEL_COMMAND_LINUX: >
-            auditwheel repair -w {dest_dir} {wheel}
+          CIBW_BEFORE_BUILD_LINUX: python -m pip install cmake casadi setuptools wheel
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: auditwheel repair -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+
+      - name: Build wheels on macOS
+        if: matrix.os == 'macos-latest'
+        run: pipx run cibuildwheel --output-dir wheelhouse
+        env:
           CIBW_BEFORE_BUILD_MACOS: >
-            python -m pip install --upgrade cmake casadi setuptools wheel && scripts/fix_suitesparse_rpath_mac.sh
-          CIBW_REPAIR_WHEEL_COMMAND_MACOS: >
-            delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: "python -c 'import pybamm; print(pybamm.have_idaklu())' | grep 'True'"
-          CIBW_SKIP: "pp* *musllinux*"
+            python -m pip install --upgrade cmake casadi setuptools wheel &&
+            scripts/fix_suitesparse_rpath_mac.sh
+          CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
+          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
 
       - name: Upload wheels
         uses: actions/upload-artifact@v3
         with:
-          name: wheels
+          name: macos_linux_wheels
           path: ./wheelhouse/*.whl
           if-no-files-found: error
 
@@ -133,9 +142,6 @@ jobs:
         with:
           python-version: 3.11
 
-      - name: Install dependencies
-        run: pip install --upgrade pip setuptools wheel
-
       - name: Build SDist
         run: pipx run build --sdist
 
@@ -147,7 +153,8 @@ jobs:
           if-no-files-found: error
 
   publish_pypi:
-    if: github.event_name != 'schedule'
+    # This job is only of value to PyBaMM and would always be skipped in forks
+    if: github.event_name != 'schedule' && github.repository == 'pybamm-team/PyBaMM'
     name: Upload package to PyPI
     needs: [build_macos_and_linux_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
@@ -158,7 +165,7 @@ jobs:
       - name: Move all package files to files/
         run: |
           mkdir files
-          mv windows_wheels/* wheels/* sdist/* files/
+          mv windows_wheels/* macos_linux_wheels/* sdist/* files/
 
       - name: Publish on PyPI
         if: github.event.inputs.target == 'pypi' || github.event_name == 'release'

From 4a0bbd3521485fbadf210f72b0502adfc66afa3c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 27 Nov 2023 23:29:23 +0530
Subject: [PATCH 437/615] #3558 cover Windows wheel job with tests

---
 .github/workflows/publish_pypi.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 6b34a69907..75e3ebc94b 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -66,7 +66,8 @@ jobs:
         env:
           CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
           CIBW_ARCHS: "AMD64"
-          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel
+          CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Upload Windows wheels
         uses: actions/upload-artifact@v3
@@ -76,7 +77,7 @@ jobs:
           if-no-files-found: error
 
   build_macos_and_linux_wheels:
-    name: Wheels ${{ matrix.os }}
+    name: Wheels (${{ matrix.os }})
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false

From 1b97101310d5b31f050264286a540a27cf2b18bc Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 28 Nov 2023 01:07:09 +0530
Subject: [PATCH 438/615] Sync lower bounds with conda package

---
 docs/source/user_guide/installation/index.rst | 18 +++++++++---------
 pyproject.toml                                |  4 ++--
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index e771611a37..2b8b7fe304 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -62,11 +62,11 @@ PyBaMM requires the following dependencies.
 ================================================================ ==========================
 Package                                                          Minimum supported version
 ================================================================ ==========================
-`NumPy <https://numpy.org>`__                                    1.16.0
-`SciPy <https://docs.scipy.org/doc/scipy/>`__                    2.8.2
-`CasADi <https://web.casadi.org/docs/>`__                        3.6.0
-`Xarray <https://docs.xarray.dev/en/stable/>`__                  2023.04.0
-`Anytree <https://anytree.readthedocs.io/en/stable/>`__          2.4.3
+`NumPy <https://numpy.org>`__                                    1.23.5
+`SciPy <https://docs.scipy.org/doc/scipy/>`__                    1.9.3
+`CasADi <https://web.casadi.org/docs/>`__                        3.6.3
+`Xarray <https://docs.xarray.dev/en/stable/>`__                  2022.6.0
+`Anytree <https://anytree.readthedocs.io/en/stable/>`__          2.8.0
 ================================================================ ==========================
 
 .. _install.optional_dependencies:
@@ -91,8 +91,8 @@ Installable with ``pip install "pybamm[plot]"``
 =========================================================== ================== ================== ==================================================================
 Dependency                                                  Minimum Version    pip extra          Notes
 =========================================================== ================== ================== ==================================================================
-`imageio <https://imageio.readthedocs.io/en/stable/>`__     2.9.0              plot               For generating simulation GIFs.
-`matplotlib <https://matplotlib.org/stable/>`__             2.0.0              plot               To plot various battery models, and analyzing battery performance.
+`imageio <https://imageio.readthedocs.io/en/stable/>`__     2.3.0              plot               For generating simulation GIFs.
+`matplotlib <https://matplotlib.org/stable/>`__             3.6.0              plot               To plot various battery models, and analyzing battery performance.
 =========================================================== ================== ================== ==================================================================
 
 .. _install.pandas_dependencies:
@@ -105,7 +105,7 @@ Installable with ``pip install "pybamm[pandas]"``
 =========================================================== ================== ================== ==================================================================
 Dependency                                                  Minimum Version    pip extra          Notes
 =========================================================== ================== ================== ==================================================================
-`pandas <https://pandas.pydata.org/docs/>`__                0.24.0             pandas             For data manipulation and analysis.
+`pandas <https://pandas.pydata.org/docs/>`__                1.5.0              pandas             For data manipulation and analysis.
 =========================================================== ================== ================== ==================================================================
 
 .. _install.docs_dependencies:
@@ -183,7 +183,7 @@ Installable with ``pip install "pybamm[latexify]"``
 =========================================================== ================== ================== =========================
 Dependency                                                  Minimum Version    pip extra          Notes
 =========================================================== ================== ================== =========================
-`sympy <https://docs.sympy.org/latest/index.html>`__        1.8.0              latexify           For symbolic mathematics.
+`sympy <https://docs.sympy.org/latest/index.html>`__        1.9.3              latexify           For symbolic mathematics.
 =========================================================== ================== ================== =========================
 
 .. _install.bpx_dependencies:
diff --git a/pyproject.toml b/pyproject.toml
index eae0575117..f02286ad18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,7 +36,7 @@ dependencies = [
     "scipy>=1.9.3",
     "casadi>=3.6.3",
     "xarray>=2022.6.0",
-    "anytree>=2.12.0",
+    "anytree>=2.8.0",
 ]
 
 [project.urls]
@@ -72,7 +72,7 @@ examples = [
 ]
 # Plotting functionality
 plot = [
-    "imageio>=2.32.0",
+    "imageio>=2.3.0",
     # Note: matplotlib is loaded for debug plots, but to ensure PyBaMM runs
     # on systems without an attached display, it should never be imported
     # outside of plot() methods.

From 04f4230ce6ddb64a88cddb31064b891bc4a4e729 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Tue, 28 Nov 2023 11:13:31 +0000
Subject: [PATCH 439/615] Add outputs to example notebook Fixes doctests error

---
 .../notebooks/models/saving_models.ipynb      | 146 ++++++++++++++++--
 1 file changed, 130 insertions(+), 16 deletions(-)

diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index 9ac76a611e..91a6f2ae5c 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -18,9 +18,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -43,9 +51,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x2a4e10550>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Recreate the pybamm model from the JSON file\n",
     "new_dfn_model = pybamm.load_model(\"sim_model_example.json\")\n",
@@ -65,13 +84,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "tags": [
      "raises-exception"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "No variables to plot",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb Cell 7\u001b[0m line \u001b[0;36m8\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb#W6sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m     plot_sim\u001b[39m.\u001b[39msolve([\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb#W6sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m     sims\u001b[39m.\u001b[39mappend(plot_sim)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/pipliggins/Documents/repos/pybamm-local/docs/source/examples/notebooks/models/saving_models.ipynb#W6sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m pybamm\u001b[39m.\u001b[39;49mdynamic_plot(sims, time_unit\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mseconds\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/dynamic_plot.py:20\u001b[0m, in \u001b[0;36mdynamic_plot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[39mCreates a :class:`pybamm.QuickPlot` object (with arguments 'args' and keyword\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \u001b[39marguments 'kwargs') and then calls :meth:`pybamm.QuickPlot.dynamic_plot`.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[39m    The 'QuickPlot' object that was created\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m     19\u001b[0m kwargs_for_class \u001b[39m=\u001b[39m {k: v \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m kwargs\u001b[39m.\u001b[39mitems() \u001b[39mif\u001b[39;00m k \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m---> 20\u001b[0m plot \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39;49mQuickPlot(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs_for_class)\n\u001b[1;32m     21\u001b[0m plot\u001b[39m.\u001b[39mdynamic_plot(kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mtesting\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mFalse\u001b[39;00m))\n\u001b[1;32m     22\u001b[0m \u001b[39mreturn\u001b[39;00m plot\n",
+      "File \u001b[0;32m~/Documents/repos/pybamm-local/pybamm/plotting/quick_plot.py:146\u001b[0m, in \u001b[0;36mQuickPlot.__init__\u001b[0;34m(self, solutions, output_variables, labels, colors, linestyles, shading, figsize, n_rows, time_unit, spatial_unit, variable_limits)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[39m# check variables have been provided after any serialisation\u001b[39;00m\n\u001b[1;32m    145\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39mlen\u001b[39m(m\u001b[39m.\u001b[39mvariables) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m m \u001b[39min\u001b[39;00m models):\n\u001b[0;32m--> 146\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo variables to plot\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    148\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows \u001b[39m=\u001b[39m n_rows \u001b[39mor\u001b[39;00m \u001b[39mint\u001b[39m(\n\u001b[1;32m    149\u001b[0m     \u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m np\u001b[39m.\u001b[39msqrt(\u001b[39mlen\u001b[39m(output_variables))\n\u001b[1;32m    150\u001b[0m )\n\u001b[1;32m    151\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_cols \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mceil(\u001b[39mlen\u001b[39m(output_variables) \u001b[39m/\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_rows))\n",
+      "\u001b[0;31mAttributeError\u001b[0m: No variables to plot"
+     ]
+    }
+   ],
    "source": [
     "dfn_models = [dfn_model, new_dfn_model]\n",
     "sims = []\n",
@@ -94,9 +127,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81d8329fab424264bd56c65d53d34f63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=36.0), Output()), _dom_classes=…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x111963010>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# using the first simulation, save a new file which includes a list of the model variables\n",
     "dfn_sim.save_model(\"sim_model_variables\", variables=True)\n",
@@ -130,9 +188,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.models.full_battery_models.lithium_ion.spm.SPM at 0x29cf65c90>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# create the model\n",
     "spm_model = pybamm.lithium_ion.SPM()\n",
@@ -156,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -173,9 +242,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce5addf4f59c447e97d2fbee633cb6e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x29c8a3d10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# read back in\n",
     "new_spm_model = pybamm.load_model(\"example_model.json\")\n",
@@ -208,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,12 +323,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "pybamm.print_citations()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From ca63509060a895aabe812a7a1d2eebc08d8e2633 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 28 Nov 2023 11:16:07 +0000
Subject: [PATCH 440/615] style: pre-commit fixes

---
 tests/unit/test_serialisation/test_serialisation.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index e304091b22..6c43eaa9d7 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -272,7 +272,7 @@ def test_deconstruct_pybamm_dicts(self):
         ser_dict = {
             "rod": {
                 "symbol_x": {
-                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",  # noqa: E501
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
                     "py/id": mock.ANY,
                     "name": "x",
                     "id": mock.ANY,
@@ -341,7 +341,7 @@ def test_reconstruct_expression_tree(self):
             },
             "children": [
                 {
-                    "py/object": "pybamm.expression_tree.binary_operators.Multiplication",  # noqa: E501
+                    "py/object": "pybamm.expression_tree.binary_operators.Multiplication",
                     "py/id": 139691619709232,
                     "name": "*",
                     "id": 6094209803352873499,
@@ -361,7 +361,7 @@ def test_reconstruct_expression_tree(self):
                             "children": [],
                         },
                         {
-                            "py/object": "pybamm.expression_tree.state_vector.StateVector",  # noqa: E501
+                            "py/object": "pybamm.expression_tree.state_vector.StateVector",
                             "py/id": 139691619589760,
                             "name": "y[0:1]",
                             "id": 5063056989669636089,
@@ -423,7 +423,7 @@ def test_reconstruct_pybamm_dict(self):
         ser_dict = {
             "rod": {
                 "symbol_x": {
-                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",  # noqa: E501
+                    "py/object": "pybamm.expression_tree.independent_variable.SpatialVariable",
                     "py/id": mock.ANY,
                     "name": "x",
                     "id": mock.ANY,

From df35b91c894a42c1618b6a50375e4e6bc27b8d60 Mon Sep 17 00:00:00 2001
From: Pip Liggins <philippa.liggins@dtc.ox.ac.uk>
Date: Tue, 28 Nov 2023 11:23:56 +0000
Subject: [PATCH 441/615] Fix ruff errors

---
 pybamm/expression_tree/operations/serialise.py | 10 ++++++----
 pybamm/simulation.py                           |  7 +++++--
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index cd2ff15c3d..c7768217a3 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -7,6 +7,8 @@
 import numpy as np
 import re
 
+from typing import Optional
+
 
 class Serialise:
     """
@@ -78,9 +80,9 @@ class _EmptyDict(dict):
     def save_model(
         self,
         model: pybamm.BaseModel,
-        mesh: pybamm.Mesh = None,
-        variables: pybamm.FuzzyDict = None,
-        filename: str = None,
+        mesh: Optional[pybamm.Mesh] = None,
+        variables: Optional[pybamm.FuzzyDict] = None,
+        filename: Optional[str] = None,
     ):
         """Saves a discretised model to a JSON file.
 
@@ -142,7 +144,7 @@ def save_model(
             json.dump(model_json, f)
 
     def load_model(
-        self, filename: str, battery_model: pybamm.BaseModel = None
+        self, filename: str, battery_model: Optional[pybamm.BaseModel] = None
     ) -> pybamm.BaseModel:
         """
         Loads a discretised, ready to solve model into PyBaMM.
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 4fe9c32924..83a386fe98 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -10,6 +10,7 @@
 from functools import lru_cache
 from datetime import timedelta
 from pybamm.util import have_optional_dependency
+from typing import Optional
 
 from pybamm.expression_tree.operations.serialise import Serialise
 
@@ -795,7 +796,9 @@ def solve(
                     # Hacky patch to allow correct processing of end_time and next_starting time
                     # For efficiency purposes, op_conds treats identical steps as the same object
                     # regardless of the initial time. Should be refactored as part of #3176
-                    op_conds_unproc = self.experiment.operating_conditions_steps_unprocessed[idx]
+                    op_conds_unproc = (
+                        self.experiment.operating_conditions_steps_unprocessed[idx]
+                    )
 
                     start_time = current_solution.t[-1]
 
@@ -1192,7 +1195,7 @@ def save(self, filename):
 
     def save_model(
         self,
-        filename: str = None,
+        filename: Optional[str] = None,
         mesh: bool = False,
         variables: bool = False,
     ):

From b13240c5baeae256f6c34d2bc4d0fdb63659bef3 Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Tue, 28 Nov 2023 12:07:28 +0000
Subject: [PATCH 442/615] #2188 implement evaluate at in 1D

---
 examples/scripts/3E_cell.py                   |  53 ++++++++
 pybamm/__init__.py                            |   1 +
 pybamm/discretisations/discretisation.py      |   4 +
 pybamm/expression_tree/unary_operators.py     | 119 +++++++++++++++---
 pybamm/spatial_methods/finite_volume.py       |  44 +++++++
 pybamm/spatial_methods/spatial_method.py      |  22 +++-
 .../test_unary_operators.py                   |  10 +-
 .../test_base_spatial_method.py               |   2 +
 .../test_finite_volume/test_finite_volume.py  |  35 ++++++
 9 files changed, 269 insertions(+), 21 deletions(-)
 create mode 100644 examples/scripts/3E_cell.py

diff --git a/examples/scripts/3E_cell.py b/examples/scripts/3E_cell.py
new file mode 100644
index 0000000000..625e25a0a7
--- /dev/null
+++ b/examples/scripts/3E_cell.py
@@ -0,0 +1,53 @@
+#
+# Simulate insertion of a reference electrode in the middle of the cell
+#
+import pybamm
+
+# load model
+model = pybamm.lithium_ion.SPM()
+
+# load parameters and evaluate the mid-point of the cell
+parameter_values = pybamm.ParameterValues("Chen2020")
+L_n = model.param.n.L
+L_s = model.param.s.L
+L_mid = parameter_values.evaluate(L_n + L_s / 2)
+
+# extract the potential in the negative and positive electrode at the electrode/current
+# collector interfaces
+phi_n = pybamm.boundary_value(
+    model.variables["Negative electrode potential [V]"], "left"
+)
+phi_p = pybamm.boundary_value(
+    model.variables["Positive electrode potential [V]"], "right"
+)
+
+# evaluate the electrolyte potential at the mid-point of the cell
+phi_e_mid = pybamm.EvaluateAt(model.variables["Electrolyte potential [V]"], L_mid)
+
+# add the new variables to the model
+model.variables.update(
+    {
+        "Negative electrode 3E potential [V]": phi_n - phi_e_mid,
+        "Positive electrode 3E potential [V]": phi_p - phi_e_mid,
+    }
+)
+
+# solve
+sim = pybamm.Simulation(model)
+sim.solve([0, 3600])
+
+# plot a comparison of the 3E potential and the potential difference between the solid
+# and electrolyte phases at the electrode/separator interfaces
+sim.plot(
+    [
+        [
+            "Negative electrode surface potential difference at separator interface [V]",
+            "Negative electrode 3E potential [V]",
+        ],
+        [
+            "Positive electrode surface potential difference at separator interface [V]",
+            "Positive electrode 3E potential [V]",
+        ],
+        "Voltage [V]",
+    ]
+)
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 07d8a1c0ea..084128b6ff 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -54,6 +54,7 @@
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
 from .citations import Citations, citations, print_citations
+
 #
 # Classes for the Expression Tree
 #
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index bb6e678f4c..09f0e37496 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -865,6 +865,10 @@ def _process_symbol(self, symbol):
                 return child_spatial_method.boundary_value_or_flux(
                     symbol, disc_child, self.bcs
                 )
+            elif isinstance(symbol, pybamm.EvaluateAt):
+                return child_spatial_method.evaluate_at(
+                    symbol, disc_child, symbol.value
+                )
             elif isinstance(symbol, pybamm.UpwindDownwind):
                 direction = symbol.name  # upwind or downwind
                 return spatial_method.upwind_or_downwind(
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 95306ebad5..5490e06718 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -287,7 +287,14 @@ def _unary_jac(self, child_jac):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.slice.start, self.slice.stop, self.children[0].id, *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                self.slice.start,
+                self.slice.stop,
+                self.children[0].id,
+                *tuple(self.domain),
+            )
         )
 
     def _unary_evaluate(self, child):
@@ -396,7 +403,9 @@ def _unary_new_copy(self, child):
 
     def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
+        sympy_Divergence = have_optional_dependency(
+            "sympy.vector.operators", "Divergence"
+        )
         return sympy_Divergence(child)
 
 
@@ -547,7 +556,18 @@ def integration_variable(self):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, *tuple([integration_variable.id for integration_variable in self.integration_variable]), self.children[0].id, *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                *tuple(
+                    [
+                        integration_variable.id
+                        for integration_variable in self.integration_variable
+                    ]
+                ),
+                self.children[0].id,
+                *tuple(self.domain),
+            )
         )
 
     def _unary_new_copy(self, child):
@@ -687,7 +707,13 @@ def __init__(self, child, vector_type="row"):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.vector_type, self.children[0].id, *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                self.vector_type,
+                self.children[0].id,
+                *tuple(self.domain),
+            )
         )
 
     def _unary_new_copy(self, child):
@@ -757,6 +783,18 @@ def _evaluates_on_edges(self, dimension):
         return False
 
 
+class ExplicitTimeIntegral(UnaryOperator):
+    def __init__(self, children, initial_condition):
+        super().__init__("explicit time integral", children)
+        self.initial_condition = initial_condition
+
+    def _unary_new_copy(self, child):
+        return self.__class__(child, self.initial_condition)
+
+    def is_constant(self):
+        return False
+
+
 class DeltaFunction(SpatialOperator):
     """
     Delta function. Currently can only be implemented at the edge of a domain.
@@ -781,7 +819,13 @@ def __init__(self, child, side, domain):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.side, self.children[0].id, *tuple([(k, tuple(v)) for k, v in self.domains.items()]))
+            (
+                self.__class__,
+                self.name,
+                self.side,
+                self.children[0].id,
+                *tuple([(k, tuple(v)) for k, v in self.domains.items()]),
+            )
         )
 
     def _evaluates_on_edges(self, dimension):
@@ -839,7 +883,13 @@ def __init__(self, name, child, side):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, self.side, self.children[0].id, *tuple([(k, tuple(v)) for k, v in self.domains.items()]))
+            (
+                self.__class__,
+                self.name,
+                self.side,
+                self.children[0].id,
+                *tuple([(k, tuple(v)) for k, v in self.domains.items()]),
+            )
         )
 
     def _unary_new_copy(self, child):
@@ -892,18 +942,6 @@ def _sympy_operator(self, child):
             return sympy.Symbol(latex_child)
 
 
-class ExplicitTimeIntegral(UnaryOperator):
-    def __init__(self, children, initial_condition):
-        super().__init__("explicit time integral", children)
-        self.initial_condition = initial_condition
-
-    def _unary_new_copy(self, child):
-        return self.__class__(child, self.initial_condition)
-
-    def is_constant(self):
-        return False
-
-
 class BoundaryGradient(BoundaryOperator):
     """
     A node in the expression tree which gets the boundary flux of a variable.
@@ -920,6 +958,51 @@ def __init__(self, child, side):
         super().__init__("boundary flux", child, side)
 
 
+class EvaluateAt(SpatialOperator):
+    """
+    A node in the expression tree which evaluates a symbol at a given position. Only
+    implemented for variables that depend on a single spatial dimension.
+
+    Parameters
+    ----------
+    child : :class:`pybamm.Symbol`
+        The variable whose boundary value to take
+    value : float
+        The point in one-dimensional space at which to evaluate the symbol.
+    """
+
+    def __init__(self, child, value):
+        self.value = value
+
+        super().__init__("evaluate", child)
+
+        # evaluating removes the domain
+        self.clear_domains()
+
+    def set_id(self):
+        """See :meth:`pybamm.Symbol.set_id()`"""
+        self._id = hash(
+            (
+                self.__class__,
+                self.name,
+                self.value,
+                self.children[0].id,
+            )
+        )
+
+    def _unary_jac(self, child_jac):
+        """See :meth:`pybamm.UnaryOperator._unary_jac()`."""
+        return pybamm.Scalar(0)
+
+    def _unary_new_copy(self, child):
+        """See :meth:`UnaryOperator._unary_new_copy()`."""
+        return self.__class__(child, self.value)
+
+    def _evaluate_for_shape(self):
+        """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
+        return pybamm.evaluate_for_shape_using_domain(self.domains)
+
+
 class UpwindDownwind(SpatialOperator):
     """
     A node in the expression tree representing an upwinding or downwinding operator.
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 5c32e5a2c0..0e25a7b3fb 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -1023,6 +1023,50 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None):
 
         return boundary_value
 
+    def evaluate_at(self, symbol, discretised_child, value):
+        """
+        Returns the symbol evaluated at a given position in space. In the Finite
+        Volume method, the symbol is evaluated at the nearest node to the given value.
+
+        Parameters
+        ----------
+        symbol: :class:`pybamm.Symbol`
+            The boundary value or flux symbol
+        discretised_child : :class:`pybamm.StateVector`
+            The discretised variable from which to calculate the boundary value
+        value : float
+            The point in one-dimensional space at which to evaluate the symbol.
+
+        Returns
+        -------
+        :class:`pybamm.MatrixMultiplication`
+            The variable representing the value at the given point.
+        """
+        # Check dimension
+        if self._get_auxiliary_domain_repeats(discretised_child.domains) > 1:
+            raise NotImplementedError(
+                "'EvaluateAt' is only implemented for 1D variables."
+            )
+
+        # Get mesh nodes
+        domain = discretised_child.domain
+        mesh = self.mesh[domain]
+        nodes = mesh.nodes
+
+        # Find the index of the node closest to the value
+        index = np.argmin(np.abs(nodes - value))
+
+        # Create a sparse matrix with a 1 at the index
+        matrix = csr_matrix(([1], ([0], [index])), shape=(1, mesh.npts))
+
+        # Index into the discretised child
+        out = pybamm.Matrix(matrix) @ discretised_child
+
+        # `EvaluateAt` removes domain
+        out.clear_domains()
+
+        return out
+
     def process_binary_operators(self, bin_op, left, right, disc_left, disc_right):
         """Discretise binary operators in model equations.  Performs appropriate
         averaging of diffusivities if one of the children is a gradient operator, so
diff --git a/pybamm/spatial_methods/spatial_method.py b/pybamm/spatial_methods/spatial_method.py
index acb0227bc2..4945c7e1bb 100644
--- a/pybamm/spatial_methods/spatial_method.py
+++ b/pybamm/spatial_methods/spatial_method.py
@@ -331,7 +331,7 @@ def internal_neumann_condition(
 
     def boundary_value_or_flux(self, symbol, discretised_child, bcs=None):
         """
-        Returns the boundary value or flux using the approriate expression for the
+        Returns the boundary value or flux using the appropriate expression for the
         spatial method. To do this, we create a sparse vector 'bv_vector' that extracts
         either the first (for side="left") or last (for side="right") point from
         'discretised_child'.
@@ -377,6 +377,26 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None):
         out.clear_domains()
         return out
 
+    def evaluate_at(self, symbol, discretised_child, value):
+        """
+        Returns the symbol evaluated at a given position in space.
+
+        Parameters
+        ----------
+        symbol: :class:`pybamm.Symbol`
+            The boundary value or flux symbol
+        discretised_child : :class:`pybamm.StateVector`
+            The discretised variable from which to calculate the boundary value
+        value : float
+            The point in one-dimensional space at which to evaluate the symbol.
+
+        Returns
+        -------
+        :class:`pybamm.MatrixMultiplication`
+            The variable representing the value at the given point.
+        """
+        raise NotImplementedError
+
     def mass_matrix(self, symbol, boundary_conditions):
         """
         Calculates the mass matrix for a spatial method.
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index fc845cb574..51ceed8495 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -392,6 +392,11 @@ def test_index(self):
             pybamm.Index(vec, 5)
         pybamm.settings.debug_mode = debug_mode
 
+    def test_evaluate_at(self):
+        a = pybamm.Symbol("a", domain=["negative electrode"])
+        f = pybamm.EvaluateAt(a, 1)
+        self.assertEqual(f.value, 1)
+
     def test_upwind_downwind(self):
         # upwind of scalar symbol should fail
         a = pybamm.Symbol("a")
@@ -611,9 +616,10 @@ def test_not_constant(self):
         self.assertFalse((2 * a).is_constant())
 
     def test_to_equation(self):
-
         sympy = have_optional_dependency("sympy")
-        sympy_Divergence = have_optional_dependency("sympy.vector.operators", "Divergence")
+        sympy_Divergence = have_optional_dependency(
+            "sympy.vector.operators", "Divergence"
+        )
         sympy_Gradient = have_optional_dependency("sympy.vector.operators", "Gradient")
 
         a = pybamm.Symbol("a", domain="negative particle")
diff --git a/tests/unit/test_spatial_methods/test_base_spatial_method.py b/tests/unit/test_spatial_methods/test_base_spatial_method.py
index 37b4eb6a0b..d48ea69a7b 100644
--- a/tests/unit/test_spatial_methods/test_base_spatial_method.py
+++ b/tests/unit/test_spatial_methods/test_base_spatial_method.py
@@ -36,6 +36,8 @@ def test_basics(self):
             spatial_method.delta_function(None, None)
         with self.assertRaises(NotImplementedError):
             spatial_method.internal_neumann_condition(None, None, None, None)
+        with self.assertRaises(NotImplementedError):
+            spatial_method.evaluate_at(None, None, None)
 
     def test_get_auxiliary_domain_repeats(self):
         # Test the method to read number of repeats from auxiliary domains
diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
index 91a5b70044..b98cfa2abe 100644
--- a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
+++ b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
@@ -551,6 +551,41 @@ def test_full_broadcast_domains(self):
         disc = pybamm.Discretisation(mesh, spatial_methods)
         disc.process_model(model)
 
+    def test_evaluate_at(self):
+        mesh = get_p2d_mesh_for_testing()
+        spatial_methods = {
+            "macroscale": pybamm.FiniteVolume(),
+            "negative particle": pybamm.FiniteVolume(),
+            "positive particle": pybamm.FiniteVolume(),
+        }
+        disc = pybamm.Discretisation(mesh, spatial_methods)
+
+        n = mesh["negative electrode"].npts
+        var = pybamm.StateVector(slice(0, n), domain="negative electrode")
+
+        idx = 3
+        value = mesh["negative electrode"].nodes[idx]
+        evaluate_at = pybamm.EvaluateAt(var, value)
+        evaluate_at_disc = disc.process_symbol(evaluate_at)
+
+        self.assertIsInstance(evaluate_at_disc, pybamm.MatrixMultiplication)
+        self.assertIsInstance(evaluate_at_disc.left, pybamm.Matrix)
+        self.assertIsInstance(evaluate_at_disc.right, pybamm.StateVector)
+
+        y = np.arange(n)[:, np.newaxis]
+        self.assertEqual(evaluate_at_disc.evaluate(y=y), y[idx])
+
+        # test fail if not 1D
+        var = pybamm.Variable(
+            "var",
+            domain=["negative particle"],
+            auxiliary_domains={"secondary": "negative electrode"},
+        )
+        disc.set_variable_slices([var])
+        evaluate_at = pybamm.EvaluateAt(var, value)
+        with self.assertRaisesRegex(NotImplementedError, "'EvaluateAt' is only"):
+            disc.process_symbol(evaluate_at)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 1b973d3c291467cf51698084f95b0bd6cdc078ac Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Tue, 28 Nov 2023 13:57:59 +0000
Subject: [PATCH 443/615] #2188 changelog and coverage

---
 CHANGELOG.md                                                | 3 +++
 .../unit/test_expression_tree/test_operations/test_copy.py  | 1 +
 tests/unit/test_expression_tree/test_operations/test_jac.py | 6 ++++++
 3 files changed, 10 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 12c6ad8c84..7dd12bc30f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Features
+
+- Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 ## Bug fixes
 
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
diff --git a/tests/unit/test_expression_tree/test_operations/test_copy.py b/tests/unit/test_expression_tree/test_operations/test_copy.py
index 0340e56bb1..6800f9092f 100644
--- a/tests/unit/test_expression_tree/test_operations/test_copy.py
+++ b/tests/unit/test_expression_tree/test_operations/test_copy.py
@@ -60,6 +60,7 @@ def test_symbol_new_copy(self):
             pybamm.maximum(a, b),
             pybamm.SparseStack(mat, mat),
             pybamm.Equality(a, b),
+            pybamm.EvaluateAt(a, 0),
         ]:
             self.assertEqual(symbol, symbol.new_copy())
 
diff --git a/tests/unit/test_expression_tree/test_operations/test_jac.py b/tests/unit/test_expression_tree/test_operations/test_jac.py
index c6e04d331f..0271adbfad 100644
--- a/tests/unit/test_expression_tree/test_operations/test_jac.py
+++ b/tests/unit/test_expression_tree/test_operations/test_jac.py
@@ -236,6 +236,12 @@ def test_index(self):
         jac = ind.jac(vec).evaluate(y=np.linspace(0, 2, 5)).toarray()
         np.testing.assert_array_equal(jac, np.array([[0, 0, 0, 0, 0]]))
 
+    def test_evluate_at(self):
+        y = pybamm.StateVector(slice(0, 4))
+        expr = pybamm.EvaluateAt(y, 2)
+        jac = expr.jac(y).evaluate(y=np.linspace(0, 2, 4))
+        np.testing.assert_array_equal(jac, 0)
+
     def test_jac_of_number(self):
         """Jacobian of a number should be zero"""
         a = pybamm.Scalar(1)

From 9e829903f86c3af0f35b4a362bb86ddcfaf1e712 Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Wed, 29 Nov 2023 13:18:57 +0530
Subject: [PATCH 444/615] reverted changes from 
 3c59897a3ef85e0753997dbb7cc9d3e1c1814835

---
 pybamm/step/_steps_util.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/step/_steps_util.py b/pybamm/step/_steps_util.py
index eaa9c64636..e524bc6064 100644
--- a/pybamm/step/_steps_util.py
+++ b/pybamm/step/_steps_util.py
@@ -37,7 +37,7 @@ class _Step:
         or "resistance".
     value : float
         The value of the step, corresponding to the type of step. Can be a number, a
-        2-tuple (for cccv_ode), or a 2-column array. Can pass list as argument (for drive cycles)
+        2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     duration : float, optional
         The duration of the step in seconds.
     termination : str or list, optional

From 23d6e9ae30c07ba73f43a0d54c114cdf3900fc5b Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 29 Nov 2023 11:22:35 +0000
Subject: [PATCH 445/615] #2188 valentin comments

---
 .../api/expression_tree/unary_operator.rst    |   6 +
 .../notebooks/models/simulate-3E-cell.ipynb   | 153 ++++++++++++++++++
 ...simulating-ORegan-2022-parameter-set.ipynb |   6 +-
 examples/scripts/3E_cell.py                   |  53 ------
 pybamm/discretisations/discretisation.py      |   2 +-
 pybamm/expression_tree/unary_operators.py     |  28 ++--
 .../lithium_ion/base_lithium_ion_model.py     |  48 ++++++
 pybamm/parameters/parameter_values.py         |   9 ++
 pybamm/spatial_methods/finite_volume.py       |  20 +--
 pybamm/spatial_methods/spatial_method.py      |   4 +-
 .../test_operations/test_jac.py               |   2 +-
 .../test_unary_operators.py                   |   2 +-
 .../test_base_lithium_ion_model.py            |  19 +++
 .../test_finite_volume/test_finite_volume.py  |  15 +-
 14 files changed, 269 insertions(+), 98 deletions(-)
 create mode 100644 docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
 delete mode 100644 examples/scripts/3E_cell.py

diff --git a/docs/source/api/expression_tree/unary_operator.rst b/docs/source/api/expression_tree/unary_operator.rst
index ad5bb0a48f..e6a3cbe554 100644
--- a/docs/source/api/expression_tree/unary_operator.rst
+++ b/docs/source/api/expression_tree/unary_operator.rst
@@ -34,6 +34,9 @@ Unary Operators
 .. autoclass:: pybamm.Mass
   :members:
 
+.. autoclass:: pybamm.BoundaryMass
+  :members:
+
 .. autoclass:: pybamm.Integral
   :members:
 
@@ -58,6 +61,9 @@ Unary Operators
 .. autoclass:: pybamm.BoundaryGradient
   :members:
 
+.. autoclass:: pybamm.EvaluateAt
+  :members:
+
 .. autoclass:: pybamm.UpwindDownwind
   :members:
 
diff --git a/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb b/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
new file mode 100644
index 0000000000..501c54265d
--- /dev/null
+++ b/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
@@ -0,0 +1,153 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulating a 3E cell\n",
+    "\n",
+    "In this notebook we show how to insert a reference electrode to mimic a 3E cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first load a model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.DFN()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we use the helper function `insert_reference_electrode` to insert a reference electrode into the model. This function takes the position of the reference electrode as an optional argument. If no position is given, the reference electrode is inserted at the midpoint of the separator. The helper function adds the new variables \"Reference electrode potential [V]\", \"Negative electrode 3E potential [V]\" and \"Positive electrode 3E potential [V]\" to the model.\n",
+    "\n",
+    "In this example we will explicitly pass a position to show how it is done"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "L_n = model.param.n.L  # Negative electrode thickness [m]\n",
+    "L_s = model.param.s.L  # Separator thickness [m]\n",
+    "L_ref = L_n + L_s / 2  # Reference electrode position [m]\n",
+    "\n",
+    "model.insert_reference_electrode(L_ref)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we can set up a simulation and solve the model as usual"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim = pybamm.Simulation(model)\n",
+    "sim.solve([0, 3600])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot a comparison of the 3E potentials and the potential difference between the solid and electrolyte phases at the electrode/separator interfaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.plot(\n",
+    "    [\n",
+    "        [\n",
+    "            \"Negative electrode surface potential difference at separator interface [V]\",\n",
+    "            \"Negative electrode 3E potential [V]\",\n",
+    "        ],\n",
+    "        [\n",
+    "            \"Positive electrode surface potential difference at separator interface [V]\",\n",
+    "            \"Positive electrode 3E potential [V]\",\n",
+    "        ],\n",
+    "        \"Voltage [V]\",\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
index 7eb647fc97..f20f385601 100644
--- a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
+++ b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
@@ -163,7 +163,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.7.4 ('dev': venv)",
+   "display_name": "dev",
    "language": "python",
    "name": "python3"
   },
@@ -177,7 +177,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.16"
   },
   "toc": {
    "base_numbering": 1,
@@ -194,7 +194,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "0f0e5a277ebcf03e91e138edc3d4774b5dee64e7d6640c0d876f99a9f6b2a4dc"
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
    }
   }
  },
diff --git a/examples/scripts/3E_cell.py b/examples/scripts/3E_cell.py
deleted file mode 100644
index 625e25a0a7..0000000000
--- a/examples/scripts/3E_cell.py
+++ /dev/null
@@ -1,53 +0,0 @@
-#
-# Simulate insertion of a reference electrode in the middle of the cell
-#
-import pybamm
-
-# load model
-model = pybamm.lithium_ion.SPM()
-
-# load parameters and evaluate the mid-point of the cell
-parameter_values = pybamm.ParameterValues("Chen2020")
-L_n = model.param.n.L
-L_s = model.param.s.L
-L_mid = parameter_values.evaluate(L_n + L_s / 2)
-
-# extract the potential in the negative and positive electrode at the electrode/current
-# collector interfaces
-phi_n = pybamm.boundary_value(
-    model.variables["Negative electrode potential [V]"], "left"
-)
-phi_p = pybamm.boundary_value(
-    model.variables["Positive electrode potential [V]"], "right"
-)
-
-# evaluate the electrolyte potential at the mid-point of the cell
-phi_e_mid = pybamm.EvaluateAt(model.variables["Electrolyte potential [V]"], L_mid)
-
-# add the new variables to the model
-model.variables.update(
-    {
-        "Negative electrode 3E potential [V]": phi_n - phi_e_mid,
-        "Positive electrode 3E potential [V]": phi_p - phi_e_mid,
-    }
-)
-
-# solve
-sim = pybamm.Simulation(model)
-sim.solve([0, 3600])
-
-# plot a comparison of the 3E potential and the potential difference between the solid
-# and electrolyte phases at the electrode/separator interfaces
-sim.plot(
-    [
-        [
-            "Negative electrode surface potential difference at separator interface [V]",
-            "Negative electrode 3E potential [V]",
-        ],
-        [
-            "Positive electrode surface potential difference at separator interface [V]",
-            "Positive electrode 3E potential [V]",
-        ],
-        "Voltage [V]",
-    ]
-)
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index 09f0e37496..62110b1676 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -867,7 +867,7 @@ def _process_symbol(self, symbol):
                 )
             elif isinstance(symbol, pybamm.EvaluateAt):
                 return child_spatial_method.evaluate_at(
-                    symbol, disc_child, symbol.value
+                    symbol, disc_child, symbol.position
                 )
             elif isinstance(symbol, pybamm.UpwindDownwind):
                 direction = symbol.name  # upwind or downwind
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index ce2e8c6245..608cf070a2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -904,7 +904,8 @@ def evaluate_for_shape(self):
 
 class BoundaryOperator(SpatialOperator):
     """
-    A node in the expression tree which gets the boundary value of a variable.
+    A node in the expression tree which gets the boundary value of a variable on its
+    primary domain.
 
     Parameters
     ----------
@@ -961,7 +962,8 @@ def _evaluate_for_shape(self):
 
 class BoundaryValue(BoundaryOperator):
     """
-    A node in the expression tree which gets the boundary value of a variable.
+    A node in the expression tree which gets the boundary value of a variable on its
+    primary domain.
 
     Parameters
     ----------
@@ -1036,7 +1038,8 @@ def to_json(self):
 
 class BoundaryGradient(BoundaryOperator):
     """
-    A node in the expression tree which gets the boundary flux of a variable.
+    A node in the expression tree which gets the boundary flux of a variable on its
+    primary domain.
 
     Parameters
     ----------
@@ -1052,19 +1055,20 @@ def __init__(self, child, side):
 
 class EvaluateAt(SpatialOperator):
     """
-    A node in the expression tree which evaluates a symbol at a given position. Only
-    implemented for variables that depend on a single spatial dimension.
+    A node in the expression tree which evaluates a symbol at a given position in space
+    in its primary domain. Currently this is only implemented for 1D primary domains.
 
     Parameters
     ----------
     child : :class:`pybamm.Symbol`
-        The variable whose boundary value to take
-    value : float
-        The point in one-dimensional space at which to evaluate the symbol.
+        The variable to evaluate
+    position : :class:`pybamm.Symbol`
+        The position in space on the symbol's primary domain at which to evaluate
+        the symbol.
     """
 
-    def __init__(self, child, value):
-        self.value = value
+    def __init__(self, child, position):
+        self.position = position
 
         super().__init__("evaluate", child)
 
@@ -1077,7 +1081,7 @@ def set_id(self):
             (
                 self.__class__,
                 self.name,
-                self.value,
+                self.position,
                 self.children[0].id,
             )
         )
@@ -1088,7 +1092,7 @@ def _unary_jac(self, child_jac):
 
     def _unary_new_copy(self, child):
         """See :meth:`UnaryOperator._unary_new_copy()`."""
-        return self.__class__(child, self.value)
+        return self.__class__(child, self.position)
 
     def _evaluate_for_shape(self):
         """See :meth:`pybamm.Symbol.evaluate_for_shape_using_domain()`"""
diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
index cc736d6d04..fbe19b0d42 100644
--- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
+++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
@@ -462,3 +462,51 @@ def set_convection_submodel(self):
         self.submodels[
             "through-cell convection"
         ] = pybamm.convection.through_cell.NoConvection(self.param, self.options)
+
+    def insert_reference_electrode(self, position=None):
+        """
+        Insert a reference electrode to measure the electrolyte potential at a given
+        position in space. Adds model variables for the electrolyte potential at the
+        reference electrode and for the potential difference between the electrode
+        potentials measured at the electrode/current collector interface and the
+        reference electrode. Only implemented for 1D models (i.e. where the
+        'dimensionality' option is 0).
+
+        Parameters
+        ----------
+        position : :class:`pybamm.Symbol`, optional
+            The position in space at which to measure the electrolyte potential. If
+            None, defaults to the mid-point of the separator.
+        """
+        if self.options["dimensionality"] != 0:
+            raise NotImplementedError(
+                "Reference electrode can only be inserted for models where "
+                "'dimensionality' is 0. For other models, please add a reference "
+                "electrode manually."
+            )
+
+        param = self.param
+        if position is None:
+            position = param.n.L + param.s.L / 2
+
+        phi_e_ref = pybamm.EvaluateAt(
+            self.variables["Electrolyte potential [V]"], position
+        )
+        phi_p = pybamm.boundary_value(
+            self.variables["Positive electrode potential [V]"], "right"
+        )
+        variables = {
+            "Positive electrode 3E potential [V]": phi_p - phi_e_ref,
+            "Reference electrode potential [V]": phi_e_ref,
+        }
+
+        if self.options["working electrode"] == "both":
+            phi_n = pybamm.boundary_value(
+                self.variables["Negative electrode potential [V]"], "left"
+            )
+            variables.update(
+                {
+                    "Negative electrode 3E potential [V]": phi_n - phi_e_ref,
+                }
+            )
+        self.variables.update(variables)
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index d5f12f362f..049910ae9e 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -721,6 +721,15 @@ def _process_symbol(self, symbol):
             # f_a_dist in the size average needs to be processed
             if isinstance(new_symbol, pybamm.SizeAverage):
                 new_symbol.f_a_dist = self.process_symbol(new_symbol.f_a_dist)
+            # position in evaluate at needs to be processed, and should be a Scalar
+            if isinstance(new_symbol, pybamm.EvaluateAt):
+                new_symbol_position = self.process_symbol(new_symbol.position)
+                if not isinstance(new_symbol_position, pybamm.Scalar):
+                    raise ValueError(
+                        "'position' in 'EvaluateAt' must evaluate to a scalar"
+                    )
+                else:
+                    new_symbol.position = new_symbol_position
             return new_symbol
 
         # Functions
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 0e25a7b3fb..636243f829 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -1023,10 +1023,9 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None):
 
         return boundary_value
 
-    def evaluate_at(self, symbol, discretised_child, value):
+    def evaluate_at(self, symbol, discretised_child, position):
         """
-        Returns the symbol evaluated at a given position in space. In the Finite
-        Volume method, the symbol is evaluated at the nearest node to the given value.
+        Returns the symbol evaluated at a given position in space.
 
         Parameters
         ----------
@@ -1034,7 +1033,7 @@ def evaluate_at(self, symbol, discretised_child, value):
             The boundary value or flux symbol
         discretised_child : :class:`pybamm.StateVector`
             The discretised variable from which to calculate the boundary value
-        value : float
+        position : :class:`pybamm.Scalar`
             The point in one-dimensional space at which to evaluate the symbol.
 
         Returns
@@ -1042,22 +1041,19 @@ def evaluate_at(self, symbol, discretised_child, value):
         :class:`pybamm.MatrixMultiplication`
             The variable representing the value at the given point.
         """
-        # Check dimension
-        if self._get_auxiliary_domain_repeats(discretised_child.domains) > 1:
-            raise NotImplementedError(
-                "'EvaluateAt' is only implemented for 1D variables."
-            )
-
         # Get mesh nodes
         domain = discretised_child.domain
         mesh = self.mesh[domain]
         nodes = mesh.nodes
+        repeats = self._get_auxiliary_domain_repeats(discretised_child.domains)
 
         # Find the index of the node closest to the value
-        index = np.argmin(np.abs(nodes - value))
+        index = np.argmin(np.abs(nodes - position.value))
 
         # Create a sparse matrix with a 1 at the index
-        matrix = csr_matrix(([1], ([0], [index])), shape=(1, mesh.npts))
+        sub_matrix = csr_matrix(([1], ([0], [index])), shape=(1, mesh.npts))
+        # repeat across auxiliary domains
+        matrix = csr_matrix(kron(eye(repeats), sub_matrix))
 
         # Index into the discretised child
         out = pybamm.Matrix(matrix) @ discretised_child
diff --git a/pybamm/spatial_methods/spatial_method.py b/pybamm/spatial_methods/spatial_method.py
index 4945c7e1bb..a461d6c150 100644
--- a/pybamm/spatial_methods/spatial_method.py
+++ b/pybamm/spatial_methods/spatial_method.py
@@ -377,7 +377,7 @@ def boundary_value_or_flux(self, symbol, discretised_child, bcs=None):
         out.clear_domains()
         return out
 
-    def evaluate_at(self, symbol, discretised_child, value):
+    def evaluate_at(self, symbol, discretised_child, position):
         """
         Returns the symbol evaluated at a given position in space.
 
@@ -387,7 +387,7 @@ def evaluate_at(self, symbol, discretised_child, value):
             The boundary value or flux symbol
         discretised_child : :class:`pybamm.StateVector`
             The discretised variable from which to calculate the boundary value
-        value : float
+        position : :class:`pybamm.Scalar`
             The point in one-dimensional space at which to evaluate the symbol.
 
         Returns
diff --git a/tests/unit/test_expression_tree/test_operations/test_jac.py b/tests/unit/test_expression_tree/test_operations/test_jac.py
index 0271adbfad..503e7321ea 100644
--- a/tests/unit/test_expression_tree/test_operations/test_jac.py
+++ b/tests/unit/test_expression_tree/test_operations/test_jac.py
@@ -236,7 +236,7 @@ def test_index(self):
         jac = ind.jac(vec).evaluate(y=np.linspace(0, 2, 5)).toarray()
         np.testing.assert_array_equal(jac, np.array([[0, 0, 0, 0, 0]]))
 
-    def test_evluate_at(self):
+    def test_evaluate_at(self):
         y = pybamm.StateVector(slice(0, 4))
         expr = pybamm.EvaluateAt(y, 2)
         jac = expr.jac(y).evaluate(y=np.linspace(0, 2, 4))
diff --git a/tests/unit/test_expression_tree/test_unary_operators.py b/tests/unit/test_expression_tree/test_unary_operators.py
index f39dba335e..6ae6b62d05 100644
--- a/tests/unit/test_expression_tree/test_unary_operators.py
+++ b/tests/unit/test_expression_tree/test_unary_operators.py
@@ -457,7 +457,7 @@ def test_index(self):
     def test_evaluate_at(self):
         a = pybamm.Symbol("a", domain=["negative electrode"])
         f = pybamm.EvaluateAt(a, 1)
-        self.assertEqual(f.value, 1)
+        self.assertEqual(f.position, 1)
 
     def test_upwind_downwind(self):
         # upwind of scalar symbol should fail
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py
index 315896b29f..fbc916d4a5 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_base_lithium_ion_model.py
@@ -29,6 +29,25 @@ def test_default_parameters(self):
         )
         os.chdir(cwd)
 
+    def test_insert_reference_electrode(self):
+        model = pybamm.lithium_ion.SPM()
+        model.insert_reference_electrode()
+        self.assertIn("Negative electrode 3E potential [V]", model.variables)
+        self.assertIn("Positive electrode 3E potential [V]", model.variables)
+        self.assertIn("Reference electrode potential [V]", model.variables)
+
+        model = pybamm.lithium_ion.SPM({"working electrode": "positive"})
+        model.insert_reference_electrode()
+        self.assertNotIn("Negative electrode potential [V]", model.variables)
+        self.assertIn("Positive electrode 3E potential [V]", model.variables)
+        self.assertIn("Reference electrode potential [V]", model.variables)
+
+        model = pybamm.lithium_ion.SPM({"dimensionality": 2})
+        with self.assertRaisesRegex(
+            NotImplementedError, "Reference electrode can only be"
+        ):
+            model.insert_reference_electrode()
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
index b98cfa2abe..16a3bbde2c 100644
--- a/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
+++ b/tests/unit/test_spatial_methods/test_finite_volume/test_finite_volume.py
@@ -564,8 +564,8 @@ def test_evaluate_at(self):
         var = pybamm.StateVector(slice(0, n), domain="negative electrode")
 
         idx = 3
-        value = mesh["negative electrode"].nodes[idx]
-        evaluate_at = pybamm.EvaluateAt(var, value)
+        position = pybamm.Scalar(mesh["negative electrode"].nodes[idx])
+        evaluate_at = pybamm.EvaluateAt(var, position)
         evaluate_at_disc = disc.process_symbol(evaluate_at)
 
         self.assertIsInstance(evaluate_at_disc, pybamm.MatrixMultiplication)
@@ -575,17 +575,6 @@ def test_evaluate_at(self):
         y = np.arange(n)[:, np.newaxis]
         self.assertEqual(evaluate_at_disc.evaluate(y=y), y[idx])
 
-        # test fail if not 1D
-        var = pybamm.Variable(
-            "var",
-            domain=["negative particle"],
-            auxiliary_domains={"secondary": "negative electrode"},
-        )
-        disc.set_variable_slices([var])
-        evaluate_at = pybamm.EvaluateAt(var, value)
-        with self.assertRaisesRegex(NotImplementedError, "'EvaluateAt' is only"):
-            disc.process_symbol(evaluate_at)
-
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 7c5995f1b6a42aa54bc5203ffcdd10a06fdd668f Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Wed, 29 Nov 2023 17:56:38 +0530
Subject: [PATCH 446/615] value can be a 2-column array added in steps.py

---
 pybamm/step/steps.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 0b8123ddb0..4852131eff 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,7 +120,8 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A.
+        The current value in A. 
+        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -142,6 +143,7 @@ def c_rate(value, **kwargs):
     ----------
     value : float
         The C-rate value.
+        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -163,6 +165,7 @@ def voltage(value, **kwargs):
     ----------
     value : float
         The voltage value in V.
+        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -184,6 +187,7 @@ def power(value, **kwargs):
     ----------
     value : float
         The power value in W.
+        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -205,6 +209,7 @@ def resistance(value, **kwargs):
     ----------
     value : float
         The resistance value in Ohm.
+        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.

From 53efe92d2622841f88bdd3b24e637ab5ed4fa1ed Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 29 Nov 2023 12:29:36 +0000
Subject: [PATCH 447/615] style: pre-commit fixes

---
 pybamm/step/steps.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 4852131eff..1765d68085 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,7 +120,7 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A. 
+        The current value in A.
         Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`

From 6120caf744f6e8988029c352bc555b6f324cf69a Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 29 Nov 2023 14:30:03 +0000
Subject: [PATCH 448/615] #2188 debug domains

---
 .../notebooks/models/simulate-3E-cell.ipynb   | 83 ++++++++++++++++---
 pybamm/expression_tree/unary_operators.py     | 13 ++-
 2 files changed, 80 insertions(+), 16 deletions(-)

diff --git a/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb b/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
index 501c54265d..c92cb53465 100644
--- a/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
+++ b/docs/source/examples/notebooks/models/simulate-3E-cell.ipynb
@@ -12,9 +12,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm"
@@ -30,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,9 +77,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x169dc5950>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sim = pybamm.Simulation(model)\n",
     "sim.solve([0, 3600])"
@@ -87,9 +106,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1f4e1ed03764660b87ee56b135b24a7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x169816a90>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "sim.plot(\n",
     "    [\n",
@@ -108,9 +152,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "pybamm.print_citations()"
    ]
@@ -125,7 +182,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -139,12 +196,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
    }
   }
  },
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 608cf070a2..319429183c 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -1070,10 +1070,16 @@ class EvaluateAt(SpatialOperator):
     def __init__(self, child, position):
         self.position = position
 
-        super().__init__("evaluate", child)
+        # "evaluate at" of a child takes the primary domain from secondary domain
+        # of the child
+        # tertiary auxiliary domain shift down to secondary, quarternary to tertiary
+        domains = {
+            "primary": child.domains["secondary"],
+            "secondary": child.domains["tertiary"],
+            "tertiary": child.domains["quaternary"],
+        }
 
-        # evaluating removes the domain
-        self.clear_domains()
+        super().__init__("evaluate", child, domains)
 
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
@@ -1083,6 +1089,7 @@ def set_id(self):
                 self.name,
                 self.position,
                 self.children[0].id,
+                *tuple([(k, tuple(v)) for k, v in self.domains.items()]),
             )
         )
 

From d4211624cdd87cdcc5adb3e09e1c5a4ee53a0de2 Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 29 Nov 2023 14:35:15 +0000
Subject: [PATCH 449/615] #2188 add 3E notebook to toctree

---
 docs/source/examples/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 4afaa6eeeb..e025ea71b4 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -65,6 +65,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/models/rate-capability.ipynb
     notebooks/models/saving_models.ipynb
     notebooks/models/SEI-on-cracks.ipynb
+    notebooks/models/simulate-3E-cell.ipynb
     notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
     notebooks/models/SPM.ipynb
     notebooks/models/SPMe.ipynb

From a1ae912c873a2f9c3eeec1fc2cf6e30efe47261b Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Wed, 29 Nov 2023 15:18:53 +0000
Subject: [PATCH 450/615] #2188 coverage

---
 tests/unit/test_parameters/test_parameter_values.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 37ec89068f..40964e8d6d 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -267,6 +267,15 @@ def test_process_symbol(self):
         self.assertEqual(processed_a.value, 4)
         self.assertEqual(processed_x, x)
 
+        # process EvaluateAt
+        evaluate_at = pybamm.EvaluateAt(x, aa)
+        processed_evaluate_at = parameter_values.process_symbol(evaluate_at)
+        self.assertIsInstance(processed_evaluate_at, pybamm.EvaluateAt)
+        self.assertEqual(processed_evaluate_at.children[0], x)
+        self.assertEqual(processed_evaluate_at.position, 4)
+        with self.assertRaisesRegex(ValueError, "'position' in 'EvaluateAt'"):
+            parameter_values.process_symbol(pybamm.EvaluateAt(x, x))
+
         # process broadcast
         whole_cell = ["negative electrode", "separator", "positive electrode"]
         broad = pybamm.PrimaryBroadcast(a, whole_cell)

From 562815b7363acd4646095b8655a41263cb8a6a62 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Wed, 29 Nov 2023 23:47:10 +0530
Subject: [PATCH 451/615] Added "parameter_info" and modified
 "print_parameter_info"

---
 pybamm/models/base_model.py | 52 +++++++++++++++++++++++--------------
 1 file changed, 32 insertions(+), 20 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index c4ae414e7c..55fa5317df 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -421,28 +421,40 @@ def input_parameters(self):
             self._input_parameters = self._find_symbols(pybamm.InputParameter)
         return self._input_parameters
 
+    def parameter_info(self, param_class, param_type):
+        parameter_info = ""
+        parameters = self._find_symbols(param_class)
+        for parameter in parameters:
+            if isinstance(parameter, pybamm.FunctionParameter):
+                if parameter.name not in parameter_info:
+                    input_names = "'" + "', '".join(parameter.input_names) + "'"
+                    parameter_info += (
+                        f"{parameter.name} ({param_type} with input(s) {input_names})\n"
+                    )
+
+            elif isinstance(parameter, pybamm.InputParameter):
+                if not parameter.domain:
+                    parameter_info += f"{parameter.name} ({param_type})\n"
+                else:
+                    parameter_info += (
+                        f"{parameter.name} ({param_type} in {parameter.domain})\n"
+                    )
+
+            elif isinstance(parameter, pybamm.Parameter):
+                parameter_info += f"{parameter.name} ({param_type})\n"
+        return parameter_info
+
     def print_parameter_info(self):
         self._parameter_info = ""
-        parameters = self._find_symbols(pybamm.Parameter)
-        for param in parameters:
-            self._parameter_info += f"{param.name} (Parameter)\n"
-        input_parameters = self._find_symbols(pybamm.InputParameter)
-        for input_param in input_parameters:
-            if input_param.domain == []:
-                self._parameter_info += f"{input_param.name} (InputParameter)\n"
-            else:
-                self._parameter_info += (
-                    f"{input_param.name} (InputParameter in {input_param.domain})\n"
-                )
-        function_parameters = self._find_symbols(pybamm.FunctionParameter)
-        for func_param in function_parameters:
-            # don't double count function parameters
-            if func_param.name not in self._parameter_info:
-                input_names = "'" + "', '".join(func_param.input_names) + "'"
-                self._parameter_info += (
-                    f"{func_param.name} (FunctionParameter "
-                    f"with input(s) {input_names})\n"
-                )
+        parameter_types = [
+            ("Parameter", pybamm.Parameter),
+            ("inputParameter", pybamm.InputParameter),
+            ("FunctionParameter", pybamm.FunctionParameter),
+        ]
+
+        for param_type, param_class in parameter_types:
+            parameter_info = self.parameter_info(param_class, param_type)
+            self._parameter_info += parameter_info
 
         print(self._parameter_info)
 

From 09f6bc882f9686fbb7facee78f495cf9247f453b Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Wed, 29 Nov 2023 14:57:57 -0800
Subject: [PATCH 452/615] add omega

---
 pybamm/models/submodels/particle/base_particle.py    |  3 ++-
 .../submodels/particle_mechanics/base_mechanics.py   |  4 +++-
 pybamm/parameters/lithium_ion_parameters.py          | 12 +++++++++---
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index dd5a94afc6..9fe08bdaf6 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -56,7 +56,8 @@ def _get_effective_diffusivity(self, c, T, current):
 
         if stress_option == "true":
             # Ai2019 eq [12]
-            Omega = domain_param.Omega
+            sto = c / phase_param.c_max
+            Omega = pybamm.r_average(domain_param.Omega(sto))
             E = domain_param.E
             nu = domain_param.nu
             theta_M = Omega / (param.R * T) * (2 * Omega * E) / (9 * (1 - nu))
diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
index feffbdd380..72ed9bebb8 100644
--- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py
+++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
@@ -45,7 +45,9 @@ def _get_mechanical_results(self, variables):
         T_xav = variables["X-averaged cell temperature [K]"]
         eps_s = variables[f"{Domain} electrode active material volume fraction"]
 
-        Omega = domain_param.Omega
+        #use a tangential approximation for omega
+        sto = variables[f"{Domain} particle concentration"]
+        Omega = pybamm.r_average(domain_param.Omega(sto))
         R0 = domain_param.prim.R
         c_0 = domain_param.c_0
         E0 = domain_param.E
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index c459a4ef1e..918a993be1 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -308,9 +308,7 @@ def _set_parameters(self):
             f"{Domain} electrode reference concentration for free of deformation "
             "[mol.m-3]"
         )
-        self.Omega = pybamm.Parameter(
-            f"{Domain} electrode partial molar volume [m3.mol-1]"
-        )
+
         self.l_cr_0 = pybamm.Parameter(f"{Domain} electrode initial crack length [m]")
         self.w_cr = pybamm.Parameter(f"{Domain} electrode initial crack width [m]")
         self.rho_cr = pybamm.Parameter(
@@ -349,6 +347,14 @@ def C_dl(self, T):
             f"{Domain} electrode double-layer capacity [F.m-2]", inputs
         )
 
+    def Omega(self, sto):
+        """Dimensional partial molar volume of Li in solid solution [m3.mol-1]"""
+        Domain = self.domain.capitalize()
+        inputs = {f"{Domain} particle stoichiometry": sto}
+        return pybamm.FunctionParameter(
+            f"{Domain} electrode partial molar volume [m3.mol-1]"
+        )
+
     def sigma(self, T):
         """Dimensional electrical conductivity in electrode"""
         inputs = {"Temperature [K]": T}

From 242c1c1c4214b663147eba901d4ad1d6232e14e2 Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Wed, 29 Nov 2023 15:02:53 -0800
Subject: [PATCH 453/615] fix test failure

---
 pybamm/parameters/lithium_ion_parameters.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 918a993be1..5827062e4a 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -352,7 +352,7 @@ def Omega(self, sto):
         Domain = self.domain.capitalize()
         inputs = {f"{Domain} particle stoichiometry": sto}
         return pybamm.FunctionParameter(
-            f"{Domain} electrode partial molar volume [m3.mol-1]"
+            f"{Domain} electrode partial molar volume [m3.mol-1]", inputs
         )
 
     def sigma(self, T):

From 65c6dcb181acd020bab7c6c246d16ecca230682e Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Wed, 29 Nov 2023 15:27:02 -0800
Subject: [PATCH 454/615] add youngs modulus

---
 pybamm/models/submodels/particle/base_particle.py        | 2 +-
 .../submodels/particle_mechanics/base_mechanics.py       | 7 ++++++-
 pybamm/parameters/lithium_ion_parameters.py              | 9 ++++++++-
 3 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index 9fe08bdaf6..cd862518f0 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -58,7 +58,7 @@ def _get_effective_diffusivity(self, c, T, current):
             # Ai2019 eq [12]
             sto = c / phase_param.c_max
             Omega = pybamm.r_average(domain_param.Omega(sto))
-            E = domain_param.E
+            E = pybamm.r_average(domain_param.E(sto, T))
             nu = domain_param.nu
             theta_M = Omega / (param.R * T) * (2 * Omega * E) / (9 * (1 - nu))
             stress_factor = 1 + theta_M * (c - domain_param.c_0)
diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
index 72ed9bebb8..48f8a08d8f 100644
--- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py
+++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
@@ -43,6 +43,11 @@ def _get_mechanical_results(self, variables):
         sto_rav = variables[f"R-averaged {domain} particle concentration"]
         c_s_surf = variables[f"{Domain} particle surface concentration [mol.m-3]"]
         T_xav = variables["X-averaged cell temperature [K]"]
+        phase_name = self.phase_name
+        T = pybamm.PrimaryBroadcast(
+            variables[f"{Domain} electrode temperature [K]"],
+            [f"{domain} {phase_name}particle"],
+        )
         eps_s = variables[f"{Domain} electrode active material volume fraction"]
 
         #use a tangential approximation for omega
@@ -50,7 +55,7 @@ def _get_mechanical_results(self, variables):
         Omega = pybamm.r_average(domain_param.Omega(sto))
         R0 = domain_param.prim.R
         c_0 = domain_param.c_0
-        E0 = domain_param.E
+        E0 = pybamm.r_average(domain_param.E(sto, T))
         nu = domain_param.nu
         L0 = domain_param.L
         sto_init = pybamm.r_average(domain_param.prim.c_init / domain_param.prim.c_max)
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 5827062e4a..8bab4b2c4a 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -303,7 +303,6 @@ def _set_parameters(self):
 
         # Mechanical parameters
         self.nu = pybamm.Parameter(f"{Domain} electrode Poisson's ratio")
-        self.E = pybamm.Parameter(f"{Domain} electrode Young's modulus [Pa]")
         self.c_0 = pybamm.Parameter(
             f"{Domain} electrode reference concentration for free of deformation "
             "[mol.m-3]"
@@ -355,6 +354,14 @@ def Omega(self, sto):
             f"{Domain} electrode partial molar volume [m3.mol-1]", inputs
         )
 
+    def E(self, sto, T):
+        """Dimensional Young's modulus"""
+        Domain = self.domain.capitalize()
+        inputs = {f"{Domain} particle stoichiometry": sto, "Temperature [K]": T}
+        return pybamm.FunctionParameter(
+            f"{Domain} electrode Young's modulus [Pa]", inputs
+        )
+
     def sigma(self, T):
         """Dimensional electrical conductivity in electrode"""
         inputs = {"Temperature [K]": T}

From d150be3dc647f37399cf1c037afae36d37db7a7e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:32:07 +0530
Subject: [PATCH 455/615] Use `next(iter())` to evaluate `casadi` search paths

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index cd10b0cf9d..17c85a81bf 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -68,7 +68,7 @@ endif()
 # to find the path for the build-time dependency, not the run-time dependency.
 execute_process(
     COMMAND "${PYTHON_EXECUTABLE}" -c
-    "import importlib.util; print(importlib.util.find_spec('casadi').submodule_search_locations[0])"
+    "import importlib.util; print(next(iter(importlib.util.find_spec('casadi').submodule_search_locations)))"
     OUTPUT_VARIABLE CASADI_DIR
     OUTPUT_STRIP_TRAILING_WHITESPACE)
 

From 5df9f8a624880de6d63a190beb346db893ee5fb8 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 09:00:22 +0530
Subject: [PATCH 456/615] #3558 try to initialise IDAKLU solver instead of just
 importing it

---
 .github/workflows/publish_pypi.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 75e3ebc94b..a1db0e9a39 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -114,7 +114,7 @@ jobs:
             bash scripts/install_sundials.sh 6.0.3 6.5.0
           CIBW_BEFORE_BUILD_LINUX: python -m pip install cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_LINUX: auditwheel repair -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Build wheels on macOS
         if: matrix.os == 'macos-latest'
@@ -124,7 +124,7 @@ jobs:
             python -m pip install --upgrade cmake casadi setuptools wheel &&
             scripts/fix_suitesparse_rpath_mac.sh
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
-          CIBW_TEST_COMMAND: python -c "import pybamm; print(pybamm.have_idaklu())" | grep True
+          CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 
       - name: Upload wheels
         uses: actions/upload-artifact@v3

From 8274a49796fcda31eae3dd61be8bca4dd261f530 Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Fri, 1 Dec 2023 10:44:50 +0700
Subject: [PATCH 457/615] pre commit command change

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 .github/workflows/test_on_push.yml       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 4545dc26df..ee946ed93a 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -36,7 +36,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run ruff
+          pre-commit run --files $(git diff --name-only HEAD^) 
 
   build:
     needs: style
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2f7f94c9bc..910e27f9b6 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -28,7 +28,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run ruff
+          pre-commit run --files $(git diff --name-only HEAD^) 
 
   run_unit_tests:
     needs: style

From 05cc75466c2b3ed005a3fbc282b5a7c0b5d8e166 Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Fri, 1 Dec 2023 10:46:46 +0700
Subject: [PATCH 458/615] pre commit command change

---
 .github/workflows/run_periodic_tests.yml | 2 +-
 .github/workflows/test_on_push.yml       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index ee946ed93a..04873b1f4b 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -36,7 +36,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run --files $(git diff --name-only HEAD^) 
+          pre-commit run --files $(git diff --name-only HEAD^)
 
   build:
     needs: style
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 910e27f9b6..55694f292a 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -28,7 +28,7 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run --files $(git diff --name-only HEAD^) 
+          pre-commit run --files $(git diff --name-only HEAD^)
 
   run_unit_tests:
     needs: style

From 5a9fee1627b02f5d57e69eac6e2b2bb606633d2b Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Fri, 1 Dec 2023 11:20:29 +0700
Subject: [PATCH 459/615] pre commit command change

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bd9c751af7..5ab597458d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,7 +5,7 @@
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
 
 ## Bug fixes
-
+- Fixed Style checker on PRs does not actually check anything ([#3571](https://github.com/pybamm-team/PyBaMM/pull/3580))
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))

From 684bc9685b9225f05072ef011384f123b78cf2f4 Mon Sep 17 00:00:00 2001
From: Scott Marquis <scottmarquis93@googlemail.com>
Date: Fri, 1 Dec 2023 14:25:33 +0100
Subject: [PATCH 460/615]  added simple example and fix

---
 .../minimal_example_of_lookup_tables.py       | 51 +++++++++++++++++++
 pybamm/expression_tree/interpolant.py         |  5 +-
 .../test_expression_tree/test_interpolant.py  | 15 +++++-
 .../test_parameters/test_parameter_values.py  |  2 +-
 4 files changed, 68 insertions(+), 5 deletions(-)
 create mode 100644 examples/scripts/minimal_example_of_lookup_tables.py

diff --git a/examples/scripts/minimal_example_of_lookup_tables.py b/examples/scripts/minimal_example_of_lookup_tables.py
new file mode 100644
index 0000000000..324ec10538
--- /dev/null
+++ b/examples/scripts/minimal_example_of_lookup_tables.py
@@ -0,0 +1,51 @@
+import pybamm
+import pandas as pd
+import numpy as np
+
+
+def process_2D(name, data):
+    data = data.to_numpy()
+    x1 = np.unique(data[:, 0])
+    x2 = np.unique(data[:, 1])
+
+    value = data[:, 2]
+
+    x = (x1, x2)
+
+    value_data = value.reshape(len(x1), len(x2), order="C")
+
+    formatted_data = (name, (x, value_data))
+
+    return formatted_data
+
+
+parameter_values = pybamm.ParameterValues(pybamm.parameter_sets.Chen2020)
+
+# overwrite the diffusion coefficient with a 2D lookup table
+D_s_n = parameter_values["Negative electrode diffusivity [m2.s-1]"]
+df = pd.DataFrame(
+    {
+        "sto": [0, 1, 0, 1, 0, 1],
+        "T": [0, 0, 25, 25, 45, 45],
+        "D_s_n": [D_s_n, D_s_n, D_s_n, D_s_n, D_s_n, D_s_n],
+    }
+)
+df["T"] = df["T"] + 273.15
+D_s_n_data = process_2D("Negative electrode diffusivity [m2.s-1]", df)
+
+
+def D_s_n(sto, T):
+    name, (x, y) = D_s_n_data
+    return pybamm.Interpolant(x, y, [sto, T], name)
+
+
+parameter_values["Negative electrode diffusivity [m2.s-1]"] = D_s_n
+
+k_n = parameter_values["Negative electrode exchange-current density [A.m-2]"]
+
+model = pybamm.lithium_ion.DFN()
+sim = pybamm.Simulation(model, parameter_values=parameter_values)
+
+sim.solve([0, 30])
+
+sim.plot(["Negative particle surface concentration [mol.m-3]"])
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 8296625da0..1cb5e70d05 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -302,10 +302,11 @@ def _function_evaluate(self, evaluated_children):
                     new_evaluated_children, self.function.grid
                 ):
                     nan_children.append(np.ones_like(child) * interp_range.mean())
-                return self.function(np.transpose(nan_children)) * np.nan
+                nan_eval = self.function(np.transpose(nan_children))
+                return np.reshape(nan_eval, shape)
             else:
                 res = self.function(np.transpose(new_evaluated_children))
-                return res[:, np.newaxis]
+                return np.reshape(res, shape)
 
         else:  # pragma: no cover
             raise ValueError("Invalid dimension: {0}".format(self.dimension))
diff --git a/tests/unit/test_expression_tree/test_interpolant.py b/tests/unit/test_expression_tree/test_interpolant.py
index 5fa078cffc..1d1a55e85c 100644
--- a/tests/unit/test_expression_tree/test_interpolant.py
+++ b/tests/unit/test_expression_tree/test_interpolant.py
@@ -131,7 +131,7 @@ def f(x, y):
 
         value = interp.evaluate(y=np.array([[1, 1, x[1]], [5, 4, y[1]]]))
         np.testing.assert_array_equal(
-            value, np.array([[f(1, 5)], [f(1, 4)], [f(x[1], y[1])]])
+            value, np.array([[f(1, 5), f(1, 4), f(x[1], y[1])]])
         )
 
         # check also works for cubic
@@ -192,6 +192,17 @@ def f(x, y):
         evaluated_children = [1, 4]
         value = interp._function_evaluate(evaluated_children)
 
+        # Test that the interpolant shape is the same as the input data shape
+        interp = pybamm.Interpolant(x_in, data, (var1, var2), interpolator="linear")
+
+        evaluated_children = [np.array([[1, 1]]), np.array([[7, 7]])]
+        value = interp._function_evaluate(evaluated_children)
+        self.assertEqual(value.shape, evaluated_children[0].shape)
+
+        evaluated_children = [np.array([[1, 1], [1, 1]]), np.array([[7, 7], [7, 7]])]
+        value = interp._function_evaluate(evaluated_children)
+        self.assertEqual(value.shape, evaluated_children[0].shape)
+
     def test_interpolation_3_x(self):
         def f(x, y, z):
             return 2 * x**3 + 3 * y**2 - z
@@ -216,7 +227,7 @@ def f(x, y, z):
 
         value = interp.evaluate(y=np.array([[1, 1, 1], [5, 4, 4], [8, 7, 7]]))
         np.testing.assert_array_equal(
-            value, np.array([[f(1, 5, 8)], [f(1, 4, 7)], [f(1, 4, 7)]])
+            value, np.array([[f(1, 5, 8), f(1, 4, 7), f(1, 4, 7)]])
         )
 
         # check also works for cubic
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index 37ec89068f..3610b53424 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -559,7 +559,7 @@ def test_process_interpolant_2d(self):
         processed_func = parameter_values.process_symbol(func)
         self.assertIsInstance(processed_func, pybamm.Interpolant)
         self.assertAlmostEqual(
-            processed_func.evaluate(inputs={"a": 3.01, "b": 4.4})[0][0], 14.82
+            processed_func.evaluate(inputs={"a": 3.01, "b": 4.4}), 14.82
         )
 
         # process differentiated function parameter

From 52697f2ea5acd1ed46ea3adf63d17320b3d80824 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 20:30:18 +0530
Subject: [PATCH 461/615] #3558 #3100 keep equal `casadi` dependency versions

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f02286ad18..19c8800a63 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ requires = [
     "setuptools>=64",
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
-    "casadi>=3.6.0; platform_system!='Windows'",
+    "casadi>=3.6.3; platform_system!='Windows'",
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"

From 8608682903c4bba0c6abf4911912f3a91ca1d879 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 1 Dec 2023 20:31:26 +0530
Subject: [PATCH 462/615] #3558 #3100 Don't use a default path to search for
 alternative `casadi` installations

Co-Authored-By: jsbrittain <98161205+jsbrittain@users.noreply.github.com>
---
 CMakeLists.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 17c85a81bf..e9b3675e59 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -79,7 +79,7 @@ endif()
 
 if(${USE_PYTHON_CASADI})
   message("Trying to link against Python casadi package")
-  find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED)
+  find_package(casadi CONFIG PATHS ${CASADI_DIR} REQUIRED NO_DEFAULT_PATH)
 else()
   message("Trying to link against any casadi package apart from the Python one")
   set(CMAKE_IGNORE_PATH "${CASADI_DIR}/cmake")

From 258a1883db05c953e6af3cb697438bd9e72203b3 Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Sun, 3 Dec 2023 12:23:45 +0700
Subject: [PATCH 463/615] test commands

---
 pybamm/spatial_methods/scikit_finite_element.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 2d51e16c32..003ef36e30 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -353,10 +353,13 @@ def indefinite_integral(self, child, discretised_child, direction):
     def boundary_integral(self, child, discretised_child, region):
         """Implementation of the boundary integral operator.
         See :meth:`pybamm.SpatialMethod.boundary_integral`
+
+        hviyougougou
         """
         # Calculate integration vector
-        integration_vector = self.boundary_integral_vector(child.domain, region=region)
-
+        integration_vector = self.boundary_integral_vector(child.domain, region=region)   
+        jlbkhvb 
+         jo[and]  
         out = integration_vector @ discretised_child
         out.clear_domains()
         return out

From 396c046bb48833e10ccec15b3257d152adc70254 Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Sun, 3 Dec 2023 12:29:04 +0700
Subject: [PATCH 464/615] reverted change log

---
 CHANGELOG.md                                    | 2 +-
 pybamm/spatial_methods/scikit_finite_element.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5ab597458d..bd9c751af7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,7 +5,7 @@
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
 
 ## Bug fixes
-- Fixed Style checker on PRs does not actually check anything ([#3571](https://github.com/pybamm-team/PyBaMM/pull/3580))
+
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 003ef36e30..bbed122d31 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -357,9 +357,9 @@ def boundary_integral(self, child, discretised_child, region):
         hviyougougou
         """
         # Calculate integration vector
-        integration_vector = self.boundary_integral_vector(child.domain, region=region)   
-        jlbkhvb 
-         jo[and]  
+        integration_vector = self.boundary_integral_vector(child.domain, region=region)
+        jlbkhvb
+         jo[and]
         out = integration_vector @ discretised_child
         out.clear_domains()
         return out

From d9a1d94a599724e041cfa112132820ef3950cdca Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Sun, 3 Dec 2023 13:46:30 +0530
Subject: [PATCH 465/615] Added docstrings and exception handling to
 parameter_info and print_parameter_info

---
 pybamm/models/base_model.py | 71 ++++++++++++++++++++++---------------
 1 file changed, 42 insertions(+), 29 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 55fa5317df..2941c1c557 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -422,41 +422,54 @@ def input_parameters(self):
         return self._input_parameters
 
     def parameter_info(self, param_class, param_type):
-        parameter_info = ""
-        parameters = self._find_symbols(param_class)
-        for parameter in parameters:
-            if isinstance(parameter, pybamm.FunctionParameter):
-                if parameter.name not in parameter_info:
-                    input_names = "'" + "', '".join(parameter.input_names) + "'"
-                    parameter_info += (
-                        f"{parameter.name} ({param_type} with input(s) {input_names})\n"
-                    )
+        """Extracts and returns all the parameter information in the model"""
+        try:
+            parameter_info = ""
+            parameters = self._find_symbols(param_class)
+            for parameter in parameters:
+                if not isinstance(parameter, (pybamm.FunctionParameter, pybamm.InputParameter, pybamm.Parameter)):
+                    raise ValueError(f"ERROR: Invalid Parameter Type: {type(parameter)}")
+                if isinstance(parameter, pybamm.FunctionParameter):
+                    if parameter.name not in parameter_info:
+                        input_names = "'" + "', '".join(parameter.input_names) + "'"
+                        parameter_info += (
+                            f"{parameter.name} ({param_type} with input(s) {input_names})\n"
+                        )
 
-            elif isinstance(parameter, pybamm.InputParameter):
-                if not parameter.domain:
-                    parameter_info += f"{parameter.name} ({param_type})\n"
-                else:
-                    parameter_info += (
-                        f"{parameter.name} ({param_type} in {parameter.domain})\n"
-                    )
+                elif isinstance(parameter, pybamm.InputParameter):
+                    if not parameter.domain:
+                        parameter_info += f"{parameter.name} ({param_type})\n"
+                    else:
+                        parameter_info += (
+                            f"{parameter.name} ({param_type} in {parameter.domain})\n"
+                        )
 
-            elif isinstance(parameter, pybamm.Parameter):
-                parameter_info += f"{parameter.name} ({param_type})\n"
-        return parameter_info
+                elif isinstance(parameter, pybamm.Parameter):
+                    parameter_info += f"{parameter.name} ({param_type})\n"
+            return parameter_info
+        except Exception as e:
+            raise ValueError(f"ERROR in parameter_info: {e}")
 
     def print_parameter_info(self):
-        self._parameter_info = ""
-        parameter_types = [
-            ("Parameter", pybamm.Parameter),
-            ("inputParameter", pybamm.InputParameter),
-            ("FunctionParameter", pybamm.FunctionParameter),
-        ]
+        """Prints all the extracted parameter information of the model"""
+        try:
+            self._parameter_info = ""
+            parameter_types = [
+                ("Parameter", pybamm.Parameter),
+                ("inputParameter", pybamm.InputParameter),
+                ("FunctionParameter", pybamm.FunctionParameter),
+            ]
 
-        for param_type, param_class in parameter_types:
-            parameter_info = self.parameter_info(param_class, param_type)
-            self._parameter_info += parameter_info
+            for param_type, param_class in parameter_types:
+                parameter_info = self.parameter_info(param_class, param_type)
+                if parameter_info is not None:
+                    self._parameter_info += parameter_info
+                else:
+                    print(f"WARNING: parameter_info is NONE for {param_type}")
 
-        print(self._parameter_info)
+            print(self._parameter_info)
+        except Exception as e:
+            print(f"ERROR in print_parameter_info: {e}")
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From b86f58cf445252d5cc1bc430c1e6dcabbe14942a Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Sun, 3 Dec 2023 17:53:43 +0530
Subject: [PATCH 466/615] Added Issue 3361 to features' section of CHANGELOG.md

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bd9c751af7..220520031b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,7 +3,7 @@
 ## Features
 
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
-
+- Created "parameter_info" method and modified "print_parameter_info" to extract all parameters and print out required ones. ([#3361](https://github.com/pybamm-team/PyBaMM/issues/3361))
 ## Bug fixes
 
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))

From 4e72df1c4e2cc28e1ed743a7d49a30e995364821 Mon Sep 17 00:00:00 2001
From: Matthias Baur <matthias.t.baur@gmail.com>
Date: Mon, 4 Dec 2023 09:07:47 +0100
Subject: [PATCH 467/615] Fix bug #3543 by summing the irreversible and
 reversible heating terms over the phases in the BaseThermal class. To do so,
 the entropic change dUdT variables are stored for each phase in the
 BaseOpenCircuitPotential class.

---
 .../open_circuit_potential/base_ocp.py        |  4 +-
 .../models/submodels/thermal/base_thermal.py  | 65 ++++++++++++-------
 2 files changed, 45 insertions(+), 24 deletions(-)

diff --git a/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py
index e93dcefa3e..35f3894dfe 100644
--- a/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py
+++ b/pybamm/models/submodels/interface/open_circuit_potential/base_ocp.py
@@ -90,8 +90,8 @@ def _get_standard_ocp_variables(self, ocp_surf, ocp_bulk, dUdT):
         if self.reaction in ["lithium-ion main", "lead-acid main"]:
             variables.update(
                 {
-                    f"{Domain} electrode entropic change [V.K-1]": dUdT,
-                    f"X-averaged {domain} electrode entropic change [V.K-1]": dUdT_av,
+                    f"{Domain} electrode {reaction_name}entropic change [V.K-1]": dUdT,
+                    f"X-averaged {domain} electrode {reaction_name}entropic change [V.K-1]": dUdT_av,
                 }
             )
 
diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index 4c476ee897..2da2f205cf 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -117,38 +117,59 @@ def _get_standard_coupled_variables(self, variables):
         # Total Ohmic heating
         Q_ohm = Q_ohm_s + Q_ohm_e
 
-        # Irreversible electrochemical heating
-        a_j_p = variables[
-            "Positive electrode volumetric interfacial current density [A.m-3]"
-        ]
-        eta_r_p = variables["Positive electrode reaction overpotential [V]"]
+        num_phases = int(getattr(self.options, 'positive')["particle phases"])
+        phase_names = [""]
+        if num_phases > 1:
+            phase_names = ["primary ", "secondary "]
+        
+        Q_rxn_p, Q_rev_p = 0, 0
+        T_p = variables["Positive electrode temperature [K]"]
+        for phase in phase_names:
+            a_j_p = variables[
+                f"Positive electrode {phase}volumetric interfacial current density [A.m-3]"
+            ]
+            eta_r_p = variables[f"Positive electrode {phase}reaction overpotential [V]"]
+            # Irreversible electrochemical heating
+            Q_rxn_p += a_j_p * eta_r_p
+            # Reversible electrochemical heating
+            dUdT_p = variables[f"Positive electrode {phase}entropic change [V.K-1]"]
+            Q_rev_p += a_j_p * T_p * dUdT_p
+
+
+        num_phases = int(getattr(self.options, 'negative')["particle phases"])
+        phase_names = [""]
+        if num_phases > 1:
+            phase_names = ["primary", "secondary"]       
+
         if self.options.electrode_types["negative"] == "planar":
             Q_rxn_n = pybamm.FullBroadcast(
                 0, ["negative electrode"], "current collector"
             )
+            Q_rev_n = pybamm.FullBroadcast(
+                0, ["negative electrode"], "current collector"
+            )
         else:
-            a_j_n = variables[
-                "Negative electrode volumetric interfacial current density [A.m-3]"
-            ]
-            eta_r_n = variables["Negative electrode reaction overpotential [V]"]
-            Q_rxn_n = a_j_n * eta_r_n
-        Q_rxn_p = a_j_p * eta_r_p
+            T_n = variables["Negative electrode temperature [K]"]
+            Q_rxn_n = 0
+            Q_rev_n = 0
+            for phase in phase_names:
+                a_j_n = variables[
+                    f"Negative electrode {phase}volumetric interfacial current density [A.m-3]"
+                ]
+                eta_r_n = variables[f"Negative electrode {phase}reaction overpotential [V]"]
+                # Irreversible electrochemical heating
+                Q_rxn_n += a_j_n * eta_r_n
+                
+                # Reversible electrochemical heating
+                dUdT_n = variables[f"Negative electrode {phase}entropic change [V.K-1]"]
+                Q_rev_n += a_j_n * T_n * dUdT_n
+        
+        # Irreversible electrochemical heating
         Q_rxn = pybamm.concatenation(
             Q_rxn_n, pybamm.FullBroadcast(0, "separator", "current collector"), Q_rxn_p
         )
 
         # Reversible electrochemical heating
-        T_p = variables["Positive electrode temperature [K]"]
-        dUdT_p = variables["Positive electrode entropic change [V.K-1]"]
-        if self.options.electrode_types["negative"] == "planar":
-            Q_rev_n = pybamm.FullBroadcast(
-                0, ["negative electrode"], "current collector"
-            )
-        else:
-            T_n = variables["Negative electrode temperature [K]"]
-            dUdT_n = variables["Negative electrode entropic change [V.K-1]"]
-            Q_rev_n = a_j_n * T_n * dUdT_n
-        Q_rev_p = a_j_p * T_p * dUdT_p
         Q_rev = pybamm.concatenation(
             Q_rev_n, pybamm.FullBroadcast(0, "separator", "current collector"), Q_rev_p
         )

From 030c99b1f679587f5778c85c9bbdae1aebb2b81b Mon Sep 17 00:00:00 2001
From: Matthias Baur <matthias.t.baur@gmail.com>
Date: Mon, 4 Dec 2023 09:36:55 +0100
Subject: [PATCH 468/615] Run pre-commit checks that were forgotten in the last
 commit

---
 pybamm/models/submodels/thermal/base_thermal.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index 2da2f205cf..27e52d6638 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -121,7 +121,7 @@ def _get_standard_coupled_variables(self, variables):
         phase_names = [""]
         if num_phases > 1:
             phase_names = ["primary ", "secondary "]
-        
+
         Q_rxn_p, Q_rev_p = 0, 0
         T_p = variables["Positive electrode temperature [K]"]
         for phase in phase_names:
@@ -139,7 +139,7 @@ def _get_standard_coupled_variables(self, variables):
         num_phases = int(getattr(self.options, 'negative')["particle phases"])
         phase_names = [""]
         if num_phases > 1:
-            phase_names = ["primary", "secondary"]       
+            phase_names = ["primary", "secondary"]
 
         if self.options.electrode_types["negative"] == "planar":
             Q_rxn_n = pybamm.FullBroadcast(
@@ -159,11 +159,11 @@ def _get_standard_coupled_variables(self, variables):
                 eta_r_n = variables[f"Negative electrode {phase}reaction overpotential [V]"]
                 # Irreversible electrochemical heating
                 Q_rxn_n += a_j_n * eta_r_n
-                
+
                 # Reversible electrochemical heating
                 dUdT_n = variables[f"Negative electrode {phase}entropic change [V.K-1]"]
                 Q_rev_n += a_j_n * T_n * dUdT_n
-        
+
         # Irreversible electrochemical heating
         Q_rxn = pybamm.concatenation(
             Q_rxn_n, pybamm.FullBroadcast(0, "separator", "current collector"), Q_rxn_p

From 0176a2692c5ef292bca5ee4c371fc91bc1179af3 Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Mon, 4 Dec 2023 18:00:02 +0700
Subject: [PATCH 469/615] reverted changes

---
 .github/workflows/run_periodic_tests.yml        | 3 ++-
 .github/workflows/test_on_push.yml              | 3 ++-
 pybamm/spatial_methods/scikit_finite_element.py | 5 +----
 3 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 04873b1f4b..020ac37f86 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -36,7 +36,8 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run --files $(git diff --name-only HEAD^)
+          git add .
+          pre-commit run ruff
 
   build:
     needs: style
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 55694f292a..10fc5f53a8 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -28,7 +28,8 @@ jobs:
       - name: Check style
         run: |
           python -m pip install pre-commit
-          pre-commit run --files $(git diff --name-only HEAD^)
+          git add .
+          pre-commit run ruff
 
   run_unit_tests:
     needs: style
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index bbed122d31..2d51e16c32 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -353,13 +353,10 @@ def indefinite_integral(self, child, discretised_child, direction):
     def boundary_integral(self, child, discretised_child, region):
         """Implementation of the boundary integral operator.
         See :meth:`pybamm.SpatialMethod.boundary_integral`
-
-        hviyougougou
         """
         # Calculate integration vector
         integration_vector = self.boundary_integral_vector(child.domain, region=region)
-        jlbkhvb
-         jo[and]
+
         out = integration_vector @ discretised_child
         out.clear_domains()
         return out

From f33204cba763fa8915d5027181405a800054db13 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 5 Dec 2023 00:28:54 +0000
Subject: [PATCH 470/615] docs: update README.md [skip ci]

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 474b528bb6..1ae2aecebd 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-66-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-67-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -270,6 +270,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://aitorres.com"><img src="https://avatars.githubusercontent.com/u/26191851?v=4?s=100" width="100px;" alt="Andrés Ignacio Torres"/><br /><sub><b>Andrés Ignacio Torres</b></sub></a><br /><a href="#infra-aitorres" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/chmabaur"><img src="https://avatars.githubusercontent.com/u/127507466?v=4?s=100" width="100px;" alt="chmabaur"/><br /><sub><b>chmabaur</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achmabaur" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chmabaur" title="Code">💻</a></td>
     </tr>
   </tbody>
 </table>

From ae288bc610661d4b325f9e69a5ee2d4887d2fde7 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 5 Dec 2023 00:28:54 +0000
Subject: [PATCH 471/615] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 5cbd6f5ebd..8dfbde917c 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -727,6 +727,16 @@
         "code",
         "test"
       ]
+    },
+    {
+      "login": "chmabaur",
+      "name": "chmabaur",
+      "avatar_url": "https://avatars.githubusercontent.com/u/127507466?v=4",
+      "profile": "https://github.com/chmabaur",
+      "contributions": [
+        "bug",
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

From 31e08c78a75a52cbabe774deff98d8a50cdf5c46 Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Mon, 4 Dec 2023 17:10:49 -0800
Subject: [PATCH 472/615] update changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index bd9c751af7..0a8d46fc26 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,7 @@
 ## Features
 
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
+- Mechanical parameters are now a function of stoichiometry ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 
 ## Bug fixes
 

From 0fd0385f014abb6b4bf555449a18455e990971ea Mon Sep 17 00:00:00 2001
From: Alec Bills <alexanderallenbills@gmail.com>
Date: Mon, 4 Dec 2023 17:15:34 -0800
Subject: [PATCH 473/615] make partial molar volume also a function of
 temperature

---
 CHANGELOG.md                                                 | 2 +-
 pybamm/models/submodels/particle/base_particle.py            | 2 +-
 pybamm/models/submodels/particle_mechanics/base_mechanics.py | 2 +-
 pybamm/parameters/lithium_ion_parameters.py                  | 4 ++--
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0a8d46fc26..1b8e1a869b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,7 +3,7 @@
 ## Features
 
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
-- Mechanical parameters are now a function of stoichiometry ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 
 ## Bug fixes
 
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index cd862518f0..0f46615724 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -57,7 +57,7 @@ def _get_effective_diffusivity(self, c, T, current):
         if stress_option == "true":
             # Ai2019 eq [12]
             sto = c / phase_param.c_max
-            Omega = pybamm.r_average(domain_param.Omega(sto))
+            Omega = pybamm.r_average(domain_param.Omega(sto, T))
             E = pybamm.r_average(domain_param.E(sto, T))
             nu = domain_param.nu
             theta_M = Omega / (param.R * T) * (2 * Omega * E) / (9 * (1 - nu))
diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
index 48f8a08d8f..35adadf47d 100644
--- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py
+++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
@@ -52,7 +52,7 @@ def _get_mechanical_results(self, variables):
 
         #use a tangential approximation for omega
         sto = variables[f"{Domain} particle concentration"]
-        Omega = pybamm.r_average(domain_param.Omega(sto))
+        Omega = pybamm.r_average(domain_param.Omega(sto, T))
         R0 = domain_param.prim.R
         c_0 = domain_param.c_0
         E0 = pybamm.r_average(domain_param.E(sto, T))
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 8bab4b2c4a..12196c4044 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -346,10 +346,10 @@ def C_dl(self, T):
             f"{Domain} electrode double-layer capacity [F.m-2]", inputs
         )
 
-    def Omega(self, sto):
+    def Omega(self, sto, T):
         """Dimensional partial molar volume of Li in solid solution [m3.mol-1]"""
         Domain = self.domain.capitalize()
-        inputs = {f"{Domain} particle stoichiometry": sto}
+        inputs = {f"{Domain} particle stoichiometry": sto, "Temperature [K]": T}
         return pybamm.FunctionParameter(
             f"{Domain} electrode partial molar volume [m3.mol-1]", inputs
         )

From e2f8a43d43e0239c5a63fa62b06d30fae988a08f Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Tue, 5 Dec 2023 10:55:09 +0530
Subject: [PATCH 474/615] Implemented "get_parameter_info" to return a
 dictionary of parameters and modified "print_parameter_info" to print the
 dictionary

---
 pybamm/models/base_model.py | 66 ++++++++++++-------------------------
 1 file changed, 21 insertions(+), 45 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 2941c1c557..c8bd93f3e3 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -421,55 +421,31 @@ def input_parameters(self):
             self._input_parameters = self._find_symbols(pybamm.InputParameter)
         return self._input_parameters
 
-    def parameter_info(self, param_class, param_type):
-        """Extracts and returns all the parameter information in the model"""
-        try:
-            parameter_info = ""
-            parameters = self._find_symbols(param_class)
-            for parameter in parameters:
-                if not isinstance(parameter, (pybamm.FunctionParameter, pybamm.InputParameter, pybamm.Parameter)):
-                    raise ValueError(f"ERROR: Invalid Parameter Type: {type(parameter)}")
-                if isinstance(parameter, pybamm.FunctionParameter):
-                    if parameter.name not in parameter_info:
-                        input_names = "'" + "', '".join(parameter.input_names) + "'"
-                        parameter_info += (
-                            f"{parameter.name} ({param_type} with input(s) {input_names})\n"
-                        )
+    def get_parameter_info(self):
+        parameter_info = []
+        parameters = self._find_symbols(pybamm.Parameter)
+        for param in parameters:
+            parameter_info.append((param.name, "Parameter"))
+
+        input_parameters = self._find_symbols(pybamm.InputParameter)
+        for input_param in input_parameters:
+            if input_param.domain == []:
+                parameter_info.append((input_param.name, "InputParameter"))
+            else:
+                parameter_info.append((input_param.name, f"InputParameter in {input_param.domain}"))
 
-                elif isinstance(parameter, pybamm.InputParameter):
-                    if not parameter.domain:
-                        parameter_info += f"{parameter.name} ({param_type})\n"
-                    else:
-                        parameter_info += (
-                            f"{parameter.name} ({param_type} in {parameter.domain})\n"
-                        )
+        function_parameters = self._find_symbols(pybamm.FunctionParameter)
+        for func_param in function_parameters:
+            if func_param.name not in [name for name, _ in parameter_info]:
+                input_names =  "', '".join(func_param.input_names)
+                parameter_info.append((func_param.name, f"FunctionParameter with inputs(s) '{input_names}'"))
 
-                elif isinstance(parameter, pybamm.Parameter):
-                    parameter_info += f"{parameter.name} ({param_type})\n"
-            return parameter_info
-        except Exception as e:
-            raise ValueError(f"ERROR in parameter_info: {e}")
+        return parameter_info
 
     def print_parameter_info(self):
-        """Prints all the extracted parameter information of the model"""
-        try:
-            self._parameter_info = ""
-            parameter_types = [
-                ("Parameter", pybamm.Parameter),
-                ("inputParameter", pybamm.InputParameter),
-                ("FunctionParameter", pybamm.FunctionParameter),
-            ]
-
-            for param_type, param_class in parameter_types:
-                parameter_info = self.parameter_info(param_class, param_type)
-                if parameter_info is not None:
-                    self._parameter_info += parameter_info
-                else:
-                    print(f"WARNING: parameter_info is NONE for {param_type}")
-
-            print(self._parameter_info)
-        except Exception as e:
-            print(f"ERROR in print_parameter_info: {e}")
+        info = self.get_parameter_info()
+        for param , details in info:
+            print(f"{param} ({details})")
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From bc7dc95ee8b59326681d90dcbd869871bb2f89bc Mon Sep 17 00:00:00 2001
From: Scott Marquis <scottmarquis93@googlemail.com>
Date: Tue, 5 Dec 2023 09:30:10 +0100
Subject: [PATCH 475/615]  fixed T and sto order in example

---
 examples/scripts/minimal_example_of_lookup_tables.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/scripts/minimal_example_of_lookup_tables.py b/examples/scripts/minimal_example_of_lookup_tables.py
index 324ec10538..1c93e311c0 100644
--- a/examples/scripts/minimal_example_of_lookup_tables.py
+++ b/examples/scripts/minimal_example_of_lookup_tables.py
@@ -25,8 +25,8 @@ def process_2D(name, data):
 D_s_n = parameter_values["Negative electrode diffusivity [m2.s-1]"]
 df = pd.DataFrame(
     {
-        "sto": [0, 1, 0, 1, 0, 1],
         "T": [0, 0, 25, 25, 45, 45],
+        "sto": [0, 1, 0, 1, 0, 1],
         "D_s_n": [D_s_n, D_s_n, D_s_n, D_s_n, D_s_n, D_s_n],
     }
 )
@@ -36,7 +36,7 @@ def process_2D(name, data):
 
 def D_s_n(sto, T):
     name, (x, y) = D_s_n_data
-    return pybamm.Interpolant(x, y, [sto, T], name)
+    return pybamm.Interpolant(x, y, [T, sto], name)
 
 
 parameter_values["Negative electrode diffusivity [m2.s-1]"] = D_s_n

From efba2e0922b4006b3dcbd2c628d759684a1fe5a0 Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Tue, 5 Dec 2023 16:13:40 +0530
Subject: [PATCH 476/615] removed cccv_ode

---
 pybamm/step/steps.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 1765d68085..1f642864f7 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,7 +120,7 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A.
+        The current value in A. 
         Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
@@ -142,8 +142,7 @@ def c_rate(value, **kwargs):
     Parameters
     ----------
     value : float
-        The C-rate value.
-        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        The C-rate value. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -164,8 +163,7 @@ def voltage(value, **kwargs):
     Parameters
     ----------
     value : float
-        The voltage value in V.
-        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        The voltage value in V. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -186,8 +184,7 @@ def power(value, **kwargs):
     Parameters
     ----------
     value : float
-        The power value in W.
-        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        The power value in W. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -208,8 +205,7 @@ def resistance(value, **kwargs):
     Parameters
     ----------
     value : float
-        The resistance value in Ohm.
-        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        The resistance value in Ohm. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.

From 6b6ad0d60479794134108aff31fc6fe7c19054d1 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 5 Dec 2023 10:58:11 +0000
Subject: [PATCH 477/615] style: pre-commit fixes

---
 pybamm/step/steps.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 1f642864f7..89b36b7895 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,7 +120,7 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A. 
+        The current value in A.
         Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`

From 0aa035800ae23b69d0a7aedadb4cc9df5cadc427 Mon Sep 17 00:00:00 2001
From: Abhishek Chaudhari
 <91185083+AbhishekChaudharii@users.noreply.github.com>
Date: Tue, 5 Dec 2023 16:36:22 +0530
Subject: [PATCH 478/615] removed cccv_ode - fix

---
 pybamm/step/steps.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 89b36b7895..2f2e9e31a4 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,8 +120,7 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A.
-        Value can be a number, a 2-tuple (for cccv_ode), or a 2-column array (for drive cycles)
+        The current value in A. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.

From d38a8b51f5f1fec42cf92fa37a2f806634dc865f Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Tue, 5 Dec 2023 18:43:39 +0530
Subject: [PATCH 479/615] Improved readability

---
 pybamm/models/base_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index c8bd93f3e3..fb9cdfeeb2 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -429,7 +429,7 @@ def get_parameter_info(self):
 
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
-            if input_param.domain == []:
+            if not input_param.domain:
                 parameter_info.append((input_param.name, "InputParameter"))
             else:
                 parameter_info.append((input_param.name, f"InputParameter in {input_param.domain}"))
@@ -444,8 +444,8 @@ def get_parameter_info(self):
 
     def print_parameter_info(self):
         info = self.get_parameter_info()
-        for param , details in info:
-            print(f"{param} ({details})")
+        for param, param_type in info:
+            print(f"{param} ({param_type})")
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From b2848f51eb4dbdf99806ccbf7b7199472ed9fafb Mon Sep 17 00:00:00 2001
From: Robert Timms <robertwtimms@gmail.com>
Date: Tue, 5 Dec 2023 14:42:37 +0000
Subject: [PATCH 480/615] #2188 changelog

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cdd99125fd..a7bd875f63 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,6 +3,7 @@
 ## Features
 
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
+- Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
 
 ## Bug fixes

From ba23d413d21710c45426ac312f50d588f2f44591 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Tue, 5 Dec 2023 19:39:24 -0500
Subject: [PATCH 481/615] #3530 add classes to handle termination

---
 pybamm/__init__.py                          |  2 +
 pybamm/experiment/experiment.py             |  2 +-
 pybamm/{ => experiment}/step/__init__.py    |  1 +
 pybamm/{ => experiment}/step/_steps_util.py | 12 ++--
 pybamm/experiment/step/step_termination.py  | 78 +++++++++++++++++++++
 pybamm/{ => experiment}/step/steps.py       |  0
 pybamm/simulation.py                        | 57 ++-------------
 7 files changed, 94 insertions(+), 58 deletions(-)
 rename pybamm/{ => experiment}/step/__init__.py (58%)
 rename pybamm/{ => experiment}/step/_steps_util.py (96%)
 create mode 100644 pybamm/experiment/step/step_termination.py
 rename pybamm/{ => experiment}/step/steps.py (100%)

diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 07d8a1c0ea..019d657054 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -54,6 +54,7 @@
 from .logger import logger, set_logging_level, get_new_logger
 from .settings import settings
 from .citations import Citations, citations, print_citations
+
 #
 # Classes for the Expression Tree
 #
@@ -222,6 +223,7 @@
 #
 from .experiment.experiment import Experiment
 from . import experiment
+from .experiment import step
 
 
 #
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 9b02e3a20f..898d9b0f79 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -3,7 +3,7 @@
 #
 
 import pybamm
-from pybamm.step._steps_util import (
+from .step._steps_util import (
     _convert_time_to_seconds,
     _convert_temperature_to_kelvin,
 )
diff --git a/pybamm/step/__init__.py b/pybamm/experiment/step/__init__.py
similarity index 58%
rename from pybamm/step/__init__.py
rename to pybamm/experiment/step/__init__.py
index eea47a54a7..e3b9ff8bd0 100644
--- a/pybamm/step/__init__.py
+++ b/pybamm/experiment/step/__init__.py
@@ -1,2 +1,3 @@
 from .steps import *
 from .steps import _Step
+from .step_termination import *
diff --git a/pybamm/step/_steps_util.py b/pybamm/experiment/step/_steps_util.py
similarity index 96%
rename from pybamm/step/_steps_util.py
rename to pybamm/experiment/step/_steps_util.py
index e524bc6064..1bf98e5083 100644
--- a/pybamm/step/_steps_util.py
+++ b/pybamm/experiment/step/_steps_util.py
@@ -4,6 +4,7 @@
 import pybamm
 import numpy as np
 from datetime import datetime
+from .step_termination import read_termination
 
 _examples = """
 
@@ -136,8 +137,10 @@ def __init__(
             termination = [termination]
         self.termination = []
         for term in termination:
-            typ, value = _convert_electric(term)
-            self.termination.append({"type": typ, "value": value})
+            if isinstance(term, str):
+                term = _convert_electric(term)
+            term = read_termination(term)
+            self.termination.append(term)
 
         self.temperature = _convert_temperature_to_kelvin(temperature)
 
@@ -193,10 +196,7 @@ def to_dict(self):
         }
 
     def __eq__(self, other):
-        return (
-            isinstance(other, _Step)
-            and self.hash_args == other.hash_args
-        )
+        return isinstance(other, _Step) and self.hash_args == other.hash_args
 
     def __hash__(self):
         return hash(self.basic_repr())
diff --git a/pybamm/experiment/step/step_termination.py b/pybamm/experiment/step/step_termination.py
new file mode 100644
index 0000000000..0d5ce9c55f
--- /dev/null
+++ b/pybamm/experiment/step/step_termination.py
@@ -0,0 +1,78 @@
+import pybamm
+import numpy as np
+
+
+class BaseTermination:
+    def __init__(self, value):
+        self.value = value
+
+    def get_event(self, variables, step_value):
+        raise NotImplementedError
+
+
+class CrateTermination(BaseTermination):
+    def get_event(self, variables, step_value):
+        event = pybamm.Event(
+            "C-rate cut-off [A] [experiment]",
+            abs(variables["C-rate"]) - self.value,
+        )
+        return event
+
+
+class CurrentTermination(BaseTermination):
+    def get_event(self, variables, step_value):
+        event = pybamm.Event(
+            "Current cut-off [A] [experiment]",
+            abs(variables["Current [A]"]) - self.value,
+        )
+        return event
+
+
+class VoltageTermination(BaseTermination):
+    def get_event(self, variables, step_value):
+        # The voltage event should be positive at the start of charge/
+        # discharge. We use the sign of the current or power input to
+        # figure out whether the voltage event is greater than the starting
+        # voltage (charge) or less (discharge) and set the sign of the
+        # event accordingly
+        if isinstance(step_value, pybamm.Symbol):
+            inpt = {"start time": 0}
+            init_curr = step_value.evaluate(t=0, inputs=inpt).flatten()[0]
+        else:
+            init_curr = step_value
+        sign = np.sign(init_curr)
+        if sign > 0:
+            name = "Discharge"
+        else:
+            name = "Charge"
+        if sign != 0:
+            # Event should be positive at initial conditions for both
+            # charge and discharge
+            event = pybamm.Event(
+                f"{name} voltage cut-off [V] [experiment]",
+                sign * (variables["Battery voltage [V]"] - self.value),
+            )
+            return event
+
+
+class CustomTermination(BaseTermination):
+    def __init__(self, name, event_function):
+        self.name = name
+        self.event_function = event_function
+
+    def get_event(self, variables, step_value):
+        return pybamm.Event(self.name, self.event_function(variables))
+
+
+def read_termination(termination):
+    if isinstance(termination, tuple):
+        typ, value = termination
+    else:
+        return termination
+
+    termination_class = {
+        "current": CurrentTermination,
+        "voltage": VoltageTermination,
+        "C-rate": CrateTermination,
+    }[typ]
+    return termination_class(value)
diff --git a/pybamm/step/steps.py b/pybamm/experiment/step/steps.py
similarity index 100%
rename from pybamm/step/steps.py
rename to pybamm/experiment/step/steps.py
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index f9aebb1c54..8eb34aebf1 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -173,16 +173,6 @@ def set_up_and_parameterise_experiment(self):
                 op_conds.type = "current"
                 op_conds.value = op_conds.value * capacity
 
-            # Update terminations
-            termination = op_conds.termination
-            for term in termination:
-                term_type = term["type"]
-                if term_type == "C-rate":
-                    # Change type to current
-                    term["type"] = "current"
-                    # Scale C-rate with capacity to obtain current
-                    term["value"] = term["value"] * capacity
-
             # Add time to the experiment times
             dt = op_conds.duration
             if dt is None:
@@ -275,46 +265,9 @@ def set_up_and_parameterise_model_for_experiment(self):
 
     def update_new_model_events(self, new_model, op):
         for term in op.termination:
-            if term["type"] == "current":
-                new_model.events.append(
-                    pybamm.Event(
-                        "Current cut-off [A] [experiment]",
-                        abs(new_model.variables["Current [A]"]) - term["value"],
-                    )
-                )
-
-            # add voltage events to the model
-            if term["type"] == "voltage":
-                # The voltage event should be positive at the start of charge/
-                # discharge. We use the sign of the current or power input to
-                # figure out whether the voltage event is greater than the starting
-                # voltage (charge) or less (discharge) and set the sign of the
-                # event accordingly
-                if isinstance(op.value, pybamm.Interpolant) or isinstance(
-                    op.value, pybamm.Multiplication
-                ):
-                    inpt = {"start time": 0}
-                    init_curr = op.value.evaluate(t=0, inputs=inpt).flatten()[0]
-                    sign = np.sign(init_curr)
-                else:
-                    sign = np.sign(op.value)
-                if sign > 0:
-                    name = "Discharge"
-                else:
-                    name = "Charge"
-                if sign != 0:
-                    # Event should be positive at initial conditions for both
-                    # charge and discharge
-                    new_model.events.append(
-                        pybamm.Event(
-                            f"{name} voltage cut-off [V] [experiment]",
-                            sign
-                            * (
-                                new_model.variables["Battery voltage [V]"]
-                                - term["value"]
-                            ),
-                        )
-                    )
+            event = term.get_event(new_model.variables, op.value)
+            if event is not None:
+                new_model.events.append(event)
 
         # Keep the min and max voltages as safeguards but add some tolerances
         # so that they are not triggered before the voltage limits in the
@@ -777,7 +730,9 @@ def solve(
                     # Hacky patch to allow correct processing of end_time and next_starting time
                     # For efficiency purposes, op_conds treats identical steps as the same object
                     # regardless of the initial time. Should be refactored as part of #3176
-                    op_conds_unproc = self.experiment.operating_conditions_steps_unprocessed[idx]
+                    op_conds_unproc = (
+                        self.experiment.operating_conditions_steps_unprocessed[idx]
+                    )
 
                     start_time = current_solution.t[-1]
 

From 4fa8933c00c762dde9abacfe8052add238c22356 Mon Sep 17 00:00:00 2001
From: Abhishek Chaudhari
 <91185083+AbhishekChaudharii@users.noreply.github.com>
Date: Wed, 6 Dec 2023 15:11:13 +0530
Subject: [PATCH 482/615] Issue 3392 improve documentation (#3474)

* Added docstring for print_parameter_info method

* PEP8 adherence for One-line docstring

* Mentioned that arrays can be passed as values for drive cycles.

* reverted changes from  3c59897a3ef85e0753997dbb7cc9d3e1c1814835

* value can be a 2-column array added in steps.py

* style: pre-commit fixes

* removed cccv_ode

* style: pre-commit fixes

* removed cccv_ode - fix

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 pybamm/models/base_model.py |  1 +
 pybamm/step/steps.py        | 10 +++++-----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index c4ae414e7c..257bc30ef8 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -422,6 +422,7 @@ def input_parameters(self):
         return self._input_parameters
 
     def print_parameter_info(self):
+        """Returns parameters used in the model"""
         self._parameter_info = ""
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
diff --git a/pybamm/step/steps.py b/pybamm/step/steps.py
index 0b8123ddb0..2f2e9e31a4 100644
--- a/pybamm/step/steps.py
+++ b/pybamm/step/steps.py
@@ -120,7 +120,7 @@ def current(value, **kwargs):
     Parameters
     ----------
     value : float
-        The current value in A.
+        The current value in A. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -141,7 +141,7 @@ def c_rate(value, **kwargs):
     Parameters
     ----------
     value : float
-        The C-rate value.
+        The C-rate value. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -162,7 +162,7 @@ def voltage(value, **kwargs):
     Parameters
     ----------
     value : float
-        The voltage value in V.
+        The voltage value in V. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -183,7 +183,7 @@ def power(value, **kwargs):
     Parameters
     ----------
     value : float
-        The power value in W.
+        The power value in W. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.
@@ -204,7 +204,7 @@ def resistance(value, **kwargs):
     Parameters
     ----------
     value : float
-        The resistance value in Ohm.
+        The resistance value in Ohm. It can be a number or a 2-column array (for drive cycles).
     **kwargs
         Any other keyword arguments are passed to the :class:`pybamm.step._Step`
         class.

From 189bf5ef07c4a7fd1c03e21c15098d4b8c92c424 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 6 Dec 2023 09:41:35 +0000
Subject: [PATCH 483/615] docs: add AbhishekChaudharii as a contributor for doc
 (#3594)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 9 +++++++++
 README.md           | 3 ++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 8dfbde917c..70673ac9af 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -737,6 +737,15 @@
         "bug",
         "code"
       ]
+    },
+    {
+      "login": "AbhishekChaudharii",
+      "name": "Abhishek Chaudhari",
+      "avatar_url": "https://avatars.githubusercontent.com/u/91185083?v=4",
+      "profile": "https://github.com/AbhishekChaudharii",
+      "contributions": [
+        "doc"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/README.md b/README.md
index 1ae2aecebd..dc3022e76f 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-67-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-68-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -271,6 +271,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chmabaur"><img src="https://avatars.githubusercontent.com/u/127507466?v=4?s=100" width="100px;" alt="chmabaur"/><br /><sub><b>chmabaur</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achmabaur" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chmabaur" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AbhishekChaudharii"><img src="https://avatars.githubusercontent.com/u/91185083?v=4?s=100" width="100px;" alt="Abhishek Chaudhari"/><br /><sub><b>Abhishek Chaudhari</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Documentation">📖</a></td>
     </tr>
   </tbody>
 </table>

From 32fad0048f785a9e0a597f5e9545a3f795c00bfb Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 6 Dec 2023 12:08:46 +0000
Subject: [PATCH 484/615] docs: add shubhambhar007 as a contributor for infra
 (#3595)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 9 +++++++++
 README.md           | 3 ++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 70673ac9af..2ae94f2cfa 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -746,6 +746,15 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "shubhambhar007",
+      "name": "Shubham Bhardwaj",
+      "avatar_url": "https://avatars.githubusercontent.com/u/32607282?v=4",
+      "profile": "https://github.com/shubhambhar007",
+      "contributions": [
+        "infra"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/README.md b/README.md
index dc3022e76f..5790060936 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-68-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-69-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -272,6 +272,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chmabaur"><img src="https://avatars.githubusercontent.com/u/127507466?v=4?s=100" width="100px;" alt="chmabaur"/><br /><sub><b>chmabaur</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achmabaur" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chmabaur" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/AbhishekChaudharii"><img src="https://avatars.githubusercontent.com/u/91185083?v=4?s=100" width="100px;" alt="Abhishek Chaudhari"/><br /><sub><b>Abhishek Chaudhari</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/shubhambhar007"><img src="https://avatars.githubusercontent.com/u/32607282?v=4?s=100" width="100px;" alt="Shubham Bhardwaj"/><br /><sub><b>Shubham Bhardwaj</b></sub></a><br /><a href="#infra-shubhambhar007" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>
   </tbody>
 </table>

From a465ad52be7014e176da157385b3c9fab43dc304 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Wed, 6 Dec 2023 11:24:06 -0500
Subject: [PATCH 485/615] #3530 docs and example

---
 CHANGELOG.md                                  |   4 +-
 .../api/experiment/experiment_steps.rst       |  25 ++
 docs/source/api/plotting/quick_plot.rst       |   3 +
 .../tutorial-1-how-to-run-a-model.ipynb       |   2 +-
 .../callbacks.ipynb                           |   0
 .../custom_experiments.ipynb                  | 235 ++++++++++++++++++
 .../experiments-start-time.ipynb              |   0
 .../rpt-experiment.ipynb                      |   0
 .../simulating-long-experiments.ipynb         |   0
 .../simulation-class.ipynb                    |   0
 pybamm/experiment/step/_steps_util.py         |   4 +-
 pybamm/experiment/step/step_termination.py    |  83 ++++++-
 pybamm/plotting/quick_plot.py                 |  41 ++-
 13 files changed, 389 insertions(+), 8 deletions(-)
 rename docs/source/examples/notebooks/{ => simulations_and_experiments}/callbacks.ipynb (100%)
 create mode 100644 docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
 rename docs/source/examples/notebooks/{ => simulations_and_experiments}/experiments-start-time.ipynb (100%)
 rename docs/source/examples/notebooks/{ => simulations_and_experiments}/rpt-experiment.ipynb (100%)
 rename docs/source/examples/notebooks/{ => simulations_and_experiments}/simulating-long-experiments.ipynb (100%)
 rename docs/source/examples/notebooks/{ => simulations_and_experiments}/simulation-class.ipynb (100%)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 5204e0bc82..de16b30849 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,10 +2,12 @@
 
 ## Features
 
+- Added method to get QuickPlot axes by variable ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+- Added custom experiment terminations ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
-- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 
 ## Bug fixes
 
diff --git a/docs/source/api/experiment/experiment_steps.rst b/docs/source/api/experiment/experiment_steps.rst
index 55de9d17bf..6a2e2abc31 100644
--- a/docs/source/api/experiment/experiment_steps.rst
+++ b/docs/source/api/experiment/experiment_steps.rst
@@ -18,3 +18,28 @@ directly:
 
 .. autoclass:: pybamm.step._Step
     :members:
+
+Step terminations
+-----------------
+
+Standard step termination events are implemented by the following classes, which are
+called when the termination is specified by a specific string. These classes can be
+either be called directly or via the string format specified in the class docstring
+
+.. autoclass:: pybamm.step.CrateTermination
+    :members:
+
+.. autoclass:: pybamm.step.CurrentTermination
+    :members:
+
+.. autoclass:: pybamm.step.VoltageTermination
+    :members:
+
+The following classes can be used to define custom terminations for an experiment
+step:
+
+.. autoclass:: pybamm.step.BaseTermination
+    :members:
+
+.. autoclass:: pybamm.step.CustomTermination
+    :members:
diff --git a/docs/source/api/plotting/quick_plot.rst b/docs/source/api/plotting/quick_plot.rst
index ff7576a00d..870a569e9d 100644
--- a/docs/source/api/plotting/quick_plot.rst
+++ b/docs/source/api/plotting/quick_plot.rst
@@ -7,3 +7,6 @@ Quick Plot
   :members:
 
 .. autofunction:: pybamm.dynamic_plot
+
+.. autoclass:: pybamm.QuickPlotAxes
+  :members:
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
index aa50147343..226e016300 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-1-how-to-run-a-model.ipynb
@@ -205,7 +205,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/callbacks.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/callbacks.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
new file mode 100644
index 0000000000..85e869c352
--- /dev/null
+++ b/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Custom experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom steps\n",
+    "\n",
+    "This feature is in development"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom termination\n",
+    "\n",
+    "Termination of a step can be specified using a few standard strings (e.g. \"4.2V\" for voltage, \"1 A\" for current, \"C/2\" for C-rate), or via a custom termination step. The custom termination step can be specified based on any variable in the model.\n",
+    "Below, we show an example where we specify a custom termination step based on keeping the anode potential above 0V, which is a common limit used to avoid lithium plating,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x14d326510>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set up model and parameters\n",
+    "model = pybamm.lithium_ion.DFN()\n",
+    "# add anode potential as a variable\n",
+    "# we use the potential at the separator interface since that is the minimum potential\n",
+    "# during charging (plating is most likely to occur first at the separator interface)\n",
+    "model.variables[\"Anode potential [V]\"] = model.variables[\n",
+    "    \"Negative electrode surface potential difference at separator interface [V]\"\n",
+    "]\n",
+    "parameter_values = pybamm.ParameterValues(\"Chen2020\")\n",
+    "\n",
+    "\n",
+    "# Create a custom termination event for the anode potential cut-off at 0.02V\n",
+    "# We use 0.02V instead of 0V to give a safety factor\n",
+    "def anode_potential_cutoff(variables):\n",
+    "    return variables[\"Anode potential [V]\"] - 0.02\n",
+    "\n",
+    "# The CustomTermination class takes a name and function\n",
+    "anode_potential_termination = pybamm.step.CustomTermination(\n",
+    "    name=\"Anode potential cut-off [V]\", event_function=anode_potential_cutoff\n",
+    ")\n",
+    "\n",
+    "# Provide a list of termination events, each step will stop whenever the first\n",
+    "# termination event is reached\n",
+    "terminations = [anode_potential_termination, \"4.2V\"]\n",
+    "\n",
+    "# Set up multi-step CC experiment with the customer terminations followed\n",
+    "# by a voltage hold\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            pybamm.step.c_rate(-1, termination=terminations),\n",
+    "            pybamm.step.c_rate(-0.5, termination=terminations),\n",
+    "            pybamm.step.c_rate(-0.25, termination=terminations),\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Set up simulation\n",
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n",
+    "\n",
+    "# for a charge we start as SOC 0\n",
+    "sim.solve(initial_soc=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x16a271d90>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Plot\n",
+    "plot = pybamm.QuickPlot(\n",
+    "    sim.solution,\n",
+    "    [\n",
+    "        \"Current [A]\",\n",
+    "        \"Voltage [V]\",\n",
+    "        \"Anode potential [V]\",\n",
+    "    ]\n",
+    ")\n",
+    "plot.plot(0)\n",
+    "\n",
+    "# Plot the limits used in the termination events to check they are not surpassed\n",
+    "plot.axes.by_variable(\"Voltage [V]\").axhline(4.2, color=\"k\", linestyle=\":\")\n",
+    "plot.axes.by_variable(\"Anode potential [V]\").axhline(0.02, color=\"k\", linestyle=\":\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check which events were reached by each step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Step 0: event: Anode potential cut-off [V] [experiment]\n",
+      "Step 1: event: Anode potential cut-off [V] [experiment]\n",
+      "Step 2: event: Charge voltage cut-off [V] [experiment]\n",
+      "Step 3: event: C-rate cut-off [experiment]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, step in enumerate(sim.solution.cycles[0].steps):\n",
+    "    print(f\"Step {i}: {step.termination}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pybamm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/examples/notebooks/experiments-start-time.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/experiments-start-time.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb
diff --git a/docs/source/examples/notebooks/rpt-experiment.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/rpt-experiment.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
diff --git a/docs/source/examples/notebooks/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/simulating-long-experiments.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
diff --git a/docs/source/examples/notebooks/simulation-class.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/simulation-class.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
diff --git a/pybamm/experiment/step/_steps_util.py b/pybamm/experiment/step/_steps_util.py
index 1bf98e5083..f44cf52113 100644
--- a/pybamm/experiment/step/_steps_util.py
+++ b/pybamm/experiment/step/_steps_util.py
@@ -4,7 +4,7 @@
 import pybamm
 import numpy as np
 from datetime import datetime
-from .step_termination import read_termination
+from .step_termination import _read_termination
 
 _examples = """
 
@@ -139,7 +139,7 @@ def __init__(
         for term in termination:
             if isinstance(term, str):
                 term = _convert_electric(term)
-            term = read_termination(term)
+            term = _read_termination(term)
             self.termination.append(term)
 
         self.temperature = _convert_temperature_to_kelvin(temperature)
diff --git a/pybamm/experiment/step/step_termination.py b/pybamm/experiment/step/step_termination.py
index 0d5ce9c55f..0787454c71 100644
--- a/pybamm/experiment/step/step_termination.py
+++ b/pybamm/experiment/step/step_termination.py
@@ -3,24 +3,64 @@
 
 
 class BaseTermination:
+    """
+    Base class for a termination event for an experiment step. To create a custom
+    termination, a class must implement `get_event` to return a :class:`pybamm.Event`
+    corresponding to the desired termination. In most cases the class
+    :class:`pybamm.step.CustomTermination` can be used to assist with this.
+
+    Parameters
+    ----------
+    value : float
+        The value at which the event is triggered
+    """
+
     def __init__(self, value):
         self.value = value
 
     def get_event(self, variables, step_value):
+        """
+        Return a :class:`pybamm.Event` object corresponding to the termination event
+
+        Parameters
+        ----------
+        variables : dict
+            Dictionary of model variables, to be used for selecting the variable(s) that
+            determine the event
+        step_value : float or :class:`pybamm.Symbol`
+            Value of the step for which this is a termination event, to be used in some
+            cases to determine the sign of the event.
+        """
         raise NotImplementedError
 
 
 class CrateTermination(BaseTermination):
+    """
+    Termination based on C-rate, created when a string termination of the C-rate type
+    (e.g. "C/10") is provided
+    """
+
     def get_event(self, variables, step_value):
+        """
+        See :meth:`BaseTermination.get_event`
+        """
         event = pybamm.Event(
-            "C-rate cut-off [A] [experiment]",
+            "C-rate cut-off [experiment]",
             abs(variables["C-rate"]) - self.value,
         )
         return event
 
 
 class CurrentTermination(BaseTermination):
+    """
+    Termination based on current, created when a string termination of the current type
+    (e.g. "1A") is provided
+    """
+
     def get_event(self, variables, step_value):
+        """
+        See :meth:`BaseTermination.get_event`
+        """
         event = pybamm.Event(
             "Current cut-off [A] [experiment]",
             abs(variables["Current [A]"]) - self.value,
@@ -29,7 +69,15 @@ def get_event(self, variables, step_value):
 
 
 class VoltageTermination(BaseTermination):
+    """
+    Termination based on voltage, created when a string termination of the voltage type
+    (e.g. "4.2V") is provided
+    """
+
     def get_event(self, variables, step_value):
+        """
+        See :meth:`BaseTermination.get_event`
+        """
         # The voltage event should be positive at the start of charge/
         # discharge. We use the sign of the current or power input to
         # figure out whether the voltage event is greater than the starting
@@ -56,15 +104,46 @@ def get_event(self, variables, step_value):
 
 
 class CustomTermination(BaseTermination):
+    """
+    Define a custom termination event using a function. This can be used to create an
+    event based on any variable in the model.
+
+    Parameters
+    ----------
+    name : str
+        Name of the event
+    event_function : callable
+        A function that takes in a dictionary of variables and evaluates the event
+        value. Must be positive before the event is triggered and zero when the
+        event is triggered.
+
+    Example
+    -------
+    Add a cut-off based on negative electrode stoichiometry. The event will trigger
+    when the negative electrode stoichiometry reaches 10%.
+
+    >>> def neg_stoich_cutoff(variables):
+    >>>    return variables["Negative electrode stoichiometry"] - 0.1
+
+    >>> neg_stoich_termination = pybamm.step.CustomTermination(
+    >>>    name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
+    >>> )
+    """
+
     def __init__(self, name, event_function):
+        if not name.endswith(" [experiment]"):
+            name += " [experiment]"
         self.name = name
         self.event_function = event_function
 
     def get_event(self, variables, step_value):
+        """
+        See :meth:`BaseTermination.get_event`
+        """
         return pybamm.Event(self.name, self.event_function(variables))
 
 
-def read_termination(termination):
+def _read_termination(termination):
     if isinstance(termination, tuple):
         typ, value = termination
     else:
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index f731a58e0e..f2475d4df9 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -486,14 +486,14 @@ def plot(self, t, dynamic=False):
         self.plots = {}
         self.time_lines = {}
         self.colorbars = {}
-        self.axes = []
+        self.axes = QuickPlotAxes()
 
         # initialize empty handles, to be created only if the appropriate plots are made
         solution_handles = []
 
         for k, (key, variable_lists) in enumerate(self.variables.items()):
             ax = self.fig.add_subplot(self.gridspec[k])
-            self.axes.append(ax)
+            self.axes.add(key, ax)
             x_min, x_max, y_min, y_max = self.axis_limits[key]
             ax.set_xlim(x_min, x_max)
             if y_min is not None and y_max is not None:
@@ -803,3 +803,40 @@ def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gi
         # remove the generated images
         for image in images:
             os.remove(image)
+
+
+class QuickPlotAxes:
+    """
+    Class to store axes for the QuickPlot
+    """
+
+    _by_variable = {}
+    _axes = []
+
+    def add(self, keys, axis):
+        """
+        Add axis
+
+        Parameters
+        ----------
+        keys : iter
+            Iterable of keys of variables being plotted on the axis
+        axis : matplotlib Axis object
+            The axis object
+        """
+        self._axes.append(axis)
+        for k in keys:
+            self._by_variable[k] = axis
+
+    def __getitem__(self, index):
+        """
+        Get axis by index
+        """
+        return self._axes[index]
+
+    @property
+    def by_variable(self, key):
+        """
+        Get axis by variable name
+        """
+        return self._by_variable[key]

From d80aca4ccf00691992c5dc6d697e5b1aa63fd810 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Wed, 6 Dec 2023 11:31:55 -0500
Subject: [PATCH 486/615] #3530 test

---
 .../test_experiments/test_experiment_steps.py | 12 ++++++++++
 .../test_simulation_with_experiment.py        | 22 +++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/tests/unit/test_experiments/test_experiment_steps.py b/tests/unit/test_experiments/test_experiment_steps.py
index 53b61d637f..03c95ef0ac 100644
--- a/tests/unit/test_experiments/test_experiment_steps.py
+++ b/tests/unit/test_experiments/test_experiment_steps.py
@@ -264,6 +264,18 @@ def test_start_times(self):
         with self.assertRaisesRegex(TypeError, "`start_time` should be"):
             pybamm.step._Step("current", 1, duration=3600, start_time="bad start_time")
 
+    def test_custom_termination(self):
+        def neg_stoich_cutoff(variables):
+            return variables["Negative electrode stoichiometry"] - 1
+
+        neg_stoich_termination = pybamm.step.CustomTermination(
+            name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
+        )
+        variables = {"Negative electrode stoichiometry": 3}
+        event = neg_stoich_termination.get_event(variables, None)
+        self.assertEqual(event.name, "Negative stoichiometry cut-off [experiment]")
+        self.assertEqual(event.expression, 2)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py
index 6688fae5b1..cc04177ba2 100644
--- a/tests/unit/test_experiments/test_simulation_with_experiment.py
+++ b/tests/unit/test_experiments/test_simulation_with_experiment.py
@@ -765,6 +765,28 @@ def test_experiment_start_time_identical_steps(self):
         # Check that there are only 3 built models (unique steps + padding rest)
         self.assertEqual(len(sim.op_conds_to_built_models), 3)
 
+    def test_experiment_custom_termination(self):
+        def neg_stoich_cutoff(variables):
+            return variables["Negative electrode stoichiometry"] - 0.5
+
+        neg_stoich_termination = pybamm.step.CustomTermination(
+            name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
+        )
+
+        model = pybamm.lithium_ion.SPM()
+        experiment = pybamm.Experiment(
+            [pybamm.step.c_rate(1, termination=neg_stoich_termination)]
+        )
+        sim = pybamm.Simulation(model, experiment=experiment)
+        sol = sim.solve(calc_esoh=False)
+        self.assertEqual(
+            sol.cycles[0].steps[0].termination,
+            "event: Negative stoichiometry cut-off [experiment]",
+        )
+
+        neg_stoich = sol["Negative electrode stoichiometry"].data
+        self.assertAlmostEqual(neg_stoich[-1], 0.5, places=4)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")

From 024f8f56d2d19831c2b5adf2aee860c38d19169f Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Wed, 6 Dec 2023 11:39:25 -0500
Subject: [PATCH 487/615] changelog

---
 CHANGELOG.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index de16b30849..acf4b4dd4e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,8 +2,8 @@
 
 ## Features
 
-- Added method to get QuickPlot axes by variable ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
-- Added custom experiment terminations ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+- Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
+- Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))

From 6bcc50c31ce7dcbe462e0420fb51cddb1185b5a8 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 7 Dec 2023 00:07:15 +0530
Subject: [PATCH 488/615] Added docstrings and modified "get_parameter_info" to
 store the entire parameters instead of their names

---
 pybamm/models/base_model.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index fb9cdfeeb2..bd81e48348 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -422,30 +422,32 @@ def input_parameters(self):
         return self._input_parameters
 
     def get_parameter_info(self):
+        """Extract the parameter information and return its dictionary"""
         parameter_info = []
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
-            parameter_info.append((param.name, "Parameter"))
+            parameter_info.append((param, "Parameter"))
 
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
             if not input_param.domain:
-                parameter_info.append((input_param.name, "InputParameter"))
+                parameter_info.append((input_param, "InputParameter"))
             else:
-                parameter_info.append((input_param.name, f"InputParameter in {input_param.domain}"))
+                parameter_info.append((input_param, f"InputParameter in {input_param.domain}"))
 
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
-            if func_param.name not in [name for name, _ in parameter_info]:
+            if func_param.name not in [p for p, _ in parameter_info]:
                 input_names =  "', '".join(func_param.input_names)
-                parameter_info.append((func_param.name, f"FunctionParameter with inputs(s) '{input_names}'"))
+                parameter_info.append((func_param, f"FunctionParameter with inputs(s) '{input_names}'"))
 
         return parameter_info
 
     def print_parameter_info(self):
+        """Print parameter information from the dictionary"""
         info = self.get_parameter_info()
         for param, param_type in info:
-            print(f"{param} ({param_type})")
+            print(f"{param.name} ({param_type})")
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From 5fb4ed22dee63c4d948ae309bc823467d3b4c5f6 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 7 Dec 2023 01:26:23 +0530
Subject: [PATCH 489/615] Implemented a parameter table to print parameter
 info, from"print_parameter_info" (initial version)

---
 pybamm/models/base_model.py | 29 +++++++++++++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index bd81e48348..64bae9ca1e 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -444,11 +444,36 @@ def get_parameter_info(self):
         return parameter_info
 
     def print_parameter_info(self):
-        """Print parameter information from the dictionary"""
+        """Print parameter information in a formatted table from the dictionary"""
         info = self.get_parameter_info()
+        header_format = 280 * "="
+        row_format = "| {:<90} | {:<90} | {:<90} |"
+
+        print(header_format)
+        print(row_format.format("Parameter", "Type of parameter", "Parameter inputs"))
+        print(header_format)
+
         for param, param_type in info:
-            print(f"{param.name} ({param_type})")
+            if isinstance(param, pybamm.FunctionParameter):
+                input_string = param_type.split("with inputs(s) ")[1]
+            else:
+                input_string = ""
+
+            param_name = getattr(param, 'name', str(param))
+
+            # Split long strings into multiline strings with a max of 90 characters per line
+            param_name_lines = [param_name[i:i + 90] for i in range(0, len(param_name), 90)]
+            param_type_lines = [param_type[i:i + 90] for i in range(0, len(param_type), 90)]
+            input_string_lines = [input_string[i:i + 90] for i in range(0, len(input_string), 90)]
+
+            max_lines = max(len(param_name_lines), len(param_type_lines), len(input_string_lines))
+            for i in range(max_lines):
+                param_line = param_name_lines[i] if i < len(param_name_lines) else ""
+                type_line = param_type_lines[i] if i < len(param_type_lines) else ""
+                input_line = input_string_lines[i] if i < len(input_string_lines) else ""
+                print(row_format.format(param_line, type_line, input_line))
 
+        print(header_format)
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""
         unpacker = pybamm.SymbolUnpacker(typ)

From 01723a5d4c2067fb7e614be3db36be9483a1ef10 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 7 Dec 2023 09:46:53 +0530
Subject: [PATCH 490/615] parameter table size optimisation and code formatting

---
 pybamm/models/base_model.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 64bae9ca1e..451bd4a8f6 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -447,8 +447,7 @@ def print_parameter_info(self):
         """Print parameter information in a formatted table from the dictionary"""
         info = self.get_parameter_info()
         header_format = 280 * "="
-        row_format = "| {:<90} | {:<90} | {:<90} |"
-
+        row_format = "| {:<70} | {:<110} | {:<90} |"
         print(header_format)
         print(row_format.format("Parameter", "Type of parameter", "Parameter inputs"))
         print(header_format)
@@ -462,18 +461,19 @@ def print_parameter_info(self):
             param_name = getattr(param, 'name', str(param))
 
             # Split long strings into multiline strings with a max of 90 characters per line
-            param_name_lines = [param_name[i:i + 90] for i in range(0, len(param_name), 90)]
-            param_type_lines = [param_type[i:i + 90] for i in range(0, len(param_type), 90)]
+            param_name_lines = [param_name[i:i + 70] for i in range(0, len(param_name), 70)]
+            param_type_lines = [param_type[i:i + 110] for i in range(0, len(param_type), 110)]
             input_string_lines = [input_string[i:i + 90] for i in range(0, len(input_string), 90)]
-
             max_lines = max(len(param_name_lines), len(param_type_lines), len(input_string_lines))
+
             for i in range(max_lines):
                 param_line = param_name_lines[i] if i < len(param_name_lines) else ""
                 type_line = param_type_lines[i] if i < len(param_type_lines) else ""
                 input_line = input_string_lines[i] if i < len(input_string_lines) else ""
-                print(row_format.format(param_line, type_line, input_line))
 
+                print(row_format.format(param_line, type_line, input_line))
         print(header_format)
+
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""
         unpacker = pybamm.SymbolUnpacker(typ)

From 292086d79dd48573132573cfa933e13757591b08 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 7 Dec 2023 18:14:52 +0530
Subject: [PATCH 491/615] Made the parameter table dynamic and modified
 docstrings of "get_parameter_info" and "print_parameter_info"

---
 pybamm/models/base_model.py | 39 +++++++++++++++++--------------------
 1 file changed, 18 insertions(+), 21 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 451bd4a8f6..23884cb49d 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -422,7 +422,7 @@ def input_parameters(self):
         return self._input_parameters
 
     def get_parameter_info(self):
-        """Extract the parameter information and return its dictionary"""
+        """Extract the parameter information and returns it as a list of tuples"""
         parameter_info = []
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
@@ -444,35 +444,32 @@ def get_parameter_info(self):
         return parameter_info
 
     def print_parameter_info(self):
-        """Print parameter information in a formatted table from the dictionary"""
+        """Print parameter information in a formatted table from the list of tuples"""
         info = self.get_parameter_info()
-        header_format = 280 * "="
-        row_format = "| {:<70} | {:<110} | {:<90} |"
-        print(header_format)
-        print(row_format.format("Parameter", "Type of parameter", "Parameter inputs"))
-        print(header_format)
-
+        max_param_name_length = 0
+        max_param_type_length = 0
         for param, param_type in info:
-            if isinstance(param, pybamm.FunctionParameter):
-                input_string = param_type.split("with inputs(s) ")[1]
-            else:
-                input_string = ""
+            param_name_length = len(getattr(param, 'name', str(param)))
+            param_type_length = len(param_type)
+            max_param_name_length = max(max_param_name_length, param_name_length)
+            max_param_type_length = max(max_param_type_length, param_type_length)
 
-            param_name = getattr(param, 'name', str(param))
+        header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        print(header_format.format("Parameter", "Type of parameter"))
+        print(header_format.format("=" * max_param_name_length, "=" * max_param_type_length))
 
-            # Split long strings into multiline strings with a max of 90 characters per line
-            param_name_lines = [param_name[i:i + 70] for i in range(0, len(param_name), 70)]
-            param_type_lines = [param_type[i:i + 110] for i in range(0, len(param_type), 110)]
-            input_string_lines = [input_string[i:i + 90] for i in range(0, len(input_string), 90)]
-            max_lines = max(len(param_name_lines), len(param_type_lines), len(input_string_lines))
+        for param, param_type in info:
+            param_name = getattr(param, 'name', str(param))
+            param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
+            param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
+            max_lines = max(len(param_name_lines), len(param_type_lines))
 
             for i in range(max_lines):
                 param_line = param_name_lines[i] if i < len(param_name_lines) else ""
                 type_line = param_type_lines[i] if i < len(param_type_lines) else ""
-                input_line = input_string_lines[i] if i < len(input_string_lines) else ""
 
-                print(row_format.format(param_line, type_line, input_line))
-        print(header_format)
+                print(row_format.format(param_line, type_line))
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From 3ab36c06ea738bdc090f955abc9a33976829fea6 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 7 Dec 2023 18:18:52 +0530
Subject: [PATCH 492/615] Added "get_parameter_info" and modified information
 about "print_parameter_info" in  CHANGELOG.md

---
 CHANGELOG.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 220520031b..325b6582b7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,7 +3,7 @@
 ## Features
 
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
-- Created "parameter_info" method and modified "print_parameter_info" to extract all parameters and print out required ones. ([#3361](https://github.com/pybamm-team/PyBaMM/issues/3361))
+- Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3361](https://github.com/pybamm-team/PyBaMM/pull/3584))
 ## Bug fixes
 
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))

From 3f422bdd5b1091008ae4cf1ac0f0e864f44b3607 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Dec 2023 19:08:25 +0530
Subject: [PATCH 493/615] Update changelog about breaking change for Jax solver

---
 CHANGELOG.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6b95d66bd3..c91272494b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,10 @@
 
 - Updated `jax` and `jaxlib` to the latest available versions and added Windows (Python 3.9+) support for the Jax solver ([#3550](https://github.com/pybamm-team/PyBaMM/pull/3550))
 
+## Breaking changes
+
+- Dropped support for the `[jax]` extra, i.e., the Jax solver when running on Python 3.8. The Jax solver is now available on Python 3.9 and above ([#3550](https://github.com/pybamm-team/PyBaMM/pull/3550))
+
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
 
 ## Features

From ae9a637522e277af3f5db3c8d00aa910c9acc38d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 7 Dec 2023 19:44:52 +0530
Subject: [PATCH 494/615] #3443 Add minimal docs about Windows and Python
 support

---
 docs/source/user_guide/installation/GNU-linux.rst |  5 ++++-
 docs/source/user_guide/installation/index.rst     |  6 +++---
 docs/source/user_guide/installation/windows.rst   | 15 +++++++++++++++
 3 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index ca95bbe1b5..cf027db587 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -133,7 +133,10 @@ Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~
 
 Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
-Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
 
 .. code:: bash
 
diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 983f66842e..272061a7a6 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -216,13 +216,13 @@ Dependency                                                  Minimum Version    p
 Jax dependencies
 ^^^^^^^^^^^^^^^^^
 
-Installable with ``pip install "pybamm[jax]"``
+Installable with ``pip install "pybamm[jax]"``, currently supported on Python 3.9-3.11.
 
 ========================================================================= ================== ================== =======================
 Dependency                                                                Minimum Version    pip extra          Notes
 ========================================================================= ================== ================== =======================
-`JAX <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`__  0.4.8              jax                For JAX solvers
-`jaxlib <https://pypi.org/project/jaxlib/>`__                             0.4.7              jax                Support library for JAX
+`JAX <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`__  0.4.20             jax                For the JAX solver
+`jaxlib <https://pypi.org/project/jaxlib/>`__                             0.4.20             jax                Support library for JAX
 ========================================================================= ================== ================== =======================
 
 .. _install.odes_dependencies:
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 5b104e91bd..5ad77b6f7f 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -66,6 +66,21 @@ installed automatically when you install PyBaMM using ``pip``.
 For an introduction to virtual environments, see
 (https://realpython.com/python-virtual-environments-a-primer/).
 
+Optional - JaxSolver
+~~~~~~~~~~~~~~~~~~~~
+
+Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
+
+.. note::
+
+   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
+
+.. code:: bash
+
+	  pip install "pybamm[jax]"
+
+The ``pip install "pybamm[jax]"`` command automatically downloads and installs ``pybamm`` and the compatible versions of ``jax`` and ``jaxlib`` on your system. (``pybamm_install_jax`` is deprecated.)
+
 Uninstall PyBaMM
 ----------------
 

From 0da48b299b8143bbb7512b5cb6344239e41ccfd3 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 13:05:40 -0500
Subject: [PATCH 495/615] Update pybamm/models/base_model.py

---
 pybamm/models/base_model.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 23884cb49d..806887ef70 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -422,7 +422,11 @@ def input_parameters(self):
         return self._input_parameters
 
     def get_parameter_info(self):
-        """Extract the parameter information and returns it as a list of tuples"""
+        """
+        Extract the parameter information and returns it as a list of tuples.
+        To get a list of all parameter-like objects without extra information,
+        use `model.parameters`.
+        """
         parameter_info = []
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:

From 03878d07c39dcc50943d8ad2754af8a0d0746e9c Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 18:47:16 -0500
Subject: [PATCH 496/615] #3530 fix tests and examples, try fixing docs

---
 docs/source/examples/index.rst                   | 16 +++++++++++-----
 .../custom_experiments.ipynb                     |  8 ++++----
 pybamm/__init__.py                               |  2 +-
 pybamm/experiment/step/step_termination.py       | 13 ++++++++++---
 pybamm/plotting/quick_plot.py                    |  1 -
 .../test_experiments/test_experiment_steps.py    | 10 +++++-----
 6 files changed, 31 insertions(+), 19 deletions(-)

diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index e025ea71b4..6123e25388 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -85,6 +85,17 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/parameterization/parameter-values.ipynb
     notebooks/parameterization/parameterization.ipynb
 
+.. nbgallery::
+    :caption: Simulations and Experiments
+    :glob:
+
+    notebooks/simulation_and_experiments/callbacks.ipynb
+    notebooks/simulation_and_experiments/custom-experiments.ipynb
+    notebooks/simulation_and_experiments/experiments-start-time.ipynb
+    notebooks/simulation_and_experiments/rpt-experiment.ipynb
+    notebooks/simulation_and_experiments/simulating-long-experiments.ipynb
+    notebooks/simulation_and_experiments/simulation-class.ipynb
+
 .. nbgallery::
     :caption: Plotting
     :glob:
@@ -111,11 +122,6 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     :glob:
 
     notebooks/batch_study.ipynb
-    notebooks/callbacks.ipynb
     notebooks/change-settings.ipynb
     notebooks/initialize-model-with-solution.ipynb
-    notebooks/rpt-experiment.ipynb
-    notebooks/simulating-long-experiments.ipynb
-    notebooks/simulation-class.ipynb
     notebooks/solution-data-and-processed-variables.ipynb
-    notebooks/experiments-start-time.ipynb
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
index 85e869c352..888c002c31 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
@@ -52,7 +52,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x14d326510>"
+       "<pybamm.solvers.solution.Solution at 0x11fcd1e10>"
       ]
      },
      "execution_count": 2,
@@ -114,7 +114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x16a271d90>"
+       "<matplotlib.lines.Line2D at 0x16e37f9d0>"
       ]
      },
      "execution_count": 3,
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index f5c8b6fd70..00c596314a 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -234,7 +234,7 @@
 #
 # Plotting
 #
-from .plotting.quick_plot import QuickPlot, close_plots
+from .plotting.quick_plot import QuickPlot, close_plots, QuickPlotAxes
 from .plotting.plot import plot
 from .plotting.plot2D import plot2D
 from .plotting.plot_voltage_components import plot_voltage_components
diff --git a/pybamm/experiment/step/step_termination.py b/pybamm/experiment/step/step_termination.py
index 0787454c71..e2537f2396 100644
--- a/pybamm/experiment/step/step_termination.py
+++ b/pybamm/experiment/step/step_termination.py
@@ -33,6 +33,13 @@ def get_event(self, variables, step_value):
         """
         raise NotImplementedError
 
+    def __eq__(self, other):
+        # objects are equal if they have the same type and value
+        if isinstance(other, self.__class__):
+            return self.value == other.value
+        else:
+            return False
+
 
 class CrateTermination(BaseTermination):
     """
@@ -123,11 +130,11 @@ class CustomTermination(BaseTermination):
     when the negative electrode stoichiometry reaches 10%.
 
     >>> def neg_stoich_cutoff(variables):
-    >>>    return variables["Negative electrode stoichiometry"] - 0.1
+           return variables["Negative electrode stoichiometry"] - 0.1
 
     >>> neg_stoich_termination = pybamm.step.CustomTermination(
-    >>>    name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
-    >>> )
+           name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
+        )
     """
 
     def __init__(self, name, event_function):
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index f2475d4df9..ed5f4e6c27 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -834,7 +834,6 @@ def __getitem__(self, index):
         """
         return self._axes[index]
 
-    @property
     def by_variable(self, key):
         """
         Get axis by variable name
diff --git a/tests/unit/test_experiments/test_experiment_steps.py b/tests/unit/test_experiments/test_experiment_steps.py
index 03c95ef0ac..b99ae22395 100644
--- a/tests/unit/test_experiments/test_experiment_steps.py
+++ b/tests/unit/test_experiments/test_experiment_steps.py
@@ -34,7 +34,7 @@ def test_step(self):
         self.assertEqual(step.type, "voltage")
         self.assertEqual(step.value, 1)
         self.assertEqual(step.duration, 3600)
-        self.assertEqual(step.termination, [{"type": "voltage", "value": 2.5}])
+        self.assertEqual(step.termination, [pybamm.step.VoltageTermination(2.5)])
         self.assertEqual(step.period, 60)
         self.assertEqual(step.temperature, 298.15)
         self.assertEqual(step.tags, ["test"])
@@ -155,25 +155,25 @@ def test_step_string(self):
                 "type": "C-rate",
                 "value": -1,
                 "duration": None,
-                "termination": [{"type": "voltage", "value": 4.1}],
+                "termination": [pybamm.step.VoltageTermination(4.1)],
             },
             {
                 "value": 4.1,
                 "type": "voltage",
                 "duration": None,
-                "termination": [{"type": "current", "value": 0.05}],
+                "termination": [pybamm.step.CurrentTermination(0.05)],
             },
             {
                 "value": 3,
                 "type": "voltage",
                 "duration": None,
-                "termination": [{"type": "C-rate", "value": 0.02}],
+                "termination": [pybamm.step.CrateTermination(0.02)],
             },
             {
                 "type": "C-rate",
                 "value": 1 / 3,
                 "duration": 7200.0,
-                "termination": [{"type": "voltage", "value": 2.5}],
+                "termination": [pybamm.step.VoltageTermination(2.5)],
             },
         ]
 

From 2aa120e41beda93be9899500a470824464e07a81 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 18:59:06 -0500
Subject: [PATCH 497/615] more doctest formatting

---
 pybamm/experiment/step/step_termination.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pybamm/experiment/step/step_termination.py b/pybamm/experiment/step/step_termination.py
index e2537f2396..082711a305 100644
--- a/pybamm/experiment/step/step_termination.py
+++ b/pybamm/experiment/step/step_termination.py
@@ -130,11 +130,11 @@ class CustomTermination(BaseTermination):
     when the negative electrode stoichiometry reaches 10%.
 
     >>> def neg_stoich_cutoff(variables):
-           return variables["Negative electrode stoichiometry"] - 0.1
+    ...    return variables["Negative electrode stoichiometry"] - 0.1
 
     >>> neg_stoich_termination = pybamm.step.CustomTermination(
-           name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
-        )
+    ...    name="Negative stoichiometry cut-off", event_function=neg_stoich_cutoff
+    ... )
     """
 
     def __init__(self, name, event_function):

From 601e9def16f247e3a15ece26af3ffa44ee01c75b Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 20:54:57 -0500
Subject: [PATCH 498/615] configure doctest so we don't have to import pybamm

---
 docs/conf.py                                       |  1 +
 pybamm/citations.py                                |  1 -
 pybamm/expression_tree/symbol.py                   | 10 +++++++---
 .../equivalent_circuit/thevenin.py                 |  1 -
 .../models/full_battery_models/lithium_ion/dfn.py  |  1 -
 .../models/full_battery_models/lithium_ion/mpm.py  |  1 -
 .../models/full_battery_models/lithium_ion/spm.py  |  1 -
 .../models/full_battery_models/lithium_ion/spme.py |  1 -
 pybamm/parameters/parameter_sets.py                | 14 ++++++--------
 pybamm/parameters/parameter_values.py              |  1 -
 10 files changed, 14 insertions(+), 18 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 8e86dcc48d..55692309dc 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -76,6 +76,7 @@
 
 doctest_global_setup = """
 from docs import *
+import pybamm
 """
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/pybamm/citations.py b/pybamm/citations.py
index e73351a4c6..70bf4ba9d3 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -21,7 +21,6 @@ class Citations:
 
     Examples
     --------
-    >>> import pybamm
     >>> pybamm.citations.register("Sulzer2021")
     >>> pybamm.citations.register("@misc{Newton1687, title={Mathematical...}}")
     >>> pybamm.print_citations("citations.txt")
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index e67ea6a89f..57376f1b2e 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -423,7 +423,12 @@ def set_id(self):
         need to hash once.
         """
         self._id = hash(
-            (self.__class__, self.name, *tuple([child.id for child in self.children]), *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]))
+            (
+                self.__class__,
+                self.name,
+                *tuple([child.id for child in self.children]),
+                *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]),
+            )
         )
 
     @property
@@ -532,8 +537,7 @@ def pre_order(self):
         Examples
         --------
 
-        >>> import pybamm
-        >>> a = pybamm.Symbol('a')
+            >>> a = pybamm.Symbol('a')
         >>> b = pybamm.Symbol('b')
         >>> for node in (a*b).pre_order():
         ...     print(node.name)
diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
index cc456c8765..407039b6f6 100644
--- a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
+++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
@@ -52,7 +52,6 @@ class Thevenin(pybamm.BaseModel):
 
     Examples
     --------
-    >>> import pybamm
     >>> model = pybamm.equivalent_circuit.Thevenin()
     >>> model.name
     'Thevenin Equivalent Circuit Model'
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 5f0a2cfb3e..db4e0282d8 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -13,7 +13,6 @@ class DFN(BaseModel):
 
     Examples
     --------
-    >>> import pybamm
     >>> model = pybamm.lithium_ion.DFN()
     >>> model.name
     'Doyle-Fuller-Newman model'
diff --git a/pybamm/models/full_battery_models/lithium_ion/mpm.py b/pybamm/models/full_battery_models/lithium_ion/mpm.py
index fa2062a95c..40a533536f 100644
--- a/pybamm/models/full_battery_models/lithium_ion/mpm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/mpm.py
@@ -13,7 +13,6 @@ class MPM(SPM):
 
     Examples
     --------
-    >>> import pybamm
     >>> model = pybamm.lithium_ion.MPM()
     >>> model.name
     'Many-Particle Model'
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index bdebf12aef..386c55ded9 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -13,7 +13,6 @@ class SPM(BaseModel):
 
     Examples
     --------
-    >>> import pybamm
     >>> model = pybamm.lithium_ion.SPM()
     >>> model.name
     'Single Particle Model'
diff --git a/pybamm/models/full_battery_models/lithium_ion/spme.py b/pybamm/models/full_battery_models/lithium_ion/spme.py
index e293a5fabd..103f13415a 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spme.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spme.py
@@ -14,7 +14,6 @@ class SPMe(SPM):
 
     Examples
     --------
-    >>> import pybamm
     >>> model = pybamm.lithium_ion.SPMe()
     >>> model.name
     'Single Particle Model with electrolyte'
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 6c6201d9af..1b4a116d6a 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -16,20 +16,18 @@ class ParameterSets(Mapping):
 
     .. doctest::
 
-        >>> import pybamm
         >>> list(pybamm.parameter_sets)
-        ['Ai2020', 'Chen2020', ...]
+        2020', 'Chen2020', ...]
 
     Get the docstring for a parameter set:
 
     .. doctest::
 
-        >>> import pybamm
         >>> print(pybamm.parameter_sets.get_docstring("Ai2020"))
-        <BLANKLINE>
-        Parameters for the Enertech cell (Ai2020), from the papers :footcite:t:`Ai2019`,
-        :footcite:t:`rieger2016new` and references therein.
-        ...
+        NKLINE>
+        meters for the Enertech cell (Ai2020), from the papers :footcite:t:`Ai2019`,
+        tcite:t:`rieger2016new` and references therein.
+
 
     See also: :ref:`adding-parameter-sets`
 
@@ -44,7 +42,7 @@ def __init__(self):
     @staticmethod
     def get_entries(group_name):
         # Wrapper for the importlib version logic
-        if sys.version_info < (3, 10): # pragma: no cover
+        if sys.version_info < (3, 10):  # pragma: no cover
             return importlib.metadata.entry_points()[group_name]
         else:
             return importlib.metadata.entry_points(group=group_name)
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index 049910ae9e..a5ed9b66fb 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -24,7 +24,6 @@ class ParameterValues:
 
     Examples
     --------
-    >>> import pybamm
     >>> values = {"some parameter": 1, "another parameter": 2}
     >>> param = pybamm.ParameterValues(values)
     >>> param["some parameter"]

From 13af373a33916b3106443dfcd33525716cfa4dc0 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 20:57:33 -0500
Subject: [PATCH 499/615] fix accidentally removed code

---
 pybamm/parameters/parameter_sets.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index 1b4a116d6a..ea45f2df5c 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -17,17 +17,17 @@ class ParameterSets(Mapping):
     .. doctest::
 
         >>> list(pybamm.parameter_sets)
-        2020', 'Chen2020', ...]
+        ['Ai2020', 'Chen2020', ...]
 
     Get the docstring for a parameter set:
 
     .. doctest::
 
         >>> print(pybamm.parameter_sets.get_docstring("Ai2020"))
-        NKLINE>
-        meters for the Enertech cell (Ai2020), from the papers :footcite:t:`Ai2019`,
-        tcite:t:`rieger2016new` and references therein.
-
+        <BLANKLINE>
+        Parameters for the Enertech cell (Ai2020), from the papers :footcite:t:`Ai2019`,
+        :footcite:t:`rieger2016new` and references therein.
+        ...
 
     See also: :ref:`adding-parameter-sets`
 

From 20a94929e3b75e2e0c158b7bb8dc4fe9f24f9f09 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Thu, 7 Dec 2023 21:43:11 -0500
Subject: [PATCH 500/615] Update pybamm/expression_tree/symbol.py

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 pybamm/expression_tree/symbol.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 57376f1b2e..2c3166582e 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -537,7 +537,7 @@ def pre_order(self):
         Examples
         --------
 
-            >>> a = pybamm.Symbol('a')
+        >>> a = pybamm.Symbol('a')
         >>> b = pybamm.Symbol('b')
         >>> for node in (a*b).pre_order():
         ...     print(node.name)

From d5106c77299c03853d84dde65268cf8f9e9df20a Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Fri, 8 Dec 2023 12:58:10 +0530
Subject: [PATCH 501/615] Added documentation for filename argrument in
 simulation.py

---
 pybamm/simulation.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index bf418f068d..46ef3f330d 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -1166,7 +1166,13 @@ def solution(self):
         return self._solution
 
     def save(self, filename):
-        """Save simulation using pickle"""
+        """Save simulation using pickle module.
+
+        Parameters
+        ----------
+        filename : str
+            The file extension can be arbitrary, but it is common to use ".pkl" or ".pickle"
+        """
         if self._model.convert_to_format == "python":
             # We currently cannot save models in the 'python' format
             raise NotImplementedError(

From 2415a7239a4ca459cf972c1dc8b56c11ca4dfa96 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 8 Dec 2023 09:11:26 -0500
Subject: [PATCH 502/615] docs

---
 docs/source/examples/index.rst                       | 12 ++++++------
 docs/source/examples/notebooks/batch_study.ipynb     |  2 +-
 docs/source/examples/notebooks/change-settings.ipynb |  2 +-
 docs/source/examples/notebooks/models/DFN.ipynb      |  2 +-
 docs/source/examples/notebooks/models/SPM.ipynb      |  2 +-
 .../simulation-class.ipynb                           |  6 +++---
 6 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 6123e25388..e0f2bd5832 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -89,12 +89,12 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     :caption: Simulations and Experiments
     :glob:
 
-    notebooks/simulation_and_experiments/callbacks.ipynb
-    notebooks/simulation_and_experiments/custom-experiments.ipynb
-    notebooks/simulation_and_experiments/experiments-start-time.ipynb
-    notebooks/simulation_and_experiments/rpt-experiment.ipynb
-    notebooks/simulation_and_experiments/simulating-long-experiments.ipynb
-    notebooks/simulation_and_experiments/simulation-class.ipynb
+    notebooks/simulations_and_experiments/callbacks.ipynb
+    notebooks/simulations_and_experiments/custom-experiments.ipynb
+    notebooks/simulations_and_experiments/experiments-start-time.ipynb
+    notebooks/simulations_and_experiments/rpt-experiment.ipynb
+    notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
+    notebooks/simulations_and_experiments/simulation-class.ipynb
 
 .. nbgallery::
     :caption: Plotting
diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb
index 807a368fcc..f02d1154ad 100644
--- a/docs/source/examples/notebooks/batch_study.ipynb
+++ b/docs/source/examples/notebooks/batch_study.ipynb
@@ -523,7 +523,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The difference in the individual plots is not very well visible in the above slider plot, but we can access all the simulations created by `BatchStudy` (`batch_study.sims`) and pass it to `pybamm.plot_summary_variables` to plot the summary variables (more details on \"summary variables\" are available in the [`simulating-long-experiments`](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/simulating-long-experiments.ipynb) notebook).\n",
+    "The difference in the individual plots is not very well visible in the above slider plot, but we can access all the simulations created by `BatchStudy` (`batch_study.sims`) and pass it to `pybamm.plot_summary_variables` to plot the summary variables (more details on \"summary variables\" are available in the [`simulating-long-experiments`](./simulations_and_experiments/simulating-long-experiments.ipynb) notebook).\n",
     "\n",
     "## Comparing summary variables"
    ]
diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb
index 5b21f4dd6b..c54da8754c 100644
--- a/docs/source/examples/notebooks/change-settings.ipynb
+++ b/docs/source/examples/notebooks/change-settings.ipynb
@@ -7,7 +7,7 @@
    "source": [
     "# Changing settings when solving PyBaMM models\n",
     "\n",
-    "[This](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/models/SPM.ipynb) example notebook showed how to run an SPM battery model, using the default parameters, discretisation and solvers that were defined for that particular model. Naturally we would like the ability to alter these options on a case by case basis, and this notebook gives an example of how to do this, again using the SPM model. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/simulation-class.ipynb).\n",
+    "[This](./models/SPM.ipynb) example notebook showed how to run an SPM battery model, using the default parameters, discretisation and solvers that were defined for that particular model. Naturally we would like the ability to alter these options on a case by case basis, and this notebook gives an example of how to do this, again using the SPM model. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](./simulations_and_experiments/simulation-class.ipynb).\n",
     "\n",
     "\n",
     "### Table of Contents\n",
diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb
index 682adc8c21..d77a0856e3 100644
--- a/docs/source/examples/notebooks/models/DFN.ipynb
+++ b/docs/source/examples/notebooks/models/DFN.ipynb
@@ -107,7 +107,7 @@
    "source": [
     "Below we show how to solve the DFN model, using the default geometry, mesh, parameters, discretisation and solver provided with PyBaMM. For a more detailed example, see the notebook on the [SPM](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/models/SPM.ipynb).\n",
     "\n",
-    "In order to show off all the different points at which the process of setting up and solving a model in PyBaMM can be customised we explicitly handle the stages of choosing a geometry, setting parameters, discretising the model and solving the model. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulation-class.ipynb).\n",
+    "In order to show off all the different points at which the process of setting up and solving a model in PyBaMM can be customised we explicitly handle the stages of choosing a geometry, setting parameters, discretising the model and solving the model. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulations_and_experiments/simulation-class.ipynb).\n",
     "\n",
     "First we need to import pybamm, along with numpy which we will use in this notebook."
    ]
diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb
index 91a09a11b6..e373bdafb5 100644
--- a/docs/source/examples/notebooks/models/SPM.ipynb
+++ b/docs/source/examples/notebooks/models/SPM.ipynb
@@ -54,7 +54,7 @@
    "source": [
     "## Example solving SPM using PyBaMM\n",
     "\n",
-    "Below we show how to solve the Single Particle Model, using the default geometry, mesh, parameters, discretisation and solver provided with PyBaMM. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulation-class.ipynb).\n",
+    "Below we show how to solve the Single Particle Model, using the default geometry, mesh, parameters, discretisation and solver provided with PyBaMM. In this notebook we explicitly handle all the stages of setting up, processing and solving the model in order to explain them in detail. However, it is often simpler in practice to use the `Simulation` class, which handles many of the stages automatically, as shown [here](../simulations_and_experiments/simulation-class.ipynb).\n",
     "\n",
     "First we need to import `pybamm`, and then change our working directory to the root of the pybamm folder. "
    ]
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
index bb93ec207a..df82fa8175 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "# A step-by-step look at the Simulation class\n",
-    "The simplest way to solve a model is to use the `Simulation` class. This automatically processes the model (setting of parameters, setting up the mesh and discretisation, etc.) for you, and provides built-in functionality for solving and plotting. Changing things such as parameters in handled by passing options to the `Simulation`, as shown in the [Getting Started](getting_started/tutorial-1-how-to-run-a-model.ipynb) guides, [example notebooks](../index.rst) and [documentation](https://docs.pybamm.org/en/latest/source/api/simulation.html).\n",
+    "The simplest way to solve a model is to use the `Simulation` class. This automatically processes the model (setting of parameters, setting up the mesh and discretisation, etc.) for you, and provides built-in functionality for solving and plotting. Changing things such as parameters in handled by passing options to the `Simulation`, as shown in the [Getting Started](../getting_started/tutorial-1-how-to-run-a-model.ipynb) guides, [example notebooks](../../index.rst) and [documentation](https://docs.pybamm.org/en/latest/source/api/simulation.html).\n",
     "\n",
     "In this notebook we show how to solve a model using a `Simulation` and compare this to manually handling the different stages of the process, such as setting parameters, ourselves step-by-step."
    ]
@@ -152,7 +152,7 @@
    "metadata": {},
    "source": [
     "## Processing the model step-by-step\n",
-    "One way of gaining more control over the simulation processing is by passing options, as outlined in the [documentation](https://docs.pybamm.org/en/latest/source/api/simulation.html). However, you can also process the model step-by-step yourself. A detailed example of this can be found in the [SPM notebook](./models/SPM.ipynb), but here we outline the basic steps.\n",
+    "One way of gaining more control over the simulation processing is by passing options, as outlined in the [documentation](https://docs.pybamm.org/en/latest/source/api/simulation.html). However, you can also process the model step-by-step yourself. A detailed example of this can be found in the [SPM notebook](../models/SPM.ipynb), but here we outline the basic steps.\n",
     "\n",
     "First we pick a model"
    ]
@@ -171,7 +171,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next we must set up the geometry. We'll use the default geometry for the SPM. In all of the following steps we will also use the default settings provided by the model. For a look at changing these options, see the [change settings](./change-settings.ipynb) notebook."
+    "Next we must set up the geometry. We'll use the default geometry for the SPM. In all of the following steps we will also use the default settings provided by the model. For a look at changing these options, see the [change settings](../change-settings.ipynb) notebook."
    ]
   },
   {

From a10984889df4822e4b447a746a3bb23b5828f784 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 8 Dec 2023 09:30:36 -0500
Subject: [PATCH 503/615] fix docs and coverage

---
 ..._experiments.ipynb => custom-experiments.ipynb} |  0
 .../test_experiment_step_termination.py            | 13 +++++++++++++
 tests/unit/test_plotting/test_quick_plot.py        | 14 ++++++++++++--
 3 files changed, 25 insertions(+), 2 deletions(-)
 rename docs/source/examples/notebooks/simulations_and_experiments/{custom_experiments.ipynb => custom-experiments.ipynb} (100%)
 create mode 100644 tests/unit/test_experiments/test_experiment_step_termination.py

diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb
similarity index 100%
rename from docs/source/examples/notebooks/simulations_and_experiments/custom_experiments.ipynb
rename to docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb
diff --git a/tests/unit/test_experiments/test_experiment_step_termination.py b/tests/unit/test_experiments/test_experiment_step_termination.py
new file mode 100644
index 0000000000..5708b92444
--- /dev/null
+++ b/tests/unit/test_experiments/test_experiment_step_termination.py
@@ -0,0 +1,13 @@
+#
+# Test the experiment steps
+#
+import pybamm
+import unittest
+
+
+class TestExperimentStepTermination(unittest.TestCase):
+    def test_base_termination(self):
+        term = pybamm.step.BaseTermination(1)
+        self.assertEqual(term.value, 1)
+        self.assertNotEqual(term, pybamm.step.BaseTermination(2))
+        self.assertNotEqual(term, pybamm.step.CurrentTermination(1))
diff --git a/tests/unit/test_plotting/test_quick_plot.py b/tests/unit/test_plotting/test_quick_plot.py
index f569f00152..7e2a088de6 100644
--- a/tests/unit/test_plotting/test_quick_plot.py
+++ b/tests/unit/test_plotting/test_quick_plot.py
@@ -294,8 +294,9 @@ def test_spm_simulation(self):
         with TemporaryDirectory() as dir_name:
             test_stub = os.path.join(dir_name, "spm_sim_test")
             test_file = f"{test_stub}.gif"
-            quick_plot.create_gif(number_of_images=3, duration=3,
-                                  output_filename=test_file)
+            quick_plot.create_gif(
+                number_of_images=3, duration=3, output_filename=test_file
+            )
             assert not os.path.exists(f"{test_stub}*.png")
             assert os.path.exists(test_file)
         pybamm.close_plots()
@@ -508,6 +509,15 @@ def test_model_with_inputs(self):
         pybamm.close_plots()
 
 
+class TestQuickPlotAxes(unittest.TestCase):
+    def test_quick_plot_axes(self):
+        axes = pybamm.QuickPlotAxes()
+        axes.add(("test 1", "test 2"), 1)
+        self.assertEqual(axes[0], 1)
+        self.assertEqual(axes.by_variable("test 1"), 1)
+        self.assertEqual(axes.by_variable("test 2"), 1)
+
+
 if __name__ == "__main__":
     print("Add -v for more debug output")
     import sys

From fa679d1ac8fe1b40b2560d41377d1bdc8678d35a Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 8 Dec 2023 09:45:06 -0500
Subject: [PATCH 504/615] try fixing docs

---
 pybamm/plotting/quick_plot.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index ed5f4e6c27..686c58f3c5 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -810,8 +810,9 @@ class QuickPlotAxes:
     Class to store axes for the QuickPlot
     """
 
-    _by_variable = {}
-    _axes = []
+    def __init__(self):
+        self._by_variable = {}
+        self._axes = []
 
     def add(self, keys, axis):
         """

From 3aa0892fb3820f80a4b1783ac48e5733e8da0d77 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:24:43 +0530
Subject: [PATCH 505/615] Check coverage on 3.11 for now

---
 .github/workflows/run_periodic_tests.yml |  4 ++--
 .github/workflows/test_on_push.yml       | 18 +++++++++---------
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index b3a0a762fd..b01e9a39e8 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -80,7 +80,7 @@ jobs:
         if: matrix.os != 'windows-latest'
         run: python -m nox -s pybamm-requires
 
-      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
+      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, 3.10, and 3.12; and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
         run: python -m nox -s unit
 
@@ -89,7 +89,7 @@ jobs:
         run: python -m nox -s coverage
 
       - name: Upload coverage report
-        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.12
+        if: matrix.os == 'ubuntu-latest' && matrix.python-version == 3.11
         uses: codecov/codecov-action@v3.1.4
 
       - name: Run integration tests
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index b3a22a17a8..8d6f5a16e5 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -40,7 +40,7 @@ jobs:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here
-        # TODO: check coverage with Python 3.12 when [odes] and [jax] support it
+        # TODO: check coverage with Python 3.12 when [odes] supports it
         exclude:
           - os: ubuntu-latest
             python-version: "3.11"
@@ -117,7 +117,7 @@ jobs:
         run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
-  # TODO: check coverage with Python 3.12 when [odes] and [jax] support it
+  # TODO: check coverage with Python 3.12 when [odes] supports it
   check_coverage:
     needs: style
     runs-on: ubuntu-latest
@@ -272,15 +272,15 @@ jobs:
       - name: Install Linux system dependencies
         uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
-          packages: gfortran gcc graphviz pandoc
+          packages: graphviz pandoc
           execute_install_scripts: true
 
       # dot -c is for registering graphviz fonts and plugins
-      - name: Install OpenBLAS and TeXLive for Linux
+      - name: Install TeXLive for Linux
         run: |
           sudo apt-get update
           sudo dot -c
-          sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
+          sudo apt-get install texlive-latex-extra dvipng
 
       - name: Set up Python 3.12
         id: setup-python
@@ -292,10 +292,10 @@ jobs:
       - name: Install nox
         run: python -m pip install nox
 
-      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
+      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.12
         run: python -m nox -s doctests
 
-      - name: Check if the documentation can be built for GNU/Linux with Python 3.11
+      - name: Check if the documentation can be built for GNU/Linux with Python 3.12
         run: python -m nox -s docs
 
   # Runs only on Ubuntu with Python 3.12
@@ -350,7 +350,7 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example notebooks tests for GNU/Linux with Python 3.11
+      - name: Run example notebooks tests for GNU/Linux with Python 3.12
         run: python -m nox -s examples
 
   # Runs only on Ubuntu with Python 3.12
@@ -405,5 +405,5 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example scripts tests for GNU/Linux with Python 3.11
+      - name: Run example scripts tests for GNU/Linux with Python 3.12
         run: python -m nox -s scripts

From b647035a591681a6ac37c2309f34f339705b081a Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:25:01 +0530
Subject: [PATCH 506/615] Ignore pytest cache

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 612dc777b1..7e28f2da7d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -110,6 +110,7 @@ setup.log
 # test
 test.c
 test.json
+.pytest_cache/
 
 # tox
 .tox/

From d681070f5a1537e0776a3c019a6329d9d0dafeb7 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:25:37 +0530
Subject: [PATCH 507/615] Mention unavailability of `[odes]` on Python 3.12

---
 docs/source/user_guide/installation/GNU-linux.rst | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index cf9afcac1c..8c3c00f133 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -105,7 +105,15 @@ Optional - scikits.odes solver
 Users can install `scikits.odes <https://github.com/bmcage/odes>`__ in
 order to use the wrapped SUNDIALS ODE and DAE
 `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__.
-Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   The ``scikits.odes`` solver is not supported on Python 3.12 yet, please refer to https://github.com/bmcage/odes/issues/162.
+   There is support for Python 3.8, 3.9, 3.10, and 3.11.
 
 .. tab:: GNU/Linux
 

From d4a8e9f9c68048af03c36a0fcd8ae10fb2b7c933 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:25:57 +0530
Subject: [PATCH 508/615] Add Python 3.12 support and trove classifier

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7d25c8e140..bc8fe267e9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
-requires-python = ">=3.8, <3.12"
+requires-python = ">=3.8, <3.13"
 readme = {file = "README.md", content-type = "text/markdown"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -29,6 +29,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [

From 3bc79c737a960545c66ee51d7b291cea9ca7209c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:27:14 +0530
Subject: [PATCH 509/615] Remove references to and imports of `distutils`

---
 setup.py | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/setup.py b/setup.py
index ef82e65e70..db5be76181 100644
--- a/setup.py
+++ b/setup.py
@@ -6,14 +6,9 @@
 from platform import system
 import wheel.bdist_wheel as orig
 
-try:
-    from setuptools import setup, Extension
-    from setuptools.command.install import install
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.core import setup
-    from distutils.command.install import install
-    from distutils.command.build_ext import build_ext
+from setuptools import setup, Extension
+from setuptools.command.install import install
+from setuptools.command.build_ext import build_ext
 
 
 default_lib_dir = (
@@ -71,9 +66,9 @@ def finalize_options(self):
             self.sundials_root = os.path.join(default_lib_dir)
 
     def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
+        # setuptools outputs object files in directory self.build_temp
         # (typically build/temp.*). This is our CMake build directory.
-        # On Windows, distutils is too smart and appends "Release" or
+        # On Windows, setuptools is too smart and appends "Release" or
         # "Debug" to self.build_temp. So in this case we want the
         # build directory to be the parent directory.
         if system() == "Windows":

From 168fcb08a3af80f42efeaea1cfd3025bb0416b28 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:47:27 +0530
Subject: [PATCH 510/615] Add checks revolving Python 3.12 and `[odes]`

---
 noxfile.py | 66 +++++++++++++++++++++++++++++-------------------------
 1 file changed, 35 insertions(+), 31 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 7756b57984..27f972d7b7 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -61,7 +61,10 @@ def run_coverage(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -77,7 +80,10 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -98,7 +104,10 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -111,8 +120,6 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    # TODO: remove this when PyBaMM moves to pyproject.toml
-    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
@@ -121,8 +128,6 @@ def run_examples(session):
 @nox.session(name="scripts")
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
-    # TODO: remove this when PyBaMM moves to pyproject.toml
-    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
@@ -132,32 +137,30 @@ def run_scripts(session):
 def set_dev(session):
     """Install PyBaMM in editable mode."""
     set_environment_variables(PYBAMM_ENV, session=session)
-    # TODO: remove this when PyBaMM moves to pyproject.toml
-    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    session.run(
-        python,
-        "-m",
-        "pip",
-        "install",
-        "--upgrade",
-        "pip",
-        "setuptools",
-        "wheel",
-        external=True,
-    )
     if sys.platform == "linux":
-        session.run(
-            python,
-            "-m",
-            "pip",
-            "install",
-            "-e",
-            ".[all,dev,jax,odes]",
-            external=True,
-        )
+        if sys.version_info > (3, 12):
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax]",
+                external=True,
+            )
+        else:
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax,odes]",
+                external=True,
+            )
     else:
         if sys.version_info < (3, 9):
             session.run(
@@ -184,6 +187,9 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
             session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
@@ -197,8 +203,6 @@ def run_tests(session):
 def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
-    # TODO: remove this when PyBaMM moves to pyproject.toml
-    session.install("--upgrade", "pip", "setuptools", "wheel", silent=False)
     session.install("-e", ".[all,docs]", silent=False)
     session.chdir("docs")
     # Local development

From 5753dbb9fdfda3d46fc6a8b3e08f773948825fc6 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 8 Dec 2023 20:47:49 +0530
Subject: [PATCH 511/615] Update CHANGELOG to reflect Python 3.12 support

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index e9160e5081..6b44971452 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Features
 
+- Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531))
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))

From 99ad3d06ae31a4c8e5151b47be34e2d4432d64c5 Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 8 Dec 2023 10:46:55 -0500
Subject: [PATCH 512/615] coverage again

---
 .../test_experiments/test_experiment_step_termination.py     | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tests/unit/test_experiments/test_experiment_step_termination.py b/tests/unit/test_experiments/test_experiment_step_termination.py
index 5708b92444..7499061184 100644
--- a/tests/unit/test_experiments/test_experiment_step_termination.py
+++ b/tests/unit/test_experiments/test_experiment_step_termination.py
@@ -9,5 +9,10 @@ class TestExperimentStepTermination(unittest.TestCase):
     def test_base_termination(self):
         term = pybamm.step.BaseTermination(1)
         self.assertEqual(term.value, 1)
+
+        with self.assertRaises(NotImplementedError):
+            term.get_event(None, None)
+
+        self.assertEqual(term, pybamm.step.BaseTermination(1))
         self.assertNotEqual(term, pybamm.step.BaseTermination(2))
         self.assertNotEqual(term, pybamm.step.CurrentTermination(1))

From 3468e086212e3f86853872762b6eff23f5d8b0bf Mon Sep 17 00:00:00 2001
From: Valentin Sulzer <valentinsulzer@hotmail.com>
Date: Fri, 8 Dec 2023 10:47:37 -0500
Subject: [PATCH 513/615] update comment

---
 tests/unit/test_experiments/test_experiment_step_termination.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit/test_experiments/test_experiment_step_termination.py b/tests/unit/test_experiments/test_experiment_step_termination.py
index 7499061184..ee45bcc9f8 100644
--- a/tests/unit/test_experiments/test_experiment_step_termination.py
+++ b/tests/unit/test_experiments/test_experiment_step_termination.py
@@ -1,5 +1,5 @@
 #
-# Test the experiment steps
+# Test the experiment step termination classes
 #
 import pybamm
 import unittest

From 61cc1c897efc82dcf8fc380722ea06645b82e8a4 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 9 Dec 2023 19:53:18 +0530
Subject: [PATCH 514/615] Fix editable installation error

---
 noxfile.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/noxfile.py b/noxfile.py
index 27f972d7b7..4805bff83c 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -168,6 +168,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev]",
                 external=True,
             )
@@ -177,6 +178,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev,jax]",
                 external=True,
             )

From af265266c8b22ab81c29e5e6451d64ca74ccdb88 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Sun, 10 Dec 2023 17:30:41 +0530
Subject: [PATCH 515/615] Modified "get_parameter_info" and
 "print_parameter_info" to store and print parameter info from a dictionary.
 Modified "print_parameter_info" to store the info in a list "table" and then
 print it.

---
 pybamm/models/base_model.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 23884cb49d..cf5e44220c 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -423,23 +423,23 @@ def input_parameters(self):
 
     def get_parameter_info(self):
         """Extract the parameter information and returns it as a list of tuples"""
-        parameter_info = []
+        parameter_info = {}
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
-            parameter_info.append((param, "Parameter"))
+            parameter_info[param.name] = (param, "Parameter")
 
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
             if not input_param.domain:
-                parameter_info.append((input_param, "InputParameter"))
+                parameter_info[input_param.name] = (input_param, "InputParameter")
             else:
-                parameter_info.append((input_param, f"InputParameter in {input_param.domain}"))
+                parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}")
 
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
-            if func_param.name not in [p for p, _ in parameter_info]:
-                input_names =  "', '".join(func_param.input_names)
-                parameter_info.append((func_param, f"FunctionParameter with inputs(s) '{input_names}'"))
+            if func_param.name not in parameter_info:
+                input_names = "', '".join(func_param.input_names)
+                parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'")
 
         return parameter_info
 
@@ -448,7 +448,8 @@ def print_parameter_info(self):
         info = self.get_parameter_info()
         max_param_name_length = 0
         max_param_type_length = 0
-        for param, param_type in info:
+
+        for param, param_type in info.values():
             param_name_length = len(getattr(param, 'name', str(param)))
             param_type_length = len(param_type)
             max_param_name_length = max(max_param_name_length, param_name_length)
@@ -456,10 +457,11 @@ def print_parameter_info(self):
 
         header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
         row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
-        print(header_format.format("Parameter", "Type of parameter"))
-        print(header_format.format("=" * max_param_name_length, "=" * max_param_type_length))
 
-        for param, param_type in info:
+        table = [header_format.format("Parameter", "Type of parameter"),
+                 header_format.format("=" * max_param_name_length, "=" * max_param_type_length)]
+
+        for param, param_type in info.values():
             param_name = getattr(param, 'name', str(param))
             param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
             param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
@@ -468,8 +470,10 @@ def print_parameter_info(self):
             for i in range(max_lines):
                 param_line = param_name_lines[i] if i < len(param_name_lines) else ""
                 type_line = param_type_lines[i] if i < len(param_type_lines) else ""
+                table.append(row_format.format(param_line, type_line))
 
-                print(row_format.format(param_line, type_line))
+        for line in table:
+            print(line)
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From 5ccd1855ff925c3b410f28d24289e277915f0c52 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Sun, 10 Dec 2023 17:45:57 +0530
Subject: [PATCH 516/615] Modified "get_parameter_info" and
 "print_parameter_info" to store and print parameter info from a dictionary.
 Modified "print_parameter_info" to store the info in a list "table" and then
 print it.

---
 pybamm/models/base_model.py | 28 ++++++++++++++++------------
 1 file changed, 16 insertions(+), 12 deletions(-)

diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 806887ef70..ef61aec33b 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -423,27 +423,27 @@ def input_parameters(self):
 
     def get_parameter_info(self):
         """
-        Extract the parameter information and returns it as a list of tuples.
+        Extract the parameter information and returns it as a dictionary.
         To get a list of all parameter-like objects without extra information,
         use `model.parameters`.
         """
-        parameter_info = []
+        parameter_info = {}
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
-            parameter_info.append((param, "Parameter"))
+            parameter_info[param.name] = (param, "Parameter")
 
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
             if not input_param.domain:
-                parameter_info.append((input_param, "InputParameter"))
+                parameter_info[input_param.name] = (input_param, "InputParameter")
             else:
-                parameter_info.append((input_param, f"InputParameter in {input_param.domain}"))
+                parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}")
 
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
-            if func_param.name not in [p for p, _ in parameter_info]:
+            if func_param.name not in parameter_info:
                 input_names =  "', '".join(func_param.input_names)
-                parameter_info.append((func_param, f"FunctionParameter with inputs(s) '{input_names}'"))
+                parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'")
 
         return parameter_info
 
@@ -452,7 +452,8 @@ def print_parameter_info(self):
         info = self.get_parameter_info()
         max_param_name_length = 0
         max_param_type_length = 0
-        for param, param_type in info:
+
+        for param, param_type in info.values():
             param_name_length = len(getattr(param, 'name', str(param)))
             param_type_length = len(param_type)
             max_param_name_length = max(max_param_name_length, param_name_length)
@@ -460,10 +461,11 @@ def print_parameter_info(self):
 
         header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
         row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
-        print(header_format.format("Parameter", "Type of parameter"))
-        print(header_format.format("=" * max_param_name_length, "=" * max_param_type_length))
 
-        for param, param_type in info:
+        table = [header_format.format("Parameter", "Type of parameter"),
+                 header_format.format("=" * max_param_name_length, "=" * max_param_type_length)]
+
+        for param, param_type in info.values():
             param_name = getattr(param, 'name', str(param))
             param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
             param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
@@ -472,8 +474,10 @@ def print_parameter_info(self):
             for i in range(max_lines):
                 param_line = param_name_lines[i] if i < len(param_name_lines) else ""
                 type_line = param_type_lines[i] if i < len(param_type_lines) else ""
+                table.append(row_format.format(param_line, type_line))
 
-                print(row_format.format(param_line, type_line))
+        for line in table:
+            print(line)
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""

From d365023d094d258ad3024e37ded31c4c8fc9dab1 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Sun, 10 Dec 2023 17:50:02 +0530
Subject: [PATCH 517/615] Updated all instances of "_parameter_info" with
 "get_parameter_info" and corrected method mentioned in "1.9. Finding the
 parameters in a model" in "parameterization.ipynb" notebook

---
 .../parameterization/parameterization.ipynb   | 664 ++++++------------
 1 file changed, 209 insertions(+), 455 deletions(-)

diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index f3db45aa44..03803656e0 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -29,13 +29,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.822760400Z",
+     "start_time": "2023-12-10T12:14:16.732217100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zsh:1: no matches found: pybamm[plot,cite]\n",
+      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\r\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -60,7 +65,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.832156400Z",
+     "start_time": "2023-12-10T12:14:18.822760400Z"
+    }
+   },
    "outputs": [],
    "source": [
     "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
@@ -83,7 +93,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.841423200Z",
+     "start_time": "2023-12-10T12:14:18.827008900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model = pybamm.BaseModel()\n",
@@ -119,7 +134,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.843095800Z",
+     "start_time": "2023-12-10T12:14:18.841423200Z"
+    }
+   },
    "outputs": [],
    "source": [
     "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
@@ -145,16 +165,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.852037800Z",
+     "start_time": "2023-12-10T12:14:18.845139Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial concentration [mol.m-3] (Parameter)\n",
-      "Interfacial current density [A.m-2] (InputParameter)\n",
-      "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n",
-      "\n",
+      "| Parameter                           | Type of parameter                                          |\n",
+      "| =================================== | ========================================================== |\n",
+      "| Initial concentration [mol.m-3]     | Parameter                                                  |\n",
+      "| Interfacial current density [A.m-2] | InputParameter                                             |\n",
+      "| Diffusion coefficient [m2.s-1]      | FunctionParameter with inputs(s) 'Concentration [mol.m-3]' |\n",
       "Particle radius [m] (Parameter)\n"
      ]
     }
@@ -185,7 +211,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.854076300Z",
+     "start_time": "2023-12-10T12:14:18.849343800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def D_fun(c):\n",
@@ -210,19 +241,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.889781200Z",
+     "start_time": "2023-12-10T12:14:18.853120600Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 2.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 2.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 7,
      "metadata": {},
@@ -248,19 +276,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.890819200Z",
+     "start_time": "2023-12-10T12:14:18.859679800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 1.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 1.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 8,
      "metadata": {},
@@ -294,16 +319,16 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.864911Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n",
-       " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n",
-       " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
-      ]
+      "text/plain": "[Parameter(-0x60748912cbf94f86, Initial concentration [mol.m-3], children=[], domains={}),\n InputParameter(0x650425db234f99f4, Interfacial current density [A.m-2], children=[], domains={}),\n FunctionParameter(-0x302b1e5afcbfd4d9, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
      },
      "execution_count": 9,
      "metadata": {},
@@ -326,7 +351,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.868969800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "param.process_model(model)\n",
@@ -344,7 +374,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.951625100Z",
+     "start_time": "2023-12-10T12:14:18.875173500Z"
+    }
+   },
    "outputs": [],
    "source": [
     "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
@@ -367,14 +402,17 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.168402100Z",
+     "start_time": "2023-12-10T12:14:18.890819200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x400 with 2 Axes>"
-      ]
+      "text/plain": "<Figure size 1300x400 with 2 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -424,7 +462,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.267027300Z",
+     "start_time": "2023-12-10T12:14:19.197131800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "spm = pybamm.lithium_ion.SPM()"
@@ -437,59 +480,65 @@
    "source": [
     "## Finding the parameters in a model\n",
     "\n",
-    "We can print the `parameters` of a model by using the `get_parameters_info` function."
+    "We can print the `parameters` of a model by using the `print_parameter_info` function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.268048600Z",
+     "start_time": "2023-12-10T12:14:19.202421100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Lower voltage cut-off [V] (Parameter)\n",
-      "Faraday constant [C.mol-1] (Parameter)\n",
-      "Ideal gas constant [J.K-1.mol-1] (Parameter)\n",
-      "Electrode width [m] (Parameter)\n",
-      "Positive electrode thickness [m] (Parameter)\n",
-      "Separator Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Upper voltage cut-off [V] (Parameter)\n",
-      "Number of electrodes connected in parallel to make a cell (Parameter)\n",
-      "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n",
-      "Nominal cell capacity [A.h] (Parameter)\n",
-      "Reference temperature [K] (Parameter)\n",
-      "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n",
-      "Separator thickness [m] (Parameter)\n",
-      "Initial concentration in electrolyte [mol.m-3] (Parameter)\n",
-      "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Electrode height [m] (Parameter)\n",
-      "Number of cells connected in series to make a battery (Parameter)\n",
-      "Negative electrode thickness [m] (Parameter)\n",
-      "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n",
-      "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n",
-      "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n",
-      "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n",
-      "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n",
-      "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n",
-      "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n",
-      "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n",
-      "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "\n"
+      "| Parameter                                                 | Type of parameter                                                                                                                                                                                           |\n",
+      "| ========================================================= | =========================================================================================================================================================================================================== |\n",
+      "| Positive electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode width [m]                                       | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Initial concentration in electrolyte [mol.m-3]            | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of cells connected in series to make a battery     | Parameter                                                                                                                                                                                                   |\n",
+      "| Lower voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Ideal gas constant [J.K-1.mol-1]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator Bruggeman coefficient (electrolyte)             | Parameter                                                                                                                                                                                                   |\n",
+      "| Upper voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator thickness [m]                                   | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in negative electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Faraday constant [C.mol-1]                                | Parameter                                                                                                                                                                                                   |\n",
+      "| Reference temperature [K]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode height [m]                                      | Parameter                                                                                                                                                                                                   |\n",
+      "| Nominal cell capacity [A.h]                               | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in positive electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of electrodes connected in parallel to make a cell | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator porosity                                        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode OCP [V]                                | FunctionParameter with inputs(s) 'Negative particle stoichiometry'                                                                                                                                          |\n",
+      "| Positive electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Positive particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode OCP [V]                                | FunctionParameter with inputs(s) 'Positive particle stoichiometry'                                                                                                                                          |\n",
+      "| Negative electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Negative electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Current function [A]                                      | FunctionParameter with inputs(s) 'Time[s]'                                                                                                                                                                  |\n",
+      "| Initial concentration in positive electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Initial concentration in negative electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Positive electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Positive electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Negative electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Ambient temperature [K]                                   | FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]'                                                                                  |\n"
      ]
     }
    ],
@@ -517,53 +566,16 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.401195400Z",
+     "start_time": "2023-12-10T12:14:19.232194200Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 0.0001,\n",
-       " 'Separator thickness [m]': 2.5e-05,\n",
-       " 'Positive electrode thickness [m]': 0.0001,\n",
-       " 'Electrode height [m]': 0.137,\n",
-       " 'Electrode width [m]': 0.207,\n",
-       " 'Nominal cell capacity [A.h]': 0.680616,\n",
-       " 'Current function [A]': 0.680616,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n",
-       " 'Negative electrode porosity': 0.3,\n",
-       " 'Negative electrode active material volume fraction': 0.6,\n",
-       " 'Negative particle radius [m]': 1e-05,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n",
-       " 'Positive electrode porosity': 0.3,\n",
-       " 'Positive electrode active material volume fraction': 0.5,\n",
-       " 'Positive particle radius [m]': 1e-05,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Separator porosity': 1.0,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 3.105,\n",
-       " 'Upper voltage cut-off [V]': 4.1,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 0.0001,\n 'Separator thickness [m]': 2.5e-05,\n 'Positive electrode thickness [m]': 0.0001,\n 'Electrode height [m]': 0.137,\n 'Electrode width [m]': 0.207,\n 'Nominal cell capacity [A.h]': 0.680616,\n 'Current function [A]': 0.680616,\n 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n 'Negative electrode porosity': 0.3,\n 'Negative electrode active material volume fraction': 0.6,\n 'Negative particle radius [m]': 1e-05,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n 'Positive electrode porosity': 0.3,\n 'Positive electrode active material volume fraction': 0.5,\n 'Positive particle radius [m]': 1e-05,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n 'Separator porosity': 1.0,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 3.105,\n 'Upper voltage cut-off [V]': 4.1,\n 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
      },
      "execution_count": 15,
      "metadata": {},
@@ -571,7 +583,7 @@
     }
    ],
    "source": [
-    "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}"
+    "{k: v for k,v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -585,251 +597,16 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.460184100Z",
+     "start_time": "2023-12-10T12:14:19.418960800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ambient temperature [K]': 298.15,\n",
-       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-       " 'Initial temperature [K]': 298.15,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n",
-       "       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n",
-       "       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n",
-       "       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n",
-       "       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n",
-       "       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n",
-       "       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n",
-       "       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n",
-       "       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n",
-       "       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n",
-       "       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n",
-       "       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n",
-       "       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n",
-       "       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n",
-       "       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n",
-       "       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n",
-       "       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n",
-       "       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n",
-       "       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n",
-       "       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n",
-       "       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n",
-       "       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n",
-       "       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n",
-       "       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n",
-       "       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n",
-       "       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n",
-       "       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n",
-       "       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n",
-       "       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n",
-       "       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n",
-       "       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n",
-       "       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n",
-       "       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n",
-       "       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n",
-       "       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n",
-       "       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n",
-       "       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n",
-       "       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n",
-       "       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n",
-       "       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n",
-       "       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n",
-       "       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n",
-       "       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n",
-       "       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n",
-       "       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n",
-       "       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n",
-       "       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n",
-       "       0.89774404, 0.9014468 , 1.        ])],\n",
-       "                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n",
-       "       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n",
-       "       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n",
-       "       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n",
-       "       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n",
-       "       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n",
-       "       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n",
-       "       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n",
-       "       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n",
-       "       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n",
-       "       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n",
-       "       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n",
-       "       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n",
-       "       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n",
-       "       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n",
-       "       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n",
-       "       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n",
-       "       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n",
-       "       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n",
-       "       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n",
-       "       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n",
-       "       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n",
-       "       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n",
-       "       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n",
-       "       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n",
-       "       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n",
-       "       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n",
-       "       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n",
-       "       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n",
-       "       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n",
-       "       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n",
-       "       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n",
-       "       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n",
-       "       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n",
-       "       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n",
-       "       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n",
-       "       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n",
-       "       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n",
-       "       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n",
-       "       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n",
-       "       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n",
-       "       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n",
-       "       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n",
-       "       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n",
-       "       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n",
-       "       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n",
-       "       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n",
-       "       0.08709427, 0.08503284, 0.07601531]))),\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode electrons in reaction': 1.0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x10403e4c0>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n",
-       "       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n",
-       "       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n",
-       "       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n",
-       "       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n",
-       "       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n",
-       "       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n",
-       "       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n",
-       "       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n",
-       "       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n",
-       "       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n",
-       "       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n",
-       "       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n",
-       "       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n",
-       "       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n",
-       "       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n",
-       "       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n",
-       "       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n",
-       "       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n",
-       "       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n",
-       "       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n",
-       "       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n",
-       "       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n",
-       "       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n",
-       "       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n",
-       "       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n",
-       "       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n",
-       "       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n",
-       "       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n",
-       "       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n",
-       "       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n",
-       "       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n",
-       "       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n",
-       "       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n",
-       "       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n",
-       "       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n",
-       "       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n",
-       "       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n",
-       "       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n",
-       "       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n",
-       "       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n",
-       "       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n",
-       "       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n",
-       "       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n",
-       "       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n",
-       "       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n",
-       "       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n",
-       "       0.90320364, 0.90592613, 1.        ])],\n",
-       "                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n",
-       "       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n",
-       "       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n",
-       "       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n",
-       "       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n",
-       "       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n",
-       "       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n",
-       "       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n",
-       "       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n",
-       "       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n",
-       "       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n",
-       "       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n",
-       "       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n",
-       "       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n",
-       "       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n",
-       "       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n",
-       "       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n",
-       "       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n",
-       "       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n",
-       "       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n",
-       "       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n",
-       "       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n",
-       "       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n",
-       "       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n",
-       "       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n",
-       "       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n",
-       "       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n",
-       "       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n",
-       "       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n",
-       "       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n",
-       "       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n",
-       "       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n",
-       "       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n",
-       "       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n",
-       "       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n",
-       "       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n",
-       "       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n",
-       "       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n",
-       "       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n",
-       "       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n",
-       "       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n",
-       "       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n",
-       "       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n",
-       "       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n",
-       "       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n",
-       "       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n",
-       "       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n",
-       "       3.5684922 , 3.5672133 , 3.52302167]))),\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode electrons in reaction': 1.0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x1411e7700>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Typical current [A]': 5.0,\n",
-       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-       " 'Upper voltage cut-off [V]': 4.4}"
-      ]
+      "text/plain": "{'Ambient temperature [K]': 298.15,\n 'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Current function [A]': 5.0,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration in electrolyte [mol.m-3]': 1000,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n 'Initial temperature [K]': 298.15,\n 'Lower voltage cut-off [V]': 2.5,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n       0.89774404, 0.9014468 , 1.        ])],\n                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n       0.08709427, 0.08503284, 0.07601531]))),\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode electrons in reaction': 1.0,\n 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f380>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Negative particle radius [m]': 5.86e-06,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n       0.90320364, 0.90592613, 1.        ])],\n                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n       3.5684922 , 3.5672133 , 3.52302167]))),\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode electrons in reaction': 1.0,\n 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f240>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Positive particle radius [m]': 5.22e-06,\n 'Reference temperature [K]': 298.15,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Separator porosity': 0.47,\n 'Separator thickness [m]': 1.2e-05,\n 'Typical current [A]': 5.0,\n 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n 'Upper voltage cut-off [V]': 4.4}"
      },
      "execution_count": 16,
      "metadata": {},
@@ -1405,52 +1182,16 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.608102800Z",
+     "start_time": "2023-12-10T12:14:19.450757200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Upper voltage cut-off [V]': 4.2,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Separator thickness [m]': 1.2e-05,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Current function [A]': 5.0,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative particle radius [m]': 5.86e-06,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 0,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive particle radius [m]': 5.22e-06,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 0,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Separator porosity': 0.47,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 2.5,\n 'Upper voltage cut-off [V]': 4.2,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
      },
      "execution_count": 17,
      "metadata": {},
@@ -1459,7 +1200,7 @@
    ],
    "source": [
     "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
-    "{k: v for k,v in param_same.items() if k in spm._parameter_info}"
+    "{k: v for k,v in param_same.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -1489,7 +1230,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.611194400Z",
+     "start_time": "2023-12-10T12:14:19.609138100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1500,9 +1246,7 @@
     },
     {
      "data": {
-      "text/plain": [
-       "4.0"
-      ]
+      "text/plain": "4.0"
      },
      "execution_count": 18,
      "metadata": {},
@@ -1528,13 +1272,16 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.641429500Z",
+     "start_time": "2023-12-10T12:14:19.616345800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<function __main__.curren_func(time)>"
-      ]
+      "text/plain": "<function __main__.curren_func(time)>"
      },
      "execution_count": 19,
      "metadata": {},
@@ -1572,23 +1319,24 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.700673700Z",
+     "start_time": "2023-12-10T12:14:19.627406900Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 20,
      "metadata": {},
@@ -1616,23 +1364,24 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.785875100Z",
+     "start_time": "2023-12-10T12:14:19.699175500Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 21,
      "metadata": {},
@@ -1661,27 +1410,28 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.137222900Z",
+     "start_time": "2023-12-10T12:14:19.775429Z"
+    }
+   },
    "outputs": [
     {
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eea07489478640aab13bd2aab1fe5020",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…"
-      ]
+       "version_minor": 0,
+       "model_id": "e3e2a10c3de140de8cc785ae5421b534"
+      }
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x1412c2e50>"
-      ]
+      "text/plain": "<pybamm.plotting.quick_plot.QuickPlot at 0x7f481ffd4990>"
      },
      "execution_count": 22,
      "metadata": {},
@@ -1707,7 +1457,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.184199300Z",
+     "start_time": "2023-12-10T12:14:21.136110400Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1718,8 +1473,7 @@
       "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
       "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
       "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
-      "\n"
+      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n"
      ]
     }
    ],

From 331fdf3276eb1784abd7a08385d9df6202fa4a16 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 11 Dec 2023 19:36:59 +0000
Subject: [PATCH 518/615] chore: update pre-commit hooks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.6 → v0.1.7](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.6...v0.1.7)
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ed837e6fdb..41b19d7073 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.6"
+    rev: "v0.1.7"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From 55a70daab5876f0c4363aae139ee21e061cf241f Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Tue, 12 Dec 2023 10:36:30 +0530
Subject: [PATCH 519/615] Style and docstring modifications

---
 CHANGELOG.md                | 2 +-
 pybamm/models/base_model.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index cf7483b481..a9ae714b62 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,7 +8,7 @@
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
-- Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3361](https://github.com/pybamm-team/PyBaMM/pull/3584))
+- Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3584](https://github.com/pybamm-team/PyBaMM/pull/3584))
 - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 
 
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index ef61aec33b..74c51f9579 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -423,9 +423,9 @@ def input_parameters(self):
 
     def get_parameter_info(self):
         """
-        Extract the parameter information and returns it as a dictionary.
+        Extracts the parameter information and returns it as a dictionary.
         To get a list of all parameter-like objects without extra information,
-        use `model.parameters`.
+        use :py:attr:`model.parameters`.
         """
         parameter_info = {}
         parameters = self._find_symbols(pybamm.Parameter)
@@ -448,7 +448,7 @@ def get_parameter_info(self):
         return parameter_info
 
     def print_parameter_info(self):
-        """Print parameter information in a formatted table from the list of tuples"""
+        """Print parameter information in a formatted table from a dictionary of parameters"""
         info = self.get_parameter_info()
         max_param_name_length = 0
         max_param_type_length = 0

From a129e02413bf9c2238f21730b977658b85fab3f3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 13 Dec 2023 01:10:47 +0530
Subject: [PATCH 520/615] Bump actions/setup-python from 4 to 5 (#3608)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/benchmark_on_push.yml           |  2 +-
 .github/workflows/periodic_benchmarks.yml         |  4 ++--
 .github/workflows/publish_pypi.yml                |  6 +++---
 .github/workflows/run_benchmarks_over_history.yml |  4 ++--
 .github/workflows/run_periodic_tests.yml          |  4 ++--
 .github/workflows/test_on_push.yml                | 14 +++++++-------
 .github/workflows/update_version.yml              |  2 +-
 .github/workflows/work_precision_sets.yml         |  2 +-
 8 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/benchmark_on_push.yml b/.github/workflows/benchmark_on_push.yml
index 11ed419572..49fcdee116 100644
--- a/.github/workflows/benchmark_on_push.yml
+++ b/.github/workflows/benchmark_on_push.yml
@@ -15,7 +15,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index b0b27d0fe3..9bd105ae92 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -22,7 +22,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
@@ -59,7 +59,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 3073c95f09..7bb7134dbf 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -22,7 +22,7 @@ jobs:
     runs-on: windows-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
@@ -72,7 +72,7 @@ jobs:
         os: [ubuntu-latest, macos-latest]
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
@@ -122,7 +122,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: 3.11
 
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index 6752e38800..f1348759d2 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -24,7 +24,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
       - name: Install nox and asv
@@ -53,7 +53,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
       - name: Install asv
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 020ac37f86..1b3d952c89 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -29,7 +29,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
 
@@ -51,7 +51,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 10fc5f53a8..77d28d8f88 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -21,7 +21,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Setup Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
 
@@ -86,7 +86,7 @@ jobs:
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
@@ -143,7 +143,7 @@ jobs:
 
       - name: Set up Python 3.11
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
           cache: 'pip'
@@ -224,7 +224,7 @@ jobs:
 
       - name: Set up Python ${{ matrix.python-version }}
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
@@ -282,7 +282,7 @@ jobs:
 
       - name: Set up Python 3.11
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
           cache: 'pip'
@@ -324,7 +324,7 @@ jobs:
 
       - name: Set up Python 3.11
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
           cache: 'pip'
@@ -379,7 +379,7 @@ jobs:
 
       - name: Set up Python 3.11
         id: setup-python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.11
           cache: 'pip'
diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml
index 0d63e68007..a6c35c0333 100644
--- a/.github/workflows/update_version.yml
+++ b/.github/workflows/update_version.yml
@@ -40,7 +40,7 @@ jobs:
           ref: '${{ env.NON_RC_VERSION }}'
 
       - name: Set up Python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
 
diff --git a/.github/workflows/work_precision_sets.yml b/.github/workflows/work_precision_sets.yml
index 87eb068947..7dd435c669 100644
--- a/.github/workflows/work_precision_sets.yml
+++ b/.github/workflows/work_precision_sets.yml
@@ -11,7 +11,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: 3.9
       - name: Get current date

From e29e75b4e10b8417e19360c637b03efa56b70cd9 Mon Sep 17 00:00:00 2001
From: Jonathan Lauber <jlauber98@gmail.com>
Date: Thu, 14 Dec 2023 07:08:01 -0300
Subject: [PATCH 521/615] Added conditions to workflows to be skiped (#3616)

Added conditions to workflows of value only to PyBaMM so they always be skipped in forks.
---
 .github/workflows/publish_pypi.yml                | 2 +-
 .github/workflows/release_reminder.yml            | 1 +
 .github/workflows/run_benchmarks_over_history.yml | 1 +
 .github/workflows/run_periodic_tests.yml          | 1 +
 .github/workflows/validation_benchmarks.yml       | 1 +
 .github/workflows/work_precision_sets.yml         | 1 +
 6 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 7bb7134dbf..b003152802 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -140,7 +140,7 @@ jobs:
           if-no-files-found: error
 
   publish_pypi:
-    if: github.event_name != 'schedule'
+    if: github.event_name != 'schedule' && github.repository_owner == 'pybamm-team'
     name: Upload package to PyPI
     needs: [build_wheels, build_windows_wheels, build_sdist]
     runs-on: ubuntu-latest
diff --git a/.github/workflows/release_reminder.yml b/.github/workflows/release_reminder.yml
index ac3f4b4865..f838c8d57a 100644
--- a/.github/workflows/release_reminder.yml
+++ b/.github/workflows/release_reminder.yml
@@ -11,6 +11,7 @@ permissions:
 
 jobs:
   remind:
+    if: github.repository_owner == 'pybamm-team'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index f1348759d2..cb16f65847 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -48,6 +48,7 @@ jobs:
           path: results
 
   publish-results:
+    if: github.repository_owner == 'pybamm-team'
     name: Push and publish results
     needs: benchmarks
     runs-on: ubuntu-latest
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 1b3d952c89..6e4f34927a 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -113,6 +113,7 @@ jobs:
 
   #M-series Mac Mini
   build-apple-mseries:
+    if: github.repository_owner == 'pybamm-team'
     needs: style
     runs-on: [self-hosted, macOS, ARM64]
     env:
diff --git a/.github/workflows/validation_benchmarks.yml b/.github/workflows/validation_benchmarks.yml
index dc47e8d670..dc5d18a98c 100644
--- a/.github/workflows/validation_benchmarks.yml
+++ b/.github/workflows/validation_benchmarks.yml
@@ -9,6 +9,7 @@ on:
 
 jobs:
   build:
+    if: github.repository_owner == 'pybamm-team'
     name: Dispatch to `pybamm-validation`
     runs-on: ubuntu-latest
     steps:
diff --git a/.github/workflows/work_precision_sets.yml b/.github/workflows/work_precision_sets.yml
index 7dd435c669..ba587b6d89 100644
--- a/.github/workflows/work_precision_sets.yml
+++ b/.github/workflows/work_precision_sets.yml
@@ -7,6 +7,7 @@ on:
 
 jobs:
   benchmarks_on_release:
+    if: github.repository_owner == 'pybamm-team'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4

From 903323efca7fef8f630924d114ce70dcf8a92363 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Thu, 14 Dec 2023 10:13:09 +0000
Subject: [PATCH 522/615] docs: add jlauber18 as a contributor for infra
 (#3619)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 9 +++++++++
 README.md           | 3 ++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 2ae94f2cfa..317cb38667 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -755,6 +755,15 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "jlauber18",
+      "name": "Jonathan Lauber",
+      "avatar_url": "https://avatars.githubusercontent.com/u/28939653?v=4",
+      "profile": "https://github.com/jlauber18",
+      "contributions": [
+        "infra"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/README.md b/README.md
index 5790060936..31c6257473 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-69-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-70-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -273,6 +273,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chmabaur"><img src="https://avatars.githubusercontent.com/u/127507466?v=4?s=100" width="100px;" alt="chmabaur"/><br /><sub><b>chmabaur</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achmabaur" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chmabaur" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/AbhishekChaudharii"><img src="https://avatars.githubusercontent.com/u/91185083?v=4?s=100" width="100px;" alt="Abhishek Chaudhari"/><br /><sub><b>Abhishek Chaudhari</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/shubhambhar007"><img src="https://avatars.githubusercontent.com/u/32607282?v=4?s=100" width="100px;" alt="Shubham Bhardwaj"/><br /><sub><b>Shubham Bhardwaj</b></sub></a><br /><a href="#infra-shubhambhar007" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/jlauber18"><img src="https://avatars.githubusercontent.com/u/28939653?v=4?s=100" width="100px;" alt="Jonathan Lauber"/><br /><sub><b>Jonathan Lauber</b></sub></a><br /><a href="#infra-jlauber18" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>
   </tbody>
 </table>

From d900a81c8345b9e97803c969fc6107bebd17e2e5 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:08:23 +0530
Subject: [PATCH 523/615] #3558 build SuiteSparse with INSTALL_RPATH

---
 scripts/install_KLU_Sundials.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 8f41f5969a..5a09421c2b 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -70,20 +70,24 @@ def download_extract_library(url, download_dir):
 # - BTF
 suitesparse_dir = "SuiteSparse-{}".format(suitesparse_version)
 suitesparse_src = os.path.join(download_dir, suitesparse_dir)
+# Build with INSTALL_RPATH set to install_dir and set
+# INSTALL_RPATH_USE_LINK_PATH to TRUE to use RPATH when linking
 print("-" * 10, "Building SuiteSparse_config", "-" * 40)
 make_cmd = [
     "make",
     "library",
-    'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={}"'.format(install_dir),
 ]
 install_cmd = [
     "make",
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
+# # Set CMAKE_OPTIONS as environment variables to pass to GNU Make
+env = os.environ.copy()
+env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir} -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE"
 for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
     build_dir = os.path.join(suitesparse_src, libdir)
-    subprocess.run(make_cmd, cwd=build_dir, check=True)
+    subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 
 # 2 --- Download SUNDIALS
@@ -140,10 +144,7 @@ def download_extract_library(url, download_dir):
         "-DLDFLAGS=" + LDFLAGS,
         "-DCPPFLAGS=" + CPPFLAGS,
         "-DOpenMP_C_FLAGS=" + OpenMP_C_FLAGS,
-        "-DOpenMP_CXX_FLAGS=" + OpenMP_CXX_FLAGS,
         "-DOpenMP_C_LIB_NAMES=" + OpenMP_C_LIB_NAMES,
-        "-DOpenMP_CXX_LIB_NAMES=" + OpenMP_CXX_LIB_NAMES,
-        "-DOpenMP_libomp_LIBRARY=" + OpenMP_libomp_LIBRARY,
         "-DOpenMP_omp_LIBRARY=" + OpenMP_omp_LIBRARY,
     ]
 

From 6a9743d8a38aefb93c60cbcb47b8febed5133e43 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:09:05 +0530
Subject: [PATCH 524/615] #3558 Remove some unused CMake arguments

CMake showed a warning about these arguments not being used during the compilation of the project.
---
 scripts/install_KLU_Sundials.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 5a09421c2b..5f6f2ff110 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -108,13 +108,15 @@ def download_extract_library(url, download_dir):
 cmake_args = [
     "-DENABLE_LAPACK=ON",
     "-DSUNDIALS_INDEX_SIZE=32",
-    "-DEXAMPLES_ENABLE:BOOL=OFF",
+    "-DEXAMPLES_ENABLE_C=OFF",
+    "-DEXAMPLES_ENABLE_CXX=OFF",
+    "-DEXAMPLES_INSTALL=OFF",
     "-DENABLE_KLU=ON",
     "-DENABLE_OPENMP=ON",
     "-DKLU_INCLUDE_DIR={}".format(KLU_INCLUDE_DIR),
     "-DKLU_LIBRARY_DIR={}".format(KLU_LIBRARY_DIR),
     "-DCMAKE_INSTALL_PREFIX=" + install_dir,
-    # on mac use fixed paths rather than rpath
+    # on macOS use fixed paths rather than rpath
     "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
 ]
 
@@ -125,9 +127,7 @@ def download_extract_library(url, download_dir):
         LDFLAGS = "-L/opt/homebrew/opt/libomp/lib"
         CPPFLAGS = "-I/opt/homebrew/opt/libomp/include"
         OpenMP_C_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
-        OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/opt/homebrew/opt/libomp/include"
         OpenMP_C_LIB_NAMES = "omp"
-        OpenMP_CXX_LIB_NAMES = "omp"
         OpenMP_libomp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
         OpenMP_omp_LIBRARY = "/opt/homebrew/opt/libomp/lib/libomp.dylib"
     elif platform.processor() == "i386":
@@ -137,7 +137,6 @@ def download_extract_library(url, download_dir):
         OpenMP_CXX_FLAGS = "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include"
         OpenMP_C_LIB_NAMES = "omp"
         OpenMP_CXX_LIB_NAMES = "omp"
-        OpenMP_libomp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
         OpenMP_omp_LIBRARY = "/usr/local/opt/libomp/lib/libomp.dylib"
 
     cmake_args += [

From 29941cec3fd33093e809782a0e07526683f809a0 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:09:23 +0530
Subject: [PATCH 525/615] #3558 Remove SuiteSparse macOS RPATH fixer script

---
 scripts/fix_suitesparse_rpath_mac.sh | 17 -----------------
 1 file changed, 17 deletions(-)
 delete mode 100755 scripts/fix_suitesparse_rpath_mac.sh

diff --git a/scripts/fix_suitesparse_rpath_mac.sh b/scripts/fix_suitesparse_rpath_mac.sh
deleted file mode 100755
index 987d936ef5..0000000000
--- a/scripts/fix_suitesparse_rpath_mac.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-#!/usr/bin/env bash
-
-LIBDIR=${HOME}/.local/lib
-
-otool -L ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libamd.3.dylib ${LIBDIR}/libamd.3.dylib ${LIBDIR}/libklu.2.dylib
-install_name_tool -change @rpath/libcolamd.3.dylib ${LIBDIR}/libcolamd.3.dylib ${LIBDIR}/libklu.2.dylib
-install_name_tool -change @rpath/libbtf.2.dylib ${LIBDIR}/libbtf.2.dylib ${LIBDIR}/libklu.2.dylib
-
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libcolamd.3.dylib
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libbtf.2.dylib
-install_name_tool -change @rpath/libsuitesparseconfig.6.dylib ${LIBDIR}/libsuitesparseconfig.6.dylib ${LIBDIR}/libcolamd.3.dylib
-
-otool -L ${LIBDIR}/libklu.2.dylib

From 8e59c1b6f92e560fab4c653b33d6513cc9d0e735 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:10:22 +0530
Subject: [PATCH 526/615] #3361 #3558 Improve caching and remove `examples/`

---
 .github/workflows/test_on_push.yml | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 2f7f94c9bc..0ac4acf80b 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -104,8 +104,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -160,8 +159,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -242,8 +240,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux and macOS
         if: matrix.os != 'windows-latest'
@@ -341,8 +338,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
@@ -396,8 +392,7 @@ jobs:
             # Headers and dynamic library files for SuiteSparse and SUNDIALS
             ${{ env.HOME }}/.local/lib/
             ${{ env.HOME }}/.local/include/
-            ${{ env.HOME }}/.local/examples/
-          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py') }}
+          key: nox-${{ matrix.os }}-pybamm-requires-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/install_KLU_Sundials.py', '**/noxfile.py', '**/test_on_push.yml') }}
 
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires

From 7f1f74f7697d220e7649cf69da02c13db6ef8886 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 19:19:22 +0530
Subject: [PATCH 527/615] #3558 Remove `scripts/fix_suitesparse_rpath_mac.sh`

---
 .github/workflows/publish_pypi.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 45569b0dd9..534b8a1905 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -121,8 +121,7 @@ jobs:
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
           CIBW_BEFORE_BUILD_MACOS: >
-            python -m pip install --upgrade cmake casadi setuptools wheel &&
-            scripts/fix_suitesparse_rpath_mac.sh
+            python -m pip install --upgrade cmake casadi setuptools wheel
           CIBW_REPAIR_WHEEL_COMMAND_MACOS: delocate-listdeps {wheel} && delocate-wheel -v -w {dest_dir} {wheel}
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 

From f27aa2c5e399c65d3073b929489a45e88e6df909 Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:49:53 +0530
Subject: [PATCH 528/615] Changing pyproject config

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7d25c8e140..31e0b3e9bf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-  # "UP",          # pyupgrade
+   "UP",          # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [
@@ -214,6 +214,7 @@ ignore = [
   "RET506",      # Unnecessary `elif`
   "B018",        # Found useless expression
   "RUF002",      # Docstring contains ambiguous
+    "UP007",     # For pyupgrade
 ]
 
 [tool.ruff.lint.per-file-ignores]

From ff6d81c01331c7d269303b4a8321d9881bdf98fa Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:56:39 +0530
Subject: [PATCH 529/615] changed string formatting using pyupgrade

Signed-off-by: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
---
 .../work_precision_sets/time_vs_abstols.py    |    2 +-
 .../work_precision_sets/time_vs_dt_max.py     |    2 +-
 .../work_precision_sets/time_vs_mesh_size.py  |    2 +-
 .../time_vs_no_of_states.py                   |    2 +-
 .../work_precision_sets/time_vs_reltols.py    |    2 +-
 docs/conf.py                                  |    3 +-
 .../3-negative-particle-problem.ipynb         |    2 +-
 .../tutorial-8-solver-options.ipynb           |    4 +-
 .../compare-comsol-discharge-curve.ipynb      |    4 +-
 .../models/compare-lithium-ion.ipynb          |    2 +-
 .../compare-particle-diffusion-models.ipynb   |    4 +-
 .../examples/notebooks/models/lead-acid.ipynb |    2 +-
 .../notebooks/models/pouch-cell-model.ipynb   | 1754 ++++++++---------
 .../notebooks/models/rate-capability.ipynb    |    4 +-
 .../models/unsteady-heat-equation.ipynb       |    2 +-
 .../change-input-current.ipynb                |    2 +-
 .../parameterization/parameter-values.ipynb   |   10 +-
 .../parameterization/parameterization.ipynb   |    2 +-
 .../callbacks.ipynb                           |    2 +-
 .../notebooks/solvers/speed-up-solver.ipynb   |    6 +-
 .../spatial_methods/finite-volumes.ipynb      |   30 +-
 .../compare_comsol/compare_comsol_DFN.py      |    2 +-
 .../scripts/compare_comsol/discharge_curve.py |    4 +-
 examples/scripts/compare_particle_models.py   |    4 +-
 .../scripts/experimental_protocols/cccv.py    |    2 +-
 examples/scripts/heat_equation.py             |    2 +-
 examples/scripts/rate_capability.py           |    4 +-
 pybamm/callbacks.py                           |    2 +-
 pybamm/citations.py                           |    4 +-
 pybamm/discretisations/discretisation.py      |   58 +-
 pybamm/experiment/experiment.py               |    2 +-
 pybamm/expression_tree/array.py               |    2 +-
 pybamm/expression_tree/averages.py            |   12 +-
 pybamm/expression_tree/binary_operators.py    |   32 +-
 pybamm/expression_tree/concatenations.py      |    6 +-
 pybamm/expression_tree/functions.py           |    8 +-
 .../expression_tree/independent_variable.py   |    2 +-
 pybamm/expression_tree/input_parameter.py     |    6 +-
 pybamm/expression_tree/interpolant.py         |    6 +-
 pybamm/expression_tree/matrix.py              |    2 +-
 .../operations/convert_to_casadi.py           |    8 +-
 .../operations/evaluate_python.py             |   68 +-
 pybamm/expression_tree/operations/jacobian.py |    6 +-
 .../expression_tree/operations/serialise.py   |    2 +-
 .../operations/unpack_symbols.py              |    2 +-
 pybamm/expression_tree/state_vector.py        |   12 +-
 pybamm/expression_tree/symbol.py              |   20 +-
 pybamm/expression_tree/unary_operators.py     |   42 +-
 pybamm/expression_tree/vector.py              |    2 +-
 pybamm/geometry/battery_geometry.py           |    4 +-
 pybamm/install_odes.py                        |   20 +-
 pybamm/meshes/meshes.py                       |    6 +-
 pybamm/meshes/scikit_fem_submeshes.py         |   10 +-
 pybamm/models/base_model.py                   |   50 +-
 .../full_battery_models/base_battery_model.py |   30 +-
 .../equivalent_circuit/thevenin.py            |    2 +-
 pybamm/models/submodels/base_submodel.py      |    4 +-
 pybamm/parameters/parameter_sets.py           |    2 +-
 pybamm/parameters/parameter_values.py         |   34 +-
 pybamm/parameters/process_parameter_data.py   |    2 +-
 pybamm/plotting/quick_plot.py                 |   22 +-
 pybamm/settings.py                            |    2 +-
 pybamm/solvers/algebraic_solver.py            |    8 +-
 pybamm/solvers/base_solver.py                 |   46 +-
 pybamm/solvers/casadi_algebraic_solver.py     |    4 +-
 pybamm/solvers/casadi_solver.py               |    8 +-
 pybamm/solvers/jax_solver.py                  |    8 +-
 pybamm/solvers/processed_variable.py          |    6 +-
 pybamm/solvers/processed_variable_computed.py |    4 +-
 pybamm/solvers/scikits_dae_solver.py          |    2 +-
 pybamm/solvers/scikits_ode_solver.py          |    2 +-
 pybamm/solvers/scipy_solver.py                |    2 +-
 pybamm/solvers/solution.py                    |   11 +-
 pybamm/spatial_methods/finite_volume.py       |   28 +-
 .../spatial_methods/scikit_finite_element.py  |   12 +-
 pybamm/spatial_methods/spectral_volume.py     |    8 +-
 pybamm/util.py                                |   14 +-
 run-tests.py                                  |    4 +-
 scripts/install_KLU_Sundials.py               |   12 +-
 setup.py                                      |   20 +-
 .../test_models/standard_model_tests.py       |    4 +-
 .../test_models/standard_output_comparison.py |    4 +-
 .../test_models/standard_output_tests.py      |    4 +-
 .../test_asymptotics_convergence.py           |    2 +-
 tests/unit/test_callbacks.py                  |   12 +-
 tests/unit/test_citations.py                  |    4 +-
 .../test_expression_tree/test_functions.py    |    2 +-
 .../test_operations/test_evaluate_python.py   |   24 +-
 .../test_parameters/test_current_functions.py |    2 +-
 .../test_serialisation/test_serialisation.py  |    2 +-
 tests/unit/test_simulation.py                 |    2 +-
 .../test_solvers/test_processed_variable.py   |    2 +-
 .../test_processed_variable_computed.py       |    2 +-
 tests/unit/test_timer.py                      |    2 +-
 94 files changed, 1260 insertions(+), 1360 deletions(-)

diff --git a/benchmarks/work_precision_sets/time_vs_abstols.py b/benchmarks/work_precision_sets/time_vs_abstols.py
index 9a96f07514..d680766c43 100644
--- a/benchmarks/work_precision_sets/time_vs_abstols.py
+++ b/benchmarks/work_precision_sets/time_vs_abstols.py
@@ -98,7 +98,7 @@
 
 content = f"# PyBaMM {pybamm.__version__}\n## Solve Time vs Abstols\n<img src='./benchmark_images/time_vs_abstols_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_dt_max.py b/benchmarks/work_precision_sets/time_vs_dt_max.py
index 3e428b702c..a1f8ca06bc 100644
--- a/benchmarks/work_precision_sets/time_vs_dt_max.py
+++ b/benchmarks/work_precision_sets/time_vs_dt_max.py
@@ -100,7 +100,7 @@
 
 content = f"## Solve Time vs dt_max\n<img src='./benchmark_images/time_vs_dt_max_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_mesh_size.py b/benchmarks/work_precision_sets/time_vs_mesh_size.py
index f0f13f706b..cbab18d16c 100644
--- a/benchmarks/work_precision_sets/time_vs_mesh_size.py
+++ b/benchmarks/work_precision_sets/time_vs_mesh_size.py
@@ -80,7 +80,7 @@
 
 content = f"## Solve Time vs Mesh size\n<img src='./benchmark_images/time_vs_mesh_size_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_no_of_states.py b/benchmarks/work_precision_sets/time_vs_no_of_states.py
index eb27aba322..febc69f0a1 100644
--- a/benchmarks/work_precision_sets/time_vs_no_of_states.py
+++ b/benchmarks/work_precision_sets/time_vs_no_of_states.py
@@ -84,7 +84,7 @@
 
 content = f"## Solve Time vs Number of states\n<img src='./benchmark_images/time_vs_no_of_states_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/benchmarks/work_precision_sets/time_vs_reltols.py b/benchmarks/work_precision_sets/time_vs_reltols.py
index 93964910a8..42e9a1bab1 100644
--- a/benchmarks/work_precision_sets/time_vs_reltols.py
+++ b/benchmarks/work_precision_sets/time_vs_reltols.py
@@ -104,7 +104,7 @@
 
 content = f"## Solve Time vs Reltols\n<img src='./benchmark_images/time_vs_reltols_{pybamm.__version__}.png'>\n"
 
-with open("./benchmarks/release_work_precision_sets.md", "r") as original:
+with open("./benchmarks/release_work_precision_sets.md") as original:
     data = original.read()
 with open("./benchmarks/release_work_precision_sets.md", "w") as modified:
     modified.write(f"{content}\n{data}")
diff --git a/docs/conf.py b/docs/conf.py
index 55692309dc..35edadb249 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 #
 # Configuration file for the Sphinx documentation builder.
 #
@@ -168,7 +167,7 @@
     ],
 }
 
-html_title = "%s v%s Manual" % (project, version)
+html_title = f"{project} v{version} Manual"
 html_last_updated_fmt = "%Y-%m-%d"
 html_css_files = ["pybamm.css"]
 html_context = {"default_mode": "light"}
diff --git a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
index 2c338149e7..b04616c5f9 100644
--- a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
+++ b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
@@ -307,7 +307,7 @@
     "\n",
     "r = mesh[\"negative particle\"].nodes # radial position\n",
     "time = 1000  # time in seconds\n",
-    "ax2.plot(r * 1e6, c(t=time, r=r), label=\"t={}[s]\".format(time))\n",
+    "ax2.plot(r * 1e6, c(t=time, r=r), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
     "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
     "ax2.legend()\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
index 46a7b24346..2e55321659 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-8-solver-options.ipynb
@@ -98,9 +98,9 @@
     "\n",
     "# solve\n",
     "safe_sim.solve([0, 3600])\n",
-    "print(\"Safe mode solve time: {}\".format(safe_sim.solution.solve_time))\n",
+    "print(f\"Safe mode solve time: {safe_sim.solution.solve_time}\")\n",
     "fast_sim.solve([0, 3600])\n",
-    "print(\"Fast mode solve time: {}\".format(fast_sim.solution.solve_time))\n",
+    "print(f\"Fast mode solve time: {fast_sim.solution.solve_time}\")\n",
     "\n",
     "# plot solutions\n",
     "pybamm.dynamic_plot([safe_sim, fast_sim])"
diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
index 90611a91a0..462f03827b 100644
--- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
+++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
@@ -167,7 +167,7 @@
     "\n",
     "    # load the comsol results\n",
     "    comsol_results_path = pybamm.get_parameters_filepath(\n",
-    "        \"input/comsol_results/comsol_{}C.pickle\".format(key),\n",
+    "        f\"input/comsol_results/comsol_{key}C.pickle\",\n",
     "    )\n",
     "    comsol_variables = pickle.load(open(comsol_results_path, 'rb'))\n",
     "    comsol_time = comsol_variables[\"time\"]\n",
@@ -203,7 +203,7 @@
     "        voltage_sol,\n",
     "        color=color,\n",
     "        linestyle=\"-\",\n",
-    "        label=\"{} C\".format(C_rate),\n",
+    "        label=f\"{C_rate} C\",\n",
     "    )\n",
     "    voltage_difference_plot.plot(\n",
     "        discharge_capacity_sol[0:end_index], voltage_difference, color=color\n",
diff --git a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
index f194a62d02..74157628f8 100644
--- a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -272,7 +272,7 @@
     "    solver = pybamm.CasadiSolver()\n",
     "    timer.reset()\n",
     "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
-    "    print(\"Solved the {} in {}\".format(model.name, timer.time()))\n",
+    "    print(f\"Solved the {model.name} in {timer.time()}\")\n",
     "    solutions[model_name] = solution"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
index 6bd9f4cf63..da6f05870e 100644
--- a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
+++ b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
@@ -124,8 +124,8 @@
     "for sim in simulations:\n",
     "    sim.solve(t_eval, inputs={\"Current function [A]\": 0.68})\n",
     "    solutions_1C.append(sim.solution)\n",
-    "    print(\"Particle model: {}\".format(sim.model.name))\n",
-    "    print(\"Solve time: {}s\".format(sim.solution.solve_time))"
+    "    print(f\"Particle model: {sim.model.name}\")\n",
+    "    print(f\"Solve time: {sim.solution.solve_time}s\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb
index f550540182..0dd20126a6 100644
--- a/docs/source/examples/notebooks/models/lead-acid.ipynb
+++ b/docs/source/examples/notebooks/models/lead-acid.ipynb
@@ -228,7 +228,7 @@
     "    solver = pybamm.CasadiSolver()\n",
     "    timer.reset()\n",
     "    solution = solver.solve(model, t_eval, inputs={\"Current function [A]\": 1})\n",
-    "    print(\"Solved the {} in {}\".format(model.name, timer.time()))\n",
+    "    print(f\"Solved the {model.name} in {timer.time()}\")\n",
     "    solutions[model] = solution"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index a9431211af..2c58b1861f 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -1,879 +1,879 @@
 {
-      "cells": [
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "# Pouch cell model"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this notebook we compare the solutions of two reduced-order models of a lithium-ion pouch cell with the full solution obtained using COMSOL. This example is based on the results in [[6]](#References). The code used to produce the results in [[6]](#References) can be found [here](https://github.com/rtimms/asymptotic-pouch-cell).\n",
-                        "\n",
-                        "The full model is based on the Doyle-Fuller-Newman model [[2]](#References) and, in the interest of simplicity, considers a one-dimensional current collector (i.e. variation in one of the current collector dimensions is ignored), resulting in a 2D macroscopic model.\n",
-                        "\n",
-                        "The first of the reduced order models, which is applicable in the limit of large conductivity in the current collectors, solves a one-dimensional problem in the current collectors coupled to a one-dimensional DFN model describing the through-cell electrochemistry at each point. We refer to this as a 1+1D model, though since the DFN is already a pseudo-two-dimensional model, perhaps it is more properly a 1+1+1D model.\n",
-                        "\n",
-                        "The second reduced order model, which is applicable in the limit of very large conductivity in the current collectors, solves a single (averaged) one-dimensional DFN model for the through-cell behaviour and an uncoupled problem for the distribution of potential in the current collectors (from which the resistance and heat source can be calculated). We refer to this model as the DFNCC, where the \"CC\" indicates the additional (uncoupled) current collector problem.\n",
-                        "\n",
-                        "All of the model equations, and derivations of the reduced-order models, can be found in [[6]](#References)."
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Solving the reduced-order pouch cell models in PyBaMM"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We begin by importing PyBaMM along with the other packages required in this notebook"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 1,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "name": "stdout",
-                              "output_type": "stream",
-                              "text": [
-                                    "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
-                                    "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
-                                    "\u001b[0m\n",
-                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
-                                    "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
-                                    "Note: you may need to restart the kernel to use updated packages.\n"
-                              ]
-                        }
-                  ],
-                  "source": [
-                        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
-                        "import pybamm\n",
-                        "import pickle\n",
-                        "import matplotlib.pyplot as plt\n",
-                        "import numpy as np\n",
-                        "import scipy.interpolate as interp"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then need to load up the appropriate models. For the DFNCC we require a 1D model of the current collectors and an average 1D DFN model for the through-cell electrochemistry. The 1+1D pouch cell model is built directly into PyBaMM and are accessed by passing the model option \"dimensionality\" which can be 1 or 2, corresponding to 1D or 2D current collectors. This option can be passed to any existing electrochemical model (e.g. [SPM](./SPM.ipynb), [SPMe](./SPMe.ipynb), [DFN](./DFN.ipynb)). Here we choose the DFN model. \n",
-                        "\n",
-                        "For both electrochemical models we choose an \"x-lumped\" thermal model, meaning we assume that the temperature is uniform in the through-cell direction $x$, but account for the variation in temperature in the transverse direction $z$."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 2,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "name": "stderr",
-                              "output_type": "stream",
-                              "text": [
-                                    "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
-                                    "  options = BatteryModelOptions(extra_options)\n"
-                              ]
-                        }
-                  ],
-                  "source": [
-                        "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
-                        "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
-                        "dfn = pybamm.lithium_ion.DFN(\n",
-                        "    {\"current collector\": \"potential pair\", \"dimensionality\": 1, \"thermal\": \"x-lumped\"},\n",
-                        "    name=\"1+1D DFN\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then add the models to a dictionary for easy access later"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 3,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "models = {\"Current collector\": cc_model, \"Average DFN\": dfn_av, \"1+1D DFN\": dfn}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we update the parameters to match those used in the COMSOL simulation. In particular, we set the current to correspond to a 3C discharge and assume uniform Newton cooling on all boundaries."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 4,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "param = dfn.default_parameter_values\n",
-                        "I_1C = param[\"Nominal cell capacity [A.h]\"]  # 1C current is cell capacity multipled by 1 hour\n",
-                        "param.update(\n",
-                        "    {\n",
-                        "        \"Current function [A]\": I_1C * 3,       \n",
-                        "        \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n",
-                        "        \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n",
-                        "        \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "        \"Total heat transfer coefficient [W.m-2.K-1]\": 10,\n",
-                        "    }\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this example we choose to discretise in space using 16 nodes per domain."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 5,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "npts = 16\n",
-                        "var_pts = {\n",
-                        "    \"x_n\": npts,\n",
-                        "    \"x_s\": npts,\n",
-                        "    \"x_p\": npts,\n",
-                        "    \"r_n\": npts,\n",
-                        "    \"r_p\": npts,\n",
-                        "    \"z\": npts,\n",
-                        "}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Before solving the models we load the COMSOL data so that we can request the output at the times in the COMSOL solution"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 6,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_results_path = pybamm.get_parameters_filepath(\n",
-                        "    \"input/comsol_results/comsol_1plus1D_3C.pickle\"\n",
-                        ")\n",
-                        "comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we loop over the models, creating and solving a simulation for each."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 7,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "simulations = {}\n",
-                        "solutions = {}  # store solutions in a separate dict for easy access later\n",
-                        "for name, model in models.items():\n",
-                        "    sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n",
-                        "    simulations[name] = sim  # store simulation for later\n",
-                        "    if name == \"Current collector\":\n",
-                        "        # model is independent of time, so just solve arbitrarily at t=0 using \n",
-                        "        # the default algebraic solver\n",
-                        "        t_eval = np.array([0])\n",
-                        "        solutions[name] = sim.solve(t_eval=t_eval)        \n",
-                        "    else:\n",
-                        "        # solve at COMSOL times using Casadi solver in \"fast\" mode\n",
-                        "        t_eval = comsol_variables[\"time\"]       \n",
-                        "        solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Creating the COMSOL model"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "In this section we show how to create a PyBaMM \"model\" from the COMSOL solution. If you are just interested in seeing the comparison the skip ahead to the section \"Comparing the full and reduced-order models\".\n"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "To create a PyBaMM model from the COMSOL data we must create a `pybamm.Function` object for each variable. We do this by interpolating in space to match the PyBaMM mesh and then creating a function to interpolate in time. The following cell defines the function that handles the creation of the `pybamm.Function` object."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 8,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "# set up times\n",
-                        "comsol_t = comsol_variables[\"time\"]\n",
-                        "pybamm_t = comsol_t\n",
-                        "# set up space\n",
-                        "mesh = simulations[\"1+1D DFN\"].mesh\n",
-                        "L_z = param.evaluate(dfn.param.L_z)\n",
-                        "pybamm_z = mesh[\"current collector\"].nodes\n",
-                        "z_interp = pybamm_z\n",
-                        "\n",
-                        "\n",
-                        "def get_interp_fun_curr_coll(variable_name):\n",
-                        "    \"\"\"\n",
-                        "    Create a :class:`pybamm.Function` object using the variable (interpolate in space \n",
-                        "    to match nodes, and then create function to interpolate in time)\n",
-                        "    \"\"\"\n",
-                        "\n",
-                        "    comsol_z = comsol_variables[variable_name + \"_z\"]\n",
-                        "    variable = comsol_variables[variable_name]\n",
-                        "    variable = interp.interp1d(comsol_z, variable, axis=0, kind=\"linear\")(z_interp)\n",
-                        "\n",
-                        "    # Make sure to use dimensional time\n",
-                        "    fun = pybamm.Interpolant(\n",
-                        "        comsol_t,\n",
-                        "        variable.T,\n",
-                        "        pybamm.t,\n",
-                        "        name=variable_name + \"_comsol\"\n",
-                        "    )\n",
-                        "    fun.domains = {\"primary\": \"current collector\"}\n",
-                        "    fun.mesh = mesh.combine_submeshes(\"current collector\")\n",
-                        "    fun.secondary_mesh = None\n",
-                        "\n",
-                        "    return fun"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then pass the variables of interest to the interpolating function"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 9,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_voltage = pybamm.Interpolant(\n",
-                        "    comsol_t, \n",
-                        "    comsol_variables[\"voltage\"],\n",
-                        "    pybamm.t,\n",
-                        "    name=\"voltage_comsol\",\n",
-                        ")\n",
-                        "comsol_voltage.mesh = None\n",
-                        "comsol_voltage.secondary_mesh = None\n",
-                        "comsol_phi_s_cn = get_interp_fun_curr_coll(\"phi_s_cn\")\n",
-                        "comsol_phi_s_cp = get_interp_fun_curr_coll(\"phi_s_cp\")\n",
-                        "comsol_current = get_interp_fun_curr_coll(\"current\")\n",
-                        "comsol_temperature = get_interp_fun_curr_coll(\"temperature\")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and add them to a `pybamm.BaseModel` object"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 10,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_model = pybamm.BaseModel()\n",
-                        "comsol_model._geometry = pybamm.battery_geometry(options={\"dimensionality\": 1})\n",
-                        "comsol_model.variables = {\n",
-                        "    \"Voltage [V]\": comsol_voltage,\n",
-                        "    \"Negative current collector potential [V]\": comsol_phi_s_cn,\n",
-                        "    \"Positive current collector potential [V]\": comsol_phi_s_cp,\n",
-                        "    \"Current collector current density [A.m-2]\": comsol_current,\n",
-                        "    \"X-averaged cell temperature [K]\": comsol_temperature,\n",
-                        "    # Add spatial variables to match pybamm model\n",
-                        "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],   \n",
-                        "}"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 11,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## Comparing the full and reduced-order models"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "The DFNCC requires some post-processing to extract the solution variables. In particular, we need to pass the current and voltage from the average DFN model to the current collector model in order to compute the distribution of the potential in the current collectors and to account for the effect of the current collector resistance in the voltage. \n",
-                        "\n",
-                        "This process is automated by the method `post_process` which accepts the current collector solution object, the parameters and the voltage and current from the average DFN model. The results are stored in the dictionary `dfncc_vars`"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 12,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n",
-                        "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n",
-                        "\n",
-                        "dfncc_vars = cc_model.post_process(\n",
-                        "    solutions[\"Current collector\"], param, V_av, I_av\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "Next we create a function to create some custom plots. For a given variable the plots will show: (a) the COMSOL results as a function of position in the current collector $z$ and time $t$; (b) a comparison of the full and reduced-order models and a sequence of times; (c) the time-averaged error between the full and reduced-order models as a function of space; and (d) the space-averaged error between the full and reduced-order models as a function of time."
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 13,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "def plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    var_name,\n",
-                        "    units,\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"viridis\",\n",
-                        "):\n",
-                        "\n",
-                        "    fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n",
-                        "    fig.subplots_adjust(\n",
-                        "        left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n",
-                        "    )\n",
-                        "    # plot comsol var\n",
-                        "    comsol_var = comsol_var_fun(t=t_plot, z=z_plot)\n",
-                        "    comsol_var_plot = ax[0, 0].pcolormesh(\n",
-                        "        z_plot * 1e3, t_plot, np.transpose(comsol_var), shading=\"gouraud\", cmap=cmap\n",
-                        "    )\n",
-                        "    if \"cn\" in var_name:\n",
-                        "        format = \"%.0e\"\n",
-                        "    elif \"cp\" in var_name:\n",
-                        "        format = \"%.0e\"\n",
-                        "    else:\n",
-                        "        format = None\n",
-                        "    fig.colorbar(\n",
-                        "        comsol_var_plot,\n",
-                        "        ax=ax,\n",
-                        "        format=format,\n",
-                        "        location=\"top\",\n",
-                        "        shrink=0.42,\n",
-                        "        aspect=20,\n",
-                        "        anchor=(0.0, 0.0),\n",
-                        "    )\n",
-                        "\n",
-                        "    # plot slices\n",
-                        "    ccmap = plt.get_cmap(\"inferno\")\n",
-                        "    for ind, t in enumerate(t_slices):\n",
-                        "        color = ccmap(float(ind) / len(t_slices))\n",
-                        "        comsol_var_slice = comsol_var_fun(t=t, z=z_plot)\n",
-                        "        dfn_var_slice = dfn_var_fun(t=t, z=z_plot)\n",
-                        "        dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot)\n",
-                        "        ax[0, 1].plot(\n",
-                        "            z_plot * 1e3, comsol_var_slice, \"o\", fillstyle=\"none\", color=color\n",
-                        "        )\n",
-                        "        ax[0, 1].plot(\n",
-                        "            z_plot * 1e3,\n",
-                        "            dfn_var_slice,\n",
-                        "            \"-\",\n",
-                        "            color=color,\n",
-                        "            label=\"{:.0f} s\".format(t_slices[ind]),\n",
-                        "        )\n",
-                        "        ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n",
-                        "    # add dummy points for legend of styles\n",
-                        "    comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
-                        "    pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
-                        "    dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
-                        "\n",
-                        "    # compute errors\n",
-                        "    dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n",
-                        "    dfncc_var = dfncc_var_fun(t=t_plot, z=z_plot)\n",
-                        "    error = np.abs(comsol_var - dfn_var)\n",
-                        "    error_bar = np.abs(comsol_var - dfncc_var)\n",
-                        "\n",
-                        "    # plot time averaged error\n",
-                        "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error, axis=1), \"k-\", label=r\"$1+1$D\")\n",
-                        "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error_bar, axis=1), \"k:\", label=\"DFNCC\")\n",
-                        "\n",
-                        "    # plot z averaged error\n",
-                        "    ax[1, 1].plot(t_plot, np.nanmean(error, axis=0), \"k-\", label=r\"$1+1$D\")\n",
-                        "    ax[1, 1].plot(t_plot, np.nanmean(error_bar, axis=0), \"k:\", label=\"DFNCC\")\n",
-                        "\n",
-                        "    # set ticks\n",
-                        "    ax[0, 0].tick_params(which=\"both\")\n",
-                        "    ax[0, 1].tick_params(which=\"both\")\n",
-                        "    ax[1, 0].tick_params(which=\"both\")\n",
-                        "    if var_name in [\"$\\mathcal{I}^*$\"]:\n",
-                        "        ax[1, 0].set_yscale(\"log\")\n",
-                        "        ax[1, 0].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
-                        "    else:\n",
-                        "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
-                        "    ax[1, 1].tick_params(which=\"both\")\n",
-                        "    if var_name in [\"$\\phi^*_{\\mathrm{s,cn}}$\", \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\"]:\n",
-                        "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
-                        "    else:\n",
-                        "        ax[1, 1].set_yscale(\"log\")\n",
-                        "        ax[1, 1].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
-                        "\n",
-                        "    # set labels\n",
-                        "    ax[0, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[0, 0].set_ylabel(r\"$t^*$ [s]\")\n",
-                        "    ax[0, 0].set_title(r\"{} {}\".format(var_name, units), y=1.5)\n",
-                        "    ax[0, 1].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[0, 1].set_ylabel(r\"{}\".format(var_name))\n",
-                        "    ax[1, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
-                        "    ax[1, 0].set_ylabel(\"Time-averaged\" + \"\\n\" + r\"absolute error {}\".format(units))\n",
-                        "    ax[1, 1].set_xlabel(r\"$t^*$ [s]\")\n",
-                        "    ax[1, 1].set_ylabel(\"Space-averaged\" + \"\\n\" + r\"absolute error {}\".format(units))\n",
-                        "\n",
-                        "    ax[0, 0].text(-0.1, 1.6, \"(a)\", transform=ax[0, 0].transAxes)\n",
-                        "    ax[0, 1].text(-0.1, 1.6, \"(b)\", transform=ax[0, 1].transAxes)\n",
-                        "    ax[1, 0].text(-0.1, 1.2, \"(c)\", transform=ax[1, 0].transAxes)\n",
-                        "    ax[1, 1].text(-0.1, 1.2, \"(d)\", transform=ax[1, 1].transAxes)\n",
-                        "\n",
-                        "    leg1 = ax[0, 1].legend(\n",
-                        "        bbox_to_anchor=(0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower left\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "        mode=\"expand\",\n",
-                        "    )\n",
-                        "\n",
-                        "    ax[0, 1].legend(\n",
-                        "        [comsol_p, pybamm_p, dfncc_p],\n",
-                        "        [\"COMSOL\", r\"$1+1$D\", \"DFNCC\"],\n",
-                        "        bbox_to_anchor=(0, 1.5, 1.0, 0.102),\n",
-                        "        loc=\"lower left\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "        mode=\"expand\",\n",
-                        "    )\n",
-                        "    ax[0, 1].add_artist(leg1)\n",
-                        "\n",
-                        "    ax[1, 0].legend(\n",
-                        "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower right\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "    )\n",
-                        "    ax[1, 1].legend(\n",
-                        "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
-                        "        loc=\"lower right\",\n",
-                        "        borderaxespad=0.0,\n",
-                        "        ncol=3,\n",
-                        "    )"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We then set up the times and points in space to use in the plots "
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 14,
-                  "metadata": {},
-                  "outputs": [],
-                  "source": [
-                        "t_plot = comsol_t\n",
-                        "z_plot = z_interp\n",
-                        "t_slices = np.array([600, 1200, 1800, 2400, 3000]) / 3"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and plot the negative current collector potential"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 15,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Negative current collector potential [V]\"\n",
-                        "comsol_var_fun = comsol_solution[var]\n",
-                        "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "dfncc_var_fun = dfncc_vars[var]\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\phi^*_{\\mathrm{s,cn}}$\",\n",
-                        "    \"[V]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"cividis\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "the positive current collector potential with respect to voltage"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 16,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Positive current collector potential [V]\"\n",
-                        "comsol_var = comsol_solution[var]\n",
-                        "V_comsol = comsol_solution[\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def comsol_var_fun(t, z):\n",
-                        "    return comsol_var(t=t, z=z) - V_comsol(t=t)\n",
-                        "\n",
-                        "\n",
-                        "dfn_var = solutions[\"1+1D DFN\"][var]\n",
-                        "V = solutions[\"1+1D DFN\"][\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def dfn_var_fun(t, z):\n",
-                        "    return dfn_var(t=t, z=z) - V(t=t)\n",
-                        "\n",
-                        "\n",
-                        "dfncc_var =  dfncc_vars[var]\n",
-                        "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    return dfncc_var(t=t, z=z) - V_dfncc(t)\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\",\n",
-                        "    \"[V]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"viridis\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "the through-cell current "
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 17,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "var = \"Current collector current density [A.m-2]\"\n",
-                        "comsol_var_fun = comsol_solution[var]\n",
-                        "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "I_av = solutions[\"Average DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    \"In the DFNCC the current is just the average current\"\n",
-                        "    return np.transpose(np.repeat(I_av(t)[:, np.newaxis], len(z), axis=1))\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\mathcal{I}^*$\",\n",
-                        "    \"[A/m${}^2$]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"plasma\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "and the temperature with respect to reference temperature"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 18,
-                  "metadata": {
-                        "scrolled": true
-                  },
-                  "outputs": [
-                        {
-                              "data": {
-                                    "image/png": 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",
-                                    "text/plain": [
-                                          "<Figure size 1300x700 with 5 Axes>"
-                                    ]
-                              },
-                              "metadata": {},
-                              "output_type": "display_data"
-                        }
-                  ],
-                  "source": [
-                        "T_ref = param.evaluate(dfn.param.T_ref)\n",
-                        "var = \"X-averaged cell temperature [K]\"\n",
-                        "comsol_var = comsol_solution[var]\n",
-                        "\n",
-                        "\n",
-                        "def comsol_var_fun(t, z):\n",
-                        "    return comsol_var(t=t, z=z) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "dfn_var = solutions[\"1+1D DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfn_var_fun(t, z):\n",
-                        "    return dfn_var(t=t, z=z) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "T_av = solutions[\"Average DFN\"][var]\n",
-                        "\n",
-                        "\n",
-                        "def dfncc_var_fun(t, z):\n",
-                        "    \"In the DFNCC the temperature is just the average temperature\"\n",
-                        "    return np.transpose(np.repeat(T_av(t)[:, np.newaxis], len(z), axis=1)) - T_ref\n",
-                        "\n",
-                        "\n",
-                        "plot(\n",
-                        "    t_plot,\n",
-                        "    z_plot,\n",
-                        "    t_slices,\n",
-                        "    \"$\\\\bar{T}^* - \\\\bar{T}_0^*$\",\n",
-                        "    \"[K]\",\n",
-                        "    comsol_var_fun,\n",
-                        "    dfn_var_fun,\n",
-                        "    dfncc_var_fun,\n",
-                        "    param,\n",
-                        "    cmap=\"inferno\",\n",
-                        ")"
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "We see that the electrical conductivity of the current collectors is sufficiently\n",
-                        "high that the potentials remain fairly uniform in space, and both the 1+1D DFN and DFNCC models are able to accurately capture the potential distribution in the current collectors.\n",
-                        "\n",
-                        "\n",
-                        "In the plot of the current we see that positioning both tabs at the top of the cell means that for most of the simulation the current preferentially travels through the upper part of the cell. Eventually, as the cell continues to discharge, this part becomes more (de)lithiated until the resultant local increase in through-cell resistance is sufficient for it to become preferential for the current to travel further along the current collectors and through the lower part of the cell. This behaviour is well captured by the 1+1D model. In the DFNCC formulation the through-cell current density is assumed uniform,\n",
-                        "so the greatest error is found at the ends of the current collectors where the current density deviates most from its average.\n",
-                        "\n",
-                        "For the parameters used in this example we find that the temperature exhibits a relatively weak variation along the length of the current collectors.  "
-                  ]
-            },
-            {
-                  "attachments": {},
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": [
-                        "## References\n",
-                        "\n",
-                        "The relevant papers for this notebook are:"
-                  ]
-            },
-            {
-                  "cell_type": "code",
-                  "execution_count": 19,
-                  "metadata": {},
-                  "outputs": [
-                        {
-                              "name": "stdout",
-                              "output_type": "stream",
-                              "text": [
-                                    "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-                                    "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-                                    "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-                                    "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-                                    "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-                                    "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
-                                    "\n"
-                              ]
-                        }
-                  ],
-                  "source": [
-                        "pybamm.print_citations()"
-                  ]
-            }
-      ],
-      "metadata": {
-            "kernelspec": {
-                  "display_name": "dev",
-                  "language": "python",
-                  "name": "python3"
-            },
-            "language_info": {
-                  "codemirror_mode": {
-                        "name": "ipython",
-                        "version": 3
-                  },
-                  "file_extension": ".py",
-                  "mimetype": "text/x-python",
-                  "name": "python",
-                  "nbconvert_exporter": "python",
-                  "pygments_lexer": "ipython3",
-                  "version": "3.9.16"
-            },
-            "toc": {
-                  "base_numbering": 1,
-                  "nav_menu": {},
-                  "number_sections": true,
-                  "sideBar": true,
-                  "skip_h1_title": false,
-                  "title_cell": "Table of Contents",
-                  "title_sidebar": "Contents",
-                  "toc_cell": false,
-                  "toc_position": {},
-                  "toc_section_display": true,
-                  "toc_window_display": true
-            },
-            "vscode": {
-                  "interpreter": {
-                        "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
-                  }
-            }
-      },
-      "nbformat": 4,
-      "nbformat_minor": 2
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pouch cell model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook we compare the solutions of two reduced-order models of a lithium-ion pouch cell with the full solution obtained using COMSOL. This example is based on the results in [[6]](#References). The code used to produce the results in [[6]](#References) can be found [here](https://github.com/rtimms/asymptotic-pouch-cell).\n",
+    "\n",
+    "The full model is based on the Doyle-Fuller-Newman model [[2]](#References) and, in the interest of simplicity, considers a one-dimensional current collector (i.e. variation in one of the current collector dimensions is ignored), resulting in a 2D macroscopic model.\n",
+    "\n",
+    "The first of the reduced order models, which is applicable in the limit of large conductivity in the current collectors, solves a one-dimensional problem in the current collectors coupled to a one-dimensional DFN model describing the through-cell electrochemistry at each point. We refer to this as a 1+1D model, though since the DFN is already a pseudo-two-dimensional model, perhaps it is more properly a 1+1+1D model.\n",
+    "\n",
+    "The second reduced order model, which is applicable in the limit of very large conductivity in the current collectors, solves a single (averaged) one-dimensional DFN model for the through-cell behaviour and an uncoupled problem for the distribution of potential in the current collectors (from which the resistance and heat source can be calculated). We refer to this model as the DFNCC, where the \"CC\" indicates the additional (uncoupled) current collector problem.\n",
+    "\n",
+    "All of the model equations, and derivations of the reduced-order models, can be found in [[6]](#References)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving the reduced-order pouch cell models in PyBaMM"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by importing PyBaMM along with the other packages required in this notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy.interpolate as interp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then need to load up the appropriate models. For the DFNCC we require a 1D model of the current collectors and an average 1D DFN model for the through-cell electrochemistry. The 1+1D pouch cell model is built directly into PyBaMM and are accessed by passing the model option \"dimensionality\" which can be 1 or 2, corresponding to 1D or 2D current collectors. This option can be passed to any existing electrochemical model (e.g. [SPM](./SPM.ipynb), [SPMe](./SPMe.ipynb), [DFN](./DFN.ipynb)). Here we choose the DFN model. \n",
+    "\n",
+    "For both electrochemical models we choose an \"x-lumped\" thermal model, meaning we assume that the temperature is uniform in the through-cell direction $x$, but account for the variation in temperature in the transverse direction $z$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
+      "  options = BatteryModelOptions(extra_options)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
+    "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
+    "dfn = pybamm.lithium_ion.DFN(\n",
+    "    {\"current collector\": \"potential pair\", \"dimensionality\": 1, \"thermal\": \"x-lumped\"},\n",
+    "    name=\"1+1D DFN\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then add the models to a dictionary for easy access later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = {\"Current collector\": cc_model, \"Average DFN\": dfn_av, \"1+1D DFN\": dfn}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we update the parameters to match those used in the COMSOL simulation. In particular, we set the current to correspond to a 3C discharge and assume uniform Newton cooling on all boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param = dfn.default_parameter_values\n",
+    "I_1C = param[\"Nominal cell capacity [A.h]\"]  # 1C current is cell capacity multipled by 1 hour\n",
+    "param.update(\n",
+    "    {\n",
+    "        \"Current function [A]\": I_1C * 3,       \n",
+    "        \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n",
+    "        \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n",
+    "        \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "        \"Total heat transfer coefficient [W.m-2.K-1]\": 10,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example we choose to discretise in space using 16 nodes per domain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "npts = 16\n",
+    "var_pts = {\n",
+    "    \"x_n\": npts,\n",
+    "    \"x_s\": npts,\n",
+    "    \"x_p\": npts,\n",
+    "    \"r_n\": npts,\n",
+    "    \"r_p\": npts,\n",
+    "    \"z\": npts,\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before solving the models we load the COMSOL data so that we can request the output at the times in the COMSOL solution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_results_path = pybamm.get_parameters_filepath(\n",
+    "    \"input/comsol_results/comsol_1plus1D_3C.pickle\"\n",
+    ")\n",
+    "comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we loop over the models, creating and solving a simulation for each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "simulations = {}\n",
+    "solutions = {}  # store solutions in a separate dict for easy access later\n",
+    "for name, model in models.items():\n",
+    "    sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n",
+    "    simulations[name] = sim  # store simulation for later\n",
+    "    if name == \"Current collector\":\n",
+    "        # model is independent of time, so just solve arbitrarily at t=0 using \n",
+    "        # the default algebraic solver\n",
+    "        t_eval = np.array([0])\n",
+    "        solutions[name] = sim.solve(t_eval=t_eval)        \n",
+    "    else:\n",
+    "        # solve at COMSOL times using Casadi solver in \"fast\" mode\n",
+    "        t_eval = comsol_variables[\"time\"]       \n",
+    "        solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the COMSOL model"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we show how to create a PyBaMM \"model\" from the COMSOL solution. If you are just interested in seeing the comparison the skip ahead to the section \"Comparing the full and reduced-order models\".\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a PyBaMM model from the COMSOL data we must create a `pybamm.Function` object for each variable. We do this by interpolating in space to match the PyBaMM mesh and then creating a function to interpolate in time. The following cell defines the function that handles the creation of the `pybamm.Function` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up times\n",
+    "comsol_t = comsol_variables[\"time\"]\n",
+    "pybamm_t = comsol_t\n",
+    "# set up space\n",
+    "mesh = simulations[\"1+1D DFN\"].mesh\n",
+    "L_z = param.evaluate(dfn.param.L_z)\n",
+    "pybamm_z = mesh[\"current collector\"].nodes\n",
+    "z_interp = pybamm_z\n",
+    "\n",
+    "\n",
+    "def get_interp_fun_curr_coll(variable_name):\n",
+    "    \"\"\"\n",
+    "    Create a :class:`pybamm.Function` object using the variable (interpolate in space \n",
+    "    to match nodes, and then create function to interpolate in time)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    comsol_z = comsol_variables[variable_name + \"_z\"]\n",
+    "    variable = comsol_variables[variable_name]\n",
+    "    variable = interp.interp1d(comsol_z, variable, axis=0, kind=\"linear\")(z_interp)\n",
+    "\n",
+    "    # Make sure to use dimensional time\n",
+    "    fun = pybamm.Interpolant(\n",
+    "        comsol_t,\n",
+    "        variable.T,\n",
+    "        pybamm.t,\n",
+    "        name=variable_name + \"_comsol\"\n",
+    "    )\n",
+    "    fun.domains = {\"primary\": \"current collector\"}\n",
+    "    fun.mesh = mesh.combine_submeshes(\"current collector\")\n",
+    "    fun.secondary_mesh = None\n",
+    "\n",
+    "    return fun"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then pass the variables of interest to the interpolating function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_voltage = pybamm.Interpolant(\n",
+    "    comsol_t, \n",
+    "    comsol_variables[\"voltage\"],\n",
+    "    pybamm.t,\n",
+    "    name=\"voltage_comsol\",\n",
+    ")\n",
+    "comsol_voltage.mesh = None\n",
+    "comsol_voltage.secondary_mesh = None\n",
+    "comsol_phi_s_cn = get_interp_fun_curr_coll(\"phi_s_cn\")\n",
+    "comsol_phi_s_cp = get_interp_fun_curr_coll(\"phi_s_cp\")\n",
+    "comsol_current = get_interp_fun_curr_coll(\"current\")\n",
+    "comsol_temperature = get_interp_fun_curr_coll(\"temperature\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and add them to a `pybamm.BaseModel` object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_model = pybamm.BaseModel()\n",
+    "comsol_model._geometry = pybamm.battery_geometry(options={\"dimensionality\": 1})\n",
+    "comsol_model.variables = {\n",
+    "    \"Voltage [V]\": comsol_voltage,\n",
+    "    \"Negative current collector potential [V]\": comsol_phi_s_cn,\n",
+    "    \"Positive current collector potential [V]\": comsol_phi_s_cp,\n",
+    "    \"Current collector current density [A.m-2]\": comsol_current,\n",
+    "    \"X-averaged cell temperature [K]\": comsol_temperature,\n",
+    "    # Add spatial variables to match pybamm model\n",
+    "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],   \n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing the full and reduced-order models"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The DFNCC requires some post-processing to extract the solution variables. In particular, we need to pass the current and voltage from the average DFN model to the current collector model in order to compute the distribution of the potential in the current collectors and to account for the effect of the current collector resistance in the voltage. \n",
+    "\n",
+    "This process is automated by the method `post_process` which accepts the current collector solution object, the parameters and the voltage and current from the average DFN model. The results are stored in the dictionary `dfncc_vars`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n",
+    "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n",
+    "\n",
+    "dfncc_vars = cc_model.post_process(\n",
+    "    solutions[\"Current collector\"], param, V_av, I_av\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we create a function to create some custom plots. For a given variable the plots will show: (a) the COMSOL results as a function of position in the current collector $z$ and time $t$; (b) a comparison of the full and reduced-order models and a sequence of times; (c) the time-averaged error between the full and reduced-order models as a function of space; and (d) the space-averaged error between the full and reduced-order models as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    var_name,\n",
+    "    units,\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"viridis\",\n",
+    "):\n",
+    "\n",
+    "    fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n",
+    "    fig.subplots_adjust(\n",
+    "        left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n",
+    "    )\n",
+    "    # plot comsol var\n",
+    "    comsol_var = comsol_var_fun(t=t_plot, z=z_plot)\n",
+    "    comsol_var_plot = ax[0, 0].pcolormesh(\n",
+    "        z_plot * 1e3, t_plot, np.transpose(comsol_var), shading=\"gouraud\", cmap=cmap\n",
+    "    )\n",
+    "    if \"cn\" in var_name:\n",
+    "        format = \"%.0e\"\n",
+    "    elif \"cp\" in var_name:\n",
+    "        format = \"%.0e\"\n",
+    "    else:\n",
+    "        format = None\n",
+    "    fig.colorbar(\n",
+    "        comsol_var_plot,\n",
+    "        ax=ax,\n",
+    "        format=format,\n",
+    "        location=\"top\",\n",
+    "        shrink=0.42,\n",
+    "        aspect=20,\n",
+    "        anchor=(0.0, 0.0),\n",
+    "    )\n",
+    "\n",
+    "    # plot slices\n",
+    "    ccmap = plt.get_cmap(\"inferno\")\n",
+    "    for ind, t in enumerate(t_slices):\n",
+    "        color = ccmap(float(ind) / len(t_slices))\n",
+    "        comsol_var_slice = comsol_var_fun(t=t, z=z_plot)\n",
+    "        dfn_var_slice = dfn_var_fun(t=t, z=z_plot)\n",
+    "        dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot)\n",
+    "        ax[0, 1].plot(\n",
+    "            z_plot * 1e3, comsol_var_slice, \"o\", fillstyle=\"none\", color=color\n",
+    "        )\n",
+    "        ax[0, 1].plot(\n",
+    "            z_plot * 1e3,\n",
+    "            dfn_var_slice,\n",
+    "            \"-\",\n",
+    "            color=color,\n",
+    "            label=f\"{t_slices[ind]:.0f} s\",\n",
+    "        )\n",
+    "        ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n",
+    "    # add dummy points for legend of styles\n",
+    "    comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
+    "    pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
+    "    dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
+    "\n",
+    "    # compute errors\n",
+    "    dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n",
+    "    dfncc_var = dfncc_var_fun(t=t_plot, z=z_plot)\n",
+    "    error = np.abs(comsol_var - dfn_var)\n",
+    "    error_bar = np.abs(comsol_var - dfncc_var)\n",
+    "\n",
+    "    # plot time averaged error\n",
+    "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error, axis=1), \"k-\", label=r\"$1+1$D\")\n",
+    "    ax[1, 0].plot(z_plot * 1e3, np.nanmean(error_bar, axis=1), \"k:\", label=\"DFNCC\")\n",
+    "\n",
+    "    # plot z averaged error\n",
+    "    ax[1, 1].plot(t_plot, np.nanmean(error, axis=0), \"k-\", label=r\"$1+1$D\")\n",
+    "    ax[1, 1].plot(t_plot, np.nanmean(error_bar, axis=0), \"k:\", label=\"DFNCC\")\n",
+    "\n",
+    "    # set ticks\n",
+    "    ax[0, 0].tick_params(which=\"both\")\n",
+    "    ax[0, 1].tick_params(which=\"both\")\n",
+    "    ax[1, 0].tick_params(which=\"both\")\n",
+    "    if var_name in [\"$\\mathcal{I}^*$\"]:\n",
+    "        ax[1, 0].set_yscale(\"log\")\n",
+    "        ax[1, 0].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
+    "    else:\n",
+    "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
+    "    ax[1, 1].tick_params(which=\"both\")\n",
+    "    if var_name in [\"$\\phi^*_{\\mathrm{s,cn}}$\", \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\"]:\n",
+    "        ax[1, 0].ticklabel_format(style=\"sci\", scilimits=(-2, 2), axis=\"y\")\n",
+    "    else:\n",
+    "        ax[1, 1].set_yscale(\"log\")\n",
+    "        ax[1, 1].set_yticks = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e-2, 1e-1, 1]\n",
+    "\n",
+    "    # set labels\n",
+    "    ax[0, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[0, 0].set_ylabel(r\"$t^*$ [s]\")\n",
+    "    ax[0, 0].set_title(rf\"{var_name} {units}\", y=1.5)\n",
+    "    ax[0, 1].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[0, 1].set_ylabel(rf\"{var_name}\")\n",
+    "    ax[1, 0].set_xlabel(r\"$z^*$ [mm]\")\n",
+    "    ax[1, 0].set_ylabel(\"Time-averaged\" + \"\\n\" + rf\"absolute error {units}\")\n",
+    "    ax[1, 1].set_xlabel(r\"$t^*$ [s]\")\n",
+    "    ax[1, 1].set_ylabel(\"Space-averaged\" + \"\\n\" + rf\"absolute error {units}\")\n",
+    "\n",
+    "    ax[0, 0].text(-0.1, 1.6, \"(a)\", transform=ax[0, 0].transAxes)\n",
+    "    ax[0, 1].text(-0.1, 1.6, \"(b)\", transform=ax[0, 1].transAxes)\n",
+    "    ax[1, 0].text(-0.1, 1.2, \"(c)\", transform=ax[1, 0].transAxes)\n",
+    "    ax[1, 1].text(-0.1, 1.2, \"(d)\", transform=ax[1, 1].transAxes)\n",
+    "\n",
+    "    leg1 = ax[0, 1].legend(\n",
+    "        bbox_to_anchor=(0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower left\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "        mode=\"expand\",\n",
+    "    )\n",
+    "\n",
+    "    ax[0, 1].legend(\n",
+    "        [comsol_p, pybamm_p, dfncc_p],\n",
+    "        [\"COMSOL\", r\"$1+1$D\", \"DFNCC\"],\n",
+    "        bbox_to_anchor=(0, 1.5, 1.0, 0.102),\n",
+    "        loc=\"lower left\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "        mode=\"expand\",\n",
+    "    )\n",
+    "    ax[0, 1].add_artist(leg1)\n",
+    "\n",
+    "    ax[1, 0].legend(\n",
+    "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower right\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "    )\n",
+    "    ax[1, 1].legend(\n",
+    "        bbox_to_anchor=(0.0, 1.1, 1.0, 0.102),\n",
+    "        loc=\"lower right\",\n",
+    "        borderaxespad=0.0,\n",
+    "        ncol=3,\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then set up the times and points in space to use in the plots "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_plot = comsol_t\n",
+    "z_plot = z_interp\n",
+    "t_slices = np.array([600, 1200, 1800, 2400, 3000]) / 3"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and plot the negative current collector potential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Negative current collector potential [V]\"\n",
+    "comsol_var_fun = comsol_solution[var]\n",
+    "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "dfncc_var_fun = dfncc_vars[var]\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\phi^*_{\\mathrm{s,cn}}$\",\n",
+    "    \"[V]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"cividis\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the positive current collector potential with respect to voltage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Positive current collector potential [V]\"\n",
+    "comsol_var = comsol_solution[var]\n",
+    "V_comsol = comsol_solution[\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def comsol_var_fun(t, z):\n",
+    "    return comsol_var(t=t, z=z) - V_comsol(t=t)\n",
+    "\n",
+    "\n",
+    "dfn_var = solutions[\"1+1D DFN\"][var]\n",
+    "V = solutions[\"1+1D DFN\"][\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def dfn_var_fun(t, z):\n",
+    "    return dfn_var(t=t, z=z) - V(t=t)\n",
+    "\n",
+    "\n",
+    "dfncc_var =  dfncc_vars[var]\n",
+    "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    return dfncc_var(t=t, z=z) - V_dfncc(t)\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\phi^*_{\\mathrm{s,cp}} - V^*$\",\n",
+    "    \"[V]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"viridis\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "the through-cell current "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "var = \"Current collector current density [A.m-2]\"\n",
+    "comsol_var_fun = comsol_solution[var]\n",
+    "dfn_var_fun = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "I_av = solutions[\"Average DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    \"In the DFNCC the current is just the average current\"\n",
+    "    return np.transpose(np.repeat(I_av(t)[:, np.newaxis], len(z), axis=1))\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\mathcal{I}^*$\",\n",
+    "    \"[A/m${}^2$]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"plasma\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and the temperature with respect to reference temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "T_ref = param.evaluate(dfn.param.T_ref)\n",
+    "var = \"X-averaged cell temperature [K]\"\n",
+    "comsol_var = comsol_solution[var]\n",
+    "\n",
+    "\n",
+    "def comsol_var_fun(t, z):\n",
+    "    return comsol_var(t=t, z=z) - T_ref\n",
+    "\n",
+    "\n",
+    "dfn_var = solutions[\"1+1D DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfn_var_fun(t, z):\n",
+    "    return dfn_var(t=t, z=z) - T_ref\n",
+    "\n",
+    "\n",
+    "T_av = solutions[\"Average DFN\"][var]\n",
+    "\n",
+    "\n",
+    "def dfncc_var_fun(t, z):\n",
+    "    \"In the DFNCC the temperature is just the average temperature\"\n",
+    "    return np.transpose(np.repeat(T_av(t)[:, np.newaxis], len(z), axis=1)) - T_ref\n",
+    "\n",
+    "\n",
+    "plot(\n",
+    "    t_plot,\n",
+    "    z_plot,\n",
+    "    t_slices,\n",
+    "    \"$\\\\bar{T}^* - \\\\bar{T}_0^*$\",\n",
+    "    \"[K]\",\n",
+    "    comsol_var_fun,\n",
+    "    dfn_var_fun,\n",
+    "    dfncc_var_fun,\n",
+    "    param,\n",
+    "    cmap=\"inferno\",\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the electrical conductivity of the current collectors is sufficiently\n",
+    "high that the potentials remain fairly uniform in space, and both the 1+1D DFN and DFNCC models are able to accurately capture the potential distribution in the current collectors.\n",
+    "\n",
+    "\n",
+    "In the plot of the current we see that positioning both tabs at the top of the cell means that for most of the simulation the current preferentially travels through the upper part of the cell. Eventually, as the cell continues to discharge, this part becomes more (de)lithiated until the resultant local increase in through-cell resistance is sufficient for it to become preferential for the current to travel further along the current collectors and through the lower part of the cell. This behaviour is well captured by the 1+1D model. In the DFNCC formulation the through-cell current density is assumed uniform,\n",
+    "so the greatest error is found at the ends of the current collectors where the current density deviates most from its average.\n",
+    "\n",
+    "For the parameters used in this example we find that the temperature exhibits a relatively weak variation along the length of the current collectors.  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/models/rate-capability.ipynb b/docs/source/examples/notebooks/models/rate-capability.ipynb
index fa01342f1d..056362b8f9 100644
--- a/docs/source/examples/notebooks/models/rate-capability.ipynb
+++ b/docs/source/examples/notebooks/models/rate-capability.ipynb
@@ -97,8 +97,8 @@
     "\n",
     "for i, C_rate in enumerate(C_rates):\n",
     "    experiment = pybamm.Experiment(\n",
-    "        [\"Discharge at {:.4f}C until 3.2V\".format(C_rate)],\n",
-    "        period=\"{:.4f} seconds\".format(10 / C_rate)\n",
+    "        [f\"Discharge at {C_rate:.4f}C until 3.2V\"],\n",
+    "        period=f\"{10 / C_rate:.4f} seconds\"\n",
     "    )\n",
     "    sim = pybamm.Simulation(\n",
     "        model,\n",
diff --git a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
index 2de30eedfe..cf7bef3b47 100644
--- a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
+++ b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
@@ -407,7 +407,7 @@
     "        T_exact(xx, t),\n",
     "        \"-\",\n",
     "        color=color,\n",
-    "        label=\"Exact (t={})\".format(plot_times[i]),\n",
+    "        label=f\"Exact (t={plot_times[i]})\",\n",
     "    )\n",
     "plt.xlabel(\"x\", fontsize=16)\n",
     "plt.ylabel(\"T\", fontsize=16)\n",
diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
index 0285ab69dd..4b3ef7846e 100644
--- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
+++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
@@ -307,7 +307,7 @@
     "npts = int(50 * simulation_time * omega)  # need enough timesteps to resolve output\n",
     "t_eval = np.linspace(0, simulation_time, npts)\n",
     "solution = simulation.solve(t_eval)\n",
-    "label = [\"Frequency: {} Hz\".format(omega)]\n",
+    "label = [f\"Frequency: {omega} Hz\"]\n",
     "\n",
     "# plot current and voltage\n",
     "output_variables = [\"Current [A]\", \"Voltage [V]\"]\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
index f0a770af08..6d2b6f707f 100644
--- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
@@ -68,7 +68,7 @@
    "source": [
     "param_dict = {\"a\": 1, \"b\": 2, \"c\": 3}\n",
     "parameter_values = pybamm.ParameterValues(param_dict)\n",
-    "print(\"parameter values are {}\".format(parameter_values))"
+    "print(f\"parameter values are {parameter_values}\")"
    ]
   },
   {
@@ -131,7 +131,7 @@
     "\n",
     "\n",
     "parameter_values.update({\"cube function\": cubed}, check_already_exists=False)\n",
-    "print(\"parameter values are {}\".format(parameter_values))"
+    "print(f\"parameter values are {parameter_values}\")"
    ]
   },
   {
@@ -200,7 +200,7 @@
    ],
    "source": [
     "expr_eval = parameter_values.process_symbol(expr)\n",
-    "print(\"{} = {}\".format(expr_eval, expr_eval.evaluate()))"
+    "print(f\"{expr_eval} = {expr_eval.evaluate()}\")"
    ]
   },
   {
@@ -218,7 +218,7 @@
    ],
    "source": [
     "func_eval = parameter_values.process_symbol(func)\n",
-    "print(\"{} = {}\".format(func_eval, func_eval.evaluate()))"
+    "print(f\"{func_eval} = {func_eval.evaluate()}\")"
    ]
   },
   {
@@ -396,7 +396,7 @@
     "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
     "param_eval = parameter_values.print_parameters(parameters)\n",
     "for name, value in param_eval.items():\n",
-    "    print(\"{}: {}\".format(name, value))"
+    "    print(f\"{name}: {value}\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index f3db45aa44..3ec04e9654 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -400,7 +400,7 @@
     "\n",
     "rsol = mesh[\"negative particle\"].nodes # radial position\n",
     "time = 1000  # time in seconds\n",
-    "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n",
+    "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
     "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
     "ax2.legend()\n",
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
index e4c4295ce1..366d99c1f8 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
@@ -154,7 +154,7 @@
     "sim.solve(callbacks=callback)\n",
     "\n",
     "# Read the file that has been written, which was saved to callback.logfile\n",
-    "with open(callback.logfile, \"r\") as f:\n",
+    "with open(callback.logfile) as f:\n",
     "    print(f.read())\n",
     "    \n",
     "# Remove the log file\n",
diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 2bd7f47ae1..0955b68310 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -944,9 +944,9 @@
    ],
    "source": [
     "pybamm.settings.set_smoothing_parameters(10)\n",
-    "print(\"Smooth minimum (softminus):\\t {!s}\".format(pybamm.minimum(x,y)))\n",
-    "print(\"Smooth heaviside (sigmoid):\\t {!s}\".format(x < y))\n",
-    "print(\"Smooth absolute value: \\t\\t {!s}\".format(abs(x)))\n",
+    "print(f\"Smooth minimum (softminus):\\t {pybamm.minimum(x,y)!s}\")\n",
+    "print(f\"Smooth heaviside (sigmoid):\\t {x < y!s}\")\n",
+    "print(f\"Smooth absolute value: \\t\\t {abs(x)!s}\")\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")"
    ]
   },
diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
index 7afd4da6f9..a0725d4dd3 100644
--- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
+++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
@@ -207,8 +207,8 @@
     "# Discretise\n",
     "x_disc = disc.process_symbol(x_var)\n",
     "r_disc = disc.process_symbol(r_var)\n",
-    "print(\"x_disc is a {}\".format(type(x_disc)))\n",
-    "print(\"r_disc is a {}\".format(type(r_disc)))\n",
+    "print(f\"x_disc is a {type(x_disc)}\")\n",
+    "print(f\"r_disc is a {type(r_disc)}\")\n",
     "\n",
     "# Evaluate\n",
     "x = x_disc.evaluate()\n",
@@ -343,9 +343,9 @@
     "w_disc = disc.process_symbol(w)\n",
     "\n",
     "# Print the outcome \n",
-    "print(\"Discretised u is the StateVector {}\".format(u_disc))\n",
-    "print(\"Discretised v is the StateVector {}\".format(v_disc))\n",
-    "print(\"Discretised w is the StateVector {}\".format(w_disc))"
+    "print(f\"Discretised u is the StateVector {u_disc}\")\n",
+    "print(f\"Discretised v is the StateVector {v_disc}\")\n",
+    "print(f\"Discretised w is the StateVector {w_disc}\")"
    ]
   },
   {
@@ -405,7 +405,7 @@
     }
    ],
    "source": [
-    "print(\"w = {}\".format(w_disc.evaluate(y=y)))"
+    "print(f\"w = {w_disc.evaluate(y=y)}\")"
    ]
   },
   {
@@ -484,7 +484,7 @@
    "source": [
     "macro_mesh = mesh.combine_submeshes(*macroscale)\n",
     "print(\"gradient matrix is:\\n\")\n",
-    "print(\"1/dx *\\n{}\".format(macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()))"
+    "print(f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\")"
    ]
   },
   {
@@ -600,7 +600,7 @@
     "\n",
     "micro_mesh = mesh[\"negative particle\"]\n",
     "print(\"\\n gradient matrix is:\\n\")\n",
-    "print(\"1/dr *\\n{}\".format(micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()))\n",
+    "print(f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\")\n",
     "\n",
     "r_edge = micro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n",
     "\n",
@@ -661,8 +661,8 @@
     "(grad_u_disc.render())\n",
     "u_eval = grad_u_disc.evaluate(y=y)\n",
     "dx = np.diff(macro_mesh.nodes)[-1]\n",
-    "print(\"The value of u on the left-hand boundary is {}\".format(y[0] - dx*u_eval[0]/2))\n",
-    "print(\"The value of u on the right-hand boundary is {}\".format(y[1] + dx*u_eval[-1]/2))"
+    "print(f\"The value of u on the left-hand boundary is {y[0] - dx*u_eval[0]/2}\")\n",
+    "print(f\"The value of u on the right-hand boundary is {y[1] + dx*u_eval[-1]/2}\")"
    ]
   },
   {
@@ -704,8 +704,8 @@
     "print(\"The gradient object is:\")\n",
     "(grad_u_disc.render())\n",
     "grad_u_eval = grad_u_disc.evaluate(y=y)\n",
-    "print(\"The gradient on the left-hand boundary is {}\".format(grad_u_eval[0]))\n",
-    "print(\"The gradient of u on the right-hand boundary is {}\".format(grad_u_eval[-1]))"
+    "print(f\"The gradient on the left-hand boundary is {grad_u_eval[0]}\")\n",
+    "print(f\"The gradient of u on the right-hand boundary is {grad_u_eval[-1]}\")"
    ]
   },
   {
@@ -745,8 +745,8 @@
     "(grad_u_disc.render())\n",
     "grad_u_eval = grad_u_disc.evaluate(y=y)\n",
     "u_eval = grad_u_disc.children[1].evaluate(y=y)\n",
-    "print(\"The value of u on the left-hand boundary is {}\".format((u_eval[0] + u_eval[1])/2))\n",
-    "print(\"The gradient on the right-hand boundary is {}\".format(grad_u_eval[-1]))"
+    "print(f\"The value of u on the left-hand boundary is {(u_eval[0] + u_eval[1])/2}\")\n",
+    "print(f\"The gradient on the right-hand boundary is {grad_u_eval[-1]}\")"
    ]
   },
   {
@@ -889,7 +889,7 @@
    "source": [
     "int_u = pybamm.Integral(u, x_var)\n",
     "int_u_disc = disc.process_symbol(int_u)\n",
-    "print(\"int(u) = {} is approximately equal to 1/12, {}\".format(int_u_disc.evaluate(y=y), 1/12))\n",
+    "print(f\"int(u) = {int_u_disc.evaluate(y=y)} is approximately equal to 1/12, {1/12}\")\n",
     "\n",
     "# We divide v by r to evaluate the integral more easily\n",
     "int_v_over_r2 = pybamm.Integral(v/r_var**2, r_var)\n",
diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py
index afdb9eacbf..45bc4182ef 100644
--- a/examples/scripts/compare_comsol/compare_comsol_DFN.py
+++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py
@@ -17,7 +17,7 @@
 
 # load the comsol results
 comsol_results_path = pybamm.get_parameters_filepath(
-    "input/comsol_results/comsol_{}C.pickle".format(C_rate)
+    f"input/comsol_results/comsol_{C_rate}C.pickle"
 )
 comsol_variables = pickle.load(open(comsol_results_path, "rb"))
 
diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py
index b5cc23d946..7544730eea 100644
--- a/examples/scripts/compare_comsol/discharge_curve.py
+++ b/examples/scripts/compare_comsol/discharge_curve.py
@@ -59,7 +59,7 @@
     current = 24 * C_rate
     # load the comsol results
     comsol_results_path = pybamm.get_parameters_filepath(
-        "input/comsol_results/comsol_{}C.pickle".format(key)
+        f"input/comsol_results/comsol_{key}C.pickle"
     )
     comsol_variables = pickle.load(open(comsol_results_path, "rb"))
     comsol_time = comsol_variables["time"]
@@ -95,7 +95,7 @@
         voltage_sol,
         color=color,
         linestyle="-",
-        label="{} C".format(C_rate),
+        label=f"{C_rate} C",
     )
     voltage_difference_plot.plot(
         discharge_capacity_sol[0:end_index], voltage_difference, color=color
diff --git a/examples/scripts/compare_particle_models.py b/examples/scripts/compare_particle_models.py
index d780452004..1be5bbdfd9 100644
--- a/examples/scripts/compare_particle_models.py
+++ b/examples/scripts/compare_particle_models.py
@@ -28,8 +28,8 @@
     sim = pybamm.Simulation(model, parameter_values=parameter_values)
     sim.solve([0, 3600])
     sims.append(sim)
-    print("Particle model: {}".format(model.name))
-    print("Solve time: {}s".format(sim.solution.solve_time))
+    print(f"Particle model: {model.name}")
+    print(f"Solve time: {sim.solution.solve_time}s")
 
 # plot results
 pybamm.dynamic_plot(sims)
diff --git a/examples/scripts/experimental_protocols/cccv.py b/examples/scripts/experimental_protocols/cccv.py
index c99780c4d8..c020588d07 100644
--- a/examples/scripts/experimental_protocols/cccv.py
+++ b/examples/scripts/experimental_protocols/cccv.py
@@ -37,7 +37,7 @@
     t = sol["Time [h]"].entries
     V = sol["Voltage [V]"].entries
     # Plot
-    ax.plot(t - t[0], V, label="Discharge {}".format(i + 1))
+    ax.plot(t - t[0], V, label=f"Discharge {i + 1}")
     ax.set_xlabel("Time [h]")
     ax.set_ylabel("Voltage [V]")
     ax.set_xlim([0, t[-1] - t[0]])
diff --git a/examples/scripts/heat_equation.py b/examples/scripts/heat_equation.py
index 4e80d6adec..20f9601090 100644
--- a/examples/scripts/heat_equation.py
+++ b/examples/scripts/heat_equation.py
@@ -120,7 +120,7 @@ def T_exact(x, t):
         label="Numerical" if i == 0 else "",
     )
     plt.plot(
-        xx, T_exact(xx, t), "-", color=color, label="Exact (t={})".format(plot_times[i])
+        xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})"
     )
 plt.xlabel("x", fontsize=16)
 plt.ylabel("T", fontsize=16)
diff --git a/examples/scripts/rate_capability.py b/examples/scripts/rate_capability.py
index 93d93f1cce..0ce5be4263 100644
--- a/examples/scripts/rate_capability.py
+++ b/examples/scripts/rate_capability.py
@@ -15,8 +15,8 @@
 
 for i, C_rate in enumerate(C_rates):
     experiment = pybamm.Experiment(
-        ["Discharge at {:.4f}C until 3.2V".format(C_rate)],
-        period="{:.4f} seconds".format(10 / C_rate),
+        [f"Discharge at {C_rate:.4f}C until 3.2V"],
+        period=f"{10 / C_rate:.4f} seconds",
     )
     sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver())
     sim.solve()
diff --git a/pybamm/callbacks.py b/pybamm/callbacks.py
index 09329b201f..32607bb716 100644
--- a/pybamm/callbacks.py
+++ b/pybamm/callbacks.py
@@ -217,7 +217,7 @@ def on_cycle_end(self, logs):
 
     def on_experiment_end(self, logs):
         elapsed_time = logs["elapsed time"]
-        self.logger.notice("Finish experiment simulation, took {}".format(elapsed_time))
+        self.logger.notice(f"Finish experiment simulation, took {elapsed_time}")
 
     def on_experiment_error(self, logs):
         error = logs["error"]
diff --git a/pybamm/citations.py b/pybamm/citations.py
index 70bf4ba9d3..ca260c5cfd 100644
--- a/pybamm/citations.py
+++ b/pybamm/citations.py
@@ -238,8 +238,8 @@ def print(self, filename=None, output_format="text", verbose=False):
             citations = "\n".join(self._cited)
         else:
             raise pybamm.OptionError(
-                "Output format {} not recognised."
-                "It should be 'text' or 'bibtex'.".format(output_format)
+                f"Output format {output_format} not recognised."
+                "It should be 'text' or 'bibtex'."
             )
 
         if filename is None:
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index 62110b1676..7f20cee348 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -19,7 +19,7 @@ def has_bc_of_form(symbol, side, bcs, form):
         return False
 
 
-class Discretisation(object):
+class Discretisation:
     """The discretisation class, with methods to process a model and replace
     Spatial Operators with Matrices and Variables with StateVectors
 
@@ -54,9 +54,7 @@ def __init__(self, mesh=None, spatial_methods=None):
                     if not isinstance(mesh[domain], pybamm.SubMesh0D):
                         raise pybamm.DiscretisationError(
                             "Zero-dimensional spatial method for the "
-                            "{} domain requires a zero-dimensional submesh".format(
-                                domain
-                            )
+                            f"{domain} domain requires a zero-dimensional submesh"
                         )
 
         self._bcs = {}
@@ -74,7 +72,7 @@ def y_slices(self):
     @y_slices.setter
     def y_slices(self, value):
         if not isinstance(value, dict):
-            raise TypeError("""y_slices should be dict, not {}""".format(type(value)))
+            raise TypeError(f"""y_slices should be dict, not {type(value)}""")
 
         self._y_slices = value
 
@@ -144,7 +142,7 @@ def process_model(
                 "to discretise it more times (e.g. for convergence studies)."
             )
 
-        pybamm.logger.info("Start discretising {}".format(model.name))
+        pybamm.logger.info(f"Start discretising {model.name}")
 
         # Make sure model isn't empty
         if (
@@ -169,20 +167,20 @@ def process_model(
                 if var.domain != []:
                     raise pybamm.DiscretisationError(
                         "Spatial method has not been given "
-                        "for variable {} with domain {}".format(var.name, var.domain)
+                        f"for variable {var.name} with domain {var.domain}"
                     )
 
         # Set the y split for variables
-        pybamm.logger.verbose("Set variable slices for {}".format(model.name))
+        pybamm.logger.verbose(f"Set variable slices for {model.name}")
         self.set_variable_slices(variables)
 
         # set boundary conditions (only need key ids for boundary_conditions)
         pybamm.logger.verbose(
-            "Discretise boundary conditions for {}".format(model.name)
+            f"Discretise boundary conditions for {model.name}"
         )
         self._bcs = self.process_boundary_conditions(model)
         pybamm.logger.verbose(
-            "Set internal boundary conditions for {}".format(model.name)
+            f"Set internal boundary conditions for {model.name}"
         )
         self.set_internal_boundary_conditions(model)
 
@@ -202,7 +200,7 @@ def process_model(
 
         model_disc.bcs = self.bcs
 
-        pybamm.logger.verbose("Discretise initial conditions for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise initial conditions for {model.name}")
         ics, concat_ics = self.process_initial_conditions(model)
         model_disc.initial_conditions = ics
         model_disc.concatenated_initial_conditions = concat_ics
@@ -210,11 +208,11 @@ def process_model(
         # Discretise variables (applying boundary conditions)
         # Note that we **do not** discretise the keys of model.rhs,
         # model.initial_conditions and model.boundary_conditions
-        pybamm.logger.verbose("Discretise variables for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise variables for {model.name}")
         model_disc.variables = self.process_dict(model.variables)
 
         # Process parabolic and elliptic equations
-        pybamm.logger.verbose("Discretise model equations for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise model equations for {model.name}")
         rhs, concat_rhs, alg, concat_alg = self.process_rhs_and_algebraic(model)
         model_disc.rhs, model_disc.concatenated_rhs = rhs, concat_rhs
         model_disc.algebraic, model_disc.concatenated_algebraic = alg, concat_alg
@@ -226,9 +224,9 @@ def process_model(
 
         # Process events
         processed_events = []
-        pybamm.logger.verbose("Discretise events for {}".format(model.name))
+        pybamm.logger.verbose(f"Discretise events for {model.name}")
         for event in model.events:
-            pybamm.logger.debug("Discretise event '{}'".format(event.name))
+            pybamm.logger.debug(f"Discretise event '{event.name}'")
             processed_event = pybamm.Event(
                 event.name, self.process_symbol(event.expression), event.event_type
             )
@@ -236,21 +234,21 @@ def process_model(
         model_disc.events = processed_events
 
         # Create mass matrix
-        pybamm.logger.verbose("Create mass matrix for {}".format(model.name))
+        pybamm.logger.verbose(f"Create mass matrix for {model.name}")
         model_disc.mass_matrix, model_disc.mass_matrix_inv = self.create_mass_matrix(
             model_disc
         )
 
         # Save geometry
-        pybamm.logger.verbose("Save geometry for {}".format(model.name))
+        pybamm.logger.verbose(f"Save geometry for {model.name}")
         model_disc._geometry = getattr(self.mesh, "_geometry", None)
 
         # Check that resulting model makes sense
         if check_model:
-            pybamm.logger.verbose("Performing model checks for {}".format(model.name))
+            pybamm.logger.verbose(f"Performing model checks for {model.name}")
             self.check_model(model_disc)
 
-        pybamm.logger.info("Finish discretising {}".format(model.name))
+        pybamm.logger.info(f"Finish discretising {model.name}")
 
         # Record that the model has been discretised
         model_disc.is_discretised = True
@@ -354,9 +352,7 @@ def set_internal_boundary_conditions(self, model):
 
         def boundary_gradient(left_symbol, right_symbol):
             pybamm.logger.debug(
-                "Calculate boundary gradient ({} and {})".format(
-                    left_symbol, right_symbol
-                )
+                f"Calculate boundary gradient ({left_symbol} and {right_symbol})"
             )
             left_domain = left_symbol.domain[0]
             right_domain = right_symbol.domain[0]
@@ -478,7 +474,7 @@ def process_boundary_conditions(self, model):
             # Process boundary conditions
             for side, bc in bcs.items():
                 eqn, typ = bc
-                pybamm.logger.debug("Discretise {} ({} bc)".format(key, side))
+                pybamm.logger.debug(f"Discretise {key} ({side} bc)")
                 processed_eqn = self.process_symbol(eqn)
                 processed_bcs[key][side] = (processed_eqn, typ)
 
@@ -513,10 +509,8 @@ def check_tab_conditions(self, symbol, bcs):
 
         if domain != "current collector":
             raise pybamm.ModelError(
-                """Boundary conditions can only be applied on the tabs in the domain
-            'current collector', but {} has domain {}""".format(
-                    symbol, domain
-                )
+                f"""Boundary conditions can only be applied on the tabs in the domain
+            'current collector', but {symbol} has domain {domain}"""
             )
         # Replace keys with "left" and "right" as appropriate for 1D meshes
         if isinstance(mesh, pybamm.SubMesh1D):
@@ -694,7 +688,7 @@ def process_dict(self, var_eqn_dict, ics=False):
                 else:
                     eqn = pybamm.FullBroadcast(eqn, broadcast_domains=eqn_key.domains)
 
-            pybamm.logger.debug("Discretise {!r}".format(eqn_key))
+            pybamm.logger.debug(f"Discretise {eqn_key!r}")
             processed_eqn = self.process_symbol(eqn)
             # Calculate scale if the key has a scale
             scale = getattr(eqn_key, "scale", 1)
@@ -1001,7 +995,7 @@ def _concatenate_in_order(self, var_eqn_dict, check_complete=False, sparse=False
                 given_variable_names = [v.name for v in var_eqn_dict.keys()]
                 raise pybamm.ModelError(
                     "Initial conditions are insufficient. Only "
-                    "provided for {} ".format(given_variable_names)
+                    f"provided for {given_variable_names} "
                 )
 
         equations = list(var_eqn_dict.values())
@@ -1024,7 +1018,7 @@ def check_initial_conditions(self, model):
             if not isinstance(ic_eval, np.ndarray):
                 raise pybamm.ModelError(
                     "initial conditions must be numpy array after discretisation but "
-                    "they are {} for variable '{}'.".format(type(ic_eval), var)
+                    f"they are {type(ic_eval)} for variable '{var}'."
                 )
 
             # Check that the initial condition is within the bounds
@@ -1035,7 +1029,7 @@ def check_initial_conditions(self, model):
             ):
                 raise pybamm.ModelError(
                     "initial condition is outside of variable bounds "
-                    "{} for variable '{}'.".format(bounds, var)
+                    f"{bounds} for variable '{var}'."
                 )
 
         # Check initial conditions and model equations have the same shape
@@ -1135,7 +1129,7 @@ def remove_independent_variables_from_rhs(self, model):
             )
             if this_var_is_independent:
                 if len(model.rhs) != 1:
-                    pybamm.logger.info("removing variable {} from rhs".format(var))
+                    pybamm.logger.info(f"removing variable {var} from rhs")
                     my_initial_condition = model.initial_conditions[var]
                     model.variables[var.name] = pybamm.ExplicitTimeIntegral(
                         model.rhs[var], my_initial_condition
diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 898d9b0f79..ca2d266bf0 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -135,7 +135,7 @@ def copy(self):
         return Experiment(*self.args)
 
     def __repr__(self):
-        return "pybamm.Experiment({!s})".format(self)
+        return f"pybamm.Experiment({self!s})"
 
     def read_termination(self, termination):
         """
diff --git a/pybamm/expression_tree/array.py b/pybamm/expression_tree/array.py
index 92d86af46c..7694cbc170 100644
--- a/pybamm/expression_tree/array.py
+++ b/pybamm/expression_tree/array.py
@@ -49,7 +49,7 @@ def __init__(
         if entries.ndim == 1:
             entries = entries[:, np.newaxis]
         if name is None:
-            name = "Array of shape {!s}".format(entries.shape)
+            name = f"Array of shape {entries.shape!s}"
         self._entries = entries.astype(float)
         # Use known entries string to avoid re-hashing, where possible
         self.entries_string = entries_string
diff --git a/pybamm/expression_tree/averages.py b/pybamm/expression_tree/averages.py
index e063b16c2a..81834d5871 100644
--- a/pybamm/expression_tree/averages.py
+++ b/pybamm/expression_tree/averages.py
@@ -188,10 +188,8 @@ def z_average(symbol):
     # Symbol must have domain [] or ["current collector"]
     if symbol.domain not in [[], ["current collector"]]:
         raise pybamm.DomainError(
-            """z-average only implemented in the 'current collector' domain,
-            but symbol has domains {}""".format(
-                symbol.domain
-            )
+            f"""z-average only implemented in the 'current collector' domain,
+            but symbol has domains {symbol.domain}"""
         )
     # If symbol doesn't have a domain, its average value is itself
     if symbol.domain == []:
@@ -224,10 +222,8 @@ def yz_average(symbol):
     # Symbol must have domain [] or ["current collector"]
     if symbol.domain not in [[], ["current collector"]]:
         raise pybamm.DomainError(
-            """y-z-average only implemented in the 'current collector' domain,
-            but symbol has domains {}""".format(
-                symbol.domain
-            )
+            f"""y-z-average only implemented in the 'current collector' domain,
+            but symbol has domains {symbol.domain}"""
         )
     # If symbol doesn't have a domain, its average value is itself
     if symbol.domain == []:
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index be0aa2f517..20c0fc66bd 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -94,16 +94,16 @@ def __str__(self):
             or (self.left.name == "+" and self.name == "-")
             or self.name == "+"
         ):
-            left_str = "({!s})".format(self.left)
+            left_str = f"({self.left!s})"
         else:
-            left_str = "{!s}".format(self.left)
+            left_str = f"{self.left!s}"
         if isinstance(self.right, pybamm.BinaryOperator) and not (
             (self.name == "*" and self.right.name in ["*", "/"]) or self.name == "+"
         ):
-            right_str = "({!s})".format(self.right)
+            right_str = f"({self.right!s})"
         else:
-            right_str = "{!s}".format(self.right)
-        return "{} {} {}".format(left_str, self.name, right_str)
+            right_str = f"{self.right!s}"
+        return f"{left_str} {self.name} {right_str}"
 
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
@@ -337,11 +337,9 @@ def _binary_jac(self, left_jac, right_jac):
             return left @ right_jac
         else:
             raise NotImplementedError(
-                """jac of 'MatrixMultiplication' is only
+                f"""jac of 'MatrixMultiplication' is only
              implemented for left of type 'pybamm.Array',
-             not {}""".format(
-                    left.__class__
-                )
+             not {left.__class__}"""
             )
 
     def _binary_evaluate(self, left, right):
@@ -557,7 +555,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} <= {!s}".format(self.left, self.right)
+        return f"{self.left!s} <= {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -574,7 +572,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} < {!s}".format(self.left, self.right)
+        return f"{self.left!s} < {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -614,7 +612,7 @@ def _binary_jac(self, left_jac, right_jac):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{!s} mod {!s}".format(self.left, self.right)
+        return f"{self.left!s} mod {self.right!s}"
 
     def _binary_evaluate(self, left, right):
         """See :meth:`pybamm.BinaryOperator._binary_evaluate()`."""
@@ -629,7 +627,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "minimum({!s}, {!s})".format(self.left, self.right)
+        return f"minimum({self.left!s}, {self.right!s})"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -666,7 +664,7 @@ def __init__(self, left, right):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "maximum({!s}, {!s})".format(self.left, self.right)
+        return f"maximum({self.left!s}, {self.right!s})"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -1370,10 +1368,8 @@ def source(left, right, boundary=False):
 
     if left.domain != ["current collector"] or right.domain != ["current collector"]:
         raise pybamm.DomainError(
-            """'source' only implemented in the 'current collector' domain,
-            but symbols have domains {} and {}""".format(
-                left.domain, right.domain
-            )
+            f"""'source' only implemented in the 'current collector' domain,
+            but symbols have domains {left.domain} and {right.domain}"""
         )
     if boundary:
         return pybamm.BoundaryMass(right) @ left
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index 71d776f03e..afd9bdc1d5 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -58,7 +58,7 @@ def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
         out = self.name + "("
         for child in self.children:
-            out += "{!s}, ".format(child)
+            out += f"{child!s}, "
         out = out[:-2] + ")"
         return out
 
@@ -77,11 +77,11 @@ def get_children_domains(self, children):
         domain = []
         for child in children:
             if not isinstance(child, pybamm.Symbol):
-                raise TypeError("{} is not a pybamm symbol".format(child))
+                raise TypeError(f"{child} is not a pybamm symbol")
             child_domain = child.domain
             if child_domain == []:
                 raise pybamm.DomainError(
-                    "Cannot concatenate child '{}' with empty domain".format(child)
+                    f"Cannot concatenate child '{child}' with empty domain"
                 )
             if set(domain).isdisjoint(child_domain):
                 domain += child_domain
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index d6767f1aa9..d8248eabe8 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -47,9 +47,9 @@ def __init__(
             self.name = name
         else:
             try:
-                name = "function ({})".format(function.__name__)
+                name = f"function ({function.__name__})"
             except AttributeError:
-                name = "function ({})".format(function.__class__)
+                name = f"function ({function.__class__})"
         domains = self.get_children_domains(children)
 
         self.function = function
@@ -60,9 +60,9 @@ def __init__(
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        out = "{}(".format(self.name[10:-1])
+        out = f"{self.name[10:-1]}("
         for child in self.children:
-            out += "{!s}, ".format(child)
+            out += f"{child!s}, "
         out = out[:-2] + ")"
         return out
 
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index 146751928e..ee8afac38e 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -146,7 +146,7 @@ def __init__(
             ["particle" in dom for dom in domain]
         ):
             raise pybamm.DomainError(
-                "domain cannot be particle if name is '{}'".format(name)
+                f"domain cannot be particle if name is '{name}'"
             )
 
     def create_copy(self):
diff --git a/pybamm/expression_tree/input_parameter.py b/pybamm/expression_tree/input_parameter.py
index e66a4c8cdc..2680276c60 100644
--- a/pybamm/expression_tree/input_parameter.py
+++ b/pybamm/expression_tree/input_parameter.py
@@ -91,7 +91,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
             input_eval = inputs[self.name]
         # raise more informative error if can't find name in dict
         except KeyError:
-            raise KeyError("Input parameter '{}' not found".format(self.name))
+            raise KeyError(f"Input parameter '{self.name}' not found")
 
         if isinstance(input_eval, numbers.Number):
             input_size = 1
@@ -109,9 +109,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
                 "Input parameter '{}' was given an object of size '{}'".format(
                     self.name, input_size
                 )
-                + " but was expecting an object of size '{}'.".format(
-                    self._expected_size
-                )
+                + f" but was expecting an object of size '{self._expected_size}'."
             )
 
     def to_json(self):
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 1cb5e70d05..5de21da089 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -59,7 +59,7 @@ def __init__(
 
         # Check interpolator is valid
         if interpolator not in ["linear", "cubic", "pchip"]:
-            raise ValueError("interpolator '{}' not recognised".format(interpolator))
+            raise ValueError(f"interpolator '{interpolator}' not recognised")
 
         # Perform some checks on the data
         if isinstance(x, (tuple, list)) and len(x) == 2:
@@ -186,7 +186,7 @@ def __init__(
                     fill_value=fill_value,
                 )
         else:
-            raise ValueError("Invalid dimension of x: {0}".format(len(x)))
+            raise ValueError(f"Invalid dimension of x: {len(x)}")
 
         # Set name
         if name is None:
@@ -309,7 +309,7 @@ def _function_evaluate(self, evaluated_children):
                 return np.reshape(res, shape)
 
         else:  # pragma: no cover
-            raise ValueError("Invalid dimension: {0}".format(self.dimension))
+            raise ValueError(f"Invalid dimension: {self.dimension}")
 
     def to_json(self):
         """
diff --git a/pybamm/expression_tree/matrix.py b/pybamm/expression_tree/matrix.py
index d491fd129d..8b36bca53e 100644
--- a/pybamm/expression_tree/matrix.py
+++ b/pybamm/expression_tree/matrix.py
@@ -24,7 +24,7 @@ def __init__(
         if isinstance(entries, list):
             entries = np.array(entries)
         if name is None:
-            name = "Matrix {!s}".format(entries.shape)
+            name = f"Matrix {entries.shape!s}"
             if issparse(entries):
                 name = "Sparse " + name
         # Convert all sparse matrices to csr
diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py
index b3a048b1f1..6461a9267f 100644
--- a/pybamm/expression_tree/operations/convert_to_casadi.py
+++ b/pybamm/expression_tree/operations/convert_to_casadi.py
@@ -7,7 +7,7 @@
 from scipy import special
 
 
-class CasadiConverter(object):
+class CasadiConverter:
     def __init__(self, casadi_symbols=None):
         self._casadi_symbols = casadi_symbols or {}
 
@@ -144,7 +144,7 @@ def _convert(self, symbol, t, y, y_dot, inputs):
                     )
                 else:  # pragma: no cover
                     raise NotImplementedError(
-                        "Unknown interpolator: {0}".format(symbol.interpolator)
+                        f"Unknown interpolator: {symbol.interpolator}"
                     )
 
                 if len(converted_children) == 1:
@@ -159,9 +159,7 @@ def _convert(self, symbol, t, y, y_dot, inputs):
                     return res
                 else:  # pragma: no cover
                     raise ValueError(
-                        "Invalid converted_children count: {0}".format(
-                            len(converted_children)
-                        )
+                        f"Invalid converted_children count: {len(converted_children)}"
                     )
 
             elif symbol.function.__name__.startswith("elementwise_grad_of_"):
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index d0cd4c776d..f65ecc7159 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -203,62 +203,44 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
-                symbol_str = "{1}.multiply({0})".format(
-                    children_vars[0], children_vars[1]
-                )
+                symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})"
             else:
-                symbol_str = "{0} * {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} * {children_vars[1]}"
         elif isinstance(symbol, pybamm.Division):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply(1/{1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply(1/{1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply(1/{children_vars[1]})"
             else:
-                symbol_str = "{0} / {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} / {children_vars[1]}"
 
         elif isinstance(symbol, pybamm.Inner):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = "{0}.scalar_multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
                 else:
-                    symbol_str = "{0}.multiply({1})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
                 if output_jax and is_scalar(dummy_eval_left):
-                    symbol_str = "{1}.scalar_multiply({0})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[1]}.scalar_multiply({children_vars[0]})"
                 else:
-                    symbol_str = "{1}.multiply({0})".format(
-                        children_vars[0], children_vars[1]
-                    )
+                    symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})"
             else:
-                symbol_str = "{0} * {1}".format(children_vars[0], children_vars[1])
+                symbol_str = f"{children_vars[0]} * {children_vars[1]}"
 
         elif isinstance(symbol, pybamm.Minimum):
-            symbol_str = "np.minimum({},{})".format(children_vars[0], children_vars[1])
+            symbol_str = f"np.minimum({children_vars[0]},{children_vars[1]})"
         elif isinstance(symbol, pybamm.Maximum):
-            symbol_str = "np.maximum({},{})".format(children_vars[0], children_vars[1])
+            symbol_str = f"np.maximum({children_vars[0]},{children_vars[1]})"
 
         elif isinstance(symbol, pybamm.MatrixMultiplication):
             dummy_eval_left = symbol.children[0].evaluate_for_shape()
@@ -281,9 +263,7 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
     elif isinstance(symbol, pybamm.UnaryOperator):
         # Index has a different syntax than other univariate operations
         if isinstance(symbol, pybamm.Index):
-            symbol_str = "{}[{}:{}]".format(
-                children_vars[0], symbol.slice.start, symbol.slice.stop
-            )
+            symbol_str = f"{children_vars[0]}[{symbol.slice.start}:{symbol.slice.stop}]"
         else:
             symbol_str = symbol.name + children_vars[0]
 
@@ -296,13 +276,13 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
                 children_str += ", " + child_var
         if isinstance(symbol.function, np.ufunc):
             # write any numpy functions directly
-            symbol_str = "np.{}({})".format(symbol.function.__name__, children_str)
+            symbol_str = f"np.{symbol.function.__name__}({children_str})"
         else:
             # unknown function, store it as a constant and call this in the
             # generated code
             constant_symbols[symbol.id] = symbol.function
             funct_var = id_to_python_variable(symbol.id, True)
-            symbol_str = "{}({})".format(funct_var, children_str)
+            symbol_str = f"{funct_var}({children_str})"
 
     elif isinstance(symbol, pybamm.Concatenation):
         # no need to concatenate if there is only a single child
@@ -334,9 +314,7 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
                     for child_dom, child_slice in slices.items():
                         slice_starts.append(symbol._slices[child_dom][i].start)
                         child_vectors.append(
-                            "{}[{}:{}]".format(
-                                child_var, child_slice[i].start, child_slice[i].stop
-                            )
+                            f"{child_var}[{child_slice[i].start}:{child_slice[i].stop}]"
                         )
                 all_child_vectors.extend(
                     [v for _, v in sorted(zip(slice_starts, child_vectors))]
@@ -353,18 +331,18 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
         indices = np.argwhere(symbol.evaluation_array).reshape(-1).astype(np.int32)
         consecutive = np.all(indices[1:] - indices[:-1] == 1)
         if len(indices) == 1 or consecutive:
-            symbol_str = "y[{}:{}]".format(indices[0], indices[-1] + 1)
+            symbol_str = f"y[{indices[0]}:{indices[-1] + 1}]"
         else:
             indices_array = pybamm.Array(indices)
             constant_symbols[indices_array.id] = indices
             index_name = id_to_python_variable(indices_array.id, True)
-            symbol_str = "y[{}]".format(index_name)
+            symbol_str = f"y[{index_name}]"
 
     elif isinstance(symbol, pybamm.Time):
         symbol_str = "t"
 
     elif isinstance(symbol, pybamm.InputParameter):
-        symbol_str = 'inputs["{}"]'.format(symbol.name)
+        symbol_str = f'inputs["{symbol.name}"]'
 
     else:
         raise NotImplementedError(
@@ -448,7 +426,7 @@ def __init__(self, symbol):
         # extract constants in generated function
         for i, symbol_id in enumerate(constants.keys()):
             const_name = id_to_python_variable(symbol_id, True)
-            python_str = "{} = constants[{}]\n".format(const_name, i) + python_str
+            python_str = f"{const_name} = constants[{i}]\n" + python_str
 
         # constants passed in as an ordered dict, convert to list
         self._constants = list(constants.values())
@@ -574,7 +552,7 @@ def __init__(self, symbol):
         args = "t=None, y=None, inputs=None"
         if self._arg_list:
             args = ",".join(self._arg_list) + ", " + args
-        python_str = "def evaluate_jax({}):\n".format(args) + python_str
+        python_str = f"def evaluate_jax({args}):\n" + python_str
 
         # calculate the final variable that will output the result of calling `evaluate`
         # on `symbol`
diff --git a/pybamm/expression_tree/operations/jacobian.py b/pybamm/expression_tree/operations/jacobian.py
index 56511827b0..a191e2c74d 100644
--- a/pybamm/expression_tree/operations/jacobian.py
+++ b/pybamm/expression_tree/operations/jacobian.py
@@ -4,7 +4,7 @@
 import pybamm
 
 
-class Jacobian(object):
+class Jacobian:
     """
     Helper class to calculate the Jacobian of an expression.
 
@@ -87,9 +87,7 @@ def _jac(self, symbol, variable):
                 jac = symbol._jac(variable)
             except NotImplementedError:
                 raise NotImplementedError(
-                    "Cannot calculate Jacobian of symbol of type '{}'".format(
-                        type(symbol)
-                    )
+                    f"Cannot calculate Jacobian of symbol of type '{type(symbol)}'"
                 )
 
         # Jacobian by default removes the domain(s)
diff --git a/pybamm/expression_tree/operations/serialise.py b/pybamm/expression_tree/operations/serialise.py
index c7768217a3..53505dbb1f 100644
--- a/pybamm/expression_tree/operations/serialise.py
+++ b/pybamm/expression_tree/operations/serialise.py
@@ -175,7 +175,7 @@ def load_model(
             `battery_model`.
         """
 
-        with open(filename, "r") as f:
+        with open(filename) as f:
             model_data = json.load(f)
 
         recon_model_dict = {
diff --git a/pybamm/expression_tree/operations/unpack_symbols.py b/pybamm/expression_tree/operations/unpack_symbols.py
index 96cbca39fd..825cb2db40 100644
--- a/pybamm/expression_tree/operations/unpack_symbols.py
+++ b/pybamm/expression_tree/operations/unpack_symbols.py
@@ -3,7 +3,7 @@
 #
 
 
-class SymbolUnpacker(object):
+class SymbolUnpacker:
     """
     Helper class to unpack a (set of) symbol(s) to find all instances of a class.
     Uses caching to speed up the process.
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 7354f0ae3f..2f51d4bda1 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -47,17 +47,13 @@ def __init__(
                 raise TypeError("all y_slices must be slice objects")
         if name is None:
             if y_slices[0].start is None:
-                name = base_name + "[0:{:d}".format(y_slice.stop)
+                name = base_name + f"[0:{y_slice.stop:d}"
             else:
-                name = base_name + "[{:d}:{:d}".format(
-                    y_slices[0].start, y_slices[0].stop
-                )
+                name = base_name + f"[{y_slices[0].start:d}:{y_slices[0].stop:d}"
             if len(y_slices) > 1:
-                name += ",{:d}:{:d}".format(y_slices[1].start, y_slices[1].stop)
+                name += f",{y_slices[1].start:d}:{y_slices[1].stop:d}"
                 if len(y_slices) > 2:
-                    name += ",...,{:d}:{:d}]".format(
-                        y_slices[-1].start, y_slices[-1].stop
-                    )
+                    name += f",...,{y_slices[-1].start:d}:{y_slices[-1].stop:d}]"
                 else:
                     name += "]"
             else:
diff --git a/pybamm/expression_tree/symbol.py b/pybamm/expression_tree/symbol.py
index 2c3166582e..9d68b5f439 100644
--- a/pybamm/expression_tree/symbol.py
+++ b/pybamm/expression_tree/symbol.py
@@ -206,7 +206,7 @@ def __init__(
         auxiliary_domains=None,
         domains=None,
     ):
-        super(Symbol, self).__init__()
+        super().__init__()
         self.name = name
 
         if children is None:
@@ -466,9 +466,9 @@ def render(self):  # pragma: no cover
         anytree = have_optional_dependency("anytree")
         for pre, _, node in anytree.RenderTree(self):
             if isinstance(node, pybamm.Scalar) and node.name != str(node.value):
-                print("{}{} = {}".format(pre, node.name, node.value))
+                print(f"{pre}{node.name} = {node.value}")
             else:
-                print("{}{}".format(pre, node.name))
+                print(f"{pre}{node.name}")
 
     def visualise(self, filename):
         """
@@ -491,7 +491,7 @@ def visualise(self, filename):
 
         try:
             DotExporter(
-                new_node, nodeattrfunc=lambda node: 'label="{}"'.format(node.label)
+                new_node, nodeattrfunc=lambda node: f'label="{node.label}"'
             ).to_picture(filename)
         except FileNotFoundError:  # pragma: no cover
             # raise error but only through logger so that test passes
@@ -718,7 +718,7 @@ def jac(self, variable, known_jacs=None, clear_domain=True):
         if not isinstance(variable, (pybamm.StateVector, pybamm.StateVectorDot)):
             raise TypeError(
                 "Jacobian can only be taken with respect to a 'StateVector' "
-                "or 'StateVectorDot', but {} is a {}".format(variable, type(variable))
+                f"or 'StateVectorDot', but {variable} is a {type(variable)}"
             )
         return jac.jac(self, variable)
 
@@ -752,7 +752,7 @@ def _base_evaluate(self, t=None, y=None, y_dot=None, inputs=None):
         """
         raise NotImplementedError(
             "method self.evaluate() not implemented for symbol "
-            "{!s} of type {}".format(self, type(self))
+            f"{self!s} of type {type(self)}"
         )
 
     def evaluate(self, t=None, y=None, y_dot=None, inputs=None):
@@ -910,10 +910,8 @@ def create_copy(self):
         copy.deepcopy(), which is slow.
         """
         raise NotImplementedError(
-            """method self.new_copy() not implemented
-            for symbol {!s} of type {}""".format(
-                self, type(self)
-            )
+            f"""method self.new_copy() not implemented
+            for symbol {self!s} of type {type(self)}"""
         )
 
     def new_copy(self):
@@ -996,7 +994,7 @@ def test_shape(self):
         try:
             self.shape_for_testing
         except ValueError as e:
-            raise pybamm.ShapeError("Cannot find shape (original error: {})".format(e))
+            raise pybamm.ShapeError(f"Cannot find shape (original error: {e})")
 
     @property
     def print_name(self):
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 319429183c..435bd5dce2 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -49,7 +49,7 @@ def _from_json(cls, snippet: dict):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{}({!s})".format(self.name, self.child)
+        return f"{self.name}({self.child!s})"
 
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
@@ -115,7 +115,7 @@ def __init__(self, child):
 
     def __str__(self):
         """See :meth:`pybamm.Symbol.__str__()`."""
-        return "{}{!s}".format(self.name, self.child)
+        return f"{self.name}{self.child!s}"
 
     def _diff(self, variable):
         """See :meth:`pybamm.Symbol._diff()`."""
@@ -272,9 +272,9 @@ def __init__(self, child, index, name=None, check_size=True):
             self.slice = index
             if name is None:
                 if index.start is None:
-                    name = "Index[:{:d}]".format(index.stop)
+                    name = f"Index[:{index.stop:d}]"
                 else:
-                    name = "Index[{:d}:{:d}]".format(index.start, index.stop)
+                    name = f"Index[{index.start:d}:{index.stop:d}]"
         else:
             raise TypeError("index must be integer or slice")
 
@@ -416,13 +416,13 @@ class Gradient(SpatialOperator):
     def __init__(self, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot take gradient of '{}' since its domain is empty. ".format(child)
+                f"Cannot take gradient of '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.grad(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is True:
             raise TypeError(
-                "Cannot take gradient of '{}' since it evaluates on edges".format(child)
+                f"Cannot take gradient of '{child}' since it evaluates on edges"
             )
         super().__init__("grad", child)
 
@@ -448,15 +448,13 @@ class Divergence(SpatialOperator):
     def __init__(self, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot take divergence of '{}' since its domain is empty. ".format(
-                    child
-                )
+                f"Cannot take divergence of '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.div(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is False:
             raise TypeError(
-                "Cannot take divergence of '{}' since it does not ".format(child)
+                f"Cannot take divergence of '{child}' since it does not "
                 + "evaluate on edges. Usually, a gradient should be taken before the "
                 "divergence."
             )
@@ -577,9 +575,9 @@ def __init__(self, child, integration_variable):
             else:
                 raise TypeError(
                     "integration_variable must be of type pybamm.SpatialVariable, "
-                    "not {}".format(type(var))
+                    f"not {type(var)}"
                 )
-            name += " d{}".format(var.name)
+            name += f" d{var.name}"
 
         if self._integration_dimension == "primary":
             # integral of a child takes the domain from auxiliary domain of the child
@@ -613,7 +611,7 @@ def __init__(self, child, integration_variable):
                 "tertiary": child.domains["tertiary"],
             }
         if any(isinstance(var, pybamm.SpatialVariable) for var in integration_variable):
-            name += " {}".format(child.domain)
+            name += f" {child.domain}"
 
         self._integration_variable = integration_variable
         super().__init__(name, child, domains)
@@ -712,11 +710,9 @@ class IndefiniteIntegral(BaseIndefiniteIntegral):
     def __init__(self, child, integration_variable):
         super().__init__(child, integration_variable)
         # Overwrite the name
-        self.name = "{} integrated w.r.t {}".format(
-            child.name, self.integration_variable[0].name
-        )
+        self.name = f"{child.name} integrated w.r.t {self.integration_variable[0].name}"
         if isinstance(integration_variable, pybamm.SpatialVariable):
-            self.name += " on {}".format(self.integration_variable[0].domain)
+            self.name += f" on {self.integration_variable[0].domain}"
 
 
 class BackwardIndefiniteIntegral(BaseIndefiniteIntegral):
@@ -744,7 +740,7 @@ def __init__(self, child, integration_variable):
             child.name, self.integration_variable[0].name
         )
         if isinstance(integration_variable, pybamm.SpatialVariable):
-            self.name += " on {}".format(self.integration_variable[0].domain)
+            self.name += f" on {self.integration_variable[0].domain}"
 
 
 class DefiniteIntegralVector(SpatialOperator):
@@ -923,10 +919,8 @@ def __init__(self, name, child, side):
         if side in ["negative tab", "positive tab"]:
             if child.domain[0] != "current collector":
                 raise pybamm.ModelError(
-                    """Can only take boundary value on the tabs in the domain
-                'current collector', but {} has domain {}""".format(
-                        child, child.domain[0]
-                    )
+                    f"""Can only take boundary value on the tabs in the domain
+                'current collector', but {child} has domain {child.domain[0]}"""
                 )
         self.side = side
         # boundary value of a child takes the primary domain from secondary domain
@@ -1115,13 +1109,13 @@ class UpwindDownwind(SpatialOperator):
     def __init__(self, name, child):
         if child.domain == []:
             raise pybamm.DomainError(
-                "Cannot upwind '{}' since its domain is empty. ".format(child)
+                f"Cannot upwind '{child}' since its domain is empty. "
                 + "Try broadcasting the object first, e.g.\n\n"
                 "\tpybamm.div(pybamm.PrimaryBroadcast(symbol, 'domain'))"
             )
         if child.evaluates_on_edges("primary") is True:
             raise TypeError(
-                "Cannot upwind '{}' since it does not ".format(child)
+                f"Cannot upwind '{child}' since it does not "
                 + "evaluate on nodes."
             )
         super().__init__(name, child)
diff --git a/pybamm/expression_tree/vector.py b/pybamm/expression_tree/vector.py
index 758b988ca7..66fe7d8c12 100644
--- a/pybamm/expression_tree/vector.py
+++ b/pybamm/expression_tree/vector.py
@@ -34,7 +34,7 @@ def __init__(
                 )
             )
         if name is None:
-            name = "Column vector of length {!s}".format(entries.shape[0])
+            name = f"Column vector of length {entries.shape[0]!s}"
 
         super().__init__(
             entries, name, domain, auxiliary_domains, domains, entries_string
diff --git a/pybamm/geometry/battery_geometry.py b/pybamm/geometry/battery_geometry.py
index 0dfe3fd256..e15c358128 100644
--- a/pybamm/geometry/battery_geometry.py
+++ b/pybamm/geometry/battery_geometry.py
@@ -140,9 +140,7 @@ def battery_geometry(
             )
     else:
         raise pybamm.GeometryError(
-            "Invalid form factor '{}' (should be 'pouch' or 'cylindrical'".format(
-                form_factor
-            )
+            f"Invalid form factor '{form_factor}' (should be 'pouch' or 'cylindrical'"
         )
 
     return pybamm.Geometry(geometry)
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 0fbbcdc637..a51c9eea76 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -66,7 +66,7 @@ def install_sundials(download_dir, install_dir):
 
     print("-" * 10, "Running CMake prepare", "-" * 40)
     subprocess.run(
-        ["cmake", "../sundials-{}".format(sundials_version), *cmake_args],
+        ["cmake", f"../sundials-{sundials_version}", *cmake_args],
         cwd=build_directory,
         check=True,
     )
@@ -81,9 +81,7 @@ def update_LD_LIBRARY_PATH(install_dir):
     # for LD_LIBRARY_PATH in activate script.  If no virtual env found,
     # then the current user's .bashrc file is modified instead.
 
-    export_statement = "export LD_LIBRARY_PATH={}/lib:$LD_LIBRARY_PATH".format(
-        install_dir
-    )
+    export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
     venv_path = os.environ.get("VIRTUAL_ENV")
     if venv_path:
@@ -91,10 +89,10 @@ def update_LD_LIBRARY_PATH(install_dir):
     else:
         script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
 
-    if os.getenv("LD_LIBRARY_PATH") and "{}/lib".format(install_dir) in os.getenv(
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv(
         "LD_LIBRARY_PATH"
     ):
-        print("{}/lib was found in LD_LIBRARY_PATH.".format(install_dir))
+        print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         print("--> Not updating venv activate or .bashrc scripts")
     else:
         with open(script_path, "a+") as fh:
@@ -102,8 +100,8 @@ def update_LD_LIBRARY_PATH(install_dir):
             if export_statement not in fh.read():
                 fh.write(export_statement)
                 print(
-                    "Adding {}/lib to LD_LIBRARY_PATH"
-                    " in {}".format(install_dir, script_path)
+                    f"Adding {install_dir}/lib to LD_LIBRARY_PATH"
+                    f" in {script_path}"
                 )
 
 
@@ -146,18 +144,18 @@ def main(arguments=None):
     if args.sundials_libs:
         SUNDIALS_LIB_DIRS.insert(0, args.sundials_libs)
     for DIR in SUNDIALS_LIB_DIRS:
-        logger.info("Looking for sundials at {}".format(DIR))
+        logger.info(f"Looking for sundials at {DIR}")
         SUNDIALS_FOUND = isfile(join(DIR, "lib", "libsundials_ida.so")) or isfile(
             join(DIR, "lib", "libsundials_ida.dylib")
         )
         if SUNDIALS_FOUND:
             SUNDIALS_LIB_DIR = DIR
-            logger.info("Found sundials at {}".format(SUNDIALS_LIB_DIR))
+            logger.info(f"Found sundials at {SUNDIALS_LIB_DIR}")
             break
 
     if not SUNDIALS_FOUND:
         logger.info("Could not find sundials libraries.")
-        logger.info("Installing sundials in {}".format(install_dir))
+        logger.info(f"Installing sundials in {install_dir}")
         download_dir = os.path.join(pybamm_dir, "sundials")
         if not os.path.exists(download_dir):
             os.makedirs(download_dir)
diff --git a/pybamm/meshes/meshes.py b/pybamm/meshes/meshes.py
index 182282319f..7fdcd0eede 100644
--- a/pybamm/meshes/meshes.py
+++ b/pybamm/meshes/meshes.py
@@ -74,9 +74,7 @@ def __init__(self, geometry, submesh_types, var_pts):
                             and var.domain[0] in geometry.keys()
                         ):
                             raise KeyError(
-                                "Points not given for a variable in domain '{}'".format(
-                                    domain
-                                )
+                                f"Points not given for a variable in domain '{domain}'"
                             )
                         # Otherwise add to the dictionary of submesh points
                         submesh_pts[domain][var.name] = var_name_pts[var.name]
@@ -272,4 +270,4 @@ def __call__(self, lims, npts):
         return self.submesh_type(lims, npts, **self.submesh_params)
 
     def __repr__(self):
-        return "Generator for {}".format(self.submesh_type.__name__)
+        return f"Generator for {self.submesh_type.__name__}"
diff --git a/pybamm/meshes/scikit_fem_submeshes.py b/pybamm/meshes/scikit_fem_submeshes.py
index 8f80d6f5ce..82a7bd72f1 100644
--- a/pybamm/meshes/scikit_fem_submeshes.py
+++ b/pybamm/meshes/scikit_fem_submeshes.py
@@ -79,7 +79,7 @@ def read_lims(self, lims):
         # check that two variables have been passed in
         if len(lims) != 2:
             raise pybamm.GeometryError(
-                "lims should contain exactly two variables, not {}".format(len(lims))
+                f"lims should contain exactly two variables, not {len(lims)}"
             )
 
         # get spatial variables
@@ -181,7 +181,7 @@ def __init__(self, lims, npts):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             else:
                 edges[var.name] = np.linspace(
@@ -240,7 +240,7 @@ def __init__(self, lims, npts, side="top", stretch=2.3):
         # check side is top
         if side != "top":
             raise pybamm.GeometryError(
-                "At present, side can only be 'top', but is set to {}".format(side)
+                f"At present, side can only be 'top', but is set to {side}"
             )
 
         spatial_vars, tabs = self.read_lims(lims)
@@ -251,7 +251,7 @@ def __init__(self, lims, npts, side="top", stretch=2.3):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             elif var.name == "y":
                 edges[var.name] = np.linspace(
@@ -305,7 +305,7 @@ def __init__(self, lims, npts):
         for var in spatial_vars:
             if var.name not in ["y", "z"]:
                 raise pybamm.DomainError(
-                    "spatial variable must be y or z not {}".format(var.name)
+                    f"spatial variable must be y or z not {var.name}"
                 )
             else:
                 # Create N Chebyshev nodes in the interval (a,b)
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 257bc30ef8..8e4c80a625 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -550,9 +550,7 @@ def build_coupled_variables(self):
                         else:
                             # try setting coupled variables on next loop through
                             pybamm.logger.debug(
-                                "Can't find {}, trying other submodels first".format(
-                                    key
-                                )
+                                f"Can't find {key}, trying other submodels first"
                             )
         # Convert variables back into FuzzyDict
         self.variables = pybamm.FuzzyDict(self._variables)
@@ -561,14 +559,12 @@ def build_model_equations(self):
         # Set model equations
         for submodel_name, submodel in self.submodels.items():
             pybamm.logger.verbose(
-                "Setting rhs for {} submodel ({})".format(submodel_name, self.name)
+                f"Setting rhs for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_rhs(self.variables)
             pybamm.logger.verbose(
-                "Setting algebraic for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting algebraic for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_algebraic(self.variables)
@@ -580,14 +576,12 @@ def build_model_equations(self):
 
             submodel.set_boundary_conditions(self.variables)
             pybamm.logger.verbose(
-                "Setting initial conditions for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting initial conditions for {submodel_name} submodel ({self.name})"
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
             pybamm.logger.verbose(
-                "Updating {} submodel ({})".format(submodel_name, self.name)
+                f"Updating {submodel_name} submodel ({self.name})"
             )
             self.update(submodel)
             self.check_no_repeated_keys()
@@ -595,7 +589,7 @@ def build_model_equations(self):
     def build_model(self):
         self._build_model()
         self._built = True
-        pybamm.logger.info("Finish building {}".format(self.name))
+        pybamm.logger.info(f"Finish building {self.name}")
 
     def _build_model(self):
         # Check if already built
@@ -605,7 +599,7 @@ def _build_model(self):
                 `model.update` instead."""
             )
 
-        pybamm.logger.info("Start building {}".format(self.name))
+        pybamm.logger.info(f"Start building {self.name}")
 
         if self._built_fundamental is False:
             self.build_fundamental()
@@ -740,7 +734,7 @@ def check_and_combine_dict(self, dict1, dict2):
         if len(ids1.intersection(ids2)) != 0:
             variables = ids1.intersection(ids2)
             raise pybamm.ModelError(
-                "Submodel incompatible: duplicate variables '{}'".format(variables)
+                f"Submodel incompatible: duplicate variables '{variables}'"
             )
         dict1.update(dict2)
 
@@ -778,12 +772,12 @@ def check_for_time_derivatives(self):
                 if isinstance(node, pybamm.VariableDot):
                     raise pybamm.ModelError(
                         "time derivative of variable found "
-                        "({}) in rhs equation {}".format(node, key)
+                        f"({node}) in rhs equation {key}"
                     )
                 if isinstance(node, pybamm.StateVectorDot):
                     raise pybamm.ModelError(
                         "time derivative of state vector found "
-                        "({}) in rhs equation {}".format(node, key)
+                        f"({node}) in rhs equation {key}"
                     )
 
         # Check that no variable time derivatives exist in the algebraic equations
@@ -791,13 +785,13 @@ def check_for_time_derivatives(self):
             for node in eq.pre_order():
                 if isinstance(node, pybamm.VariableDot):
                     raise pybamm.ModelError(
-                        "time derivative of variable found ({}) in algebraic"
-                        "equation {}".format(node, key)
+                        f"time derivative of variable found ({node}) in algebraic"
+                        f"equation {key}"
                     )
                 if isinstance(node, pybamm.StateVectorDot):
                     raise pybamm.ModelError(
-                        "time derivative of state vector found ({}) in algebraic"
-                        "equation {}".format(node, key)
+                        f"time derivative of state vector found ({node}) in algebraic"
+                        f"equation {key}"
                     )
 
     def check_well_determined(self, post_discretisation):
@@ -887,7 +881,7 @@ def check_ics_bcs(self):
         for var in self.rhs.keys():
             if var not in self.initial_conditions.keys():
                 raise pybamm.ModelError(
-                    """no initial condition given for variable '{}'""".format(var)
+                    f"""no initial condition given for variable '{var}'"""
                 )
 
     def check_variables(self):
@@ -909,13 +903,11 @@ def check_variables(self):
         for var in all_vars:
             if var not in vars_in_keys:
                 raise pybamm.ModelError(
-                    """
-                    No key set for variable '{}'. Make sure it is included in either
+                    f"""
+                    No key set for variable '{var}'. Make sure it is included in either
                     model.rhs or model.algebraic, in an unmodified form
                     (e.g. not Broadcasted)
-                    """.format(
-                        var
-                    )
+                    """
                 )
 
     def check_no_repeated_keys(self):
@@ -970,7 +962,7 @@ def check_discretised_or_discretise_inplace_if_0D(self):
             except pybamm.DiscretisationError as e:
                 raise pybamm.DiscretisationError(
                     "Cannot automatically discretise model, model should be "
-                    "discretised before exporting casadi functions ({})".format(e)
+                    f"discretised before exporting casadi functions ({e})"
                 )
 
     def export_casadi_objects(self, variable_names, input_parameter_order=None):
@@ -1287,9 +1279,7 @@ def check_and_convert_equations(self, equations):
                 equations[var] = eqn
             if not (var.domain == eqn.domain or var.domain == [] or eqn.domain == []):
                 raise pybamm.DomainError(
-                    "variable and equation in '{}' must have the same domain".format(
-                        self.name
-                    )
+                    f"variable and equation in '{self.name}' must have the same domain"
                 )
 
         # For initial conditions, check that the equation doesn't contain any
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index b174ef581c..94ea006aa4 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -631,7 +631,7 @@ def __init__(self, extra_options):
                 value = (value,)
             else:
                 if not (
-                    (
+
                         option
                         in [
                             "diffusivity",
@@ -652,7 +652,7 @@ def __init__(self, extra_options):
                         ]
                         and isinstance(value, tuple)
                         and len(value) == 2
-                    )
+
                 ):
                     # more possible options that can take 2-tuples to be added
                     # as they come
@@ -1021,10 +1021,8 @@ def options(self, extra_options):
             and options["hydrolysis"] == "true"
         ):
             raise pybamm.OptionError(
-                """must use surface formulation to solve {!s} with hydrolysis
-                    """.format(
-                    self
-                )
+                f"""must use surface formulation to solve {self!s} with hydrolysis
+                    """
             )
 
         self._options = options
@@ -1053,14 +1051,12 @@ def build_model_equations(self):
         # Set model equations
         for submodel_name, submodel in self.submodels.items():
             pybamm.logger.verbose(
-                "Setting rhs for {} submodel ({})".format(submodel_name, self.name)
+                f"Setting rhs for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_rhs(self.variables)
             pybamm.logger.verbose(
-                "Setting algebraic for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting algebraic for {submodel_name} submodel ({self.name})"
             )
 
             submodel.set_algebraic(self.variables)
@@ -1072,14 +1068,12 @@ def build_model_equations(self):
 
             submodel.set_boundary_conditions(self.variables)
             pybamm.logger.verbose(
-                "Setting initial conditions for {} submodel ({})".format(
-                    submodel_name, self.name
-                )
+                f"Setting initial conditions for {submodel_name} submodel ({self.name})"
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
             pybamm.logger.verbose(
-                "Updating {} submodel ({})".format(submodel_name, self.name)
+                f"Updating {submodel_name} submodel ({self.name})"
             )
             self.update(submodel)
             self.check_no_repeated_keys()
@@ -1089,18 +1083,18 @@ def build_model(self):
         self._build_model()
 
         # Set battery specific variables
-        pybamm.logger.debug("Setting voltage variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting voltage variables ({self.name})")
         self.set_voltage_variables()
 
-        pybamm.logger.debug("Setting SoC variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting SoC variables ({self.name})")
         self.set_soc_variables()
 
-        pybamm.logger.debug("Setting degradation variables ({})".format(self.name))
+        pybamm.logger.debug(f"Setting degradation variables ({self.name})")
         self.set_degradation_variables()
         self.set_summary_variables()
 
         self._built = True
-        pybamm.logger.info("Finish building {}".format(self.name))
+        pybamm.logger.info(f"Finish building {self.name}")
 
     @property
     def summary_variables(self):
diff --git a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
index 407039b6f6..9d01f89ffd 100644
--- a/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
+++ b/pybamm/models/full_battery_models/equivalent_circuit/thevenin.py
@@ -207,7 +207,7 @@ def build_model(self):
         self._build_model()
 
         self._built = True
-        pybamm.logger.info("Finished building {}".format(self.name))
+        pybamm.logger.info(f"Finished building {self.name}")
 
     @property
     def default_parameter_values(self):
diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py
index ab095b9be2..225ae83705 100644
--- a/pybamm/models/submodels/base_submodel.py
+++ b/pybamm/models/submodels/base_submodel.py
@@ -128,9 +128,7 @@ def domain(self, domain):
                 self._Domain = domain.capitalize()
         else:
             raise pybamm.DomainError(
-                "Domain '{}' not recognised (must be one of {})".format(
-                    domain, ok_domain_list
-                )
+                f"Domain '{domain}' not recognised (must be one of {ok_domain_list})"
             )
 
     @property
diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py
index ea45f2df5c..20c20de091 100644
--- a/pybamm/parameters/parameter_sets.py
+++ b/pybamm/parameters/parameter_sets.py
@@ -50,7 +50,7 @@ def get_entries(group_name):
     def __new__(cls):
         """Ensure only one instance of ParameterSets exists"""
         if not hasattr(cls, "instance"):
-            cls.instance = super(ParameterSets, cls).__new__(cls)
+            cls.instance = super().__new__(cls)
         return cls.instance
 
     def __getitem__(self, key) -> dict:
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index a5ed9b66fb..be842a7bca 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -222,9 +222,7 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
                 and not (self[name] == float(value) or self[name] == value)
             ):
                 raise ValueError(
-                    "parameter '{}' already defined with value '{}'".format(
-                        name, self[name]
-                    )
+                    f"parameter '{name}' already defined with value '{self[name]}'"
                 )
             # check parameter already exists (for updating parameters)
             if check_already_exists is True:
@@ -232,8 +230,8 @@ def update(self, values, check_conflict=False, check_already_exists=True, path="
                     self._dict_items[name]
                 except KeyError as err:
                     raise KeyError(
-                        "Cannot update parameter '{}' as it does not ".format(name)
-                        + "have a default value. ({}). If you are ".format(err.args[0])
+                        f"Cannot update parameter '{name}' as it does not "
+                        + f"have a default value. ({err.args[0]}). If you are "
                         + "sure you want to update this parameter, use "
                         + "param.update({{name: value}}, check_already_exists=False)"
                     )
@@ -395,7 +393,7 @@ def process_model(self, unprocessed_model, inplace=True):
 
         """
         pybamm.logger.info(
-            "Start setting parameters for {}".format(unprocessed_model.name)
+            f"Start setting parameters for {unprocessed_model.name}"
         )
 
         # set up inplace vs not inplace
@@ -417,7 +415,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_rhs = {}
         for variable, equation in unprocessed_model.rhs.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (rhs)".format(variable)
+                f"Processing parameters for {variable!r} (rhs)"
             )
             new_variable = self.process_symbol(variable)
             new_rhs[new_variable] = self.process_symbol(equation)
@@ -426,7 +424,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_algebraic = {}
         for variable, equation in unprocessed_model.algebraic.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (algebraic)".format(variable)
+                f"Processing parameters for {variable!r} (algebraic)"
             )
             new_variable = self.process_symbol(variable)
             new_algebraic[new_variable] = self.process_symbol(equation)
@@ -435,7 +433,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_initial_conditions = {}
         for variable, equation in unprocessed_model.initial_conditions.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (initial conditions)".format(variable)
+                f"Processing parameters for {variable!r} (initial conditions)"
             )
             new_variable = self.process_symbol(variable)
             new_initial_conditions[new_variable] = self.process_symbol(equation)
@@ -446,7 +444,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_variables = {}
         for variable, equation in unprocessed_model.variables.items():
             pybamm.logger.verbose(
-                "Processing parameters for {!r} (variables)".format(variable)
+                f"Processing parameters for {variable!r} (variables)"
             )
             new_variables[variable] = self.process_symbol(equation)
         model.variables = new_variables
@@ -454,7 +452,7 @@ def process_model(self, unprocessed_model, inplace=True):
         new_events = []
         for event in unprocessed_model.events:
             pybamm.logger.verbose(
-                "Processing parameters for event '{}''".format(event.name)
+                f"Processing parameters for event '{event.name}''"
             )
             new_events.append(
                 pybamm.Event(
@@ -465,7 +463,7 @@ def process_model(self, unprocessed_model, inplace=True):
         interpolant_events = self._get_interpolant_events(model)
         for event in interpolant_events:
             pybamm.logger.verbose(
-                "Processing parameters for event '{}''".format(event.name)
+                f"Processing parameters for event '{event.name}''"
             )
             new_events.append(
                 pybamm.Event(
@@ -475,7 +473,7 @@ def process_model(self, unprocessed_model, inplace=True):
 
         model.events = new_events
 
-        pybamm.logger.info("Finish setting parameters for {}".format(model.name))
+        pybamm.logger.info(f"Finish setting parameters for {model.name}")
 
         return model
 
@@ -523,7 +521,7 @@ def process_boundary_conditions(self, model):
                 try:
                     bc, typ = bcs[side]
                     pybamm.logger.verbose(
-                        "Processing parameters for {!r} ({} bc)".format(variable, side)
+                        f"Processing parameters for {variable!r} ({side} bc)"
                     )
                     processed_bc = (self.process_symbol(bc), typ)
                     new_boundary_conditions[processed_variable][side] = processed_bc
@@ -608,7 +606,7 @@ def _process_symbol(self, symbol):
                 new_value.copy_domains(symbol)
                 return new_value
             else:
-                raise TypeError("Cannot process parameter '{}'".format(value))
+                raise TypeError(f"Cannot process parameter '{value}'")
 
         elif isinstance(symbol, pybamm.FunctionParameter):
             function_name = self[symbol.name]
@@ -658,7 +656,7 @@ def _process_symbol(self, symbol):
 
                 else:  # pragma: no cover
                     raise ValueError(
-                        "Invalid function name length: {0}".format(len(function_name))
+                        f"Invalid function name length: {len(function_name)}"
                     )
 
             elif isinstance(function_name, numbers.Number):
@@ -682,7 +680,7 @@ def _process_symbol(self, symbol):
                 function = function_name
             else:
                 raise TypeError(
-                    "Parameter provided for '{}' ".format(symbol.name)
+                    f"Parameter provided for '{symbol.name}' "
                     + "is of the wrong type (should either be scalar-like or callable)"
                 )
             # Differentiate if necessary
@@ -897,7 +895,7 @@ def print_evaluated_parameters(self, evaluated_parameters, output_file):
         """
         # Get column width for pretty printing
         column_width = max(len(name) for name in evaluated_parameters.keys())
-        s = "{{:>{}}}".format(column_width)
+        s = f"{{:>{column_width}}}"
         with open(output_file, "w") as file:
             for name, value in sorted(evaluated_parameters.items()):
                 if 0.001 < abs(value) < 1000:
diff --git a/pybamm/parameters/process_parameter_data.py b/pybamm/parameters/process_parameter_data.py
index 8de8f32ba8..8998c6e583 100644
--- a/pybamm/parameters/process_parameter_data.py
+++ b/pybamm/parameters/process_parameter_data.py
@@ -49,7 +49,7 @@ def process_2D_data(name, path=None):
     """
     filename, name = _process_name(name, path, ".json")
 
-    with open(filename, "r") as jsonfile:
+    with open(filename) as jsonfile:
         json_data = json.load(jsonfile)
     data = json_data["data"]
     data[0] = [np.array(el) for el in data[0]]
diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py
index 686c58f3c5..9b082fd6d4 100644
--- a/pybamm/plotting/quick_plot.py
+++ b/pybamm/plotting/quick_plot.py
@@ -52,7 +52,7 @@ def close_plots():
     plt.close("all")
 
 
-class QuickPlot(object):
+class QuickPlot:
     """
     Generates a quick plot of a subset of key outputs of the model so that the model
     outputs can be easily assessed.
@@ -165,7 +165,7 @@ def __init__(
             self.spatial_factor = 1e6
             self.spatial_unit = "$\mu$m"
         else:
-            raise ValueError("spatial unit '{}' not recognized".format(spatial_unit))
+            raise ValueError(f"spatial unit '{spatial_unit}' not recognized")
 
         # Time parameters
         self.ts_seconds = [solution.t for solution in solutions]
@@ -191,7 +191,7 @@ def __init__(
             time_scaling_factor = 3600
             self.time_unit = "h"
         else:
-            raise ValueError("time unit '{}' not recognized".format(time_unit))
+            raise ValueError(f"time unit '{time_unit}' not recognized")
         self.time_scaling_factor = time_scaling_factor
         self.min_t = min_t / time_scaling_factor
         self.max_t = max_t / time_scaling_factor
@@ -283,7 +283,7 @@ def set_output_variables(self, output_variables, solutions):
                     sol = solution[var]
                     # Check variable isn't all-nan
                     if np.all(np.isnan(sol.entries)):
-                        raise ValueError("All-NaN variable '{}' provided".format(var))
+                        raise ValueError(f"All-NaN variable '{var}' provided")
                     # If ok, add to the list of solutions
                     else:
                         variables[i].append(sol)
@@ -324,7 +324,7 @@ def set_output_variables(self, output_variables, solutions):
                 if len(variables) > 1:
                     raise NotImplementedError(
                         "Cannot plot 2D variables when comparing multiple solutions, "
-                        "but '{}' is 2D".format(variable_tuple[0])
+                        f"but '{variable_tuple[0]}' is 2D"
                     )
                 # But do allow if just a single solution
                 else:
@@ -387,7 +387,7 @@ def get_spatial_var(self, key, variable, dimension):
                 domain = variable.domains["secondary"][0]
 
         if domain == "current collector":
-            domain += " {}".format(spatial_var_name)
+            domain += f" {spatial_var_name}"
 
         return spatial_var_name, spatial_var_value
 
@@ -504,7 +504,7 @@ def plot(self, t, dynamic=False):
             # Set labels for the first subplot only (avoid repetition)
             if variable_lists[0][0].dimensions == 0:
                 # 0D plot: plot as a function of time, indicating time t with a line
-                ax.set_xlabel("Time [{}]".format(self.time_unit))
+                ax.set_xlabel(f"Time [{self.time_unit}]")
                 for i, variable_list in enumerate(variable_lists):
                     for j, variable in enumerate(variable_list):
                         if len(variable_list) == 1:
@@ -540,7 +540,7 @@ def plot(self, t, dynamic=False):
                 spatial_vars = self.spatial_variable_dict[key]
                 spatial_var_name = next(iter(spatial_vars.keys()))
                 ax.set_xlabel(
-                    "{} [{}]".format(spatial_var_name, self.spatial_unit),
+                    f"{spatial_var_name} [{self.spatial_unit}]",
                 )
                 for i, variable_list in enumerate(variable_lists):
                     for j, variable in enumerate(variable_list):
@@ -582,8 +582,8 @@ def plot(self, t, dynamic=False):
                     x = self.first_spatial_variable[key]
                     y = self.second_spatial_variable[key]
                     var = variable(t_in_seconds, **spatial_vars, warn=False).T
-                ax.set_xlabel("{} [{}]".format(x_name, self.spatial_unit))
-                ax.set_ylabel("{} [{}]".format(y_name, self.spatial_unit))
+                ax.set_xlabel(f"{x_name} [{self.spatial_unit}]")
+                ax.set_ylabel(f"{y_name} [{self.spatial_unit}]")
                 vmin, vmax = self.variable_limits[key]
                 # store the plot and the var data (for testing) as cant access
                 # z data from QuadMesh or QuadContourSet object
@@ -684,7 +684,7 @@ def dynamic_plot(self, testing=False, step=None):
             ax_slider = plt.axes([0.315, 0.02, 0.37, 0.03], facecolor=axcolor)
             self.slider = Slider(
                 ax_slider,
-                "Time [{}]".format(self.time_unit),
+                f"Time [{self.time_unit}]",
                 self.min_t,
                 self.max_t,
                 valinit=self.min_t,
diff --git a/pybamm/settings.py b/pybamm/settings.py
index bdc9c1a137..591d9fd101 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -3,7 +3,7 @@
 #
 
 
-class Settings(object):
+class Settings:
     _debug_mode = False
     _simplify = True
     _min_smoothing = "exact"
diff --git a/pybamm/solvers/algebraic_solver.py b/pybamm/solvers/algebraic_solver.py
index 2a364907f7..d241d5b24c 100644
--- a/pybamm/solvers/algebraic_solver.py
+++ b/pybamm/solvers/algebraic_solver.py
@@ -34,7 +34,7 @@ def __init__(self, method="lm", tol=1e-6, extra_options=None):
         super().__init__(method=method)
         self.tol = tol
         self.extra_options = extra_options or {}
-        self.name = "Algebraic solver ({})".format(method)
+        self.name = f"Algebraic solver ({method})"
         self.algebraic_solver = True
         pybamm.citations.register("Virtanen2020")
 
@@ -215,7 +215,7 @@ def jac_norm(y):
                     success = True
                 elif not sol.success:
                     raise pybamm.SolverError(
-                        "Could not find acceptable solution: {}".format(sol.message)
+                        f"Could not find acceptable solution: {sol.message}"
                     )
                 else:
                     y0_alg = sol.x
@@ -223,9 +223,7 @@ def jac_norm(y):
                         raise pybamm.SolverError(
                             "Could not find acceptable solution: solver terminated "
                             "successfully, but maximum solution error "
-                            "({}) above tolerance ({})".format(
-                                np.max(abs(sol.fun)), self.tol
-                            )
+                            f"({np.max(abs(sol.fun))}) above tolerance ({self.tol})"
                         )
                 itr += 1
 
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index dbc2bfe875..36f101b1d0 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -16,7 +16,7 @@
 from pybamm.expression_tree.binary_operators import _Heaviside
 
 
-class BaseSolver(object):
+class BaseSolver:
     """Solve a discretised model.
 
     Parameters
@@ -314,7 +314,7 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
         # Check model.algebraic for ode solvers
         if self.ode_solver is True and len(model.algebraic) > 0:
             raise pybamm.SolverError(
-                "Cannot use ODE solver '{}' to solve DAE model".format(self.name)
+                f"Cannot use ODE solver '{self.name}' to solve DAE model"
             )
         # Check model.rhs for algebraic solvers
         if self.algebraic_solver is True and len(model.rhs) > 0:
@@ -338,16 +338,14 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
             except pybamm.DiscretisationError as e:
                 raise pybamm.DiscretisationError(
                     "Cannot automatically discretise model, "
-                    "model should be discretised before solving ({})".format(e)
+                    f"model should be discretised before solving ({e})"
                 )
 
         if (
             isinstance(self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver))
         ) and model.convert_to_format != "casadi":
             pybamm.logger.warning(
-                "Converting {} to CasADi for solving with CasADi solver".format(
-                    model.name
-                )
+                f"Converting {model.name} to CasADi for solving with CasADi solver"
             )
             model.convert_to_format = "casadi"
         if (
@@ -355,9 +353,7 @@ def _check_and_prepare_model_inplace(self, model, inputs, ics_only):
             and model.convert_to_format != "casadi"
         ):
             pybamm.logger.warning(
-                "Converting {} to CasADi for calculating ICs with CasADi".format(
-                    model.name
-                )
+                f"Converting {model.name} to CasADi for calculating ICs with CasADi"
             )
             model.convert_to_format = "casadi"
 
@@ -689,7 +685,7 @@ def calculate_consistent_state(self, model, time=0, inputs=None):
             root_sol = self.root_method._integrate(model, np.array([time]), inputs)
         except pybamm.SolverError as e:
             raise pybamm.SolverError(
-                "Could not find consistent states: {}".format(e.args[0])
+                f"Could not find consistent states: {e.args[0]}"
             )
         pybamm.logger.debug("Found consistent states")
 
@@ -748,7 +744,7 @@ def solve(
             If multiple calls to `solve` pass in different models
 
         """
-        pybamm.logger.info("Start solving {} with {}".format(model.name, self.name))
+        pybamm.logger.info(f"Start solving {model.name} with {self.name}")
 
         # get a list-only version of calculate_sensitivities
         if isinstance(calculate_sensitivities, bool):
@@ -788,7 +784,7 @@ def solve(
                     "'t_eval' can be provided as an array of times at which to "
                     "return the solution, or as a list [t0, tf] where t0 is the "
                     "initial time and tf is the final time, but has been provided "
-                    "as a list of length {}.".format(len(t_eval))
+                    f"as a list of length {len(t_eval)}."
                 )
             else:
                 t_eval = np.linspace(t_eval[0], t_eval[-1], 100)
@@ -981,7 +977,7 @@ def solve(
 
         # Report times
         if len(solutions) == 1:
-            pybamm.logger.info("Finish solving {} ({})".format(model.name, termination))
+            pybamm.logger.info(f"Finish solving {model.name} ({termination})")
             pybamm.logger.info(
                 (
                     "Set-up time: {}, Solve time: {} (of which integration time: {}), "
@@ -994,7 +990,7 @@ def solve(
                 )
             )
         else:
-            pybamm.logger.info("Finish solving {} for all inputs".format(model.name))
+            pybamm.logger.info(f"Finish solving {model.name} for all inputs")
             pybamm.logger.info(
                 ("Set-up time: {}, Solve time: {}, Total time: {}").format(
                     solutions[0].set_up_time,
@@ -1054,7 +1050,7 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval):
         discontinuities = [v for v in discontinuities if v < t_eval[-1]]
 
         pybamm.logger.verbose(
-            "Discontinuity events found at t = {}".format(discontinuities)
+            f"Discontinuity events found at t = {discontinuities}"
         )
         if isinstance(inputs, list):
             raise pybamm.SolverError(
@@ -1215,7 +1211,7 @@ def step(
             and old_solution.termination is None
         ):
             pybamm.logger.verbose(
-                "Start stepping {} with {}".format(model.name, self.name)
+                f"Start stepping {model.name} with {self.name}"
             )
 
         if isinstance(old_solution, pybamm.EmptySolution):
@@ -1246,7 +1242,7 @@ def step(
 
         # Step
         pybamm.logger.verbose(
-            "Stepping for {:.0f} < t < {:.0f}".format(t_start_shifted, t_end)
+            f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}"
         )
         timer.reset()
         solution = self._integrate(model, t_eval, model_inputs)
@@ -1263,7 +1259,7 @@ def step(
         solution.set_up_time = set_up_time
 
         # Report times
-        pybamm.logger.verbose("Finish stepping {} ({})".format(model.name, termination))
+        pybamm.logger.verbose(f"Finish stepping {model.name} ({termination})")
         pybamm.logger.verbose(
             (
                 "Set-up time: {}, Step time: {} (of which integration time: {}), "
@@ -1332,7 +1328,7 @@ def get_termination_reason(self, solution, events):
                         "(possibly due to NaNs)"
                     )
                 # Add the event to the solution object
-                solution.termination = "event: {}".format(termination_event)
+                solution.termination = f"event: {termination_event}"
             # Update t, y and inputs to include event time and state
             # Note: if the final entry of t is equal to the event time we skip
             # this (having duplicate entries causes an error later in ProcessedVariable)
@@ -1484,10 +1480,10 @@ def report(string):
         jacp = None
         if model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities} using jax"
-                )
+
             )
             jacp = func.get_sensitivities()
         if use_jacobian:
@@ -1505,10 +1501,10 @@ def report(string):
         # to python evaluator
         if model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities}"
-                )
+
             )
             jacp_dict = {
                 p: symbol.diff(pybamm.InputParameter(p))
@@ -1611,11 +1607,11 @@ def jacp(*args, **kwargs):
                     casadi_expression = casadi.vertcat(x0, Sx_0, z0, Sz_0)
         elif model.calculate_sensitivities:
             report(
-                (
+
                     f"Calculating sensitivities for {name} with respect "
                     f"to parameters {model.calculate_sensitivities} using "
                     "CasADi"
-                )
+
             )
             # Compute derivate wrt p-stacked (can be passed to solver to
             # compute sensitivities online)
diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py
index e7983b7f87..cdde5bb99c 100644
--- a/pybamm/solvers/casadi_algebraic_solver.py
+++ b/pybamm/solvers/casadi_algebraic_solver.py
@@ -129,7 +129,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             # If there are no symbolic inputs, check the function is below the tol
             # Skip this check if there are symbolic inputs
             if success and (
-                (not any(np.isnan(fun)) and np.all(casadi.fabs(fun) < self.tol))
+                not any(np.isnan(fun)) and np.all(casadi.fabs(fun) < self.tol)
             ):
                 # update initial guess for the next iteration
                 y0_alg = y_alg_sol
@@ -141,7 +141,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
                     y_alg = casadi.horzcat(y_alg, y_alg_sol)
             elif not success:
                 raise pybamm.SolverError(
-                    "Could not find acceptable solution: {}".format(message)
+                    f"Could not find acceptable solution: {message}"
                 )
             elif any(np.isnan(fun)):
                 raise pybamm.SolverError(
diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py
index 4cf863ede1..6ee8758de3 100644
--- a/pybamm/solvers/casadi_solver.py
+++ b/pybamm/solvers/casadi_solver.py
@@ -102,9 +102,9 @@ def __init__(
             self.mode = mode
         else:
             raise ValueError(
-                "invalid mode '{}'. Must be 'safe', for solving with events, "
+                f"invalid mode '{mode}'. Must be 'safe', for solving with events, "
                 "'fast', for solving quickly without events, or 'safe without grid' or "
-                "'fast with events' (both experimental)".format(mode)
+                "'fast with events' (both experimental)"
             )
         self.max_step_decrease_count = max_step_decrease_count
         self.dt_max = dt_max or 600
@@ -126,7 +126,7 @@ def __init__(
             self.perturb_algebraic_initial_conditions = (
                 perturb_algebraic_initial_conditions
             )
-        self.name = "CasADi solver with '{}' mode".format(mode)
+        self.name = f"CasADi solver with '{mode}' mode"
 
         # Initialize
         self.integrators_maxcount = integrators_maxcount
@@ -184,7 +184,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             t_f = t_eval[-1]
 
             pybamm.logger.debug(
-                "Start solving {} with {}".format(model.name, self.name)
+                f"Start solving {model.name} with {self.name}"
             )
 
             if self.mode == "safe without grid":
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 313fddc208..5e98c5bf07 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -71,12 +71,12 @@ def __init__(
         )
         method_options = ["RK45", "BDF"]
         if method not in method_options:
-            raise ValueError("method must be one of {}".format(method_options))
+            raise ValueError(f"method must be one of {method_options}")
         self.ode_solver = False
         if method == "RK45":
             self.ode_solver = True
         self.extra_options = extra_options or {}
-        self.name = "JAX solver ({})".format(method)
+        self.name = f"JAX solver ({method})"
         self._cached_solves = dict()
         pybamm.citations.register("jax2018")
 
@@ -136,11 +136,11 @@ def create_solve(self, model, t_eval):
             raise RuntimeError(
                 "Terminate events not supported for this solver."
                 " Model has the following events:"
-                " {}.\nYou can remove events using `model.events = []`."
+                f" {model.events}.\nYou can remove events using `model.events = []`."
                 " It might be useful to first solve the model using a"
                 " different solver to obtain the time of the event, then"
                 " re-solve using no events and a fixed"
-                " end-time".format(model.events)
+                " end-time"
             )
 
         # Initial conditions, make sure they are an 0D array
diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py
index f9d967c4b0..c5d0683d75 100644
--- a/pybamm/solvers/processed_variable.py
+++ b/pybamm/solvers/processed_variable.py
@@ -8,7 +8,7 @@
 import xarray as xr
 
 
-class ProcessedVariable(object):
+class ProcessedVariable:
     """
     An object that can be evaluated at arbitrary (scalars or vectors) t and x, and
     returns the (interpolated) value of the base variable at that t and x.
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            "Shape not recognized for {} ".format(base_variables[0])
+                            f"Shape not recognized for {base_variables[0]} "
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
@@ -363,7 +363,7 @@ def _process_spatial_variable_names(self, spatial_variable):
             return raw_names[0]
         else:
             raise NotImplementedError(
-                "Spatial variable name not recognized for {}".format(spatial_variable)
+                f"Spatial variable name not recognized for {spatial_variable}"
             )
 
     def __call__(self, t=None, x=None, r=None, y=None, z=None, R=None, warn=True):
diff --git a/pybamm/solvers/processed_variable_computed.py b/pybamm/solvers/processed_variable_computed.py
index 78d16c27fb..fd17dfab7b 100644
--- a/pybamm/solvers/processed_variable_computed.py
+++ b/pybamm/solvers/processed_variable_computed.py
@@ -8,7 +8,7 @@
 import xarray as xr
 
 
-class ProcessedVariableComputed(object):
+class ProcessedVariableComputed:
     """
     An object that can be evaluated at arbitrary (scalars or vectors) t and x, and
     returns the (interpolated) value of the base variable at that t and x.
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            "Shape not recognized for {} ".format(base_variables[0])
+                            f"Shape not recognized for {base_variables[0]} "
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
diff --git a/pybamm/solvers/scikits_dae_solver.py b/pybamm/solvers/scikits_dae_solver.py
index 56b3ff42c3..a5bf1e5a4f 100644
--- a/pybamm/solvers/scikits_dae_solver.py
+++ b/pybamm/solvers/scikits_dae_solver.py
@@ -61,7 +61,7 @@ def __init__(
             raise ImportError("scikits.odes is not installed")
 
         super().__init__(method, rtol, atol, root_method, root_tol, extrap_tol)
-        self.name = "Scikits DAE solver ({})".format(method)
+        self.name = f"Scikits DAE solver ({method})"
 
         self.extra_options = extra_options or {}
 
diff --git a/pybamm/solvers/scikits_ode_solver.py b/pybamm/solvers/scikits_ode_solver.py
index 66132f39bb..9f5ee67604 100644
--- a/pybamm/solvers/scikits_ode_solver.py
+++ b/pybamm/solvers/scikits_ode_solver.py
@@ -57,7 +57,7 @@ def __init__(
         super().__init__(method, rtol, atol, extrap_tol=extrap_tol)
         self.extra_options = extra_options or {}
         self.ode_solver = True
-        self.name = "Scikits ODE solver ({})".format(method)
+        self.name = f"Scikits ODE solver ({method})"
 
         pybamm.citations.register("Malengier2018")
         pybamm.citations.register("Hindmarsh2000")
diff --git a/pybamm/solvers/scipy_solver.py b/pybamm/solvers/scipy_solver.py
index be228e054a..e0065cf4ec 100644
--- a/pybamm/solvers/scipy_solver.py
+++ b/pybamm/solvers/scipy_solver.py
@@ -43,7 +43,7 @@ def __init__(
         )
         self.ode_solver = True
         self.extra_options = extra_options or {}
-        self.name = "Scipy solver ({})".format(method)
+        self.name = f"Scipy solver ({method})"
         pybamm.citations.register("Virtanen2020")
 
     def _integrate(self, model, t_eval, inputs_dict=None):
diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py
index d7a27f142c..90712960cc 100644
--- a/pybamm/solvers/solution.py
+++ b/pybamm/solvers/solution.py
@@ -25,7 +25,7 @@ def default(self, obj):
         return json.JSONEncoder.default(self, obj)  # pragma: no cover
 
 
-class Solution(object):
+class Solution:
     """
     Class containing the solution of, and various attributes associated with, a PyBaMM
     model.
@@ -321,8 +321,7 @@ def check_ys_are_not_too_large(self):
                 # there will always be a statevector, but just in case
                 if statevector is None:  # pragma: no cover
                     raise RuntimeError(
-                        "Cannot find statevector corresponding to variable {}"
-                        .format(var.name)
+                        f"Cannot find statevector corresponding to variable {var.name}"
                     )
                 y_var = y[statevector.y_slices[0]]
                 if np.any(y_var > pybamm.settings.max_y_value):
@@ -470,7 +469,7 @@ def update(self, variables):
         # Process
         for key in variables:
             cumtrapz_ic = None
-            pybamm.logger.debug("Post-processing {}".format(key))
+            pybamm.logger.debug(f"Post-processing {key}")
             vars_pybamm = [model.variables_and_events[key] for model in self.all_models]
 
             # Iterate through all models, some may be in the list several times and
@@ -689,7 +688,7 @@ def save_data(
                         or (i > 0 and 48 <= ord(s) <= 57)
                     ):
                         raise ValueError(
-                            "Invalid character '{}' found in '{}'. ".format(s, name)
+                            f"Invalid character '{s}' found in '{name}'. "
                             + "MATLAB variable names must only contain a-z, A-Z, _, "
                             "or 0-9 (except the first position). "
                             "Use the 'short_names' argument to pass an alternative "
@@ -716,7 +715,7 @@ def save_data(
                 with open(filename, "w") as outfile:
                     json.dump(data, outfile, cls=NumpyEncoder)
         else:
-            raise ValueError("format '{}' not recognised".format(to_format))
+            raise ValueError(f"format '{to_format}' not recognised")
 
     @property
     def sub_solutions(self):
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 636243f829..84f76a2bbd 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -641,9 +641,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs):
             lbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    lbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{lbc_type}'"
             )
 
         if rbc_type == "Dirichlet":
@@ -662,9 +660,7 @@ def add_ghost_nodes(self, symbol, discretised_symbol, bcs):
             rbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -756,9 +752,7 @@ def add_neumann_values(self, symbol, discretised_gradient, bcs, domain):
             lbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
         if rbc_type == "Neumann" and rbc_value != 0:
             rbc_sub_matrix = coo_matrix(
@@ -774,9 +768,7 @@ def add_neumann_values(self, symbol, discretised_gradient, bcs, domain):
             rbc_vector = pybamm.Vector(np.zeros((n + n_bcs) * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    rbc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -1222,7 +1214,7 @@ def arithmetic_mean(array):
             elif shift_key == "edge to node":
                 sub_matrix = diags([0.5, 0.5], [0, 1], shape=(n, n + 1))
             else:
-                raise ValueError("shift key '{}' not recognised".format(shift_key))
+                raise ValueError(f"shift key '{shift_key}' not recognised")
             # Second dimension length
             second_dim_repeats = self._get_auxiliary_domain_repeats(
                 discretised_symbol.domains
@@ -1366,7 +1358,7 @@ def harmonic_mean(array):
                 return D_eff
 
             else:
-                raise ValueError("shift key '{}' not recognised".format(shift_key))
+                raise ValueError(f"shift key '{shift_key}' not recognised")
 
         # If discretised_symbol evaluates to number there is no need to average
         if discretised_symbol.size == 1:
@@ -1376,7 +1368,7 @@ def harmonic_mean(array):
         elif method == "harmonic":
             out = harmonic_mean(discretised_symbol)
         else:
-            raise ValueError("method '{}' not recognised".format(method))
+            raise ValueError(f"method '{method}' not recognised")
         return out
 
     def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
@@ -1404,7 +1396,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
         if symbol not in bcs:
             raise pybamm.ModelError(
                 "Boundary conditions must be provided for "
-                "{}ing '{}'".format(direction, symbol)
+                f"{direction}ing '{symbol}'"
             )
 
         if direction == "upwind":
@@ -1412,7 +1404,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
             if typ != "Dirichlet":
                 raise pybamm.ModelError(
                     "Dirichlet boundary conditions must be provided for "
-                    "upwinding '{}'".format(symbol)
+                    f"upwinding '{symbol}'"
                 )
 
             concat_bc = pybamm.NumpyConcatenation(bc, discretised_symbol)
@@ -1429,7 +1421,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
             if typ != "Dirichlet":
                 raise pybamm.ModelError(
                     "Dirichlet boundary conditions must be provided for "
-                    "downwinding '{}'".format(symbol)
+                    f"downwinding '{symbol}'"
                 )
 
             concat_bc = pybamm.NumpyConcatenation(discretised_symbol, bc)
diff --git a/pybamm/spatial_methods/scikit_finite_element.py b/pybamm/spatial_methods/scikit_finite_element.py
index 2d51e16c32..07a3c0e1be 100644
--- a/pybamm/spatial_methods/scikit_finite_element.py
+++ b/pybamm/spatial_methods/scikit_finite_element.py
@@ -59,7 +59,7 @@ def spatial_variable(self, symbol):
             entries = symbol_mesh["current collector"].coordinates[1, :][:, np.newaxis]
         else:
             raise pybamm.GeometryError(
-                "Spatial variable must be 'y' or 'z' not {}".format(symbol.name)
+                f"Spatial variable must be 'y' or 'z' not {symbol.name}"
             )
         return pybamm.Vector(entries, domains=symbol.domains)
 
@@ -221,9 +221,7 @@ def unit_bc_load_form(v, w):
             boundary_load = boundary_load + neg_bc_value * pybamm.Vector(neg_bc_load)
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    neg_bc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{neg_bc_type}'"
             )
 
         if pos_bc_type == "Neumann":
@@ -238,9 +236,7 @@ def unit_bc_load_form(v, w):
             boundary_load = boundary_load + pos_bc_value * pybamm.Vector(pos_bc_load)
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, not '{}'".format(
-                    pos_bc_type
-                )
+                f"boundary condition must be Dirichlet or Neumann, not '{pos_bc_type}'"
             )
 
         return -stiffness_matrix @ discretised_symbol + boundary_load
@@ -281,7 +277,7 @@ def stiffness_form(u, v, w):
             _, pos_bc_type = boundary_conditions[symbol]["positive tab"]
         except KeyError:
             raise pybamm.ModelError(
-                "No boundary conditions provided for symbol `{}``".format(symbol)
+                f"No boundary conditions provided for symbol `{symbol}``"
             )
 
         # adjust matrix for Dirichlet boundary conditions
diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py
index 7f7cfdb37a..a10422813f 100644
--- a/pybamm/spatial_methods/spectral_volume.py
+++ b/pybamm/spatial_methods/spectral_volume.py
@@ -528,7 +528,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(lbc_type)
+                f"not '{lbc_type}'"
             )
 
         if rbc_type == "Dirichlet":
@@ -544,7 +544,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(rbc_type)
+                f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -622,7 +622,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(lbc_type)
+                f"not '{lbc_type}'"
             )
 
         if rbc_type == "Neumann":
@@ -638,7 +638,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
         else:
             raise ValueError(
                 "boundary condition must be Dirichlet or Neumann, "
-                "not '{}'".format(rbc_type)
+                f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
diff --git a/pybamm/util.py b/pybamm/util.py
index af278d752a..71883e3d27 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -122,16 +122,16 @@ def search(self, key, print_values=False):
             )
         elif print_values:
             # Else print results, including dict items
-            print("\n".join("{}\t{}".format(k, v) for k, v in results.items()))
+            print("\n".join(f"{k}\t{v}" for k, v in results.items()))
         else:
             # Just print keys
-            print("\n".join("{}".format(k) for k in results.keys()))
+            print("\n".join(f"{k}" for k in results.keys()))
 
     def copy(self):
         return FuzzyDict(super().copy())
 
 
-class Timer(object):
+class Timer:
     """
     Provides accurate timing.
 
@@ -171,13 +171,13 @@ def __str__(self):
         """
         time = self.value
         if time < 1e-6:
-            return "{:.3f} ns".format(time * 1e9)
+            return f"{time * 1e9:.3f} ns"
         if time < 1e-3:
-            return "{:.3f} us".format(time * 1e6)
+            return f"{time * 1e6:.3f} us"
         if time < 1:
-            return "{:.3f} ms".format(time * 1e3)
+            return f"{time * 1e3:.3f} ms"
         elif time < 60:
-            return "{:.3f} s".format(time)
+            return f"{time:.3f} s"
         output = []
         time = int(round(time))
         units = [(604800, "week"), (86400, "day"), (3600, "hour"), (60, "minute")]
diff --git a/run-tests.py b/run-tests.py
index 25b1731b18..c523554fc9 100755
--- a/run-tests.py
+++ b/run-tests.py
@@ -40,7 +40,7 @@ def run_code_tests(executable=False, folder: str = "unit", interpreter="python")
         result = unittest.TextTestRunner(verbosity=2).run(suite)
         ret = int(not result.wasSuccessful())
     else:
-        print("Running {} tests with executable '{}'".format(folder, interpreter))
+        print(f"Running {folder} tests with executable '{interpreter}'")
         cmd = [interpreter, "-m", "unittest", "discover", "-v", tests]
         p = subprocess.Popen(cmd)
         try:
@@ -178,7 +178,7 @@ def test_script(path, executable="python"):
         sys.exit(1)
 
     # Sucessfully run
-    print("ok ({})".format(b.time()))
+    print(f"ok ({b.time()})")
     return True
 
 
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 8f41f5969a..e46831eb5e 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -59,7 +59,7 @@ def download_extract_library(url, download_dir):
 suitesparse_version = "6.0.3"
 suitesparse_url = (
     "https://github.com/DrTimothyAldenDavis/"
-    + "SuiteSparse/archive/v{}.tar.gz".format(suitesparse_version)
+    + f"SuiteSparse/archive/v{suitesparse_version}.tar.gz"
 )
 download_extract_library(suitesparse_url, download_dir)
 
@@ -68,13 +68,13 @@ def download_extract_library(url, download_dir):
 # - AMD
 # - COLAMD
 # - BTF
-suitesparse_dir = "SuiteSparse-{}".format(suitesparse_version)
+suitesparse_dir = f"SuiteSparse-{suitesparse_version}"
 suitesparse_src = os.path.join(download_dir, suitesparse_dir)
 print("-" * 10, "Building SuiteSparse_config", "-" * 40)
 make_cmd = [
     "make",
     "library",
-    'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={}"'.format(install_dir),
+    f'CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX={install_dir}"',
 ]
 install_cmd = [
     "make",
@@ -107,8 +107,8 @@ def download_extract_library(url, download_dir):
     "-DEXAMPLES_ENABLE:BOOL=OFF",
     "-DENABLE_KLU=ON",
     "-DENABLE_OPENMP=ON",
-    "-DKLU_INCLUDE_DIR={}".format(KLU_INCLUDE_DIR),
-    "-DKLU_LIBRARY_DIR={}".format(KLU_LIBRARY_DIR),
+    f"-DKLU_INCLUDE_DIR={KLU_INCLUDE_DIR}",
+    f"-DKLU_LIBRARY_DIR={KLU_LIBRARY_DIR}",
     "-DCMAKE_INSTALL_PREFIX=" + install_dir,
     # on mac use fixed paths rather than rpath
     "-DCMAKE_INSTALL_NAME_DIR=" + KLU_LIBRARY_DIR,
@@ -154,7 +154,7 @@ def download_extract_library(url, download_dir):
     print("\n-" * 10, "Creating build dir", "-" * 40)
     os.makedirs(build_dir)
 
-sundials_src = "../sundials-{}".format(sundials_version)
+sundials_src = f"../sundials-{sundials_version}"
 print("-" * 10, "Running CMake prepare", "-" * 40)
 subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
diff --git a/setup.py b/setup.py
index ef82e65e70..6b62aacc99 100644
--- a/setup.py
+++ b/setup.py
@@ -24,13 +24,13 @@
 
 def set_vcpkg_environment_variables():
     if not os.getenv("VCPKG_ROOT_DIR"):
-        raise EnvironmentError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
+        raise OSError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
     if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise EnvironmentError(
+        raise OSError(
             "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
         )
     if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise EnvironmentError(
+        raise OSError(
             "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
         )
     return (
@@ -91,17 +91,17 @@ def run(self):
 
         build_type = os.getenv("PYBAMM_CPP_BUILD_TYPE", "RELEASE")
         cmake_args = [
-            "-DCMAKE_BUILD_TYPE={}".format(build_type),
-            "-DPYTHON_EXECUTABLE={}".format(sys.executable),
+            f"-DCMAKE_BUILD_TYPE={build_type}",
+            f"-DPYTHON_EXECUTABLE={sys.executable}",
             "-DUSE_PYTHON_CASADI={}".format("TRUE" if use_python_casadi else "FALSE"),
         ]
         if self.suitesparse_root:
             cmake_args.append(
-                "-DSuiteSparse_ROOT={}".format(os.path.abspath(self.suitesparse_root))
+                f"-DSuiteSparse_ROOT={os.path.abspath(self.suitesparse_root)}"
             )
         if self.sundials_root:
             cmake_args.append(
-                "-DSUNDIALS_ROOT={}".format(os.path.abspath(self.sundials_root))
+                f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}"
             )
 
         build_dir = self.get_build_directory()
@@ -264,12 +264,12 @@ def compile_KLU():
     pybind11_dir = os.path.join(pybamm_project_dir, "pybind11")
     try:
         open(os.path.join(pybind11_dir, "tools", "pybind11Tools.cmake"))
-        logger.info("Found pybind11 directory ({})".format(pybind11_dir))
+        logger.info(f"Found pybind11 directory ({pybind11_dir})")
     except FileNotFoundError:
         PyBind11Found = False
         msg = (
-            "Could not find PyBind11 directory ({})."
-            " Skipping compilation of KLU module.".format(pybind11_dir)
+            f"Could not find PyBind11 directory ({pybind11_dir})."
+            " Skipping compilation of KLU module."
         )
         logger.info(msg)
 
diff --git a/tests/integration/test_models/standard_model_tests.py b/tests/integration/test_models/standard_model_tests.py
index d4074e15ef..43eba8894e 100644
--- a/tests/integration/test_models/standard_model_tests.py
+++ b/tests/integration/test_models/standard_model_tests.py
@@ -8,7 +8,7 @@
 import os
 
 
-class StandardModelTest(object):
+class StandardModelTest:
     """Basic processing test for the models."""
 
     def __init__(
@@ -195,7 +195,7 @@ def test_all(
             self.test_outputs()
 
 
-class OptimisationsTest(object):
+class OptimisationsTest:
     """Test that the optimised models give the same result as the original model."""
 
     def __init__(self, model, parameter_values=None, disc=None):
diff --git a/tests/integration/test_models/standard_output_comparison.py b/tests/integration/test_models/standard_output_comparison.py
index 66c1ccc0ef..4d4d16e5ca 100644
--- a/tests/integration/test_models/standard_output_comparison.py
+++ b/tests/integration/test_models/standard_output_comparison.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class StandardOutputComparison(object):
+class StandardOutputComparison:
     """Calls all the tests comparing standard output variables."""
 
     def __init__(self, solutions):
@@ -56,7 +56,7 @@ def test_all(self, skip_first_timestep=False):
             self.run_test_class(PorosityComparison, skip_first_timestep)
 
 
-class BaseOutputComparison(object):
+class BaseOutputComparison:
     def __init__(self, time, solutions):
         self.t = time
         self.solutions = solutions
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 05cb86f249..83b88c0ff0 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 
-class StandardOutputTests(object):
+class StandardOutputTests:
     """Calls all the tests on the standard output variables."""
 
     def __init__(self, model, parameter_values, disc, solution):
@@ -58,7 +58,7 @@ def test_all(self, skip_first_timestep=False):
             self.run_test_class(VelocityTests)
 
 
-class BaseOutputTest(object):
+class BaseOutputTest:
     def __init__(self, model, param, disc, solution, operating_condition):
         self.model = model
         self.param = param
diff --git a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
index c264e26543..c78e7f9223 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lead_acid/test_asymptotics_convergence.py
@@ -41,7 +41,7 @@ def test_leading_order_convergence(self):
         full_disc.process_model(full_model)
 
         def get_max_error(current):
-            pybamm.logger.info("current = {}".format(current))
+            pybamm.logger.info(f"current = {current}")
             # Solve, make sure times are the same and use tight tolerances
             t_eval = np.linspace(0, 3600 * 17 / current)
             solver = pybamm.CasadiSolver()
diff --git a/tests/unit/test_callbacks.py b/tests/unit/test_callbacks.py
index 94a00b15d9..b36fef9ec6 100644
--- a/tests/unit/test_callbacks.py
+++ b/tests/unit/test_callbacks.py
@@ -63,9 +63,9 @@ def test_callback_list(self):
             ]
         )
         callback.on_experiment_end(None)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertEqual(f.read(), "first\n")
-        with open("test_callback_2.log", "r") as f:
+        with open("test_callback_2.log") as f:
             self.assertEqual(f.read(), "second\n")
 
     def test_logging_callback(self):
@@ -89,19 +89,19 @@ def test_logging_callback(self):
             self.assertEqual(f.read(), "")
 
         callback.on_cycle_start(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Cycle 5/12", f.read())
 
         callback.on_step_start(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Cycle 5/12, step 1/4", f.read())
 
         callback.on_experiment_infeasible(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("Experiment is infeasible: 'event'", f.read())
 
         callback.on_experiment_end(logs)
-        with open("test_callback.log", "r") as f:
+        with open("test_callback.log") as f:
             self.assertIn("took 0.45", f.read())
 
         # Calling start again should clear the log
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index b3e2c88422..d8c1de3718 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -50,13 +50,13 @@ def test_print_citations(self):
         # Text Style
         with temporary_filename() as filename:
             pybamm.print_citations(filename, "text")
-            with open(filename, "r") as f:
+            with open(filename) as f:
                 self.assertTrue(len(f.readlines()) > 0)
 
         # Bibtext Style
         with temporary_filename() as filename:
             pybamm.print_citations(filename, "bibtex")
-            with open(filename, "r") as f:
+            with open(filename) as f:
                 self.assertTrue(len(f.readlines()) > 0)
 
         # Write to stdout
diff --git a/tests/unit/test_expression_tree/test_functions.py b/tests/unit/test_expression_tree/test_functions.py
index e9bd8522e6..33e11459ab 100644
--- a/tests/unit/test_expression_tree/test_functions.py
+++ b/tests/unit/test_expression_tree/test_functions.py
@@ -46,7 +46,7 @@ def test_function_of_one_variable(self):
         b = pybamm.Scalar(1)
         sina = pybamm.Function(np.sin, b)
         self.assertEqual(sina.evaluate(), np.sin(1))
-        self.assertEqual(sina.name, "function ({})".format(np.sin.__name__))
+        self.assertEqual(sina.name, f"function ({np.sin.__name__})")
 
         c = pybamm.Vector(np.linspace(0, 1))
         cosb = pybamm.Function(np.cos, c)
diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
index df33e0fe27..426e7811f6 100644
--- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
+++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
@@ -46,7 +46,7 @@ def test_find_symbols(self):
         var_a = pybamm.id_to_python_variable(a.id)
         var_b = pybamm.id_to_python_variable(b.id)
         self.assertEqual(
-            list(variable_symbols.values())[2], "{} + {}".format(var_a, var_b)
+            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
         )
 
         # test identical subtree
@@ -66,12 +66,12 @@ def test_find_symbols(self):
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
         self.assertEqual(
-            list(variable_symbols.values())[2], "{} + {}".format(var_a, var_b)
+            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
         )
 
         var_child = pybamm.id_to_python_variable(expr.children[0].id)
         self.assertEqual(
-            list(variable_symbols.values())[3], "{} + {}".format(var_child, var_b)
+            list(variable_symbols.values())[3], f"{var_child} + {var_b}"
         )
 
         # test unary op
@@ -90,7 +90,7 @@ def test_find_symbols(self):
         # test values of variable_symbols
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
-        self.assertEqual(list(variable_symbols.values())[2], "-{}".format(var_b))
+        self.assertEqual(list(variable_symbols.values())[2], f"-{var_b}")
         var_child = pybamm.id_to_python_variable(expr.children[1].id)
         self.assertEqual(
             list(variable_symbols.values())[3], f"np.maximum({var_a},{var_child})"
@@ -108,7 +108,7 @@ def test_find_symbols(self):
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         var_funct = pybamm.id_to_python_variable(expr.id, True)
         self.assertEqual(
-            list(variable_symbols.values())[1], "{}({})".format(var_funct, var_a)
+            list(variable_symbols.values())[1], f"{var_funct}({var_a})"
         )
 
         # test matrix
@@ -144,7 +144,7 @@ def test_find_symbols(self):
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{}))".format(var_a, var_b),
+            f"np.concatenate(({var_a},{var_b}))",
         )
 
         # test domain concatentate
@@ -158,7 +158,7 @@ def test_find_symbols(self):
         self.assertEqual(list(variable_symbols.keys())[2], expr.id)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{}))".format(var_a, var_b),
+            f"np.concatenate(({var_a},{var_b}))",
         )
 
         # test that Concatentation throws
@@ -203,7 +203,7 @@ def test_domain_concatenation(self):
         self.assertEqual(len(constant_symbols), 0)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({}[0:{}],{}[0:{}]))".format(var_a, a_pts, var_b, b_pts),
+            f"np.concatenate(({var_a}[0:{a_pts}],{var_b}[0:{b_pts}]))",
         )
 
         evaluator = pybamm.EvaluatorPython(expr)
@@ -237,14 +237,14 @@ def test_domain_concatenation(self):
         variable_symbols = OrderedDict()
         pybamm.find_symbols(expr, constant_symbols, variable_symbols)
 
-        b0_str = "{}[0:{}]".format(var_b, b0_pts)
-        a0_str = "{}[0:{}]".format(var_a, a0_pts)
-        b1_str = "{}[{}:{}]".format(var_b, b0_pts, b0_pts + b1_pts)
+        b0_str = f"{var_b}[0:{b0_pts}]"
+        a0_str = f"{var_a}[0:{a0_pts}]"
+        b1_str = f"{var_b}[{b0_pts}:{b0_pts + b1_pts}]"
 
         self.assertEqual(len(constant_symbols), 0)
         self.assertEqual(
             list(variable_symbols.values())[2],
-            "np.concatenate(({},{},{}))".format(a0_str, b0_str, b1_str),
+            f"np.concatenate(({a0_str},{b0_str},{b1_str}))",
         )
 
         evaluator = pybamm.EvaluatorPython(expr)
diff --git a/tests/unit/test_parameters/test_current_functions.py b/tests/unit/test_parameters/test_current_functions.py
index d8bac9cc58..10a311fc2c 100644
--- a/tests/unit/test_parameters/test_current_functions.py
+++ b/tests/unit/test_parameters/test_current_functions.py
@@ -77,7 +77,7 @@ def user_current(t):
         )
 
 
-class StandardCurrentFunctionTests(object):
+class StandardCurrentFunctionTests:
     def __init__(self, function_list, always_array=False):
         self.function_list = function_list
         self.always_array = always_array
diff --git a/tests/unit/test_serialisation/test_serialisation.py b/tests/unit/test_serialisation/test_serialisation.py
index 6c43eaa9d7..75ea33fe66 100644
--- a/tests/unit/test_serialisation/test_serialisation.py
+++ b/tests/unit/test_serialisation/test_serialisation.py
@@ -512,7 +512,7 @@ def test_save_load_model(self):
         )
 
         # Test for error if no model type is provided
-        with open("test_model.json", "r") as f:
+        with open("test_model.json") as f:
             model_data = json.load(f)
             del model_data["py/object"]
 
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index ac70f0b43b..4375e745ad 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -227,7 +227,7 @@ def test_solve_with_initial_soc(self):
         options = {"working electrode": "positive"}
         parameter_values["Current function [A]"] = 0.0
         sim = pybamm.Simulation(model, parameter_values=parameter_values)
-        sol = sim.solve([0,1], initial_soc = "{} V".format(ucv))
+        sol = sim.solve([0,1], initial_soc = f"{ucv} V")
         voltage = sol["Terminal voltage [V]"].entries
         self.assertAlmostEqual(voltage[0], ucv, places=5)
 
diff --git a/tests/unit/test_solvers/test_processed_variable.py b/tests/unit/test_solvers/test_processed_variable.py
index 79de9b0368..d8b4ccfd0c 100644
--- a/tests/unit/test_solvers/test_processed_variable.py
+++ b/tests/unit/test_solvers/test_processed_variable.py
@@ -233,7 +233,7 @@ def test_processed_variable_1D_unknown_domain(self):
             model,
             {},
             np.linspace(0, 1, 1),
-            np.zeros((var_pts[x])),
+            np.zeros(var_pts[x]),
             "test",
         )
 
diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py
index e31f51ab1e..c8b1f2597d 100644
--- a/tests/unit/test_solvers/test_processed_variable_computed.py
+++ b/tests/unit/test_solvers/test_processed_variable_computed.py
@@ -207,7 +207,7 @@ def test_processed_variable_1D_unknown_domain(self):
             pybamm.BaseModel(),
             {},
             np.linspace(0, 1, 1),
-            np.zeros((var_pts[x])),
+            np.zeros(var_pts[x]),
             "test",
         )
 
diff --git a/tests/unit/test_timer.py b/tests/unit/test_timer.py
index 3a5e37b435..228cdd5dce 100644
--- a/tests/unit/test_timer.py
+++ b/tests/unit/test_timer.py
@@ -15,7 +15,7 @@ class TestTimer(TestCase):
     """
 
     def __init__(self, name):
-        super(TestTimer, self).__init__(name)
+        super().__init__(name)
 
     def test_timing(self):
         t = pybamm.Timer()

From 75b58bc50646e5599027e73f5c8254714a20754e Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Thu, 14 Dec 2023 22:59:07 +0530
Subject: [PATCH 530/615] added commit hash

Signed-off-by: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
---
 .git-blame-ignore-revs | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 9e59bd7f07..0583211dda 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -6,3 +6,5 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693
 5273214b585c5a4286609aed40e0b092d0e05f42
 # migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
 12c5d77203bd93542785d237bac00bad5ed5469a
+# activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579
+ff6d81c01331c7d269303b4a8321d9881bdf98fa
\ No newline at end of file

From ade8e7d973b9053bcf66e603e2c3085609f30d9c Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 14 Dec 2023 23:07:50 +0530
Subject: [PATCH 531/615] #3558 Set BUILD AND INSTALL RPATHs correctly

SuiteSparse dynamic libraries were being repeated in the list of paths without this configuration. Setting build rpaths for AMD, COLAMD, BTF, and KLU ensures that they do not reference the SuiteSparse config in the build folder but the one that is installed into the install prefix.
---
 scripts/install_KLU_Sundials.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 5f6f2ff110..8793eb09da 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -82,11 +82,21 @@ def download_extract_library(url, download_dir):
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
-# # Set CMAKE_OPTIONS as environment variables to pass to GNU Make
+# Set CMAKE_OPTIONS as environment variables to pass to the GNU Make command
 env = os.environ.copy()
-env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir} -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=TRUE"
 for libdir in ["SuiteSparse_config", "AMD", "COLAMD", "BTF", "KLU"]:
     build_dir = os.path.join(suitesparse_src, libdir)
+    # We want to ensure that libsuitesparseconfig.dylib is not repeated in
+    # multiple paths at the time of wheel repair. Therefore, it should not be
+    # built with an RPATH since it is copied to the install prefix.
+    if libdir == "SuiteSparse_config":
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
+    else:
+    # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
+    # INSTALL RPATH in order to ensure that the dynamic libraries are found
+    # at runtime just once. Otherwise delocate complains about multiple
+    # references to the SuiteSparse_config dynamic libaries.
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 

From 58d81a79e4fc96956c72fd0594867af00b36e50b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 14 Dec 2023 18:46:54 +0000
Subject: [PATCH 532/615] style: pre-commit fixes

---
 .git-blame-ignore-revs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index 0583211dda..ec0f52cbfd 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -7,4 +7,4 @@ a63e49ece0f9336d1f5c2562f7459e555c6e6693
 # migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
 12c5d77203bd93542785d237bac00bad5ed5469a
 # activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579
-ff6d81c01331c7d269303b4a8321d9881bdf98fa
\ No newline at end of file
+ff6d81c01331c7d269303b4a8321d9881bdf98fa

From 40a9dcfe292213acd154a6e636d588485ba54b8a Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+prady0t@users.noreply.github.com>
Date: Fri, 15 Dec 2023 01:09:04 +0530
Subject: [PATCH 533/615] Apply suggestions from code review

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 pybamm/discretisations/discretisation.py                | 2 +-
 pybamm/models/full_battery_models/base_battery_model.py | 3 +--
 pybamm/solvers/processed_variable.py                    | 2 +-
 pyproject.toml                                          | 4 ++--
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index 7f20cee348..c250d06e9c 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -72,7 +72,7 @@ def y_slices(self):
     @y_slices.setter
     def y_slices(self, value):
         if not isinstance(value, dict):
-            raise TypeError(f"""y_slices should be dict, not {type(value)}""")
+            raise TypeError(f"y_slices should be dict, not {type(value)}")
 
         self._y_slices = value
 
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 94ea006aa4..dea066db08 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -1021,8 +1021,7 @@ def options(self, extra_options):
             and options["hydrolysis"] == "true"
         ):
             raise pybamm.OptionError(
-                f"""must use surface formulation to solve {self!s} with hydrolysis
-                    """
+                f"must use surface formulation to solve {self!s} with hydrolysis"
             )
 
         self._options = options
diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py
index c5d0683d75..d33d6894dd 100644
--- a/pybamm/solvers/processed_variable.py
+++ b/pybamm/solvers/processed_variable.py
@@ -106,7 +106,7 @@ def __init__(
                     else:
                         # Raise error for 3D variable
                         raise NotImplementedError(
-                            f"Shape not recognized for {base_variables[0]} "
+                            f"Shape not recognized for {base_variables[0]}"
                             + "(note processing of 3D variables is not yet implemented)"
                         )
 
diff --git a/pyproject.toml b/pyproject.toml
index 31e0b3e9bf..aa22064e47 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-   "UP",          # pyupgrade
+  "UP",           # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [
@@ -214,7 +214,7 @@ ignore = [
   "RET506",      # Unnecessary `elif`
   "B018",        # Found useless expression
   "RUF002",      # Docstring contains ambiguous
-    "UP007",     # For pyupgrade
+  "UP007",       # For pyupgrade
 ]
 
 [tool.ruff.lint.per-file-ignores]

From 6e9b3734f71feccc84d2b70d34360bd248f64eab Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+prady0t@users.noreply.github.com>
Date: Fri, 15 Dec 2023 18:07:33 +0530
Subject: [PATCH 534/615] Update pyproject.toml

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index aa22064e47..69fb9bfc1e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -196,7 +196,7 @@ extend-select = [
   "RUF",         # Ruff-specific
   # "SIM",         # flake8-simplify
   # "T20",         # flake8-print
-  "UP",           # pyupgrade
+  "UP",          # pyupgrade
   "YTT",         # flake8-2020
 ]
 ignore = [

From e2d8792685bc994a4f44cd3bf3e0fa2ba6a16285 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Fri, 15 Dec 2023 22:24:33 +0530
Subject: [PATCH 535/615] docs: add prady0t as a contributor for infra (#3620)

* docs: update README.md [skip ci]

* docs: update .all-contributorsrc [skip ci]

---------

Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com>
---
 .all-contributorsrc | 9 +++++++++
 README.md           | 5 ++++-
 2 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 317cb38667..7cc68678e0 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -764,6 +764,15 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "prady0t",
+      "name": "Pradyot Ranjan",
+      "avatar_url": "https://avatars.githubusercontent.com/u/99216956?v=4",
+      "profile": "https://github.com/prady0t",
+      "contributions": [
+        "infra"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/README.md b/README.md
index 31c6257473..8bad257378 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-70-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-71-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -275,6 +275,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/shubhambhar007"><img src="https://avatars.githubusercontent.com/u/32607282?v=4?s=100" width="100px;" alt="Shubham Bhardwaj"/><br /><sub><b>Shubham Bhardwaj</b></sub></a><br /><a href="#infra-shubhambhar007" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/jlauber18"><img src="https://avatars.githubusercontent.com/u/28939653?v=4?s=100" width="100px;" alt="Jonathan Lauber"/><br /><sub><b>Jonathan Lauber</b></sub></a><br /><a href="#infra-jlauber18" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+    </tr>
   </tbody>
 </table>
 

From 60c6e02896fcf239268545f2ce646e1761d0b590 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Mon, 18 Dec 2023 02:58:30 +0530
Subject: [PATCH 536/615] Prevent separate function to install dependencies

---
 pybamm/install_odes.py | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index e01be3a7f3..f7b50150ca 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,7 +5,6 @@
 import sys
 import logging
 import subprocess
-from importlib import import_module
 
 from pybamm.util import root_dir
 
@@ -14,22 +13,21 @@
 
 SUNDIALS_VERSION = "6.5.0"
 
-def install_required_module(module):
-    try:
-        import_module(module)
-    except ModuleNotFoundError:
-        print(f"{module} module not found. Installing {module}...")
-        subprocess.run(["pip", "install", module], check=True)
-
-required_modules = ["wget", "cmake"]
-
-for module in required_modules:
-    install_required_module(module)
-
-import wget  # noqa: E402
+try:
+    # wget module is required to download SUNDIALS or SuiteSparse.
+    import wget
+    NO_WGET = False
+except ModuleNotFoundError:
+    NO_WGET = True
 
 def download_extract_library(url, directory):
     # Download and extract archive at url
+    if NO_WGET:
+        error_msg = (
+            "Could not find wget module."
+            " Please install wget module (pip install wget)."
+        )
+        raise ModuleNotFoundError(error_msg)
     archive = wget.download(url, out=directory)
     tar = tarfile.open(archive)
     tar.extractall(directory)

From d97df926649db42ef290aaa641726184aca59027 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 18 Dec 2023 19:45:42 +0000
Subject: [PATCH 537/615] Bump actions/download-artifact from 3 to 4

Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 3 to 4.
- [Release notes](https://github.com/actions/download-artifact/releases)
- [Commits](https://github.com/actions/download-artifact/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/download-artifact
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/periodic_benchmarks.yml         | 2 +-
 .github/workflows/publish_pypi.yml                | 2 +-
 .github/workflows/run_benchmarks_over_history.yml | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 9bd105ae92..09a73cd7cb 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -73,7 +73,7 @@ jobs:
           token: ${{ secrets.BENCH_PAT }}
 
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: asv_new_results
           path: new_results
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b003152802..7c6e14975f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -146,7 +146,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Download all artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
 
       - name: Move all package files to files/
         run: |
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index cb16f65847..deefc534c5 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -65,7 +65,7 @@ jobs:
           repository: pybamm-team/pybamm-bench
           token: ${{ secrets.BENCH_PAT }}
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
           name: asv_new_results
           path: new_results

From b667fecddbcd27e27a169791c5520d2e65372950 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 18 Dec 2023 19:45:45 +0000
Subject: [PATCH 538/615] Bump actions/upload-artifact from 3 to 4

Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 3 to 4.
- [Release notes](https://github.com/actions/upload-artifact/releases)
- [Commits](https://github.com/actions/upload-artifact/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/upload-artifact
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/periodic_benchmarks.yml         | 2 +-
 .github/workflows/publish_pypi.yml                | 6 +++---
 .github/workflows/run_benchmarks_over_history.yml | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 9bd105ae92..91947e8983 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -48,7 +48,7 @@ jobs:
           LD_LIBRARY_PATH: $HOME/.local/lib
 
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: asv_new_results
           path: results
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b003152802..97496c6aba 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -57,7 +57,7 @@ jobs:
           CIBW_ARCHS: "AMD64"
 
       - name: Upload Windows wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: windows_wheels
           path: ./wheelhouse/*.whl
@@ -110,7 +110,7 @@ jobs:
           CIBW_SKIP: "pp* *musllinux*"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: wheels
           path: ./wheelhouse/*.whl
@@ -133,7 +133,7 @@ jobs:
         run: pipx run build --sdist
 
       - name: Upload SDist
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: sdist
           path: ./dist/*.tar.gz
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index cb16f65847..db379b5b40 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -42,7 +42,7 @@ jobs:
           asv run -m "GitHubRunner" -s ${{ github.event.inputs.ncommits }} \
           ${{ github.event.inputs.commit_start }}..${{ github.event.inputs.commit_end }}
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: asv_new_results
           path: results

From 96b0886eba7c45e6721748444fdeaddb70b22f4a Mon Sep 17 00:00:00 2001
From: XuboGU <guxubo@sjtu.edu.cn>
Date: Tue, 19 Dec 2023 10:50:35 +0800
Subject: [PATCH 539/615] fix a value error in function
 `electrolyte_diffusivity_Ai2020`

---
 pybamm/input/parameters/lithium_ion/Ai2020.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py
index abae3087ea..31b9ab228d 100644
--- a/pybamm/input/parameters/lithium_ion/Ai2020.py
+++ b/pybamm/input/parameters/lithium_ion/Ai2020.py
@@ -451,7 +451,7 @@ def electrolyte_diffusivity_Ai2020(c_e, T):
         Solid diffusivity
     """
 
-    D_c_e = 10 ** (-8.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
+    D_c_e = 10 ** (-4.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
 
     return D_c_e
 

From b6042015716eb0130f1efd5cdb26176d0c68f8a0 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 14:34:53 +0530
Subject: [PATCH 540/615] chore: update pre-commit hooks (#3632)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.7 → v0.1.8](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.7...v0.1.8)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 41b19d7073..9b3a8f9d4b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.7"
+    rev: "v0.1.8"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From c2be73849c3a50ba319fb14fbe15063e0a2c63d6 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 19 Dec 2023 14:56:57 +0530
Subject: [PATCH 541/615] Unique names for artifacts

---
 .github/workflows/periodic_benchmarks.yml         | 2 +-
 .github/workflows/run_benchmarks_over_history.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 91947e8983..43d74d822d 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -50,7 +50,7 @@ jobs:
       - name: Upload results as artifact
         uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: results
 
   publish-results:
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index db379b5b40..e854a1fffc 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -44,7 +44,7 @@ jobs:
       - name: Upload results as artifact
         uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: results
 
   publish-results:

From bcd9798aee9e6220fcada2983c1384a688bc6a5e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 19 Dec 2023 14:57:29 +0530
Subject: [PATCH 542/615] Sync artifact names with the upload action

---
 .github/workflows/periodic_benchmarks.yml         | 2 +-
 .github/workflows/run_benchmarks_over_history.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 09a73cd7cb..c4878d200b 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -75,7 +75,7 @@ jobs:
       - name: Download results artifact
         uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: new_results
 
       - name: Copy new results and push to pybamm-bench repo
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index deefc534c5..767a837ac7 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -67,7 +67,7 @@ jobs:
       - name: Download results artifact
         uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: new_results
       - name: Copy new results and push to pybamm-bench repo
         env:

From f365ea557b1dc2b676c53d7e1f30ac66f0fd6ee3 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 19 Dec 2023 16:39:13 +0530
Subject: [PATCH 543/615] #3100 bump `vcpkg` baseline for `casadi` `3.6.4`

---
 vcpkg-configuration.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index 8ab4e738fc..f33d9205b0 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -13,7 +13,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/casadi-vcpkg-registry.git",
-      "baseline": "70f49f3c22fee4874fb8a36ef1a559f2c185ef1f",
+      "baseline": "baa26c2e629ea18fbb1aefa7d27c6612c4068fa7",
       "packages": ["casadi"]
     }
   ]

From 65a9d6edde2c3661f054d7f813e1333b8c555e12 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 19 Dec 2023 16:45:24 +0530
Subject: [PATCH 544/615] #3100 #3193 Add note for keeping `casadi` version in
 sync

---
 pyproject.toml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index e95017eb75..bd912ba23a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,6 +4,9 @@ requires = [
     "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.3; platform_system!='Windows'",
+    # Note: the version of CasADi as a build-time dependency should be matched
+    # cross platforms, so updates to its minimum version here should be accompanied
+    # by a version bump in https://github.com/pybamm-team/casadi-vcpkg-registry.
     "cmake; platform_system!='Windows'",
     ]
 build-backend = "setuptools.build_meta"

From 0080c1a8718858dbfafcd526bf1e56aa8e4a8e39 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 19:00:01 +0000
Subject: [PATCH 545/615] docs: update README.md [skip ci]

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8bad257378..d5050cfe55 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-71-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-72-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -277,6 +277,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     </tr>
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
     </tr>
   </tbody>
 </table>

From 8a3e9565da043a5c84b166bad58f59da01326b4a Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 19:00:02 +0000
Subject: [PATCH 546/615] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 7cc68678e0..1cc25d48f8 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -773,6 +773,16 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "XuboGU",
+      "name": "XuboGU",
+      "avatar_url": "https://avatars.githubusercontent.com/u/53944452?v=4",
+      "profile": "https://github.com/XuboGU",
+      "contributions": [
+        "code",
+        "bug"
+      ]
     }
   ],
   "contributorsPerLine": 7,

From f2938b3a6a6c481a08b825165565f8fd0cf40ce1 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 19 Dec 2023 14:29:32 -0500
Subject: [PATCH 547/615] Remove initial conditions

---
 pybamm/solvers/base_solver.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 36f101b1d0..76cf3e9367 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -698,7 +698,6 @@ def solve(
         model,
         t_eval=None,
         inputs=None,
-        initial_conditions=None,
         nproc=None,
         calculate_sensitivities=False,
     ):
@@ -717,14 +716,10 @@ def solve(
         inputs : dict or list, optional
             A dictionary or list of dictionaries describing any input parameters to
             pass to the model when solving
-        initial_conditions : :class:`pybamm.Symbol`, optional
-            Initial conditions to use when solving the model. If None (default),
-            `model.concatenated_initial_conditions` is used. Otherwise, must be a symbol
-            of size `len(model.rhs) + len(model.algebraic)`.
         nproc : int, optional
             Number of processes to use when solving for more than one set of input
             parameters. Defaults to value returned by "os.cpu_count()".
-        calculate_sensitivites : list of str or bool
+        calculate_sensitivities : list of str or bool
             If true, solver calculates sensitivities of all input parameters.
             If only a subset of sensitivities are required, can also pass a
             list of input parameter names

From 6a315fd28a08c64ba62a1f163277a7a6b654a600 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 20:53:40 +0000
Subject: [PATCH 548/615] style: pre-commit fixes

---
 .../examples/notebooks/solvers/speed-up-solver.ipynb      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 60335d9e02..418534401c 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -1120,11 +1120,11 @@
    ],
    "source": [
     "pybamm.settings.set_smoothing_parameters(10)\n",
-    "print(\"Soft minimum (softminus):\\t {!s}\".format(pybamm.minimum(x,y)))\n",
-    "print(\"Smooth heaviside (sigmoid):\\t {!s}\".format(x < y))\n",
-    "print(\"Smooth absolute value: \\t\\t {!s}\".format(abs(x)))\n",
+    "print(f\"Soft minimum (softminus):\\t {pybamm.minimum(x,y)!s}\")\n",
+    "print(f\"Smooth heaviside (sigmoid):\\t {x < y!s}\")\n",
+    "print(f\"Smooth absolute value: \\t\\t {abs(x)!s}\")\n",
     "pybamm.settings.min_max_mode = \"smooth\"\n",
-    "print(\"Smooth minimum:\\t\\t\\t {!s}\".format(pybamm.minimum(x,y)))\n",
+    "print(f\"Smooth minimum:\\t\\t\\t {pybamm.minimum(x,y)!s}\")\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")"
    ]
   },

From 0c6c5dc1a1eac095897cd94bdb2539c544920156 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 19 Dec 2023 17:16:20 -0500
Subject: [PATCH 549/615] Change function names

---
 .../notebooks/solvers/speed-up-solver.ipynb   | 19 +++++++++----------
 pybamm/expression_tree/binary_operators.py    | 10 +++++-----
 .../test_binary_operators.py                  | 12 ++++++------
 3 files changed, 20 insertions(+), 21 deletions(-)

diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 418534401c..80dc0ddb88 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -922,7 +922,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the minimum and maximum functions, an alternative smoothing functions (smooth_plus, smooth_minus) are provided.\n",
+    "For the minimum and maximum functions, an alternative smoothing functions (smooth_max, smooth_min) are provided.\n",
     "$$\n",
     " \\textrm{min}(x, y) = 0.5 * (\\sqrt((x - y)^2 + \\sigma) + (x + y))\n",
     " \\quad , \\quad\n",
@@ -962,7 +962,7 @@
     "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Smooth plus can be called explicitly\n",
-    "print(\"Smooth plus (k=100): \", pybamm.smooth_plus(x,y,100))\n",
+    "print(\"Smooth plus (k=100): \", pybamm.smooth_max(x,y,100))\n",
     "\n",
     "# Smooth plus and smooth minus will be used when the mode is set to \"smooth\"\n",
     "pybamm.settings.min_max_mode = \"smooth\"\n",
@@ -982,7 +982,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here is the plot of smooth_plus with different values of `k`"
+    "Here is the plot of smooth_max with different values of `k`"
    ]
   },
   {
@@ -1006,9 +1006,9 @@
     "\n",
     "fig, ax = plt.subplots(figsize=(10,5))\n",
     "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,5).evaluate(), \":\", lw=2, label=\"smooth_plus (k=5)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,10).evaluate(), \":\", lw=2, label=\"smooth_plus (k=10)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_plus(pts,1,100).evaluate(), \":\", lw=2, label=\"smooth_plus (k=100)\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,5).evaluate(), \":\", lw=2, label=\"smooth_max (k=5)\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,10).evaluate(), \":\", lw=2, label=\"smooth_max (k=10)\")\n",
+    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,100).evaluate(), \":\", lw=2, label=\"smooth_max (k=100)\")\n",
     "ax.legend();"
    ]
   },
@@ -1059,9 +1059,9 @@
     "\n",
     "model_smooth = pybamm.BaseModel()\n",
     "k = pybamm.InputParameter(\"k\")\n",
-    "model_smooth.rhs = {x: pybamm.smooth_plus(x, 1, k)}\n",
+    "model_smooth.rhs = {x: pybamm.smooth_max(x, 1, k)}\n",
     "model_smooth.initial_conditions = {x: 0.5}\n",
-    "model_smooth.variables = {\"x\": x, \"max(x,1)\": pybamm.smooth_plus(x, 1, k)}\n",
+    "model_smooth.variables = {\"x\": x, \"max(x,1)\": pybamm.smooth_max(x, 1, k)}\n",
     "\n",
     "\n",
     "# Exact solution\n",
@@ -1153,8 +1153,7 @@
       "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
       "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
       "[7] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
-      "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n",
-      "\n"
+      "[8] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n"
      ]
     }
    ],
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 6d19d33c6c..e8e1b421c2 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1246,7 +1246,7 @@ def minimum(left, right):
     if mode == "exact" or (left.is_constant() and right.is_constant()):
         out = Minimum(left, right)
     elif mode == "smooth":
-        out = pybamm.smooth_minus(left, right, k)
+        out = pybamm.Smooth_min(left, right, k)
     else:
         out = pybamm.softminus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1270,7 +1270,7 @@ def maximum(left, right):
     if mode == "exact" or (left.is_constant() and right.is_constant()):
         out = Maximum(left, right)
     elif mode == "smooth":
-        out = pybamm.smooth_plus(left, right, k)
+        out = pybamm.smooth_max(left, right, k)
     else:
         out = pybamm.softplus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1331,7 +1331,7 @@ def softplus(left, right, k):
     return pybamm.log(pybamm.exp(k * left) + pybamm.exp(k * right)) / k
 
 
-def smooth_minus(left, right, k):
+def Smooth_min(left, right, k):
     """
     Smooth_minus approximation to the minimum function. k is the smoothing parameter,
     set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
@@ -1340,9 +1340,9 @@ def smooth_minus(left, right, k):
     return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2
 
 
-def smooth_plus(left, right, k):
+def smooth_max(left, right, k):
     """
-    Smooth_plus approximation to the maximum function. k is the smoothing parameter,
+    Smooth_max approximation to the maximum function. k is the smoothing parameter,
     set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
     sigma = (1.0 / k) ** 2
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 4af0b7a102..9d61521b1f 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -431,20 +431,20 @@ def test_smooth_minus_plus(self):
         a = pybamm.Scalar(1)
         b = pybamm.StateVector(slice(0, 1))
 
-        minimum = pybamm.smooth_minus(a, b, 3000)
+        minimum = pybamm.Smooth_min(a, b, 3000)
         self.assertAlmostEqual(minimum.evaluate(y=np.array([2]))[0, 0], 1)
         self.assertAlmostEqual(minimum.evaluate(y=np.array([0]))[0, 0], 0)
 
-        maximum = pybamm.smooth_plus(a, b, 3000)
+        maximum = pybamm.smooth_max(a, b, 3000)
         self.assertAlmostEqual(maximum.evaluate(y=np.array([2]))[0, 0], 2)
         self.assertAlmostEqual(maximum.evaluate(y=np.array([0]))[0, 0], 1)
 
-        minimum = pybamm.smooth_minus(a, b, 1)
+        minimum = pybamm.Smooth_min(a, b, 1)
         self.assertEqual(
             str(minimum),
             "0.5 * (1.0 + y[0:1] - sqrt(1.0 + (1.0 - y[0:1]) ** 2.0))",
         )
-        maximum = pybamm.smooth_plus(a, b, 1)
+        maximum = pybamm.smooth_max(a, b, 1)
         self.assertEqual(
             str(maximum),
             "0.5 * (sqrt(1.0 + (1.0 - y[0:1]) ** 2.0) + 1.0 + y[0:1])",
@@ -454,8 +454,8 @@ def test_smooth_minus_plus(self):
         pybamm.settings.min_max_mode = "smooth"
 
         pybamm.settings.min_max_smoothing = 1
-        self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.smooth_minus(a, b, 1)))
-        self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.smooth_plus(a, b, 1)))
+        self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.Smooth_min(a, b, 1)))
+        self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.smooth_max(a, b, 1)))
 
         pybamm.settings.min_max_smoothing = 3000
         a = pybamm.Scalar(1)

From 6927fce36ca5df7ecfcd22fe292dc603bfed43e8 Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Tue, 19 Dec 2023 18:38:13 -0500
Subject: [PATCH 550/615] Fix typo

---
 pybamm/expression_tree/binary_operators.py               | 6 +++---
 tests/unit/test_expression_tree/test_binary_operators.py | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index e8e1b421c2..7348e08712 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1246,7 +1246,7 @@ def minimum(left, right):
     if mode == "exact" or (left.is_constant() and right.is_constant()):
         out = Minimum(left, right)
     elif mode == "smooth":
-        out = pybamm.Smooth_min(left, right, k)
+        out = pybamm.smooth_min(left, right, k)
     else:
         out = pybamm.softminus(left, right, k)
     return pybamm.simplify_if_constant(out)
@@ -1331,9 +1331,9 @@ def softplus(left, right, k):
     return pybamm.log(pybamm.exp(k * left) + pybamm.exp(k * right)) / k
 
 
-def Smooth_min(left, right, k):
+def smooth_min(left, right, k):
     """
-    Smooth_minus approximation to the minimum function. k is the smoothing parameter,
+    Smooth_min approximation to the minimum function. k is the smoothing parameter,
     set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
     sigma = (1.0 / k)**2
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index 9d61521b1f..ab582ade12 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -431,7 +431,7 @@ def test_smooth_minus_plus(self):
         a = pybamm.Scalar(1)
         b = pybamm.StateVector(slice(0, 1))
 
-        minimum = pybamm.Smooth_min(a, b, 3000)
+        minimum = pybamm.smooth_min(a, b, 3000)
         self.assertAlmostEqual(minimum.evaluate(y=np.array([2]))[0, 0], 1)
         self.assertAlmostEqual(minimum.evaluate(y=np.array([0]))[0, 0], 0)
 
@@ -439,7 +439,7 @@ def test_smooth_minus_plus(self):
         self.assertAlmostEqual(maximum.evaluate(y=np.array([2]))[0, 0], 2)
         self.assertAlmostEqual(maximum.evaluate(y=np.array([0]))[0, 0], 1)
 
-        minimum = pybamm.Smooth_min(a, b, 1)
+        minimum = pybamm.smooth_min(a, b, 1)
         self.assertEqual(
             str(minimum),
             "0.5 * (1.0 + y[0:1] - sqrt(1.0 + (1.0 - y[0:1]) ** 2.0))",
@@ -454,7 +454,7 @@ def test_smooth_minus_plus(self):
         pybamm.settings.min_max_mode = "smooth"
 
         pybamm.settings.min_max_smoothing = 1
-        self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.Smooth_min(a, b, 1)))
+        self.assertEqual(str(pybamm.minimum(a, b)), str(pybamm.smooth_min(a, b, 1)))
         self.assertEqual(str(pybamm.maximum(a, b)), str(pybamm.smooth_max(a, b, 1)))
 
         pybamm.settings.min_max_smoothing = 3000

From 366dcda4bb6a42f32dd72d30e9ace94be8f61b1e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 20 Dec 2023 14:38:38 +0530
Subject: [PATCH 551/615] #3639 Fix Python CI by pre-installing `setuptools`

---
 noxfile.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/noxfile.py b/noxfile.py
index 4805bff83c..e4bf157dc8 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -60,6 +60,10 @@ def run_coverage(session):
     """Run the coverage tests and generate an XML report."""
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     if sys.platform != "win32":
         if sys.version_info > (3, 12):
             session.install("-e", ".[all,jax]", silent=False)
@@ -79,6 +83,10 @@ def run_coverage(session):
 def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     if sys.platform != "win32":
         if sys.version_info > (3, 12):
             session.install("-e", ".[all,jax]", silent=False)
@@ -103,6 +111,10 @@ def run_doctests(session):
 def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     if sys.platform != "win32":
         if sys.version_info > (3, 12):
             session.install("-e", ".[all,jax]", silent=False)
@@ -120,6 +132,10 @@ def run_unit(session):
 def run_examples(session):
     """Run the examples tests for Jupyter notebooks."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     session.install("-e", ".[all,dev]", silent=False)
     notebooks_to_test = session.posargs if session.posargs else []
     session.run("pytest", "--nbmake", *notebooks_to_test, external=True)
@@ -129,6 +145,10 @@ def run_examples(session):
 def run_scripts(session):
     """Run the scripts tests for Python scripts."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     session.install("-e", ".[all]", silent=False)
     session.run("python", "run-tests.py", "--scripts")
 
@@ -140,6 +160,10 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.run(python, "-m", "pip", "install", "setuptools", external=True)
     if sys.platform == "linux":
         if sys.version_info > (3, 12):
             session.run(
@@ -188,6 +212,10 @@ def set_dev(session):
 def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     if sys.platform != "win32":
         if sys.version_info > (3, 12):
             session.install("-e", ".[all,jax]", silent=False)
@@ -206,6 +234,10 @@ def build_docs(session):
     """Build the documentation and load it in a browser tab, rebuilding on changes."""
     envbindir = session.bin
     session.install("-e", ".[all,docs]", silent=False)
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     session.chdir("docs")
     # Local development
     if session.interactive:

From 86e0e15391983940816228e770e3d8e1accc0e3e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Wed, 20 Dec 2023 14:59:29 +0530
Subject: [PATCH 552/615] #3639 fix some more doctests failures

---
 noxfile.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/noxfile.py b/noxfile.py
index e4bf157dc8..a670b48e17 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -103,6 +103,10 @@ def run_integration(session):
 @nox.session(name="doctests")
 def run_doctests(session):
     """Run the doctests and generate the output(s) in the docs/build/ directory."""
+    # Temporary fix for Python 3.12 CI. TODO: remove after
+    # https://bitbucket.org/pybtex-devs/pybtex/issues/169/replace-pkg_resources-with
+    # is fixed
+    session.install("setuptools", silent=False)
     session.install("-e", ".[all,docs]", silent=False)
     session.run("python", "run-tests.py", "--doctest")
 

From c8266ed8551b08b96094cd5806df414122598142 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Wed, 20 Dec 2023 21:05:20 +0530
Subject: [PATCH 553/615] Check for shell files directly

---
 .all-contributorsrc                           |  10 +
 .github/workflows/periodic_benchmarks.yml     |   8 +-
 .github/workflows/publish_pypi.yml            |  10 +-
 .../workflows/run_benchmarks_over_history.yml |   8 +-
 .github/workflows/run_periodic_tests.yml      |   8 +-
 .github/workflows/test_on_push.yml            |  46 +-
 .gitignore                                    |   1 +
 .pre-commit-config.yaml                       |   2 +-
 .readthedocs.yaml                             |   2 +-
 CHANGELOG.md                                  |   4 +
 README.md                                     |   3 +-
 .../parameterization/parameterization.ipynb   | 664 ++++++------------
 .../user_guide/installation/GNU-linux.rst     |  14 +-
 .../installation/install-from-source.rst      |   2 +-
 .../user_guide/installation/windows.rst       |   2 +-
 noxfile.py                                    |  62 +-
 pybamm/input/parameters/lithium_ion/Ai2020.py |   2 +-
 pybamm/install_odes.py                        |  17 +-
 pybamm/models/base_model.py                   |  66 +-
 pybamm/solvers/base_solver.py                 |   7 +-
 pyproject.toml                                |   3 +-
 setup.py                                      |  15 +-
 22 files changed, 391 insertions(+), 565 deletions(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 7cc68678e0..1cc25d48f8 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -773,6 +773,16 @@
       "contributions": [
         "infra"
       ]
+    },
+    {
+      "login": "XuboGU",
+      "name": "XuboGU",
+      "avatar_url": "https://avatars.githubusercontent.com/u/53944452?v=4",
+      "profile": "https://github.com/XuboGU",
+      "contributions": [
+        "code",
+        "bug"
+      ]
     }
   ],
   "contributorsPerLine": 7,
diff --git a/.github/workflows/periodic_benchmarks.yml b/.github/workflows/periodic_benchmarks.yml
index 9bd105ae92..c778c934bf 100644
--- a/.github/workflows/periodic_benchmarks.yml
+++ b/.github/workflows/periodic_benchmarks.yml
@@ -48,9 +48,9 @@ jobs:
           LD_LIBRARY_PATH: $HOME/.local/lib
 
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: results
 
   publish-results:
@@ -73,9 +73,9 @@ jobs:
           token: ${{ secrets.BENCH_PAT }}
 
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_periodic_results
           path: new_results
 
       - name: Copy new results and push to pybamm-bench repo
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index b003152802..90b67e9f87 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -57,7 +57,7 @@ jobs:
           CIBW_ARCHS: "AMD64"
 
       - name: Upload Windows wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: windows_wheels
           path: ./wheelhouse/*.whl
@@ -110,7 +110,7 @@ jobs:
           CIBW_SKIP: "pp* *musllinux*"
 
       - name: Upload wheels
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: wheels
           path: ./wheelhouse/*.whl
@@ -124,7 +124,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Install dependencies
         run: pip install --upgrade pip setuptools wheel
@@ -133,7 +133,7 @@ jobs:
         run: pipx run build --sdist
 
       - name: Upload SDist
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: sdist
           path: ./dist/*.tar.gz
@@ -146,7 +146,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Download all artifacts
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
 
       - name: Move all package files to files/
         run: |
diff --git a/.github/workflows/run_benchmarks_over_history.yml b/.github/workflows/run_benchmarks_over_history.yml
index cb16f65847..4f7302a4a5 100644
--- a/.github/workflows/run_benchmarks_over_history.yml
+++ b/.github/workflows/run_benchmarks_over_history.yml
@@ -42,9 +42,9 @@ jobs:
           asv run -m "GitHubRunner" -s ${{ github.event.inputs.ncommits }} \
           ${{ github.event.inputs.commit_start }}..${{ github.event.inputs.commit_end }}
       - name: Upload results as artifact
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: results
 
   publish-results:
@@ -65,9 +65,9 @@ jobs:
           repository: pybamm-team/pybamm-bench
           token: ${{ secrets.BENCH_PAT }}
       - name: Download results artifact
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
         with:
-          name: asv_new_results
+          name: asv_over_history_results
           path: new_results
       - name: Copy new results and push to pybamm-bench repo
         env:
diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 3f041de65d..1c402d312e 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -31,7 +31,7 @@ jobs:
       - name: Setup python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -46,7 +46,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
@@ -80,7 +80,7 @@ jobs:
         if: matrix.os != 'windows-latest'
         run: python -m nox -s pybamm-requires
 
-      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, and 3.10, and for macOS and Windows with all Python versions
+      - name: Run unit tests for GNU/Linux with Python 3.8, 3.9, 3.10, and 3.12; and for macOS and Windows with all Python versions
         if: (matrix.os == 'ubuntu-latest' && matrix.python-version != 3.11) || (matrix.os != 'ubuntu-latest')
         run: python -m nox -s unit
 
@@ -121,7 +121,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/test_on_push.yml b/.github/workflows/test_on_push.yml
index 77d28d8f88..53942acd31 100644
--- a/.github/workflows/test_on_push.yml
+++ b/.github/workflows/test_on_push.yml
@@ -23,7 +23,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
 
       - name: Check style
         run: |
@@ -38,8 +38,9 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         # We check coverage on Ubuntu with Python 3.11, so we skip unit tests for it here
+        # TODO: check coverage with Python 3.12 when [odes] supports it
         exclude:
           - os: ubuntu-latest
             python-version: "3.11"
@@ -116,6 +117,7 @@ jobs:
         run: python -m nox -s unit
 
   # Runs only on Ubuntu with Python 3.11
+  # TODO: check coverage with Python 3.12 when [odes] supports it
   check_coverage:
     needs: style
     runs-on: ubuntu-latest
@@ -180,7 +182,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
     name: Integration tests (${{ matrix.os }} / Python ${{ matrix.python-version }})
 
     steps:
@@ -253,14 +255,14 @@ jobs:
       - name: Run integration tests for ${{ matrix.os }} with Python ${{ matrix.python-version }}
         run: python -m nox -s integration
 
-# Runs only on Ubuntu with Python 3.11. Skips IDAKLU module compilation
+# Runs only on Ubuntu with Python 3.12. Skips IDAKLU module compilation
 # for speedups, which is already tested in other jobs.
   run_doctests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Doctests (ubuntu-latest / Python 3.11)
+    name: Doctests (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -270,39 +272,39 @@ jobs:
       - name: Install Linux system dependencies
         uses: awalsh128/cache-apt-pkgs-action@v1.3.1
         with:
-          packages: gfortran gcc graphviz pandoc
+          packages: graphviz pandoc
           execute_install_scripts: true
 
       # dot -c is for registering graphviz fonts and plugins
-      - name: Install OpenBLAS and TeXLive for Linux
+      - name: Install TeXLive for Linux
         run: |
           sudo apt-get update
           sudo dot -c
-          sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
+          sudo apt-get install texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
 
       - name: Install nox
         run: python -m pip install nox
 
-      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.11
+      - name: Install docs dependencies and run doctests for GNU/Linux with Python 3.12
         run: python -m nox -s doctests
 
-      - name: Check if the documentation can be built for GNU/Linux with Python 3.11
+      - name: Check if the documentation can be built for GNU/Linux with Python 3.12
         run: python -m nox -s docs
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_example_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example notebooks (ubuntu-latest / Python 3.11)
+    name: Example notebooks (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -322,11 +324,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
 
       - name: Install nox
@@ -348,16 +350,16 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example notebooks tests for GNU/Linux with Python 3.11
+      - name: Run example notebooks tests for GNU/Linux with Python 3.12
         run: python -m nox -s examples
 
-  # Runs only on Ubuntu with Python 3.11
+  # Runs only on Ubuntu with Python 3.12
   run_scripts_tests:
     needs: style
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
-    name: Example scripts (ubuntu-latest / Python 3.11)
+    name: Example scripts (ubuntu-latest / Python 3.12)
 
     steps:
       - name: Check out PyBaMM repository
@@ -377,11 +379,11 @@ jobs:
           sudo dot -c
           sudo apt-get install libopenblas-dev texlive-latex-extra dvipng
 
-      - name: Set up Python 3.11
+      - name: Set up Python 3.12
         id: setup-python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: 3.12
           cache: 'pip'
 
       - name: Install nox
@@ -403,5 +405,5 @@ jobs:
       - name: Install SuiteSparse and SUNDIALS on GNU/Linux
         run: python -m nox -s pybamm-requires
 
-      - name: Run example scripts tests for GNU/Linux with Python 3.11
+      - name: Run example scripts tests for GNU/Linux with Python 3.12
         run: python -m nox -s scripts
diff --git a/.gitignore b/.gitignore
index 3dfafa2a8f..46c7e02b9f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -113,6 +113,7 @@ scikits_odes_setup.log
 # test
 test.c
 test.json
+.pytest_cache/
 
 # tox
 .tox/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 41b19d7073..9b3a8f9d4b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.7"
+    rev: "v0.1.8"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f907ac23d5..fb84bce9cb 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -24,7 +24,7 @@ build:
     - "graphviz"
   os: ubuntu-22.04
   tools:
-    python: "3.11"
+    python: "3.12"
     # You can also specify other tool versions:
     # nodejs: "19"
     # rust: "1.64"
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2151b72324..a6b3f53e16 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -3,12 +3,16 @@
 ## Features
 
 - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531))
 - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
 - Added a new unary operator, `EvaluateAt`, that evaluates a spatial variable at a given position ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Added a method, `insert_reference_electrode`, to `pybamm.lithium_ion.BaseModel` that insert a reference electrode to measure the electrolyte potential at a given position in space and adds new variables that mimic a 3E cell setup. ([#3573](https://github.com/pybamm-team/PyBaMM/pull/3573))
 - Serialisation added so models can be written to/read from JSON ([#3397](https://github.com/pybamm-team/PyBaMM/pull/3397))
+- Added a `get_parameter_info` method for models and modified "print_parameter_info" functionality to extract all parameters and their type in a tabular and readable format ([#3584](https://github.com/pybamm-team/PyBaMM/pull/3584))
+- Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576))
+
 
 ## Bug fixes
 
diff --git a/README.md b/README.md
index 8bad257378..d5050cfe55 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-71-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-72-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -277,6 +277,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     </tr>
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
     </tr>
   </tbody>
 </table>
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index 3ec04e9654..50be5e8ed9 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -29,13 +29,18 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.822760400Z",
+     "start_time": "2023-12-10T12:14:16.732217100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zsh:1: no matches found: pybamm[plot,cite]\n",
+      "/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8)\r\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -60,7 +65,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.832156400Z",
+     "start_time": "2023-12-10T12:14:18.822760400Z"
+    }
+   },
    "outputs": [],
    "source": [
     "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
@@ -83,7 +93,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.841423200Z",
+     "start_time": "2023-12-10T12:14:18.827008900Z"
+    }
+   },
    "outputs": [],
    "source": [
     "model = pybamm.BaseModel()\n",
@@ -119,7 +134,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.843095800Z",
+     "start_time": "2023-12-10T12:14:18.841423200Z"
+    }
+   },
    "outputs": [],
    "source": [
     "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
@@ -145,16 +165,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.852037800Z",
+     "start_time": "2023-12-10T12:14:18.845139Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial concentration [mol.m-3] (Parameter)\n",
-      "Interfacial current density [A.m-2] (InputParameter)\n",
-      "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n",
-      "\n",
+      "| Parameter                           | Type of parameter                                          |\n",
+      "| =================================== | ========================================================== |\n",
+      "| Initial concentration [mol.m-3]     | Parameter                                                  |\n",
+      "| Interfacial current density [A.m-2] | InputParameter                                             |\n",
+      "| Diffusion coefficient [m2.s-1]      | FunctionParameter with inputs(s) 'Concentration [mol.m-3]' |\n",
       "Particle radius [m] (Parameter)\n"
      ]
     }
@@ -185,7 +211,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.854076300Z",
+     "start_time": "2023-12-10T12:14:18.849343800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def D_fun(c):\n",
@@ -210,19 +241,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.889781200Z",
+     "start_time": "2023-12-10T12:14:18.853120600Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 2.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 2.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 7,
      "metadata": {},
@@ -248,19 +276,16 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.890819200Z",
+     "start_time": "2023-12-10T12:14:18.859679800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration [mol.m-3]': 1.5,\n",
-       " 'Particle radius [m]': 2}"
-      ]
+      "text/plain": "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x7f48221f0220>,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration [mol.m-3]': 1.5,\n 'Particle radius [m]': 2}"
      },
      "execution_count": 8,
      "metadata": {},
@@ -294,16 +319,16 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.864911Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n",
-       " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n",
-       " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
-      ]
+      "text/plain": "[Parameter(-0x60748912cbf94f86, Initial concentration [mol.m-3], children=[], domains={}),\n InputParameter(0x650425db234f99f4, Interfacial current density [A.m-2], children=[], domains={}),\n FunctionParameter(-0x302b1e5afcbfd4d9, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
      },
      "execution_count": 9,
      "metadata": {},
@@ -326,7 +351,12 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.891821400Z",
+     "start_time": "2023-12-10T12:14:18.868969800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "param.process_model(model)\n",
@@ -344,7 +374,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:18.951625100Z",
+     "start_time": "2023-12-10T12:14:18.875173500Z"
+    }
+   },
    "outputs": [],
    "source": [
     "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
@@ -367,14 +402,17 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.168402100Z",
+     "start_time": "2023-12-10T12:14:18.890819200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1300x400 with 2 Axes>"
-      ]
+      "text/plain": "<Figure size 1300x400 with 2 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -424,7 +462,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.267027300Z",
+     "start_time": "2023-12-10T12:14:19.197131800Z"
+    }
+   },
    "outputs": [],
    "source": [
     "spm = pybamm.lithium_ion.SPM()"
@@ -437,59 +480,65 @@
    "source": [
     "## Finding the parameters in a model\n",
     "\n",
-    "We can print the `parameters` of a model by using the `get_parameters_info` function."
+    "We can print the `parameters` of a model by using the `print_parameter_info` function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.268048600Z",
+     "start_time": "2023-12-10T12:14:19.202421100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Lower voltage cut-off [V] (Parameter)\n",
-      "Faraday constant [C.mol-1] (Parameter)\n",
-      "Ideal gas constant [J.K-1.mol-1] (Parameter)\n",
-      "Electrode width [m] (Parameter)\n",
-      "Positive electrode thickness [m] (Parameter)\n",
-      "Separator Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-      "Upper voltage cut-off [V] (Parameter)\n",
-      "Number of electrodes connected in parallel to make a cell (Parameter)\n",
-      "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n",
-      "Nominal cell capacity [A.h] (Parameter)\n",
-      "Reference temperature [K] (Parameter)\n",
-      "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n",
-      "Separator thickness [m] (Parameter)\n",
-      "Initial concentration in electrolyte [mol.m-3] (Parameter)\n",
-      "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n",
-      "Electrode height [m] (Parameter)\n",
-      "Number of cells connected in series to make a battery (Parameter)\n",
-      "Negative electrode thickness [m] (Parameter)\n",
-      "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n",
-      "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n",
-      "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n",
-      "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n",
-      "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n",
-      "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n",
-      "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-      "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n",
-      "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-      "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n",
-      "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-      "\n"
+      "| Parameter                                                 | Type of parameter                                                                                                                                                                                           |\n",
+      "| ========================================================= | =========================================================================================================================================================================================================== |\n",
+      "| Positive electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode width [m]                                       | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrolyte)    | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Initial concentration in electrolyte [mol.m-3]            | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of cells connected in series to make a battery     | Parameter                                                                                                                                                                                                   |\n",
+      "| Lower voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Ideal gas constant [J.K-1.mol-1]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator Bruggeman coefficient (electrolyte)             | Parameter                                                                                                                                                                                                   |\n",
+      "| Upper voltage cut-off [V]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Positive electrode Bruggeman coefficient (electrode)      | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator thickness [m]                                   | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in negative electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Faraday constant [C.mol-1]                                | Parameter                                                                                                                                                                                                   |\n",
+      "| Reference temperature [K]                                 | Parameter                                                                                                                                                                                                   |\n",
+      "| Electrode height [m]                                      | Parameter                                                                                                                                                                                                   |\n",
+      "| Nominal cell capacity [A.h]                               | Parameter                                                                                                                                                                                                   |\n",
+      "| Maximum concentration in positive electrode [mol.m-3]     | Parameter                                                                                                                                                                                                   |\n",
+      "| Number of electrodes connected in parallel to make a cell | Parameter                                                                                                                                                                                                   |\n",
+      "| Negative electrode thickness [m]                          | Parameter                                                                                                                                                                                                   |\n",
+      "| Separator porosity                                        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode OCP [V]                                | FunctionParameter with inputs(s) 'Negative particle stoichiometry'                                                                                                                                          |\n",
+      "| Positive electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Positive particle radius [m]                              | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode OCP [V]                                | FunctionParameter with inputs(s) 'Positive particle stoichiometry'                                                                                                                                          |\n",
+      "| Negative electrode exchange-current density [A.m-2]       | FunctionParameter with inputs(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]' |\n",
+      "| Negative electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Current function [A]                                      | FunctionParameter with inputs(s) 'Time[s]'                                                                                                                                                                  |\n",
+      "| Initial concentration in positive electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Initial concentration in negative electrode [mol.m-3]     | FunctionParameter with inputs(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]'                                                                                                                 |\n",
+      "| Positive electrode OCP entropic change [V.K-1]            | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]'                                                                             |\n",
+      "| Positive electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Positive particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Negative electrode porosity                               | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Positive electrode active material volume fraction        | FunctionParameter with inputs(s) 'Through-cell distance (x) [m]'                                                                                                                                            |\n",
+      "| Negative electrode diffusivity [m2.s-1]                   | FunctionParameter with inputs(s) 'Negative particle stoichiometry', 'Temperature [K]'                                                                                                                       |\n",
+      "| Ambient temperature [K]                                   | FunctionParameter with inputs(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]'                                                                                  |\n"
      ]
     }
    ],
@@ -517,53 +566,16 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.401195400Z",
+     "start_time": "2023-12-10T12:14:19.232194200Z"
+    }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 0.0001,\n",
-       " 'Separator thickness [m]': 2.5e-05,\n",
-       " 'Positive electrode thickness [m]': 0.0001,\n",
-       " 'Electrode height [m]': 0.137,\n",
-       " 'Electrode width [m]': 0.207,\n",
-       " 'Nominal cell capacity [A.h]': 0.680616,\n",
-       " 'Current function [A]': 0.680616,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n",
-       " 'Negative electrode porosity': 0.3,\n",
-       " 'Negative electrode active material volume fraction': 0.6,\n",
-       " 'Negative particle radius [m]': 1e-05,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n",
-       " 'Positive electrode porosity': 0.3,\n",
-       " 'Positive electrode active material volume fraction': 0.5,\n",
-       " 'Positive particle radius [m]': 1e-05,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n",
-       " 'Separator porosity': 1.0,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 3.105,\n",
-       " 'Upper voltage cut-off [V]': 4.1,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 0.0001,\n 'Separator thickness [m]': 2.5e-05,\n 'Positive electrode thickness [m]': 0.0001,\n 'Electrode height [m]': 0.137,\n 'Electrode width [m]': 0.207,\n 'Nominal cell capacity [A.h]': 0.680616,\n 'Current function [A]': 0.680616,\n 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n 'Negative electrode porosity': 0.3,\n 'Negative electrode active material volume fraction': 0.6,\n 'Negative particle radius [m]': 1e-05,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n 'Positive electrode porosity': 0.3,\n 'Positive electrode active material volume fraction': 0.5,\n 'Positive particle radius [m]': 1e-05,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n 'Separator porosity': 1.0,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 3.105,\n 'Upper voltage cut-off [V]': 4.1,\n 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
      },
      "execution_count": 15,
      "metadata": {},
@@ -571,7 +583,7 @@
     }
    ],
    "source": [
-    "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}"
+    "{k: v for k,v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -585,251 +597,16 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.460184100Z",
+     "start_time": "2023-12-10T12:14:19.418960800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ambient temperature [K]': 298.15,\n",
-       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Electron charge [C]': 1.602176634e-19,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-       " 'Initial temperature [K]': 298.15,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n",
-       "       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n",
-       "       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n",
-       "       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n",
-       "       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n",
-       "       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n",
-       "       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n",
-       "       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n",
-       "       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n",
-       "       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n",
-       "       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n",
-       "       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n",
-       "       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n",
-       "       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n",
-       "       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n",
-       "       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n",
-       "       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n",
-       "       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n",
-       "       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n",
-       "       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n",
-       "       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n",
-       "       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n",
-       "       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n",
-       "       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n",
-       "       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n",
-       "       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n",
-       "       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n",
-       "       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n",
-       "       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n",
-       "       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n",
-       "       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n",
-       "       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n",
-       "       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n",
-       "       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n",
-       "       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n",
-       "       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n",
-       "       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n",
-       "       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n",
-       "       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n",
-       "       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n",
-       "       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n",
-       "       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n",
-       "       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n",
-       "       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n",
-       "       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n",
-       "       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n",
-       "       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n",
-       "       0.89774404, 0.9014468 , 1.        ])],\n",
-       "                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n",
-       "       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n",
-       "       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n",
-       "       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n",
-       "       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n",
-       "       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n",
-       "       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n",
-       "       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n",
-       "       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n",
-       "       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n",
-       "       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n",
-       "       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n",
-       "       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n",
-       "       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n",
-       "       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n",
-       "       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n",
-       "       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n",
-       "       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n",
-       "       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n",
-       "       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n",
-       "       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n",
-       "       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n",
-       "       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n",
-       "       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n",
-       "       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n",
-       "       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n",
-       "       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n",
-       "       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n",
-       "       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n",
-       "       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n",
-       "       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n",
-       "       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n",
-       "       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n",
-       "       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n",
-       "       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n",
-       "       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n",
-       "       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n",
-       "       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n",
-       "       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n",
-       "       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n",
-       "       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n",
-       "       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n",
-       "       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n",
-       "       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n",
-       "       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n",
-       "       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n",
-       "       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n",
-       "       0.08709427, 0.08503284, 0.07601531]))),\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode electrons in reaction': 1.0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x10403e4c0>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n",
-       "                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n",
-       "       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n",
-       "       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n",
-       "       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n",
-       "       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n",
-       "       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n",
-       "       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n",
-       "       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n",
-       "       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n",
-       "       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n",
-       "       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n",
-       "       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n",
-       "       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n",
-       "       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n",
-       "       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n",
-       "       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n",
-       "       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n",
-       "       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n",
-       "       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n",
-       "       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n",
-       "       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n",
-       "       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n",
-       "       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n",
-       "       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n",
-       "       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n",
-       "       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n",
-       "       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n",
-       "       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n",
-       "       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n",
-       "       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n",
-       "       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n",
-       "       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n",
-       "       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n",
-       "       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n",
-       "       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n",
-       "       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n",
-       "       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n",
-       "       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n",
-       "       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n",
-       "       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n",
-       "       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n",
-       "       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n",
-       "       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n",
-       "       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n",
-       "       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n",
-       "       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n",
-       "       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n",
-       "       0.90320364, 0.90592613, 1.        ])],\n",
-       "                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n",
-       "       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n",
-       "       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n",
-       "       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n",
-       "       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n",
-       "       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n",
-       "       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n",
-       "       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n",
-       "       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n",
-       "       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n",
-       "       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n",
-       "       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n",
-       "       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n",
-       "       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n",
-       "       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n",
-       "       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n",
-       "       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n",
-       "       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n",
-       "       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n",
-       "       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n",
-       "       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n",
-       "       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n",
-       "       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n",
-       "       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n",
-       "       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n",
-       "       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n",
-       "       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n",
-       "       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n",
-       "       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n",
-       "       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n",
-       "       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n",
-       "       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n",
-       "       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n",
-       "       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n",
-       "       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n",
-       "       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n",
-       "       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n",
-       "       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n",
-       "       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n",
-       "       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n",
-       "       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n",
-       "       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n",
-       "       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n",
-       "       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n",
-       "       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n",
-       "       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n",
-       "       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n",
-       "       3.5684922 , 3.5672133 , 3.52302167]))),\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode electrons in reaction': 1.0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x1411e7700>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Typical current [A]': 5.0,\n",
-       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-       " 'Upper voltage cut-off [V]': 4.4}"
-      ]
+      "text/plain": "{'Ambient temperature [K]': 298.15,\n 'Boltzmann constant [J.K-1]': 1.380649e-23,\n 'Current function [A]': 5.0,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Electron charge [C]': 1.602176634e-19,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Initial concentration in electrolyte [mol.m-3]': 1000,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n 'Initial temperature [K]': 298.15,\n 'Lower voltage cut-off [V]': 2.5,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n       0.89774404, 0.9014468 , 1.        ])],\n                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n       0.08709427, 0.08503284, 0.07601531]))),\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode electrons in reaction': 1.0,\n 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f380>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Negative particle radius [m]': 5.86e-06,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n       0.90320364, 0.90592613, 1.        ])],\n                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n       3.5684922 , 3.5672133 , 3.52302167]))),\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode electrons in reaction': 1.0,\n 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f481dd1f240>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Positive particle radius [m]': 5.22e-06,\n 'Reference temperature [K]': 298.15,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Separator porosity': 0.47,\n 'Separator thickness [m]': 1.2e-05,\n 'Typical current [A]': 5.0,\n 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n 'Upper voltage cut-off [V]': 4.4}"
      },
      "execution_count": 16,
      "metadata": {},
@@ -1405,52 +1182,16 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.608102800Z",
+     "start_time": "2023-12-10T12:14:19.450757200Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
-       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
-       " 'Negative electrode thickness [m]': 8.52e-05,\n",
-       " 'Separator thickness [m]': 1.2e-05,\n",
-       " 'Positive electrode thickness [m]': 7.56e-05,\n",
-       " 'Electrode height [m]': 0.065,\n",
-       " 'Electrode width [m]': 1.58,\n",
-       " 'Nominal cell capacity [A.h]': 5.0,\n",
-       " 'Current function [A]': 5.0,\n",
-       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Negative electrode porosity': 0.25,\n",
-       " 'Negative electrode active material volume fraction': 0.75,\n",
-       " 'Negative particle radius [m]': 5.86e-06,\n",
-       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n",
-       " 'Positive electrode porosity': 0.335,\n",
-       " 'Positive electrode active material volume fraction': 0.665,\n",
-       " 'Positive particle radius [m]': 5.22e-06,\n",
-       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
-       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-       " 'Separator porosity': 0.47,\n",
-       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-       " 'Reference temperature [K]': 298.15,\n",
-       " 'Ambient temperature [K]': 298.15,\n",
-       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-       " 'Number of cells connected in series to make a battery': 1.0,\n",
-       " 'Lower voltage cut-off [V]': 2.5,\n",
-       " 'Upper voltage cut-off [V]': 4.2,\n",
-       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
-      ]
+      "text/plain": "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n 'Faraday constant [C.mol-1]': 96485.33212,\n 'Negative electrode thickness [m]': 8.52e-05,\n 'Separator thickness [m]': 1.2e-05,\n 'Positive electrode thickness [m]': 7.56e-05,\n 'Electrode height [m]': 0.065,\n 'Electrode width [m]': 1.58,\n 'Nominal cell capacity [A.h]': 5.0,\n 'Current function [A]': 5.0,\n 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n 'Negative electrode porosity': 0.25,\n 'Negative electrode active material volume fraction': 0.75,\n 'Negative particle radius [m]': 5.86e-06,\n 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Negative electrode Bruggeman coefficient (electrode)': 0,\n 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n 'Positive electrode porosity': 0.335,\n 'Positive electrode active material volume fraction': 0.665,\n 'Positive particle radius [m]': 5.22e-06,\n 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n 'Positive electrode Bruggeman coefficient (electrode)': 0,\n 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n 'Separator porosity': 0.47,\n 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n 'Reference temperature [K]': 298.15,\n 'Ambient temperature [K]': 298.15,\n 'Number of electrodes connected in parallel to make a cell': 1.0,\n 'Number of cells connected in series to make a battery': 1.0,\n 'Lower voltage cut-off [V]': 2.5,\n 'Upper voltage cut-off [V]': 4.2,\n 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
      },
      "execution_count": 17,
      "metadata": {},
@@ -1459,7 +1200,7 @@
    ],
    "source": [
     "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
-    "{k: v for k,v in param_same.items() if k in spm._parameter_info}"
+    "{k: v for k,v in param_same.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -1489,7 +1230,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.611194400Z",
+     "start_time": "2023-12-10T12:14:19.609138100Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1500,9 +1246,7 @@
     },
     {
      "data": {
-      "text/plain": [
-       "4.0"
-      ]
+      "text/plain": "4.0"
      },
      "execution_count": 18,
      "metadata": {},
@@ -1528,13 +1272,16 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.641429500Z",
+     "start_time": "2023-12-10T12:14:19.616345800Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<function __main__.curren_func(time)>"
-      ]
+      "text/plain": "<function __main__.curren_func(time)>"
      },
      "execution_count": 19,
      "metadata": {},
@@ -1572,23 +1319,24 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.700673700Z",
+     "start_time": "2023-12-10T12:14:19.627406900Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 20,
      "metadata": {},
@@ -1616,23 +1364,24 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:19.785875100Z",
+     "start_time": "2023-12-10T12:14:19.699175500Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
+      "text/plain": "<Axes: >"
      },
      "execution_count": 21,
      "metadata": {},
@@ -1661,27 +1410,28 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.137222900Z",
+     "start_time": "2023-12-10T12:14:19.775429Z"
+    }
+   },
    "outputs": [
     {
      "data": {
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…",
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eea07489478640aab13bd2aab1fe5020",
        "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…"
-      ]
+       "version_minor": 0,
+       "model_id": "e3e2a10c3de140de8cc785ae5421b534"
+      }
      },
      "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x1412c2e50>"
-      ]
+      "text/plain": "<pybamm.plotting.quick_plot.QuickPlot at 0x7f481ffd4990>"
      },
      "execution_count": 22,
      "metadata": {},
@@ -1707,7 +1457,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-12-10T12:14:21.184199300Z",
+     "start_time": "2023-12-10T12:14:21.136110400Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1718,8 +1473,7 @@
       "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
       "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
       "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
-      "\n"
+      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n"
      ]
     }
    ],
diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index db3f6c3bb1..3bfd3b6de6 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -6,7 +6,7 @@ GNU-Linux & MacOS
 Prerequisites
 -------------
 
-To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed.
 
 .. tab:: Debian-based distributions (Debian, Ubuntu, Linux Mint)
 
@@ -50,7 +50,7 @@ User install
 
 We recommend to install PyBaMM within a virtual environment, in order
 not to alter any distribution Python files.
-First, make sure you are using Python 3.8, 3.9, 3.10, or 3.11.
+First, make sure you are using Python 3.8, 3.9, 3.10, 3.11, or 3.12.
 To create a virtual environment ``env`` within your current directory type:
 
 .. code:: bash
@@ -105,7 +105,15 @@ Optional - scikits.odes solver
 Users can install `scikits.odes <https://github.com/bmcage/odes>`__ in
 order to use the wrapped SUNDIALS ODE and DAE
 `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__.
-Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   Currently, only GNU/Linux and macOS are supported.
+
+.. note::
+
+   The ``scikits.odes`` solver is not supported on Python 3.12 yet, please refer to https://github.com/bmcage/odes/issues/162.
+   There is support for Python 3.8, 3.9, 3.10, and 3.11.
 
 .. tab:: GNU/Linux
 
diff --git a/docs/source/user_guide/installation/install-from-source.rst b/docs/source/user_guide/installation/install-from-source.rst
index 003c7f143a..26b6b5cf20 100644
--- a/docs/source/user_guide/installation/install-from-source.rst
+++ b/docs/source/user_guide/installation/install-from-source.rst
@@ -25,7 +25,7 @@ or download the source archive on the repository's homepage.
 
 To install PyBaMM, you will need:
 
-- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, and 3.11)
+- Python 3 (PyBaMM supports versions 3.8, 3.9, 3.10, 3.11, and 3.12)
 - The Python headers file for your current Python version.
 - A BLAS library (for instance `openblas <https://www.openblas.net/>`_).
 - A C compiler (ex: ``gcc``).
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 5ad77b6f7f..6e815b33c8 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -6,7 +6,7 @@ Windows
 Prerequisites
 -------------
 
-To use PyBaMM, you must have Python 3.8, 3.9, 3.10, or 3.11 installed.
+To use PyBaMM, you must have Python 3.8, 3.9, 3.10, 3.11, or 3.12 installed.
 
 To install Python 3 download the installation files from `Python’s
 website <https://www.python.org/downloads/windows/>`__. Make sure to
diff --git a/noxfile.py b/noxfile.py
index 297fc5b3d7..4805bff83c 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -22,7 +22,7 @@
 
 def set_environment_variables(env_dict, session):
     """
-    Sets environment variables for a nox session object.
+    Sets environment variables for a nox Session object.
 
     Parameters
     -----------
@@ -61,7 +61,10 @@ def run_coverage(session):
     set_environment_variables(PYBAMM_ENV, session=session)
     session.install("coverage", silent=False)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -77,7 +80,10 @@ def run_integration(session):
     """Run the integration tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -98,7 +104,10 @@ def run_unit(session):
     """Run the unit tests."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
-        session.install("-e", ".[all,jax,odes]", silent=False)
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
+            session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
             session.install("-e", ".[all]", silent=False)
@@ -131,27 +140,27 @@ def set_dev(session):
     session.install("virtualenv", "cmake")
     session.run("virtualenv", os.fsdecode(VENV_DIR), silent=True)
     python = os.fsdecode(VENV_DIR.joinpath("bin/python"))
-    session.run(
-        python,
-        "-m",
-        "pip",
-        "install",
-        "--upgrade",
-        "pip",
-        "setuptools",
-        "wheel",
-        external=True,
-    )
     if sys.platform == "linux":
-        session.run(
-            python,
-            "-m",
-            "pip",
-            "install",
-            "-e",
-            ".[all,dev,jax,odes]",
-            external=True,
-        )
+        if sys.version_info > (3, 12):
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax]",
+                external=True,
+            )
+        else:
+            session.run(
+                python,
+                "-m",
+                "pip",
+                "install",
+                "-e",
+                ".[all,dev,jax,odes]",
+                external=True,
+            )
     else:
         if sys.version_info < (3, 9):
             session.run(
@@ -159,6 +168,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev]",
                 external=True,
             )
@@ -168,6 +178,7 @@ def set_dev(session):
                 "-m",
                 "pip",
                 "install",
+                "-e",
                 ".[all,dev,jax]",
                 external=True,
             )
@@ -178,6 +189,9 @@ def run_tests(session):
     """Run the unit tests and integration tests sequentially."""
     set_environment_variables(PYBAMM_ENV, session=session)
     if sys.platform != "win32":
+        if sys.version_info > (3, 12):
+            session.install("-e", ".[all,jax]", silent=False)
+        else:
             session.install("-e", ".[all,jax,odes]", silent=False)
     else:
         if sys.version_info < (3, 9):
diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py
index abae3087ea..31b9ab228d 100644
--- a/pybamm/input/parameters/lithium_ion/Ai2020.py
+++ b/pybamm/input/parameters/lithium_ion/Ai2020.py
@@ -451,7 +451,7 @@ def electrolyte_diffusivity_Ai2020(c_e, T):
         Solid diffusivity
     """
 
-    D_c_e = 10 ** (-8.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
+    D_c_e = 10 ** (-4.43 - 54 / (T - 229 - 5e-3 * c_e) - 0.22e-3 * c_e)
 
     return D_c_e
 
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index f7b50150ca..798482c94f 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -80,20 +80,29 @@ def update_LD_LIBRARY_PATH(install_dir):
 
     export_statement = f"export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH"
 
+    home_dir = os.environ.get("HOME")
+    bashrc_path = os.path.join(home_dir, ".bashrc")
+    zshrc_path = os.path.join(home_dir, ".zshrc")
     venv_path = os.environ.get("VIRTUAL_ENV")
+
     if venv_path:
         script_path = os.path.join(venv_path, "bin/activate")
     else:
-        if 'BASH' in os.environ:
+        if os.path.exists(bashrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
-        if 'ZSH' in os.environ:
+        elif os.path.exists(zshrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
+        elif os.path.exists(bashrc_path) and os.path.exists(zshrc_path):
+            print("Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path")
+            script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
+        else:
+            print("Neither .bashrc nor .zshrc found in the home directory.")
 
     if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
-        if 'BASH' in os.environ:
+        if os.path.exists(bashrc_path):
             print("--> Not updating venv activate or .bashrc scripts")
-        if 'ZSH' in os.environ:
+        if os.path.exists(zshrc_path):
             print("--> Not updating venv activate or .zshrc scripts")
     else:
         with open(script_path, "a+") as fh:
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 8e4c80a625..3da6b53618 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -421,31 +421,63 @@ def input_parameters(self):
             self._input_parameters = self._find_symbols(pybamm.InputParameter)
         return self._input_parameters
 
-    def print_parameter_info(self):
-        """Returns parameters used in the model"""
-        self._parameter_info = ""
+    def get_parameter_info(self):
+        """
+        Extracts the parameter information and returns it as a dictionary.
+        To get a list of all parameter-like objects without extra information,
+        use :py:attr:`model.parameters`.
+        """
+        parameter_info = {}
         parameters = self._find_symbols(pybamm.Parameter)
         for param in parameters:
-            self._parameter_info += f"{param.name} (Parameter)\n"
+            parameter_info[param.name] = (param, "Parameter")
+
         input_parameters = self._find_symbols(pybamm.InputParameter)
         for input_param in input_parameters:
-            if input_param.domain == []:
-                self._parameter_info += f"{input_param.name} (InputParameter)\n"
+            if not input_param.domain:
+                parameter_info[input_param.name] = (input_param, "InputParameter")
             else:
-                self._parameter_info += (
-                    f"{input_param.name} (InputParameter in {input_param.domain})\n"
-                )
+                parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}")
+
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
-            # don't double count function parameters
-            if func_param.name not in self._parameter_info:
-                input_names = "'" + "', '".join(func_param.input_names) + "'"
-                self._parameter_info += (
-                    f"{func_param.name} (FunctionParameter "
-                    f"with input(s) {input_names})\n"
-                )
+            if func_param.name not in parameter_info:
+                input_names =  "', '".join(func_param.input_names)
+                parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'")
 
-        print(self._parameter_info)
+        return parameter_info
+
+    def print_parameter_info(self):
+        """Print parameter information in a formatted table from a dictionary of parameters"""
+        info = self.get_parameter_info()
+        max_param_name_length = 0
+        max_param_type_length = 0
+
+        for param, param_type in info.values():
+            param_name_length = len(getattr(param, 'name', str(param)))
+            param_type_length = len(param_type)
+            max_param_name_length = max(max_param_name_length, param_name_length)
+            max_param_type_length = max(max_param_type_length, param_type_length)
+
+        header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+
+        table = [header_format.format("Parameter", "Type of parameter"),
+                 header_format.format("=" * max_param_name_length, "=" * max_param_type_length)]
+
+        for param, param_type in info.values():
+            param_name = getattr(param, 'name', str(param))
+            param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
+            param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
+            max_lines = max(len(param_name_lines), len(param_type_lines))
+
+            for i in range(max_lines):
+                param_line = param_name_lines[i] if i < len(param_name_lines) else ""
+                type_line = param_type_lines[i] if i < len(param_type_lines) else ""
+                table.append(row_format.format(param_line, type_line))
+
+        for line in table:
+            print(line)
 
     def _find_symbols(self, typ):
         """Find all the instances of `typ` in the model"""
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 36f101b1d0..76cf3e9367 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -698,7 +698,6 @@ def solve(
         model,
         t_eval=None,
         inputs=None,
-        initial_conditions=None,
         nproc=None,
         calculate_sensitivities=False,
     ):
@@ -717,14 +716,10 @@ def solve(
         inputs : dict or list, optional
             A dictionary or list of dictionaries describing any input parameters to
             pass to the model when solving
-        initial_conditions : :class:`pybamm.Symbol`, optional
-            Initial conditions to use when solving the model. If None (default),
-            `model.concatenated_initial_conditions` is used. Otherwise, must be a symbol
-            of size `len(model.rhs) + len(model.algebraic)`.
         nproc : int, optional
             Number of processes to use when solving for more than one set of input
             parameters. Defaults to value returned by "os.cpu_count()".
-        calculate_sensitivites : list of str or bool
+        calculate_sensitivities : list of str or bool
             If true, solver calculates sensitivities of all input parameters.
             If only a subset of sensitivities are required, can also pass a
             list of input parameter names
diff --git a/pyproject.toml b/pyproject.toml
index 69fb9bfc1e..e95017eb75 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
 maintainers = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
-requires-python = ">=3.8, <3.12"
+requires-python = ">=3.8, <3.13"
 readme = {file = "README.md", content-type = "text/markdown"}
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -29,6 +29,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "Topic :: Scientific/Engineering",
 ]
 dependencies = [
diff --git a/setup.py b/setup.py
index 6b62aacc99..2c89603b74 100644
--- a/setup.py
+++ b/setup.py
@@ -6,14 +6,9 @@
 from platform import system
 import wheel.bdist_wheel as orig
 
-try:
-    from setuptools import setup, Extension
-    from setuptools.command.install import install
-    from setuptools.command.build_ext import build_ext
-except ImportError:
-    from distutils.core import setup
-    from distutils.command.install import install
-    from distutils.command.build_ext import build_ext
+from setuptools import setup, Extension
+from setuptools.command.install import install
+from setuptools.command.build_ext import build_ext
 
 
 default_lib_dir = (
@@ -71,9 +66,9 @@ def finalize_options(self):
             self.sundials_root = os.path.join(default_lib_dir)
 
     def get_build_directory(self):
-        # distutils outputs object files in directory self.build_temp
+        # setuptools outputs object files in directory self.build_temp
         # (typically build/temp.*). This is our CMake build directory.
-        # On Windows, distutils is too smart and appends "Release" or
+        # On Windows, setuptools is too smart and appends "Release" or
         # "Debug" to self.build_temp. So in this case we want the
         # build directory to be the parent directory.
         if system() == "Windows":

From 503f8960d50f3ebc92eb33717a545ca68bac99be Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Thu, 21 Dec 2023 11:16:40 +0530
Subject: [PATCH 554/615] Improved error handling by raising TypeError when a
 str is passed to Experiment instead of list of strings

---
 pybamm/experiment/experiment.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 9b02e3a20f..618610677d 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -42,13 +42,17 @@ class Experiment:
 
     def __init__(
         self,
-        operating_conditions,
-        period="1 minute",
-        temperature=None,
-        termination=None,
+        operating_conditions: list,
+        period: str = "1 minute",
+        temperature: float = None,
+        termination: list = None,
         drive_cycles=None,
         cccv_handling=None,
     ):
+        if not (isinstance(operating_conditions, list)):
+            raise TypeError("operating_conditions must be list of strings. For example:"
+                            f"\n\n [{operating_conditions}]")
+        
         if cccv_handling is not None:
             raise ValueError(
                 "cccv_handling has been deprecated, use "

From ae05b4f41f415c9add700a17d5e3016a0ef97412 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 21 Dec 2023 06:02:50 +0000
Subject: [PATCH 555/615] style: pre-commit fixes

---
 pybamm/experiment/experiment.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 618610677d..239f3cfbb4 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -52,7 +52,7 @@ def __init__(
         if not (isinstance(operating_conditions, list)):
             raise TypeError("operating_conditions must be list of strings. For example:"
                             f"\n\n [{operating_conditions}]")
-        
+
         if cccv_handling is not None:
             raise ValueError(
                 "cccv_handling has been deprecated, use "

From a04fce6b994c62fe2aeff8e1d8ce85ee6df38b48 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 21 Dec 2023 18:18:11 +0530
Subject: [PATCH 556/615] #3646 fix parallel level, set environment variable

---
 scripts/install_KLU_Sundials.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 34148920e6..0bfa02cefa 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -37,6 +37,9 @@ def download_extract_library(url, download_dir):
 except OSError:
     raise RuntimeError("CMake must be installed.")
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
 # Create download directory in PyBaMM dir
 pybamm_dir = os.path.split(os.path.abspath(os.path.dirname(__file__)))[0]
 download_dir = os.path.join(pybamm_dir, "install_KLU_Sundials")
@@ -78,6 +81,7 @@ def download_extract_library(url, download_dir):
 ]
 install_cmd = [
     "make",
+    f"-j{cpu_count()}",
     "install",
 ]
 print("-" * 10, "Building SuiteSparse", "-" * 40)
@@ -89,13 +93,13 @@ def download_extract_library(url, download_dir):
     # multiple paths at the time of wheel repair. Therefore, it should not be
     # built with an RPATH since it is copied to the install prefix.
     if libdir == "SuiteSparse_config":
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_BUILD_PARALLEL_LEVEL={cpu_count()}"
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
     else:
     # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
     # INSTALL RPATH in order to ensure that the dynamic libraries are found
     # at runtime just once. Otherwise, delocate complains about multiple
     # references to the SuiteSparse_config dynamic library (auditwheel does not).
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE -DCMAKE_BUILD_PARALLEL_LEVEL={cpu_count()}"
+        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 
@@ -168,5 +172,5 @@ def download_extract_library(url, download_dir):
 subprocess.run(["cmake", sundials_src, *cmake_args], cwd=build_dir, check=True)
 
 print("-" * 10, "Building the sundials", "-" * 40)
-make_cmd = ["make", "install"]
+make_cmd = ["make", f"-j{cpu_count()}", "install"]
 subprocess.run(make_cmd, cwd=build_dir, check=True)

From 3dc8c8c8a55ecc5a8c10ab34ca94342fdfb714cb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Thu, 21 Dec 2023 18:19:11 +0530
Subject: [PATCH 557/615] #3646 set parallel variable for `build_ext` (IDAKLU)

---
 setup.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/setup.py b/setup.py
index 2c89603b74..d0b96c7951 100644
--- a/setup.py
+++ b/setup.py
@@ -2,6 +2,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 from pathlib import Path
 from platform import system
 import wheel.bdist_wheel as orig
@@ -79,6 +80,9 @@ def run(self):
         if not self.extensions:
             return
 
+        # Build in parallel wherever possible
+        os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
         if system() == "Windows":
             use_python_casadi = False
         else:

From 139e34dfed33de1fb859d8572507d48ec76e14fb Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 22 Dec 2023 14:30:33 +0530
Subject: [PATCH 558/615] #3646 set parallel jobs for `pybamm_install_odes`

---
 pybamm/install_odes.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index a51c9eea76..fa2d3af289 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 
 from pybamm.util import root_dir
 
@@ -16,6 +17,8 @@
 except ModuleNotFoundError:
     NO_WGET = True
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
 
 def download_extract_library(url, directory):
     # Download and extract archive at url

From 971ef8a277a1f5f025a8c2b81235f7d21b0ed85d Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 22 Dec 2023 14:31:04 +0530
Subject: [PATCH 559/615] #3646 set parallel jobs for `install_sundials.sh`

for Linux wheel builds
---
 scripts/install_sundials.sh | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/scripts/install_sundials.sh b/scripts/install_sundials.sh
index 0fdd4cdc6a..020166a188 100644
--- a/scripts/install_sundials.sh
+++ b/scripts/install_sundials.sh
@@ -43,6 +43,10 @@ download $SUNDIALS_ROOT_ADDR $SUNDIALS_ARCHIVE_NAME
 extract $SUITESPARSE_ARCHIVE_NAME
 extract $SUNDIALS_ARCHIVE_NAME
 
+# Build in parallel wherever possible
+export MAKEFLAGS="-j$(nproc)"
+export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
+
 ### Compile and install SUITESPARSE ###
 # SuiteSparse is required to compile SUNDIALS's
 # KLU solver.

From 7e0cc70c0f9d2d54f16d7eb2c8a4102e56d3c5ae Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Fri, 22 Dec 2023 18:53:19 +0530
Subject: [PATCH 560/615] Add note to avoid installation failure

---
 .../user_guide/installation/GNU-linux.rst     | 20 +++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 3bfd3b6de6..4af1e58144 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -102,9 +102,7 @@ For an introduction to virtual environments, see
 Optional - scikits.odes solver
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Users can install `scikits.odes <https://github.com/bmcage/odes>`__ in
-order to use the wrapped SUNDIALS ODE and DAE
-`solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__.
+Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize the wrapped SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ in PyBaMM.
 
 .. note::
 
@@ -112,7 +110,7 @@ order to use the wrapped SUNDIALS ODE and DAE
 
 .. note::
 
-   The ``scikits.odes`` solver is not supported on Python 3.12 yet, please refer to https://github.com/bmcage/odes/issues/162.
+   The ``scikits.odes`` solver is not supported on Python 3.12 yet. Please refer to https://github.com/bmcage/odes/issues/162.
    There is support for Python 3.8, 3.9, 3.10, and 3.11.
 
 .. tab:: GNU/Linux
@@ -121,10 +119,10 @@ order to use the wrapped SUNDIALS ODE and DAE
 
    .. code:: bash
 
-	   apt install libopenblas-dev
-	   pybamm_install_odes
+      apt install libopenblas-dev
+      pybamm_install_odes
 
-   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 .. tab:: macOS
@@ -136,9 +134,15 @@ order to use the wrapped SUNDIALS ODE and DAE
       brew install openblas
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command is installed with PyBaMM. It automatically downloads and installs the SUNDIALS library on your
+   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
+   To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
+
+   .. code:: bash
+
+      export SUNDIALS_INST=$(brew --prefix sundials)
+
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~
 

From fade9004b5d8073cda4d18bae85662e270427c17 Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Fri, 22 Dec 2023 19:40:59 +0530
Subject: [PATCH 561/615] improved type hinting and used annotations

---
 pybamm/experiment/experiment.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 239f3cfbb4..a0404312ff 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -2,6 +2,7 @@
 # Experiment class
 #
 
+from __future__ import annotations
 import pybamm
 from pybamm.step._steps_util import (
     _convert_time_to_seconds,
@@ -42,16 +43,13 @@ class Experiment:
 
     def __init__(
         self,
-        operating_conditions: list,
+        operating_conditions: list[str],
         period: str = "1 minute",
-        temperature: float = None,
-        termination: list = None,
+        temperature: float | None = None,
+        termination: list[str] | None = None,
         drive_cycles=None,
         cccv_handling=None,
     ):
-        if not (isinstance(operating_conditions, list)):
-            raise TypeError("operating_conditions must be list of strings. For example:"
-                            f"\n\n [{operating_conditions}]")
 
         if cccv_handling is not None:
             raise ValueError(

From 6f50b40939375f2a1b5feae6db1039e2eef5b7df Mon Sep 17 00:00:00 2001
From: Abhishek <abhishek4080@gmail.com>
Date: Sat, 23 Dec 2023 10:30:16 +0530
Subject: [PATCH 562/615] Updated docstrings to be in line with the type hints
 added

---
 pybamm/experiment/experiment.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index a0404312ff..371bb670b2 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -22,8 +22,8 @@ class Experiment:
 
     Parameters
     ----------
-    operating_conditions : list
-        List of operating conditions
+    operating_conditions : list[str]
+        List of strings representing the operating conditions.
     period : string, optional
         Period (1/frequency) at which to record outputs. Default is 1 minute. Can be
         overwritten by individual operating conditions.
@@ -31,8 +31,8 @@ class Experiment:
         The ambient air temperature in degrees Celsius at which to run the experiment.
         Default is None whereby the ambient temperature is taken from the parameter set.
         This value is overwritten if the temperature is specified in a step.
-    termination : list, optional
-        List of conditions under which to terminate the experiment. Default is None.
+    termination : list[str], optional
+        List of strings representing the conditions to terminate the experiment. Default is None.
         This is different from the termination for individual steps. Termination for
         individual steps is specified in the step itself, and the simulation moves to
         the next step when the termination condition is met

From 15e059769d839d4f90bab2c4ef37884529e8c61b Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 16:13:30 +0530
Subject: [PATCH 563/615] Add note for path validation

---
 .../user_guide/installation/GNU-linux.rst      | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 4af1e58144..479cbeeecf 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -137,11 +137,23 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
    The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
-   To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
+To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
 
-   .. code:: bash
+.. code:: bash
+
+   export SUNDIALS_INST=$(brew --prefix sundials)
+
+Ensure that the path matches the installation location on your system. You can verify the installation location by running:
+
+.. code:: bash
+
+   brew info sundials
+
+Look for the installation path, and use that path to set the ``SUNDIALS_INST`` variable.
+
+Note: The location where Homebrew installs SUNDIALS might vary based on the system architecture (ARM or Intel). Adjust the path in the ``export SUNDIALS_INST`` command accordingly.
 
-      export SUNDIALS_INST=$(brew --prefix sundials)
+To avoid manual setup of path the ``pybamm_install_odes`` is recommended for a smoother installation process, as it takes care of automatically downloading and installing the SUNDIALS library on your system.
 
 Optional - JaxSolver
 ~~~~~~~~~~~~~~~~~~~~

From 62210175832968d55b82dac608d90e03a95ce359 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sat, 23 Dec 2023 18:44:14 +0530
Subject: [PATCH 564/615] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/run_periodic_tests.yml          | 1 -
 docs/source/user_guide/installation/GNU-linux.rst | 5 ++---
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 1c402d312e..f247176e40 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -178,7 +178,6 @@ jobs:
           NONINTERACTIVE: 1
         run: |
           brew analytics off
-          brew update
           brew install openblas
           brew reinstall gcc gfortran
 
diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/GNU-linux.rst
index 479cbeeecf..ee1d5b3f8a 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/GNU-linux.rst
@@ -102,7 +102,7 @@ For an introduction to virtual environments, see
 Optional - scikits.odes solver
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize the wrapped SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ in PyBaMM.
+Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize its interfaced SUNDIALS ODE and DAE `solvers <https://docs.pybamm.org/en/latest/source/api/solvers/scikits_solvers.html>`__ wrapped in PyBaMM.
 
 .. note::
 
@@ -122,7 +122,6 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
       apt install libopenblas-dev
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
 
 .. tab:: macOS
@@ -131,7 +130,7 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize t
 
    .. code:: bash
 
-      brew install openblas
+      brew install openblas gcc gfortran
       pybamm_install_odes
 
    The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your

From b8eaaabcfb783ae0de8185ffcea79118eb9f011a Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:07:28 +0530
Subject: [PATCH 565/615] Rename file & suggested fixes

---
 .../installation/{GNU-linux.rst => gnu-linux-mac.rst}         | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename docs/source/user_guide/installation/{GNU-linux.rst => gnu-linux-mac.rst} (92%)

diff --git a/docs/source/user_guide/installation/GNU-linux.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
similarity index 92%
rename from docs/source/user_guide/installation/GNU-linux.rst
rename to docs/source/user_guide/installation/gnu-linux-mac.rst
index ee1d5b3f8a..c8e26369b8 100644
--- a/docs/source/user_guide/installation/GNU-linux.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -133,8 +133,8 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
       brew install openblas gcc gfortran
       pybamm_install_odes
 
-   The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
-   system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
+The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
+system (under ``~/.local``), before installing `scikits.odes <https://scikits-odes.readthedocs.io/en/stable/installation.html>`__ . (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with `scikits.odes <https://scikits-odes.readthedocs.io/en/stable/installation.html>`__)
 
 To avoid installation failures when using ``pip install pybamm[odes]``, make sure to set the ``SUNDIALS_INST`` environment variable. If you have installed SUNDIALS using Homebrew, set the variable to the appropriate location. For example:
 

From 4e1dbec54fdab4f42da5b9a2fd648d47d30cbde6 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:11:11 +0530
Subject: [PATCH 566/615] Set `CMAKE_BUILD_PARALLEL_LEVEL`

---
 pybamm/install_odes.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 798482c94f..2e33cf0994 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -5,6 +5,7 @@
 import sys
 import logging
 import subprocess
+from multiprocessing import cpu_count
 
 from pybamm.util import root_dir
 
@@ -13,6 +14,9 @@
 
 SUNDIALS_VERSION = "6.5.0"
 
+# Build in parallel wherever possible
+os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
+
 try:
     # wget module is required to download SUNDIALS or SuiteSparse.
     import wget

From e770c9250914b3098e58504ec0882cac9dda547d Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sat, 23 Dec 2023 19:23:26 +0530
Subject: [PATCH 567/615] Fix broken doctree due to rename

---
 README.md                                             |  6 +++---
 docs/source/user_guide/installation/gnu-linux-mac.rst |  2 +-
 docs/source/user_guide/installation/index.rst         | 10 +++++-----
 docs/source/user_guide/installation/windows-wsl.rst   |  2 +-
 pybamm/expression_tree/operations/evaluate_python.py  |  4 ++--
 pybamm/solvers/jax_bdf_solver.py                      |  2 +-
 pybamm/solvers/jax_solver.py                          |  2 +-
 7 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index d5050cfe55..e176d4f54c 100644
--- a/README.md
+++ b/README.md
@@ -104,7 +104,7 @@ PyBaMM makes releases every four months and we use [CalVer](https://calver.org/)
 
 PyBaMM is available on GNU/Linux, MacOS and Windows.
 We strongly recommend to install PyBaMM within a python virtual environment, in order not to alter any distribution python files.
-For instructions on how to create a virtual environment for PyBaMM, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#user-install).
+For instructions on how to create a virtual environment for PyBaMM, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#user-install).
 
 ### Using pip
 
@@ -130,8 +130,8 @@ conda install -c conda-forge pybamm
 
 Following GNU/Linux and macOS solvers are optionally available:
 
-- [scikits.odes](https://scikits-odes.readthedocs.io/en/latest/)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver).
-- [jax](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver).
+- [scikits.odes](https://scikits-odes.readthedocs.io/en/latest/)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver).
+- [jax](https://jax.readthedocs.io/en/latest/notebooks/quickstart.html)-based solver, see [the documentation](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver).
 
 ## 📖 Citing PyBaMM
 
diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index c8e26369b8..0e765a37a3 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,4 +1,4 @@
-GNU-Linux & MacOS
+gnu-linux-mac & MacOS
 =================
 
 .. contents::
diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 65cbad33fb..5f1b5eaab8 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -47,8 +47,8 @@ Optional solvers
 
 Following GNU/Linux and macOS solvers are optionally available:
 
-*  `scikits.odes <https://scikits-odes.readthedocs.io/en/latest/>`_ -based solver, see `Optional - scikits.odes solver <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`_.
-*  `jax <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_ -based solver, see `Optional - JaxSolver <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver>`_.
+*  `scikits.odes <https://scikits-odes.readthedocs.io/en/latest/>`_ -based solver, see `Optional - scikits.odes solver <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`_.
+*  `jax <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_ -based solver, see `Optional - JaxSolver <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver>`_.
 
 Dependencies
 ------------
@@ -236,12 +236,12 @@ Installable with ``pip install "pybamm[odes]"``
 ================================================================================================================================ ================== ================== =============================
 Dependency                                                                                                                       Minimum Version    pip extra          Notes
 ================================================================================================================================ ================== ================== =============================
-`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
+`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
 ================================================================================================================================ ================== ================== =============================
 
 .. note::
 
-   Before running ``pip install "pybamm[odes]"``, make sure to install ``scikits.odes`` build-time requirements as described `here <https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-scikits-odes-solver>`_ .
+   Before running ``pip install "pybamm[odes]"``, make sure to install ``scikits.odes`` build-time requirements as described `here <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`_ .
 
 Full installation guide
 -----------------------
@@ -251,7 +251,7 @@ Installing a specific version? Installing from source? Check the advanced instal
 .. toctree::
    :maxdepth: 1
 
-   GNU-linux
+   gnu-linux-mac
    windows
    windows-wsl
    install-from-source
diff --git a/docs/source/user_guide/installation/windows-wsl.rst b/docs/source/user_guide/installation/windows-wsl.rst
index 6453c92211..6692789176 100644
--- a/docs/source/user_guide/installation/windows-wsl.rst
+++ b/docs/source/user_guide/installation/windows-wsl.rst
@@ -37,7 +37,7 @@ Get PyBaMM's Source Code
 5. Follow the Installation Steps
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Follow the `installation instructions for PyBaMM on Linux <GNU-linux.html>`__.
+Follow the `installation instructions for PyBaMM on Linux <gnu-linux-mac.html>`__.
 
 Using Visual Studio Code with the WSL
 ---------------------------------------
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index f65ecc7159..bd6dbd0165 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -42,7 +42,7 @@ class JaxCooMatrix:
     def __init__(self, row, col, data, shape):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         self.row = jax.numpy.array(row)
@@ -515,7 +515,7 @@ class EvaluatorJax:
     def __init__(self, symbol):
         if not pybamm.have_jax():  # pragma: no cover
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         constants, python_str = pybamm.to_python(symbol, debug=False, output_jax=True)
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 8f5b8ed817..9fb2b64f39 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -1005,7 +1005,7 @@ def jax_bdf_integrate(func, y0, t_eval, *args, rtol=1e-6, atol=1e-6, mass=None):
     """
     if not pybamm.have_jax():
         raise ModuleNotFoundError(
-            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+            "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
         )
 
     def _check_arg(arg):
diff --git a/pybamm/solvers/jax_solver.py b/pybamm/solvers/jax_solver.py
index 5e98c5bf07..6c89bed4dd 100644
--- a/pybamm/solvers/jax_solver.py
+++ b/pybamm/solvers/jax_solver.py
@@ -61,7 +61,7 @@ def __init__(
     ):
         if not pybamm.have_jax():
             raise ModuleNotFoundError(
-                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/GNU-linux.html#optional-jaxsolver"
+                "Jax or jaxlib is not installed, please see https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-jaxsolver"
             )
 
         # note: bdf solver itself calculates consistent initial conditions so can set

From 2a97c7c641cb3bdfb90dd09f6319894c4a6c9bb2 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:20:27 +0530
Subject: [PATCH 568/615] Fix configurations and adhere to scientific python
 guidelines

---
 .github/dependabot.yml | 5 +++++
 pyproject.toml         | 7 ++++++-
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 6fddca0d6e..c4fbd385c1 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -5,3 +5,8 @@ updates:
     directory: "/"
     schedule:
       interval: "weekly"
+    # group updates in a single PR
+    groups:
+      actions:
+        patterns:
+          - "*"
diff --git a/pyproject.toml b/pyproject.toml
index e95017eb75..2cff90cc62 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,6 @@
 [build-system]
 requires = [
     "setuptools>=64",
-    "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.3; platform_system!='Windows'",
     "cmake; platform_system!='Windows'",
@@ -226,6 +225,7 @@ ignore = [
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]
+minversion = "6"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [
@@ -234,11 +234,16 @@ required_plugins = [
 addopts = [
   "-nauto",
   "-v",
+  "-ra",
+  "--strict-config",
+  "--strict-markers",
 ]
 testpaths = [
   "docs/source/examples/",
 ]
 console_output_style = "progress"
+xfail_strict = true
+filterwarnings = ["error"]
 
 # Logging configuration
 log_cli = "true"

From a7045711866f1ddc3edb7c99ad650f0324efdbfe Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:28:41 +0530
Subject: [PATCH 569/615] List wheel as a build-dep, cleanup tabs v/s spaces

---
 pyproject.toml | 86 +++++++++++++++++++++++++++-----------------------
 1 file changed, 46 insertions(+), 40 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 2cff90cc62..aee9f677c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,10 +1,11 @@
 [build-system]
 requires = [
     "setuptools>=64",
+    "wheel",
     # On Windows, use the CasADi vcpkg registry and CMake bundled from MSVC
     "casadi>=3.6.3; platform_system!='Windows'",
     "cmake; platform_system!='Windows'",
-    ]
+]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -181,40 +182,40 @@ extend-exclude = ["__init__.py"]
 
 [tool.ruff.lint]
 extend-select = [
-  # "B",           # flake8-bugbear
-  # "I",           # isort
-  # "ARG",         # flake8-unused-arguments
-  # "C4",          # flake8-comprehensions
-  # "ICN",         # flake8-import-conventions
-  # "ISC",         # flake8-implicit-str-concat
-  # "PGH",         # pygrep-hooks
-  # "PIE",         # flake8-pie
-  # "PL",          # pylint
-  # "PT",          # flake8-pytest-style
-  # "PTH",         # flake8-use-pathlib
-  # "RET",         # flake8-return
-  "RUF",         # Ruff-specific
-  # "SIM",         # flake8-simplify
-  # "T20",         # flake8-print
-  "UP",          # pyupgrade
-  "YTT",         # flake8-2020
+    # "B",           # flake8-bugbear
+    # "I",           # isort
+    # "ARG",         # flake8-unused-arguments
+    # "C4",          # flake8-comprehensions
+    # "ICN",         # flake8-import-conventions
+    # "ISC",         # flake8-implicit-str-concat
+    # "PGH",         # pygrep-hooks
+    # "PIE",         # flake8-pie
+    # "PL",          # pylint
+    # "PT",          # flake8-pytest-style
+    # "PTH",         # flake8-use-pathlib
+    # "RET",         # flake8-return
+    "RUF",         # Ruff-specific
+    # "SIM",         # flake8-simplify
+    # "T20",         # flake8-print
+    "UP",          # pyupgrade
+    "YTT",         # flake8-2020
 ]
 ignore = [
-  "E741",        # Ambiguous variable name
-  "RUF012",      # Mutable class attributes should be annotated with `typing.ClassVar`
-  "SIM108",      # Use ternary operator
-  "ARG001",      # Unused function argument:
-  "ARG002",      # Unused method arguments
-  "PLR2004",     # Magic value used in comparison
-  "PLR0915",     # Too many statements
-  "PLR0913",     # Too many arguments
-  "PLR0912",     # Too many branches
-  "RET504",      # Unnecessary assignment
-  "RET505",      # Unnecessary `else`
-  "RET506",      # Unnecessary `elif`
-  "B018",        # Found useless expression
-  "RUF002",      # Docstring contains ambiguous
-  "UP007",       # For pyupgrade
+    "E741",        # Ambiguous variable name
+    "RUF012",      # Mutable class attributes should be annotated with `typing.ClassVar`
+    "SIM108",      # Use ternary operator
+    "ARG001",      # Unused function argument:
+    "ARG002",      # Unused method arguments
+    "PLR2004",     # Magic value used in comparison
+    "PLR0915",     # Too many statements
+    "PLR0913",     # Too many arguments
+    "PLR0912",     # Too many branches
+    "RET504",      # Unnecessary assignment
+    "RET505",      # Unnecessary `else`
+    "RET506",      # Unnecessary `elif`
+    "B018",        # Found useless expression
+    "RUF002",      # Docstring contains ambiguous
+    "UP007",       # For pyupgrade
 ]
 
 [tool.ruff.lint.per-file-ignores]
@@ -229,17 +230,17 @@ minversion = "6"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [
-  "pytest-xdist",
+    "pytest-xdist",
 ]
 addopts = [
-  "-nauto",
-  "-v",
-  "-ra",
-  "--strict-config",
-  "--strict-markers",
+    "-nauto",
+    "-v",
+    "-ra",
+    "--strict-config",
+    "--strict-markers",
 ]
 testpaths = [
-  "docs/source/examples/",
+    "docs/source/examples/",
 ]
 console_output_style = "progress"
 xfail_strict = true
@@ -254,3 +255,8 @@ log_date_format = "%Y-%m-%d %H:%M:%S"
 [tool.coverage.run]
 source = ["pybamm"]
 concurrency = ["multiprocessing"]
+
+[tool.repo-review]
+ignore = [
+    "PP003"  # list wheel as a build-dep
+]

From 758a0522af7e7c742b6378bba7131d5b0a9d3c6f Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:32:56 +0530
Subject: [PATCH 570/615] Move to ruff format

---
 .pre-commit-config.yaml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9b3a8f9d4b..5c63ec7b76 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,12 +9,14 @@ repos:
       - id: ruff
         args: [--fix, --show-fixes]
         types_or: [python, pyi, jupyter]
+    - id: ruff-format
+        types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/adamchainz/blacken-docs
     rev: "1.16.0"
     hooks:
        - id: blacken-docs
-         additional_dependencies: [black==22.12.0]
+         additional_dependencies: [black==23.*]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0

From c60fd5053b243d8abfc7dfcf4a365febcf816ec1 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:33:32 +0530
Subject: [PATCH 571/615] Fix config

---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5c63ec7b76..5cfbdf4710 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,7 +9,7 @@ repos:
       - id: ruff
         args: [--fix, --show-fixes]
         types_or: [python, pyi, jupyter]
-    - id: ruff-format
+      - id: ruff-format
         types_or: [python, pyi, jupyter]
 
   - repo: https://github.com/adamchainz/blacken-docs

From 20271815dfd2464b3e555a2ef18d17c51a44dede Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Sat, 23 Dec 2023 21:36:14 +0530
Subject: [PATCH 572/615] add try with finally block

---
 run-tests.py | 51 +++++++++++++++++++++++----------------------------
 1 file changed, 23 insertions(+), 28 deletions(-)

diff --git a/run-tests.py b/run-tests.py
index 25b1731b18..55a02a7297 100755
--- a/run-tests.py
+++ b/run-tests.py
@@ -64,36 +64,31 @@ def run_doc_tests():
     used).
     """
     print("Checking if docs can be built.")
-    p = subprocess.Popen(
-        [
-            "sphinx-build",
-            "-j",
-            "auto",
-            "-b",
-            "doctest",
-            "docs",
-            "docs/build/html",
-            "-W",
-            "--keep-going",
-        ]
-    )
     try:
-        ret = p.wait()
-    except KeyboardInterrupt:
+        subprocess.run(
+            [
+                "sphinx-build",
+                "-j",
+                "auto",
+                "-b",
+                "doctest",
+                "docs",
+                "docs/build/html",
+                "-W",
+                "--keep-going",
+            ],
+            check=True,
+        )
+    except subprocess.CalledProcessError as e:
+        print(f"FAILED with exit code {e.returncode}")
+        sys.exit(e.returncode)
+    finally:
+        # Regardless of whether the doctests pass or fail, attempt to remove the built files.
+        print("Deleting built files.")
         try:
-            p.terminate()
-        except OSError:
-            pass
-        p.wait()
-        print("")
-        sys.exit(1)
-    if ret != 0:
-        print("FAILED")
-        sys.exit(ret)
-    # delete the entire docs/source/build folder + files since it currently
-    # causes problems with nbsphinx in further docs or doctest builds
-    print("Deleting built files.")
-    shutil.rmtree("docs/build")
+            shutil.rmtree("docs/build")
+        except Exception as e:
+            print(f"Error deleting built files: {e}")
 
 
 def run_scripts(executable="python"):

From d2edbcaafa8970ac3a82212cf4d641c51a831ed2 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:40:37 +0530
Subject: [PATCH 573/615] Ignore F821 for lithium-plating.ipynb

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index e95017eb75..12134e966c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -223,6 +223,7 @@ ignore = [
 "docs/*" = ["T20"]
 "examples/*" = ["T20"]
 "**.ipynb" = ["E402", "E703"]
+"docs/source/examples/notebooks/models/lithium-plating.ipynb" = ["F821"]
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]

From 60ebd4148059a95428a496f4f55c1175ead362d3 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:40:45 +0530
Subject: [PATCH 574/615] Format everything

---
 .../examples/notebooks/batch_study.ipynb      |   54 +-
 .../examples/notebooks/change-settings.ipynb  |   23 +-
 .../creating_models/1-an-ode-model.ipynb      |    2 +-
 .../creating_models/2-a-pde-model.ipynb       |    4 +-
 .../3-negative-particle-problem.ipynb         |   12 +-
 ...paring-full-and-reduced-order-models.ipynb |   42 +-
 .../creating_models/5-half-cell-model.ipynb   |   67 +-
 .../6-a-simple-SEI-model.ipynb                |   56 +-
 .../expression_tree/broadcasts.ipynb          |    8 +-
 .../expression_tree/expression-tree.ipynb     |   21 +-
 .../tutorial-10-creating-a-model.ipynb        |   40 +-
 .../tutorial-11-creating-a-submodel.ipynb     |   59 +-
 .../tutorial-3-basic-plotting.ipynb           | 1811 +++++++++--------
 .../tutorial-4-setting-parameter-values.ipynb |   17 +-
 .../tutorial-5-run-experiments.ipynb          |   25 +-
 ...torial-6-managing-simulation-outputs.ipynb |   10 +-
 .../tutorial-7-model-options.ipynb            |    6 +-
 .../tutorial-9-changing-the-mesh.ipynb        |   12 +-
 .../initialize-model-with-solution.ipynb      |   14 +-
 ...DFN-with-particle-size-distributions.ipynb |  118 +-
 .../examples/notebooks/models/DFN.ipynb       |    6 +-
 .../examples/notebooks/models/MPM.ipynb       |  129 +-
 .../examples/notebooks/models/MSMR.ipynb      |   18 +-
 .../notebooks/models/SEI-on-cracks.ipynb      |   53 +-
 .../examples/notebooks/models/SPM.ipynb       |   94 +-
 .../examples/notebooks/models/SPMe.ipynb      |    2 +-
 ...ating_mechanical_models_Enertech_DFN.ipynb |  215 +-
 .../compare-comsol-discharge-curve.ipynb      |   10 +-
 .../notebooks/models/compare-ecker-data.ipynb |   29 +-
 .../models/compare-lithium-ion.ipynb          |   19 +-
 .../compare-particle-diffusion-models.ipynb   |   63 +-
 .../notebooks/models/composite_particle.ipynb |  218 +-
 .../models/coupled-degradation.ipynb          |   57 +-
 .../models/electrode-state-of-health.ipynb    |  102 +-
 .../examples/notebooks/models/half-cell.ipynb |   70 +-
 .../notebooks/models/jelly-roll-model.ipynb   |   85 +-
 .../examples/notebooks/models/lead-acid.ipynb |    5 +-
 .../notebooks/models/lithium-plating.ipynb    |  133 +-
 .../models/loss_of_active_materials.ipynb     |  141 +-
 .../notebooks/models/pouch-cell-model.ipynb   |   44 +-
 .../notebooks/models/rate-capability.ipynb    |   15 +-
 .../notebooks/models/saving_models.ipynb      |    1 +
 ...simulating-ORegan-2022-parameter-set.ipynb |    4 +-
 .../models/submodel_cracking_DFN_or_SPM.ipynb |   82 +-
 .../models/unsteady-heat-equation.ipynb       |   14 +-
 .../using-model-options_thermal-example.ipynb |   13 +-
 .../notebooks/models/using-submodels.ipynb    |   80 +-
 .../change-input-current.ipynb                |   11 +-
 .../parameterization/parameter-values.ipynb   |   15 +-
 .../parameterization/parameterization.ipynb   | 1122 +++++-----
 .../plotting/customize-quick-plot.ipynb       |   13 +-
 .../callbacks.ipynb                           |    6 +-
 .../custom-experiments.ipynb                  |    8 +-
 .../experiments-start-time.ipynb              |    4 +-
 .../rpt-experiment.ipynb                      |   95 +-
 .../simulating-long-experiments.ipynb         |   59 +-
 .../simulation-class.ipynb                    |    4 +-
 ...olution-data-and-processed-variables.ipynb |   47 +-
 .../notebooks/solvers/dae-solver.ipynb        |   40 +-
 .../notebooks/solvers/ode-solver.ipynb        |   24 +-
 .../notebooks/solvers/speed-up-solver.ipynb   |  198 +-
 .../spatial_methods/finite-volumes.ipynb      |  111 +-
 .../scripts/compare_comsol/discharge_curve.py |    2 +-
 examples/scripts/heat_equation.py             |    4 +-
 pybamm/discretisations/discretisation.py      |   12 +-
 pybamm/expression_tree/binary_operators.py    |    6 +-
 pybamm/expression_tree/concatenations.py      |    8 +-
 pybamm/expression_tree/functions.py           |    3 +-
 .../expression_tree/independent_variable.py   |    4 +-
 pybamm/expression_tree/interpolant.py         |    8 +-
 .../operations/convert_to_casadi.py           |    4 +-
 .../operations/evaluate_python.py             |   16 +-
 pybamm/expression_tree/parameter.py           |    8 +-
 .../printing/sympy_overrides.py               |    1 +
 pybamm/expression_tree/scalar.py              |    1 +
 pybamm/expression_tree/state_vector.py        |    7 +-
 pybamm/expression_tree/unary_operators.py     |    3 +-
 pybamm/expression_tree/variable.py            |    8 +-
 pybamm/expression_tree/vector.py              |    4 +-
 pybamm/input/parameters/lithium_ion/Ai2020.py |    8 +-
 .../input/parameters/lithium_ion/Chen2020.py  |    8 +-
 .../lithium_ion/Chen2020_composite.py         |   12 +-
 .../input/parameters/lithium_ion/Ecker2015.py |    8 +-
 .../Ecker2015_graphite_halfcell.py            |    4 +-
 .../parameters/lithium_ion/Marquis2019.py     |    8 +-
 .../parameters/lithium_ion/Mohtat2020.py      |   14 +-
 .../parameters/lithium_ion/NCA_Kim2011.py     |   16 +-
 .../input/parameters/lithium_ion/OKane2022.py |    8 +-
 .../OKane2022_graphite_SiOx_halfcell.py       |    4 +-
 .../input/parameters/lithium_ion/Prada2013.py |    8 +-
 .../parameters/lithium_ion/Ramadass2004.py    |    8 +-
 pybamm/install_odes.py                        |    4 +-
 pybamm/meshes/one_dimensional_submeshes.py    |    4 +-
 pybamm/models/base_model.py                   |   50 +-
 .../full_battery_models/base_battery_model.py |   46 +-
 .../through_cell/explicit_convection.py       |    6 +-
 .../interface/lithium_plating/base_plating.py |   12 +-
 .../interface/total_interfacial_current.py    |    2 +-
 .../submodels/particle/base_particle.py       |    4 +-
 .../submodels/particle/msmr_diffusion.py      |    4 +-
 .../particle/x_averaged_polynomial_profile.py |    3 +-
 .../particle_mechanics/base_mechanics.py      |    2 +-
 .../models/submodels/thermal/base_thermal.py  |    9 +-
 pybamm/parameters/bpx.py                      |   12 +-
 pybamm/parameters/parameter_values.py         |   27 +-
 pybamm/parameters/process_parameter_data.py   |    6 +-
 pybamm/plotting/plot2D.py                     |    2 +-
 pybamm/plotting/plot_voltage_components.py    |   11 +-
 pybamm/settings.py                            |   12 +-
 pybamm/simulation.py                          |    4 +-
 pybamm/solvers/base_solver.py                 |   36 +-
 pybamm/solvers/casadi_algebraic_solver.py     |    4 +-
 pybamm/solvers/casadi_solver.py               |    4 +-
 pybamm/solvers/idaklu_solver.py               |    8 +-
 pybamm/solvers/jax_bdf_solver.py              |   14 +-
 pybamm/solvers/scipy_solver.py                |    2 +-
 pybamm/spatial_methods/finite_volume.py       |    3 +-
 pybamm/spatial_methods/spectral_volume.py     |   12 +-
 pybamm/util.py                                |   10 +-
 scripts/fix_casadi_rpath_mac.py               |   14 +-
 scripts/update_version.py                     |    5 +-
 setup.py                                      |   45 +-
 .../base_lithium_ion_tests.py                 |   21 +-
 .../test_lithium_ion/test_mpm.py              |   10 +-
 .../unit/test_experiments/test_experiment.py  |    1 +
 .../test_binary_operators.py                  |    4 +-
 .../test_operations/test_evaluate_python.py   |   16 +-
 .../test_operations/test_jac.py               |    4 +-
 .../test_meshes/test_scikit_fem_submesh.py    |    2 +-
 .../test_base_battery_model.py                |   19 +-
 .../test_lithium_ion/test_mpm.py              |    4 +-
 .../test_lithium_ion/test_mpm_half_cell.py    |    4 +-
 tests/unit/test_parameters/test_bpx.py        |    9 +-
 .../test_parameter_sets/test_Ecker2015.py     |    2 +-
 .../test_Ecker2015_graphite_halfcell.py       |    2 +-
 .../test_size_distribution_parameters.py      |    1 -
 tests/unit/test_simulation.py                 |   10 +-
 tests/unit/test_solvers/test_idaklu_solver.py |   26 +-
 .../test_processed_variable_computed.py       |   12 +-
 tests/unit/test_solvers/test_solution.py      |   27 +-
 .../test_scikit_finite_element.py             |    8 +-
 tests/unit/test_util.py                       |   21 +-
 142 files changed, 3617 insertions(+), 3028 deletions(-)

diff --git a/docs/source/examples/notebooks/batch_study.ipynb b/docs/source/examples/notebooks/batch_study.ipynb
index f02d1154ad..0c0d216763 100644
--- a/docs/source/examples/notebooks/batch_study.ipynb
+++ b/docs/source/examples/notebooks/batch_study.ipynb
@@ -136,7 +136,9 @@
     "parameter_values = {\"Chen2020\": pybamm.ParameterValues(\"Chen2020\")}\n",
     "\n",
     "# creating a BatchStudy object and solving the simulation\n",
-    "batch_study = pybamm.BatchStudy(models=models, parameter_values=parameter_values, permutations=True)\n",
+    "batch_study = pybamm.BatchStudy(\n",
+    "    models=models, parameter_values=parameter_values, permutations=True\n",
+    ")\n",
     "batch_study.solve(t_eval=[0, 3600])\n",
     "batch_study.plot()"
    ]
@@ -195,13 +197,17 @@
     "# different values for \"Current function [A]\"\n",
     "current_values = [4.5, 4.75, 5]\n",
     "\n",
-    "# changing the value of \"Current function [A]\" in all the parameter values present in the \n",
+    "# changing the value of \"Current function [A]\" in all the parameter values present in the\n",
     "# parameter_values dictionary\n",
-    "for k, v, current_value in zip(parameter_values.keys(), parameter_values.values(), current_values):\n",
-    "    v[\"Current function [A]\"] = current_value \n",
+    "for k, v, current_value in zip(\n",
+    "    parameter_values.keys(), parameter_values.values(), current_values\n",
+    "):\n",
+    "    v[\"Current function [A]\"] = current_value\n",
     "\n",
     "# creating a BatchStudy object with permutations set to True to create a cartesian product\n",
-    "batch_study = pybamm.BatchStudy(models=model, parameter_values=parameter_values, permutations=True)\n",
+    "batch_study = pybamm.BatchStudy(\n",
+    "    models=model, parameter_values=parameter_values, permutations=True\n",
+    ")\n",
     "batch_study.solve(t_eval=[0, 3600])\n",
     "\n",
     "# generating the required labels and plotting\n",
@@ -474,19 +480,19 @@
     "# using the cccv experiment with 10 cycles\n",
     "cccv = pybamm.Experiment(\n",
     "    [\n",
-    "        (\"Discharge at C/10 for 10 hours or until 3.3 V\",\n",
-    "        \"Rest for 1 hour\",\n",
-    "        \"Charge at 1 A until 4.1 V\",\n",
-    "        \"Hold at 4.1 V until 50 mA\",\n",
-    "        \"Rest for 1 hour\")\n",
+    "        (\n",
+    "            \"Discharge at C/10 for 10 hours or until 3.3 V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at 1 A until 4.1 V\",\n",
+    "            \"Hold at 4.1 V until 50 mA\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "        )\n",
     "    ]\n",
     "    * 10,\n",
     ")\n",
     "\n",
     "# creating the experiment dict\n",
-    "experiment = {\n",
-    "    \"cccv\": cccv\n",
-    "}\n",
+    "experiment = {\"cccv\": cccv}\n",
     "\n",
     "# populating a dictionary with 3 same parameter values (Mohtat2020 chemistry)\n",
     "parameter_values = {\n",
@@ -499,23 +505,31 @@
     "inner_sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n",
     "\n",
     "# updating the value of \"Inner SEI open-circuit potential [V]\" in all the dictionary items\n",
-    "for k, v, inner_sei_oc_v in zip(parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values):\n",
+    "for k, v, inner_sei_oc_v in zip(\n",
+    "    parameter_values.keys(), parameter_values.values(), inner_sei_oc_v_values\n",
+    "):\n",
     "    v.update(\n",
-    "        {\n",
-    "            \"Inner SEI open-circuit potential [V]\": inner_sei_oc_v\n",
-    "        },\n",
+    "        {\"Inner SEI open-circuit potential [V]\": inner_sei_oc_v},\n",
     "    )\n",
     "\n",
     "# creating a Single Particle Model with \"electron-mitigation limited\" SEI\n",
     "model = {\"spm\": pybamm.lithium_ion.SPM({\"SEI\": \"electron-migration limited\"})}\n",
     "\n",
     "# creating a BatchStudy object with the given experimen, model and parameter_values\n",
-    "batch_study = pybamm.BatchStudy(models=model, experiments=experiment, parameter_values=parameter_values, permutations=True)\n",
+    "batch_study = pybamm.BatchStudy(\n",
+    "    models=model,\n",
+    "    experiments=experiment,\n",
+    "    parameter_values=parameter_values,\n",
+    "    permutations=True,\n",
+    ")\n",
     "\n",
-    "#solving and plotting the result\n",
+    "# solving and plotting the result\n",
     "batch_study.solve(initial_soc=1)\n",
     "\n",
-    "labels = [f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\" for inner_sei_oc_v in inner_sei_oc_v_values]\n",
+    "labels = [\n",
+    "    f\"Inner SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n",
+    "    for inner_sei_oc_v in inner_sei_oc_v_values\n",
+    "]\n",
     "batch_study.plot(labels=labels)"
    ]
   },
diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb
index c54da8754c..1a23da86fc 100644
--- a/docs/source/examples/notebooks/change-settings.ipynb
+++ b/docs/source/examples/notebooks/change-settings.ipynb
@@ -48,7 +48,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
     "\n",
     "# create the model\n",
     "model = pybamm.lithium_ion.SPM()\n",
@@ -378,9 +379,9 @@
     }
    ],
    "source": [
-    "format_str = '{:<75}  {:>20}'\n",
-    "print(format_str.format('PARAMETER', 'VALUE'))\n",
-    "print(\"-\"*97)\n",
+    "format_str = \"{:<75}  {:>20}\"\n",
+    "print(format_str.format(\"PARAMETER\", \"VALUE\"))\n",
+    "print(\"-\" * 97)\n",
     "for key, value in model.default_parameter_values.items():\n",
     "    try:\n",
     "        print(format_str.format(key, value))\n",
@@ -417,8 +418,8 @@
     "old_value = param[variable]\n",
     "param[variable] = 1.4\n",
     "new_value = param[variable]\n",
-    "print(variable,'was',old_value)\n",
-    "print(variable,'now is',param[variable])"
+    "print(variable, \"was\", old_value)\n",
+    "print(variable, \"now is\", param[variable])"
    ]
   },
   {
@@ -514,8 +515,8 @@
     }
    ],
    "source": [
-    "print(format_str.format('DOMAIN', 'DISCRETISED BY'))\n",
-    "print(\"-\"*82)\n",
+    "print(format_str.format(\"DOMAIN\", \"DISCRETISED BY\"))\n",
+    "print(\"-\" * 82)\n",
     "for key, value in model.default_spatial_methods.items():\n",
     "    print(format_str.format(key, value.__class__.__name__))"
    ]
@@ -553,7 +554,9 @@
    "outputs": [],
    "source": [
     "submesh_types = model.default_submesh_types\n",
-    "submesh_types[\"negative particle\"] = pybamm.MeshGenerator(pybamm.SpectralVolume1DSubMesh)"
+    "submesh_types[\"negative particle\"] = pybamm.MeshGenerator(\n",
+    "    pybamm.SpectralVolume1DSubMesh\n",
+    ")"
    ]
   },
   {
@@ -621,7 +624,7 @@
     }
    ],
    "source": [
-    "print('Default solver for SPM model:',type(model.default_solver).__name__)"
+    "print(\"Default solver for SPM model:\", type(model.default_solver).__name__)"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
index a610700887..dd97a4aad8 100644
--- a/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/1-an-ode-model.ipynb
@@ -117,7 +117,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.rhs = {x: dxdt, y: dydt} "
+    "model.rhs = {x: dxdt, y: dydt}"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
index c427fd4fe6..4926d19432 100644
--- a/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/2-a-pde-model.ipynb
@@ -180,7 +180,9 @@
     "r = pybamm.SpatialVariable(\n",
     "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
     ")\n",
-    "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}"
+    "geometry = {\n",
+    "    \"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}\n",
+    "}"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
index b04616c5f9..c14c1279e6 100644
--- a/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
+++ b/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb
@@ -106,14 +106,14 @@
     "# governing equations\n",
     "N = -D * pybamm.grad(c)  # flux\n",
     "dcdt = -pybamm.div(N)\n",
-    "model.rhs = {c: dcdt}  \n",
+    "model.rhs = {c: dcdt}\n",
     "\n",
-    "# boundary conditions \n",
+    "# boundary conditions\n",
     "lbc = pybamm.Scalar(0)\n",
     "rbc = -j / F / D\n",
     "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n",
     "\n",
-    "# initial conditions \n",
+    "# initial conditions\n",
     "model.initial_conditions = {c: c0}"
    ]
   },
@@ -193,7 +193,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
+    "r = pybamm.SpatialVariable(\n",
+    "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
+    ")\n",
     "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}"
    ]
   },
@@ -305,7 +307,7 @@
     "ax1.set_xlabel(\"Time [s]\")\n",
     "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n",
     "\n",
-    "r = mesh[\"negative particle\"].nodes # radial position\n",
+    "r = mesh[\"negative particle\"].nodes  # radial position\n",
     "time = 1000  # time in seconds\n",
     "ax2.plot(r * 1e6, c(t=time, r=r), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
diff --git a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
index 15d9e8e027..f180d16f0d 100644
--- a/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
+++ b/docs/source/examples/notebooks/creating_models/4-comparing-full-and-reduced-order-models.ipynb
@@ -144,11 +144,11 @@
     "# governing equations for full model\n",
     "N = -D * pybamm.grad(c)  # flux\n",
     "dcdt = -pybamm.div(N)\n",
-    "full_model.rhs = {c: dcdt} \n",
+    "full_model.rhs = {c: dcdt}\n",
     "\n",
     "# governing equations for reduced model\n",
     "dc_avdt = -3 * j / R / F\n",
-    "reduced_model.rhs = {c_av: dc_avdt} \n",
+    "reduced_model.rhs = {c_av: dc_avdt}\n",
     "\n",
     "# initial conditions (these are the same for both models)\n",
     "full_model.initial_conditions = {c: c0}\n",
@@ -157,7 +157,9 @@
     "# boundary conditions (only required for full model)\n",
     "lbc = pybamm.Scalar(0)\n",
     "rbc = -j / F / D\n",
-    "full_model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}"
+    "full_model.boundary_conditions = {\n",
+    "    c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}\n",
+    "}"
    ]
   },
   {
@@ -186,7 +188,7 @@
     "# reduced model\n",
     "reduced_model.variables = {\n",
     "    \"Concentration [mol.m-3]\": pybamm.PrimaryBroadcast(c_av, \"negative particle\"),\n",
-    "    \"Surface concentration [mol.m-3]\": c_av,  # in this model the surface concentration is just equal to the scalar average concentration \n",
+    "    \"Surface concentration [mol.m-3]\": c_av,  # in this model the surface concentration is just equal to the scalar average concentration\n",
     "    \"Average concentration [mol.m-3]\": c_av,\n",
     "}"
    ]
@@ -239,7 +241,9 @@
    "outputs": [],
    "source": [
     "# geometry\n",
-    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
+    "r = pybamm.SpatialVariable(\n",
+    "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
+    ")\n",
     "geometry = {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}\n",
     "param.process_geometry(geometry)\n",
     "\n",
@@ -273,7 +277,7 @@
     "\n",
     "# process models\n",
     "for model in models:\n",
-    "    disc.process_model(model);"
+    "    disc.process_model(model)"
    ]
   },
   {
@@ -346,38 +350,38 @@
     "c_av_reduced = solutions[1][\"Average concentration [mol.m-3]\"]\n",
     "\n",
     "# plot\n",
-    "r = mesh[\"negative particle\"].nodes # radial position\n",
+    "r = mesh[\"negative particle\"].nodes  # radial position\n",
     "\n",
     "\n",
     "def plot(t):\n",
     "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
-    "    \n",
+    "\n",
     "    # Plot concetration as a function of r\n",
-    "    ax1.plot(r * 1e6, c_full(t=t,r=r), label=\"Full Model\")\n",
-    "    ax1.plot(r * 1e6, c_reduced(t=t,r=r), label=\"Reduced Model\")    \n",
+    "    ax1.plot(r * 1e6, c_full(t=t, r=r), label=\"Full Model\")\n",
+    "    ax1.plot(r * 1e6, c_reduced(t=t, r=r), label=\"Reduced Model\")\n",
     "    ax1.set_xlabel(\"Particle radius [microns]\")\n",
     "    ax1.set_ylabel(\"Concentration [mol.m-3]\")\n",
     "    ax1.legend()\n",
-    "    \n",
+    "\n",
     "    # Plot average concentration over time\n",
     "    t_hour = np.linspace(0, 3600, 600)  # plot over full hour\n",
-    "    c_min = c_av_reduced(t=3600) * 0.98  # minimum axes limit \n",
-    "    c_max = param[\"Initial concentration [mol.m-3]\"] * 1.02   # maximum axes limit \n",
-    "    \n",
+    "    c_min = c_av_reduced(t=3600) * 0.98  # minimum axes limit\n",
+    "    c_max = param[\"Initial concentration [mol.m-3]\"] * 1.02  # maximum axes limit\n",
+    "\n",
     "    ax2.plot(t_hour, c_av_full(t=t_hour), label=\"Full Model\")\n",
-    "    ax2.plot(t_hour, c_av_reduced(t=t_hour), label=\"Reduced Model\") \n",
+    "    ax2.plot(t_hour, c_av_reduced(t=t_hour), label=\"Reduced Model\")\n",
     "    ax2.plot([t, t], [c_min, c_max], \"k--\")  # plot line to track time\n",
     "    ax2.set_xlabel(\"Time [s]\")\n",
-    "    ax2.set_ylabel(\"Average concentration [mol.m-3]\") \n",
+    "    ax2.set_ylabel(\"Average concentration [mol.m-3]\")\n",
     "    ax2.legend()\n",
     "\n",
     "    plt.tight_layout()\n",
     "    plt.show()\n",
-    "                   \n",
+    "\n",
     "\n",
     "import ipywidgets as widgets\n",
-    "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=3600,step=1,value=0));\n",
-    "   "
+    "\n",
+    "widgets.interact(plot, t=widgets.FloatSlider(min=0, max=3600, step=1, value=0));"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
index b28d6add1a..685ac0b8d1 100644
--- a/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/5-half-cell-model.ipynb
@@ -114,7 +114,9 @@
    "outputs": [],
    "source": [
     "phi_e_s = pybamm.Variable(\"Separator electrolyte potential [V]\", domain=\"separator\")\n",
-    "phi_e_p = pybamm.Variable(\"Positive electrolyte potential [V]\", domain=\"positive electrode\")"
+    "phi_e_p = pybamm.Variable(\n",
+    "    \"Positive electrolyte potential [V]\", domain=\"positive electrode\"\n",
+    ")"
    ]
   },
   {
@@ -231,7 +233,9 @@
    "source": [
     "c_surf = pybamm.surf(c)  # get the surface concentration\n",
     "inputs = {\"Positive particle surface concentration [mol.m-3]\": c_surf}\n",
-    "j0 =  pybamm.FunctionParameter(\"Positive electrode exchange-current density [A.m-2]\", inputs)\n",
+    "j0 = pybamm.FunctionParameter(\n",
+    "    \"Positive electrode exchange-current density [A.m-2]\", inputs\n",
+    ")\n",
     "U = pybamm.FunctionParameter(\"Positive electrode OCP [V]\", inputs)"
    ]
   },
@@ -252,7 +256,7 @@
    "outputs": [],
    "source": [
     "j_s = pybamm.PrimaryBroadcast(0, \"separator\")\n",
-    "j_p =  2 * j0 * pybamm.sinh((F / 2 / R / T) * (phi - phi_e_p - U))\n",
+    "j_p = 2 * j0 * pybamm.sinh((F / 2 / R / T) * (phi - phi_e_p - U))\n",
     "j = pybamm.concatenation(j_s, j_p)"
    ]
   },
@@ -272,7 +276,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# charge conservation equations \n",
+    "# charge conservation equations\n",
     "i = -sigma * pybamm.grad(phi)\n",
     "i_e = -kappa * pybamm.grad(phi_e)\n",
     "model.algebraic = {\n",
@@ -282,23 +286,25 @@
     "# particle equations (mass conservation)\n",
     "N = -D * pybamm.grad(c)  # flux\n",
     "dcdt = -pybamm.div(N)\n",
-    "model.rhs = {c: dcdt}  \n",
+    "model.rhs = {c: dcdt}\n",
     "\n",
-    "# boundary conditions \n",
+    "# boundary conditions\n",
     "model.boundary_conditions = {\n",
-    "    phi: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-I_app / A / sigma, \"Neumann\")},\n",
-    "    phi_e: {\"left\": (pybamm.Scalar(0), \"Dirichlet\"), \"right\": (pybamm.Scalar(0), \"Neumann\")},\n",
-    "    c: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-j_p / F / D, \"Neumann\")}\n",
+    "    phi: {\n",
+    "        \"left\": (pybamm.Scalar(0), \"Neumann\"),\n",
+    "        \"right\": (-I_app / A / sigma, \"Neumann\"),\n",
+    "    },\n",
+    "    phi_e: {\n",
+    "        \"left\": (pybamm.Scalar(0), \"Dirichlet\"),\n",
+    "        \"right\": (pybamm.Scalar(0), \"Neumann\"),\n",
+    "    },\n",
+    "    c: {\"left\": (pybamm.Scalar(0), \"Neumann\"), \"right\": (-j_p / F / D, \"Neumann\")},\n",
     "}\n",
     "\n",
     "# initial conditions\n",
     "inputs = {\"Initial concentration [mol.m-3]\": c0}\n",
     "U_init = pybamm.FunctionParameter(\"Positive electrode OCP [V]\", inputs)\n",
-    "model.initial_conditions = {\n",
-    "    phi: U_init,\n",
-    "    phi_e: 0,\n",
-    "    c: c0\n",
-    "}"
+    "model.initial_conditions = {phi: U_init, phi_e: 0, c: c0}"
    ]
   },
   {
@@ -322,9 +328,11 @@
     "    \"Electrolyte potential [V]\": phi_e,\n",
     "    \"Positive particle concentration [mol.m-3]\": c,\n",
     "    \"Positive particle surface concentration [mol.m-3]\": c_surf,\n",
-    "    \"Average positive particle surface concentration [mol.m-3]\": pybamm.x_average(c_surf),\n",
+    "    \"Average positive particle surface concentration [mol.m-3]\": pybamm.x_average(\n",
+    "        c_surf\n",
+    "    ),\n",
     "    \"Positive electrode interfacial current density [A.m-2]\": j_p,\n",
-    "    \"Positive electrode OCP [V]\":pybamm.boundary_value(U, \"right\"),\n",
+    "    \"Positive electrode OCP [V]\": pybamm.boundary_value(U, \"right\"),\n",
     "    \"Voltage [V]\": pybamm.boundary_value(phi, \"right\"),\n",
     "}"
    ]
@@ -356,20 +364,20 @@
    "source": [
     "from pybamm import tanh\n",
     "\n",
-    "# both functions will depend on the maximum concentration \n",
+    "# both functions will depend on the maximum concentration\n",
     "c_max = pybamm.Parameter(\"Maximum concentration in positive electrode [mol.m-3]\")\n",
     "\n",
     "\n",
     "def exchange_current_density(c_surf):\n",
-    "    k = 6 * 10 ** (-7)   # reaction rate [(A/m2)(m3/mol)**1.5]\n",
+    "    k = 6 * 10 ** (-7)  # reaction rate [(A/m2)(m3/mol)**1.5]\n",
     "    c_e = 1000  # (constant) electrolyte concentration [mol.m-3]\n",
-    "    return k * c_e** 0.5 * c_surf ** 0.5 * (c_max - c_surf) ** 0.5\n",
+    "    return k * c_e**0.5 * c_surf**0.5 * (c_max - c_surf) ** 0.5\n",
     "\n",
     "\n",
     "def open_circuit_potential(c_surf):\n",
     "    stretch = 1.062\n",
     "    sto = stretch * c_surf / c_max\n",
-    "    \n",
+    "\n",
     "    u_eq = (\n",
     "        2.16216\n",
     "        + 0.07645 * tanh(30.834 - 54.4806 * sto)\n",
@@ -400,7 +408,7 @@
    "source": [
     "param = pybamm.ParameterValues(\n",
     "    {\n",
-    "        \"Surface area per unit volume [m-1]\":0.15e6,\n",
+    "        \"Surface area per unit volume [m-1]\": 0.15e6,\n",
     "        \"Positive particle radius [m]\": 10e-6,\n",
     "        \"Separator thickness [m]\": 25e-6,\n",
     "        \"Positive electrode thickness [m]\": 100e-6,\n",
@@ -437,18 +445,19 @@
    "outputs": [],
    "source": [
     "r = pybamm.SpatialVariable(\n",
-    "    \"r\", \n",
-    "    domain=[\"positive particle\"],     \n",
-    "    auxiliary_domains={\n",
-    "        \"secondary\": \"positive electrode\"\n",
-    "    },\n",
-    "    coord_sys=\"spherical polar\")\n",
+    "    \"r\",\n",
+    "    domain=[\"positive particle\"],\n",
+    "    auxiliary_domains={\"secondary\": \"positive electrode\"},\n",
+    "    coord_sys=\"spherical polar\",\n",
+    ")\n",
     "x_s = pybamm.SpatialVariable(\"x_s\", domain=[\"separator\"], coord_sys=\"cartesian\")\n",
-    "x_p = pybamm.SpatialVariable(\"x_p\", domain=[\"positive electrode\"], coord_sys=\"cartesian\")\n",
+    "x_p = pybamm.SpatialVariable(\n",
+    "    \"x_p\", domain=[\"positive electrode\"], coord_sys=\"cartesian\"\n",
+    ")\n",
     "\n",
     "\n",
     "geometry = {\n",
-    "    \"separator\": {x_s: {\"min\": -L_s, \"max\": 0}},        \n",
+    "    \"separator\": {x_s: {\"min\": -L_s, \"max\": 0}},\n",
     "    \"positive electrode\": {x_p: {\"min\": 0, \"max\": L_p}},\n",
     "    \"positive particle\": {r: {\"min\": 0, \"max\": R_p}},\n",
     "}"
diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
index e383498065..fbbe0cac5e 100644
--- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
@@ -127,7 +127,8 @@
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -201,7 +202,9 @@
     "\n",
     "\n",
     "def D(cc):\n",
-    "    return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})"
+    "    return pybamm.FunctionParameter(\n",
+    "        \"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc}\n",
+    "    )"
    ]
   },
   {
@@ -249,8 +252,8 @@
     "R = k * pybamm.BoundaryValue(c, \"left\")\n",
     "\n",
     "# solvent concentration equation\n",
-    "N = - 1/L * D(c) * pybamm.grad(c)\n",
-    "dcdt = (V_hat * R) / L * pybamm.inner(xi, pybamm.grad(c)) - 1/L * pybamm.div(N)\n",
+    "N = -1 / L * D(c) * pybamm.grad(c)\n",
+    "dcdt = (V_hat * R) / L * pybamm.inner(xi, pybamm.grad(c)) - 1 / L * pybamm.div(N)\n",
     "\n",
     "# SEI thickness equation\n",
     "dLdt = V_hat * R"
@@ -305,7 +308,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "D_left = pybamm.BoundaryValue(D(c), \"left\") # pybamm requires BoundaryValue(D(c)) and not D(BoundaryValue(c)) \n",
+    "D_left = pybamm.BoundaryValue(\n",
+    "    D(c), \"left\"\n",
+    ")  # pybamm requires BoundaryValue(D(c)) and not D(BoundaryValue(c))\n",
     "grad_c_left = R * L / D_left"
    ]
   },
@@ -351,7 +356,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.boundary_conditions = {c: {\"left\": (grad_c_left, \"Neumann\"), \"right\": (c_right, \"Dirichlet\")}}"
+    "model.boundary_conditions = {\n",
+    "    c: {\"left\": (grad_c_left, \"Neumann\"), \"right\": (c_right, \"Dirichlet\")}\n",
+    "}"
    ]
   },
   {
@@ -437,7 +444,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.variables = {\"SEI thickness [m]\": L, \"SEI growth rate [m]\": dLdt, \"Solvent concentration [mol.m-3]\": c}"
+    "model.variables = {\n",
+    "    \"SEI thickness [m]\": L,\n",
+    "    \"SEI growth rate [m]\": dLdt,\n",
+    "    \"Solvent concentration [mol.m-3]\": c,\n",
+    "}"
    ]
   },
   {
@@ -488,7 +499,7 @@
     "\n",
     "\n",
     "def Diffusivity(cc):\n",
-    "    return cc * 10**(-12)\n",
+    "    return cc * 10 ** (-12)\n",
     "\n",
     "\n",
     "# parameter values (not physically based, for example only!)\n",
@@ -510,7 +521,7 @@
     "submesh_types = {\"SEI layer\": pybamm.Uniform1DSubMesh}\n",
     "var_pts = {xi: 100}\n",
     "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
-    "                   \n",
+    "\n",
     "spatial_methods = {\"SEI layer\": pybamm.FiniteVolume()}\n",
     "disc = pybamm.Discretisation(mesh, spatial_methods)\n",
     "disc.process_model(model)"
@@ -524,7 +535,7 @@
    "source": [
     "# solve\n",
     "solver = pybamm.ScipySolver()\n",
-    "t = [0, 100] # solve for 100s\n",
+    "t = [0, 100]  # solve for 100s\n",
     "solution = solver.solve(model, t)\n",
     "\n",
     "# post-process output variables\n",
@@ -566,28 +577,31 @@
     "# plot SEI thickness in microns as a function of t in microseconds\n",
     "# and concentration in mol/m3 as a function of x in microns\n",
     "L_0_eval = param.evaluate(L_0)\n",
-    "xi = np.linspace(0, 1, 100) # dimensionless space\n",
+    "xi = np.linspace(0, 1, 100)  # dimensionless space\n",
     "\n",
     "\n",
     "def plot(t):\n",
-    "    _, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n",
+    "    _, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
     "    ax1.plot(solution.t, L_out(solution.t) * 1e6)\n",
-    "    ax1.plot(t, L_out(t) * 1e6, 'r.')\n",
-    "    ax1.set_ylabel(r'SEI thickness [$\\mu$m]')\n",
-    "    ax1.set_xlabel(r't [s]')  \n",
-    "    \n",
+    "    ax1.plot(t, L_out(t) * 1e6, \"r.\")\n",
+    "    ax1.set_ylabel(r\"SEI thickness [$\\mu$m]\")\n",
+    "    ax1.set_xlabel(r\"t [s]\")\n",
+    "\n",
     "    ax2.plot(xi * L_out(t) * 1e6, c_out(t, xi))\n",
     "    ax2.set_ylim(0, 1.1)\n",
-    "    ax2.set_xlim(0, L_out(solution.t[-1]) * 1e6)    \n",
-    "    ax2.set_ylabel('Solvent concentration [mol.m-3]')\n",
-    "    ax2.set_xlabel(r'x [$\\mu$m]')\n",
+    "    ax2.set_xlim(0, L_out(solution.t[-1]) * 1e6)\n",
+    "    ax2.set_ylabel(\"Solvent concentration [mol.m-3]\")\n",
+    "    ax2.set_xlabel(r\"x [$\\mu$m]\")\n",
     "\n",
     "    plt.tight_layout()\n",
     "    plt.show()\n",
-    "    \n",
+    "\n",
     "\n",
     "import ipywidgets as widgets\n",
-    "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1],step=0.1,value=0));"
+    "\n",
+    "widgets.interact(\n",
+    "    plot, t=widgets.FloatSlider(min=0, max=solution.t[-1], step=0.1, value=0)\n",
+    ");"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
index 035fe77ed7..466baa3c7a 100644
--- a/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
+++ b/docs/source/examples/notebooks/expression_tree/broadcasts.ipynb
@@ -102,7 +102,7 @@
     "T = pybamm.Variable(\"T\", domain=\"negative electrode\")\n",
     "disc.set_variable_slices([T])\n",
     "disc_T = disc.process_symbol(T)\n",
-    "disc_T.evaluate(y=np.linspace(0,1,5))"
+    "disc_T.evaluate(y=np.linspace(0, 1, 5))"
    ]
   },
   {
@@ -145,7 +145,7 @@
    "source": [
     "primary_broad_T = pybamm.PrimaryBroadcast(T, \"negative particle\")\n",
     "disc_T = disc.process_symbol(primary_broad_T)\n",
-    "disc_T.evaluate(y=np.linspace(0,1,5))"
+    "disc_T.evaluate(y=np.linspace(0, 1, 5))"
    ]
   },
   {
@@ -192,7 +192,7 @@
     "c_s = pybamm.Variable(\"c_s\", domain=\"negative particle\")\n",
     "disc.set_variable_slices([c_s])\n",
     "disc_c_s = disc.process_symbol(c_s)\n",
-    "disc_c_s.evaluate(y=np.linspace(0,1,3))"
+    "disc_c_s.evaluate(y=np.linspace(0, 1, 3))"
    ]
   },
   {
@@ -235,7 +235,7 @@
    "source": [
     "secondary_broad_c_s = pybamm.SecondaryBroadcast(c_s, \"negative electrode\")\n",
     "disc_broad_c_s = disc.process_symbol(secondary_broad_c_s)\n",
-    "disc_broad_c_s.evaluate(y=np.linspace(0,1,3))"
+    "disc_broad_c_s.evaluate(y=np.linspace(0, 1, 3))"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
index b15c8b1d32..a5b38efd9e 100644
--- a/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
+++ b/docs/source/examples/notebooks/expression_tree/expression-tree.ipynb
@@ -39,10 +39,10 @@
     "import pybamm\n",
     "import numpy as np\n",
     "\n",
-    "y = pybamm.StateVector(slice(0,1))\n",
+    "y = pybamm.StateVector(slice(0, 1))\n",
     "t = pybamm.t\n",
-    "equation = 2*y * (1 - y) + t\n",
-    "equation.visualise('expression_tree1.png')"
+    "equation = 2 * y * (1 - y) + t\n",
+    "equation.visualise(\"expression_tree1.png\")"
    ]
   },
   {
@@ -90,7 +90,7 @@
    "outputs": [],
    "source": [
     "diff_wrt_equation = equation.diff(t)\n",
-    "diff_wrt_equation.visualise('expression_tree2.png')"
+    "diff_wrt_equation.visualise(\"expression_tree2.png\")"
    ]
   },
   {
@@ -152,11 +152,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "D = pybamm.Parameter('D')\n",
-    "c = pybamm.Variable('c', domain=['negative electrode'])\n",
+    "D = pybamm.Parameter(\"D\")\n",
+    "c = pybamm.Variable(\"c\", domain=[\"negative electrode\"])\n",
     "\n",
     "dcdt = D * pybamm.div(pybamm.grad(c))\n",
-    "dcdt.visualise('expression_tree3.png')"
+    "dcdt.visualise(\"expression_tree3.png\")"
    ]
   },
   {
@@ -183,9 +183,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "parameter_values = pybamm.ParameterValues({'D': 2})\n",
+    "parameter_values = pybamm.ParameterValues({\"D\": 2})\n",
     "dcdt = parameter_values.process_symbol(dcdt)\n",
-    "dcdt.visualise('expression_tree4.png')"
+    "dcdt.visualise(\"expression_tree4.png\")"
    ]
   },
   {
@@ -210,13 +210,14 @@
    "source": [
     "# Here, we import a dummy discretisation from the PyBaMM tests directory.\n",
     "import sys\n",
+    "\n",
     "sys.path.insert(0, pybamm.root_dir())\n",
     "from tests import get_discretisation_for_testing\n",
     "\n",
     "disc = get_discretisation_for_testing()\n",
     "disc.y_slices = {c: [slice(0, 40)]}\n",
     "dcdt = disc.process_symbol(dcdt)\n",
-    "dcdt.visualise('expression_tree5.png')"
+    "dcdt.visualise(\"expression_tree5.png\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
index 8744e94f7e..c788a773b8 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-10-creating-a-model.ipynb
@@ -107,7 +107,7 @@
     "N = -pybamm.grad(c)  # define the flux\n",
     "dcdt = -pybamm.div(N)  # define the rhs equation\n",
     "\n",
-    "model.rhs = {c: dcdt}  # add the equation to rhs dictionary with the variable as the key "
+    "model.rhs = {c: dcdt}  # add the equation to rhs dictionary with the variable as the key"
    ]
   },
   {
@@ -126,12 +126,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# boundary conditions \n",
+    "# boundary conditions\n",
     "c_surf = pybamm.surf(c)  # concentration at the surface of the sphere\n",
     "j = j0 * (1 - c_surf) ** (1 / 2) * c_surf ** (1 / 2)  # prescribed boundary flux\n",
     "model.boundary_conditions = {c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}}\n",
     "\n",
-    "# initial conditions \n",
+    "# initial conditions\n",
     "model.initial_conditions = {c: c0}"
    ]
   },
@@ -177,7 +177,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")"
+    "r = pybamm.SpatialVariable(\n",
+    "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
+    ")"
    ]
   },
   {
@@ -219,7 +221,7 @@
     "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
     "var_pts = {r: 20}\n",
     "# create a mesh of our geometry, using a uniform grid with 20 volumes\n",
-    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts) "
+    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)"
    ]
   },
   {
@@ -240,10 +242,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "parameter_values = pybamm.ParameterValues({\n",
-    "    \"Initial concentration\": 0.9,\n",
-    "    \"Flux parameter\": 0.8,\n",
-    "})"
+    "parameter_values = pybamm.ParameterValues(\n",
+    "    {\n",
+    "        \"Initial concentration\": 0.9,\n",
+    "        \"Flux parameter\": 0.8,\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -282,13 +286,13 @@
    "outputs": [],
    "source": [
     "sim = pybamm.Simulation(\n",
-    "        model,\n",
-    "        geometry=geometry,\n",
-    "        parameter_values=parameter_values,\n",
-    "        submesh_types=submesh_types,\n",
-    "        var_pts=var_pts,\n",
-    "        spatial_methods=spatial_methods,\n",
-    "        solver=solver,\n",
+    "    model,\n",
+    "    geometry=geometry,\n",
+    "    parameter_values=parameter_values,\n",
+    "    submesh_types=submesh_types,\n",
+    "    var_pts=var_pts,\n",
+    "    spatial_methods=spatial_methods,\n",
+    "    solver=solver,\n",
     ")"
    ]
   },
@@ -373,8 +377,8 @@
     }
    ],
    "source": [
-    "# pass in a list of the variables we want to plot \n",
-    "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])  "
+    "# pass in a list of the variables we want to plot\n",
+    "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
index a38c0c90ee..45dd6a7702 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-11-creating-a-submodel.ipynb
@@ -90,14 +90,14 @@
     "        c = pybamm.Variable(\"Concentration\", domain=\"negative particle\")\n",
     "\n",
     "        # define concentration at the surface of the sphere\n",
-    "        c_surf = pybamm.surf(c)  \n",
+    "        c_surf = pybamm.surf(c)\n",
     "\n",
-    "        # define flux \n",
+    "        # define flux\n",
     "        N = -pybamm.grad(c)\n",
     "\n",
     "        # create dictionary of model variables\n",
     "        variables = {\n",
-    "            \"Concentration\": c, \n",
+    "            \"Concentration\": c,\n",
     "            \"Surface concentration\": c_surf,\n",
     "            \"Flux\": N,\n",
     "        }\n",
@@ -105,7 +105,7 @@
     "        return variables\n",
     "\n",
     "    def get_coupled_variables(self, variables):\n",
-    "        return variables  \n",
+    "        return variables\n",
     "\n",
     "    def set_rhs(self, variables):\n",
     "        # extract the variables we need\n",
@@ -115,8 +115,8 @@
     "        # define the rhs of the PDE\n",
     "        dcdt = -pybamm.div(N)\n",
     "\n",
-    "        # add it to the submodel dictionary \n",
-    "        self.rhs = {c: dcdt} \n",
+    "        # add it to the submodel dictionary\n",
+    "        self.rhs = {c: dcdt}\n",
     "\n",
     "    def set_algebraic(self, variables):\n",
     "        pass\n",
@@ -127,7 +127,9 @@
     "        j = variables[\"Boundary flux\"]\n",
     "\n",
     "        # add the boundary conditions to the submodel dictionary\n",
-    "        self.boundary_conditions = {c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}}\n",
+    "        self.boundary_conditions = {\n",
+    "            c: {\"left\": (0, \"Neumann\"), \"right\": (-j, \"Neumann\")}\n",
+    "        }\n",
     "\n",
     "    def set_initial_conditions(self, variables):\n",
     "        # extract the variable we need\n",
@@ -135,7 +137,7 @@
     "\n",
     "        # define the initial concentration parameter\n",
     "        c0 = pybamm.Parameter(\"Initial concentration\")\n",
-    " \n",
+    "\n",
     "        # add the initial conditions to the submodel dictionary\n",
     "        self.initial_conditions = {c: c0}"
    ]
@@ -183,7 +185,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels = {\"Particle\": Particle(None, \"Negative\"), \"Boundary flux\": BoundaryFlux(None, \"Negative\")}"
+    "model.submodels = {\n",
+    "    \"Particle\": Particle(None, \"Negative\"),\n",
+    "    \"Boundary flux\": BoundaryFlux(None, \"Negative\"),\n",
+    "}"
    ]
   },
   {
@@ -285,12 +290,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
+    "r = pybamm.SpatialVariable(\n",
+    "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
+    ")\n",
     "geometry = {\"negative particle\": {r: {\"min\": 0, \"max\": 1}}}\n",
     "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n",
     "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
     "var_pts = {r: 20}\n",
-    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts) "
+    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)"
    ]
   },
   {
@@ -309,21 +316,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "parameter_values = pybamm.ParameterValues({\n",
-    "    \"Initial concentration\": 0.9,\n",
-    "    \"Flux parameter\": 0.8,\n",
-    "})\n",
+    "parameter_values = pybamm.ParameterValues(\n",
+    "    {\n",
+    "        \"Initial concentration\": 0.9,\n",
+    "        \"Flux parameter\": 0.8,\n",
+    "    }\n",
+    ")\n",
     "\n",
     "solver = pybamm.ScipySolver()\n",
     "\n",
     "sim = pybamm.Simulation(\n",
-    "        model,\n",
-    "        geometry=geometry,\n",
-    "        parameter_values=parameter_values,\n",
-    "        submesh_types=submesh_types,\n",
-    "        var_pts=var_pts,\n",
-    "        spatial_methods=spatial_methods,\n",
-    "        solver=solver,\n",
+    "    model,\n",
+    "    geometry=geometry,\n",
+    "    parameter_values=parameter_values,\n",
+    "    submesh_types=submesh_types,\n",
+    "    var_pts=var_pts,\n",
+    "    spatial_methods=spatial_methods,\n",
+    "    solver=solver,\n",
     ")"
    ]
   },
@@ -354,7 +363,7 @@
     }
    ],
    "source": [
-    "sim.solve([0, 1])  "
+    "sim.solve([0, 1])"
    ]
   },
   {
@@ -406,8 +415,8 @@
     }
    ],
    "source": [
-    "# pass in a list of the variables we want to plot \n",
-    "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])  "
+    "# pass in a list of the variables we want to plot\n",
+    "sim.plot([\"Concentration\", \"Surface concentration\", \"Flux\", \"Boundary flux\"])"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
index 40a02f682a..022a11e48a 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-3-basic-plotting.ipynb
@@ -1,942 +1,947 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Tutorial 3 - Basic plotting"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In [Tutorial 2](./tutorial-2-compare-models.ipynb), we made use of PyBaMM's automatic plotting function when comparing models. This gave a good quick overview of many of the key variables in the model. However, by passing in just a few arguments it is easy to plot any of the many other variables that may be of interest to you. We start by building and solving a model as before:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.solvers.solution.Solution at 0x139a534f0>"
-            ]
-          },
-          "execution_count": 1,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
-        "import pybamm\n",
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "model_dfn = pybamm.lithium_ion.DFN()\n",
-        "sim_dfn = pybamm.Simulation(model_dfn)\n",
-        "sim_dfn.solve([0, 3600])"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We now want to plot a selection of the model variables. To see a full list of the available variables just type:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['Time [s]',\n",
-              " 'Time [min]',\n",
-              " 'Time [h]',\n",
-              " 'x [m]',\n",
-              " 'x_n [m]',\n",
-              " 'x_s [m]',\n",
-              " 'x_p [m]',\n",
-              " 'r_n [m]',\n",
-              " 'r_p [m]',\n",
-              " 'Current variable [A]',\n",
-              " 'Total current density [A.m-2]',\n",
-              " 'Current [A]',\n",
-              " 'C-rate',\n",
-              " 'Discharge capacity [A.h]',\n",
-              " 'Discharge energy [W.h]',\n",
-              " 'Throughput energy [W.h]',\n",
-              " 'Throughput capacity [A.h]',\n",
-              " 'Porosity',\n",
-              " 'Negative electrode porosity',\n",
-              " 'X-averaged negative electrode porosity',\n",
-              " 'Separator porosity',\n",
-              " 'X-averaged separator porosity',\n",
-              " 'Positive electrode porosity',\n",
-              " 'X-averaged positive electrode porosity',\n",
-              " 'Porosity change',\n",
-              " 'Negative electrode porosity change [s-1]',\n",
-              " 'X-averaged negative electrode porosity change [s-1]',\n",
-              " 'Separator porosity change [s-1]',\n",
-              " 'X-averaged separator porosity change [s-1]',\n",
-              " 'Positive electrode porosity change [s-1]',\n",
-              " 'X-averaged positive electrode porosity change [s-1]',\n",
-              " 'Negative electrode interface utilisation variable',\n",
-              " 'X-averaged negative electrode interface utilisation variable',\n",
-              " 'Negative electrode interface utilisation',\n",
-              " 'X-averaged negative electrode interface utilisation',\n",
-              " 'Positive electrode interface utilisation variable',\n",
-              " 'X-averaged positive electrode interface utilisation variable',\n",
-              " 'Positive electrode interface utilisation',\n",
-              " 'X-averaged positive electrode interface utilisation',\n",
-              " 'Negative particle crack length [m]',\n",
-              " 'X-averaged negative particle crack length [m]',\n",
-              " 'Negative particle cracking rate [m.s-1]',\n",
-              " 'X-averaged negative particle cracking rate [m.s-1]',\n",
-              " 'Positive particle crack length [m]',\n",
-              " 'X-averaged positive particle crack length [m]',\n",
-              " 'Positive particle cracking rate [m.s-1]',\n",
-              " 'X-averaged positive particle cracking rate [m.s-1]',\n",
-              " 'Negative electrode active material volume fraction',\n",
-              " 'X-averaged negative electrode active material volume fraction',\n",
-              " 'Negative electrode capacity [A.h]',\n",
-              " 'Negative particle radius',\n",
-              " 'Negative particle radius [m]',\n",
-              " 'X-averaged negative particle radius [m]',\n",
-              " 'Negative electrode surface area to volume ratio [m-1]',\n",
-              " 'X-averaged negative electrode surface area to volume ratio [m-1]',\n",
-              " 'Negative electrode active material volume fraction change [s-1]',\n",
-              " 'X-averaged negative electrode active material volume fraction change [s-1]',\n",
-              " 'Loss of lithium due to loss of active material in negative electrode [mol]',\n",
-              " 'Positive electrode active material volume fraction',\n",
-              " 'X-averaged positive electrode active material volume fraction',\n",
-              " 'Positive electrode capacity [A.h]',\n",
-              " 'Positive particle radius',\n",
-              " 'Positive particle radius [m]',\n",
-              " 'X-averaged positive particle radius [m]',\n",
-              " 'Positive electrode surface area to volume ratio [m-1]',\n",
-              " 'X-averaged positive electrode surface area to volume ratio [m-1]',\n",
-              " 'Positive electrode active material volume fraction change [s-1]',\n",
-              " 'X-averaged positive electrode active material volume fraction change [s-1]',\n",
-              " 'Loss of lithium due to loss of active material in positive electrode [mol]',\n",
-              " 'Separator pressure [Pa]',\n",
-              " 'X-averaged separator pressure [Pa]',\n",
-              " 'negative electrode transverse volume-averaged velocity [m.s-1]',\n",
-              " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-1]',\n",
-              " 'separator transverse volume-averaged velocity [m.s-1]',\n",
-              " 'X-averaged separator transverse volume-averaged velocity [m.s-1]',\n",
-              " 'positive electrode transverse volume-averaged velocity [m.s-1]',\n",
-              " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-1]',\n",
-              " 'Transverse volume-averaged velocity [m.s-1]',\n",
-              " 'negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'separator transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'Transverse volume-averaged acceleration [m.s-2]',\n",
-              " 'Negative electrode volume-averaged velocity [m.s-1]',\n",
-              " 'Negative electrode volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged negative electrode volume-averaged acceleration [m.s-2]',\n",
-              " 'Negative electrode pressure [Pa]',\n",
-              " 'X-averaged negative electrode pressure [Pa]',\n",
-              " 'Positive electrode volume-averaged velocity [m.s-1]',\n",
-              " 'Positive electrode volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n",
-              " 'Positive electrode pressure [Pa]',\n",
-              " 'X-averaged positive electrode pressure [Pa]',\n",
-              " 'Negative particle stoichiometry',\n",
-              " 'Negative particle concentration',\n",
-              " 'Negative particle concentration [mol.m-3]',\n",
-              " 'X-averaged negative particle concentration',\n",
-              " 'X-averaged negative particle concentration [mol.m-3]',\n",
-              " 'R-averaged negative particle concentration',\n",
-              " 'R-averaged negative particle concentration [mol.m-3]',\n",
-              " 'Average negative particle concentration',\n",
-              " 'Average negative particle concentration [mol.m-3]',\n",
-              " 'Negative particle surface stoichiometry',\n",
-              " 'Negative particle surface concentration',\n",
-              " 'Negative particle surface concentration [mol.m-3]',\n",
-              " 'X-averaged negative particle surface concentration',\n",
-              " 'X-averaged negative particle surface concentration [mol.m-3]',\n",
-              " 'Negative electrode extent of lithiation',\n",
-              " 'X-averaged negative electrode extent of lithiation',\n",
-              " 'Minimum negative particle concentration',\n",
-              " 'Maximum negative particle concentration',\n",
-              " 'Minimum negative particle concentration [mol.m-3]',\n",
-              " 'Maximum negative particle concentration [mol.m-3]',\n",
-              " 'Minimum negative particle surface concentration',\n",
-              " 'Maximum negative particle surface concentration',\n",
-              " 'Minimum negative particle surface concentration [mol.m-3]',\n",
-              " 'Maximum negative particle surface concentration [mol.m-3]',\n",
-              " 'Positive particle stoichiometry',\n",
-              " 'Positive particle concentration',\n",
-              " 'Positive particle concentration [mol.m-3]',\n",
-              " 'X-averaged positive particle concentration',\n",
-              " 'X-averaged positive particle concentration [mol.m-3]',\n",
-              " 'R-averaged positive particle concentration',\n",
-              " 'R-averaged positive particle concentration [mol.m-3]',\n",
-              " 'Average positive particle concentration',\n",
-              " 'Average positive particle concentration [mol.m-3]',\n",
-              " 'Positive particle surface stoichiometry',\n",
-              " 'Positive particle surface concentration',\n",
-              " 'Positive particle surface concentration [mol.m-3]',\n",
-              " 'X-averaged positive particle surface concentration',\n",
-              " 'X-averaged positive particle surface concentration [mol.m-3]',\n",
-              " 'Positive electrode extent of lithiation',\n",
-              " 'X-averaged positive electrode extent of lithiation',\n",
-              " 'Minimum positive particle concentration',\n",
-              " 'Maximum positive particle concentration',\n",
-              " 'Minimum positive particle concentration [mol.m-3]',\n",
-              " 'Maximum positive particle concentration [mol.m-3]',\n",
-              " 'Minimum positive particle surface concentration',\n",
-              " 'Maximum positive particle surface concentration',\n",
-              " 'Minimum positive particle surface concentration [mol.m-3]',\n",
-              " 'Maximum positive particle surface concentration [mol.m-3]',\n",
-              " 'Negative electrode potential [V]',\n",
-              " 'X-averaged negative electrode potential [V]',\n",
-              " 'Negative electrode ohmic losses [V]',\n",
-              " 'X-averaged negative electrode ohmic losses [V]',\n",
-              " 'Gradient of negative electrode potential [V.m-1]',\n",
-              " 'Positive electrode potential [V]',\n",
-              " 'X-averaged positive electrode potential [V]',\n",
-              " 'Positive electrode ohmic losses [V]',\n",
-              " 'X-averaged positive electrode ohmic losses [V]',\n",
-              " 'Gradient of positive electrode potential [V.m-1]',\n",
-              " 'Porosity times concentration [mol.m-3]',\n",
-              " 'Negative electrode porosity times concentration [mol.m-3]',\n",
-              " 'Separator porosity times concentration [mol.m-3]',\n",
-              " 'Positive electrode porosity times concentration [mol.m-3]',\n",
-              " 'Total lithium in electrolyte [mol]',\n",
-              " 'Electrolyte potential [V]',\n",
-              " 'X-averaged electrolyte potential [V]',\n",
-              " 'X-averaged electrolyte overpotential [V]',\n",
-              " 'Gradient of electrolyte potential [V.m-1]',\n",
-              " 'Negative electrolyte potential [V]',\n",
-              " 'X-averaged negative electrolyte potential [V]',\n",
-              " 'Gradient of negative electrolyte potential [V.m-1]',\n",
-              " 'Separator electrolyte potential [V]',\n",
-              " 'X-averaged separator electrolyte potential [V]',\n",
-              " 'Gradient of separator electrolyte potential [V.m-1]',\n",
-              " 'Positive electrolyte potential [V]',\n",
-              " 'X-averaged positive electrolyte potential [V]',\n",
-              " 'Gradient of positive electrolyte potential [V.m-1]',\n",
-              " 'Ambient temperature [K]',\n",
-              " 'Cell temperature [K]',\n",
-              " 'Negative current collector temperature [K]',\n",
-              " 'Positive current collector temperature [K]',\n",
-              " 'X-averaged cell temperature [K]',\n",
-              " 'Volume-averaged cell temperature [K]',\n",
-              " 'Negative electrode temperature [K]',\n",
-              " 'X-averaged negative electrode temperature [K]',\n",
-              " 'Separator temperature [K]',\n",
-              " 'X-averaged separator temperature [K]',\n",
-              " 'Positive electrode temperature [K]',\n",
-              " 'X-averaged positive electrode temperature [K]',\n",
-              " 'Ambient temperature [C]',\n",
-              " 'Cell temperature [C]',\n",
-              " 'Negative current collector temperature [C]',\n",
-              " 'Positive current collector temperature [C]',\n",
-              " 'X-averaged cell temperature [C]',\n",
-              " 'Volume-averaged cell temperature [C]',\n",
-              " 'Negative electrode temperature [C]',\n",
-              " 'X-averaged negative electrode temperature [C]',\n",
-              " 'Separator temperature [C]',\n",
-              " 'X-averaged separator temperature [C]',\n",
-              " 'Positive electrode temperature [C]',\n",
-              " 'X-averaged positive electrode temperature [C]',\n",
-              " 'Negative current collector potential [V]',\n",
-              " 'Inner SEI thickness [m]',\n",
-              " 'Outer SEI thickness [m]',\n",
-              " 'X-averaged inner SEI thickness [m]',\n",
-              " 'X-averaged outer SEI thickness [m]',\n",
-              " 'SEI [m]',\n",
-              " 'Total SEI thickness [m]',\n",
-              " 'X-averaged SEI thickness [m]',\n",
-              " 'X-averaged total SEI thickness [m]',\n",
-              " 'X-averaged negative electrode resistance [Ohm.m2]',\n",
-              " 'Inner SEI interfacial current density [A.m-2]',\n",
-              " 'X-averaged inner SEI interfacial current density [A.m-2]',\n",
-              " 'Outer SEI interfacial current density [A.m-2]',\n",
-              " 'X-averaged outer SEI interfacial current density [A.m-2]',\n",
-              " 'SEI interfacial current density [A.m-2]',\n",
-              " 'X-averaged SEI interfacial current density [A.m-2]',\n",
-              " 'Inner SEI on cracks thickness [m]',\n",
-              " 'Outer SEI on cracks thickness [m]',\n",
-              " 'X-averaged inner SEI on cracks thickness [m]',\n",
-              " 'X-averaged outer SEI on cracks thickness [m]',\n",
-              " 'SEI on cracks [m]',\n",
-              " 'Total SEI on cracks thickness [m]',\n",
-              " 'X-averaged SEI on cracks thickness [m]',\n",
-              " 'X-averaged total SEI on cracks thickness [m]',\n",
-              " 'Inner SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'Outer SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'Lithium plating concentration [mol.m-3]',\n",
-              " 'X-averaged lithium plating concentration [mol.m-3]',\n",
-              " 'Dead lithium concentration [mol.m-3]',\n",
-              " 'X-averaged dead lithium concentration [mol.m-3]',\n",
-              " 'Lithium plating thickness [m]',\n",
-              " 'X-averaged lithium plating thickness [m]',\n",
-              " 'Dead lithium thickness [m]',\n",
-              " 'X-averaged dead lithium thickness [m]',\n",
-              " 'Loss of lithium to lithium plating [mol]',\n",
-              " 'Loss of capacity to lithium plating [A.h]',\n",
-              " 'Negative electrode lithium plating reaction overpotential [V]',\n",
-              " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n",
-              " 'Lithium plating interfacial current density [A.m-2]',\n",
-              " 'X-averaged lithium plating interfacial current density [A.m-2]',\n",
-              " 'Negative crack surface to volume ratio [m-1]',\n",
-              " 'Negative electrode roughness ratio',\n",
-              " 'X-averaged negative electrode roughness ratio',\n",
-              " 'Positive crack surface to volume ratio [m-1]',\n",
-              " 'Positive electrode roughness ratio',\n",
-              " 'X-averaged positive electrode roughness ratio',\n",
-              " 'Electrolyte transport efficiency',\n",
-              " 'Negative electrolyte transport efficiency',\n",
-              " 'X-averaged negative electrolyte transport efficiency',\n",
-              " 'Separator electrolyte transport efficiency',\n",
-              " 'X-averaged separator electrolyte transport efficiency',\n",
-              " 'Positive electrolyte transport efficiency',\n",
-              " 'X-averaged positive electrolyte transport efficiency',\n",
-              " 'Electrode transport efficiency',\n",
-              " 'Negative electrode transport efficiency',\n",
-              " 'X-averaged negative electrode transport efficiency',\n",
-              " 'Separator electrode transport efficiency',\n",
-              " 'X-averaged separator electrode transport efficiency',\n",
-              " 'Positive electrode transport efficiency',\n",
-              " 'X-averaged positive electrode transport efficiency',\n",
-              " 'Separator volume-averaged velocity [m.s-1]',\n",
-              " 'Separator volume-averaged acceleration [m.s-2]',\n",
-              " 'X-averaged separator volume-averaged acceleration [m.s-2]',\n",
-              " 'Volume-averaged velocity [m.s-1]',\n",
-              " 'Volume-averaged acceleration [m.s-1]',\n",
-              " 'X-averaged volume-averaged acceleration [m.s-1]',\n",
-              " 'Pressure [Pa]',\n",
-              " 'Negative electrode open-circuit potential [V]',\n",
-              " 'X-averaged negative electrode open-circuit potential [V]',\n",
-              " 'Negative electrode entropic change [V.K-1]',\n",
-              " 'X-averaged negative electrode entropic change [V.K-1]',\n",
-              " 'Positive electrode open-circuit potential [V]',\n",
-              " 'X-averaged positive electrode open-circuit potential [V]',\n",
-              " 'Positive electrode entropic change [V.K-1]',\n",
-              " 'X-averaged positive electrode entropic change [V.K-1]',\n",
-              " 'Negative electrode effective conductivity',\n",
-              " 'Negative electrode current density [A.m-2]',\n",
-              " 'Positive electrode effective conductivity',\n",
-              " 'Positive electrode current density [A.m-2]',\n",
-              " 'Electrode current density [A.m-2]',\n",
-              " 'Positive current collector potential [V]',\n",
-              " 'Local voltage [V]',\n",
-              " 'Voltage [V]',\n",
-              " 'Contact overpotential [V]',\n",
-              " 'Electrolyte concentration concatenation [mol.m-3]',\n",
-              " 'Negative electrolyte concentration [mol.m-3]',\n",
-              " 'X-averaged negative electrolyte concentration [mol.m-3]',\n",
-              " 'Separator electrolyte concentration [mol.m-3]',\n",
-              " 'X-averaged separator electrolyte concentration [mol.m-3]',\n",
-              " 'Positive electrolyte concentration [mol.m-3]',\n",
-              " 'X-averaged positive electrolyte concentration [mol.m-3]',\n",
-              " 'Negative electrolyte concentration',\n",
-              " 'Negative electrolyte concentration [Molar]',\n",
-              " 'X-averaged negative electrolyte concentration',\n",
-              " 'X-averaged negative electrolyte concentration [Molar]',\n",
-              " 'Separator electrolyte concentration',\n",
-              " 'Separator electrolyte concentration [Molar]',\n",
-              " 'X-averaged separator electrolyte concentration',\n",
-              " 'X-averaged separator electrolyte concentration [Molar]',\n",
-              " 'Positive electrolyte concentration',\n",
-              " 'Positive electrolyte concentration [Molar]',\n",
-              " 'X-averaged positive electrolyte concentration',\n",
-              " 'X-averaged positive electrolyte concentration [Molar]',\n",
-              " 'Electrolyte concentration [mol.m-3]',\n",
-              " 'X-averaged electrolyte concentration [mol.m-3]',\n",
-              " 'Electrolyte concentration',\n",
-              " 'Electrolyte concentration [Molar]',\n",
-              " 'X-averaged electrolyte concentration',\n",
-              " 'X-averaged electrolyte concentration [Molar]',\n",
-              " 'Electrolyte current density [A.m-2]',\n",
-              " 'X-averaged concentration overpotential [V]',\n",
-              " 'X-averaged electrolyte ohmic losses [V]',\n",
-              " 'Negative electrode surface potential difference [V]',\n",
-              " 'X-averaged negative electrode surface potential difference [V]',\n",
-              " 'Positive electrode surface potential difference [V]',\n",
-              " 'X-averaged positive electrode surface potential difference [V]',\n",
-              " 'Ohmic heating [W.m-3]',\n",
-              " 'X-averaged Ohmic heating [W.m-3]',\n",
-              " 'Volume-averaged Ohmic heating [W.m-3]',\n",
-              " 'Irreversible electrochemical heating [W.m-3]',\n",
-              " 'X-averaged irreversible electrochemical heating [W.m-3]',\n",
-              " 'Volume-averaged irreversible electrochemical heating [W.m-3]',\n",
-              " 'Reversible heating [W.m-3]',\n",
-              " 'X-averaged reversible heating [W.m-3]',\n",
-              " 'Volume-averaged reversible heating [W.m-3]',\n",
-              " 'Total heating [W.m-3]',\n",
-              " 'X-averaged total heating [W.m-3]',\n",
-              " 'Volume-averaged total heating [W.m-3]',\n",
-              " 'Current collector current density [A.m-2]',\n",
-              " 'Inner SEI concentration [mol.m-3]',\n",
-              " 'X-averaged inner SEI concentration [mol.m-3]',\n",
-              " 'Outer SEI concentration [mol.m-3]',\n",
-              " 'X-averaged outer SEI concentration [mol.m-3]',\n",
-              " 'SEI concentration [mol.m-3]',\n",
-              " 'X-averaged SEI concentration [mol.m-3]',\n",
-              " 'Loss of lithium to SEI [mol]',\n",
-              " 'Loss of capacity to SEI [A.h]',\n",
-              " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n",
-              " 'Negative electrode SEI interfacial current density [A.m-2]',\n",
-              " 'Positive electrode SEI interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n",
-              " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
-              " 'Inner SEI on cracks concentration [mol.m-3]',\n",
-              " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n",
-              " 'Outer SEI on cracks concentration [mol.m-3]',\n",
-              " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n",
-              " 'SEI on cracks concentration [mol.m-3]',\n",
-              " 'X-averaged SEI on cracks concentration [mol.m-3]',\n",
-              " 'Loss of lithium to SEI on cracks [mol]',\n",
-              " 'Loss of capacity to SEI on cracks [A.h]',\n",
-              " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n",
-              " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n",
-              " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n",
-              " 'Lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n",
-              " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode interfacial current density [A.m-2]',\n",
-              " 'X-averaged negative electrode interfacial current density [A.m-2]',\n",
-              " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n",
-              " 'X-averaged negative electrode total volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative electrode exchange current density [A.m-2]',\n",
-              " 'X-averaged negative electrode exchange current density [A.m-2]',\n",
-              " 'Negative electrode reaction overpotential [V]',\n",
-              " 'X-averaged negative electrode reaction overpotential [V]',\n",
-              " 'Negative electrode volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n",
-              " 'SEI film overpotential [V]',\n",
-              " 'X-averaged SEI film overpotential [V]',\n",
-              " 'Positive electrode interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n",
-              " 'X-averaged positive electrode total volumetric interfacial current density [A.m-3]',\n",
-              " 'Positive electrode exchange current density [A.m-2]',\n",
-              " 'X-averaged positive electrode exchange current density [A.m-2]',\n",
-              " 'Positive electrode reaction overpotential [V]',\n",
-              " 'X-averaged positive electrode reaction overpotential [V]',\n",
-              " 'Positive electrode volumetric interfacial current density [A.m-3]',\n",
-              " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n",
-              " 'Negative particle rhs [mol.m-3.s-1]',\n",
-              " 'Negative particle bc [mol.m-2]',\n",
-              " 'Negative particle effective diffusivity [m2.s-1]',\n",
-              " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n",
-              " 'Negative particle flux [mol.m-2.s-1]',\n",
-              " 'Negative electrode stoichiometry',\n",
-              " 'Negative electrode volume-averaged concentration',\n",
-              " 'Negative electrode volume-averaged concentration [mol.m-3]',\n",
-              " 'Total lithium in primary phase in negative electrode [mol]',\n",
-              " 'Positive particle rhs [mol.m-3.s-1]',\n",
-              " 'Positive particle bc [mol.m-2]',\n",
-              " 'Positive particle effective diffusivity [m2.s-1]',\n",
-              " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n",
-              " 'Positive particle flux [mol.m-2.s-1]',\n",
-              " 'Positive electrode stoichiometry',\n",
-              " 'Positive electrode volume-averaged concentration',\n",
-              " 'Positive electrode volume-averaged concentration [mol.m-3]',\n",
-              " 'Total lithium in primary phase in positive electrode [mol]',\n",
-              " 'Electrolyte flux [mol.m-2.s-1]',\n",
-              " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n",
-              " 'Electrolyte migration flux [mol.m-2.s-1]',\n",
-              " 'Electrolyte convection flux [mol.m-2.s-1]',\n",
-              " 'Sum of negative electrode electrolyte reaction source terms [A.m-3]',\n",
-              " 'Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]',\n",
-              " 'Sum of negative electrode volumetric interfacial current densities [A.m-3]',\n",
-              " 'Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]',\n",
-              " 'Sum of positive electrode electrolyte reaction source terms [A.m-3]',\n",
-              " 'Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]',\n",
-              " 'Sum of positive electrode volumetric interfacial current densities [A.m-3]',\n",
-              " 'Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]',\n",
-              " 'Interfacial current density [A.m-2]',\n",
-              " 'Exchange current density [A.m-2]',\n",
-              " 'Sum of volumetric interfacial current densities [A.m-3]',\n",
-              " 'Sum of electrolyte reaction source terms [A.m-3]',\n",
-              " 'X-averaged open-circuit voltage [V]',\n",
-              " 'Measured open-circuit voltage [V]',\n",
-              " 'X-averaged reaction overpotential [V]',\n",
-              " 'X-averaged solid phase ohmic losses [V]',\n",
-              " 'X-averaged battery open-circuit voltage [V]',\n",
-              " 'Measured battery open-circuit voltage [V]',\n",
-              " 'X-averaged battery reaction overpotential [V]',\n",
-              " 'X-averaged battery solid phase ohmic losses [V]',\n",
-              " 'X-averaged battery electrolyte ohmic losses [V]',\n",
-              " 'X-averaged battery concentration overpotential [V]',\n",
-              " 'Battery voltage [V]',\n",
-              " 'Change in measured open-circuit voltage [V]',\n",
-              " 'Local ECM resistance [Ohm]',\n",
-              " 'Terminal power [W]',\n",
-              " 'Power [W]',\n",
-              " 'Resistance [Ohm]',\n",
-              " 'Total lithium in negative electrode [mol]',\n",
-              " 'LAM_ne [%]',\n",
-              " 'Loss of active material in negative electrode [%]',\n",
-              " 'Total lithium in positive electrode [mol]',\n",
-              " 'LAM_pe [%]',\n",
-              " 'Loss of active material in positive electrode [%]',\n",
-              " 'LLI [%]',\n",
-              " 'Loss of lithium inventory [%]',\n",
-              " 'Loss of lithium inventory, including electrolyte [%]',\n",
-              " 'Total lithium [mol]',\n",
-              " 'Total lithium in particles [mol]',\n",
-              " 'Total lithium capacity [A.h]',\n",
-              " 'Total lithium capacity in particles [A.h]',\n",
-              " 'Total lithium lost [mol]',\n",
-              " 'Total lithium lost from particles [mol]',\n",
-              " 'Total lithium lost from electrolyte [mol]',\n",
-              " 'Total lithium lost to side reactions [mol]',\n",
-              " 'Total capacity lost to side reactions [A.h]']"
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "model_dfn.variable_names()"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are a _lot_ of variables. You can also search the list of variables for a particular string (e.g. \"electrolyte\")"
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 3 - Basic plotting"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [Tutorial 2](./tutorial-2-compare-models.ipynb), we made use of PyBaMM's automatic plotting function when comparing models. This gave a good quick overview of many of the key variables in the model. However, by passing in just a few arguments it is easy to plot any of the many other variables that may be of interest to you. We start by building and solving a model as before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Electrolyte concentration\n",
-            "Electrolyte concentration [Molar]\n",
-            "Electrolyte concentration [mol.m-3]\n",
-            "Electrolyte concentration concatenation [mol.m-3]\n",
-            "Electrolyte convection flux [mol.m-2.s-1]\n",
-            "Electrolyte current density [A.m-2]\n",
-            "Electrolyte diffusion flux [mol.m-2.s-1]\n",
-            "Electrolyte flux [mol.m-2.s-1]\n",
-            "Electrolyte migration flux [mol.m-2.s-1]\n",
-            "Electrolyte potential [V]\n",
-            "Electrolyte transport efficiency\n",
-            "Gradient of electrolyte potential [V.m-1]\n",
-            "Gradient of negative electrolyte potential [V.m-1]\n",
-            "Gradient of positive electrolyte potential [V.m-1]\n",
-            "Gradient of separator electrolyte potential [V.m-1]\n",
-            "Loss of lithium inventory, including electrolyte [%]\n",
-            "Negative electrolyte concentration\n",
-            "Negative electrolyte concentration [Molar]\n",
-            "Negative electrolyte concentration [mol.m-3]\n",
-            "Negative electrolyte potential [V]\n",
-            "Negative electrolyte transport efficiency\n",
-            "Positive electrolyte concentration\n",
-            "Positive electrolyte concentration [Molar]\n",
-            "Positive electrolyte concentration [mol.m-3]\n",
-            "Positive electrolyte potential [V]\n",
-            "Positive electrolyte transport efficiency\n",
-            "Separator electrolyte concentration\n",
-            "Separator electrolyte concentration [Molar]\n",
-            "Separator electrolyte concentration [mol.m-3]\n",
-            "Separator electrolyte potential [V]\n",
-            "Separator electrolyte transport efficiency\n",
-            "Sum of electrolyte reaction source terms [A.m-3]\n",
-            "Sum of negative electrode electrolyte reaction source terms [A.m-3]\n",
-            "Sum of positive electrode electrolyte reaction source terms [A.m-3]\n",
-            "Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n",
-            "Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n",
-            "Total lithium in electrolyte [mol]\n",
-            "Total lithium lost from electrolyte [mol]\n",
-            "X-averaged battery electrolyte ohmic losses [V]\n",
-            "X-averaged electrolyte concentration\n",
-            "X-averaged electrolyte concentration [Molar]\n",
-            "X-averaged electrolyte concentration [mol.m-3]\n",
-            "X-averaged electrolyte ohmic losses [V]\n",
-            "X-averaged electrolyte overpotential [V]\n",
-            "X-averaged electrolyte potential [V]\n",
-            "X-averaged negative electrolyte concentration\n",
-            "X-averaged negative electrolyte concentration [Molar]\n",
-            "X-averaged negative electrolyte concentration [mol.m-3]\n",
-            "X-averaged negative electrolyte potential [V]\n",
-            "X-averaged negative electrolyte transport efficiency\n",
-            "X-averaged positive electrolyte concentration\n",
-            "X-averaged positive electrolyte concentration [Molar]\n",
-            "X-averaged positive electrolyte concentration [mol.m-3]\n",
-            "X-averaged positive electrolyte potential [V]\n",
-            "X-averaged positive electrolyte transport efficiency\n",
-            "X-averaged separator electrolyte concentration\n",
-            "X-averaged separator electrolyte concentration [Molar]\n",
-            "X-averaged separator electrolyte concentration [mol.m-3]\n",
-            "X-averaged separator electrolyte potential [V]\n",
-            "X-averaged separator electrolyte transport efficiency\n"
-          ]
-        }
-      ],
-      "source": [
-        "model_dfn.variables.search(\"electrolyte\")"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We have tried to make variables names fairly self explanatory."
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x139a534f0>"
       ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As a first example, we choose to plot the voltage. We add this to a list and then pass this list to the `plot` method of our simulation:"
-      ]
-    },
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "model_dfn = pybamm.lithium_ion.DFN()\n",
+    "sim_dfn = pybamm.Simulation(model_dfn)\n",
+    "sim_dfn.solve([0, 3600])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now want to plot a selection of the model variables. To see a full list of the available variables just type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "8c87342bdc1e425ba87715d286d799d0",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x1077ee910>"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "output_variables = [\"Voltage [V]\"]\n",
-        "sim_dfn.plot(output_variables=output_variables)"
+     "data": {
+      "text/plain": [
+       "['Time [s]',\n",
+       " 'Time [min]',\n",
+       " 'Time [h]',\n",
+       " 'x [m]',\n",
+       " 'x_n [m]',\n",
+       " 'x_s [m]',\n",
+       " 'x_p [m]',\n",
+       " 'r_n [m]',\n",
+       " 'r_p [m]',\n",
+       " 'Current variable [A]',\n",
+       " 'Total current density [A.m-2]',\n",
+       " 'Current [A]',\n",
+       " 'C-rate',\n",
+       " 'Discharge capacity [A.h]',\n",
+       " 'Discharge energy [W.h]',\n",
+       " 'Throughput energy [W.h]',\n",
+       " 'Throughput capacity [A.h]',\n",
+       " 'Porosity',\n",
+       " 'Negative electrode porosity',\n",
+       " 'X-averaged negative electrode porosity',\n",
+       " 'Separator porosity',\n",
+       " 'X-averaged separator porosity',\n",
+       " 'Positive electrode porosity',\n",
+       " 'X-averaged positive electrode porosity',\n",
+       " 'Porosity change',\n",
+       " 'Negative electrode porosity change [s-1]',\n",
+       " 'X-averaged negative electrode porosity change [s-1]',\n",
+       " 'Separator porosity change [s-1]',\n",
+       " 'X-averaged separator porosity change [s-1]',\n",
+       " 'Positive electrode porosity change [s-1]',\n",
+       " 'X-averaged positive electrode porosity change [s-1]',\n",
+       " 'Negative electrode interface utilisation variable',\n",
+       " 'X-averaged negative electrode interface utilisation variable',\n",
+       " 'Negative electrode interface utilisation',\n",
+       " 'X-averaged negative electrode interface utilisation',\n",
+       " 'Positive electrode interface utilisation variable',\n",
+       " 'X-averaged positive electrode interface utilisation variable',\n",
+       " 'Positive electrode interface utilisation',\n",
+       " 'X-averaged positive electrode interface utilisation',\n",
+       " 'Negative particle crack length [m]',\n",
+       " 'X-averaged negative particle crack length [m]',\n",
+       " 'Negative particle cracking rate [m.s-1]',\n",
+       " 'X-averaged negative particle cracking rate [m.s-1]',\n",
+       " 'Positive particle crack length [m]',\n",
+       " 'X-averaged positive particle crack length [m]',\n",
+       " 'Positive particle cracking rate [m.s-1]',\n",
+       " 'X-averaged positive particle cracking rate [m.s-1]',\n",
+       " 'Negative electrode active material volume fraction',\n",
+       " 'X-averaged negative electrode active material volume fraction',\n",
+       " 'Negative electrode capacity [A.h]',\n",
+       " 'Negative particle radius',\n",
+       " 'Negative particle radius [m]',\n",
+       " 'X-averaged negative particle radius [m]',\n",
+       " 'Negative electrode surface area to volume ratio [m-1]',\n",
+       " 'X-averaged negative electrode surface area to volume ratio [m-1]',\n",
+       " 'Negative electrode active material volume fraction change [s-1]',\n",
+       " 'X-averaged negative electrode active material volume fraction change [s-1]',\n",
+       " 'Loss of lithium due to loss of active material in negative electrode [mol]',\n",
+       " 'Positive electrode active material volume fraction',\n",
+       " 'X-averaged positive electrode active material volume fraction',\n",
+       " 'Positive electrode capacity [A.h]',\n",
+       " 'Positive particle radius',\n",
+       " 'Positive particle radius [m]',\n",
+       " 'X-averaged positive particle radius [m]',\n",
+       " 'Positive electrode surface area to volume ratio [m-1]',\n",
+       " 'X-averaged positive electrode surface area to volume ratio [m-1]',\n",
+       " 'Positive electrode active material volume fraction change [s-1]',\n",
+       " 'X-averaged positive electrode active material volume fraction change [s-1]',\n",
+       " 'Loss of lithium due to loss of active material in positive electrode [mol]',\n",
+       " 'Separator pressure [Pa]',\n",
+       " 'X-averaged separator pressure [Pa]',\n",
+       " 'negative electrode transverse volume-averaged velocity [m.s-1]',\n",
+       " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-1]',\n",
+       " 'separator transverse volume-averaged velocity [m.s-1]',\n",
+       " 'X-averaged separator transverse volume-averaged velocity [m.s-1]',\n",
+       " 'positive electrode transverse volume-averaged velocity [m.s-1]',\n",
+       " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-1]',\n",
+       " 'Transverse volume-averaged velocity [m.s-1]',\n",
+       " 'negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'separator transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'Transverse volume-averaged acceleration [m.s-2]',\n",
+       " 'Negative electrode volume-averaged velocity [m.s-1]',\n",
+       " 'Negative electrode volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged negative electrode volume-averaged acceleration [m.s-2]',\n",
+       " 'Negative electrode pressure [Pa]',\n",
+       " 'X-averaged negative electrode pressure [Pa]',\n",
+       " 'Positive electrode volume-averaged velocity [m.s-1]',\n",
+       " 'Positive electrode volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged positive electrode volume-averaged acceleration [m.s-2]',\n",
+       " 'Positive electrode pressure [Pa]',\n",
+       " 'X-averaged positive electrode pressure [Pa]',\n",
+       " 'Negative particle stoichiometry',\n",
+       " 'Negative particle concentration',\n",
+       " 'Negative particle concentration [mol.m-3]',\n",
+       " 'X-averaged negative particle concentration',\n",
+       " 'X-averaged negative particle concentration [mol.m-3]',\n",
+       " 'R-averaged negative particle concentration',\n",
+       " 'R-averaged negative particle concentration [mol.m-3]',\n",
+       " 'Average negative particle concentration',\n",
+       " 'Average negative particle concentration [mol.m-3]',\n",
+       " 'Negative particle surface stoichiometry',\n",
+       " 'Negative particle surface concentration',\n",
+       " 'Negative particle surface concentration [mol.m-3]',\n",
+       " 'X-averaged negative particle surface concentration',\n",
+       " 'X-averaged negative particle surface concentration [mol.m-3]',\n",
+       " 'Negative electrode extent of lithiation',\n",
+       " 'X-averaged negative electrode extent of lithiation',\n",
+       " 'Minimum negative particle concentration',\n",
+       " 'Maximum negative particle concentration',\n",
+       " 'Minimum negative particle concentration [mol.m-3]',\n",
+       " 'Maximum negative particle concentration [mol.m-3]',\n",
+       " 'Minimum negative particle surface concentration',\n",
+       " 'Maximum negative particle surface concentration',\n",
+       " 'Minimum negative particle surface concentration [mol.m-3]',\n",
+       " 'Maximum negative particle surface concentration [mol.m-3]',\n",
+       " 'Positive particle stoichiometry',\n",
+       " 'Positive particle concentration',\n",
+       " 'Positive particle concentration [mol.m-3]',\n",
+       " 'X-averaged positive particle concentration',\n",
+       " 'X-averaged positive particle concentration [mol.m-3]',\n",
+       " 'R-averaged positive particle concentration',\n",
+       " 'R-averaged positive particle concentration [mol.m-3]',\n",
+       " 'Average positive particle concentration',\n",
+       " 'Average positive particle concentration [mol.m-3]',\n",
+       " 'Positive particle surface stoichiometry',\n",
+       " 'Positive particle surface concentration',\n",
+       " 'Positive particle surface concentration [mol.m-3]',\n",
+       " 'X-averaged positive particle surface concentration',\n",
+       " 'X-averaged positive particle surface concentration [mol.m-3]',\n",
+       " 'Positive electrode extent of lithiation',\n",
+       " 'X-averaged positive electrode extent of lithiation',\n",
+       " 'Minimum positive particle concentration',\n",
+       " 'Maximum positive particle concentration',\n",
+       " 'Minimum positive particle concentration [mol.m-3]',\n",
+       " 'Maximum positive particle concentration [mol.m-3]',\n",
+       " 'Minimum positive particle surface concentration',\n",
+       " 'Maximum positive particle surface concentration',\n",
+       " 'Minimum positive particle surface concentration [mol.m-3]',\n",
+       " 'Maximum positive particle surface concentration [mol.m-3]',\n",
+       " 'Negative electrode potential [V]',\n",
+       " 'X-averaged negative electrode potential [V]',\n",
+       " 'Negative electrode ohmic losses [V]',\n",
+       " 'X-averaged negative electrode ohmic losses [V]',\n",
+       " 'Gradient of negative electrode potential [V.m-1]',\n",
+       " 'Positive electrode potential [V]',\n",
+       " 'X-averaged positive electrode potential [V]',\n",
+       " 'Positive electrode ohmic losses [V]',\n",
+       " 'X-averaged positive electrode ohmic losses [V]',\n",
+       " 'Gradient of positive electrode potential [V.m-1]',\n",
+       " 'Porosity times concentration [mol.m-3]',\n",
+       " 'Negative electrode porosity times concentration [mol.m-3]',\n",
+       " 'Separator porosity times concentration [mol.m-3]',\n",
+       " 'Positive electrode porosity times concentration [mol.m-3]',\n",
+       " 'Total lithium in electrolyte [mol]',\n",
+       " 'Electrolyte potential [V]',\n",
+       " 'X-averaged electrolyte potential [V]',\n",
+       " 'X-averaged electrolyte overpotential [V]',\n",
+       " 'Gradient of electrolyte potential [V.m-1]',\n",
+       " 'Negative electrolyte potential [V]',\n",
+       " 'X-averaged negative electrolyte potential [V]',\n",
+       " 'Gradient of negative electrolyte potential [V.m-1]',\n",
+       " 'Separator electrolyte potential [V]',\n",
+       " 'X-averaged separator electrolyte potential [V]',\n",
+       " 'Gradient of separator electrolyte potential [V.m-1]',\n",
+       " 'Positive electrolyte potential [V]',\n",
+       " 'X-averaged positive electrolyte potential [V]',\n",
+       " 'Gradient of positive electrolyte potential [V.m-1]',\n",
+       " 'Ambient temperature [K]',\n",
+       " 'Cell temperature [K]',\n",
+       " 'Negative current collector temperature [K]',\n",
+       " 'Positive current collector temperature [K]',\n",
+       " 'X-averaged cell temperature [K]',\n",
+       " 'Volume-averaged cell temperature [K]',\n",
+       " 'Negative electrode temperature [K]',\n",
+       " 'X-averaged negative electrode temperature [K]',\n",
+       " 'Separator temperature [K]',\n",
+       " 'X-averaged separator temperature [K]',\n",
+       " 'Positive electrode temperature [K]',\n",
+       " 'X-averaged positive electrode temperature [K]',\n",
+       " 'Ambient temperature [C]',\n",
+       " 'Cell temperature [C]',\n",
+       " 'Negative current collector temperature [C]',\n",
+       " 'Positive current collector temperature [C]',\n",
+       " 'X-averaged cell temperature [C]',\n",
+       " 'Volume-averaged cell temperature [C]',\n",
+       " 'Negative electrode temperature [C]',\n",
+       " 'X-averaged negative electrode temperature [C]',\n",
+       " 'Separator temperature [C]',\n",
+       " 'X-averaged separator temperature [C]',\n",
+       " 'Positive electrode temperature [C]',\n",
+       " 'X-averaged positive electrode temperature [C]',\n",
+       " 'Negative current collector potential [V]',\n",
+       " 'Inner SEI thickness [m]',\n",
+       " 'Outer SEI thickness [m]',\n",
+       " 'X-averaged inner SEI thickness [m]',\n",
+       " 'X-averaged outer SEI thickness [m]',\n",
+       " 'SEI [m]',\n",
+       " 'Total SEI thickness [m]',\n",
+       " 'X-averaged SEI thickness [m]',\n",
+       " 'X-averaged total SEI thickness [m]',\n",
+       " 'X-averaged negative electrode resistance [Ohm.m2]',\n",
+       " 'Inner SEI interfacial current density [A.m-2]',\n",
+       " 'X-averaged inner SEI interfacial current density [A.m-2]',\n",
+       " 'Outer SEI interfacial current density [A.m-2]',\n",
+       " 'X-averaged outer SEI interfacial current density [A.m-2]',\n",
+       " 'SEI interfacial current density [A.m-2]',\n",
+       " 'X-averaged SEI interfacial current density [A.m-2]',\n",
+       " 'Inner SEI on cracks thickness [m]',\n",
+       " 'Outer SEI on cracks thickness [m]',\n",
+       " 'X-averaged inner SEI on cracks thickness [m]',\n",
+       " 'X-averaged outer SEI on cracks thickness [m]',\n",
+       " 'SEI on cracks [m]',\n",
+       " 'Total SEI on cracks thickness [m]',\n",
+       " 'X-averaged SEI on cracks thickness [m]',\n",
+       " 'X-averaged total SEI on cracks thickness [m]',\n",
+       " 'Inner SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'X-averaged inner SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'Outer SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'X-averaged outer SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'X-averaged SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'Lithium plating concentration [mol.m-3]',\n",
+       " 'X-averaged lithium plating concentration [mol.m-3]',\n",
+       " 'Dead lithium concentration [mol.m-3]',\n",
+       " 'X-averaged dead lithium concentration [mol.m-3]',\n",
+       " 'Lithium plating thickness [m]',\n",
+       " 'X-averaged lithium plating thickness [m]',\n",
+       " 'Dead lithium thickness [m]',\n",
+       " 'X-averaged dead lithium thickness [m]',\n",
+       " 'Loss of lithium to lithium plating [mol]',\n",
+       " 'Loss of capacity to lithium plating [A.h]',\n",
+       " 'Negative electrode lithium plating reaction overpotential [V]',\n",
+       " 'X-averaged negative electrode lithium plating reaction overpotential [V]',\n",
+       " 'Lithium plating interfacial current density [A.m-2]',\n",
+       " 'X-averaged lithium plating interfacial current density [A.m-2]',\n",
+       " 'Negative crack surface to volume ratio [m-1]',\n",
+       " 'Negative electrode roughness ratio',\n",
+       " 'X-averaged negative electrode roughness ratio',\n",
+       " 'Positive crack surface to volume ratio [m-1]',\n",
+       " 'Positive electrode roughness ratio',\n",
+       " 'X-averaged positive electrode roughness ratio',\n",
+       " 'Electrolyte transport efficiency',\n",
+       " 'Negative electrolyte transport efficiency',\n",
+       " 'X-averaged negative electrolyte transport efficiency',\n",
+       " 'Separator electrolyte transport efficiency',\n",
+       " 'X-averaged separator electrolyte transport efficiency',\n",
+       " 'Positive electrolyte transport efficiency',\n",
+       " 'X-averaged positive electrolyte transport efficiency',\n",
+       " 'Electrode transport efficiency',\n",
+       " 'Negative electrode transport efficiency',\n",
+       " 'X-averaged negative electrode transport efficiency',\n",
+       " 'Separator electrode transport efficiency',\n",
+       " 'X-averaged separator electrode transport efficiency',\n",
+       " 'Positive electrode transport efficiency',\n",
+       " 'X-averaged positive electrode transport efficiency',\n",
+       " 'Separator volume-averaged velocity [m.s-1]',\n",
+       " 'Separator volume-averaged acceleration [m.s-2]',\n",
+       " 'X-averaged separator volume-averaged acceleration [m.s-2]',\n",
+       " 'Volume-averaged velocity [m.s-1]',\n",
+       " 'Volume-averaged acceleration [m.s-1]',\n",
+       " 'X-averaged volume-averaged acceleration [m.s-1]',\n",
+       " 'Pressure [Pa]',\n",
+       " 'Negative electrode open-circuit potential [V]',\n",
+       " 'X-averaged negative electrode open-circuit potential [V]',\n",
+       " 'Negative electrode entropic change [V.K-1]',\n",
+       " 'X-averaged negative electrode entropic change [V.K-1]',\n",
+       " 'Positive electrode open-circuit potential [V]',\n",
+       " 'X-averaged positive electrode open-circuit potential [V]',\n",
+       " 'Positive electrode entropic change [V.K-1]',\n",
+       " 'X-averaged positive electrode entropic change [V.K-1]',\n",
+       " 'Negative electrode effective conductivity',\n",
+       " 'Negative electrode current density [A.m-2]',\n",
+       " 'Positive electrode effective conductivity',\n",
+       " 'Positive electrode current density [A.m-2]',\n",
+       " 'Electrode current density [A.m-2]',\n",
+       " 'Positive current collector potential [V]',\n",
+       " 'Local voltage [V]',\n",
+       " 'Voltage [V]',\n",
+       " 'Contact overpotential [V]',\n",
+       " 'Electrolyte concentration concatenation [mol.m-3]',\n",
+       " 'Negative electrolyte concentration [mol.m-3]',\n",
+       " 'X-averaged negative electrolyte concentration [mol.m-3]',\n",
+       " 'Separator electrolyte concentration [mol.m-3]',\n",
+       " 'X-averaged separator electrolyte concentration [mol.m-3]',\n",
+       " 'Positive electrolyte concentration [mol.m-3]',\n",
+       " 'X-averaged positive electrolyte concentration [mol.m-3]',\n",
+       " 'Negative electrolyte concentration',\n",
+       " 'Negative electrolyte concentration [Molar]',\n",
+       " 'X-averaged negative electrolyte concentration',\n",
+       " 'X-averaged negative electrolyte concentration [Molar]',\n",
+       " 'Separator electrolyte concentration',\n",
+       " 'Separator electrolyte concentration [Molar]',\n",
+       " 'X-averaged separator electrolyte concentration',\n",
+       " 'X-averaged separator electrolyte concentration [Molar]',\n",
+       " 'Positive electrolyte concentration',\n",
+       " 'Positive electrolyte concentration [Molar]',\n",
+       " 'X-averaged positive electrolyte concentration',\n",
+       " 'X-averaged positive electrolyte concentration [Molar]',\n",
+       " 'Electrolyte concentration [mol.m-3]',\n",
+       " 'X-averaged electrolyte concentration [mol.m-3]',\n",
+       " 'Electrolyte concentration',\n",
+       " 'Electrolyte concentration [Molar]',\n",
+       " 'X-averaged electrolyte concentration',\n",
+       " 'X-averaged electrolyte concentration [Molar]',\n",
+       " 'Electrolyte current density [A.m-2]',\n",
+       " 'X-averaged concentration overpotential [V]',\n",
+       " 'X-averaged electrolyte ohmic losses [V]',\n",
+       " 'Negative electrode surface potential difference [V]',\n",
+       " 'X-averaged negative electrode surface potential difference [V]',\n",
+       " 'Positive electrode surface potential difference [V]',\n",
+       " 'X-averaged positive electrode surface potential difference [V]',\n",
+       " 'Ohmic heating [W.m-3]',\n",
+       " 'X-averaged Ohmic heating [W.m-3]',\n",
+       " 'Volume-averaged Ohmic heating [W.m-3]',\n",
+       " 'Irreversible electrochemical heating [W.m-3]',\n",
+       " 'X-averaged irreversible electrochemical heating [W.m-3]',\n",
+       " 'Volume-averaged irreversible electrochemical heating [W.m-3]',\n",
+       " 'Reversible heating [W.m-3]',\n",
+       " 'X-averaged reversible heating [W.m-3]',\n",
+       " 'Volume-averaged reversible heating [W.m-3]',\n",
+       " 'Total heating [W.m-3]',\n",
+       " 'X-averaged total heating [W.m-3]',\n",
+       " 'Volume-averaged total heating [W.m-3]',\n",
+       " 'Current collector current density [A.m-2]',\n",
+       " 'Inner SEI concentration [mol.m-3]',\n",
+       " 'X-averaged inner SEI concentration [mol.m-3]',\n",
+       " 'Outer SEI concentration [mol.m-3]',\n",
+       " 'X-averaged outer SEI concentration [mol.m-3]',\n",
+       " 'SEI concentration [mol.m-3]',\n",
+       " 'X-averaged SEI concentration [mol.m-3]',\n",
+       " 'Loss of lithium to SEI [mol]',\n",
+       " 'Loss of capacity to SEI [A.h]',\n",
+       " 'X-averaged negative electrode SEI interfacial current density [A.m-2]',\n",
+       " 'Negative electrode SEI interfacial current density [A.m-2]',\n",
+       " 'Positive electrode SEI interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode SEI volumetric interfacial current density [A.m-2]',\n",
+       " 'Positive electrode SEI volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged negative electrode SEI volumetric interfacial current density [A.m-3]',\n",
+       " 'Inner SEI on cracks concentration [mol.m-3]',\n",
+       " 'X-averaged inner SEI on cracks concentration [mol.m-3]',\n",
+       " 'Outer SEI on cracks concentration [mol.m-3]',\n",
+       " 'X-averaged outer SEI on cracks concentration [mol.m-3]',\n",
+       " 'SEI on cracks concentration [mol.m-3]',\n",
+       " 'X-averaged SEI on cracks concentration [mol.m-3]',\n",
+       " 'Loss of lithium to SEI on cracks [mol]',\n",
+       " 'Loss of capacity to SEI on cracks [A.h]',\n",
+       " 'X-averaged negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'Negative electrode SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'Positive electrode SEI on cracks interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode SEI on cracks volumetric interfacial current density [A.m-2]',\n",
+       " 'Positive electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged negative electrode SEI on cracks volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode lithium plating interfacial current density [A.m-2]',\n",
+       " 'X-averaged negative electrode lithium plating interfacial current density [A.m-2]',\n",
+       " 'Lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged positive electrode lithium plating interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'Positive electrode lithium plating interfacial current density [A.m-2]',\n",
+       " 'Positive electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode interfacial current density [A.m-2]',\n",
+       " 'X-averaged negative electrode interfacial current density [A.m-2]',\n",
+       " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n",
+       " 'X-averaged negative electrode total volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative electrode exchange current density [A.m-2]',\n",
+       " 'X-averaged negative electrode exchange current density [A.m-2]',\n",
+       " 'Negative electrode reaction overpotential [V]',\n",
+       " 'X-averaged negative electrode reaction overpotential [V]',\n",
+       " 'Negative electrode volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged negative electrode volumetric interfacial current density [A.m-3]',\n",
+       " 'SEI film overpotential [V]',\n",
+       " 'X-averaged SEI film overpotential [V]',\n",
+       " 'Positive electrode interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n",
+       " 'X-averaged positive electrode total volumetric interfacial current density [A.m-3]',\n",
+       " 'Positive electrode exchange current density [A.m-2]',\n",
+       " 'X-averaged positive electrode exchange current density [A.m-2]',\n",
+       " 'Positive electrode reaction overpotential [V]',\n",
+       " 'X-averaged positive electrode reaction overpotential [V]',\n",
+       " 'Positive electrode volumetric interfacial current density [A.m-3]',\n",
+       " 'X-averaged positive electrode volumetric interfacial current density [A.m-3]',\n",
+       " 'Negative particle rhs [mol.m-3.s-1]',\n",
+       " 'Negative particle bc [mol.m-2]',\n",
+       " 'Negative particle effective diffusivity [m2.s-1]',\n",
+       " 'X-averaged negative particle effective diffusivity [m2.s-1]',\n",
+       " 'Negative particle flux [mol.m-2.s-1]',\n",
+       " 'Negative electrode stoichiometry',\n",
+       " 'Negative electrode volume-averaged concentration',\n",
+       " 'Negative electrode volume-averaged concentration [mol.m-3]',\n",
+       " 'Total lithium in primary phase in negative electrode [mol]',\n",
+       " 'Positive particle rhs [mol.m-3.s-1]',\n",
+       " 'Positive particle bc [mol.m-2]',\n",
+       " 'Positive particle effective diffusivity [m2.s-1]',\n",
+       " 'X-averaged positive particle effective diffusivity [m2.s-1]',\n",
+       " 'Positive particle flux [mol.m-2.s-1]',\n",
+       " 'Positive electrode stoichiometry',\n",
+       " 'Positive electrode volume-averaged concentration',\n",
+       " 'Positive electrode volume-averaged concentration [mol.m-3]',\n",
+       " 'Total lithium in primary phase in positive electrode [mol]',\n",
+       " 'Electrolyte flux [mol.m-2.s-1]',\n",
+       " 'Electrolyte diffusion flux [mol.m-2.s-1]',\n",
+       " 'Electrolyte migration flux [mol.m-2.s-1]',\n",
+       " 'Electrolyte convection flux [mol.m-2.s-1]',\n",
+       " 'Sum of negative electrode electrolyte reaction source terms [A.m-3]',\n",
+       " 'Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]',\n",
+       " 'Sum of negative electrode volumetric interfacial current densities [A.m-3]',\n",
+       " 'Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]',\n",
+       " 'Sum of positive electrode electrolyte reaction source terms [A.m-3]',\n",
+       " 'Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]',\n",
+       " 'Sum of positive electrode volumetric interfacial current densities [A.m-3]',\n",
+       " 'Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]',\n",
+       " 'Interfacial current density [A.m-2]',\n",
+       " 'Exchange current density [A.m-2]',\n",
+       " 'Sum of volumetric interfacial current densities [A.m-3]',\n",
+       " 'Sum of electrolyte reaction source terms [A.m-3]',\n",
+       " 'X-averaged open-circuit voltage [V]',\n",
+       " 'Measured open-circuit voltage [V]',\n",
+       " 'X-averaged reaction overpotential [V]',\n",
+       " 'X-averaged solid phase ohmic losses [V]',\n",
+       " 'X-averaged battery open-circuit voltage [V]',\n",
+       " 'Measured battery open-circuit voltage [V]',\n",
+       " 'X-averaged battery reaction overpotential [V]',\n",
+       " 'X-averaged battery solid phase ohmic losses [V]',\n",
+       " 'X-averaged battery electrolyte ohmic losses [V]',\n",
+       " 'X-averaged battery concentration overpotential [V]',\n",
+       " 'Battery voltage [V]',\n",
+       " 'Change in measured open-circuit voltage [V]',\n",
+       " 'Local ECM resistance [Ohm]',\n",
+       " 'Terminal power [W]',\n",
+       " 'Power [W]',\n",
+       " 'Resistance [Ohm]',\n",
+       " 'Total lithium in negative electrode [mol]',\n",
+       " 'LAM_ne [%]',\n",
+       " 'Loss of active material in negative electrode [%]',\n",
+       " 'Total lithium in positive electrode [mol]',\n",
+       " 'LAM_pe [%]',\n",
+       " 'Loss of active material in positive electrode [%]',\n",
+       " 'LLI [%]',\n",
+       " 'Loss of lithium inventory [%]',\n",
+       " 'Loss of lithium inventory, including electrolyte [%]',\n",
+       " 'Total lithium [mol]',\n",
+       " 'Total lithium in particles [mol]',\n",
+       " 'Total lithium capacity [A.h]',\n",
+       " 'Total lithium capacity in particles [A.h]',\n",
+       " 'Total lithium lost [mol]',\n",
+       " 'Total lithium lost from particles [mol]',\n",
+       " 'Total lithium lost from electrolyte [mol]',\n",
+       " 'Total lithium lost to side reactions [mol]',\n",
+       " 'Total capacity lost to side reactions [A.h]']"
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_dfn.variable_names()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a _lot_ of variables. You can also search the list of variables for a particular string (e.g. \"electrolyte\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, we may be interested in plotting both the electrolyte concentration and the voltage. In which case, we would do:"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Electrolyte concentration\n",
+      "Electrolyte concentration [Molar]\n",
+      "Electrolyte concentration [mol.m-3]\n",
+      "Electrolyte concentration concatenation [mol.m-3]\n",
+      "Electrolyte convection flux [mol.m-2.s-1]\n",
+      "Electrolyte current density [A.m-2]\n",
+      "Electrolyte diffusion flux [mol.m-2.s-1]\n",
+      "Electrolyte flux [mol.m-2.s-1]\n",
+      "Electrolyte migration flux [mol.m-2.s-1]\n",
+      "Electrolyte potential [V]\n",
+      "Electrolyte transport efficiency\n",
+      "Gradient of electrolyte potential [V.m-1]\n",
+      "Gradient of negative electrolyte potential [V.m-1]\n",
+      "Gradient of positive electrolyte potential [V.m-1]\n",
+      "Gradient of separator electrolyte potential [V.m-1]\n",
+      "Loss of lithium inventory, including electrolyte [%]\n",
+      "Negative electrolyte concentration\n",
+      "Negative electrolyte concentration [Molar]\n",
+      "Negative electrolyte concentration [mol.m-3]\n",
+      "Negative electrolyte potential [V]\n",
+      "Negative electrolyte transport efficiency\n",
+      "Positive electrolyte concentration\n",
+      "Positive electrolyte concentration [Molar]\n",
+      "Positive electrolyte concentration [mol.m-3]\n",
+      "Positive electrolyte potential [V]\n",
+      "Positive electrolyte transport efficiency\n",
+      "Separator electrolyte concentration\n",
+      "Separator electrolyte concentration [Molar]\n",
+      "Separator electrolyte concentration [mol.m-3]\n",
+      "Separator electrolyte potential [V]\n",
+      "Separator electrolyte transport efficiency\n",
+      "Sum of electrolyte reaction source terms [A.m-3]\n",
+      "Sum of negative electrode electrolyte reaction source terms [A.m-3]\n",
+      "Sum of positive electrode electrolyte reaction source terms [A.m-3]\n",
+      "Sum of x-averaged negative electrode electrolyte reaction source terms [A.m-3]\n",
+      "Sum of x-averaged positive electrode electrolyte reaction source terms [A.m-3]\n",
+      "Total lithium in electrolyte [mol]\n",
+      "Total lithium lost from electrolyte [mol]\n",
+      "X-averaged battery electrolyte ohmic losses [V]\n",
+      "X-averaged electrolyte concentration\n",
+      "X-averaged electrolyte concentration [Molar]\n",
+      "X-averaged electrolyte concentration [mol.m-3]\n",
+      "X-averaged electrolyte ohmic losses [V]\n",
+      "X-averaged electrolyte overpotential [V]\n",
+      "X-averaged electrolyte potential [V]\n",
+      "X-averaged negative electrolyte concentration\n",
+      "X-averaged negative electrolyte concentration [Molar]\n",
+      "X-averaged negative electrolyte concentration [mol.m-3]\n",
+      "X-averaged negative electrolyte potential [V]\n",
+      "X-averaged negative electrolyte transport efficiency\n",
+      "X-averaged positive electrolyte concentration\n",
+      "X-averaged positive electrolyte concentration [Molar]\n",
+      "X-averaged positive electrolyte concentration [mol.m-3]\n",
+      "X-averaged positive electrolyte potential [V]\n",
+      "X-averaged positive electrolyte transport efficiency\n",
+      "X-averaged separator electrolyte concentration\n",
+      "X-averaged separator electrolyte concentration [Molar]\n",
+      "X-averaged separator electrolyte concentration [mol.m-3]\n",
+      "X-averaged separator electrolyte potential [V]\n",
+      "X-averaged separator electrolyte transport efficiency\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_dfn.variables.search(\"electrolyte\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have tried to make variables names fairly self explanatory."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a first example, we choose to plot the voltage. We add this to a list and then pass this list to the `plot` method of our simulation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a6cc55c5b66b4ce78ca2cae475435df1",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x13a8fbe80>"
-            ]
-          },
-          "execution_count": 5,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n",
-        "sim_dfn.plot(output_variables=output_variables)"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c87342bdc1e425ba87715d286d799d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can also plot multiple variables on the same plot by nesting lists"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x1077ee910>"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_variables = [\"Voltage [V]\"]\n",
+    "sim_dfn.plot(output_variables=output_variables)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we may be interested in plotting both the electrolyte concentration and the voltage. In which case, we would do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "974616973e534d219b0d196b893f522b",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x13aaadd60>"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "sim_dfn.plot([[\"Electrode current density [A.m-2]\", \"Electrolyte current density [A.m-2]\"], \"Voltage [V]\"])"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6cc55c5b66b4ce78ca2cae475435df1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "467c303add6f439fa35d549653026823",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x13ab92e20>"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "sim_dfn.plot()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x13a8fbe80>"
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_variables = [\"Electrolyte concentration [mol.m-3]\", \"Voltage [V]\"]\n",
+    "sim_dfn.plot(output_variables=output_variables)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also plot multiple variables on the same plot by nesting lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For plotting the voltage components you can use the `plot_votage_components` function"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "974616973e534d219b0d196b893f522b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time [h]'>)"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pybamm.plot_voltage_components(sim_dfn.solution)"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x13aaadd60>"
       ]
-    },
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_dfn.plot(\n",
+    "    [\n",
+    "        [\"Electrode current density [A.m-2]\", \"Electrolyte current density [A.m-2]\"],\n",
+    "        \"Voltage [V]\",\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "And with a few modifications (by creating subplots and by providing the axes on which the voltage components have to be plotted), it can also be used to compare the voltage components of different simulations"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "467c303add6f439fa35d549653026823",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1500x600 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# simulating and solving Single Particle Model\n",
-        "model_spm = pybamm.lithium_ion.SPM()\n",
-        "sim_spm = pybamm.Simulation(model_spm)\n",
-        "sim_spm.solve([0, 3700])\n",
-        "\n",
-        "# comparing voltage components for Doyle-Fuller-Newman model and Single Particle Model\n",
-        "fig, axes = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n",
-        "\n",
-        "pybamm.plot_voltage_components(sim_dfn.solution, ax=axes.flat[0])\n",
-        "pybamm.plot_voltage_components(sim_spm.solution, ax=axes.flat[1])\n",
-        "\n",
-        "axes.flat[0].set_title(\"Doyle-Fuller-Newman Model\")\n",
-        "axes.flat[1].set_title(\"Single Particle Model\")\n",
-        "\n",
-        "plt.show()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x13ab92e20>"
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim_dfn.plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For plotting the voltage components you can use the `plot_votage_components` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this tutorial we have seen how to use the plotting functionality in PyBaMM.\n",
-        "\n",
-        "In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <AxesSubplot: xlabel='Time [h]'>)"
       ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pybamm.plot_voltage_components(sim_dfn.solution)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And with a few modifications (by creating subplots and by providing the axes on which the voltage components have to be plotted), it can also be used to compare the voltage components of different simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
-            "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
-            "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-            "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[6] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x600 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "pybamm",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
-      }
+   ],
+   "source": [
+    "# simulating and solving Single Particle Model\n",
+    "model_spm = pybamm.lithium_ion.SPM()\n",
+    "sim_spm = pybamm.Simulation(model_spm)\n",
+    "sim_spm.solve([0, 3700])\n",
+    "\n",
+    "# comparing voltage components for Doyle-Fuller-Newman model and Single Particle Model\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(15, 6), sharey=True)\n",
+    "\n",
+    "pybamm.plot_voltage_components(sim_dfn.solution, ax=axes.flat[0])\n",
+    "pybamm.plot_voltage_components(sim_spm.solution, ax=axes.flat[1])\n",
+    "\n",
+    "axes.flat[0].set_title(\"Doyle-Fuller-Newman Model\")\n",
+    "axes.flat[1].set_title(\"Single Particle Model\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we have seen how to use the plotting functionality in PyBaMM.\n",
+    "\n",
+    "In [Tutorial 4](./tutorial-4-setting-parameter-values.ipynb) we show how to change parameter values."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
+      "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n",
+      "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[6] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pybamm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
+  "vscode": {
+   "interpreter": {
+    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
index 64a345c312..8ac3cf2eda 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb
@@ -35,7 +35,8 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -323,8 +324,8 @@
    "outputs": [],
    "source": [
     "parameter_values[\"Current function [A]\"] = 10\n",
-    "parameter_values[\"Open-circuit voltage at 100% SOC [V]\"]=3.4\n",
-    "parameter_values[\"Open-circuit voltage at 0% SOC [V]\"]=3.0"
+    "parameter_values[\"Open-circuit voltage at 100% SOC [V]\"] = 3.4\n",
+    "parameter_values[\"Open-circuit voltage at 0% SOC [V]\"] = 3.0"
    ]
   },
   {
@@ -366,8 +367,8 @@
     }
    ],
    "source": [
-    "sim = pybamm.Simulation(model,parameter_values=parameter_values)\n",
-    "sim.solve([0, 3600],initial_soc=1)\n",
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n",
+    "sim.solve([0, 3600], initial_soc=1)\n",
     "sim.plot()"
    ]
   },
@@ -401,10 +402,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd    # needed to read the csv data file\n",
+    "import pandas as pd  # needed to read the csv data file\n",
     "\n",
     "# Import drive cycle from file\n",
-    "drive_cycle = pd.read_csv(\"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None).to_numpy()\n",
+    "drive_cycle = pd.read_csv(\n",
+    "    \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n",
+    ").to_numpy()\n",
     "\n",
     "# Create interpolant\n",
     "current_interpolant = pybamm.Interpolant(drive_cycle[:, 0], drive_cycle[:, 1], pybamm.t)\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
index 3aad616445..9ec8d79cf1 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-5-run-experiments.ipynb
@@ -62,12 +62,15 @@
    "source": [
     "experiment = pybamm.Experiment(\n",
     "    [\n",
-    "        (\"Discharge at C/10 for 10 hours or until 3.3 V\",\n",
-    "        \"Rest for 1 hour\",\n",
-    "        \"Charge at 1 A until 4.1 V\",\n",
-    "        \"Hold at 4.1 V until 50 mA\",\n",
-    "        \"Rest for 1 hour\"),\n",
-    "    ] * 3\n",
+    "        (\n",
+    "            \"Discharge at C/10 for 10 hours or until 3.3 V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at 1 A until 4.1 V\",\n",
+    "            \"Hold at 4.1 V until 50 mA\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "        ),\n",
+    "    ]\n",
+    "    * 3\n",
     ")"
    ]
   },
@@ -196,7 +199,9 @@
     }
    ],
    "source": [
-    "[(\"Discharge at 1C for 0.5 hours\", \"Discharge at C/20 for 0.5 hours\")] * 3 + [(\"Charge at 0.5 C for 45 minutes\",)]"
+    "[(\"Discharge at 1C for 0.5 hours\", \"Discharge at C/20 for 0.5 hours\")] * 3 + [\n",
+    "    (\"Charge at 0.5 C for 45 minutes\",)\n",
+    "]"
    ]
   },
   {
@@ -224,7 +229,9 @@
     }
    ],
    "source": [
-    "pybamm.step.string(\"Discharge at 1C for 1 hour\", period=\"1 minute\", temperature=\"25oC\", tags=[\"tag1\"])"
+    "pybamm.step.string(\n",
+    "    \"Discharge at 1C for 1 hour\", period=\"1 minute\", temperature=\"25oC\", tags=[\"tag1\"]\n",
+    ")"
    ]
   },
   {
@@ -336,7 +343,7 @@
    "source": [
     "t = np.linspace(0, 1, 60)\n",
     "sin_t = 0.5 * np.sin(2 * np.pi * t)\n",
-    "drive_cycle_power = np.column_stack([t,sin_t])\n",
+    "drive_cycle_power = np.column_stack([t, sin_t])\n",
     "experiment = pybamm.Experiment([pybamm.step.power(drive_cycle_power)])\n",
     "sim = pybamm.Simulation(model, experiment=experiment)\n",
     "sim.solve()\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
index 3599c37abb..f2e1b9be75 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-6-managing-simulation-outputs.ipynb
@@ -44,6 +44,7 @@
    "source": [
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
+    "\n",
     "model = pybamm.lithium_ion.SPMe()\n",
     "sim = pybamm.Simulation(model)\n",
     "sim.solve([0, 3600])"
@@ -387,8 +388,12 @@
    "source": [
     "sol.save_data(\"sol_data.csv\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"csv\")\n",
     "# matlab needs names without spaces\n",
-    "sol.save_data(\"sol_data.mat\", [\"Current [A]\", \"Voltage [V]\"], to_format=\"matlab\",\n",
-    "              short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"})"
+    "sol.save_data(\n",
+    "    \"sol_data.mat\",\n",
+    "    [\"Current [A]\", \"Voltage [V]\"],\n",
+    "    to_format=\"matlab\",\n",
+    "    short_names={\"Current [A]\": \"I\", \"Voltage [V]\": \"V\"},\n",
+    ")"
    ]
   },
   {
@@ -414,6 +419,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.remove(\"SPMe.pkl\")\n",
     "os.remove(\"SPMe_sol.pkl\")\n",
     "os.remove(\"sol_data.pkl\")\n",
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
index 96f6e203f2..8969afc15a 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-7-model-options.ipynb
@@ -70,7 +70,7 @@
     }
    ],
    "source": [
-    "model = pybamm.lithium_ion.SPMe(options=options) # loading in options\n",
+    "model = pybamm.lithium_ion.SPMe(options=options)  # loading in options\n",
     "\n",
     "sim = pybamm.Simulation(model)\n",
     "sim.solve([0, 3600])"
@@ -115,7 +115,9 @@
     }
    ],
    "source": [
-    "sim.plot([\"Cell temperature [K]\", \"Total heating [W.m-3]\", \"Current [A]\", \"Voltage [V]\"])"
+    "sim.plot(\n",
+    "    [\"Cell temperature [K]\", \"Total heating [W.m-3]\", \"Current [A]\", \"Voltage [V]\"]\n",
+    ")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
index ee4cdc7f63..7cee8dd679 100644
--- a/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
+++ b/docs/source/examples/notebooks/getting_started/tutorial-9-changing-the-mesh.ipynb
@@ -101,10 +101,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create our dictionary \n",
+    "# create our dictionary\n",
     "var_pts = {\n",
     "    \"x_n\": 10,  # negative electrode\n",
-    "    \"x_s\": 10,  # separator \n",
+    "    \"x_s\": 10,  # separator\n",
     "    \"x_p\": 10,  # positive electrode\n",
     "    \"r_n\": 10,  # negative particle\n",
     "    \"r_p\": 10,  # positive particle\n",
@@ -219,7 +219,7 @@
     "model = pybamm.lithium_ion.DFN()\n",
     "parameter_values = pybamm.ParameterValues(\"Ecker2015\")\n",
     "\n",
-    "# choose solver \n",
+    "# choose solver\n",
     "solver = pybamm.CasadiSolver(mode=\"fast\")\n",
     "\n",
     "# loop over number of mesh points\n",
@@ -227,11 +227,11 @@
     "for N in npts:\n",
     "    var_pts = {\n",
     "        \"x_n\": N,  # negative electrode\n",
-    "        \"x_s\": N,  # separator \n",
+    "        \"x_s\": N,  # separator\n",
     "        \"x_p\": N,  # positive electrode\n",
     "        \"r_n\": N,  # negative particle\n",
     "        \"r_p\": N,  # positive particle\n",
-    "    }    \n",
+    "    }\n",
     "    sim = pybamm.Simulation(\n",
     "        model, solver=solver, parameter_values=parameter_values, var_pts=var_pts\n",
     "    )\n",
@@ -278,7 +278,7 @@
     }
    ],
    "source": [
-    "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], time_unit=\"seconds\", labels=npts)    "
+    "pybamm.dynamic_plot(solutions, [\"Voltage [V]\"], time_unit=\"seconds\", labels=npts)"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
index 8691439334..aa7bea4d5c 100644
--- a/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
+++ b/docs/source/examples/notebooks/initialize-model-with-solution.ipynb
@@ -23,8 +23,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001B[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001B[0m\n",
+      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1 is available.\n",
+      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -36,7 +36,7 @@
     "import pandas as pd\n",
     "import os\n",
     "\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -265,8 +265,12 @@
    ],
    "source": [
     "pybamm.dynamic_plot(\n",
-    "    [sol_US06_1, sol_US06_2, sol_US06_3], \n",
-    "    labels=[\"Default initial conditions\", \"Fully charged (from DFN)\", \"Fully charged (from SPM)\"]\n",
+    "    [sol_US06_1, sol_US06_2, sol_US06_3],\n",
+    "    labels=[\n",
+    "        \"Default initial conditions\",\n",
+    "        \"Fully charged (from DFN)\",\n",
+    "        \"Fully charged (from SPM)\",\n",
+    "    ],\n",
     ")"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
index 59e1e47e97..d3553ed278 100644
--- a/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
+++ b/docs/source/examples/notebooks/models/DFN-with-particle-size-distributions.ipynb
@@ -214,43 +214,93 @@
    ],
    "source": [
     "# The discrete sizes or \"bins\" used\n",
-    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0,0] # const in the x and current collector direction\n",
-    "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:,0,0]\n",
+    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[\n",
+    "    :, 0, 0\n",
+    "]  # const in the x and current collector direction\n",
+    "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:, 0, 0]\n",
     "\n",
     "# The distributions (number, area, and volume-weighted)\n",
-    "f_a_p = sim.solution[\"X-averaged positive area-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_num_p = sim.solution[\"X-averaged positive number-based particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_v_p = sim.solution[\"X-averaged positive volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_a_n = sim.solution[\"X-averaged negative area-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_num_n = sim.solution[\"X-averaged negative number-based particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_v_n = sim.solution[\"X-averaged negative volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
+    "f_a_p = sim.solution[\n",
+    "    \"X-averaged positive area-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_num_p = sim.solution[\n",
+    "    \"X-averaged positive number-based particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_v_p = sim.solution[\n",
+    "    \"X-averaged positive volume-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_a_n = sim.solution[\n",
+    "    \"X-averaged negative area-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_num_n = sim.solution[\n",
+    "    \"X-averaged negative number-based particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_v_n = sim.solution[\n",
+    "    \"X-averaged negative volume-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
     "\n",
     "# plot\n",
-    "f, axs = plt.subplots(1, 2 ,figsize=(10,4))\n",
+    "f, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
     "\n",
     "# negative electrode\n",
-    "width_n = (R_n[-1] - R_n[-2])/ 1e-6\n",
-    "axs[0].bar(R_n / 1e-6, f_a_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:blue\",\n",
-    "           label=\"area-weighted\")\n",
-    "axs[0].bar(R_n / 1e-6, f_num_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:red\",\n",
-    "           label=\"number-weighted\")\n",
-    "axs[0].bar(R_n / 1e-6, f_v_n * 1e-6, width=width_n, alpha=0.3, color=\"tab:green\",\n",
-    "           label=\"volume-weighted\")\n",
-    "axs[0].set_xlim((0,25))\n",
+    "width_n = (R_n[-1] - R_n[-2]) / 1e-6\n",
+    "axs[0].bar(\n",
+    "    R_n / 1e-6,\n",
+    "    f_a_n * 1e-6,\n",
+    "    width=width_n,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"area-weighted\",\n",
+    ")\n",
+    "axs[0].bar(\n",
+    "    R_n / 1e-6,\n",
+    "    f_num_n * 1e-6,\n",
+    "    width=width_n,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"number-weighted\",\n",
+    ")\n",
+    "axs[0].bar(\n",
+    "    R_n / 1e-6,\n",
+    "    f_v_n * 1e-6,\n",
+    "    width=width_n,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:green\",\n",
+    "    label=\"volume-weighted\",\n",
+    ")\n",
+    "axs[0].set_xlim((0, 25))\n",
     "axs[0].set_xlabel(\"Particle size $R_{\\mathrm{n}}$ [$\\mu$m]\", fontsize=12)\n",
     "axs[0].set_ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n",
     "axs[0].legend(fontsize=10)\n",
     "axs[0].set_title(\"Discretized distributions (histograms) in negative electrode\")\n",
     "\n",
     "# positive electrode\n",
-    "width_p = (R_p[-1] - R_p[-2])/ 1e-6\n",
-    "axs[1].bar(R_p / 1e-6, f_a_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:blue\",\n",
-    "           label=\"area-weighted\")\n",
-    "axs[1].bar(R_p / 1e-6, f_num_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:red\",\n",
-    "           label=\"number-weighted\")\n",
-    "axs[1].bar(R_p / 1e-6, f_v_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:green\",\n",
-    "           label=\"volume-weighted\")\n",
-    "axs[1].set_xlim((0,25))\n",
+    "width_p = (R_p[-1] - R_p[-2]) / 1e-6\n",
+    "axs[1].bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_a_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"area-weighted\",\n",
+    ")\n",
+    "axs[1].bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_num_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"number-weighted\",\n",
+    ")\n",
+    "axs[1].bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_v_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:green\",\n",
+    "    label=\"volume-weighted\",\n",
+    ")\n",
+    "axs[1].set_xlim((0, 25))\n",
     "axs[1].set_xlabel(\"Particle size $R_{\\mathrm{p}}$ [$\\mu$m]\", fontsize=12)\n",
     "axs[1].set_ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n",
     "axs[1].set_title(\"Positive electrode\")\n",
@@ -297,6 +347,7 @@
     "def f_a_dist_p_dim(R):\n",
     "    return pybamm.lognormal(R, R_av_p_dim, sd_p_dim)\n",
     "\n",
+    "\n",
     "# Note: the only argument must be the particle size R"
    ]
   },
@@ -310,8 +361,7 @@
     "distribution_params = {\n",
     "    \"Positive minimum particle radius [m]\": R_min_p,\n",
     "    \"Positive maximum particle radius [m]\": R_max_p,\n",
-    "    \"Positive area-weighted \"\n",
-    "    + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
+    "    \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
     "}\n",
     "params.update(distribution_params, check_already_exists=False)"
    ]
@@ -353,10 +403,12 @@
     "output_variables = [\n",
     "    \"X-averaged negative area-weighted particle-size distribution [m-1]\",\n",
     "    \"X-averaged positive area-weighted particle-size distribution [m-1]\",\n",
-    "    \"Voltage [V]\"\n",
+    "    \"Voltage [V]\",\n",
     "]\n",
     "quickplot = pybamm.QuickPlot(\n",
-    "    [sim, sim_custom], output_variables=output_variables, labels=[\"default lognormals\", \"custom\"]\n",
+    "    [sim, sim_custom],\n",
+    "    output_variables=output_variables,\n",
+    "    labels=[\"default lognormals\", \"custom\"],\n",
     ")\n",
     "quickplot.plot(0)"
    ]
@@ -386,15 +438,15 @@
     "models = [\n",
     "    pybamm.lithium_ion.DFN(options={\"particle size\": \"distribution\"}, name=\"MP-DFN\"),\n",
     "    pybamm.lithium_ion.MPM(name=\"MPM\"),\n",
-    "    pybamm.lithium_ion.DFN(name=\"DFN\")\n",
+    "    pybamm.lithium_ion.DFN(name=\"DFN\"),\n",
     "]\n",
     "\n",
     "# parameters\n",
     "params = pybamm.ParameterValues(\"Marquis2019\")\n",
-    "params = pybamm.get_size_distribution_parameters(params) \n",
+    "params = pybamm.get_size_distribution_parameters(params)\n",
     "\n",
     "# define current function\n",
-    "t_cutoff = 3450   # [s]\n",
+    "t_cutoff = 3450  # [s]\n",
     "t_rest = 3600  # [s]\n",
     "I_typ = params[\"Nominal cell capacity [A.h]\"]  # current for 1C\n",
     "\n",
@@ -433,7 +485,7 @@
     }
    ],
    "source": [
-    "# plot current, voltage \n",
+    "# plot current, voltage\n",
     "qp = pybamm.QuickPlot(sims, output_variables=[\"Current [A]\", \"Voltage [V]\"])\n",
     "qp.plot(0)"
    ]
diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb
index d77a0856e3..a6237a2f3f 100644
--- a/docs/source/examples/notebooks/models/DFN.ipynb
+++ b/docs/source/examples/notebooks/models/DFN.ipynb
@@ -197,7 +197,7 @@
    "source": [
     "# solve model\n",
     "solver = model.default_solver\n",
-    "t_eval = np.linspace(0, 3600, 300) # time in seconds\n",
+    "t_eval = np.linspace(0, 3600, 300)  # time in seconds\n",
     "solution = solver.solve(model, t_eval)"
    ]
   },
@@ -230,7 +230,9 @@
     }
    ],
    "source": [
-    "quick_plot = pybamm.QuickPlot(solution, [\"Positive electrode interfacial current density [A.m-2]\"])\n",
+    "quick_plot = pybamm.QuickPlot(\n",
+    "    solution, [\"Positive electrode interfacial current density [A.m-2]\"]\n",
+    ")\n",
     "quick_plot.dynamic_plot();"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/MPM.ipynb b/docs/source/examples/notebooks/models/MPM.ipynb
index c7e1068dc2..82e5a9502d 100644
--- a/docs/source/examples/notebooks/models/MPM.ipynb
+++ b/docs/source/examples/notebooks/models/MPM.ipynb
@@ -180,7 +180,9 @@
     }
    ],
    "source": [
-    "c_n_R_dependent = model.variables[\"X-averaged negative particle concentration distribution [mol.m-3]\"]\n",
+    "c_n_R_dependent = model.variables[\n",
+    "    \"X-averaged negative particle concentration distribution [mol.m-3]\"\n",
+    "]\n",
     "c_n_R_dependent.domains"
    ]
   },
@@ -282,9 +284,9 @@
    ],
    "source": [
     "for k, t in model.default_submesh_types.items():\n",
-    "    print(k,'is of type',t.__name__)\n",
+    "    print(k, \"is of type\", t.__name__)\n",
     "for var, npts in model.default_var_pts.items():\n",
-    "    print(var,'has',npts,'mesh points')"
+    "    print(var, \"has\", npts, \"mesh points\")"
    ]
   },
   {
@@ -371,38 +373,42 @@
    ],
    "source": [
     "# Concentrations as a function of t, r and R\n",
-    "c_s_n = sim.solution[\"X-averaged negative particle concentration distribution [mol.m-3]\"]\n",
-    "c_s_p = sim.solution[\"X-averaged positive particle concentration distribution [mol.m-3]\"]\n",
+    "c_s_n = sim.solution[\n",
+    "    \"X-averaged negative particle concentration distribution [mol.m-3]\"\n",
+    "]\n",
+    "c_s_p = sim.solution[\n",
+    "    \"X-averaged positive particle concentration distribution [mol.m-3]\"\n",
+    "]\n",
     "\n",
     "# r_n, r_p\n",
-    "r_n = sim.solution[\"r_n [m]\"].entries[:,0,0]\n",
-    "r_p = sim.solution[\"r_p [m]\"].entries[:,0,0]\n",
+    "r_n = sim.solution[\"r_n [m]\"].entries[:, 0, 0]\n",
+    "r_p = sim.solution[\"r_p [m]\"].entries[:, 0, 0]\n",
     "# dimensional R_n, R_p\n",
-    "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:,0]\n",
-    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0]\n",
+    "R_n = sim.solution[\"Negative particle sizes [m]\"].entries[:, 0]\n",
+    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:, 0]\n",
     "t = sim.solution[\"Time [s]\"].entries\n",
     "\n",
     "\n",
     "def plot_concentrations(t):\n",
-    "    f, axs = plt.subplots(1, 2 ,figsize=(10,3))    \n",
+    "    f, axs = plt.subplots(1, 2, figsize=(10, 3))\n",
     "    plot_c_n = axs[0].pcolormesh(\n",
     "        R_n, r_n, c_s_n(r=r_n, R=R_n, t=t), vmin=0.15, vmax=0.8\n",
     "    )\n",
     "    plot_c_p = axs[1].pcolormesh(\n",
     "        R_p, r_p, c_s_p(r=r_p, R=R_p, t=t), vmin=0.6, vmax=0.95\n",
     "    )\n",
-    "    axs[0].set_xlabel(r'$R_n$ [$\\mu$m]')\n",
-    "    axs[1].set_xlabel(r'$R_p$ [$\\mu$m]')\n",
-    "    axs[0].set_ylabel(r'$r_n / R_n$')\n",
-    "    axs[1].set_ylabel(r'$r_p / R_p$')\n",
-    "    axs[0].set_title('Concentration in negative particles [mol.m-3]')\n",
-    "    axs[1].set_title('Concentration in positive particles [mol.m-3]')\n",
+    "    axs[0].set_xlabel(r\"$R_n$ [$\\mu$m]\")\n",
+    "    axs[1].set_xlabel(r\"$R_p$ [$\\mu$m]\")\n",
+    "    axs[0].set_ylabel(r\"$r_n / R_n$\")\n",
+    "    axs[1].set_ylabel(r\"$r_p / R_p$\")\n",
+    "    axs[0].set_title(\"Concentration in negative particles [mol.m-3]\")\n",
+    "    axs[1].set_title(\"Concentration in positive particles [mol.m-3]\")\n",
     "    plt.colorbar(plot_c_n, ax=axs[0])\n",
     "    plt.colorbar(plot_c_p, ax=axs[1])\n",
-    "    \n",
+    "\n",
     "    plt.show()\n",
-    "    \n",
-    "    \n",
+    "\n",
+    "\n",
     "# initial time\n",
     "plot_concentrations(t[0])"
    ]
@@ -464,7 +470,7 @@
     "R_a_p_dim = params[\"Positive particle radius [m]\"]\n",
     "\n",
     "# Standard deviations (dimensional)\n",
-    "sd_a_n_dim = 0.2 * R_a_n_dim \n",
+    "sd_a_n_dim = 0.2 * R_a_n_dim\n",
     "sd_a_p_dim = 0.6 * R_a_p_dim\n",
     "\n",
     "# Minimum and maximum particle sizes (dimensional)\n",
@@ -484,6 +490,7 @@
     "def f_a_dist_p_dim(R):\n",
     "    return pybamm.lognormal(R, R_a_p_dim, sd_a_p_dim)\n",
     "\n",
+    "\n",
     "# Note: the only argument must be the particle size R"
    ]
   },
@@ -499,10 +506,8 @@
     "    \"Positive minimum particle radius [m]\": R_min_p,\n",
     "    \"Negative maximum particle radius [m]\": R_max_n,\n",
     "    \"Positive maximum particle radius [m]\": R_max_p,\n",
-    "    \"Negative area-weighted \"\n",
-    "    + \"particle-size distribution [m-1]\": f_a_dist_n_dim,\n",
-    "    \"Positive area-weighted \"\n",
-    "    + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
+    "    \"Negative area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_n_dim,\n",
+    "    \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
     "}\n",
     "params.update(distribution_params, check_already_exists=False)"
    ]
@@ -572,22 +577,48 @@
    ],
    "source": [
     "# The discrete sizes or \"bins\" used, and the distributions\n",
-    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[:,0] # const in the current collector direction\n",
+    "R_p = sim.solution[\"Positive particle sizes [m]\"].entries[\n",
+    "    :, 0\n",
+    "]  # const in the current collector direction\n",
     "# The distributions\n",
-    "f_a_p = sim.solution[\"X-averaged positive area-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_num_p = sim.solution[\"X-averaged positive number-based particle-size distribution [m-1]\"].entries[:,0]\n",
-    "f_v_p = sim.solution[\"X-averaged positive volume-weighted particle-size distribution [m-1]\"].entries[:,0]\n",
+    "f_a_p = sim.solution[\n",
+    "    \"X-averaged positive area-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_num_p = sim.solution[\n",
+    "    \"X-averaged positive number-based particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
+    "f_v_p = sim.solution[\n",
+    "    \"X-averaged positive volume-weighted particle-size distribution [m-1]\"\n",
+    "].entries[:, 0]\n",
     "\n",
     "\n",
     "# plot\n",
-    "width_p = (R_p[-1] - R_p[-2])/ 1e-6\n",
-    "plt.bar(R_p / 1e-6, f_a_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:blue\",\n",
-    "           label=\"area-weighted\")\n",
-    "plt.bar(R_p / 1e-6, f_num_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:red\",\n",
-    "           label=\"number-weighted\")\n",
-    "plt.bar(R_p / 1e-6, f_v_p * 1e-6, width=width_p, alpha=0.3, color=\"tab:green\",\n",
-    "           label=\"volume-weighted\")\n",
-    "plt.xlim((0,30))\n",
+    "width_p = (R_p[-1] - R_p[-2]) / 1e-6\n",
+    "plt.bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_a_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:blue\",\n",
+    "    label=\"area-weighted\",\n",
+    ")\n",
+    "plt.bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_num_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:red\",\n",
+    "    label=\"number-weighted\",\n",
+    ")\n",
+    "plt.bar(\n",
+    "    R_p / 1e-6,\n",
+    "    f_v_p * 1e-6,\n",
+    "    width=width_p,\n",
+    "    alpha=0.3,\n",
+    "    color=\"tab:green\",\n",
+    "    label=\"volume-weighted\",\n",
+    ")\n",
+    "plt.xlim((0, 30))\n",
     "plt.xlabel(\"Particle size $R_{\\mathrm{p}}$ [$\\mu$m]\", fontsize=12)\n",
     "plt.ylabel(\"[$\\mu$m$^{-1}$]\", fontsize=12)\n",
     "plt.legend(fontsize=10)\n",
@@ -611,7 +642,9 @@
    "outputs": [],
    "source": [
     "# Define standard deviation in negative electrode to vary\n",
-    "sd_a_p_dim = pybamm.Parameter(\"Positive electrode area-weighted particle-size standard deviation [m]\")\n",
+    "sd_a_p_dim = pybamm.Parameter(\n",
+    "    \"Positive electrode area-weighted particle-size standard deviation [m]\"\n",
+    ")\n",
     "\n",
     "# Set the area-weighted particle-size distribution\n",
     "\n",
@@ -624,8 +657,7 @@
     "distribution_params = {\n",
     "    \"Positive electrode area-weighted particle-size \"\n",
     "    + \"standard deviation [m]\": \"[input]\",\n",
-    "    \"Positive area-weighted \"\n",
-    "    + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
+    "    \"Positive area-weighted \" + \"particle-size distribution [m-1]\": f_a_dist_p_dim,\n",
     "}\n",
     "params.update(distribution_params, check_already_exists=False)"
    ]
@@ -666,7 +698,7 @@
     "\n",
     "sim = pybamm.Simulation(model, parameter_values=params, experiment=experiment)\n",
     "solutions = []\n",
-    "for sd_a_p in [0.4, 0.6, 0.8]: \n",
+    "for sd_a_p in [0.4, 0.6, 0.8]:\n",
     "    solution = sim.solve(\n",
     "        inputs={\n",
     "            \"Positive electrode area-weighted particle-size \"\n",
@@ -679,7 +711,7 @@
     "pybamm.dynamic_plot(\n",
     "    solutions,\n",
     "    output_variables=output_variables,\n",
-    "    labels=[\"MPM, sd_a_p=0.4\", \"MPM, sd_a_p=0.6\", \"MPM, sd_a_p=0.8\"]\n",
+    "    labels=[\"MPM, sd_a_p=0.4\", \"MPM, sd_a_p=0.6\", \"MPM, sd_a_p=0.8\"],\n",
     ")"
    ]
   },
@@ -711,7 +743,7 @@
    ],
    "source": [
     "print(\"The mean of the input lognormal was:\", R_a_p_dim)\n",
-    "print(\"The means of discretized distributions are:\") \n",
+    "print(\"The means of discretized distributions are:\")\n",
     "for solution in solutions:\n",
     "    R = solution[\"Positive area-weighted mean particle radius [m]\"]\n",
     "    print(\"Positive area-weighted mean particle radius [m]\", R.entries[0])"
@@ -742,7 +774,7 @@
     "print(0.4 * R_a_p_dim)\n",
     "print(0.6 * R_a_p_dim)\n",
     "print(0.8 * R_a_p_dim)\n",
-    "print(\"The standard deviations of discretized distributions are:\") \n",
+    "print(\"The standard deviations of discretized distributions are:\")\n",
     "for solution in solutions:\n",
     "    sd = solution[\"Positive area-weighted particle-size standard deviation [m]\"]\n",
     "    print(\"Positive area-weighted particle-size standard deviation [m]\", sd.entries[0])"
@@ -788,11 +820,7 @@
     }
    ],
    "source": [
-    "models = [\n",
-    "    pybamm.lithium_ion.SPM(),\n",
-    "    pybamm.lithium_ion.MPM(),\n",
-    "    pybamm.lithium_ion.DFN()\n",
-    "]\n",
+    "models = [pybamm.lithium_ion.SPM(), pybamm.lithium_ion.MPM(), pybamm.lithium_ion.DFN()]\n",
     "\n",
     "# solve\n",
     "sims = []\n",
@@ -856,8 +884,7 @@
    "source": [
     "model_Fickian = pybamm.lithium_ion.MPM(name=\"MPM Fickian\")\n",
     "model_Uniform = pybamm.lithium_ion.MPM(\n",
-    "    name=\"MPM Uniform\",\n",
-    "    options={\"particle\": \"uniform profile\"}\n",
+    "    name=\"MPM Uniform\", options={\"particle\": \"uniform profile\"}\n",
     ")\n",
     "\n",
     "sim_Fickian = pybamm.Simulation(model_Fickian)\n",
@@ -914,7 +941,7 @@
     "    options={\n",
     "        \"current collector\": \"potential pair\",\n",
     "        \"dimensionality\": 1,\n",
-    "        \"particle\": \"uniform profile\", # to reduce computation time\n",
+    "        \"particle\": \"uniform profile\",  # to reduce computation time\n",
     "    }\n",
     ")\n",
     "\n",
diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb
index 6dbe14f484..3f009a045a 100644
--- a/docs/source/examples/notebooks/models/MSMR.ipynb
+++ b/docs/source/examples/notebooks/models/MSMR.ipynb
@@ -373,7 +373,7 @@
     "        \"Negative particle stoichiometry\",\n",
     "        \"Positive particle stoichiometry\",\n",
     "        \"X-averaged negative electrode open-circuit potential [V]\",\n",
-    "        \"X-averaged positive electrode open-circuit potential [V]\",        \n",
+    "        \"X-averaged positive electrode open-circuit potential [V]\",\n",
     "        \"Negative particle potential [V]\",\n",
     "        \"Positive particle potential [V]\",\n",
     "        \"Current [A]\",\n",
@@ -422,8 +422,12 @@
     }
    ],
    "source": [
-    "xns = [f\"Average x_n_{i}\" for i in range(6)]  # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n",
-    "xps = [f\"Average x_p_{i}\" for i in range(4)]  # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n",
+    "xns = [\n",
+    "    f\"Average x_n_{i}\" for i in range(6)\n",
+    "]  # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n",
+    "xps = [\n",
+    "    f\"Average x_p_{i}\" for i in range(4)\n",
+    "]  # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n",
     "sim.plot(\n",
     "    [\n",
     "        xns,\n",
@@ -483,9 +487,7 @@
     "    bottom = top\n",
     "ax[0].set_xlabel(\"Time [h]\")\n",
     "ax[0].set_ylabel(\"x_n [-]\")\n",
-    "ax[0].legend(\n",
-    "    loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n",
-    ")\n",
+    "ax[0].legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)\n",
     "ax[1].plot(time, sol[\"Average positive particle stoichiometry\"].data, \"k-\", label=\"x_p\")\n",
     "bottom = 0\n",
     "for xp in xps:\n",
@@ -494,9 +496,7 @@
     "    bottom = top\n",
     "ax[1].set_xlabel(\"Time [h]\")\n",
     "ax[1].set_ylabel(\"x_p [-]\")\n",
-    "ax[1].legend(\n",
-    "    loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n",
-    ")"
+    "ax[1].legend(loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3)"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
index d70c7032a3..f9d41ffc54 100644
--- a/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
+++ b/docs/source/examples/notebooks/models/SEI-on-cracks.ipynb
@@ -21,8 +21,8 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.0.1\u001B[0m\n",
-      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -48,12 +48,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\", \"particle mechanics\": \"swelling only\"})\n",
-    "model2 = pybamm.lithium_ion.DFN({\n",
-    "    \"particle mechanics\": \"swelling and cracking\",\n",
-    "    \"SEI\": \"solvent-diffusion limited\",\n",
-    "    \"SEI on cracks\": \"true\",\n",
-    "})"
+    "model1 = pybamm.lithium_ion.DFN(\n",
+    "    {\"SEI\": \"solvent-diffusion limited\", \"particle mechanics\": \"swelling only\"}\n",
+    ")\n",
+    "model2 = pybamm.lithium_ion.DFN(\n",
+    "    {\n",
+    "        \"particle mechanics\": \"swelling and cracking\",\n",
+    "        \"SEI\": \"solvent-diffusion limited\",\n",
+    "        \"SEI on cracks\": \"true\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -74,7 +78,7 @@
     "param = pybamm.ParameterValues(\"OKane2022\")\n",
     "var_pts = {\n",
     "    \"x_n\": 20,  # negative electrode\n",
-    "    \"x_s\": 20,  # separator \n",
+    "    \"x_s\": 20,  # separator\n",
     "    \"x_p\": 20,  # positive electrode\n",
     "    \"r_n\": 26,  # negative particle\n",
     "    \"r_p\": 26,  # positive particle\n",
@@ -107,10 +111,16 @@
     }
    ],
    "source": [
-    "exp = pybamm.Experiment([\"Hold at 4.2 V until C/100\", \"Rest for 1 hour\", \"Discharge at 1C until 2.5 V\"])\n",
-    "sim1 = pybamm.Simulation(model1, parameter_values=param, experiment=exp, var_pts=var_pts)\n",
+    "exp = pybamm.Experiment(\n",
+    "    [\"Hold at 4.2 V until C/100\", \"Rest for 1 hour\", \"Discharge at 1C until 2.5 V\"]\n",
+    ")\n",
+    "sim1 = pybamm.Simulation(\n",
+    "    model1, parameter_values=param, experiment=exp, var_pts=var_pts\n",
+    ")\n",
     "sol1 = sim1.solve(calc_esoh=False)\n",
-    "sim2 = pybamm.Simulation(model2, parameter_values=param, experiment=exp, var_pts=var_pts)\n",
+    "sim2 = pybamm.Simulation(\n",
+    "    model2, parameter_values=param, experiment=exp, var_pts=var_pts\n",
+    ")\n",
     "sol2 = sim2.solve(calc_esoh=False)"
    ]
   },
@@ -128,7 +138,10 @@
     "lithium_pos1 = sol1[\"Total lithium in positive electrode [mol]\"].entries\n",
     "t2 = sol2[\"Time [s]\"].entries\n",
     "V2 = sol2[\"Voltage [V]\"].entries\n",
-    "SEI2 = sol2[\"Loss of lithium to negative SEI [mol]\"].entries + sol2[\"Loss of lithium to negative SEI on cracks [mol]\"].entries\n",
+    "SEI2 = (\n",
+    "    sol2[\"Loss of lithium to negative SEI [mol]\"].entries\n",
+    "    + sol2[\"Loss of lithium to negative SEI on cracks [mol]\"].entries\n",
+    ")\n",
     "lithium_neg2 = sol2[\"Total lithium in negative electrode [mol]\"].entries\n",
     "lithium_pos2 = sol2[\"Total lithium in positive electrode [mol]\"].entries"
    ]
@@ -153,14 +166,14 @@
     }
    ],
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n",
-    "ax1.plot(t1,V1,label=\"without cracking\")\n",
-    "ax1.plot(t2,V2,label=\"with cracking\")\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 4))\n",
+    "ax1.plot(t1, V1, label=\"without cracking\")\n",
+    "ax1.plot(t2, V2, label=\"with cracking\")\n",
     "ax1.set_xlabel(\"Time [s]\")\n",
     "ax1.set_ylabel(\"Voltage [V]\")\n",
     "ax1.legend()\n",
-    "ax2.plot(t1,SEI1,label=\"without cracking\")\n",
-    "ax2.plot(t2,SEI2,label=\"with cracking\")\n",
+    "ax2.plot(t1, SEI1, label=\"without cracking\")\n",
+    "ax2.plot(t2, SEI2, label=\"with cracking\")\n",
     "ax2.set_xlabel(\"Time [s]\")\n",
     "ax2.set_ylabel(\"Loss of lithium to SEI [mol]\")\n",
     "ax2.legend()\n",
@@ -196,8 +209,8 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.plot(t2,lithium_neg2+lithium_pos2)\n",
-    "ax.plot(t2,lithium_neg2[0]+lithium_pos2[0]-SEI2,linestyle=\"dashed\")\n",
+    "ax.plot(t2, lithium_neg2 + lithium_pos2)\n",
+    "ax.plot(t2, lithium_neg2[0] + lithium_pos2[0] - SEI2, linestyle=\"dashed\")\n",
     "ax.set_xlabel(\"Time [s]\")\n",
     "ax.set_ylabel(\"Total lithium in electrodes [mol]\")\n",
     "plt.show()"
diff --git a/docs/source/examples/notebooks/models/SPM.ipynb b/docs/source/examples/notebooks/models/SPM.ipynb
index e373bdafb5..9b01b13a80 100644
--- a/docs/source/examples/notebooks/models/SPM.ipynb
+++ b/docs/source/examples/notebooks/models/SPM.ipynb
@@ -78,7 +78,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -122,9 +123,9 @@
    "source": [
     "variable = list(model.rhs.keys())[1]\n",
     "equation = list(model.rhs.values())[1]\n",
-    "print('rhs equation for variable \\'',variable,'\\' is:')\n",
-    "path = 'docs/source/examples/notebooks/models/'\n",
-    "equation.visualise(path+'spm1.png')"
+    "print(\"rhs equation for variable '\", variable, \"' is:\")\n",
+    "path = \"docs/source/examples/notebooks/models/\"\n",
+    "equation.visualise(path + \"spm1.png\")"
    ]
   },
   {
@@ -186,14 +187,14 @@
     }
    ],
    "source": [
-    "print('SPM domains:')\n",
+    "print(\"SPM domains:\")\n",
     "for i, (k, v) in enumerate(geometry.items()):\n",
-    "    print(str(i+1)+'.',k,'with variables:')\n",
+    "    print(str(i + 1) + \".\", k, \"with variables:\")\n",
     "    for var, rng in v.items():\n",
-    "        if 'min' in rng:\n",
-    "            print('  -(',rng['min'],') <=',var,'<= (',rng['max'],')')\n",
+    "        if \"min\" in rng:\n",
+    "            print(\"  -(\", rng[\"min\"], \") <=\", var, \"<= (\", rng[\"max\"], \")\")\n",
     "        else:\n",
-    "            print(var, '=', rng['position'])"
+    "            print(var, \"=\", rng[\"position\"])"
    ]
   },
   {
@@ -282,9 +283,9 @@
    ],
    "source": [
     "for k, t in model.default_submesh_types.items():\n",
-    "    print(k,'is of type',t.__name__)\n",
+    "    print(k, \"is of type\", t.__name__)\n",
     "for var, npts in model.default_var_pts.items():\n",
-    "    print(var,'has',npts,'mesh points')"
+    "    print(var, \"has\", npts, \"mesh points\")"
    ]
   },
   {
@@ -336,7 +337,7 @@
    ],
    "source": [
     "for k, method in model.default_spatial_methods.items():\n",
-    "    print(k,'is discretised using',method.__class__.__name__,'method')"
+    "    print(k, \"is discretised using\", method.__class__.__name__, \"method\")"
    ]
   },
   {
@@ -382,7 +383,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.concatenated_rhs.children[1].visualise(path+'spm2.png')"
+    "model.concatenated_rhs.children[1].visualise(path + \"spm2.png\")"
    ]
   },
   {
@@ -414,9 +415,9 @@
     "solver = model.default_solver\n",
     "n = 250\n",
     "t_eval = np.linspace(0, 3600, n)\n",
-    "print('Solving using',type(solver).__name__,'solver...')\n",
+    "print(\"Solving using\", type(solver).__name__, \"solver...\")\n",
     "solution = solver.solve(model, t_eval)\n",
-    "print('Finished.')"
+    "print(\"Finished.\")"
    ]
   },
   {
@@ -906,9 +907,9 @@
     }
    ],
    "source": [
-    "print('SPM model variables:')\n",
+    "print(\"SPM model variables:\")\n",
     "for v in model.variables.keys():\n",
-    "    print('\\t-',v)"
+    "    print(\"\\t-\", v)"
    ]
   },
   {
@@ -925,9 +926,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "voltage = solution['Voltage [V]']\n",
-    "c_s_n_surf = solution['Negative particle surface concentration']\n",
-    "c_s_p_surf = solution['Positive particle surface concentration']"
+    "voltage = solution[\"Voltage [V]\"]\n",
+    "c_s_n_surf = solution[\"Negative particle surface concentration\"]\n",
+    "c_s_p_surf = solution[\"Positive particle surface concentration\"]"
    ]
   },
   {
@@ -957,19 +958,23 @@
    "source": [
     "t = solution[\"Time [s]\"].entries\n",
     "x = solution[\"x [m]\"].entries[:, 0]\n",
-    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13, 4))\n",
     "\n",
     "ax1.plot(t, voltage(t))\n",
-    "ax1.set_xlabel(r'$Time [s]$')\n",
-    "ax1.set_ylabel('Voltage [V]')\n",
+    "ax1.set_xlabel(r\"$Time [s]$\")\n",
+    "ax1.set_ylabel(\"Voltage [V]\")\n",
     "\n",
-    "ax2.plot(t, c_s_n_surf(t=t, x=x[0]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "ax2.set_xlabel(r'$Time [s]$')\n",
-    "ax2.set_ylabel('Negative particle surface concentration')\n",
+    "ax2.plot(\n",
+    "    t, c_s_n_surf(t=t, x=x[0])\n",
+    ")  # can evaluate at arbitrary x (single representative particle)\n",
+    "ax2.set_xlabel(r\"$Time [s]$\")\n",
+    "ax2.set_ylabel(\"Negative particle surface concentration\")\n",
     "\n",
-    "ax3.plot(t, c_s_p_surf(t=t, x=x[-1]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "ax3.set_xlabel(r'$Time [s]$')\n",
-    "ax3.set_ylabel('Positive particle surface concentration')\n",
+    "ax3.plot(\n",
+    "    t, c_s_p_surf(t=t, x=x[-1])\n",
+    ")  # can evaluate at arbitrary x (single representative particle)\n",
+    "ax3.set_xlabel(r\"$Time [s]$\")\n",
+    "ax3.set_ylabel(\"Positive particle surface concentration\")\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()"
@@ -989,8 +994,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "c_s_n = solution['Negative particle concentration']\n",
-    "c_s_p = solution['Positive particle concentration']\n",
+    "c_s_n = solution[\"Negative particle concentration\"]\n",
+    "c_s_p = solution[\"Positive particle concentration\"]\n",
     "r_n = solution[\"r_n [m]\"].entries[:, 0]\n",
     "r_p = solution[\"r_p [m]\"].entries[:, 0]"
    ]
@@ -1016,27 +1021,34 @@
     }
    ],
    "source": [
-    "c_s_n = solution['Negative particle concentration']\n",
-    "c_s_p = solution['Positive particle concentration']\n",
+    "c_s_n = solution[\"Negative particle concentration\"]\n",
+    "c_s_p = solution[\"Positive particle concentration\"]\n",
     "r_n = solution[\"r_n [m]\"].entries[:, 0, 0]\n",
     "r_p = solution[\"r_p [m]\"].entries[:, 0, 0]\n",
     "\n",
     "\n",
     "def plot_concentrations(t):\n",
-    "    f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n",
-    "    plot_c_n, = ax1.plot(r_n, c_s_n(r=r_n,t=t,x=x[0]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "    plot_c_p, = ax2.plot(r_p, c_s_p(r=r_p,t=t,x=x[-1]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "    ax1.set_ylabel('Negative particle concentration')\n",
-    "    ax2.set_ylabel('Positive particle concentration')\n",
-    "    ax1.set_xlabel(r'$r_n$ [m]')\n",
-    "    ax2.set_xlabel(r'$r_p$ [m]')\n",
+    "    f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "    (plot_c_n,) = ax1.plot(\n",
+    "        r_n, c_s_n(r=r_n, t=t, x=x[0])\n",
+    "    )  # can evaluate at arbitrary x (single representative particle)\n",
+    "    (plot_c_p,) = ax2.plot(\n",
+    "        r_p, c_s_p(r=r_p, t=t, x=x[-1])\n",
+    "    )  # can evaluate at arbitrary x (single representative particle)\n",
+    "    ax1.set_ylabel(\"Negative particle concentration\")\n",
+    "    ax2.set_ylabel(\"Positive particle concentration\")\n",
+    "    ax1.set_xlabel(r\"$r_n$ [m]\")\n",
+    "    ax2.set_xlabel(r\"$r_p$ [m]\")\n",
     "    ax1.set_ylim(0, 1)\n",
     "    ax2.set_ylim(0, 1)\n",
     "    plt.show()\n",
     "\n",
     "\n",
     "import ipywidgets as widgets\n",
-    "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));"
+    "\n",
+    "widgets.interact(\n",
+    "    plot_concentrations, t=widgets.FloatSlider(min=0, max=3600, step=10, value=0)\n",
+    ");"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb
index a9542d89ec..1e60568055 100644
--- a/docs/source/examples/notebooks/models/SPMe.ipynb
+++ b/docs/source/examples/notebooks/models/SPMe.ipynb
@@ -177,7 +177,7 @@
    ],
    "source": [
     "# solve simulation\n",
-    "simulation.solve([0, 3600]) # time interval in seconds"
+    "simulation.solve([0, 3600])  # time interval in seconds"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
index 8bdfa76f60..8e1b742c15 100644
--- a/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
+++ b/docs/source/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
@@ -27,7 +27,8 @@
     "import pybamm\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -45,10 +46,10 @@
    "outputs": [],
    "source": [
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options = {\n",
-    "        \"particle\": \"Fickian diffusion\", \n",
-    "        \"cell geometry\": \"arbitrary\", \n",
-    "        \"thermal\": \"lumped\", \n",
+    "    options={\n",
+    "        \"particle\": \"Fickian diffusion\",\n",
+    "        \"cell geometry\": \"arbitrary\",\n",
+    "        \"thermal\": \"lumped\",\n",
     "        \"particle mechanics\": \"swelling only\",\n",
     "    }\n",
     ")"
@@ -87,34 +88,32 @@
     "# update parameters, making C-rate and input\n",
     "param = pybamm.ParameterValues(\"Ai2020\")\n",
     "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
-    "param.update({\n",
-    "    \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n",
-    "})\n",
+    "param.update({\"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")})\n",
     "\n",
     "# update the mesh\n",
     "var = pybamm.standard_spatial_vars\n",
     "var_pts = {\n",
-    "  var.x_n: 50,\n",
-    "  var.x_s: 50,\n",
-    "  var.x_p: 50,\n",
-    "  var.r_n: 21,\n",
-    "  var.r_p: 21,\n",
+    "    var.x_n: 50,\n",
+    "    var.x_s: 50,\n",
+    "    var.x_p: 50,\n",
+    "    var.r_n: 21,\n",
+    "    var.r_p: 21,\n",
     "}\n",
     "\n",
     "# define the simulation\n",
     "sim = pybamm.Simulation(\n",
-    "        model,\n",
-    "        var_pts=var_pts,\n",
-    "        parameter_values=param,\n",
-    "        solver=pybamm.CasadiSolver(mode=\"fast\")\n",
-    "    )\n",
+    "    model,\n",
+    "    var_pts=var_pts,\n",
+    "    parameter_values=param,\n",
+    "    solver=pybamm.CasadiSolver(mode=\"fast\"),\n",
+    ")\n",
     "\n",
     "# solve for different C-rates\n",
     "Crates = [0.5, 1, 2]\n",
     "solutions = []\n",
     "for Crate in Crates:\n",
     "    print(f\"{Crate} C\")\n",
-    "    sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n",
+    "    sol = sim.solve(t_eval=[0, 3600 / Crate * 1.05], inputs={\"C-rate\": Crate})\n",
     "    solutions.append(sol)\n",
     "\n",
     "# unpack solutions\n",
@@ -137,18 +136,27 @@
    "source": [
     "# load experimental results\n",
     "import pandas as pd\n",
+    "\n",
     "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n",
-    "data_Disp_01C=pd.read_csv (path + \"0.1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_05C=pd.read_csv (path + \"0.5C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_1C=pd.read_csv (path + \"1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_Disp_2C=pd.read_csv (path + \"2C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_01C=pd.read_csv (path + \"0.1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_05C=pd.read_csv (path + \"0.5C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_1C=pd.read_csv (path + \"1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_V_2C=pd.read_csv (path + \"2C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_05C=pd.read_csv (path + \"0.5C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_1C=pd.read_csv (path + \"1C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
-    "data_T_2C=pd.read_csv (path + \"2C_discharge_T.txt\", delimiter= '\\s+',header=None)"
+    "data_Disp_01C = pd.read_csv(\n",
+    "    path + \"0.1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n",
+    ")\n",
+    "data_Disp_05C = pd.read_csv(\n",
+    "    path + \"0.5C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n",
+    ")\n",
+    "data_Disp_1C = pd.read_csv(\n",
+    "    path + \"1C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n",
+    ")\n",
+    "data_Disp_2C = pd.read_csv(\n",
+    "    path + \"2C_discharge_displacement.txt\", delimiter=\"\\s+\", header=None\n",
+    ")\n",
+    "data_V_01C = pd.read_csv(path + \"0.1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_V_05C = pd.read_csv(path + \"0.5C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_V_1C = pd.read_csv(path + \"1C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_V_2C = pd.read_csv(path + \"2C_discharge_U.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_T_05C = pd.read_csv(path + \"0.5C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_T_1C = pd.read_csv(path + \"1C_discharge_T.txt\", delimiter=\"\\s+\", header=None)\n",
+    "data_T_2C = pd.read_csv(path + \"2C_discharge_T.txt\", delimiter=\"\\s+\", header=None)"
    ]
   },
   {
@@ -178,59 +186,116 @@
    "source": [
     "t_all2C = solution2C[\"Time [h]\"].entries\n",
     "V_n2C = solution2C[\"Voltage [V]\"].entries\n",
-    "T_n2C = solution2C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "T_n2C = (\n",
+    "    solution2C[\"Volume-averaged cell temperature [K]\"].entries\n",
+    "    - param[\"Initial temperature [K]\"]\n",
+    ")\n",
     "L_x2C = solution2C[\"Cell thickness change [m]\"].entries\n",
     "\n",
     "t_all1C = solution1C[\"Time [h]\"].entries\n",
     "V_n1C = solution1C[\"Voltage [V]\"].entries\n",
-    "T_n1C = solution1C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "T_n1C = (\n",
+    "    solution1C[\"Volume-averaged cell temperature [K]\"].entries\n",
+    "    - param[\"Initial temperature [K]\"]\n",
+    ")\n",
     "L_x1C = solution1C[\"Cell thickness change [m]\"].entries\n",
     "\n",
     "t_all05C = solution05C[\"Time [h]\"].entries\n",
     "V_n05C = solution05C[\"Voltage [V]\"].entries\n",
-    "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
+    "T_n05C = (\n",
+    "    solution05C[\"Volume-averaged cell temperature [K]\"].entries\n",
+    "    - param[\"Initial temperature [K]\"]\n",
+    ")\n",
     "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n",
     "\n",
-    "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n",
+    "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))\n",
     "\n",
-    "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n",
-    "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax1.plot(t_all05C, V_n05C,'g-')\n",
-    "ax1.plot(data_V_05C.values[::100,0]/3600, data_V_05C.values[::100,1],'go',markerfacecolor='none')\n",
-    "ax1.plot(t_all1C, V_n1C,'b-')\n",
-    "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n",
+    "ax1.plot(t_all2C, V_n2C, \"r-\", label=\"Simulation\")\n",
+    "ax1.plot(\n",
+    "    data_V_2C.values[::30, 0] / 3600,\n",
+    "    data_V_2C.values[::30, 1],\n",
+    "    \"ro\",\n",
+    "    markerfacecolor=\"none\",\n",
+    "    label=\"Experiment\",\n",
+    ")\n",
+    "ax1.plot(t_all05C, V_n05C, \"g-\")\n",
+    "ax1.plot(\n",
+    "    data_V_05C.values[::100, 0] / 3600,\n",
+    "    data_V_05C.values[::100, 1],\n",
+    "    \"go\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
+    "ax1.plot(t_all1C, V_n1C, \"b-\")\n",
+    "ax1.plot(\n",
+    "    data_V_1C.values[::50, 0] / 3600,\n",
+    "    data_V_1C.values[::50, 1],\n",
+    "    \"bo\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
     "ax1.legend()\n",
     "ax1.set_xlabel(\"Time [h]\")\n",
     "ax1.set_ylabel(\"Voltage [V]\")\n",
-    "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax1.text(1.1, 3.2, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax1.text(1.6, 3.2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "ax1.text(0.1, 3.2, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n",
+    "ax1.text(1.1, 3.2, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n",
+    "ax1.text(1.6, 3.2, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n",
     "\n",
-    "ax2.plot(t_all2C, T_n2C,'r-',label=\"Simulation\")\n",
-    "ax2.plot(data_T_2C.values[0:1754:50,0]/3600, data_T_2C.values[0:1754:50,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax2.plot(t_all05C, T_n05C,'g-')\n",
-    "ax2.plot(data_T_05C.values[0:7301:200,0]/3600, data_T_05C.values[0:7301:200,1],'go',markerfacecolor='none')\n",
-    "ax2.plot(t_all1C, T_n1C,'b-')\n",
-    "ax2.plot(data_T_1C.values[0:3598:100,0]/3600, data_T_1C.values[0:3598:100,1],'bo',markerfacecolor='none')\n",
+    "ax2.plot(t_all2C, T_n2C, \"r-\", label=\"Simulation\")\n",
+    "ax2.plot(\n",
+    "    data_T_2C.values[0:1754:50, 0] / 3600,\n",
+    "    data_T_2C.values[0:1754:50, 1],\n",
+    "    \"ro\",\n",
+    "    markerfacecolor=\"none\",\n",
+    "    label=\"Experiment\",\n",
+    ")\n",
+    "ax2.plot(t_all05C, T_n05C, \"g-\")\n",
+    "ax2.plot(\n",
+    "    data_T_05C.values[0:7301:200, 0] / 3600,\n",
+    "    data_T_05C.values[0:7301:200, 1],\n",
+    "    \"go\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
+    "ax2.plot(t_all1C, T_n1C, \"b-\")\n",
+    "ax2.plot(\n",
+    "    data_T_1C.values[0:3598:100, 0] / 3600,\n",
+    "    data_T_1C.values[0:3598:100, 1],\n",
+    "    \"bo\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
     "ax2.legend()\n",
     "ax2.set_xlabel(\"Time [h]\")\n",
     "ax2.set_ylabel(\"Temperature rise [K]\")\n",
-    "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "ax2.text(0.5, 8, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n",
+    "ax2.text(0.8, 4.4, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n",
+    "ax2.text(1.5, 2, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n",
     "\n",
-    "ax3.plot(t_all2C, L_x2C,'r-',label=\"Simulation\")\n",
-    "ax3.plot(data_Disp_2C.values[0:1754:5,0]/3600, data_Disp_2C.values[0:1754:5,1]-data_Disp_2C.values[0,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
-    "ax3.plot(t_all05C, L_x05C,'g-')\n",
-    "ax3.plot(data_Disp_05C.values[0:1754:10,0]/3600, data_Disp_05C.values[0:1754:10,1]-data_Disp_05C.values[0,1],'go',markerfacecolor='none')\n",
-    "ax3.plot(t_all1C, L_x1C,'b-')\n",
-    "ax3.plot(data_Disp_1C.values[0:1754:10,0]/3600, data_Disp_1C.values[0:1754:10,1]-data_Disp_1C.values[0,1],'bo',markerfacecolor='none')\n",
+    "ax3.plot(t_all2C, L_x2C, \"r-\", label=\"Simulation\")\n",
+    "ax3.plot(\n",
+    "    data_Disp_2C.values[0:1754:5, 0] / 3600,\n",
+    "    data_Disp_2C.values[0:1754:5, 1] - data_Disp_2C.values[0, 1],\n",
+    "    \"ro\",\n",
+    "    markerfacecolor=\"none\",\n",
+    "    label=\"Experiment\",\n",
+    ")\n",
+    "ax3.plot(t_all05C, L_x05C, \"g-\")\n",
+    "ax3.plot(\n",
+    "    data_Disp_05C.values[0:1754:10, 0] / 3600,\n",
+    "    data_Disp_05C.values[0:1754:10, 1] - data_Disp_05C.values[0, 1],\n",
+    "    \"go\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
+    "ax3.plot(t_all1C, L_x1C, \"b-\")\n",
+    "ax3.plot(\n",
+    "    data_Disp_1C.values[0:1754:10, 0] / 3600,\n",
+    "    data_Disp_1C.values[0:1754:10, 1] - data_Disp_1C.values[0, 1],\n",
+    "    \"bo\",\n",
+    "    markerfacecolor=\"none\",\n",
+    ")\n",
     "ax3.legend()\n",
     "ax3.set_xlabel(\"Time [h]\")\n",
     "ax3.set_ylabel(\"Thickness change [m]\")\n",
-    "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n",
-    "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n",
-    "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
+    "ax3.text(0.1, -0.0001, r\"2 C\", {\"color\": \"r\", \"fontsize\": 14})\n",
+    "ax3.text(0.9, -0.0001, r\"1 C\", {\"color\": \"b\", \"fontsize\": 14})\n",
+    "ax3.text(1.8, -0.0001, r\"0.5 C\", {\"color\": \"g\", \"fontsize\": 14})\n",
     "\n",
     "f.tight_layout()\n",
     "f.show()"
@@ -266,22 +331,32 @@
     "\n",
     "cs_n_xav = solution2C[\"X-averaged negative particle concentration [mol.m-3]\"].entries\n",
     "cs_p_xav = solution2C[\"X-averaged positive particle concentration [mol.m-3]\"].entries\n",
-    "st_surf_n =  solution2C[\"Negative particle surface tangential stress [Pa]\"].entries / E_n\n",
-    "st_surf_p =  solution2C[\"Positive particle surface tangential stress [Pa]\"].entries / E_p\n",
+    "st_surf_n = solution2C[\"Negative particle surface tangential stress [Pa]\"].entries / E_n\n",
+    "st_surf_p = solution2C[\"Positive particle surface tangential stress [Pa]\"].entries / E_p\n",
     "\n",
-    "data_st_n_2C=pd.read_csv (path + \"stn_2C.txt\", delimiter= ',',header=3)\n",
-    "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n",
+    "data_st_n_2C = pd.read_csv(path + \"stn_2C.txt\", delimiter=\",\", header=3)\n",
+    "data_st_p_2C = pd.read_csv(path + \"stp_2C.txt\", delimiter=\",\", header=3)\n",
     "\n",
-    "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n",
+    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3.5))\n",
     "\n",
-    "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n",
-    "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n",
+    "ax1.plot(t_all2C, st_surf_n[-1, :], \"ro\", markerfacecolor=\"none\", label=\"Current\")\n",
+    "ax1.plot(\n",
+    "    data_st_n_2C.values[:, 0] / 3600,\n",
+    "    data_st_n_2C.values[:, 1],\n",
+    "    \"r-\",\n",
+    "    label=\"Ai et al. 2020\",\n",
+    ")\n",
     "ax1.legend()\n",
     "ax1.set_xlabel(\"Time [h]\")\n",
     "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n",
     "\n",
-    "ax2.plot(t_all2C, st_surf_p[0,:],'ro',markerfacecolor='none',label=\"Current\")\n",
-    "ax2.plot(data_st_p_2C.values[0:3601,0]/3600, data_st_p_2C.values[0:3601,1],'r-',label=\"Ai et al. 2020\")\n",
+    "ax2.plot(t_all2C, st_surf_p[0, :], \"ro\", markerfacecolor=\"none\", label=\"Current\")\n",
+    "ax2.plot(\n",
+    "    data_st_p_2C.values[0:3601, 0] / 3600,\n",
+    "    data_st_p_2C.values[0:3601, 1],\n",
+    "    \"r-\",\n",
+    "    label=\"Ai et al. 2020\",\n",
+    ")\n",
     "ax2.legend()\n",
     "ax2.set_xlabel(\"Time [h]\")\n",
     "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n",
diff --git a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
index 462f03827b..2faac3bb1d 100644
--- a/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
+++ b/docs/source/examples/notebooks/models/compare-comsol-discharge-curve.ipynb
@@ -38,6 +38,7 @@
     "import os\n",
     "import pickle\n",
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
@@ -160,16 +161,15 @@
     "plt.grid(True)\n",
     "plt.xlabel(r\"Discharge Capacity (Ah)\")\n",
     "plt.ylabel(r\"$\\vert V - V_{comsol} \\vert$\")\n",
-    "colors = iter(plt.cycler(color='bgrcmyk'))\n",
+    "colors = iter(plt.cycler(color=\"bgrcmyk\"))\n",
     "\n",
     "# loop over C_rates dict to create plot\n",
     "for key, C_rate in C_rates.items():\n",
-    "\n",
     "    # load the comsol results\n",
     "    comsol_results_path = pybamm.get_parameters_filepath(\n",
     "        f\"input/comsol_results/comsol_{key}C.pickle\",\n",
     "    )\n",
-    "    comsol_variables = pickle.load(open(comsol_results_path, 'rb'))\n",
+    "    comsol_variables = pickle.load(open(comsol_results_path, \"rb\"))\n",
     "    comsol_time = comsol_variables[\"time\"]\n",
     "    comsol_voltage = comsol_variables[\"voltage\"]\n",
     "\n",
@@ -178,7 +178,9 @@
     "\n",
     "    # solve model at comsol times\n",
     "    solver = pybamm.CasadiSolver(mode=\"fast\")\n",
-    "    solution = solver.solve(model, comsol_time, inputs={\"Current function [A]\": current})\n",
+    "    solution = solver.solve(\n",
+    "        model, comsol_time, inputs={\"Current function [A]\": current}\n",
+    "    )\n",
     "    time_in_seconds = solution[\"Time [s]\"].entries\n",
     "    # discharge capacity\n",
     "    discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n",
diff --git a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
index 05a375fa45..b0db095926 100644
--- a/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
+++ b/docs/source/examples/notebooks/models/compare-ecker-data.ipynb
@@ -38,7 +38,8 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -55,8 +56,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "voltage_data_1C = pd.read_csv(\"pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv\", header=None).to_numpy()\n",
-    "voltage_data_5C = pd.read_csv(\"pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv\", header=None).to_numpy()"
+    "voltage_data_1C = pd.read_csv(\n",
+    "    \"pybamm/input/discharge_data/Ecker2015/Ecker_1C.csv\", header=None\n",
+    ").to_numpy()\n",
+    "voltage_data_5C = pd.read_csv(\n",
+    "    \"pybamm/input/discharge_data/Ecker2015/Ecker_5C.csv\", header=None\n",
+    ").to_numpy()"
    ]
   },
   {
@@ -127,7 +132,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sim =  pybamm.Simulation(model, parameter_values=parameter_values, var_pts=var_pts)"
+    "sim = pybamm.Simulation(model, parameter_values=parameter_values, var_pts=var_pts)"
    ]
   },
   {
@@ -147,15 +152,19 @@
     "C_rates = [1, 5]  # C-rates to solve for\n",
     "capacity = parameter_values[\"Nominal cell capacity [A.h]\"]\n",
     "t_evals = [\n",
-    "    np.linspace(0, 3800, 100), \n",
-    "    np.linspace(0, 720, 100)\n",
-    "] # times to return the solution at\n",
+    "    np.linspace(0, 3800, 100),\n",
+    "    np.linspace(0, 720, 100),\n",
+    "]  # times to return the solution at\n",
     "solutions = [None] * len(C_rates)  # empty list that will hold solutions\n",
     "\n",
     "# loop over C-rates\n",
     "for i, C_rate in enumerate(C_rates):\n",
     "    current = C_rate * capacity\n",
-    "    sim.solve(t_eval=t_evals[i], solver=pybamm.CasadiSolver(mode=\"fast\"),inputs={\"Current function [A]\": current})\n",
+    "    sim.solve(\n",
+    "        t_eval=t_evals[i],\n",
+    "        solver=pybamm.CasadiSolver(mode=\"fast\"),\n",
+    "        inputs={\"Current function [A]\": current},\n",
+    "    )\n",
     "    solutions[i] = sim.solution"
    ]
   },
@@ -193,7 +202,7 @@
     "# plot the 1C results\n",
     "t_sol = solutions[0][\"Time [s]\"].entries\n",
     "ax1.plot(t_sol, solutions[0][\"Voltage [V]\"](t_sol))\n",
-    "ax1.plot(voltage_data_1C[:,0], voltage_data_1C[:,1], \"o\")\n",
+    "ax1.plot(voltage_data_1C[:, 0], voltage_data_1C[:, 1], \"o\")\n",
     "ax1.set_xlabel(\"Time [s]\")\n",
     "ax1.set_ylabel(\"Voltage [V]\")\n",
     "ax1.set_title(\"1C\")\n",
@@ -202,7 +211,7 @@
     "# plot the 5C results\n",
     "t_sol = solutions[1][\"Time [s]\"].entries\n",
     "ax2.plot(t_sol, solutions[1][\"Voltage [V]\"](t_sol))\n",
-    "ax2.plot(voltage_data_5C[:,0], voltage_data_5C[:,1], \"o\")\n",
+    "ax2.plot(voltage_data_5C[:, 0], voltage_data_5C[:, 1], \"o\")\n",
     "ax2.set_xlabel(\"Time [s]\")\n",
     "ax2.set_ylabel(\"Voltage [V]\")\n",
     "ax2.set_title(\"5C\")\n",
diff --git a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
index 74157628f8..36dbe5a6af 100644
--- a/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/docs/source/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -51,7 +51,8 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
@@ -138,7 +139,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "geometry = {\"DFN\": dfn.default_geometry, \"SPM\": spm.default_geometry, \"SPMe\": spme.default_geometry}"
+    "geometry = {\n",
+    "    \"DFN\": dfn.default_geometry,\n",
+    "    \"SPM\": spm.default_geometry,\n",
+    "    \"SPMe\": spme.default_geometry,\n",
+    "}"
    ]
   },
   {
@@ -207,7 +212,9 @@
    "source": [
     "mesh = {}\n",
     "for model_name, model in models.items():\n",
-    "    mesh[model_name] = pybamm.Mesh(geometry[model_name], model.default_submesh_types, model.default_var_pts)"
+    "    mesh[model_name] = pybamm.Mesh(\n",
+    "        geometry[model_name], model.default_submesh_types, model.default_var_pts\n",
+    "    )"
    ]
   },
   {
@@ -402,9 +409,11 @@
    "source": [
     "# update parameter values and solve again\n",
     "# simulate for shorter time\n",
-    "t_eval = np.linspace(0,800,300)\n",
+    "t_eval = np.linspace(0, 800, 300)\n",
     "for model_name, model in models.items():\n",
-    "    solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"Current function [A]\": 3})\n",
+    "    solutions[model_name] = model.default_solver.solve(\n",
+    "        model, t_eval, inputs={\"Current function [A]\": 3}\n",
+    "    )\n",
     "\n",
     "# Plot\n",
     "list_of_solutions = list(solutions.values())\n",
diff --git a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
index da6f05870e..2d74940e3d 100644
--- a/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
+++ b/docs/source/examples/notebooks/models/compare-particle-diffusion-models.ipynb
@@ -40,7 +40,8 @@
     "import os\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -57,8 +58,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "particle_options = [\"Fickian diffusion\", \"uniform profile\", \"quadratic profile\", \"quartic profile\"]\n",
-    "models = [pybamm.lithium_ion.DFN(options={'particle': opt}, name=opt) for opt in particle_options]"
+    "particle_options = [\n",
+    "    \"Fickian diffusion\",\n",
+    "    \"uniform profile\",\n",
+    "    \"quadratic profile\",\n",
+    "    \"quartic profile\",\n",
+    "]\n",
+    "models = [\n",
+    "    pybamm.lithium_ion.DFN(options={\"particle\": opt}, name=opt)\n",
+    "    for opt in particle_options\n",
+    "]"
    ]
   },
   {
@@ -156,14 +165,18 @@
    ],
    "source": [
     "plt.figure(figsize=(15, 15))\n",
-    "style = ['k', 'r*', 'b^', 'g--']\n",
+    "style = [\"k\", \"r*\", \"b^\", \"g--\"]\n",
     "for i in range(len(models)):\n",
-    "    plt.plot(solutions_1C[i]['Time [s]'].entries,\n",
-    "             solutions_1C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n",
+    "    plt.plot(\n",
+    "        solutions_1C[i][\"Time [s]\"].entries,\n",
+    "        solutions_1C[i][\"Voltage [V]\"].entries,\n",
+    "        style[i],\n",
+    "        label=particle_options[i],\n",
+    "    )\n",
     "plt.legend()\n",
-    "plt.title('Model Comparison 1C')\n",
-    "plt.xlabel('Time [s]')\n",
-    "plt.ylabel('Voltage [V]')\n",
+    "plt.title(\"Model Comparison 1C\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
     "plt.grid()"
    ]
   },
@@ -184,7 +197,7 @@
     "t_eval = np.linspace(0, 1800, 72)\n",
     "solutions_2C = []\n",
     "for sim in simulations:\n",
-    "    sim.solve(t_eval, inputs={\"Current function [A]\": 2*0.68})\n",
+    "    sim.solve(t_eval, inputs={\"Current function [A]\": 2 * 0.68})\n",
     "    solutions_2C.append(sim.solution)"
    ]
   },
@@ -209,12 +222,16 @@
    "source": [
     "plt.figure(figsize=(15, 15))\n",
     "for i in range(len(models)):\n",
-    "    plt.plot(solutions_2C[i]['Time [s]'].entries,\n",
-    "             solutions_2C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n",
+    "    plt.plot(\n",
+    "        solutions_2C[i][\"Time [s]\"].entries,\n",
+    "        solutions_2C[i][\"Voltage [V]\"].entries,\n",
+    "        style[i],\n",
+    "        label=particle_options[i],\n",
+    "    )\n",
     "plt.legend()\n",
-    "plt.title('Model Comparison 2C')\n",
-    "plt.xlabel('Time [s]')\n",
-    "plt.ylabel('Voltage [V]')\n",
+    "plt.title(\"Model Comparison 2C\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
     "plt.grid()"
    ]
   },
@@ -235,7 +252,7 @@
     "t_eval = np.linspace(0, 360, 72)\n",
     "solutions_6C = []\n",
     "for sim in simulations:\n",
-    "    sim.solve(t_eval, inputs={\"Current function [A]\": 6*0.68})\n",
+    "    sim.solve(t_eval, inputs={\"Current function [A]\": 6 * 0.68})\n",
     "    solutions_6C.append(sim.solution)"
    ]
   },
@@ -260,12 +277,16 @@
    "source": [
     "plt.figure(figsize=(15, 15))\n",
     "for i in range(len(models)):\n",
-    "    plt.plot(solutions_6C[i]['Time [s]'].entries,\n",
-    "             solutions_6C[i]['Voltage [V]'].entries, style[i], label=particle_options[i])\n",
+    "    plt.plot(\n",
+    "        solutions_6C[i][\"Time [s]\"].entries,\n",
+    "        solutions_6C[i][\"Voltage [V]\"].entries,\n",
+    "        style[i],\n",
+    "        label=particle_options[i],\n",
+    "    )\n",
     "plt.legend()\n",
-    "plt.title('Model Comparison 6C')\n",
-    "plt.xlabel('Time [s]')\n",
-    "plt.ylabel('Voltage [V]')\n",
+    "plt.title(\"Model Comparison 6C\")\n",
+    "plt.xlabel(\"Time [s]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
     "plt.grid()"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb
index 59fa9c957e..5057d57589 100644
--- a/docs/source/examples/notebooks/models/composite_particle.ipynb
+++ b/docs/source/examples/notebooks/models/composite_particle.ipynb
@@ -36,15 +36,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "# %pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pybamm\n",
     "import timeit\n",
     "from matplotlib import style\n",
-    "style.use('ggplot')\n",
-    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "style.use(\"ggplot\")\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
     "pybamm.set_logging_level(\"INFO\")"
    ]
   },
@@ -74,23 +75,27 @@
    ],
    "source": [
     "start = timeit.default_timer()\n",
-    "model = pybamm.lithium_ion.DFN({\n",
-    "    \"particle phases\": (\"2\", \"1\"),\n",
-    "    \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\")\n",
-    "})\n",
+    "model = pybamm.lithium_ion.DFN(\n",
+    "    {\n",
+    "        \"particle phases\": (\"2\", \"1\"),\n",
+    "        \"open-circuit potential\": ((\"single\", \"current sigmoid\"), \"single\"),\n",
+    "    }\n",
+    ")\n",
     "param = pybamm.ParameterValues(\"Chen2020_composite\")\n",
     "\n",
     "param.update({\"Upper voltage cut-off [V]\": 4.5})\n",
     "param.update({\"Lower voltage cut-off [V]\": 2.5})\n",
     "\n",
-    "param.update({\n",
-    "    \"Primary: Maximum concentration in negative electrode [mol.m-3]\":28700,\n",
-    "    \"Primary: Initial concentration in negative electrode [mol.m-3]\":23000,\n",
-    "    \"Primary: Negative electrode diffusivity [m2.s-1]\":5.5E-14,\n",
-    "    \"Secondary: Negative electrode diffusivity [m2.s-1]\":1.67E-14,\n",
-    "    \"Secondary: Initial concentration in negative electrode [mol.m-3]\":277000,\n",
-    "    \"Secondary: Maximum concentration in negative electrode [mol.m-3]\":278000\n",
-    "})"
+    "param.update(\n",
+    "    {\n",
+    "        \"Primary: Maximum concentration in negative electrode [mol.m-3]\": 28700,\n",
+    "        \"Primary: Initial concentration in negative electrode [mol.m-3]\": 23000,\n",
+    "        \"Primary: Negative electrode diffusivity [m2.s-1]\": 5.5e-14,\n",
+    "        \"Secondary: Negative electrode diffusivity [m2.s-1]\": 1.67e-14,\n",
+    "        \"Secondary: Initial concentration in negative electrode [mol.m-3]\": 277000,\n",
+    "        \"Secondary: Maximum concentration in negative electrode [mol.m-3]\": 278000,\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -120,9 +125,9 @@
    "source": [
     "C_rate = 0.5\n",
     "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
-    "I_load = C_rate * capacity  \n",
+    "I_load = C_rate * capacity\n",
     "\n",
-    "t_eval = np.linspace(0,10000,1000)\n",
+    "t_eval = np.linspace(0, 10000, 1000)\n",
     "\n",
     "param[\"Current function [A]\"] = I_load"
    ]
@@ -242,21 +247,25 @@
     }
    ],
    "source": [
-    "v_si=[0.001,0.04,0.1]\n",
+    "v_si = [0.001, 0.04, 0.1]\n",
     "total_am_volume_fraction = 0.75\n",
-    "solution=[]\n",
+    "solution = []\n",
     "for v in v_si:\n",
-    "    param.update({\n",
-    "        \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n",
-    "        \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n",
-    "    })\n",
+    "    param.update(\n",
+    "        {\n",
+    "            \"Primary: Negative electrode active material volume fraction\": (1 - v)\n",
+    "            * total_am_volume_fraction,  # primary\n",
+    "            \"Secondary: Negative electrode active material volume fraction\": v\n",
+    "            * total_am_volume_fraction,\n",
+    "        }\n",
+    "    )\n",
     "    print(v)\n",
     "    sim = pybamm.Simulation(\n",
     "        model,\n",
     "        parameter_values=param,\n",
-    "        solver=pybamm.CasadiSolver(dt_max = 5),\n",
+    "        solver=pybamm.CasadiSolver(dt_max=5),\n",
     "    )\n",
-    "    solution.append(sim.solve(t_eval = t_eval))\n",
+    "    solution.append(sim.solve(t_eval=t_eval))\n",
     "stop = timeit.default_timer()\n",
     "print(\"running time: \" + str(stop - start) + \"s\")"
    ]
@@ -301,13 +310,13 @@
     }
    ],
    "source": [
-    "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n",
-    "for i in range(0,len(v_si)):\n",
+    "ltype = [\"k-\", \"r--\", \"b-.\", \"g:\", \"m-\", \"c--\", \"y-.\"]\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
     "    V_i = solution[i][\"Voltage [V]\"].entries\n",
-    "    plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Voltage [V]')\n",
+    "    plt.plot(t_i, V_i, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
     "plt.legend()"
    ]
   },
@@ -359,24 +368,28 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    j_n_p1_av = solution[i][\"X-averaged negative electrode primary interfacial current density [A.m-2]\"].entries\n",
-    "    plt.plot(t_i, j_n_p1_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n",
+    "    j_n_p1_av = solution[i][\n",
+    "        \"X-averaged negative electrode primary interfacial current density [A.m-2]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, j_n_p1_av, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Averaged interfacial current density [A/m$^{2}$]\")\n",
     "plt.legend()\n",
-    "plt.title('Graphite')\n",
+    "plt.title(\"Graphite\")\n",
     "\n",
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    j_n_p2_av = solution[i][\"X-averaged negative electrode secondary interfacial current density [A.m-2]\"].entries\n",
-    "    plt.plot(t_i, j_n_p2_av,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Averaged interfacial current density [A/m$^{2}$]')\n",
+    "    j_n_p2_av = solution[i][\n",
+    "        \"X-averaged negative electrode secondary interfacial current density [A.m-2]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, j_n_p2_av, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Averaged interfacial current density [A/m$^{2}$]\")\n",
     "plt.legend()\n",
-    "plt.title('Silicon')"
+    "plt.title(\"Silicon\")"
    ]
   },
   {
@@ -427,24 +440,28 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    j_n_p1_Vav = solution[i][\"X-averaged negative electrode primary volumetric interfacial current density [A.m-3]\"].entries\n",
-    "    plt.plot(t_i, j_n_p1_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n",
+    "    j_n_p1_Vav = solution[i][\n",
+    "        \"X-averaged negative electrode primary volumetric interfacial current density [A.m-3]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, j_n_p1_Vav, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Averaged volumetric interfacial current density [A/m$^{3}$]\")\n",
     "plt.legend()\n",
-    "plt.title('Graphite')\n",
+    "plt.title(\"Graphite\")\n",
     "\n",
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    j_n_p2_Vav = solution[i][\"X-averaged negative electrode secondary volumetric interfacial current density [A.m-3]\"].entries\n",
-    "    plt.plot(t_i, j_n_p2_Vav,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Averaged volumetric interfacial current density [A/m$^{3}$]')\n",
+    "    j_n_p2_Vav = solution[i][\n",
+    "        \"X-averaged negative electrode secondary volumetric interfacial current density [A.m-3]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, j_n_p2_Vav, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Averaged volumetric interfacial current density [A/m$^{3}$]\")\n",
     "plt.legend()\n",
-    "plt.title('Silicon')"
+    "plt.title(\"Silicon\")"
    ]
   },
   {
@@ -495,24 +512,28 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    c_s_xrav_n_p1 = solution[i][\"Average negative primary particle concentration\"].entries\n",
-    "    plt.plot(t_i, c_s_xrav_n_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
+    "    c_s_xrav_n_p1 = solution[i][\n",
+    "        \"Average negative primary particle concentration\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, c_s_xrav_n_p1, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
     "plt.ylabel(\"$c_\\mathrm{g}/c_\\mathrm{g,max}$\")\n",
     "plt.legend()\n",
-    "plt.title('Graphite')\n",
+    "plt.title(\"Graphite\")\n",
     "\n",
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    c_s_xrav_n_p2 = solution[i][\"Average negative secondary particle concentration\"].entries\n",
-    "    plt.plot(t_i, c_s_xrav_n_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
+    "    c_s_xrav_n_p2 = solution[i][\n",
+    "        \"Average negative secondary particle concentration\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, c_s_xrav_n_p2, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
     "plt.ylabel(\"$c_\\mathrm{si}/c_\\mathrm{si,max}$\")\n",
     "plt.legend()\n",
-    "plt.title('Silicon')"
+    "plt.title(\"Silicon\")"
    ]
   },
   {
@@ -573,34 +594,45 @@
    ],
    "source": [
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    ocp_p1 = solution[i][\"X-averaged negative electrode primary open-circuit potential [V]\"].entries\n",
-    "    plt.plot(t_i, ocp_p1 ,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
+    "    ocp_p1 = solution[i][\n",
+    "        \"X-averaged negative electrode primary open-circuit potential [V]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, ocp_p1, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
     "plt.ylabel(\"Equilibruim potential [V]\")\n",
     "plt.legend()\n",
-    "plt.title('Graphite')\n",
+    "plt.title(\"Graphite\")\n",
     "\n",
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
-    "    ocp_p2 = solution[i][\"X-averaged negative electrode secondary open-circuit potential [V]\"].entries\n",
-    "    plt.plot(t_i, ocp_p2,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
+    "    ocp_p2 = solution[i][\n",
+    "        \"X-averaged negative electrode secondary open-circuit potential [V]\"\n",
+    "    ].entries\n",
+    "    plt.plot(t_i, ocp_p2, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
     "plt.ylabel(\"Equilibruim potential [V]\")\n",
     "plt.legend()\n",
-    "plt.title('Silicon')\n",
+    "plt.title(\"Silicon\")\n",
     "\n",
     "plt.figure()\n",
-    "for i in range(0,len(v_si)):\n",
-    "    t_i = solution[len(v_si)- 1 - i][\"Time [s]\"].entries / 3600\n",
-    "    ocp_p = solution[len(v_si)- 1 - i][\"X-averaged positive electrode open-circuit potential [V]\"].entries\n",
-    "    plt.plot(t_i, ocp_p,ltype[len(v_si)- 1 - i],label=\"$V_\\mathrm{si}=$\"+str(v_si[len(v_si)- 1 - i]))\n",
-    "plt.xlabel('Time [h]')\n",
+    "for i in range(0, len(v_si)):\n",
+    "    t_i = solution[len(v_si) - 1 - i][\"Time [s]\"].entries / 3600\n",
+    "    ocp_p = solution[len(v_si) - 1 - i][\n",
+    "        \"X-averaged positive electrode open-circuit potential [V]\"\n",
+    "    ].entries\n",
+    "    plt.plot(\n",
+    "        t_i,\n",
+    "        ocp_p,\n",
+    "        ltype[len(v_si) - 1 - i],\n",
+    "        label=\"$V_\\mathrm{si}=$\" + str(v_si[len(v_si) - 1 - i]),\n",
+    "    )\n",
+    "plt.xlabel(\"Time [h]\")\n",
     "plt.ylabel(\"Equilibrium potential [V]\")\n",
     "plt.legend()\n",
-    "plt.title('NMC811')"
+    "plt.title(\"NMC811\")"
    ]
   },
   {
@@ -840,18 +872,22 @@
     }
    ],
    "source": [
-    "solution=[]\n",
+    "solution = []\n",
     "for v in v_si:\n",
-    "    param.update({\n",
-    "        \"Primary: Negative electrode active material volume fraction\": (1-v) * total_am_volume_fraction, #primary\n",
-    "        \"Secondary: Negative electrode active material volume fraction\": v * total_am_volume_fraction,\n",
-    "    })\n",
+    "    param.update(\n",
+    "        {\n",
+    "            \"Primary: Negative electrode active material volume fraction\": (1 - v)\n",
+    "            * total_am_volume_fraction,  # primary\n",
+    "            \"Secondary: Negative electrode active material volume fraction\": v\n",
+    "            * total_am_volume_fraction,\n",
+    "        }\n",
+    "    )\n",
     "    print(v)\n",
     "    sim = pybamm.Simulation(\n",
     "        model,\n",
     "        experiment=experiment,\n",
     "        parameter_values=param,\n",
-    "        solver=pybamm.CasadiSolver(dt_max = 5)\n",
+    "        solver=pybamm.CasadiSolver(dt_max=5),\n",
     "    )\n",
     "    solution.append(sim.solve(calc_esoh=False))\n",
     "stop = timeit.default_timer()\n",
@@ -896,13 +932,13 @@
     }
    ],
    "source": [
-    "ltype=['k-','r--','b-.','g:','m-','c--','y-.']\n",
-    "for i in range(0,len(v_si)):\n",
+    "ltype = [\"k-\", \"r--\", \"b-.\", \"g:\", \"m-\", \"c--\", \"y-.\"]\n",
+    "for i in range(0, len(v_si)):\n",
     "    t_i = solution[i][\"Time [s]\"].entries / 3600\n",
     "    V_i = solution[i][\"Voltage [V]\"].entries\n",
-    "    plt.plot(t_i, V_i,ltype[i],label=\"$V_\\mathrm{si}=$\"+str(v_si[i]))\n",
-    "plt.xlabel('Time [h]')\n",
-    "plt.ylabel('Voltage [V]')\n",
+    "    plt.plot(t_i, V_i, ltype[i], label=\"$V_\\mathrm{si}=$\" + str(v_si[i]))\n",
+    "plt.xlabel(\"Time [h]\")\n",
+    "plt.ylabel(\"Voltage [V]\")\n",
     "plt.legend()"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/coupled-degradation.ipynb b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
index 00b524c041..1551a79a64 100644
--- a/docs/source/examples/notebooks/models/coupled-degradation.ipynb
+++ b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
@@ -80,7 +80,7 @@
     "param = pybamm.ParameterValues(\"OKane2022\")\n",
     "var_pts = {\n",
     "    \"x_n\": 5,  # negative electrode\n",
-    "    \"x_s\": 5,  # separator \n",
+    "    \"x_s\": 5,  # separator\n",
     "    \"x_p\": 5,  # positive electrode\n",
     "    \"r_n\": 30,  # negative particle\n",
     "    \"r_p\": 30,  # positive particle\n",
@@ -104,16 +104,23 @@
    "source": [
     "cycle_number = 10\n",
     "exp = pybamm.Experiment(\n",
-    "    [\"Hold at 4.2 V until C/100\",\n",
-    "    \"Rest for 4 hours\",\n",
-    "    \"Discharge at 0.1C until 2.5 V\",  # initial capacity check\n",
-    "    \"Charge at 0.3C until 4.2 V\",\n",
-    "    \"Hold at 4.2 V until C/100\",]\n",
-    "    + [(\"Discharge at 1C until 2.5 V\",  # ageing cycles\n",
-    "    \"Charge at 0.3C until 4.2 V\",\n",
-    "    \"Hold at 4.2 V until C/100\",)] * cycle_number\n",
+    "    [\n",
+    "        \"Hold at 4.2 V until C/100\",\n",
+    "        \"Rest for 4 hours\",\n",
+    "        \"Discharge at 0.1C until 2.5 V\",  # initial capacity check\n",
+    "        \"Charge at 0.3C until 4.2 V\",\n",
+    "        \"Hold at 4.2 V until C/100\",\n",
+    "    ]\n",
+    "    + [\n",
+    "        (\n",
+    "            \"Discharge at 1C until 2.5 V\",  # ageing cycles\n",
+    "            \"Charge at 0.3C until 4.2 V\",\n",
+    "            \"Hold at 4.2 V until C/100\",\n",
+    "        )\n",
+    "    ]\n",
+    "    * cycle_number\n",
     "    + [\"Discharge at 0.1C until 2.5 V\"],  # final capacity check\n",
-    "    period=\"5 minutes\"\n",
+    "    period=\"5 minutes\",\n",
     ")\n",
     "sim = pybamm.Simulation(model, parameter_values=param, experiment=exp, var_pts=var_pts)\n",
     "sol = sim.solve()"
@@ -152,13 +159,15 @@
     "Q_SEI_cr = sol[\"Loss of capacity to negative SEI on cracks [A.h]\"].entries\n",
     "Q_plating = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n",
     "Q_side = sol[\"Total capacity lost to side reactions [A.h]\"].entries\n",
-    "Q_LLI = sol[\"Total lithium lost [mol]\"].entries * 96485.3 / 3600  # convert from mol to A.h\n",
+    "Q_LLI = (\n",
+    "    sol[\"Total lithium lost [mol]\"].entries * 96485.3 / 3600\n",
+    ")  # convert from mol to A.h\n",
     "plt.figure()\n",
-    "plt.plot(Qt,Q_SEI,label=\"SEI\",linestyle=\"dashed\")\n",
-    "plt.plot(Qt,Q_SEI_cr,label=\"SEI on cracks\",linestyle=\"dashdot\")\n",
-    "plt.plot(Qt,Q_plating,label=\"Li plating\",linestyle=\"dotted\")\n",
-    "plt.plot(Qt,Q_side,label=\"All side reactions\",linestyle=(0,(6,1)))\n",
-    "plt.plot(Qt,Q_LLI,label=\"All LLI\")\n",
+    "plt.plot(Qt, Q_SEI, label=\"SEI\", linestyle=\"dashed\")\n",
+    "plt.plot(Qt, Q_SEI_cr, label=\"SEI on cracks\", linestyle=\"dashdot\")\n",
+    "plt.plot(Qt, Q_plating, label=\"Li plating\", linestyle=\"dotted\")\n",
+    "plt.plot(Qt, Q_side, label=\"All side reactions\", linestyle=(0, (6, 1)))\n",
+    "plt.plot(Qt, Q_LLI, label=\"All LLI\")\n",
     "plt.xlabel(\"Throughput capacity [A.h]\")\n",
     "plt.ylabel(\"Capacity loss [A.h]\")\n",
     "plt.legend()\n",
@@ -206,9 +215,9 @@
     "LAM_neg = sol[\"Loss of active material in negative electrode [%]\"].entries\n",
     "LAM_pos = sol[\"Loss of active material in positive electrode [%]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(Qt,LLI,label=\"LLI\")\n",
-    "plt.plot(Qt,LAM_neg,label=\"LAM (negative)\")\n",
-    "plt.plot(Qt,LAM_pos,label=\"LAM (positive)\")\n",
+    "plt.plot(Qt, LLI, label=\"LLI\")\n",
+    "plt.plot(Qt, LAM_neg, label=\"LAM (negative)\")\n",
+    "plt.plot(Qt, LAM_pos, label=\"LAM (positive)\")\n",
     "plt.xlabel(\"Throughput capacity [A.h]\")\n",
     "plt.ylabel(\"Degradation modes [%]\")\n",
     "plt.legend()\n",
@@ -252,12 +261,12 @@
    ],
    "source": [
     "eps_neg_avg = sol[\"X-averaged negative electrode porosity\"].entries\n",
-    "eps_neg_sep = sol[\"Negative electrode porosity\"].entries[-1,:]\n",
-    "eps_neg_CC = sol[\"Negative electrode porosity\"].entries[0,:]\n",
+    "eps_neg_sep = sol[\"Negative electrode porosity\"].entries[-1, :]\n",
+    "eps_neg_CC = sol[\"Negative electrode porosity\"].entries[0, :]\n",
     "plt.figure()\n",
-    "plt.plot(Qt,eps_neg_avg,label=\"Average\")\n",
-    "plt.plot(Qt,eps_neg_sep,label=\"Separator\",linestyle=\"dotted\")\n",
-    "plt.plot(Qt,eps_neg_CC,label=\"Current collector\",linestyle=\"dashed\")\n",
+    "plt.plot(Qt, eps_neg_avg, label=\"Average\")\n",
+    "plt.plot(Qt, eps_neg_sep, label=\"Separator\", linestyle=\"dotted\")\n",
+    "plt.plot(Qt, eps_neg_CC, label=\"Current collector\", linestyle=\"dashed\")\n",
     "plt.xlabel(\"Throughput capacity [A.h]\")\n",
     "plt.ylabel(\"Negative electrode porosity\")\n",
     "plt.legend()\n",
diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
index 54b71157f7..5528b74830 100644
--- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
+++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
@@ -76,22 +76,26 @@
    ],
    "source": [
     "spm = pybamm.lithium_ion.SPM()\n",
-    "experiment = pybamm.Experiment([\n",
-    "    \"Charge at 1C until 4.2V\", \n",
-    "    \"Hold at 4.2V until C/50\",\n",
-    "    \"Discharge at 1C until 2.8V\",\n",
-    "    \"Hold at 2.8V until C/50\",\n",
-    "])\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        \"Charge at 1C until 4.2V\",\n",
+    "        \"Hold at 4.2V until C/50\",\n",
+    "        \"Discharge at 1C until 2.8V\",\n",
+    "        \"Hold at 2.8V until C/50\",\n",
+    "    ]\n",
+    ")\n",
     "parameter_values = pybamm.ParameterValues(\"Mohtat2020\")\n",
     "\n",
     "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
     "spm_sol = sim.solve()\n",
-    "spm_sol.plot([\n",
-    "    \"Voltage [V]\", \n",
-    "    \"Current [A]\", \n",
-    "    \"Negative electrode stoichiometry\",\n",
-    "    \"Positive electrode stoichiometry\",\n",
-    "])"
+    "spm_sol.plot(\n",
+    "    [\n",
+    "        \"Voltage [V]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Negative electrode stoichiometry\",\n",
+    "        \"Positive electrode stoichiometry\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -179,16 +183,13 @@
     "\n",
     "y_100_min = 1e-10\n",
     "\n",
-    "x_100_upper_limit = (Q_Li - y_100_min*Q_p)/Q_n\n",
+    "x_100_upper_limit = (Q_Li - y_100_min * Q_p) / Q_n\n",
     "\n",
     "model.algebraic = {x_100: U_p(y_100, T_ref) - U_n(x_100, T_ref) - Vmax}\n",
-    "    \n",
+    "\n",
     "model.initial_conditions = {x_100: x_100_upper_limit}\n",
     "\n",
-    "model.variables = {\n",
-    "    \"x_100\": x_100,\n",
-    "    \"y_100\": y_100\n",
-    "}\n",
+    "model.variables = {\"x_100\": x_100, \"y_100\": y_100}\n",
     "\n",
     "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n",
     "sol = sim.solve([0])\n",
@@ -204,7 +205,7 @@
     "\n",
     "x_0 = pybamm.Variable(\"x_0\")\n",
     "Q = Q_n * (x_100 - x_0)\n",
-    "y_0 = y_100 + Q/Q_p\n",
+    "y_0 = y_100 + Q / Q_p\n",
     "\n",
     "model.algebraic = {x_0: U_p(y_0, T_ref) - U_n(x_0, T_ref) - Vmin}\n",
     "model.initial_conditions = {x_0: 0.1}\n",
@@ -250,7 +251,7 @@
    "source": [
     "esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n",
     "\n",
-    "inputs={ \"V_min\": Vmin, \"V_max\": Vmax, \"Q_n\": Q_n, \"Q_p\": Q_p, \"Q_Li\": Q_Li}\n",
+    "inputs = {\"V_min\": Vmin, \"V_max\": Vmax, \"Q_n\": Q_n, \"Q_p\": Q_p, \"Q_Li\": Q_Li}\n",
     "\n",
     "esoh_sol = esoh_solver.solve(inputs)\n",
     "\n",
@@ -298,23 +299,23 @@
     "x_100 = esoh_sol[\"x_100\"].data * np.ones_like(t)\n",
     "y_100 = esoh_sol[\"y_100\"].data * np.ones_like(t)\n",
     "\n",
-    "fig, axes = plt.subplots(1,2)\n",
+    "fig, axes = plt.subplots(1, 2)\n",
     "\n",
     "axes[0].plot(t, x_spm, \"b\")\n",
     "axes[0].plot(t, x_0, \"k:\")\n",
     "axes[0].plot(t, x_100, \"k:\")\n",
     "axes[0].set_ylabel(\"x\")\n",
-    "    \n",
+    "\n",
     "axes[1].plot(t, y_spm, \"r\")\n",
     "axes[1].plot(t, y_0, \"k:\")\n",
     "axes[1].plot(t, y_100, \"k:\")\n",
     "axes[1].set_ylabel(\"y\")\n",
-    "    \n",
+    "\n",
     "for k in range(2):\n",
-    "    axes[k].set_xlim([t[0],t[-1]])\n",
-    "    axes[k].set_ylim([0,1])    \n",
+    "    axes[k].set_xlim([t[0], t[-1]])\n",
+    "    axes[k].set_ylim([0, 1])\n",
     "    axes[k].set_xlabel(\"Time [h]\")\n",
-    "    \n",
+    "\n",
     "fig.tight_layout()"
    ]
   },
@@ -341,8 +342,13 @@
     "all_parameter_sets = [\n",
     "    k\n",
     "    for k, v in pybamm.parameter_sets.items()\n",
-    "    if v[\"chemistry\"] == \"lithium_ion\" and k not in [\n",
-    "        \"Xu2019\", \"Chen2020_composite\", \"Ecker2015_graphite_halfcell\", \"OKane2022_graphite_SiOx_halfcell\"\n",
+    "    if v[\"chemistry\"] == \"lithium_ion\"\n",
+    "    and k\n",
+    "    not in [\n",
+    "        \"Xu2019\",\n",
+    "        \"Chen2020_composite\",\n",
+    "        \"Ecker2015_graphite_halfcell\",\n",
+    "        \"OKane2022_graphite_SiOx_halfcell\",\n",
     "    ]\n",
     "]\n",
     "\n",
@@ -350,19 +356,21 @@
     "def solve_esoh_sweep_QLi(parameter_set, param):\n",
     "    parameter_values = pybamm.ParameterValues(parameter_set)\n",
     "\n",
-    "   # Vmin = parameter_values[\"Lower voltage cut-off [V]\"]\n",
-    "   # Vmax = parameter_values[\"Upper voltage cut-off [V]\"]\n",
+    "    # Vmin = parameter_values[\"Lower voltage cut-off [V]\"]\n",
+    "    # Vmax = parameter_values[\"Upper voltage cut-off [V]\"]\n",
     "    Vmin = parameter_values[\"Open-circuit voltage at 0% SOC [V]\"]\n",
     "    Vmax = parameter_values[\"Open-circuit voltage at 100% SOC [V]\"]\n",
-    "    \n",
+    "\n",
     "    Q_n = parameter_values.evaluate(param.n.Q_init)\n",
     "    Q_p = parameter_values.evaluate(param.p.Q_init)\n",
-    "    \n",
-    "    Q = parameter_values.evaluate(param.Q/param.n_electrodes_parallel)\n",
-    "    esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param, known_value=\"cell capacity\")\n",
+    "\n",
+    "    Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel)\n",
+    "    esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(\n",
+    "        parameter_values, param, known_value=\"cell capacity\"\n",
+    "    )\n",
     "    inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q\": Q, \"Q_n\": Q_n, \"Q_p\": Q_p}\n",
     "    sol_init_Q = esoh_solver.solve(inputs)\n",
-    "    \n",
+    "\n",
     "    Q_Li_init = parameter_values.evaluate(param.Q_Li_particles_init)\n",
     "    esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)\n",
     "    inputs = {\"V_max\": Vmax, \"V_min\": Vmin, \"Q_Li\": Q_Li_init, \"Q_n\": Q_n, \"Q_p\": Q_p}\n",
@@ -384,7 +392,7 @@
     "            pass\n",
     "\n",
     "    return sweep, sol_init_QLi, sol_init_Q\n",
-    "    \n",
+    "\n",
     "\n",
     "for parameter_set in [\"Chen2020\"]:\n",
     "    sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)"
@@ -408,33 +416,33 @@
    ],
    "source": [
     "def plot_sweep(sweep, sol_init, sol_init_Q, parameter_set):\n",
-    "    fig, axes = plt.subplots(1,3,figsize=(10,3))\n",
+    "    fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
     "    parameter_values = pybamm.ParameterValues(parameter_set)\n",
     "    parameter_values.evaluate(param.n.Q_init)\n",
     "    parameter_values.evaluate(param.p.Q_init)\n",
     "    # Plot min/max stoichimetric limits, including the value with the given Q_Li\n",
-    "    for i,ks in enumerate([[\"x_0\",\"x_100\"],[\"y_0\",\"y_100\"],[\"Q\"]]):\n",
+    "    for i, ks in enumerate([[\"x_0\", \"x_100\"], [\"y_0\", \"y_100\"], [\"Q\"]]):\n",
     "        ax = axes.flat[i]\n",
-    "        for j,k in enumerate(ks):\n",
+    "        for j, k in enumerate(ks):\n",
     "            if i == 0 and j == 0:\n",
     "                label1 = \"Stoichiometric envelope\"\n",
     "                label2 = \"Calculation from cyclable lithium\"\n",
     "                label3 = \"Calculation from cell capacity\"\n",
     "            else:\n",
     "                label1 = label2 = label3 = None\n",
-    "            ax.plot(sweep[\"Q_Li\"], sweep[k],\"b-\", label=label1)\n",
-    "            ax.axhline(sol_init_QLi[k],c=\"k\",linestyle=\"--\", label=label2)\n",
-    "            ax.axhline(sol_init_Q[k],c=\"r\",linestyle=\"--\", label=label3)\n",
+    "            ax.plot(sweep[\"Q_Li\"], sweep[k], \"b-\", label=label1)\n",
+    "            ax.axhline(sol_init_QLi[k], c=\"k\", linestyle=\"--\", label=label2)\n",
+    "            ax.axhline(sol_init_Q[k], c=\"r\", linestyle=\"--\", label=label3)\n",
     "        ax.set_xlabel(\"Cyclable lithium [A.h]\")\n",
     "        ax.set_ylabel(ks[0][0])\n",
-    "        ax.set_xlim([np.min(sweep[\"Q_Li\"]),np.max(sweep[\"Q_Li\"])])\n",
-    "        ax.axvline(sol_init_QLi[\"Q_Li\"],c=\"k\",linestyle=\"--\")\n",
-    "        ax.axvline(sol_init_Q[\"Q_Li\"],c=\"r\",linestyle=\"--\")\n",
+    "        ax.set_xlim([np.min(sweep[\"Q_Li\"]), np.max(sweep[\"Q_Li\"])])\n",
+    "        ax.axvline(sol_init_QLi[\"Q_Li\"], c=\"k\", linestyle=\"--\")\n",
+    "        ax.axvline(sol_init_Q[\"Q_Li\"], c=\"r\", linestyle=\"--\")\n",
     "        # Plot capacities of electrodes\n",
     "        # ax.axvline(Qn,c=\"b\",linestyle=\"--\")\n",
     "        # ax.axvline(Qp,c=\"r\",linestyle=\"--\")\n",
     "    axes[-1].set_ylabel(\"Cell capacity [A.h]\")\n",
-    "    \n",
+    "\n",
     "    # Plot initial values of stoichometries\n",
     "    parameter_values.evaluate(param.n.prim.sto_init_av)\n",
     "    parameter_values.evaluate(param.p.prim.sto_init_av)\n",
@@ -442,7 +450,7 @@
     "    # axes[1].axhline(sto_p_init,c=\"g\",linestyle=\"--\")\n",
     "\n",
     "    axes[1].set_title(parameter_set)\n",
-    "    fig.legend(loc=\"center left\", bbox_to_anchor=(1.01,0.5))\n",
+    "    fig.legend(loc=\"center left\", bbox_to_anchor=(1.01, 0.5))\n",
     "    fig.tight_layout()\n",
     "    return fig, axes\n",
     "\n",
diff --git a/docs/source/examples/notebooks/models/half-cell.ipynb b/docs/source/examples/notebooks/models/half-cell.ipynb
index 7eda7e2491..2085162694 100644
--- a/docs/source/examples/notebooks/models/half-cell.ipynb
+++ b/docs/source/examples/notebooks/models/half-cell.ipynb
@@ -77,13 +77,15 @@
     }
    ],
    "source": [
-    "exp_slow = pybamm.Experiment([\"Discharge at C/25 until 3.5 V\", \"Charge at C/25 until 4.2 V\"])\n",
+    "exp_slow = pybamm.Experiment(\n",
+    "    [\"Discharge at C/25 until 3.5 V\", \"Charge at C/25 until 4.2 V\"]\n",
+    ")\n",
     "sim1 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_slow)\n",
     "sol1 = sim1.solve()\n",
     "t = sol1[\"Time [s]\"].entries\n",
     "V = sol1[\"Voltage [V]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,V)\n",
+    "plt.plot(t, V)\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Voltage [V]\")\n",
     "plt.show()"
@@ -124,13 +126,15 @@
     }
    ],
    "source": [
-    "exp_fast = pybamm.Experiment([\"Discharge at 1C until 3.5 V\", \"Charge at 1C until 4.2 V\"])\n",
+    "exp_fast = pybamm.Experiment(\n",
+    "    [\"Discharge at 1C until 3.5 V\", \"Charge at 1C until 4.2 V\"]\n",
+    ")\n",
     "sim2 = pybamm.Simulation(model, parameter_values=param_nmc, experiment=exp_fast)\n",
     "sol2 = sim2.solve()\n",
     "t = sol2[\"Time [s]\"].entries\n",
     "V = sol2[\"Voltage [V]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,V)\n",
+    "plt.plot(t, V)\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Voltage [V]\")\n",
     "plt.show()"
@@ -164,25 +168,34 @@
     }
    ],
    "source": [
-    "model_with_degradation = pybamm.lithium_ion.DFN({\n",
-    "    \"working electrode\": \"positive\",\n",
-    "    \"SEI\": \"reaction limited\",  # SEI on both electrodes\n",
-    "    \"SEI porosity change\": \"true\",\n",
-    "    \"particle mechanics\": \"swelling and cracking\",\n",
-    "    \"SEI on cracks\": \"true\",\n",
-    "    \"lithium plating\": \"partially reversible\",\n",
-    "    \"lithium plating porosity change\": \"true\",  # alias for \"SEI porosity change\"\n",
-    "})\n",
+    "model_with_degradation = pybamm.lithium_ion.DFN(\n",
+    "    {\n",
+    "        \"working electrode\": \"positive\",\n",
+    "        \"SEI\": \"reaction limited\",  # SEI on both electrodes\n",
+    "        \"SEI porosity change\": \"true\",\n",
+    "        \"particle mechanics\": \"swelling and cracking\",\n",
+    "        \"SEI on cracks\": \"true\",\n",
+    "        \"lithium plating\": \"partially reversible\",\n",
+    "        \"lithium plating porosity change\": \"true\",  # alias for \"SEI porosity change\"\n",
+    "    }\n",
+    ")\n",
     "param_GrSi = pybamm.ParameterValues(\"OKane2022_graphite_SiOx_halfcell\")\n",
     "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 1.5e-07})\n",
     "var_pts = {\"x_n\": 1, \"x_s\": 5, \"x_p\": 7, \"r_n\": 1, \"r_p\": 30}\n",
-    "exp_degradation = pybamm.Experiment([\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"])\n",
-    "sim3 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n",
+    "exp_degradation = pybamm.Experiment(\n",
+    "    [\"Charge at 0.3C until 1.5 V\", \"Discharge at 0.3C until 0.005 V\"]\n",
+    ")\n",
+    "sim3 = pybamm.Simulation(\n",
+    "    model_with_degradation,\n",
+    "    parameter_values=param_GrSi,\n",
+    "    experiment=exp_degradation,\n",
+    "    var_pts=var_pts,\n",
+    ")\n",
     "sol3 = sim3.solve()\n",
     "t = sol3[\"Time [s]\"].entries\n",
     "V = sol3[\"Voltage [V]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,V)\n",
+    "plt.plot(t, V)\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Voltage [V]\")\n",
     "plt.show()"
@@ -221,10 +234,10 @@
     "Q_SEI_cr = sol3[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n",
     "Q_pl = sol3[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n",
-    "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n",
-    "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n",
-    "plt.plot(t,Q_pl,label=\"Lithium plating\")\n",
+    "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n",
+    "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n",
+    "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n",
+    "plt.plot(t, Q_pl, label=\"Lithium plating\")\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n",
     "plt.legend()\n",
@@ -260,12 +273,17 @@
    ],
    "source": [
     "param_GrSi.update({\"SEI reaction exchange current density [A.m-2]\": 6e-07})\n",
-    "sim4 = pybamm.Simulation(model_with_degradation, parameter_values=param_GrSi, experiment=exp_degradation, var_pts=var_pts)\n",
+    "sim4 = pybamm.Simulation(\n",
+    "    model_with_degradation,\n",
+    "    parameter_values=param_GrSi,\n",
+    "    experiment=exp_degradation,\n",
+    "    var_pts=var_pts,\n",
+    ")\n",
     "sol4 = sim4.solve()\n",
     "t = sol4[\"Time [s]\"].entries\n",
     "V = sol4[\"Voltage [V]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,V)\n",
+    "plt.plot(t, V)\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Voltage [V]\")\n",
     "plt.show()"
@@ -296,10 +314,10 @@
     "Q_SEI_cr = sol4[\"Loss of capacity to positive SEI on cracks [A.h]\"].entries\n",
     "Q_pl = sol4[\"Loss of capacity to positive lithium plating [A.h]\"].entries\n",
     "plt.figure()\n",
-    "plt.plot(t,Q_SEI_n,label=\"Negative SEI\")\n",
-    "plt.plot(t,Q_SEI_p,label=\"Positive SEI\")\n",
-    "plt.plot(t,Q_SEI_cr,label=\"SEI on cracks\")\n",
-    "plt.plot(t,Q_pl,label=\"Lithium plating\")\n",
+    "plt.plot(t, Q_SEI_n, label=\"Negative SEI\")\n",
+    "plt.plot(t, Q_SEI_p, label=\"Positive SEI\")\n",
+    "plt.plot(t, Q_SEI_cr, label=\"SEI on cracks\")\n",
+    "plt.plot(t, Q_pl, label=\"Lithium plating\")\n",
     "plt.xlabel(\"Time [s]\")\n",
     "plt.ylabel(\"Loss of lithium inventory [A.h]\")\n",
     "plt.legend()\n",
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index 557366099a..43e65fbe7d 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -58,9 +58,9 @@
    "source": [
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
-    "import numpy as np \n",
+    "import numpy as np\n",
     "from numpy import pi\n",
-    "import matplotlib.pyplot as plt "
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -84,7 +84,7 @@
     "delta = pybamm.Parameter(\"Current collector thickness\")\n",
     "delta_p = delta  # assume same thickness\n",
     "delta_n = delta  # assume same thickness\n",
-    "l = 1/2 - delta_p - delta_n  # active material thickness\n",
+    "l = 1 / 2 - delta_p - delta_n  # active material thickness\n",
     "sigma_p = pybamm.Parameter(\"Positive current collector conductivity\")\n",
     "sigma_n = pybamm.Parameter(\"Negative current collector conductivity\")\n",
     "sigma_a = pybamm.Parameter(\"Active material conductivity\")"
@@ -114,11 +114,11 @@
     "phi_p = pybamm.Variable(\"Positive potential\", domain=\"cell\")\n",
     "phi_n = pybamm.Variable(\"Negative potential\", domain=\"cell\")\n",
     "\n",
-    "A_p = (2 * sigma_a / eps ** 4 / l) / (delta_p * sigma_p / 2 / pi ** 2)\n",
-    "A_n = (2 * sigma_a / eps ** 4 / l) / (delta_n * sigma_n / 2 / pi ** 2)\n",
+    "A_p = (2 * sigma_a / eps**4 / l) / (delta_p * sigma_p / 2 / pi**2)\n",
+    "A_n = (2 * sigma_a / eps**4 / l) / (delta_n * sigma_n / 2 / pi**2)\n",
     "model.algebraic = {\n",
-    "        phi_p: pybamm.div((1 / r ** 2) * pybamm.grad(phi_p)) + A_p * (phi_n - phi_p),\n",
-    "        phi_n: pybamm.div((1 / r ** 2) * pybamm.grad(phi_n)) - A_n * (phi_n - phi_p),\n",
+    "    phi_p: pybamm.div((1 / r**2) * pybamm.grad(phi_p)) + A_p * (phi_n - phi_p),\n",
+    "    phi_n: pybamm.div((1 / r**2) * pybamm.grad(phi_n)) - A_n * (phi_n - phi_p),\n",
     "}\n",
     "\n",
     "model.boundary_conditions = {\n",
@@ -129,7 +129,7 @@
     "    phi_n: {\n",
     "        \"left\": (0, \"Dirichlet\"),\n",
     "        \"right\": (0, \"Neumann\"),\n",
-    "    }    \n",
+    "    },\n",
     "}\n",
     "\n",
     "model.initial_conditions = {phi_p: 1, phi_n: 0}  # initial guess for solver\n",
@@ -165,7 +165,7 @@
    "source": [
     "params = pybamm.ParameterValues(\n",
     "    {\n",
-    "        \"Number of winds\":20,\n",
+    "        \"Number of winds\": 20,\n",
     "        \"Inner radius\": 0.25,\n",
     "        \"Current collector thickness\": 0.05,\n",
     "        \"Positive current collector conductivity\": 5e6,\n",
@@ -218,7 +218,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# solver \n",
+    "# solver\n",
     "solver = pybamm.CasadiAlgebraicSolver()\n",
     "solution = solver.solve(model)"
    ]
@@ -253,7 +253,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# post-process homogenised potential \n",
+    "# post-process homogenised potential\n",
     "phi_n = solution[\"Negative potential\"]\n",
     "phi_p = solution[\"Positive potential\"]\n",
     "\n",
@@ -263,13 +263,17 @@
     "\n",
     "\n",
     "def phi_am1(r, theta):\n",
-    "    # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape \n",
-    "    return alpha(r) * (r[:,np.newaxis]/eps - r0/eps - delta - theta / 2 / pi) / (1 - 4*delta) + phi_p(r=r)\n",
+    "    # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape\n",
+    "    return alpha(r) * (r[:, np.newaxis] / eps - r0 / eps - delta - theta / 2 / pi) / (\n",
+    "        1 - 4 * delta\n",
+    "    ) + phi_p(r=r)\n",
     "\n",
     "\n",
     "def phi_am2(r, theta):\n",
-    "    # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape \n",
-    "    return alpha(r) * (r0/eps + 1 - delta + theta / 2 / pi - r[:,np.newaxis]/eps) / (1 - 4*delta) + phi_p(r=r)"
+    "    # careful here - phi always returns a column vector so we need to add a new axis to r to get the right shape\n",
+    "    return alpha(r) * (\n",
+    "        r0 / eps + 1 - delta + theta / 2 / pi - r[:, np.newaxis] / eps\n",
+    "    ) / (1 - 4 * delta) + phi_p(r=r)"
    ]
   },
   {
@@ -279,7 +283,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# define spiral \n",
+    "# define spiral\n",
     "\n",
     "\n",
     "def spiral_pos_inner(t):\n",
@@ -324,22 +328,22 @@
     "# Setup fine mesh with nr points per layer\n",
     "nr = 10\n",
     "rr = np.linspace(r0, 1, nr)\n",
-    "tt = np.arange(0, (N+1)*2*pi, 2*pi)\n",
+    "tt = np.arange(0, (N + 1) * 2 * pi, 2 * pi)\n",
     "# N+1 winds of pos c.c.\n",
-    "r_mesh_pos = np.zeros((len(tt),len(rr)))\n",
+    "r_mesh_pos = np.zeros((len(tt), len(rr)))\n",
     "for i in range(len(tt)):\n",
-    "    r_mesh_pos[i,:] = np.linspace(spiral_pos_inner(tt[i]), spiral_pos_outer(tt[i]), nr)\n",
+    "    r_mesh_pos[i, :] = np.linspace(spiral_pos_inner(tt[i]), spiral_pos_outer(tt[i]), nr)\n",
     "# N winds of neg, am1, am2\n",
-    "r_mesh_neg = np.zeros((len(tt)-1, len(rr)))\n",
-    "r_mesh_am1 = np.zeros((len(tt)-1, len(rr)))\n",
-    "r_mesh_am2 = np.zeros((len(tt)-1, len(rr)))\n",
-    "for i in range(len(tt)-1):\n",
-    "    r_mesh_am2[i,:] = np.linspace(spiral_am2_inner(tt[i]), spiral_am2_outer(tt[i]), nr)\n",
-    "    r_mesh_neg[i,:] = np.linspace(spiral_neg_inner(tt[i]), spiral_neg_outer(tt[i]), nr)\n",
-    "    r_mesh_am1[i,:] = np.linspace(spiral_am1_inner(tt[i]), spiral_am1_outer(tt[i]), nr)\n",
-    "# Combine and sort    \n",
-    "r_total_mesh = np.vstack((r_mesh_pos,r_mesh_neg,r_mesh_am1, r_mesh_am2))\n",
-    "r_total_mesh = np.sort(r_total_mesh,axis=None)"
+    "r_mesh_neg = np.zeros((len(tt) - 1, len(rr)))\n",
+    "r_mesh_am1 = np.zeros((len(tt) - 1, len(rr)))\n",
+    "r_mesh_am2 = np.zeros((len(tt) - 1, len(rr)))\n",
+    "for i in range(len(tt) - 1):\n",
+    "    r_mesh_am2[i, :] = np.linspace(spiral_am2_inner(tt[i]), spiral_am2_outer(tt[i]), nr)\n",
+    "    r_mesh_neg[i, :] = np.linspace(spiral_neg_inner(tt[i]), spiral_neg_outer(tt[i]), nr)\n",
+    "    r_mesh_am1[i, :] = np.linspace(spiral_am1_inner(tt[i]), spiral_am1_outer(tt[i]), nr)\n",
+    "# Combine and sort\n",
+    "r_total_mesh = np.vstack((r_mesh_pos, r_mesh_neg, r_mesh_am1, r_mesh_am2))\n",
+    "r_total_mesh = np.sort(r_total_mesh, axis=None)"
    ]
   },
   {
@@ -362,17 +366,22 @@
     }
    ],
    "source": [
-    "# plot homogenised potential \n",
-    "fig, ax = plt.subplots(1, 1, figsize=(8,6))\n",
+    "# plot homogenised potential\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
     "\n",
-    "ax.plot(r_total_mesh, phi_n(r=r_total_mesh), 'b', label=r\"$\\phi^-$\")\n",
-    "ax.plot(r_total_mesh, phi_p(r=r_total_mesh), 'r', label=r\"$\\phi^+$\")\n",
+    "ax.plot(r_total_mesh, phi_n(r=r_total_mesh), \"b\", label=r\"$\\phi^-$\")\n",
+    "ax.plot(r_total_mesh, phi_p(r=r_total_mesh), \"r\", label=r\"$\\phi^+$\")\n",
     "for i in range(len(tt)):\n",
-    "    ax.plot(r_mesh_pos[i,:], phi_p(r=r_mesh_pos[i,:]), 'k', label=r\"$\\phi$\" if i ==0 else \"\")\n",
-    "for i in range(len(tt)-1):\n",
-    "    ax.plot(r_mesh_neg[i,:], phi_n(r=r_mesh_neg[i,:]), 'k')\n",
-    "    ax.plot(r_mesh_am1[i,:], phi_am1(r_mesh_am1[i,:], tt[i]), 'k')\n",
-    "    ax.plot(r_mesh_am2[i,:], phi_am2(r_mesh_am2[i,:], tt[i]), 'k')\n",
+    "    ax.plot(\n",
+    "        r_mesh_pos[i, :],\n",
+    "        phi_p(r=r_mesh_pos[i, :]),\n",
+    "        \"k\",\n",
+    "        label=r\"$\\phi$\" if i == 0 else \"\",\n",
+    "    )\n",
+    "for i in range(len(tt) - 1):\n",
+    "    ax.plot(r_mesh_neg[i, :], phi_n(r=r_mesh_neg[i, :]), \"k\")\n",
+    "    ax.plot(r_mesh_am1[i, :], phi_am1(r_mesh_am1[i, :], tt[i]), \"k\")\n",
+    "    ax.plot(r_mesh_am2[i, :], phi_am2(r_mesh_am2[i, :], tt[i]), \"k\")\n",
     "ax.set_xlabel(r\"$r$\")\n",
     "ax.set_ylabel(r\"$\\phi$\")\n",
     "ax.legend();"
diff --git a/docs/source/examples/notebooks/models/lead-acid.ipynb b/docs/source/examples/notebooks/models/lead-acid.ipynb
index 0dd20126a6..ccfa35b091 100644
--- a/docs/source/examples/notebooks/models/lead-acid.ipynb
+++ b/docs/source/examples/notebooks/models/lead-acid.ipynb
@@ -37,7 +37,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -223,7 +224,7 @@
    "source": [
     "timer = pybamm.Timer()\n",
     "solutions = {}\n",
-    "t_eval = np.linspace(0, 3600 * 17, 100) # time in seconds\n",
+    "t_eval = np.linspace(0, 3600 * 17, 100)  # time in seconds\n",
     "for model in models:\n",
     "    solver = pybamm.CasadiSolver()\n",
     "    timer.reset()\n",
diff --git a/docs/source/examples/notebooks/models/lithium-plating.ipynb b/docs/source/examples/notebooks/models/lithium-plating.ipynb
index 1e14513620..e84fdbb1ac 100644
--- a/docs/source/examples/notebooks/models/lithium-plating.ipynb
+++ b/docs/source/examples/notebooks/models/lithium-plating.ipynb
@@ -18,7 +18,8 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -36,16 +37,18 @@
    "source": [
     "# choose models\n",
     "plating_options = [\"reversible\", \"irreversible\", \"partially reversible\"]\n",
-    "models = {option: pybamm.lithium_ion.DFN(options={\"lithium plating\": option}, name=option) \n",
-    "          for option in plating_options}\n",
+    "models = {\n",
+    "    option: pybamm.lithium_ion.DFN(options={\"lithium plating\": option}, name=option)\n",
+    "    for option in plating_options\n",
+    "}\n",
     "\n",
     "# pick parameters\n",
     "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n",
     "parameter_values.update({\"Ambient temperature [K]\": 268.15})\n",
     "parameter_values.update({\"Upper voltage cut-off [V]\": 4.21})\n",
-    "#parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n",
+    "# parameter_values.update({\"Lithium plating kinetic rate constant [m.s-1]\": 1E-9})\n",
     "parameter_values.update({\"Lithium plating transfer coefficient\": 0.5})\n",
-    "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1E-4})"
+    "parameter_values.update({\"Dead lithium decay constant [s-1]\": 1e-4})"
    ]
   },
   {
@@ -67,14 +70,18 @@
     "s = pybamm.step.string\n",
     "experiment_discharge = pybamm.Experiment(\n",
     "    [\n",
-    "        (s(\"Discharge at C/20 until 2.5 V\", period=\"10 minutes\"),\n",
-    "        s(\"Rest for 1 hour\", period=\"3 minutes\")),\n",
+    "        (\n",
+    "            s(\"Discharge at C/20 until 2.5 V\", period=\"10 minutes\"),\n",
+    "            s(\"Rest for 1 hour\", period=\"3 minutes\"),\n",
+    "        ),\n",
     "    ]\n",
     ")\n",
     "\n",
     "sims_discharge = []\n",
     "for model in models.values():\n",
-    "    sim_discharge = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment_discharge)\n",
+    "    sim_discharge = pybamm.Simulation(\n",
+    "        model, parameter_values=parameter_values, experiment=experiment_discharge\n",
+    "    )\n",
     "    sol_discharge = sim_discharge.solve(calc_esoh=False)\n",
     "    model.set_initial_conditions_from(sol_discharge, inplace=True)\n",
     "    sims_discharge.append(sim_discharge)"
@@ -97,12 +104,14 @@
     "experiments = {}\n",
     "for C_rate in C_rates:\n",
     "    experiments[C_rate] = pybamm.Experiment(\n",
-    "    [\n",
-    "        (f\"Charge at {C_rate} until 4.2 V\",\n",
-    "        \"Hold at 4.2 V until C/20\",\n",
-    "        \"Rest for 1 hour\")\n",
-    "    ]\n",
-    ")"
+    "        [\n",
+    "            (\n",
+    "                f\"Charge at {C_rate} until 4.2 V\",\n",
+    "                \"Hold at 4.2 V until C/20\",\n",
+    "                \"Rest for 1 hour\",\n",
+    "            )\n",
+    "        ]\n",
+    "    )"
    ]
   },
   {
@@ -121,14 +130,18 @@
     "def define_and_solve_sims(model, experiments, parameter_values):\n",
     "    sims = {}\n",
     "    for C_rate, experiment in experiments.items():\n",
-    "        sim = pybamm.Simulation(model, experiment=experiment, parameter_values=parameter_values)\n",
+    "        sim = pybamm.Simulation(\n",
+    "            model, experiment=experiment, parameter_values=parameter_values\n",
+    "        )\n",
     "        sim.solve(calc_esoh=False)\n",
     "        sims[C_rate] = sim\n",
     "\n",
     "    return sims\n",
     "\n",
     "\n",
-    "sims_reversible = define_and_solve_sims(models[\"reversible\"], experiments, parameter_values)"
+    "sims_reversible = define_and_solve_sims(\n",
+    "    models[\"reversible\"], experiments, parameter_values\n",
+    ")"
    ]
   },
   {
@@ -138,7 +151,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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lj1Dv168ftWrVYvHixfj6+mIwGGjcuHGJG3OsWLGCKVOmMHfuXNq1a4ejoyMffvgh+/btK/U8FVWez+pG13ptdOL1D36vrQX5X//9HIsry8zMpF+/fsyePbtIXR8fH9OfK/L3fStIktBC3Gy9gDhylUT0BQbUmiq9PKQQQghxyymKUqFpv7eT0WjkxRdfZNWqVWzfvt30BeCanj174uHhwQcffMCqVauKtE9NTZV1CSncJbp169YUFBTw6quv0rNnT06cOGG6uZ44cSLr1q3jl19+wdnZmbFjx/LII4+we/du4Oqi6H379sXb25s9e/YQGxvLkCFD0Gq1pik9UVFR9O3bl+eff57vv/+eLVu28Mwzz+Dj42NaTPynn35i0qRJfP7557Rt25Z58+bRq1cvTp06hZeXl2U+oDKKO5cGRjAajJIgFELcccpzT3Cr6lbE1q1bOXr0KBMnTgSgRYsWxMXFodFoKnXDCSsrK9Oagde0aNGCEydOlHt9vaZNm7JlyxazKcLXJCUlcerUKRYvXkynTleTsdfPoijO7t27ad++PaNHjzaV/XfUZHHx/1eDBg1YuXKl2cjM3bt34+joaPaQtjwcHR0JDAxky5Yt3HfffUXevza1NzY2ltDQUACzTUzKq0WLFqxcuZLAwMCb2rVZq9Xe8POqDJKZspBgz2AAcqzSSbuSbeFohBBCCFEWY8aM4bvvvuOHH37A0dGRuLg44uLiyMnJAa4+PV6yZAnr1q3jwQcf5M8//+T8+fMcPHiQl19+meeff97CV1A13GiX6LS0NJYuXcpHH31E165dadmyJcuWLWPPnj38/fffAGzatIkTJ07w3Xff0bx5c/r06cO7777LwoULTSMbPv/8c4KCgpg7dy4NGjRg7NixPProo3z88cemWD766CNGjRrF8OHDadiwIZ9//jl2dnZ89dVXJcafl5dHenq62csSWvSqxcOTW9BhYIipzKA3YNDf/pEHQghxN8rLyyMuLo6YmBgOHz7Me++9x0MPPcQDDzzAkCFDAOjevTvt2rWjf//+bNq0ifPnz7Nnzx5ee+01Dh48WOFzBwYG8tdffxETE2PaKfeVV15hz549jB07lrCwMCIjI1mzZk2RjUv+a9q0aRw4cIDRo0cTHh5OREQEixYtIjExEVdXV9zd3fnyyy85c+YMW7duZdKkSaX2FxISwsGDB9m4cSOnT5/mjTfeKLLZRmBgIOHh4Zw6dYrExMRiR+uNHj2aixcv8uKLLxIREcGaNWt48803mTRpkmnEX0W89dZbzJ07l/nz5xMZGcnhw4f59NNPgasjDe+55x7ThiQ7duzg9ddfr/C5xowZQ3JyMk888QQHDhzg7NmzbNy4keHDh5cr6XctsRkXF2fadfpWkCShhTTyvzpvP802j3OX0ywcjRBCCCHKYtGiRaSlpdGlSxd8fHxMr59++slU56GHHmLPnj1otVqefPJJ6tevzxNPPEFaWlqR3fjEVf/dJfrQoUPodDq6d+9uqlO/fn0CAgLYu3cvAHv37qVJkyZm04979epFeno6x48fN9W5vo9rda71kZ+fz6FDh8zqqFQqunfvbqpTnFmzZuHs7Gx6+fv738zl3xTfEBesbQtHJuz65Qy/LwwnN6v4qVFCCCEqz4YNG/Dx8SEwMJDevXuzbds25s+fz5o1a0zrCSqKwh9//MG9997L8OHDqVu3LoMGDeLChQtmv8PK65133uH8+fPUrl3bNPqtadOm7Nixg9OnT9OpUydCQ0OZPn06vr6+pfZVt25dNm3axJEjR2jTpg3t2rVjzZo1aDQaVCoVK1as4NChQzRu3JiJEyfy4Ycfltrfc889xyOPPMLjjz9O27ZtSUpKMhtVCDBq1Cjq1atHq1at8PT0NM0UuJ6fnx9//PEH+/fvp1mzZjz//POMHDnyppJ2AEOHDmXevHl89tlnNGrUiAceeMC0+Rxc3VikoKCAli1bMmHChJu6f/P19WX37t3o9Xp69uxJkyZNmDBhAi4uLuVKdM6dO5fNmzfj7+9vGuF4KyhGWWG7iPT0dJydnUlLS8PJyemWnOO38MNM2/oZzqozvOQ1k4GDOtyS8wghhBBVUW5uLlFRUQQFBZl22hPmSvuMbse9yu1iMBh48MEHSU1NNU1f+uGHHxg+fDh5eXlmddu0acN9993H7NmzefbZZ7lw4QIbN240vZ+dnY29vT1//PEHffr0oW7dugwfPpxp06aZ6vzxxx/07duX7OxsUlJS8PPzY8+ePWZrSL788svs2LGjxLWT8vLyzGJLT0/H39/f4n8f6Yk5/PjOPgryDTwwthm1GrvfuJEQQliY3BMIUTkq495R1iS0kLWH8smLe5g071XEXowAJEkohBBCiLtPcbtEV3XW1tZYW1tbOowinDxseeSllsSdTZMEoRBCCCHKTaYbW0iwx9XMrSHfg8S00xaORgghhBDi9ru2S/S2bdvMFiD39vYmPz+f1NRUs/rx8fF4e3ub6sTHxxd5/9p7pdVxcnLC1tYWDw8P1Gp1sXWu9VGVXMjJI6Og9PWLPP0dadKl8LPMyyng+M4YZPKQEEIIIW5EkoQWUsvdDgBjvhtZeRctHI0QQgghxO1jNBoZO3Ysq1atYuvWrUV2iW7ZsiVarZYtW7aYyk6dOkV0dLRpWnC7du04evQoCQkJpjqbN2/GycmJhg0bmupc38e1Otf6sLKyomXLlmZ1DAYDW7ZsMZt+XBVk6w0MORpFj4OniMjKKVMbo8HI5qXH2f79Kfb+dvbGDYQQQghxV5PpxhYS8G+S0KBzJ09JQF9gQK2RnK0QQggh7nxjxozhhx9+YM2aNaZdogGcnZ2xtbXF2dmZkSNHMmnSJNzc3HBycuLFF1+kXbt23HPPPQD07NmThg0b8vTTT/PBBx8QFxfH66+/zpgxY0xTgZ9//nkWLFjAyy+/zIgRI9i6dSs///wz69atM8UyadIkhg4dSqtWrWjTpg3z5s0jKyuL4cOH3/4PphSX8/LJLNCTbzTiri3jLbwCAY3cuHwmlTqtvG5tgEIIIYSo9iRJaCG13P5NEua7ka1OIz0xB1dvewtHJYQQQghx6y1atAiALl26mJUvW7aMYcOGAfDxxx+jUqkYMGAAeXl59OrVi88++8xUV61W8/vvv/PCCy/Qrl077O3tGTp0KO+8846pTlBQEOvWrWPixIl88skn1KxZkyVLltCrVy9Tnccff5wrV64wffp04uLiaN68ORs2bLipHSdvhTp2NmxpXY/o3Hw8rbSm8jyDAesSdkdUFIWm9/kT0roGtg5WpnKD3oBKLQ+nhRBCCGFOkoQWUtPVDgUjRqMVybZaLsSmS5JQCCGEEHeFsqyPZ2Njw8KFC1m4cGGJdWrVqsUff/xRaj9dunThn3/+KbXO2LFjGTt27A1jsjQXrQaX60YR7kvN5IUTF5hXP4B73RxLbHd9gjA9MYe1n4TR6fG6srmJEEIIIczII0QLsdKo8HS6epMXZ+/G2bPRFo5ICCGEEEJUJ59ciOdyno6f45LL3ObwxgukXclh39pzGA2ymYkQQgghCslIQgsKdHcgIT2NVK07cRcigbaWDkkIIYQQQlQTSxoH8emFeMYElH29wU6P10VrraZZtwAUlXILoxNCCCFEdSMjCS2otqcTAIZ8dxJTTlk4GiGEEEIIUZUZsrMxGgymYzu1ileCfXDQqE1lC6MTOJyWVWIfao2KDo+G4OBqbSqLPZNKgU5/a4IWQgghRLUhSUILquV+dQ1Cg86dzLwLFo5GCCGEEEJUVcaCAi69OI6YCRMx5OQUW2fdlVTePXuZR8LOcCk3v0z9xp5NY828MNZ8HEZeTkFlhiyEEKIcunTpwoQJEyq1z/Pnz6MoCmFhYZXar7hzSZLQgq7f4TiXBPR6ww1aCCGEEMLS/vrrL/r164evry+KorB69eoidc6cOcPw4cOpWbMm1tbWBAUF8cQTT3Dw4MHbH7C4I+QeO0bW/v1k7txJfvTFYut0cXWku7sTz/l7UdPGqtg6/2UoMKDWqrBx0KK1Vt+4gRBCCACuXLnCCy+8QEBAANbW1nh7e9OrVy92795tqlPSfUJxfvvtN959991KjdHf35/Y2FgaN25cqf3eCYYNG0b//v0tHUaVI2sSWlCA+9UkoVHnTq4mhYykXFy87CwclRBCCCFKk5WVRbNmzRgxYgSPPPJIkfcPHjxIt27daNy4MV988QX169cnIyODNWvWMHnyZHbs2GGBqEV1Z9u8ObWWfYU+PQObenWLrWOvUbO8SRDXrzSYqzegVhS0Jaw/6FfPlYFTW2HvYo1K1igUQogyGzBgAPn5+Sxfvpzg4GDi4+PZsmULSUlJ5eonPz8fKysr3NzcKj1GtVqNt7d3pfd7O1z7XK6n1+tRFAWVSsa73SryyVrQtenGRr09aVZ6LsVlWjgiIYQQQtxInz59mDFjBg8//HCR94xGI8OGDSMkJISdO3fSt29fateuTfPmzXnzzTdZs2aNBSI2t3bt2nK/ckqY3ipuL7tWrXDsep/pOP/SJTK2bjOro1YUVMrVZJ/BaOTFk9EMDj9Lmq7kqcQuNezMRhEe3nSBM4cSKjl6IYS4c6SmprJz505mz57NfffdR61atWjTpg3Tpk3jwQcfBCAwMBCAhx9+GEVRTMdvvfUWzZs3Z8mSJQQFBWFjYwMUnW4cGBjIu+++yxNPPIG9vT1+fn4sXLjQLA5FUVi0aBF9+vTB1taW4OBgfv31V9P7/51uvH37dhRFYcuWLbRq1Qo7Ozvat2/PqVPmeyTMmDEDLy8vHB0deeaZZ5g6dSrNmzcv9TM5fvw4DzzwAE5OTjg6OtKpUyfOnj1b7LUB9O/fn2HDhhW53iFDhuDk5MSzzz7L119/jYuLC2vXrqVhw4ZYW1sTHR1NXl4eU6ZMwc/PD3t7e9q2bcv27dtNfV1rt3HjRho0aICDgwO9e/cmNjbW9HewfPly1qxZg6IoKIpi1v5uJiMJLcjBWoOzrUJajpFYezfOnLtI46Zl351OCCGEuJMYjUayDbd/6Q07lQpFqZwRVGFhYRw/fpwffvih2KfcLi4ulXKem1HeqTWKohAZGUlwcPCtCUhUiD4zk4vPP0/+2XP4zn4f53+/lF4vMjuPLcnp6AxGTmfn0dr5xrf+lyKS2fvbWVDApUZrPGo63orwhRDihnR5VzdU0lgV/p7WFxgw6I2oVApqrapoXa3KtHO7Xm/AUGBEUYFGq75hXbW67GOoHBwccHBwYPXq1dxzzz1YW1sXqXPgwAG8vLxYtmwZvXv3Rq0ujOHMmTOsXLmS3377zaz8vz788ENeffVV3n77bTZu3Mj48eOpW7cuPXr0MNV54403eP/99/nkk0/49ttvGTRoEEePHqVBgwYl9vvaa68xd+5cPD09ef755xkxYoRpmvT333/PzJkz+eyzz+jQoQMrVqxg7ty5BAUFldhfTEwM9957L126dGHr1q04OTmxe/duCgrKt9btnDlzmD59Om+++SYAO3fuJDs7m9mzZ7NkyRLc3d3x8vJi7NixnDhxghUrVuDr68uqVavo3bs3R48eJSQkBIDs7GzmzJnDt99+i0ql4qmnnmLKlCl8//33TJkyhZMnT5Kens6yZcsAbslIzupIkoQWFuBux9FLWSRZexB3/izQ0tIhCSGEEBaRbTBQ+6+jt/28Z+9tgn0pN+jlERkZCUD9+vUrpb9bJS4uDi+vsj2YdHSUJFFVpLKxwa51awzpGdi1bVtsnXr2NqwJrcO5nDxaO9uXqV/fuq406VITtVYlCUIhhEV9Of7q8hwjPuyIrePVaaf/bIpm39pzNOzgw31PFybBvnppJwX5Bp6e0Q4nD1sAjm2PYdcvkYS0rkHPkY1Mdb95bQ+5mToGTW+Du68DABF7YmnUya/MsWk0Gr7++mtGjRrF559/TosWLejcuTODBg2iadOmAHh6egJXHxD+d8pvfn4+33zzjalOSTp06MDUqVMBqFu3Lrt37+bjjz82SxIOHDiQZ555BoB3332XzZs38+mnn/LZZ5+V2O/MmTPp3LkzAFOnTqVv377k5uZiY2PDp59+ysiRIxk+fDgA06dPZ9OmTWRmljzzceHChTg7O7NixQq0Wq0p3vLq2rUrkydPNh3v3LkTnU7HZ599RrNmzQCIjo5m2bJlREdH4+vrC8CUKVPYsGEDy5Yt47333gNAp9Px+eefU7t2bQDGjh3LO++8A1xN8tra2pKXl1dtp2PfKjLd2MJqezgDUFDgzpWkUzeoLYQQQoiqzGg0WjqEGxo6dCi2trZlrv/UU0/h5OR0CyMSFaFoNHhPn07Q6lVoa9QwlRv/Mxq3iaMdD3m5mo6v5Ov4Mym9xH5VKoVOj4fQ/pHapjK9zoAuX1+J0QshRPU3YMAALl++zNq1a+nduzfbt2+nRYsWfP311zdsW6tWrRsmCAHatWtX5PjkyZPlrvNf1xKZAD4+PgAkJFxdZuLUqVO0adPGrP5/j/8rLCyMTp06mRKEFdWqVasiZVZWVmbxHj16FL1eT926dU0jOh0cHNixY4dpejOAnZ2dKUEIV6/z2jWKkslIQgszrUuY705m7nnLBiOEEEJYkJ1Kxdl7m1jkvJXl2lPziIgIQkNDK63fynRtWk1ZLVq06BZFIsprf+x+3GzcqONaB7g6FVxz3fSo7H/+If7dGdT8dD5av6IjYrL0egaHn+NYRg7zGwTwqHfxU6uun35vNBrZ8s1J0hNz6Du6qWk0jxBC3GrPfnJ1pJvGqvD3dGjPAJp18y+y0dKIDztdrXvdFOTGXfxo2NEX5T+/5ofMbF+kbv32PhWK0cbGhh49etCjRw/eeOMNnnnmGd58802ztfaKY29fttHdt8r1ybxr/+YbbmLJlxs9fFSpVEUepOp0uiL1ivtcbG1tzX4vZWZmolarOXToUJGp2g4ODqY//zdhqShKtXiYa2kyktDCav27w7FB50aeMQ6D/vavxSSEEEJUBYqiYK9W3/ZXZa1HCNC8eXMaNmzI3Llzi73ZTk1NrbRzibtLZEok47eNZ8j6IZxKLjr7xGg0EvfOu+SeOMGVhcVPMbNWVDR1sMNFq6aFU9m+oGYk5RJ9IokrFzJIipFN9oQQt4/WWo3W2vz3tFqjQmutNluP0KzudclDtfpq3evXIyytbmVo2LAhWVlZhefSatHrKz4S+++//y5y/N+1BstSpzzq1avHgQMHzMr+e/xfTZs2NU0NLo6np6dp0xC4ukvxsWPHKhRfaGgoer2ehIQE6tSpY/Yqz9RhKyurm/q7uVNJktDCro0kNOS7k6dOISM518IRCSGEEKI0mZmZhIWFmXYKjIqKIiwsjOjoaBRFYdmyZZw+fZpOnTrxxx9/cO7cOcLDw5k5cyYPPfSQZYO/zpIlSxg6dKhpZOFPP/1EgwYNCA4ONi0YLqoOD1sPQlxDqOdWjyDnoovHK4qC/6LPcO7fH+/XXyu2D41K4cN6Ndncqh7BdkUX2S+Ok4ctA15qSfcRDalZXxZ1F0IIgKSkJLp27cp3331HeHg4UVFR/PLLL3zwwQdmv+sDAwPZsmULcXFxpKSklPs8u3fv5oMPPuD06dMsXLiQX375hfHjx5vV+eWXX/jqq684ffo0b775Jvv372fs2LEVvrYXX3yRpUuXsnz5ciIjI5kxYwbh4eGlPlQdO3Ys6enpDBo0iIMHDxIZGcm3335r2jW5a9eurFu3jnXr1hEREcELL7xQ4QendevWZfDgwQwZMoTffvuNqKgo9u/fz6xZs1i3bl2Z+wkMDCQ8PJxTp06RmJhYYoLzbiPTjS3s2khCY4ET6VZ5xMZl4expZ+GohBBCCFGSgwcPct9995mOJ02aBFxd6+/rr7+mTZs2HDx4kJkzZzJq1CgSExPx8fGhffv2zJs3z0JRm5s3bx6vv/46vXr14rXXXuPy5ct8/PHHTJw4Eb1ez9y5c/Hz8+PZZ5+1dKjiX642rizuuZg8fR5W6sIpv0aj0fTFTevtje/7s8za5Z8/j1VgoOlYURT8bArbn8zMYc75OObVD8BRU/wGPq7e9rh6F448zMnIJy4qnaCmHpVxaUIIUe04ODjQtm1bPv74Y86ePYtOp8Pf359Ro0bx6quvmurNnTuXSZMmsXjxYvz8/Dh//ny5zjN58mQOHjzI22+/jZOTEx999BG9evUyq/P222+zYsUKRo8ejY+PDz/++CMNGzas8LUNHjyYc+fOMWXKFHJzc3nssccYNmwY+/fvL7GNu7s7W7du5aWXXqJz586o1WqaN29Ohw4dABgxYgRHjhxhyJAhaDQaJk6caHYvVV7Lli1jxowZTJ48mZiYGDw8PLjnnnt44IEHytzHqFGj2L59O61atSIzM5Nt27bRpUuXCsd0p1CMMim7iPT0dJydnUlLS7vlC3UbjUYaTP+DXB3Udp7L+Caf8OADt389JiGEEOJ2ys3NJSoqiqCgIGxsbCwdTpVU2md0s/cqDRo04I033uDJJ5/kn3/+oU2bNnz++eeMHDkSgKVLl7Jo0SIOHjxYKddyp7ud947X+yniJ44mHuXNdm+iVRddLD5t7VouT51GjWnTcHv6qSLv641GOu+P4Ex2HkN83fmgnv8Nz1mQr2fNvH+Ii0rnvsH1adjRt1KuRQhx95J7guIFBgYyYcIEJkyYUGIdRVFYtWoV/fv3v6Wx9OjRA29vb7799ttbeh5xcyrj3lGmG1uYoijUdL36l5dg50Zc1NkbtBBCCCGEuDkXLlygY8eOwNW1fdRqNffcc4/p/c6dO5vtECiqnviseGYfmM2as2vYcH5DsXVyjx8HgwFdTEyx76sVhYUNa9HRxYFXg8u2aL9Ko8IzwAlrWw3etZ0rHL8QQoiqKTs7m48++ojjx48TERHBm2++yZ9//snQoUMtHZq4DWS6cRUQ7OnEmYRc8g0eJCaetnQ4QgghhLjD2dnZmS2s7unpabYjIEBBQcHtDkuUQw37Gnxy3yfsvrybB4KLn17lNXUqdq1b49C1a4n9NHO049fQOmZlifkFeFgV/zVBpVLo9HgILXoF4OAqI36EEOJOoygKf/zxBzNnziQ3N5d69eqxcuVKunfvbunQxG0gScIqIMjdAUjAkO9GRs45S4cjhBBCiDtc/fr1CQ8PN+1+ePHiRbP3IyIiCLxuHTtRNXWq2YlONTuZjvUGPRczLhLoHAhc/aLneN2XOqPRSMo33+Dy6KOo7Ivf3XhjYhrPH7/A/AYB9PNyKbaOoihmCcLky1ns/Pk03Yc3xN65bBuiCCGEKF1Z1i+8FavH2dra8ueff1Z6v6J6kOnGVcD1OxznGmMx6A0WjkgIIYQQd7LZs2dTr169Et+Pjo7mueeeu40RiZtlNBqZfWA2g9YNYn9s8YvLJ376KfGz3id65DMY9fpi66xNSCXHYGB7cnqZz7tl+QkuRaSw+9czFY5fCCGEEJYnIwmrANMOxzo38lVJZKbk4eRha+GohBBCCHGnurbbYElGjx59myIRlUVn0HEm9QzZumyScpOKrePQuTMpP67AZeBAFHXxOxnPbxBAG2d7nvRxL9N5FUWhx8hG7Polknsfr1vh+IUQQghheTKSsAoIcLuaJDTku5GlzSI+PusGLYQQQgghKtfo0aNJTEy0dBiigqzUVizqvogF3RbQJ6hPsXVsmzWj9ob1uAx4pMR+1IrCUD8PtCoFuDpS8Oe4ZPIMJc90cfGy44ExzbBxKNxhOe1KdgWvRAghhBCWIknCKsDXxRa1yghoSHCw5/SFeEuHJIQQQoi7zHfffUd6etmmmIqqyVptzb017zUdZ+myWBW5ymzNKrVz4Y7EhtxcLj7/AtkHD5bY56KLVxh3MponjpxDX8a1r6KPJ/HDm/vY979zt2S9LCGEEELcGjLduApQqxS8na2ISdERa+tO7PnzQCNLhyWEEEKIu4gkc+4seoOeCdsm8Hfs38RmxTK6edEp5IlffEHm9u3kHj9O7c2bUNkU3a24iYMtjmoVPd2dUCtKmc4dfz4dg8FIRlLuTV+HEEIIIW4fSRJWEcEejsSkJJOtuJMUL4s+CyGEEEKIilOr1Nxb816OJh6lk1+nYut4PPccugsXcH3yyWIThACd3BzZ2bYB3tbaYt8vTuu+Qbj7OVCrsTtKGROLQgghhLA8mW5cRQR7OAJg1LmTniVJQiGEEELcXhkZGQQHB1s6DFGJnm74NOseXkcTzybFvq+yscHvo4+wa9XKVFaQklJkVOn1CUKdwcgzx6LYkZxR6rmDm3ui1hR+1Ti88QKpCbJOoRBC3AxFUVi9erWlwxB3MEkSVhEB7vbA1c1Lcg2XMRhkyo8QQgghbo+EhASOHTtGeHi42UtUf+62hbsUx2TGMGn7JNLy0oqtq4tP4PyAR4l/dwZGvb7YOosvXeH3K2mMOh5Fmq6gTDGc2H2ZvavO8tuHh8jN0pX/IoQQoopRFKXU11tvvVVi2/Pnz6MoCmFhYbct3qpKPouqR6YbVxG1TDscu5OvSiQzORcnD1sLRyWEEEKI4sTFxTFz5kzWrVtHTEwMXl5eNG/enAkTJtCtWzdTvaCgIBYvXoxGo+Hjjz9m//79pKenExISwksvvcTgwYMteBVw6NAhhg4dysmTJ02jxxRFwWg0oigK+hISRcIyIuLScbWzooZT8VODS2M0Gnl5x8uEJ4ajUlTM6TynSJ3s/fvQxcaStXs3hsxMs01OrhlZ04MTmTk86OWCs7ZsXyVqNXbHq5YjwaGe2NiXfdqyEEJUVbGxsaY///TTT0yfPp1Tp06ZyhwcHCwRVqXT6/UoioJKZT6+LD8/HysrKwtFJW4lGUlYRdRy/zdJqHMnW5NBokzHEEIIIaqk8+fP07JlS7Zu3cqHH37I0aNH2bBhA/fddx9jxowx1QsPDyclJYXOnTuzZ88emjZtysqVKwkPD2f48OEMGTKE33//3YJXAiNGjKBu3brs2bOHc+fOERUVZfZfUXVcTM7m6aX7eeSzPZxPzCp3e0VReLP9mzTzbMZLrV4qto5zv374zZuH/9IlxSYIAaxVKhY0rEVPj8L34/N05BsMJZ7b3tmah6e0oEWvWqayAp1eNssRQlRb3t7eppezszOKopiOvby8+Oijj6hZsybW1tY0b96cDRs2mNoGBQUBEBoaiqIodOnSBYADBw7Qo0cPPDw8cHZ2pnPnzhw+fLhccRkMBj744APq1KmDtbU1AQEBzJw5E4Dt27ejKAqpqamm+mFhYSiKwvnz5wH4+uuvcXFxYe3atTRs2BBra2uio6MJDAzk3XffZciQITg5OfHss88CsGvXLjp16oStrS3+/v6MGzeOrKzC31GBgYG89957jBgxAkdHRwICAvjyyy9v+FkIy5GRhFWE/78jCTHYkGSvJTImieCG7qU3EkIIIe4gRqMRY07ObT+vYmtbrs0VRo8ejaIo7N+/H3t7e1N5o0aNGDFihOl4zZo19O7dG61Wy6uvvmrWx/jx49m0aRO//fYbDzzwwM1fRAWdO3eOlStXUqdOHYvFIMrGaAQHaw3WGhVuDhUbvVHXtS7f9vnW7P/3nIIcbDWFs1ecevU0a5N94ADagAC0NWoU22eqroBHw87gY61lSeMgnDTqYutptIXlBr2BDV8cw9peQ9enGqDWyrgFIUQho9FIQX7JDx5uJY2V6qY3XPrkk0+YO3cuX3zxBaGhoXz11Vc8+OCDHD9+nJCQEPbv30+bNm34888/adSokWlEXkZGBkOHDuXTTz/FaDQyd+5c7r//fiIjI3F0dCzTuadNm8bixYv5+OOP6dixI7GxsURERJQr/uzsbGbPns2SJUtwd3fHy8sLgDlz5jB9+nTefPNNAM6ePUvv3r2ZMWMGX331FVeuXGHs2LGMHTuWZcuWmfqbO3cu7777Lq+++iq//vorL7zwAp07d6ZevXolfhbCciRJWEXYaNW4O6hJytRz2d6dS+eigLqWDksIIYS4bYw5OZxq0fK2n7fe4UModnZlqpucnMyGDRuYOXOmWYLwGhcXF9Of165dy6RJk0rsKy0tjQYNGpQ73srUrVs3jhw5IknCaiDA3Y6VL7Qnv8CAk03Fp+xe/+X3cPxhJm2fxIedP6S1d+sidXOOHefic8+jcnEm8Ntv0fr5FalzKiuXmDwdWXoDWXp9iUnC68VHpRN9Ihm1WiG0Ry08at4Z0/KEEJWjIN/Al+N3WOTcz37SGa31jf8dK82cOXN45ZVXGDRoEACzZ89m27ZtzJs3j4ULF+Lp6QmAu7s73t7epnZdu3Y16+fLL7/ExcWFHTt2lOmBYkZGBp988gkLFixg6NChANSuXZuOHTuWK36dTsdnn31Gs2bNzMq7du3K5MmTTcfPPPMMgwcPZsKECQCEhIQwf/58OnfuzKJFi7Cxubo0xv3338/o0aMBeOWVV/j444/Ztm0b9erVK/GzEJYjScIqJMjDnqTMdNI17iTHRlk6HCGEEEL8x5kzZzAajdSvX7/UejExMYSHh9OnT59i3//55585cOAAX3zxxa0Is8yWLFnC0KFDOXbsGI0bN0arNU8+PfjggxaKTBTHzd58hMXG43EkpOfydLvACvW3/PhyknKT+DHix2KThGoXFzReXmh8vFH/+0Xuv9q6OLAmtA4aRcHHumwjQHzquNBvbDN0eXpJEAoh7ijp6elcvnyZDh06mJV36NCBI0eOlNo2Pj6e119/ne3bt5OQkIBeryc7O5vo6OgynfvkyZPk5eWZrY1cEVZWVjRt2rRIeatWrcyOjxw5Qnh4ON9//72pzGg0YjAYiIqKMj0Ivb6va9OyExISbipGcetIkrAKCfZw4uD5dAz57qRlnbZ0OEIIIcRtpdjaUu/wIYuct6zKuoba2rVr6dixo9nIwmu2bdvG8OHDWbx4MY0aNSrzuW+FvXv3snv3btavX1/kPdm4pGqLSsxi3I//kFdgwMvJhl6Nyj8C44POH/Bl+Jc80+SZYt+3qulHrR9/QNFqUZUyBayJo/lI3P2pmWTqDXR1dyqxjX9DN7PjjORcUhOy8a/vVkILIcTdQmOl4tlPOlvs3JYydOhQkpKS+OSTT6hVqxbW1ta0a9eO/Pz8MrW3vcH9zLXNR66/l9Hpiu44b1vCMiz/nUGRmZnJc889x7hx44rUDQgIMP35vw8gFUXBUMo6tsKyJElYhdRyv/pDZ9C5kVtwHKPBiKK6ufUQhBBCiOpCUZQyT/u1lJCQEBRFueH6PmvXri12FN6OHTvo168fH3/8MUOGDLlVYZbZiy++yFNPPcUbb7xBjRLWnBNVU6C7HWPvq8ORS6l0q+9VoT6s1da8GPqiWdmxxGM09mhsOta4upq9n/zDDygaDa6PPVZsn9E5eQw7FkWqTs+3TYPpVkqi8Jr83ALWLTxCcmw2PUY0JKSV/L8oxN1MUZSbnvJrKU5OTvj6+rJ79246dy5MdO7evZs2bdoAmNbd+++DuN27d/PZZ59x//33A3Dx4kUSExPLfO6QkBBsbW3ZsmULzzxT9OHPtam9sbGxuP77b3tYWFjZL+4/WrRowYkTJ25qyZKSPgthObJKcBUS8O/mJYZ8d3SqK2Sm5lk4IiGEEEJcz83NjV69erFw4UKz3fuuSU1NJTMzk23btvHQQw+Zvbd9+3b69u3L7NmzTbsCWlpSUhITJ06UBGE1pCgKL3YL4YunW6FRF44OydVV/IvW6jOreWLdE8w5MKfYUbM54eHEvzuDuOlvkn3wYLF9eFtr6eHuTFNHO9q5lG0qsVqtwr2mA7YOWryDi99VWQghqouXXnqJ2bNn89NPP3Hq1CmmTp1KWFgY48ePB8DLywtbW1s2bNhAfHw8aWlpwNUk37fffsvJkyfZt28fgwcPvuHowOvZ2Njwyiuv8PLLL/PNN99w9uxZ/v77b5YuXQpAnTp18Pf356233iIyMpJ169Yxd+7cCl/nK6+8wp49exg7dixhYWFERkayZs0axo4dW+Y+SvoshOVIkrAKqeV+NUlozHcnV5NGUnzRLx9CCCGEsKyFCxei1+tp06YNK1euJDIykpMnTzJ//nzatWvHhg0bqFu3LoGBgaY227Zto2/fvowbN44BAwYQFxdHXFwcycnJlrsQ4JFHHmHbtm0WjUHcHPV1s04W7TjLo5/v4UpGxR40p+elA6BSit/d06ZJE9yfexaXJwZh27L4TYasVCrm1ffn1+a1sVMXftUoMJQ8VV+tVdF9WEMGTmuNo5uNqbys0/uFEKIqGTduHJMmTWLy5Mk0adKEDRs2sHbtWkJCQgDQaDTMnz+fL774Al9fX9NDxaVLl5KSkkKLFi14+umnGTdunGln4bJ64403mDx5MtOnT6dBgwY8/vjjpvX/tFotP/74IxERETRt2pTZs2czY8aMCl9n06ZN2bFjB6dPn6ZTp06EhoYyffp0fH19y9xHSZ+FsBzFKL99i0hPT8fZ2Zm0tDScnG48RaKypGXraPbOJgDaWL/BqHt/oUe32rft/EIIIcTtkpubS1RUFEFBQabd76qT2NhYZs6cye+//05sbCyenp60bNmSiRMnsnTpUmrVqmV24z1s2DCWL19epJ/OnTuzffv2Ys9R2mdUWfcqM2fOZN68efTt25cmTZoUWTeouHWGRFGWune8Xlq2jm4fbScxM5/ZA5rweOuAGzcqxqH4Q4R6haJSSh5LYDQaTUlEY0EBxrw8VMXs9n3NL3HJLL50hW+aBONtXbadmeOi0ti54jS9RjXGyaPsI2mEENVPdb8nEKKqqIx7xyozkvD9999HURTT9tnFOX78OAMGDCAwMBBFUZg3b16ROtfe++9rzJgxty74SuJsp8XB5uoNV4yjG5eizls2ICGEEEIUy8fHhwULFnD+/Hny8vK4dOkSa9asoWPHjvzxxx9F1iP8+uuvMRqNRV4lJQhvlyVLluDg4MCOHTtYsGABH3/8selV3H2WqLqc7bT8+nx7Xru/QYUThAAta7Q0JQgNRgNfHPmC5FzzEa+mBKHRSNy7Mzj/9NPoStipMkdvYNa5WMIzcvgptmwjZ41GI3/9eJqECxkc+D2qwtcihBBCiPKpEhuXHDhwgC+++KLYbbavl52dTXBwMAMHDmTixIkl9nX9opfHjh2jR48eDBw4sFJjvlUC3Ow4cTmLZCt3Ui6ft3Q4QgghhCiH5ORkJk6cSOvWrS0dSplERUkC5k4S6GHPqHuDTcf5BQaOX04jNMC1lFYl+/zI5yw6soj1Uev5pd8vaNXmowALEq6QsXkz+pQUco8fR1vMtDhbtYpVoXVYHpPEi7XKNm1OURTuf6Epf68+S6dBdSsUuxBCCCHKz+IjCTMzMxk8eDCLFy827bBTktatW/Phhx8yaNAgrK2ti63j6emJt7e36fX7779Tu3Zts52F/isvL4/09HSzl6XU9rg67NNQ4E5aRqTF4hBCCCFE+Xl5efH6668Xu56bELeTwWDk5V+PMPDzvawJi6lQH32C+uDn4MfwxsOLJAgBtDW8CFzxI77vz8LxvvtK7KeWrTXT6/ii+vfnwmA0sjkxrdQ1Bx1crek+vCFWNoVjGqJPJGEoZW1DIYQQQtwciycJx4wZQ9++fenevXul952fn893333HiBEjSr1ZnzVrFs7OzqaXv79/pcdSVrXcr67nYsh3J7fgEka5ERJCCCHEbbZmzRq++eYbS4chboLeaOTabaSzbdnWAfyvIOcgVj20iofqFC4k/9/EnlVAAM7XLTSvz8ggc8eOUvv9MCqOp49GMf1M2ZOXZw4l8L/5R1i3MBy93lDmdkIIIYQoO4smCVesWMHhw4eZNWvWLel/9erVpKamMmzYsFLrTZs2jbS0NNPr4sWLtySesgj4d4djg86dfOLJTK3Y7nRCCCGEEBX1yiuvMHz4cEuHIW6CVq1i3uPN+fWF9nSpV77dMa9nqyncNCSnIIdnNj3DpvObiq1rzM/n0tgXufj8C6T89HOJfTpo1ChAYwe7csWi0apw9bZDrbb4OAchxC0ge6oKcXMq42fIYmsSXrx4kfHjx7N58+ZbtoPR0qVL6dOnzw234La2ti5x+vLtFmgaSehGnjaNlIRsHN1khychhBBC3D4RERGWDkFUApVKobm/i+k4OSufxTvPMbF7Xaw05U+0rYhYwf64/USmRNLetz0OVg7mFdRqrOvUIffoUWybNimxnzEBXvRwd6Kufdnvceu09MLN1x6XGoWJxet3WRZCVF9a7dXRztnZ2djaym7mQlRUdnY2UPgzVREWSxIeOnSIhIQEWrRoYSrT6/X89ddfLFiwgLy8PNRqdYX7v3DhAn/++Se//fZbZYR729T6dyShUedCmnUuZ+PSCKjvZuGohBBCCHE3SU1N5bvvvmPs2LGWDkVUEqPRyPPfHmL/+WQS0vOY+1izcvcxpOEQErIT6FGrR9EEIaCo1dR4/TXchg/DqmbNUvu6PkGYozcw4lgUYwK86OjqWGIbNx97s+vZuvwknrUcadKlpiQLhajG1Go1Li4uJPy7S7qdnZ38TAtRDkajkezsbBISEnBxcbmpXJrFkoTdunXj6NGjZmXDhw+nfv36vPLKKzd1UQDLli3Dy8uLvn373lQ/t5uXozVWGsgvUBPr6MqFqIvQJcjSYQkhhBDiLrBlyxaWLl3KqlWrsLOzkyThHURRFMZ0rUPMb0d5oUvwjRsUQ61S80qbV8zKLmVcwtPOE2u1tek81ycI86OjiXvrbXxmvYe2Ro1i+10QHc+25AyOZ+bw9z0NsSvDdOILR5OI+DuO0/vj8W/ghqu3/Q3bCCGqLm9vbwBTolAIUX4uLi6mn6WKsliS0NHRkcaNG5uV2dvb4+7ubiofMmQIfn5+pjUL8/PzOXHihOnPMTExhIWF4eDgQJ06dUz9GAwGli1bxtChQ9FoLHaJFaIoCn6uNkRdySXBxp2Uy+eBey0dlhBCCCHuUBcvXmTZsmUsW7aM6OhoBg0axKpVq+jWrZulQxOVrHNdT7ZO6Yy1pvBhvE5vQFvBNf4SshMYvnE4fg5+fHLfJzhbOxepE/va62QfOEDcW2/jv+izYvt5MaAGl3J1PO7tVqYEIUCtJu60H1AHrZVKEoRC3AEURcHHxwcvLy90Op2lwxGi2tFqtTc92A4smCQsi+joaFSqwhuFy5cvExoaajqeM2cOc+bMoXPnzmzfvt1U/ueffxIdHc2IESNuZ7iVpraHE1FXctHp3UlLOWPpcIQQQghxh9HpdKxevZolS5awc+dOevfuzYcffsgTTzzBa6+9RsOGDS0dorhFrk8QRsZnMHL5QeYMbEaboPIvbxOTGUNmfiZJOUklLpbuM2sWcW+/jffbb5XYj41axScNAszKTmflUsNKg7O2+K8riqIQ2sO8TXZ6PpkpuXjVcirfhQghqgy1Wl0piQ4hRMVUqSTh9Ym+4o4DAwPLtFtLz549q/XOSLWubV6icydXF01Gcq5sXiKEEEKISuPn50f9+vV56qmnWLFiBa6urgA88cQTFo5M3E7zt54hOjmbjzef5odRbcu9BlioVyhf9/4ae609LjYuxdaxqulHwOIvzcryo6OxCggotj7AlXwdg46cxUal4sdmwdSyvfEGg/oCAxu+OEpCdAY9RzYiuLlnua5FCCGEEFCxuQXilgq8tnlJvjs6VTwXTyRbOCIhhBBCXC8uLo4XX3yR4OBgrK2t8ff3p1+/fmzZssWsXlBQEH/++adZ2ZkzZ3B0dMTFxeU2RmyuoKAARVFQFEVGbNzFPny0KcM7BLJwcIsKbxJQz60eNR0L1yDcHbObPZf3lFg/++BBzvV9gPjZH2DU64utk6QrQAFUCriWMJLwv/QFBqzttajVCq7edjduIIQQQogiqtRIQnFVgGkkoRs6JYnwiEQadvS1cFRCCCHErWU0GskpyLnt57XV2JYrQXL+/Hk6dOiAi4sLH374IU2aNEGn07Fx40bGjBlDREQEAOHh4aSkpNC5c2dTW51OxxNPPEGnTp3Ys6fkRMqtdvnyZVauXMnSpUsZP348ffr04amnnpLdJO8yNlo1b/ZrZFb2T3QKzWq6oFKV//+FMylnmLh9Ijq9jiW9ltCyRssidXKOHsOo06G7fBlK+P+tvr0tG1rVJbPAgJOmbElsKxsN9z/fhJT4bLM1Co0GI0oFrkUIIYS4G0mSsAqq5Xb16ach340sqywOX77IY/rGqCq4qLQQQghRHeQU5ND2h7a3/bz7ntyHnbbsI49Gjx6Noijs378fe/vCZESjRo3M1kNes2YNvXv3RqvVmspef/116tevT7du3SyaJLSxsWHw4MEMHjyYs2fPsmzZMsaNG0dBQQEzZ85k2LBhdO3aVUYZ3mV2nL7CiK8P0LNhDT4ZFIqVpnz3ngFOAXTx70JybjJNPZsWW8d9+DCsggKxb9sWRVVy/55WWjytCo83JaaxJiGVD+v5l7i5iaJScPMp/JlMvpzFpqXH6T68AR41Hct1LUIIIcTdSLJOVZCfqy0qBTBaEe3qhDblBPFR6ZYOSwghhLjrJScns2HDBsaMGWOWILzm+inEa9eu5aGHHjIdb926lV9++YWFCxfejlDLrHbt2syYMYMLFy6wbt068vLyeOCBB6hRo4alQxO3WVqODpUCtlo1WnX5R99Zqa14v9P7zL9vPlrV1eS40WhEZzDfqdSxSxdUtram46QlS8g5cqTEfjML9Iw/Gc3K+BSWXrpS5nh2r4wkKSaTfWvOlfNKhBBCiLuTjCSsgrRqFT7O1sSk5nHJyZM+J45w/kQSPnVcLB2aEEIIccvYamzZ9+Q+i5y3rM6cOYPRaKR+/fql1ouJiSE8PJw+ffoAkJSUxLBhw/juu+9wcqqaO6+qVCr69OlDnz59uHLlCt9++62lQxK32YPNfAl0t6Oet2OFp56rFJXZyNzvTn7HpvObmN91Pq42rkXqZ/z5Jwlz5qJYW1P7j3Vo/fyK1HHQqFnaOIivYq7wvL9XmWPpMaIRu1eeof3DtSt0LUIIIcTdRpKEVVSQhyMxqXkY891Q54ez51wS7ZAbHCGEEHcuRVHKNe3XEoxGY5nqrV27lo4dO5pGFo4aNYonn3ySe++99xZGV3k8PT2ZNGmSpcMQFtC0povZ8UebT9Mu2J12td3L3VdaXhpfhH9BWl4amy9s5rF6jxWpY3dPOxzuuw/rkJBiE4TXtHd1oL2rg+nYaDSyOzWTjq4lTyO2sdfSbUgDs7IzhxLwq+uCraNVCa2EEEKIu5dMN66iarlfW5fQnTO+ecRciSQnM9/CUQkhhBB3t5CQEBRFMW1OUpK1a9fy4IMPmo63bt3KnDlz0Gg0aDQaRo4cSVpaGhqNhq+++upWh23Gzc2NxMTEMtcPCAjgwoULtzAiUVX9Hn6Z+VsiGfrVfmJSy7+pkLO1M9/0/obRzUYzsO7AYuuoHeypueBTPCeMN5UZcnMx5OaW2vfSmEQeDTvLy6culjl5fykimU1Lj/PzewfITpf7aiGEEOK/ZCRhFWVKEurcOBKk0P3ESS6eTKZua28LRyaEEELcvdzc3OjVqxcLFy5k3LhxRdYlTE1NRaPRsG3bNhYtWmQq37t3L3q93nS8Zs0aZs+ezZ49e/ArZfTUrZCamsr69etxdnYuU/2kpCSz2MXdo3uDGtzfxJtGvs74uZR9Wv71gl2CeaH5C6ZjnUHHgdgDtPdrbypTrtsgx2gwcPmVqejiYqn56adovYqfXpxvMKICgm2tyzw12s7JGicPG7yDnLF11N64gRBCCHGXkSRhFRXgdvVLhzHfkxRHBdeUfzh1IkmShEIIIYSFLVy4kA4dOtCmTRveeecdmjZtSkFBAZs3b2bRokW8++671K1bl8DAQFObBg3MpzwePHgQlUpF48aNb3P0Vw0dOtQi5xXVi41WzYInWnB9Di47vwCNSlXunY/h6hThWftm8cvpXxjfYjzPNHmmSB3dpUtk/f03huxsdJdiSkwSjg7w4l5XBxo5FCYvDUYjqlIShm6+9gyc1hq1WjElFvUFBjCCWisTrIQQQghJElZRjf2uLmpuyPXBqLcmR3uBPTHxPGBsWOGFpIUQQghx84KDgzl8+DAzZ85k8uTJxMbG4unpScuWLVm0aBFLly41m2pc1RgMBkuHIKoRlarwvlNvMPLiD/+QmVfA50+1xNW+/Ov6OVo5oqBQx6VOse9bBQQQ9PNP5J6MwK5FaKl9NXYsXMO0wGDkyfCzdHd3YlRNzxLvl61tzb/+7F55hvhzafR6tjFO7hUbLSmEEELcKeSRWRVV09WOIA97jKgoyK7NsUAjBemnSYrJtHRoQgghxF3Px8eHBQsWcP78efLy8rh06RJr1qyhY8eO/PHHHzdMEg4bNozU1NTbE6wQleRMQib7opIJu5jKxZTscrdXFIWJLSfy24O/0cW/S4n1rGrVwql3L9OxLiGBK599hrGUae9rElL4KyWT2VFxxObpyhRPdno+p/fHkXAhg+TLWWW+DiGEEOJOJSMJq7COdTyISsxCn1WHE/7HabzvGNHHk/GoWfIubkIIIYSwnOTkZCZOnEjr1q0tHYoQla6etyMrX2hPdHJ2kV2Qy6OOa+EowvT8dCZtm8SElhNo7FF0+r1RryfmxXHkHDmCPjkF79dfK7bPR2q4klKgx8dai69N2UY42jlZ8dirrbl4IpnAJh4VuxghhBDiDiIjCauwjiFXb1aMWfXIs1Kwzwjj4JkkC0clhBBCiJJ4eXnx+uuvy9Ig4o5Vz9uRHg1rmI5jUnP4cX90hfv79PCn7Ivbx9SdUykwFBR5X1GrcR3yNJoaNXB7+qkS+1EUhWdqetLX08VUdjorl02JaaWe38ndlkadCjcPysvWse27CHIyZfdjIYQQdx8ZSViFtavtjlqlUJDvjkHnTJxLChlXLpGf2xQrG/mrE0IIIYQQlpOr0/PM8oOcjE0nLUfH851rl7uPCS0nkJKXwsjGI9Goir+/de7bF8fu3VFZW5vKClJS0Li6lthvVoGekceiiMzOY159fwb5uJcpnh0/nibyQDyp8dk8PLlF+S5GCCGEqOZkJGEV5mSjpVlNZwAKskIID1KwTz9JzOlUywYmhBBCCCHuetYaFQ8198XL0Zp+zXwr1Ie91p45nefQwL1wB/BTyafI1pmveXh9gjD31CnO9uhJ0pIlGI3GYvvVqhQ6uTribaWlm7tTmeNp2bsWbr72dHi0+I1VhBBCiDuZJAmruI4hngDos0KI8gavhHDOHU+0cFRCCCGEuBN07tyZb775hpycHEuHIqohRVF4vnNttkzujJ9L4c7AubqSNxi5kYsZF3lm0zMM2zCMxJzi73nTN2zAkJlJ1p69UMJu3VYqFe/VrcnWNvXwtNKayhNusKmJu58Dg15vg1etwsRiwoV08nOKToUWQggh7jSSJKziOl1blzC7LgZUqHUn2HUp2cJRCSGEEOJOEBoaypQpU/D29mbUqFH8/ffflg5JVEOONoVJuLCLqXT+cBs7I69UqK+U3BRUigqVosJB61BsHc9x4/B57z18585BUatL7c9NWziFeW9qJm3+PsGi6IQSRyACKKrCNUUzU3L536dH+Om9A6RdkWS6EEKIO5skCau45v4uOFhr0BfYYsj1IdI3n+T0c6QmZN+4sRBCCCFEKebNm8fly5dZtmwZCQkJ3HvvvTRs2JA5c+YQHx9v6fBENbRo+xni0/P47u8LFWrf1LMpP/b9kXn3zcNGY1NsHUVRcHnkYbM1CZO/+ZaMbdtK7Xv9lTRyDUaOZZY92ZebpUNjpcLKRo29S9l2TRZCCCGqK0kSVnFatYp7gt2Aq+sSHglScEs+wcUTMppQCCGEEDdPo9HwyCOPsGbNGi5dusSTTz7JG2+8gb+/P/3792fr1q2WDlFUI58MCuXFrnWYM7BZhfvwdfDF297bdLz6zGre2P0Gefq8YutnH/6H+Pff59ILo8k5drzEft+u48unDQL4oF7NMu9A7lHTkcdfa0Of55ug0RaOWizIr/iUaiGEEKKqkiRhNdCxztUpx4asEJKcFTyTwjgeIesSCiGEEKLy7N+/nzfffJO5c+fi5eXFtGnT8PDw4IEHHmDKlCmWDk9UEzZaNZN71jObgrxifzSXUys2VTc5N5n39r3H6jOrWXNmTbF1bBs3wnXQ4zgPeASbRg1L7EtRFAZ6u2F/3RTlt8/EsCwmsdTpxzb2WpzcC9dcPLknlhUz9pN4KaMCVySEEEJUXZobVxGWdm3zEkNOEEaDhmzNBSITrzBQZ0CtlTyvEEIIISomISGBb7/9lmXLlhEZGUm/fv348ccf6dWrl2mk1bBhw+jduzdz5syxcLSiOtp4PI6pvx3Fw8GKjRPuxd3B+saNruNm48a8++axPmo9j9Z9tNg6ipUV3tOnY9TrTf/fGvLz0V2KwTo4qMS+96Vmsuji1bUTQx3taO5kd8N4DHoDhzdeIC0hh/NHk/Co6Viu6xFCCCGqMskwVQO1Pe3xcbbBYFSjzw7iaCCoMyOJPZtq6dCEEEKIu1JcXBwvvvgiwcHBWFtb4+/vT79+/diyZYtZvaCgIP78808ANm7cyD333IOjoyOenp4MGDCA8+fPWyD6QjVr1mTJkiUMHTqUS5cu8euvv9K7d2+zqZhNmzaldevWFoxSVGcNfZxo4ONEv2a+5U4QXtPetz3vdngXlXL1q4vBaOCvS38VqXf9Jibx771H1IABpK9fX2K/bZzteau2L+Nr1ShTghBApVYx4KWWtH4giJa9apXzSoQQQoiqTUYSVgOKotCxjge/HLpEQVYIJwJOM3DfMS4cT6JmfTdLhyeEEEJUCqPRSI7u9q/zZatVl3l9MoDz58/ToUMHXFxc+PDDD2nSpAk6nY6NGzcyZswYIiIiAAgPDyclJYXOnTsTFRXFQw89xKRJk/j+++9JS0tj4sSJPPLIIxw+fPhWXdoNbdmyhU6dOpVax8nJiW032BBCiJL4u9mx8oV2WKkLxybk6vSoFAUrTcXGKyw6sojPj3zOk/WfZFrbaUXeN+Tno4uOxpibi8qh+B2S4eo99vMBXmZl6QV61iSk8JSPe4n/Ltg4aGnzQOEIRaPByLbvImjY0RfvYOcKXZMQQghRFUiSsJroVNeTXw5dgqx65Nb4A+e0MP6+kEoHSwcmhBBCVJIcnZ6G0zfe9vOeeKcXdlZlvyUaPXo0iqKwf/9+7O3tTeWNGjVixIgRpuM1a9bQu3dvtFothw4dQq/XM2PGDFSqq4mRKVOm8NBDD6HT6dBqtUXOczu8+eab/Pbbb7i4uJiVp6eny6YlotJc//NlNBqZ9ttRzidl8dngFvg425bSsnjWamsUFBp7NC72fZWVFf6LF5N94AD299xjdu7SHggYjUYmRUTz+5U0TmTmMqtuzTLFc+yvGE7uieXs4QSGvNceazvL/DwLIYQQN0umG1cTHWq7A6DLq4GhwIHLbmlEZcSQlVr8Lm9CCCGEqHzJycls2LCBMWPGmCUIr7k+2bZ27VoeeughAFq2bIlKpWLZsmXo9XrS0tL49ttv6d69u8UShAA7duwgPz+/SHlubi47d+60QETiTncpJYctJ+MJv5RGdFJ2hfp4pskzrH5oNf1q9zOV5evN/z9W1GqzBKE+LY3zAx4lY2vpo2I7uDpir1Yx0Nu1zPHUa+tNSOsadHg0RBKEQgghqjUZSVhNuDtY08jXieOX09Fn1SE86B/qX4kg+kRbGrT3sXR4QgghxE2z1ao58U4vi5y3rM6cOYPRaKR+/fql1ouJiSE8PJw+ffoAV9cm3LRpE4899hjPPfccer2edu3a8ccff9xU7BUVHh4OXB05deLECeLi4kzv6fV6NmzYgJ+fn0ViE3c2fzc7fn+xE4eik2kb7F7hfoJdgk1/zi3IZcj6Idxb815eaPYCalXRn+mkpV+Re+IE8bNmYd+xAyorqyJ1FEVhuJ8H/b1ccNUWfk2KzMolyNYajar4UYhWthp6jDDfVTk1IZvUuGwCm3pU9BKFEEKI205GElYjHUOu3mQUZIVw1gd8448QeTLRwlEJIYQQlUNRFOysNLf9VZ71CI1GY5nqrV27lo4dO5pGFsbFxTFq1CiGDh3KgQMH2LFjB1ZWVjz66KNl7rMyNW/enNDQUBRFoWvXrjRv3tz0atmyJTNmzGD69Om3NIa//vqLfv364evri6IorF692ux9o9HI9OnT8fHxwdbWlu7duxMZGWlWJzk5mcGDB+Pk5ISLiwsjR44kMzPTrE54eDidOnXCxsYGf39/PvjggyKx/PLLL9SvXx8bGxuaNGliseTt3SLA3Y6HQwun8l7JyGPab+Gk5+oq1N/W6K2cTD7Jz6d+JiUvpdg6nmPH4DZsGH4ff1xsgvB61ycI4/J0PPRPJI+EneFKfsnxKYpi+rdEX2Bg89LjrPssnGM7LlXgioQQQgjLkJGE1UinOp58seMcZNfDgIIm/yS7ElJ4wGBEVcKTTSGEEEJUnpCQEBRFMW1OUpK1a9fy4IMPmo4XLlyIs7OzWYLqu+++w9/fn3379nHPddMib4eoqCiMRiPBwcHs378fT09P03tWVlZ4eXmhVpd9hGVFZGVl0axZM0aMGMEjjzxS5P0PPviA+fPns3z5coKCgnjjjTfo1asXJ06cwMbGBoDBgwcTGxvL5s2b0el0DB8+nGeffZYffvgBuLq2Ys+ePenevTuff/45R48eZcSIEbi4uPDss88CsGfPHp544glmzZrFAw88wA8//ED//v05fPgwjRsXv+adqFxTfjnCjtNXuJKRx5Kh5d9J+/7g+zFgwMPWAw/b4kfuKVZW1Jj6illZ5u7dKIqCffv2JfZ9OiuXfIORbL0BJ00ZfyaM4FPbhfTEXBlJKIQQolpRjJZ4fF3Fpaen4+zsTFpaGk5OTpYOxyRXp6fZ25vIKzBgF/wRPU/EoXeayrSn++Eb4mLp8IQQQogyy83NJSoqiqCgIFPCp7ro06cPR48e5dSpU0XWJUxNTUWj0eDh4UFERASBgYEATJ48mV27drFv3z5T3djYWHx9fdm9ezfti0lSlPYZVdV7lYpSFIVVq1bRv39/4OooQl9fXyZPnsyUKVMASEtLo0aNGnz99dcMGjSIkydP0rBhQw4cOECrVq0A2LBhA/fffz+XLl3C19eXRYsW8dprrxEXF4fVv6PHpk6dyurVq02J3scff5ysrCx+//13Uzz33HMPzZs35/PPPy823ry8PPLyCteFTk9Px9/f/475+7jdjlxM5aVfj/DZ4BbU8XKslD6PJR7jWOIxHq/3eLGjhXWxsUT1fxh9ejr+ny/CoXPnEvu6kJOH3gjBdtbA1f8/cw1GbNWlT8rKyczH1qFw1GLChXQ8/R1R5OG+EEKI26ys944y3bgasdGqaRPkBoA+K4TwIAWPpOOc3HPZwpEJIYQQd4+FCxei1+tp06YNK1euJDIykpMnTzJ//nzatWvHhg0bqFu3rilBCNC3b18OHDjAO++8Q2RkJIcPH2b48OHUqlWL0NDQ2xr/2rVr0el0pj+X9rKUqKgo4uLi6N69u6nM2dmZtm3bsnfvXgD27t2Li4uLKUEI0L17d1QqlSkZu3fvXu69915TghCgV69enDp1ipSUFFOd689zrc618xRn1qxZODs7m17+/v43f9F3sWb+LmwYf69ZgvDopTTyCvQV6i8jP4NJ2ycxc99Mfoj4odg6ahcXHHv1wqZhQ+zatSu1v1q21qYEIcBPccl0O3CKsPTSN165PkF4JTqDlR8cYu38MHR5FbsuIYQQ4laT6cbVTMc6HuyMTMSQFcIV/934xIdx4MT9dMzWyW5qQgghxG0QHBzM4cOHmTlzJpMnTyY2NhZPT09atmzJokWLWLp0qdlUY4CuXbvyww8/8MEHH/DBBx9gZ2dnSija2tre1vj79+9PXFwcXl5eppF7xVEUBb3eMsmMaxup1KhRw6y8Ro0apveuXcP1NBoNbm5uZnWCgoKK9HHtPVdXV+Li4ko9T3GmTZvGpEmTTMfXRhKKirt+6ZyzVzJ5YvHfBHrYsWxYGzwdrUtpWZSD1oGnGjzFysiVPFT7oeLPZ2uLzztvY8jONq1RaDQayT16FNumTUvsW280sjA6gXM5efyVkkFzJ7syxZSakI1KpWBlq0FjJeM0hBBCVE2SJKxmOoZ4wHowZNfGaFSTaXWRCPdcTu2Lp+l9NW/cgRBCCCFumo+PDwsWLGDBggVm5QUFBQwYMID169cXaTNo0CAGDRp0u0IskcFgKPbPouysra2xti5f4kqUXUJ6HlYaFXZWGlwr8BBcURSGNBrCE/WfQKsubH8w7iAta7Q0m36ssitM8qX99huxr72O65Cn8X711WL7VisK/2sRwlcxiYwJ8Cq2TnFCWtXAo6YDNg5asw1O9AUGrGzkK5kQQoiqQR5jVTMNvJ1wt7dCb9CizwngaBDYZxzl2K4Yi+yOKIQQQohCycnJTJw4kdaty7/5QlWRmppq6RDw9vYGID4+3qw8Pj7e9J63tzcJCQlm7xcUFJCcnGxWp7g+rj9HSXWuvS9uv3a13fljXCc+fSIUzb/r/hmNRnJ15RvZen2CcMfFHQzfOJxx28ZRYCgotr4u5uoSPhoPz2Lfv8ZFq2FSoDfqf5N9BqOR4Uej+CE2qdT7cVdve7MpyPv/d46fZuwn7lxama9JCCGEuJUkSVjNqFQKHepc3SVNn1mH4wEK9x7azsGCPOKj0i0cnRBCCHF38/Ly4vXXXy92o4SqaPbs2fz000+m44EDB+Lm5oafnx9HjhyxWFxBQUF4e3uzZcsWU1l6ejr79u2j3b/rx7Vr147U1FQOHTpkqrN161YMBgNt27Y11fnrr79MazACbN68mXr16uHq6mqqc/15rtVpd4N16sSt5e1sQw2nwg17lu6Kov/C3Zy9klmh/pJzk9GqtNR0qIlGVfzIPc9xLxK44kfcnxlpKjNkZ9/wQfzahFTWJ6bx2ulLxObpSq17jS5Pz5lDCaQn5pKdnl/2CxFCCCFuIUkSVkMdQ64mCcmpT46Ngkp/ljinRI7vkg1MhBBCCFF2n3/+uWktvc2bN/Pnn3+yYcMG+vTpw0svvXRLz52ZmUlYWBhhYWHA1c1KwsLCiI6ORlEUJkyYwIwZM1i7di1Hjx5lyJAh+Pr6mtZRbNCgAb1792bUqFHs37+f3bt3M3bsWAYNGoSvry8ATz75JFZWVowcOZLjx4/z008/8cknn5itJzh+/Hg2bNjA3LlziYiI4K233uLgwYOMHTv2ll6/KLtcnZ6lu6KIiMtgz9mkCvXxcMjD/PTAT0xsObGw34JcDEbzKfe2zZujqP4dvWgwcHHMGGLGT6Dg341uitPPy4U3avvyTogfvjZWJda7ntZazWOvteG+p+oT3Lxw5KJeJ0sACCGEsBxZAKMa6vRvklCX7YuV3pZtTbNocGYXYbHudMwJwdpW/lqFEEJUD7JURslux2cTFxdnShL+/vvvPPbYY/Ts2ZPAwEDTaLxb5eDBg9x3332m42uJu6FDh/L111/z8ssvk5WVxbPPPktqaiodO3Zkw4YN2NgUji77/vvvGTt2LN26dUOlUjFgwADmz59vet/Z2ZlNmzYxZswYWrZsiYeHB9OnT+fZZ5811Wnfvj0//PADr7/+Oq+++iohISGsXr2axo0b39LrF2Vno1WzZkwHfj54kafaBlS4nxDXENOfjUYjb+55k8ScRGZ2nIm3fdHp5bnHj5N94CCKVos+JRXNv6NP/0utKEXWJzybncsXF6/wRm1fHDXqYttZ22po2NHXdKzL0/PLrAPUbulFqz6BqDUynkMIIcTtJdmkasjH2ZbanvacvZJFQVYwf9c7xpylf/H1gIc4vS+OJl1kAxMhhBBVm1Z7da2w7Ozs2767b3WRnZ0NFH5Wt4KrqysXL17E39+fDRs2MGPGDOBqAuVW72zcpUuXUhOhiqLwzjvv8M4775RYx83NjR9++KHU8zRt2pSdO3eWWmfgwIEMHDiw9ICFRXk52TC2a2GST6c3MOWXIzzTMZgmNZ3L3d/FjItsu7iNfH0+8dnxxSYJbZs0IfCnFeiio7EOLtwl22g0lrqkgNFoZPzJaA6mZ5NjMPBpg1pliunMoXhS4rKJ2BNL8+4BkiQUQghx20mSsJrqFOLJ2StZ2Otaku90nKOB2WhzjnNspy2NO/tVm7WQhBBC3J3UajUuLi6mjSfs7Ozkd9e/jEYj2dnZJCQk4OLiglpd/CikyvDII4/w5JNPEhISQlJSEn369AHgn3/+oU6dOrfsvELcrC//OseasMvsPpPIrle6YqMt389JgFMAPz/wM/8k/EMzz2am8v8mAG0bNcK2USPTcV5UFLFTp+H9zjvY1KtbbN+KovB6bV/eiIxhapBPmWNq0N4XjZUaGwet2cygGyUlhRBCiMoiScJqqlOIB1/vOY8hux64w9ZmKgbs28E/jRvT9XwGNYKcLB2iEEIIUapru8f+d4dacZWLi8st32H3448/JjAwkIsXL/LBBx/g4OAAQGxsLKNHj76l5xbiZjzZJoCjl9Lo18y33AnCawKdAwl0DjQdJ+YkMmHbBF5q/ZJZ4vB6Ce/PJufIERLmziHgyy9L7PseFwc2taprltz7JiaRYDtrOro6ltgupFUNs+OLJ5PZ/79z3Pd0A9x87Mt4ZUIIIUTFSJKwmmob7I5GpZCSqcZJ58U5nwQ8ko9y3j2d47tiJEkohBCiylMUBR8fH7y8vMx2nxVXpxjfyhGE159nypQpRconTpxYTG0hqg5XeysWPdXCLAl3LCaN6ORs7m9S9tF715t/eD5Hrhxh5t8z+emBn4odveczcwbx78/Ga/KkYnowd337k5k5vBYZg85o5I+WIbRwunHCz2g0svvXSJJisji+M4ZOjxU/clEIIYSoLJIkrKYcrDW0DXZj95kk/HiISyxmW1OF2hf2cuSiIx0fDcFKNjARQghRDajV6tuSEBPFi4yMZNu2bSQkJGAwmO+sOn36dAtFJcSNXZ+Ey8nXM27FP5y7ksW7/Rvz9D1lWwfwelNaT8FgNDCs0bASp/dqPDzwm/OhWVnyN9+gsnfA+ZGHS2znb2PFkz5uxOfrCHW0K1M8iqLQd0wzDqyLou2DwaZymX4shBDiVpEsUjU2sKU/u88kkZhQB6Ovws5GCu9+t53vHurN6QPxNL7Xz9IhCiGEEKIKW7x4MS+88AIeHh54e3ubJR4URZEkoag2NGqFPo29+e1wDA9UcCShk5UTMzrOMCv739n/kVOQw8C6A4tNzOWdO0f8h3NAp0PjXQOHDh2K7dtBo2Z2PX8KDIUJvjyDgfkX4nne36vEHZAd3Wzo+nQDs7Kt30Zg52hF676BaKzkAYsQQojKI0nCaqx3Y28c12i4kl6AX42WpNse5IJnCnpDJMd2WtOok688ZRRCCCFEiWbMmMHMmTN55ZVXLB2KEDdFq1bxUq/6jO5SB3vrwq842yISaF/HHesSknClSchOYOa+mWTpsnDQOnB/8P1F6lgFBOA57kVyT5zAvn37G/apURXem887H8/HF+LZkpTB+pYhZbpvvxKdQcSeWBQFgkM9qREoSwwJIYSoPCpLByAqzkar5qHmvgA45fUGYGszhY5hOziSncuV6AxLhieEEEKIKi4lJYWBAwdaOgwhKs31CcL9UcmMWH6AhxbsJju/oNx9edh6MKb5GNr6tKVXYK9i6ygaDR6jRuH30UemJJ8xP5/492dTkJhYav+dXB2pZWPF6ACvMj/Y9wxwpM/zTWj7ULBZgtBoNJbxqoQQQoiSSZKwmnuslT8AZ2McMeptORqkIuT8IU7XyOP4zssWjk4IIYQQVdnAgQPZtGmTpcMQ4pbIyi/A1c6KpjWdsbMq/wQqlaLi6YZPs7jHYtSqqyMRDUYDC/5ZwJXsK2Z1r0/yXVm0iOSvv+bC0GEY/7PO5/Xauzqwo019+nk6m8r2p2by7eVEDKUk/YKbe9Kyd6DpODs9n59mHuBc2JUS2wghhBBlIdONq7kmfs7U93YkIi6DIB7iMivY1diI/+V9hF+wosOjdbCykb9mIYQQQhRVp04d3njjDf7++2+aNGmCVqs1e3/cuHEWikyIm3dfPS82TbwXa03huIjMvAJiU3MIqeFY5n6uTwCuiFjBF+FfsObsGtY9vA4rtVWR+k69epG5dRseo0ejqEofk2GjLnw/z2Bg8qmLRGbnkabTM7ZWjTLFd3jTBZIuZbL/9ygCm3qgUslyQ0IIISpGskfVnKIoPNbKn3d+P0FOcnPwXMG2Jgovr9rKr326EHkgnkadZAMTIYQQQhT15Zdf4uDgwI4dO9ixY4fZe4qiSJJQVHseDtZmx+/9cZJfD17i3f6NeLx1QLn7a+3dmgZuDXg45OFiE4QANvXrE7TyVxRN4Vet7MOHyTt7FpdHHy1552RF4Wlfd5bFJPKUr3uZY2r7YDBqjYrgZp6mBKHRaMRoMKJSy8QxIYQQZSdJwjtA/1A/Zq0/SXSiES/X2iQ5nyXNJo4MTTTHd1pLklAIIYQQxYqKirJ0CELcNjq9gbi0XPL1Bvzd7CrUR4hrCN/3/R61UrgRytnUs4RfCad/nf6mBOD1CUJDXh6xr79B/rlzGNLTcR85sti+1YrCs/5eDPfzRHvdaMBPzsfTxsWedi4OxbbTWqlp17+2WVnE3jjCt12ky5P1qREkm5sIIYQomyrzaOn9999HURQmTJhQYp3jx48zYMAAAgMDURSFefPmFVsvJiaGp556Cnd3d2xtbWnSpAkHDx68NYFXAW72VvRs6A2Al6E/cHUDk3ZH/yI8I5uEC+kWjE4IIYQQVV1+fj6nTp2ioKD8mzsIUV1o1SqWDm3Fyhfa0762h6n8TEIG+QUlrx1YpB+VFpVy9WuUwWjgzT1vMn3PdD478lmx9RWNBpeBA9H6++Py6KNl6L8wQfh3aiazomJ55J8zRGXnlSk+o8HI4Y0XSLyYSczplDK1EUIIIaCKJAkPHDjAF198QdOmTUutl52dTXBwMO+//z7e3t7F1klJSaFDhw5otVrWr1/PiRMnmDt3Lq6urrci9CpjYKuaAFyIqYHRoOZAXYWWJ/Zyws/AsR0xFo5OCCGEEFVRdnY2I0eOxM7OjkaNGhEdHQ3Aiy++yPvvv2/h6ISofIqi0LJW4feCtBwdTy7eR79Pd3ExObvc/RmNRroHdMfdxp0BIQOKP6dajfvwYdT+Yx1q58JNSpKWLiXn2PFS+69nb8MQX3ee9HEjyM661Lqm86kUHpnSgpa9a9Gsu7+pPDdLJ7sgCyGEKJXFk4SZmZkMHjyYxYsX3zCR17p1az788EMGDRqEtXXxvyRnz56Nv78/y5Yto02bNgQFBdGzZ09q165dbH2AvLw80tPTzV7VTacQT3ycbcjINVCDHujVCgfq6vC8coijB+JIT8qxdIhCCCGEqGKmTZvGkSNH2L59OzY2Nqby7t2789NPP1kwMiFuj7NXMikwGNHpDXg6li0Jdz21Ss2wxsPYMGAD3vaFgxhWRa4iIjnCrK5y3cZA2QcPkvDhHM4//jj5ly6V2L+rVsMH9fyZU68w2ZeqK2BQ2FkOpWWV2M7W0Yp7+tdG/e+ahEajkQ1fHmXVnMOkxJXcTgghxN3N4knCMWPG0LdvX7p3714p/a1du5ZWrVoxcOBAvLy8CA0NZfHixaW2mTVrFs7OzqaXv79/qfWrIrVK4dGWV0cTqjI6ArC1qYqe+7ZxrKaWwxujLRmeEEIIIaqg1atXs2DBAjp27Gi2mUKjRo04e/asBSMT4vZoEeDKn5M6s+iplthoC9cZPHoprVz92GgKk+xnUs7wzt/vMOj3QZxOOV1sfaugIJz69sXlkYexqlnzhv1f//P58fl4tqdkMOnURQxlHBmYGp9NfFQ6CdEZqDUW/woohBCiirLob4gVK1Zw+PBhZs2aVWl9njt3jkWLFhESEsLGjRt54YUXGDduHMuXLy+xzbRp00hLSzO9Ll68WGnx3E4DW15Nbp6J1aAp8OSil4JV3jliHRM4sfsyGcm5Fo5QCCGEEFXJlStX8PLyKlKelZVV4g6sQtxp3OytqOftaDpefzSWfgt2MeWXIxWanuti40JX/6508utEiEtIsXU07u74zZ2D95tvmsr0aWnETJ5C/oULpfb/Yq0aDPJ2Y2aIHyqlcDfjAkPJsbp62zP47XvoMaIhTh62pvKEC+kYSmknhBDi7mKxJOHFixcZP34833//vdn0lptlMBho0aIF7733HqGhoTz77LOMGjWKzz//vMQ21tbWODk5mb2qowB3O9oFu2M0gp9ydVHkrc1UtD+yg5PeGg5vLP2GQwghhBB3l1atWrFu3TrT8bXE4JIlS2jXrp2lwhLCos5eyUSlgI+zTYWS5R62HsztMpc5XeaY2ufp83h377tcyjCfWnz9LshX5n9K+rp1XJo4sdTkpIeVhnkNAujoWpjYXJ2QSo+Dp9idklFiOwdXG2qHFj4USLuSzcoPD/Hr+wfJzdKV+zqFEELceTQ3rnJrHDp0iISEBFq0aGEq0+v1/PXXXyxYsIC8vDzUanUpPRTPx8eHhg0bmpU1aNCAlStX3nTM1cFjrWuy91wS8XFBGH0VdjdUmP/5Vl5u/yANdl+mZe9AHFzLv96KEEIIIe487733Hn369OHEiRMUFBTwySefcOLECfbs2cOOHTssHZ4QFjG2awid63oRUsPBVBaTmkNSZh5Na7qUuR9rdeE991fHvuLn0z/zd+zfrO2/FrWq6Pcct6efIv/CBdxHjSpXctJoNLIgOp6TWbkcSMuiw3XJw9KkxGaj0aqxsddgbWexr4VCCCGqEIuNJOzWrRtHjx4lLCzM9GrVqhWDBw8mLCysQglCgA4dOnDq1CmzstOnT1OrVq3KCLvK69PYB0cbDVfSDbgZ25JjrfBPbT1dDm3klJeGw5tkNKEQQgghrurYsSNhYWEUFBTQpEkTNm3ahJeXF3v37qVly5aWDk8Ii2lS09lsjcK31x7noYW7Wb7nfIX661mrJ61qtOLF0BeLTRACWAUGErBkMfZt25jK0tev5+LYsaVubqIoCr80r8OEWjV4zr9wpGBcno6MAn2J7QKbejD47Xvo8lR9U1KyQKfn8MYL5OcWlPcShRBC3AEs9sjI0dGRxo0bm5XZ29vj7u5uKh8yZAh+fn6mNQvz8/M5ceKE6c8xMTGEhYXh4OBAnTp1AJg4cSLt27fnvffe47HHHmP//v18+eWXfPnll7fx6izHRqvmwWa+fL8vGsfcnqTY/c36Viqm/7CJiVP6UH/nZVr2qoW9i4wmFEIIIQTUrl37hpu8CXE3yy8wYGelRq0otKvtXqE+arvU5qteX5mV7Y/dz8+nf+alVi9Rw75GkTbGggISPpyD7vJlbBs3xuP550vs302rYWqwj1nZ5IiLhGdms6BBLTq7FT+60M7Jyuw4fNsl9q46y5lDCQyc1krWJhVCiLtMld7aKjo6mtjYWNPx5cuXCQ0NJTQ0lNjYWObMmUNoaCjPPPOMqU7r1q1ZtWoVP/74I40bN+bdd99l3rx5DB482BKXYBGPt766gUnkJXusFBeivBVO++XT4Z8/Oe2h5p9NstOxEEIIcbdKT08v80sIAVYaFfMGhbJtShfq1ihMtv1xNJaTsWX/OVEUxZR0MxqNvH/gfTae38g3J74pvr5GQ83PF+H0YD/chg0zlevT0jAaDKWeK1lXQFROHqk6PTVttGWO0c3bHidPW5p0qSkJQiGEuAspxops2XWHS09Px9nZmbS0tGq5iYnRaKTPJzuJiMvg3uax/JP3CfUuGXnpV2smT/qYwfsNDJnRDntnGU0ohBBCVEc3c6+iUqnK/OVfry95qqIoVN3vHUX5xaXl0m3udnJ0ela+0J7QANdy9xGRHMGn/3zKex3fw9naGYCcghxs1KVvmBL93HPok5LxmTkDm3r1SqyXbzAUWaNwZVwyDRxsaehgW2I7fYEBlUpBUV2NIeZUCv/8GU37R+rg5mNf3ssUQghRBZT1XkVWqL0DKYrCY638eef3EyTE18bKzYpTNfO56JlDm2NbOePWlX82R9Px0RBLhyqEEEKI22zbtm2mP58/f56pU6cybNgw027Ge/fuZfny5ablXoQQRakU6FzPk5jUXJqVYzOT69V3q8/CbgvNymb+PZML6Rd4/Z7XqedWNAGou3yZnEOHMeTloVhZFXn/elYqlVmCMC5Px5RTl8g1GFjfsi7NneyKbafWmE8227v6LPFR6Th52HLv43XLenlCCCGqIUkS3qH6h/oxa/1JImKzebjek/wZ/zW/tVd48fc/mDKhOyE7YmjRs1aRdUiEEEIIcWfr3Lmz6c/vvPMOH330EU888YSp7MEHH6RJkyZ8+eWXDB061BIhClHleTnZ8NnglmTnF6D6d8Sd3mBkzPeHeaSFHz0a1ij3dN3k3GQ2X9hMdkE2efq8YutofX2pvWE92QcPYR0UZCrP3L0b26ZNUTuWvLOxwWikh4cTsbk6mjkWjiQ0Go2lxtp9WEP2/e8crfoEmspyMvJBAVsH+S4hhBB3kiq9JqGoODd7K3o29AZAyeiIRtFwNEjFFacMQiP+4qyLirDNsjahEEIIcTfbu3cvrVq1KlLeqlUr9u/fb4GIhKhe7KwKx1ysPHyJDcfjmPLLEdIrsDuwm40ba/uvZXq76TT1bGoqP550nGxdtulY4+GBU+9epmNdfAKXxozlbI+e5F+KKbF/XxsrvmwUyC/NaxfuZmww0u9wJJ9FJ5BXwjqHLjXs6PVMY7PBBX+vOce3r+/l1L64cl+nEEKIqkuShHewwW0DAFh3JJmufv0B+K29isc3/4+9ddUc3XHp6lNAIYQQQtyV/P39i93ZeMmSJfj7+1sgIiGqr/ub+DDmvtq81KsezraFm4Vk5OrK3EcN+xoMrDvQdJyen84Lm1+g76q+nEs7V2ybgitX0Pr6YhUUhNbP94bnsFEXfgVck5DCwfRsFkYnUGAo21L1er2BxIsZ6HL1OHmUvLahEEKI6kemG9/B2tV2p5m/C0cupmKV0QuFlRwKUTFoRyoNz+7hnFNbwv68SLuHa1s6VCGEEEJYwMcff8yAAQNYv349bdu2BWD//v1ERkaycuVKC0cnRPXiYK3hpV71zcqOxaTx+Bd7GdkxiIk96pZ7CvKljEvYae2wUlsR4BhQbB3bxo0IXruGguTkwt2TdTpiJk/BZeCj2HfsWOJ5+9dwJf/ffSztNWpT+fbkdDq6OKJRFW2nVqt49JVWXI5Mxae2s6n89P44CnQG6t3jjVotY1GEEKI6kn+972CKojD2vjoA/O+fNO7z6wvAqvYqnti0lj31tBzdfonczLI/3RRCCCHEneP+++8nMjKSBx98kOTkZJKTk+nXrx+nT5/m/vvvt3R4QlR7vx2OIStfz4Xk7HInCAEaujfkf/3/x4KuC9Coro7vMBqNvLXnLfZe3muqp2g0aL28TMepv60iY9MmLr8yFWNOTon9qxWFJ3zcecLH3VR2OD2LQUfOcd+BCHL1xU9BVlQKfvUKd3TW5enZ9esZtn0bQeSB+HJfpxBCiKpBRhLe4brV96K+tyMRcRk45TwI/M7eBioe25lI7eh9RDmEErYlmnsektGEQgghxN2oZs2azJw509JhCHFHeuOBBrQJcqWhT+GIu7RsHZtPxvNwqB/qYkbq/ZdWrSXAqXAU4ZboLayMXMkfUX+w+dHNOFs7F2nj2LMH+VFRWAUGorIr3MU4Pzoaq4DiRyReE5enw02rprmTndnU5NIoKmjRM4DIgwmEtKphKs9KzcPGUSsjC4UQopqQf63vcCqVwhjTaMIMOvp0w6jA6nYqBm9Yw6761oRvu0R2uqxNKIQQQgghRGVSFIXejX0IcC9M1C3acZYpvxzhxR8PV6jPUK9QBjcYzIjGI8wShPFZhSP4NK6u1Jj6Cq6DHjeV5YSFcbZnLy5NmIixhE1KAO73dOHAPQ2ZXrtwfcOk/AL6HYpkdXwKBmPRtQs1WjXNuwfw6CstUWuufsU0Go1sXHKMH97aR9y5tApdqxBCiNtLkoR3gfub+BDsYU9qtg7vgqsLIe9spMIqPw7/2ENcsIX9/yt+IWQhhBBCCCFE5fFxtsHZVssjoTVNZQaDEWMxybfiuNu6M7XNVJ5v9ryp7HzaeXqv7M2k7ZPQGYpfSij70GFQFFR2diiq0r8G2mvUeFoVbr7yxcUEDqRnsehiAqWNfbx+SnVWaj6p8dlkpeTh6GZTpmsTQghhWZIkvAuoVQrPd7k6nfh/h3No7dUevRrW3qPiqfWr2dXQmhO7LpN4KdPCkQohhBBCCHFnG9o+kF2v3Ee3BoVrCP4v/DIPf7aHXZGJFepzb+xe9EY9Or0OrUpbbB33kSMI/t9aPF8cayorSEoi5qWXyTtzptT+Rwd48VKgNy8H+ZgSgQUGIz9cTiKnhHULHVyteXpGex4Y2xR7F2tT+b615wj7M5r83ILyXqYQQohbTJKEd4mHQ/3wc7ElMTOPINWTAGxrqsIl/RKeiUeJdtOw+9fIMj/BFEIIIYQQQlSMo43WbNTd4p3nCLuYStjFlAr190T9J1j54EomtppoKsvWZfP6rtc5nXLaVGZdpw5aHx/TcdKSpaT/739cfu21Uvt30WqYHORNN3cnU9nqhBQmnbpIn0OnS/wOobVWU7O+m+k4MyWPwxsvsPvXMyTFZJX7OoUQQtxakiS8S2jVKp7rHAzA+n8KaObRAp0G/tdWxdN/rGJzqC0XI1I4H16xp5dCCCGEqH66du1KampqkfL09HS6du16+wMS4i61bFgbXuhSm6HtA01lp+Mz2BoRX+aH+CGuIQQ7B5uOV5xawZqza5i8fTIGY/Gj/Zz7P4Rjjx54jhljKjPm55O1f/8Nz2ulUlHTRsvDXq5mCc9kXckjBG0dtHR+oh7123njU7twPcVLp1LISM694TUKIYS4tSRJeBd5rJU/Hg7WXE7NpYF2CACbW6jwToyibtRBjtWyYvevZ9AXlLyQsRBCCCHuHNu3byc/v+jmZbm5uezcudMCEQlxd/J0tOaV3vVxtCmcKjx30ylGfH2QOZtOVajPe/3upVdgL55t+iwqpXAzkYNxB00JQJt69aj56Xwc7r3X1C7t93VEDxnKpRdfLLX/B71c2Nu2Ic/6e5rKjmRk03z3cV4+dbHYJKNaq6JhR1+6DW1oKtPrDPz51XG+e30vlyMrNpJSCCFE5ZAk4V3ERqtmVKcgADYfUVHftSF5WvijtYpRq39kV0MNick5HN1+ycKRCiGEEOJWCg8PJzw8HIATJ06YjsPDw/nnn39YunQpfn5+Fo5SiLuX0Wgk0MMeeys1D4cW/ixm5hWQq9OXqY86rnWY03kO/Wr3M5XtjNnJ8I3DGbFxRIkjBQsSE1GsrLBr3tw8Jn3R82pVCrbqwq+UmxLTyDcaydYbzEYXliYnMx+XGnbYOllRI7BwdGF2ej5GgyyFJIQQt5PG0gGI22vwPbVYtOMs55Oy6Wk/goiUKWxopabvgSt0/3sTe+t1x37deeq19cbW0crS4QohhBDiFmjevDmKoqAoSrHTim1tbfn0008tEJkQAq7uEjytTwPGdQ3B3rrwK9vn28+y4sBFXutbn4ev2x25rGIzY7HV2NLYo7FZEq/AUIBGdfU8Hs+OwuWRh1FsbE3v5xw5wqUJE3EfPgy3IUNK7P+lIB+6ujnholWbyq7k6xh6NIrhfh4MqOGK6j/JQwdXG/pPakFORj5qbWHCccMXR8nOyKf78IZ4BzkjhBDi1pORhHcZB2sNw9tfHU3411Fb6rrUI9vayMoOKgavX80J/3wS0bP/f1EWjlQIIYQQt0pUVBRnz57FaDSyf/9+oqKiTK+YmBjS09MZMWKEpcMU4q53fYLQaDSyJSKBxMw8bK9LwpXH4/UfZ9OATYxsPNJUdiH9Aj1+7cGiI4tMows1Hh6oHexNdVJ+XEFBbCy5p2489bmlsz217WxMx9/EJHE4PZsll65Q2tjC6wcoZKXlkXQ5i4ykXBzdCvuSTRaFEOLWkpGEd6Fh7QNZvPMcEXEZTGn1IqdTx7KxpYqeh3MYvH4lW9o9hfPOGBp39sPdz8HS4QohhBCiktWqVQsAg0HWIRaiulAUhbVjO7DpeDw9Gnqbytceucy2iARGdgyisd+NR9y52LiYHa+MXEliTiInEk+UOEXY++23sGvTBtumTUxluoQE4t6YjutTT2HfsUOJbUfU9ECrUqhvb2OqozMYmXb6EgO8XbnH2b5IW3tna4bOak/cuTTsna1N5Tt+OEVupo7WDwTJ9xQhhLgFJEl4F3K20/LUPbX4fMdZ/jyioVO9e9kZ8xffd1ExafVWVnXpRYyrI7t+ieTB8c3LvJ6IEEIIIaqfyMhItm3bRkJCQpGk4fTp0y0UlRCiOFq1ir5NfUzHRqORz7adISIug7o1HMuUJPyvF0NfpIFbA/wcCtc+zNJlMXHbRB4OeZietXqitrbG5ZGHzdqlrlhB5o4d6DMycOjUscT+XbUaxtWqYVa2PjGN72KT2JiUxqF2DbEq5vuGlY2GgIbupuO8bB0Re+PQFxho3iOg3NcphBDixiRJeJca2TGIZbujCLuYylMdnmfP5d0cqAcRNfU8/9v3fPb4ZHy2pHDhaBKBTT0sHa4QQgghboHFixfzwgsv4OHhgbe3t9mDQUVRJEkoRBWnKAofPNqUb/ZeYFBrf1N52MVUtkUkMLhtAF5ONqX0AFqVlj5BfczKVp9Zzd7YvcRlx9E7sHex7Zz798eQlYVd27amMkN+Plc++hjnhx/Gpl7dEs/ZwN6Gp33dCbCxwkpVuALWFxcTuNfVkQYOtkXaWNtpGTitFVFHrlAjyKnwWv+MJikmk+bdA2R0oRBC3KQyJQnd3NzK1amiKBw+fNg0lUVUPZ6O1jzRJoCv95zn1/2ZPNr0UX469RPfdFcz66swfu16ghP+wbiuPIN/QzfUGlm+UgghhLjTzJgxg5kzZ/LKK69YOhQhRAU1renCnIEuZmVLdp7j9/BY4tNzeX9A03L32SuwF+l56dR0rGl6eGAwGnh///t0C+hGa+/WWAUEUGPaNLN2GRs3kfz116Rv2ECdLX+iqItfOzHE3oYP6/mblR3LyObNM5fRKgpHOzTCRVv0q6q7n4NZItBgMHJk60Uyk/Pwq+cqSUIhhLhJZUoSpqamMm/ePJydbzx83Wg0Mnr0aPR6/U0HJ26tZ+8N5vt9F/j7XDJPt3+Sddp1RNXI5K8mCi+s/I6XJ7xHvfXpHNsRQ7Nu/jfuUAghhBDVSkpKCgMHDrR0GEKISnZ/Ex9i03IZ0i7QVBaTmsPm43E8HFoTZzttqe09bD14ofkLZmX7YvfxY8SP/O/s/9j62FZsNUVH+1kF1sKxVy9sGjY0SxAmLf0Kh/vuwzo4qMRzqhSF+z2csVYpZgnC7y8nUd/ehhZOdkWWQVIU6PVMY07ujaVOCy9T+dnDCUQdSaRxZz+8g2VnZCGEKCvFWIYtolQqFXFxcXh5ed2oKgCOjo4cOXKE4ODgmw7QEtLT03F2diYtLQ0nJ6cbN6jG3v39BEt3RVGvhiOPdTvLvH8+wi1TYd7nOj59bBQ5TvfQ7UIBg9+5B1sHqxt3KIQQQohbrrLuVUaOHEnr1q15/vnnKzG6u8/ddO8oqq+PNp9m/pZI7q3ryTcj2pS7/fm083x74lscrByY2HKiqfzL8C9p7NGYe3zuQaUUnX2Uc/QY5wcORLG2pu6e3ajs7YvUuZ7RaDQlA1N1BTTbc5w8g5E/W9WlsaNdmWJdM+8fLkWk0KpvIG37Vc/vpEIIUZnKeq9SppGE5d35LiMjo1z1heWM6xrCysOXOBWfgTbrXvwcfiKGGH5vozDyfz8z8o17aB6lZ++qs3R9uoGlwxVCCCFEJapTpw5vvPEGf//9N02aNEGrNR9dNG7cOAtFJoSobMEe9tT3dmRAi8INSnJ1er77+wL9Q/3wcLAupTUEOgfyRrs3zMouZVzi038+BWD9I+up6VizSDtFo8bhvvtQOTiYJQiTv/0O67p1sWvdCuW6dQmvHy2YrTfwoJcLUdl5NLpuncJf45LRKAo9PZyxUxdNTLZ9MBgnz1gatCvc5CXhQjp7V52l8b1+1G5RtsEvQghxtynTSEKA33//nf+zd9/xURVrA8d/Z3vJpvdeqaGE3hFBQVCk2BABETsKKqLXLl4Ve39VRBFBERQboIIU6b33UEII6XWTbHaz9bx/LG7IpQiKhjLfz4cbMjs759nkss55dmae/v37o1Bc+mfTXW6fBn++5giT5u8l1E/DUze5eHbdY2hdEu9+5OTnboPY2uJ6rttkZdAjGcQ0CmrocAVBEAThsne+5ipJSaff+idJEllZWX957MvJ5TZ3FC5esiwjy6BQeBNxP27L46E520kJM7J0whXnPF5hTSHTdk+j1FbKW1e85Wv/Ys8XGNVG+ib2xaQxea/tdvu2ILsqKjjYoyc4nSTPn4c2Le2M1/HIMgrf2Ygy7dftJc/uZErzBK4PP7v7kxWzMtm9Mo9GHSO4anRzX7vskZEUJ1dXFgRBuJSc15WEAIMGDSIiIoLbb7+d0aNHk5qael4CFRrebZ0SmLnuKFmlNRzMSqFVWCt2lOxgTg8Fdyz+mQXd+lAQqGL5V5nc/HR7VOpTH0AsCIIgCMLF5ciRIw0dgiAI/yJJkjjxWL8AvZpWsQH0bFy3sk6WZd787QA9G4fRLiHopHMATxRpjOTJjk/Wa7O77UzZMYVqZzXxpng6RHm3Np94RqHscBA4aBCOY8fqJQgrvvkGPB5MffuiCqpL/ilOiMHukbkpMpjfyiq5OqTuvMGfS8xsrqxhWFQIjYwnV3TOuDoevUlNTOO6ca1VDma/uJGU1mF0vzkNxSlWJQqCIFxOzvpd8MiRI9xzzz3Mnj2bxo0b07NnT2bOnInNZvsn4xP+BWqlgif6e7cSf7r6CKMae88YWd5SQUGQkzHzZrOwox/lxVa2LDzakKEKgiAIgvAPcDgcZGZm4nK5GjoUQRD+Rb2ahPPTA90Yd2XdApCduZV88PshRny2AYv93N8T3B43d7e8m67RXWkX2c7X/t2B73h5w8tklmeijogg6r8vEP/5NN/jsttN6Qf/R+Hzk7Bt23ba8fVKBY8nR7G0fRP0JyT1pueV8tGxEhaWVtaNKcv8sXHOP1RPh+uS6+2Mytpegq3KQfHRqnoJQkuFnbPccCcIgnBJOeskYVxcHM8++yyHDx9myZIlJCYmct999xEVFcW9997Lpk2b/sk4hX9Yn6bhdE4OweHyMH+jgmsSr0GWYEZvJVdtXI2f+QibU7VsXXiU8vyahg5XEARBEITzwGq1MmbMGAwGA82bNycnJweABx98kFdeeaWBoxME4d+iOiFBZtQquaFtLIMzYjDp6s4p/e+CvXy84jBlFvsZxzKoDdyefjsfX/VxvUIms/bP4uv9X7O7dLevzSN78Mje8+9lh4PgUSMxtG+PsVs3Xx/zd99x7IEHsKxZc8brjokJY0BYANeFBfra1lfW0GXDPt4/WnTK5zTrGsV141rR8fq64iYet4c5L23ky2fWUVliPeM1BUEQLjV/aT11r169+OKLLygoKOD1119n165ddOrUiVatWp3v+IR/iSRJPDWgKZIE83bk0zviTjQKDbsTJbamSjw8axor03VUaCSWf7Uf2SM+WRMEQRCEi90TTzzBjh07WL58OTpd3fa8Pn36MGfOnAaMTBCEhpIabuKNG1sxeUhLX5vZ6uCLtdm88ut+Km1OX7vLfXYFLmVZZnyb8QxMGUjv+N6+9t+P/U7f7/ry2a7PUOj1hIwZQ8LMGSg0Gl+fyh9/wrJkKfb9mXXjuVy4/6dYZr+wAD5LTyLJUFeA5ecSM0dsDg5aa+v13Wexec85VCqIbxZCfLMQ32MVhVZcdjd2mwtTcN37Yu7+cvIyK/CI+yBBEC5hf+vQBZPJRO/evenVqxeBgYHs3bv3fMUlNID0mABuaOOtSDZlWQm3NR0BwMw+KpLzjtB/1SIWtTeSf7iSPavzGzJUQRAEQRDOgx9//JEPPviAbt261Tt3rHnz5hw+fLgBIxME4UKiUip4fmBzbmkfR3KYn6/9pV/20f/dVSzZe+qVen+QJIkesT14qdtLBOoCfe3LcpZRWFOI2W72tcmyzKbCTTg93mRkxNNPEXLPPfj3v8bXx7pxIwe6dCXvscfOeN0nkqL4uFkCd8SE+doK7U56bcqkzdq91LjdJz0nJMaPO97ozsBxrettQd4wL4sf397GXnEfJAjCJewvJQltNhszZszgiiuuIC0tjdmzZ/PII4+QnZ19nsMT/m2P9m2MXq1ka46ZOMVAgnXB5AfJLGorMWb+N1RoK9kfq2bd94eoMZ95q4EgCIIgCBe2kpISwsPDT2qvqak5Y7ECQRAuL35aFbd1SuCVoXWrC2VZZvHeIvYWVHHi2royi53N2eW4z2LF3XNdnuOdXu8wJG2Ir+2Q+RB3LLqDq+dejcvjQte4MeEPP4Q6KsrXx7plKzidSCesOAQwf/8D9iNHfOcJGlVKBkUE0drf4OuTWVOLn1JBtE6N8YRiKp/mlvB1QRkVThdqrZLwhLrqnx6PTFCUEZ2fmqSWob727F2lzH9/Bwc3nTlJKgiCcLE4pyTh+vXrufvuu33nEMbGxrJkyRIOHTrEU089RUxMzD8Vp/AvifDXcW/PFADeWZzNvS0eBGDOFWpqtHbGzf6c39oZqXJ7WPXNgYYMVRAEQRCEv6ldu3b8/PPPvu//SAx++umndO7cuaHCEgThIiBJEvMe6MarQ1vQPa0ucfbL7kJu+Hgdd37x52fWa5Vaesf3JikgydeWb8knWBdMs5BmqBQqX/v/bf8/Zu+fTaW9krAHHyD5l58JvfNO3+POwkIKnnySrP4DcJvNp71mz2ATe7ul83GzBF+byyPzVnYhD+8/xj5L3dZkq9uDW5ZRKCSuHNGU0a91wxhYt535yPYScvaUUZhVWe8axUerxLZkQRAuSqo/7+LVrFkzMjMzycjIYPLkydx6660EBAT8+ROFi85dPZL4emMOuRU2zMWtyQjPYFvxNj7rq+Kxb7fSes8mlrVqjW5zCUd2lJDUKuzPBxUEQRAE4YLz8ssvc80117B3715cLhfvvvsue/fuZe3ataxYsaKhwxME4QIXbNRwc/v4em1Wuwt/nYqOyXXn/DndHh6ctY1OycEM6xiPVqX836F8esb1ZNmNy6h01CXerE4r03ZNw+Fx0CGyAwHaALTJyZhrzXhctehUOtyVVRi7dEZ2OFEF1VUwLnjmGVzFJYTedy/61q0B0CgUxOvrkn122cOYmDDWmC10CDD62qceK+GjY8WMS4jg/vhwFIr6K6xbXxVPQLihXsVkc5GVbydvRu+vYdTkLiiVf+uEL0EQhH/VWb9j9enTh61bt7J582buu+8+kSC8hBk0Kib2bQzAh78fZlzLp1ApVGxJhQ2NJR78Zjr7o93khKpYOfsAjlpXA0csCIIgCMJf0a1bN3bs2IHL5aJFixb89ttvhIeHs27dOtq2bdvQ4QmCcBG6p2cKW565ipGd61bqbc6uYOGeQt5fdgi1ou4W9FBxdb1CKH9QKpQE64J937tlN2MzxtInvk+9VYdTd02l2+xufLHnC3SNGxE/bRrxM77wPS673VT/thjLihXIJ5w/6MjJoXrpUl/xE6NSyYSkSL7PSEV1QiJwQ6UFs8uN4YREX43LzQuH8llaVkVghIE2fROISKrbmmwutqI1qAiOMtRLEK6YlcnyWZlUFNac/Q9TEAThX3bWScL33ntPVC++jAzOiCE9xp9qu4sfNjq4I/0OAD7vp8ZQa2bMT3P4tZMf5ko7G37KauBoBUEQBEE4V06nkzvuuANJkpg6dSobN25k7969fPnll7Ro0aKhwxME4SKmViowaOo2rSWFGvnPNU24o1tSvdV4E77ZQZv/LmbpvjOf6WfSmLgj/Q7e7vV2vfNSMysysbvtRBgjfG1FtmKeWv0U8w/PB4WC+BkzCJ84Ef0J97JVv/xC7tgHKHjyqXrXkZ31E5YzWiQzv00aA8LqFsisMVv48FgxTx/MrRfLIWstNreHxBah3PF6N666o7nvMbfLw/4NhexZmYfLUVcRuqKwhqN7ysSiC0EQLhhnlSRs06YNFRUVZz1ot27dyMvL+8tBCQ1PoZB4ekAzAL7emMMVEbeS4J9AhcHDrJ4Krl+5mJCiw6xtomPn8lyKjlQ1cMSCIAiCIJwLtVrNd99919BhCIJwGYgM8J57PrZXqq/N7nJjdbhxe2TSY+qScL/uKmDktI18vzX3T8edetVUfhj4A91juvvaNhRsYN7heczePxtJktA1bkTImDvYUrqdYmsxAAqjH5rERAydO/me57ZYONCxE0dHj8Zj9xZoVCkk2gcYCdOoff3CNWqGRQUzOKJuizHAiJ1ZNF61i41mCwqlAmNA3XZmJLh6THNaXxVPaGxddej96wtZ8P4OVs2uf9a7035y1WVBEIR/w1mdSbh9+3Z27NhBcHDwn3c+3t9uF5VvL3adkkPo2zyCRXuKeGFeJk8NfJq7F9/F4jYKeuxxMWHWp4z9z0s0O+Zg2cx93PREe5RqceaGIAiCIFwsBg0axI8//sjDDz/c0KEIgnCZ0aqULH6kJ0VVtUT463ztKw6UsPJACU0iTb42WZb5bPUR2iYE0TI2EOXx1YiSJJEalFpv3KYhTbmzxZ1EGeuqIXtkDw8tf4hKeyWzB8ym+YjbCB5xG3ZnLbIsI0kStq1b8VitOPPyUWjrEnxln32G21xJwKDr0aak0NrfQGv/+ucwVrnc1HpkXLJMEz+9r/2LvFJmFZRxW3QII1qG1quMDKDRKfEP1RHdKNDXZq1yMP0/awhPMDHk0TYoxJmGgiD8i866cEnv3r19peT/zInLroWL2zPXNmPVwVI2ZVdw5FgLrk+5np8O/8QnAzS88mkOg5f9wq8d+xOypIqNC7LoPDj1zwcVBEEQBOGCkJaWxgsvvMCaNWto27YtRqOx3uPjxo1roMgEQbhcnJggBLizexKNI020ia9bqXe4xMKLP+9Dr1ay8/mrUeK938wqsRCgVxPiV5fUaxTUiEZBjeqNWV5bToxfDG6Pm0bBdY9N2/M5szNnMyZ9DCO6jyD55wW4iovrPdf87Vwc2dnoMzLQpqQA4MzLw7plC/qMDDRxcfirlGzt3IxcuxP/E4qyrDVb2FFto5+jbjux3ePhscxc2vgbGH51Am37JSKfUAm5KLsK2SPjcrjrJQhXfXMAa5WD1n3iiUisOwNREAThfDqrJOGRI0fOeeDY2Nhzfo5w4YkNMjDh6sb8d8FeJv+6j+/HjmNl7kpyQipY0EFi1ILvWN6mE9uTjSh+yyGxRShRqYENHbYgCIIgCGfhs88+IzAwkC1btrBly5Z6j0mSJJKEgiD861LDTaSGm+q1uTwyfZtHICGhPiFx9ty8Paw6WMpbN7ViSBvv/afT7UECVCf0C9WHMufaOdhcNtSKuq3DO0t3Ul5bjkapQZIktCkp1MaGMuKXEbQIa8GEthMIufturJs3Y2iT4XueZdUqCp+fhLFLF+KnfQZ43zNDM/fhTkxEebzI57Mp0fQNDaDFCasLd1XbmFNYzpKyKkZGeytASwqJn0vMOD0yXZsEMGpyF2oqHfV+BlnbSrBU2GnePcbXVnKsmsz1hcQ0DjpplaIgCMJfcVZJwoSEhD/vJFyybu+SyE/b89iZW8nbvx1jYqeJPLn6Seb2UNFpv4OHv/6M5+//D8kFTpZ8sY+bn2qPRnfWi1QFQRAEQWgAsiyzfPlywsPD0ev1f/4EQRCEBtIk0p8pI9qd1G6xu3yP/2F5ZgkPfr2V/i2ieOum1r52WZbRq+q/173b6132lu0l1lS3wGVHyQ62l2zHbDfzWPvHCBwymMAhg/ls12fUZNVwbcq1hPiZ0LdujaFD+7rxnU6OjrodubaWlN8WoYmPJ0anYWBNNZLbAXhXTIZpVDyc4C20cuIOvA+OFrOt2srHzRIYFBGEX5COIruTjZU1tPTT0fv2ZhQeriQ8oS6BmpdZwY6lx6gqtdVLEu5Yegy/IC1xzYLFfZkgCOdEHHAg/CmlQuKVIS1RKiR+2VWItrY9HaM64lDKTL1GRfu9O+m0dR2/dDVRWWJj3feHGzpkQRAEQRD+hCzLpKWlkZv758UBBEEQLkQ/3N+VHc9eTeMTzi/ck19JrdOD4n+OwLrm3VXcPGUdR8tqfG0apYbW4a0J1dcl2JqFNOPlbi9zd8u76z3/+4PfM3XXVAosBQRcO4DE2V9TcXNv3tryFiuOrcBVXIwqPByFvz/qE3bVlU39lAMdOlLy3nsAJOi1PJYUyQR/db3xOwUayTAZyPA3+NpWV1Rz155sHth3jNjGQbTrn4hGp+L3sir2WWwEx/vR4opYkjPCfM9xOdys+e4QCz/Zjd1at8254HAl+9YWYC6yntPPWBCEy4v4WEE4K82i/bmrezIfrzjMc/P2MO3OpxhRfCO7Eu2sai4x7tsvGP10czalaZFW5pHUKpT45iENHbYgCIIgCKehUChIS0ujrKyMtLS0hg5HEAThLwkw1E+2jbsyjetaRXNiirDMYmd/YTUAQUaNr33a6iN8uyWXYR3iGNk5EfBuTb4u5bp6Y8qyzKjmo9hdupvmIc197ZsKN/H57s/Jis2iZ+8PSP1tEW5LDVN3f4pJY+KaxGtwFRcBoI6pSxy68vM51LsPqshIUpctRVIoeC41BmdRMUo8vn5qhYKWJj1tAwz1Yrlv71HMLjdL2jWixy3eMxYP1tRyxGYnVVLRtEsUVaU2/ILqzmo8sKGQ3SvzyLg6ni5DvOfIe9weti89RnCUkfjmISgUoraAIFzuxEpC4ayN751GfLCBgspa5qyzck/LewCY0VeN0lXFhFmfsqy1gVKTgmUz9lFb42zgiAVBEARBOJNXXnmFiRMnsnv37oYORRAE4bxQKCRSwvxIDvPztQUaNCx6qAf/d2sb/HUnnEmYa2ZfQRWV1rr7FqvDRceXlzD80/XUOt2Ad1vw4NQbeKHrCwTqAn1904LSuKnRTVwRd4WvzWPQ8PGOj3l5w8tYnBZi3nqLRuvXsaOVickbJrMydyX2rCyQJJSBgUiKulvywueeI7NNW8w//AjAwPBAfm0Sy+O1ZjxW7wpAi9tDE6OOIJWSVENd0Zefis2M3HWE94rL6HVbE65/KANJkvg0t4TfSisxRumJaRRIZHKA7zl/7AJb9OmeeknVA5sK2frbUcrzaxAE4fIiVhIKZ02vUfLS4HRGfLaRL9ZlM6fFIFIDf+GQ+RAz+qgYO38zV25YyYJu3Rmx0MzK2Qe4ekzzPx9YEARBEIQGMXLkSKxWK61atUKj0Zx0NmF5eXkDRSYIgnD+KBUSjSNN9bYlAzzWrwn9W0SREl6XUDxcXENRlR2XW0anrqtU/MyPu1myr4jH+jbhpvZxALQOa0uYqhmxQXXvnXaXnZHNRpJdlU20X7T3+oGBrD+wlVn7Z6FUKOnR/TEab9mMo6yUOxfdSYwphontJuIqKQFZRh0T7RvPtn07x+68E01KCik/L8CkUvJjmzSqV63CvXkz7ubNUPr5EahW0txPR0tTXSwVThdPH8zzvq7uLWjXKx6An0vM7Ky20QE1qe3CkSQJ6YRVhPvXFnBsXwU6o5rgaG/V+6oyG2vnHiI0zo92/ZP+3i9EEIQL1l9KEprNZubOncvhw4eZOHEiwcHBbN26lYiICGJiYv58AOGi1T0tjCFtYvh+ax7P/LiPl255ljt+G8WKdJmO+yQe/PYL7mjcnDXNDCg3FZHcOozUtuENHbYgCIIgCKfwzjvvNHQIgiAIDSY6UE90YP0PR9Ii/PhxbFcqaupXFz5YbKHU4kCnqUscHiq2cM27qwj107L56T4A+Gn86BI8kvYBHiqtLt/25u4x3VFKSjpHdwZAYTBQ5lGyoXAD6mI1z3R6hqTv5uIqLeXD7C9Z/MN/Gdl8JH0tJpRBQaiTE8mqzCLWLxaNUkPxy5NxHDlC/OfTMHbuzJ2xYdxmKaPy6+lUtWiJf7++2Nwerg8PpNLpxqiqi/vXkkrmFlXwZHIU4+5MB6DS6eKmHYdJ0mu5u0UIWoOa8AQTVrcHvUKiPL+Gw9tKMBfb6iUJF3++h6oSG52uTyGmcRDgPRfRbnNh8NfUK84iCMKF75yThDt37qRPnz4EBASQnZ3NXXfdRXBwMN9//z05OTnMmDHjn4hTuIA8PaAZyzNLyCyqZsP+aEY1H8X0PdP5ZKCGNz6y8fiMj3nswSdJzXeyYlYmUakBGAO0fz6wIAiCIAj/qlGjRjV0CIIgCBcUnVpJ67jAk9pnjunAoWILcUF15wMWV9vRqhTEBddPNL61OJP1WeW8c3NrBmV4F9HE6FpRctSPAw4TXY+vq/HX+DO52ytUOsyoFN5bc1VoKAd3Hia7Khun24l/v3749+tHfkUOA34cgEahYcOtG9AkJSF73Gz1K6Xi8DwywjLw27aN8s+m4XfFFfj360u0TsOU5onk3HkXOR43EU8/jTY5mT4h/gTU2ujotiPLMpIkkV3rYEe1jQK7k4+vTIcrvTE+sPcoC0rM/CcynK43pKLWKqlxu1lcWkW8XkNhVhVVJbZ6rz/voJkF7+8gPMHEjU/UVYA+tKUYSYLotED0Jg2CIFx4zvlMwkceeYTbb7+dgwcPotPVnYHQv39/Vq5ceV6DEy5MwUYNz1zbFIB3lx7k2rg7SA1Mxax1M+VaNRmZe7h+xW8s6OZPda2T37/cjyzLDRy1IAiCIAj/Kycn54x/BEEQBC+DRkXL2MB6hU96Ngpj3wv9mH57h3p9Y4MMpIQZSQw1+tr2F1YzZ/MxftyW52szaUzMWR7Kx/OjWH2w1Nf+QIv/MLbRByQbOvvayt1VGFQGIo2RqJQq4j78P1IXLeL70mU8tfopVuatRJuWRtCIEdh6teP2hbfz/NrnkT0erBs3UrN2HSWeSipqK7g+PJBHD2zHv39f8saNByBRp2F6ehLvbV1FxZxvcJvNAOTWOqj1yEQG6mjdJ57m3WPIstq5d+9RRu48Qr+70+l7VzohsX68m13EA3uPsr7CgiSBMVCL0yNT7nQhyzIb5mWx8JPdlJ1w1mH+ITPz39vO5l+O1PsZVpXaqK1xivtIQfiXnfNKwk2bNjFlypST2mNiYigsLDwvQQkXvkGtvVuOVx0s5fmfMpk8dDLDfhnGplQXK1pI3PPjLDY1a8HvrUJQby1j35oCmnWL/vOBBUEQBEH41yQmJp5xK5jb7f4XoxEEQbj4KBTSSRWW37ix1Un9kkONPNQnjRBj/RV02WU1HCu3oVPXrd85WqzklZ8stIrL56ex3q296aHpXGOaRonFyr6CKppG+QOQZGpKiwAPscYUDE3bYWjXjryiLWxZ+A7F1mKQZeI+nYozN4/nj37O0jW/81THp+hjNoMkURlp4utNrxPjF8OwJsPI/HQKhXY76k7tMAYE8G3rVHK+no37lf9Q3P8awsePxy1DxwAj7bZvwqirJSQ9HYVOze+ZVayvrKFPswTuef8KnLVu9tXYuHrzAeJ1GianBKAzqvAP1TGnoJxih5PkvFpy9pajUCnwyDIyoJQk5r23ncpiG4MeySCmkXcbc/HRKg5sLCIs3kTjjpG+n5fT7kalUYitzYJwHpzzSkKtVktVVdVJ7QcOHCAsLOy8BCVc+CRJ4qVBLdCpFazLKmPnESNjW48F4PN+GswGJ09O/5DNySqOhKtY/e1BKkusDRy1IAiCIAgn2rZtG1u3bvX92bBhAx9//DGNGjXi22+/bejwBEEQLhlpESYe6tOIEZ0T67XPubszc+/t7Ev6gfdeq3GEibQTCqoALN1fxM87i6mxu3xtjXTXsXb91by7oO5DnUT/RLrqXiG05n6yym0YO3QgcMhgqh0ePPZQTMpwQu+9l8Y7tmMdfg0z9s7gy31fIjscBAwehF/Pnjx58C06fNWB37J/wVRchPvoUUosRby39T0OFf7Cjxmp3PjOqxy9bQTVRbm4PC7ujw/ng8M7aTL2HszTp6HzU1Nk91aO7rFjE53SzAwel45/iJ6vC8p4KasAc5SWXiOa0Lx7NNurrcQt38HVmzJxOz0A+AVp+bawnA+OFrEpu4IdS49xaEsxTo9MlcuNLMvMfnEjU8atoCi7Lk9RfLSKdT8c5uDmono/Q5vF4RtbEISTnXOScODAgbzwwgs4nd5/7JIkkZOTw+OPP87QoUP/ciCvvPIKkiTx0EMPnbbPnj17GDp0qO9T71Mdtv388897qzOd8KdJkyZ/OS7h9OJDDDzcpxEA/12wl74xt5ARnoFN5ebD6zU0ysli+MKf+LmriWqPh98+3YPbJd6QBUEQBOFC0apVq3p/2rVrx1133cUbb7zBe++919DhCYIgXPKiA/W0SwzGqK3b5HdVswgWPdzjpBWJ/+nXlCeuaUJyWF3y0Opwo1crCTPVnQEfog9hz1E9K/dImK11BVhuiH2KmqxHmbbE21eh0RAWFEtC7eO4C0aTWW4n6vnniZvyMfnVVizmRErMGoJvG07CzBlU9+vE1F1T+Xz358i1tehbtkQdH8+TB94kY2YGNvNyelSXI+/eRV7xYZ5e/TSHcmdxpHsLxnzwBjmj7+DAsW3sK9vHFQFKntuxlowHRhG29XsSW4RS6nDhAXr9+iPXtTjKnS9m4B+i56v8Mt7YewSzppbWvWNJbh3KrmorjVbtosuGfVirvIk/rV7FB0eLmJh5jJVHyti66CgHNxVhcbnZYLZwsKaWua9s5uMHl1N4pNL3c8k/ZGbJ9L3sWp5b7+ddmFVJ8dEqnHaxql64fJzzduM333yTG264gfDwcGw2Gz179qSwsJDOnTvz0ksv/aUg/tjC3LJlyzP2s1qtJCcnc+ONN/Lwww+ftl/z5s1ZsmSJ73uV6i8VcRbOwphuSfyyu5Adx8w8Nnc3r9z8IjcuuIG9MTYWtJcY8esPrE/PYHH7WK5bW836n7LoOjS1ocMWBEEQBOEMGjduzKZNmxo6DEEQBOEEA1pGndQ2pE0sQ9rE4vifxRj39kwhr8JGXHBdoRW7y4NJqyLUry6hmBiQSGVlFsfKbVgddSsUhyc9x8MbdjN/g57b24ShCgsj3JyFYdnTHM3Wsam1lY5fzgQg79v7qS0cyIZMHdcMGYy+RTo7/cx8v/0zIg0FDEscgaF1a1zmCt488gWr1q7hhS4v0FV2UJaTQ5Ylh9u/H0CMXyzbe7xPxYMjKHK5yGzioSCvmgxtS67dsYE206eiGdifmitGsd9cBsBD779G50A/TOMnYgrVsWRHHkV793FFSTFtUsIIaNGEfTW1XL/tEOkOG8OrLCAr0RnVjNt3lK1VVkbYNLjWF+KsdRPWOYKpuSVEatRoPz1IdXktQx9vS3W4FrvHg+tANbt+OExs02B6Da9bjLTttxycDjdNu0RhCvbWbrBZHFSV1KI3qfEPrV/cRhAuVOecPQsICGDx4sWsXr2anTt3YrFYaNOmDX369PlLAVgsFoYPH87UqVN58cUXz9i3ffv2tG/vrY70n//857T9VCoVkZGRp338f9ntdux2u+/7U22nFk5NpVTwzs2t6f/uKtZllfHbjnAea/8Yk9ZNYnYvFa2PeLcd3/3EyyTHaWBxDrGNg0hID2no0AVBEAThsve/cx5ZlikoKOD5558nLS2tgaISBEEQzpVGVX+T4PCOCSf1GZQRw6CMGNye+sVAnurflKIqO4khdYVW/LU6WsQE0CgiwNeWHJiMRs6myGZDpay73ui0Jxm/ZQeZOUY0AxLQJCSQaMnDf4WKrGw12zpa6XU8oVjz3StY9vfjkyot1956K37durJTWcDBpTK5ciyFaZVEDB6E21zJT7mbWXu4lluaR3G9UUMpUBQscecPDxGgCWbHDV9R/uAWalwuPhmkZ92urXRNHUOrwlKSP/0I96Crme5eQ+0OA4n6m3hl4t0YLdUUTnuBpeaFHKoIJHrdQdr+/iuK5FaYWwxga1k2/5dTw4O//UT3LBu5Ud3RGdU8djif7QezeCS/FCnbQW1sBgdqarlu60Eau+zctKSKykoPCc1DmFVTxa5qG10rofyLw8Q0DuTKB1vxfVEFASolni+yqC6z0feudFyxBmxuD4oiG1m/5xMSY6Rtv0Tfz/bw1mIctW7imgbjF+RN7jpqXVgq7Gh0Kl+bIJwvf3mJXbdu3ejWrdvfDmDs2LEMGDCAPn36/GmS8GwdPHiQ6OhodDodnTt3ZvLkycTHx5+2/+TJk5k0adJ5ufblKCnUyDPXNuPJH3bx+qJMfhx7FT1jl7MidwUfDNbw0md5jJk3h+lDRhBV7l3KfcvTHTAGijc0QRAEQWhIgYGBJx30LssycXFxzJ49u4Giahj/93//x+uvv05hYSGtWrXi/fffp0OHDn/+REEQhIuMUlH/fb9f+skrFHs3jaB304iT2mfd2Qmz1UlyWF1CsVGEPw/0SiUqUOdri/GLIT0yH7VUTaC+rrDLzY1GsWLTVvzUAagjwlFHhJPusBC7XEtWsYsKu0zr//4XgJRl3/F7kY4VHjWTJjxAyD13s69kN/L0NAqqY9l4qJQer7yCu6qSw+bdHN46Hmu2xKMtQ6js2ZPCJnH8sKEMhT2Kj28Kxc/lRAbmlmzit5U6Wkc353E9GA5lYm0Rw9jtr6HYHMZNnScwYOmvaKsqWTwK3vhhFsFh3bnygJ2OX3yMo2sn/hu6AvciPZV+o5nw3wmElxSz+N7b+CR3I1ukRig3m0n7fhbVhiRWdEynIPMQk4qSeeT7r+mSWUZOUjt2VbuZs0/Pzv0lPL5jG/J+JQVXtEfbSce12wvodmAvt26uJNcVTb9HuzO9ysyqvEKuLTTj+KkY/yaJ9J/YhkmH8zE6HLRaWEppvo0+d6RTEKfjQE0tCTUyFfOPERCup+dtTVhTYUGjkNBuK6em1E7jTpGoI/XYPB6UVjclu8vRGdWktAn3/c7K8iw4at0EhuvRm7yFd9xuD/YaF0qVhPZ/CvcIF7dzThKe7nwaSZLQ6XSkpqbSo0cPlErln441e/Zstm7del63s3Ts2JHp06fTuHFjCgoKmDRpEt27d2f37t2YTKZTPueJJ57gkUce8X1fVVVFXFzceYvpcjCsQxxL9xWxdH8xj3yzg8/GPMuOkqFkh5j5tpuCW5b9yob0DOb1bMaIhWYWf76XgeNbo1CIClSCIAiC0FCWLVtWL0moUCgICwsjNTX1sjquZc6cOTzyyCN8/PHHdOzYkXfeeYe+ffuSmZlJeHj4nw8gCIJwmYgLNhAXXL+taZR/vcIrf/h0VPuT2q5sEs6qx3rVa/PT+PHY1S0prKylUWTdPfugxn0oKsoiKsCbfFRoNDSPaUP3OA9bc8yY9GoCrh0AwK27D7Buy0EMSj3+/a7Ev19fDLYy4qasIqtcTZXNyRXbtiLX1hK5/luc5hCOuCVajOqGvWkj8vycsHgPNVWN6CRriLntVtzmSkodDsqP3EptvofvO2so29QMV+M4th/Q4KrK4KGrTIR7vFu9cxKtzP25E2q1zGtxSozFBRiuTGJWaR7OOUG0aOmh+57tBBbkU3JHGPfNl9FpQ+igttD8x69wpKVzs2Ifrg/TcCZ2Y9i3X5J0LJulo6/grZ83YjAkEu3W0O7DV7H4xzOufQyvT4kmN6wX78x4n8aHMtlyVW/uXPs7br94qDHx1tQ30SjCefPWJnw2N5zf1W158JcfuXrfYdY2bc0MZzDl/tFkVQfw8rw5qAo1fH9NCvHWaD6ojOP6LZu5ZVcO2/zjsF0XQUlkBLvM/ty1ehmabTY2dUik8/BUPi0LpEnmYfpszuGQw5/AwbHUNopgu9lAj+1bUa01U9A0nl6jG/NtmRJTXjEZe4oor1QTfmU8ntQg9lRJJB09inpzBbVRkXS5MY3fK2qRKy0kHq3CVg7RrWNQJZo4YKklyFKDtLcKyWCkefcY9ltrcbrd+Jc7cZY5CI4yoovQU+p0oZMknNkWJIVEVEoAVR4PMiDVuHBZnOj9NOgDNLhlUEpgKatFUkgYA7W+nIXL6cbtklGqJFTqunyX7JGRLqG8hiTLsvzn3eokJSVRUlKC1WolKMhbiryiogKDwYCfnx/FxcUkJyfz+++/nzHRduzYMdq1a8fixYt9ZxFeccUVtG7d+pQFSf5XYmIiDz300BkLnQCYzWYSEhJ46623GDNmzFm9xqqqKgICAqisrMTf/+Q3O+HUSi12+r2zklKLgzu7JdGpRT4PL38YhQzPz3QRUR3AmKdeoUmxnj5baug4MJl2/RMbOmxBEARBuOiIucr51bFjR9q3b88HH3wAgMfjIS4ujgcffPCMR9z84d/4fdz++ptYHM5/ZGxBEISLnQdwySpAQiPVvVda3Hqcshqj0uZrd8hqSh1BKCU3EZoyX98CRyg1bgPh6jL8VTUA1MgqsqwJqCQ3TQ3Z3mtJHg7WRlHtCCVeW0i4pgxJ9lAieciuzkDCRTvDbpQuBy4l7PLEYLfHEKMtIMFxGMnt5miQitzKroCHjprNaKsrcWpUbDTE47alEK4ponHVDlROJ4diDByr8iZWWyvXE1JagFujZVV4Cu6axgRqimhZthGtvZaDiYFkW64CoKlyE7FF2bjUWlZFp+KqaYZRXUzrik0Yaq0cjvMny9YbUJGo2kpa4WFcSjWr41JwWlqgU5fSunIjJmsNR6L9OeS8AmQtcZqdNMnPxCMpWZWQgsPSCq2qjNZVm/C3VnM0wp8DcnfwGIjW7qV53h5kScHqhCRqLS3RqCppVbOZwOoqcsNM7Fe1B5eRcE02LfN2AbAmKR6rNR2twka6ZRvB1WbyQ0zs07VAdvkTqi6gde4OANYnxWKxNUGjcNKsZiehlRUUB5rYbWoMzkAC1WW0zN0FSGyLi6DamYYKD43sewg3V1BhMrLLPwWPK5AAZTVNijORPB72R4VgdiWjQibVuZ8wcwVVeh17ApNwuwMxSTZSyg+jdHs4HBaAWY5HKUskefYTWlmJVa1hb1ACblcQJslOXOVR1E43OSFGKohD6VGQIGcSZKnEo9Bgik3kswmnr73xd53tXOWck4Rff/01n3zyCZ9++ikpKSkAHDp0iHvuuYe7776brl27cssttxAZGcncuXNPO86PP/7I4MGD6604dLvdSJKEQqHAbrefcTXi2SYJwXuWYZ8+fZg8efJZvUYx8f7rlu4rYswXmwH46s6O/FL4NvMOzyPSouLVKbXsSU7nsQef4KY1NTQudDHokQyiUwMbNmhBEARBuMicr7nK5MmTiYiI4I477qjXPm3aNEpKSnj88cf/bqgXPIfDgcFgYO7cuQwaNMjXPmrUKMxmMz/99NNJzznVedZxcXH/6Nwx7anpON1h/8jYgiAIgiA0LK2qgMwX7/zHxj/bueM57yN5+umn+e6773wJQoDU1FTeeOMNhg4dSlZWFq+99hpDhw494zi9e/dm165d9dpGjx5NkyZNePzxx89qu/LZsFgsHD58mBEjRpyX8YQz6900gls7xjNrQw4TvtnB3LET2FS4iQIK+GSAmnE/7ObWRfP46epBRPxsZvFne7j56Q7ojOIcA0EQBEH4t02ZMoVZs2ad1N68eXNuueWWyyJJWFpaitvtJiKi/tlbERER7N+//5TPaYjzrEMVBVil8n/1msL5IAHntCZDEITz7o+toPLx/1XgkTWAhIx0/KvC+3dZQinZUUq1x/uqsHsCjz92vA/4nqdWWFArqkACt6zC5ooBWTph7Lq+KqUZjboYAI+sotZ++gJhCmUFam0+MiDLSpy2JqftK6kqUepyfddyW5qdvq+yCqX+mO97l6UpoDh1Z6UFlf7oCX2bAKfJ0yisqAxHTujbCDjNPb5Ui8p4uK5vTRrImtP0daAyHjyhbyrIp6tt4ELll3lC3xSQdafp60Hlt6+urzUZPP9bgbruvVvlt/eEvongMZzw+B9fvb9vpd9+pONtLls8uA3HH5VP6H28r/EAkuRB6YYwd1118YZ0zknCgoICXK6Tg3e5XBQWFgIQHR1NdXX1GccxmUykp6fXazMajYSEhPjaR44cSUxMjG8FoMPhYO/evb6/5+XlsX37dvz8/EhNTQXg0Ucf5brrriMhIYH8/Hyee+45lEolw4YNO9eXKvxFTw9oyrrDZRwpreHVX47yau9XGb1wNGuauGneSuKO+d+yM7UJ83qkMfy3SpbN2Mc197Y46eB0QRAEQRD+WYWFhURFnXxgfVhYGAUFBQ0Q0cWhIc6zXvffJ/7R8QVBEBqCxyNjc7qpcbiwOdwEGTX467zJpcLKWtYeLqXG4cbmcGFzeLA6XdQ63Nicbga1jqFLaigA24+ZeeL7Xdid3sdqj3+1O71nz43q4UfvlhI2l429ebW8O//0qZDU2F3ERm6h1lVLpSuEfbtvPG1fKWArqohfvd84gvAcPv2Ha5L/AZSR3tXpkssPDj7te0zhcaOSPTjUbpDcKP33oYmcB4DsUeM+8iAKhRa120BscSEqj5sDsTJIHpSGw2jDlnn7ylCbNxwlfmg8MXTYtxOF7GFdU/AoPGjUBQQY1qP0gEdppFx5NR5FEEpFCgPWLEMC1jbz4FDJ6CQzoZ7daDwSdk0YBSYLVm0gNkNTBq5agixJ7I234FR5UEs2gpzHCHYqsEkh5BvsVBhMVASmc83635GROBZagVsno5bcBEgVRNjAZguk0KjAbNJTFpBO551rkSSJElM5qkAJjVLCT20nqgbcVj/KQ82Ua9WUmdJokrkNSZKo1FZgiFSjViswalRE1oBk0VEbq6VcBaW6OKKy96FQKKhRVuMfo0ar06JTJxNS40FpUSEHR1GlgHJ1KKbCHJRKJTZFDcGxBnQ6NQpFdwKsMiqLhDIwBCtQrtCjqTKjUEh4JCeBkQZ0Rg0KqRUGu4yy2o3CaMIJmGVQuV0oJFAqFJhC9ej0aiSpLyqHB4VTJiDU8Cf/Wv4d55wk7NWrF/fccw+ffvopGRkZAGzbto377ruPK6+8EoBdu3aRlJT0t4PLyclBoajLaufn5/uuCfDGG2/wxhtv0LNnT5YvXw5Abm4uw4YNo6ysjLCwMLp168b69esJCxPbM/4tBo2Kt29uzdCP1jJ/Rz59mrZmXJtxvL3lbT7vpya1wMGz095nzFOTWdlCj2JHKbtX5NHiitiGDl0QBEEQLitxcXGsWbPmpHnbmjVriI6ObqCo/l2hoaEolUqKiorqtRcVFREZGXnK52i1WrTa061kEARBuLyYrQ4Ol9RQXeukqtZFda2T6hO+3tA2lpaxgQCsPFDCpPl7sNhd1Ni9ycETD0B7YVAavdONWBwWVmSW8PJPlae97p7qxYQf2IWltoqS6mCyC4actu/X+77j+5LfAXDXRiCpRuOvMxDqVKAoLADJSVa0GxQucj27KarxriT3uEpRB68m3JREUqmRlE1rcSndzO0hg+RCpclHZ5fRuEClUxCUNI9abRpReQGMmf8tssLNV1fKaFxu9KVW9MUyao8SS2ACtsBlZEUk4mcP4Z4fv8auVrE5tQYkFXKtEk9JGilVSiq0jajV17KpkT9l/npabCnEpVARhQ2VVodCikZhHkO3LJnK6lAc2nC2JxvIDfbgVjfGJSnpoFSR0i4KlVqDSj2O1M3V1OS5Ueh15PgrKDQqkIeMRqWQ6KNU0P6qeDQKCZUkYdhbiaPEjlKtwKKCWpWEplkztCoFQ1VKmrQNR61QoJYkzAUWHDY3SqUChVJCoZJQXtfF+1WpwOCv8RX5kD0ySPzPYqGe5/D/vE7n0LflOfQ9/QrPk52HDwj/dwFjAzvnMwkLCwsZMWIES5cuRa32ZvhdLhe9e/dm5syZRERE8Pvvv+N0Orn66qv/kaD/aeJMwvPj3SUHeXvJAUw6Fb+M78bkLRNZlbeK6CoVk6fWsr1RBk/dN5FbV1aTWubmhsfbERZ36grUgiAIgiDUOV9zlddee43XXnuN119/3fdh79KlS3nssceYMGECTzxxeaxe69ixIx06dOD9998HvIVL4uPjeeCBBy6YwiWCIAj/BFmWsdhdmK1Ogo0ajFrvOqK9+VUs3F2A2eak8n/+VNmcvDq0Jb2beo9p+Gl7HuNnbz/tNUZdoSA92UKlvZId2W7mrznN4hDJjjZiAZqgTQC4a6OwF19DanA8abVaFEezsGFhRVwpKJyojAdR6vO8r8Otw22Lo3fSVVy5X43h6y+pMjp59UY3ercTvdOJ3ulB54SApi0whUZxgOYkrqxg7KzPsGlgYRsFClkNkhoFasJlHa6W1+DyT2F2qAllWQW9N63BptVQo1XgVBlwaPXUarQM3itToo7HoQ3kh45GCgKdhJnNyJIGpVZLeGIIGr0WvVpNl7mFWAqsAByIVlNmUqJxy+gUCgL9NPQakopeqUCvUFC9KI9aswO1VoFbq0CjVaLXKNGoVWiNKpp1rftArySnGrfLg0qjQKVWolQrUGkUKFUKVBqlr0qvcHn6xwqX/GH//v0cOHAAgMaNG9O4ceO/FukFSEz0zg+X28ONU9axLcdMx6Rg/m9EY275+SaKrEV03QfjfnTx0dDbWHTFAMb8XEGMSceNT7ZHqz/nBa6CIAiCcFk5X3MVWZb5z3/+w3vvvYfD4QBAp9Px+OOP8+yzz56vcC94c+bMYdSoUUyZMoUOHTrwzjvv8M0337B///6Tzio8FTF3FAThQmJ1uCiptlNSbSc13I9Ag/e8t/VZZXy1IYfyGjtlFgdlNQ7MVgdOtzclMHVkO65q5n3P+3FbHg/N2X7aa1zfuZLY6DzKa8s5mK9k1950wk0mYvyDMWmU2BwlbKhYg6SoRWXag1KfD4DsMuC2RzK82RD673Jh/2oGBXozz46SkSRvHGqnjMEOgaEx+PuF0C72OtotLSbsk4+p0sPCdgr0dhm9HQwOMLhVJDz6BCHpbRh3oArDhp30W7eSGr0ei96AVaenRm/AbTBwS7mBcv9ULC4DU9LVHPN3oXU6sOp0KCUVIUYtfiolASol188porbS+9/GXfEazH4KtE4ZjUsm2KTlmlsa46dUYlQqODg9E7vFhVavRKNXodEd/6NXYgzU0rx7jO9nV5pbDUiotUo0OiVqrTehJ47fEv5J/3iS8FImJnrnT3ZpDf3fW4XV4eaRqxrRvYWF0QtH45bd3P2rm167FDzw6PO4TEncvLSSlJahXHNPC98SZEEQBEEQTna+5yoWi4V9+/ah1+tJS0u7LLfSfvDBB7z++usUFhbSunVr3nvvPTp27HhWzxVzR0EQ/mmyLFNtd1FUWUtkgA7T8XP71h4u5av1ORRX1/oSgzUOt+95Jyb+zrTiT62UGdi5msiIXEpsJRwpdnI4J4r20c1oF90Mf72KgspMphz4AElpQ6EuR1La640xvs14Bi61UPbxFI6GwYvDlJis4FcLfjYZv1qIvuo6gqOSCPJvReSPGwmb8hEOJRQHKlAqjbjVJlwGE+nRkYQ/OgFtWhrXbD5Axf5MUnOzsRiMVBv8sBgMVBuMuI0m3tinorrCjq3KwcxWWvJDlOgcMga7TJBaRXp6KIEqJYFqFYYPD2I1e+OuMHqPNtO5ZAJUKiIT/bnuwVa+17NlYTZul4zOqEJrUKM1qNAZ1Wj03q8G/9MU2xCEC9A/miTMzc1l3rx55OTk+D51/sNbb7117tFeYMRE7/yauyWXR7/dgSTBzDs6csA+j7e3vI3ao+Clzx3o3GHc+eRk2h9R0H23jU6DkmnbL7GhwxYEQRCEC5aYq1xYxO9DEIS/S5Zl30qy3XmV/LyrgMLKWgoraymqqqWwqhbr8eTf2ST+9GoFgUYFN3dVExNRRlFNEQeLK9l/TEmPxDZ0j88g2Kgh27Kb/6x9AElx6sqq4zLGMXidTOmUKRzTW3l/oBJ/q0xADd4/Vpnk0WMJT0kn0pSEZ9Yv8N67eJRK7IFBVAcGUelnwuofQK+UBIJuG44mNpb+Ww6QlVuAsdaK2c8fq06PfLwegV4h8elRNdVldmrMdubESxT5eVcPGu0ewg0auvaMI0StIkStIuv1ndRU1CUsJQl0Jg0Gk5qweBO9R9VV+z20pRiFQkLnp0bnp0ZvUqM1qMVWXOGSd7ZzlXPe17l06VIGDhxIcnIy+/fvJz09nezsbGRZpk2bNn8raOHSdEPbWDZnlzN70zHGzd7G/AdvZkvRFlbmruTtG7W8MrWEiV9+wqS7HiK61IX0Uxbhif7ENQlu6NAFQRAEQRAEQRD+thq7i8MlFo6V28gpt5JntpJXYSPfXEu+2cY7t7T2nfGXVVrDR8sPn3Icf50Km9NNpb2SPEseNYo8+revoUVEAm1j0ggzaTli2c74FfdiAT7LArJOGEANIWGBtD7kwvzd95gsOcR2chFY5SGoBgItEGSRSRsznphGGSQFJCFtXoBstRJrhVe+1eMICcUSFExNSCgZLeMJTL0eTWws/TYf4GBiS5Svf0K1wehL+gFoFRK/6cI5trOW6t/34a+rJVjlh8GuJ7bCQ5QRrugTR5hGTZhGxdaF23yJv77H60ppdEoMAVrCE7RcFVtXmDRheBMUKgmDSYPBX4PWePqkX2rb8L/8OxSEy8E5JwmfeOIJHn30USZNmoTJZOK7774jPDyc4cOH069fv38iRuES8PzA5uzMrWRvQRXjvt7BxyP/y7BfbqKAIj7pr2Lcjxu5fsVv/NS9L6N/NfPbp3u46cn2mIJ1DR26IAiCIAiCIAjCGXk8MvmVNo6U1nCktIacMivXt46hRWwAAEv2FZ2xuEe+2eb7e7MoE6M6JxDuryEm0EiEvw63opS5Rz6jwHqUV/Ye46nt1fWe3yL1ftqYUqjdt43K7K0A+DtVhFgkgitcBJvdpFw7jNikFrQIbYFz20pq1qwhHHh7h3cMRVAQcmQkxuhowiJ6ootqwtMHc9ka1wTzC2+T7x9Irbbu/kwjSWT3bInb4aEsz0JgrQeFzkiQCxItMmEo6Ng6gkitmiiNmrWv78R6PPHX/cTgJQiPV3JjVEjd2NcnI0kSfoFajIFajEFa1BrlKX92Cekhp2wXBOHcnXOScN++fXz99dfeJ6tU2Gw2/Pz8eOGFF7j++uu57777znuQwsVPp1by0W1tuPb91Ww5WsGU3wt5o+cb3L7wdtY0ddM8W2Ls3C85FJfID1ekMWKhmYWf7GbIhDYo1Yo/v4AgCIIgCIIgCMI/zOORfavUdudV8v6ygxwprSG7zIrD5anXNz7E4EsSxgcbCDdpiQs2EB9sIDZIT0ygnkA/GY+iFKu0k/e2ziO7KpsjlUc4Vn2MuxLvYlCrewA4UlnF0pW/1hs/yK0jJiiB2OAkUgJSqPxpHsWvv45aghkq0DlP2EIsScRe3xVTai9ybHZ2pLfGPOFxsgOD2W8MZKfBj6OyAq1CIqtHSxTHtz4X2J1sVekhTI8CiFQqSfTTEafXEKfT8NXLm6g6ZgGgqwTdTzjMzC9Iy6jrW9Y1tA7DYXdjCtZhCtbhH6LDFKLDGKRFqax/z9ekU9Rf/h0JgvDXnXOS0Gg0+s4hjIqK4vDhwzRv3hyA0tLS8xudcElJCDHyxo2tuGfmFj5dfYR2iW0Y32Y8b215i8/7qknNd/DC1He46z8vsbCjH9eurWL1twfpeeulUzlbEARBEARBEIQLmyzLlNU4OFBUzaFiCweLLBws9v79kasac2vHeADsLg+L9hT5nqdWSiSEGEkMMZIQYqBpVN25X/FhHj64wx9/rT9NgpsAkGXO4vqfrj9tHMeqj2Hdtg3znG9wHj3EKKOK8CI7ERUy4ZWgc1qIeecu/Hv2BaA6ZTnatFTUCQlIsXGUR0SRFxrG4cBQ7uvQEuXxolTPHcrn12ogtfUJL9r7RSVJZGabkQprKc+vobXZQmyVA22+Db8aN4GBOka+3ML3tB90BVQBWoOKgDA9/mF6AkKPfw3T13s93W9udI6/CUEQ/m3nnCTs1KkTq1evpmnTpvTv358JEyawa9cuvv/+ezp16vRPxChcQvo2j+TuHsl8sjKLid/u5McHbmBz7GZW5q7krZt1vDzVzKSp7/DwQ88QmaZFWplHRLK/+CRJEARBEM6TefPmnXXfgQMH/oORCIIgNLxapxu700OAwVsteHdeJSM+20CF1XnK/geL67b5pkX48dx1zUgKNZIc6kd0oA4ZNztLd3KoYhe/Ff7Ah/sPcdh8mAp7BQBD0oYwqcskAGJMMShQEKw0EevyJ9qsICrPSvM+N9Gk8wCi/KKwLllG5Y8/AjAAQKFAExePpmUKmsRENPFxAKyuqGZlXCP2v/Qu+2pqOVZ7QoFRF9yAgsjj36b76cmx2YmTVITXyoTVylzRIYZEvYZQtYrvXttC0ZEqABTAH6f4qTQKdH5qPG4PiuMr/3rf3hSNzlvtVxCEi985JwnfeustLBbvcuJJkyZhsViYM2cOaWlpl0RlY+GfN7FvY7blVLApu4KxX21j+pgXuN08nDzyeOcGDU98dZAHvv2C94bdSWSFm+VfZRIS40dYnKmhQxcEQRCEi96gQYPOqp8kSbjd7n82GEEQhH9RqcXOrrxK9hVUsa+gmn0FVWSVWLirRzJPXNMUgMgAHRVWJ5IEcUEG0sL9SIswHf/qR0qYH+BdbWh1l5Ecn4NaoSY+pAsAFoeV2xfeftK1JSRiTbEEaAOw7dpF8ZtvYT9wgOnVDnTOMqDM1zcsVUFoX2/yT9+iBaHjHkSbnII1PoH9waHssrvZY7ExuVEsOrX3ln5JWRUfHyupd81wjYpUg45Ug5aio1UUZNdQmltNdK6FoYVWZI93+aAkQZsrUn3HPEWnBqJSKwiOMhIUZSQw0kBQhAFjoNZXgfkP/iH1VwsKgnBxO+ckYXJysu/vRqORjz/++LwGJFz61EoFH9zahgHvrWJ/YTVvL8rj3SvfZcSvI9gZZ+OrKxWMXLqUA/FJ/NCjN6N/qWDhlF3c+ER78QmVIAiCIPxNHo/nzzsJgiBc5FxuD6rjq91KLXYGvr+a/MraU/Y9Vm71/T3EqGHBg91ICfNDf7xQhizLFNYUsrtsE0t372Zv2V4yyzN9qwPbRbSjS4w3Sein8aNdeFu0dg8JVj2xBU6iMstpfuWNRA0ZDkBtZibW9esB0EkSmsQEtI0aHf+Thr5VKwB2Vlv51Qa7u/Vjt8VGQVEtFOX6Yh0VE0rnQG/SsnuQCavbQ5KkIrzSjX9hLX0GpCAdPz9x8bQ9HNhYtzUaQGtUERLtR3C0EafD7UsSdhma+ld/7IIgXOT+UpJw06ZNhITUryBkNptp06YNWVlZp3mmINSJ8Nfx7i0Z3PbZBuZsPkbbxJa82PVFJqyYwIIOChKLZMbPmU5WdDw/9kzm1sWVLJm+lwH3tfT9h04QBEEQBEEQBMHucrMnv4qtRyvYfszMztxKWsQE8H/D2wDexJ/F7kKSIDnUSLPoAJpGmWga5U+zKH/CTVrfWJIkERsik119gKYhTX3tNy+42ZcU/INSUpIUkERqYCqusjJKp0zBtmMHj+/dh+ysv11ZDtkDt3j/rk1OJuqlF9E2aow2NQWzSs32KivbqqwMjQwiUe+NZ0uVlbeP1k/sJeu1pJv0tPDTE+qWOLq7jOKjVdQerabp0SqslQ4KgUKgXfsogiKNAMQ2CcLtkgmN9SM0zo/QWL9TrgwUBOHyds5Jwuzs7FNuPbHb7eTl5Z2XoITLQ9fUUB7p04g3Fx/gmR9388P9XbmrxV1M3TWVKQPURJc5+O/Ut7nrPy+zJMNI3y1lbJiXRadBKQ0duiAIgiBcMmpqalixYgU5OTm+4nR/GDduXANFJQiCcGayLPPaokw2HilnV17lSZWFZerK7EqSxJx7OhMXbMBPW/8W2CN7OGw+zLaSbWwv3s724u3kVOcQbghn6Y1Lfc9vFd6KopoimgU0Is3iR2KOndTQxkRc7838uS0WKmZ+CbL3uoqAAHRNm6Jr1gxds2boW3mr/Do8HnZZHWzp0J1tVVa27ThCtq3uvTdKq/YlCTsGGLklMph0k57mBh1R1R6iI4xodN7XsGFeFpt/ya73eiQJgqONhCf410sANu0STdMu0X/55y0IwuXhrJOEJx5yvWjRIgICAnzfu91uli5dSmJi4nkNTrj0je2VypacCpZnlnDPl5v54f67OVBxgBW5K3jzJi2TP63g+anvMOGhp4kq1cDCowTHGGnUPvLPBxcEQRAE4Yy2bdtG//79sVqt1NTUEBwcTGlpKQaDgfDwcJEkFAShwcmyzNEyK+uzysg323jk6saAN3G3+mApu/IqAe9qwYz4IDLiA2kVG0iLmIB645xYafgPr296nR8P/UiVo+qkx/QqPRaHBV1VLdbNW3hySyS2LXnY9/8Ax49tsLVpAzd4k4RKPz/Cxo9DHROLvlVL1HFxSJKE2enCIcsEaLzHJq2ssHDbzpN336XotWT4G4jXawCorXFizLYyLMtFweFcdmZXsdXuZsDYliS2CAUgItGfwAgD4QkmwhP8CU8wERpvQn18m7QgCMK5Ousk4R+HXEuSxKhRo+o9plarSUxM5M033zyvwQmXPoVC4u2bWnP9/60hp9zK/V9t4+MRLzFq0W1kk81bN6h5dmYm9303kyk3jSa0upJlM/YTGG4gPOHk/9ALgiAIgnD2Hn74Ya677jo+/vhjAgICWL9+PWq1mttuu43x48c3dHiCIFyGTkwKev+UU1jlPUtQpZC474pU31mBd/dIxuHy0DYhiIQQwym3ztrddnYU72Bj4Ua2FW/jwz4folV6V+q5ZTdVjip0Sh0twlrQOqw1rYyNaJ3YmQBtALIsc3BgX9zl5fXGVMfGom/VCkP7dvXaQ++9l/xaB4vNFjYeyGVDZQ2ZNbWMiw/nyRTvKr52/gZC1Cra+hto428gw99IK5OewOMFSAoOmZn9wQbK8mpOei0anRJrVd2qw8SWoSS2DP2rP2pBEISTnHWS8I9DrpOSkti0aROhoeLNSDg/gowaPhvVjiEfrmXjkXJe+/Uo7/Z+l+G/DGd/tIVpVyu4e+FvHIhPZu4VPbn91wp++WgXNz7RDmOA9s8vIAiCIAjCKW3fvp0pU6agUChQKpXY7XaSk5N57bXXGDVqFEOGDGnoEAVBuMw8/t1OvtmcW69NrZTIiAuiY3IwDrcHPd4k4XWtTt4+63Q72V22mw0FG9hUuIntxdtxeOoSazuKd9AhqgMAtzS+hWtCuhO7rwz7qo1Y181DdjjwX7kC8C6QMbRrh+PoUQxt22Jo1xZ923aoI8LrXbPa5eaFw/msrqjmiK3+sQ0Aufa68wkD1Sp2d22OrdpJ/kEz+esLKE0LJLCtd0ydn9qXIAyMMBCZ5E9kSgCRyQEERRlRiPPZBUH4B53zmYRHjhz5J+IQLnNpESbeuzWDMdM3MXvTMRpFNOPVHq/ywNIHWJLhLWQy4evPeDg8iu96pTJ8kZlfPtrF4AkZqNRiOb0gCIIg/BVqtRqFwlvNMjw8nJycHJo2bUpAQADHjh1r4OgEQbhU1TrdbDhSzqoDJaw8WMJno9oTF2wAvNuC/0gKdkoOplNyCBnxQb7Vg/9LlmU8sgelwvv4rP2zeGPzG/X6hOnDaB/Zng6RHUgJTMG6eTPVixfjWbcezYEDFJ/YWaXCVVyMOiICgJi330JS1l3b7HSxutiM1ePhpshgAIxKBQuKzVS43CiAliYDnQKNdAww0i7ASJhGjdPuJv+gmWP7yjm2r5zy/LqVgjaLg9TjScLACAP97kknKiUQg7/m7/yYBUEQztlZJQnfe++9sx5QnF0j/FW9GofzZP+mvPjzPl78eS/Tbm/PuDbjeHfru3x+tYq4EicvTXmTex/7Lwu6BDF4VRXLv8yk9+1NRVUuQRAEQfgLMjIy2LRpE2lpafTs2ZNnn32W0tJSZs6cSXp6ekOHJwjCJUKWZQ4WW1iR6U0KbjhSXq/QyKqDpdzaMR6Am9rFcUv7+NMmBQEsDgsbCjewJm8Na/LW8EDGA1yXch0AHSI7EKwLpl1EOzpEdqB9ZHuiipzokpKQNN6kW9HiTyj/YoZvPG2TJhg7d8bYuROGtm1RGI2+xzwKBTsqa1hWXs3y8iq2VlnxANFaNTdGBCFJEgpJ4tnUaELUKjoF+uGvqh+70+Fm2qOrcDnrF1cJiTES3SiIhPQQX5skSaRk1F+pKAiC8G+RZFmW/6xTUlLS2Q0mSWRlnXwI68WmqqqKgIAAKisr8fcX5979m2RZ9m0xMGlV/HB/Fz7eP4lF2YsIsCt56TM7Nn0MYydOok22git22eg8JIU2Vyc0dOiCIAiC8K85X3OVzZs3U11dTa9evSguLmbkyJGsXbuWtLQ0pk2bRqtWrc5j1JcuMXcUhDNbuLuQe7/cUq8t0l9Hj0ah9GgURvfUMAIM6tM+X5ZlDpsPszx3OWvy1rC9eDsu2eV7/Nrka5ncfbKvr6emBuuGDVhWrMSyciWuwkLip0/H2KkjADUbNlK1YAHGLp0xdOyIKjj4lNd98XA+X+WXUeFy12tPM2jpHmTi2ZRodEqFr91mcZCzu4zs3WW4HB4G3N/S99jcVzdTU2knvmkwsU2DiW0ShN5PrBQUBOHfcbZzlbNaSSi2GAv/FkmS+O+gdI6U1rApu4I7Z2xm1t3PcLTqKPvL9/PKMC0vTM/juU/f5YmxjxFS6YYfDhMcZfRV+RIEQRAE4ey0a1d36H54eDgLFy5swGgEQbjYVVqdLD9QzOK9RWTEBzGmm3exSZfUEPy0KtokBNEjLZSejcJIDfc7691ApbZSBs8bXK8twT+BrtFd6RrTlXYR7XCVlVG1YAHVy5dj3bwFnHXnAEpaLY5jOb4kobFjB4wdO/ge98gyO6ttLC6rZHxCBJrjxzA4PDIVLjf+KgU9gkz0CvbnimATMTpvck+WZcryLGTvKiV7ZxmFRyrh+BIcSSFht7nQ6r233Nc92AqNXiV2QAmCcEE7q5WEp/PHUy+1NzrxaXDDK7PYuf7/1pBbYaNLSgiv35zAyEW3UWwtpvkxiadmOfmla28+uGUMty2rIqkGbni8HcFRxj8fXBAEQRAucmKucmERvw/hclZYWcuvuwtYtKeQTdkVuD3ee8RWcYH8NLarr5/D5UGjUpxuGACsTitr89fy+7HfcXlcvNrjVd9jo34dhVFtpEdsD7pGdyXWFItcW4tCrwfAtnsP2Tfc4OuvjovDr2dP/Hr2wNC+PQqdrt61at0eVpst/FZayW+lVRQ6vEnFOa1S6BlsAuCwtZYyh4s2/kZUpygYsnTGPvavLajXFhLrR2J6CPHpIUQm+aNQnvk1C4Ig/BvO60rC/zVjxgxef/11Dh48CECjRo2YOHEiI0aM+GvRCsL/CPHT8umodgz9cC1rD5fx8TI/Puz9IaMWjmJPXA0fD1Awdv5ScsOj+K5nf25fWMnPH+7kxv+0Q2c8/VYFQRAEQbjctWnThqVLlxIUFERGRsYZP+zdunXrvxiZIAgXE1mWGTltI6sOltZrbxxhonfTcPo0i6jXfroEocVhYXnuchZlL2Jt3lpfJWKVQsUzjmfw0/gB8Hm/z5FksG3fTvVHX3N4yRIM7doRPfllAHTNm2G6ph/6Vq3w69kTTWLiKd/f9lhsvJVdyO/l1VjddWcEGpUKegWbMJ2Q1Esx6EgxgNPu5tDuMrK2FdN5SCqmYG/CMTLJn4ObiohtEkRii1AS0kN8jwmCIFyMzjlJ+NZbb/HMM8/wwAMP0LWr95Oh1atXc++991JaWsrDDz983oMULk9NIv1595YM7pq5mZnrj9Ioojlv9nyTsUvHsjIdwivg3u+/Ij80nG+vaMvI38z8+vEuBo5rjVItPrETBEEQhFO5/vrr0Wq1AAwaNKhhgxEE4aKRZ7ax+mAJN7f3FhiRJAl/vffD+bYJQVyTHknf5pG+KsVn44NtH/D57s99iUGAWL9YesX3oldcL/QqPbLHg237dqp+/oWq3xbhLqlLStY4HMiyjCRJSJJE7Ntvn3SNcqeLGreHOF3d+X8/l1QCEKVVc3WIP31DA+ga5IdWUXcPYbe5yN5ZyuGtxeTsLcd9vOhIRHIAra6MA6BRh0gadYxEfYYiK4IgCBeTc95unJSUxKRJkxg5cmS99i+++ILnn3/+kji/UGwZubB8vOIwr/y6H6VCYurItpQrVjFp3SQA7l/gplOmhvGPPItCE8cNK6tp3CGCPqObXXLb4AVBEAThD2KucmERvw/hUlVe42DBznx+2JbHthwzAIsf7kFahHc77pHSGnRqBVEB+j8dy+q0sjJvJR0jOxKkCwLg6/1f8/KGl0n0T6RvYl+uTryatMC0evP4o7ePxrp+ve97hcmE3xVXYOrTB79uXetVIvbF7XTxa0kl84vNrDZXMyg8iA+aeQsdyrLM/+UU0yPYRAs//Un3DJUlNlbOPkDu/nI87rpbZf9QHckZ4TTpFElIjN9Z/gQFQRAuDP/YduOCggK6dOlyUnuXLl0oKCg4xTME4e+5p0cyh4stfLsll/u/2srXd/XmzhZ5fLrrU6b0VxFS5eDlj97g3sf/y5LWRqSNRfiH6uk4MLmhQxcEQRCEC9qmTZvweDx07NixXvuGDRtQKpX1CpsIgnB5qHW6WbqvmB+25bI8swSX549z6KF9YjA1jrpKv0mhZz4P3O1xs7FwI/MPz2dJzhJsLhvPdn6WGxvdCED/pP60CW9Do6BGSJKE/dAhSr/6kJC77kRxfMWzoU0Gtbt2YerTB/8B/TF26oSkObkqcJXLzYISM/OKzKwyV3NCfo/c2vorDh9IqNsK7XK4sVTYCYzwroDU+6nJzfQmCIMiDaS0CSc5I4zQ2LMvtCIIgnCxOuckYWpqKt988w1PPvlkvfY5c+aQlpZ23gIThD9IksTLQ1pQYrGzPLOEO6Zv4tt77yCvOo9fs3/lrRtUvPBFBZM/fJ0HJzyPv1ULv2RjCtHRrGt0Q4cvCIIgCBessWPH8thjj52UJMzLy+PVV19lw4YNDRSZIAgNZeWBEsbOqjuPND3Gn8EZsVzXMopw/7M7b+9QxSHmZ81nQdYCiq3FvvZYv1hUUt0taIA2AGONm4qZMzH/8CP2ffsA0DVpjKlPHwCCR48m5J57fEnD0xmy7RC7Lba6uP30XBcWyHXhgSQb6j/X4/aQu7+CA5uKyNpWQlCkgRufaA+ARq+iz6hmhMT6iaKIgiBcds45SThp0iRuvvlmVq5c6TuTcM2aNSxdupRvvvnmvAcoCABqpYIPh7dh2Cfr2ZFbye2fb+Lru5+myFrE1uKtTL5FzUufH2XS1Ld56r6JmKweFF9lYgrSEdcsuKHDFwRBEIQL0t69e2nTps1J7RkZGezdu7cBIhIE4d+UZ7bx7eZjBBk0jOqSCMAVjcNpFuVPz8ZhDMmI8W0tPlsVtRXcMP8G3LJ3xaG/xp9rkq7hupTraBnaEkmSkJ1OLCtXYv7hByzLV4DL5X2yWo1ft24og4J84ylN9a8vyzJbqqz8WFzBU8nR6I8XGhkQFoDDIzM0IuiUiUFZlinOriZzQyGHthRhq3b6HrNVO7FbnWgN3jMW09rXL7oiCIJwuTjrMwl3795Neno6AFu2bOHtt99m3/FPepo2bcqECRPIyMj45yL9F4lzZS5cZRY7Qz9aS3aZlaZR/kwd1ZT7fh9NdlU2SUUwaaaLVRldeWPk/QxbbiHFIjN0YltxboggCIJwSTlfc5WQkBAWLFhA586d67WvXbuWAQMGUFFR8XdDvSyIuaNwMXG6PSzdV8zsTTmsOFCCLENcsJ6VE3ud83Zat8fN2vy17Cnbw72t7vW1j1s2DoCBKQPpEdsDjbL+9mD7oUNkXXud73tdejoBgwfh378/qhMShCfKsdmZU1jOd0UVZNu8hU4+bpbAoAhvf6dHRiVx2tew8utMdq3Iq7umn5q0tuGktY8gMjkASSG2EguCcOk627nKWScJFQoF7du358477+SWW27BZDq3T5QuJmKid2HLKbMy5KO1lFrsdE0N4YUhUYz+bQQV9gpaHYHHv3HxQ6/+TL9+OCOXVJGkVHPD4+0wBp55i4IgCIIgXCzO11xl2LBhFBQU8NNPPxEQEACA2Wxm0KBBhIeHi10iZ0nMHYWLwdGyGmZvOsbcLbmUVNt97Z2SgxnWIZ5rW0ajPMtEWVFNET8c+oHvD35PQU0BEhK/Dv2VGL8YADyyB4XkXeHnttRQtWA+ruJiwsaN841x7L770SQlETDoenSNGp3yOnaPh4WllczKL2dlRTV/3LgalAr6hwZwV1wYrUwnV1N2uz0c3VVGeIIJvyDvFumsbSX8Nm0Pya3DaNwpktgmQSiVipOeKwiCcCk670nCVatW8fnnnzN37lw8Hg833HADY8aMoXv37uct6AuFmOhd+HbnVXLzlHXUONxc1yqaO3pL3LP4bmwuG533ehg/z8PUQcNY1OM6Ri0ykxRmZPCENmh057zDXhAEQRAuOOdrrpKXl0ePHj0oKyvz7QjZvn07ERERLF68mLi4uPMV8iVNzB2Fi8HEb3fw7ZZcAEL9NAxtG8st7eP/tPjIH1weF2vy1jD3wFxW5q3EI3sA73bigSkDub357UQY67bp1u7dS8XsOVQuWIBstSJptaStWonyHP6NHLLW0m3Dft/3PYL8uDkymH5hARiVypP6l+fXsG9tPpkbCrFVO2k/IJEO13mLGbrdHlx2t29LsSAIwuXkvCcJ/1BTU8M333zD9OnTWbVqFampqYwZM4ZRo0YRGRn5twO/EIiJ3sVh1cESRn++CZdH5s5uSVyZYWbssrG4PC76bPNw10IPr468l53NuzFicSVpjUMYcH8LFOITQ0EQBOEidz7nKjU1NXz11Vfs2LEDvV5Py5YtGTZsGGq1uJE+W2LuKFxoyix2vt6Yw9XNI2l0/EzBrTkVvLvkIMM6xNG7aQTqc5wTzzs8j6dWP+X7vm1EW25odANXJVyFVundseOx2aj65Vcq5syhdudOX19NUhJBt9xM4A03oDCeOilZ43LzU7GZXLuDx5KifO337skmSa/llqhgEvQn7wxyOz0c3l7MnpX55B80+9r1/hoy+sSTcXX8Ob1OQRCES9E/liQ80aFDh/j888+ZOXMmhYWF9OvXj3nz5v3V4S4YYqJ38fhxWx4PzdkOwFP9m5IQf4iJKyfikT0MWuvh5lXw9L0TKA5vwS0rq2nRNZorbm18zuetCIIgCMKFRMxVLizi9yFcKHblVjJ9bTbzd+bjcHkY1iGeyUNa/KWxdpfupsZZQ8cob/Vzq9PK0HlD6R3fmyGNhpAckHzSc8o+/ZTiN970fqNW439VHwJvvgVDh/annX8fstbyeW4p3xSWU+32oJYktnVpTqjmz3cAeTwyXz27jqrSWgAkhURiixCado0mvnmw2E4sCIJw3L+SJIS6T5+feOIJzGYzbrf77wx3QRATvYvLJysP8/Iv3m0Ib97YCkwbeH7d8wAMX+am71Y1j45/CoUyges31NDhhG0HgiAIgnAxOt9zlb1795KTk4PD4ajXPnDgwL899uVAzB2FhuR0e/h1dyFfrM1my9G6YkMtYwO4u0cy17aMPvuxPE4WZy/mq/1fsbNkJ6mBqXw/8Htfgk+W5Xp/r1m7FoXegKGN97gCV2kpR28bQcCQIQQOGYwqNPSU13HLMotLq/g8r5QVFdW+9mS9luHRIYyIDsFfdfJ2Yo9HJv+gmZhGgb44fv9yP0d3ldKsWzTNusXgFyTOIRcEQfhfZztX+csHtK1cuZJp06bx3XffoVAouOmmmxgzZsxfHU4Q/rK7uidTVGXns9VHmDh3B+8N68wjbR/hrS1v8dWVSoy1Tl7+8DXGT3iOZS1CkX7OxuCvIb1nbEOHLgiCIAgNKisri8GDB7Nr1y4kSeKPz47/uPm+FD78FYRLmSzLDPxgDfsKqgBQKSQGtIxiVJdEMuICz3r3TJmtjLkH5vJN5jcU24qPj6WiaXBTrC4rRrV3i7AkSXhsNirnz6di5kzsBw9haN+ehJkzvM8JDSX511/+9LrT80p56qC30rAEXB3qzx0xYXQP8kNxiufarU72ri5g1/JcqstrueHxdkQkeW9yOw9OocewRmLVoCAIwnlwTknC/Px8pk+fzvTp0zl06BBdunThvffe46abbsJ4mrMlBOGfJkkST/VvSo3dxexNxxg/ezsfDr+GMemVfLb7M6Zeo8Rot/LqB6/wwKMvYHDoYfYBdH4aUtuGN3T4giAIgtBgxo8fT1JSEkuXLiUpKYmNGzdSVlbGhAkTeOONNxo6PEEQTqG4upZQoxaFQkKSJPo2j6Ck2s7wjvEM7xhPuL/unMb7at9XvLX5LRwe70riUH0oNzW+iRsb3Uiovm4loLOwkIpZX2OeMwd3ZSUACoMBbZMmyC4Xksp7a3mqBOEeiw27x0Mbf+894+CIIP4vp5ghEUGMjA4h/hRnDQKYi63sXJbLvnUFuOzeDy20BhVVZTZfklBnFOenCoIgnC9nvd34mmuuYcmSJYSGhjJy5EjuuOMOGjdu/E/H1yDElpGLk9sjM/HbHXy/LQ+1UmLKbW1ZZf6YuQfmonLD49+6CbJG8dAjz9B1v4q2x5xc90ArYpsEN3TogiAIgnBOztdcJTQ0lGXLltGyZUsCAgLYuHEjjRs3ZtmyZUyYMIFt27adx6gvXWLuKPwbDpdYmLoyi++35vHRbW3o3dRbSdjqcKFUSGhPsT33dNweN0qFt//a/LXcs/ge0kPSGd5sOH0T+qJW1k+8lbz3PqWffAIuFwDq2FiCR9xGwJAhKE2mU15DlmWWl1fz0bFiVlZYaOdvYEHbRnUxyDLK06w4tFY5+P3L/WTvKoXjd6vB0UZa9Y6jUfsIVJqzf62CIAjCP7DdWK1WM3fuXK699lqUpyg3LwgNTamQeO2GlthdHn7eVcC9X23l05H3UZ1YzaLsRbwxVMkzX+fz+nsvM2H8M2idKpQf7WLwhDaExZ96ciMIgiAIlzK3243p+A1+aGgo+fn5NG7cmISEBDIzMxs4OkEQALYcrWDKisMs3lfEH8s7Vh4o8SUJDWdR4AO8Sbt1+euYtnsaLcNaMq7NOAA6R3Xm6wFf0zykeb1VgLLHg6TwbuHVJCWBy4WhfXuCR43Er1cvpNPcE9o9Hn4oquDjYyXsr/EWFFFKEKPTYHN70B/fFny6BCGAzqiiNLcaZEhID6FV7zhimwSJ4oOCIAj/sLNOEl4KVYuFS59KqeCdW1rjcHtYvLeIu2duYdrtj2FxWliTt4aXb1bz9NfHeO39yUwc9yTaLQrmv7+dIRPbEhhuaOjwBUEQBOFflZ6ezo4dO0hKSqJjx4689tpraDQaPvnkE5KTRZEvQWhI67PKeHfJQdZllfna+jSN4N6eybRLPPudMC6Pi8VHF/P57s/ZV74PgAMVB7iv1X2olWokSSI9NB3wJhItK1ZQ9slU/PteTfCoUQD4X9MPbWoKuqZNz3ituYXlvHA4n2KHd8WhUalgeFQId8aGnnZLsaPWxZ6V+WRtL2bQhDYolQoUSgVXjmiKX5CWoEhxrJUgCMK/5W9XN74UiS0jFz+7y83dM7aw4kAJRo2ST29vxceZ/2Fr8VYMdnhmlotafRpPjv0PQ9a7aI6KIRPbYgwQ1dAEQRCEC9/5mqssWrSImpoahgwZwqFDh7j22ms5cOAAISEhzJkzhyuvvPI8Rn3pEnNH4XzzeGSueXcVmUXVqJUSg1rHcE/PZFLDz373i8vj4pcjvzBlxxRyqnMA0Kv0DE0byshmI4nyi/L1lV0uqn5dSNnUqdgPHABAHR9PyqKF57R6b16xmbv3ZBOlVXNnbBi3RQUToD71uhRbtYOdv+eya3kudqs3qXjVmGY0ah951tcTBEEQzs7ZzlVEkvAUxETv0lDrdHPH9E2sPVyGSafi89GteX/vY2wt3oqx1psorPZvwnP3PM5Nq+0099MzeEIbtPq/XPRbEARBEP4Vf2eusnPnTtLT01EoTl0JtLy8nKAgsa3vXIi5o/B3ybLM+qxyWsUF+LYPL9xdyOpDJdx3RSoxgfpzHvPtLW8zbfc0AAK1gdza9FaGNR5GoC6w7rpOJ5U//UTplE9wHjsGeIuRBA67heCRo1BHnL7IX7bNzgdHi2nip+PO2DAAXB6ZBSVm+ocFoDnNe0x1eS3bFuewb3U+LqfHG1+EgYyr42ncMRKlSlQpFgRBON9EkvBvEBO9S4fV4WLUtI1syq4g0KDm89GteGvXo2wv2Y6xFp6d5aIsKJ2Xx0xg2IpaWkT5c92DrcRhyIIgCMIF7e/MVZRKJQUFBYSHh5OcnMymTZsICQn5hyK9PIi5o/BXybLMusNlvLPkIBuzy3mqf1Pu6vHXtvq7PC4sDosvCXis+hijfh3Fbc1u45bGt2BQn3y0TsGzz2H+5hsAlEFBBI8cQdCtt6IMCDjtdfbX2Hj/aDE/FFXgAcI1KjZ1bob2NEnBE1WV2vjq2fV4PN5b0LB4E237JZDUOgyFQnwwIQiC8E8574VLBOFiZNComHZ7e0Z8tpHtx8yMmb6TT0a+zjt7HmVHyQ7+O0zFs7N2M/GL93hzxEOollehnrqba+5pIT7FFARBEC5JgYGBHDlyhPDwcLKzs/F4PA0dkiBcljZnl/Pawkw2ZpcDoFEpsNhd5zyOy+Pi56yfmbJzCk2Dm/LmFW8CEGeKY9ENi1Ar6ioVyy4Xnlo7Sj/vOX+BN99E9bJlhIwZQ9DNN6EwnP6M7h3VVt7NLuKX0kpf25XBJh5KiDhjgrDW4kTn543BP1RPVGoAkkKiTd8EUYxEEAThAiNWEp6C+DT40lNpczLisw3szK3EX6diysgWfLB/IjtLduJn864oPBrTgQ9vGstty2to2TyMq8c0Q6EUiUJBEAThwvN35ip33303M2bMICoqipycHGJjY1GepkppVlbW+Qj3kifmjsK52F9YxRuLMlmyrxjwJgdv7RDPvT1TiAzQnfU4HtnDwiML+b/t/+c7czBYF8z8wfPx19T//6HsdlP1yy+U/t+HGLt0JvLZZ+secziQNJozXuvt7EJePVIIgAT0DwtgfEIELU2nTyqW5VvY/HM2R3eXcdt/O2Pw917D6XCjFrt2BEEQ/lViJaEgnCBAr+bLOzsy+vNNbDlawV1f7OKD4a8yVX6cnaU7j68o3Mhd36v5dPA9sKIE1cz99B7ZFElsfRAEQRAuIZ988omvUMm4ceO46667MJnOvhiCIAh/z1u/HWDJvmKUComb2sUxvnfaOSUHZVlmXf463tn6jq9acZA2iNvTbz9pW7Hs8VC9aBEl73+A43jS31NTQ/hjj6HQea95ugShR5ZRHF/ld3VoAG9lFzEoIpAH4yNoZDx9vGX5Fjb/ks2hLcVwfDlKzt4ymnTyFkoRCUJBEIQLl1hJeAri0+BLV43dxZ1fbGZdVhl6tZJ3b23G9Kz/sKt0FyYrPDfLxYHErky7/i5uXWmlQ6doegxrJLZBCIIgCBeU8zVXGT16NO+9955IEv5NYu4onEmZxY4MhPppAThQVM27Sw7yyNWNSAnzO+fx5h+ez5OrnwTAqDYyuvloRjQbcdKZgzXr11P8xpvU7t4NgCIggJDRowm67TbfduNT2V9j4/UjhYSqVbzaOM7XXuJwEqZRn/Z55fk1bPrlSL3kYEpGGO0GJBEae+6vUxAEQTh/ROGSv0FM9C5ttU439365heWZJWhUCt66uSlfHv0Pe8r2YLLCM1+7yI7tyNTB93LrKhude8bRZUiKSBQKgiAIF4zzMVdxOp3o9Xq2b99Oenr6eY7w8iLmjsKp1NhdfLrqCFNXZXFdqygmD2n5l8dyepy+swWtTiuDfxrMlfFXcnfLuwnSBZ3Uv3zWLIpe+C/grVYcfMcdBN8+CqXf6ZN1h621vJldxA9FFciAViGxrUtzgtV/vvms1uJk+hNrcB+vViySg4IgCBcWsd1YEE5Dp1YyZURbxn29jUV7injo6728csNLyDzJXvYyabiKJ+Zs4P5vXXw8dCwsP4Zaq6TDtUkNHbogCIIgnDdqtZr4+HjcbndDhyIIlxSPR+b7bXm8tnA/xdV2APYWVON0e1Cf43nXJdYSPtzxIfvK9jFrwCwUkgKD2sD8wfPRKOtvE5Y9HqTjBUT8+/Wj9L338b/2WkLvuxfVGSqYH6t18MaRQr4tLOePMkYDwgKYmBR5xgShw+ZCo/c+rvNT06xbNDVmO+0HJBIaK1YnC4IgXIzESsJTEJ8GXx6cbg8TvtnBvB35KCR4cUhjFpa+wLbibWgd8NhcNzV+rfjoxge5eY2dK69LIePq+IYOWxAEQRDO21zls88+4/vvv2fmzJkEBwefxwgvL2LuKPxhU3Y5/12wl5253grAccF6HuvbhAEtolCcwznXta5aZuydwae7PsXmsgHw2dWf0SGqw0l9XRUVlH70Ec6jOcRN+djX7qmpQWE8/bZigEWlldy1OxvH8VvCq0L8eSwpkhZnKEhitzrZ9lsOO3/PZfCENoTFexOCskcWZ3kLgiBcoMRKQkH4E2qlgrdvbo1OreCbzbk89X0mkwY+i075GusK1jH5JiWP/LCDB2e/zf/d9BDMP4xKo6DFFbENHbogCIIgnBcffPABhw4dIjo6moSEBIz/k1DYunVrA0UmCBefbzcfY+LcnQD4aVU8eGUqt3dNRKs6+0Idsiyz6Ogi3t78Nvk1+QC0DG3JhHYTaBPRpl5fj8NBxcyZlH70MR6LBQDbzp3oW3q3Nf9ZghCgnb8RrUKivcnIU8lRtAk4/XNcDjc7l+eydeFR7FYXAJkbC31JQpEgFARBuPhdMEnCV155hSeeeILx48fzzjvvnLLPnj17ePbZZ9myZQtHjx7l7bff5qGHHvpbYwqXN6VC4pUhLdGplcxYd5Rnf8pkYt9H0cd9yLJjS3ljqJIH5u/hoa/f4P2bH0GaewCVRknTLlENHbogCIIg/G2DBg1q6BAE4ZJxVbMIQowarm4eySNXNSLMpD2n51fUVjD+9/FsK94GQIQhgofbPkz/pP71zsaWZRnL0qUUvfY6zpwcALTNmhI+YYIvQXgqblnmu6IKVldU826TeCRJIkSjYmn7xsTpNKc9f9vj9rBvbQGbFhyhptIBQFCUkU7XJ5PUKvScXqMgCIJwYbsgkoSbNm1iypQptDzDf9QArFYrycnJ3HjjjTz88MPnZUxBUCgkJg1sjl6jZMqKLF5fdJBRnccwIMnAz0fm8/5AJWMWZfLQrNd5/5YJ8PU+AJEoFARBEC56zz33XEOHIAgXJY9H5rutuaw6WMq7t7RGkiQCDRqWT7wCk+70FYDPJEAbgNPtRK/SMzp9NLc3vx29Sl+vj7O4mPyJj2HdsAEAVVgYYY88QsD1A33nEf4vWZb5vbyaFw/ns7emFoChEcH0DPauAIzXnzmZOe+9HeRlVgBgCtbR4bokGnWMPKft04IgCMLF4dxOzv0HWCwWhg8fztSpUwkKOrky14nat2/P66+/zi233IJWe/r/mJ3LmIIAIEkST1zTlKcHNAXgi3U5VOYM5YbUYcgSfNpPyaHwQzz01ev81E7BL7P3s29tfgNHLQiCIAh/n9ls5tNPP+WJJ56gvLwc8G4zzsvLa+DIBOHCtDe/ihunrGPi3J3M25HP75nFvsfOJUFod9uZvns6VqcVAIWk4MVuLzJv0Dzua3XfSQlCAFVgIM7CAiSNhpB77iFl4a8EDh502gThPouNW3ZkcevOLPbW1OKvUvB0chQdzrCt+H+lZIShNajodmMawyd1oknncztfURAEQbh4NPhKwrFjxzJgwAD69OnDiy++2CBj2u127Ha77/uqqqrzEodw8bmzezIR/jomfLODX3cX0qGmFyNa+TMzcwqzeikZvOYIj854ibduexx5TiYATbtEN3DUgiAIgvDX7Ny5kz59+hAQEEB2djZ33XUXwcHBfP/99+Tk5DBjxoyGDlEQLhjVtU7eWnyAL9Zm45HBoFEyvncaXVPPfcvt6rzVTN4wmZzqHKqd1TyY8SAAKYEp9frJDgfmH38kcPBgJLUaSaMh5rXXUIaEoIk9/TnZFpebF7MKmJFXigfQSBKjY0MZnxBxxorF1ioHmxYcIbZpECkZ4QA07x5NWvsIdMa/tkJSEARBuHg0aJJw9uzZbN26lU2bNjXomJMnT2bSpEnnLQbh4nZdq2hC/DTcM2MLG4+UY7a24I4ujzBt/1v80FVBjT6fpz6bxGujHsf9TSayDM26ikShIAiCcPF55JFHuP3223nttdcwmUy+9v79+3Prrbc2YGSCcOGQZZl5O/J58ed9lFR7FxYMaBHF09c2JSrg5NV+Z1JYU8hrm15j8dHFAITpw0gLSjtl35r1Gyh84QUcWVnItXaCR44AQN+q1Z9eR6OQWFlejQcYEBbAsynRJJxhW7HL6Wbnsly2/JqNo9ZNzt4yklqGolAqUCgV6IwNvgFNEARB+Bc0WJLw2LFjjB8/nsWLF6PT6Rp0zCeeeIJHHnnE931VVRVxcXHnJSbh4tQlJZRv7u3M7Z9v5ECRhepl8dx5xbN8duC//NZGQblfOU9Me4E3RjyG5zvvikKRKBQEQRAuNn+c4fy/YmJiKCwsbICIBOHC43TLvLv0ICXVdpJCjUwa2JwejcLObQyPky/3fslHOz7C5rKhlJTc2vRW7m91P34av/p9i4spfu11qhYsAEAZEoLyLI5QWlVeTadAP9QKCY1CweuNvSsNuwaZTvscWZbJ2l7CmrmHqC7znlcYFm+i242pKJQiMSgIgnC5abAk4ZYtWyguLqZNmza+NrfbzcqVK/nggw+w2+0olcp/ZUytVnvGMw6Fy1PTKH++v78ro6Zt5FCxhc8W+XNP39eZdugJNjdyUmm0MmHmS3x40yO4v/eeyyIShYIgCMLFRKvVnvKYlQMHDhAWdm5JEEG4lNgcbtRKCZVSgUal4MXr09lytIK7eyajVZ3bPQrA21veZubemQC0DmvN052epnFw43p9ZJeLillfU/Lee3gsFpAkgoYNI+yh8Sj9/U879sGaWp47lMey8mpeTIvhzljvv90zJQcBKgprWDn7ALn7vUVJjIFaOg9KplGHSCRx5qAgCMJlqcGShL1792bXrl312kaPHk2TJk14/PHHzzlB+E+NKVzeYgL1zL23M3d+sZnNRyt4d4GCcf0+YHbuRA7GVPHyzU4mzn2NrwaM5dsf4UYZmnUTiUJBEATh4jBw4EBeeOEFvvnmG8BbyCsnJ4fHH3+coUOHNnB0gtAwVh0s4ckfdjGqcyJ3dk8GoEtqKF3+wtmDfxjZbCTLcpZxT8t7uD71ehTSyav0Cl/4L+bj/xZ1LVoQ+dxz6NObn3bMGpebN7OL+CS3GJcMakmi0uk+65iqy2rJ3V+BUqUg4+p42vRNQK0V90uCIAiXswZLEppMJtLT0+u1GY1GQkJCfO0jR44kJiaGyZMnA+BwONi7d6/v73l5eWzfvh0/Pz9SU1PPakxBOFeBBg1f3tmR8bO3sWhPEW/8XMHt3d5mre0ZCshn0nCZx+a+x+Iuo5k9X+JmWaZ595iGDlsQBEEQ/tSbb77JDTfcQHh4ODabjZ49e1JYWEjnzp156aWXGjo8QfhXVdQ4ePHnfXy3NReAWRtzuL1LIqpz3HYryzLzDs9jf/l+Hu/wOACRxkgWDF6ASnH626+g4cOpXrKEsHHjCLzxBqTTLHCQZZkFJZU8dyiPfLsTgKtC/JmUGkOy4fS7o2RZxlxkJSjSW9k4vnkInQenkNImnICwcztbURAEQbg0NXh14zPJyclBoaj7j3J+fj4ZGRm+79944w3eeOMNevbsyfLlyxsgQuFyoVMr+XB4W17+ZR+frT7C9NUlXNX8efwD3ieTXbwwTMH4nz5nf1oVs36Wudkl07LX6SvOCYIgCMKFICAggMWLF7NmzRp27NiBxWKhTZs29OnTp6FDE4R/jSzLLNhZwKT5eyi1OJAkGNkpgYn9mpxzgjC3OpcX1r3AuoJ1APSO7027yHYA9RKEsixTNX8+zvwCQu+9BwBd40ak/r4MxZ8cg/RyVgHv5xQDEK/T8FJaDFeFBpzxOSU51ayac4DSPAvDJ3XCGOC9Rpu+Cef0+gRBEIRLmyTLstzQQVxoqqqqCAgIoLKyEv8znP8hXJ6+2XSMp37chdMt0yzaj4jkn9hcthhJlhn9mwe731WUhw/m5m6JtO2X2NDhCoIgCJeg8zVXmTFjBjfffPNJZzM7HA5mz57NyJEj/26olwUxd7x4FVTaePqH3Szd7026pYb78erQFrRNCD6ncVweF1/t+4r/2/5/2Fw2tEot97e+n5HNRp60etCRm0fh889Ts3o1KBQkff8duiZNzvpa+yw2Bmw9yL1xYTwYH4H+DIlMu83F+h8Ps2dlHrIMKo2Cvnelk9jir2+dFgRBEC4+ZztXEUnCUxATPeHPbDxSzr1fbqG8xkGYSUPH1jtYUfoFANet95Bc2prdzUdzc6skOg1KRpLE4c+CIAjC+XO+5ipKpZKCggLCw8PrtZeVlREeHo7bffbnm13OxNzx4pVZWM21768CYGyvVO67IuWcC5Nklmfy3Nrn2FO2B4D2ke15rvNzJPjXX6Unu91UfPklxe+8i2yzIWk0hI4dS8gdo5HU6tOO/1tpJZk1tTyYEOFrMztdBKpPvylMlmUObSlm9TcHsVY5AEhrF06Xoan4BenO6fUJgiAIF7+znatc0NuNBeFC1SEpmJ/GduXOLzaTWVTNb+uac037x1lmfpX5nRS0Pbidaza+zizG4bS76X5TmqgSJwiCIFxwZFk+5QdZubm5BAScefuiIFysLHYXflrvbVDjSBMvD25B67hA0iLOXA34VJweJw8se4DCmkJMahOPtn+UwamDT/p3VZuZScHTz1B7vMiioV07Iv/7AtqkpNOOnV/r4KmDefxaWolSgt4h/jTz854deMYEoUfm5492cnRXmbdvhIGewxoR2+TcVkcKgiAIlx+RJBSEvygu2MB393fhodnbWbKviJ/WBXF161fYbH+WLWkOigPzGPPbi3wrP4RjppMrb2uK4hzPtREEQRCEf0JGRgaSJCFJEr1790alqpsSut1ujhw5Qr9+/RowQkE4/zwema82HOW1RZl8fVcn0mO8ifAb28X95THVCjWPtX+MX4/8yhMdniDMEHbydW02ckaOwl1ZicLPj/CJE72FSRSnnhe6ZZkv8kp5OasAi9uDSoJ748JJ0GvOKiZJIREYZuCYqpy2/RJp2zcBpVrMQQVBEIQ/J7Ybn4LYMiKcC49H5vXfMvlo+WEAOqboKdRPptxdiJ9NZuwCJVtb3E+fpM70vSMdpUpM0gRBEIS/5+/OVSZNmuT7OmHCBPz8/HyPaTQaEhMTGTp0KBrN2SUlLndi7njhyzPbeGzuDtYc8q6uG94xnpcGtzjncRxuBx/t+IjGQY3pl3T2ifTyWbOoWbuWyGeeRR0Rftp++yw2Hs08xpYqKwDt/A283jiOpn5nrj6cf8iM3k/tq1zsqHVhrXQQGGE46xgFQRCES5c4k/BvEBM94a/4YVsuj3+3C4fLQ1ywlsDYb8l2rkXhkRm1WKYydDitGvVn4N0tUGnO7awbQRAEQTjR+ZqrfPHFF//P3n2HR1WlDxz/Ti+Z9B5SSOi9C4gFAUXF/dnWrrj2ggV117L2XV10ddcusrqKu6tiWwuuDRGwIdJ7CRASEtLrZPrMvb8/JkyICSEhA0nI+3me+9x25syZCyQv75zCRRddhNksc5R1hMSOXZeqqry/qpA/fbaFeo8fs0HLfWcM4ooJWWjbORXMlsot3P/D/eys2Um0KZrPz/ucKGPzP2/F46H8+eexnXACERMnhtpxqDmqnQGFscs3U+ULYNNp+WNOKr/rlYC2lde5HT5++u9Otv5YTGrfaM69c7RMcSOEEKIZmZNQiKPs3FHpZCfYmPXWGvZWuSi3n8OIgf3Z4p/PG9M1nLrmLfasLubdF6/kgptHYjTLPz8hhBCd68orrwSCqxmXlZWhKEqT+5mZmZ3RLCHCorTOzb0fbmDJ9nIAxmTF8vQFI8hOiGhXPT7Fx2sbXuMfG/6BX/UTZ47joQkPtZggdG3YwL5778O7ezf2L74k54vP0ZpMbVrEzqrTclfvFL6vtvOXfumkmVvvybtrbRnfvbMjtDBJbLKVgF+RL6OFEEIcNulJ2AL5Nlh0RLXDy+x317FsRzAgHdXHyU7dX0DnZ3C+ypTNg9GPuJVLbhmPNUqGcQkhhGi/cMUqubm5XH311fz0009Nru/v9SSrG7eNxI5d07+W7+GhTzZj1Gv5/Wn9ueaEHHTt7GWXW53L/T/cz9aqrQCcmnUqD0x4gDhz00VAFK+XipdepvK11yAQQJeYQOqjfyJyyikHrbvOH+DRnUX8X1IsJ8cFF03Z/1+z1pKKzjov3y3Yzq41wVgzJtnKKZcPJK1fTLs+mxBCiJ5Dhht3gAR6oqMUReXFJTt5X/k/iQABAABJREFU5psdqCpkJWqpj/o7XmMJiTUqly1NwDnoTmbeMY3oRJkrRgghRPuEK1aZNGkSer2ee++9l9TU1GaJiREjRnS0qT2CxI5dk6Ko/OmzLVw2PvOwVi4ucZQw478z8Cpeok3R3D/+fk7vfXrzlYu3bGHfvffh2bEDgKizziL5/j+ij409aN2LK+v4/fa9FHt89LYY+eG4QejbkMCsLKrno7+vwePwo9FqGH1aJmNn9EZvkN6DQgghDk6ShB0ggZ4Il+9zy7l9wTqqHF5sJi2Rqf+l3vIzBp/K5UsN6OJu4tLfX0JiZvsDVyGEED1XuGKViIgIVq9ezcCBA8PYup5HYseu4Ze8Kl74Npd5V4zBagzPtC6P/fwYpY5SHpr4UIsrF3tyc9l97nng96OLiyPl4YeJmn7aQeur9fl5eOc+FpRUAZBtMfLMwEwmxNgO+poDBQIK789ZhUYDU2YOIjFDYkghhBCHJknCDpBAT4TTvhoXs95ew9qCGgB6p2+jwvYvNBqFyesVBlSfw//94U4yB8d3bkOFEEJ0G+GKVcaNG8czzzzDCSecEMbW9TwSO3YuX0DhuW9yeXnpThQVbp7ch7tPP7zE9+e7P2dU0ihSbanBuhUfeo3+oMN/VVWl6LbbQasl5eGH0MfFtVgOYFFFLX/YXkiJ14cGuD49kXtyUrHqtAd9jaqo7PilhL5jk9Hpg+Xqqz1YogzoWnmdEEIIcSBJEnaABHoi3Lx+hTlfbOWNH/cA0CveQ230s2hM1WSVqpy5bgin3PQEg07M7tyGCiGE6BbCFat8++23PPDAA/zlL39h2LBhGAyGJvclDmobiR07T16Fg9kL1rK+sBaA345J55H/G4LN1L6ehHXeOh7/+XE+z/ucscljee2019Bpmw/hVVWV2g8/JHLaNHQxMUBwPkKNwdDqPIKrah2ctSYXgD4WE88OymRcdOsLqNSWu/j2X1vZl1vDuBm9Oe43Oe36TEIIIcR+srqxEF2IUa/l4d8MYUxWLPd+uJGiSrDY78UY/yH5yb8w/+TNOP8xk7p9TzP+ovGd3VwhhBA9xLRp0wCYOnVqk+uycIno6lRV5f1VhTyycDNOb4Aos56/nDeMs4antbuu1aWrue/7+yh2FKPT6Dgu9ThUmvej8FdUUHz/A9QvW0b99z/Q69ln0Gg0aI2HXohuTJSVsxKjyTAbuTs7FUtrvQdVlc3f7+PHD3fi9wTQm3RYo03t/lxCCCFEe0mSUIij6KzhaYxIj2H2u+tYnV+Nq/g8YlzDcCb9hzen1VD84w3UFNzDab+/uNVvo4UQQohwWLJkSWc3QYjDMnfZLv765XYAxmfH8cxFI0mLsbSrDp/iY+66ufxz0z9RVIV0WzpPnPQEIxKbL9hjX7KE4vsfIFBVhcZoxDpmdKt11/kD/DWvmDt7pxBnCA5X/seQ3mgPEd/Zq9ws+c829m4JzlmY1i+GKTMHEZ3Yvs8mhBBCHA4ZbtwCGTIijjR/QOHlpbt4bnEuAUXFavagJr+O3prPgL0qv604m98+9SdZqU4IIUSLJFbpWuTP4+grrXPzmxd+4HeTenPDSX3QtWFl4Cavd5Qye8lsNlVuAuDsPmdz3/j7iDA0HQKsuFyUPvkkNQveBcDUvz9pTz2FeUD/g9b9Y7Wd27YWUOTxcXZSDPOG9G5Tm/I3VfL1PzfjdfnRGbRMPKcPw09JR9POzyaEEEL8mgw3FqIL0+u03Da1Hyf0S2D2gnUUVIGm4EbMsUvZlr6I5+M/ofjm9Vz/6Hwi0hI6u7lCCCGOIRs2bGDo0KFotVo2bNjQatnhw4cfpVYJ0TpFUflpVyUn9AvGRclRZpb+YfJhr2IcZYrCHXATZYzioYkPMb339GZlPLvzKJw1C29eHgBxv/sdiXfMRmtqeeivO6AwJ6+YeXvLAcgyG7mmV9vjuOgkC0pAITk7iqlXDiI2pfU5C4UQQohwk56ELZBvg8XRVO/x8/Anm/lwTSEARtM+DOlvoTVWcvImI78/7e/0PvXkTm6lEEKIrqQjsYpWq6WkpISkpCS0Wi0ajYaWwkGZk7DtJHY8sqocXu56bx1Ltpcz97LRnDEs9bDqcfvdGHVGtJrgfID5dfmYdCZSIlJaLB+orWX3OedCIEDaE3OIOP74g9a90e7klq0FbHe4Abg8NZ5H+6YRoW99VEhFoZ2E9MjQeVl+HQnpNrSycrEQQogwkp6EQnQTNpOev104gskDErn/o43UudPw592BIfFzlg5ZTu7GWcze8FtOv/NhmadQCCFEh+Xl5ZGYmBg6FqIr+yWvitveWUtJnRuTXovDe3iJ6101u/j9st9zZvaZXDf8OgCyorKalQvY7WhtNjQaDbroaDJefgl9Sgr62NiD1v11RS3XbNqDT1VJNOr5+4AMTk2IbrU9Hpef7xZsZ8cvpZx75yjS+gXrT8qSJLMQQojOI19RCdFF/GZEGl/OPomJOfEoih5P6f/hybuRQlsi98V+wON/PBNfdVVnN1MIIUQ3l5WVFfrSKSsrq9XtSHn88cc5/vjjsVqtxMTEtFimoKCAGTNmYLVaSUpK4g9/+AN+v79JmaVLlzJ69GhMJhN9+/Zl/vz5zep56aWX6N27N2azmfHjx/PLL780ue92u5k1axbx8fHYbDbOP/98SktLw/VRxWFSFJUXv83l4n8sp6TOTU5iBB/PmsRvx6S3qx5VVfko9yMu/uxidtbs5N3t7+L0OVss6/j5Z3afOYPa//43dM08aFCrCUKA8dERJBr1zEiMZum4gYdMEO7LrWbBn1ewY0UpGqCisL5dn0kIIYQ4UiRJKEQXkhZj4a1rx/PYOUOJMOrwebJw5s3GVXMyCwYUccXLU8n/RVaiFEII0b15vV4uuOACbrrpphbvBwIBZsyYgdfr5aeffuLNN99k/vz5PPTQQ6EyeXl5zJgxg1NOOYV169Yxe/Zsrr32Wr766qtQmXfffZc777yThx9+mDVr1jBixAimT59OWVlZqMwdd9zBwoULef/991m2bBn79u3jvPPOO3IfXhxSud3DlW/8wtNf70BR4bxRvVh4ywkMSm1fLzuHz8Eff/gjD/30EO6Am4mpE1lw1gKsBmuTcqrfT9lzz1Fw1dX4y8upXvAuqqIctF5VVfm+yh4aph9t0PPlmP68NqQ38a3MkRjwKyz/eBcf/X0t9VUeohLMnPeHMQw/JaNdn0sIIYQ4UmROwhbIvDKiKyisdnLffzfyfW4FADpTAaZeHxCtlPIH/W849+rH0Wglzy+EED3RsRKrzJ8/n9mzZ1NTU9Pk+hdffMFZZ53Fvn37SE5OBuCVV17hnnvuoby8HKPRyD333MP//vc/Nm3aFHrdxRdfTE1NDV9++SUA48ePZ9y4cbz44osAKIpCRkYGt956K/feey+1tbUkJiby9ttv89vf/haAbdu2MWjQIJYvX86ECRPa9DmOlT+PruKrzSXc8O/VmA1a/nT2UC4Yk97uKVe2VW3jD8v+wJ66Peg0Om4ZdQtXD706NB/hfr7iYop+/wdcq1cDEHPBb0n+4x/RWiwt1lvr83P3jkI+KavhbwMyuCwtvk3tqS5xsOj1LZQX2AEYeHwqJ17YD6NZZn8SQghx5LU1VpEMgxBdVHqslX9dfRx/PX84kWY9AU8mzt23U+mYykP6z7ntr1OoLNzV2c0UQgghwm758uUMGzYslCAEmD59OnV1dWzevDlUZtq0aU1eN336dJYvXw4EeyuuXr26SRmtVsu0adNCZVavXo3P52tSZuDAgWRmZobKtMTj8VBXV9dkE+EzfUgKD8wYxMJbTuDCsRntThDWeeu46sur2FO3h2RrMq9Pf51rh13bLEFoX7yY3eeci2v1arQREaT97WlS//zngyYIV9TUM2Xldj4pq0GnAbu/7fMjluyuo7zAjilCz+nXD2XqzEGSIBRCCNHlSJJQiC5Mo9Fw4bgMFt1xMlMHJgE6vOWn4cy7hcUxFs799Fw+/+Tvnd1MIYQQIqxKSkqaJAiB0HlJSUmrZerq6nC5XFRUVBAIBFosc2AdRqOx2byIB5ZpyZw5c4iOjg5tGRkyXLQjHB4/93+0kZJad+jatSfm0C85spVXHVyUMYrZo2dzUvpJfPCbDxidPLpZGc/uPApvvQ2lthbz0KFkf/RfomfMaLE+v6LyVF4x567dSZHHR5bZyMJR/bgxM6nVdhw4YGvgxBQmnJPDJQ+Op8/o1l8nhBBCdBZJEgrRDaREm3ntyrE8e9FIIkx6FE8azj03U1x3NndXLmD286dTVVXU2c0UQgjRzaxcuZIVK1Y0u75ixQpWrVrVrrruvfdeNBpNq9u2bdvC1fROdd9991FbWxva9u7d29lN6rZ2ltk5+6UfeWtFAbcvWMvhzoS0176XHdU7QucXDriQF6a8QIw5psXyppxsEm6+mbirrqL3229hzMxssVyBy8O5a3fytz2lKMAFKbEsHjeA0dERrbanYEslHzyxCrfDBwS/+B1zem8iYkyH9fmEEEKIo0H6uAvRTWg0Gs4Z1YtJfRN48JNNfLmpBF/NBPz2oXyZ9AWrFpzJ/QNnccaU6zu7qUIIIbqJWbNmcffddzN+/Pgm14uKinjyySdbTCAezF133cXvfve7Vsvk5OS0qa6UlJRmqxDvX3E4JSUltP/1KsSlpaVERUVhsVjQ6XTodLoWyxxYh9frpaampklvwgPLtMRkMmEySbKno/63oZi7P1iPwxsgOcrEH6YPaPfQYoBle5dx3w/3EWWM4t2z3iXaFB1MTNO0rvrvf8CYlRlKCCbeMuuQdRd5fKyucxCp0/LXARmcm9z6SscBv8KKT3azdlEBAKu/2MOk3/Zr92cSQgghOoMkCYXoZhIjTbxy+Rh+yC3n5nfWUue04S6+gGLLWO7SvcdXuf/j4UtfJTZShrIIIYRo3ZYtWxg9uvlQzFGjRrFly5Z21ZWYmEhiYmJY2jVx4kQef/xxysrKSEoK/j5btGgRUVFRDB48OFTm888/b/K6RYsWMXHiRACMRiNjxoxh8eLFnHPOOUBw4ZLFixdzyy23ADBmzBgMBgOLFy/m/PPPB2D79u0UFBSE6hHh5wsoPPHFNv75Qx4AE3LieOGS0SRGti/xGlACvLTuJV7d+CoA2dHZeAKeZuXUQICKl16iYu4rmAYNpPc776BtJcmrqmooWTkxxsbTAzM4IcZGpqX19tWWO/n6tc2U5QcXJxl6ci/G/1/bEuNCCCFEVyDDjYXopk7ol8iqP07jzMEpoIWAKxtn3m38r3YQM/5zFv/7+V+HPWRHCCFEz2AymZr1tAMoLi5Grz9y3yUXFBSwbt06CgoKCAQCrFu3jnXr1lFfXw/AaaedxuDBg7niiitYv349X331FQ888ACzZs0K9eC78cYb2b17N3fffTfbtm3j5Zdf5r333uOOO+4Ivc+dd97Jq6++yptvvsnWrVu56aabcDgcXHXVVQBER0dzzTXXcOedd7JkyRJWr17NVVddxcSJE9u8srFon8p6D5e9uiKUILzx5D7855rx7U4QVrmruOGbG0IJwksHXsr86fNJsjb9ktRfWUnBtddS8fJcUFUsw4e3Wu82h4sZa3LZ5WycH/HS1PhDJgi3ryjh3cdXUpZvx2TVc8YNwzj5kgHojbp2fS4hhBCiM2lUySI009aloYXoKpZtKOGGLzbhrg5+e67R12JK+pxJEdU8ct6LZMRmd3ILhRBChFO4YpVLLrmE4uJiPvnkE6KjowGoqanhnHPOISkpiffeey9cTW7id7/7HW+++Waz60uWLGHy5MkA5Ofnc9NNN7F06VIiIiK48soreeKJJ5okL5cuXcodd9zBli1bSE9P58EHH2w25PnFF1/kqaeeoqSkhJEjR/L88883GV7tdru56667eOedd/B4PEyfPp2XX3651eHGvyaxY9vVe/yc9/KPFNe4efrCEUwf0vbnvN/G8o3csfQOSp2lWPQWHp74MDNymi864lyzhqLZd+AvK0NjsZD6p0eJ/s1vWqxTVVXeKani/h2FuBSVU+IieWdEnza1Z9OyQpa9E5wPMbVvNKdePYTIOHO7P5cQQghxpLQ1VpEkYQsk0BPdUX2dh0vfXsPG4irUhpE2Wks+toTPuXHABK495W6MOmPnNlIIIURYhCtWKSoq4qSTTqKyspJRo0YBsG7dOpKTk1m0aJGs2ttGEju2T36lg4CikpNoO6zX37L4FpYVLqN3VG+emfwMfWP7NrmvqipV89+k7OmnIRDAmJND+vPPYerbt8X66v0B7tlRyIel1QBMjo3khcGZJBoNbWqP2+Hj/TkrGTAhlbFnZKHVyWAtIYQQXYskCTtAAj3RXQUCCs9+tJUXyyvQ5NeiEBzioo9cT3bCjzx2xh8Znzmpk1sphBCio8IZqzgcDt566y3Wr1+PxWJh+PDhXHLJJRgMbUuQCIkdW+MPKMz5YhtJkSZuOLltPfMOpcZdw4vrXmT26NnYjM0TjYrXS/6ll+HetImoGTNI/dOjaCNaXo14c72L6zftYZfLg04D92SncktmEtpWFlBRVZW9W6vIGBQXmrvQ5w1gkKHFQgghuihJEnaABHqiu/txeSF37SykvKyGQJkfNBrQ+DHE/shZfZ08cMajxFviO7uZQgghDpPEKl2L/Hm0rNrhZdbba/hpVyU6rYZv7zqZrPiWk3WtqXRV8uWeL7ls0GVtfo1v3z7qv/+BmAsvOOiKyatqHZy/biceRSXVZOCVwVmMj2m9d6PH5WfJv7exa00Zp1w+kMEnpLXrswghhBCdoa2xiqxuLMQxaNLEdBZmRPPA59v4LEclbm0+dq8VX9XJfLy6nm/33M89U0/gomGXoNPKt95CCNGTfPrpp5xxxhkYDAY+/fTTVsv+3//931FqlTjWbC2u47p/raKw2oXVqOPvF444rATh5srNzF4ymxJHCWadmfP7n99iOfuSJXhyd5Jw/XUAGNLSiL3owlbrHhFpZZjNQoxBz3MDM4k3tv5fo/ICO1++uom6chdarQa/T2n35xFCCCG6MulJ2AL5NlgcK7xuP2++u5Xnoty47PVEbCqmThucmF5rLCMrZRVzzpnJhLTxh6hJCCFEV9KRWEWr1VJSUkJSUhJa7cHnTtNoNAQCgY42tUeQ2LGp/20o5vfvr8flC5AZZ+XVmWMZkBLZ7noW7lrIo8sfxRPw0DuqN89NeY6c6JwmZVRFoeLluVS8+CIAmfPfIKKVlal3Oz1kmI0YtMHehbU+P5F63SGHF2/+fh8/vJdLwK9gizMx/dqhpOREt/szCSGEEJ1BehIKITCa9Vw7cygjlxby5/Iifpk+iOyV26iu0uH2JpFXcCaXztvA2L7/46n/u4as6KzObrIQQogjTFGUFo+FCIdnFu3gucW5AJzYL4EXLhlFjLV9C6f5FT/PrH6Gf235FwAnpZ/EEyc+QaSxaaIxUFfHvrvvoX7pUgBiL70U6+jRB633w5Iqfr+9kCt7xfNI314ARBta/++Q1+VnyVvb2LmqDIDewxOYeuUgzBEyZ6cQQohjjyy9JcQxTqPRMO6UDP5x6iCuWu+hbMQA3CemM9S/C73qRvGk8cvm8Ux55nNuW/gitZ7azm6yEEKIo8Dn8zF16lRyc3M7uyniGBJvCyYErz8phzd+N67dCcJaTy03fXNTKEF43bDreGHKC80ShJ7cXPZccCH1S5eiMRpJnTOHlIceRGNs/n4eReGe7XuZtbUAl6Kwtd6NT2nbYKqKQjs7V5eh1Wo4/vy+nHnTMEkQCiGEOGZJT0IheoiU7Ggevn4MQ/+1mVei9KyacRKZBXvp+8tafo4cQsCdyac/wldr3+KqkxP5/YnnotfKjwghhDhWGQwGNmzY0NnNEMeYmRN7M7RXNKMzYw/r9ZsqNrGieAUWvYXHJj3Gab1Pa1am7uuv2XfvfahOJ/q0VNKffwHL0CEt1rfX7eW6TXtYZ3cCcEdWMr/PTkHXyvDiA6X1i+XEC/uRlBUlw4uFEEIc86QnoRA9iNlm4NIbR/D3+ETO/sVBaUovvj1/BuOjSxhfuwINfjzOLF75wsqoJ1/l9VVLkGlLhRDi2HX55Zfzz3/+s7ObIbqx9XtruOQfP1Pr9IWuHW6CEGBSr0k8OPFB/n3Gv1tMEAIoDieq04l1wgSyP/jgoAnCbyrrOHXldtbZncTqdbw1PId7clJbTRD6vQG+W7CDmjJn6NrwUzIkQSiEEKJHkIVLWiCTT4ueoGR3LR+8tYV3c3TkphmJcDm57qvP+CGgY5NtHBBc9TgmupTbpw3kd2Mnomnjt+5CCCGOrHDFKrfeeiv/+te/6NevH2PGjCEiounqs3//+9872tQeoafGjgvX7+P376/H41e4fEImj50z7LDq+WDHB0xMm0gvW682v8a+eDG2k09Go2951EO1z8+45VuoDyiMjLTy6tDeZJhbH/pcW+7ky39somJvPQkZNi68bxwarcQ+Qgghur+2xiqSJGxBTw30RM/jdflZ+s42Piir4etRVtxGLQMK87nhiw95LSWH3cbR7E8WRkdVM3vqIK4cNxqtBMxCCNGpwhWrnHLKKa3eX7JkyWHX3ZP0tNhRVVWe/SY3tEDJlIFJPHfxSCLN7Zurz6f4ePKXJ3l3+7v0i+3HW2e+hUVvaVbOW1hI6WOPk/r4Y+jj49tc/8el1fxc6+DRvmmYWlnJGyBvfTnfzN+K1+XHEmng1GuGkDEwrl2fRwghhOiqJEnYAT0t0BNi+4oSFn60g0+GmsntZUSjKFy98RfGfbuQF/oPZ5fhOFCD375H2RzcNmUAvxs/DL1OZiwQQojOILFK19KT/jzcvgB3vb+e/20oBuC6E7O594xB6Nr5BWKtp5a7lt3FiuIVaNAwe8xsrhpyVbNRC44Vv1B0++0EamqInD6d9OeePWidm+tduAIKY6MjDlrm15SAwoqFeaz5Mh+AlJwopl83FFusuV2fRwghhOjK2hqryP/whRAMGJ/CdXeN5bYiLWf/XI/JD/8cMYE7Zz3EjbGxPLfyb+Soi0Hrpq4+gsc+LWTk4x/z8rKNOL3+zm6+EEKIw3T11Vdjt9ubXXc4HFx99dWd0CLRlZXZ3Vw0bzn/21CMXqvhyfOHcf+Mwe1OEObV5nHZ55exongFVr2V5055jquHXt0kQaiqKlVvv03BNdcQqKnBPGQIyffde9A6Pyqt5qzVO7hqUx7FHm+b2uF2+Pj0+fWhBOHwKemcc+doSRAKIYTosaQnYQt60rfBQhwoEFBY+VkeS5cW8OWoCLZlBHsPDnXZeeLbheSv+YYXJhxPvuZE1IANAJPBz4XjenHTSUNIi2k+REgIIUT4hStW0el0FBcXk5SU1OR6RUUFKSkp+P3yRVBb9JTYsczu5tyXfsLh9fPK5WOYkNP2ob/7/Vz8M3cuvRO7105qRCovTHmBAXEDmpRRvV5KHnucmvfeAyDqrLNIfezPaM3Nk3d+ReXPu/Yxr7AcgFPiInl5cBaxhpbnKjxQwK/w0d/WULnPwZQrBtJvbHK7P48QQgjRHchw4w7oKYGeEAdTtL2ab97cwhqTwpdjrNRGBOclvKq+gqvf+xdrStfx0oQJ7FVPQPUF/4Og0aicMjCGW08ZwqgOrGoohBDi0Doaq9TV1aGqKrGxseTm5pKYmBi6FwgEWLhwIffeey/79u0LZ7OPWT0pdswttWPQaemd0PYhvfupqsrln1/OhooNjEwcybOnPEu8pWmi0V9dTdGtt+FctQo0GpLuupO4a65pcfG0cq+PGzbn81NNPQC3ZyVzd3ZKq6sXq6oKKqEFSexVbnyeAHGp7f88QgghRHchScIO6EmBnhAH43X5+eGDXNb/XMx3Qy2sGGBG0WiwaTU8VZbHsH/OY20gn39OGEKu4UQCzj6h1w5Os3DTyQM5fWgKBpm3UAghwq6jsYpWq211xXqNRsOjjz7K/fff35Fm9hjHcuz43sq96HUazhudHpb6ypxlvLHpDWaPmY1JZ2p2P1Bby56LLsZfUUHa008ROXlyi/WsqXNw7aY97PP4iNBpeX5QJjMSY1p9b78vwHfv7MASaWTiuX1aLSuEEEIcSyRJ2AHHcqAnRHvt2VjBkv9sY7cmwOdjIyiKDw7fGW018dfctZj++RpbtMW8ObEXGyJOxF83EtRgmYRIPTMn5HDRuAySo2R+HyGECJeOxirLli1DVVWmTJnChx9+SFxc4yquRqORrKws0tLSwtnkY9qxGDuqqsrfvt7Bi0t2YtBp+OzWExmQEtnuejwBDz8U/cDUzKltfo13zx5Uvx9T374HLXPLlnw+KK2mj8XE68OyGRDRepxhr3Lz5byNlOXb0Wg1XPrIeGKSrG1ukxBCCNGdSZKwA47FQE+IjnA7fHy3YAfbV5Wypo+JJSOsuPUatMCVCZHcvOoHXK+9Sq6hkncmRvFL/PH4qsejBoL/mdBqYNqgZC4dn8lJ/RLRtnOCcyGEEE2FK1bJz88nMzOz1V6F4tCOtdjR4w/wh/c38On64HDz26b05Y5T+7f770mVu4rbv72ddeXr+OtJf+WM7DNaLvfWW2i0WmIvuaTNdTv8Af6aV8Jd2SlE6XWtli3aUc1Xr27CZfdhitAz/dqhZAyKa/U1QgghxLFEkoQdcKwFekKEy641ZSx9ezvlfj/fjIpgU2ZwYZNovY67k6M469svqXn9dfJMdbx/vJEf04bjqxlPwJUdqiM91swlx2Vxwdh0kiKld6EQQhyOcMYq33//PfPmzWP37t28//779OrVi3//+99kZ2dzwgknhKnFx7ZjKXasdni5/t+rWLmnGr1Ww1/OG8aFYzPaXU9ebR43f3MzhfWFRBojeXbysxyXelyTMqrfT+mcJ6h+6y3Q6cj+74eYBwxosb4qn5+39lVyS2ZSm5OVqqqy4dtCfvxwJ6qikpBh44wbhhGVIAutCSGE6FnaGqscetkvIYRo0Gd0Eql9Y1j2znYil5czeqeexcfZKLLB/UXVzB91Eo+9N4PRH31A73//m8uNq/h83BoWDUrFYT8OX+1oCqvhqa+288yiHZw6OJmLxmVwYr9EdNK7UAghjroPP/yQK664gssuu4w1a9bg8XgAqK2t5S9/+Quff/55J7dQHE17KhxcNX8leRUOIs16Xrl8DJP6JrS7npUlK5m9ZDZ13jp62Xrx8rSXyYnOaVImYLdTdMedOH74AYDE227D1L9/i/VtrXfxu4155Lu9ANya1bZViJe+vZ0t3wd7Q/Yfn8zkywZiMLbe61AIIYToyaQnYQuOpW+DhTgSVFUld1UpP7yXi6Pex7ocE9+NjsDesEbJ9IQoHo4xE7HgLWreWUCd4mTRKA2fj7NS4RuCt2Y8iisrVF9SpIlzR/Xi/DHp9E9u/3xHQgjR04QrVhk1ahR33HEHM2fOJDIykvXr15OTk8PatWs544wzKCkpCWOrj13HSuz44re5PP31DnrFWJh/1Tj6Hcbv5IW7FvLQTw/hV/yMSBzB81OeJ87cdGivt7CQvTfeiHfnLjRmM2lPPknU9NNarO/L8lpmbc3HEVDINBt5c1g2g2xt6wm4Y2UJi9/YyqQL+jJscroMqxdCCNFjtTVW6TLLjj7xxBNoNBpmz5590DKbN2/m/PPPp3fv3mg0Gp599tlmZebOncvw4cOJiooiKiqKiRMn8sUXXxy5hgvRA2k0GvqPS+HShycweEIKo3d5uOHjaibt8aEDvqqoY/Kucl497zKSvv6a3lffzPmbI3n5JQe3/7CSwdGvYs1+FkPsT2h0TsrsHuZ9t5vTnvmOs174njd+zKOy3tPZH1MIIY5527dv56STTmp2PTo6mpqamqPfINGpZp3SlztP7c9Hs44/rATh1sqt/PGHP+JX/EzvPZ3XTnutWYLQuWYtey64EO/OXeiTksj6z39aTBCqqsrf95Twu015OAIKk2JsfDGm/yEThH5fIHTcf1wKlz46geGnZEiCUAghhGiDLpEkXLlyJfPmzWP48OGtlnM6neTk5PDEE0+QkpLSYpn09HSeeOIJVq9ezapVq5gyZQpnn302mzdvPhJNF6JHM9sMTL1yMP83eyTJMWamrLBz3ec1DHWAV1V5saCMSdv28dG5F5H5zSLSZt/J1KJY/vqKm4c+LmKi7wsi+j2Oude/0ds2gybApqI6Hl24hfF/Wcy1b67ii43FuA8I+IUQQoRPSkoKO3fubHb9hx9+ICcnp4VXiGPNl5saf89qNBpum9rvsOcMHhQ/iGuGXsNVQ67iryf9FbO+eT2utWsIVFdjHjyY3u+/h2XokGZlHIEA123ew1/zgj1Zr+6VwIIRfYg3HnymJFVV2bi0kLcfWYGjtvGLxuhEmX9QCCGEaKtOH25cX1/P6NGjefnll3nssccYOXJkiz0Ef613797Mnj271Z6H+8XFxfHUU09xzTXXtKlNx8qQESGOJr83wMrP97Du6wICikpebzPfjY9kL8H/eGSYjdybncLZUWbqPviQyn/+E39pKSWx8M04I0tG6alV9PjrRuKvHU3AnR6qO8Ko49TBycwYnsZJ/RMwHWIVQyGEONaFK1aZM2cO//nPf3j99dc59dRT+fzzz8nPz+eOO+7gwQcf5NZbbw1jq49d3TF2VFWVvy/awQvf7mT6kGRevmzMYc0P7PQ58Sk+ok3RoXpb67Wnqio1775H9P/9Bq3V2mKZNXUOzl4TTF4/0T+dy9LiW21DwK/w3bs7QvMPjv+/bMaemd3qa4QQQoiepNssXDJr1ixmzJjBtGnTeOyxx8JadyAQ4P3338fhcDBx4sSDlvN4PKGJuiH48IQQ7aM36ph4Th/6jU1myb+3otljp3e+m11jo1ncz8het5dZWwt42WbmgRlnc9JFF2JfuBDj/Plc/vUuLvzWy/JBWr45ZRPbs38i4EnCXzsa1T4GhzeSj9ft4+N1+4g06zltcApnDU9lUt8EjPou0SFaCCG6pXvvvRdFUZg6dSpOp5OTTjoJk8nE73//e0kQHsN8AYV7P9zIh2sKARiYEsXhrB9W6ark1m9vRa/V8+ppr2LSmZolCFWfj8rXXiP2ipnobBFoNBpiL76o1XpHR0Xw94EZZJqNjI+xtVrWWefly3kbKd5VCxqYeE4fRp2W2f4PI4QQQojOTRIuWLCANWvWsHLlyrDWu3HjRiZOnIjb7cZms/HRRx8xePDgg5afM2cOjz76aFjbIERPlZBu4/x7xrJxSSErFu6m38paeq+FvGlJfB6nsLnezSUbdnNCjI0HTjuTEeefj+OHH6maP5+Tf/yRkzdWsTsZvp3mZVnmYtyJX2FwZRCwj0TjGI3dbebDNYV8uKaQaIuB04ekcPqwFI7vEy89DIUQop00Gg33338/f/jDH9i5cyf19fUMHjwYm631xIzovuo9fm76z2q+z61Ap9Xw+DlDufi49ifV8uvyuembm9hr30u0KZq9dXvpG9u3SZmA3U7R7Dtw/PgjrvUbSJ/78kF7GX5UWs0gm5mBEcHhwRekxLVY7kBl+XV88cpG6qs9GC16Tr16ML2HtX81ZiGEEEIEddpw47179zJ27FgWLVoUmotw8uTJYRlu7PV6KSgooLa2lg8++IDXXnuNZcuWHTRR2FJPwoyMjG41ZESIrshR62H5f3exfUVwTqFAtIHtpyeyUOPB2/CjZ0ZiNL/vncIgmwX3jh1UvfkmdZ8uRPX5cJhg+fExLDvOynZtGaqqIeDKQu84joB9OE5P4/ccEUYdkwckcergZE4ZkES01dApn1kIIY6G7ji89VjWXf48yurcXDV/JZv31WEx6Hj5stGcMjCp3fWsL1/PrYtvpdpTTS9bL+ZOm0t2dNPhvb59+9h7w414cnPRWCz0+tvfiJxySrO6FFXlr3klPJtfSqbZyNdj+xNjOHQ/hqLt1Sx8cT0Bn0JMspUzbxpGbEpEuz+LEEII0RO0NVbptCThxx9/zLnnnotO19jzJxAIoNFo0Gq1eDyeJvd+rT1zEk6bNo0+ffowb968NrWtuwR6QnQX+3bW8N2CHVQW1gOg6Wtj9YlxfO6sZ/8PoN8kxnBn72QG2Sz4KyqofmcB1W+/TaC6GoCCFD0/zchkSWo11QF7MGHozCbKOxlXXV/qnI3DjvVaDeNz4jhtcAqnDk4mLUYmLRdCHFvCFau43W5eeOEFlixZQllZGYqiNLm/Zs2ajja1R+gOsaOqqpz90o9sKKwlPsLI678bx4iMmHbXs6RgCXd/dzfugJvB8YN5aepLJFia9t5zbd5M4Y034S8vR5eYQMbcVw66QMltWwv4X3ktALdkJvHHnFS0bViJ2Ovy88GTq4hKsHDqNUMwWTp9FiUhhBCiy+rySUK73U5+fn6Ta1dddRUDBw7knnvuYejQoa2+vj1JwilTppCZmcn8+fPb1LbuEOgJ0d0oAYXN3+9jxae78Tj9AFhPTOL7wRY+r7GHyh2YLFTcbuq+/JKadxbgWr8eAL8WNkxK4fsTYvhZk0dADaCqGvBkkqROxVnTj5Kapv+5GJwaxeQBiUwekMTozBj0OpnHUAjRvYUrVrnsssv4+uuv+e1vf0tycnKzoaAPP/xwR5vaI3SX2HF1fjX3f7SReVeMISu+/b3uPtv9Gff/cD+KqnBirxN5+uSnsRqaLj5iX7KEort+j+p0YurXj4x5r2BIS2tWV7HHy5Ub8thQ78Kg0fD0gAwuSm19iLHfG0Bn0Ib+njrrvJhtBrSHM6GiEEII0YN0+SRhS3493HjmzJn06tWLOXPmAMFhxFu2bAHgzDPP5LLLLuOyyy7DZrPRt29wDpT77ruPM844g8zMTOx2O2+//TZPPvkkX331Faeeemqb2tFdAj0huiOX3cvPH+9iy0/FoILepCNuei++StfxWWVtqNyByUIA99atVC94l9qFC1GdTgBq4kysOncg32c52erKC71W50shXTcDd21/dpWqHPhTLtKs58R+CUzun8TJAxJJjjIfnQ8uhBBhFK5YJTo6ms8//5xJkyaFsXU9T1eOHevcPqLMjVNwKIp62Em13bW7ufKLK5mSOYUHJzyIXtu0957idrPr9DPwl5QQcfzx9HruWXSRkc3qWVfn5MqNuyn1+okz6HhjaPYhFyipq3Tx+csbGDgxlZHTZGESIYQQoj2OiSTh5MmT6d27d6gH4J49e8jOzm72upNPPpmlS5cCcM0117B48WKKi4uJjo5m+PDh3HPPPW1OEELXDvSEOFaU5tXx3bs7KNsTXE08ItpIwlmZfBIbYGHDsCMNcFZiDLdmJTE8MthTIVBfT+2nn1LzzgI8ubmh+iqGpfPL6VksjS5mj6MgdN2sSaSv4TcozkFsK9RQ6/I3aceg1ChO7p/Iif0SGJMVi9kgi58IIbq+cMUqgwcPZsGCBaH5ocXh6aqx4werC/nzZ1t469rxDO0VHZY6i+uLSYlIOegCJO4tW6j58L8k33sPGkPL8wNfvmE331TWMSDCzL+GZZNlMbX+njtr+GLeRlx2HxHRRi59dAJGswwvFkIIIdqqWyYJu4quGugJcaxRVZWdq8tY/tEu7JVuAOJ7RZD0f5m8o/OwsLwmVPakWBuzMpM5KdaGRqNBVVVca9dS8+671H31Nao7+HpVq6X0tJGsOD6WxZptlDhLQ3WYdVaG2M7A6h1HQWkkm/fZm/QyNOq1jMmMZVLfeI7vm8DwXtEyNFkI0SWFK1b54osveP7553nllVfIysoKYwt7lq4WO6qqyivLdvPkl9sAuGpSbx7+TfM5AQ/FE/Dw4A8Pcm6/c5mYNrHl9/J6cW/bhqUdieZqn585u4t5sE8akfrWv5zb+lMxS9/ahhJQSciwceZNw4mMk1EAQgghRHtIkrADulqgJ8SxLuBT2LC0kNVf7AnNV5g5OI6EszL4j7uej8uqCTT8pBpmszArM4mzEmPQNwyXCtTXY//qK2o++gjXqtWNFUdFUnjeeH4ZbmaZcz3FjuLQLYPWwKj4k0jRTKamOpVVe+yU1jWucg4QadIzPieO4/skMCEnngEpkehk3iMhRBcQrlilvLycCy+8kO+++w6r1YrhVz2/qqqqOtrUHqErxY6KovLn/23hjR/3AHD9STnce/rAdg8xrvPWcdu3t7G6dDUxphi+Ov+rZvMPBurrKbrtNpyr15A5/w2so0a1WJdPUfm8ooazk2Lb9TmWf7SLdYuCowNyRiUy7XeDMZikx78QQgjRXpIk7ICuFOgJ0ZO4HT5Wfb6HjUsLUQIqGg0MnJhK2mnp/Mdey1v7qnA1rLyZaTZyY0YiF6fGYz2gt583P5+ajz+m9uNP8Bc3JgX16b0o/c14fhmsZ6ljLXm1jXMYajVahsUPZ0jUFPSewewq1rF8dxW1Ll+T9kWa9YzNimVs7ziOy45jeHo0pkP0gBBCiCMhXLHKtGnTKCgo4Jprrmlx4ZIrr7yyo03tEbpK7OjxB7jrvfV8tiH4+++BGYO49sScdtdT5izjpm9uYkf1DmwGG8+d8hzHpR7XpIyvtJS919+AZ/t2NFYr6c89h+3EE5rVVevzc82mPfxQU89f+vXi6vTEQ76/qqp88cpG8tZXADD2zN4cd1Y2GvmiTgghhDgskiTsgK4S6AnRU9WWO1n+0W52rSkDQKvXMOSEXuSc2ot36+z8s6icKl8AgDiDjivTEriyVwIppsYeMKqi4Fyxgpr/foR98eLQYicAxr59qJ4xkZWD9SypW83Wqq1N3j/ZmsykXifS23gC9XVprNxjZ/WeKhzeQJNyRr2WEenRjOsdx7jecYzKjCHGajxSj0UIIULCFatYrVaWL1/OiBEjwti6nqcrxI4Oj5/r/72KH3dWYtBpePqCEZw9sle769lTu4cbFt3APsc+EiwJzJ02l4FxA5uU8eTmUnD9DfiLi9ElJJDxyitYhjYfzpzv8nD5ht3kOj1E6LS8MjiLUxPaNjfihiV7+em/u5g6cxD9xiW3+3MIIYQQopEkCTugKwR6Qggo2V3Lz5/spmh7NQA6g5Zhk9MZNDWdj+vtvLK3nAK3FwC9JrjIybXpiYyJsjbpDaO4XNQvXUrd559Tv3QZqq+xh6B5yBDcZ57IuiEWfnJs5Ofin3EH3KH7Rq2RcanjmJhyAkn6URRXWFi5p5qVe6qoqPc2a3NOQgQjM2MYlRnLqIwYBqZEyryGQoiwC1esMnr0aF5++WUmTJgQxtb1PF0hdvQFFK55cxWr91TxyhVjOLHfoXvs/dqmik3c/M3NVHuqyYzMZN6p80iPTG9SxrHiFwpvuQXFbseYnU3Gq//AmJ7erK7VtQ5mbsyj0ucn1WTgP8NzGGKztPr+B668rKoq9ko3UQmtv0YIIYQQhyZJwg7oCoGeEKJR4bYqVnyaR8nu4KrHepOOEVPSGTo1g2+dTl4vLOfnWkeo/PBIC9emJ3J2UgwmbdMEXcBux/7NYur+9z8cy5dDoLF3oGngQIynTmb7mGR+ZhffF31PUX1Rk9cnWZKYkDaB8SkTSDONYHeJhl/2VLE6v5q8Cge/ZjZoGd4rhlGZMQxPj2F4ejTpsZaDrgophBBtEa5Y5euvv+bRRx/l8ccfZ9iwYc3mJJQ4qG26Suzo8PgpqHIyKPXw2vDo8kf5YMcHDIkfwktTXyLeEt/kvmvjJvIvvRTV58MyejTpL72IPrb5PIOfltVw29Z83IrKMJuFfw3PJtXUek/7HStLWPt1AefcMQqTteVVkYUQQghxeCRJ2AFdJdATQjRSVZWCLVWs+GQ35QV2AIwWPSOnZTBiSgbb/V5eL6rgv6XVeJTgj7V4g56ZafHM7BXf4n9O/FVV2L/+mrrPv8C5ahU0zHcIYMjMxDZtKhUnDGZlZDnLi5ezpmwNnkDTxU36xvRlQuoEJqZNJMc2jNxSH2sLalhbUM26vTXY3f5m7xtjNTCsV3Tjlh5NrxhJHAoh2i5csYq24YuUX//8UVUVjUZDIBBo6WXiVzordly1p4pvtpZxz+kDwvI7xKf4eG3Da8wcMpMIQ0Sz+2ogQNHsOwBIe+qvaM3NVxne7fRwwoqtKMBp8VHMHZxFRCvz96qqysr/7WHlZ8G5go/7TTbjZmR3+LMIIYQQopEkCTtAkoRCdF2qqpK3voJfFu6msijYc89k1TNscjrDp6TjNGp5u7iSN4oq2OcJDivWaWBafBSXpcYzJS4qtCrygfzV1dR/uwT7N9/g+PFHVG/jUGJdYgKRk0/BcNJEcvtY+Ll6Lcv3LWdb1TZUGn+EajVaBsYNZGzyWMaljGNk4igq6nSsLahm7d4aNhbWsq2kDl+g+Y/dWKuBob2iGZwaxeC0KAalRpGTECFDlYUQLQpXrLJs2bJW75988smHXXdP0hmx45ebSrh9wVo8foW//nY4F47NOKx6fiz6kQmpE9BpW07kqaoKgQAavR4AxetFo9Oh0R088fdSQRklHi+P9O2FrpXkpd8X4Nt/bSN3ZSkAI0/NZOK5fdq9ErMQQgghWidJwg6QJKEQXZ+qqOxcU8bKz/KoLgkuSqI3ahlyQi9GnpqBOdrEFxW1/PNXQ5FTTQYuTonjktQ4Mi2mFutWHA7qv/8e+6JvqF+2DKW+PnRPYzBgHTeWiJNOwj9xFGuNxfxc/DMrildQWF/YpB4NGgbEDWBs8ljGJo9lVPIoIvTR7CipZ0NRDZuKatlQWMv2Ejt+pfmPYqNey4DkSAalRjI4NZg4HJASKYujCCEkVulijvafx7+W7+HhTzejqjBtUBIvXDIai/HgSbuWqKrKvA3zeGndS1zY/0IemPBA8x6lPh/FDzwIQOoTcw7aW9HhD1AfUEhuWEBsf0/U1jjrvHzxygZKdteh1Wo4+dIBDD4hrV2fQQghhBBtI0nCDpDAW4juQ1VUdq8vZ/UX+aFhyFqdhoETUhg1PYuYJCu5DjdvFVfyXklVaFVkDXBybCSXpcUzPSEKo7blHnuK14tzxQrql31H/bJl+PbubXLfkJGB7aSTsJ18EvbBGayu28yqklWsLl3Nnro9zerLjMxkZNJIRiSOYGTSSPpE98EXgO0ldjbvq2NrcR1biuvYVlzXbDXl/ZIiTfRPjqRfso0ByZH0S46kf7KNSLPM4SRETxGuWOW7775r9f5JJ5102HX3JEcrdlRVlb9+tZ25S3cBcMlxGfz57KHt7nWuqApP/vIkb297G4Abht/ArJGzmi765XBQOPsOHN9/DzodvRe8g2XYsGZ1lXp8XLFhNz5V5ZPR/YhqZWjxftUlDhY+vx57lRuTVc/p1w8lfWBcuz6DEEIIIdpOkoQdIElCIbofVVXZu7WK1V/ksy+3BgCNBvqOSWL06VkkpEfiURS+rKjlrX2VfFfd2Dsw3qDnvOQYfpsSx3DbwecGVFUVb94e6r9bhuO773CsXAUHrJSMwYB15Egijp9IxMSJ2Psks6ZiPatKV7GqZBW7anc1q9NmsDEsYVgocTgkfggx5hgURaWgysnW4sbE4dZiO0U1roM+g7RoM32TI+mTGEGfRFvDFkFipEnmOxTiGBPuOQkPdODPC5mTsG2ORuzo9Svc++EG/rs2uKDWnaf259Ypfdv9890X8PHAjw/wed7nANx73L1cNuiyJmX8VVXsveFG3Bs3ojGb6fXsM0ROntysrm0OF5et302Rx0e8Qc8HI/sw6BArGEOwF+H7T6xEp9MyY9ZwYlOaz38ohBBCiPCRJGEHSJJQiO6teGcNq7/KJ39jZeharwGxjJyaQdbQeDRaDfkuD28XV7GguJJSb+PiIv2sJi5IieO85FjSza0P61UcDhw//0z90mXU//gD/n3FTe5rbTas48cTMXEiEcdPxJ0Wz8bKjawrW8e68nVsLN+I0+9sVm+6LZ2hCUMZmjCUIfFDGBw/GKvBCkC9x09uqZ0dpXZ2lNY37O2U1nma1bNfpElPTpItlDzsHR9B7wQrveMjiDDp2/RMhRBdS7hildra2ibnPp+PtWvX8uCDD/L4448zderUjja1RzgasePKPVVcNG85Go2GOecO48Jx7Z+D0OlzcueyO/mx6Ef0Gj2PnfAYM3JmNCnj3buXgmuvxZdfgC4mhox5r2AZMaJZXT9U27l6Ux51foW+VhNvDc8h6yDTeLSkusSBxWbEbJNe8EIIIcSRJknCDpAkoRDHhvK9dtZ8lc+uNeWoDXP+xSRbGX5KOgMnpmIw6fArKt9W1fFhaTVfVdTiPmBuwIkxEVyQHMdZSTGHHD6lqiq+ggIcy5fj+Gk5jhUrUH71n29dYgLWsWOxjhuHdexY9DnZ7KrbzbqydawtX8umik3k1+U3q1ur0ZITncOQ+CEMih/EwLiBDIgdgM1oC5WpdfrYUWZnV1k9u8rr2VXuYFd5PXurnLQw3WFIYqSJ3vHWhsRhBL3jI8iKt5IRayXaKv9xE6KrOtKxyrJly7jzzjtZvXp12Os+Fh2t2PHdlQUkRZo5ZWBSu1+rqirXfX0dK0pWYNaZ+fvkv3Ni+olNyri3bKHg+hsIVFRg6NWLjFdfxZTTfKXhD0qquGPbXnyqyvjoCOYPyybWcPAvnVRFZfnHu4jvZWPA+JR2t10IIYQQHSNJwg6QJKEQxxZ7lZsNSwrZ8sM+vK5gr0GTVc/gE9IYNjmdyDgzAHX+AJ+V1/BBSTU/1TQORzZrNUyNj+I3iTGcGh9FRBvmW1IDAdxbtgaThst/wrV6TZMVkwF00dFYxo4NJQ7NAwdQF3CwpXILmys3s6liE5sqNlHqLG3xPTIiMxgYN7DJlmhJbDL0zOMPkF/pDCUPd5c72FPpYE+lkyqHt8V694sy68lsSBhmxllJjwvuM2ItpMVYMBvaN0m+ECJ8jnSssm3bNsaOHUv9AQs3iYPrLrHj0r1Lefinh3nulOcYmTSy2X3HTz9RcMONmPr0IeMf8zAkNU9GvrWvkru2B+fnPTsphucGZmJuZU5EvzfAN29sYdfacrR6DZf/aWLo964QQgghjg5JEnZAdwn0hBDt43X72ba8hA3f7qW2PDi3n0aroe/oRIZOTie1T3QowVbo9vLf0mreL6ki19k4lNei1TClnQlDAMXjwb1hA46VK3GtWoVz7TpUV9P5BTVWK5Zhw7CMHIll5AgsI0eij42l3FnO5srNbK7czLbKbWyr3kaJo6TF94k1xdI3ti/9Yvo17mP6Nul1uF+ty0d+pYO8Cgd7KpzB40oHe6ucVNS3nkAESLAZ6RVjoVesJbiPCSYPe8VaSIu2EGM1yFyIQhwh4YpVNmzY0ORcVVWKi4t54okn8Pv9/PDDDx1tao/QlWNHRVXQahqTeE6fMzSFRUscP/2EedgwdJGRLd7f5/YyY00u5yXHcn9OKtpWfs4767x8PncDpXl1aPUaplwxSHoSCiGEEJ1AkoQd0JUDPSFExymKSv7GCtYv3kvRjprQ9bi0CIae1Iv+41MwWYLDplRVZVO9i0/LalhYXsMeV2Py7HB6GO6n+ny4N2/GuWoVzpWrcK5Zg2K3NytnzMoKJg1HjcQyciSmvn3R6PVUu6vZXr09lDTcVrmNvLo8FFVp8f1SI1LpFxtMGOZE55AdnU12dDaRxpb/E+j0+tlb5WJvlZOCKid7q52h48JqF86DrLx8ILNBS2q0hZQoM6nRZlKig/vUaAsp0WaSokzER5jQaSWRKER7hXPhEo1Gw6/DwQkTJvD6668zcODAjja1R+iqsePmys089ONDPHfKc6RHprdYpvq997COHdfisOL9AqqK7oBkYLXP3+rwYoCqfQ4+e2k99ko3pgg9Z944jLR+sYf3QYQQQgjRIZIk7ICuGugJIcKvvMDOxqWF5K4sxe8LJtj0Ri39xyUz5KReJGU1/gzYnzBc2JAwzDsgYWjSajgxNpLpCVGcFh9Nsql98/mpgQCenbtwrVsX2rx5ec3KacxmzIMGYR46FMvQIZiHDsXYuzcanQ63382u2l3srN7Jzpqd5FbnkluTS5mz7KDvm2hJbJI0zInJoXdUb5KsSU16njRpq6pS6/JRWO2iqMZF0a/2+2pcVB5iKPN+Oq2GpEgTSVFmkiNNJEeZSY4KnidFmkhs2CSZKERT4YpV8vObzoOq1WpJTEzEbJbhoO3RFWPHFcUruO3b23D6nZyWdRp/m/y3JvdVVaXihReoeHku+rRUcv77X3QxMc3qKff6uHJjHjdlJPGbpOb3W7J3WxVfztuE1+UnOtHCWbeMICb54L0XhRBCCHFkSZKwA7pioCeEOLI8Th/bV5Sw6bt9VBc7QteTsiIZclIv+o1NxmBq7Cmoqiqb610sLK9lYVkNu11NVxceFWllekIU0xOiGRhhPqxht4GaGlwbNjQmDjdsRGlhfjCt1Yp58GDMQ4ZgHjIY08CBmLKz0RiCicpaTy07a3ays3onuTW57Kndw+7a3ZS7yg/63madmYyoDLIis8iKCm6ZUZlkRWURb44/5Odx+wKU1rkprnVTUhvcF9e6QvvSOg8V9R7a+htIq4F4m4lEWzBpmBRpIiHSRHyEkQSbiQSbiXhb8DjWakDfyvxYQhwLJFbpWrran8ei/EXc8909+BQf41PG8+wpzzaZdkINBCh59E/UvPceAAm33ELCrJub/Wzf7fRwyfpd5Lu9pJoM/DR+EJY2/HxdsXA3q/63h9Q+0Zxx0zAsNmN4P6AQQggh2kWShB3Q1QI9IcTRo6oqxTtr2fRdEbvWlqH4gz8iDSYdfcckMXBiCql9Y5r8R0pVVbY73XxdUceXFbWsqXM2qTPTbGR6QhTT4qMZHx3R6gTvrbZNUfDm5+PetBn3pk24Nm/CvWUrqtPZrKzGYMDUrx+mgQMxDxzQsB+I7oCfaXavnbzaPHbX7m6yL7IX4Vf9B22HVW8lPTKddFt6cB+ZTkZkBum2dNJsaRh1bfvPoD+gUFHvpbTOfcDmCe7tHsobtkpH25OJABoNxFqNxEcYiTtg238eG2EkPsLUcGwg1mqURVhEt9ORWOX5559vc9nbbrutvU3rkbpS7Pj+jvf58/I/o6IyLXMaT5z0BCadKXRf8XjY9/vfY1/0DWg0pDz8ELEXX9ysnjW1Di7fuJsqX4BMs5F3RuTQx9q2HqaqqrJpWRGDJqWil5+vQgghRKeTJGEHdKVATwjReVx2L1uXF7P5+33UlTcuMhKVYGbgxFQGjE8hKsHS7HVlHh+LKoMJw++r7biVxh+zFq2GiTE2psRHcUpcJDkWU4cW91ADAbx5ebg2bcK9cRPubdvwbNuG4nC0WF6flhpMHvbt27Dvh6lPDlpL4+fwKT6K64vJr8sPbQX2AvLr8tlXvw+Vg//a0KAhyZpEL1svUm2ppEWkNdmnRqRi0Td/Zq3xBxSqHF7K7B7K6xuThxX1HirqvVTWe6is91JR76HK6W1XQnE/s0FLrNVIjNVIrNXQcNy4j7IYiLEYiGk4j7YEN0kuis7SkVglO/vgc88dSKPRsHv37sNpXo/TFWJHVVV5deOrvLD2BQB+2/+3PDD+AXTaxp9Tgbo6Cm+ehXPVKjQGA2lPP03U9NOa1fVVRS03bt6DS1EZHmnhreE5JBoPPo1GwK+w9usCRk7LQG+Un4tCCCFEVyNJwg7oCoGeEKLr2N+7cNvPxexcXYbP3bhoR6/+MQyYkEqf0YkYzc0ncXcEAnxfVc/XlbUsqbJT7PE1uZ9hNnJKXCSnxEVyQmwkke1Y/OSg7VUUfEVFwYTh1m24t2/Hs3Urvn37Wn6BRoMhIyOYOOzbF1PfPhizszFmZ6OzNV0V2RvwUlRfRKG9kML6Qgrthey17w0du/yult/jAHHmOFIiUki2JpMSkdLsOMmShEHXvjkd9wsoKtVOb2PS0OE96Fbp8FLj9OJXDv/XoEmvDSUMoywGosz60HG0xUCU2UCkWU+UJbiPNO/f64kyGzDptbICtDgsEqt0LV3hz8Mb8HLlF1eyqXIT1w27jltH3drs50vxQw9T8957aG020l96iYjxxzWr582iCu7bUYgCTImL5NUhvVtdmMvj9PHFvI0Uba+h39gkTrt2aLg/mhBCCCE6SJKEHdAVAj0hRNfk8wTYva6cbcuLKdxezf5OdXqjluzhCfQdm0zWkHh0huZDilVVZZvDzZIqO0ur6vi5xoH3gB/BOg2MjLRyQmwkk2JsjI2OwBrGufUCtbV4duzAs3MnntydDftcAtXVB32NPjERY04OxuzemHJyQslDQ2oqGl3T/zSqqkqVu4rC+kKK64vZ59jHvvp9FDuKQ3uHr+UejgfSoCHeEk+SNYkkSxKJ1sTgccOWaEkk2ZpMtCm6wwk2VVWp9/ipcfqodgaTh/uPq50+ap1ealw+al0+apzBffDYSwdyiyEGnYZIswGbSR/czHoiG/YHnkfs34x6Ikw6bA3nttA9HaYwJJhF93EkYpX9IaEkrtuvq8SOFa4Klu1dxvn9z2/xfqC2lqI77iDp7rsxH2Tl6od3FjFvbzmXpMbx1/4ZGFpZNKqu0sVnL26gutiBwaTj9OuHkjkkPiyfRQghhBDhI0nCDugqgZ4QomuzV7nZvqKEbcuLqS1r7EFntOjJGZlAv7HJpA+MRXuQRJ8jEOCn6nqWVNlZUlXXZLVkAKNGw5hoK5NiIjkh1sboKCtGbfgX5PBXVuLJzQ0mDnNz8ebl4cnLI1BRcfAXGQwY09MxZmZiyMrEmJmFMSsLY1YmhrQ0NPrmvSpVVaXOW0exo5gSRwkljhJKnaVNjksdpXiVtq2MrNfqSbAkkGBOIMGaEDw+4DzeHE+8JZ54czxWQ3hX1VQUlXqvn9qGxGGd20edy0edy0+du+FaQ0LR7g5es7v9oeN6j/+whkW3xqDTYDXqiTDqsJoa9g1Jxf17i0GP1ajDatJhNQTLWY06Iox6zAZd8J5RFzq2GHWY9Tq0srJ0lxPOWOVf//oXTz31FLm5uQD079+fP/zhD1xxxRXhaGqP0FmxY7mznO8KvztoUhDAt28fhrS0NtepqCqfldfym8TWv4gpy6/jfy9twFnnJSLGxFm3DCchPbJd7RdCCCHE0SFJwg6QJKEQoj1UVaUs307uqlJ2rirDUdO40rHZZqDv6CT6jUsitU8MmlaSLXvdXn6stvNDdT0/1tQ3G5ps0WoYHRXBcdERjI+JYExURFiGJx9MoK4ulDD07s5rON6NL78A1ec7+Av1egwpKRjS0zGk98KYno6hV/DY0KsX+sTEg/7HU1VVqj3VlDhKKHeWU+Yqo8xZRrmznFJnafCas4xqz8F7P7bEorcQZ44jzhxHvDmeOEvwONYUS6w5ljhzHDHmGOJMccSaYzHr2zY5/+Han2QMJg59ODzB43qPn/qGfZNzrx+Hx4/TE6De48fRcF7v8eP2KUe0rQAWQzBhaDHoMBu0BxzrfnVv/6YN7vXBsmZDsKdj6LpBh0mvDZUN3ZOEZJuFK1b5+9//zoMPPsgtt9zCpEmTAPjhhx946aWXeOyxx7jjjjvC1eRjWmfEjjurd3Lz4pspdhQz58Q5nJVzVrMy9m+/pejOu0i66y7irri8xXrq/QGezy/lruwUTG38ImrPhgq+em0Tfq9CfC8bZ90yHFvskf25KYQQQojDJ0nCDpAkoRDicKmKSvGuWnJXlbJrTRkue2MyzRJlJHtEAjkjE0kfEItOf/D/jKmqSp7Lyw/Vdn6sqeeH6noqfU1XHNYCQ2wWjouO4LiYCMZH20gxHd5cfu2hBgL4S0rwFhTgzS9o2OfjK8jHW7AX1eNp9fUakwlDWlrDloo+NRVDauO5ITkZjbH1FZK9AS+VrkoqXBVUuCood5U3Od+/VbmrcAfc7f6MFr2FWFMsMeYYYkwxRJuiiTE1PQ7tjdFEmaKINEai1YS/p+eh+AMKTl8ApycQSh46PAGcXj8Ob6Dh3I/LG8DhDeBquO7yNpZxeoP3Xd4ALl8ApzeAx3/kk48tMeg0mPSNSUSTXotRr8XUcLx/M+q1GHXBBKPxwGtN7h14rmty78C9Qaf51Xlwr9dquuzQ23DFKtnZ2Tz66KPMnDmzyfU333yTRx55hLy8vI42tUc42rHjz8U/c8eSO6j31dM7qjcvT32ZjKiMJmWq33uPkkceBUXBNnky6S+/hOZXScBSj4/LNuxmU72Li1PieHZQ5iHf2+cJ8O8Hl+Oq85IxKJbTrx+G0dK897gQQgghug5JEnaAJAmFEOGgBBSKtteQu6qU3evK8Tgbk3xGs46sofFkj0wka2h8i4ueHEhVVbY73aysdbCixsEvtQ4K3M2H5aabDYyKjGB0lJXRUVaGRVrDOq/hoaiKgr+sDF9hId7CQnyFRfiKioLnRYX4S0pBOUTySaNBn5CAPiUFQ0oy+uT9++BmSElBn5yM1mQ6dHtUFZffRaWrkkp3JVXuquDeFdzXuGuo8lRR466h2l1NlacKv+I/ZL0tNhsNkcZIooxRRJuiiTJGEWWKCu6NwSTi/vsH7m1GG5HGSEy6Q3+eo0lRVFy+YNIwmFAM4N5/7gvg9gaa3Hd5A7j9Adw+JVTO03Ds9jfc9yl4/I17j0/B7Q/gC3TdUMSoCyYRDQ1JRMMBiUXD/nOdFoO+8Xz/vb9fOBLdEeoZGa5YxWw2s2nTJvr27dvkem5uLsOGDcPtbn+SvSc6mrHjJzs/4ZGfHsGv+hmdNJrnTnmOGHNM6L6qqlS8/DIVL7wIQPR555H66CNoDE2/RMp1uLlkwy4K3T7iDXr+MzyHUVFtm5qheFctO1aUcMJF/dAdxd8xQgghhDg8kiTsAEkSCiHCLeBXKNpRTd66CnavL8dZ25jg0+m1pA+KJXt4AllDE7DFti1ZVOzx8kutg19qHKysdbCp3sWv0286DQyKsDA6ysqohq2f1Yyuk3pHqT4fvuJifPuK8e3bh694H77iYvyh8+JD9kTcTxcdjT4pEX1iEvrExMbjpET0SUnBRGNCAlpr2+cjVFUVh88RShjWemqp8dRQ466hxlMTOg9d99RQ561r06rOh2LQGoJJQ4MNm9EW3B9wHGGIIMIQgc1gw2qwhu5ZDVYi9BGh+2a9uVN6NHZEQFGbJQ89/oZjv9JwHrzvDQTvewPKAfsAngPOvf4DtgPOPQ3Hvl/tvQfswxkV5c0584j1RAxXrDJ06FAuvfRS/vjHPza5/thjj/Huu++ycePGjja1RzgasaOqqsxdP5e56+cCcEbvM/jzCX9u8gWD6vdT8qc/U/PeewDE33gDibff3uzv4S819Vy5MY9qf4Aci4m3R+TQ23Lw3z1+X4DqYieJmTLnoBBCCNEdSZKwAyRJKIQ4klRFpXRPHbvXlbN7XXmTRU8A4tNtZA2NJ2toPCnZUQdd+OTX6v0B1tmdrK1zsqbOyZo6B6Xe5r3iLFotQ2xmhkVaGRZpYbjNQv8I8xFZFKW9VFUlUF2Nr2gf/rJSfKWl+EtK8ZeW4CspxV9Sgq+0FLUdvZs0Viv6+PiGpGE8uvh49AmJweO4OPRxceji4tHHxaKNimo2HK8tfAEftd5a6jx1Tfa1nlrsXjt2r506b12Lx/W++na/X6ufFw0WvSWUNNx/bDVYseqtLe4tegtWfXBv0VuwGCzNrhm0hi479Dac/IFgstAXUEOJxAOTifuvH1huf5kDj/0BlatPyD5i7QxXrPLhhx9y0UUXMW3atNCchD/++COLFy/mvffe49xzzw1Xk49pRyN2XFWyiqu+ugqAa4ddy62jbm3yhYCqKBTedhv13ywGjYbkBx8g7tJLm9Xzv/Iabt6Sj0dRGR1l5V/DckgwHrw3u9vh4/O5G6jYW8+5vx9NYoYkCoUQQojuRpKEHSBJQiHE0aKqKlXFDvLWlbNnYyWle+rggJ/KJquejMFxZA2NJ3NwPNao1ufq+3Xd+zy+JknDDfUunIHmw32NGg0DbWaG26wMtpkZbLMwKMJMtKHrzTOlqipKbS3+8nJ8ZWX4y8uDW9n+feO19iQTAdDr0cXGoI+NQxcfF9zHxKCLjQ1uMTHoYmPQxcSgbzjXWCwdSp4FlAAOvwOH14HdZ6feW0+9rz60t3vtOHwOHD4H9b56nD4n9b76xmveepx+Jw6fA5Uj9ytdp9Fh0Vsw681N9hZd02smnan5sc6MSW8K7nWm0D2z3tzsmlFn7HY9ITtDR2OVTZs2MXToUABWr17NM888w9atWwEYNGgQd911F6NGjQprm49lRyt2nLt+LgmWBC7of0GL9ytff4PyZ58l7emniDrttObt9AeY8PMWqnwBpidEMXdw71anpKircLHwhfXUlDoxmnWcedNweg2IDdvnEUIIIcTRIUnCDpAkoRCis7jqvRRsriJ/UyUFWyrxOJr2BEzIsJE+MI70gbGk9Y3BYGrf6sYBVWW308Omehcb7E422l1srHdR6w+0WL6XycCghoThYJuFQTYzfSxmDN1gBVpVVVEcTgIV5fgrK/FXVOKvKCdQWYm/vAJ/ZSWBqir8VVUEqqpQ6g+vR5/GaEQXHY0uJhptdDS66JjgeXQ0uugotFFR6KKi0UVFoovafx7caw+xQEt7P6/L78Lpd+L0OUNJxP3nh9q7/K7Q5vQ3nh/uHI0dYdQaMelMmPSm4P5Xm1FnDG0HXjPpTBi1Rgw6Q6gOo67x3KgzNt5vOP71NYO2cd+Vk5UdjVW0Wi3jxo3j2muv5eKLLyYyUnqHdURnxo6qqjb5osK7dy/GjIyDll9eU89nZTU82rcX+lZ+lpfl1/HZi+tx2X3YYk2cdcsI4nvZwtp2IYQQQhwdkiTsAEkSCiG6AkVRKdtTR/6mSvI3VVJeYG9yX6vTkJITTfrAWDIGxZGUFdnmockHUlWVArc3lDDc0rAVeXwtljdoNGRbTPSPMNE/wkx/q5n+EWZyLCbM3XgCe8XrJdCQMPRXVhGoriJQXY2/uppATQ2B6hoCoeNq/DU14Gv5GbWVxmRCGxWJzhaJNjISnc2GNjISbaSt4ZoNXWQk2ggbWpsNrS0CXUREw7ENbUQEWqsVja59yeL28Cm+YMLQ58IdcOPyu3D73aEkYpPjgBu3340n4MHld+EJeHD7g9cOvOcOuPH4G/YBDx6/B7969JORh6LX6DHoDE0Sh78+1mv1Tc73lzdoDTw88WF02iPzZ9PRWOX777/njTfe4IMPPkBRFH77299yzTXXcOKJJx6B1h77Oit2tH+7hIp5r5D52mvoDpLodfgD5Do9jGzjoiQAezZU8NVrm/B7FeLTbZw1a0Sb58sVQgghRNcjScIOkCShEKIrctZ5KdxeReG2avZuraK+qukCHwazjrR+MaT1jSGtXwyJmZHo9IeftKv1+dnmcLPF4WZrvYut9W62OFw4WhiuDKAFeltM9Isw0cdiJsdqIsdiIsdqItmoP+bms9vfU1GprSFQV0egtpZATW1wX1tLoLaGQE0NSp2dgN1OoK42eFxXh2K3E84VMjRWK9oIKzprBJqGvTZi/2ZFa92/twbLhraIhr0FrcWCxmIJnlssRzTx2BK/4g8mDBuShqHjAze/B6/ixRPw4A14Q9f3Hx+49ypefAEfXsUbPA948Sk+PAEPPsUXPD/wvuINe6/JDTM3dPmFSxwOB++99x7z58/n+++/p2/fvlxzzTVceeWVpKSkhLHFx7ajHTuqqkrVG/Mpe+opUFUSbr6JxNtua1au2ONl5oY89rg8LBzTj4ERlkPWXbi9mk+fXYuqQubgOKZfPxSjuetNPSGEEEKItpMkYQdIklAI0dWpqkptuYvCbdUUbquicHt1s6HJeoOW5JyoUNIwOScag7FjiR+lYZ7DHQ43Oxxucp1udjg87HC6DzpkGSBCpyXHYiLbaqKPxURvi4neFiO9LSaSjsEE4qGoioLicBCorUOx1xGw21Hq61HsdgL2epT6YGJR2X/scKDUO4JlGraAwwH+I9f7TmM0BpOFDUnDYBLRjNZiRWs2B4/NB1w3W9BazGhMZjRmE1qzpWFvDpY3m4M9JxuOtSYTGpPpqCcjW6OoSmMCUfGFkoih44br+xOKvoAveO/A1yg+/Iofv+LnmmHXHLG2HolYZefOnbzxxhv8+9//pqSkhNNPP51PP/00LHUf645m7Kj6fMEVjN9/H4CYiy4i5YH70RgMTcptsju5YmMexR4f8QY9/x6ezeioiEPWHwgofP7SBiJiTJx82QB03biHuBBCCCGCJEnYAZIkFEJ0N6qiUlFYz77cmtDmdjQdCqvVakjMiiQlO5rknChScqKxxZrCkqBTVZVyr58dTjfbHW7yXB52OT3kuTwUuLy03PcwyKLVkNmQNMwym8iyGMmymMgwG0k3G4joQkmkrkRVVVSvF8VuR3E6URyOxu3A8/3HTieK09Ww/9XmcKC6XCguV1h7OLaJwRBMGB6YODSZ0BqNDYlFY/C6seGe0RBMYJpMaAxGNMaGzRTca40HXDMaG8sYDA3XDMG5IA3B/f7r6LtXsvpIxSoOh4O33nqL++67j5qaGgKBgyf/RaOjFTsGamspvH02zp9/Bq2W5HvuJnbmzGZ/dxdV1HLDlnycAYV+VhP/GZ5DluXgw4X9vgBarSY0ZYXfF0Cn13arfxNCCCGEOLi2xioydkAIIY4BGq2GxMxIEjMjGTE1A1VVqS52sm9nMGFYvLOG+moPpXl1lObVwbfB11mjjY1Jw+xoErMiD6u3oUajIclkIMlk4ITYpvNieRWFfJc3lDjc7fKwx+Vhj8tLkduLS1HZ7ggmF1sSb9CTbjaQYTY2JA4b92kmA9F6XY/8j6xGowkm00zhmydMVVVUjwfF5UJ1OlEaEoeK04XicqK63SguN6rbheJyo7hdDclFd8N9D4rHjepyB/duT7CM24Pidgdf7/E0ncvR50Px+aC+ns5OR4WSib/ejAb49TX9gcf6A+4Fj9HrSbrzzi7VU7I13333Ha+//joffvghWq2WCy+8kGuuOXI9IUX7eQsK2Hv9DXj37EFrtZL2978ROXlys3KvFZbzUG4RCnBirI3XhvRudaV6V72Xz1/eSHy6jZMv6Y9Go0Fv6B5/b4UQQggRXtKTsAXSk1AIcaxRVRV7pZviXbWU7q6lJK+OisJ6VKXprwCtVkNsWgRJDQnHpKwo4tMjjth/GH2KSqHbS747mDTMd3nIdwXPC92+Vocw72fVaellMpBqMpBmMpJmNtDLFEwgpjRsMT00kdhVqYFAMBnp8QQTh243qtcbSiKqHi+q19NQxovq8aB6PShuT7Ccp2Hv8wbLe32N1xo2xecNXt+/P+Ce6gueH+lekwO3bunScxLu27eP+fPnM3/+fHbu3Mnxxx/PNddcw4UXXkhExKGHpYpGRyN29JWVsefCi0CrIWPuXMwDBjQr81FpNTdtyQfg0tQ4nuyf0epq9DWlTj57cT215S5MVj0XPXAckXHmI9J+IYQQQnQe6UkohBAiRKPREJVgISrBwoDxwcUIfN4A5fl2SvJqKc2ro2R3Lc5aL5WF9VQW1rP1p2KgeeIwMTOS+F42DKaOJw4NWg3Z1uBchS2p9fkp9PgodHvZ6/ay1+Wl0OOlwOWlyOOlyhfAGVDIdXrIdXparAPArNWQbAwmDJNNBlKNwX2yUU+S0UCiKbiPlWTiUaHR6UILqHQm1e8PJQxVn6/x2OttvNdkO6C83x9MQPr9sP++3x9MSPr9qAF/l/67dMYZZ/DNN9+QkJDAzJkzufrqqxnQQtJJdB2GpCQyX/0HupgY9ImJLZaZkRjNCTE2To6L5JbMpFb/Du7bWcPnczfgcfiJjDdz1i0jJEEohBBC9HCSJBRCiB7KYGxYDblfDBDsbVhf7aE8305ZQR3lBXbKC+y47L5miUM0EJ1oIaGXjfh0G/G9bCSk24iMN4c1MRJt0BNt0DPE1vKKnK6AQrHHxz6PlyJ3cL/P46PI7aXY46PU66PKF8CtqOS7veS7va0/E42GRKOexP3JQ6OeBIOeBKOeBKOh8digJ86gR99KDx3R9Wn0ejR6PVgOveLrscZgMPDBBx9w1llnoesmQ6IFmPr1a3btuyo7E2IiMGq1GLVa3h3ZB90hfg7nrirlm/lbUPwqSVmRzJg1AmuU8Ug1WwghhBDdhAw3boEMNxZCiKCWEocVe+tx1rWcbDOadcSn24hLjSAuLYLY1AjiUiOwRhk7rVeVO6BQ5vVR4vFR4vVT6vGFEohlXh9lXj/lDcnE9orV64g3BhOGcQZdw77xPN6gJ9agJ9agI0avJ1qvk8SiCAuJVbqWzvjzcAcU/rRrH68XVXBjRiKP9O3Vptet+6aAHz/YCUD2iAROvWbIYc1FK4QQQojuQ4YbCyGE6DCNRkNknJnIODM5oxqHtznrgsOSK4qCPQwrCuupLnHgdQco3llL8c7aJvWYrHpiUyKIS7USmxpBbEoEMclWIuPNaI9w0sys05JpMZHZysqeEFxgpdzrDyUNS70+Krz+4OZruq/y+VGBan+Aan8AOPhQ51+L0muJ1euJMehC+yi9jhh9w96g/9W5jkhd8Li1ucWEED1HrsPNjVv2sLk+uOCThuCXOm35MiYmyYpGA8NPyeD43/Y94j+DhRBCCNF9SE/CFsi380II0X4Bv0J1iZPKonqqix1UNWx15a6Drg+h1WuITrQSk2QhNsVKdJKV2GQrMclWzDZDl53TLaCqVPsClHt9VPsCVPn8B2zB88qG82pfgBq/nzq/0uH3tWg1RDUkDyP1OqJ0DXu9Fps+mEyM1GuJ1OkazoPXbQfudZJsPBZIrNK1HK0/D1VVeaekivt3FOFSFOINep4flMnU+Nbf89cJxMqieuJ72Y5YO4UQQgjRtUhPQiGEEEeVTq8lIT04N+GB/L4ANaWuUOKwuthBdamT2jJXMLHYcC1vfdP6DGYd0YkWohMsRCcFF12JTrQQlWjBFnvkeyC2RqfRNMxT2PZfo35FpcYfTBjW+AJU+/xU+wPU+PzU+gONmy9AnT9Ajb9x7wwEE4wuRcXl9VPq9Xeo/UaNBpteS4SuMXEYodMSoddi0WqDxzodVt3+Yy3WAzaLtuVjg0bTZRO7QnR3df4Ad2/fy8dlNQCcGGvjxUFZJJsMrb6uorCeb/+1lenXDSU6MTj/piQIhRBCCNESSRIKIYQ4ovQGXYvJQ0VRqa9yU1PqpKbMSU1JcF9d6qS+yoPPHaBibz0Ve+ub1anVNw6Djoo3ExlvITK+8Tgi2oimi/WW02vbn1jcz6+o2APBpOH+ze5XqAvsPw6e1wcC1AeUhvMDjgMBHAEFjxLs0ulV1YYej+2fh7E1Og1YtFrMWi2WhgSiRadp2Aevm7UazAceh+4Fj01aLaaGY7NO0+TcpNVg1GoxaTSNx1oNWklMih6gyufnm8o6dBq4JzuVWzKTDvl3f/fachbN34LfE+DHD3I586bhR6m1QgghhOiOJEkohBCiU2i1GqISgj0EM4fEN7nn9wWoq3BTV+6ittxFbYUrdFxX6ULxq9SWuagtc7Vct16DLdZMZKwJW6wZW6wJW1zDvuGayarvNr3e9FoNsdrgIigd4VPUUCLREQjg8CvUB4LJRUdAwRlQcDTccx5w7my45gqoOJXGey4luPc3DCcPqDTUp4AvDB+8jfQaQslEo0aLUasJbhoNBm0w0WjQBK+ZtBoMDWX2X2s8bixnaHhtk30Lx/pfXdcfcL2XqesOmRfdT2+LiRcGZZJoNDA2OqLVsqqisuqLPfyyMA+A9IGxTJk56Gg0UwghhBDdmCQJhRBCdDl6gy64QnJq8/8IK4qKo8aDvdJFXaUbe6W7Ye/CXunGXuVB8avUlQcTiwd9D5OOiGgjthgT1mgTthgTEfu3aCMRMSas0Ub0hmNn1U9DKNkY3nq9DclCt6LiCii4FQVXQMHZsHcrKq7QsYI7oAbLKMF7HkXB3VDOrQR7PHoO2IfKKApeRcWtNJ3k0q+CP6DgCACEt3dkR+ybPAJJETa3Z88e/vznP/Ptt99SUlJCWloal19+Offffz9GozFUbsOGDcyaNYuVK1eSmJjIrbfeyt13392krvfff58HH3yQPXv20K9fP5588knOPPPM0H1VVXn44Yd59dVXqampYdKkScydO5d+/fqFylRVVXHrrbeycOFCtFot559/Ps899xw2W9cbkntGYswhy/g8ARa/uYVda8oBGD4lnUnn90Wr0x7h1gkhhBCiu5MkoRBCiG5Fq20capzWr/l9JaDgqPVir3RTX+2mvtpDfZUbe7UneF7lwe3w4fcEWu2NuJ/JqscaZcQSacQabcQatX8zNVw3YIkM7o+lhGJ7GLVajNqjl4BQVRWfquJV1FAi0asGj70NiURvw31vwz1fQ1mvGrzvUxrr2L8P3VMb7zfbN5T1Nxz7G97L31Bm/3VFVWUY9EFs27YNRVGYN28effv2ZdOmTVx33XU4HA6efvppIDi59mmnnca0adN45ZVX2LhxI1dffTUxMTFcf/31APz0009ccsklzJkzh7POOou3336bc845hzVr1jB06FAA/vrXv/L888/z5ptvkp2dzYMPPsj06dPZsmULZrMZgMsuu4zi4mIWLVqEz+fjqquu4vrrr+ftt9/unAfUAY5aDwtfWE9lYT1anYaTLx3A4Elpnd0sIYQQQnQTsrpxC2TFQCGEOLb5vAEc1R4cNR4ctR4cNd6GvadxX+Ml0M4ViQ1mXTBhaAsmDq2RBsw2I2abAXOEAYvNEDo22wyYLPouN3ei6B6OtVjlqaeeYu7cuezevRuAuXPncv/991NSUhLqXXjvvffy8ccfs23bNgAuuugiHA4Hn332WaieCRMmMHLkSF555RVUVSUtLY277rqL3//+9wDU1taSnJzM/Pnzufjii9m6dSuDBw9m5cqVjB07FoAvv/ySM888k8LCQtLSWk6weTwePB5P6Lyuro6MjIxO//Pw+wJ89Le12CtdnHHDMFL7xnRaW4QQQgjRdcjqxkIIIcRBGIw6YpKtxCRbD1pGVVW8Lj/OOi/OWm9wX+fFWecJnTvqvLjtXlx2H4qi4nMH8LlbH+Z8II1WgzlCj8lqwGTVY44wYIrQY244N0UEE4omix6TVY/RqsdkCd7TG7Uy3504ZtTW1hIXFxc6X758OSeddFKT4cfTp0/nySefpLq6mtjYWJYvX86dd97ZpJ7p06fz8ccfA5CXl0dJSQnTpk0L3Y+Ojmb8+PEsX76ciy++mOXLlxMTExNKEAJMmzYNrVbLihUrOPfcc1ts75w5c3j00UfD8dE7zOPyo9Nr0Bt06A06zrxxGIqiEhln7uymCSGEEKKbkSShEEII0QKNRtOQvDMQm3KIRQJUFY/Tj7veh9PuxW0P7l12L+56H26HD3e9D9cBxz5PAFVRcdl9uOztX+VDq9UEk4ZWPSaLHqNFj9Gsx2jRBY8bzhvv6TCY9+91wbJmncxTJjrdzp07eeGFF0JDjQFKSkrIzs5uUi45OTl0LzY2lpKSktC1A8uUlJSEyh34uoOVSUpKanJfr9cTFxcXKtOS++67r0mCcn9PwqOtYEslS/69jX5jkzn+/L4ARMSYjno7hBBCCHFs6DJJwieeeIL77ruP22+/nWeffbbFMps3b+ahhx5i9erV5Ofn88wzzzB79uwmZebMmcN///tftm3bhsVi4fjjj+fJJ59kwIABR/5DCCGE6JE0Gk1wCHGEodXeiQcK+BTcjmDi0OP0BZOMDh8ehx+P04fb6cfjCCYVvS4/Hqcfj8uP1+lHUVQURQ0mIOs7toyw3qDFYNFjNAWThwbT/k3/q3MdBmPjsd6kw2DUNuwbrhl16I1aDEadDKPuge69916efPLJVsts3bqVgQMHhs6Lioo4/fTTueCCC7juuuuOdBPDxmQyYTJ1XjLO6/Lz44c72fLDPgDyNlRw3G+y0Rt75ryoQgghhAiPLpEkXLlyJfPmzWP48OGtlnM6neTk5HDBBRdwxx13tFhm2bJlzJo1i3HjxuH3+/njH//IaaedxpYtW4iIaL0niBBCCHG06Aza0GrK7aGqKn6vEkwaNiQXva5gAtHnbkgkugKha1538L7XHcDn3r8PhOZb9PsU/D4vbRsg3b7Ptz9huD95qDfs32vRGYJJRp1Rh76hrN6gC77OoA3t918LXdcHj3X6X59rpFdkJ7vrrrv43e9+12qZnJyc0PG+ffs45ZRTOP744/nHP/7RpFxKSgqlpaVNru0/T0lJabXMgff3X0tNTW1SZuTIkaEyZWVlTerw+/1UVVWFXt/V7N1axbf/3kp9VXBOxOGnpDPhnD6SIBRCCCFEh3V6krC+vp7LLruMV199lccee6zVsuPGjWPcuHFA8Nvqlnz55ZdNzufPn09SUhKrV6/mpJNOCk+jhRBCiE6i0WhCvflssYffkyngV/C5A8EkojuAzxPA5/EH51X0NG5etz907PcE8HmVYDmPgt/bcD20b1zoJeBTCPgUPA5/OD52m2i0GnR6TTBxqNeiPeC4cdM03GtMLDa5ptM02Wt1GnQNZQ481+o0DVvDNX3jsVanQavVEptq7VHzRiYmJpKYmNimskVFRZxyyimMGTOGN954A+2vVseeOHEi999/Pz6fD4PBAMCiRYsYMGAAsbGxoTKLFy9uMqpk0aJFTJw4EYDs7GxSUlJYvHhxKClYV1fHihUruOmmm0J11NTUsHr1asaMGQPAt99+i6IojB8//rCfxZHgdfv56cOdbP4+2HswKsHMlJmD6NU/tpNbJoQQQohjRacnCWfNmsWMGTOYNm3aIZOEh6O2thagyWTYv9bSCnVCCCHEsUyn16KzaTHbDGGrU1XVYM9EbzBheGDyMHTN9+t9wz2fQsAbwO9XCHiV4N6n4PcqBPzBcgFfIHjNrxDwqwR8CqqiNr6/ouL3qk2SlZ3p5pdPgZ6TI2yzoqIiJk+eTFZWFk8//TTl5eWhe/t771166aU8+uijXHPNNdxzzz1s2rSJ5557jmeeeSZU9vbbb+fkk0/mb3/7GzNmzGDBggWsWrUq1CtRo9Ewe/ZsHnvsMfr160d2djYPPvggaWlpnHPOOQAMGjSI008/neuuu45XXnkFn8/HLbfcwsUXX3zQlY07i8fpZ8fKYM/JYSf3YsK5fTCaOz2UF0IIIcQxpFMjiwULFrBmzRpWrlx5ROpXFIXZs2czadIkhg4detByXWmFOiGEEKK70mg0wfkJj+KwRyXQkDBsSCoG/AdsPpVAQAndU/aXCxxw7FdQAmrj3td4TQkoBAIqyv57DceBwP776gFlg+X3l1MVVeZlPIhFixaxc+dOdu7cSXp6epN7qhpM+kZHR/P1118za9YsxowZQ0JCAg899BDXX399qOzxxx/P22+/zQMPPMAf//hH+vXrx8cff9wk5rv77rtxOBxcf/311NTUcMIJJ/Dll19iNjeu/PvWW29xyy23MHXqVLRaLeeffz7PP//8EX4K7RcZZ+aUywdiiTSSPkB6DwohhBAi/DTq/mjsKNu7dy9jx45l0aJFobkIJ0+ezMiRIw+6cMmBevfuzezZs5stXHKgm266iS+++IIffvihWRB6oJZ6EmZkZFBbW0tUVFSbP5MQQgghxNFQV1dHdHS0xCpdhPx5CCGEEKIra2us0mk9CVevXk1ZWRmjR48OXQsEAnz33Xe8+OKLeDwedLrD74lwyy238Nlnn/Hdd9+1miCEzl+hTgghhBBCCCGEEEKIztRpScKpU6eycePGJteuuuoqBg4cyD333HPYCUJVVbn11lv56KOPWLp0KdnZ2eForhBCCCGEEEIIIYQQx6xOSxJGRkY2mycwIiKC+Pj40PWZM2fSq1cv5syZA4DX62XLli2h46KiItatW4fNZqNv375AcCGUt99+m08++YTIyEhKSkqA4Nw2FovlaH08IYQQQgghhBBCCCG6DW1nN6A1BQUFFBcXh8737dvHqFGjGDVqFMXFxTz99NOMGjWKa6+9NlRm7ty51NbWMnnyZFJTU0Pbu+++2xkfQQghhBBCCCGEEEKILq9TVzf+taVLl7Z63rt3bw61zkonrcMihBBCCCGEEEIIIUS31aV7EgohhBBCCCGEEEIIIY48SRIKIYQQQgghhBBCCNHDSZJQCCGEEEIIIYQQQogeTpKEQgghhBBCCCGEEEL0cJIkFEIIIYQQQgghhBCih5MkoRBCCCGEEEIIIYQQPZwkCYUQQgghhBBCCCGE6OEkSSiEEEIIIYQQQgghRA8nSUIhhBBCCCGEEEIIIXo4fWc3oCtSVRWAurq6Tm6JEEIIIURz+2OU/TGL6FwSOwohhBCiK2tr7ChJwhbY7XYAMjIyOrklQgghhBAHZ7fbiY6O7uxm9HgSOwohhBCiOzhU7KhR5SvoZhRFYd++fURGRqLRaI7Ie9TV1ZGRkcHevXuJioo6Iu/Rk8jzDD95puElzzO85HmGlzzP8Doaz1NVVex2O2lpaWi1MntMZ5PYsfuR5xle8jzDS55n+MkzDS95nuHVlWJH6UnYAq1WS3p6+lF5r6ioKPlHFUbyPMNPnml4yfMML3me4SXPM7yO9POUHoRdh8SO3Zc8z/CS5xle8jzDT55peMnzDK+uEDvKV89CCCGEEEIIIYQQQvRwkiQUQgghhBBCCCGEEKKHkyRhJzGZTDz88MOYTKbObsoxQZ5n+MkzDS95nuElzzO85HmGlzxPcSTI36vwkucZXvI8w0ueZ/jJMw0veZ7h1ZWepyxcIoQQQgghhBBCCCFEDyc9CYUQQgghhBBCCCGE6OEkSSiEEEIIIYQQQgghRA8nSUIhhBBCCCGEEEIIIXo4SRIKIYQQQgghhBBCCNHDSZJQCCGEEEIIIYQQQogeTpKEneSll16id+/emM1mxo8fzy+//NLZTeoWvvvuO37zm9+QlpaGRqPh448/bnJfVVUeeughUlNTsVgsTJs2jdzc3M5pbDcwZ84cxo0bR2RkJElJSZxzzjls3769SRm3282sWbOIj4/HZrNx/vnnU1pa2kkt7trmzp3L8OHDiYqKIioqiokTJ/LFF1+E7suz7JgnnngCjUbD7NmzQ9fkmbbdI488gkajabINHDgwdF+eZfsVFRVx+eWXEx8fj8ViYdiwYaxatSp0X34niXCS2PHwSOwYXhI7hpfEjkeWxI4dI7Fj+HWH2FGShJ3g3Xff5c477+Thhx9mzZo1jBgxgunTp1NWVtbZTevyHA4HI0aM4KWXXmrx/l//+leef/55XnnlFVasWEFERATTp0/H7XYf5ZZ2D8uWLWPWrFn8/PPPLFq0CJ/Px2mnnYbD4QiVueOOO1i4cCHvv/8+y5YtY9++fZx33nmd2OquKz09nSeeeILVq1ezatUqpkyZwtlnn83mzZsBeZYdsXLlSubNm8fw4cObXJdn2j5DhgyhuLg4tP3www+he/Is26e6uppJkyZhMBj44osv2LJlC3/729+IjY0NlZHfSSJcJHY8fBI7hpfEjuElseORI7FjeEjsGD7dJnZUxVF33HHHqbNmzQqdBwIBNS0tTZ0zZ04ntqr7AdSPPvoodK4oipqSkqI+9dRToWs1NTWqyWRS33nnnU5oYfdTVlamAuqyZctUVQ0+P4PBoL7//vuhMlu3blUBdfny5Z3VzG4lNjZWfe211+RZdoDdblf79eunLlq0SD355JPV22+/XVVV+fvZXg8//LA6YsSIFu/Js2y/e+65Rz3hhBMOel9+J4lwktgxPCR2DD+JHcNPYseOk9gxPCR2DK/uEjtKT8KjzOv1snr1aqZNmxa6ptVqmTZtGsuXL+/ElnV/eXl5lJSUNHm20dHRjB8/Xp5tG9XW1gIQFxcHwOrVq/H5fE2e6cCBA8nMzJRnegiBQIAFCxbgcDiYOHGiPMsOmDVrFjNmzGjy7ED+fh6O3Nxc0tLSyMnJ4bLLLqOgoACQZ3k4Pv30U8aOHcsFF1xAUlISo0aN4tVXXw3dl99JIlwkdjxy5N9px0nsGD4SO4aPxI7hI7Fj+HSX2FGShEdZRUUFgUCA5OTkJteTk5MpKSnppFYdG/Y/P3m2h0dRFGbPns2kSZMYOnQoEHymRqORmJiYJmXlmR7cxo0bsdlsmEwmbrzxRj766CMGDx4sz/IwLViwgDVr1jBnzpxm9+SZts/48eOZP38+X375JXPnziUvL48TTzwRu90uz/Iw7N69m7lz59KvXz+++uorbrrpJm677TbefPNNQH4nifCR2PHIkX+nHSOxY3hI7BheEjuGj8SO4dVdYkf9UXsnIUSXNmvWLDZt2tRkngnRfgMGDGDdunXU1tbywQcfcOWVV7Js2bLObla3tHfvXm6//XYWLVqE2Wzu7OZ0e2eccUboePjw4YwfP56srCzee+89LBZLJ7ase1IUhbFjx/KXv/wFgFGjRrFp0yZeeeUVrrzyyk5unRBCHHkSO4aHxI7hI7FjeEnsGF7dJXaUnoRHWUJCAjqdrtmqP6WlpaSkpHRSq44N+5+fPNv2u+WWW/jss89YsmQJ6enpoespKSl4vV5qamqalJdnenBGo5G+ffsyZswY5syZw4gRI3juuefkWR6G1atXU1ZWxujRo9Hr9ej1epYtW8bzzz+PXq8nOTlZnmkHxMTE0L9/f3bu3Cl/Pw9DamoqgwcPbnJt0KBBoWE48jtJhIvEjkeO/Ds9fBI7ho/EjuEjseORJbFjx3SX2FGShEeZ0WhkzJgxLF68OHRNURQWL17MxIkTO7Fl3V92djYpKSlNnm1dXR0rVqyQZ3sQqqpyyy238NFHH/Htt9+SnZ3d5P6YMWMwGAxNnun27dspKCiQZ9pGiqLg8XjkWR6GqVOnsnHjRtatWxfaxo4dy2WXXRY6lmd6+Orr69m1axepqany9/MwTJo0ie3btze5tmPHDrKysgD5nSTCR2LHI0f+nbafxI5HnsSOh09ixyNLYseO6Tax41FbIkWELFiwQDWZTOr8+fPVLVu2qNdff70aExOjlpSUdHbTujy73a6uXbtWXbt2rQqof//739W1a9eq+fn5qqqq6hNPPKHGxMSon3zyibphwwb17LPPVrOzs1WXy9XJLe+abrrpJjU6OlpdunSpWlxcHNqcTmeozI033qhmZmaq3377rbpq1Sp14sSJ6sSJEzux1V3Xvffeqy5btkzNy8tTN2zYoN57772qRqNRv/76a1VV5VmGw4Er1KmqPNP2uOuuu9SlS5eqeXl56o8//qhOmzZNTUhIUMvKylRVlWfZXr/88ouq1+vVxx9/XM3NzVXfeust1Wq1qv/5z39CZeR3kggXiR0Pn8SO4SWxY3hJ7HjkSex4+CR2DK/uEjtKkrCTvPDCC2pmZqZqNBrV4447Tv355587u0ndwpIlS1Sg2XbllVeqqhpcNvzBBx9Uk5OTVZPJpE6dOlXdvn175za6C2vpWQLqG2+8ESrjcrnUm2++WY2NjVWtVqt67rnnqsXFxZ3X6C7s6quvVrOyslSj0agmJiaqU6dODQV5qirPMhx+HejJM227iy66SE1NTVWNRqPaq1cv9aKLLlJ37twZui/Psv0WLlyoDh06VDWZTOrAgQPVf/zjH03uy+8kEU4SOx4eiR3DS2LH8JLY8ciT2PHwSewYft0hdtSoqqoevX6LQgghhBBCCCGEEEKIrkbmJBRCCCGEEEIIIYQQooeTJKEQQgghhBBCCCGEED2cJAmFEEIIIYQQQgghhOjhJEkohBBCCCGEEEIIIUQPJ0lCIYQQQgghhBBCCCF6OEkSCiGEEEIIIYQQQgjRw0mSUAghhBBCCCGEEEKIHk6ShEIIIYQQQgghhBBC9HCSJBRCdFu/+93vOOecc476+86fPx+NRoNGo2H27NkdrismJiYs7TrSJk+eHPrc69at6+zmCCGEEEK0i8SOR5fEjkJ0P/rOboAQQrREo9G0ev/hhx/mueeeQ1XVo9SipqKioti+fTsREREdqueiiy7izDPPDFOrGmk0Gj766KOwBsL//e9/2bVrF8cdd1zY6hRCCCGECAeJHTtGYkchBEiSUAjRRRUXF4eO3333XR566CG2b98eumaz2bDZbJ3RNCAYSKWkpHS4HovFgsViCUOLjry4uDjq6uo6uxlCCCGEEM1I7Nj1SOwoRPcjw42FEF1SSkpKaIuOjg4FVvs3m83WbMjI5MmTufXWW5k9ezaxsbEkJyfz6quv4nA4uOqqq4iMjKRv37588cUXTd5r06ZNnHHGGdhsNpKTk7niiiuoqKhod5t79+7NY489xsyZM7HZbGRlZfHpp59SXl7O2Wefjc1mY/jw4axatSr0ml8PGXnkkUcYOXIk//73v+nduzfR0dFcfPHF2O32Ju/z7LPPNnnvkSNH8sgjj4TuA5x77rloNJrQOcAnn3zC6NGjMZvN5OTk8Oijj+L3+wFQVZVHHnmEzMxMTCYTaWlp3Hbbbe1+DkIIIYQQR5vEjhI7CiE6TpKEQohjyptvvklCQgK//PILt956KzfddBMXXHABxx9/PGvWrOG0007jiiuuwOl0AlBTU8OUKVMYNWoUq1at4ssvv6S0tJQLL7zwsN7/mWeeYdKkSaxdu5YZM2ZwxRVXMHPmTC6//HLWrFlDnz59mDlzZqtDXXbt2sXHH3/MZ599xmeffcayZct44okn2tyGlStXAvDGG29QXFwcOv/++++ZOXMmt99+O1u2bGHevHnMnz+fxx9/HIAPP/yQZ555hnnz5pGbm8vHH3/MsGHDDus5CCGEEEJ0BxI7SuwohGgkSUIhxDFlxIgRPPDAA/Tr14/77rsPs9lMQkIC1113Hf369eOhhx6isrKSDRs2APDiiy8yatQo/vKXvzBw4EBGjRrF66+/zpIlS9ixY0e73//MM8/khhtuCL1XXV0d48aN44ILLqB///7cc889bN26ldLS0oPWoSgK8+fPZ+jQoZx44olcccUVLF68uM1tSExMBCAmJoaUlJTQ+aOPPsq9997LlVdeSU5ODqeeeip//vOfmTdvHgAFBQWkpKQwbdo0MjMzOe6447juuuva/QyEEEIIIboLiR0ldhRCNJIkoRDimDJ8+PDQsU6nIz4+vsk3msnJyQCUlZUBsH79epYsWRKap8ZmszFw4EAg+K1sR95//3u19v4t6d27N5GRkaHz1NTUVsu31fr16/nTn/7U5LNed911FBcX43Q6ueCCC3C5XOTk5HDdddfx0UcfhYaTCCGEEEIciyR2PDiJHYXoeWThEiHEMcVgMDQ512g0Ta7tX/lOURQA6uvr+c1vfsOTTz7ZrK7U1NQOvf/+92rt/Q9Vx/7XHFheq9U2G3Li8/kO2bb6+noeffRRzjvvvGb3zGYzGRkZbN++nW+++YZFixZx880389RTT7Hs/9m77/goyvyB45/Zmk3vlZCE3ntHUWyo2MXCKSIqFhQLp6dYz3KivzsVTlEsgKBiBw8LAtIs9N5DqCG9Z1M2W+f3xyZLIoEsCNmEfN+vm3tmnnmeyXcJhsl3nnmeVauOi0kIIYQQ4lwg944nJveOQrQ8kiQUQrRoffr04dtvvyU5ORmdrnn8SIyKiqqzgp/ZbObQoUN12uj1epxOZ526Pn36kJqaSrt27U54bZPJxNVXX83VV1/Ngw8+SKdOndixYwd9+vQ5sx9CCCGEEKIZknvHuuTeUYhzi7xuLIRo0R588EGKiooYPXo0GzZs4MCBAyxevJhx48Ydd6PUVFx00UV88skn/Pbbb+zYsYOxY8ei1WrrtElOTmbZsmXk5ORQXFwMwPPPP8/cuXN58cUX2bVrF3v27OGLL77g2WefBdyr5c2cOZOdO3dy8OBBPv30U0wmE0lJSY3+GYUQQgghmiK5d5R7RyHOZZIkFEK0aPHx8fzxxx84nU4uu+wyunfvzqOPPkpoaCgaTdP8ETl58mQuuOACrrrqKkaOHMl1111H27Zt67R54403WLp0KYmJifTu3RuAESNG8MMPP7BkyRL69+/PoEGDeOuttzw3cqGhoXz44YcMHTqUHj168Msvv/D9998TERHR6J9RCCGEEKIpkntHuXcU4lymqCdbS10IIcRxPv74Yx599FFKSkp8HUqjO3z4MCkpKWzZsoVevXr5OhwhhBBCiCZP7h3l3lGI5qJpPuoQQogmrrS0lMDAQJ588klfh9JorrjiCrp27errMIQQQgghmh25dxRCNAcyklAIIU5RWVkZubm5gPs1i8jISB9H1DgyMzOxWCwAtG7dGoPB4OOIhBBCCCGaPrl3lHtHIZoLSRIKIYQQQgghhBBCCNHCyevGQgghhBBCCCGEEEK0cJIkFEIIIYQQQgghhBCihZMkoRBCCCGEEEIIIYQQLZwkCYUQQgghhBBCCCGEaOEkSSiEEEIIIYQQQgghRAsnSUIhhBBCCCGEEEIIIVo4SRIKIYQQQgghhBBCCNHCSZJQCCGEEEIIIYQQQogWTpKEQgghhBBCCCGEEEK0cJIkFEIIIYQQQgghhBCihZMkoRBCCCGEEEIIIYQQLZwkCYUQQgghhBBCCCGEaOEkSSiEEEIIIYQQQgghRAun83UATZHL5SIrK4ugoCAURfF1OEIIIYQQdaiqSllZGfHx8Wg08szX1+TeUQghhBBNmbf3jpIkrEdWVhaJiYm+DkMIIYQQ4qSOHj1Kq1atfB1Giyf3jkIIIYRoDhq6d5QkYT2CgoIA9x9ecHCwj6MRQgghhKjLbDaTmJjouWdpKTIzM3nyySdZtGgRlZWVtGvXjtmzZ9OvX7962995553MmTPnuPouXbqwa9cuAKZMmcL8+fPZu3cvJpOJIUOG8Prrr9OxY0ev45J7RyGEEEI0Zd7eO0qSsB41r4kEBwfLjZ4QQgghmqyW9GprcXExQ4cOZfjw4SxatIioqCjS0tIICws7YZ9p06bx2muveY4dDgc9e/bkpptu8tStWrWKBx98kP79++NwOHj66ae57LLL2L17NwEBAV7FJveOQgghhGgOGrp3lCShEEIIIYRo8l5//XUSExOZPXu2py4lJeWkfUJCQggJCfEcf/fddxQXFzNu3DhP3c8//1ynz8cff0x0dDSbNm1i2LBhZyh6IYQQQoimT2a6FkIIIYQQTd7ChQvp168fN910E9HR0fTu3ZsPP/zwlK4xc+ZMLrnkEpKSkk7YprS0FIDw8PATtrFarZjN5jqbEEIIIURzJ0lCIYQQQgjR5B08eJD33nuP9u3bs3jxYh544AEefvjheuccrE9WVhaLFi3innvuOWEbl8vFo48+ytChQ+nWrdsJ202ZMsUzSjEkJEQWLRFCCCHEOUFRVVX1dRBNjdlsJiQkhNLSUplXRgghhBBNTku8VzEYDPTr14/Vq1d76h5++GE2bNjAmjVrGuw/ZcoU3njjDbKysjAYDPW2eeCBB1i0aBG///77SVf+s1qtWK1Wz3HNZOAt6fshhBBCiObD23tHGUkohBBCCCGavLi4OLp06VKnrnPnzqSnpzfYV1VVZs2axZgxY06YIHzooYf44YcfWLFixUkThABGo9GzSIksViKEEEKIc4UsXCKEEEIIIZq8oUOHkpqaWqdu3759J51fsMaqVavYv38/d99993HnVFVl4sSJLFiwgJUrVza4GIoQQgghxLlKRhIKIYQQQogm77HHHmPt2rW8+uqr7N+/n3nz5vHBBx/w4IMPetpMnjyZO+6447i+M2fOZODAgfXOM/jggw/y6aefMm/ePIKCgsjJySEnJweLxXJWP48QQgghRFMjSUIhhBBCCNHk9e/fnwULFvD555/TrVs3Xn75ZaZOncptt93maZOdnX3c68elpaV8++239Y4iBHjvvfcoLS3lwgsvJC4uzrN9+eWXZ/XzCCGEEEI0NbJwST1a4mTgQgghhGg+5F6laZHvhxBCCCGaMlm4RAghhBBCCCGEEEII4RVJEgohhBBCCCGEEEII0cJJklAIIYQQQgghhBBCiBZOkoRCCCGEEEIIIYQQQrRwOl8HADB9+nT+/e9/k5OTQ8+ePXn77bcZMGDACdt//fXXPPfccxw+fJj27dvz+uuvc+WVV3rO33nnncyZM6dOnxEjRvDzzz+ftc9wqpwOF0d2FpLYORy9UevrcIQQQgghhBBCCCGaBFVVcanuUgVUFVRUd1l7n7ptOMk5lepKOP6atb5u7eV9Pf08+7Xa1XzN6iuq9VybWm2Ona9bD+Cn19AuOuiv/rH9ZT5PEn755ZdMmjSJGTNmMHDgQKZOncqIESNITU0lOjr6uParV69m9OjRTJkyhauuuop58+Zx3XXXsXnzZrp16+Zpd/nllzN79mzPsdFobJTP460Fb2wm95CZS8Z1oePAWF+HI4QQQgghhBBCiGpOl4rd6cLhUnF4ShWHy1V9TvW0cbpUHC61uqx17FRxqmqt8y6cLv5UqjjVP9eBU1Vxudz9XTX7LnBVX8+pqqg1+9X1NedU9Vi7mkSbS3V/HdVzPXebmrYutXZb/nRcK2FX61xNf09Cj+PbqYDLdSwR5/rTOWrt11ynJeocF8yiR873dRi+TxK++eabjB8/nnHjxgEwY8YMfvzxR2bNmsVTTz11XPtp06Zx+eWX88QTTwDw8ssvs3TpUt555x1mzJjhaWc0GomNbbrJt6RuEeQeMrN3TbYkCYUQQgghhBBCtDiqqmJ1uKo3J1Z7rX2Hq/rYia26jc3hwuZ0VR+7620OF1anC7tDxeZ0Yne4E3c17exOF3anis3p3nc4j52vc+w4lgi0u1x1RnmJc4eigAIoioJSp859Qqk+Bndd7fbuupr/q3WdWsfUc23P1Y5rV/11gchAw5n8mKfNp0lCm83Gpk2bmDx5sqdOo9FwySWXsGbNmnr7rFmzhkmTJtWpGzFiBN99912dupUrVxIdHU1YWBgXXXQRr7zyChEREfVe02q1YrVaPcdms/k0P5H3Og6MZf33h8hILaasqIqgcL+z/jWFEEIIIYQQQghv2Z0uKm1OKm0OKqzHSov92LHF5sRid2GxObDYnVTanFjsTqrszupz7vPW6roqu4sqx7H95kSrUdBqFPQ1pVaDVqOg0yhotQp6jcbTRqdV0Go07nM1bWo2RUFTXVdT/rlOqyhoFDz72tr1fzqvURS0GtAoNft1z2mUY+c0tdrVnFNqt9G4E1cazbG6mmRYzTUVapXV11EUd9LLs8+xayt1ruPeh/rboxxLztXuo6CgaOom+JRabaH2tf7U5limTjTAp0nCgoICnE4nMTExdepjYmLYu3dvvX1ycnLqbZ+Tk+M5vvzyy7nhhhtISUnhwIEDPP3001xxxRWsWbMGrfb4+f+mTJnCiy++eAY+kfeCI03Etw8lK62Efetz6Ht5cqN+fSGEEEIIIYQQ5yabw4W5yk5ZlYOyWqW5ykFZlQOzxU651UF5lYNym4OKmn2rg4rqRGC51YHN0XhJPEUBo06DUad1l3r3vkFbs6/BUHOsqzmu3rTuUu8pFQxaDfqaOq271GuV6tK9r6s+p9Mq7mNNzb47uaeraadxl5JsEuc6n79ufDbceuutnv3u3bvTo0cP2rZty8qVK7n44ouPaz958uQ6oxPNZjOJiYlnPc6Og2LJSishdW0OfUYkyQ8cIYQQQgghhBCA+1XcSpuT4kobJZV2iittFFfaKam0UVxhp9RybDNb6h5b7M4zGotBq8Fk0BJg0LpLow5/gxZ/gw6TQYtJr8W/uvSr2Te492vq/PQad6nTYjK4E4B+ei1GvQY/nVaScEI0AT5NEkZGRqLVasnNza1Tn5ube8L5BGNjY0+pPUCbNm2IjIxk//799SYJjUajTxY2adcnmt++2EdxTiV5h8uISQlu9BiEEEIIIYQQQpx9qqpSYXOSX2aloNxKYbmNogobheVWCivc+0UVNgrKre6EYIUdm/OvjeQLNOoI8qvZ9H8qdQQZdQQYdQRWbwFGHYF+x/YDqhOBBp3mDP0pCCGaMp8mCQ0GA3379mXZsmVcd911ALhcLpYtW8ZDDz1Ub5/BgwezbNkyHn30UU/d0qVLGTx48Am/TkZGBoWFhcTFxZ3J8P8yg0lHm95R7Fufy9612ZIkFEIIIYQQQohmRlVViips5JiryDVXkVNqJb/MSn55lbsss5JfbqWgzHZaI/wMWg2h/nrC/A2eMixAT4jJQIhJf9wWbNIRYtIT5KdHq5GReUII7/n8deNJkyYxduxY+vXrx4ABA5g6dSoVFRWe1Y7vuOMOEhISmDJlCgCPPPIIF1xwAW+88QYjR47kiy++YOPGjXzwwQcAlJeX8+KLL3LjjTcSGxvLgQMH+Mc//kG7du0YMWKEzz7niXQcFMu+9bmkbczlvFHt0erlCY0QQgghhBBCNAU1CcDMEgtZJRYyS6rIKrG4E4KlVeSYq8gzW09pxF+AQUtEoJGIQAMRAQbCAwzu41r74dWJwDB/A/4GrbyGK4RoFD5PEt5yyy3k5+fz/PPPk5OTQ69evfj55589i5Okp6ej0RxLnA0ZMoR58+bx7LPP8vTTT9O+fXu+++47unXrBoBWq2X79u3MmTOHkpIS4uPjueyyy3j55Zd98kpxQ1p1Cicg1EhFiZXDOwto2zva1yEJIYQQQgghRItQkwQ8UlTJ0aJK0gsrySi2kFVq8SQGvV2BNzLQQEywH7HBfkQHG4kKNBIVZCSyuqzZDzD6/NdwIYSol6KqqurrIJoas9lMSEgIpaWlBAef/VeA1yzYz+bF6ST3iGTkhB5n/esJIYQQonlr7HsVcXLy/RCiaXO5VLLNVRzKr+BQQTnpRZUcKawkvToxWGFr+BXg6CAj8aEmEsJMxIf4ERtiIjbYj9gQIzHBfkQH+cm8fUKIJsvbexV5hNEEdBwYx+bF6aTvLMRSZsMUZPB1SEIIIYQQQgjRrJRa7BzIL+dQfgUHC8o5VFDBwfwKDhdWNDgaMC7Ej8RwfxLD/EkMN5EQWr2FmYgN8cOo0zbSpxBCCN+RJGETEB4fQHRSEHlHyti3IZeeFyX6OiQhhBBCCCGEaJLMVXbScstJyy1jX245aXll7MstI9dsPWEfvVahdbg/KZEBJEUE0Drcn9bh/iSG+9MqzISfXpKAQgghScImouOgOPKOlJG6NkeShEIIIYQQQogWz+lSOVxYwe4sM7uyzOzJNrMvt4zs0qoT9okN9qNNVAApke6tbVQgKZEBtAozodPK68BCCHEykiRsItr3j+aPb9LITy+jMLOciIRAX4ckhBBCCCGEEI2iyu5kb04Zu7JK2Z1lZne2mb3ZZVjs9c8XGBNspENMEO2jg+gYG0j7mCDaRwcS5Kdv5MiFEOLcIUnCJsIUaCCpWwSHthWQujaHITe283VIQgghhBBCCHHGOV0q+/PK2Xa0hK0ZJWw7WkJqThkO1/Frapr0WjrFBdElLpjOccF0ig2ifUwQISZJBgohxJkmScImpNPgOHeScH0Og65rg0aGwwshhBBCCCGauTxzFRuPFLP1aAlbj5awM7OUynpWFI4MNNAlPoQuccF0iQ+mS1wwKZEBaDWKD6IWQoiWR5KETUhStwj8AvRUlto4ureYpK4Rvg5JCCGEEEIIIbymqioH8ivYeLiIDYeL2XikiCOFlce1CzBo6d4qhJ6JofRqFUrPxFDiQvxQFEkICiGEr0iSsAnR6jS07x/DjpUZpK7JliShEEIIIYQQoklzulR2Z5lZe7CQ9YeL2HSkmKIKW502igKdYoPp3TqUXonurW1UoIwQFEKIJkaShE1Mp8Gx7FiZwcFtBVgtDowm+RYJIYQQQgghmgb3SMFy/thfyOoDBaw9WESpxV6njVGnoVdiKP2Tw+mXHEafpDCCZUERIYRo8iQD1cREtQ4iLC6A4uwKDmzKo8t58b4OSQghhBBCCNGCZZVY+H1/Aav3F7D6QCF5ZdY654OMOgakhDMgJZz+KeF0iw/BoJP51YUQormRJGEToygKnQbFsmbBAfauzZYkoRBCCCGEEKJR2Z0uNh8pZkVqPiv25pGaW1bnvFGnoV9yGEPaRjKkbQTdE0LQyaKLQgjR7EmSsAnqMCCWtd8dIHt/KaX5lYRE+fs6JCGEEEIIIcQ5LL/Myqp97qTgr2n5lFU5POc0CvRMDGVo20iGtIugT+sw/PRaH0YrhBDibJAkYRMUGGakVedwju4uInVtDgOubuPrkIQQQgghhBDnmP155SzelcOSXTlsyyitcy7MX8+FHaO5sGMUF3SIItTf4KMohRBCNBZJEjZRnQbFcnR3EXvX5tB/ZAqKrPwlhBBCCCGE+AtUVWV3tpmfd+bw884c0vLK65zvlhDMRR2jubBTND1bhcrqw0II0cJIkrCJSukVhcFPS1lhFZn7imnVKdzXIQkhhBBCCCGaGZdLZcvREhbvcicG04sqPef0WoUhbSO5vFssF3eKJjrYz4eRCiGE8DVJEjZReoOW9gNi2fVrJntWZ0uSUAghhBBCCOG1vTlmvtuSxcKtmWSVVnnq/fQaLugQxRXd4hjeKZoQk96HUQohhGhKJEnYhHUeEseuXzM5sCWfYZV2jP7yD7gQQgghhBCiflklFhZuy+K7LZnszTm2InGgUcdFnaK5olssF3SMwt8gvwYKIYQ4nvzr0IRFJwURkRBAYWYFaRty6XZBK1+HJIQQQgghhGhCSi12Fu3I5rutmaw7VISquusNWg0Xdozi+t4JDO8ULasRCyGEaJAkCZswRVHoPCSe379OY/cf2ZIkFEIIIYQQQqCqKmsPFvHFhnQW7czB5nB5zg1MCee63glc2S2OEHkTSQghxCmQJGET12FgDKvn7yc/vYyCjDIiWwX5OiQhhBBCCCGED+SXWflmUwZfbkjncOGxBUg6xgRxXe8ErukVT0KoyYcRCiGEaM4kSdjEmQINpPSM5MDmfPaszub8myVJKIQQQgghREvhdKn8lpbPF+uP8sueXBwu9/vEAQYt1/RKYPSARLonhKAoio8jFUII0dxJkrAZ6DwkngOb80ldl8OQ69uh1Wt8HZIQQgghhBDiLCqusDFvfTrz1qWTWWLx1PdKDGX0gESu6hFPgFF+nRNCCHHmyL8qzUBil3ACQo1UlFg5tL2Adn2jfR2SEEIIIYQQ4ixIzSlj9h+HWLAlE2v1XIPBfjpu6NOKWwck0ik22McRCiGEOFdJkrAZ0GgUOg2OZdOiI+z5I0uShEIIIYQQQpxDXC6V5XvzmL36EH/sL/TUd40PZtzQFK7qESerEwshhDjrJEnYTHQaHMemRUdI31NEWVEVQeF+vg5JCCGEEEII8ReUWx18vfEoc1Yf9ixEolFgRNdYxg1NoX9ymMw1KIQQotFIktBHFuaV8GlWAa93SCTF39hg+9Bof+Lbh5KVVkLq2mz6XZnSCFEKIYQQQgghzrSiChuzfj/EnDWHKatyABDkp2P0gNaMGZREYri/jyMUQoi/RlVVVFRcqgtVVXHh8uw7VScu1T2dgkutrq/Vtma/pr7OtWqOq69Zc67mGJVj7Wr1rX2N2vHVvt6f+9RpV+u8+38nqa/dv/q4of0QQwgXJ13cqN+j+kiS0Ec+zy7k1+JyPs8u5Om28V716TI0jqy0Evaszqbv5ckoGnmqKIQQQgghRHORU1rFB78e5PP16VjsTgDaRAYwbmgyN/RpJQuRCNEMOV1O7C67Z3O4HJ6t9rFnX3XgdDmPtVOPtXeqzrqly+lpX/tczfGf912qC4fLgUt1ec7V7LtUFw61+pzLiUp1ss7lPl/7uCah9+d2NfU111TVY4k7p8vpSdrV1AnvdQzrKEnCluz2+AhWFJXxRU4RT6TEofci4demTzT6L/ZhLqgiM62EVh3DGiFSIYQQQgghxF9xpLCCGasO8M2mDOxO9y/O3RKCeWh4Oy7rEotGHv4LccqcLidVziqqHFVUOauwOq1YHVZ3WWurclRhc9qwOq3YnDZsLve+3Wn3tLG77J46m8t2XGlz2uokAT3nXXbPyDTx1ygoaBQNiqKgobpUNCgonjoUPHUaRQNQp43nGtXH9Z33lLXa1P6aCsfqardTUHD/70911W1rPkPtfidqX7u+pn+rwFaN9md9MpIk9JFLI4KJ1OvIszn4pbCUK6JCG+yjN2hp3z+G3b9lsWd1liQJhRBCCCGEaMJSc8p4b+V+Fm7LwlU9qGZAcjgPXtSOYe0jZb5Bcc5zuBxUOiqptFdvjmNlhb3Cc2xxWLA4LFQ5qjz7deqc7rImIVjlqMLusvv649VLo2jQKTr0Wj06jQ69xl3qFJ27rK7TKlrPsVajrdNGq2jddcqx8zXta65fU1fTtqZeo2g8xzXn/1xXu6zZatcrilLnfO26moSaRqNBw7Fzta9Vk8CruYanj1I3+VfTtnbCTviWJAl9xKDRcEtcONPT8/g0q8irJCFAlyHx7P4tiwOb8xl2ix2jv/7sBiqEEEIIIYQ4JQfyy3lz6T5+3J7tqbugQxQPDm/HgJRwH0YmhPfsTjtmm5lyezlltjL3vs29X2Yro8zuLivsFZTbyt2lvW5pcVgaJVaj1ohRa8RP64dBa8BP5+epq9kMWoOnrNnXa/R16vQavXtfY0Cv1ddfavTotXp3WbNpjyUCtRpZiVw0X5Ik9KHb4iKYnp7HiiIzmVU2EvwMDfaJTg4iPD6AoqwK0jbm0W1YQiNEKoQQQgghhGhIZomF//6SxjebM3C6VBQFLu8ay4PD29EtIcTX4YkWyqW6KLOVUVxVTIm15FhpLabUWkqptRSzzYzZaqbUVuqpq3RUnrEYdIoOf70/AfoA/HX++Ourt+p9k86ESWfCT+vnOfbT+rlLnd+x89XJv5rzNccyCk2IM0OShD7Uxt/I4NAA1pRU8EV2EX9PiW2wj6IodB4Sxx/f7GfPH1mSJBRCCCGEEMLHCsqtTF+xn8/WpmNzuucnu6RzNH+/rCOd44J9HJ04F1XaKym0FFJYVUhRVZG7tBQdO7YUUlxV7EkEOlXnaX+tAH0AQYYg96YPItgQTKAhkCBDEIF6dxmgDyBQH0igIdCzH6AP8OzrtfIGnBDNgSQJfez2uAjWlFQwL7uQR5Nj0HrxBKTjwFjWzD9A3pEyCjLKiWwV2AiRCiGEEEIIIWozV9n58NeDzPz9EJU2dxJmUJtwnhjRib5JMn+4OHVVjipyK3PJrcgl35JPfmW+u6zeL7AUkG/Jp8JeccrXDtQHEmoMJcwvjFBjKKHGUEKMIYQYQwg2BHv2QwwhBBuDCTGEEGgIRKeRtIEQLYX81+5jI6NCeSYtk0yrnV+Lyhge0fCTRlOQgeSekRzcks/e1dmcd3P7RohUCCGEEEIIAWB1OJmz+jDTVxyg1OJePKFHqxCeGNGR89rJgiSifnaXndyKXLIrssmuyCanIofcilxyK3Pd+5W5lFhLvL6en9aPCFMEEX4RhPuFE2GqW4b7hRPmF0aY0Z0UlNF8QoiGSJLQx/y0GkbFhvFRRgGfZhd6lSQE6DwkjoNb8kldl8Pg69ui1WvOcqRCCCGEEEK0bKqqsnR3Lv/6aQ9HCt3ztbWPDuTvl3VkRNcYSQ62cE6Xk9zKXI6WHSWrPIvM8kxPmV2RTW5lLi7V1eB1TDoTMf4xRPlHEWWq3vyPlZGmSKJMUQQa5I0yIcSZJUnCJuC2uAg+yihgcUEp+TY7UYaGn/C07hJOQIiBilIbB7fl075fTCNEKoQQQgghRMu0N8fMyz/s5o/9hQBEBRl5YkRHbuzTCq1GkoMthd1lJ6s8i3RzOkfLjnK07CjpZemkm9PJLM/E7rKftL9BYyAuMI7YgFhi/WOJDYglJiCGGP8Y975/DMGGYEk4CyF8QpKETUDnQBN9gv3ZbK7ky+wiHkpqOOGn0WroPDSejT8dZtdvWZIkFEIIIYQQ4iwoqrDx5tJU5q1Lx6WCQafhnvNSmDC8HYFG+XXqXGW2mTlcephDpYeObeZDHDUfxaE6TthPp9HRKrAVCUEJxAfEEx8YT0JgAnEBcSQEJhBhikCjyFtgQoimSf5VayJuj4tgs7mSedlFPNg62qsnR52HxrFx0WEyU4spya0kNMa/ESIVQgghhBDi3Gd3upi75gjTftmHucqdFLqiWyxPX9mZxHC57z5XVNgrSCtOI60kjf3F+9lfsp+DpQcpsBScsI9JZ6JVUCtaB7WmdVBr935waxKDEon1j0Wr0TbiJxBCiDNHkoRNxLXRoTy3P5ODFitrSioYEtbw/BLBESZad4kgfVchu3/PYsiN7RohUiGEEEIIIc5tqw8U8Nx3OzmQ715BtnNcMC9c3YVBbSJ8HJk4XU6XkyNlR9hbuJe0kjTSitPYX7KfzPLME/aJ9o8mJSSFlOAUkkOSSQlJoU1IG6L9o2U0oBDinCRJwiYiQKfl+ugwPs0u5LPsQq+ShABdz48nfVche9ZkM/CaNrKAiRBCCCGEEKepqMLGv37cw7ebMwCIDDTw+GUdualfosw72IzYXXYOlhxkd+Fu9hTtYU/hHlKLU7E4LPW2jzZF0z6sPe1C29E+rD1tQ9uSHJwsC4MIIVocSRI2IbfFR/BpdiE/5Jfwij2BMH3D357k7hEEhBqpKLFycGs+7fvL3IRCCCGEEEKcClVV+WZTBq/+tIfiSjuKAmMGJfH4iI4E+zW8qKDwHZfq4nDpYbYXbGd7/nZ2Fe4irTit3gVETDoTHcI60CGsA+3D2tM+tD3tw9oTYgzxQeRCCNH0SJKwCekVZKJLgB+7K6r4NreYe1pFNdjHvYBJHBt/PMyu3zIlSSiEEEIIIcQpOJBfztPzd7DuUBEAnWKDmHJDd3q3DvNxZKI+JVUlnoTgjoId7MjfQZm97Lh2QfogOkd0pnN4Z0+ZFJwk8wUKIcRJSJKwCVEUhdviI3gmLZPPsgq5OyHSqwVMugyNZ9NPh8ncV0JxTgVhsQGNEK0QQgghhBDNV5XdyXsrD/DeygPYnC789Boeu6QDd52Xgl4rU/g0BaqqklmeyabcTWzK3cTmvM0cMR85rp2f1o8uEV3oEdWDbpHd6BLRhVaBrbz6XUoIIcQxkiRsYm6MCePlA1nsqahii7mSPiENJ/yCwv1I6hbB4R3uBUyGjmrfCJEKIYQQQgjRPK07WMjk+Ts4WOBemOTCjlG8fG03WbXYx1RV5VDpITbmbvQkBnMrc49rlxycTI+oHvSI7EGPqB60C2uHXiOvhQshxF8lScImJlSv46qoUL7JLebT7EKvkoQAXc5P4PCOQvauyWHgtW3Q6WUYvRBCCCGEELVV2Z38e3Eqs/44hKpCVJCRf17dlSu7x8qoMx/JLM9kTdYa1mStYWPuRoqqiuqc1yk6ukZ2pW9MX/rG9KVnVE+ZQ1AIIc4SSRI2QbfFR/BNbjHf5ZXwUrsEAnUNJ/ySuoYTGGakvNjKwS35dBgQ2wiRCiGEEEII0TxsO1rCpK+2ciDfPXrwln6JPD2yMyEmGYHWmMw2MxuyN7Am250YTC9Lr3PeqDXSM6qnJynYPbI7/noZ4SmEEI1BkoRN0KCQANr5G9lfaeW7vBJuj49osI97AZN4NvxwiF2/ZUmSUAghhBBCCMDmcPH28jTeXXkAp0slKsjI6zd256JOsuBfY3CpLvYU7mFVxipWZ61mZ8FOnKrTc16raOke2Z3B8YMZFDeIbpHdMGgNPoxYCCFaLpmRtwlSFIW/xbkTg59kFXjdr8vQOBQFstJKKMquOFvhCSGEEEL4RGZmJrfffjsRERGYTCa6d+/Oxo0bT9j+zjvvRFGU47auXbvWaTd9+nSSk5Px8/Nj4MCBrF+//mx/FNFIUnPKuP7dP3h7+X6cLpWre8az5NFhkiA8yyrtlSxPX84/V/+Ti7++mFt/vJX3tr3HtvxtOFUnycHJ3NrxVqYNn8Zvt/7GJ1d+woReE+gT00cShEII4UMykrCJujk2nNcOZrOtzMK2skp6BjU8xD4wzI+k7pEc3l7A7t+zOO8mWcBECCGEEOeG4uJihg4dyvDhw1m0aBFRUVGkpaURFhZ2wj7Tpk3jtdde8xw7HA569uzJTTfd5Kn78ssvmTRpEjNmzGDgwIFMnTqVESNGkJqaSnR09Fn9TOLscbpUPvztIG8u2YfN6SLMX8/L13Xjqh7xvg7tnJVTkcOqo6tYmbGS9dnrsblsnnP+On+GJgzlvITzGBw3mLjAOB9GKoQA90JBOByoLhc4nahOp7t0uVAdDqipr690uVCdLnA53ft/qqs5rnPO5QKnC1TXn86r7n1PvVr/vkqtdn/qU3MO9eTHqoqqVh+r6vFtVNW9obr/fFzqsbra9bXbnla9+1ilpg4MiYnEPv9c4/4lqIckCZuoSIOOq6JDmZ9bzJzMAt7s1Nqrfl3Pj+fw9gL2rs1m0HWygIkQQgghGt/ChQtPuc+ll16KyWQ64fnXX3+dxMREZs+e7alLSUk56TVDQkIICTm2wMF3331HcXEx48aN89S9+eabjB8/3lM3Y8YMfvzxR2bNmsVTTz11yp9D+F5GcSWPfrGVjUeKAbi4UzRTbuxOdJCfjyM79xw1H2XJkSUsPbKUXYW76pxLCEzgglYXcEHiBfSL6ScjBMU5Q1VVVLsd1WZDtVrdZc1mtx/b/nxc55wd1eFw7zuO1eNwoNod7nMOB6rjT3VOh/vY4U7quc/XJPiq2zmd7nZOl7u+JhHoqD5Xve9JVokmwdGpk69DACRJ2KSNjY9gfm4xC3KLeb5tPKH6hr9drbtGEBhupLzIyoHN+XQcKHMTCiGEEKJxXXfddafUXlEU0tLSaNOmzQnbLFy4kBEjRnDTTTexatUqEhISmDBhAuPHj/f668ycOZNLLrmEpKQkAGw2G5s2bWLy5MmeNhqNhksuuYQ1a9ac8DpWqxWr1eo5NpvNXscgzq6fd+bwj2+2Ya5yEGjU8fzVXbipbytZufgMOmI+wtIjS1lyeAl7ivZ46jWKhp5RPRnWahgXtrqQtqFt5c9dNArV6cRlsaBaLLiqqnBVWlCrLLgsVbgslahVVe79KgtqlRXVWoWryuqut1ahVlndpbU66We14rLV3rdW97N6En/nPEUBrRZFozm+1GhAq0HRaOuWSt22aBT3OY3G/bPgz3UaBTTVdUr1dWvqFQ0oCopWAyjV52q3U9xfp865Y/08xyc6X18bxf25lZrznrYcq0M51r92vaI59uf25+ugnKCeOtfT1nqo6UuSJGzCBoQE0CnAj70VVXydU8z4xKgG+2g0Cl2GxrP++0Ps+i1TkoRCCCGE8ImcnByvX9cNCgpqsM3Bgwd57733mDRpEk8//TQbNmzg4YcfxmAwMHbs2Ab7Z2VlsWjRIubNm+epKygowOl0EhNTd366mJgY9u7de8JrTZkyhRdffLHBrykaT5XdyZSf9jBnzREAeiWG8vbo3iSGy6q4Z8Lh0sMsPryYJUeWsK94n6deq2jpH9ufS5Mu5aLWFxFpivRhlKK5UG02nOXluMrK3GV5Ba6KE2yVlcdKi6W6rESttHjq1FoPbXxCr0ej16MYDO5Nrz+21T42GFB0OhTDsfPodO59XXWdToei17nrddXHNXVabXWdFkVX00aHotWCVueur7Xvbl9z3n2u9n6dsr5koGiRJEnYhCmKwtiESCbvy2BuVgH3tIr06mlcl6HxbPjxMNn7SynMKiciPrARohVCCCGEcBs7duxJXx3+s9tvv53g4OCTtnG5XPTr149XX30VgN69e7Nz505mzJjhVZJwzpw5hIaGnvIox/pMnjyZSZMmeY7NZjOJiYl/+bri9BwqqOCheZvZleUe0XnfsDY8PqIjeq38kvtXlFSVsOjwIr4/8D07CnZ46rWKloFxA7ks6TKGtx5OuF+4D6MUvqA6nTjNZpwlJbhKS9375jKc5lJc5jKcZjOuMjPOUjPOMrO7rrzMnQwsK0O12Rr+IqdDUdCYTCgmExo/PxSTHxqTv3vfz69WaUQxHisVPyMaox+K0Vhd59401aViMKIYDWgMhupjQ92EoCTUxDlEkoRN3KiYMF45kEVapZU/Sso5L6zhJ+0BoUaSu0dwaJt7AZPzb+7QCJEKIYQQQrjVnjfQG++9916DbeLi4ujSpUudus6dO/Ptt9822FdVVWbNmsWYMWMwGI7NixYZGYlWqyU3N7dO+9zcXGJjT/w2htFoxGg0Nvh1xdn3v62ZPD1/BxU2J+EBBt64uSfDO8qCM6fL5rTxa8avfH/ge37N/BWHywG4E4OD4gcxImkEwxOHE+oX6ttAxRmjulw4S0txFhfjLCrCUVSEs6gYZ3ERjqJinCUl7q201FO6zOYzMp+d4u+PNjAQTWAgmoCAWpu/u/R3l9qAABR/f/exqbr0N1UfmzznFINBXnEX4i+SJGETF6TTcmNMGHOzCpmTWehVkhCg67AEDm0rIHVtDoOva4vOIAuYCCGEEKL5Gjp0KKmpqXXq9u3b55lf8GRWrVrF/v37ufvuu+vUGwwG+vbty7JlyzwjDF0uF8uWLeOhhx46Y7GLM89ic/LPhbv4cuNRAAamhDPt1t7EhsjiJKdKVVW2F2zn+wPfs+jQIsy2Y3Nsdg7vzNVtr+aKlCvkVeJmRFVVXKWl2PPycBYW4igowJFfgKOwAKdn313vLCkBp/O0vo4mMBBtSAiakGC0QcFog4PRBAehDQ5BGxyEJri6LjAQbVAQmqAgNAGBaIPcSUFFJ+kIIZoa+a+yGbgzIZK5WYUsKigh12onxqhvsE/rzuEEhftRVlTF/s15dBoU1wiRCiGEEELUVVFRwWuvvcayZcvIy8vD5XLVOX/w4EGvrvPYY48xZMgQXn31VW6++WbWr1/PBx98wAcffOBpM3nyZDIzM5k7d26dvjNnzmTgwIF069btuOtOmjSJsWPH0q9fPwYMGMDUqVOpqKioswKyaFr255XxwKebScsrR1Hg4Yva8/DF7dFqZATRqTDbzHx/4Hu+2fcN+0v2e+qjTdGMbDuSq9tcTfuw9j6MUNRHtdmw5+XhyM7GnpOLIy8PR14e9rxcHHn5OHLddaf6Sq8mKAhteBi60DC04eHu/fBwtKFhaEND0YaFog0Jce+HhqINDnbPqSeEOKdIkrAZ6BJoon9wABvMFczLLuSx5IYXI1E0Cl3Oi2fdwoPsXJUpSUIhhBBC+MQ999zDqlWrGDNmDHFxcaf9Klj//v1ZsGABkydP5qWXXiIlJYWpU6dy2223edpkZ2eTnp5ep19paSnffvst06ZNq/e6t9xyC/n5+Tz//PPk5OTQq1cvfv755+MWMxFNw+JdOUz6cisVNifRQUam3tqLIW1lhJu3VFVlR8EOvkr9isWHF1PlrALAT+vHpUmXcnXbqxkQOwCtRt5C8hVneTn2jAz3lpWNPTsbe042jup9R36+16/6akND0UVFoo2IRBcZiS4iou5xZATa8Ah0YaEotaZiEEK0XIqqnoHJBM4xZrOZkJAQSktLG5xE+3SVr1pF8edfEPv8c+jj4xts/01OEQ/tSSfeqGf9oC7ovHhSWmm2MWfyH7icKjdN7kd00tn5LEIIIYRoXI1xr3KmhIaG8uOPPzJ06FBfh3LWNKfvR3PlcqlM/WUf/13uHvE2uE0Eb/+tN5GBMjekN8pt5fx48Ee+3vc1qcXHXttvF9qOmzrcxFVtryLYIH93G4PqcGDPzMSWfhR7pjsZaDua4UkMOktLG7yGoteji4tDHxODLiYGXUw0+uho9350NLroGHTRUWgk8SeEqObtvYqMJPSRwtkfU7l2LcWdOhL96KMNtr8qKpTn92eSZbXzS6GZy6NCGuzjH2ygXd9o9q3PZceqTC6+Q/7hF0IIIUTjCgsLIzxcVj8Vp89cZeexL7aybG8eAHcNTeHpKzuhk9WLG3So9BCf7v6U7w9+j8VhAcCoNTIieQQ3dbiJnlE9ZaGHs0B1OrFn52A7chjbkSPYjxzBevgw9sNHsGVmgsNx0v7asDD0rVqhj49HHxuLPj7OnRSs3rTh4bKirhDirJAkoY+EjR5N5dq1lHz9DVETJjQ4vNtPq2F0XATT0/OYk1XgVZIQoPuFrdi3Ppe09bkMvaEdfoEyb4QQQgghGs/LL7/M888/z5w5c/D39/d1OKKZ2Z9Xxr1zN3GwoAKjTsOUG7pzQ59Wvg6rSVNVlfU565m7ey6/ZvzqqU8JSeGmDjdxTdtrCDF697uEODnVZsOWno71wEGsB/Zj238A64ED2A4dOumcgIrRiD6xFYZWiehbtcKQ2MqdFGyViD4hAW1gQCN+CiGEOEaShD4SdNFwdNHROPLyMC9dSsjIkQ32uSM+gnfT81hRVMZhi5VkU8OvV8SkBBPVOoj89DJ2/5FFnxENrwAohBBCCPFX9O7du87opP379xMTE0NycjL6P010v3nz5sYOTzQTS3fn8tiXWym3OogP8eP9Mf3o3kqSWydic9pYdGgRc3fPZV/xPgAUFC5IvIA7utxBv5h+MmrwNKkuF/aMDKpSU7HuTcWaluZOBh45cuJRgXo9hsREDElJ7i05ybOvi42VkYBCiCZJkoQ+ouj1hN50EwXTp1P8+edeJQmTTEYuDA9iRVEZczMLeb5dw3MZKopC9wsTWD53LztXZdLr0tZoZOU3IYQQQpxF1113na9DEM2Yy6UybVka05alATAwJZzpt/WR+QdPoLiqmK/3fc3nez+nwFIAgEln4tq213J7l9tJCpZBAqfCVVFBVeo+rPtSqdq7F2vqPqypqbgqK+ttr/H3x9CuHcY2bTC2a4uhTVuMbdugT0hA0cmv20KI5kV+avlQ6M03UTBjBpaNm6hK3Ydfxw4N9rkzIZIVRWV8kVPIP1Ji8fNiLpb2/WL449v9lBVVcWRHASk9o85E+EIIIYQQ9XrhhRd8HYJopiptDh75YitLd+cCcOeQZJ4Z2Rm9zD94nLzKPGbvnM03+77xrFIcbYpmdOfR3NThJnml2Asui4WqPXup2rmTql07sezche3gwXpXD1YMBozt2mHs1Aljh/YY27XH2K4tupgYGaEphDhnSJLQh/QxMQRdfDFlS5ZQ8uUXxD7/fIN9LokIJsGoJ9Nq54f8EkbFNjwRuM6gpcvQeLYsSWfHqkxJEgohhBBCiCYnz1zF3XM2siOzFINOw5Tru3NjX5l/8M+yy7OZuXMmC9IWYHO5573rHN6ZO7rewYikEei1Mgd5fVSHg6rUVCzbtlG1cxdVO3di3b8fXK7j2uqiozF26ohfx04YO3bEr1NHDMnJMjJQCHHOk59yPhb2t9GULVlC6Xf/I2rS3xucpFarKIyJj+C1Qzl8nFngVZIQoNuwBLYsTefo7iKKcyoIi5XJcIUQQgjhO2PHjuXo0aMsX77c16GIJmBfbhnjZm8gs8RCRICBD8f2o0/rMF+H1aQcNR9l5s6Z/O/A/3C43PPg9Y7uzX097mNI/BAZzfYnjuJiLNu2YdmyFcuWLVh27EC1WI5rp4uKwq9bN/y6dcXUrRt+Xbuii4z0QcRCCOF7kiT0Mf+BAzGkpGA7dAjz9wsJGz26wT5/i4vgP4dz2GiuZFe5ha6Bpgb7BEeaSO4eyeHtBexclcn5tzT8arMQQgghxNkSHx+PRibuF8DvaQU88OkmyqwO2kQGMHtcf5Ii5IF2jUOlh/hw+4f8dOgnnKoTgIGxA7mv532yGEk1VVWxZ2ZSuW49lZs3Ydmy1f3a8J9ogoMx9eyJqXt3d2Kwa1f0MdE+iFgIIZqmJnFnNn36dJKTk/Hz82PgwIGsX7/+pO2//vprOnXqhJ+fH927d+enn346Ydv7778fRVGYOnXqGY76zFAUxZMYLJ73OWo981/8WbRRz5VRoQDMySzw+mt1vzABgL1rsrFVnWAVLiGEEEKIRjBlyhRmz57t6zCEj3218Sh3zl5PmdXBgORw5k8YIgnCahllGUz+bTLXfnct3x/8HqfqZGjCUD654hM+GvER/WP7t+gEoT0zk5L5C8h6ajL7L76YA5dcSvYzz1D67XxPgtCQkkLIDTcQ+/JLtPnhezqsXUPrDz8g6uGJBF00XBKEQgjxJz4fSfjll18yadIkZsyYwcCBA5k6dSojRowgNTWV6Ojjf2ivXr2a0aNHM2XKFK666irmzZvHddddx+bNm+nWrVudtgsWLGDt2rXExze8CrAvhVx3LXlvvYU1LQ3Lpk349+vXYJ+x8REszCvhm9xinmsbT5BO22CfxE7hhMb4U5Jbyb51OXS7QOZ4EUIIIUTjM5vNfPbZZ8ycOZONGzf6OhzhA6qq8ubSfby9fD8A1/aK5/9G9cDoxT3tua7QUsgH2z/gq31feV4rvjDxQu7rcR/dIrs10Pvc5cjPp2L1airWrady3TrsmZl1G+h0mLp3x79fP0x9emPq1QtdmLyyLoQQp8LnScI333yT8ePHM27cOABmzJjBjz/+yKxZs3jqqaeOaz9t2jQuv/xynnjiCQBefvllli5dyjvvvMOMGTM87TIzM5k4cSKLFy9m5MiRJ43BarVitVo9x2az+Ux8NK9pg4MJuWokJV9/Q/G8z71KEg4JDaS9v5G0Sitf5hRxT6uGFyNRNArdLkjg96/S2LEqk67DElr000chhBBCNK4VK1Ywa9Ys5s+fT0hICNdff72vQxI+YHU4efKb7Xy3NQuAiRe1Y9KlHVr8fWmFvYI5u+YwZ9ccKh2VAAyOG8wjfR+ha0RXH0fX+FSbjcotW6n4/TfKf/sd6969dRtotZi6dcN/4ED8BwzAv09vNP7+vglWCCHOEV4lCbdv337KF+7SpQu6BlZ/stlsbNq0icmTJ3vqNBoNl1xyCWvWrKm3z5o1a5g0aVKduhEjRvDdd995jl0uF2PGjOGJJ56ga9eG/0GdMmUKL774YoPtzqbQW2+l5OtvMC9dSkxBQYOT5SqKwt2tonhqXwazMgq4KyESjRc3Vp0Gx7H2fwcpyqoga18JCR3l6ZoQQgghzp7MzEw+/vhjZs+eTUlJCcXFxcybN4+bb765xSeFWqLSSjvjP9nI+kNF6DQKr17fnZv7J/o6LJ+yOW18ve9rPtj+AUVVRQB0jejKo30fZVDcIB9H17hsR49S/ttvVPz+B5Vr1+KqrDx2UlHw69qVgMGD8B8w0J0UDJBX04UQ4kzyKknYq1cvFEXxar48cCf69u3bR5s2bU7arqCgAKfTSUxMTJ36mJgY9v75SVG1nJycetvn5OR4jl9//XV0Oh0PP/ywV/FOnjy5TuLRbDaTmNi4Nyumrl0x9eyJZds2Sr75lsj772uwz00xYbx6MIuDFivLi8q4JCK4wT5Gk46OA2PZ9WsmO1ZmSJJQCCGEEGfFt99+y8yZM/n111+54ooreOONN7jiiisICAige/fukiBsgfLKqrhj5nr25pQRZNTx3u19Oa99y11FVlVVfjr0E29veZvMcvers0nBSUzsPZHLki5rEf+NqC4Xlm3bKF++nLJly49bbEQbEUHgeUMJOO98AoYOQRce7qNIhRCiZfD6deN169YRFdXwK62qqh43N2Bj2rRpE9OmTWPz5s1e/8NqNBoxGo1nObKGhf1tNJZt2yj+8ksixt+Doj35nCwBOi1/i4tgxtF8Pjqa71WSEKD7BQns+jWTg9sKKCuqIijc70yEL4QQQgjhccstt/Dkk0/y5ZdfEhQU5OtwhI8dLark9pnrOFJYSVSQkbl3DaBznHf3rueiXQW7mLJ+CtvytwEQZYri/p73c33769Fr9D6O7uxyWa1UrFlD+bLllK1YgbOg1kKMOh3+vXsTcP75BJ43FGOnTiiyCroQQjQar5KEF1xwAe3atSM0NNSriw4bNgyTydRgu8jISLRaLbm5uXXqc3NziY2NrbdPbGzsSdv/9ttv5OXl0bp1a895p9PJ3//+d6ZOncrhw4e9+gy+EHT55WinvIYjO5vyVasIuuiiBvuMS4jkg6P5rCwuY19FFR0CGk74RSQEktAhlMx9Jez6LZNB17Y9E+ELIYQQQnjcfffdTJ8+nZUrVzJmzBhuueUWwmQRgRYpLbeM22euI9dsJTHcxKd3D2yxKxgXWgr575b/siBtASoqJp2J8d3Hc3uX2zHpGv79qblylpe7Rwv+sozy339HrfUasSYwkMBhwwi65GICzj8frTxUEEIIn/HqscyKFSu8ThAC/PTTT8TFxTXYzmAw0LdvX5YtW+apc7lcLFu2jMGDB9fbZ/DgwXXaAyxdutTTfsyYMWzfvp2tW7d6tvj4eJ544gkWL17s9WfwBY3RSMiNNwBQPO9zr/okmYyMiAwBYGZGvtdfq2Zl492/Z+G0u04xUiGEEEKIk3v//ffJzs7m3nvv5fPPPycuLo5rr70WVVVxueTeo6XYdrSEm99fQ67ZSoeYQL65f0iLTBDaXXbm7prLVQuuYn7afFRUrm5zNT9c/wPje4w/JxOELosF86JFZEycSNqQoWT940nKlixBraxEFxND2N9GkzjzIzqs/oOEN98g+MorJUEohBA+5vPVjSdNmsTYsWPp168fAwYMYOrUqVRUVHhWO77jjjtISEhgypQpADzyyCNccMEFvPHGG4wcOZIvvviCjRs38sEHHwAQERFBREREna+h1+uJjY2lY8eOjfvhTkPYLbdQNGs2Fb//ji09HUOtEZEncnerSBYVlPJVTjFPt4kjRN/wtzWlVyQBoUYqSqzs35xHx4H1j9wUQgghhDhdJpOJsWPHMnbsWNLS0pg9ezYbN25k6NChjBw5klGjRnHDDTf4Okxxlqw+UMD4ORupsDnpmRjKx3f2JyzA4OuwGt3qzNW8vuF1Dpa659vrEtGFyQMm0yu6l28DOwtcNhsVv/+O+cefKFuxos6IQUNKCkGXjyDo4kvw69qlRcy5KIQQzc0pJwmdTicff/wxy5YtIy8v77gnwcuXLz+l691yyy3k5+fz/PPPk5OTQ69evfj55589i5Okp6ejqTUPxZAhQ5g3bx7PPvssTz/9NO3bt+e7777z6TyIZ5KhdWsCzj+Pil9/o/iLL4n5xxMN9hkaGkinAD/2VlTxeXYR97eObrCPVquh27B41i08xI6VGZIkFEIIIcRZ1b59e1599VVeeeUVfvzxR2bOnMno0aOxWq2+Dk2cBUt25fDQ51uwOVwMbRfBB2P6EWD0+fiERpVZnsnr619nxdEVAIT7hfNIn0e4rt11aJRzZ5491eWicv16Shd+T9nSpbjKyjzn9K1aEXzFFQSPvBJjx46SGBRCiCZOUb1dsrjaQw89xMcff8zIkSOJi4s77gf9W2+9dUYD9AWz2UxISAilpaUEBzf+hMplK1aQ8cAEtCEhtFu1Eo1fw/MMfpZVyN9Tj5LoZ2DtoM5ovfgHuNJsY87kP3A5VUY92Y+YlJY7ebQQQgjRnPj6XuVMycvLIzq64YebTd258v04U+ZvzuCJb7bjdKlc1iWG/47ujZ/+5AvynUscLgef7fmM6VunY3FY0Ck6Rncezf097yfYcO78/bAdPUrpgu8o/e477FlZnnpddLQnMegnK5kLIUST4O29yik/zvviiy/46quvuPLKK/9SgOLEAocNQx8fjz0rC/Oinwm9/roG+1wfE8YrB7I4WmVjaYGZy6NCGuzjH2ygff8YUtfmsG1ZOpfdc26MxhRCCCGEby1cuJArrrgCvf7kq7TWJAh/+uknhg8f7tXCd6Jpm7vmMM//bxcAo/q24rUbuqPTnjuj5hqyt2gvL6x+gd2FuwHoG9OX5wY9R9vQc2OhQFdFBebFSyidP5/KjRs99ZqgIIKvuIKQq6/C1LevrEgshBDN1CknCQ0GA+3atTsbsYhqilZL6K23kv/mmxR/9hkh113b4BM4f62G2+MjeDs9jw8z8r1KEgL0vDiR1LU57N+cz+CiKoLCGx61KIQQQghxMtdffz05OTlERUV51f7WW29l69attGnT5ixHJs6mOasP88JCd4LwrqEpPDuyMxpNyxhFZnFYeG/be8zdNRen6iRIH8SkfpO4of0Nzf7VYlVVsWzaRMm38zEvXnxsnkFFIWDIEEJuuJ6giy/26u0nIYQQTdspJwn//ve/M23aNN555x0ZOn4WhY66kYJ33qFq504sW7fi37t3g33GJkTy7tE8/igpZ0+5hc6BDT+Nj0oMIqFjKJmpJexYkcGQGyUBLIQQQoi/RlVV7rzzToxGo1ftq6qqznJE4myrnSCccGFbnhjRcuafW5O1hpfWvERGeQYAlyVdxlMDniLK37skeVPlLK/A/P1Ciud9jjUtzVNvSEoi5PrrCbnuWvSxMq+5EEKcS7xKEv551bnly5ezaNEiunbtetxrJPPnzz9z0bVguvBwgq+6itL58yn+5BOvkoSt/AxcERnCD/mlfJSRzxudGl4ZGaDnxa3JTC1h1+9Z9BuZjMGvZU0qLYQQQogza+zYsafU/rbbbpO5/JqxlpogLKkq4d8b/83CAwsBiPGP4dlBz3Jh4oW+Dewvsu7fT/G8zyn93/9wVVQAoJhMBI+8ktAbbsDUu3eL+P4KIURL5FU2KCSk7qur119//VkJRtQVfscYSufPx7x4CdE5OV49qRvfKoof8kv5NreYZ9rGE65v+Fuc3C2CkGgTpXkW9q7JpsfwxDMRvhBCCCFaqNmzZ/s6BNFIaicIH2hBCcJVR1fxwuoXKKwqREHh1k638nDvhwk0BPo6tNOi2u2ULVtO8bx5VK5f76k3JCcT9rfRhFx3HVpJ5AshxDnPqySh3Oided/t/45Pd3/Kfy74D8khyfW28evUCf/+/ancsIHieZ8TPemxBq87ICSA7oEmdpRb+CyrkIlJMQ32UTQKPS9K5Ncv9rFteQbdLmjVYuaPEUIIIYQQp2fumroJwn+0gARhhb2Cf2/4N9+mfQtA25C2/HPIP+kV3cu3gZ0mp9lM8RdfUvzppzjy8tyVGg2BFw0n/G9/w3/w4HP+eyqEEOKY5j2LbjP2y5FfSC1OZd7eeSdtF3bHGABKvvoKlxfz9SiKwj2t3POffJxZgMOlehVPp8FxGP11mPMtHN5e4FUfIYQQQgjRMtVexfj+C1pGgnBL3hZGLRzFt2nfoqBwR5c7+PLqL5tlgtCelUXulNfYf+Fw8t98E0deHtqICCLuv492y34h8Z13CBgy5Jz/ngohhKjrjCUJn376ae66664zdblz3m2dbwPcIwrNNvMJ2wVddBH6hAScJSWUfv+9V9e+NjqUCL2OTKudRQWlXvXRG7V0PT8BgG3LjnrVRwghhBBCtDx/ThA+efm5nSC0O+1M2zyNO3++k4zyDOIC4pg5YiZP9H8Co9a7xXmaiqq9e8l84h/sv/QyiubMwVVZibF9e+KmTKHdiuVEP/oo+rg4X4cphBDCR85YkjAjI4NDhw6dqcud8wbFDaJdaDssDgsL0hacsJ2i1RJ2mzuhWDz3E1S14ZGBfloNd8RHAPBRRr7XMXW/0P2acVZaCXlHTpy4FEIIIYQQLVPtBOF9F7Q55xOEacVp/O2nv/HRjo9wqS6uaXsN317zLf1j+/s6NK+pqkr5H3+QftfdHLrueszffw9OJ/4DB5L4wfukLPwfoddfh8Zg8HWoQgghfOyMJQnnzp3LihUrztTlznmKonhGE36+93OcLucJ24aOuhHF3x9rWhqVa9d6df2xCZHoFFhXWsH2skqv+gSGGWnbNxqQ0YRCCCGEODMOHjzo6xDEGfLVhqN1EoRPXd7pnE0QulQXc3bN4ZYfbmFv0V5CjaG8eeGb/Ou8fxFkCPJ1eF5RVZWy5Ss4POomjt59DxWrV4NGQ/CVV5L8zTckzfmYwGHDztnvoRBCiFN3RpKEqqqyaNEiRo0adSYu12KMbDOSEGMImeWZrMxYecJ22uBgQq+7DoCiuZ94de1Yo56ro0IB+PAURhP2usS9svH+jXmUF1u97ieEEEIIUZ927doxfPhwPv30U6q8mF9ZNE0/7cjmqfnbAbjnvJRzOkFYVFXEhGUT+M/G/2B32Tk/4XwWXLuAS5Mu9XVoXlFVlbIV7uRgxoQJVO3ahWIyETZmDG2XLCHhzTcwdevq6zCFEEI0QX8pSXjo0CGee+45WrduzfXXXy83fqfIpDMxqr07sfrp7k9P2jbs9tsBKF+5Elt6ulfXvzfRPSrwu9wSsq02r/pEJwUT1y4El0tlx6oMr/oIIYQQQpzI5s2b6dGjB5MmTSI2Npb77ruP9evX+zoscQp+S8vnkS+24FLh1v6JPDOy8zmbINyUu4mbFt7EH5l/YNQaeW7Qc0y/eDqRpkhfh9agOsnBB6qTg/7+RIy/h3bLfiH2macxtErwdZhCCCGasFNOElqtVj777DMuuugiOnbsyKuvvsqkSZPIy8vjhx9+OBsxntNu7XQrWkXLxtyN7C3ae8J2xjYpBAw7H1SVok9PnlCs0TvYn0EhAdhVlY8yvF+xuNfFrQHY9WsmduuJX4MWQgghhGhIr169mDZtGllZWcyaNYvs7GzOO+88unXrxptvvkl+vvdvPIjGt+lIMffO3YTdqTKyexz/ur77OZkgdKkuPtrxEXcvvps8Sx4pISnMGzmPmzve3OQ/r6qqlK1cyeGbbj4+OfjLUqL//nd04eG+DlMIIUQzoKjerIQBbNq0iZkzZ/L555/Trl07xowZwy233EKrVq3Ytm0bXbp0OduxNhqz2UxISAilpaUEBwef9a/3xKon+Pnwz1zX7jpeHvryCduV//Y7R8ePRxMQQLtVK9EGBjZ47SUFpdyx4xBBWg2bh3QlSKdtsI/LpfLZ82swF1RxwegOdLug1Sl9HiGEEEKcXY19r3ImWa1W3n33XSZPnozNZsNgMHDzzTfz+uuvE9dMV1Vtzt+Pk9mTbeaW99dgrnJwfvtIPhrbD6MX95LNTVFVEU//9jR/ZP0BwFVtruK5Qc/hr/f3cWQNq1i7jrw33qBqxw4AFJOJ8Nv+Rvhdd0liUAjRJKiqilMFJ+7Spao4VRUn4FRV1FrnnKqKWl3vVMGFiqu6jwtwqu7ruarbuHCfU8HdrqZ9nfqadniO65S19tWaNtXXqd3u2PljX6+m3lWdVqvdxlXrejV9arJvx65X3Q6INegZnxh11r4P3t6r6Ly94MCBA5k4cSJr166lY8eOZyRI4XZb59v4+fDP/HjwRx7t8ygRpoh62wWcNxRDmzbYDh6kdP4Cwu8Y0+C1L4kIpr2/kbRKK59lFXJ/6+gG+2g0Cj0uSuT3r9LYtjyDrucnoGia9hNUIYQQQjRtGzduZNasWXzxxRcEBATw+OOPc/fdd5ORkcGLL77ItddeK68hNyGHCyq4Y9Z6zFUO+iaF8f6YvudkgnBT7ib+seof5FnyMGqNPDPwGa5rd12THz1YtW8feW+8QcWqXwFJDgpxLlBVFZuqYnepWF0qdlXF5nJ56jxlrXOO6j6OWuftqoqjet+h1hzzp+Nam8t9vnads7qPs04dtc65z7tqnXNxbN9Zq3T5+g+2mega6HdWk4Te8jpJePHFFzNz5kzy8vIYM2YMI0aMaPL/eDYXPaN60j2yOzsKdvD1vq+5v+f99bZTFIXwMbeT8+JLFH32KWG334aiOfkb4xpF4f7EaP6eepQPM/K5u1UUei8Sfp2HxLF+4UFKcis5srOQ5B5Nfx4WIYQQQjQ9b775JrNnzyY1NZUrr7ySuXPncuWVV6KpvodJSUnh448/Jjk52beBCo+c0ipun7mO/DIrnWKDmDW2P/4Gr39taBZcqotZO2fx9pa3cakuUkJS+M8F/6FDWAdfh3ZS9txc8t9+m9L5C8DlAp2OsFtuIXLCA+gi6h9oIIQ4NWp1Iq3S6cLicmFxqlQ6nVhcKlU1dS4XVU61unRR5XJR5VKpcrmw1iqt1e2stY6tqru0VScDbdXnbN695HnO0QAaBbSKggYFrVJ9jIJGUdAoNW2UOqVWUVCq+x475+6nKKCpLo8dg6KAwrG2Cu5r1G5f+1xN6qTmnELNdRTP3H2evp7rV7erjo/qY6X62LPV6ofiHknYFHj9r/3ixYs5evQos2fP5oEHHsBisXDLLbcASLLwL1IUhds638ZTvz3Fl6lfcne3u9Fr6/8LEnLtteS9NRX7kXTKV60iaPjwBq9/Y0wYrx3KJtNqZ2FeMTfGNvx00eCno8v5CWxdms7WZUclSSiEEEKI0/Lee+9x1113ceedd57wdeLo6GhmzpzZyJGJ+hRX2Bgzcx0ZxRaSI/yZe/cAQvybxi8uZ0qZrYynfnuKXzPco/Caw+vFzvJyCj/6iKKP56BWLxYZNGIE0Y89ikES7KKFq3K6KHM6qXC6KHc4KasuK5wuyqv3y50uKqrbVFZvFZ7SSaXrWL3F5cLZBPJ1WgUMigaDRkGvKBg0imdfryjoNQoGRYNO426nq26jqz6nU0Cv1D5Wjjt2b9TaV9BpFLSArlYbba12WqX6vOJO4umqE3zu7Vi9lurE35/6aarbaasTbZJPalq8npPwz5YuXcrs2bNZsGABiYmJjBo1ilGjRtGnT58zHWOj88W8MnannRHfjiDfks+r573K1W2vPmHb3P/7N0WzZuE/eBBJs2d7df2ph3N47VAO3QJNLO3Xwav/EMuKqvjk2TWoLpVbnu1PZKsgrz+PEEIIIc6e5jQH3uHDh2ndurVn5GANVVU5evQorVu39lFkZ05z+n6cTLnVwW0frmVbRimxwX58ff9gEsObbuLsdBwqPcTDyx/msPlws3i9WLXbKf7qKwqmv4uzqAgAU58+RD/xOP69e/s4OiH+OlVVqXC6KHE4MTuclNjdZanDSanDQanj2HGZw4XZ4aTM6aTM4cTscFHmcJ7VEXg6Bfy1GkwaDabq0k+jwU+r4Kc5Vu+nUarrNRir940aBaOm/mODomDUatylxp0IrDlvqE7iaZvozyXRPHl7r3LaScIaxcXFfPrpp8yaNYvt27fjdDb/1XB9daP3wfYPeHvL23SN6MrnIz8/4c2KPTOT/ZdeBi4XKQv/h1+Hhl+LKLI76Lt6NxaXi697tuX8cO8Sfos/3Mn+TXl0GBjDpeO6ntLnEUIIIcTZ0ZySUlqtluzsbKKj686LXFhYSHR0tNw7NhF2p4u7Pt7Ab2kFhPnr+eq+wbSPObceEP+a8StP/vok5fZyYvxjmHbRNLpGNN3724q1a8l5+RVsBw4AYEhOJvrxvxN48cVNNqkpWjZVVSl1OCm0Oyi0Odyl3UmR3UGx3UGx3Umxw0GJp85JicOB4wzl+Py1GgK1GgK1WgJ11aVWQ6DOXfprNQRoNQRotbX2a+rddf7VSb+axKA3U3UJ0Ryc8YVLTiQsLIyJEycyceJENm/e/Fcv16KN6jCK97e9z67CXWzL30av6F71ttMnJBB0ySWULVlC8SefEvfySw1eO1yvY3RcOLMyC3j3aJ7XScLel7Vm/6Y80jbkMfCaNgRHmE7lIwkhhBCihTvR8+jy8nL8/PwaORpRH1VVeWbBDn5LK8Ck1/LxuAHnVIJQVVVm7pzJfzf/FxWVPtF9eOPCN4g0Nc3pdOw5OeS+/jpli34GQBseTtTEhwgdNQpFf269+i2aPodLpdDuIN9mJ8/mIN/mIM9mp6C6zPckAx0U2U8/4adXFEJ0WkL1WoJ1WkJqbcF/2gK1Gs9+kE5LUHUiUEbeCfHXeZUk3L59O926dTvuNZE/q3nVeNeuXXTs2BGd7tya4PhsC/cLZ2SbkSzYv4BPdn9ywiQhQPgdYyhbsoTShQuJmvQYurCwBq9/X2IUH2cWsKKojN3lFroENpzwi04KplWnMDL2FrP1l6MMu6VpT+YshBBCiKZh0qRJgHuuoeeffx5//2OvrTqdTtatW0evXr18FJ2o7Z3l+/lqYwYaBd75W296Job6OqQzxuKw8Pwfz/PzYXfC7aYONzF5wOQTzv/tS6rNRuGcORS8NwO1shI0GsJGjybq4YloQ0J8HZ44x6iqSqHdSa7NTrbVTq61urTZybFWb9XJwFPN+wVqNUTodUQYdETodYTrdYTptYTrdYTqtIRVH4fpdYTptITqdZg0ioyQFaIJ8CqL17t3b3JycoiK8m455sGDB7N161batGnzl4JriW7rfBsL9i9gWfoyssuziQusf4JvU9+++HXpQtXu3RR//jlREyY0eO0kk5GRUaF8n1/CjKN5/Ldzklcx9RmRRMbeYvb8nkX/kcmYAg2n9JmEEEII0fJs2bIFcP8iumPHDgyGY/cPBoOBnj178vjjj/sqPFFtwZYM3li6D4AXr+3GxZ1jfBzRmZNVnsUjKx5hb9FedIqOyQMnc3PHm30dVr3K//iD3Ff+he3QIcA972Dsc8/i17mzjyMTzZXF6SLTaiOzyk5mle3YvtVGVnVZ5fIu/acBIgw6og06ovR6oozuMtqgI8pwLBlYkxg0NjC4SAjRdHmVJFRVleeee67OE+CTsdlsfymolqxjeEcGxA5gfc56Pk/9nEl9J9XbTlEUwu+6i6zHH6f408+IuOsuNF68svNA6yi+zy9hQW4Jk9vEEWdsOOHXqlMYUa2DyE8vY8eKDAZcLclfIYQQQpzcihUrABg3bhzTpk1rtnP1nctWHyjgH99sB+C+YW0YM8i7B8jNwYacDfx95d8pthYT7hfOmxe+Sd+Yvr4O6zj2rCxyX3udsiVLANBGRBD9xOOEXHutjKoSJ+VUVbKsdtItVtKrbKRbbLVKK7k2h1fXidDriDPqiTHoiTXqiDXq3ZvBXcYY9EQYdPIqrxAthFdJwmHDhpGamur1RQcPHozJJHPXna7bOt/G+pz1fLvvW+7vcT/++vqTs8GXjyD/zTexZ2VR+t3/CLv1lgav3Sc4gEEhAawtreCjjAKeaxvfYB9FUeh9WWuWfLSL7Ssz6H1ZEnqj9pQ/lxBCCCFantmzZ/s6BFGPfbll3PfJJuxOlZE94njy8k6+DumMWXhgIS/88QIO1UHn8M5MGz7thG/n+IrqclH82Tzy3nrr2KvFt91G1MSH0EpCXVRzqSqZVjuHKq0ctFiPlRYrRyw27A2sQRqg1dDKz0C8UU8rPwMJRj3x1WWCn4E4o15G/Qkh6vAqSbhy5cqzHIao7YJWF9AqsBUZ5Rn8cPCHE74Woeh0hN85ltxXp1A0ezahN41C0TacvJvQOpq1Ow4xN7OAR5NiCNI13Kdtn2iCow5izrew+/csel6ceMqfSwghhBAtww033MDHH39McHAwN9xww0nbzp8/v5GiEjXyzFWMm72BsioH/ZLCeOOmnmjOgRU8VVVlxvYZvLv1XQAuT76cl4a+hEnXtAYvWA8cIPvZ57BUv5Jv6tuX2Oefw69jRx9HJnylyunigMVKWkUVqRVV7KusYn+llcMWK9aTvBJsUBRa+Rlo7WegtammNHqOw3RaGZEqhDglsrJIE6TVaPlb57/xfxv+j0/3fMqoDqPQKPU/4Qm98Ubyp7+L7cgRypYtI/iyyxq8/iURwbT3N5JWaeWzrELubx3dYB+NRqH3pa1ZNS+Vrb+k0+3CBLRaeeokhBBCiOOFhIR4fjENkQUXmpQKq4O75mwgs8RCSmQAH97RDz99839DxO6y89Kal/hu/3cA3N3tbh7u8/AJ76F9QbXbKZw5i4Lp01HtdjT+/kQ/8Tiht9yCIqO5WgSry8X+Siu7yy3sragirTohmG6x4TpBH50CSX5G2vgbaWMyklKrjDfq5TVgIcQZpahqA2OUWyCz2UxISAilpaU+mz+n3FbOpd9cSrm9nLcvepsLEy88Ydu8qVMpnPE+pp49Sfric6+eFn2WVcjfU4+SYNSzdlAX9F48PXbYncx9Zg0Ws42L7+xMp0FN67UNIYQQoqVoCvcq4pjm8v1wOF3c+8kmlu/NIyLAwPwJQ0iKCPB1WH9Zua2cSSsnsSZ7DRpFwzMDn2lyC5RU7d5N1jPPYt2zB4CAYecT989/oo9veOof0fyoqkqOzc7u8ip2l1vYU+Eu91dW4TjBb98hOi0d/P1oH2CsLv1o62+kldGA7hwY6SuE8C1v71VkJGETFWgI5KaONzF752xm75x90iRh+O23UzRrNpZt27Bs3ox/34YnZb4xJozXDmWTabXzfX4JN8SENdhHp9fS86JWrP3uIFuWpNNxQCyK/IMlhBBCiJM4dOgQDoeD9u3b16lPS0tDr9eTnJzsm8BaGFVV+ef3u1i+Nw+jTsOHY/udEwnC3IpcJiybwL7ifZh0Jv5zwX8Y1mqYr8PycFmtFEx/l8KZM8HpRBsSQswzTxN89dXyGug5QlVVjlbZ2F5mYXtZJdvKLOwor6TI7qy3fYhOS+cAPzoFmujgb6RDgB8d/P2IMujk74QQwuckSdiE3d75dj7Z/Qmb8zazNW8rvaJ71dtOFxlJyLXXUvL11xTOnOVVktBPq+HuhEheO5TDu+l5XB8d6tU/St2GJbDp5yMUZVVweGchKT0iT/VjCSGEEKIFufPOO7nrrruOSxKuW7eOjz76SOa+biSfrD3Cp2vTURSYdmtv+rRu+AFxU7eveB8TfplAbmUuEX4RTL94Ol0ju/o6LA/Ltm1kPTUZ26FDAARdfjmxzz6DLlLun5uzbKuNLWZ3MnCbuZLtJ0gIahVoa/KjS6AfXQJNdA5wl/FGvSQDhRBN1iknCSsqKggIaP5PHZuDaP9ormpzFd/t/46Pd33M1OipJ2wbPm4cJd98Q/ny5VgPHMDYtm2D178jIZJpR/LYWW5hZVEZwyMafj3G6K+n27AEtixJZ8viI5IkFEIIIcRJbdmyhaFDhx5XP2jQIB566CEfRNTyrN5fwIvf7wbgycs7cXm3WB9H9NetzV7LYyseo9xeTkpICu9d8h4JgQm+DgsA1eGg4P33KXj3PffowahIYp9/nuBLL/V1aOIU2VwudpZZ2GiuYKO5kk2lFWRa7ce10ysKnQP86BHkT48gEz2C/OkU4IefzOEuhGhmTjlJGBMTw80338xdd93FeeeddzZiErXc2fVOvtv/HcvTl3O49DDJIcn1tjO2SSHwoosoX7aMwtmziX/llQavHa7XcUd8BO9n5DPtSK5XSUKAnhcnsm35UbIPlJK1v4T4dqGn8ImEEEII0ZIoikJZWdlx9aWlpTid9b+OJ86c9MJKJszbjNOlcn3vBO4b1sbXIf1lPx78kWd/fxaH6qBvTF+mDZ9GiLFpLJBjO3qUrCf+gWXrVgCCr7yS2BeeRysL+DQLRXYHa0vK2VBawSZzJdvKKo9bXVirQOcAEz2DTPQM8qdHkD+dA/0wyuIzQohzwCn/JPv0008pKirioosuokOHDrz22mtkZWWdjdgE0Da0LRe0ugAVlTm755y0bcTddwNg/t9C7Hl5Xl3/gdbRGBSFtaUVrC0p96pPQIiRTgPdT6C3LD7iVR8hhBBCtEzDhg1jypQpdRKCTqeTKVOmyAPns6zc6mD83I2UVNrp2SqEKTd0b/avOX6590sm/zYZh+rgiuQr+ODSD5pEglBVVUrmL+DQtddh2boVTWAg8f/+PxLefEMShE1Ygc3BD3klPLMvg+Hr99Ll953ctfMw7x3NZ31pBVaXSrhey6URwUxOiePbXm3Zd153funfkTc6teaOhEh6BftLglAIcc447dWN8/Pz+eSTT/j444/Zs2cPI0aM4K677uKaa65Bp2veUx02tRXqNuZsZNzicRg0BhaPWkyk6cSv+B4e/TcsW7YQce+9RE96zKvr/yP1KHOzChkeHsTnPRt+TRmgJLeSz/65FlS49bkBRCQEetVPCCGEEH9dU7tXOZndu3czbNgwQkNDOf/88wH47bffMJvNLF++nG7duvk4wr+uKX4/XC6V+z/dxJLduUQHGVn40HnEhvj5Oqy/5KMdHzFt8zQAbu14K5MHTkaj+D454ywpIfuFf1K2eDEApn59SXj9dfQJTeP1Z3FMkd3BH8XlrC5xb6kVVce16eDvx6DQAPoGB9A/JIAUk6HZJ9eFEMLbe5XTThLW9vbbb/PEE09gs9mIjIzk/vvv56mnnsLf3/+vXtonmtqNnqqq3P7T7Wwv2M747uN5uM/DJ2xb9ssvZDw0EU1QEO1WrEAb2PD8kUcsVoas24NThZ/7dqBXsHfft5/f38GBLfl0HBjLJeO6eP15hBBCCPHXNLV7lYZkZWXxzjvvsG3bNkwmEz169OChhx4iPDzc16GdEU3x+/HmklT+u3w/Bp2GL+8dRO9mvFCJqqpM2zyNmTtnAjC++3gm9p7YJBI3FWvWkPXUZBy5uaDTEfXww0TcfReKVuvr0ARgd6lsNFewqqiMlUVlbCur5M+//HYK8GNIaCCDQwMZFBpAlEHvk1iFEOJs8vZe5bSH/OXm5jJnzhw+/vhjjhw5wqhRo7j77rvJyMjg9ddfZ+3atSxZsuR0Ly9qURSFO7vdyaSVk/gy9Uvu6X4P/vr6E3mBF12EITkZ2+HDlHzzNRF33tng9ZNMRq6PDuOb3GL+eySXWd1TvIqr94gkDmzJJ21DLgOvbUNQePN+Oi2EEEKIsyM+Pp5XX33V12G0GD9uz+a/y/cDMOX67s06QehSXby67lW+TP0SgMf6PsZd3e7ycVSg2u3kTZ1K0cxZABhSUoj/978xdWs6qyu3RKqqcshiY2WRmVXFZfxeXE6F01WnTccAP84LDWRIWCCDQgKJMDTvt+CEEOJMOuWfiPPnz2f27NksXryYLl26MGHCBG6//XZCQ0M9bYYMGULnzp3PZJwt3kWJF9E6qDXpZenMT5vP7V1ur7edotEQftc4cp5/gaI5cwm/7TYUfcNPwyYmxfBNbjE/FZSSWlFFx4CGE34xycEkdAwjM7WYrUvTOf+WDqf8uYQQQgjRMlRWVpKeno7NZqtT36NHDx9FdG7alVXK419vA2D8+Snc2LeVjyM6fQ6Xg+f/eJ7vD36PgsKzg57l5o43+zos7Lm5ZD42CcvmzQCE3noLMf/4B5pm+hZVc2d3qawrLWdJgZklhaUcttT9GROu13JBWBAXhgdzQXgQsUYZKSiEECdyyknCcePGceutt/LHH3/Qv3//etvEx8fzzDPP/OXgxDFajZaxXcfy8tqX+WT3J9za6VZ0mvq/fSHXXkv+f9/GkZ2N+eefCbn66gav3zHAj5FRIfyYX8rbR3J5p0uSV3H1GdGazNRidv+eRd8rkvEPNpzS5xJCCCHEuS0/P59x48axaNGies/LCsdnTkG5lXvnbsJidzKsQxRPXdF8H9rbnDaeWPUEy48uR6to+dd5/2Jkm5G+DouK1avJfPwJnEVFaIKCiHv1XwRfeqmvw2pxzA4nywvNLCk0s6zQTKnj2M8RvaLQPySAC8ODuCA8iO6BJjRN4NV0IYRoDk55pt/s7Gzef//9EyYIAUwmEy+88MJfCkwc75q21xDuF05WRRZLDp/4VW6N0Uj47bcBUPjRTLyddvLhpBgAFuQVc8Ri9apPYudwopOCcNhdbP0l3as+QgghhGg5Hn30UUpKSli3bh0mk4mff/6ZOXPm0L59exYuXOjr8M4ZNoeLBz7dRGaJhTaRAbw9ujdaTfNMjFTaK3lw2YMsP7ocg8bAWxe+5fMEoepykT99Oul334OzqAhjl86kfPuNJAgbUbbVxkcZ+dy8dT9dft/B/buPMD+3mFKHk3C9lptjw5jZLZk953Vjfu92PJwUQ88gf0kQCiHEKTjlJGFQUBB5eXnH1RcWFqKVCXrPKj+dH6M7jQZg9q7ZJ03+hd16K4q/P9bUVCr+WO3V9XsG+TM8PAinCu+kH/89ro+iKPQf6Z7DcMeqTCzltgZ6CCGEEKIlWb58OW+++Sb9+vVDo9GQlJTE7bffzv/93/8xZcoUX4d3zvjXj7vZcLiYID8dH47tR4ipeb5SWWGv4P5f7mdt9lpMOhPTL5nO8NbDfRqTo7iYo/feR8Hb74CqEnrzzSR//jmG1q19GldLkGO181FGPtduTqP36t08m5bJr8XlOFRo72/kwdbRLOzdjh1Du/HfzkmMjAolUCe/kwohxOk65SThiRJTVqsVg0FeNT3bbu14Kyadib1Fe1mbvfaE7bShoYSOuhGAwg8/9Pr6j1SPJvwyu4hsq3cJv6TuEUS1DsJhdbLtl6Nefy0hhBBCnPsqKiqIjo4GICwsjPz8fAC6d+/O5uo53cRf87+tmcxZcwSAabf2om1UoI8jOj0V9goe+OUBtuRtIcgQxIeXfciguEE+jalyyxYOXX8DFb//juLnR9xrU4h76UU0RqNP4zqX5VntzMrI57rNafRevYtn0zJZV1oBQP/gAF5oG88fAzvx28DOPNc2ngGhgWhltKAQQpwRXs9J+N///hdwjxz76KOPCAw8dvPhdDr59ddf6dSp05mPUNQR6hfK9e2uZ97eeczeOZvB8YNP2Dbizjsp/vwLKteto3LzFvz79G7w+oNCAxkUEsDa0gpmpOfzYvuEBvsoikK/K5NZNGMH21dm0OvS1vgFNM+n10IIIYQ4szp27EhqairJycn07NmT999/n+TkZGbMmEFcXJyvw2v20nLLeOrbHQBMvKgdF3WK8XFEp6fSXsmEXya4E4R6d4Kwa4TvVgpWVZXiuXPJ/fd/wOHAkJJCwrSp+HWQhfrOhlK7g4X5JSzILWFNSTm1h6X0C/bnmuhQrooKJd5PBqUI0VypqorLpaK6VFxOd6m6OFZXXapqdb1T9fRBPdaupo3LRXVbFVXlT6X7Gqr6p/3qErXmXK1+f67/0zGe+hOf89QBuEDlWL2nP8f6164LDDMy8Oo2vvr2eHidJHzrrbcA94eZMWNGnVeLDQaD52ZPnH1juozhi9QvWJO9hr1Fe+kUXn9yVh8fT+h111Ly9TcUzHiP1h984NX1H0mKYe32g8zNKuThpBgiDA3/NUnpGUlEq0AKM8rZtvxok/jLLYQQQgjfe+SRR8jOzgbghRde4PLLL+ezzz7DYDDw8ccf+za4Zq7c6uD+T90LlZzXLpJHL2meCayaOQg3520mUB/IB5d94NMEoctqJfu55zAv/B6A4CuvIPall9EGBvgspnORw6WysriMr3KKWFxQitV1LDXYJ9ifa6JCuSo6lFaSGBQCcCfJnHYXTsexzeVQax2r1XUunE61ujzWxuU81tblVKu36rbV7WvXu5wqTmdNUq/WOZdaZ7/O+VpJQJdLRXXWTv75+k+waYtoFdgk8iheJwkPHToEwPDhw5k/fz5hYWFnLShxcq2CWjEiaQSLDi9i9s7ZvD7s9RO2jRg/npJv51Px629Ydu7C1K3hG64Lw4PoEWRie5mFDzPyeapNw0/5FUWh/5XJ/PzBTrYvz6DXxYkY/WU0oRBCCNHS3X777Z79vn37cuTIEfbu3Uvr1q2JjIz0YWTNm6qqPPXtdg7kVxAb7Me0W3s1y4VKLA4LE5dPZGPuRgL1gbx/6ft0i+zms3jsuXlkTJxI1fbtoNUS89RThN1+G4q8znrG7C638GVOEfNzi8m3OTz1HQP8uCkmjGuiQ2ltkte5RdPncqk4bE7sVvfmsDlx2FzYq0vHn0t7dRu7C4fdhdNWXdqd1aXr2DmH+9hpd+FwuHDZXe4RdecoRaOgaECjKCgaBY1WQVHcdYrm2L7Gs1+9Ke7zmlr7x8o/1bn/D40GqH2OY2089dUlNefr7Ndti+fa1ecARQNQz3lqfw2oqVQUMAU1jQciXicJa6xYseJsxCFO0Z3d7mTR4UUsPryYh/s8TEJg/a8FG1q3JviqkZgXfk/h+zNo9fbbDV5bURQeTYrhrp2HmZWZz4TW0QR7MQFwm15RhMcHUJRVwfYVGZ4FTYQQQgghwJ3YMplM9OnT57T6Z2Zm8uSTT7Jo0SIqKytp164ds2fPpl+/fifsY7Vaeemll/j000/JyckhLi6O559/nrvuusvTZurUqbz33nukp6cTGRnJqFGjmDJlCn5+fqcVZ2P4ePVhftiejU6jMP22PkQENr+kSpWjionLJ7I+Zz3+On/eu+Q9ekT18Fk8lh07yXjwQRx5eWhDQkiYNo2AQQN9Fs+5pNju4OucIr7KKWZnucVTH67XckNMGDfHhtM90CTJWHFWqS4Vu9WJrcqBzVJTOrBVHdu3W53Yq5zYrE7sVgf2Kmd1n5pkoAOH1Z0IdNpdvvswCmh1GrRaBa1eg1anQaNzl1qd4j7WKmi07uM6pVZBo9eg1biPNTqlbltNTd/aW606Ta1jjYLiqatO7tUk7TTH2tdO/NWuq5PAE02CV0nCSZMm8fLLLxMQEMCkSZNO2vbNN988I4GJk+sS0YWBcQNZl72O2Ttn8+ygZ0/YNvLeezF//wNlS3+hat8+r+ZSuTwyhA7+fuyrrGJ2RgGPJDc8v42icc9NuOSjXWxbdpSeFyViMJ1yHloIIYQQ55iZM2fy1ltvkZaWBkD79u159NFHueeee7y+RnFxMUOHDmX48OEsWrSIqKgo0tLSGny75eabbyY3N5eZM2fSrl07srOzcbmO/WI3b948nnrqKWbNmsWQIUPYt28fd955J4qiNNn72k1HivnXj3sAeGZkZ/omNb83fKocVTy8/GHWZa/DpDMx49IZ9Iru5bN4Sn/4kexnnkG1WjG0a0viu+/K6sV/kaqqbDJXMiergIV5JZ7XiQ2KwqWRwdwcG85F4cHom+EIWOE7DrsTa4WDqkq7u6ywY620Y610uDeLA1t1aa20Y7M4sVrs2Cod2KxOOBuD8RTQG7TojFr0Bg06g9a96d37x+o06PRatAYNOr0Grd59rDO4k3vHSq27rE7+afUKWp22unT3qxlRJ8SZ5lUGZ8uWLdjtds/+ichf0sZ1X4/7WJe9jvlp87m3x71E+0fX287Yrh1Bl11G2eLFFL7/AQlv/KfBa2sUhYeTonloTzrvZ+QxPjEKf23Di2G37RNNWOwhinMq2b4yg35XJJ/qxxJCCCHEOeT555/nzTffZOLEiQwe7F5wbc2aNTz22GOkp6fz0ksveXWd119/ncTERGbPnu2pS0k5+VsLP//8M6tWreLgwYOEh4cDkJycXKfN6tWrGTp0KH/7298850ePHs26deu8/YiNqrDcyoOfbcbhUhnZI447hyT7OqRTZnVaeWTFI6zJXoNJZ+K9S96jd3TDC+ydDarLRf60/1L4/vsABF54IfH/+TfawOa5QnRTUO5w8m1uMXOzCthVXuWp7xZo4rb4CK6LDiVMLwMJBDjtLizlNixldizlNqrK7VjK7VRVb5ZyO1UV7vqqCgfWCjuOMzB6T6NRMJh0GExa9H46jCYdBj/3vt5Pi96oxWDUoje6jw3VdfrqOp1Rg95wrE6r10guRJwzFFWV6SP/zGw2ExISQmlpKcHBwb4O54RUVeXOn+9kc95mbu98O08OePKEbav27OHQ9TeARkObH3/A2MBNNbgnEx66bg9Hqmy82C6e+xLrT0L+Weq6HH6ZvRtjgI47/jUEg5/cBAghhBBnUnO5VwGIioriv//9L6NHj65T//nnnzNx4kQKCgq8uk6XLl0YMWIEGRkZrFq1ioSEBCZMmMD48eNP2GfChAns27ePfv368cknnxAQEMA111zDyy+/jMlkAtwjCSdMmMCSJUsYMGAABw8eZOTIkYwZM4ann3663utarVasVqvn2Gw2k5iYeNa/H06Xyh2z1vHH/kLaRgXwv4fOI9DYvO6zbE4bj6x4hN8zf8ekM/Huxe/SL/bEr4ufTc7yCrKefJLyZcsAiLjnbqIeewxF2/A0O+J4O8sqmZtVyLe5xVQ43YkcP43CddFh3JEQQe8gf0mktAAupwtLmZ2KUisVpTYqS61Umm1YzDYqy2zu/TI7ljIb1kpHwxesh6KAMUCPX4Aeo78Oo7+79PPXYfDXYTC5E39Gf707AehfnQisTgxqdZLUEy2Pt/eOp3xXUVpaitPp9DyNrVFUVIROp2vyN6pNxeGCCj787SD/uLwTIabTW+BDURTu7XEv9/9yP9/s+4Z7ut9DhCmi3rZ+nTsTeOGFlK9cSeGHHxH/6r8avL5Oo/BwUgx/Tz3K20fyuD0+ggAvbpra94tmw4+HKM2zsHNVJn1GJJ3yZxNCCCHEucFut9c7Z2Dfvn1xOLz/BfHgwYO89957TJo0iaeffpoNGzbw8MMPYzAYGDt27An7/P777/j5+bFgwQIKCgqYMGEChYWFnhGJf/vb3ygoKOC8885DVVUcDgf333//CROEAFOmTOHFF1/0OvYzZeov+/hjfyEmvZb3bu/b7BKEDpeDJ399kt8zf8dP68f0i6f7LEFoy8gg44EJWNPSUAwG4l55mZBrrvFJLM2ZU1VZXFDK+0fzWVda4alv729kbEIko2LCCJVRg+cMm8VBebGV8uIqykuslBdbqSiuoqLU5kkKWspsp/RKr0aj4BekxxRowC9QjylQj1/15tkPOLYZA/QYjFoUeU1diLPilEcSXnHFFVx99dVMmDChTv2MGTNYuHAhP/300xkN0BfO9tN5VVW58r+/syfbzKRLO/Dwxe3/0rVG/ziaXYW7uLvb3Tza99ETtrVs3crhW0eDTkfbn3/G0Kr+xU5qs1ePJkyvsvF823gmtPZuNOHeNdksm7MHU5CeMa8MQW+UJ7JCCCHEmdKcRhJOnDgRvV5/3Px+jz/+OBaLhenTp3t1HYPBQL9+/Vi9erWn7uGHH2bDhg2sWbOm3j6XXXYZv/32Gzk5OYSEhAAwf/58Ro0aRUVFBSaTiZUrV3LrrbfyyiuvMHDgQPbv388jjzzC+PHjee655+q9ri9GEq7Ym8e4jzcAMO3WXlzbq+H7uKZEVVX+ueafzE+bj16jZ/rF0xkcP9gnsVh27uLofffhLCxEGxVJ4jvvYOrZ0yexNFflDidf5BTx4dF8jlTZANArCiOjQrgjPpLBoQEyUquZUV0qlWYb5sIqygot1WUV5UVVlFUnA21VTq+upSjgH2zAP8RIQKgR/yA9pmAD/sEGTEEG/IMM7uMgA0Z/nST8hGgEZ20k4bp16+qdxPnCCy/kmWeeOdXLtUiKovDAhW15+PMtzPz9EOOGJhPkd/qjCe/rcR8Pr3iYz/d+zrhu4wgxhtTb1tSrFwFDBlOxeg2FMz8i7oUXGry+XqMwKTmGR/ce5Z30XMbGRxDgxUrHHQbEsOHHQ5gLqtj1Wya9LpGJn4UQQoiWaubMmSxZsoRBgwYB7vvJ9PR07rjjjjqL4p1soZC4uDi6dOlSp65z5858++23J+2TkJDgSRDW9FFVlYyMDNq3b89zzz3HmDFjPIuodO/enYqKCu69916eeeYZNJrj52Q2Go0YjY23mnBxhY1Hv9wKwB2Dk5pdghDgrc1vMT9tPhpFw/8N+z+fJQjLf/2VjEcfQ62sxNipE4kz3kMfG+uTWJqjjCobMzPy+Sy7ELPD/UpxuF7LHfGRjEuIJMZ4er/TiMZhq3JgLrBQmmehNN+9eRKCRVW4HA2PHzKYdASGGas3PwLDjASEGPEPMXhKU5ABjST+hGiWTjlJaLVa6301xG63Y7FY6ukh6jOyexxTf9nHwfwKPll7hAkXtjvta12YeCEdwjqwr3gfn+35jAm9JpywbcT991Oxeg2l33xL5P0PoI9peGTgqJhwph3J5ZDFxqzMAiYmNbzSsUaroe8Vyaz4ZC+bl6TTbVgCOoOMJhRCCCFamp07d9KnTx8ADhw4AEBkZCSRkZHs3LnT066hUUdDhw4lNTW1Tt2+fftISjrxtCZDhw7l66+/pry8nMDqhSj27duHRqOhVatWAFRWVh6XCNRWT6/SVKbuDgsw8OzIzny7OYNnRnb2dTinbNbOWcze6X69+4XBL3BJ0iU+iaPk2/lkP/88OJ0EDBlCwn+nyQIlXtpqrmTG0Ty+zy/BWf2fRTt/I/e2imJUbLhXCxyKxuGwOSnJs1CcU0FpXmWdhGCl2XbSvooCgWF+BEX4ERzhLgPD/QgK8yMw3D0qUOabF+LcdsqvGw8fPpxu3brx9ttv16l/8MEH2b59O7/99tsZDdAXGusVnvmbM5j01TbCAwz8/uRw/A2n/wN38eHFPL7qcYIMQSy5cQmBhvpveFRV5cjtY7Bs2kT42LHETH7Kq+t/k1PEQ3vSCdNpWT+4C0FejCZ0Ol189txayoqqOO/m9vS8KPGUPpMQQggh6tecXjc+UzZs2MCQIUN48cUXufnmm1m/fj3jx4/ngw8+4LbbbgNg8uTJZGZmMnfuXADKy8vp3LkzgwYN4sUXX6SgoIB77rmHCy64gA8//BCAf/7zn7z55pt88MEHnteNH3jgAfr27cuXX37pVWyN9f1QVbXZvcI5P20+L6x2v70yqe8kxnUb1+gxqKpKwbvvUvD2OwCEXHsNcS+/jGIwNHoszc26knLeOpzLyuIyT915oYHclxjFxRHBaJrZ38dziaXMRnFOBcU5lZ6tJLcCc2HVSecE9AvQExxlIqR6C440eRKCAWFGtJLwFeKcdNZeN37llVe45JJL2LZtGxdffDEAy5YtY8OGDSxZsuT0I26BrukZz7RlaRwprOSztemMH9bmtK91SetLSAlJ4VDpIb5I/YJ7ut9TbztFUYi8/36Ojh9P8ZdfEnHfvej+tAhNfa6PCWPqkVz2V1r5KCOfx5Ibfi1Dq9XQ94okVn6WypbFR+h6fjw6vYwmFEIIIcSp69+/PwsWLGDy5Mm89NJLpKSkMHXqVE+CECA7O5v09HTPcWBgIEuXLmXixIn069ePiIgIbr75Zl555RVPm2effRZFUXj22WfJzMwkKiqKq6++mn/9q+FF3hpbc0sQ/nLkF15c417gZVy3cb5JEDoc5Lz4EiVffw1AxL33EvXYo83uz7IxqarK78XlvHkkhzUl7sVItApcHx3GA62j6Rpo8nGELYvd6qQoq4LCrHKKMt1lYVYFlpOMCjT66wiN8Scsxp+QaH9Coo8lBP0C5JVwIcSJnfJIQoCtW7fy73//m61bt2IymejRoweTJ0+mffvTX4CjKWnMp/NfbkjnyW93EBlo5Pcnh+P3F5Jo3x/4nqd/f5owYxg/3/gz/nr/etupqsrhm26maudOIu69l+hJj3l1/e9yi7l/9xFCdFrWD+pMiBcrlTkdLj59bg3lxVbOu6k9PS+W0YRCCCHEX9XcRhJu3LiRr776ivT0dGy2ur/Yzp8/30dRnTnN7fvRGNZmr2XCLxOwu+zc2P5GXhj8QqMn5lyVlWQ+NonyVatAoyH2uWcJGz26UWNoTlRVZXlRGW8dzmGjuRJwL0Zya1w4D7WOJsnUePNwtkSqqlJRYiU/vYz8o+UUHC2jMLP8xCMDFQgK9yMsNoCwWH/PFhoTgClIL4lwIUQd3t6rnFaS8FzXmDd6NoeL4f9ZSWaJhX9e3YU7h6ac9rUcLgdXL7iajPIMHu/3OGO7jj1h27Jly8h48CE0AQG0W74MbUj9i53U5lRVLtqQSmpFFX9PjuGJlDiv4tr1WyYrP0uVlY6FEEKIM6Q5JaW++OIL7rjjDkaMGMGSJUu47LLL2LdvH7m5uVx//fXMnj3b1yH+Zc3p+9EYdhbs5O7Fd1PpqOSS1pfwnwv+g1bTuPd/jsJCjt7/AFU7dqAYjSS88R+CLvHNXIhNnaqq/FxQyltHctle5p5j3k+jcFtcBBNaR5PgJ69ln2mqqmIusJCfXk5+ehkFR8vIP1qGpcxeb3tTsIGI+AAi4gMJT6gu4wPk9yohhNfO2uvGtVVVVR33NFhujE6NQafhgQvb8ux3O5mx6iCjB7bG6MV8f/XRaXTc0/0e/rnmn3y862Nu7XQrRm39T/wChw/H2KED1n37KPr0U6IefLDB62sVhceTYxm/6zAfHM3nnlZRhHkxmrDTkDg2L0nHnG9h+4qj9L08+VQ/mhBCCCGaqVdffZW33nqLBx98kKCgIKZNm0ZKSgr33XcfcXHePXAUzcfBkoM88MsDVDoqGRg3kNeHvd7oCUJbRibpd9+F/Ug62pAQWr33Hv59ejdqDM3Fr0Vl/OtgFtuqk4MmjYaxCRFMSIwmWlYqPmMs5TZyD5nd22EzeYfNWCuPXwxU0SiEx/kTmRhEVGIQEQkBRCQEYgqSRK0QonGc8kjCyspK/vGPf/DVV19RWFh43Hmn03nGgvOVxn4abHU4ueD/VpJjruKV67px+6ATr9LXELvTzpULriSnIoenBz7N6E4nfqXC/NNPZE76O5qQENot+8Wr1d1cqsolG1LZXVHFo0kxPNXGu5v71HU5/DJ7N0Z/HWNeGYzRX246hBBCiNPVnEauBQQEsGvXLpKTk4mIiGDlypV0796dPXv2cNFFF5Gdne3rEP+y5vT9OJvyK/O57afbyK7IpltENz4a8REB+oBGjcF66BDp4+7CkZODPiGBxA8/xNjm9N/UOVdtMVfy6sEsfisuByBAq+HuhEjuTYwm8i8spijcizcWpJeTc6jUkxQ051uOa6fRKUQmBBLZ2p0QrEkK6gwyOlAIceadtZGETzzxBCtWrOC9995jzJgxTJ8+nczMTN5//31ee+21vxR0S2XUabnvgja8+P1u3lt5gJv7JWLQnd6qUnqtnru63cWr615l1s5ZjGo/Cr22/oRc0IgRGNpMx3bwIEVz5ng1mlCjKDyREsu4nYf5MCOf8a2iiPDiRqJ9/xg2Lz5CUVYFW5amM+jatqf82YQQQgjR/ISFhVFW5l4ZNSEhgZ07d9K9e3dKSkqorKz0cXTiTKm0V/LgsgfJrsgmOTiZdy95t9EThFWp+0i/+26cBQUY2rSh9exZ6GNiGjWGpi6toorXDmXzY34pAAZFYWxCBA8nxRBlkIf4p8Nuc5J7sJSs/aVk7y8h52ApDpvruHahMf7EpAQTkxxMTEowEa0CZSVhIUSTc8pJwu+//565c+dy4YUXMm7cOM4//3zatWtHUlISn332WZ0V5oT3Rg9ozfQVB8gssbBgSwa39G992te6of0NfLD9A3Iqcvj+4Pfc0P6GetspWi1REx8i87FJFM3+mPDbbkMbGtrg9S+PDKF7oIkd5RbeO5rHs23jG+yj0SgMvLoNi97fwbblGfQYnoh/sAybF0IIIc51w4YNY+nSpXTv3p2bbrqJRx55hOXLl7N06VIuvvhiX4cnzgCny8mTvz7JnqI9hBnDePfidwnzC2vUGCw7dpB+z3hcpaUYO3em9cyP0IWHN2oMTVlWlY3/HM7hi+wiXIAC3BQbxuPJsbSWBUlOidXiIDuthKy0ErL2l5B/pAyXq+7LecYAHbEpIe6kYEow0UnBsqqwEKJZOOUkYVFREW3atAHc8w8WFRUBcN555/HAAw+c2ehaED+9lvuGteFfP+1h+ooD3NinFbrTfLJk1Bq5s+ud/Gfjf/hw+4dc0/YadJr6v9VBI0Zg7NgRa2oqhbM/JvqxRxu8vlI9mvCOHYeYmVHAfYlRXj15TOkVSXRSEHlHyti8+Ajn3XRurIYthBBCiBN75513qKr6f/buOzzKYnvg+Hc3u+k9pPcQCB1CFxBFQAEpigUEBZFrAemogBQFBASliogKgl1E6SCKFKVJC50kEJKQ3nvf9vuDKz+5ErIJJJvA+TzPPpe8mXfmrMslk/POzCkBYPr06ajVao4cOcJTTz3FjBkzTByduFMGg4GFJxZyIOEAFmYWrHhkBb72vjUaQ9HJk8S/+hr6wkKsWrbE97NPjSrKdz/I1+pYdi2VNQnplP43kfVYPXumBnrS2NbKxNHVDY7mWYIAAM+4SURBVDqNnpSYXBIisokPzyItNo//PbDLxtECrwaOeAU74NnAEWcPGxRKqS4shKh7Kp0kDAoKIiYmBj8/Pxo1asSPP/5I+/bt2b59O45GrEIT5Rva0Y9P/rhKXFYR284mMbC1T5X7eqbhM6w9v5aEggR+ifmFfvX73bKdQqnEddxYEl4fQ9bXX+M8fJhRT117utjTys6aM/lFfByXxrvB3hXeo1Ao6DAgiO0rznLhj0Ra9fDF1smy0u9NCCGEEHWH8z/mFUqlkqlTp5owGnG3fRP+Dd9HfA/A/C7zaeXWqkbHLzh0mIQxYzCUlGDdvj0+q1ZhZluz25xrI73BwIaULOZHJ5Nedr1ARkcHG6bX96Kdg/z3uR2D3kBmUgHx4dkkRGSRdCXnX9uHHVyt8Gro+N/EoCN2LpYoFJIUFELUfZVOEo4YMYKzZ8/y0EMPMXXqVPr168fKlSvRaDQsWbKkOmK8b1ibq/jPg4Es2h3Jyn1RDGjljVkVn0BZq60Z1nQYy8OW8+m5T+kd2Lvc1YS2jzyCZbNmlFy4QObna3Cf8laF/SsUCt4K9GDIuWjWJ2YwytcNdyMqoPk2dsargSNJV3I4sSuWbkMbVfq9CSGEEKLu2LVrF2ZmZjz22GM3Xf/tt9/Q6XT07t3bRJGJO7U3bi8fnPgAgMltJvNowKM1On7+77+TOHESBo0Gm4e64rN8OUpLeQB9IreQGVcSblQsDrQyZ3awNz1d7CWRVY6yYi1xl7K4diGDaxezKM4ru+n7VnZqfBo549vYCZ9Gztg5y98zIcS9qdLVjf9XbGwsYWFhBAcH06JFi7sVl0mZskJdQamWzu/vI7dYw4rnQunfsuLz/spTqCmk98+9yS7NZk6nOTzZ4Mnyxz14kPiXX0FhYUH9335D7e5WYf8Gg4F+YVc4mVfEf3zq8V4D41Y+JkXlsPnDMJRKBc+92wFHN2uj35MQQggh6lY13RYtWvD+++/Tp0+fm67v3r2bKVOmcPbsWRNFdvfUpc/jbjmffp6Xfn2JEl0JzzZ8lhkdZ9RoAip3+w6Spk4FnQ67Rx/F+8MPUJjf3+ddJ5WU8V50MptSswGwNVMyKcCD//jUw1wpBTL+V05qEbHnM4g9n0lyVA563f//WqyyMMO7gSM+jZzwbeyMs5eNJFiFEHVatVU3/l8BAQEEBATcaTfiv2wtVLzUOZClv19m5b4r9G3uibKKqwlt1DaMbD6SD09+yOqzq+kb1LfcSsc2XbpgFRpK8enTZH72GR4zKz4j6PpqQk+ePXuVrxIzedXXDV/LiidnXsGO+DV1Ie5iJid2xNDzpaaVfm9CCCGEqBuuXLlCkyZN/nW9UaNGREVFmSAicacS8hMYs28MJboSunh3YVqHaTWaQMnZtJnk6dPBYMBhQH88581DobrjX2vqrGKdntXxaay4lkaxXo8CeM7TmWlBnlKx+B/0egPJUTnEnMkg9kIGuWnFN33f0d0a/+YuBDRzwTPYETOVJFaFEPefKv3Lt3fvXvr27Uv9+vWpX78+ffv25ffff7/bsd23XuwcgJ2FisupBfx6MeWO+no25FlcrVxJKkxi05VN5bZTKBS4jh8PQPaPP6JJTDSq/wedbOniaEuZwcAHMclGx9VxwPXiN5dPpJKZWGD0fUIIIYSoWxwcHIiOjv7X9aioKGxs5Gy0uia3NJfX975OVkkWjZwb8eFDH5Z7pE21jL91640EoePgQXguWHBfJwj3ZOTS9XgEC2NSKNbrae9gw+62DVnSyE8ShIBOpyf+UhYHvo1g/ZRDbFlymrP74slNK0ZppsCnkRNdnmnA0DkdGTq7I12eboBPI2dJEAoh7luV/tdv1apV9OrVCzs7O8aPH8/48eOxt7enT58+fPzxx1UK4uOPPyYgIABLS0s6dOjA8ePHb9t+48aNNGrUCEtLS5o3b86uXbtu+v67775Lo0aNsLGxwcnJiR49enDs2LEqxWYKDlZqXuwcAMDyvVfQ66u+I9xKZcXLLV4G4LNzn1GiLSm3rU3HDlh36AAaDRmrVxvVv0KhYHr961uiN6ZkE15QXMEd17n62VE/1BUMcHx7jFH3CCGEEKLuGTBgABMmTODq1as3rkVFRTF58mT69+9vwshEZWl0GiYdmER0bjRu1m6sfGQlNuqaS/Tm7thJ0rS3rycInxuMxzvvoLhPt9Gmlmp4+UIsL5yPIb6kDC8LNaub+LM1NJiWdvf3UT46jZ7Y8xns/SqcdW8dYtuKM1w8mERxvgYLaxWNOnrQ+9XmjFz8IAMmhNKyu68cfySEEP9V6TMJfXx8mDp1KmPGjLnp+scff8z8+fNJNHIF2t82bNjAsGHDWL16NR06dGDZsmVs3LiRyMhI3Nz+fS7ekSNH6Nq1KwsWLKBv37589913LFy4kLCwMJo1awbAd999h5ubG0FBQRQXF7N06VI2btxIVFQUrq6uFcZUG86VyS4so+ui/eSXalk5JJS+Lap+NmGZroy+m/uSXJjMG23fYHjT4eW2LQoL49qQoWBmRv1dOzH39zdqjFcuxrItLYceLvZ80yLIqHuykgr5Ye4xDAZ4empb3APujzN8hBBCiDtVG+YqxsrNzaVXr16cPHkSH5/r5xcnJCTw4IMPsmnTJhwdHU0b4F1Qlz6POzH36Fx+vPwjNmobvuz1JSHOITU2dt7uX0mcPBl0OhyfeRqP2bPvywSh3mDg66RM5kUnkafVY6aAV3xceSPQAxszM1OHZzJ6nZ6EiGwuH08l5mw6ZSW6G9+zslMT2MqV4FA3vEIcMTO7//7eCCGEsXOVSicJbW1tOXPmDMHBwTddv3LlCqGhoRQUVG7raIcOHWjXrh0rV64EQK/X4+vry9ixY5k6deq/2g8aNIjCwkJ27Nhx41rHjh1p1aoVq8tZ/fb3f4zff/+d7t27VxhTbZnoLfv9Mst+v0KQqw2/TeiK6g5+oG2+splZR2bhZOHE7qd2Y60u/2lZ3MuvUHjwIA4D+uO1cKFR/UcXldL1eDhaA2xqFUwnJ1uj7vt9/SUi/0rBt4kz/ce1MuoeIYQQ4n5XW+YqxjIYDOzZs4ezZ89iZWVFixYt6Nq1q6nDumvq2udRFT9G/sjcv+aiQMHK7ivp6lNzn1/+3r0kjJ8AWi0OTzyB5/x592WCMLygmLciEziRVwhASzsrFof40uw+XTloMBhIj8vn8rFULp9MvakisY2DOUGhbtRv7YpnsGOVz3gXQoh7hbFzlUr/dO3fvz+bN2/+1/WtW7fSt2/fSvVVVlbGqVOn6NGjx/8HpFTSo0cPjh49est7jh49elN7gMcee6zc9mVlZXz22Wc4ODjQsmXLW7YpLS0lLy/vpldtMLJLIE7WaqLTC9l8unIrNP9Xv/r98LPzI7s0m2/Dv71tW9dx44DrVeNK/7E16HaCrC0Y6ukCwHvRSRibe273eCBKpYL4S1kkXs426h4hhBBC1C0KhYJHH32UN998kzFjxtxTCcL7wanUUyw4tgCAca3H1WyCcP9+EiZMBK0W+3798Jz33n2XICzW6Zl/NYmeJyM5kVeIjZmS9xp4s6tNw/syQZiXUczJXbF8P/sYGxec5Oy+eIrzyrC0UdP8IW8GvtmG4Qs603VwQ7wbOkmCUAghKqHSp/w2adKEefPmceDAAR544AEA/vrrLw4fPszkyZNZsWLFjbbj/ptsKk9GRgY6nQ53d/ebrru7uxMREXHLe1JSUm7ZPiXl5gIfO3bsYPDgwRQVFeHp6cmePXuoV6/eLftcsGABs2fPvm2spmBnqea1h+qz4JcIlu+9woBW3phX8RBdlVLF6FajmXpwKusurmNQo0HYm986e2zVvBm23btTsHcv6StX4rN0qVFjTA7w4MeUbMLyitiVkcvjro4V3uPgakWTLl5c+DORo5uv8tRbbWq0Op4QQgghhChfckEykw5MQmvQ0iugFyObjayxsQsOHiJx3HjQaLDv0xuvBfNR3Gdbag9l5/NGZDyxxddXyfWqZ8+8Bj54W5qbOLKapSnVEXUqlfAjySRH5d64bqZWEtSyHg3be+Db1Fm2EgshxB2qdJJw7dq1ODk5cenSJS5dunTjuqOjI2vXrr3xtUKhqDBJWJ26devGmTNnyMjI4PPPP+fZZ5/l2LFjtzzncNq0aUyaNOnG13l5efj6+tZkuOUa9kAAaw7FkJBdzIYTcbzwQECV++oV0Is159cQlRPFVxe/YkzomHLbuo4bS8HeveT/spuSV1/FslGjCvt3s1Dzmq8rS6+lsiA6mcdcHFAZ8eSu7eMBRPyVTGpMHlfD0glu8+/PSAghhBBC1KxibTHj948nqySLxs6NmdN5To09zC08epSEMWMwaDTY9eyJ18KF91UV4yKdnnlXk1ibmAGAp4WaeQ286WPEQ/h7SXpcPhcPJXHleMr/nzOoAJ8QJ0I6eBDUyhVzq/vn74UQQlS3Sv+LGhNz9yrR1qtXDzMzM1JTU2+6npqaioeHxy3v8fDwMKq9jY0NwcHBBAcH07FjRxo0aMDatWuZNm3av/q0sLDAwsLiDt9N9bAyN2PsI8HM2nqRj/ZF8XQbX6zMq/YE1UxpxuutXmfigYl8felrhjYeipOl0y3bWoaEYNe7F/m/7Cb9o5X4frzSqDFG+7nxZVIGUUWl/JCSxfNeLhXeY+NgQWhPP07sjOXolqsEtqyHWRVXTAohhBBCiDtnMBh45/A7hGeF42zpzPJuy7FSWdXI2IXHjhM/ajSG0lJsu3XDe/GHKNTqGhm7NjiZW8i48Diii0sBeMHLhVn1vbBT3R+rKMuKtVw+kcqlQ0mkx+XfuG7vakWTzp6EdPDE1ql2/u4mhBB1nUkzMebm5rRp04a9e/feuKbX69m7d++Nrcz/64EHHripPcCePXvKbf/PfktLS+88aBMY3M4Pb0cr0vJL+epo7B311d2vO42dG1OkLWLdhXW3bes6ZgwolRTs3Uvx+fNG9W+nMmNSwPWE7QcxyRTp9Ebd16qnH9b25uSlF3Phjzs7f1EIIYQQQtyZLy58wS+xv6BSqFj80GI8bT1rZNzi8xdIGDUKQ0kJNg91xXv5MhTm98fW2lL99dWD/cOuEF1ciqeFmu9bBPFBiO99kSBMjc1j71fhrJtyiD++iyQ9Lh+lSkGDtm4MmNCK52d3pE2vAEkQCiFENarS2uyEhAS2bdtGXFwcZWVlN31vyZIllepr0qRJDB8+nLZt29K+fXuWLVtGYWEhI0aMAGDYsGF4e3uzYMH1w5LHjx/PQw89xOLFi3n88cf54YcfOHnyJJ999hkAhYWFzJs3j/79++Pp6UlGRgYff/wxiYmJPPPMM1V5uyZnrlIyvkcD3vrpHJ/8cZUhHfyws6za01SFQsHY0LGM3jua7yO+54UmL+Bq7XrLthb16+PQrx+5W7eS9uFi/NavM2qLyQteLnwWn05cSRlrEtIZ5+9e4T3mlira9wvkwLeRnNgVQ6MHPLCwvn+eGAshhBD3urS0NNLS0tDrb36A2KJFCxNFJMrzZ8KfLA9bDsC0DtNo69G2RsYtjY4m/pVX0BcVYf1AR3xWrEB5nyQIL+QXMTY8jvDCEgCednfivQbeOKrv7a20Op2e6LB0zu6LJzXm/4tHOnlY06SLFyEdPbCyvT/+DgghRG1Q6Z86e/fupX///gQFBREREUGzZs2IjY3FYDDQunXrSgcwaNAg0tPTmTVrFikpKbRq1Yrdu3ffKE4SFxeH8h8VzDp16sR3333HjBkzePvtt2nQoAFbtmyhWbNmAJiZmREREcGXX35JRkYGLi4utGvXjoMHD9K0adNKx1dbDAz1ZvUfV4lOL2TtoRgm9GhY5b66eHehlWsrzqSf4fPzn/N2h7fLbVtv7Fjydu2i6NgxCg8exNaIaoQWSiVTAj14PTyOj66l8ryXC85GTHAad/Lk7L4EspMLObX7Gp0GBlfqfQkhhBCi9jl16hTDhw8nPDwcg8EAXH9oaTAYUCgU6HQ6E0co/ikmN4Ypf07BgIFnGj7DsyHP1si4mpQU4kb+B112NpbNmuHz0UqUtfQ4oLtJqzfwUVwqi2NT0BrARa1iUYiPUQUA67LigjIuHkziwh+JFOZc3+2lVCkIbuNGswe98ajvIMUMhRDCBBSGv2drRmrfvj29e/dm9uzZ2NnZcfbsWdzc3Bg6dCi9evVi1KhR1RVrjcnLy8PBwYHc3Fzs7W9dAdgUtp9NYuz3p7G1UHHwrW442VT9qdrx5OOM/G0kKqWKnU/uxMvWq9y2qQsXkbVuHRYNGhC4ZbNRVeX0BgM9T0ZysaCEV31dmR3sbVRcsecz2PnxOcxUSobM7oC9S82cfSOEEELUJbV1rnIrLVu2pH79+kyZMgV3d/d//eLv7+9vosjunrr0edxOXlkeQ3cOJTYvltZurVnz6BrUZtW/s0Obnc2151+g7OpVzAMD8f/2G1TOztU+rqnFFpcy6uI1TucXAdCnngMLQ3xwNb93d9NkJBRwbn88l4+notNcX1VsZW9Os67eNOvqjbW9rBoUQojqYOxcpdJnEoaHhzNs2DAAVCoVxcXF2NraMmfOHBYuXFj1iEWFHm/uSWNPewpKtaz+8+od9dXesz0dPDqg1Wv57Nxnt21b79VXUNrbU3rlCrlbthrVv1KhYEbQ9cTjuoQM4kvKKrjjOv9mLniHOKLT6jm2Ndqoe4QQQghRe0VHR7No0SI6dOhAQEAA/v7+N71E7aA36Jl+cDqxebF42Hiw+OHFNZIg1BcWEv/aa5RdvYrK3R2/tWvuiwThltRsepyI5HR+EfYqJSsb+7G2WcA9mSA0GAzEXcxky9IwNrx3nPDDyeg0etz87egxognD53Wifd9ASRAKIUQtUOkkoY2NzY1zCD09Pbl69f+TVRkZGXcvMvEvSqWCyT2vbzP+8kgsaXkld9TfmNAxAGyJ2kJ0bvkJOTNHR+q9+ioA6StWoC8xbtyHne3o4mhLmcHABzHJRt2jUCjo/FQDAC4fTyXtWl4FdwghhBCiNuvevTtnz541dRiiAusurONAwgHMleYs67aMelb1qn1MQ1kZCeMnUHL2HGYODvitXYPaq/zdLfeCIp2eyRFxvHbpGgU6PR0cbNjXrhFPezjfc9tr9XoDUafS2LjgJNs/OktiZA4K5fUtxQPfbMPTU9sS0sEDM7VJa2kKIYT4h0qfSdixY0cOHTpE48aN6dOnD5MnT+b8+fNs2rSJjh07VkeM4h+6N3Yj1M+R03E5fLw/itkDmlW5r1ZurXjY92EOxB9g+anlLH9kebltnZ4fSta336BNSibrq6+p98rLFfavUCiYXt+L3qcuszElm9d83WhiW/H2YVc/Oxp2cOfysVSO/BzFgImh99ykSQghhLhfrFmzhuHDh3PhwgWaNWuGWn3zSqn+/fubKDLxtxMpJ1hxegUAUztMpalL9Z/jbdDrSZr2NoWHDqGwssL309VYBN/b51GHFxTz6sVrXC4qQQFM8HdncoAHKuW9Nc/VafVEHkvh9G9x5KRe30qtMlfS9EFvWnb3xc7Z0sQRCiGEKE+lk4RLliyhoKAAgNmzZ1NQUMCGDRto0KBBpSsbi8pTKBS8+WgIQ9Yc47vjcbzcNQgfJ+sq9zex9UQOJhxkX/w+TqWeoo17m1u2U1pY4DZhAklvTSHzs89wfOZpVE5OFfYfam/NADdHtqblMOtKIhtb1Tcq4dehfxBXT6WTeDmHa+czCWhR/U+zhRBCCHH3HT16lMOHD/PLL7/863tSuMT0MoozeOvPt9Ab9PQL6sfTDZ6u9jENBgOp8+aTt3MnqFT4rFiOVatW1T6uqRgMBr5JzmTmlURK9AbczFWsauJPFyc7U4d2V2lKdVw6lMSZ3+MoyL5ejMTCWkWLbj606OaLpe29t5VaCCHuNZVe2x0UFESLFi2A61uPV69ezblz5/j555/lXJka0im4Hp3qu6DRGVix98od9RXkGMTABgMBWHJyCberY2Pfty8WjRujLygg45NPjB5jepAnFkoFh3IK+DXDuO3D9i5WtOzuA8CRTVHodXqjxxNCCCFE7TF27Fief/55kpOT0ev1N70kQWhaWr2Wt/58i4ziDIIdg5nRcUaN7N7IWLWK7G+/BYUCr/ffx/bBB6t9TFPJ1Wh55eI13oxMoERvoJuzHXvbhdxTCcKyEi2ndsfy1dtHOLTxCgXZpVg7mNPpqWCGze9E+35BkiAUQog6otJJwhMnTnDs2LF/XT927BgnT568K0GJir3xWAgAP4clcjW94I76Gt1qNFYqK85lnOPXa7+W206hVOL+5hsAZH//A2VxcUb172dlwWu+bgC8ezWRUr1xCb/WvQKwtFGTnVJE+BHjzjQUQgghRO2SmZnJxIkTcXd3N3Uo4n98fOZjTqScwFplzZKHl2CtrvruFGNlb9xIxkcrAXCfPh2Hvo9X+5imciaviB4nL7M9PQeVAmbV9+LbFkH3THESrUbHmd/j+GbmUf7aEk1JoQZ7VyseHhrCsPc6EdrTD3PLSm9cE0IIYUKVThK+/vrrxMfH/+t6YmIir7/++l0JSlSstZ8TPRq7odMbWPhLxB31Vc+qHiOajQBg+anllOnKr0Rs06kTNl26gEZD+rJlRo8xzs8NN3MVscVlrE0wrsCNhZWKto8HAHBsewxlJVqjxxNCCCFE7TBw4ED2799v6jDE//gj/g/WnF8DwOxOswl0CKz2MQsOHybl3dkAuIx6Defnh1b7mKbyfXImA05fIb6kDD9Lc7a1bsBoPzeU98A52zqtngt/JvLNzL84/FMUxfnXk4M9RjRh6LsdaPqgtxQjEUKIOqrSj3YuXbpE69at/3U9NDSUS5cu3ZWghHGm9m7E/sh0fruUyrHoTDoEuVS5r+FNhvNj5I8kFCTwY+SPPN/k+XLbur0xmZjDh8nb9QvOI0Zg1bx5hf3bqMx4O8iTCRHxLI1N4RkPJ6Oeojbr6s35/Qnkphdzek8cHfoFVep9CSGEEMK0GjZsyLRp0zh06BDNmzf/V+GScePGmSiy+1dCfgLTDk0DYEijIfQK7FXtY5Zcvkzi+Amg02Hfvx+u9+jnXqbXM/NKIl8mZQLwqIs9HzX2w0Fd91fU6XV6Io+lcmJnDPmZJQDYOlvQ7vFAQjp6YGYmiUEhhKjrFIbbHUJ3Cy4uLuzYsYMHHnjgputHjhzh8ccfJzs7+64GaAp5eXk4ODiQm5uLvb29qcO5rembz/PtsTha+jiweXRnlHdQHe2nyz8x++hsHCwc2DVwF/bm5b/3pKnTyN2yBet27fD76kujzq/RGwz0OnWZc/nFvODlwgchvkbFdTUsjd2fXUBlrmTo7I7YOklFNCGEEPe3ujRXCQwsf4WaQqEgOjq6BqOpHnXp8yjTlTHsl2FczLxIi3otWN9rPWqz6t3+qk1PJ2bQILRJyVi3bYvvF2tRmptX65imkFKq4eULsZzIK0QBvBHgwcQA9zq/etBgMBB1Ko3j22NuVCu2tjenbZ8AmnT2klWDQghRBxg7V6n0v+iPPvoo06ZNIzc398a1nJwc3n77bXr27Fm1aEWVTejREBtzM84m5LL9XNId9fVE8BPUd6hPbmnuje0n5XEdPw6FuTlFJ05Q8McfRvWvVCiYG+wNwLdJmVwsKDbqvqBQVzyDHdCW6Tmy6apR9wghhBCidoiJiSn3dS8kCOuaRScWcTHzIg4WDnz40IfVniDUFxURP2o02qRkzAMC8Fn50T2ZIDyeU8CjJyM5kVeIvUrJV80DmRzoUecThCnRufy86BS/rblITmoRljZqOg0M5vn3HqD5wz6SIBRCiHtMpf9V//DDD4mPj8ff359u3brRrVs3AgMDSUlJYfHixdURo7gNVzsLRj1cH4BFuyMp0VS9SqBKqWJS20kAfHvpW5IKyk86qj09cR72AgBpH36IQWvceYEdHG3p7+aIHph1JfG21ZT/plAoePDZhqCAKydSSbqSY9RYQgghhBDi/+2M3smGyA0oUPD+g+/jaetZreMZdDoS33yLkgsXMHN0xPfT1Zg5OlbrmDXNYDCwLjGDgWeiSCvT0sjGkl/bhNCznoOpQ7sjeRnF/LrmAj8vOkVqTB4qCzPa9wvkhXkPEPqoH2pzM1OHKIQQohpU+nAMb29vzp07x7fffsvZs2exsrJixIgRPPfcc/86Y0bUjJFdgvjmrzgSc4r58kgsrz5Uv8p9Pej9IO082nEi5QQfnf6IBQ8uKLetyyuvkLPxJ8qirpK7ZQuOTz9t1Bgz63vxa0Yuh3MK2J2RS29XxwrvcfWzo2kXLy4eTOLPDZd59u12d7S1WgghhBA146WXXrrt97/44osaiuT+Fpsby+yj14uGvNLiFbp4d6n2MdM++JCCvXtRqNX4rPoYc3//ah+zJhXr9Ey9nMCGlCwA+rs5sjTEFxtV3U2glRZrCdsdy9m9Cei0elBAk06etO8fhI2DhanDE0IIUc2qdIKujY0Nr7zyyt2ORVSRlbkZbzwWwhsbz7JyfxTPtvXFyaZq2zgUCgWT205m8I7B7IjewbAmw2js0viWbc3s7XEZ9Rpp7y8kffkK7Hr1xszWpsIxfC3NGeXrxrJrqcy+msQjLvZYKCte1NphQBBRp9LITCjg0qEkmnX1rvT7E0IIIUTN+t/zqjUaDRcuXCAnJ4dHHnnERFHdXzQ6DVMOTqFYW0w7j3aMajmq2sfM+u47stavB8BzwQKsb1H4sC5LLdUw7Hw0Z/OLUQIz6nsxytfVqHO6ayO9Ts+lw8kc3x5Ncb4GAJ9GTnR+Oph6PnYmjk4IIURNkUMk7hFPhnrT2NOe/BItK/ZduaO+mro0pU9gHwAWn1p82y3BTkOGoPb3Q5ueTuannxo9xlg/N9zNVcQWl7EmIcOoe6xszWn/3+rGf229SkmhxujxhBBCCGEamzdvvum1Y8cOoqOjGTRoEB07djR1ePeFFadXcCnzEg4WDizosgAzZfWudCv44w9S35sHgOuE8Tj0fbxax6tpFwuK6XPqMmfzi3FWm7GhZX1G+7nV2QRhQkQWG+ad4I/vIinO1+Dobs3jr7eg//hWkiAUQoj7jCQJ7xFmSgXT+1xf8ff10WvEZBTeUX/jWo9DrVRzLPkYhxIPldtOaW6O+5SpAGStX0/ZtWtG9W+jMuPtIC8AlsamkF5mXMKvWVcvnL1sKC3UcmybHHYuhBBC1EVKpZJJkyaxdOlSU4dyzzuSeIT1F9cDMKfTHNxt3Kt1vJKICBInTgK9Hocnn8Tl1Verdbya9ntmHv3DrpBYqqGBtQW72jTkQee6mUgrzC3lt7UX2brsDFlJhVjaqHlwUEMGz2pPQPN6dTbpKYQQouokSXgP6dKgHg+HuKLVG1i0O+KO+vK29WZIoyEALDm1BJ2+/IIott0exqZLFwwaDakLFxk9xjMeTrS0s6JAp2dhdIpR9yjNlHQd1BCAi38mkpGQb/R4QgghhKg9rl69itbIwmeiajKLM3n70NsADAoZxCN+1bu9W5uZSfyo0eiLirDu2BHP2e/eU4mmNQnpDDsXTaFOTxdHW7a3bkCAVd07p0+vN3BufwLfvfMXV06kggKaP+TN0DkdadHNBzMz+RVRCCHuV1U6k1DUXtN6N+bPy+n8ciGFk7FZtA1wrnJfL7d4mc1Rm4nKiWJz1GaebnjrwiQKhQL3t6cR3X8ABfv2UXDwELYPVnwYtlKhYG6wN/1PR/FtciYvervQzM66wvu8Q5wIbuNG1Kk0Dm64whOTQu+pCagQQghxL5k0adJNXxsMBpKTk9m5cyfDhw83UVT3PoPBwMzDM8ksySTYMZg32r5RveOVlZEwfjza5GTM/f3xWb4MhXnVzsiubbR6A7OiEvki8foROUM8nVnY0Bd1HSyilxqTxx/fR5Ied/1Bu5u/HQ8NCcHN397EkQkhhKgN7ugxUUFBAXl5eTe9hGmFeNgxqJ0vAO/tDL/teYIVcbBw4LWWrwHw0emPyCsr//O1CArCeehQAFIXLMCgMW77cHtHWwa4OWIAZlxJNDreTk8Fo1IrSbqSQ9SpNKPuEUIIIUTNO3369E2vc+fOAbB48WKWLVtm2uDuYd+Gf8vBxIOYK81Z2HUhlirLah0vZcECik+eQmljg8+qjzFzcKjW8WpKvlbHsPPRNxKEM4I8WRxS9xKEJYUaDnwXyU+LTpIel4+5lYqHnmvIU1PaSoJQCCHEDZVeSRgTE8OYMWM4cOAAJSUlN64bDAYUCgU6XfnbUkXNmNijIVvPJHEmPoed55Pp28Kryn0NbjSYny7/RHRuNJ+c+YQp7aeU27be66PJ3b6dsuhosr79FpcXXzRqjJn1vfgtI4+/cgvZmJrNsx4Vr360c7akdS9/jm+P4cjPUQQ0r4faonoP4RZCCCFE5e3fv9/UIdx3IrIiWHJqCQBvtHuDhk4Nq3W87B9/JOf7H0ChwOuDD7CoX79ax6spCSVlvHAumvDCEqyUClY28edxV0dTh1UpBoOBy8dTOfzTlRtVi0M6eNDpqWCs7e+NlZ5CCCHunkqvJHz++efJzs7miy++YO/evezbt499+/axf/9+9u3bVx0xikpys7fkla7XqwAv2h1JqbbqiVu1Un0jMfh9xPdEZUeV29bM3h7XiRMAyFj5MdrMTKPG8LE0Z3LA9UO0Z0clkaMx7nyi0J5+2LlYUpBdyqndsUbdI4QQQghxLyvSFPHWn2+h0Wt42OdhBocMrt7xwsJImfseAK7jxmL3SLdqHa+mnM8vos+py4QXluBmrmJTaIM6lyAszCll16pz/L7uEsX5Gpw8rHliYig9RjSRBKEQQohbqvRKwrNnz3Lq1ClCQkKqIx5xl7zSNYjvjsURl1XEV0eu8fJ/k4ZV0cmrE939urM3bi/vn3ifz3t+Xu4ZgI4DB5Lz/Q+UXLpE+rJleM6da1y8vq78mJLN5aISFkQnszDEt8J7VOZmdHm6Ab98ep7Te+Jo3MkTB9eKzzQUQgghRPVq3bo1e/fuxcnJidDQ258dHBYWVoOR3fsWnVhETG4MrlauzOk8p1rPbdakpJAwbjxoNNg9+igur71WbWPVpEPZ+bx4PoYCnZ7GNpZ83SIIH8u6k1QzGAxE/pXCoY1XKC3SojRT0O7xAEIf9cdMJUVJhBBClK/SScJ27doRHx8vScJaztpcxeRHGzLl5/Os2HuFJ0K9cbWrevW1N9q+wcGEgxxLPsbvcb/T07/nLdspzMxwnzGda0OGkvPTzzgOGoxVs6YV9m+uVPJ+Qx8Gnoniq6RMBnu6EGpfccIvsFU9fBo5kRCRzeGfougzqkWl35sQQggh7q4BAwZgYXF93vHEE0+YNpj7yG+xv/HzlZ9RoGD+g/NxsnSqtrH0paUkjB2HLiMDi4YN8Vow/54oJLczPYdRF69RZjDQydGW9c0DsVfVnSNtCrJL2P9NJHEXr+/ocfO345FhjXHxtjVxZEIIIeoChaGSlS2uXr3Ka6+9xvPPP0+zZs1Qq9U3fb9Fi7qfpMnLy8PBwYHc3Fzs7evuQb46vYEnVx3mXEIuT7fx4cNnWt5RfytPr+TTc5/iaePJ1ie2YqWyKrdt4htvkrdjB1atW+P/7TdGTxrHXLrGT6nZtLCz4pc2DTEz4r6s5EI2zD2OXm+g79iW+Dd1Mfo9CSGEEHXRvTJXuVfUhs8jpTCFgdsGkl+Wz0vNXmJim4nVNpbBYCB56lRyt27DzMGBgJ9/wtzHp9rGqylfJ2UwJTIBPdCnngOrmvhjaVY3Vt4ZDAbCjyRzeOMVykp0KFUKOvQLolUPX5R15D0IIYSoPsbOVSr9EyM9PZ2rV68yYsQI2rVrR6tWrQgNDb3xv6L2MFMqmN3/+iq+n04lcOpa9h31N7L5SDxsPEguTGb9hfW3bev2xmQUVlYUh4WRt2On0WO8E+yFvUrJufxivkoy7kxDZ08bmne7PjH98/tItGVSPEcIIYSobcrKykhISCAuLu6ml7hzeoOeGYdnkF+WTzOXZowJHVOt42V9+SW5W7eBmRney5bW+QShwWBgaWwKb/43Qfi8pwufNwuoMwnC/KwStn90lv1fR1BWosM90J5Bb7en9WP+kiAUQghRKZX+qfHSSy8RGhrK0aNHiY6OJiYm5qb/FbVLqJ8Tz7S5PnF7d9tFdPpKLRy9iZXKijfavgHA2gtrSSxILLet2sODeq++AkDahx+iLyoyagxXczXTgq5XY14QnUR6mcao+9r3DcTG0YK8jBJO7oo16h4hhBBCVL/Lly/z4IMPYmVlhb+/P4GBgQQGBhIQEEBgYKCpw7snfB/xPceSj2FpZsn7Xd9HrVRXfFMVFR45QtqiDwBwf+tNbB54oNrGqgl6g4HpVxJZGJMCwER/dz4I8TFqN4upXT97MJnv5xwj/lIWZiolnQYGM/DNNjh72Zg6PCGEEHVQpZOE165dY+HChXTo0IGAgAD8/f1veona561ejbCzVHE+MZcNJ+LvqK9H/R+lnUc7SnWlLD65+LZtnUeMQO3jgzY1lYzPPjN6jGFeLrSwsyJPq2d2VJJR95hbqeg6uCEAp3+LIzOpwOjxhBBCCFF9RowYgVKpZMeOHZw6dYqwsDDCwsI4ffq0FC25C2JzY1l2ahkAE9tMxN+++ubjZQmJJE6cBHo9Dk88gdOwYdU2Vk0o0+sZfekaXyRmAPBeA2+mBHnWibMVS4u17PniEr+vD0dTosMjyJ5BM9oR+qgfSmXtj18IIUTtVOkk4SOPPMLZs2erIxZRTVztLJjY43oC7YNfI8gpKqtyXwqFgqntp2KmMGPPtT38lfxXuW2VFha4TXkLgKwv1lFm5JYiM4WChQ19UQA/pWZzJNu4hF9QK1cCW9ZDrzdw4JtIDHewalIIIYQQd8eZM2f49NNP6d27N61ataJly5Y3vUTVafVaph+aTomuhI6eHRncaHC1jaUvKyNxwgR0ublYNmuGx+x360QyrTyFWh3DzsWwJS0HtULBJ038+Y+Pq6nDMkpyVA4b3jvOlROpKJQKOvQP5Mk32uDkIasHhRBC3JlKJwn79evHxIkTeffdd/n555/Ztm3bTS9ROw17wJ8QdzuyizQs/u3yHfXV0Kkhg0IGAfD+sffR6MvfEmzXowc2nR7AUFZGypy5GFsnJ9TemmFe1wuQTL2cQJleb9R9Dw5qiNrCjJToXC4dNm4VohBCCCGqT5MmTcjIyDB1GPekdRfWcS7jHHZqO+Z2notSUX3nz6W9v5CSCxdQOjjgs3wZyv9Wr66LcjVanj17lQPZ+VibKfm6RSBPuldfJei7Ra/Tc3x7NJsXh5GfWYJ9PUsGvtGatn0CZfWgEEKIu6LS1Y2VyvInHwqFAp2u7heNqA0V6qrD0auZPPf5XygVsH1sF5p6OVS5r9zSXPpu7ktOaQ5T2k3h+SbPl9u2NCaGmP4DMGg0eC9ZjH2fPkaNkaPR0vlYBJkaLTOCPBnj727UfWf3xnNo4xUsrFU8904HbBzq7iRWCCGEuJXaPlfJy8u78eeTJ08yY8YM5s+fT/PmzVGrbz4vrzbGX1mm+DwisiJ4budzaPVa5nWZR//6/attrNydO0mafP1cap/Vn2D38MPVNlZ1y9ZoGXz2Kmfzi3FSmfFtiyBaO9T+FXh5GcXs+eISKdG5AIR08KDr4IaYW6lMHJkQQoi6oNqqG+v1+nJf90KC8F72QH0X+rbwRG+Ad7ZeNHpV3604WDgwrvU4AFadWUVmcfmViC0CA3F57VUAUuYvQPePXxxux1Gt4p3g60VMFsemklBi3Dbp5t18cPWzo7RIy+GNV4y6RwghhBB3j6OjI05OTjg5OdGzZ0/++usvunfvjpub243rf7cRlVemK2PawWlo9Vq6+3WnX1C/ahurNDqa5JmzAHB59dU6nSDMLNPyzJnrCUJntRk/hQbXiQTh5eMpbHjvOCnRuZhbmtHzpSb0GNFEEoRCCCHuOvnJcp+Z/nhj9oancfJaNlvOJPJkqE+V+xoYPJCNkRsJzwpnedhy5nSeU25bl5dfJm/HTspiYkhbuhTPd94xaoxn3J34LimTv3ILmXklkXXNK66CqFQqeHhoCD+9f5IrJ9MIeSAT/6YuRr8vIYQQQtyZ/fv3mzqEe9rHZz4mKicKZ0tnZj0wq9rOBtQXFZE4fjyGoiKs27fHdeyYahmnJqSXaXjmzFUiCktwNVexsVV9GtlYmTqs29KU6fjz+0gijl6vvOwR5EDPl5pgX692xy2EEKLuqvR24zlzyk8EAcyaNeuOAqoNavsWnjv18f4oPvg1Elc7C/ZNfgg7S3XFN5XjTNoZXvjlBQDW91pPG/c25bYt/OsYcS++CAoFAd9/h1WrVkaNEV5QTM+TkWgNsK5ZAL1dHY2679CPVzi7Lx77epYMntUBtbmZUfcJIYQQtV1dmqvExcXh6+v7r0SWwWAgPj4ePz8/E0V299Tk53E67TQv7n4RvUHPsm7L6O7XvVrGMRgMJE+dRu7WrZi51iNo0yZUrnWjsMf/Si3V8PSZKK4UleJuruKnVsE0sLE0dVi3lZNWxO5PL5CZWIBCAW37BNC2TwBKs+o7d1IIIcS9q9q2G2/evPmm148//sjChQtZvHgxW7ZsuZOYRQ35z4OBBLhYk55fykf7ou6or1ZurRjYYCAAc47OQaMrv4iJTccOOAwYAAYDye+8i0FTftt/amxrxWhfNwCmXE4gR6M16r72/QOxdbIgL6OEkztjjLpHCCGEEHdXYGAg6enp/7qelZVFYGDFOwTE/yvSFDH90HT0Bj396/evtgQhQM5PP5G7dSsolXh/uLjOJgiTSsp48vT1BKGXhZrNoQ1qfYIw+kw6G+efIDOxACs7Nf0nhNK+X5AkCIUQQlS7Sv+kOX369E2vCxcukJycTPfu3Zk4cWJ1xCjuMguVGe/0awrAF4diiEoruKP+JrWZhLOlM9G50Xxx4YvbtnWb8hZmDg6URkaS9dXXxo8R4EGwtQVpZVpmXzWuarG5pYqugxsCcGZPPJmJd/Y+hRBCCFF5BoPhltthCwoKsLSs3cma2mbJqSXE58fjYePB1PZTq22ckvBwUue+B4DrhAnYdGhfbWNVp/j/Jgiji0vxsVSzOTSYIOvaW9BOr9NzZFMUv6w+T1mJDo8gB559uz0+IXJ2pxBCiJpR6e3G5Tl//jz9+vUjNjb2bnRnUnVpC8+dGLn+BHsj0ugSXI+vR7a/o/NsdkbvZOrBqZgrzfm5/88EOASU2zbn559Jnj4DhZUV9XdsR+3tbdQYx3MKGHA6CgPwY8v6dHW2M+q+X1afJ/pMOu6B9jz1ZhsUyuo5t0cIIYSoKXVhrjJp0iQAli9fzssvv4y1tfWN7+l0Oo4dO4aZmRmHDx82VYh3TU18HocTD/Pa768B8Pmjn9PRs2O1jKPLzyfmqafRxMVh+/DD+Kz6GIWy7q1gu1ZcylNnokgo0eBvac5PocH4WpqbOqxyFeaWsmftRRIv5wDQsrsvDwysj5msHhRCCHEXVNt24/Lk5uaSm5t7t7oTNWBWvyaYq5Qcispg21njVueVp09gHzp5daJMX8bcv+betnKyw8CBWLVtg6G4mJS57xldZbm9oy0jvOsBMDkynkKtcdW0HxzUELWlGakxeVw8dGfvUwghhBDG+XvXicFg4Pz58zftRImIiKBly5asX7/e1GHWCXllecw6cv3c7yGNhlRbgtBgMJD89tto4uJQe3nh9f6COpsgHHj6eoIwyMqCzbU8QZgUlcOP80+QeDkHtYUZj73cjC7PNJAEoRBCiBpX6erGK1asuOlrg8FAcnIyX3/9Nb17975rgYnq5+9iw5huwSzZc5k52y/xUENXHK2rNoFSKBTM6DiDgVsHcjzlONuubmNA8IBy23q++y7RTw6k4MAB8vfswf7RR40aZ3qQJ79m5BJfUsb7McnMbVBxdWZbJws6Dgji4IYrHNkUhX8zF+ycZXuTEEIIUZ3+rnA8YsQIli9fXmtXPNYFNiobnm/8PNujtzOhzYRqGyf766/J3/M7qNV4L1+GmaNjtY1VXZJKynjmzFUSSzU0sLbgp1bBuFtUvUhfdTIYDJzbl8Dhn6Mw6A04edrQ+9VmOHnYmDo0IYQQ96lKbzf+3wOmlUolrq6uPPLII0ybNg07O+O2gNZmdWELz91SptXz+IqDXEkr4Nm2Pix6uuUd9bf2/FqWhS3D0cKRbU9sw8my/DNU0pYtI3P1p6jc3QnauQMzW1ujxjiQlcfgs9EogG2tG9DOoeKJlF5vYNMHp0iNycOviTN9x7a8o+3VQgghhCndT3OVuqCmPg+tXotKWeln/EYpuXSJmEGDQaPBfcYMnJ8fWi3jVKf0Mg1Pno4iqqiUACtztoQ2wKOWJgh1Gj0Hvo0g4q8UABq0c+fhoSGYW1bP5yuEEOL+Zuxc5a6dSXgvud8m3qeuZfHUJ0cB+O7lDnSqX6/KfWn0GgbvGMzl7Mv0r9+feV3mldtWX1JCdP8BaOLicHrhBTymv230OOPD49iQkkUDawv2tA3B0ojtGNkphWx47wQ6rZ5uLzSiSWcvo8cTQgghapPaPlcZOHAg69evx97enoEDB9627aZNm2ooqupT2z+PiuiLioh56mnKYmKw7d4dn5Uf1bmHqVkaLU+djiK8sARvCzVbWjeotVuMi/LK+GX1eVKic1EoFXR+OpgW3Xzq3H9zIYQQdUeNn0ko6q42/s4839EPgLc3nadEY9xZf7eiVqp554F3UKBg29Vt/JX8V7ltlZaWeMy6fr5O9rffUnzhotHjvBvshau5iitFpSy7lmrUPU4eNnToHwTA4Y1XyM8qMXo8IYQQQhjPwcHhRsLDwcHhti9heqkL3qcsJgaVmxue782tc8mqPK2O585eJbywBHdzFT+1qr1nEGYkFLDx/ROkROdibqWi35iWtHzEt879NxdCCHFvMmoloTwNvvfllWjoueQPUvNKGdMtmDceC7mj/ub9NY8fIn/Az86PTQM2YWFmUW7bxMlvkLdzJxaNGhG48UcUauO2hexIy+E/F2NRKWB3m4Y0s7Ou8B7ZdiyEEOJecD/OVQASExOZMmUKv/zyC0VFRQQHB7Nu3Tratm1b7j2lpaXMmTOHb775hpSUFDw9PZk1axYvvfTSjTY5OTlMnz6dTZs2kZWVhb+/P8uWLaNPnz5GxVWXP4+83b+SOGECKBT4rfsCm47VUxSluhTqdDx3NprjuYU4q83YHNqAEJvaefZ09Jl09qy7hLZUh4ObFY+PbiHnDwohhKgRd3UloTwNvvfZW6qZ3b8ZAKv/uEpESt4d9Teu9ThcrVyJy4/js3Of3bat+7SpmDk4UBoRQcant2/7T33dHHnc1QGtASZFxKPVV7xzXqlU0H14Y8xUSuIuZRF+JNno8YQQQghhOtnZ2XTu3Bm1Ws0vv/zCpUuXWLx4MU5O5Z9/DPDss8+yd+9e1q5dS2RkJN9//z0hIf//MLSsrIyePXsSGxvLTz/9RGRkJJ9//jne3t7V/ZZMTpOcTPJ/d3W4/Oc/dS5BWKzTM/xcDMdzC3FQmbGhZf1amSA0GAyc2h3LL5+eR1uqw6eRE09PaSsJQiGEELWOnEl4C3X5afCdeuWrk/x2KZVWvo78PKoTZsqqr7Lbc20Pkw5MQqVU8VO/n6jvWL/ctrk7dpL0xhugUhH400YsGzUyaoy0Ug1dj0eQo9UxPciTsf7uRt13+rc4jmyKwtzSjMGzOki1YyGEEHVKbZ+rhIaGGr1SPywszKh2U6dO5fDhwxw8eNDoOHbv3s3gwYOJjo7G2dn5lm1Wr17NBx98QEREBGojdzP8r9r+edyKQacjbviLFJ08iWXz5gR8963RuzlqgzK9nhHnY9mblYeNmZKNLevT2ohidjVNq9Gx/+sILh+/fjxO84e86fxsA8yMOE9bCCGEuFukcMkdqIsTvbslObeYnkv+pKBUy5wBTRn2QECV+zIYDIzbN44DCQcIdQtlfa/1KBW3nhAZDAYSx40jf8/vWDRuTOCPG4yeqG5IzmJ8RBwWSgV724UQbF1xwk+2HQshhKjLavtcZfbs2Ua3feedd4xq16RJEx577DESEhL4448/8Pb2ZvTo0bz88svl3jN69GguX75M27Zt+frrr7GxsaF///7MnTsXKysrAPr06YOzszPW1tZs3boVV1dXhgwZwpQpUzAzM7tlv6WlpZSWlt74Oi8vD19f31r7edxKxiefkL58BUprawK3bMbcz8/UIRlNqzfw6qVYdqbnYqVU8G2L+nRysjV1WP9SmFvKL6vPkxqTh0KpoOugBjR7yMfUYQkhhLgPGTt3VFW249TUVN544w327t1LWloa/5tj1OmqXvRCmJ6ngxVv9Qph1taLLNodSc8m7ng6WFWpL4VCwdsd3uZYyjFOp51mQ+QGnmv0XLltPWbNouj4CUrDw8n47DNcX3/dqHGe9XBiS1o2+7PyGXspju2tG6CqYAXk39uON7x34vq248PJNOki1Y6FEEKIu8HYxF9lREdH88knnzBp0iTefvttTpw4wbhx4zA3N2f48OHl3nPo0CEsLS3ZvHkzGRkZjB49mszMTNatW3ejzb59+xg6dCi7du0iKiqK0aNHo9Foyn0fCxYsqFQitLYpOn2a9JUfA+Dxzqw6lSA0GAxMjoxnZ3ou5goF65oH1soEYU5qEdtWnCE/swQLaxW9XmmGT6Nbr2YVQgghaotKryTs3bs3cXFxjBkzBk9Pz3+tvhowYMBdDdAUavvT+eqm1xt4evURwuJy6NHYnc+HtbmjVXbfhX/HguMLsFJZ8VO/n/CzL38iWtVtx4klZXQ7EUGeVs+bAR5MDvQw6r6/tx2rLc14TrYdCyGEqCPux7mKubk5bdu25ciRIzeujRs3jhMnTnD06NFb3vPoo49y8OBBUlJSbpydvWnTJp5++mkKCwuxsrKiYcOGlJSUEBMTc2Pl4JIlS/jggw9ITr712cV1eSWhLj+fmCeeRJOYiH2/fnh/sMjUIVXK/KtJrIhLw0wBa5sG0su19p2JnhKdy86Pz1FSqMHe1Yp+Y1ri6F5xgT0hhBCiutzVwiX/dOjQIb799ltGjRrFE088wYABA256ibpPqVSwYGALVEoFv4en8uvFlDvqb3CjwbT3aE+xtpiZh2eiN+jLbWv/eB/sevYArZakaW9j0GiMGsPb0pz3G/oCsORaCqfzioy6r2UPX9wD7dGU6DjwTcS/VsYKIYQQonbw9PSkSZMmN11r3LgxcXFxt73H29v7puJ6jRs3xmAwkJCQcKNNw4YNb9pa3LhxY1JSUigrK7tlvxYWFtjb29/0qgsMBgMp77yLJjERtY8PHu/MMnVIlbImIZ0VcWkAfNDQt1YmCGPOprN16WlKCjW4+dvx1JttJEEohBCizqh0ktDX11cSKfeBEA87XnvoeqGRWVsvkltsXLLuVpQKJXM6z8FaZU1YWhjfXPqm3LZ/bzs2c3C4se3YWE+6OTLAzRGdAcZcukaRrvxk5I3Y/rfa8WGpdiyEEELURp07dyYyMvKma5cvX8bf3/+29yQlJVFQUHDTPUqlEh8fnxttoqKi0Ov1N7Xx9PTE3Nz8Lr8L08rdspW8XbvAzAzvDz/AzLb2bdMtz9a0bGZeSQRgaqAHQ7xcTBzRv134M5FfVp9Hq9Hj38yFARNDsba/t/4OCSGEuLdVOkm4bNkypk6dSmxsbDWEI2qTMY8EE1jPhrT8UubtvHRHfXnbevNGuzcAWHF6BTG5MeW2Vbm64j5zJgAZn6ymJCLCqDEUCgXvN/TBw1zN1eJS5l5NMuo+Jw8bOvQPAuDQT1fIzyox6j4hhBBC1JyJEyfy119/MX/+fKKiovjuu+/47LPPeP0fZxhPmzaNYcOG3fh6yJAhuLi4MGLECC5dusSff/7Jm2++yUsvvXSjcMmoUaPIyspi/PjxXL58mZ07dzJ//vyb+r0XlCUkkDp3LgCuY8dg1aqVaQOqhEPZ18+dNgAjvOsx3t/d1CHdxGAwcGxbNH98F4nBAI07edJnVHPMLSt9/LsQQghhUkYlCZ2cnHB2dsbZ2ZnBgwdz4MAB6tevj52d3Y3rf7/EvcNSbcbCp1qgUMCPJxPYF5F6R/093eBpOnl1olRXyoxDM9DqteW2tX+8D7Y9uld627GTWsXyxtfPPFyXmMG+zDyj7vvntuPf111Cr5fVskIIIURt0q5dOzZv3sz3339Ps2bNmDt3LsuWLWPo0KE32iQnJ9+0/djW1pY9e/aQk5ND27ZtGTp0KP369WPFihU32vj6+vLrr79y4sQJWrRowbhx4xg/fjxTp06t0fdXnQx6PclTp6EvKsKqbRtcblMRura5kF/Ei+djKDMYeNzVgfcaeN/RWdl3m06nZ9/XEZzcFQtA28cD6PZCI5RmlV6LIYQQQpicUYVLvvzyS6M7LK+6XF1yPx4Gfjvv7bjEmkMxuNpZ8NuErjjZVH3bREphCk9ufZICTQETWk9gZPOR5bbVpqcT3bcfutxc6o0dY3S1Y4DplxNYm5iBu7mK/e0b4ayu+EluTloRP847gaZUR4cBQbTtHWD0eEIIIURNqu1zlUmTJhnddsmSJdUYSc2o7Z9H5rr1pC1ciMLamqCtWzD39TV1SEa5VlxKv7ArpJVpecDRhu9b1MeyFiXfykq0/Pr5ReIuZqJQwENDQmj6oLepwxJCCCH+xdi5SqWrG98PavtEr6aVaHT0/egQUWkF9G3hycohre+ovy1RW5h5eCZqpZoNfTfQwKlBuW2rWu24SKfnsZORXCkq5XFXB9Y0DTDqqXP4kWT2fRWOUqlg4JttcA+Uz18IIUTtU9vnKt26dbvp67CwMLRaLSEhIcD1M//MzMxo06YN+/btM0WId1Vt/jxKo6KIGfgUhrIyPGbPxmnQs6YOySgZZVr6h10huriUxjaWbAkNxsGIh741paRQw/YVZ0i7lo9KreTRl5sR2KKeqcMSQgghbqnaqhubmZmRlpb2r+uZmZk3VYUT9w5LtRlLnm2JmVLBjnPJbD9r3Fl/5RlQfwAP+TyERq9h+qHpaPTlbyX+17bjcqoM/i9rMyUrm/ijUsDO9Fx+Ss026r5GD3hQv7Uber2BPV9cpKyk/C3RQgghhLi1/fv333j169ePhx56iISEBMLCwggLCyM+Pp5u3brx+OOPmzrUe5pBoyFpylQMZWXYdH0Qx2efMXVIRinU6nj+XDTRxaV4W6j5vmX9WpUgLMorY8uSMNKu5WNpo2bAxFBJEAohhLgnVDpJWN7Cw9LS0nuuApz4fy18HHm9WzAAM7deIC2v6sU9FAoF7zzwDvbm9oRnhbP2/NrbtvV8550b1Y7TP/rI6HFa2lnzRoAHAG9fTiC+pOIEo0Kh4OGhIdg6WZCbXsyhH68YPZ4QQggh/m3x4sUsWLAAJyenG9ecnJx47733WLx4sQkju/dlfPoZJRcvonRwwHPue7XqLL/yaPUGXrl4jTP5RTirzfihZX08LNSmDuuG/KwSNi8OIzOxEGsHc56YHIpHkIOpwxJCCCHuCqMfyf19wLNCoWDNmjXY2tre+J5Op+PPP/+kkZFbQUXdNKZbMHvDU7mYlMe0TedZM7xtlSebrtauvN3hbaYenMqnZz/lYd+HaeR8678/KldXPN6bS+LYcWSuWYtN5y7YdOxgXMx+7vyemcfJvCLGhV/j51bBKCuI2dJGTY8RTdiy9DThR5Lxa+pCcBu3Sr9HIYQQQlzf3pKenv6v6+np6eTn55sgovtD8YWLZKxeDYDHzJmo3evGXGZmVCJ7s/KwUir4unkQDWwsTR3SDbnpRWxdeob8rBJsnS0YMCEURzdrU4clhBBC3DVGryRcunQpS5cuxWAwsHr16htfL126lNWrV1NUVMTq/05ExL3JXKVkybOtMDdTsjcijY0nE+6ovz6Bfejh1wOtQXt927HuNtuOe/bE8ZlnwGAgacoUtNnGbR9WKRV81NgfazMlR3MKWR3/719SbsW7oROtH/MH4MC3EeRnVX3lpBBCCHE/e/LJJxkxYgSbNm0iISGBhIQEfv75Z0aOHMnAgQNNHd49SV9aStLUKaDVYterF/aP9zF1SEZZk5DOusQMAFY28aeNg42JI/p/WUmFbPowjPysEhzcrBj4RhtJEAohhLjnGJ0kjImJISYmhoceeoizZ8/e+DomJobIyEh+/fVXOnQwbnWXqLtCPOyY9GhDAObsuERCdlGV+1IoFMzoOAMnCycuZ19m1dlVt23vPm0q5oGBaFNTSZk1q9yt7/8r0NqCOcHXK80tiE7mTJ5xMbfvF4ibvx2lRVr2rr+EXi81foQQQojKWr16Nb1792bIkCH4+/vj7+/PkCFD6NWrF6tW3f5nv6ia9GXLKYu6ilm9eni8M6tObDP+PTOPWVcSAZge5Mnjro6mDegf0uPy2bwkjKLcMpy9bHhycmvsnGvPCkchhBDibqn0mYT79++/6UwZcf95+cEg2vg7UVCq5c2N5+4oeeZi5cLMB2YCsPb8Wk6knCi3rdLaGq8PPwC1mvw9v5OzcaPR4wz1dOZxVwc0BgOvXowlT6ur8B4zMyU9X2qKysKMxMs5nNkTZ/R4QgghhLjO2tqaVatWkZmZyenTpzl9+jRZWVmsWrUKG5vas1LsXlF04gRZ69cD4Dl3Dqo6MG8PLyjmtYux6IHnPJ0Z41d7tkanROeyZelpSgo0uPnb8eSk1tg4WJg6LCGEEKJaKAxGLMeaNGkSc+fOxcbGhkmTJt227ZIlS+5acKZibGno+1lsRiG9lx+kWKPj3X5NeLFz4B31986Rd9h0ZRNuVm781P8nnCzLn9Bmrl1L2gcforCyIvDnn7EIMm7sXI2WHicvE19SRl9XBz5vGmDUk/VLh5PY/3UESqWCp6a0wc1f/k4IIYQwrbo4V4mKiuLq1at07doVKysrDAZDnVjhZoza8nnoCgqJeeIJNAkJODw1EK9580wWi7HSSjX0PnWZxFINnRxt+aFlEObKSq9jqBYJEVns/OQ82lIdnsEO9H29JeZWtafKshBCCGEsY+cqRv0EPn36NBqN5safy3udOXPmrgQvar+AejZM63O90Mj7uyOITi+4o/6mtJtCgH0AacVpzDpy+63EziNGYP1ARwzFxSS98QaGsoqrFgM4qFV82sQflQJ2pOeyPinTqPsad/Kkfqgrer2BPV9cQlNa8SpEIYQQQlyXmZlJ9+7dadiwIX369CE5ORmAkSNHMnnyZBNHd29J++ADNAkJqL28cJ82zdThVKhYp+fFCzEklmoIsrJgbbOAWpMgjLuUyY6V59CW6vBt7ES/sa0kQSiEEOKeZ9RP4f379+Po6Hjjz+W99u3bV52xilrm+Q7+dAmuR4lGz8Qfz6LR6avcl7XamkVdF6FWqjkQf4AfIn8ot61CqcTr/YWYOTpScukSacuXGz1OawcbZgR5AfDOlUTO51d8PqFCoeDh5xth42hBTmoRBzdcNno8IYQQ4n43ceJE1Go1cXFxWFv/f6GHQYMGsXv3bhNGdm8pOHiInA0bAPCcPx8zW1sTR3R7eoOB8RFxhOUV4agy45sWQTipa0cSLj48i12fnEen1RPQoh6Pj26J2sLM1GEJIYQQ1a52PKoTdZJSqWDh0y2ws1RxNj6Hxb/dWfKssUtjJrW5vp39wxMfcjm7/P7U7m54znsPgKy1X1B45IjR47zq68qjLvaUGQy8cjGWAiPOJ7S0UdNjRBNQQPiRZMKPJBs9nhBCCHE/++2331i4cCE+Pj43XW/QoAHXrl0zUVT3Fl1BIcnvzALA6YUXsOlY+4sJfhCTwra0HNQKBWubBRBkXTvO+UuIyGLXqnPoNNcThL1eaYaZWn5lEkIIcX8w6nHdwIEDje5w06ZNVQ5G1D3ejlYsfKoFo78NY/UfV+lU34WuDV2r3N/QxkM5mnyUPxP+5K0/3uL7vt9jpbK6ZVu77t1xHDSInA0bSJoylcBtW406nFuhULC8sR89TkQSU1zGm5HxrGriX+G5SD4hTnToF8ixbTH8+X0krn521POp3U/phRBCCFMrLCy8aQXh37KysrCwqB2JoboufckStEnJqH18cJs4wdThVGhjShZLr6UCsCjEh85OdiaO6LrEy9nsXHUOrUaPf3MXer3cDDOVJAiFEELcP4z6qefg4GD0S9x/+jT3ZGgHPwAm/XiGtPySKvelUCiY23ku9azqcTX3Kh+c+OC27d2nTsE8KAhtejrJM2be9izDf3JSq1jdNAAzBWxOy+Hb5Cyj7mvTKwC/ps5oNXp2f3aesmKtUfcJIYQQ96sHH3yQr7766sbXCoUCvV7PokWL6NatmwkjuzcUnTxJ9nffAderGStvkZCtTU7kFjI5Ih6AMX5uPOfpYuKIrkuKymHHx+fQlunxa+osKwiFEELcl4yqbny/qS0V6uqSEo2OASsPE5maz4MN6vHliPYolVWvWHg06Siv7nkVAwaWPLyEnv49yx/70iViBg0GjQb3WTNxHjLE6HFWXkvlvehkLJUKdrVpSBPbW69a/KfigjJ+nHeCguxS6rd25bGXm90z1RmFEELUDXVprnLhwgW6d+9O69at2bdvH/379+fixYtkZWVx+PBh6tevb+oQ75ipPg99SQkxA56g7No1HJ95Gs+5c2ts7KpIKdXw6MlI0sq09KnnwJpmAShrwRwq+Wou21ecQfPfIiV9RrdApZYzCIUQQtw77mp1YyEqYqk2Y+WQUCzVSg5eyeDTP6PvqL8HvB5gRLMRALxz5B2SC8o/A9CySRPcJl8/yzBtwfsUnz9v9Dij/dx4xNmOEv318wkLjTif0MrWnMdebobSTMHVsHTO7UswejwhhBDiftOsWTMuX75Mly5dGDBgAIWFhQwcOJDTp0/fEwlCU8r4+GPKrl1D5eaG25tvmjqc2yrV6xl5IYa0Mi2NbCz5qLFfrUgQpsTksv2j6wlC7xAn+oySBKEQQoj7V61IEn788ccEBARgaWlJhw4dOH78+G3bb9y4kUaNGmFpaUnz5s3ZtWvXje9pNBqmTJlC8+bNsbGxwcvLi2HDhpGUlFTdb+O+18Ddjtn9mwLw4W+RnLqWfUf9jQkdQ/N6zckvy2fqwalo9eVv7XUePhy7nj0waDQkjB+PNtu4sZUKBR819sfTQk1UUSlTLicYtWXZI8iBzk8HA3Dk5yhSonONe1NCCCHEfSYuLg57e3umT5/Ojz/+yK5du3jvvffw9PQkLi7O1OHVWcUXLpL5xToAPN59B7NavqJ0+uVETuUV4aAyY33zQGxUpk/EpcbmsX35GTQlOrwbOvL46y1QmZs+LiGEEMJUTJ4k3LBhA5MmTeKdd94hLCyMli1b8thjj5GWlnbL9keOHOG5555j5MiRnD59mieeeIInnniCCxcuAFBUVERYWBgzZ84kLCyMTZs2ERkZSf/+/Wvybd23nm3rS7+WXuj0BsZ9f5rcYk2V+1Ir1Sx8cCE2ahvC0sL4/Nzn5bZVKBR4zp+P2t8PbVIySW9NwaDXGzWOi7mKT5r4owR+Ss3mm+RMo+5r/rAPwW3c0OsN/Pr5BYrzy4y6TwghhLifBAYGkp6e/q/rmZmZBAYGmiCius+g0ZA8YwbodNj36YPdI4+YOqTb+ioxg2+SM1EAnzTxJ8DK9AVr0uPy2b7iDGUlOjyDHXj89ZaoJUEohBDiPmfyMwk7dOhAu3btWLlyJQB6vR5fX1/Gjh3L1KlT/9V+0KBBFBYWsmPHjhvXOnbsSKtWrVi9evUtxzhx4gTt27fn2rVr+Pn5/ev7paWllJaW3vg6Ly8PX1/fOnHOT22UV6Kh74pDxGUV8XhzT1YOCb2jM/t2RO9g2sFpKBVKVvdYzQNeD5TbtiQykthBgzGUlFBv7BhcX3/d6HE+upbKvOhk1AoFm0KDaedgU+E9ZSVaNi44SU5qEb5NnOk7puUdncUohBBCGKMunUmoVCpJTU3F1dX1puvXrl2jSZMmFBYWmiiyu6emP4+MTz4hffkKzBwdCdq1E5Wzc7WPWVUncgsZeDoKjcHA20GejPN3N3VIZKcUsunDMEoKNHjWd6Dv2JaYW6pMHZYQQghRberEmYRlZWWcOnWKHj163LimVCrp0aMHR48eveU9R48evak9wGOPPVZue4Dc3FwUCgWOjo63/P6CBQtuqtDs6+tb+TcjbrC3VPPRc6GolAp2nk/m++Pxd9Rf36C+PBH8BHqDnil/Trn9+YQhIXi8+w4AGSs/puDgIaPHGePnRl9XBzQGAyMvxJBSWvEqSHNLFb1eaYZKrST+UhYnd8UaPZ4QQghxL5s0aRKTJk1CoVAwc+bMG19PmjSJ8ePHM2jQIFq1amXqMOuc0qgoMlZ9AoD79Om1OkGYUqph5IUYNAYDfV0dGOvnZuqQyM8qYdvyM5QUaHDzt6PvGEkQCiGEEH8z6ifiihUrjO5w3LhxRrfNyMhAp9Ph7n7zE0V3d3ciIiJueU9KSsot26ekpNyyfUlJCVOmTOG5554rN1s6bdo0Jk2adOPrv1cSiqpr6evIlF6NmLcrnNnbL9LG34kQD7sq9ze9w3QisyIJzwpn0oFJrO+9HguzW29VcXziCYrDTpPz448kvfkmgZt+Ru3lVeEYCoWC5Y38iCq6QkRhCSMvxLApNBgL5e1z6S7etjw8NITf14dzYmcMHkH2+DVxqdL7FEIIIe4Vp0+fBsBgMHD+/HnMzc1vfM/c3JyWLVvyxhtvmCq8Osmg05E8fQYGjQbbhx7Cvu/jpg6pXP8sVBJiY8nyRn53tLPkbijOL2Pb8jMUZJfi5GF9fQWhlSQIhRBCiL8Z9VNx6dKlN32dnp5OUVHRjZV5OTk5WFtb4+bmVqkkYXXTaDQ8++yzGAwGPvnkk3LbWVhYYGFh+rNR7jUjuwRy+GoGByLTGfNdGFvHdMbavGoTMUuVJUu7LWXQjkFcyLzA+8ff550H3im3vfv0tym5cIGSS5dImDAR/2++RvmPX07KY/Pfw7QfO3mZU3lFTL+cyIeNKk4Yh3T0JOlqLpcOJrFn7SWemdYW+3pWlXqPQgghxL1k//79AIwYMYLly5fX+m3RdUH2t99SfPYsShsbPGa/a/Kk2+3MuPKPQiXNTF+opKxEy/aPzpKTWoStkwX9xrXCyrbiuaEQQghxPzFqu3FMTMyN17x582jVqhXh4eFkZWWRlZVFeHg4rVu3Zu7cuZUavF69epiZmZGamnrT9dTUVDw8PG55j4eHh1Ht/04QXrt2jT179sjE1ASUSgUfPtMSNzsLrqQVMG3TeaMqB5fH29abhQ8uRIGCny7/xOYrm8sf28IC7xXLUTo4UHLuHGnvLzR6nAArCz5p4o8C+CY5k68SM4y678FnG+DqZ0dJoYZdn5ynrKT8asxCCCHE/WLdunUyD7sLyuLjSVu6DAC3N99EXc5cuTb4OimDr5OuFypZ1cSfQGvTPozXanTs+uQ86XH5WNqq6T++FXbOliaNSQghhKiNKn0m4cyZM/noo48ICQm5cS0kJISlS5cyY8aMSvVlbm5OmzZt2Lt3741rer2evXv38sADty5O8cADD9zUHmDPnj03tf87QXjlyhV+//13XFxk66ep1LO1uHE+4dYzSXxxOPaO+uvs3ZnXW10vRvLeX+9xMfNiuW3NfXzwWvg+ANnffUfu9h3ltv1fj7jY83aQJwDTryRyPKegwntUajN6v9YcKzs1mYkF7P0yHIPepHWBhBBCiFrh5MmTvPXWWwwePJiBAwfe9BIVMxgMJM+ahaG4GOv27XF89hlTh1Suk7mFvH05EYCpgZ50dzFtgliv07Nn7SUSI7NRW5jRb2xLnDwqLk4nhBBC3I8qnSRMTk5Gq/33CimdTvevFX7GmDRpEp9//jlffvkl4eHhjBo1isLCQkaMGAHAsGHDmDZt2o3248ePZ/fu3SxevJiIiAjeffddTp48yZgxY4DrCcKnn36akydP8u2336LT6UhJSSElJYWysrJKxyfuXIcgF6Y/3hiA+bvC+Ss68476e7nFyzzs8zBl+jIm7Z9ETklOuW3tHn4Yl1GvAZA8axalV64YPc4YPzf6uTpeL2RyMZbk0or//tg5W9L7tRYoVQqiT6dzYmeM0eMJIYQQ96IffviBTp06ER4ezubNm9FoNFy8eJF9+/bh4OBg6vDqBH1eHoaSUhSWlnjOnYOigvOSTSW9TMN/LsSiMRh43NWBcf6mLVRiMBg48G0k0WfSUaoU9BndAjd/WdUqhBBClKfSM4zu3bvz6quvEhYWduPaqVOnGDVq1L+qDhtj0KBBfPjhh8yaNYtWrVpx5swZdu/efaM4SVxcHMnJ/1/NtlOnTnz33Xd89tlntGzZkp9++oktW7bQrFkzABITE9m2bRsJCQm0atUKT0/PG68jR45UOj5xd7zYKYAnQ73R6Q2M+S6M5NziKvelVCiZ9+A8/Oz8SCpMYsrBKej0unLbu44Zg02nBzAUF5Mwdhy63FyjxlEoFCxr5EtjG0vSy7SMvBBLqV5f4X2e9R14eMj1lbYndsYSdSrNuDcmhBBC3IPmz5/P0qVL2b59O+bm5ixfvpyIiAieffZZ/Pz8TB1enWDm4ID/t98Q8N23mPv7mzqcW9IbDIy9FEdKmYZga4taUajk6KarhB9JRqGAx/7TDJ8QJ5PGI4QQQtR2CkMlD4lLT09n+PDh7N69G7VaDYBWq+Wxxx5j/fr1uLmZ9onh3ZCXl4eDgwO5ublyhs5dVFym46lPjnApOY+Wvo78+GpHLO7gEOvL2ZcZunMoJboSXmnxCmNDx5bbVpuVRcxTT6NNTsamc2d8P12NQmVcEZVrxaU8dvIyOVodQzydWRzia9Sk99DGK5zdG49KrWTgm21w9at6dWchhBDin+rSXMXGxoaLFy8SEBCAi4sLBw4coHnz5oSHh/PII4/c9DC4rqpLn0d1WRqbwsKYFKyUCna1aUhjW9MWcAv79RpHN18F4JFhjWjcycuk8QghhBCmZOxcpdIrCV1dXdm1axcRERFs3LiRjRs3Eh4ezq5du+6JBKGoPlbmZnz6QhscrdWcjc/hna3lnydojIZODXm307sAfHbuM/bH7S+3rcrZGd9VH6OwsqLw8GFSK1HIxN/KgtVN/VEC3yVnsT7JuO3SnQbWx6+JM1qNnl2fnKMoT7a7CyGEuP84OTmRn58PgLe3NxcuXAAgJyeHoqIiU4Ym7pJD2fl8EJMCwPyGPiZPEEYeS7mRIOw0MFgShEIIIYSRqnygSUBAACEhIfTp04eGDRvezZjEPczX2ZoVg0NRKuCHE/F8dyzujvp7POhxhjYeCsDbh97mWt61cttaNm78/4VMvvmG7B82GD3Ow87/X8hkxpUE9mXmVXiP0kzJo/9piqO7NQXZpez+9Dw6TcXblYUQQoh7SdeuXdmzZw8AzzzzDOPHj+fll1/mueeeo3v37iaOTtyptFINoy5dQw8M8nDmOU/TFgxMiMxm31fhALTq6Ufoo7KlXQghhDBWpZOERUVFjBw5Emtra5o2bUpc3PUkz9ixY3n//ffveoDi3tO1oStvPHb9zL53tl0gLC77jvqb3GYyoW6hFGgKGLN3DLml5Z85aP/oo7hOGA9AynvvUfjXMaPHed3PjUEezugM8PLFWC4WVHyuooW1mj6jmmNupSL5ai5/fB9JJXf4CyGEEHXaypUrGTx4MADTp09n0qRJpKam8tRTT7F27VoTRyfuhM5gYNSla6SXaQmxsWRBQx+TxpOVVMgvq8+j1xkIbuNGpyfrmzQeIYQQoq6pdJJw2rRpnD17lgMHDmBpaXnjeo8ePdiwwfiVWeL+Nuqh+vRu5oFGZ2D0N2Gk55dWuS+1mZolDy/Bw8aD2LxYJh+YjEavKbe9y6uvYt+3L2i1JIwfT9m18lcf/pNCoeCDEB+6ONpSqNPz/LlooyoeO3nY8Nh/mqJQQPiRZM7tSzD6vQkhhBB1nbOzM15e17d7KpVKpk6dyrZt21i8eDFOTlJIoi77MCaFwzkFWJsp+bxpANZmpqu6XJhbyo6VZykr1uIR5ED3FxujUJq2cIoQQghR11T6J/mWLVtYuXIlXbp0ual4Q9OmTbl69epdDU7cuxQKBR8805JgN1tS8kp4/dswNLqqb8WtZ1WPlY+sxFplzbGUY8z7a165K/YUCgWe783FskUL9Lm5xI8aje6/ZyVVxFypZG2zABpYW5BcquGFczEUaMuvrPw3v6YudHoqGIDDP10h7qJx5xoKIYQQdVFeXp7RL1E3HcjKY9m1VAA+aOhDQxvLCu6oPppSHbtWnSM/qwQHNyv6jG6OSl314nhCCCHE/arSScL09PRbFigpLCw0quKrEH+ztVDx6QttsLNQcTw2iznbL93RVtwQ5xA+eOgDlAolP1/5mS8vflluW6WlJT4rP0Ll7k5ZdDSJkyZj0GqNGsdBreLbFkHUU6u4UFDMKxdj0eorjrtld18adfLEYIDdn10gPc64xKQQQghR1zg6OuLk5HTb199tRN2TXFrG65fiMAAveLnwlIezyWLR6w38tvYiadfysbRR03dMS6xszU0WjxBCCFGXVTpJ2LZtW3bu3Hnj678Tg2vWrOGBBx64e5GJ+0J9V1uWDGqFQgFf/3WNdYdj76i/rj5debPtmwAsObWEvXF7y22rdnPDZ9XHKCwtKTx4kLQPPjB6HD8rC75qEYiVUsG+rHymX0moMMGpUCh4eEgI3iFOaEp17Fh5lryMis81FEIIIeqa/fv3s2/fvtu+/m4j6hat3sCoi9fI1GhpamvJnGBvk8ViMBg49OMVYs9lYKZS0md0CxzdrE0WjxBCCFHXKQyVXLp16NAhevfuzfPPP8/69et59dVXuXTpEkeOHOGPP/6gTZs21RVrjcnLy8PBwYHc3Fzs7e1NHc594bM/rzJ/VwQKBXz2Qlt6NnGvcl8Gg4F5x+axIXIDVior1vdaTxOXJuW2z9v9K4kTJgDgMXcOTs88Y/RYu9JzGHkhFgPwTn0vRvn9e5Xt/yot1rL5wzAyEwtwdLfmqTfbYGmrNnpMIYQQQuYqtcv99HnMu5rER3Fp2Jop+a1tCEHWFiaL5czvcRz+KQqAx15uRnCbiudhQgghxP3I2LlKpVcSdunShTNnzqDVamnevDm//fYbbm5uHD169J5IEArTePnBIIZ08MNggHHfn+Z8QvkViiuiUCiY2n4qnbw6UawtZuzesaQWppbb3r7XY9QbOwaAlNlzKDx61Oix+rg68k7964exz7maxI60nArvsbBS0XdMS2ydLMhJLWLnqnNoyyo+11AIIYSoy4qKioiIiODcuXM3vUTdcSArj4/i0gBY0sjPpAnCq6fTOPzz9QRhp4HBkiAUQggh7oJKryS8H9xPT4NrE41Oz0vrT3DwSgZudhZseb0zXo5WVe4vvyyfF3a9wNXcqzR2bsz6XuuxVt96C4rBYCBp8hvk7dqF0sYG/6+/wrJJ+asP//feaVcSWZ+YgaVSwaZWwbR2sKnwvsykAjZ/GEZpkZagVq489kozlFKFTwghhBHq0lwlPT2dESNG8Msvv9zy+zpd3X9QVpc+j6rKKNPyyIkI0sq0DPdyYWGIr8liSYnJZcuS0+g0epo95E3XwQ3lbHQhhBDiNqptJaGZmRlpaWn/up6ZmYmZmVQRE1WnNlPy8dDWhLjbkZZfykvrT1BQalwxkVuxM7fjo+4f4WThRHhWONMOTkNvuHUFZYVCgeeC+Vi3b4++sJC4V16lLCHBqHEUCgXvBXvT3dmeEr2BF87HEF1UWuF9Ll629H6tOUqVgugz6Rz68codFW4RQgghaqMJEyaQk5PDsWPHsLKyYvfu3Xz55Zc0aNCAbdu2mTo8YQSDwcDEiDjSyrQ0tLbkXROeQ1iQXcovn5xHp9Hj39yFB59tIAlCIYQQ4i6pdJKwvCRGaWkp5uZSSUzcGXtLNV+MaIernQURKfmM+S4Mre7WiT1j+Nr5svyR5aiVavbF72PZqWXltlVaWODz8UosQkLQZWQQP/I/aLOyjBpHpVTwWVN/mttakanR8syZKJJKyiq8z7uhEz1evL5i8fyBBE7/FmfUeEIIIURdsW/fPpYsWULbtm1RKpX4+/vz/PPPs2jRIhYsWGDq8IQR1idlsiczD3OFgtVN/bEyq/SvEHeFtkzHL6vPUZRXhou3DY+ObIrSRLEIIYQQ9yKVsQ1XrFgBXF81tWbNGmxtbW98T6fT8eeff9KoUaO7H6G473g7WrF2eFue/fQoByLTeXf7ReYOaFblp8ShbqHM6TyHaQense7iOupZ1WNY02G3bGtmZ4fvZ59x7bnnKLt2jfhXX8N//TqUNhVvH7ZRmfFdyyAGhEURXVzKoLNX2RzagHrmt/+/WYO27hTmlHL4pyiObr6KrZMFDdt7VOm9CiGEELVNYWEhbm7Xz4tzcnIiPT2dhg0b0rx5c8LCwkwcnahIRGExs6MSAZhR35MmtlU/CuZOGAwG9n0dQdq1fCxt1PQZ1QJzS6N/lRFCCCGEEYz+ybp06VLg+g/o1atX37S12NzcnICAAFavXn33IxT3pRY+jiwbFMqob0/xzV9xBLjY8J8Hg6rcX9+gvqQUprA8bDkfnPwAR0tH+tfvf8u2anc3fNes4dqQIZScP0/ChIn4rvoYhbriCsSu5mp+bFWf/mFXuFJUypBzV/m5VTB2qttvxW/Vw4+C7FLO7o1n75fhWNmb49vIuUrvVQghhKhNQkJCiIyMJCAggJYtW/Lpp5/emDd6enqaOjxxGyU6PaMvXqNEb6Cbsx3/8XE1WSynf4vjyolUlEoFvV5phn090yQrhRBCiHuZ0evzY2JiiImJ4aGHHuLs2bM3vo6JiSEyMpJff/2VDh06VGes4j7Tq5kH0/s0BmDernB+vZhyR/2NbDaSF5q8AMCsw7P4I/6PcttaBAXiu/oTFJaWFB48SPKMmUafF+hjac6GlvVxVptxLr+YYeejKTZiy3Tnp65X5tPrDPzyyXlSY/KMe2NCCCFELTZ+/HiSk5MBeOedd/jll1/w8/NjxYoVzJ8/38TRiduZH53MpcISXNQqVjT2Q2mis/9iz2VwdMtVAB4c3BDvECeTxCGEEELc66S68S3cDxXq6gqDwcDMrRf45q84LNVKvhnZgbYBVV9hpzfomXFoBtujt2NhZsFnPT+jtXvrctvnHzhAwutjQKfD5eX/4DZ5stFjncsv4qnTUeTr9PR0seeLZoGoK6herNXo2LHyHImR2VhYqxgwMRRXXzujxxRCCHF/qMtzlaKiIiIiIvDz86NevXqmDueuqMufR3n2ZeYx5Fw0AF83D6RnPQeTxJGVVMhPi06iKdHRrKs3Dw0JMUkcQgghRF1m7FylSknChIQEtm3bRlxcHGVlNxdnWLJkSeWjrWXuxYleXabV6Xn5q5Psj0zHzlLFhlceoIlX1T8XjV7DhP0T+DPhT+zUdqzrtY4Q5/InnDk/byJ5+nQA3N+ehvOwW59neCtHcwp47uxVSvQGBro7sdKIp/BlJVq2rzhLSnQulrZqnpzcGmfPis9EFEIIcf+QuUrtcq99HullGh45EUl6mZaXvOsxv6GPSeIoKdSw8f2T5KUX493QkX7jW2EmhUqEEEKISjN2rlLpn7J79+4lJCSETz75hMWLF7N//37WrVvHF198wZkzZ+4kZiFuSWWmZNXQNrQLcCK/RMuwL44Tk1FY5f7USjUfPvQhrd1ak6/J57XfXyM+P77c9o5PDcR1wgQAUhe8T+7OnUaP9YCjLWuaBaJSwKbUbN6+kljhtmVzSxV9x7bE1c+OkgINW5edJje9yOgxhRBCiNrkqaeeYuHChf+6vmjRIp555hkTRCRux2AwMCkinvQyLY1sLJlZ38skceh1en79/AJ56cXYuVjy2CvNJEEohBBCVLNK/6SdNm0ab7zxBufPn8fS0pKff/6Z+Ph4HnroIZnoiWpjZW7GmuHtaOJpT0ZBKc+vOUZybnHV+1NZ8VH3j2jg1ICM4gxe3fMqGcUZ5bZ3efUVnIYMAYOBpLemkLdnj9Fj9XCxZ2VjfxTA+sQMFsZUfLaihZWK/uNa4exlQ1FuGVuXniE/q8ToMYUQQoja4s8//6RPnz7/ut67d2/+/PNPE0QkbmddYgZ7MvOwUCr4pIk/ViZKzB3+KYqEiGxUFmY8ProFVrbmJolDCCGEuJ9U+qd+eHg4w/673VKlUlFcXIytrS1z5sy55VNiIe4WBys1X77UnsB6NiTmFPPC2uNkFZZVfGM57M3t+bTHp3jbehOfH89re14jvyz/lm0VCgXu09/Gvn8/0OlInDSZ/P37jR7rCXcn3v/vVp1l11L56FpqhfdY2qrpP74Vju7W5GeVsHXpaQpzS40eUwghhKgNCgoKMDf/d4JHrVaTlydFumqTiMJi5lxNAmBmfS8a25qmgvClQ0mc258AQM8RTXDxtjVJHEIIIcT9ptJJQhsbmxvnEHp6enL16tUb38vIKH8llhB3g6udBV+PbI+ngyVRaQW8uO44+SWaqvdn7cpnPT/D2dKZyOxIxu4bS4n21iv2FGZmeM2fj32f3qDRkDhuPAUHDxk91nDvekwP8gRgXnQyy2MrThTaOFjQf3wr7FwsyU0vZuuyMxQXVD0xKoQQQtS05s2bs2HDhn9d/+GHH2jSpIkJIhK3UqbXM+ZSHCV6A4842zHS2zRFZVJj8vjj+0gAOvQPJKiVq0niEEIIIe5HlU4SduzYkUOHridG+vTpw+TJk5k3bx4vvfQSHTt2vOsBCvG/fJys+XpkB5xtzDmXkMvLX52kRKOrcn9+9n6s7rEaW7Utp1JPMW7fuPIThSoVXgsXYtezJwaNhoQxYyg8etToscb6uzM10AOABTHJLImteOuxnbMlAyaEYuNoQXZyIduWn6G0qOqJUSGEEKImzZw5k7lz5zJ8+HC+/PJLvvzyS4YNG8a8efOYOXOmqcMT/7U0NpULBcU4q81Y1sgPRQWF1qpDcUEZuz87j15nICjUlTa9A2o8BiGEEOJ+Vukk4ZIlS+jQoQMAs2fPpnv37mzYsIGAgADWrl171wMU4laC3Wz5ckR7bC1U/BWdxZjvwtDo9FXur7FLY1Z2X4mVyoqjyUdvnyhUq/Fe/CG23bphKC0lftRoCo8fN3qsCQEeN1YULopJ4UMjzih0cLViwIRWWNmpyYgvYPtHZykr0Ro9phBCCGEq/fr1Y8uWLURFRTF69GgmT55MQkICv//+O0888YSpwxPAmbwiVsRd3+HwfkNf3CzUNR6DXm9gz9qLFGSX4uBmRfdhjU2SqBRCCCHuZwpDRaVW70PGloYWpvdXdCbDvjhOmVbPk6HeLH6mJUpl1SeUJ1NOMnrvaIq1xXTy6sTybsuxVFnesq2+rIyE18dQePAgCmtr/NZ8jnXr1kaPtfJaKu9FJwMw0d+dtwI9KpwMZyQUsGVJGKVFWtwD7ek3tiUW1jU/kRdCCGFadWWuotVqmT9/Pi+99BI+Pj6mDqfa1JXP41ZKdHp6nozkSlEpT7g5srppgEniOLYtmpO7YlGZK3l6Sls5h1AIIYS4i4ydq5imXJkQd0nHIBdWDWmNmVLB5tOJTN9yHr2+6nnvth5tWdV9FVYqK44kHWHC/gmU6m5dLERpbo7PRyuw6fQAhqIi4l9+heJz54wea4y/O+/U9wJg6bVUFsakUFHOvp6PLf3Ht8LCRkVqTB5blp6WMwqFEELUWiqVikWLFqHVyur32mphTDJXikpxM1cxv6FpErmx5zM4uSsWgIeHNpIEoRBCCGEiRiUJnZyccHZ2NuolRE3r0cSdJc+2RKGA74/HM3XTubuWKDycdJjx+8aXnyi0tMTn44+xbt8efWEhcSP/Q/HFi0aPNcrPjdnB1xOFy66lMj86ucJEoZu/PU9MbH1j6/GWJVL1WAghRO3VvXt3/vjjD1OHIW7hWE4Bq+PTAfgwxBdntarGY8jLKOb3dZcAaP6QNyEdPGo8BiGEEEJcZ9R24y+//NLoDocPH35HAdUGdXnLyP1sy+lEJv14Br0Bnmrtw6KnW2B2B1uPT6Sc4PW9r1OsLaazV2eWP7IcCzOLW7bVFxYS9/IrFIeFoXRwwG/N51g1b270WGsS0plxJRGA0b5uzKzvWeHW4+yUQrYuPU1hbhkOblYMmBCKnfOtt0YLIYS4t9Slucrq1auZPXs2Q4cOpU2bNtjY2Nz0/f79+5sosrunLn0efyvU6njkRCTXSsoY7OHMssZ+NR6DVqPj50WnyIgvwD3Qnicnt8ZMJRudhBBCiLvN2LmKnEl4C3Vxoieu2342iQkbzqDTG3iilRcfPtMSlVnVJ5s3JQq9O7O8W/mJQl1BAfEj/0Px2bMora3xWf0JNu3bGz3WFwnpvP3fROGrPq68G+xVYaIwN72IrUvPkJ9Vgp2LJU9MDMW+npXxb1AIIUSdVJfmKkpl+T+HFQoFOp2uBqOpHnXp8/jb1MsJrE/MwNtCzf72jbBXmdV4DPu+Dif8cDKWtmqefbudPOwUQgghqsldP5NQr9ezcOFCOnfuTLt27Zg6dSrFxcV3JVgh7pZ+Lb346LlQVEoFW84kMfHHs2jvoOpxO492fNz94+tbjxMP3/aMQjNbW3zXrsW6Qwf0/z2jsKAS26te8nHl/f+eBfRpQjoTIuLRVrBt2sHVmiffaI2DqxX5mSVs+jCMnNQi49+gEEIIUc30en25r3shQVgX/ZGVz/rEDACWNvIzSYLw0uEkwg8no1DAoyObSoJQCCGEqAWMThLOmzePt99+G1tbW7y9vVm+fDmvv/56dcYmRJX0ae7JyiGtUSkVbD+bxPgfzqC5C4lCSzNLDiUeYuzesRRpbp2IM7O1wfezT7Ht1g1DaSnxr48hb9cuo8d60bseyxr5YqaADSlZjLgQQ1EFsds5W/LkG61x8rShMKeUTYvDyEwsqNR7FEIIIWpCSUmJqUO47+VpdUyMiANghHc9ujrb1XgM6XH5/Pn9ZQDa9w/Ct7Gcay6EEELUBkYnCb/66itWrVrFr7/+ypYtW9i+fTvffvsten3Vky9CVJdezTz45Pk2qM0U7DyfzNjvTlOmvbNE4aoe14uZHE0+ysu/vUxOSc4t2yotLPBZsRz7xx8HrZbEyW+QvXGj0WMN9nRhXbNALJUK9mTmMejMVbI1t68KaeNgwZOTQnHxsaU4r4wtS06Tdi2vMm9RCCGEqBY6nY65c+fi7e2Nra0t0dHRAMycOZO1a9eaOLr7z6wriSSVagiwMmdGfc8aH7+kUMMvn55Hp9UT0NyFNo/513gMQgghhLg1o5OEcXFx9OnT58bXPXr0QKFQkJSUVC2BCXGnejZx59MX2mBupmT3xRRe/y7sjhOFax5dg4OFA+cyzjF893BSClNu2VahVuO1aCGOgwaBwUDKzFlkrltv9FiP1nPgx5b1cVCZcSKvkCdOR5FUUnbbe6zszHliYihuAfaUFGrYsuQ08ZeyKvMWhRBCiLtu3rx5rF+/nkWLFmFubn7jerNmzVizZo0JI7v//JaRyw8pWSiAFY38sDGr2W3GBoOB/V9HkJ9Zgn09S7q/2ATFHRSZE0IIIcTdZXSSUKvVYml581kharUajUZz14MS4m55pJE7nw1rg7lKyZ5Lqbz69UmKy6p+/lEL1xZ82etL3KzdiM6NZtgvw4jJjbllW4WZGR7vvoPLf0YCkLZwIekrPsLYWkHtHW3ZEhqMh7mayMIS+oVd4Urh7bdpWdqoGTC+Fd4hjmhKdexYeZaIv5Ir9yaFEEKIu+irr77is88+Y+jQoZj9IynVsmVLIiIiTBjZ/SVHo+WNyHgAXvN1pb2jbY3HcOGPRKLPpKM0U/DYy82wtFHXeAxCCCGEKJ/RSUKDwcCLL77IwIEDb7xKSkp47bXXbromRG3zcIgba4e3xUKlZH9kOkPX/EVO0e1X5d1Ofcf6fN37awLsA0guTGb4L8O5mHnxlm0VCgWukyfjOmECABmrVpG6YAEGI7fpN7a1YnubBtS3siCxVMOA01cIyyu87T3mVir6jWlFg3bu6PUG9q4P5+QvsUYnJ4UQQoi7KTExkeDg4H9d1+v18rC5Br0blURamZYG1hZMCaz5bcYZCfkc/ikKgE4Dg3HzrxtVoIUQQoj7idFJwuHDh+Pm5oaDg8ON1/PPP4+Xl9dN14SojR5s4Mq3/+mAg5WasLgcnl59lKScqlfn9rL14sveX9LEpQnZpdm8tPsljicfv2VbhUJBvddexX3GDACyv/qapClT0ZcZl6j0tTRna+sGtLSzIkuj4+kzVzmQdfvzBs3USnqOaELoo34AHNsazR/fRaK/gwIuQgghRFU0adKEgwcP/uv6Tz/9RGhoqAkiuv/8kZV/Y5vxkkZ+WJoZ/SvAXaEp1fHr5xdvnEPY4hGfGh1fCCGEEMZRGGR50b/k5eXh4OBAbm4u9vbylPNecjk1n+FfHCc5twRPB0u+fKk9Dd2rXtWvoKyACfsncCzlGGqlmkVdF9HDv0e57XO2bCF5+gzQ6bBu2xaflR9h5uho3FhaHS9diOHP7AJUCvgwxJfBni4V3ndufwIHf7wMBghoUY9HRzZFbVGzZxAJIYS4u+rSXGXr1q0MHz6cadOmMWfOHGbPnk1kZCRfffUVO3bsoGfPnqYO8Y7V5s+jUKvj4RORxJeUMdK7HvMa1nyCbu9X4UQcScbGwZxBM9tjZWte8U1CCCGEuGuMnavU7GNEIUysobsdP4/qRLCbLcm5JTz9yRFOxla9uIetuS0f9/iYHn490Og1TP5jMj9f/rnc9o5PPIHvZ5+itLWl6ORJYgc/R1lcnHFjqcz4pkUQT7g5ojXAhIh45l5NQldBnr9FNx96vdIMM7WS2HMZbFl6muL8qm+3FkIIISpjwIABbN++nd9//x0bGxtmzZpFeHg427dvr3SCMDExkeeffx4XFxesrKxo3rw5J0+evO09paWlTJ8+HX9/fywsLAgICOCLL764ZdsffvgBhULBE088Uam4arOFMSnEl5ThbaHm7aCa32YceSyFiCPJKBTQc2RTSRAKIYQQtZisJLyF2vw0WNwdOUVlvLT+BGFxOViolKwc0pqeTdyr3J9Or2PuX3P5+cr1BOF/mv+HsaFjUSpunYcvuXyZ+NdeQ5uUjJmTEz6rPsbayC1XeoOBD2JSWHotFYDH6tmzqrE/Nqrbrw5Mjsph5yfnKC3U4uBqRb9xLXFwta7EuxRCCFFb3I9zlezsbEJDQ+nWrRujRo3C1dWVK1euUL9+ferXr1/ufQMGDCA1NZX33nuP4OBgkpOT0ev1dO7c+aZ2sbGxdOnShaCgIJydndmyZYvRsdXWz+NUbiF9w65gAL5rEcQjLjUbW05aET/OO4GmVEe7xwNo3y+oRscXQgghxHXGzlUkSXgLtXWiJ+6u4jIdY74LY29EGkoFLBjYnEHt/Krcn8Fg4KPTH/H5+c8B6OHXg3ld5mGtvnUiTpOWRsKo0ZRcvIjC3ByvRQux79XL6PE2pWYzMSKOUr2BJjaWfNUiCB/L2z+dz04pZPtHZ8nPLMHKTk2vV5rj1cDR6DGFEELUDnVprhIUFMSJEydwcbn5iIycnBxat25NdHS0Uf1MnTqVw4cP3/J8w/Ls3r2bwYMHEx0djbOzc7ntdDodXbt25aWXXuLgwYPk5OTU+SRhqV5PzxOXuVxUwtPuTqxs4l+j4+s0en7+4BTpcfl4NXBkwIRWKGv4LEQhhBBCXCfbjYWogJW5GZ++0IZn2/qgN8CUn8+zct+VKlcBVigUjGs9jnld5qFWqvk97nde3P0iqYWpt2yvdnPD/+uvsH3kEQxlZSROmEjG558bPf5Adyc2tQrG1VzFpcISep+6zKnc21c+dvKw4am32uDqZ0dxvoatS09z4c/ESr9XIYQQwlixsbHodLp/XS8tLSUx0fifQdu2baNt27Y888wzuLm5ERoayueff27UPYsWLcLb25uGDRvyxhtvUFx8c/GyOXPm4ObmxsiRI42KpbS0lLy8vJtetc2Ka6lcLirBRa1iTgPvGh//6JarpMflY2GjoudLTSRBKIQQQtQBKlMHIIQpqcyULHyqBa52Fny8/yof/naZa5lFvPdkMywq2L5bnv71++Nj68OE/RMIzwpnyM4hrOi+gqYuTf/VVmltjc9HK0hduJDsr74mffESNHHxeMyaiUKtrnCsNg42/NKmIcPPR3OxoISBZ6JY2siPge5O5d5j42DBk5Nbs++rcKJOpfHHd5FkJBTw4LMNMFPJBF4IIcTdsW3btht//vXXX3FwcLjxtU6nY+/evQQEBBjdX3R0NJ988gmTJk3i7bff5sSJE4wbNw5zc3OGDx9e7j2HDh3C0tKSzZs3k5GRwejRo8nMzGTdunUAHDp0iLVr13LmzBmjY1mwYAGzZ882un1NCy8oZsW1NADmN/TGWV2zU/7Ycxmc3RsPQPfhTbB1sqzR8YUQQghRNbLd+BZq45YRUf2+OhrLu9suojdAW38nVr/Qhnq2FlXuLz4/nrF7x3I19yqWZpYseHDBbSsfZ339DakLFoBej02nTngt/hCVU/nJvn8q1OoYHX6NXzOur2SY6O/Om4EeKBWKcu8xGAyE/XqNv7ZGgwE8gx3o9UpzrO3lQHEhhKjt6sJcRam8/uBJoVD8a5W8Wq0mICCAxYsX07dvX6P6Mzc3p23bthw5cuTGtXHjxnHixAmOHj16y3seffRRDh48SEpKyo0k5aZNm3j66acpLCxEq9XSokULVq1aRe/evQF48cUXK9xuXFpaSmlp6Y2v8/Ly8PX1rRWfh85goO+pK5zOL+KxevasbxaI4jbzgbutILuUDe8dp6RQQ4tHfHjw2YY1NrYQQgghbk22GwtRScMeCGD9iPbYWao4eS2bASsPcymp6tuHfO18+brP13T26kyJroSJByay5vyacrcTO7/wPD4rV6KwsqLwyBFin36GkkuXjBrLRmXGF80Ced3PDYCl11L5z4VY8rT/3t71N4VCQZteATw+ugXmlmYkR+WyccEJ0uPyK/9mhRBCiP+h1+vR6/X4+fmRlpZ242u9Xk9paSmRkZFGJwgBPD09adKkyU3XGjduTFxc3G3v8fb2vmkVY+PGjTEYDCQkJHD16lViY2Pp168fKpUKlUrFV199xbZt21CpVFy9evWW/VpYWGBvb3/Tq7ZYk5DO6fwi7MyUvN/Qp0YThAa9gd/XX6SkUEM9X1s6PRlcY2MLIYQQ4s5JklCIf+ja0JUtr3cmqJ4NiTnFPPXJEXZfSKlyf3bmdqzsvpIhjYYAsDxsOTMOz6BMV3br9o90I+CH71H7+aFJTCT2uSHkbt1q1FhmCgUz63uxrJEvaoWCXRm5PHYykosFxbe9L6B5PZ6e2hZHd2sKskvZ9MEprpy49TmKQgghRGXFxMRQr169O+6nc+fOREZG3nTt8uXL+PuXX5Cjc+fOJCUlUVBQcNM9SqUSHx8fGjVqxPnz5zlz5syNV//+/enWrRtnzpzB19f3juOuSdeKS3k/+vq8ZVawF54WNbs74Oy+eBIjc1CZK3nsP80wU8uvGkIIIURdItuNb6EubOER1Su3SMOY78M4eCUDgEk9GzL2keA7ehr/Q8QPvH/8fXQGHc1cmrH44cV42Xrdsq0uN5fEt96i8I8/AXAaOhT3KW+hMDdush+WV8jLF2JJLNVgqVSwoKEPz3m63Pae0iINv629RNzFTABaP+ZPh/9r7z7D46jOv49/t+9KK616s2XJveDesekYOxSHEmocIJCQggmQ8gRI6B0SSkjyJ0ACDtVU00IzNhiDjbtx711dVl+tts7zQrKwsGxkSZZW9u9zXXNp98yZmbNnZOn2rXPmnNsLs7njRiCIiEjLdLVYZc6cOcyZM6dxROH+nn322RadY8mSJUyYMIG77rqLiy++mMWLF3PNNdfw9NNPM23aNABuueUW8vLyeP755wGoqalh4MCBjB8/nrvuuovS0lJ+/vOfc/LJJx900ZOWTDf+rmi4H4ZhcPE3W5lfXsOEBDdvDO99yMeOtLe9eTW89sASIiGDU6b157gTO36xFBEREWmephuLtIEnxsZzPx3DVRNzAXh09iZ+88oKfIGDT9/9PpcOuJT/O/3/iLfHs2bvGi5+/2Lm75nfbF2Lx0P2k0+SMn06AOUvvcTOK39KsKi4RdcaGR/L7DH9OS0pjrqIwW837Oa3G3bhC0cOeowjxsbZ04cyckoPAJZ/vJP3nlhJbVXzox5FRERa4q677mLy5MnMmTOH0tJSysvLm2wtNWbMGGbNmsUrr7zC4MGDueeee3j88ccbE4QABQUFTaYfu91uZs+eTUVFBaNHj2batGlMnTqVJ554ol0/YzR4tbCM+eU1OM0mHumf3aEJwnAwwuxn1xEJGeQOSWbQCc3/EVRERESim0YSNiMa/hos0eOVxbu47e01hCIGQ7p5ePqKUWR6XK0+X15NHr///Pes3bsWgF8M/QXXDrsWi7n51ZSrP/uM/D/eRKS6GktqCt0ff5yYUaNadK2IYfDEziIe3l5IBDjO7eTfx/WkZ8yhF2TZtKSQz17YQCgQISbezhlXD6L7gKTD+pwiInLkdKVYJTMzk4cffpjLL7+8s5tyxHT2/dgbCHHi4vWUBcP8uVcmv8lJ79DrL3hrCys+2YXTbeOy28dpETQREZEoo5GEIu3ksrE9eOnn40iKtbM6r5JznviSLzaVtPp83dzdeP7M57mk/yUAPL3qaX716a8oqytrtn7cqafS8/XXcPTtS7iklJ1X/pSyF1866AIo+zObTNyYm8Grw3qTbLOytqaOyUs38kFJxSGP6zcmg4tuHkNSViy1VQHe+dtKFr23jUhEf1MQEZHDEwgEmDBhQmc346h2z9Z8yoJhBsY6+VV2WodeO29TOStm14/ePPUnA5QgFBER6cKUJBRpgXG9knln+kQGZcaz1xvgyucW88gnGwm3Mmlmt9i5dfytPHDiA7isLr4u+JqL3ruIlcUrm6+fm0vuzFeIP+tMCIUouvde8m78LeHKyhZd78SkOD4d04+xnliqwxGuXrODO7fkEYgcfPpxUlYsF948moETM8GApf/bwbuPr8Bb6W/NRxYRkWPUz3/+c15++eXObsZRa2FFDTML6//Q+HD/bGwd+Cxhvy/EpzPWgQEDJ2bSa3hqh11bRERE2p+mGzejs6eMSPSqC4a5+/11vLyo/i/m43sl8cSlI0iLd7b6nFvKt/Dbz3/LjqodWE1Wfjf6d/xk4E+aXSTFMAzKZvyX4kcegVAIa2Ym3f7yMDGjR7foWsGIwb3b8nlqd/1IyCFuF/8YlEP/2EO3f+OiQj5/eSMhfxhXnI0zrjqO7EGafiwi0lm6Uqxyww038PzzzzN06FCGDh2KzWZrsv/RRx/tpJa1n866H4FIhNOXbGRzrZ+fZCbz1wEduxrzpzPWsfHrQuJTnFxy61jsTmuHXl9ERERapqWxipKEzehKgbd0jndW5vGnt1bjDYRJcTv426XDmdgnpdXn8wa93LngTj7a8REAk3pM4o7j7yDBmdBsfd/qNeT94fcEd+4Cs5mUX/2SlGuvxWRtWXD+YUkFv9+4m7JgGKfZxK29s7i6W8ohH3JeXujl42fWsjevBkww6gc5jD2nJ2aLBiSLiHS0rhSrnHrqqQfdZzKZmDt3bge25sjorPvxxM4i7t9WQLLNypfjBpBo67gk3ZZlxXz8zBpMJjj/9yPJ7JPQYdcWERGRw6MkYRt0pcBbOs/Wkhqmv7ScDYXVmExww+l9+c1pfbG0cpqPYRi8vOFl/rr0r4QiIVJdqdw78V4mdGv+OU4Rr5fC++6n8q23AHANH07WX/+CvXv3Fl2vyB/kxg27+KysGoCTE+N4fGA2mY6DP0soFAjz5eubWTs/H4DM3h5O/+kgPKmtX8hFREQOn2KV6NIZ92Onz8/JizdQFzH4+8AeXJTRcSP8vZV+Xrl7EX5viFE/yGH8eb077NoiIiJy+LRwicgR1jvVzdvTJ3LpmGwMAx7/dDNXPLuIkurWPbPPZDIxbeA0XjzzRXLjcynxlfDLT3/JA4seoC5Ud0B9c2wsWfffR9Yjf8XsduNbuZLt551P5fv/a9H10h02Xh7ai/v7dsNpNjGvvJpTF2/k3eKKgx5jtVs4ZdoAJv/8OGxOCwVbK5l572LWfJHXooVUREREpO0Mw+DmTXuoixhMTHBzYXpih1577vPr8XtDpPaIY8w5PTvs2iIiInJkaSRhM/TXeTlcs1bs4U9vrcEXrJ9+/PCFQzhtQHqrz+cL+Xh06aPM3DgTgF6eXjxw4gMMSh7UbP3Anjzy/9//w7diBQCec88l/bbbsLhjW3S9zd46pq/fyapqHwAXpidyf7/uxFstBz2mqtTHnP+uJ39zBQA9BiVx6uUDcSc6WvoxRUSklbpCrHLBBRe0qN5bDSPiu7KOvh/vFlfwi7U7sJtMzB3bnz4xrX828uFa/fkevpi5CYvNzMW3jCEpq2WxhoiIiHQejSQU6UDnj+jOe7+ZSL90N6U1fq6esZSb31xFjT/UqvO5rC7+PP7PPDnpSVJcKWyr3Ma0D6bx79X/JhwJH1Df3r0bOS88T8r06WA2U/nOO2w/91y8Cxa06Hp9Y528P7IvN+akYwbeKCrntCUb+LK8+qDHxKe4OO+3I5h4YR8sVjO71pUx855FbFxUqFGFIiKCx+Np0SaHpzoU5rbNewC4LietQxOEFUW1LHhzCwDHn99bCUIREZGjjEYSNqMr/HVeolNdMMxfP97If77ajmFA90QXf71oGON7Jbf6nOV15dy98G4+3fUpACPTRnLfCffRPa75Zw/WLltG/v/7I8H8+ucGei78Eel//COWFn4vL66o4br1u9hVFwDgsswk7uidRcIhHoZeVuBlzox1FO+sTyr2GpHKKT/ujyvu4M83FBGR1lOsEl068n78edMe/pNXSk+Xnc/GDMDZQQuIRSIGbz+ynIKtlXQfkMgPrx+OqZXPYRYREZGOpZGEIp3AabNw6zmDeOWa8XRPdLGn3Mdlz3zNve+voy544AjAlkh0JvLoKY9yz8R7iLHGsLx4ORe+dyGvbniViBE5oH7MqFH0eu9dEqdNA6DyjTfZds5Uqj/7rEXXG5vgZu6Y/vy0W/1qza8UlHHi4g28W1xx0BGCSZmx/OiPoxj3w56YzSa2rSjhlbsXsW1lSas+s4iIiBxoZVUtz+WVAvBQv+wOSxACrP5sDwVbK7E5LJx6+QAlCEVERI5CGknYDP11XtpDjT/Eve+vY+aS3QD0SXPz6MXDGNo9odXn3F29mz9/+WdWFNc/e3BE2gjuOP4Oeic0v6pg7dKlFPz5VgI7dwIQP3Uq6X+6BWtiyx5wvqiihj9s3M3m2vrFWKakxPNA3+5kOQ8+QrBkVzWfzlhHWb4XgN4j0zjx4r7EJuhZhSIi7UWxSnTpiPsRNgzOXLaJVdU+LkhP5P8G5RyR6zSnoriWV+9ZTCgY4eQf92fwSd067NoiIiLSdi2NVZQkbIYCb2lPczcUcdObqymp9mMxm7ju1D5cd1ofbK386384Embmxpn8bfnf8IV8WM1WrhlyDT8f8nPslgOTd5G6Okr+/nfKnpsBkQiWpCQybr+NuClTMJm+fxSAPxLhbzuL+PvOYoKGgdti5s+9s7gyKxnzQY4PByMsfn8bK2bvxogY2J0Wxp3bm8End8OskQciIm2mWCW6dMT9+PeeEm7dnEe81cyXYweS5rAdket8lxExmPXocgq2VNKtfyLn3ji8RfGDiIiIRA8lCdtAgbe0t3JvgFvfWcP/VhUAMCAjjvvOH8yonKRWn7OgpoD7Ft3HvD3zAOjp6cmdx9/JyPSRzdb3rVpFwZ//jH9z/QPH486YRPqf/oQtM7NF11tf4+P3G3ezvKoWgLGeWP7SP5v+sQd/YHrJ7mo+f2kjxTuqAEjLieOUaQNI7RHX4s8pIiIHUqwSXY70/agIhhi9cB014QgP9evOlQ2PBOkIqz7bzfxXN2N1WLjstrHEp7g67NoiIiLSPpQkbAMF3nKkvPtNPne8s4by2iAAl43twc0/GIAnpnWjAQzD4OOdH/PgogfZW7cXgIv6XcSNo24k3n7g924kEGDvv56i9OmnIRTC5HKR8stfknT1VZjt37/ISNgweC6vlPu3FVAbjmA1wc+7p/K73AzirZZmj4lEDNZ+kcfXb28lUBfGZIKhp2Yz9oc9sTsPvhiKiIgcnGKV6NIR9+OzvVW8VljGPwflHHQkf3urLPEx855FhAIRTrq0H0NOaX7RNBEREYluShK2gQJvOZLKvAEe/HA9ry3dA0CK286tZw/i3OFZrZ6+U+mv5LFlj/Hm5jcBSHWl8scxf2RKbvNTius2bqTwnnvwLV0GgC2nBxl/+hPuk09u0fX21AW4ZdMeZu+tHyGYYrPyp96ZXJqRdND/uHgr/Xz5+ma2LC0GwJ3o4MSL+9FzeIqmLYmIHCbFKtHlaLwfRsTgncdXkLepgm79Ejj3xhFarERERKSLUpKwDY7GQE+iz6Jte/nz22vYUlwDwAl9UrjnvMH0TIlt9TmXFC7h7oV3s6NqBwAj00Zy89ibGZg88IC6hmFQ9f7/KH74YUIl9asQu089lfRbbsbeo0eLrjdnbxW3b85jq69+YZNhcS7u69ud0Z6Df4Zda/cy75WNVJXWAdDjuGQmXtiHpMzWf24RkWONYpXocjTej9Wf7+GLmZuw2s1cets4PKmaZiwiItJVKUnYBkdjoCfRKRCK8Mz8bTwxZzP+UAS71cz0U/rwq1N64TjI9N3v4w/7eXbNszy7+lnqwnWYMHFB3wu4bsR1pLgOfIZRuMZL6ZP/R9l/n6+fgmy3k/zzn5F8zTWYXd//H4JAJMJ/9pTyyI5CasIRAC5MT+TW3llkHOSh6qFAmKUf7GDF7F1EwgYms4nBJ2YxZmpPXO7vn/YsInKsU6wSXY62+1FV6uOVexYT8oc58ZK+DD01u7ObJCIiIm2gJGEbHG2BnkS/nXu93Pr2GuZvLgUgNzmGW84ayORB6a2eilvoLeSxZY/xwfYPAIi1xfKrob9i2sBp2CwHJu/827ZRdO99eBcsAMCalUna739P/JlnYjJ//0rMJYEg928r4JWCMgBiLGZ+m5PONd1TcR5kJefyQi8L3trKjlX1n9vusjLm7FyGnNIdi7V1qz+LiBwLFKtEl6PpfhiGwTuPryRvYzmZfTyc/7uRmmYsIiLSxbU0VomK/4X/85//JDc3F6fTybhx41i8ePEh67/++usMGDAAp9PJkCFD+OCDD5rsf+utt5g8eTLJycmYTCZWrlx5BFsv0nY5ybE8f/VY/n7ZCFLjHOzYW8svX1jGpU9/zZq8yladMyM2g4dOeogXznyB45KPwxv08siyRzjvnfP4fPfnfPfvA45evcj+z7/p9vcnsGZlEsovIP/3f2DHhRc1Jg4PJdVu47EBPfhwVD9GxcdQG45w37YCJixaz0v5ewlFDvx7RGJGLGdfO5Qf3jic5O5uAr4QX72xhZfvWsS2FSUHtFFERESOrLXz88nbWI7VZua0KwYqQSgiInIM6fQk4auvvsrvfvc77rjjDpYvX86wYcOYMmUKxcXFzdZfsGABl112GT/72c9YsWIF5513Hueddx5r1qxprOP1ejnhhBN46KGHOupjiLSZyWRi6rAsPvvDKVx3ah8cVjOLtpcx9R9f8rvXVlJQ6WvVeYenDefls1/mnon3kOJKYVf1Ln4z9zf8YvYvWLt37QFtiD/jDHr/73+k3nA95thY6tatY9fVP2PX1T/Dt3btQa7yrRHxMbw3si9/H9iDbg4b+f4gv9+4m1OWbODd4goizST+sgckcfGfxnDq5QNwxdupKvHx4VOrefvRFZTsqm7V5xYREZHDU7XXx4I3twAw/rzeJKTFdHKLREREpCN1+nTjcePGMWbMGP7xj38AEIlEyM7O5je/+Q0333zzAfUvueQSvF4v77//fmPZ+PHjGT58OP/617+a1N2xYwc9e/ZkxYoVDB8+vMVtOpqmjEjXlVfh4y8fbeDtlfkAOG1mfnFSb355Ui9iHdZWndMb9PLMqmd4ft3zBCNBACb1mMT04dPpk9jngPqhsjJK//Uvyl+ZCcH6+vFnn03qjTdgz/7+5xPVhSM8n1/K4zuLKAuGARjqdnFLr0xOSYprdip1oC7E8o93svLT3YSD9c847D0yjbFTe2pxExGRBopVosvRcD8Mw+D9f3zDrrVlZPb2cP7vNc1YRETkaNElphsHAgGWLVvGpEmTGsvMZjOTJk1i4cKFzR6zcOHCJvUBpkyZctD6LeH3+6mqqmqyiXS2bgkuHr90BG9Pn8jonETqghGemLOZU//6Oa8t2U24mem73yfWFsuNo27knfPeYWqvqZgw8emuT7ng3Qu4Zf4t7Kra1aS+NSmJjD/9id4ffkD81KkAVP3vf2w962wK772P0N69h7ye02LmF9lpLB4/iD/kZuC2mFlV4+OyVdv40cqtLKv0HnCM3Wll/Lm9mXbXePqOSQdg6/JiZt69iNnPraWiuPawP7eIiIgc2ualRexaW4bFaubUywcoQSgiInIM6tQkYWlpKeFwmPT09Cbl6enpFBYWNntMYWHhYdVviQceeACPx9O4ZbdghJRIRxmencDrvzqeJ6eNpEdSDMXVfv745irOeGwe76zMa1WyMDsum/tPvJ9Z587ijJwzMDB4f9v7/PDtH3Lngjsp9Db992Tv3p1uf3mYnm+9SewJJ0AwSPmLL7Jl0hkUPfQwoZKSQ17PbbXwh54ZfD1+EL/snorDbGJBRQ1nL9/MT1ZtY2kzycK4JCeTf3Ycl9w6lp7DUjAM2LSoiJfvXMTcF9ZTtbd1069FRESkqTpvkC9f2wzA6LNySMzQyH0REZFjUac/kzAa3HLLLVRWVjZuu3fv7uwmiTRhMpk4c0gms393En8+ayAJMTa2lXi5YeZKpjz+Be+vyifSimRh74TePHrKo8w8ZyYndDuBsBHmzc1vcvZbZ/PQ4oco9ZU2qe8cNIge/36GHs89i3PwYAyfj7LnnmPLpDMovPc+gt+TrE+xW7mrbzcWjBvIjzOTMAOf7q3inOWb+dGKLXxRVn3AYiUp3d2c9euhXHTLaHIGJ2NEDNZ/VcBLt3/NvJc3UlPuP+zPLSIiIt9a8OYWfNVBEjNjGTE5p7ObIyIiIp2kU5OEKSkpWCwWioqKmpQXFRWRkZHR7DEZGRmHVb8lHA4H8fHxTTaRaOSwWrjmpF7M/+Op/GFyP+KdVrYU13Ddyys482/z+WhNQauShcclH8eTk57k+TOfZ3T6aAKRAC+uf5Epb0zh7oV3s7uqaeI89vjjyX39NbKf+heuYcMw/H7KX3yRrWdMpuCOOwnsyTvk9bo57Tw6oAdfNiQLbSYTX1XUcPE3Wzlr2WY+Kqk8YIGTtJx4zrluGD/64yi6D0gkEjZY80UeL962kHmvbKSyRCMLRUREDteejeWsX1AAwKnT+mOxagyBiIjIsSoqFi4ZO3Ysf//734H6hUt69OjBddddd9CFS2pra3nvvfcayyZMmMDQoUO1cIkcc6rqgjz75Xb+M3871f4QAIMy4/ntGf2YNDCt2YVBvo9hGHxd8DX/t/L/WFmyEgCzycyUnCn8bMjP6J/U/4D6tQsXUvp/T1K7dGl9odWK54c/JOWXv8Ce8/0jEvLqAjy5u5iX8vfia0hyDoh1cn1OOj9MTcDazHOR8jaVs+jdbRRsqQTAZII+o9IYMTmH1B5xh/25RUS6EsUq0aWr3o9QMMzMexZTWezjuJO6ccqP+3//QSIiItLltDRW6fQk4auvvsqVV17JU089xdixY3n88cd57bXX2LBhA+np6VxxxRV069aNBx54AIAFCxZw8skn8+CDD3L22Wczc+ZM7r//fpYvX87gwYMBKCsrY9euXeTn5zfW6d+/PxkZGS0acdhVAz05dlXWBvnPl9t49qsd1DQkCwdmxvOLk3pyztAsbJbDHxVgGAbLi5fz79X/5su8LxvLT+x2Ij8b8jNGpY864JjaJUsoffJJvAsaFhIym4k7/XSSfnolrpEjvzdpWRII8szuEp7LK6U6XL+ycY7Tzs+7p3JpZhJxVssBbczbVMGKj3eya11ZY3n2wERGTM6h+4DEViVKRUSinWKV6NJV78eid7ex9IMdxHjs/PiOcThibJ3dJBERETkCukySEOAf//gHf/nLXygsLGT48OE88cQTjBs3DoBTTjmF3NxcZsyY0Vj/9ddf59Zbb2XHjh307duXhx9+mLPOOqtx/4wZM7jqqqsOuM4dd9zBnXfe+b3t6aqBnki5N8Az87cxY8EOagNhALI8Tq4+oSeXju2B22Ft1Xk3lG3g2dXP8vHOj4kY9cm7EWkjuOq4qzip+0lYzE2Td76VKyl58km8875oLHMOHkzSlVcS/4MpmGyH/k9IZTDEc3mlPL2nhLJg/eeItZi5NCOJn3VPpVeM44BjSvdUs/zjXWxZVozRMBoxtUccIyb3oPeIVMytSJSKiEQrxSrRpSvej735Nbx23xIiYYMf/GIwvUemdXaTRERE5AjpUknCaNMVAz2R/VXUBnjx653MWLCT0pr6hT3inFamjcvhqom5pMc7W3XeXVW7eG7tc7yz5R2CkSAA3dzduGzAZZzX5zw8Dk+T+nWbNlH+wgtUvvMuRiAAgDU9ncRp00i8+CIsCQmHvJ43HOaNwnL+vaeEzbX1n8MEnJ4cz8+7p3ByYtwBIwWrSn2snLOb9V/mEwrWJzTjkp0MPrkbgyZk4XRrlISIdH2KVaJLV7sfRsTgrb8up3BbJblDUzjr10M08l5EROQopiRhG3S1QE/kYOqCYd5ekcfT87exrcQLgM1i4rzh3bjmpF70S2/ds/tKakt4Yf0LvLnpTaoCVQA4LU7O7nU2lw247IDnFobKyiifOZPyl18hXFq/YrLJ6cRz3rkk/eQnOPr0OeT1DMPgi/IantlTwqd7qxrL+8Y4+Hn3VC5MTyT2O1ORfTUBVn+ex+rP9lDnrU9oWmxm+o5OY8gp3UnL0b9tEem6FKtEl652P9Z8kce8lzdic1i47I5xxCW17o+HIiIi0jUoSdgGXS3QE/k+kYjBnA3FPP3FVpbsKG8sH98riZ+Mz2HyoAzsrVjN0Bfy8cG2D3h5w8tsKt/UWD4qfRQ/HvBjTutxGlbzt1OcI4EAVR98QNl/n8e/fn1juWvkSBIuuoj4H0zB7HId8prbav38Z08JMwvL8DY8tzDWYub8tER+nJXEiLiYJqMhgoEwm5cUsWZeHiW7qhvL03vGM+TkbvQelYbVZjngOiIi0UyxSnTpSvfDW+Hn5Tu/JlAX5oSL+zLstOzObpKIiIgcYUoStkFXCvREDtfyXeU8PW8bn6wrpOHRfaS4HVw6JptLx2bTPTHmsM+5b5GTl9e/zJxdcwgb9c8RTItJ4/w+53Nun3PJjstuUr92yRLKX3iB6rmfQbi+vjkuDs/UqSRcfBHOAQMOec2qUJhXC8p4Nq+E7b5AY/nAWCfTspL5UXoiibZvE5SGYVC0vYrVn+9hy/JiIqH6D+902xg0MYuBEzNJSDv8zy4i0hkUq0SXrnQ/Pnp6NVuXl5CWE8ePbhqN2axpxiIiIkc7JQnboCsFeiKtlV/hY+biXcxcspvi6vrn/ZlNcGr/NH4yPoeT+qViacV/HIq8Rby26TXe2PQGZXXfrjg8JmMM5/c5n0k5k3BZvx0tGCwupvKtWVS88QbBPXsay51Dh5Jw0YV4zjoLc2zsQa9nGAYLK7y8XLCX90sqqGvIfDrMJs5K8TAtK5kJCW7M+40urK0KsO6rfNZ+kUdNub+xPLOPh4ETMuk9Mg27s3WLvIiIdATFKtGlq9yP7d+U8MGTqzGZTVz8p9GkdG/dY0dERESka1GSsA26SqAn0h6C4QifrivixUU7+WrL3sby7okuLhqVzQUju5GddPgj7ALhAHN3zeXtLW+zIH8BBvU/amJtsfwg9wec3/d8hqYMbZwabEQieBcupOL1N6ieMweC9c8RNMXEEDfpdDxTf0js8eMxWQ+evKsIhnirqJyXCvaytqausTzbaeeC9ETOT09gQOy3CcpIOMKOVXtZ+2Ueu9eVse+nodVhoc+IVAYcn0lW3wRMGmUhIlFGsUp06Qr3I1AX4pW7FlFT7mfklB4cf/6hnwcsIiIiRw8lCdugKwR6IkfC1pIaXl60izeW7aHSF2wsH9cziR+N7M6ZQzKIcx7+6sCF3kLe3fouszbPYk/Nt6MFe3p68sPeP2RK7pQm05FDe/dS+fbbVLz2OoGdOxvLLcnJxJ91Fp6p5+AccvCVGA3DYFWNj5fy9zKrqJzqhmcXAgyKdXJ+eiLnpSeS7bQ3lteU+9m4qIANCwupKKptLI9PcdJ/fCb9x2XgST308xJFRDqKYpXo0hXux4K3trDik13Epzi59PZx2Ox6Hq+IiMixQknCNugKgZ7IkVQXDPPB6gLeWp7HV1tLG0fYOW1mfnBcBheM7M7EPimHPR05YkRYVrSMt7e8zeyds/GFfI37hqQMYUruFKbkTiEjNgOoT/b5Vqyk6v33qPrwI8Ll3y66Ys/JIf6cc/BMPQd7bu5Br1kbjvBJaSWzisuZu7ea4H4/8sZ5Yjk/PZGpqQkk262N1yzcVsWGhQVsXlpEsC7cWD8tJ44+o9LpMzpNK0GKSKdSrBJdov1+lOV7efXexUQiBmdfO5TcoSmd3SQRERHpQEoStkG0B3oiHSm/wsesFXm8uXwP20q8jeUZ8U7OHZ7FWUMyGdrdc9BRfQdTE6jhk52f8OH2D1lcuJiI8e1ov5FpI/lBzx9wRs4ZpLjq/yNjBIPUfPUVVe+9T/WcORh1304ndgwaSPwZZxB3xhnYe/c+aFvKgyH+V1LJW0XlLKyoYd8PP4sJJiS4OTPFw5mpHjId9SMMg4Ew21aUsGFhAXkby9n/p2VGr/j6hOGoNGITHIf12UVE2kqxSnSJ5vthGAbvPLaCvE0V5A5N4exrh3Z2k0RERKSDKUnYBtEc6Il0FsMw+GZPJW8u28O73+Q3mY7cLcHFWUMyOGtIJsOzEw47YVjqK+XTnZ/y4fYPWV68vLHcbDIzJn0Mp/Y4lVOzTyXLnQVAxOules4cKt97H++CBY2rIwPYe/YkriFh6Bx83EHbkl8X4J3iCmYVlbOqxtdk34i4GM5K9XBWqofeMfUjBmurAmxdXsyWZcXkb6mgMcNogqw+CfQZlUbPYSm4EzXCUESOPMUq0SWa78emxYXMfnYdVpuZy+4YR3yKHp0hIiJyrFGSsA2iOdATiQb+UJjPNpTw/qp85m4opjbwbZIuy+PkzCGZnDUkkxHZCZgPc0pyobeQT3Z8wkc7PmJ16eom+/on9ufUHqdySvYpDEoahMlkIlReTs3cuVR/MhvvggUYwW+Tl9bMTOImTSLu9NOJGTUSk6355ylur/XzQWklH5ZUsLSqtsm+fjFOzkr1MDklnmFxMVhMJrwVfrYsL2bL0mIKt1U2qZ/aI47coSn0HJpCSrb7sBOmIiItoVglukTr/fD7Qrx8x9fUVgUY98NejD4rt7ObJCIiIp1AScI2iNZATyQa+QJh5m0q5oPVhcxZX4R3v4RhpsfJ6QPTOH1AOsf3TsZpO7yHpO+u3s3cXXP5bPdnrChe0WRKclpMGqdm148wHJ0xGofFQbimBu8XX1A1ezY1877AqP024WeOjSV2wvHEnngi7pNOwpaR0ew1i/xBPiqt5MOSSr6sqCa030/IJJuFU5LiOS0pjpOT4ki126guq2PLsmK2ryyhYFvltyMMAXeig55DU8gdlkK3folYrObD+vwiIgejWCW6ROv9mP/aJlbN3YMnzcVlt43DYtPvIRERkWORkoRtEK2Bnki0qwuGmbephA9WFzBnfTE1/lDjPqfNzAl9UjhtQDqnDUgjw3N403LL68r5Ys8XfL77c77K/6rJoidOi5NRGaOYmDWRCVkT6OXpheH3412wkOrZs6n54gvCe/c2OZ+jXz/cJ51I7IknETNyRLOjDCuDIT7dW8WHpZXMK6tuskoywLA4F6clxXNacjwj4mII1ATZuaaU7d+Usnt9GaHAt/VtTgvZA5LIHpREj0FJmu4lIm2iWCW6ROP9KN1TzWv3LcEwYOr1w+gxKLmzmyQiIiKdREnCNojGQE+kq6kLhlmwtZS5G4qZu76Y/Mq6JvsHZcZz+sA0Tu6XyrDsBGyWlo9u8If9LCpYxGe7P2Pe7nmU+Eqa7E+PSWdC1gQmZE1gfOZ4PPZ46tauo2b+F3i/mI/vm2/YfxUSc2wsMWPGEDN+HLHjxuHo3x+TuWl7ghGDZVVe5u6t4rOyalZ/5zmGHquF8QmxnJAQx8REN31sNvI3VrB9VSk7VpVSWxVoWj/V1Zgw7NYvEbvL2uLPLyKiWCW6RNv9MCIGsx5ZTsHWSnqPTOUHvxjS2U0SERGRTqQkYRtEW6An0tUZhsGGwmrmbihmzvoiVuyuaLJScKzdwvheyUzsk8LEPin0S2/5s/wMw2BzxWYW5i9kQf4ClhUtwx/2N+43YWJg8kBGp49mdPpoRqaPJLY2gverBdR8MQ/v/C8Jl5c3OafF4yFm7Nj6pOH48dh79TqgPUX+IJ+XVTO3rIp5ZdVUhMJN9ifZLExIcDMxMY4J8bF4SgPsWV/GrnVlFG2rIhLZL0lpNpHeK57sgUl065dAWm481sOcmi0ixxbFKtEl2u7H+gUFzH1+PVaHhWl3jtOiWiIiIsc4JQnbINoCPZGjzd4aP/M2lTBnQzELtpRSXhtssj81zsGE3t8mDbsltHxqbl2ojuVFy1mQv4Cv8r9iS8WWJvtNmOiX2I/RGaMZlT6KkakjiNlWRO2iRXgXfY1v6TIitU0XL7GkphAzejQxI0bgGjES54D+TaYnhyIGq2t8fFlezYKKGhZVeqn9ztTkNLuVcR43YzwxDHc6ScyrI399ObvXlVFZ0nRUosVqJr1nPFl9E8jql0BGLw82u5KGIvItxSrRJZruR503yMt3fo2vOsjxF/Rm5OScTm2PiIiIdD4lCdsgmgI9kaNdJGKwrqCKr7aU8tXWvSzevpe6YNMEW7cEF6NzExmTm8SY3CT6prlbvGpycW0xSwqXsLRoKcuKlrG9cvsBdXp7ejM8bThDU4cyJGEQmbu91C1egnfRInzLV2D4/U3qm1wuXEOG4Bo5oj5xOHw4Fo+ncX8wYrCyupavyqv5qqKGJZVe6iJNf9S6zCaGxcUw1hPLIGyk7qmjZlMleZsr8H1narLZYiItJ56sfglk9vKQ3jMeV5y9RZ9fRI5OilWiSzTdj3kvb2TNF3kkZsZyya1jsBzG4zxERETk6KQkYRtEU6Ancqzxh8Is31nBV1tK+XJLKavzKgl/J8HmcdkYnZPImJ5JjMlN5LgsT4tXTi71lbKsaBlLC5eytGjpASMNAdw2N4NTBjcmDfvmg2P1ZmqXL8e38hsiVVUHHGPv0xvX4CE4Bw/GNWQwjgEDMDsc9Z8pEmF5VS1LKr0sqfSytNJL+XemJwP0djkYFuein8lKZmmImC017N1UibfCf0BdT6qL9F7xZPT0kNHLQ1K3WP1HUOQYolglukTL/SjeWcXrDy4FA8777Qi69U/stLaIiIhI9FCSsA2iJdATEfD6Q6zYVcGSHWUs3VnG8p0V+IJNE2w2i4kBGfEMy/YwrHsCw7MT6JXqxtKC0YbldeWsKF7B6tLVfFPyDWtK1zRZOXmfbu5uDEoexIDEAfQNJJCzzYtz5WZ8y5cT2LnzwBNbrTj69cV13GCcQwbjGjwYR58+mOx2DMNgS62fJVXfJg031x6YCDQD/WKdDLLZ6V4dITmvDsfmGrz5tQdezmYmNSeO9Nx4UnvEkdojjoS0GEwtHHEpIl2LYpXoEg33IxIxePOhpRTvrKbvmHQm/+y4TmmHiIiIRB8lCdsgGgI9EWleMBxhXX4VS3aUsWRHGct2llNaEzigXqzdwpDuHoZlJzC0WwKDsuLJSYr53mnKoUiIrRVb+abkG1aVrOKbkm/YUbWj2bppMWkMShpE/5hcepZZ6b7di2f1DgKr1xIuKzvwAJsNR69eOPr3w9l/AI4B/XH27481JYW9gRDfVNfyTXUtK6tr+abKR2EgeMApzECu005Pw0JmdYSEfD/OLdU49wb57iezOSykZLtJzY5rTBwmZsRg1ohDkS5PsUp0iYb7sXZ+Hp+/tBG708KP7xpPrMfRKe0QERGR6KMkYRtEQ6AnIi1jGAZ5FT6+2V3JN3sqWLm7gjV5ldQGDpzOG2O3MDAznkGZ8QzKqv/aPyPue6cqV/or2VC2gfV717OubB3r965nZ9VODA788emyuuiT0Ife9m7k1DjI3u0nc20hjhWbmp2mDGBJScHZvz+Ofv1w9OmNo3dv7L17U2J31icNq2obEog+9gZDzZ4j3mwm17CQ6TVIKPbj2uUjsSxIrN9okjy0WM0kZsaQ3M1NcpabpG6xJGe5iU2wt3hFaRHpfIpVoktn3486b5CX7viaupogJ1zUl2GnZ3d4G0RERCR6KUnYBp0d6IlI24QjBluKa/hmdwUrdlewLr+SDYXV+EORA+qaTdAr1U3/9Dj6pLnplx5Hv3Q3uSmx2A4x4s4b9LKxbCPry9azfu96NpZvZGvFVoKRA0f/ASQ5k+jl6k52II6scsjY5SVtbQGe9fmYD/Jj2JqWhqNPb+y9++Do3Qt7r95UZ3VjvTOWdbV+1tX4WFvjY0ttHaGD/CSPw0S3oInkyjBxBXUkloVIrg7jqY1g3u8YR4y1IXEYS2JmLIkZMSRmxBLjUfJQJBopVokunX0/5r+2iVVz95CYEcMlt43VM2pFRESkCSUJ26CzAz0RaX+hcITtpV7WFVSxLr+KdQVVrM2vosx74FRlAKvZRM+UWPo1JA/7pLnpmRJLbkosboe1+WtEQuyq2sXmis1sLm/YKjazp3pPs6MOARxmBz3s6XQPuMkqN5FWUEvK5lJSt5fj8XLAFGIAc0wMtpwc7A2buUc2hemZbEhKZ73dyaZaP5tr69jpCxzkqmA1ICUISdVh4vYGSaoKk1wdJqkmQpzv2wSizWkhMb0+YZiQEUNiRgwJ6TF4UlxY7S1bLEZE2p9ilejSmfejLN/LzHsXY0QMfnj9cLIHJXXo9UVERCT6KUnYBgq8RY4NhmFQXO1nXUEVm4uq2VxUw6biGrYUVeNtZrryPqlxDnqmxNIzOZaeqbHkJsfSMyWW7CQXMfYDE4i1wVq2V25nW+U2tlduZ0fVDrZXbmdn1c6DjjwEcJkdZJFAZq2DtL0hUnfXkLyrgrTyCClVYG9m5rE5NhZbt27YunfHlJVFZVo6+SlpbPYksyo+kXURMzvr/PgjB//RbzEg0RchvipMgjdMQk2EBG+k/rU3QkzDFObYBAeeVFf9luYiPsVFQloM8akuHK7mE6ki0j4Uq0SXzrofhmHw3hMr2b2+nJ7DUjjr10M77NoiIiLSdShJ2AYKvEWObYZhkF9Z923isKia7aVetpd62XuQkYf7pLgdZCe5yE6MoUdSTP3rpBiyE2PI9Dix7jcFLBwJk1+Tz/aq7Y1Jw13Vu9hTvYcCbwER48Dp0ftLNGJI8ztIq4Lk4jqS87ykVBkkVxkkVUOc78CRiJbERKwZGYTS06lJSaUkMZk9niS2xCewNjaeVU43deZDjxC0hQziayN4aiPE14b3ex1pfO12WIlLdhKX5Gz8Gp/sanztiLVqGrNIGyhWiS6ddT+2rSzhw3+txmw18eM7xuFJjemwa4uIiEjXoSRhGyjwFpGDqfQF2VHqZcdeL9tK6r9uL/Wyo9RLVV3zi4rsYzZBeryTrARXw+akW4KLLE/9+0yPk4QYGyaTiWA4SF5NHrurdzcmDndV7yK/Jp/8mnxqQ7Xf21abYSbF7yC5BhLLAiTtDZJUbZBYAwk1BoleSKgBx/7NNpsxJScTTE7Bm5REeUISRfEJ7I7zsDUmns2xcZTFJ1AZF0/EfPBnXjkDEdy+CHE+gzhfw+u6b8sSIpDutJPkceBOcBCb6MCd6NzvtQNnrE2JRJGDUKwSXTrjfoSCYV65axFVpXWM/EEOx5/Xu0OuKyIiIl1PS2MVzQcTETkMHpeNYdkJDMtOOGBfZW2Q3eW17C6rZXd5LbvKatld5mN3WS17yn0EwhEKKusoqKxj2c7yZs/vsJpJj3eSHu8gPd5JRnwy6fHdGOhxcmqqk9Q4B8mxNiKmWvK9+RTUFJBXk0eBt/5robeQotoiyurKCJoiFDh9FDiBFIDmk3oxQTOJtSYSKsN4aiIk1OzFU1uKpwbiiyGp1qCnF86pBWfD7GjDZCLs8eBLSKQqPoGyuHiK3PHsjo2jyB1PpTuOSnc8Fe44ijLiqHW6oJmEnz1oEFvnw13nJXaHQaw/QmxdBHedQWzQIMlqIdlhI81lI93twO1xEOtxEOOxExNvxxVnxxVnw/o9K1SLiBxtvpmzm6rSOmI9dkb9IKezmyMiIiJHASUJRUTaiSfGhifGw+BungP2RSIGpV4/+RV15Ff4yK/wkbff17xyH+W1QfyhCLvK6hOMh+K0mUmNc5DidpLqHkRK3Ah6uh2MSbGTFOsg3mUCaw1ByvFFSijxFVNUW0RRbRGlvlJKakso9ZVSF66j1hah1gN5HjhYInEfRxDiaw3ctRDvq8LtqyLOt7N+xGAp9NsFo3wQW2cQWwfuOojxg2Gx4ouLpyounvJYN3tdbipiY6mOcVMV66Y6Npaq2DiqYtzsTYqlOqZ+X8Bma0guGmD4iPHX4tprEJsXwRUwcAUiuPwG7gh4LBYSrRaS7DaSnVaSY+ykxNpJiLXVJxPddpxuG063DZsWXRGRLqym3M/SD3cCcPwFfbA7FdKLiIhI2ymiEBHpAGazibQ4J2lxToY3MwoRoC4YprjKT1F1HYWVdRRV1W+FVf7G16XVfryBMHXBSMMoRd/3XttkAo8ri6TYXJJj7STE2BnsspGQaCPGYWCx+sHiJUwVAcqpo5za8F6qQyWU15VRVlfG3rq9+MN+/DYo8Zgo8UDzay83L6bOILausmGrTyDG+Ou35GLI9jcti/EbuAJgC1uJmGOos8XgjXFTExNDjSuWmpgYvK4YvE4XXlcMtU4XBU4XW50uvLYYvLiojbjwBZwEInacZeAMGjgDBs6ggStkEGuYcJvMxJpNxFssxFktxNuteBwWEp02Elw2El02EmPsxMdYccbasbssGrUoIp1u4dtbCPnDZPSKp9/Y9M5ujoiIiBwllCQUEYkSTpuFHskx9Eg+9IPnawMhSqsDlNT4Kan2U9rwtaTGT1lNgDJvgL1eP2XeABW+IIYBFbVBKmqDbCvxfk8rEhu2XphNEO+yEe+0keG04nZacNgj2K0hzJYAZosfw+QjbPISMlUTMKqoi5RTGy6jNlKGN1xGXaQKkylCrdNErRNKgMNJLjZ8YkwRL65ACS4/uAL7NgNnAJxeSK2A7AD1ScBA/bRoRxAcgYZVoE12IjgwLE4iZgchawxBSwxBu5M6u5M6h4M6u4M8h5OtDgc+u6OxrM7uIGCzEzbbiFgcYLJhstgx2RxYLXZcZjMuk5kYswmXxUysxUys1YLbZiHWZsFtt+K2W3A7LMQ5bLidVuKdVuJcNtx2KzaznrsoIi1XuK2STYuKwAQnXtJPz24VERGRdqMkoYhIFxNjt9Ij2fq9yUSAUDhChS9YnzisCVBeG6CiNkh5bYBKX5DyhkRiRWN5kCpfkEA4QmS/5GLzzEBsw5Z20DY4rCZcdjMOG9itBjZrGKsljNkSBHMATAEipjoi+AjhJWR48VNDMFKDP1ID5gAmc5BqU4AaVxBigpjMATCHgHBzjzpsRhiobdj2+wQRA0ewPpFo3++rIwT2WgNPJaSGwNaw2Rs2W9jAHgRbGEyGhfpfp1YwWQEbhtmGgZVqs51Ksx3DbCNssRM22xu+OghZHQStdsIWGxGzFcNiI2KxYljqX5usNkxWKyarHbPVhtlmxWyzYbHasdpt2Ow2bHZ742Z32nE4bDhtVpw2M06rBZfdgtNmJsZmwWExYzObsJv2fTVhb/hqM5uwmUxKNohEOSNi8MXMTQAMPD6TtBwtWiMiIiLtR0lCEZGjmNViJsXtIMXtgBbOSDMMA38oQpUvSFVdkEpfkCpfiKq6+gRipS9IjT9MdV2QGn+ImroQ1XUhqv2hxjKvP0QwbADgDxn4Q+H9rmCiMamGq02fz2QysFrAaolgMUcwm8OYLSFMphCYgvUJSAIYRoCIyU+Y+oSkYQqCKQymEHWmEJhCYApj2pcJNIUbzlFfx2QKf8/rCFDXwoRlU9aQgSUC1jBY930NU18WAou/vtwSAUvY2O91Q539XlsiYDJM9RtmTIYZEybAgskwg8kMDeX1ry0YDWUGFjCZMWEBzGC21tc1WRo3TJb685qt9e/NFswmCyazFbPZgtlsxWSyYrbUvzZbrFjMFswWG2azBavVitlsxWK1YrHUbyarBYvVgsVqxWSpf222mrFYLPXnsVgwmUyYLRYslvpyTGbMFjMWsxmz2YzJbG7cbzaZMVnqyy0mE2ZLw36zBbPZVN8es6nxnGaTqf48FjMmkxmL2YQZMJvqv5pMYMaEueGrifqVyk2gpKp0uPULCyjZVY3daWG8VjMWERGRdqYkoYiINGEymXDaLDhtFtLina0+TyAUwesP4Q2E8PrD1PhD1AbqE4hef5jaYBhfwz5fMExtIERtIExtw766hs0XqN+/73VtMIxRn3/EMEwEQxAMWQALYGuXPmgtE5GGxGHk2wQi+xKJYQxTGIg0JCEj+yUY9x0Tadhfv5kayjE3ZADtBvWjJyOA0XDs/vW/U0YEk8nY773R5GvT+kZ9OZH6BCjBhrJ99Y0m9UyNZfudc9/WUNdk+m7Zga/NgQjmQP1HNBv1m6lha/Z9pL6vv1u+/+sm+/l2n+k7ZWbDgO8cA/WJVhrq0JAYZN9xmOqb3jBt3rzf633fBfvq/+ueJVitCrWkffh9Ib5+eysAY87pSUy8vZNbJCIiIkcbRa4iInJE2K1m7FY7ibHt+x/ZfSMd/cEIdaF9ycQIdcEw/lCkMblYF4oQCEXwh8L4g5H6Y0LhhrL6esFwfZ1AONJYvv/7YDhCMGw0vg6EIwRDDWXhyIFtw4xhmBuSSHJ49ks0mvZ14HeSiw3vTfv28d16+5WbaHocNBy7fz2+cxxNjzV/5xrfqW9qctyB5zKbD71auMjhWPK/7fiqgySkxzDklO6d3RwRERE5CilJKCIiXcr+Ix09nThy0DAMgmGDUKQ+aRgMRwg1fA2GI4Qi9cnFcOTbOqGG+qH9jgtFIoQjEI7UHxOO1J933/tQuL4sYhiN+/ff6svqzxExvrN/v/eR5l4bENmvbF+5YdB4rGHQeC7DoLFeZL969e/BwCDSUP/w7ZdQ2//wZk7VlhxsR+ZvlSRsf3l5edx00018+OGH1NbW0qdPH5577jlGjx590GP8fj933303L774IoWFhWRmZnL77bdz9dVXA/DMM8/w/PPPs2bNGgBGjRrF/fffz9ixYzvkM7WEvzbIuvn5AJxwcV8sVn1viYiISPtTklBERKQVTCYTdqsJO/rPenMMoz5huC+puC/ZaFBfZuyXVNy/jnGQ9wb7ko8N5fvOY9CkroHR5Jj6qen7HRP5ttygvnD/90bje+Pbr43n3r+8/ryw/76m55D2VV5ezsSJEzn11FP58MMPSU1NZfPmzSQmJh7yuIsvvpiioiL+85//0KdPHwoKCohEvh0J/Pnnn3PZZZcxYcIEnE4nDz30EJMnT2bt2rV069btSH+sFnHE2Ljk1rFsWVZEznHJnd0cEREROUqZDMNQHPsdVVVVeDweKisriY/XqnEiIiISXY7FWOXmm2/mq6++Yv78+S0+5qOPPuLSSy9l27ZtJCUlteiYcDhMYmIi//jHP7jiiitadMyxeD9ERESk62hprKLhDyIiIiIS9d59911Gjx7NRRddRFpaGiNGjOCZZ55p0TEPP/ww3bp1o1+/fvzhD3/A5/Md9Jja2lqCweAhk4p+v5+qqqomm4iIiEhXpyShiIiIiES9bdu28eSTT9K3b18+/vhjfv3rX3P99dfz3//+95DHfPnll6xZs4ZZs2bx+OOP88Ybb3Dttdce9JibbrqJrKwsJk2adNA6DzzwAB6Pp3HLzs5u02cTERERiQaabtwMTRkRERGRaHYsxip2u53Ro0ezYMGCxrLrr7+eJUuWsHDhwmaPmTx5MvPnz6ewsBCPxwPAW2+9xYUXXojX68XlcjWp/+CDD/Lwww/z+eefM3To0IO2xe/34/f7G99XVVWRnZ19TN0PERER6To03VhEREREjhqZmZkMGjSoSdnAgQPZtWvXIY/p1q1bY4Jw3zGGYbBnz54mdf/617/y4IMP8sknnxwyQQjgcDiIj49vsomIiIh0dUoSioiIiEjUmzhxIhs3bmxStmnTJnJycg55TH5+PjU1NU2OMZvNdO/evbHs4Ycf5p577uGjjz5i9OjR7d94ERERkS5ASUIRERERiXq//e1v+frrr7n//vvZsmULL7/8Mk8//TTTp09vrHPLLbc0WZH4xz/+McnJyVx11VWsW7eOL774gv/3//4fV199deNU44ceeojbbruNZ599ltzcXAoLCyksLGySWBQRERE5FihJKCIiIiJRb8yYMcyaNYtXXnmFwYMHc8899/D4448zbdq0xjoFBQVNph+73W5mz55NRUUFo0ePZtq0aUydOpUnnniisc6TTz5JIBDgwgsvJDMzs3H761//2qGfT0RERKSzaeGSZhyLDwMXERGRrkOxSnTR/RAREZFopoVLREREREREREREpEWUJBQRERERERERETnGKUkoIiIiIiIiIiJyjFOSUERERERERERE5BinJKGIiIiIiIiIiMgxTklCERERERERERGRY5y1sxsQjQzDAOqXiBYRERGJNvtilH0xi3QuxY4iIiISzVoaOypJ2Izq6moAsrOzO7klIiIiIgdXXV2Nx+Pp7GYc8xQ7ioiISFfwfbGjydCfoA8QiUTIz88nLi4Ok8l0RK5RVVVFdnY2u3fvJj4+/ohc41ii/mx/6tP2pf5sX+rP9qX+bF8d0Z+GYVBdXU1WVhZms54e09kUO3Y96s/2pf5sX+rP9qc+bV/qz/YVTbGjRhI2w2w207179w65Vnx8vP5RtSP1Z/tTn7Yv9Wf7Un+2L/Vn+zrS/akRhNFDsWPXpf5sX+rP9qX+bH/q0/al/mxf0RA76k/PIiIiIiIiIiIixzglCUVERERERERERI5xShJ2EofDwR133IHD4ejsphwV1J/tT33avtSf7Uv92b7Un+1L/SlHgr6v2pf6s32pP9uX+rP9qU/bl/qzfUVTf2rhEhERERERERERkWOcRhKKiIiIiIiIiIgc45QkFBEREREREREROcYpSSgiIiIiIiIiInKMU5JQRERERERERETkGKckYSf55z//SW5uLk6nk3HjxrF48eLOblKX8MUXXzB16lSysrIwmUy8/fbbTfYbhsHtt99OZmYmLpeLSZMmsXnz5s5pbBfwwAMPMGbMGOLi4khLS+O8885j48aNTerU1dUxffp0kpOTcbvd/OhHP6KoqKiTWhzdnnzySYYOHUp8fDzx8fEcf/zxfPjhh4371Zdt8+CDD2Iymbjxxhsby9SnLXfnnXdiMpmabAMGDGjcr748fHl5efzkJz8hOTkZl8vFkCFDWLp0aeN+/U6S9qTYsXUUO7YvxY7tS7HjkaXYsW0UO7a/rhA7KknYCV599VV+97vfcccdd7B8+XKGDRvGlClTKC4u7uymRT2v18uwYcP45z//2ez+hx9+mCeeeIJ//etfLFq0iNjYWKZMmUJdXV0Ht7RrmDdvHtOnT+frr79m9uzZBINBJk+ejNfrbazz29/+lvfee4/XX3+defPmkZ+fzwUXXNCJrY5e3bt358EHH2TZsmUsXbqU0047jXPPPZe1a9cC6su2WLJkCU899RRDhw5tUq4+PTzHHXccBQUFjduXX37ZuE99eXjKy8uZOHEiNpuNDz/8kHXr1vHII4+QmJjYWEe/k6S9KHZsPcWO7UuxY/tS7HjkKHZsH4od20+XiR0N6XBjx441pk+f3vg+HA4bWVlZxgMPPNCJrep6AGPWrFmN7yORiJGRkWH85S9/aSyrqKgwHA6H8corr3RCC7ue4uJiAzDmzZtnGEZ9/9lsNuP1119vrLN+/XoDMBYuXNhZzexSEhMTjX//+9/qyzaorq42+vbta8yePds4+eSTjRtuuMEwDH1/Hq477rjDGDZsWLP71JeH76abbjJOOOGEg+7X7yRpT4od24dix/an2LH9KXZsO8WO7UOxY/vqKrGjRhJ2sEAgwLJly5g0aVJjmdlsZtKkSSxcuLATW9b1bd++ncLCwiZ96/F4GDdunPq2hSorKwFISkoCYNmyZQSDwSZ9OmDAAHr06KE+/R7hcJiZM2fi9Xo5/vjj1ZdtMH36dM4+++wmfQf6/myNzZs3k5WVRa9evZg2bRq7du0C1Jet8e677zJ69Gguuugi0tLSGDFiBM8880zjfv1Okvai2PHI0b/TtlPs2H4UO7YfxY7tR7Fj++kqsaOShB2stLSUcDhMenp6k/L09HQKCws7qVVHh339p75tnUgkwo033sjEiRMZPHgwUN+ndrudhISEJnXVpwe3evVq3G43DoeDX/3qV8yaNYtBgwapL1tp5syZLF++nAceeOCAferTwzNu3DhmzJjBRx99xJNPPsn27ds58cQTqa6uVl+2wrZt23jyySfp27cvH3/8Mb/+9a+5/vrr+e9//wvod5K0H8WOR47+nbaNYsf2odixfSl2bD+KHdtXV4kdrR12JRGJatOnT2fNmjVNnjMhh69///6sXLmSyspK3njjDa688krmzZvX2c3qknbv3s0NN9zA7NmzcTqdnd2cLu/MM89sfD106FDGjRtHTk4Or732Gi6XqxNb1jVFIhFGjx7N/fffD8CIESNYs2YN//rXv7jyyis7uXUiIkeeYsf2odix/Sh2bF+KHdtXV4kdNZKwg6WkpGCxWA5Y9aeoqIiMjIxOatXRYV//qW8P33XXXcf777/PZ599Rvfu3RvLMzIyCAQCVFRUNKmvPj04u91Onz59GDVqFA888ADDhg3jb3/7m/qyFZYtW0ZxcTEjR47EarVitVqZN28eTzzxBFarlfT0dPVpGyQkJNCvXz+2bNmi789WyMzMZNCgQU3KBg4c2DgNR7+TpL0odjxy9O+09RQ7th/Fju1HseORpdixbbpK7KgkYQez2+2MGjWKOXPmNJZFIhHmzJnD8ccf34kt6/p69uxJRkZGk76tqqpi0aJF6tuDMAyD6667jlmzZjF37lx69uzZZP+oUaOw2WxN+nTjxo3s2rVLfdpCkUgEv9+vvmyF008/ndWrV7Ny5crGbfTo0UybNq3xtfq09Wpqati6dSuZmZn6/myFiRMnsnHjxiZlmzZtIicnB9DvJGk/ih2PHP07PXyKHY88xY6tp9jxyFLs2DZdJnbssCVSpNHMmTMNh8NhzJgxw1i3bp3xi1/8wkhISDAKCws7u2lRr7q62lixYoWxYsUKAzAeffRRY8WKFcbOnTsNwzCMBx980EhISDDeeecdY9WqVca5555r9OzZ0/D5fJ3c8uj061//2vB4PMbnn39uFBQUNG61tbWNdX71q18ZPXr0MObOnWssXbrUOP74443jjz++E1sdvW6++WZj3rx5xvbt241Vq1YZN998s2EymYxPPvnEMAz1ZXvYf4U6w1CfHo7f//73xueff25s377d+Oqrr4xJkyYZKSkpRnFxsWEY6svDtXjxYsNqtRr33XefsXnzZuOll14yYmJijBdffLGxjn4nSXtR7Nh6ih3bl2LH9qXY8chT7Nh6ih3bV1eJHZUk7CR///vfjR49ehh2u90YO3as8fXXX3d2k7qEzz77zAAO2K688krDMOqXDb/tttuM9PR0w+FwGKeffrqxcePGzm10FGuuLwHjueeea6zj8/mMa6+91khMTDRiYmKM888/3ygoKOi8Rkexq6++2sjJyTHsdruRmppqnH766Y1BnmGoL9vDdwM99WnLXXLJJUZmZqZht9uNbt26GZdccomxZcuWxv3qy8P33nvvGYMHDzYcDocxYMAA4+mnn26yX7+TpD0pdmwdxY7tS7Fj+1LseOQpdmw9xY7tryvEjibDMIyOG7coIiIiIiIiIiIi0UbPJBQRERERERERETnGKUkoIiIiIiIiIiJyjFOSUERERERERERE5BinJKGIiIiIiIiIiMgxTklCERERERERERGRY5yShCIiIiIiIiIiIsc4JQlFRERERERERESOcUoSioiIiIiIiIiIHOOUJBSRLuunP/0p5513Xodfd8aMGZhMJkwmEzfeeGObz5WQkNAu7TrSTjnllMbPvXLlys5ujoiIiMhhUezYsRQ7inQ91s5ugIhIc0wm0yH333HHHfztb3/DMIwOalFT8fHxbNy4kdjY2Dad55JLLuGss85qp1Z9y2QyMWvWrHYNhN966y22bt3K2LFj2+2cIiIiIu1BsWPbKHYUEVCSUESiVEFBQePrV199ldtvv52NGzc2lrndbtxud2c0DagPpDIyMtp8HpfLhcvlaocWHXlJSUlUVVV1djNEREREDqDYMfoodhTpejTdWESiUkZGRuPm8XgaA6t9m9vtPmDKyCmnnMJvfvMbbrzxRhITE0lPT+eZZ57B6/Vy1VVXERcXR58+ffjwww+bXGvNmjWceeaZuN1u0tPTufzyyyktLT3sNufm5nLvvfdyxRVX4Ha7ycnJ4d1336WkpIRzzz0Xt9vN0KFDWbp0aeMx350ycueddzJ8+HBeeOEFcnNz8Xg8XHrppVRXVze5zuOPP97k2sOHD+fOO+9s3A9w/vnnYzKZGt8DvPPOO4wcORKn00mvXr246667CIVCABiGwZ133kmPHj1wOBxkZWVx/fXXH3Y/iIiIiHQ0xY6KHUWk7ZQkFJGjyn//+19SUlJYvHgxv/nNb/j1r3/NRRddxIQJE1i+fDmTJ0/m8ssvp7a2FoCKigpOO+00RowYwdKlS/noo48oKiri4osvbtX1H3vsMSZOnMiKFSs4++yzufzyy7niiiv4yU9+wvLly+nduzdXXHHFIae6bN26lbfffpv333+f999/n3nz5vHggw+2uA1LliwB4LnnnqOgoKDx/fz587niiiu44YYbWLduHU899RQzZszgvvvuA+DNN9/kscce46mnnmLz5s28/fbbDBkypFX9ICIiItIVKHZU7Cgi31KSUESOKsOGDePWW2+lb9++3HLLLTidTlJSUrjmmmvo27cvt99+O3v37mXVqlUA/OMf/2DEiBHcf//9DBgwgBEjRvDss8/y2WefsWnTpsO+/llnncUvf/nLxmtVVVUxZswYLrroIvr168dNN93E+vXrKSoqOug5IpEIM2bMYPDgwZx44olcfvnlzJkzp8VtSE1NBSAhIYGMjIzG93fddRc333wzV155Jb169eKMM87gnnvu4amnngJg165dZGRkMGnSJHr06MHYsWO55pprDrsPRERERLoKxY6KHUXkW0oSishRZejQoY2vLRYLycnJTf6imZ6eDkBxcTEA33zzDZ999lnjc2rcbjcDBgwA6v8q25br77vWoa7fnNzcXOLi4hrfZ2ZmHrJ+S33zzTfcfffdTT7rNddcQ0FBAbW1tVx00UX4fD569erFNddcw6xZsxqnk4iIiIgcjRQ7HpxiR5FjjxYuEZGjis1ma/LeZDI1Kdu38l0kEgGgpqaGqVOn8tBDDx1wrszMzDZdf9+1DnX97zvHvmP2r282mw+YchIMBr+3bTU1Ndx1111ccMEFB+xzOp1kZ2ezceNGPv30U2bPns21117LX/7yF+bNm3dAm0RERESOBoodD06xo8ixR0lCETmmjRw5kjfffJPc3Fys1q7xIzE1NbXJCn5VVVVs3769SR2bzUY4HG5SNnLkSDZu3EifPn0Oem6Xy8XUqVOZOnUq06dPZ8CAAaxevZqRI0e274cQERER6YIUOzal2FHk6KLpxiJyTJs+fTplZWVcdtllLFmyhK1bt/Lxxx9z1VVXHRAoRYvTTjuNF154gfnz57N69WquvPJKLBZLkzq5ubnMmTOHwsJCysvLAbj99tt5/vnnueuuu1i7di3r169n5syZ3HrrrUD9ann/+c9/WLNmDdu2bePFF1/E5XKRk5PT4Z9RREREJBopdlTsKHI0U5JQRI5pWVlZfPXVV4TDYSZPnsyQIUO48cYbSUhIwGyOzh+Rt9xyCyeffDLnnHMOZ599Nueddx69e/duUueRRx5h9uzZZGdnM2LECACmTJnC+++/zyeffMKYMWMYP348jz32WGMgl5CQwDPPPMPEiRMZOnQon376Ke+99x7Jyckd/hlFREREopFiR8WOIkczk3GotdRFROQAM2bM4MYbb6SioqKzm9LhduzYQc+ePVmxYgXDhw/v7OaIiIiIRD3FjoodRbqK6PxTh4hIlKusrMTtdnPTTTd1dlM6zJlnnslxxx3X2c0QERER6XIUO4pIV6CRhCIih6m6upqioiKgfppFSkpKJ7eoY+Tl5eHz+QDo0aMHdru9k1skIiIiEv0UOyp2FOkqlCQUERERERERERE5xmm6sYiIiIiIiIiIyDFOSUIREREREREREZFjnJKEIiIiIiIiIiIixzglCUVERERERERERI5xShKKiIiIiIiIiIgc45QkFBEREREREREROcYpSSgiIiIiIiIiInKMU5JQRERERERERETkGPf/AcJ2KHGnlwcKAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -148,7 +161,6 @@
     }
    ],
    "source": [
-    "\n",
     "colors = [\"tab:purple\", \"tab:cyan\", \"tab:red\", \"tab:green\", \"tab:blue\"]\n",
     "linestyles = [\"dashed\", \"dotted\", \"solid\"]\n",
     "\n",
@@ -160,42 +172,49 @@
     "currents = [\n",
     "    \"X-averaged negative electrode volumetric interfacial current density [A.m-3]\",\n",
     "    \"X-averaged negative electrode lithium plating volumetric interfacial current density [A.m-3]\",\n",
-    "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\"\n",
+    "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
     "]\n",
     "\n",
     "\n",
     "def plot(sims):\n",
     "    import matplotlib.pyplot as plt\n",
-    "    fig, axs = plt.subplots(2, 2, figsize=(13,9))\n",
-    "    for (C_rate,sim), color in zip(sims.items(),colors):\n",
+    "\n",
+    "    fig, axs = plt.subplots(2, 2, figsize=(13, 9))\n",
+    "    for (C_rate, sim), color in zip(sims.items(), colors):\n",
     "        # Isolate final equilibration phase\n",
     "        sol = sim.solution.cycles[0].steps[2]\n",
     "\n",
     "        # Voltage vs time\n",
     "        t = sol[\"Time [min]\"].entries\n",
-    "        t = t-t[0]\n",
+    "        t = t - t[0]\n",
     "        V = sol[\"Voltage [V]\"].entries\n",
-    "        axs[0,0].plot(t, V, color=color, linestyle=\"solid\", label=C_rate)\n",
+    "        axs[0, 0].plot(t, V, color=color, linestyle=\"solid\", label=C_rate)\n",
     "\n",
     "        # Currents\n",
-    "        for current, ls in zip(currents,linestyles):\n",
+    "        for current, ls in zip(currents, linestyles):\n",
     "            j = sol[current].entries\n",
-    "            axs[0,1].plot(t, j, color=color, linestyle=ls)\n",
+    "            axs[0, 1].plot(t, j, color=color, linestyle=ls)\n",
     "\n",
     "        # Plated lithium capacity\n",
     "        Q_Li = sol[\"Loss of capacity to negative lithium plating [A.h]\"].entries\n",
-    "        axs[1,0].plot(t, Q_Li, color=color, linestyle='solid')\n",
+    "        axs[1, 0].plot(t, Q_Li, color=color, linestyle=\"solid\")\n",
     "\n",
     "        # Capacity vs time\n",
-    "        Q_main = sol[\"Negative electrode volume-averaged concentration [mol.m-3]\"].entries * F * A * L_n / 3600\n",
-    "        axs[1,1].plot(t, Q_main, color=color, linestyle='solid')\n",
+    "        Q_main = (\n",
+    "            sol[\"Negative electrode volume-averaged concentration [mol.m-3]\"].entries\n",
+    "            * F\n",
+    "            * A\n",
+    "            * L_n\n",
+    "            / 3600\n",
+    "        )\n",
+    "        axs[1, 1].plot(t, Q_main, color=color, linestyle=\"solid\")\n",
     "\n",
-    "    axs[0,0].legend()\n",
-    "    axs[0,0].set_ylabel(\"Voltage [V]\")\n",
-    "    axs[0,1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n",
-    "    axs[0,1].legend(('Deintercalation current','Stripping current','Total current'))\n",
-    "    axs[1,0].set_ylabel(\"Plated lithium capacity [A.h]\")\n",
-    "    axs[1,1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n",
+    "    axs[0, 0].legend()\n",
+    "    axs[0, 0].set_ylabel(\"Voltage [V]\")\n",
+    "    axs[0, 1].set_ylabel(\"Volumetric interfacial current density [A.m-3]\")\n",
+    "    axs[0, 1].legend((\"Deintercalation current\", \"Stripping current\", \"Total current\"))\n",
+    "    axs[1, 0].set_ylabel(\"Plated lithium capacity [A.h]\")\n",
+    "    axs[1, 1].set_ylabel(\"Intercalated lithium capacity [A.h]\")\n",
     "\n",
     "    for ax in axs.flat:\n",
     "        ax.set_xlabel(\"Time [minutes]\")\n",
@@ -228,7 +247,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sims_irreversible = define_and_solve_sims(models[\"irreversible\"], experiments, parameter_values)"
+    "sims_irreversible = define_and_solve_sims(\n",
+    "    models[\"irreversible\"], experiments, parameter_values\n",
+    ")"
    ]
   },
   {
@@ -238,22 +259,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(<Figure size 1300x900 with 4 Axes>,\n",
-       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
-       "       dtype=object))"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -263,7 +269,7 @@
     }
    ],
    "source": [
-    "plot(sims_irreversible)"
+    "plot(sims_irreversible);"
    ]
   },
   {
@@ -279,7 +285,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sims_partially_reversible = define_and_solve_sims(models[\"partially reversible\"], experiments, parameter_values)"
+    "sims_partially_reversible = define_and_solve_sims(\n",
+    "    models[\"partially reversible\"], experiments, parameter_values\n",
+    ")"
    ]
   },
   {
@@ -289,22 +297,7 @@
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(<Figure size 1300x900 with 4 Axes>,\n",
-       " array([[<Axes: xlabel='Time [minutes]', ylabel='Voltage [V]'>,\n",
-       "         <Axes: xlabel='Time [minutes]', ylabel='Volumetric interfacial current density [A.m-3]'>],\n",
-       "        [<Axes: xlabel='Time [minutes]', ylabel='Plated lithium capacity [Ah]'>,\n",
-       "         <Axes: xlabel='Time [minutes]', ylabel='Intercalated lithium capacity [Ah]'>]],\n",
-       "       dtype=object))"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x900 with 4 Axes>"
       ]
@@ -314,7 +307,7 @@
     }
    ],
    "source": [
-    "plot(sims_partially_reversible)"
+    "plot(sims_partially_reversible);"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
index 4ec9f4cc65..127763ec35 100644
--- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
+++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
@@ -36,17 +36,16 @@
     "import pybamm\n",
     "\n",
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options=\n",
-    "    {\n",
-    "        \"SEI\":\"solvent-diffusion limited\", \n",
-    "        \"SEI porosity change\":\"false\", \n",
-    "        \"particle mechanics\":\"swelling only\",\n",
-    "        \"loss of active material\":\"stress-driven\",\n",
+    "    options={\n",
+    "        \"SEI\": \"solvent-diffusion limited\",\n",
+    "        \"SEI porosity change\": \"false\",\n",
+    "        \"particle mechanics\": \"swelling only\",\n",
+    "        \"loss of active material\": \"stress-driven\",\n",
     "    }\n",
     ")\n",
     "param = pybamm.ParameterValues(\"Ai2020\")\n",
-    "param.update({\"Negative electrode LAM constant proportional term [s-1]\": 1e-4/3600})\n",
-    "param.update({\"Positive electrode LAM constant proportional term [s-1]\": 1e-4/3600})\n",
+    "param.update({\"Negative electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n",
+    "param.update({\"Positive electrode LAM constant proportional term [s-1]\": 1e-4 / 3600})\n",
     "total_cycles = 2\n",
     "experiment = pybamm.Experiment(\n",
     "    [\n",
@@ -54,13 +53,14 @@
     "        \"Rest for 600 seconds\",\n",
     "        \"Charge at 1C until 4.2 V\",\n",
     "        \"Hold at 4.199 V for 600 seconds\",\n",
-    "    ] * total_cycles\n",
+    "    ]\n",
+    "    * total_cycles\n",
     ")\n",
     "sim = pybamm.Simulation(\n",
-    "    model, \n",
-    "    experiment = experiment,\n",
-    "    parameter_values = param,\n",
-    "    solver = pybamm.CasadiSolver(\"fast with events\")\n",
+    "    model,\n",
+    "    experiment=experiment,\n",
+    "    parameter_values=param,\n",
+    "    solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     ")\n",
     "solution = sim.solve(calc_esoh=False)"
    ]
@@ -103,16 +103,18 @@
     }
    ],
    "source": [
-    "sim.plot([\n",
-    "    \"Voltage [V]\",\n",
-    "    \"Current [A]\",\n",
-    "    \"Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\",\n",
-    "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
-    "    \"X-averaged positive electrode active material volume fraction\",\n",
-    "    \"X-averaged negative electrode active material volume fraction\",\n",
-    "    \"X-averaged positive particle surface tangential stress [Pa]\",\n",
-    "    \"X-averaged negative particle surface tangential stress [Pa]\",\n",
-    "])"
+    "sim.plot(\n",
+    "    [\n",
+    "        \"Voltage [V]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Sum of x-averaged positive electrode volumetric interfacial current densities [A.m-3]\",\n",
+    "        \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
+    "        \"X-averaged positive electrode active material volume fraction\",\n",
+    "        \"X-averaged negative electrode active material volume fraction\",\n",
+    "        \"X-averaged positive particle surface tangential stress [Pa]\",\n",
+    "        \"X-averaged negative particle surface tangential stress [Pa]\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -157,18 +159,18 @@
     "solutions = []\n",
     "\n",
     "for k in ks:\n",
-    "    param.update({\"Positive electrode LAM constant proportional term [s-1]\": k/3600})\n",
-    "    param.update({\"Negative electrode LAM constant proportional term [s-1]\": k/3600})\n",
+    "    param.update({\"Positive electrode LAM constant proportional term [s-1]\": k / 3600})\n",
+    "    param.update({\"Negative electrode LAM constant proportional term [s-1]\": k / 3600})\n",
     "\n",
     "    sim = pybamm.Simulation(\n",
-    "        model, \n",
+    "        model,\n",
     "        experiment=experiment,\n",
     "        parameter_values=param,\n",
     "        solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     "    )\n",
     "    solution = sim.solve(calc_esoh=False)\n",
     "    solutions.append(solution)\n",
-    "    \n",
+    "\n",
     "pybamm.dynamic_plot(\n",
     "    solutions,\n",
     "    output_variables=[\n",
@@ -181,7 +183,7 @@
     "        \"X-averaged positive electrode surface area to volume ratio [m-1]\",\n",
     "        \"X-averaged negative electrode surface area to volume ratio [m-1]\",\n",
     "    ],\n",
-    "    labels=[f\"k={k:.0e}\" for k in ks]\n",
+    "    labels=[f\"k={k:.0e}\" for k in ks],\n",
     ")"
    ]
   },
@@ -226,14 +228,17 @@
    ],
    "source": [
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options=\n",
-    "    {\n",
-    "        \"SEI\":\"solvent-diffusion limited\", \n",
-    "        \"loss of active material\":\"reaction-driven\",\n",
+    "    options={\n",
+    "        \"SEI\": \"solvent-diffusion limited\",\n",
+    "        \"loss of active material\": \"reaction-driven\",\n",
     "    }\n",
     ")\n",
     "param = pybamm.ParameterValues(\"Chen2020\")\n",
-    "param.update({\"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3,})\n",
+    "param.update(\n",
+    "    {\n",
+    "        \"Negative electrode reaction-driven LAM factor [m3.mol-1]\": 1e-3,\n",
+    "    }\n",
+    ")\n",
     "total_cycles = 2\n",
     "experiment = pybamm.Experiment(\n",
     "    [\n",
@@ -241,24 +246,27 @@
     "        \"Rest for 600 seconds\",\n",
     "        \"Charge at 1C until 4.2 V\",\n",
     "        \"Hold at 4.199 V for 600 seconds\",\n",
-    "    ] * total_cycles\n",
+    "    ]\n",
+    "    * total_cycles\n",
     ")\n",
     "sim = pybamm.Simulation(\n",
-    "    model, \n",
-    "    experiment = experiment,\n",
-    "    parameter_values = param,\n",
-    "    solver = pybamm.CasadiSolver(\"fast with events\")\n",
+    "    model,\n",
+    "    experiment=experiment,\n",
+    "    parameter_values=param,\n",
+    "    solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     ")\n",
     "solution = sim.solve(calc_esoh=False)\n",
     "\n",
-    "sim.plot([\n",
-    "    \"Voltage [V]\",\n",
-    "    \"Current [A]\",\n",
-    "    \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
-    "    \"X-averaged negative electrode active material volume fraction\",\n",
-    "    \"Negative total SEI thickness [m]\",\n",
-    "    \"X-averaged negative total SEI thickness [m]\",\n",
-    "])"
+    "sim.plot(\n",
+    "    [\n",
+    "        \"Voltage [V]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Sum of x-averaged negative electrode volumetric interfacial current densities [A.m-3]\",\n",
+    "        \"X-averaged negative electrode active material volume fraction\",\n",
+    "        \"Negative total SEI thickness [m]\",\n",
+    "        \"X-averaged negative total SEI thickness [m]\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
@@ -313,16 +321,18 @@
     "\n",
     "\n",
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options=\n",
-    "    {\n",
-    "        \"loss of active material\":\"current-driven\",\n",
+    "    options={\n",
+    "        \"loss of active material\": \"current-driven\",\n",
     "    }\n",
     ")\n",
     "param = pybamm.ParameterValues(\"Chen2020\")\n",
-    "param.update({\n",
-    "    \"Positive electrode current-driven LAM rate\": current_LAM,\n",
-    "    \"Negative electrode current-driven LAM rate\": current_LAM,\n",
-    "}, check_already_exists=False)\n",
+    "param.update(\n",
+    "    {\n",
+    "        \"Positive electrode current-driven LAM rate\": current_LAM,\n",
+    "        \"Negative electrode current-driven LAM rate\": current_LAM,\n",
+    "    },\n",
+    "    check_already_exists=False,\n",
+    ")\n",
     "total_cycles = 2\n",
     "experiment = pybamm.Experiment(\n",
     "    [\n",
@@ -330,22 +340,25 @@
     "        \"Rest for 600 seconds\",\n",
     "        \"Charge at 1C until 4.2 V\",\n",
     "        \"Hold at 4.199 V for 600 seconds\",\n",
-    "    ] * total_cycles\n",
+    "    ]\n",
+    "    * total_cycles\n",
     ")\n",
     "sim = pybamm.Simulation(\n",
-    "    model, \n",
-    "    experiment = experiment,\n",
-    "    parameter_values = param,\n",
-    "    solver = pybamm.CasadiSolver(\"fast with events\")\n",
+    "    model,\n",
+    "    experiment=experiment,\n",
+    "    parameter_values=param,\n",
+    "    solver=pybamm.CasadiSolver(\"fast with events\"),\n",
     ")\n",
     "solution = sim.solve(calc_esoh=False)\n",
     "\n",
-    "sim.plot([\n",
-    "    \"Voltage [V]\",\n",
-    "    \"Current [A]\",\n",
-    "    \"X-averaged positive electrode active material volume fraction\",\n",
-    "    \"X-averaged negative electrode active material volume fraction\",\n",
-    "])"
+    "sim.plot(\n",
+    "    [\n",
+    "        \"Voltage [V]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"X-averaged positive electrode active material volume fraction\",\n",
+    "        \"X-averaged negative electrode active material volume fraction\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index 2c58b1861f..69cfbfec40 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -132,10 +132,12 @@
    "outputs": [],
    "source": [
     "param = dfn.default_parameter_values\n",
-    "I_1C = param[\"Nominal cell capacity [A.h]\"]  # 1C current is cell capacity multipled by 1 hour\n",
+    "I_1C = param[\n",
+    "    \"Nominal cell capacity [A.h]\"\n",
+    "]  # 1C current is cell capacity multipled by 1 hour\n",
     "param.update(\n",
     "    {\n",
-    "        \"Current function [A]\": I_1C * 3,       \n",
+    "        \"Current function [A]\": I_1C * 3,\n",
     "        \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n",
     "        \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n",
     "        \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n",
@@ -213,14 +215,16 @@
     "    sim = pybamm.Simulation(model, parameter_values=param, var_pts=var_pts)\n",
     "    simulations[name] = sim  # store simulation for later\n",
     "    if name == \"Current collector\":\n",
-    "        # model is independent of time, so just solve arbitrarily at t=0 using \n",
+    "        # model is independent of time, so just solve arbitrarily at t=0 using\n",
     "        # the default algebraic solver\n",
     "        t_eval = np.array([0])\n",
-    "        solutions[name] = sim.solve(t_eval=t_eval)        \n",
+    "        solutions[name] = sim.solve(t_eval=t_eval)\n",
     "    else:\n",
     "        # solve at COMSOL times using Casadi solver in \"fast\" mode\n",
-    "        t_eval = comsol_variables[\"time\"]       \n",
-    "        solutions[name] = sim.solve(solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval)"
+    "        t_eval = comsol_variables[\"time\"]\n",
+    "        solutions[name] = sim.solve(\n",
+    "            solver=pybamm.CasadiSolver(mode=\"fast\"), t_eval=t_eval\n",
+    "        )"
    ]
   },
   {
@@ -265,7 +269,7 @@
     "\n",
     "def get_interp_fun_curr_coll(variable_name):\n",
     "    \"\"\"\n",
-    "    Create a :class:`pybamm.Function` object using the variable (interpolate in space \n",
+    "    Create a :class:`pybamm.Function` object using the variable (interpolate in space\n",
     "    to match nodes, and then create function to interpolate in time)\n",
     "    \"\"\"\n",
     "\n",
@@ -275,10 +279,7 @@
     "\n",
     "    # Make sure to use dimensional time\n",
     "    fun = pybamm.Interpolant(\n",
-    "        comsol_t,\n",
-    "        variable.T,\n",
-    "        pybamm.t,\n",
-    "        name=variable_name + \"_comsol\"\n",
+    "        comsol_t, variable.T, pybamm.t, name=variable_name + \"_comsol\"\n",
     "    )\n",
     "    fun.domains = {\"primary\": \"current collector\"}\n",
     "    fun.mesh = mesh.combine_submeshes(\"current collector\")\n",
@@ -302,7 +303,7 @@
    "outputs": [],
    "source": [
     "comsol_voltage = pybamm.Interpolant(\n",
-    "    comsol_t, \n",
+    "    comsol_t,\n",
     "    comsol_variables[\"voltage\"],\n",
     "    pybamm.t,\n",
     "    name=\"voltage_comsol\",\n",
@@ -338,7 +339,7 @@
     "    \"Current collector current density [A.m-2]\": comsol_current,\n",
     "    \"X-averaged cell temperature [K]\": comsol_temperature,\n",
     "    # Add spatial variables to match pybamm model\n",
-    "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],   \n",
+    "    \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"],\n",
     "}"
    ]
   },
@@ -356,7 +357,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {})"
+    "comsol_solution = pybamm.Solution(\n",
+    "    solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y, comsol_model, {}\n",
+    ")"
    ]
   },
   {
@@ -386,9 +389,7 @@
     "V_av = solutions[\"Average DFN\"][\"Voltage [V]\"]\n",
     "I_av = solutions[\"Average DFN\"][\"Total current density [A.m-2]\"]\n",
     "\n",
-    "dfncc_vars = cc_model.post_process(\n",
-    "    solutions[\"Current collector\"], param, V_av, I_av\n",
-    ")"
+    "dfncc_vars = cc_model.post_process(solutions[\"Current collector\"], param, V_av, I_av)"
    ]
   },
   {
@@ -417,7 +418,6 @@
     "    param,\n",
     "    cmap=\"viridis\",\n",
     "):\n",
-    "\n",
     "    fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n",
     "    fig.subplots_adjust(\n",
     "        left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n",
@@ -462,9 +462,9 @@
     "        )\n",
     "        ax[0, 1].plot(z_plot * 1e3, dfncc_var_slice, \":\", color=color)\n",
     "    # add dummy points for legend of styles\n",
-    "    comsol_p, = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
-    "    pybamm_p, = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
-    "    dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
+    "    (comsol_p,) = ax[0, 1].plot(np.nan, np.nan, \"ko\", fillstyle=\"none\")\n",
+    "    (pybamm_p,) = ax[0, 1].plot(np.nan, np.nan, \"k-\", fillstyle=\"none\")\n",
+    "    (dfncc_p,) = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n",
     "\n",
     "    # compute errors\n",
     "    dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n",
@@ -650,7 +650,7 @@
     "    return dfn_var(t=t, z=z) - V(t=t)\n",
     "\n",
     "\n",
-    "dfncc_var =  dfncc_vars[var]\n",
+    "dfncc_var = dfncc_vars[var]\n",
     "V_dfncc = dfncc_vars[\"Voltage [V]\"]\n",
     "\n",
     "\n",
diff --git a/docs/source/examples/notebooks/models/rate-capability.ipynb b/docs/source/examples/notebooks/models/rate-capability.ipynb
index 056362b8f9..ef09a37909 100644
--- a/docs/source/examples/notebooks/models/rate-capability.ipynb
+++ b/docs/source/examples/notebooks/models/rate-capability.ipynb
@@ -97,13 +97,10 @@
     "\n",
     "for i, C_rate in enumerate(C_rates):\n",
     "    experiment = pybamm.Experiment(\n",
-    "        [f\"Discharge at {C_rate:.4f}C until 3.2V\"],\n",
-    "        period=f\"{10 / C_rate:.4f} seconds\"\n",
+    "        [f\"Discharge at {C_rate:.4f}C until 3.2V\"], period=f\"{10 / C_rate:.4f} seconds\"\n",
     "    )\n",
     "    sim = pybamm.Simulation(\n",
-    "        model,\n",
-    "        experiment=experiment,\n",
-    "        solver=pybamm.CasadiSolver(dt_max=120)\n",
+    "        model, experiment=experiment, solver=pybamm.CasadiSolver(dt_max=120)\n",
     "    )\n",
     "    sim.solve()\n",
     "\n",
@@ -118,13 +115,13 @@
     "\n",
     "plt.figure(1)\n",
     "plt.scatter(C_rates, capacities)\n",
-    "plt.xlabel('C-rate')\n",
-    "plt.ylabel('Capacity [Ah]')\n",
+    "plt.xlabel(\"C-rate\")\n",
+    "plt.ylabel(\"Capacity [Ah]\")\n",
     "\n",
     "plt.figure(2)\n",
     "plt.scatter(currents * voltage_av, capacities * voltage_av)\n",
-    "plt.xlabel('Power [W]')\n",
-    "plt.ylabel('Energy [Wh]')\n",
+    "plt.xlabel(\"Power [W]\")\n",
+    "plt.ylabel(\"Energy [Wh]\")\n",
     "\n",
     "plt.show()"
    ]
diff --git a/docs/source/examples/notebooks/models/saving_models.ipynb b/docs/source/examples/notebooks/models/saving_models.ipynb
index 91a6f2ae5c..57bda3ef85 100644
--- a/docs/source/examples/notebooks/models/saving_models.ipynb
+++ b/docs/source/examples/notebooks/models/saving_models.ipynb
@@ -307,6 +307,7 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "\n",
     "os.remove(\"example_model.json\")\n",
     "os.remove(\"sim_model_example.json\")\n",
     "os.remove(\"sim_model_variables.json\")"
diff --git a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
index f20f385601..9965a78563 100644
--- a/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
+++ b/docs/source/examples/notebooks/models/simulating-ORegan-2022-parameter-set.ipynb
@@ -131,7 +131,9 @@
     }
    ],
    "source": [
-    "sim.solve([0, 10])    # solving time kept short for testing purposes, feel free to extend it\n",
+    "sim.solve(\n",
+    "    [0, 10]\n",
+    ")  # solving time kept short for testing purposes, feel free to extend it\n",
     "sim.plot()"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
index b3725fd36f..ac92c06d15 100644
--- a/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
+++ b/docs/source/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
@@ -23,7 +23,8 @@
     "import pybamm\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -47,9 +48,9 @@
    "outputs": [],
    "source": [
     "model = pybamm.lithium_ion.DFN(\n",
-    "    options = {\n",
-    "        \"particle\": \"Fickian diffusion\",  \n",
-    "        \"particle mechanics\": \"swelling and cracking\", # other options are \"none\", \"swelling only\"\n",
+    "    options={\n",
+    "        \"particle\": \"Fickian diffusion\",\n",
+    "        \"particle mechanics\": \"swelling and cracking\",  # other options are \"none\", \"swelling only\"\n",
     "    }\n",
     ")"
    ]
@@ -87,12 +88,12 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable."
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable."
+   ]
   },
   {
    "cell_type": "code",
@@ -107,7 +108,7 @@
    "source": [
     "var_pts = {\n",
     "    \"x_n\": 20,  # negative electrode\n",
-    "    \"x_s\": 20,  # separator \n",
+    "    \"x_s\": 20,  # separator\n",
     "    \"x_p\": 20,  # positive electrode\n",
     "    \"r_n\": 26,  # negative particle\n",
     "    \"r_p\": 26,  # positive particle\n",
@@ -189,43 +190,52 @@
     "E_n = param[\"Negative electrode Young's modulus [Pa]\"]\n",
     "stress_t_n_surf = solution[\"Negative particle surface tangential stress [Pa]\"]\n",
     "x = solution[\"x [m]\"].entries[0:19, 0]\n",
-    "c_s_n = solution['Negative particle concentration']\n",
+    "c_s_n = solution[\"Negative particle concentration\"]\n",
     "r_n = solution[\"r_n [m]\"].entries[:, 0, 0]\n",
     "\n",
     "# plot\n",
     "\n",
     "\n",
     "def plot_concentrations(t):\n",
-    "    f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4 ,figsize=(20,4))\n",
-    "    ax1.plot(x, stress_t_n_surf(t=t,x=x) / E_n)\n",
-    "    ax1.set_xlabel(r'$x_n$ [m]')\n",
-    "    ax1.set_ylabel('$\\sigma_t/E_n$')\n",
-    "    \n",
-    "    plot_c_n, = ax2.plot(r_n, c_s_n(r=r_n,t=t,x=x[0]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "    ax2.set_ylabel('Negative particle concentration')\n",
-    "    ax2.set_xlabel(r'$r_n$ [m]')\n",
+    "    f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(20, 4))\n",
+    "    ax1.plot(x, stress_t_n_surf(t=t, x=x) / E_n)\n",
+    "    ax1.set_xlabel(r\"$x_n$ [m]\")\n",
+    "    ax1.set_ylabel(\"$\\sigma_t/E_n$\")\n",
+    "\n",
+    "    (plot_c_n,) = ax2.plot(\n",
+    "        r_n, c_s_n(r=r_n, t=t, x=x[0])\n",
+    "    )  # can evaluate at arbitrary x (single representative particle)\n",
+    "    ax2.set_ylabel(\"Negative particle concentration\")\n",
+    "    ax2.set_xlabel(r\"$r_n$ [m]\")\n",
     "    ax2.set_ylim(0, 1)\n",
-    "    ax2.set_title('Close to current collector')\n",
+    "    ax2.set_title(\"Close to current collector\")\n",
     "    ax2.grid()\n",
-    "    \n",
-    "    plot_c_n, = ax3.plot(r_n, c_s_n(r=r_n,t=t,x=x[10]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "    ax3.set_ylabel('Negative particle concentration')\n",
-    "    ax3.set_xlabel(r'$r_n$ [m]')\n",
-    "    ax3.set_ylim(0, 1)  \n",
-    "    ax3.set_title('In the middle')\n",
+    "\n",
+    "    (plot_c_n,) = ax3.plot(\n",
+    "        r_n, c_s_n(r=r_n, t=t, x=x[10])\n",
+    "    )  # can evaluate at arbitrary x (single representative particle)\n",
+    "    ax3.set_ylabel(\"Negative particle concentration\")\n",
+    "    ax3.set_xlabel(r\"$r_n$ [m]\")\n",
+    "    ax3.set_ylim(0, 1)\n",
+    "    ax3.set_title(\"In the middle\")\n",
     "    ax3.grid()\n",
     "\n",
-    "    plot_c_n, = ax4.plot(r_n, c_s_n(r=r_n,t=t,x=x[-1]))  # can evaluate at arbitrary x (single representative particle)\n",
-    "    ax4.set_ylabel('Negative particle concentration')\n",
-    "    ax4.set_xlabel(r'$r_n$ [m]')\n",
-    "    ax4.set_ylim(0, 1)  \n",
-    "    ax4.set_title('Close to separator')\n",
+    "    (plot_c_n,) = ax4.plot(\n",
+    "        r_n, c_s_n(r=r_n, t=t, x=x[-1])\n",
+    "    )  # can evaluate at arbitrary x (single representative particle)\n",
+    "    ax4.set_ylabel(\"Negative particle concentration\")\n",
+    "    ax4.set_xlabel(r\"$r_n$ [m]\")\n",
+    "    ax4.set_ylim(0, 1)\n",
+    "    ax4.set_title(\"Close to separator\")\n",
     "    ax4.grid()\n",
     "    plt.show()\n",
-    "    \n",
+    "\n",
     "\n",
     "import ipywidgets as widgets\n",
-    "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));"
+    "\n",
+    "widgets.interact(\n",
+    "    plot_concentrations, t=widgets.FloatSlider(min=0, max=3600, step=10, value=0)\n",
+    ");"
    ]
   },
   {
@@ -263,12 +273,14 @@
    "source": [
     "label = [\"Crack model\"]\n",
     "output_variables = [\n",
-    "    \"Negative particle crack length [m]\", \n",
+    "    \"Negative particle crack length [m]\",\n",
     "    \"Positive particle crack length [m]\",\n",
     "    \"X-averaged negative particle crack length [m]\",\n",
-    "    \"X-averaged positive particle crack length [m]\"\n",
+    "    \"X-averaged positive particle crack length [m]\",\n",
     "]\n",
-    "quick_plot = pybamm.QuickPlot(solution, output_variables, label,variable_limits='tight')\n",
+    "quick_plot = pybamm.QuickPlot(\n",
+    "    solution, output_variables, label, variable_limits=\"tight\"\n",
+    ")\n",
     "quick_plot.dynamic_plot();"
    ]
   },
diff --git a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
index cf7bef3b47..5e3d11a0ee 100644
--- a/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
+++ b/docs/source/examples/notebooks/models/unsteady-heat-equation.ipynb
@@ -138,7 +138,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.initial_conditions = {T: 2 * x - x ** 2}"
+    "model.initial_conditions = {T: 2 * x - x**2}"
    ]
   },
   {
@@ -331,24 +331,26 @@
    "outputs": [],
    "source": [
     "N = 100  # number of Fourier modes to sum\n",
-    "k_val = param[\"Thermal diffusivity\"]  # extract value of diffusivity from the parameters dictionary\n",
+    "k_val = param[\n",
+    "    \"Thermal diffusivity\"\n",
+    "]  # extract value of diffusivity from the parameters dictionary\n",
     "\n",
     "\n",
     "# Fourier coefficients\n",
     "def q(n):\n",
-    "    return (8 / (n ** 2 * np.pi ** 2)) * np.sin(n * np.pi / 2)\n",
+    "    return (8 / (n**2 * np.pi**2)) * np.sin(n * np.pi / 2)\n",
     "\n",
     "\n",
     "def c(n):\n",
-    "    return (16 / (n ** 3 * np.pi ** 3)) * (1 - np.cos(n * np.pi))\n",
+    "    return (16 / (n**3 * np.pi**3)) * (1 - np.cos(n * np.pi))\n",
     "\n",
     "\n",
     "def b(n):\n",
-    "    return c(n) - 4 * q(n) / (k_val * n ** 2 * np.pi ** 2)\n",
+    "    return c(n) - 4 * q(n) / (k_val * n**2 * np.pi**2)\n",
     "\n",
     "\n",
     "def T_n(t, n):\n",
-    "    return (4 * q(n) / (k_val * n ** 2 * np.pi ** 2)) + b(n) * np.exp(\n",
+    "    return (4 * q(n) / (k_val * n**2 * np.pi**2)) + b(n) * np.exp(\n",
     "        -k_val * (n * np.pi / 2) ** 2 * t\n",
     "    )\n",
     "\n",
diff --git a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
index 0c97752792..1f6250f760 100644
--- a/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
+++ b/docs/source/examples/notebooks/models/using-model-options_thermal-example.ipynb
@@ -35,7 +35,8 @@
     "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -147,10 +148,12 @@
     }
    ],
    "source": [
-    "simulation.plot([\n",
-    "    \"Voltage [V]\",\n",
-    "    \"X-averaged cell temperature [K]\",\n",
-    "])"
+    "simulation.plot(\n",
+    "    [\n",
+    "        \"Voltage [V]\",\n",
+    "        \"X-averaged cell temperature [K]\",\n",
+    "    ]\n",
+    ")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/models/using-submodels.ipynb b/docs/source/examples/notebooks/models/using-submodels.ipynb
index 211e3346d8..d02e5489c7 100644
--- a/docs/source/examples/notebooks/models/using-submodels.ipynb
+++ b/docs/source/examples/notebooks/models/using-submodels.ipynb
@@ -142,7 +142,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(\n",
+    "model.submodels[\n",
+    "    \"negative primary particle\"\n",
+    "] = pybamm.particle.XAveragedPolynomialProfile(\n",
     "    model.param, \"negative\", options={**model.options, \"particle\": \"uniform profile\"}\n",
     ")"
    ]
@@ -365,7 +367,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(model.param, model.options)"
+    "model.submodels[\"external circuit\"] = pybamm.external_circuit.ExplicitCurrentControl(\n",
+    "    model.param, model.options\n",
+    ")"
    ]
   },
   {
@@ -427,11 +431,19 @@
    "outputs": [],
    "source": [
     "options = {**model.options, \"particle\": \"uniform profile\"}\n",
-    "model.submodels[\"negative primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n",
-    "model.submodels[\"positive primary particle\"] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n",
+    "model.submodels[\n",
+    "    \"negative primary particle\"\n",
+    "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"negative\", options)\n",
+    "model.submodels[\n",
+    "    \"positive primary particle\"\n",
+    "] = pybamm.particle.XAveragedPolynomialProfile(model.param, \"positive\", options)\n",
     "\n",
-    "model.submodels[\"negative total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n",
-    "model.submodels[\"positive total particle concentration\"] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)"
+    "model.submodels[\n",
+    "    \"negative total particle concentration\"\n",
+    "] = pybamm.particle.TotalConcentration(model.param, \"negative\", options)\n",
+    "model.submodels[\n",
+    "    \"positive total particle concentration\"\n",
+    "] = pybamm.particle.TotalConcentration(model.param, \"positive\", options)"
    ]
   },
   {
@@ -457,14 +469,10 @@
     "] = pybamm.open_circuit_potential.SingleOpenCircuitPotential(\n",
     "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
     ")\n",
-    "model.submodels[\n",
-    "    \"negative interface\"\n",
-    "] = pybamm.kinetics.InverseButlerVolmer(\n",
+    "model.submodels[\"negative interface\"] = pybamm.kinetics.InverseButlerVolmer(\n",
     "    model.param, \"negative\", \"lithium-ion main\", options=model.options\n",
     ")\n",
-    "model.submodels[\n",
-    "    \"positive interface\"\n",
-    "] = pybamm.kinetics.InverseButlerVolmer(\n",
+    "model.submodels[\"positive interface\"] = pybamm.kinetics.InverseButlerVolmer(\n",
     "    model.param, \"positive\", \"lithium-ion main\", options=model.options\n",
     ")\n",
     "model.submodels[\n",
@@ -498,18 +506,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels[\n",
-    "    \"Negative particle mechanics\"\n",
-    "] = pybamm.particle_mechanics.NoMechanics(model.param, \"negative\", model.options)\n",
-    "model.submodels[\n",
-    "    \"Positive particle mechanics\"\n",
-    "] = pybamm.particle_mechanics.NoMechanics(model.param, \"positive\", model.options)\n",
-    "model.submodels[\"Negative sei\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options)\n",
-    "model.submodels[\"Positive sei\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options)\n",
-    "model.submodels[\"Negative sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"negative\", model.options, cracks=True)\n",
-    "model.submodels[\"Positive sei on cracks\"] = pybamm.sei.NoSEI(model.param, \"positive\", model.options, cracks=True)\n",
-    "model.submodels[\"Negative lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Negative\")\n",
-    "model.submodels[\"Positive lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param, \"Positive\")"
+    "model.submodels[\"Negative particle mechanics\"] = pybamm.particle_mechanics.NoMechanics(\n",
+    "    model.param, \"negative\", model.options\n",
+    ")\n",
+    "model.submodels[\"Positive particle mechanics\"] = pybamm.particle_mechanics.NoMechanics(\n",
+    "    model.param, \"positive\", model.options\n",
+    ")\n",
+    "model.submodels[\"Negative sei\"] = pybamm.sei.NoSEI(\n",
+    "    model.param, \"negative\", model.options\n",
+    ")\n",
+    "model.submodels[\"Positive sei\"] = pybamm.sei.NoSEI(\n",
+    "    model.param, \"positive\", model.options\n",
+    ")\n",
+    "model.submodels[\"Negative sei on cracks\"] = pybamm.sei.NoSEI(\n",
+    "    model.param, \"negative\", model.options, cracks=True\n",
+    ")\n",
+    "model.submodels[\"Positive sei on cracks\"] = pybamm.sei.NoSEI(\n",
+    "    model.param, \"positive\", model.options, cracks=True\n",
+    ")\n",
+    "model.submodels[\"Negative lithium plating\"] = pybamm.lithium_plating.NoPlating(\n",
+    "    model.param, \"Negative\"\n",
+    ")\n",
+    "model.submodels[\"Positive lithium plating\"] = pybamm.lithium_plating.NoPlating(\n",
+    "    model.param, \"Positive\"\n",
+    ")"
    ]
   },
   {
@@ -525,12 +545,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels[\"electrolyte diffusion\"] = pybamm.electrolyte_diffusion.ConstantConcentration(\n",
-    "    model.param\n",
-    ")\n",
-    "model.submodels[\"electrolyte conductivity\"] = pybamm.electrolyte_conductivity.LeadingOrder(\n",
-    "    model.param\n",
-    ")"
+    "model.submodels[\n",
+    "    \"electrolyte diffusion\"\n",
+    "] = pybamm.electrolyte_diffusion.ConstantConcentration(model.param)\n",
+    "model.submodels[\n",
+    "    \"electrolyte conductivity\"\n",
+    "] = pybamm.electrolyte_conductivity.LeadingOrder(model.param)"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
index 4b3ef7846e..28fc476e16 100644
--- a/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
+++ b/docs/source/examples/notebooks/parameterization/change-input-current.ipynb
@@ -45,7 +45,8 @@
     "import pybamm\n",
     "import numpy as np\n",
     "import os\n",
-    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
     "\n",
     "# create the model\n",
     "model = pybamm.lithium_ion.DFN()\n",
@@ -151,12 +152,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd    # needed to read the csv data file\n",
+    "import pandas as pd  # needed to read the csv data file\n",
     "\n",
     "model = pybamm.lithium_ion.DFN()\n",
     "\n",
     "# import drive cycle from file\n",
-    "drive_cycle = pd.read_csv(\"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None).to_numpy()\n",
+    "drive_cycle = pd.read_csv(\n",
+    "    \"pybamm/input/drive_cycles/US06.csv\", comment=\"#\", header=None\n",
+    ").to_numpy()\n",
     "\n",
     "# load parameter values\n",
     "param = model.default_parameter_values\n",
@@ -267,7 +270,7 @@
     "# set user defined current function\n",
     "A = model.param.I_typ\n",
     "omega = 0.1\n",
-    "param[\"Current function [A]\"] = my_fun(A,omega)"
+    "param[\"Current function [A]\"] = my_fun(A, omega)"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
index 6d2b6f707f..b13084b166 100644
--- a/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameter-values.ipynb
@@ -36,7 +36,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -93,8 +94,10 @@
    ],
    "source": [
     "chem_parameter_values = pybamm.ParameterValues(\"Marquis2019\")\n",
-    "print(\"Negative current collector thickness is {} m\".format(\n",
-    "    chem_parameter_values[\"Negative current collector thickness [m]\"])\n",
+    "print(\n",
+    "    \"Negative current collector thickness is {} m\".format(\n",
+    "        chem_parameter_values[\"Negative current collector thickness [m]\"]\n",
+    "    )\n",
     ")"
    ]
   },
@@ -127,7 +130,7 @@
    ],
    "source": [
     "def cubed(x):\n",
-    "    return x ** 3\n",
+    "    return x**3\n",
     "\n",
     "\n",
     "parameter_values.update({\"cube function\": cubed}, check_already_exists=False)\n",
@@ -325,9 +328,9 @@
     "###################################################################\n",
     "\n",
     "# Plot\n",
-    "t_fine = np.linspace(0,t_eval[-1],1000)\n",
+    "t_fine = np.linspace(0, t_eval[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "ax1.plot(t_fine, 2 * np.exp(-3 * t_fine), t_sol1, u1(t_sol1), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
     "ax1.legend([\"2 * exp(-3 * t)\", \"u1\"], loc=\"best\")\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index 50be5e8ed9..9c060ed1ff 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -76,7 +76,9 @@
     "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
     "\n",
     "R = pybamm.Parameter(\"Particle radius [m]\")\n",
-    "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n",
+    "D = pybamm.FunctionParameter(\n",
+    "    \"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c}\n",
+    ")\n",
     "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n",
     "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n",
     "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")"
@@ -106,14 +108,14 @@
     "# governing equations\n",
     "N = -D * pybamm.grad(c)  # flux\n",
     "dcdt = -pybamm.div(N)\n",
-    "model.rhs = {c: dcdt}  \n",
+    "model.rhs = {c: dcdt}\n",
     "\n",
-    "# boundary conditions \n",
+    "# boundary conditions\n",
     "lbc = pybamm.Scalar(0)\n",
     "rbc = -j\n",
     "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n",
     "\n",
-    "# initial conditions \n",
+    "# initial conditions\n",
     "model.initial_conditions = {c: c0}\n",
     "\n",
     "model.variables = {\n",
@@ -142,8 +144,12 @@
    },
    "outputs": [],
    "source": [
-    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
-    "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})"
+    "r = pybamm.SpatialVariable(\n",
+    "    \"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\"\n",
+    ")\n",
+    "geometry = pybamm.Geometry(\n",
+    "    {\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}}\n",
+    ")"
    ]
   },
   {
@@ -220,7 +226,7 @@
    "outputs": [],
    "source": [
     "def D_fun(c):\n",
-    "    return 3.9 #* pybamm.exp(-c)\n",
+    "    return 3.9  # * pybamm.exp(-c)\n",
     "\n",
     "\n",
     "values = {\n",
@@ -319,11 +325,11 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "scrolled": true,
     "ExecuteTime": {
      "end_time": "2023-12-10T12:14:18.891821400Z",
      "start_time": "2023-12-10T12:14:18.864911Z"
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
     {
@@ -411,8 +417,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1300x400 with 2 Axes>",
-      "image/png": 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"
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",
+      "text/plain": "<Figure size 1300x400 with 2 Axes>"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -436,7 +442,7 @@
     "ax1.set_xlabel(\"Time [s]\")\n",
     "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n",
     "\n",
-    "rsol = mesh[\"negative particle\"].nodes # radial position\n",
+    "rsol = mesh[\"negative particle\"].nodes  # radial position\n",
     "time = 1000  # time in seconds\n",
     "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=f\"t={time}[s]\")\n",
     "ax2.set_xlabel(\"Particle radius [microns]\")\n",
@@ -566,11 +572,11 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {
-    "scrolled": true,
     "ExecuteTime": {
      "end_time": "2023-12-10T12:14:19.401195400Z",
      "start_time": "2023-12-10T12:14:19.232194200Z"
-    }
+    },
+    "scrolled": true
    },
    "outputs": [
     {
@@ -583,7 +589,7 @@
     }
    ],
    "source": [
-    "{k: v for k,v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}"
+    "{k: v for k, v in spm.default_parameter_values.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -621,495 +627,503 @@
     "    return D_ref * arrhenius\n",
     "\n",
     "\n",
-    "neg_ocp = np.array([[0.        , 1.81772748],\n",
-    "         [0.03129623, 1.0828807 ],\n",
-    "         [0.03499902, 0.99593794],\n",
-    "         [0.0387018 , 0.90023398],\n",
-    "         [0.04240458, 0.79649431],\n",
-    "         [0.04610736, 0.73354429],\n",
-    "         [0.04981015, 0.66664314],\n",
-    "         [0.05351292, 0.64137149],\n",
-    "         [0.05721568, 0.59813869],\n",
-    "         [0.06091845, 0.5670836 ],\n",
-    "         [0.06462122, 0.54746181],\n",
-    "         [0.06832399, 0.53068399],\n",
-    "         [0.07202675, 0.51304734],\n",
-    "         [0.07572951, 0.49394092],\n",
-    "         [0.07943227, 0.47926274],\n",
-    "         [0.08313503, 0.46065259],\n",
-    "         [0.08683779, 0.45992726],\n",
-    "         [0.09054054, 0.43801501],\n",
-    "         [0.09424331, 0.42438665],\n",
-    "         [0.09794607, 0.41150269],\n",
-    "         [0.10164883, 0.40033659],\n",
-    "         [0.10535158, 0.38957134],\n",
-    "         [0.10905434, 0.37756538],\n",
-    "         [0.1127571 , 0.36292541],\n",
-    "         [0.11645985, 0.34357086],\n",
-    "         [0.12016261, 0.3406314 ],\n",
-    "         [0.12386536, 0.32299468],\n",
-    "         [0.12756811, 0.31379458],\n",
-    "         [0.13127086, 0.30795386],\n",
-    "         [0.13497362, 0.29207319],\n",
-    "         [0.13867638, 0.28697687],\n",
-    "         [0.14237913, 0.27405477],\n",
-    "         [0.14608189, 0.2670497 ],\n",
-    "         [0.14978465, 0.25857493],\n",
-    "         [0.15348741, 0.25265783],\n",
-    "         [0.15719018, 0.24826777],\n",
-    "         [0.16089294, 0.2414345 ],\n",
-    "         [0.1645957 , 0.23362778],\n",
-    "         [0.16829847, 0.22956218],\n",
-    "         [0.17200122, 0.22370236],\n",
-    "         [0.17570399, 0.22181271],\n",
-    "         [0.17940674, 0.22089651],\n",
-    "         [0.1831095 , 0.2194268 ],\n",
-    "         [0.18681229, 0.21830064],\n",
-    "         [0.19051504, 0.21845333],\n",
-    "         [0.1942178 , 0.21753715],\n",
-    "         [0.19792056, 0.21719357],\n",
-    "         [0.20162334, 0.21635373],\n",
-    "         [0.2053261 , 0.21667822],\n",
-    "         [0.20902886, 0.21738444],\n",
-    "         [0.21273164, 0.21469313],\n",
-    "         [0.2164344 , 0.21541846],\n",
-    "         [0.22013716, 0.21465495],\n",
-    "         [0.22383993, 0.2135479 ],\n",
-    "         [0.2275427 , 0.21392964],\n",
-    "         [0.23124547, 0.21074206],\n",
-    "         [0.23494825, 0.20873788],\n",
-    "         [0.23865101, 0.20465319],\n",
-    "         [0.24235377, 0.20205732],\n",
-    "         [0.24605653, 0.19774358],\n",
-    "         [0.2497593 , 0.19444147],\n",
-    "         [0.25346208, 0.19190285],\n",
-    "         [0.25716486, 0.18850531],\n",
-    "         [0.26086762, 0.18581399],\n",
-    "         [0.26457039, 0.18327537],\n",
-    "         [0.26827314, 0.18157659],\n",
-    "         [0.2719759 , 0.17814088],\n",
-    "         [0.27567867, 0.17529686],\n",
-    "         [0.27938144, 0.1719375 ],\n",
-    "         [0.28308421, 0.16934161],\n",
-    "         [0.28678698, 0.16756649],\n",
-    "         [0.29048974, 0.16609676],\n",
-    "         [0.29419251, 0.16414985],\n",
-    "         [0.29789529, 0.16260378],\n",
-    "         [0.30159806, 0.16224113],\n",
-    "         [0.30530083, 0.160027  ],\n",
-    "         [0.30900361, 0.15827096],\n",
-    "         [0.31270637, 0.1588054 ],\n",
-    "         [0.31640913, 0.15552238],\n",
-    "         [0.32011189, 0.15580869],\n",
-    "         [0.32381466, 0.15220118],\n",
-    "         [0.32751744, 0.1511132 ],\n",
-    "         [0.33122021, 0.14987253],\n",
-    "         [0.33492297, 0.14874637],\n",
-    "         [0.33862575, 0.14678037],\n",
-    "         [0.34232853, 0.14620776],\n",
-    "         [0.34603131, 0.14555879],\n",
-    "         [0.34973408, 0.14389819],\n",
-    "         [0.35343685, 0.14359279],\n",
-    "         [0.35713963, 0.14242846],\n",
-    "         [0.36084241, 0.14038612],\n",
-    "         [0.36454517, 0.13882096],\n",
-    "         [0.36824795, 0.13954628],\n",
-    "         [0.37195071, 0.13946992],\n",
-    "         [0.37565348, 0.13780934],\n",
-    "         [0.37935626, 0.13973714],\n",
-    "         [0.38305904, 0.13698858],\n",
-    "         [0.38676182, 0.13523254],\n",
-    "         [0.3904646 , 0.13441178],\n",
-    "         [0.39416737, 0.1352898 ],\n",
-    "         [0.39787015, 0.13507985],\n",
-    "         [0.40157291, 0.13647321],\n",
-    "         [0.40527567, 0.13601512],\n",
-    "         [0.40897844, 0.13435452],\n",
-    "         [0.41268121, 0.1334765 ],\n",
-    "         [0.41638398, 0.1348317 ],\n",
-    "         [0.42008676, 0.13275118],\n",
-    "         [0.42378953, 0.13286571],\n",
-    "         [0.4274923 , 0.13263667],\n",
-    "         [0.43119506, 0.13456447],\n",
-    "         [0.43489784, 0.13471718],\n",
-    "         [0.43860061, 0.13395369],\n",
-    "         [0.44230338, 0.13448814],\n",
-    "         [0.44600615, 0.1334765 ],\n",
-    "         [0.44970893, 0.13298023],\n",
-    "         [0.45341168, 0.13259849],\n",
-    "         [0.45711444, 0.13338107],\n",
-    "         [0.46081719, 0.13309476],\n",
-    "         [0.46451994, 0.13275118],\n",
-    "         [0.46822269, 0.13443087],\n",
-    "         [0.47192545, 0.13315202],\n",
-    "         [0.47562821, 0.132713  ],\n",
-    "         [0.47933098, 0.1330184 ],\n",
-    "         [0.48303375, 0.13278936],\n",
-    "         [0.48673651, 0.13225491],\n",
-    "         [0.49043926, 0.13317111],\n",
-    "         [0.49414203, 0.13263667],\n",
-    "         [0.49784482, 0.13187316],\n",
-    "         [0.50154759, 0.13265574],\n",
-    "         [0.50525036, 0.13250305],\n",
-    "         [0.50895311, 0.13324745],\n",
-    "         [0.51265586, 0.13204496],\n",
-    "         [0.51635861, 0.13242669],\n",
-    "         [0.52006139, 0.13233127],\n",
-    "         [0.52376415, 0.13198769],\n",
-    "         [0.52746692, 0.13254122],\n",
-    "         [0.53116969, 0.13145325],\n",
-    "         [0.53487245, 0.13298023],\n",
-    "         [0.53857521, 0.13168229],\n",
-    "         [0.54227797, 0.1313578 ],\n",
-    "         [0.54598074, 0.13235036],\n",
-    "         [0.5496835 , 0.13120511],\n",
-    "         [0.55338627, 0.13089971],\n",
-    "         [0.55708902, 0.13109058],\n",
-    "         [0.56079178, 0.13082336],\n",
-    "         [0.56449454, 0.13011713],\n",
-    "         [0.5681973 , 0.129869  ],\n",
-    "         [0.57190006, 0.12992626],\n",
-    "         [0.57560282, 0.12942998],\n",
-    "         [0.57930558, 0.12796026],\n",
-    "         [0.58300835, 0.12862831],\n",
-    "         [0.58671112, 0.12656689],\n",
-    "         [0.59041389, 0.12734947],\n",
-    "         [0.59411664, 0.12509716],\n",
-    "         [0.59781941, 0.12110791],\n",
-    "         [0.60152218, 0.11839751],\n",
-    "         [0.60522496, 0.11244226],\n",
-    "         [0.60892772, 0.11307214],\n",
-    "         [0.61263048, 0.1092165 ],\n",
-    "         [0.61633325, 0.10683058],\n",
-    "         [0.62003603, 0.10433014],\n",
-    "         [0.6237388 , 0.10530359],\n",
-    "         [0.62744156, 0.10056993],\n",
-    "         [0.63114433, 0.09950104],\n",
-    "         [0.63484711, 0.09854668],\n",
-    "         [0.63854988, 0.09921473],\n",
-    "         [0.64225265, 0.09541635],\n",
-    "         [0.64595543, 0.09980643],\n",
-    "         [0.64965823, 0.0986612 ],\n",
-    "         [0.653361  , 0.09560722],\n",
-    "         [0.65706377, 0.09755413],\n",
-    "         [0.66076656, 0.09612258],\n",
-    "         [0.66446934, 0.09430929],\n",
-    "         [0.66817212, 0.09661885],\n",
-    "         [0.67187489, 0.09366032],\n",
-    "         [0.67557767, 0.09522548],\n",
-    "         [0.67928044, 0.09535909],\n",
-    "         [0.68298322, 0.09316404],\n",
-    "         [0.686686  , 0.09450016],\n",
-    "         [0.69038878, 0.0930877 ],\n",
-    "         [0.69409156, 0.09343126],\n",
-    "         [0.69779433, 0.0932404 ],\n",
-    "         [0.70149709, 0.09350762],\n",
-    "         [0.70519988, 0.09339309],\n",
-    "         [0.70890264, 0.09291591],\n",
-    "         [0.7126054 , 0.09303043],\n",
-    "         [0.71630818, 0.0926296 ],\n",
-    "         [0.72001095, 0.0932404 ],\n",
-    "         [0.72371371, 0.09261052],\n",
-    "         [0.72741648, 0.09249599],\n",
-    "         [0.73111925, 0.09240055],\n",
-    "         [0.73482204, 0.09253416],\n",
-    "         [0.7385248 , 0.09209515],\n",
-    "         [0.74222757, 0.09234329],\n",
-    "         [0.74593034, 0.09366032],\n",
-    "         [0.74963312, 0.09333583],\n",
-    "         [0.75333589, 0.09322131],\n",
-    "         [0.75703868, 0.09264868],\n",
-    "         [0.76074146, 0.09253416],\n",
-    "         [0.76444422, 0.09243873],\n",
-    "         [0.76814698, 0.09230512],\n",
-    "         [0.77184976, 0.09310678],\n",
-    "         [0.77555253, 0.09165615],\n",
-    "         [0.77925531, 0.09159888],\n",
-    "         [0.78295807, 0.09207606],\n",
-    "         [0.78666085, 0.09175158],\n",
-    "         [0.79036364, 0.09177067],\n",
-    "         [0.79406641, 0.09236237],\n",
-    "         [0.79776918, 0.09241964],\n",
-    "         [0.80147197, 0.09320222],\n",
-    "         [0.80517474, 0.09199972],\n",
-    "         [0.80887751, 0.09167523],\n",
-    "         [0.81258028, 0.09322131],\n",
-    "         [0.81628304, 0.09190428],\n",
-    "         [0.81998581, 0.09167523],\n",
-    "         [0.82368858, 0.09285865],\n",
-    "         [0.82739136, 0.09180884],\n",
-    "         [0.83109411, 0.09150345],\n",
-    "         [0.83479688, 0.09186611],\n",
-    "         [0.83849965, 0.0920188 ],\n",
-    "         [0.84220242, 0.09320222],\n",
-    "         [0.84590519, 0.09131257],\n",
-    "         [0.84960797, 0.09117896],\n",
-    "         [0.85331075, 0.09133166],\n",
-    "         [0.85701353, 0.09089265],\n",
-    "         [0.86071631, 0.09058725],\n",
-    "         [0.86441907, 0.09051091],\n",
-    "         [0.86812186, 0.09033912],\n",
-    "         [0.87182464, 0.09041547],\n",
-    "         [0.87552742, 0.0911217 ],\n",
-    "         [0.87923019, 0.0894611 ],\n",
-    "         [0.88293296, 0.08999555],\n",
-    "         [0.88663573, 0.08921297],\n",
-    "         [0.89033849, 0.08881213],\n",
-    "         [0.89404126, 0.08797229],\n",
-    "         [0.89774404, 0.08709427],\n",
-    "         [0.9014468 , 0.08503284],\n",
-    "         [1.        , 0.07601531]])\n",
+    "neg_ocp = np.array(\n",
+    "    [\n",
+    "        [0.0, 1.81772748],\n",
+    "        [0.03129623, 1.0828807],\n",
+    "        [0.03499902, 0.99593794],\n",
+    "        [0.0387018, 0.90023398],\n",
+    "        [0.04240458, 0.79649431],\n",
+    "        [0.04610736, 0.73354429],\n",
+    "        [0.04981015, 0.66664314],\n",
+    "        [0.05351292, 0.64137149],\n",
+    "        [0.05721568, 0.59813869],\n",
+    "        [0.06091845, 0.5670836],\n",
+    "        [0.06462122, 0.54746181],\n",
+    "        [0.06832399, 0.53068399],\n",
+    "        [0.07202675, 0.51304734],\n",
+    "        [0.07572951, 0.49394092],\n",
+    "        [0.07943227, 0.47926274],\n",
+    "        [0.08313503, 0.46065259],\n",
+    "        [0.08683779, 0.45992726],\n",
+    "        [0.09054054, 0.43801501],\n",
+    "        [0.09424331, 0.42438665],\n",
+    "        [0.09794607, 0.41150269],\n",
+    "        [0.10164883, 0.40033659],\n",
+    "        [0.10535158, 0.38957134],\n",
+    "        [0.10905434, 0.37756538],\n",
+    "        [0.1127571, 0.36292541],\n",
+    "        [0.11645985, 0.34357086],\n",
+    "        [0.12016261, 0.3406314],\n",
+    "        [0.12386536, 0.32299468],\n",
+    "        [0.12756811, 0.31379458],\n",
+    "        [0.13127086, 0.30795386],\n",
+    "        [0.13497362, 0.29207319],\n",
+    "        [0.13867638, 0.28697687],\n",
+    "        [0.14237913, 0.27405477],\n",
+    "        [0.14608189, 0.2670497],\n",
+    "        [0.14978465, 0.25857493],\n",
+    "        [0.15348741, 0.25265783],\n",
+    "        [0.15719018, 0.24826777],\n",
+    "        [0.16089294, 0.2414345],\n",
+    "        [0.1645957, 0.23362778],\n",
+    "        [0.16829847, 0.22956218],\n",
+    "        [0.17200122, 0.22370236],\n",
+    "        [0.17570399, 0.22181271],\n",
+    "        [0.17940674, 0.22089651],\n",
+    "        [0.1831095, 0.2194268],\n",
+    "        [0.18681229, 0.21830064],\n",
+    "        [0.19051504, 0.21845333],\n",
+    "        [0.1942178, 0.21753715],\n",
+    "        [0.19792056, 0.21719357],\n",
+    "        [0.20162334, 0.21635373],\n",
+    "        [0.2053261, 0.21667822],\n",
+    "        [0.20902886, 0.21738444],\n",
+    "        [0.21273164, 0.21469313],\n",
+    "        [0.2164344, 0.21541846],\n",
+    "        [0.22013716, 0.21465495],\n",
+    "        [0.22383993, 0.2135479],\n",
+    "        [0.2275427, 0.21392964],\n",
+    "        [0.23124547, 0.21074206],\n",
+    "        [0.23494825, 0.20873788],\n",
+    "        [0.23865101, 0.20465319],\n",
+    "        [0.24235377, 0.20205732],\n",
+    "        [0.24605653, 0.19774358],\n",
+    "        [0.2497593, 0.19444147],\n",
+    "        [0.25346208, 0.19190285],\n",
+    "        [0.25716486, 0.18850531],\n",
+    "        [0.26086762, 0.18581399],\n",
+    "        [0.26457039, 0.18327537],\n",
+    "        [0.26827314, 0.18157659],\n",
+    "        [0.2719759, 0.17814088],\n",
+    "        [0.27567867, 0.17529686],\n",
+    "        [0.27938144, 0.1719375],\n",
+    "        [0.28308421, 0.16934161],\n",
+    "        [0.28678698, 0.16756649],\n",
+    "        [0.29048974, 0.16609676],\n",
+    "        [0.29419251, 0.16414985],\n",
+    "        [0.29789529, 0.16260378],\n",
+    "        [0.30159806, 0.16224113],\n",
+    "        [0.30530083, 0.160027],\n",
+    "        [0.30900361, 0.15827096],\n",
+    "        [0.31270637, 0.1588054],\n",
+    "        [0.31640913, 0.15552238],\n",
+    "        [0.32011189, 0.15580869],\n",
+    "        [0.32381466, 0.15220118],\n",
+    "        [0.32751744, 0.1511132],\n",
+    "        [0.33122021, 0.14987253],\n",
+    "        [0.33492297, 0.14874637],\n",
+    "        [0.33862575, 0.14678037],\n",
+    "        [0.34232853, 0.14620776],\n",
+    "        [0.34603131, 0.14555879],\n",
+    "        [0.34973408, 0.14389819],\n",
+    "        [0.35343685, 0.14359279],\n",
+    "        [0.35713963, 0.14242846],\n",
+    "        [0.36084241, 0.14038612],\n",
+    "        [0.36454517, 0.13882096],\n",
+    "        [0.36824795, 0.13954628],\n",
+    "        [0.37195071, 0.13946992],\n",
+    "        [0.37565348, 0.13780934],\n",
+    "        [0.37935626, 0.13973714],\n",
+    "        [0.38305904, 0.13698858],\n",
+    "        [0.38676182, 0.13523254],\n",
+    "        [0.3904646, 0.13441178],\n",
+    "        [0.39416737, 0.1352898],\n",
+    "        [0.39787015, 0.13507985],\n",
+    "        [0.40157291, 0.13647321],\n",
+    "        [0.40527567, 0.13601512],\n",
+    "        [0.40897844, 0.13435452],\n",
+    "        [0.41268121, 0.1334765],\n",
+    "        [0.41638398, 0.1348317],\n",
+    "        [0.42008676, 0.13275118],\n",
+    "        [0.42378953, 0.13286571],\n",
+    "        [0.4274923, 0.13263667],\n",
+    "        [0.43119506, 0.13456447],\n",
+    "        [0.43489784, 0.13471718],\n",
+    "        [0.43860061, 0.13395369],\n",
+    "        [0.44230338, 0.13448814],\n",
+    "        [0.44600615, 0.1334765],\n",
+    "        [0.44970893, 0.13298023],\n",
+    "        [0.45341168, 0.13259849],\n",
+    "        [0.45711444, 0.13338107],\n",
+    "        [0.46081719, 0.13309476],\n",
+    "        [0.46451994, 0.13275118],\n",
+    "        [0.46822269, 0.13443087],\n",
+    "        [0.47192545, 0.13315202],\n",
+    "        [0.47562821, 0.132713],\n",
+    "        [0.47933098, 0.1330184],\n",
+    "        [0.48303375, 0.13278936],\n",
+    "        [0.48673651, 0.13225491],\n",
+    "        [0.49043926, 0.13317111],\n",
+    "        [0.49414203, 0.13263667],\n",
+    "        [0.49784482, 0.13187316],\n",
+    "        [0.50154759, 0.13265574],\n",
+    "        [0.50525036, 0.13250305],\n",
+    "        [0.50895311, 0.13324745],\n",
+    "        [0.51265586, 0.13204496],\n",
+    "        [0.51635861, 0.13242669],\n",
+    "        [0.52006139, 0.13233127],\n",
+    "        [0.52376415, 0.13198769],\n",
+    "        [0.52746692, 0.13254122],\n",
+    "        [0.53116969, 0.13145325],\n",
+    "        [0.53487245, 0.13298023],\n",
+    "        [0.53857521, 0.13168229],\n",
+    "        [0.54227797, 0.1313578],\n",
+    "        [0.54598074, 0.13235036],\n",
+    "        [0.5496835, 0.13120511],\n",
+    "        [0.55338627, 0.13089971],\n",
+    "        [0.55708902, 0.13109058],\n",
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+    "        [0.35870932, 4.1634517],\n",
+    "        [0.3614318, 4.1598248],\n",
+    "        [0.36415428, 4.1571712],\n",
+    "        [0.36687674, 4.154079],\n",
+    "        [0.36959921, 4.1504135],\n",
+    "        [0.37232169, 4.1466532],\n",
+    "        [0.37504418, 4.1423388],\n",
+    "        [0.37776665, 4.1382346],\n",
+    "        [0.38048913, 4.1338248],\n",
+    "        [0.38321161, 4.1305799],\n",
+    "        [0.38593408, 4.1272392],\n",
+    "        [0.38865655, 4.1228104],\n",
+    "        [0.39137903, 4.1186109],\n",
+    "        [0.39410151, 4.114182],\n",
+    "        [0.39682398, 4.1096005],\n",
+    "        [0.39954645, 4.1046948],\n",
+    "        [0.40226892, 4.1004758],\n",
+    "        [0.4049914, 4.0956464],\n",
+    "        [0.40771387, 4.0909696],\n",
+    "        [0.41043634, 4.0864644],\n",
+    "        [0.41315882, 4.0818448],\n",
+    "        [0.41588129, 4.077683],\n",
+    "        [0.41860377, 4.0733309],\n",
+    "        [0.42132624, 4.0690737],\n",
+    "        [0.42404872, 4.0647216],\n",
+    "        [0.4267712, 4.0608654],\n",
+    "        [0.42949368, 4.0564747],\n",
+    "        [0.43221616, 4.0527525],\n",
+    "        [0.43493864, 4.0492401],\n",
+    "        [0.43766111, 4.0450211],\n",
+    "        [0.44038359, 4.041986],\n",
+    "        [0.44310607, 4.0384736],\n",
+    "        [0.44582856, 4.035171],\n",
+    "        [0.44855103, 4.0320406],\n",
+    "        [0.45127351, 4.0289288],\n",
+    "        [0.453996, 4.02597],\n",
+    "        [0.45671848, 4.0227437],\n",
+    "        [0.45944095, 4.0199757],\n",
+    "        [0.46216343, 4.0175133],\n",
+    "        [0.46488592, 4.0149746],\n",
+    "        [0.46760838, 4.0122066],\n",
+    "        [0.47033085, 4.009954],\n",
+    "        [0.47305333, 4.0075679],\n",
+    "        [0.47577581, 4.0050669],\n",
+    "        [0.47849828, 4.0023184],\n",
+    "        [0.48122074, 3.9995501],\n",
+    "        [0.48394321, 3.9969349],\n",
+    "        [0.48666569, 3.9926589],\n",
+    "        [0.48938816, 3.9889555],\n",
+    "        [0.49211064, 3.9834003],\n",
+    "        [0.4948331, 3.9783037],\n",
+    "        [0.49755557, 3.9755929],\n",
+    "        [0.50027804, 3.9707632],\n",
+    "        [0.50300052, 3.9681098],\n",
+    "        [0.50572298, 3.9635665],\n",
+    "        [0.50844545, 3.9594433],\n",
+    "        [0.51116792, 3.9556634],\n",
+    "        [0.51389038, 3.9521511],\n",
+    "        [0.51661284, 3.9479132],\n",
+    "        [0.51933531, 3.9438281],\n",
+    "        [0.52205777, 3.9400866],\n",
+    "        [0.52478024, 3.9362304],\n",
+    "        [0.52750271, 3.9314201],\n",
+    "        [0.53022518, 3.9283848],\n",
+    "        [0.53294765, 3.9242232],\n",
+    "        [0.53567012, 3.9192028],\n",
+    "        [0.53839258, 3.9166257],\n",
+    "        [0.54111506, 3.9117961],\n",
+    "        [0.54383753, 3.90815],\n",
+    "        [0.54656, 3.9038739],\n",
+    "        [0.54928247, 3.8995597],\n",
+    "        [0.55200494, 3.8959136],\n",
+    "        [0.5547274, 3.8909314],\n",
+    "        [0.55744986, 3.8872662],\n",
+    "        [0.56017233, 3.8831048],\n",
+    "        [0.5628948, 3.8793442],\n",
+    "        [0.56561729, 3.8747628],\n",
+    "        [0.56833976, 3.8702576],\n",
+    "        [0.57106222, 3.8666878],\n",
+    "        [0.57378469, 3.8623927],\n",
+    "        [0.57650716, 3.8581741],\n",
+    "        [0.57922963, 3.854146],\n",
+    "        [0.5819521, 3.8499846],\n",
+    "        [0.58467456, 3.8450022],\n",
+    "        [0.58739702, 3.8422534],\n",
+    "        [0.59011948, 3.8380919],\n",
+    "        [0.59284194, 3.8341596],\n",
+    "        [0.5955644, 3.8309333],\n",
+    "        [0.59828687, 3.8272109],\n",
+    "        [0.60100935, 3.823164],\n",
+    "        [0.60373182, 3.8192315],\n",
+    "        [0.60645429, 3.8159864],\n",
+    "        [0.60917677, 3.8123021],\n",
+    "        [0.61189925, 3.8090379],\n",
+    "        [0.61462172, 3.8071671],\n",
+    "        [0.61734419, 3.8040555],\n",
+    "        [0.62006666, 3.8013639],\n",
+    "        [0.62278914, 3.7970879],\n",
+    "        [0.62551162, 3.7953317],\n",
+    "        [0.62823408, 3.7920673],\n",
+    "        [0.63095656, 3.788383],\n",
+    "        [0.63367903, 3.7855389],\n",
+    "        [0.6364015, 3.7838206],\n",
+    "        [0.63912397, 3.78111],\n",
+    "        [0.64184645, 3.7794874],\n",
+    "        [0.64456893, 3.7769294],\n",
+    "        [0.6472914, 3.773608],\n",
+    "        [0.65001389, 3.7695992],\n",
+    "        [0.65273637, 3.7690265],\n",
+    "        [0.65545884, 3.7662776],\n",
+    "        [0.65818131, 3.7642922],\n",
+    "        [0.66090379, 3.7626889],\n",
+    "        [0.66362625, 3.7603791],\n",
+    "        [0.66634874, 3.7575538],\n",
+    "        [0.66907121, 3.7552056],\n",
+    "        [0.67179369, 3.7533159],\n",
+    "        [0.67451616, 3.7507198],\n",
+    "        [0.67723865, 3.7487535],\n",
+    "        [0.67996113, 3.7471499],\n",
+    "        [0.68268361, 3.7442865],\n",
+    "        [0.68540608, 3.7423012],\n",
+    "        [0.68812855, 3.7400677],\n",
+    "        [0.69085103, 3.7385788],\n",
+    "        [0.6935735, 3.7345319],\n",
+    "        [0.69629597, 3.7339211],\n",
+    "        [0.69901843, 3.7301605],\n",
+    "        [0.7017409, 3.7301033],\n",
+    "        [0.70446338, 3.7278316],\n",
+    "        [0.70718585, 3.7251589],\n",
+    "        [0.70990833, 3.723861],\n",
+    "        [0.71263081, 3.7215703],\n",
+    "        [0.71535328, 3.7191267],\n",
+    "        [0.71807574, 3.7172751],\n",
+    "        [0.72079822, 3.7157097],\n",
+    "        [0.72352069, 3.7130945],\n",
+    "        [0.72624317, 3.7099447],\n",
+    "        [0.72896564, 3.7071004],\n",
+    "        [0.7316881, 3.7045615],\n",
+    "        [0.73441057, 3.703588],\n",
+    "        [0.73713303, 3.70208],\n",
+    "        [0.73985551, 3.7002664],\n",
+    "        [0.74257799, 3.6972122],\n",
+    "        [0.74530047, 3.6952841],\n",
+    "        [0.74802293, 3.6929362],\n",
+    "        [0.7507454, 3.6898055],\n",
+    "        [0.75346787, 3.6890991],\n",
+    "        [0.75619034, 3.686522],\n",
+    "        [0.75891281, 3.6849759],\n",
+    "        [0.76163529, 3.6821697],\n",
+    "        [0.76435776, 3.6808143],\n",
+    "        [0.76708024, 3.6786573],\n",
+    "        [0.7698027, 3.6761947],\n",
+    "        [0.77252517, 3.674763],\n",
+    "        [0.77524765, 3.6712887],\n",
+    "        [0.77797012, 3.6697233],\n",
+    "        [0.78069258, 3.6678908],\n",
+    "        [0.78341506, 3.6652565],\n",
+    "        [0.78613753, 3.6630611],\n",
+    "        [0.78885999, 3.660274],\n",
+    "        [0.79158246, 3.6583652],\n",
+    "        [0.79430494, 3.6554828],\n",
+    "        [0.79702741, 3.6522949],\n",
+    "        [0.79974987, 3.6499848],\n",
+    "        [0.80247234, 3.6470451],\n",
+    "        [0.8051948, 3.6405547],\n",
+    "        [0.80791727, 3.6383405],\n",
+    "        [0.81063974, 3.635076],\n",
+    "        [0.81336221, 3.633549],\n",
+    "        [0.81608468, 3.6322317],\n",
+    "        [0.81880714, 3.6306856],\n",
+    "        [0.82152961, 3.6283948],\n",
+    "        [0.82425208, 3.6268487],\n",
+    "        [0.82697453, 3.6243098],\n",
+    "        [0.829697, 3.6223626],\n",
+    "        [0.83241946, 3.6193655],\n",
+    "        [0.83514192, 3.6177621],\n",
+    "        [0.83786439, 3.6158531],\n",
+    "        [0.84058684, 3.6128371],\n",
+    "        [0.84330931, 3.6118062],\n",
+    "        [0.84603177, 3.6094582],\n",
+    "        [0.84875424, 3.6072438],\n",
+    "        [0.8514767, 3.6049912],\n",
+    "        [0.85419916, 3.6030822],\n",
+    "        [0.85692162, 3.6012688],\n",
+    "        [0.85964409, 3.5995889],\n",
+    "        [0.86236656, 3.5976417],\n",
+    "        [0.86508902, 3.5951984],\n",
+    "        [0.86781149, 3.593843],\n",
+    "        [0.87053395, 3.5916286],\n",
+    "        [0.87325642, 3.5894907],\n",
+    "        [0.87597888, 3.587429],\n",
+    "        [0.87870135, 3.5852909],\n",
+    "        [0.88142383, 3.5834775],\n",
+    "        [0.8841463, 3.5817785],\n",
+    "        [0.88686877, 3.5801177],\n",
+    "        [0.88959124, 3.5778842],\n",
+    "        [0.89231371, 3.5763381],\n",
+    "        [0.8950362, 3.5737801],\n",
+    "        [0.89775868, 3.5721002],\n",
+    "        [0.90048116, 3.5702102],\n",
+    "        [0.90320364, 3.5684922],\n",
+    "        [0.90592613, 3.5672133],\n",
+    "        [1.0, 3.52302167],\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "from pybamm import exp, constants\n",
     "\n",
     "\n",
-    "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n",
+    "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(\n",
+    "    c_e, c_s_surf, c_n_max, T\n",
+    "):\n",
     "    m_ref = 6.48e-7  # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n",
     "    E_r = 35000\n",
     "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
     "\n",
-    "    return (\n",
-    "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n",
-    "    )\n",
+    "    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_n_max - c_s_surf) ** 0.5\n",
     "\n",
     "\n",
     "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n",
@@ -1117,55 +1131,53 @@
     "    E_r = 17800\n",
     "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
     "\n",
-    "    return (\n",
-    "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n",
-    "    )\n",
+    "    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_p_max - c_s_surf) ** 0.5\n",
     "\n",
     "\n",
     "values = {\n",
-    " 'Negative electrode thickness [m]': 8.52e-05,\n",
-    " 'Separator thickness [m]': 1.2e-05,\n",
-    " 'Positive electrode thickness [m]': 7.56e-05,\n",
-    " 'Electrode height [m]': 0.065,\n",
-    " 'Electrode width [m]': 1.58,\n",
-    " 'Nominal cell capacity [A.h]': 5.0,\n",
-    " 'Typical current [A]': 5.0,\n",
-    " 'Current function [A]': 5.0,\n",
-    " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-    " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-    " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n",
-    " 'Negative electrode porosity': 0.25,\n",
-    " 'Negative electrode active material volume fraction': 0.75,\n",
-    " 'Negative particle radius [m]': 5.86e-06,\n",
-    " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-    " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-    " 'Negative electrode electrons in reaction': 1.0,\n",
-    " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
-    " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-    " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-    " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-    " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n",
-    " 'Positive electrode porosity': 0.335,\n",
-    " 'Positive electrode active material volume fraction': 0.665,\n",
-    " 'Positive particle radius [m]': 5.22e-06,\n",
-    " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-    " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-    " 'Positive electrode electrons in reaction': 1.0,\n",
-    " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
-    " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-    " 'Separator porosity': 0.47,\n",
-    " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-    " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-    " 'Reference temperature [K]': 298.15,\n",
-    " 'Ambient temperature [K]': 298.15,\n",
-    " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-    " 'Number of cells connected in series to make a battery': 1.0,\n",
-    " 'Lower voltage cut-off [V]': 2.5,\n",
-    " 'Upper voltage cut-off [V]': 4.4,\n",
-    " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n",
-    " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-    " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-    " 'Initial temperature [K]': 298.15\n",
+    "    \"Negative electrode thickness [m]\": 8.52e-05,\n",
+    "    \"Separator thickness [m]\": 1.2e-05,\n",
+    "    \"Positive electrode thickness [m]\": 7.56e-05,\n",
+    "    \"Electrode height [m]\": 0.065,\n",
+    "    \"Electrode width [m]\": 1.58,\n",
+    "    \"Nominal cell capacity [A.h]\": 5.0,\n",
+    "    \"Typical current [A]\": 5.0,\n",
+    "    \"Current function [A]\": 5.0,\n",
+    "    \"Maximum concentration in negative electrode [mol.m-3]\": 33133.0,\n",
+    "    \"Negative electrode diffusivity [m2.s-1]\": 3.3e-14,\n",
+    "    \"Negative electrode OCP [V]\": (\"graphite_LGM50_ocp_Chen2020\", neg_ocp),\n",
+    "    \"Negative electrode porosity\": 0.25,\n",
+    "    \"Negative electrode active material volume fraction\": 0.75,\n",
+    "    \"Negative particle radius [m]\": 5.86e-06,\n",
+    "    \"Negative electrode Bruggeman coefficient (electrolyte)\": 1.5,\n",
+    "    \"Negative electrode Bruggeman coefficient (electrode)\": 1.5,\n",
+    "    \"Negative electrode electrons in reaction\": 1.0,\n",
+    "    \"Negative electrode exchange-current density [A.m-2]\": graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
+    "    \"Negative electrode OCP entropic change [V.K-1]\": 0.0,\n",
+    "    \"Maximum concentration in positive electrode [mol.m-3]\": 63104.0,\n",
+    "    \"Positive electrode diffusivity [m2.s-1]\": 4e-15,\n",
+    "    \"Positive electrode OCP [V]\": (\"nmc_LGM50_ocp_Chen2020\", pos_ocp),\n",
+    "    \"Positive electrode porosity\": 0.335,\n",
+    "    \"Positive electrode active material volume fraction\": 0.665,\n",
+    "    \"Positive particle radius [m]\": 5.22e-06,\n",
+    "    \"Positive electrode Bruggeman coefficient (electrolyte)\": 1.5,\n",
+    "    \"Positive electrode Bruggeman coefficient (electrode)\": 1.5,\n",
+    "    \"Positive electrode electrons in reaction\": 1.0,\n",
+    "    \"Positive electrode exchange-current density [A.m-2]\": nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
+    "    \"Positive electrode OCP entropic change [V.K-1]\": 0.0,\n",
+    "    \"Separator porosity\": 0.47,\n",
+    "    \"Separator Bruggeman coefficient (electrolyte)\": 1.5,\n",
+    "    \"Typical electrolyte concentration [mol.m-3]\": 1000.0,\n",
+    "    \"Reference temperature [K]\": 298.15,\n",
+    "    \"Ambient temperature [K]\": 298.15,\n",
+    "    \"Number of electrodes connected in parallel to make a cell\": 1.0,\n",
+    "    \"Number of cells connected in series to make a battery\": 1.0,\n",
+    "    \"Lower voltage cut-off [V]\": 2.5,\n",
+    "    \"Upper voltage cut-off [V]\": 4.4,\n",
+    "    \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n",
+    "    \"Initial concentration in negative electrode [mol.m-3]\": 29866.0,\n",
+    "    \"Initial concentration in positive electrode [mol.m-3]\": 17038.0,\n",
+    "    \"Initial temperature [K]\": 298.15,\n",
     "}\n",
     "param = pybamm.ParameterValues(values)\n",
     "param"
@@ -1200,7 +1212,7 @@
    ],
    "source": [
     "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
-    "{k: v for k,v in param_same.items() if k in spm.get_parameter_info()}"
+    "{k: v for k, v in param_same.items() if k in spm.get_parameter_info()}"
    ]
   },
   {
@@ -1328,8 +1340,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
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WP9Bl7cMnznoQOa5KGAqnhSjQfOJcqn/r9BSJIxmeoqnJiAvXoLHDgpJjLL4l/8eExQ9sPmKA2WrDqLgwzBoVI3U4w/L16SlQKRU4VGtEVbNZ6nCIrkir2Yo9Va0AgPnT/CNh0aiVuGuWs/Mtp4UoADBh8QPidNCdufLrvXIxcRFaXD0uHgCwiaMs5Oe2HDXAZhcwJSUKmXFhUoczbOKGiP+taEJNa5fE0RBdGSYsMlfX3o1dp1sAAHfOlGfvlYtZMMNxJfrhwXMQBDawIv/18WHHdND8afIueD/fqLhwXDs+HoLg2MWZyJ8xYZG590trIQjAVWNikRHrP1d2AFA0LRkatRIVjZ04buiQOhyiETF292LnqWYAwPzp/pWwAI4GdwDwQTkvHMi/MWGRMUEQXM3ixEZQ/iRKF4IbJiYAYPEt+a+SYw3otQkYnxiBcYmRUofjtnkTE6BVK1HX3s0LB/Jrbics27dvx4IFC5CamgqFQoGNGzde8vjvfe97UCgUF9ymTp3qOuZXv/rVBT+fNGmS228m0JRWt6Gq2YzQEBXm+8nKhPMtcK4W+ojTQuSnxNVB/jYdJArTqHHteEc92WdHGySOhmjk3E5YzGYzsrOzsXr16mEd/8c//hH19fWuW01NDWJjY3H33XcPOm7q1KmDjtuxY4e7oQWcfzk3Opw/PRkRWvntWTIcN05KQrhGhdq2bpTVtEsdDpFbzJY+bD/ZBAC4xU9WBw2lcHISAGDLMSYs5L/c/is4f/58zJ8/f9jH6/V66PV61783btyItrY2LFu2bHAgajWSk/3zCsYbenptrq6ad/nhdJAoVKPCTVOSsLH8HD4sP4eZmf6xLJsIAL440QhLnx2j4sIwOcX/poNEN05OgkJxGIdqjTAYe5Cs10kdEpHbfF7D8tprr6GwsBCjRg3eOKyiogKpqakYM2YMlixZgurqi1e0WywWmEymQbdA89mxBnT09CEtOhRXjYmTOpwrIk4L/ftwPbe7J78iTgfdMi3Zb1oKDCUhUoscZ3fezzjKQn7KpwnLuXPn8Mknn+D+++8fdH9+fj7WrVuHzZs34+WXX0ZVVRWuvfZadHQMXSC2atUq18iNXq9HRkaGL8L3qS3OueYF2alQKv33ixIArh2fAH1oCJo6LNjjXKJNJHc9vTZ8cdzRIfbrfjwdJLppimNaiAkL+SufJiyvv/46oqOjsXDhwkH3z58/H3fffTdmzJiBoqIifPzxx2hvb8fbb7895POsWLECRqPRdaupqfFB9L5jswvY5pw3v3FyosTRXDmNWomvO5eDcrUQ+YttJ5vQZbUhLToUM9L1l3+AzN3krGPZWdmCTkufxNEQuc9nCYsgCFi7di2++93vQqPRXPLY6OhoTJgwAZWVlUP+XKvVIioqatAtkJRVt6G9qxf60BDk+sEma8OxwLmD8ydHDLD2cSM2kr/Nzumgoqn+PR0kGpcYgdFxYbDa7Piv84KIyJ/4LGHZtm0bKisrcd9991322M7OTpw6dQopKf4/DDsSX5xwDENfNyEBalVgtMrJHxOHxEgtjN29+G8FvyxJ3qx9dtfUiT82ixuKQqHoXy3E5c3kh9z+a9jZ2Yny8nKUl5cDAKqqqlBeXu4qkl2xYgWWLl16weNee+015OfnY9q0aRf87PHHH8e2bdtw5swZ7Ny5E3fccQdUKhUWL17sbngB4fPjjj/oYtO1QKBSKnDrgFb9RHL25almdPT0ISFSi7wAWtkm1rF8fqIRfTaOdJJ/cTth2b9/P3Jzc5GbmwsAKC4uRm5uLlauXAkAqK+vv2CFj9FoxLvvvnvR0ZXa2losXrwYEydOxLe+9S3ExcVh9+7dSEgInD/Yw2Uw9uBYvQkKBXD9hMB6/+JqoS1HG9BttUkcDdHFbXbuHXTL1GS/L3ofKG9UDKLDQtDe1Yv9Z9ukDofILW73YZk3b94lO5auW7fugvv0ej26ui6+U+j69evdDSNgidNB2enRiIvQShyNZ+VmRCNVr8M5Yw92V7Xghon+X1BMgafPZsenR/27u+3FqFVKfG1SIt4rrcNnRxv8vmUCBZfAKJAIIOIyyq9NCrw/5gqFAtc7p7m2nWAdC8nT3qpWtHX1IiYsBHOyYqUOx+NuGtD1lttlkD9hwiIjlj4bdlQ6doUN1NGH68Y7EpbtLLwlmRKbxd08JTlgit4Hum5CAjQqJc62dKGysVPqcIiGLfA+jX5sX1Ubuqw2JERqMTU1sJZqi+aOi4dKqcDpJjNq2y4+TUgkBbtdwOavnPUrAbI66HzhWjXmjnNMBX3K1ULkR5iwyMjnzumgeRMSAqrQbyB9aIirRfj2k83SBkN0ngPVbWjqsCBSp8bVY+OlDsdr2PWW/BETFhnZeiJw61cGck0LsXkVycwnztVBN01OgkYduF+PYj+W8pp2NHb0SBwN0fAE7ifSz5xpNuN0sxlqpQJXjw/cKzsAuG6C4/19eaqZvSBIVsQRh6IAWx10vqQoHbLT9RAE4PNjjVKHQzQsTFhkQlzOPHt0LKJ0IRJH410z0qMRHRaCjp4+lNe0Sx0OEQCguqUL1a1dUCsVuGZcYF80AP2jLJwWIn/BhEUmxPqVGyYFVrO4oagG/EHYxmkhkokvTzlqqmZmxiBc63aLKr9z01RHwvLfimZ0WbkZIskfExYZMFv6sOd0K4DAr18RXTeBdSwkLzsqHAnL1UEwugIAE5MikR4TCkuf3fXeieSMCYsM7DzVAqvNjvSYUIxNiJA6HJ8QC28P1RnRarZKHA0FO7tdcI2wXDM+OLq/KhQK12ohboZI/oAJiwx8PqC7bSBsYz8cyXodJiZFQhDgapZHJJWj9Sa0d/UiQqvGjPRoqcPxGddmiMcbYbOz6y3JGxMWiQmC4FrOHKjdbS9GXC3EaSGSmpg0XzUmDiEB2N32YhxF/mq0mK0oq+ZmiCRvwfPJlKnjhg7UG3ugVStRMDY4hqJFYh3LfyuauKcJSUqs4bhmXHB9BkNUSsxzXijxwoHkjgmLxMTlzHPHxkEXopI4Gt+aPToWuhAlGkwWnGjokDocClI9vTbsPeMoer8mwHsgDeVqZ5K281SLxJEQXRoTFokF8u7Ml6MLUbm2t+fuzSSVA2fbYO2zIylKGzRF7wPNdW5BUF7TzuXNJGtMWCRk7OrFgbOOeeN5QVa/IuLuzSQ1sX7l6nHxQVP0PlBGbBjSY0LRZxew7wzrWEi+mLBIaFtFE+wCMD4xAhmxYVKHIwmxjsWxUzWv7sj3xPqVa4NwOkhUMEacFuKKPZIvJiwS2urqbhucoysAMDYhHGnRobDa7K7meUS+0ma24sg5IwAE9O7MlzPXWceym3UsJGNMWCRiswvY6qzKD7blzAMpFArX8ma26Sdf23W6BYIATEiKQGKUTupwJFMwxvEZPFxnhLG7V+JoiIbGhEUix+pNaDVbEaFVY9boGKnDkRTrWEgqA+tXglmyXocxCeGwC8DeKo50kjwxYZHI7tOOoddZo2OCqlHVUOaOi4dKqcDpJjNqWrukDoeCSH//leBOWADWsZD8BfdfSgntdtZriMt6g5k+NAS5GdEAOMpCvlPd0oXq1i6olQrk83PoWt68i3UsJFNMWCRgtwvYd4YJy0DcvZl8TdzsMDczGhFatcTRSO+qMbEAHN23WzotEkdDdCEmLBI4ZjDB2N2LcI0K01KjpA5HFsSEZWdlC3ptdomjoWDA+pXB4iK0mJQcCaB/BJhITpiwSEBcvjtrdCzUQV6/Ipqepkd0WAg6LH0or2mXOhwKcHa7gJ2VrF85n7if2a7TrGMh+eFfSwmIBbf5ziFYAlRKhesPB6eFyNuO1pvQ1tWLCK0a2c76KeqvY+G+QiRHTFh8zG4XXButsX5lsP7dm3l1R94lTgddNSY26FfpDTQnKxZKBXC6yQyDsUfqcIgG4SfVx040dKC9qxdhGhWmp+mlDkdWxGWVR+qMMFvYpp+850vWrwxJHxqCac7vJU4LkdwwYfExcToobxT7r5wvPSYUqXod+uwCyqrbpQ6HAlRPr83VHI31Kxdy1bFwWohkhn8xfWwP+69clEKhwJwsR12POG1G5GkHzrbB0mdHYqQW4xIjpA5HdljHQnLFhMWH7HYBe6ocXwJXseB2SHOyHInc3ip+WZJ37BiwOkihUEgcjfzMGhUDtVKB2rZudp4mWXE7Ydm+fTsWLFiA1NRUKBQKbNy48ZLHb926FQqF4oKbwWAYdNzq1asxevRo6HQ65OfnY+/eve6GJnsVjZ1o6+pFaIgK09OipQ5HluZkOfZVKqtuh6XPJnE0FIjE+pVrxnM6aCjhWjVynCun2Kaf5MTthMVsNiM7OxurV69263EnTpxAfX2965aY2L9D8YYNG1BcXIynnnoKpaWlyM7ORlFRERobG90NT9YG1q9o1BzcGsrYhAjEhmtg6bPjSJ1R6nAowLSZrTjs/L1iwe3FzWUdC8mQ238158+fj9/+9re444473HpcYmIikpOTXTelsv+lX3jhBTzwwANYtmwZpkyZgjVr1iAsLAxr1651NzxZ43TQ5SkUCswZ7Tg/e7hrLHnYrtMtEARgfGIEkqJ0UocjW1eNFTdCbIEgCBJHQ+Tgs8v8nJwcpKSk4KabbsKXX37put9qteLAgQMoLCzsD0qpRGFhIXbt2jXkc1ksFphMpkE3uRMEwVVwy43WLm22WHjLhIU8bI9zlFMcQaChzcx0jAI3dlhwqsksdThEAHyQsKSkpGDNmjV499138e677yIjIwPz5s1DaWkpAKC5uRk2mw1JSUmDHpeUlHRBnYto1apV0Ov1rltGRoa338YVq2zsRIvZCl2IEjPS2X/lUvKdCcuBM22w2Xl1R56z70wbgP7ibhqaLkSFWaMc9WS7TnNaiOTB6wnLxIkT8T//8z/Iy8vD3LlzsXbtWsydOxd/+MMfRvycK1asgNFodN1qamo8GLF3DKxf0apVEkcjb5NTohChVaPD0odj9fIfPSP/YOrpxXGD4/dp1ugYiaORv/46FhbekjxIUvk5Z84cVFZWAgDi4+OhUqnQ0NAw6JiGhgYkJycP+XitVouoqKhBN7kTdz/N55XdZamUCtcfFE4LkaeUnm2DXQAyY8NYvzIMAxvI2TnSSTIgScJSXl6OlJQUAIBGo0FeXh5KSkpcP7fb7SgpKUFBQYEU4XmcIAzsv8KEZThmj2YdC3nWfud0EEdXhmdGejTCNCq0dfXiuKFD6nCIoHb3AZ2dna7REQCoqqpCeXk5YmNjkZmZiRUrVqCurg5vvPEGAODFF19EVlYWpk6dip6eHvztb3/D559/jk8//dT1HMXFxbj33nsxa9YszJkzBy+++CLMZjOWLVvmgbcovVNNnWjutEKrViI7g/UrwyHWsew70wpBENjgi67YPmf3ZDEZpksLUSkxJysWW080YdfpFkxJlf9INgU2txOW/fv344YbbnD9u7i4GABw7733Yt26daivr0d1dbXr51arFT/5yU9QV1eHsLAwzJgxA5999tmg51i0aBGampqwcuVKGAwG5OTkYPPmzRcU4vorcTpoZibrV4ZreroeWrUSLWYrTjWZ2UKdroi1z47ymnYAwGyOsAxbwZg4R8Jyqhn3XZMldTgU5NxOWObNm3fJdfnr1q0b9O8nnngCTzzxxGWfd/ny5Vi+fLm74fgFseA2n/1Xhk2rViEnIxp7qlqxt6qVCQtdkSPnjLD02RETFoKxCfxdGi5xX6E9p1vRZ7NDzQ1bSUL87fMyR/0KNzwciXxXPxYuq6Qrs985HTRrdCynF90wJTUKkc4VeycaWMdC0mLC4mWnm81o6rBAo1a69ueg4RF7ZYi9M4hGSvwd4nSQe1RKBXKd/VgOnOXnkKTFhMXLxO62uRnR0IWwfsUdM0dFQ61UoK69G7Vt3DWWRkYQhEEjLOSevExHwrKfFw4kMSYsXibWr3A6yH1hGjWmpjlWVXF5M43UqSYz2rp6oVUrMS2Vq/TcJS4D5wgLSY0JixcN7L/CgtuRGbi8mWgkxN+dnIxo7pI+AjkZ0VA5Rzrrjd1Sh0NBjJ9eLzrT0oUGkwUalRIzMzl3PhLcuZmuFPuvXJlwrRqTUyIBcFqIpMWExYvE6aCcTNavjJQ4HH26yVG8TOQudri9cmIdC6eFSEpMWLxI3Mr+qixe2Y1UdJgGk5LFqzuOspB7Gkw9qG7tglLh2HiURibPOTrFhIWkxITFi/Y7P9yzmbBckTlZnBaikRFHVyYlRyFSFyJxNP5rljPZO1pvgtnSJ3E0FKyYsHhJg6kHtW3dUCrA/itXaE4WN0KkkemvX+HoypVIjQ5Fql4Hm13AQecWB0S+xoTFS0qdoysTeWV3xcTC22MGE4zdvRJHQ/5k/1n2X/EUcVpoP6eFSCJMWLyktNrxoc4bFS1tIAEgMUqH0XFhEIT+RJDocjotfTh6zgSABbeekJcZDYB1LCQdJixeIn6ouZzZM1jHQu4qq26DXQDSY0KRog+VOhy/J45SlVa3wW6/+Aa4RN7ChMULLH02HKlzXNlxZYJniD00uBEiDVf//kGcDvKEScmRCNOo0NHTh5ON3AiRfI8JixccqTPBarMjPkKDzNgwqcMJCPnOjRAP1xnRbbVJHA35g31VbBjnSWqVErnOaSE2kCMpMGHxArHOIjczhlvZe0hGbCiSo3TotQkoq+GXJV1ar83u+j3hCiHPyRvFfiwkHSYsXiB+mDkd5DkKhcLVz4ZXd3Q5X50zoafXjuiwEIxNiJA6nIAhfqcxYSEpMGHxMEEQcKCaCYs3zHQOR4srsIguRuyKPGtUDJRKjnJ6Sm5mNBQKoLq1C40dPVKHQ0GGCYuH1bZ1o6nDArVSgelp3Mrek8QVV2XV7RAErlKgixMbxrH/imdF6UIwMcmxVcYBjnSSjzFh8TDx6n9qmp4bHnrY5JQoaNVKGLt7cbrZLHU4JFOCILimDVm/4nliTxs2kCNfY8LiYWLBbR77r3icRq3EjHTHqBUbyNHFVDWb0WK2QqNWYhpHOT1u1ih2vCVpMGHxMLF+ZSY73HpFrjMRLK1ulzYQki1xdCUnPRpaNUc5PU2szfuKLQbIx5iweFCXtQ/H6h0NlVhw6x1i4W0ZC2/pIlwbHmbxM+gN6TGhSIzUos8u4FBtu9ThUBBhwuJBB2uMsNkFpOp1bAXuJWLh7cmGDnRym3saAgtuvUuhULCOhSTBhMWDSl3TQbyy85bEKB3SokNhF8Bt7ukCTR0WnGnpgkLBfby8iQ3kSApMWDyIGx76htgenIW3dD5xqnBCYiT0oSESRxO4BjaQ40aI5CtMWDxEEATXCAvrV7zL1Y+FIyx0HvF3QkxqyTumpkZBF+JoMXCqqVPqcChIMGHxkNPNZrR39UIXosSU1Cipwwlo4pRbWXUbG8jRIOIICxMW7wpRKZGdHg2A00LkO0xYPET80M5Ii0aIiqfVm6akREGjVqKtqxdVbCBHTn02Ow7VGgH0L38n72HhLfka/7J6SBkLbn1Go1a6tj1gPxYSnWzoRJfVhkitGuO44aHXzWLhLfkYExYP4Q7NvsV+LHS+shrH70J2RjQ3PPQBcdqtqtmM5k6LtMFQUHA7Ydm+fTsWLFiA1NRUKBQKbNy48ZLHv/fee7jpppuQkJCAqKgoFBQU4D//+c+gY371q19BoVAMuk2aNMnd0CRj7O7FyQZH4Rnnzn1jJjve0nnKnL8L/Az6RnSYBuMTHSNZHGUhX3A7YTGbzcjOzsbq1auHdfz27dtx00034eOPP8aBAwdwww03YMGCBSgrKxt03NSpU1FfX++67dixw93QJFPuXJkwOi4M8RFaaYMJEuLU2wmDiQ3kCAALbqUg1rGwxQD5gtrdB8yfPx/z588f9vEvvvjioH8//fTT+OCDD/DRRx8hNze3PxC1GsnJye6GIwuu/iucDvKZpCgdUvU6nDP24FBNO+aOi5c6JJKQsasXp5ocBdg5Gfwc+kreqFi8tbeGIyzkEz6vYbHb7ejo6EBs7OC22RUVFUhNTcWYMWOwZMkSVFdXX/Q5LBYLTCbToJuUStkwThK5o9iPhRzKnXvajI4LQ2y4RtpggkhORjQA4HCdEb02u7TBUMDzecLy/PPPo7OzE9/61rdc9+Xn52PdunXYvHkzXn75ZVRVVeHaa69FR0fHkM+xatUq6PV61y0jI8NX4V/AZhdcU0IsuPUtVx0Lr+6CXv90ED+DvjQmPhxROjUsfXacMAz9fU3kKT5NWN588038+te/xttvv43ExETX/fPnz8fdd9+NGTNmoKioCB9//DHa29vx9ttvD/k8K1asgNFodN1qamp89RYuIG7CF6FVY0JSpGRxBCPXSqGadjaQC3IsuJWGUqlAtnOUhSv2yNt8lrCsX78e999/P95++20UFhZe8tjo6GhMmDABlZWVQ/5cq9UiKipq0E0q4txtbmY0VFxK6VNTU/XQqJVoNVtxpqVL6nBIIvYBo5y5rF/xuVxulUE+4pOE5a233sKyZcvw1ltv4dZbb73s8Z2dnTh16hRSUlJ8EN2VKeVQtGQ0aiWmObdB4NVd8KpqMcPY3QutWolJKRzl9LVc5whLOVsMkJe5nbB0dnaivLwc5eXlAICqqiqUl5e7imRXrFiBpUuXuo5/8803sXTpUvz+979Hfn4+DAYDDAYDjEaj65jHH38c27Ztw5kzZ7Bz507ccccdUKlUWLx48RW+Pe8rZcM4SfX3Y2HCEqzE6aAZ6XpuiyEBsfDWsZ+aVdpgKKC5/enev38/cnNzXUuSi4uLkZubi5UrVwIA6uvrB63weeWVV9DX14eHH34YKSkprtsjjzziOqa2thaLFy/GxIkT8a1vfQtxcXHYvXs3EhISrvT9eVVzpwVnWrqgUPR/aMm3xKXkpWfbpQ2EJMOCW2nFhGswOi4MQH9PKiJvcLsPy7x58y5Z4Lhu3bpB/966detln3P9+vXuhiEL4pXd+MQI6ENDpA0mSIkjLMcNJpgtfQjXuv0rTX7OVXDLiwbJ5GbG4ExLF8pr2jFvYuLlH0A0Ahw/vQKuDQ95ZSeZZL0OKXod7AJcO/VS8Oiy9uG4wdGHiSMs0slxrRRqlzQOCmxMWK6AOPzJ6SBpsY4leB2sMcIuACl6HZL1OqnDCVricvJythggL2LCMkI2u4CDYsLC3g+SyuXOzUFL3KGZ/VekNSk5Chq1EsbuXlQ1m6UOhwIUE5YRqmzshNlqQ7hGhfGJXEopJVfhbTWv7oJNf/0Kp4OkNLDFAAtvyVuYsIxQufPKbnq6ng3jJDY1NQoalaOB3Fk2kAsagiCww62MuBrIsY6FvIQJywj116/wyk5qWrUKU9OcDeRqOC0ULGrbutHcaYFaqcC0NL3U4QQ9sZaPIyzkLUxYRki8imDBrTz0b4TYLm0g5DNiK/gpqVHQhaikDYZco1zH6k3o6bVJGwwFJCYsI2C29OFkg2NnUg5FywNXCgUfV8M4XjTIQlp0KOIjtOizCzhSxxYD5HlMWEbgcF3/UsqkKC6llIOZo6IBAMcNHeiy9kkbDPlEf/0Kp2XlQKFQDFix1y5pLBSYmLCMAPuvyE+KPhRJUVrY7AKO1JmkDoe8zNJnw9FzYsO4aGmDIRfWsZA3MWEZgXKuTJCl/i9LTgsFuq/OmWC12REbrkFmbJjU4ZATeyKRNzFhGQGuEJIn8b8Hr+4C38D9gxQKthWQixnp0VAogHPGHjSYeqQOhwIMExY3GYw9MJh6oFIqMJ1LKWXFNcLC+fOA59rHaxQvGuQkQqvGxCRHI03WsZCnMWFxkzjdMDEpEqEaLqWUkxnpeiidV3eNvLoLaNyhWb5Yx0LewoTFTWXcP0i2wrVq1zYJZfyyDFiNph7UtXdDoQBmMGGRnf6NEFnHQp7FhMVNbBgnb7y6C3xiMjoxKRIRWrW0wdAFxFqyQ7VG2Ozc24s8hwmLG/psdhyudTRE4lC0PIkjXweZsAQs7h8kb+MSIxChVaPLanM12CTyBCYsbjjZ0InuXhsitWqMTYiQOhwagjjCwqu7wCUW3HKUU55USgVmpDsWJLDwljyJCYsbxGmGGRl6KLlDsyxNSIpEmEaFTksfTjV1Sh0OeVifzY5DzlFOthWQL/ZEIm9gwuIG8cPHKzv5GrjcnMubA09Fo2OUM1yjwrhEjnLKlbhdAkdYyJOYsLiBDeP8g5hQcqVQ4HGNcqZHQ8VRTtkSP4OVTZ0w9fRKGwwFDCYsw9TR04uKRscUA0dY5I0rhQKXOGrGtgLylhCpRXpMKAQBOFTDnZvJM5iwDNPhWiMEwbGFekKkVupw6BLEP2YnDCbu3BxgDta2A+BFgz9gHQt5GhOWYWLDOP8h7txsF8CdmwOI2dLnWibLhEX+WMdCnsaEZZjE6QX2X/EPvLoLPIdqjbALQIpeh6QondTh0GUMnJoVBLYYoCvHhGUYBEEYUHAbLWksNDzcuTnw8DPoX6amRiFEpUCL2Yqa1m6pw6EAwIRlGM4Ze9DUYYFaqcA07tDsF7IzuLQ50BxkwuJXdCEqTEmJAgCUcaSTPIAJyzCInTUnp0RBF8Idmv3BjPRoKLhzc0ARR1iymbD4Da7YI09iwjIM5dzw0O9EaNWYwJ2bA4bB2AODqQdKBVyNAUn+clw7N7dLGgcFBiYsw8C5c/8k/vfiRoj+TyyenpAUiXDu0Ow3xFqyr86ZYO2zSxwN+TsmLJfRa7PjcJ1z7xIuafYrvLoLHOXO5mPcodm/jI4Lgz40BNY+O47Vs8UAXRm3E5bt27djwYIFSE1NhUKhwMaNGy/7mK1bt2LmzJnQarUYN24c1q1bd8Exq1evxujRo6HT6ZCfn4+9e/e6G5pXnDB0wNJnR5ROjay4cKnDITdw5+bAwX28/JNCoXDVHIlN/4hGyu2ExWw2Izs7G6tXrx7W8VVVVbj11ltxww03oLy8HI8++ijuv/9+/Oc//3Eds2HDBhQXF+Opp55CaWkpsrOzUVRUhMbGRnfD87iyAYV+3KHZv4xPjEBoCHdu9nc2u4DDzh2aWXDrf1yFt1yxR1fI7YRl/vz5+O1vf4s77rhjWMevWbMGWVlZ+P3vf4/Jkydj+fLluOuuu/CHP/zBdcwLL7yABx54AMuWLcOUKVOwZs0ahIWFYe3ate6G53Hih4wN4/yPWqXE9HQub/Z3FY0dMFsdOzSPdxZSk//I5Uoh8hCv17Ds2rULhYWFg+4rKirCrl27AABWqxUHDhwYdIxSqURhYaHrmPNZLBaYTKZBN29xDUVz7twv5XLnZr8nFk1PT9dzh2Y/JI6KnW42w9jFnZv9kSAIeHR9Gf6ytRJmi3T7s3k9YTEYDEhKShp0X1JSEkwmE7q7u9Hc3AybzTbkMQaDYcjnXLVqFfR6veuWkZHhldiN3b041WQGAGSnR3vlNci72AfC//Wv0ouRNhAakdhwDUbFhQEAylnH4pfOtHRhY/k5vPhZBUJU0q3V8ctVQitWrIDRaHTdampqvPI6CgWw8htT8L25oxEXwR2a/ZE4MnayoYM7N/upMlcfJPZf8VdsMeDfxJmGaalR0KilSxu83tAgOTkZDQ0Ng+5raGhAVFQUQkNDoVKpoFKphjwmOTl5yOfUarXQar2fQETpQvD9a7K8/jrkPeLOzQ0mC47UmTAnK1bqkMgNg3do5giLv8pOj8YH5ec40umn+punSvsZ9HqqVFBQgJKSkkH3bdmyBQUFBQAAjUaDvLy8QcfY7XaUlJS4jiG6EuJ0Hndu9j9H6hw7NCdH6ZCs5w7N/mpgTyTu3Ox/xBpAqWs53U5YOjs7UV5ejvLycgCOZcvl5eWorq4G4JiuWbp0qev4H/zgBzh9+jSeeOIJHD9+HH/5y1/w9ttv47HHHnMdU1xcjFdffRWvv/46jh07hoceeghmsxnLli27wrdHxAZy/oxdpgPDlBTHzs2t3LnZ7/T02lxN/6ReLev2lND+/ftxww03uP5dXFwMALj33nuxbt061NfXu5IXAMjKysK///1vPPbYY/jjH/+I9PR0/O1vf0NRUZHrmEWLFqGpqQkrV66EwWBATk4ONm/efEEhLtFIsA+E/yqXyZUdXRlx5+aDtUaU1bQh01mES/L31TkTem0C4iM0SI8JlTQWtxOWefPmXXJIb6gutvPmzUNZWdkln3f58uVYvny5u+EQXdb5OzcnRnFqwV+4dmjmKj2/l5MRjYO1RpTXtOP2nDSpw6FhGjjKqVBI21bAL1cJEbmDOzf7pwZTD+qNjh2aZ6RzhZC/E0fJuFLIv8hpWpYJCwUFLqv0P+IXJXdoDgziCpMj3LnZr/Tv4yX9Kj0mLBQUxG6bZaxj8RtyurKjKzdw5+bjBu7c7A9aOi2oae2GQgHMkEEfJCYsFBRyncPRh2rbuXOznxCLpLnhYWAYuHMzV+z5B/G/09iECETpQqQNBkxYKEhMSIpEmEYFs9WGykbu3Cx3NruAw3WOHZo5whI4uGLPv8htlJMJCwUFlVLhKtwsq2YDObk71dSJTksfwjQqTEjiDs2Bgjs3+xcmLEQSEYvG+GUpf+IV+PQ07tAcSLhzs/+w24UBLfmjJY1FxISFgoZYx8LCW/mTSytw8qyBOzcf5M7Nsna6uRMdlj7oQpSYlCyPUU4mLBQ0xOHok40d6LRw52Y5E5ef57BhXMDJ4bSQXygbMMqpVskjVZBHFEQ+kBilQ1p0KATBsVqI5KnbasMJcYdmjrAEnP7NSNsljYMuTfzvk5spff8VERMWCio57Mcie4frjLDZBSRFaZGil3bvEvI87tzsH+RWcAswYaEgk8udm2Wvv7NmtLSBkFdw52b567bacNzgHOWU0eeQCQsFlYEjLLy6kydx9EtOQ9HkOeLOzQBQzqlZWTpyzjHKmRipRYpePpvFMmGhoDItTQ+1UoHmTgvq2nl1J0euhEVGV3bkWWwgJ28DlzNLvUPzQExYKKjoQlSY7Ly6Yx2L/NQbu2Ew9UClVGA6d2gOWP11LGziKEflMm0rwISFgg7rWORLTCInJUciTMMdmgOVuFKIOzfLkxwLbgEmLBSE+utYeHUnN+J/k1yZXdmRZ2XFh3PnZplqNPWgrt25Q7PM+iAxYaGgIyYsvLqTn/76FRbcBrKBOzcf5EinrIhdpickRiJCK69RTiYsFHQGXt0dq+fVnVxY++yuHZo5whL4XCOdTFhkRa7TQQATFgpCCoWC7cFl6LjBBEufHfrQEGTFh0sdDnkZd26WJ9cKIRleNDBhoaDUvxEi61jkor//iryWUpJ3iBcNp5vMaO+yShsMAQBsdsG1bQlHWIhkgiMs8uMquGX9SlCICde4RtI4LSQPlY2dMFttCNOoMCFJHjs0D8SEhYKSmLCcaelCq5lXd3JQ5tpsLVrSOMh3crm3l6yIfXFmpOuhUspvlJMJCwWl6DANxjiv7rhKQXotnRacbekCANfqEQp8uaMco2mcmpWH/oJbeY5yMmGhoCUWlXE4WnriF+W4xAjoQ0OkDYZ8ZqbYxLG6HXY79/aSWtmAlvxyxISFglYuG8jJBvcPCk4TkyIRplGhw9KHisZOqcMJamZLH042OHZoluu0LBMWClribsAHa3h1J7WyGrHDrTyHosk71ColZjj3jCrlhYOkDtcZYReAFL0OSVHy2aF5ICYsFLQmJkdCq1bC1NOH081mqcMJWja7gIM1bBgXrGZmso5FDuQ+HQQwYaEgFjLg6o7Lm6VT2diJTkufbJdSkneJCUspVwpJSlwhxISFSKa4EaL0xHOfnR4ty6WU5F3iqFplYyeMXb3SBhOkBEFwJYxynpZlwkJBTfxwcoRFOgM73FLwiYvQYlRcGID+Wibyrdq2bjR1WKBWKlyjznI0ooRl9erVGD16NHQ6HfLz87F3796LHjtv3jwoFIoLbrfeeqvrmO9973sX/PyWW24ZSWhEbhFHWI4bOtBttUkbTJAS/0jNlPGVHXlXfx1Lu7SBBCmx4HlqahR0ISqJo7k4txOWDRs2oLi4GE899RRKS0uRnZ2NoqIiNDY2Dnn8e++9h/r6etftyJEjUKlUuPvuuwcdd8sttww67q233hrZOyJyQ4peh8RILWx2wbVTMPmOqafXtZxVjputkW+I/Vi4UkgapWedFw2j5H3R4HbC8sILL+CBBx7AsmXLMGXKFKxZswZhYWFYu3btkMfHxsYiOTnZdduyZQvCwsIuSFi0Wu2g42Ji5H3iKDAoFApuhCihQzVGCAKQGRuG+Ait1OGQRAZOzbLFgO8dcH735QVSwmK1WnHgwAEUFhb2P4FSicLCQuzatWtYz/Haa6/hnnvuQXj44O3jt27disTEREycOBEPPfQQWlpaLvocFosFJpNp0I1opMQ21Kxj8T3XhoccXQlqk5IjERqiQkdPHyqb2EDOl7qsfThW72gYJ/dpWbcSlubmZthsNiQlJQ26PykpCQaD4bKP37t3L44cOYL7779/0P233HIL3njjDZSUlODZZ5/Ftm3bMH/+fNhsQ9cUrFq1Cnq93nXLyMhw520QDdI/wtIuaRzByLXhoYyXUpL3DWwgx5FO3zpYY4TNLiBFr0NqdKjU4VyST1cJvfbaa5g+fTrmzJkz6P577rkHt912G6ZPn46FCxdi06ZN2LdvH7Zu3Trk86xYsQJGo9F1q6mp8UH0FKimp+mhVAAGUw/qjd1ShxM0BEEYMMIi7ys78j6xfqL0bLu0gQQZsW5I7vUrgJsJS3x8PFQqFRoaGgbd39DQgOTk5Es+1mw2Y/369bjvvvsu+zpjxoxBfHw8Kisrh/y5VqtFVFTUoBvRSIVr1ZiY7Pgd4iiL75xp6UJbVy80aiUmp/AzHOz6G8hxhMWXDpz1n1V6biUsGo0GeXl5KCkpcd1nt9tRUlKCgoKCSz72nXfegcViwXe+853Lvk5tbS1aWlqQkpLiTnhEIyauUhA/vOR94ujK9DQ9NGq2hAp24tRsRWMnjN1sIOcLjoZx/lFwC4xgSqi4uBivvvoqXn/9dRw7dgwPPfQQzGYzli1bBgBYunQpVqxYccHjXnvtNSxcuBBxcXGD7u/s7MRPf/pT7N69G2fOnEFJSQluv/12jBs3DkVFRSN8W0TumTXa8WFlwuI73KGZBoqP0CIz1tFA7iAL4H3idLMZ7V290KqVmOIHo5xqdx+waNEiNDU1YeXKlTAYDMjJycHmzZtdhbjV1dVQKgfnQSdOnMCOHTvw6aefXvB8KpUKhw4dwuuvv4729nakpqbi5ptvxm9+8xtotVzmSL4xa1QsAOCrc0b09Npk3TwpUHCHZjrfzMxoVLd2obS6DddNSJA6nIAnXqDNSPePUU63ExYAWL58OZYvXz7kz4YqlJ04cSIEYei19aGhofjPf/4zkjCIPCY9JhSJkVo0dlhwsKYd+WPiLv8gGrFuq821lJJLmkk0c1QMNpaf40aIPlLmRwW3APcSIgLgaCAnzuHu57SQ1x2ucyylTIrSIkWvkzockon+Fv1tbCDnA+IIS56fjHIyYSFyynMtq2TC4m2u5cwZMVAouEMzOUxMjoQuRImOnj6cYgM5rzJ29+Jkg+Mcc4SFyM/MGu2oYznAqzuv4w7NNJQQlRIz0qMBsMWAt4mdvUfF+c+2GExYiJwcO5Uq0d7Vi9PNvLrzloFLKVlwS+djPxbf8LfpIIAJC5FLiEqJbOfV3f4z/LL0lrr2bjR2WKBWKjA9TS91OCQz3LnZN/xlh+aBmLAQDcDCW+8Tk8GpaXqEarh8nAYTR90qGjth6mEDOW+w2QXXlJA/dLgVMWEhGkBsIMfCW+/Zd6YVADDbj67syHcSIrXIiA2FILCBnLecbOhAp6UP4RoVJiZHSh3OsDFhIRpAvNo43WxGS6dF4mgCkzjCIhY5E53PVcfCjRC9Qqxfyc2MgUrpP6v0mLAQDRAdpsH4xAgAbNPvDcauXpxocDSME0eziM7Hwlvvcu3Q7Ger9JiwEJ2H+wp5z4Fqx3TQmIRwv1lKSb4nLndnAznv8MeCW4AJC9EFxKs7Ft563t4qxzmdPYrTQXRxk1McLQZMPX043WyWOpyA0tJpwZmWLgD+11aACQvRecTaisO1Rlj6bBJHE1j2OwtuOR1ElxKiUmJGWjQATgt5mrhP0/jECOhDQ6QNxk1MWIjOMzouDHHhGlhtdhypM0odTsDo6bXhUK3jfM5mwS1dRu6oaABcsedproZxfjYdBDBhIbrAoI0Q2UDOYw7XGWG12REfocWouDCpwyGZE6cN9zpH5cgzSv1sh+aBmLAQDUGcsmAdi+e4+q+M5oaHdHniZ/B0kxnNbDHgEb02u6u3jT81jBMxYSEaQp7z6q70bBsEgasUPIH9V8gd0WEaTHI2NdtXxVEWTzh6zgRLnx3RYSEYEx8udThuY8JCNIRpaVHQqJVoMVtdFfU0cna74Cq4ncOEhYZpTpbjd2UPExaP6O+/EgOlHzWMEzFhIRqCVq3CDOfGfPs5h37FHPvC9CFMo8LkFP9pBU7SEouz9zJh8Qix4NbfGsaJmLAQXUQeG8h5jFg4OTMzBmoVv3ZoeMQRlmMGEzdC9AB/bRgn4jcH0UXMctaxsPD2yrH/Co1EUpQOo+PCIAjAAa7YuyL1xm6cM/ZApVQgOz1a6nBGhAkL0UWIS5srGzvR3mWVOBr/Jhbcsv8KuUv8nWEdy5URN5KclByJcK1a2mBGiAkL0UXEhmtclfTstjlyde3dqGvvhkqpQE5GtNThkJ8Rp4X2sZbsiuypagEAzPLT6SCACQvRJbGB3JUTp4Ompkb57ZUdSSc/Kw4AcKi2Hd1WbpUxUntOOz6HV42JkziSkWPCQnQJbCB35TgdRFciIzYUyVE69NoElNXwczgSrWYrTjR0AOgfsfJHTFiILkFsIHewph3WPrvE0fingR1uidylUCgwO4vLm6/EXud00MSkSMRFaCWOZuSYsBBdwtiEcESHhcDSZ8fRepPU4fgdY3ev68pOTP6I3MU6liuz2zkdlD/Gvz+DTFiILkGhUCAvU6xj4ZeluxxbGwBZ8eFIiPTfKzuSVr4zYTlwto0jnSOw+7RjhMWf61cAJixEl8UGciMnXhH788oEkt64hAhEh4Wgp9eOI+eMUofjV9rMVhw3+H/9CsCEheiyBjaQ40aI7mHBLXmCUqlgm/4REvvXjE+MQLwf168ATFiILmtGuh4atRJNHRacbjZLHY7fsPTZUF7bDoAdbunKidNC3LnZPWL/FX+fDgKYsBBdli5E5apj2XWqReJo/MeROiOsfXbER2iQ5Ydb2ZO8iNMZe8+0wmbnSOdwBUrBLTDChGX16tUYPXo0dDod8vPzsXfv3oseu27dOigUikE3nU436BhBELBy5UqkpKQgNDQUhYWFqKioGEloRF4xd6zj6oQJy/Dtc04HzRoVC4XC/7ayJ3mZkhKFcI0KHT19OOGsyaBLa++y4rjBsbpRbMDnz9xOWDZs2IDi4mI89dRTKC0tRXZ2NoqKitDY2HjRx0RFRaG+vt51O3v27KCfP/fcc3jppZewZs0a7NmzB+Hh4SgqKkJPT4/774jICwrEhOV0C+y8uhsWbnhInqRWKV27DHN58/DsrWqFIDjaMwTCKj23E5YXXngBDzzwAJYtW4YpU6ZgzZo1CAsLw9q1ay/6GIVCgeTkZNctKSnJ9TNBEPDiiy/il7/8JW6//XbMmDEDb7zxBs6dO4eNGzeO6E0RedqM9GiEaVSDOkbSxdntgmuEhQW35Cn5bCDnlt0B0I5/ILcSFqvVigMHDqCwsLD/CZRKFBYWYteuXRd9XGdnJ0aNGoWMjAzcfvvt+Oqrr1w/q6qqgsFgGPScer0e+fn5l3xOIl/SqJWuP7ycFrq8yqZOGLt7ERqiwpTUKKnDoQAxcOdmrti7PLHgNj8YE5bm5mbYbLZBIyQAkJSUBIPBMORjJk6ciLVr1+KDDz7AP/7xD9jtdsydOxe1tbUA4HqcO89psVhgMpkG3Yi8TZwW2smE5bLEIfvczGiEqFjbT56RnRENjUqJ5k4Lqrhi75KMXb2u7txX+Xn/FZHXv0kKCgqwdOlS5OTk4Prrr8d7772HhIQE/PWvfx3xc65atQp6vd51y8jI8GDEREMTC2/3VLVwlcJliEtPZ3E6iDxIF6JCTkY0ANaxXM6+M476lTEJ4UiM0l3+AX7ArYQlPj4eKpUKDQ0Ng+5vaGhAcnLysJ4jJCQEubm5qKysBADX49x5zhUrVsBoNLpuNTU17rwNohGZmqpHpE6Njp4+fMVumxclCAK+dI5CBcqVHcnH7CxH4e0e1rFcktiOPxBWB4ncSlg0Gg3y8vJQUlLius9ut6OkpAQFBQXDeg6bzYbDhw8jJSUFAJCVlYXk5ORBz2kymbBnz56LPqdWq0VUVNSgG5G3qZQKV/Eap4UurqKxE00dFuhC+ld1EHnKHOcfYBbeXpqY0F0VAP1XRG5PCRUXF+PVV1/F66+/jmPHjuGhhx6C2WzGsmXLAABLly7FihUrXMf/3//9Hz799FOcPn0apaWl+M53voOzZ8/i/vvvB+BYQfToo4/it7/9LT788EMcPnwYS5cuRWpqKhYuXOiZd0nkIQVMWC5rR0UzAEeBpC5EJXE0FGjyRsVAqQBq27pxrr1b6nBkydjd6xoFDpQVQgCgdvcBixYtQlNTE1auXAmDwYCcnBxs3rzZVTRbXV0NpbI/D2pra8MDDzwAg8GAmJgY5OXlYefOnZgyZYrrmCeeeAJmsxkPPvgg2tvbcc0112Dz5s0XNJgjktrccY4P/76qVlj77NCoWVB6vh2VjoTl2vHxEkdCgShCq8a0ND0O1Rqx70wrbs9Jkzok2dl/phV25y7pSQFSvwIACiEA1oaZTCbo9XoYjUZOD5FX2e0CZv/uM7SYrfjXDwpYVHqeXpsd2b/+FF1WG/7942swNVUvdUgUgH6z6She21GFb+dn4uk7pksdjuw8/fExvLL9NO6ZnYFnvjlD6nAuyZ2/37w8JHKDknUsl1Re044uqw2x4RpMTubFA3nHHDaQuySx4DaQpoMAJixEbuvvx9IscSTyI9avzB0bB6WS+weRd4gN5CobO9HSaZE4Gnnp6OnFkTpH/UogbHg4EBMWIjeJ/VhKq9vR02uTOBp5Yf0K+UJsuAYTkiIA9LefJ4f9Z9pgF4BRcWFI0YdKHY5HMWEhclNWfDiSo3Sw9tlRerZN6nBko6OnF+U17QCAq8cxYSHvumZcAgBg+8kmiSORl91VYg+kwJoOApiwELlNoVCwTf8Q9pxuhc0uYHRcGNJjwqQOhwLcdRMcSfH2iibuKzSAOOIUaNNBABMWohFhHcuFxOkgjq6QL+RnxUGjVqLe2IPKxk6pw5GFTkvfgPoVjrAQEfrrWA7VGtFp6ZM4Gnn40pmwXMOEhXwgVKNCvnO10DZOCwFw9F+x2QVkxoYhLTqw6lcAJixEI5IeE4aM2FD02QVuwgbAYOxBRWMnFApg7lgmLOQb14131LEwYXFwTQcF6B5eTFiIRmjuGMcf5l2sY3GNrsxI00MfFiJxNBQsrp/oSFj2VrVyxR4cO8kDgdd/RcSEhWiExDb9TFj6ExbWr5AvjU+MQHKUDpY+e9Dv3tzR04tDtYHZf0XEhIVohMSNEI+cM8LY1StxNNIRBMFVcMv6FfIlhULRv1ooyKeFvqxshs0uYEx8eMCu0mPCQjRCiVE6jE0IhyD09z4IRhWNnWjssECrVmLmqBipw6Egc90E9mMBgM+PNwIA5k1MlDgS72HCQnQFxALTYJ4WEtvxz8mKhS5EJXE0FGyuGRcPpcKROJ9r75Y6HEkIgoAvTjgStq9NYsJCREMQlzcHc8LC5cwkpegwDWakRwMA/lsRnKMsX50zoanDgjCNCrOzAneUkwkL0RUQq/FPNHSgqSP4NmHrtdldO8Oy4JakIk4LBevy5i+c00HXjIuHVh24o5xMWIiuQEy4BpNTogD0b+keTMpr2mG22hAbrsEU53kg8rXrnQnLjopm9NnsEkfje5+fcCQsNwTwdBDAhIXoionTQuLUSDAR61fmjo2DUqmQOBoKVtnpekTp1DD19OGgc2lvsGg1W12bjt4QwAW3ABMWoismXt2VHG+E3R5cm7CxfoXkQK1S4prxwbm8edvJRggCMDklCsl6ndTheBUTFqIrlD8mFhFaNZo6LDhUFzxXdx09vShzXtmxfoWkJrbp3x5khbdfHBdXByVIHIn3MWEhukJatcrVInzLUYPE0fjOntOOjdZGxYUhIzYwG1WR/xALbw/WtAdNI8c+m91VaBzo00EAExYij7hpchIA4LOjjRJH4js72I6fZCQ1OhTjEiNgF/p/NwNdWU07jN29iA4LQW5m4C5nFjFhIfKAGyYmQqVU4ERDB6pbuqQOxyfE+pVrmbCQTPTv3hwcFw7icubrxidAFQRF70xYiDxAHxaCOaMdG45tOdYgcTTe12DqQUVjJxQKoGBsYO4MS/5HnJrdfrIZghD4BfBiO/5A7m47EBMWIg+5aYo4LRT4CctWZ9+H6Wl6RIdpJI6GyCE/KxZatRIGZ0IdyOqN3Thu6IBC0V+/E+iYsBB5SKGzjmXvmVa0d1kljsa7PjniKC4Wa3eI5EAXosKcLMdIZ6AvbxZXB+VmRCM2PDguGpiwEHlIZlwYJiZFwmYXsPVE4H5ZGrt7XfUr86cnSxwN0WDXB0mb/i/E7rZBsDpIxISFyIPEaaEtATwt9PnxBvTaBIxLjMC4xEipwyEaRJwe2VvVip5em8TReIelz+a6aAj0dvwDMWEh8qBCZ8Ky7WQTLH2B+WX5yWHHdND8aRxdIfkZnxiB5CgdLH127KlqlTocr9hb1Youqw2JkVpMTQ2ePbyYsBB50Iw0PRIjtei09GH36cD7sjRb+lxD7fOnpUgcDdGFFAoFrpvgWGq/LUCnZsXVQTdMTIRCEfjLmUVMWIg8SKlU4MbJgbta6IsTjbD02TEqLgyTUzgdRPJ0/QTHNMnWAO3HItbI3RAE7fgHYsJC5GE3i8ubjzUEXC8IcXXQLdOSg+rKjvzLNePjoVEpcbrJjOMGk9TheFRVsxlVzWaEqBRB12V6RAnL6tWrMXr0aOh0OuTn52Pv3r0XPfbVV1/Ftddei5iYGMTExKCwsPCC47/3ve9BoVAMut1yyy0jCY1IcgVj4xCmUaHe2IOvzgXOl2VPr83VWZPTQSRn+tAQVxO5D8vPSRyNZ4mfwdmjYxGpC5E4Gt9yO2HZsGEDiouL8dRTT6G0tBTZ2dkoKipCY+PQQ29bt27F4sWL8cUXX2DXrl3IyMjAzTffjLq6ukHH3XLLLaivr3fd3nrrrZG9IyKJ6UJUrhbhnwbQtND2k03ostqQqtchO10vdThEl3RbdioA4KND5wJqpFNczhws3W0HcjtheeGFF/DAAw9g2bJlmDJlCtasWYOwsDCsXbt2yOP/+c9/4oc//CFycnIwadIk/O1vf4PdbkdJScmg47RaLZKTk123mJjA38iJAldhAHa93eycDiridBD5gRsnJyI0RIWa1m6U17RLHY5HmC192OMs5p8XRP1XRG4lLFarFQcOHEBhYWH/EyiVKCwsxK5du4b1HF1dXejt7UVsbOyg+7du3YrExERMnDgRDz30EFpaWtwJjUhWvjYpEUoFcLTehNo2/98M0dpnd+2R9PXpnA4i+QvTqF19kT48GBjTQl9WNsNqsyMzNgxjE8KlDsfn3EpYmpubYbPZkJQ0uB13UlISDAbDsJ7jZz/7GVJTUwclPbfccgveeOMNlJSU4Nlnn8W2bdswf/582GxD97GwWCwwmUyDbkRyEhuuwaxRjqS85Jj/r1TYeaoZHT19SIjUIi8ItrGnwCBOC/37UD1sdv+fFupfzpwQlKOcPl0l9Mwzz2D9+vV4//33odPpXPffc889uO222zB9+nQsXLgQmzZtwr59+7B169Yhn2fVqlXQ6/WuW0ZGho/eAdHwBVLXW7FZXNHUJCiDYBt7CgzXTohHlE6Nxg4L9lT596h9T68NHx+uBwDcNCU4mza6lbDEx8dDpVKhoWHwF3BDQwOSky99Ap9//nk888wz+PTTTzFjxoxLHjtmzBjEx8ejsrJyyJ+vWLECRqPRdaupqXHnbRD5hFjHsvt0C0w9vRJHM3J9Njs+PSp2t+V0EPkPrVrl+p396GC9xNFcmZJjjTD19CFFr0PB2Dipw5GEWwmLRqNBXl7eoIJZsYC2oKDgoo977rnn8Jvf/AabN2/GrFmzLvs6tbW1aGlpQUrK0F+OWq0WUVFRg25EcpMVH45xiRHo8/PNEPdWtaKtqxcxYSHIz4q9/AOIZOS2HMe00CdH6mHts0sczcj964DjwvzOmWlQBekop9tTQsXFxXj11Vfx+uuv49ixY3jooYdgNpuxbNkyAMDSpUuxYsUK1/HPPvssnnzySaxduxajR4+GwWCAwWBAZ2cnAKCzsxM//elPsXv3bpw5cwYlJSW4/fbbMW7cOBQVFXnobRJJozAAut6KzeJumpIEtYq9Jsm/XDUmDvERWrR39WJHpX9eODR29GB7hWOzwztnpkscjXTc/vZZtGgRnn/+eaxcuRI5OTkoLy/H5s2bXYW41dXVqK/vH3p7+eWXYbVacddddyElJcV1e/755wEAKpUKhw4dwm233YYJEybgvvvuQ15eHv773/9Cq9V66G0SSUOsY/niRCN6bf53dWe3C/jPV87pIK4OIj+kUirwjRn+PS30Qdk52OwCcjOjMTYhQupwJKMeyYOWL1+O5cuXD/mz8wtlz5w5c8nnCg0NxX/+85+RhEEkezkZ0YiP0KC504odlc24wc96J5RWt6Gxw4JInRpXjw2uNuAUOBZkp2LdzjP49CsDuq02hGpUUoc0bIIg4F8HagEA3wzi0RWAewkReZVKqcAC59LKt/ZUSxyN+z52rg4qnJwEjZpfF+SfZmZGIy06FGarzbU02F98dc6EEw0d0KiVWDAjVepwJMVvICIvW5KfCQAoOd4Ig7FH4miGTxD6p4NumRacyygpMCgU/RcOH/lZEzlxdOWmKUnQhwXX3kHnY8JC5GXjEiMxZ3QsbHYBG/b5zxL8Q7VG1LV3I0yjwvUTgmsbewo8YhO5z080+k2bAWuf3dWl964gnw4CmLAQ+cS3naMsG/ZV+03HTXF10A0TE6EL8Z85f6KhTE6JxNiEcFj77Pj0K/9YtffFiUa0mq1IiNTi2vGsIWPCQuQDt0xLRkxYCM4Ze7D1hPzn0AVBwCdHHCsq5k/ndBD5P4VCgduy0wD4z7TQu87poDty09hSAExYiHxCF6LCXXmOId03/aD4dtvJJpxt6UKEVu13K5uILmZBtmN5847KZrR0WiSO5tJazVZ84by4CfbVQSImLEQ+sniOY1roixONqGvvljiaS3ttRxUA4FuzMhCuHVH3AyLZGZMQgWlpUbDZBXx8ZHgb9krlw/I69NoETEuLwsTkSKnDkQUmLEQ+MiYhAgVj4mAXgA175TvKcsLQgf9WNEOpAJZdPVrqcIg86jY/WS30r1LHdBCLbfsxYSHyoSVXOUZZ1u+rkW3n27XO0ZWbpyQjIzZM4miIPOsbzl4m+860ot4oz5HOE4YOHKkzIUSlwG05aVKHIxtMWIh86OYpyYiP0KCxw4KSY/Irvm3utOD98joAwH3XZkkcDZHnpUaHYvboGAgCsLFMnqMs7zpHV26YmIjYcI3E0cgHExYiH9KolbgrLwMA8KYMp4X+ubsa1j47ZqTrMWtUjNThEHnF3c7P4N+/rEJPr03iaAbrs9nxfpnjokEs1CcHJixEPrZ4juPL8r8VTahp7ZI4mn6WPhv+v91nAQD3XZMFhSI4t7CnwLcwNw1p0aFo7LDg7f3yaub434pmNHVYEBuuwTyu0BuECQuRj42KC8e14+MhCMBbMhpl+bD8HJo7LUiO0uHr3JmZAphGrcQPrh8DAHh56ylY+uQzyiIW296Wncr9u87Ds0EkAXF/obf318DaJ33xrSAIrqXMS+eOQgibVFGAu3tWBhIjtag39uDdA3VShwMAMHb1YstRRxdeTgddiN9KRBK4cXISEiK1aO60ur6gpLTrVAuOGzoQGqLCt539YogCmS5EhR9cPxYA8JetlbJYtffGrjOw9tkxMSkSU1OjpA5HdpiwEEkgRKXEolli8e1ZiaPpbxT3zbw0RIdxVQIFh8VzMhEfoUFtWzc2lkk7ytLcacGabacAAA9/bRxryIbAhIVIIvfMyYBCAXxZ2YKqZrNkcZxu6kTJcccS62VXcykzBY9QjQoPXOuoZVn9RSX6JBxl+eNnFTBbbZiRrsc3WEM2JCYsRBJJjwnDvAkJAKQtvv37l2cAAF+blIixCRGSxUEkhe9cNQoxYSE409KFTYfqJYnhVFOnq83BL74+GUolR1eGwoSFSELfzh8FwJGwNHb0+Pz127us+JdzR9j7ruHoCgWfcK0a9ztHWf78RSXsdsHnMTz7yXHY7AIKJyfiqjFxPn99f8GEhUhCX5uUiOlpenT09OH/Pjrq89d/a28NunttmJQciblj+UVJwWlpwShE6dSobOzEJz7eFHFvVSs+PdoAlVKBn8+f5NPX9jdMWIgkpFIqsOrO6VApFdh0qB5fHPddu/5emx2v7zwDAPg+G8VREIvUhbjqt/70eYXPRlkEQcDTHx8DACyanYFxidyV+VKYsBBJbFqaHt937or8y41HYLb0+eR1Pz5cD4OpB/ERGtcOtkTB6vtXZyFCq8ZxQwe2HPNNq4F/H65HeU07wjQqPFo43iev6c+YsBDJwGM3TUBadCjq2rvxhy0nvf56jR09+M0mxxTUd68aDV2IyuuvSSRn+rAQLC1w1JT96fMKCIJ3R1msfXY8t/kEAODB68YgMVLn1dcLBExYiGQgTKPGbxdOAwCs/bIKR+qMXnstm13AYxvK0dxpxaTkSPyPs0U5UbC7/9oxCNOocKTOhK0nmrz6Wv/YfRbVrV1IiNS6llbTpTFhIZKJGyYl4hszUmAXgJ+/d8hrPSFe3lqJLytbEBqiwp+/PZOjK0ROseEafOcqxyjLH0u8N8pi7O7FS59XAACKb5qAcK3aK68TaJiwEMnIygVTEKVT40idCeucBbGetLeqFS84p5x+s3AaxiWy7wrRQPdfmwWtWonymnas/qLSK6/xl62VaO/qxfjECNzNPYOGjQkLkYwkRuqw4uuTAQAvbDmJ2rYujz13q9mKH79VBrsA3DkzjZurEQ0hMVKHX97q+Aw+/+lJbNjn2aaOtW1drmaNK74+CWpuNDpsPFNEMrNoVgZmj45Bl9WGlR985ZFhaUEQ8Pg7B2Ew9WBMQjh+c/s0D0RKFJi+WzAaP5zn2BhxxXuH8ZmHNigVBAHPfHIc1j47rhoTixsmJnrkeYMFExYimVE6e7OEqBT4/HgjPj585Y2sXttRhc+PN0KjVuLPi2dyzpzoMn5aNBF356XDLgDL3yrFgbNtV/R8lj4bit8+6Gr//4uvT2bvIzcxYSGSoXGJkXho3jgAwFMffgVjV++In6u8ph3PfHIcALDyG1MwhdvWE12WQuG4cPjapET09Npx3+v7UNnYMaLnMnb1Yulre/F+WR1USgWe/eZ0zEiP9mzAQYAJC5FM/XDeWIyJD0dzpwUL//Ilvqxsdvs5jN29+NFbpeizC/j69GQsyc/0QqREgUmtUuLP385FTkY02p1JR72x263nqGntwp0vf4k9Va2I0Krx9+/NxqLZ/ByOxIgSltWrV2P06NHQ6XTIz8/H3r17L3n8O++8g0mTJkGn02H69On4+OOPB/1cEASsXLkSKSkpCA0NRWFhISoqKkYSGlHA0IWo8IdFOUiI1KKq2Ywlf9uDR9eXobnTMqzHd1r68LN/HUJNazcyYkOx6s4ZHIImclOYRo2135uNMQnhOGfswffW7hv2iGdZdRvu+MuXONVkRqpeh389VIDrnDu0k/vcTlg2bNiA4uJiPPXUUygtLUV2djaKiorQ2Dj0Hig7d+7E4sWLcd9996GsrAwLFy7EwoULceTIEdcxzz33HF566SWsWbMGe/bsQXh4OIqKitDT4/vda4nkJDsjGiU/uR73FoyCQgFsLD+Hrz2/FW/uqR5yvxO7XcDOymYUbyjH7N9+hs1fGRCiUuDPi2dCHxoiwTsg8n+x4Rq88f05SIrS4kRDBx54Yz96em2XfMzmI/W455XdaO60YmpqFN5/+GpMSuZ07JVQCG4uQcjPz8fs2bPx5z//GQBgt9uRkZGBH/3oR/j5z39+wfGLFi2C2WzGpk2bXPddddVVyMnJwZo1ayAIAlJTU/GTn/wEjz/+OADAaDQiKSkJ69atwz333HPZmEwmE/R6PYxGI6Ki+AtBgelgTTt+8f5hfHXOBADIGxWD390xDZOSo3Cm2Yx3S2vxXmkd6tr7h6zHxIfjp0UTMX96ilRhEwWM4wYT7l6zCx09fYgJC0FmXDjSo0ORHiPewpAWE4rtJ5vwu4+PQRAcO7L/aXEuC90vwp2/326dQavVigMHDmDFihWu+5RKJQoLC7Fr164hH7Nr1y4UFxcPuq+oqAgbN24EAFRVVcFgMKCwsND1c71ej/z8fOzatWtYCQtRMMjOiMYHD1+N13edxQufnsCBs234xks7MCklEkfqTK7jInVqLMhOxV156cjNiOY0EJGHTEqOwqtLZ+GBN/ajrasXbV3tOFjTftHjv3NVJn61YCp7rXiIWwlLc3MzbDYbkpKSBt2flJSE48ePD/kYg8Ew5PEGg8H1c/G+ix1zPovFAoulfx7fZDINeRxRoFGrlLjvmix8fXoyfv3hUWz+yoAjdSYoFcB1ExLwzZnpuGlKEtvtE3nJVWPisHvFjTjTYkZtW7fz1oXatm7UOf+/zS7g0cIJuP/aLF4weJBfjlGtWrUKv/71r6UOg0gyKfpQrPluHnZWNuNMSxdunJyIpCju9krkC+FaNaam6jE1VT/kz+12AUolExVPc2ucKj4+HiqVCg0Ng7v+NTQ0IDk5ecjHJCcnX/J48X/dec4VK1bAaDS6bjU1Ne68DaKAMXdcPL6dn8lkhUhGmKx4h1sJi0ajQV5eHkpKSlz32e12lJSUoKCgYMjHFBQUDDoeALZs2eI6PisrC8nJyYOOMZlM2LNnz0WfU6vVIioqatCNiIiIApfbU0LFxcW49957MWvWLMyZMwcvvvgizGYzli1bBgBYunQp0tLSsGrVKgDAI488guuvvx6///3vceutt2L9+vXYv38/XnnlFQCOboKPPvoofvvb32L8+PHIysrCk08+idTUVCxcuNBz75SIiIj8ltsJy6JFi9DU1ISVK1fCYDAgJycHmzdvdhXNVldXQ6nsH7iZO3cu3nzzTfzyl7/EL37xC4wfPx4bN27EtGn9m6898cQTMJvNePDBB9He3o5rrrkGmzdvhk7HYW4iIiIaQR8WOWIfFiIiIv/jzt9vLg4nIiIi2WPCQkRERLLHhIWIiIhkjwkLERERyR4TFiIiIpI9JixEREQke0xYiIiISPaYsBAREZHsMWEhIiIi2XO7Nb8cic16TSaTxJEQERHRcIl/t4fTdD8gEpaOjg4AQEZGhsSREBERkbs6Ojqg1+sveUxA7CVkt9tx7tw5REZGQqFQePS5TSYTMjIyUFNTw32KLoPnavh4roaP58o9PF/Dx3M1fN46V4IgoKOjA6mpqYM2Th5KQIywKJVKpKene/U1oqKi+As9TDxXw8dzNXw8V+7h+Ro+nqvh88a5utzIiohFt0RERCR7TFiIiIhI9piwXIZWq8VTTz0FrVYrdSiyx3M1fDxXw8dz5R6er+HjuRo+OZyrgCi6JSIiosDGERYiIiKSPSYsREREJHtMWIiIiEj2mLAQERGR7DFhuYzVq1dj9OjR0Ol0yM/Px969e6UOSVKrVq3C7NmzERkZicTERCxcuBAnTpwYdExPTw8efvhhxMXFISIiAt/85jfR0NAgUcTy8cwzz0ChUODRRx913cdzNVhdXR2+853vIC4uDqGhoZg+fTr279/v+rkgCFi5ciVSUlIQGhqKwsJCVFRUSBixNGw2G5588klkZWUhNDQUY8eOxW9+85tB+7EE67navn07FixYgNTUVCgUCmzcuHHQz4dzXlpbW7FkyRJERUUhOjoa9913Hzo7O334LnzjUueqt7cXP/vZzzB9+nSEh4cjNTUVS5cuxblz5wY9hy/PFROWS9iwYQOKi4vx1FNPobS0FNnZ2SgqKkJjY6PUoUlm27ZtePjhh7F7925s2bIFvb29uPnmm2E2m13HPPbYY/joo4/wzjvvYNu2bTh37hzuvPNOCaOW3r59+/DXv/4VM2bMGHQ/z1W/trY2XH311QgJCcEnn3yCo0eP4ve//z1iYmJcxzz33HN46aWXsGbNGuzZswfh4eEoKipCT0+PhJH73rPPPouXX34Zf/7zn3Hs2DE8++yzeO655/CnP/3JdUywniuz2Yzs7GysXr16yJ8P57wsWbIEX331FbZs2YJNmzZh+/btePDBB331FnzmUueqq6sLpaWlePLJJ1FaWor33nsPJ06cwG233TboOJ+eK4Euas6cOcLDDz/s+rfNZhNSU1OFVatWSRiVvDQ2NgoAhG3btgmCIAjt7e1CSEiI8M4777iOOXbsmABA2LVrl1RhSqqjo0MYP368sGXLFuH6668XHnnkEUEQeK7O97Of/Uy45pprLvpzu90uJCcnC//v//0/133t7e2CVqsV3nrrLV+EKBu33nqr8P3vf3/QfXfeeaewZMkSQRB4rkQAhPfff9/17+Gcl6NHjwoAhH379rmO+eSTTwSFQiHU1dX5LHZfO/9cDWXv3r0CAOHs2bOCIPj+XHGE5SKsVisOHDiAwsJC131KpRKFhYXYtWuXhJHJi9FoBADExsYCAA4cOIDe3t5B523SpEnIzMwM2vP28MMP49Zbbx10TgCeq/N9+OGHmDVrFu6++24kJiYiNzcXr776quvnVVVVMBgMg86XXq9Hfn5+0J2vuXPnoqSkBCdPngQAHDx4EDt27MD8+fMB8FxdzHDOy65duxAdHY1Zs2a5jiksLIRSqcSePXt8HrOcGI1GKBQKREdHA/D9uQqIzQ+9obm5GTabDUlJSYPuT0pKwvHjxyWKSl7sdjseffRRXH311Zg2bRoAwGAwQKPRuH6hRUlJSTAYDBJEKa3169ejtLQU+/btu+BnPFeDnT59Gi+//DKKi4vxi1/8Avv27cOPf/xjaDQa3Hvvva5zMtRnMtjO189//nOYTCZMmjQJKpUKNpsNv/vd77BkyRIA4Lm6iOGcF4PBgMTExEE/V6vViI2NDepz19PTg5/97GdYvHixa/NDX58rJiw0Yg8//DCOHDmCHTt2SB2KLNXU1OCRRx7Bli1boNPppA5H9ux2O2bNmoWnn34aAJCbm4sjR45gzZo1uPfeeyWOTl7efvtt/POf/8Sbb76JqVOnory8HI8++ihSU1N5rsjjent78a1vfQuCIODll1+WLA5OCV1EfHw8VCrVBSs2GhoakJycLFFU8rF8+XJs2rQJX3zxBdLT0133Jycnw2q1or29fdDxwXjeDhw4gMbGRsycORNqtRpqtRrbtm3DSy+9BLVajaSkJJ6rAVJSUjBlypRB902ePBnV1dUA4Don/EwCP/3pT/Hzn/8c99xzD6ZPn47vfve7eOyxx7Bq1SoAPFcXM5zzkpycfMHCir6+PrS2tgbluROTlbNnz2LLli2u0RXA9+eKCctFaDQa5OXloaSkxHWf3W5HSUkJCgoKJIxMWoIgYPny5Xj//ffx+eefIysra9DP8/LyEBISMui8nThxAtXV1UF33m688UYcPnwY5eXlrtusWbOwZMkS1//nuep39dVXX7BE/uTJkxg1ahQAICsrC8nJyYPOl8lkwp49e4LufHV1dUGpHPz1rVKpYLfbAfBcXcxwzktBQQHa29tx4MAB1zGff/457HY78vPzfR6zlMRkpaKiAp999hni4uIG/dzn58rjZbwBZP369YJWqxXWrVsnHD16VHjwwQeF6OhowWAwSB2aZB566CFBr9cLW7duFerr6123rq4u1zE/+MEPhMzMTOHzzz8X9u/fLxQUFAgFBQUSRi0fA1cJCQLP1UB79+4V1Gq18Lvf/U6oqKgQ/vnPfwphYWHCP/7xD9cxzzzzjBAdHS188MEHwqFDh4Tbb79dyMrKErq7uyWM3PfuvfdeIS0tTdi0aZNQVVUlvPfee0J8fLzwxBNPuI4J1nPV0dEhlJWVCWVlZQIA4YUXXhDKyspcK1uGc15uueUWITc3V9izZ4+wY8cOYfz48cLixYulektec6lzZbVahdtuu01IT08XysvLB33fWywW13P48lwxYbmMP/3pT0JmZqag0WiEOXPmCLt375Y6JEkBGPL297//3XVMd3e38MMf/lCIiYkRwsLChDvuuEOor6+XLmgZOT9h4bka7KOPPhKmTZsmaLVaYdKkScIrr7wy6Od2u1148sknhaSkJEGr1Qo33nijcOLECYmilY7JZBIeeeQRITMzU9DpdMKYMWOE//3f/x30hyRYz9UXX3wx5HfUvffeKwjC8M5LS0uLsHjxYiEiIkKIiooSli1bJnR0dEjwbrzrUueqqqrqot/3X3zxhes5fHmuFIIwoDUiERERkQyxhoWIiIhkjwkLERERyR4TFiIiIpI9JixEREQke0xYiIiISPaYsBAREZHsMWEhIiIi2WPCQkRERLLHhIWIiIhkjwkLERERyR4TFiIiIpI9JixEREQke/8/v1IMEV2W6YMAAAAASUVORK5CYII=",
+      "text/plain": "<Figure size 640x480 with 1 Axes>"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1373,8 +1385,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 640x480 with 1 Axes>",
-      "image/png": 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"
+      "image/png": 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",
+      "text/plain": "<Figure size 640x480 with 1 Axes>"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -1389,10 +1401,14 @@
     }
    ],
    "source": [
-    "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n",
-    "x = pybamm.linspace(3000,6000,100)\n",
+    "negative_electrode_exchange_current_density = param[\n",
+    "    \"Negative electrode exchange-current density [A.m-2]\"\n",
+    "]\n",
+    "x = pybamm.linspace(3000, 6000, 100)\n",
     "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n",
-    "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n",
+    "evaluated = param.evaluate(\n",
+    "    negative_electrode_exchange_current_density(1000, x, c_n_max, 300)\n",
+    ")\n",
     "evaluated = pybamm.Array(evaluated)\n",
     "pybamm.plot(x, evaluated)"
    ]
@@ -1419,12 +1435,12 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3e2a10c3de140de8cc785ae5421b534",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "e3e2a10c3de140de8cc785ae5421b534"
-      }
+       "version_minor": 0
+      },
+      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…"
      },
      "metadata": {},
      "output_type": "display_data"
diff --git a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
index d7a5fda2da..67b81b4ae6 100644
--- a/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
+++ b/docs/source/examples/notebooks/plotting/customize-quick-plot.ipynb
@@ -164,6 +164,7 @@
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
+    "\n",
     "plt.style.available"
    ]
   },
@@ -279,10 +280,10 @@
     "\n",
     "mpl.rcParams[\"axes.labelsize\"] = 12\n",
     "mpl.rcParams[\"axes.titlesize\"] = 12\n",
-    "mpl.rcParams[\"xtick.labelsize\"] =  12\n",
-    "mpl.rcParams[\"ytick.labelsize\"] =  12\n",
-    "mpl.rcParams[\"legend.fontsize\"] =  12\n",
-    "mpl.rcParams[\"axes.prop_cycle\"] = cycler('color', [\"k\", \"g\", \"c\"])\n",
+    "mpl.rcParams[\"xtick.labelsize\"] = 12\n",
+    "mpl.rcParams[\"ytick.labelsize\"] = 12\n",
+    "mpl.rcParams[\"legend.fontsize\"] = 12\n",
+    "mpl.rcParams[\"axes.prop_cycle\"] = cycler(\"color\", [\"k\", \"g\", \"c\"])\n",
     "pybamm.dynamic_plot(sims)"
    ]
   },
@@ -326,8 +327,8 @@
    "source": [
     "pybamm.settings.max_words_in_line = 4\n",
     "\n",
-    "plot = pybamm.QuickPlot(sims, figsize=(14,7))\n",
-    "plot.plot(0.5) # time in hours\n",
+    "plot = pybamm.QuickPlot(sims, figsize=(14, 7))\n",
+    "plot.plot(0.5)  # time in hours\n",
     "\n",
     "# Move title to ylabel\n",
     "for ax in plot.fig.axes:\n",
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
index 366d99c1f8..f6fb7609ae 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/callbacks.ipynb
@@ -52,7 +52,8 @@
     "import pybamm\n",
     "\n",
     "model = pybamm.lithium_ion.DFN()\n",
-    "experiment = pybamm.Experiment([\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
     "        (\n",
     "            \"Discharge at C/5 for 10 hours or until 3.3 V\",\n",
     "            \"Charge at 1 A until 4.1 V\",\n",
@@ -156,9 +157,10 @@
     "# Read the file that has been written, which was saved to callback.logfile\n",
     "with open(callback.logfile) as f:\n",
     "    print(f.read())\n",
-    "    \n",
+    "\n",
     "# Remove the log file\n",
     "import os\n",
+    "\n",
     "os.remove(callback.logfile)"
    ]
   },
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb
index 888c002c31..4dfa8c8c72 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/custom-experiments.ipynb
@@ -77,6 +77,7 @@
     "def anode_potential_cutoff(variables):\n",
     "    return variables[\"Anode potential [V]\"] - 0.02\n",
     "\n",
+    "\n",
     "# The CustomTermination class takes a name and function\n",
     "anode_potential_termination = pybamm.step.CustomTermination(\n",
     "    name=\"Anode potential cut-off [V]\", event_function=anode_potential_cutoff\n",
@@ -103,7 +104,7 @@
     "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment)\n",
     "\n",
     "# for a charge we start as SOC 0\n",
-    "sim.solve(initial_soc=0)\n"
+    "sim.solve(initial_soc=0)"
    ]
   },
   {
@@ -133,7 +134,6 @@
     }
    ],
    "source": [
-    "\n",
     "# Plot\n",
     "plot = pybamm.QuickPlot(\n",
     "    sim.solution,\n",
@@ -141,13 +141,13 @@
     "        \"Current [A]\",\n",
     "        \"Voltage [V]\",\n",
     "        \"Anode potential [V]\",\n",
-    "    ]\n",
+    "    ],\n",
     ")\n",
     "plot.plot(0)\n",
     "\n",
     "# Plot the limits used in the termination events to check they are not surpassed\n",
     "plot.axes.by_variable(\"Voltage [V]\").axhline(4.2, color=\"k\", linestyle=\":\")\n",
-    "plot.axes.by_variable(\"Anode potential [V]\").axhline(0.02, color=\"k\", linestyle=\":\")\n"
+    "plot.axes.by_variable(\"Anode potential [V]\").axhline(0.02, color=\"k\", linestyle=\":\")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb
index 4af1bd6201..60699ab6b2 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/experiments-start-time.ipynb
@@ -101,7 +101,9 @@
     }
    ],
    "source": [
-    "experiment = pybamm.Experiment([\"Discharge at 1C for 20 minutes\", \"Charge at C/3 for 10 minutes\"])\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\"Discharge at 1C for 20 minutes\", \"Charge at C/3 for 10 minutes\"]\n",
+    ")\n",
     "sim = pybamm.Simulation(model, experiment=experiment)\n",
     "sim.solve()\n",
     "sim.plot()"
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
index cbe07e3a55..fe06dadffe 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/rpt-experiment.ipynb
@@ -37,7 +37,7 @@
     "import matplotlib.pyplot as plt\n",
     "import os\n",
     "\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -79,21 +79,26 @@
    "outputs": [],
    "source": [
     "N = 10\n",
-    "cccv_experiment = pybamm.Experiment([\n",
-    "    (\"Charge at 1C until 4.2V\", \n",
-    "     \"Hold at 4.2V until C/50\",\n",
-    "     \"Discharge at 1C until 3V\",\n",
-    "     \"Rest for 1 hour\",\n",
-    "    )\n",
-    "] * N)\n",
-    "charge_experiment = pybamm.Experiment([\n",
-    "    (\"Charge at 1C until 4.2V\", \n",
-    "     \"Hold at 4.2V until C/50\",\n",
-    "    )\n",
-    "])\n",
-    "rpt_experiment = pybamm.Experiment([\n",
-    "    (\"Discharge at C/3 until 3V\",)\n",
-    "])"
+    "cccv_experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Charge at 1C until 4.2V\",\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "            \"Discharge at 1C until 3V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "        )\n",
+    "    ]\n",
+    "    * N\n",
+    ")\n",
+    "charge_experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Charge at 1C until 4.2V\",\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
+    "rpt_experiment = pybamm.Experiment([(\"Discharge at C/3 until 3V\",)])"
    ]
   },
   {
@@ -111,11 +116,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n",
+    "sim = pybamm.Simulation(\n",
+    "    model, experiment=cccv_experiment, parameter_values=parameter_values\n",
+    ")\n",
     "cccv_sol = sim.solve()\n",
-    "sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n",
+    "sim = pybamm.Simulation(\n",
+    "    model, experiment=charge_experiment, parameter_values=parameter_values\n",
+    ")\n",
     "charge_sol = sim.solve(starting_solution=cccv_sol)\n",
-    "sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n",
+    "sim = pybamm.Simulation(\n",
+    "    model, experiment=rpt_experiment, parameter_values=parameter_values\n",
+    ")\n",
     "rpt_sol = sim.solve(starting_solution=charge_sol)"
    ]
   },
@@ -214,11 +225,17 @@
     "M = 5\n",
     "for i in range(M):\n",
     "    if i != 0:  # skip the first set of ageing cycles because it's already been done\n",
-    "        sim = pybamm.Simulation(model, experiment=cccv_experiment, parameter_values=parameter_values)\n",
+    "        sim = pybamm.Simulation(\n",
+    "            model, experiment=cccv_experiment, parameter_values=parameter_values\n",
+    "        )\n",
     "        cccv_sol = sim.solve(starting_solution=rpt_sol)\n",
-    "        sim = pybamm.Simulation(model, experiment=charge_experiment, parameter_values=parameter_values)\n",
+    "        sim = pybamm.Simulation(\n",
+    "            model, experiment=charge_experiment, parameter_values=parameter_values\n",
+    "        )\n",
     "        charge_sol = sim.solve(starting_solution=cccv_sol)\n",
-    "        sim = pybamm.Simulation(model, experiment=rpt_experiment, parameter_values=parameter_values)\n",
+    "        sim = pybamm.Simulation(\n",
+    "            model, experiment=rpt_experiment, parameter_values=parameter_values\n",
+    "        )\n",
     "        rpt_sol = sim.solve(starting_solution=charge_sol)\n",
     "    cccv_sols.append(cccv_sol)\n",
     "    charge_sols.append(charge_sol)\n",
@@ -310,18 +327,30 @@
     "cccv_capacities = []\n",
     "rpt_cycles = []\n",
     "rpt_capacities = []\n",
-    "for i in range (M):\n",
+    "for i in range(M):\n",
     "    for j in range(N):\n",
-    "        cccv_cycles.append(i*(N+2)+j+1)\n",
-    "        start_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[0]\n",
-    "        end_capacity = rpt_sol.cycles[i*(N+2)+j].steps[2][\"Discharge capacity [A.h]\"].entries[-1]\n",
-    "        cccv_capacities.append(end_capacity-start_capacity)\n",
-    "    rpt_cycles.append((i+1)*(N+2))\n",
-    "    start_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[0]\n",
-    "    end_capacity = rpt_sol.cycles[(i+1)*(N+2)-1][\"Discharge capacity [A.h]\"].entries[-1]\n",
-    "    rpt_capacities.append(end_capacity-start_capacity)\n",
-    "plt.scatter(cccv_cycles,cccv_capacities,label=\"Ageing cycles\")\n",
-    "plt.scatter(rpt_cycles,rpt_capacities,label=\"RPT cycles\")\n",
+    "        cccv_cycles.append(i * (N + 2) + j + 1)\n",
+    "        start_capacity = (\n",
+    "            rpt_sol.cycles[i * (N + 2) + j]\n",
+    "            .steps[2][\"Discharge capacity [A.h]\"]\n",
+    "            .entries[0]\n",
+    "        )\n",
+    "        end_capacity = (\n",
+    "            rpt_sol.cycles[i * (N + 2) + j]\n",
+    "            .steps[2][\"Discharge capacity [A.h]\"]\n",
+    "            .entries[-1]\n",
+    "        )\n",
+    "        cccv_capacities.append(end_capacity - start_capacity)\n",
+    "    rpt_cycles.append((i + 1) * (N + 2))\n",
+    "    start_capacity = rpt_sol.cycles[(i + 1) * (N + 2) - 1][\n",
+    "        \"Discharge capacity [A.h]\"\n",
+    "    ].entries[0]\n",
+    "    end_capacity = rpt_sol.cycles[(i + 1) * (N + 2) - 1][\n",
+    "        \"Discharge capacity [A.h]\"\n",
+    "    ].entries[-1]\n",
+    "    rpt_capacities.append(end_capacity - start_capacity)\n",
+    "plt.scatter(cccv_cycles, cccv_capacities, label=\"Ageing cycles\")\n",
+    "plt.scatter(rpt_cycles, rpt_capacities, label=\"RPT cycles\")\n",
     "plt.xlabel(\"Cycle number\")\n",
     "plt.ylabel(\"Discharge capacity [A.h]\")\n",
     "plt.legend()"
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
index 890107e421..c7f1f0e634 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/simulating-long-experiments.ipynb
@@ -119,13 +119,16 @@
    "source": [
     "pybamm.set_logging_level(\"NOTICE\")\n",
     "\n",
-    "experiment = pybamm.Experiment([\n",
-    "    (\"Discharge at 1C until 3V\",\n",
-    "     \"Rest for 1 hour\",\n",
-    "     \"Charge at 1C until 4.2V\", \n",
-    "     \"Hold at 4.2V until C/50\"\n",
-    "    )\n",
-    "])\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Discharge at 1C until 3V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at 1C until 4.2V\",\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "        )\n",
+    "    ]\n",
+    ")\n",
     "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
     "sol = sim.solve()"
    ]
@@ -458,13 +461,17 @@
     }
    ],
    "source": [
-    "experiment = pybamm.Experiment([\n",
-    "    (\"Discharge at 1C until 3V\",\n",
-    "     \"Rest for 1 hour\",\n",
-    "    \"Charge at 1C until 4.2V\", \n",
-    "    \"Hold at 4.2V until C/50\")\n",
-    "] * 500,\n",
-    "termination=\"80% capacity\"\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Discharge at 1C until 3V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at 1C until 4.2V\",\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "        )\n",
+    "    ]\n",
+    "    * 500,\n",
+    "    termination=\"80% capacity\",\n",
     ")\n",
     "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
     "sol = sim.solve()"
@@ -1389,7 +1396,7 @@
     "# With integer\n",
     "sol_int = sim.solve(save_at_cycles=5)\n",
     "# With list\n",
-    "sol_list = sim.solve(save_at_cycles=[30,45,55])"
+    "sol_list = sim.solve(save_at_cycles=[30, 45, 55])"
    ]
   },
   {
@@ -1573,7 +1580,7 @@
     }
    ],
    "source": [
-    "sol_list.cycles[44].plot([\"Current [A]\",\"Voltage [V]\"])"
+    "sol_list.cycles[44].plot([\"Current [A]\", \"Voltage [V]\"])"
    ]
   },
   {
@@ -1594,7 +1601,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(1,2,figsize=(10,5))\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
     "for cycle in sol_int.cycles:\n",
     "    if cycle is not None:\n",
     "        t = cycle[\"Time [h]\"].data - cycle[\"Time [h]\"].data[0]\n",
@@ -1747,13 +1754,17 @@
     }
    ],
    "source": [
-    "experiment = pybamm.Experiment([\n",
-    "    (\"Discharge at 1C until 3V\",\n",
-    "     \"Rest for 1 hour\",\n",
-    "    \"Charge at 1C until 4.2V\", \n",
-    "    \"Hold at 4.2V until C/50\")\n",
-    "] * 10,\n",
-    "termination=\"80% capacity\"\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Discharge at 1C until 3V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at 1C until 4.2V\",\n",
+    "            \"Hold at 4.2V until C/50\",\n",
+    "        )\n",
+    "    ]\n",
+    "    * 10,\n",
+    "    termination=\"80% capacity\",\n",
     ")\n",
     "sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)\n",
     "sol = sim.solve()"
diff --git a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
index df82fa8175..db2a0fb20d 100644
--- a/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
+++ b/docs/source/examples/notebooks/simulations_and_experiments/simulation-class.ipynb
@@ -134,7 +134,9 @@
    "source": [
     "# using less number of images in the example\n",
     "# for a smoother GIF use more images\n",
-    "simulation.create_gif(number_of_images=5, duration=0.2, output_filename=\"simulation.gif\")"
+    "simulation.create_gif(\n",
+    "    number_of_images=5, duration=0.2, output_filename=\"simulation.gif\"\n",
+    ")"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
index 2849fca58c..3379b05991 100644
--- a/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
+++ b/docs/source/examples/notebooks/solution-data-and-processed-variables.ipynb
@@ -47,7 +47,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')\n",
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")\n",
     "\n",
     "# load model\n",
     "model = pybamm.lithium_ion.SPMe()\n",
@@ -106,7 +107,7 @@
     }
    ],
    "source": [
-    "solution.data['Negative particle surface concentration [mol.m-3]'].shape"
+    "solution.data[\"Negative particle surface concentration [mol.m-3]\"].shape"
    ]
   },
   {
@@ -210,7 +211,7 @@
     }
    ],
    "source": [
-    "solution['Time [h]']"
+    "solution[\"Time [h]\"]"
    ]
   },
   {
@@ -268,7 +269,7 @@
     }
    ],
    "source": [
-    "solution['Time [h]'].entries"
+    "solution[\"Time [h]\"].entries"
    ]
   },
   {
@@ -284,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])"
+    "time_in_seconds = np.array([0, 600, 900, 1700, 3000])"
    ]
   },
   {
@@ -304,7 +305,7 @@
     }
    ],
    "source": [
-    "solution['Time [h]'](time_in_seconds)"
+    "solution[\"Time [h]\"](time_in_seconds)"
    ]
   },
   {
@@ -331,7 +332,7 @@
     }
    ],
    "source": [
-    "var = 'X-averaged negative electrode temperature [K]'\n",
+    "var = \"X-averaged negative electrode temperature [K]\"\n",
     "solution[var](time_in_seconds)"
    ]
   },
@@ -359,17 +360,21 @@
    "source": [
     "# to a pickle file (default)\n",
     "solution.save_data(\n",
-    "    \"outputs.pickle\", [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"]\n",
+    "    \"outputs.pickle\",\n",
+    "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"],\n",
     ")\n",
     "# to a matlab file\n",
     "# need to give variable names without space\n",
     "solution.save_data(\n",
-    "    \"outputs.mat\", \n",
-    "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"], \n",
+    "    \"outputs.mat\",\n",
+    "    [\"Time [h]\", \"Current [A]\", \"Voltage [V]\", \"Electrolyte concentration [mol.m-3]\"],\n",
     "    to_format=\"matlab\",\n",
     "    short_names={\n",
-    "        \"Time [h]\": \"t\", \"Current [A]\": \"I\", \"Voltage [V]\": \"V\", \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
-    "    }\n",
+    "        \"Time [h]\": \"t\",\n",
+    "        \"Current [A]\": \"I\",\n",
+    "        \"Voltage [V]\": \"V\",\n",
+    "        \"Electrolyte concentration [mol.m-3]\": \"c_e\",\n",
+    "    },\n",
     ")\n",
     "# to a csv file (time-dependent outputs only, no spatial dependence allowed)\n",
     "solution.save_data(\n",
@@ -430,7 +435,7 @@
     "step_simulation = pybamm.Simulation(model)\n",
     "while time < end_time:\n",
     "    step_solution = step_simulation.step(dt)\n",
-    "    print('Time', time)\n",
+    "    print(\"Time\", time)\n",
     "    print(step_solution[\"Voltage [V]\"].entries)\n",
     "    time += dt"
    ]
@@ -483,25 +488,25 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "As a final step, we will clean up the output files created by this notebook:"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "As a final step, we will clean up the output files created by this notebook:"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "os.remove(\"outputs.csv\")\n",
     "os.remove(\"outputs.mat\")\n",
     "os.remove(\"outputs.pickle\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
diff --git a/docs/source/examples/notebooks/solvers/dae-solver.ipynb b/docs/source/examples/notebooks/solvers/dae-solver.ipynb
index 324d500df3..cb8293c676 100644
--- a/docs/source/examples/notebooks/solvers/dae-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/dae-solver.ipynb
@@ -30,7 +30,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -57,8 +58,8 @@
     "model = pybamm.BaseModel()\n",
     "u = pybamm.Variable(\"u\")\n",
     "v = pybamm.Variable(\"v\")\n",
-    "model.rhs = {u: -v} # du/dt = -v\n",
-    "model.algebraic = {v: 2 * u - v} # 2*v = u\n",
+    "model.rhs = {u: -v}  # du/dt = -v\n",
+    "model.algebraic = {v: 2 * u - v}  # 2*v = u\n",
     "model.initial_conditions = {u: 1, v: 2}\n",
     "model.variables = {\"u\": u, \"v\": v}\n",
     "\n",
@@ -103,9 +104,9 @@
     "v = solution[\"v\"]\n",
     "\n",
     "# Plot\n",
-    "t_fine = np.linspace(0,t_eval[-1],1000)\n",
+    "t_fine = np.linspace(0, t_eval[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "ax1.plot(t_fine, np.exp(-2 * t_fine), t_sol, u(t_sol), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
     "ax1.legend([\"exp(-2*t)\", \"u\"], loc=\"best\")\n",
@@ -182,10 +183,10 @@
     "model = pybamm.BaseModel()\n",
     "u = pybamm.Variable(\"u\")\n",
     "v = pybamm.Variable(\"v\")\n",
-    "model.rhs = {u: -v} # du/dt = -v\n",
-    "model.algebraic = {v: 2 * u - v} # 2*v = u\n",
+    "model.rhs = {u: -v}  # du/dt = -v\n",
+    "model.algebraic = {v: 2 * u - v}  # 2*v = u\n",
     "model.initial_conditions = {u: 1, v: 2}\n",
-    "model.events.append(pybamm.Event('v=0.2', v - 0.2)) # adding event here\n",
+    "model.events.append(pybamm.Event(\"v=0.2\", v - 0.2))  # adding event here\n",
     "\n",
     "model.variables = {\"u\": u, \"v\": v}\n",
     "\n",
@@ -205,14 +206,23 @@
     "v = solution[\"v\"]\n",
     "\n",
     "# Plot\n",
-    "t_fine = np.linspace(0,t_eval[-1],1000)\n",
+    "t_fine = np.linspace(0, t_eval[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "ax1.plot(t_fine, np.exp(-2 * t_fine), t_sol, u(t_sol), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
     "ax1.legend([\"exp(-2*t)\", \"u\"], loc=\"best\")\n",
     "\n",
-    "ax2.plot(t_fine, 2 * np.exp(-2 * t_fine), t_sol, v(t_sol), \"o\", t_fine, 0.2 * np.ones_like(t_fine), \"k\")\n",
+    "ax2.plot(\n",
+    "    t_fine,\n",
+    "    2 * np.exp(-2 * t_fine),\n",
+    "    t_sol,\n",
+    "    v(t_sol),\n",
+    "    \"o\",\n",
+    "    t_fine,\n",
+    "    0.2 * np.ones_like(t_fine),\n",
+    "    \"k\",\n",
+    ")\n",
     "ax2.set_xlabel(\"t\")\n",
     "ax2.legend([\"2*exp(-2*t)\", \"v\", \"v = 0.2\"], loc=\"best\")\n",
     "\n",
@@ -275,10 +285,10 @@
     "model = pybamm.BaseModel()\n",
     "u = pybamm.Variable(\"u\")\n",
     "v = pybamm.Variable(\"v\")\n",
-    "model.rhs = {u: -v} # du/dt = -v\n",
-    "model.algebraic = {v: 2 * u - v} # 2*v = u\n",
-    "model.initial_conditions = {u: 1, v: 1} # bad initial conditions, solver fixes\n",
-    "model.events.append(pybamm.Event('v=0.2', v - 0.2))\n",
+    "model.rhs = {u: -v}  # du/dt = -v\n",
+    "model.algebraic = {v: 2 * u - v}  # 2*v = u\n",
+    "model.initial_conditions = {u: 1, v: 1}  # bad initial conditions, solver fixes\n",
+    "model.events.append(pybamm.Event(\"v=0.2\", v - 0.2))\n",
     "model.variables = {\"u\": u, \"v\": v}\n",
     "\n",
     "# Discretise using default discretisation\n",
diff --git a/docs/source/examples/notebooks/solvers/ode-solver.ipynb b/docs/source/examples/notebooks/solvers/ode-solver.ipynb
index 992dae5980..156b7b2ada 100644
--- a/docs/source/examples/notebooks/solvers/ode-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/ode-solver.ipynb
@@ -30,7 +30,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -103,9 +104,9 @@
     "v = solution[\"v\"]\n",
     "\n",
     "# Plot\n",
-    "t_fine = np.linspace(0,t_eval[-1],1000)\n",
+    "t_fine = np.linspace(0, t_eval[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "ax1.plot(t_fine, 2 * np.cos(t_fine) - np.sin(t_fine), t_sol, u(t_sol), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
     "ax1.legend([\"2*cos(t) - sin(t)\", \"u\"], loc=\"best\")\n",
@@ -184,7 +185,7 @@
     "v = pybamm.Variable(\"v\")\n",
     "model.rhs = {u: -v, v: u}\n",
     "model.initial_conditions = {u: 2, v: 1}\n",
-    "model.events.append(pybamm.Event('v=-2', v + 2)) # New termination event\n",
+    "model.events.append(pybamm.Event(\"v=-2\", v + 2))  # New termination event\n",
     "model.variables = {\"u\": u, \"v\": v}\n",
     "\n",
     "# Discretise using default discretisation\n",
@@ -203,14 +204,23 @@
     "v = solution[\"v\"]\n",
     "\n",
     "# Plot\n",
-    "t_fine = np.linspace(0,t_eval[-1],1000)\n",
+    "t_fine = np.linspace(0, t_eval[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "ax1.plot(t_fine, 2 * np.cos(t_fine) - np.sin(t_fine), t_sol, u(t_sol), \"o\")\n",
     "ax1.set_xlabel(\"t\")\n",
     "ax1.legend([\"2*cos(t) - sin(t)\", \"u\"], loc=\"best\")\n",
     "\n",
-    "ax2.plot(t_fine, 2 * np.sin(t_fine) + np.cos(t_fine), t_sol, v(t_sol), \"o\", t_fine, -2 * np.ones_like(t_fine), \"k\")\n",
+    "ax2.plot(\n",
+    "    t_fine,\n",
+    "    2 * np.sin(t_fine) + np.cos(t_fine),\n",
+    "    t_sol,\n",
+    "    v(t_sol),\n",
+    "    \"o\",\n",
+    "    t_fine,\n",
+    "    -2 * np.ones_like(t_fine),\n",
+    "    \"k\",\n",
+    ")\n",
     "ax2.set_xlabel(\"t\")\n",
     "ax2.legend([\"2*sin(t) + cos(t)\", \"v\", \"v = -2\"], loc=\"best\")\n",
     "\n",
diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 80dc0ddb88..c49c8926fb 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -98,7 +98,9 @@
     "sim = pybamm.Simulation(model, parameter_values=param)\n",
     "\n",
     "# Set up solvers. Reduce max_num_steps for the fast solver, for faster errors\n",
-    "fast_solver = pybamm.CasadiSolver(mode=\"fast\", extra_options_setup={\"max_num_steps\": 1000})\n",
+    "fast_solver = pybamm.CasadiSolver(\n",
+    "    mode=\"fast\", extra_options_setup={\"max_num_steps\": 1000}\n",
+    ")\n",
     "safe_solver = pybamm.CasadiSolver(mode=\"safe\")"
    ]
   },
@@ -134,8 +136,8 @@
     }
    ],
    "source": [
-    "safe_sol = sim.solve([0,3700], solver=safe_solver, inputs={\"Crate\": 1})\n",
-    "fast_sol = sim.solve([0,3700], solver=fast_solver, inputs={\"Crate\": 1})\n",
+    "safe_sol = sim.solve([0, 3700], solver=safe_solver, inputs={\"Crate\": 1})\n",
+    "fast_sol = sim.solve([0, 3700], solver=fast_solver, inputs={\"Crate\": 1})\n",
     "\n",
     "timer = pybamm.Timer()\n",
     "print(\"Safe:\", safe_sol.solve_time)\n",
@@ -144,7 +146,12 @@
     "cutoff = param[\"Lower voltage cut-off [V]\"]\n",
     "plt.plot(fast_sol[\"Time [h]\"].data, fast_sol[\"Voltage [V]\"].data, \"b-\", label=\"Fast\")\n",
     "plt.plot(safe_sol[\"Time [h]\"].data, safe_sol[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n",
-    "plt.plot(fast_sol[\"Time [h]\"].data, cutoff * np.ones_like(fast_sol[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n",
+    "plt.plot(\n",
+    "    fast_sol[\"Time [h]\"].data,\n",
+    "    cutoff * np.ones_like(fast_sol[\"Time [h]\"].data),\n",
+    "    \"k--\",\n",
+    "    label=\"Voltage cut-off\",\n",
+    ")\n",
     "plt.legend();"
    ]
   },
@@ -196,16 +203,21 @@
     }
    ],
    "source": [
-    "safe_sol = sim.solve([0,4500], solver=safe_solver, inputs={\"Crate\": 1})\n",
+    "safe_sol = sim.solve([0, 4500], solver=safe_solver, inputs={\"Crate\": 1})\n",
     "\n",
     "print(\"Safe:\", safe_sol.solve_time)\n",
     "\n",
     "plt.plot(safe_sol[\"Time [h]\"].data, safe_sol[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n",
-    "plt.plot(safe_sol[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n",
+    "plt.plot(\n",
+    "    safe_sol[\"Time [h]\"].data,\n",
+    "    cutoff * np.ones_like(safe_sol[\"Time [h]\"].data),\n",
+    "    \"k--\",\n",
+    "    label=\"Voltage cut-off\",\n",
+    ")\n",
     "plt.legend()\n",
     "\n",
     "try:\n",
-    "    sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n",
+    "    sim.solve([0, 4500], solver=fast_solver, inputs={\"Crate\": 1})\n",
     "except pybamm.SolverError as e:\n",
     "    print(\"Solving fast mode, error occurred:\", e.args[0])"
    ]
@@ -238,13 +250,17 @@
     }
    ],
    "source": [
-    "fast_sol = sim.solve([0,4049], solver=fast_solver, inputs={\"Crate\": 1})\n",
-    "fast_sol.plot([\n",
-    "    \"Minimum negative particle surface concentration\",\n",
-    "    \"Electrolyte concentration [mol.m-3]\",\n",
-    "    \"Maximum positive particle surface concentration\",\n",
-    "    \"Voltage [V]\",\n",
-    "], time_unit=\"seconds\", figsize=(9,9));"
+    "fast_sol = sim.solve([0, 4049], solver=fast_solver, inputs={\"Crate\": 1})\n",
+    "fast_sol.plot(\n",
+    "    [\n",
+    "        \"Minimum negative particle surface concentration\",\n",
+    "        \"Electrolyte concentration [mol.m-3]\",\n",
+    "        \"Maximum positive particle surface concentration\",\n",
+    "        \"Voltage [V]\",\n",
+    "    ],\n",
+    "    time_unit=\"seconds\",\n",
+    "    figsize=(9, 9),\n",
+    ");"
    ]
   },
   {
@@ -285,9 +301,16 @@
     }
    ],
    "source": [
-    "safe_sol_160 = sim.solve([0,160], solver=safe_solver, inputs={\"Crate\": 10})\n",
-    "plt.plot(safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"r-\", label=\"Safe\")\n",
-    "plt.plot(safe_sol_160[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol_160[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n",
+    "safe_sol_160 = sim.solve([0, 160], solver=safe_solver, inputs={\"Crate\": 10})\n",
+    "plt.plot(\n",
+    "    safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"r-\", label=\"Safe\"\n",
+    ")\n",
+    "plt.plot(\n",
+    "    safe_sol_160[\"Time [h]\"].data,\n",
+    "    cutoff * np.ones_like(safe_sol_160[\"Time [h]\"].data),\n",
+    "    \"k--\",\n",
+    "    label=\"Voltage cut-off\",\n",
+    ")\n",
     "plt.legend();"
    ]
   },
@@ -315,10 +338,25 @@
     }
    ],
    "source": [
-    "safe_sol_150 = sim.solve([0,150], solver=safe_solver, inputs={\"Crate\": 10})\n",
-    "plt.plot(safe_sol_150[\"Time [h]\"].data, safe_sol_150[\"Voltage [V]\"].data, \"r-\", label=\"Safe [0,150]\")\n",
-    "plt.plot(safe_sol_160[\"Time [h]\"].data, safe_sol_160[\"Voltage [V]\"].data, \"b.\", label=\"Safe [0,160]\")\n",
-    "plt.plot(safe_sol_150[\"Time [h]\"].data, cutoff * np.ones_like(safe_sol_150[\"Time [h]\"].data), \"k--\", label=\"Voltage cut-off\")\n",
+    "safe_sol_150 = sim.solve([0, 150], solver=safe_solver, inputs={\"Crate\": 10})\n",
+    "plt.plot(\n",
+    "    safe_sol_150[\"Time [h]\"].data,\n",
+    "    safe_sol_150[\"Voltage [V]\"].data,\n",
+    "    \"r-\",\n",
+    "    label=\"Safe [0,150]\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    safe_sol_160[\"Time [h]\"].data,\n",
+    "    safe_sol_160[\"Voltage [V]\"].data,\n",
+    "    \"b.\",\n",
+    "    label=\"Safe [0,160]\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    safe_sol_150[\"Time [h]\"].data,\n",
+    "    cutoff * np.ones_like(safe_sol_150[\"Time [h]\"].data),\n",
+    "    \"k--\",\n",
+    "    label=\"Voltage cut-off\",\n",
+    ")\n",
     "plt.legend();"
    ]
   },
@@ -329,7 +367,7 @@
    "outputs": [],
    "source": [
     "safe_solver_2 = pybamm.CasadiSolver(mode=\"safe\", dt_max=30)\n",
-    "safe_sol_2 = sim.solve([0,160], solver=safe_solver_2, inputs={\"Crate\": 10})"
+    "safe_sol_2 = sim.solve([0, 160], solver=safe_solver_2, inputs={\"Crate\": 10})"
    ]
   },
   {
@@ -365,18 +403,22 @@
     }
    ],
    "source": [
-    "for dt_max in [10,20,100,1000,3700]:\n",
+    "for dt_max in [10, 20, 100, 1000, 3700]:\n",
     "    safe_sol = sim.solve(\n",
-    "        [0,3600], \n",
+    "        [0, 3600],\n",
     "        solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=dt_max),\n",
-    "        inputs={\"Crate\": 1}\n",
+    "        inputs={\"Crate\": 1},\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"\n",
+    "        + f\"(integration time: {safe_sol.integration_time})\"\n",
     "    )\n",
-    "    print(f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"+\n",
-    "          f\"(integration time: {safe_sol.integration_time})\")\n",
     "\n",
-    "fast_sol = sim.solve([0,3600], solver=fast_solver, inputs={\"Crate\": 1})\n",
-    "print(f\"With 'fast' mode, took {fast_sol.solve_time} \"+\n",
-    "      f\"(integration time: {fast_sol.integration_time})\")"
+    "fast_sol = sim.solve([0, 3600], solver=fast_solver, inputs={\"Crate\": 1})\n",
+    "print(\n",
+    "    f\"With 'fast' mode, took {fast_sol.solve_time} \"\n",
+    "    + f\"(integration time: {fast_sol.integration_time})\"\n",
+    ")"
    ]
   },
   {
@@ -429,15 +471,19 @@
     }
    ],
    "source": [
-    "for dt_max in [10,20,100,1000,3600]:\n",
+    "for dt_max in [10, 20, 100, 1000, 3600]:\n",
     "    # Reduce max_num_steps to fail faster\n",
     "    safe_sol = sim.solve(\n",
-    "        [0,4500], \n",
-    "        solver=pybamm.CasadiSolver(mode=\"safe\", dt_max=dt_max, extra_options_setup={\"max_num_steps\": 1000}),\n",
-    "        inputs={\"Crate\": 1}\n",
+    "        [0, 4500],\n",
+    "        solver=pybamm.CasadiSolver(\n",
+    "            mode=\"safe\", dt_max=dt_max, extra_options_setup={\"max_num_steps\": 1000}\n",
+    "        ),\n",
+    "        inputs={\"Crate\": 1},\n",
     "    )\n",
-    "    print(f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"+\n",
-    "          f\"(integration time: {safe_sol.integration_time})\")"
+    "    print(\n",
+    "        f\"With dt_max={dt_max}, took {safe_sol.solve_time} \"\n",
+    "        + f\"(integration time: {safe_sol.integration_time})\"\n",
+    "    )"
    ]
   },
   {
@@ -763,7 +809,7 @@
     "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Softplus\n",
-    "print(\"Softplus (k=10): \", pybamm.softplus(x,y,10))\n",
+    "print(\"Softplus (k=10): \", pybamm.softplus(x, y, 10))\n",
     "\n",
     "# Changing the setting to call softplus automatically\n",
     "pybamm.settings.min_max_mode = \"soft\"\n",
@@ -804,9 +850,9 @@
     "a = pybamm.InputParameter(\"a\")\n",
     "pybamm.settings.max_smoothing = 20\n",
     "# Both inputs are constant so uses exact maximum\n",
-    "print(\"Exact:\", pybamm.maximum(0.999,1).evaluate())\n",
+    "print(\"Exact:\", pybamm.maximum(0.999, 1).evaluate())\n",
     "# One input is not constant (InputParameter) so uses softplus\n",
-    "print(\"Softplus:\", pybamm.maximum(a,1).evaluate(inputs={\"a\": 0.999}))\n",
+    "print(\"Softplus:\", pybamm.maximum(a, 1).evaluate(inputs={\"a\": 0.999}))\n",
     "pybamm.settings.set_smoothing_parameters(\"exact\")"
    ]
   },
@@ -836,11 +882,29 @@
    "source": [
     "pts = pybamm.linspace(0, 2, 100)\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(10,5))\n",
-    "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n",
-    "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,5).evaluate(), \":\", lw=2, label=\"softplus (k=5)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,10).evaluate(), \":\", lw=2, label=\"softplus (k=10)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.softplus(pts,1,100).evaluate(), \":\", lw=2, label=\"softplus (k=100)\")\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "ax.plot(pts.evaluate(), pybamm.maximum(pts, 1).evaluate(), lw=2, label=\"exact\")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.softplus(pts, 1, 5).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"softplus (k=5)\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.softplus(pts, 1, 10).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"softplus (k=10)\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.softplus(pts, 1, 100).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"softplus (k=100)\",\n",
+    ")\n",
     "ax.legend();"
    ]
   },
@@ -902,7 +966,7 @@
     "    exact_sol = solver.solve(model_exact, [0, 2])\n",
     "    # Report integration time, which is the time spent actually doing the integration\n",
     "    time += exact_sol.integration_time\n",
-    "print(\"Exact:\", time/100)\n",
+    "print(\"Exact:\", time / 100)\n",
     "sols = [exact_sol]\n",
     "\n",
     "ks = [5, 10, 100]\n",
@@ -912,10 +976,12 @@
     "    for _ in range(100):\n",
     "        sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n",
     "        time += sol.integration_time\n",
-    "    print(f\"Soft, k={k}:\", time/100)\n",
+    "    print(f\"Soft, k={k}:\", time / 100)\n",
     "    sols.append(sol)\n",
     "\n",
-    "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);"
+    "pybamm.dynamic_plot(\n",
+    "    sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]\n",
+    ");"
    ]
   },
   {
@@ -962,7 +1028,7 @@
     "print(f\"Exact maximum: {pybamm.maximum(x,y)}\")\n",
     "\n",
     "# Smooth plus can be called explicitly\n",
-    "print(\"Smooth plus (k=100): \", pybamm.smooth_max(x,y,100))\n",
+    "print(\"Smooth plus (k=100): \", pybamm.smooth_max(x, y, 100))\n",
     "\n",
     "# Smooth plus and smooth minus will be used when the mode is set to \"smooth\"\n",
     "pybamm.settings.min_max_mode = \"smooth\"\n",
@@ -1004,11 +1070,29 @@
    "source": [
     "pts = pybamm.linspace(0, 2, 100)\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(10,5))\n",
-    "ax.plot(pts.evaluate(), pybamm.maximum(pts,1).evaluate(), lw=2, label=\"exact\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,5).evaluate(), \":\", lw=2, label=\"smooth_max (k=5)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,10).evaluate(), \":\", lw=2, label=\"smooth_max (k=10)\")\n",
-    "ax.plot(pts.evaluate(), pybamm.smooth_max(pts,1,100).evaluate(), \":\", lw=2, label=\"smooth_max (k=100)\")\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "ax.plot(pts.evaluate(), pybamm.maximum(pts, 1).evaluate(), lw=2, label=\"exact\")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.smooth_max(pts, 1, 5).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"smooth_max (k=5)\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.smooth_max(pts, 1, 10).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"smooth_max (k=10)\",\n",
+    ")\n",
+    "ax.plot(\n",
+    "    pts.evaluate(),\n",
+    "    pybamm.smooth_max(pts, 1, 100).evaluate(),\n",
+    "    \":\",\n",
+    "    lw=2,\n",
+    "    label=\"smooth_max (k=100)\",\n",
+    ")\n",
     "ax.legend();"
    ]
   },
@@ -1072,7 +1156,7 @@
     "    exact_sol = solver.solve(model_exact, [0, 2])\n",
     "    # Report integration time, which is the time spent actually doing the integration\n",
     "    time += exact_sol.integration_time\n",
-    "print(\"Exact:\", time/100)\n",
+    "print(\"Exact:\", time / 100)\n",
     "sols = [exact_sol]\n",
     "\n",
     "ks = [10, 50, 100, 1000, 10000]\n",
@@ -1082,10 +1166,12 @@
     "    for _ in range(100):\n",
     "        sol = solver.solve(model_smooth, [0, 2], inputs={\"k\": k})\n",
     "        time += sol.integration_time\n",
-    "    print(f\"Smooth, k={k}:\", time/100)\n",
+    "    print(f\"Smooth, k={k}:\", time / 100)\n",
     "    sols.append(sol)\n",
     "\n",
-    "pybamm.dynamic_plot(sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]);"
+    "pybamm.dynamic_plot(\n",
+    "    sols, [\"x\", \"max(x,1)\"], labels=[\"exact\"] + [f\"soft (k={k})\" for k in ks]\n",
+    ");"
    ]
   },
   {
diff --git a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
index a0725d4dd3..849f1bdf47 100644
--- a/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
+++ b/docs/source/examples/notebooks/spatial_methods/finite-volumes.ipynb
@@ -67,7 +67,8 @@
     "import numpy as np\n",
     "import os\n",
     "import matplotlib.pyplot as plt\n",
-    "os.chdir(pybamm.__path__[0]+'/..')"
+    "\n",
+    "os.chdir(pybamm.__path__[0] + \"/..\")"
    ]
   },
   {
@@ -199,7 +200,7 @@
     }
    ],
    "source": [
-    "# Set up \n",
+    "# Set up\n",
     "macroscale = [\"negative electrode\", \"separator\", \"positive electrode\"]\n",
     "x_var = pybamm.SpatialVariable(\"x\", domain=macroscale)\n",
     "r_var = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"])\n",
@@ -214,7 +215,7 @@
     "x = x_disc.evaluate()\n",
     "r = r_disc.evaluate()\n",
     "\n",
-    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
+    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
     "\n",
     "ax1.plot(x, \"*\")\n",
     "ax1.set_xlabel(\"index\")\n",
@@ -242,7 +243,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_macroscale = x ** 3 / 3\n",
+    "y_macroscale = x**3 / 3\n",
     "y_microscale = np.cos(r)\n",
     "y_scalar = np.array([[5]])\n",
     "\n",
@@ -271,11 +272,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "u = pybamm.Variable(\"u\", domain=macroscale) # u is a variable in the macroscale (e.g. electrolyte potential)\n",
-    "v = pybamm.Variable(\"v\", domain=[\"negative particle\"]) # v is a variable in the negative particle (e.g. particle concentration)\n",
-    "w = pybamm.Variable(\"w\") # w is a variable without a domain (e.g. time, average concentration)\n",
+    "u = pybamm.Variable(\n",
+    "    \"u\", domain=macroscale\n",
+    ")  # u is a variable in the macroscale (e.g. electrolyte potential)\n",
+    "v = pybamm.Variable(\n",
+    "    \"v\", domain=[\"negative particle\"]\n",
+    ")  # v is a variable in the negative particle (e.g. particle concentration)\n",
+    "w = pybamm.Variable(\n",
+    "    \"w\"\n",
+    ")  # w is a variable without a domain (e.g. time, average concentration)\n",
     "\n",
-    "variables = [u,v,w]"
+    "variables = [u, v, w]"
    ]
   },
   {
@@ -304,7 +311,7 @@
    "source": [
     "try:\n",
     "    u.evaluate()\n",
-    "except NotImplementedError as e: \n",
+    "except NotImplementedError as e:\n",
     "    print(e)"
    ]
   },
@@ -342,7 +349,7 @@
     "v_disc = disc.process_symbol(v)\n",
     "w_disc = disc.process_symbol(w)\n",
     "\n",
-    "# Print the outcome \n",
+    "# Print the outcome\n",
     "print(f\"Discretised u is the StateVector {u_disc}\")\n",
     "print(f\"Discretised v is the StateVector {v_disc}\")\n",
     "print(f\"Discretised w is the StateVector {w_disc}\")"
@@ -376,8 +383,8 @@
     "x_fine = np.linspace(x[0], x[-1], 1000)\n",
     "r_fine = np.linspace(r[0], r[-1], 1000)\n",
     "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,4))\n",
-    "ax1.plot(x_fine, x_fine**3/3, x, u_disc.evaluate(y=y), \"o\")\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
+    "ax1.plot(x_fine, x_fine**3 / 3, x, u_disc.evaluate(y=y), \"o\")\n",
     "ax1.set_xlabel(\"x\")\n",
     "ax1.legend([\"x^3/3\", \"u\"], loc=\"best\")\n",
     "\n",
@@ -484,7 +491,9 @@
    "source": [
     "macro_mesh = mesh.combine_submeshes(*macroscale)\n",
     "print(\"gradient matrix is:\\n\")\n",
-    "print(f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\")"
+    "print(\n",
+    "    f\"1/dx *\\n{macro_mesh.d_nodes[:,np.newaxis] * grad_u_disc.children[0].entries.toarray()}\"\n",
+    ")"
    ]
   },
   {
@@ -512,7 +521,7 @@
     }
    ],
    "source": [
-    "x_edge = macro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n",
+    "x_edge = macro_mesh.edges[1:-1]  # note that grad_u_disc is evaluated on the node edges\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(x_fine, x_fine**2, x_edge, grad_u_disc.evaluate(y=y), \"o\")\n",
@@ -600,9 +609,11 @@
     "\n",
     "micro_mesh = mesh[\"negative particle\"]\n",
     "print(\"\\n gradient matrix is:\\n\")\n",
-    "print(f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\")\n",
+    "print(\n",
+    "    f\"1/dr *\\n{micro_mesh.d_nodes[:,np.newaxis] * grad_v_disc.children[0].entries.toarray()}\"\n",
+    ")\n",
     "\n",
-    "r_edge = micro_mesh.edges[1:-1] # note that grad_u_disc is evaluated on the node edges\n",
+    "r_edge = micro_mesh.edges[1:-1]  # note that grad_u_disc is evaluated on the node edges\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(r_fine, -np.sin(r_fine), r_edge, grad_v_disc.evaluate(y=y), \"o\")\n",
@@ -655,7 +666,12 @@
     }
    ],
    "source": [
-    "disc.bcs = {u: {\"left\": (pybamm.Scalar(1), \"Dirichlet\"), \"right\": (pybamm.Scalar(2), \"Dirichlet\")}}\n",
+    "disc.bcs = {\n",
+    "    u: {\n",
+    "        \"left\": (pybamm.Scalar(1), \"Dirichlet\"),\n",
+    "        \"right\": (pybamm.Scalar(2), \"Dirichlet\"),\n",
+    "    }\n",
+    "}\n",
     "grad_u_disc = disc.process_symbol(grad_u)\n",
     "print(\"The gradient object is:\")\n",
     "(grad_u_disc.render())\n",
@@ -699,7 +715,9 @@
     }
    ],
    "source": [
-    "disc.bcs = {u: {\"left\": (pybamm.Scalar(3), \"Neumann\"), \"right\": (pybamm.Scalar(4), \"Neumann\")}}\n",
+    "disc.bcs = {\n",
+    "    u: {\"left\": (pybamm.Scalar(3), \"Neumann\"), \"right\": (pybamm.Scalar(4), \"Neumann\")}\n",
+    "}\n",
     "grad_u_disc = disc.process_symbol(grad_u)\n",
     "print(\"The gradient object is:\")\n",
     "(grad_u_disc.render())\n",
@@ -739,7 +757,9 @@
     }
    ],
    "source": [
-    "disc.bcs = {u: {\"left\": (pybamm.Scalar(5), \"Dirichlet\"), \"right\": (pybamm.Scalar(6), \"Neumann\")}}\n",
+    "disc.bcs = {\n",
+    "    u: {\"left\": (pybamm.Scalar(5), \"Dirichlet\"), \"right\": (pybamm.Scalar(6), \"Neumann\")}\n",
+    "}\n",
     "grad_u_disc = disc.process_symbol(grad_u)\n",
     "print(\"The gradient object is:\")\n",
     "(grad_u_disc.render())\n",
@@ -779,7 +799,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "disc.bcs = {u: {\"left\": (pybamm.Scalar(-1), \"Neumann\"), \"right\": (pybamm.Scalar(1), \"Neumann\")}}"
+    "disc.bcs = {\n",
+    "    u: {\"left\": (pybamm.Scalar(-1), \"Neumann\"), \"right\": (pybamm.Scalar(1), \"Neumann\")}\n",
+    "}"
    ]
   },
   {
@@ -849,9 +871,12 @@
    ],
    "source": [
     "print(\"div(grad) matrix is:\\n\")\n",
-    "print(\"1/dx^2 * \\n{}\".format(\n",
-    "    macro_mesh.d_edges[:,np.newaxis]**2 * div_grad_u_disc.right.left.entries.toarray()\n",
-    "))"
+    "print(\n",
+    "    \"1/dx^2 * \\n{}\".format(\n",
+    "        macro_mesh.d_edges[:, np.newaxis] ** 2\n",
+    "        * div_grad_u_disc.right.left.entries.toarray()\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -892,10 +917,12 @@
     "print(f\"int(u) = {int_u_disc.evaluate(y=y)} is approximately equal to 1/12, {1/12}\")\n",
     "\n",
     "# We divide v by r to evaluate the integral more easily\n",
-    "int_v_over_r2 = pybamm.Integral(v/r_var**2, r_var)\n",
+    "int_v_over_r2 = pybamm.Integral(v / r_var**2, r_var)\n",
     "int_v_over_r2_disc = disc.process_symbol(int_v_over_r2)\n",
-    "print(\"int(v/r^2) = {} is approximately equal to 4 * pi * sin(1), {}\".format(\n",
-    "    int_v_over_r2_disc.evaluate(y=y), 4 * np.pi * np.sin(1))\n",
+    "print(\n",
+    "    \"int(v/r^2) = {} is approximately equal to 4 * pi * sin(1), {}\".format(\n",
+    "        int_v_over_r2_disc.evaluate(y=y), 4 * np.pi * np.sin(1)\n",
+    "    )\n",
     ")"
    ]
   },
@@ -999,7 +1026,7 @@
     "    c_e: {\"left\": (np.cos(0), \"Neumann\"), \"right\": (np.cos(10), \"Neumann\")},\n",
     "    c_s: {\"left\": (0, \"Neumann\"), \"right\": (-1, \"Neumann\")},\n",
     "}\n",
-    "model.initial_conditions = {c_e: 1 + 0.1 * pybamm.sin(10*x_var), c_s: 1}\n",
+    "model.initial_conditions = {c_e: 1 + 0.1 * pybamm.sin(10 * x_var), c_s: 1}\n",
     "\n",
     "# Create a new discretisation and process model\n",
     "disc2 = pybamm.Discretisation(mesh, spatial_methods)\n",
@@ -1035,8 +1062,8 @@
     "c_s_0 = model.initial_conditions[c_s].evaluate()\n",
     "y0 = model.concatenated_initial_conditions.evaluate()\n",
     "\n",
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n",
-    "ax1.plot(x_fine, 1 + 0.1*np.sin(10*x_fine), x, c_e_0, \"o\")\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13, 4))\n",
+    "ax1.plot(x_fine, 1 + 0.1 * np.sin(10 * x_fine), x, c_e_0, \"o\")\n",
     "ax1.set_xlabel(\"x\")\n",
     "ax1.legend([\"1+0.1*sin(10*x)\", \"c_e_0\"], loc=\"best\")\n",
     "\n",
@@ -1044,7 +1071,7 @@
     "ax2.set_xlabel(\"r\")\n",
     "ax2.legend([\"1\", \"c_s_0\"], loc=\"best\")\n",
     "\n",
-    "ax3.plot(y0,\"*\")\n",
+    "ax3.plot(y0, \"*\")\n",
     "ax3.set_xlabel(\"index\")\n",
     "ax3.set_ylabel(\"y0\")\n",
     "\n",
@@ -1081,8 +1108,8 @@
     "rhs_c_s = model.rhs[c_s].evaluate(0, y0)\n",
     "rhs = model.concatenated_rhs.evaluate(0, y0)\n",
     "\n",
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n",
-    "ax1.plot(x_fine, -10*np.sin(10*x_fine) - 5, x, rhs_c_e, \"o\")\n",
+    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13, 4))\n",
+    "ax1.plot(x_fine, -10 * np.sin(10 * x_fine) - 5, x, rhs_c_e, \"o\")\n",
     "ax1.set_xlabel(\"x\")\n",
     "ax1.set_ylabel(\"rhs_c_e\")\n",
     "ax1.legend([\"1+0.1*sin(10*x)\", \"c_e_0\"], loc=\"best\")\n",
@@ -1091,7 +1118,7 @@
     "ax2.set_xlabel(\"r\")\n",
     "ax2.set_ylabel(\"rhs_c_s\")\n",
     "\n",
-    "ax3.plot(rhs,\"*\")\n",
+    "ax3.plot(rhs, \"*\")\n",
     "ax3.set_xlabel(\"index\")\n",
     "ax3.set_ylabel(\"rhs\")\n",
     "\n",
@@ -1147,8 +1174,12 @@
     "model = pybamm.BaseModel()\n",
     "\n",
     "# Define concentration and velocity\n",
-    "c = pybamm.Variable(\"c\", domain=[\"negative electrode\", \"separator\", \"positive electrode\"])\n",
-    "v = pybamm.PrimaryBroadcastToEdges(1, [\"negative electrode\", \"separator\", \"positive electrode\"])\n",
+    "c = pybamm.Variable(\n",
+    "    \"c\", domain=[\"negative electrode\", \"separator\", \"positive electrode\"]\n",
+    ")\n",
+    "v = pybamm.PrimaryBroadcastToEdges(\n",
+    "    1, [\"negative electrode\", \"separator\", \"positive electrode\"]\n",
+    ")\n",
     "model.rhs = {c: -pybamm.div(c * v) + 1}\n",
     "model.initial_conditions = {c: 0}\n",
     "model.boundary_conditions = {c: {\"left\": (0, \"Dirichlet\")}}\n",
@@ -1158,13 +1189,13 @@
     "def solve_and_plot(model):\n",
     "    model_disc = disc.process_model(model, inplace=False)\n",
     "\n",
-    "    t_eval = [0,100]\n",
+    "    t_eval = [0, 100]\n",
     "    solution = pybamm.CasadiSolver().solve(model_disc, t_eval)\n",
     "\n",
     "    # plot\n",
-    "    plot = pybamm.QuickPlot(solution,[\"c\"],spatial_unit=\"m\")\n",
+    "    plot = pybamm.QuickPlot(solution, [\"c\"], spatial_unit=\"m\")\n",
     "    plot.dynamic_plot()\n",
-    "    \n",
+    "\n",
     "\n",
     "solve_and_plot(model)"
    ]
@@ -1198,7 +1229,7 @@
     }
    ],
    "source": [
-    "model.rhs = {c: -pybamm.div(pybamm.upwind(c) * v) + 1}    \n",
+    "model.rhs = {c: -pybamm.div(pybamm.upwind(c) * v) + 1}\n",
     "solve_and_plot(model)"
    ]
   },
@@ -1231,7 +1262,7 @@
     }
    ],
    "source": [
-    "model.rhs = {c: -pybamm.div(pybamm.downwind(c) * (-v)) + 1}   \n",
+    "model.rhs = {c: -pybamm.div(pybamm.downwind(c) * (-v)) + 1}\n",
     "model.boundary_conditions = {c: {\"right\": (0, \"Dirichlet\")}}\n",
     "solve_and_plot(model)"
    ]
diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py
index 7544730eea..9e437ce301 100644
--- a/examples/scripts/compare_comsol/discharge_curve.py
+++ b/examples/scripts/compare_comsol/discharge_curve.py
@@ -53,7 +53,7 @@
 plt.grid(True)
 plt.xlabel(r"Discharge Capacity (Ah)", fontsize=20)
 plt.ylabel(r"$\vert V - V_{comsol} \vert$", fontsize=20)
-colors = iter(plt.cycler(color='bgrcmyk'))
+colors = iter(plt.cycler(color="bgrcmyk"))
 
 for key, C_rate in C_rates.items():
     current = 24 * C_rate
diff --git a/examples/scripts/heat_equation.py b/examples/scripts/heat_equation.py
index 20f9601090..fd01b37f97 100644
--- a/examples/scripts/heat_equation.py
+++ b/examples/scripts/heat_equation.py
@@ -119,9 +119,7 @@ def T_exact(x, t):
         color=color,
         label="Numerical" if i == 0 else "",
     )
-    plt.plot(
-        xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})"
-    )
+    plt.plot(xx, T_exact(xx, t), "-", color=color, label=f"Exact (t={plot_times[i]})")
 plt.xlabel("x", fontsize=16)
 plt.ylabel("T", fontsize=16)
 plt.legend()
diff --git a/pybamm/discretisations/discretisation.py b/pybamm/discretisations/discretisation.py
index c250d06e9c..7be3b2bc53 100644
--- a/pybamm/discretisations/discretisation.py
+++ b/pybamm/discretisations/discretisation.py
@@ -175,13 +175,9 @@ def process_model(
         self.set_variable_slices(variables)
 
         # set boundary conditions (only need key ids for boundary_conditions)
-        pybamm.logger.verbose(
-            f"Discretise boundary conditions for {model.name}"
-        )
+        pybamm.logger.verbose(f"Discretise boundary conditions for {model.name}")
         self._bcs = self.process_boundary_conditions(model)
-        pybamm.logger.verbose(
-            f"Set internal boundary conditions for {model.name}"
-        )
+        pybamm.logger.verbose(f"Set internal boundary conditions for {model.name}")
         self.set_internal_boundary_conditions(model)
 
         # set up inplace vs not inplace
@@ -898,9 +894,7 @@ def _process_symbol(self, symbol):
                     No key set for variable '{}'. Make sure it is included in either
                     model.rhs or model.algebraic in an unmodified form
                     (e.g. not Broadcasted)
-                    """.format(
-                        symbol.name
-                    )
+                    """.format(symbol.name)
                 )
             # Add symbol's reference and multiply by the symbol's scale
             # so that the state vector is of order 1
diff --git a/pybamm/expression_tree/binary_operators.py b/pybamm/expression_tree/binary_operators.py
index 7348e08712..3d70741785 100644
--- a/pybamm/expression_tree/binary_operators.py
+++ b/pybamm/expression_tree/binary_operators.py
@@ -1336,8 +1336,8 @@ def smooth_min(left, right, k):
     Smooth_min approximation to the minimum function. k is the smoothing parameter,
     set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
-    sigma = (1.0 / k)**2
-    return ((left + right) - (pybamm.sqrt((left - right)**2 + sigma))) / 2
+    sigma = (1.0 / k) ** 2
+    return ((left + right) - (pybamm.sqrt((left - right) ** 2 + sigma))) / 2
 
 
 def smooth_max(left, right, k):
@@ -1346,7 +1346,7 @@ def smooth_max(left, right, k):
     set by `pybamm.settings.min_max_smoothing`. The recommended value is k=100.
     """
     sigma = (1.0 / k) ** 2
-    return (pybamm.sqrt((left - right)**2 + sigma) + (left + right)) / 2
+    return (pybamm.sqrt((left - right) ** 2 + sigma) + (left + right)) / 2
 
 
 def sigmoid(left, right, k):
diff --git a/pybamm/expression_tree/concatenations.py b/pybamm/expression_tree/concatenations.py
index afd9bdc1d5..40cfe617ac 100644
--- a/pybamm/expression_tree/concatenations.py
+++ b/pybamm/expression_tree/concatenations.py
@@ -191,7 +191,7 @@ def __init__(self, *children):
             *children,
             name="numpy_concatenation",
             check_domain=False,
-            concat_fun=np.concatenate
+            concat_fun=np.concatenate,
         )
 
     @classmethod
@@ -201,7 +201,7 @@ def _from_json(cls, snippet: dict):
             *snippet["children"],
             name="numpy_concatenation",
             domains=snippet["domains"],
-            concat_fun=np.concatenate
+            concat_fun=np.concatenate,
         )
 
         return instance
@@ -280,7 +280,7 @@ def _from_json(cls, snippet: dict):
         instance = super()._from_json(
             *snippet["children"],
             name="domain_concatenation",
-            domains=snippet["domains"]
+            domains=snippet["domains"],
         )
 
         def repack_defaultDict(slices):
@@ -415,7 +415,7 @@ def __init__(self, *children):
             *children,
             name="sparse_stack",
             check_domain=False,
-            concat_fun=concatenation_function
+            concat_fun=concatenation_function,
         )
 
     def _concatenation_new_copy(self, children):
diff --git a/pybamm/expression_tree/functions.py b/pybamm/expression_tree/functions.py
index d8248eabe8..f95f190b43 100644
--- a/pybamm/expression_tree/functions.py
+++ b/pybamm/expression_tree/functions.py
@@ -10,6 +10,7 @@
 import pybamm
 from pybamm.util import have_optional_dependency
 
+
 class Function(pybamm.Symbol):
     """
     A node in the expression tree representing an arbitrary function.
@@ -412,7 +413,7 @@ def _from_json(cls, snippet: dict):
 
     def _function_diff(self, children, idx):
         """See :meth:`pybamm.Function._function_diff()`."""
-        return 2 / np.sqrt(np.pi) * exp(-children[0] ** 2)
+        return 2 / np.sqrt(np.pi) * exp(-(children[0] ** 2))
 
 
 def erf(child):
diff --git a/pybamm/expression_tree/independent_variable.py b/pybamm/expression_tree/independent_variable.py
index ee8afac38e..dccb627eed 100644
--- a/pybamm/expression_tree/independent_variable.py
+++ b/pybamm/expression_tree/independent_variable.py
@@ -145,9 +145,7 @@ def __init__(
         elif name in ["x", "y", "z", "x_n", "x_s", "x_p"] and any(
             ["particle" in dom for dom in domain]
         ):
-            raise pybamm.DomainError(
-                f"domain cannot be particle if name is '{name}'"
-            )
+            raise pybamm.DomainError(f"domain cannot be particle if name is '{name}'")
 
     def create_copy(self):
         """See :meth:`pybamm.Symbol.new_copy()`."""
diff --git a/pybamm/expression_tree/interpolant.py b/pybamm/expression_tree/interpolant.py
index 5de21da089..7efe10413c 100644
--- a/pybamm/expression_tree/interpolant.py
+++ b/pybamm/expression_tree/interpolant.py
@@ -243,7 +243,13 @@ def entries_string(self, value):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`."""
         self._id = hash(
-            (self.__class__, self.name, self.entries_string, *tuple([child.id for child in self.children]), *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                self.entries_string,
+                *tuple([child.id for child in self.children]),
+                *tuple(self.domain),
+            )
         )
 
     def _function_new_copy(self, children):
diff --git a/pybamm/expression_tree/operations/convert_to_casadi.py b/pybamm/expression_tree/operations/convert_to_casadi.py
index 6461a9267f..d29ae994f2 100644
--- a/pybamm/expression_tree/operations/convert_to_casadi.py
+++ b/pybamm/expression_tree/operations/convert_to_casadi.py
@@ -209,7 +209,5 @@ def _convert(self, symbol, t, y, y_dot, inputs):
                 """
                 Cannot convert symbol of type '{}' to CasADi. Symbols must all be
                 'linear algebra' at this stage.
-                """.format(
-                    type(symbol)
-                )
+                """.format(type(symbol))
             )
diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index f65ecc7159..9c6f734553 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -203,7 +203,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
+                    symbol_str = (
+                        f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
+                    )
                 else:
                     symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
@@ -215,7 +217,9 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})"
+                    symbol_str = (
+                        f"{children_vars[0]}.scalar_multiply(1/{children_vars[1]})"
+                    )
                 else:
                     symbol_str = f"{children_vars[0]}.multiply(1/{children_vars[1]})"
             else:
@@ -226,12 +230,16 @@ def find_symbols(symbol, constant_symbols, variable_symbols, output_jax=False):
             dummy_eval_right = symbol.children[1].evaluate_for_shape()
             if scipy.sparse.issparse(dummy_eval_left):
                 if output_jax and is_scalar(dummy_eval_right):
-                    symbol_str = f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
+                    symbol_str = (
+                        f"{children_vars[0]}.scalar_multiply({children_vars[1]})"
+                    )
                 else:
                     symbol_str = f"{children_vars[0]}.multiply({children_vars[1]})"
             elif scipy.sparse.issparse(dummy_eval_right):
                 if output_jax and is_scalar(dummy_eval_left):
-                    symbol_str = f"{children_vars[1]}.scalar_multiply({children_vars[0]})"
+                    symbol_str = (
+                        f"{children_vars[1]}.scalar_multiply({children_vars[0]})"
+                    )
                 else:
                     symbol_str = f"{children_vars[1]}.multiply({children_vars[0]})"
             else:
diff --git a/pybamm/expression_tree/parameter.py b/pybamm/expression_tree/parameter.py
index 5fcb8c8ec9..787b7b5007 100644
--- a/pybamm/expression_tree/parameter.py
+++ b/pybamm/expression_tree/parameter.py
@@ -163,7 +163,13 @@ def input_names(self, inp=None):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id`"""
         self._id = hash(
-            (self.__class__, self.name, self.diff_variable, *tuple([child.id for child in self.children]), *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                self.diff_variable,
+                *tuple([child.id for child in self.children]),
+                *tuple(self.domain),
+            )
         )
 
     def diff(self, variable):
diff --git a/pybamm/expression_tree/printing/sympy_overrides.py b/pybamm/expression_tree/printing/sympy_overrides.py
index 1898822ea8..58ac356399 100644
--- a/pybamm/expression_tree/printing/sympy_overrides.py
+++ b/pybamm/expression_tree/printing/sympy_overrides.py
@@ -8,6 +8,7 @@
 
 class CustomPrint(LatexPrinter):
     """Override SymPy methods to match PyBaMM's requirements"""
+
     def _print_Derivative(self, expr):
         """Override :meth:`sympy.printing.latex.LatexPrinter._print_Derivative`"""
         eqn = super()._print_Derivative(expr)
diff --git a/pybamm/expression_tree/scalar.py b/pybamm/expression_tree/scalar.py
index 64a3893fc9..73dccf7d6c 100644
--- a/pybamm/expression_tree/scalar.py
+++ b/pybamm/expression_tree/scalar.py
@@ -6,6 +6,7 @@
 import pybamm
 from pybamm.util import have_optional_dependency
 
+
 class Scalar(pybamm.Symbol):
     """
     A node in the expression tree representing a scalar value.
diff --git a/pybamm/expression_tree/state_vector.py b/pybamm/expression_tree/state_vector.py
index 2f51d4bda1..348f908b45 100644
--- a/pybamm/expression_tree/state_vector.py
+++ b/pybamm/expression_tree/state_vector.py
@@ -118,7 +118,12 @@ def set_evaluation_array(self, y_slices, evaluation_array):
     def set_id(self):
         """See :meth:`pybamm.Symbol.set_id()`"""
         self._id = hash(
-            (self.__class__, self.name, tuple(self.evaluation_array), *tuple(self.domain))
+            (
+                self.__class__,
+                self.name,
+                tuple(self.evaluation_array),
+                *tuple(self.domain),
+            )
         )
 
     def _jac_diff_vector(self, variable):
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index 435bd5dce2..950ac16318 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -1115,8 +1115,7 @@ def __init__(self, name, child):
             )
         if child.evaluates_on_edges("primary") is True:
             raise TypeError(
-                f"Cannot upwind '{child}' since it does not "
-                + "evaluate on nodes."
+                f"Cannot upwind '{child}' since it does not " + "evaluate on nodes."
             )
         super().__init__(name, child)
 
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
index 3916ff5249..35193782e3 100644
--- a/pybamm/expression_tree/variable.py
+++ b/pybamm/expression_tree/variable.py
@@ -103,7 +103,13 @@ def bounds(self, values):
 
     def set_id(self):
         self._id = hash(
-            (self.__class__, self.name, self.scale, self.reference, *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]))
+            (
+                self.__class__,
+                self.name,
+                self.scale,
+                self.reference,
+                *tuple([(k, tuple(v)) for k, v in self.domains.items() if v != []]),
+            )
         )
 
     def create_copy(self):
diff --git a/pybamm/expression_tree/vector.py b/pybamm/expression_tree/vector.py
index 66fe7d8c12..641c098f79 100644
--- a/pybamm/expression_tree/vector.py
+++ b/pybamm/expression_tree/vector.py
@@ -29,9 +29,7 @@ def __init__(
             raise ValueError(
                 """
                 Entries must have 1 dimension or be column vector, not have shape {}
-                """.format(
-                    entries.shape
-                )
+                """.format(entries.shape)
             )
         if name is None:
             name = f"Column vector of length {entries.shape[0]!s}"
diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py
index 31b9ab228d..d13f7fb0db 100644
--- a/pybamm/input/parameters/lithium_ion/Ai2020.py
+++ b/pybamm/input/parameters/lithium_ion/Ai2020.py
@@ -69,9 +69,7 @@ def graphite_electrolyte_exchange_current_density_Dualfoil1998(
     E_r = 5000  # activation energy for Temperature Dependent Reaction Constant [J/mol]
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_entropy_Enertech_Ai2020_function(sto, c_s_max):
@@ -272,9 +270,7 @@ def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_m
     E_r = 5000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def lico2_entropic_change_Ai2020_function(sto, c_s_max):
diff --git a/pybamm/input/parameters/lithium_ion/Chen2020.py b/pybamm/input/parameters/lithium_ion/Chen2020.py
index e526b480c4..0b5420baaf 100644
--- a/pybamm/input/parameters/lithium_ion/Chen2020.py
+++ b/pybamm/input/parameters/lithium_ion/Chen2020.py
@@ -70,9 +70,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def nmc_LGM50_ocp_Chen2020(sto):
@@ -141,9 +139,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m
     E_r = 17800
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def electrolyte_diffusivity_Nyman2008(c_e, T):
diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
index f7e27c8d52..69767cbddf 100644
--- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
+++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py
@@ -37,9 +37,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def silicon_ocp_lithiation_Mark2016(sto):
@@ -167,9 +165,7 @@ def silicon_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def nmc_LGM50_ocp_Chen2020(sto):
@@ -238,9 +234,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m
     E_r = 17800
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def electrolyte_diffusivity_Nyman2008(c_e, T):
diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py
index 3f37db6e61..28b2ca21e4 100644
--- a/pybamm/input/parameters/lithium_ion/Ecker2015.py
+++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py
@@ -147,9 +147,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m
         E_r / (pybamm.constants.R * 296.15)
     )
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def nco_diffusivity_Ecker2015(sto, T):
@@ -292,9 +290,7 @@ def nco_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_max, T
         E_r / (pybamm.constants.R * 296.15)
     )
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def electrolyte_diffusivity_Ecker2015(c_e, T):
diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
index f6bc8e4d93..441ae95b8b 100644
--- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
+++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py
@@ -177,9 +177,7 @@ def graphite_electrolyte_exchange_current_density_Ecker2015(c_e, c_s_surf, c_s_m
         E_r / (pybamm.constants.R * 296.15)
     )
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def electrolyte_diffusivity_Ecker2015(c_e, T):
diff --git a/pybamm/input/parameters/lithium_ion/Marquis2019.py b/pybamm/input/parameters/lithium_ion/Marquis2019.py
index d3bddc6e30..1664e6b1b2 100644
--- a/pybamm/input/parameters/lithium_ion/Marquis2019.py
+++ b/pybamm/input/parameters/lithium_ion/Marquis2019.py
@@ -90,9 +90,7 @@ def graphite_electrolyte_exchange_current_density_Dualfoil1998(
     E_r = 37480
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_entropic_change_Moura2016(sto, c_s_max):
@@ -221,9 +219,7 @@ def lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_m
     E_r = 39570
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def lico2_entropic_change_Moura2016(sto, c_s_max):
diff --git a/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/pybamm/input/parameters/lithium_ion/Mohtat2020.py
index 29535b9f3d..86f14e39a2 100644
--- a/pybamm/input/parameters/lithium_ion/Mohtat2020.py
+++ b/pybamm/input/parameters/lithium_ion/Mohtat2020.py
@@ -86,9 +86,7 @@ def graphite_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, c_s_m
     E_r = 37480
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_entropic_change_PeymanMPM(sto, c_s_max):
@@ -208,9 +206,7 @@ def NMC_electrolyte_exchange_current_density_PeymanMPM(c_e, c_s_surf, c_s_max, T
     E_r = 39570
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def NMC_entropic_change_PeymanMPM(sto, c_s_max):
@@ -244,9 +240,9 @@ def NMC_entropic_change_PeymanMPM(sto, c_s_max):
         - 0.5623 * 10 ** (-4) * np.exp(109.451 * sto - 100.006)
     )
 
-    du_dT = (
-        -800 + 779 * u_eq - 284 * u_eq**2 + 46 * u_eq**3 - 2.8 * u_eq**4
-    ) * 10 ** (-3)
+    du_dT = (-800 + 779 * u_eq - 284 * u_eq**2 + 46 * u_eq**3 - 2.8 * u_eq**4) * 10 ** (
+        -3
+    )
 
     return du_dT
 
diff --git a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
index b5543ea6c2..123714a9da 100644
--- a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
+++ b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py
@@ -105,11 +105,7 @@ def graphite_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, c_s_max
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
     return (
-        m_ref
-        * arrhenius
-        * c_e**alpha
-        * c_s_surf**alpha
-        * (c_s_max - c_s_surf) ** alpha
+        m_ref * arrhenius * c_e**alpha * c_s_surf**alpha * (c_s_max - c_s_surf) ** alpha
     )
 
 
@@ -177,18 +173,12 @@ def nca_electrolyte_exchange_current_density_Kim2011(c_e, c_s_surf, c_s_max, T):
     c_e_ref = pybamm.Parameter("Initial concentration in electrolyte [mol.m-3]")
     alpha = 0.5  # charge transfer coefficient
 
-    m_ref = i0_ref / (
-        c_e_ref**alpha * (c_s_max - c_s_ref) ** alpha * c_s_ref**alpha
-    )
+    m_ref = i0_ref / (c_e_ref**alpha * (c_s_max - c_s_ref) ** alpha * c_s_ref**alpha)
     E_r = 3e4
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
     return (
-        m_ref
-        * arrhenius
-        * c_e**alpha
-        * c_s_surf**alpha
-        * (c_s_max - c_s_surf) ** alpha
+        m_ref * arrhenius * c_e**alpha * c_s_surf**alpha * (c_s_max - c_s_surf) ** alpha
     )
 
 
diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py
index e3718fb9ee..930848268f 100644
--- a/pybamm/input/parameters/lithium_ion/OKane2022.py
+++ b/pybamm/input/parameters/lithium_ion/OKane2022.py
@@ -162,9 +162,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_volume_change_Ai2020(sto, c_s_max):
@@ -343,9 +341,7 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_m
     E_r = 17800
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def volume_change_Ai2020(sto, c_s_max):
diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
index e13d27fad0..31081af14a 100644
--- a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
+++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py
@@ -192,9 +192,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = pybamm.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_volume_change_Ai2020(sto, c_s_max):
diff --git a/pybamm/input/parameters/lithium_ion/Prada2013.py b/pybamm/input/parameters/lithium_ion/Prada2013.py
index 2d3d0e7ceb..421256af2a 100644
--- a/pybamm/input/parameters/lithium_ion/Prada2013.py
+++ b/pybamm/input/parameters/lithium_ion/Prada2013.py
@@ -70,9 +70,7 @@ def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(
     E_r = 35000
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def LFP_ocp_Afshar2017(sto):
@@ -131,9 +129,7 @@ def LFP_electrolyte_exchange_current_density_kashkooli2017(c_e, c_s_surf, c_s_ma
     E_r = 39570
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def electrolyte_conductivity_Prada2013(c_e, T):
diff --git a/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/pybamm/input/parameters/lithium_ion/Ramadass2004.py
index 13aa86fe8e..4269acf1e9 100644
--- a/pybamm/input/parameters/lithium_ion/Ramadass2004.py
+++ b/pybamm/input/parameters/lithium_ion/Ramadass2004.py
@@ -89,9 +89,7 @@ def graphite_electrolyte_exchange_current_density_Ramadass2004(
     E_r = 37480
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def graphite_entropic_change_Moura2016(sto, c_s_max):
@@ -227,9 +225,7 @@ def lico2_electrolyte_exchange_current_density_Ramadass2004(c_e, c_s_surf, c_s_m
     E_r = 39570
     arrhenius = np.exp(E_r / pybamm.constants.R * (1 / 298.15 - 1 / T))
 
-    return (
-        m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
-    )
+    return m_ref * arrhenius * c_e**0.5 * c_s_surf**0.5 * (c_s_max - c_s_surf) ** 0.5
 
 
 def lico2_entropic_change_Moura2016(sto, c_s_max):
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index a51c9eea76..0680c5acdb 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -100,13 +100,11 @@ def update_LD_LIBRARY_PATH(install_dir):
             if export_statement not in fh.read():
                 fh.write(export_statement)
                 print(
-                    f"Adding {install_dir}/lib to LD_LIBRARY_PATH"
-                    f" in {script_path}"
+                    f"Adding {install_dir}/lib to LD_LIBRARY_PATH" f" in {script_path}"
                 )
 
 
 def main(arguments=None):
-
     log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
     logger = logging.getLogger("scikits.odes setup")
 
diff --git a/pybamm/meshes/one_dimensional_submeshes.py b/pybamm/meshes/one_dimensional_submeshes.py
index d68745daec..d6c3c7f78e 100644
--- a/pybamm/meshes/one_dimensional_submeshes.py
+++ b/pybamm/meshes/one_dimensional_submeshes.py
@@ -302,9 +302,7 @@ def __init__(self, lims, npts, edges=None):
                 """User-suppled edges has should have length (npts + 1) but has length
                 {}.Number of points (npts) for domain {} is {}.""".format(
                     len(edges), spatial_var.domain, npts
-                ).replace(
-                    "\n                ", " "
-                )
+                ).replace("\n                ", " ")
             )
 
         # check end points of edges agree with spatial_lims
diff --git a/pybamm/models/base_model.py b/pybamm/models/base_model.py
index 3da6b53618..6b45aeb083 100644
--- a/pybamm/models/base_model.py
+++ b/pybamm/models/base_model.py
@@ -437,13 +437,19 @@ def get_parameter_info(self):
             if not input_param.domain:
                 parameter_info[input_param.name] = (input_param, "InputParameter")
             else:
-                parameter_info[input_param.name] = (input_param, f"InputParameter in {input_param.domain}")
+                parameter_info[input_param.name] = (
+                    input_param,
+                    f"InputParameter in {input_param.domain}",
+                )
 
         function_parameters = self._find_symbols(pybamm.FunctionParameter)
         for func_param in function_parameters:
             if func_param.name not in parameter_info:
-                input_names =  "', '".join(func_param.input_names)
-                parameter_info[func_param.name] = (func_param, f"FunctionParameter with inputs(s) '{input_names}'")
+                input_names = "', '".join(func_param.input_names)
+                parameter_info[func_param.name] = (
+                    func_param,
+                    f"FunctionParameter with inputs(s) '{input_names}'",
+                )
 
         return parameter_info
 
@@ -454,21 +460,35 @@ def print_parameter_info(self):
         max_param_type_length = 0
 
         for param, param_type in info.values():
-            param_name_length = len(getattr(param, 'name', str(param)))
+            param_name_length = len(getattr(param, "name", str(param)))
             param_type_length = len(param_type)
             max_param_name_length = max(max_param_name_length, param_name_length)
             max_param_type_length = max(max_param_type_length, param_type_length)
 
-        header_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
-        row_format = f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        header_format = (
+            f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        )
+        row_format = (
+            f"| {{:<{max_param_name_length}}} | {{:<{max_param_type_length}}} |"
+        )
 
-        table = [header_format.format("Parameter", "Type of parameter"),
-                 header_format.format("=" * max_param_name_length, "=" * max_param_type_length)]
+        table = [
+            header_format.format("Parameter", "Type of parameter"),
+            header_format.format(
+                "=" * max_param_name_length, "=" * max_param_type_length
+            ),
+        ]
 
         for param, param_type in info.values():
-            param_name = getattr(param, 'name', str(param))
-            param_name_lines = [param_name[i:i + max_param_name_length] for i in range(0, len(param_name), max_param_name_length)]
-            param_type_lines = [param_type[i:i + max_param_type_length] for i in range(0, len(param_type), max_param_type_length)]
+            param_name = getattr(param, "name", str(param))
+            param_name_lines = [
+                param_name[i : i + max_param_name_length]
+                for i in range(0, len(param_name), max_param_name_length)
+            ]
+            param_type_lines = [
+                param_type[i : i + max_param_type_length]
+                for i in range(0, len(param_type), max_param_type_length)
+            ]
             max_lines = max(len(param_name_lines), len(param_type_lines))
 
             for i in range(max_lines):
@@ -612,9 +632,7 @@ def build_model_equations(self):
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
-            pybamm.logger.verbose(
-                f"Updating {submodel_name} submodel ({self.name})"
-            )
+            pybamm.logger.verbose(f"Updating {submodel_name} submodel ({self.name})")
             self.update(submodel)
             self.check_no_repeated_keys()
 
@@ -1360,9 +1378,7 @@ def check_and_convert_bcs(self, boundary_conditions):
                     raise pybamm.ModelError(
                         """
                         boundary condition types must be Dirichlet or Neumann, not '{}'
-                        """.format(
-                            bc[1]
-                        )
+                        """.format(bc[1])
                     )
 
         return boundary_conditions
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index dea066db08..4886251e0a 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -631,28 +631,26 @@ def __init__(self, extra_options):
                 value = (value,)
             else:
                 if not (
-
-                        option
-                        in [
-                            "diffusivity",
-                            "exchange-current density",
-                            "intercalation kinetics",
-                            "interface utilisation",
-                            "lithium plating",
-                            "loss of active material",
-                            "number of MSMR reactions",
-                            "open-circuit potential",
-                            "particle",
-                            "particle mechanics",
-                            "particle phases",
-                            "particle size",
-                            "SEI",
-                            "SEI on cracks",
-                            "stress-induced diffusion",
-                        ]
-                        and isinstance(value, tuple)
-                        and len(value) == 2
-
+                    option
+                    in [
+                        "diffusivity",
+                        "exchange-current density",
+                        "intercalation kinetics",
+                        "interface utilisation",
+                        "lithium plating",
+                        "loss of active material",
+                        "number of MSMR reactions",
+                        "open-circuit potential",
+                        "particle",
+                        "particle mechanics",
+                        "particle phases",
+                        "particle size",
+                        "SEI",
+                        "SEI on cracks",
+                        "stress-induced diffusion",
+                    ]
+                    and isinstance(value, tuple)
+                    and len(value) == 2
                 ):
                     # more possible options that can take 2-tuples to be added
                     # as they come
@@ -1071,9 +1069,7 @@ def build_model_equations(self):
             )
             submodel.set_initial_conditions(self.variables)
             submodel.set_events(self.variables)
-            pybamm.logger.verbose(
-                f"Updating {submodel_name} submodel ({self.name})"
-            )
+            pybamm.logger.verbose(f"Updating {submodel_name} submodel ({self.name})")
             self.update(submodel)
             self.check_no_repeated_keys()
 
diff --git a/pybamm/models/submodels/convection/through_cell/explicit_convection.py b/pybamm/models/submodels/convection/through_cell/explicit_convection.py
index 7d83c550ec..33b58e2b23 100644
--- a/pybamm/models/submodels/convection/through_cell/explicit_convection.py
+++ b/pybamm/models/submodels/convection/through_cell/explicit_convection.py
@@ -39,11 +39,7 @@ def get_coupled_variables(self, variables):
                 x_p = pybamm.standard_spatial_vars.x_p
                 DeltaV_k = param.p.DeltaV
                 p_k = (
-                    -DeltaV_k
-                    * a_j_k_av
-                    / param.F
-                    * ((x_p - 1) ** 2 - param.p.L**2)
-                    / 2
+                    -DeltaV_k * a_j_k_av / param.F * ((x_p - 1) ** 2 - param.p.L**2) / 2
                     + p_s
                 )
                 v_box_k = -DeltaV_k * a_j_k_av / param.F * (x_p - param.L_x)
diff --git a/pybamm/models/submodels/interface/lithium_plating/base_plating.py b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
index 5b7a7a5b7f..ebfbe46831 100644
--- a/pybamm/models/submodels/interface/lithium_plating/base_plating.py
+++ b/pybamm/models/submodels/interface/lithium_plating/base_plating.py
@@ -111,10 +111,14 @@ def _get_standard_concentration_variables(self, c_plated_Li, c_dead_Li):
             f"X-averaged {domain} lithium plating thickness [m]": L_plated_Li_av,
             f"{Domain} dead lithium thickness [m]": L_dead_Li,
             f"X-averaged {domain} dead lithium thickness [m]": L_dead_Li_av,
-            f"Loss of lithium to {domain} lithium plating "
-            "[mol]": (Q_plated_Li + Q_dead_Li),
-            f"Loss of capacity to {domain} lithium plating "
-            "[A.h]": (Q_plated_Li + Q_dead_Li) * param.F / 3600,
+            f"Loss of lithium to {domain} lithium plating " "[mol]": (
+                Q_plated_Li + Q_dead_Li
+            ),
+            f"Loss of capacity to {domain} lithium plating " "[A.h]": (
+                Q_plated_Li + Q_dead_Li
+            )
+            * param.F
+            / 3600,
         }
 
         return variables
diff --git a/pybamm/models/submodels/interface/total_interfacial_current.py b/pybamm/models/submodels/interface/total_interfacial_current.py
index a9094c4448..79e13e37f6 100644
--- a/pybamm/models/submodels/interface/total_interfacial_current.py
+++ b/pybamm/models/submodels/interface/total_interfacial_current.py
@@ -110,7 +110,7 @@ def _get_coupled_variables_by_phase_and_domain(self, variables, domain, phase_na
             new_variables[
                 f"Sum of {domain} electrode {phase_name}"
                 "electrolyte reaction source terms [A.m-3]"
-            ] += (s_k * a_j_k)
+            ] += s_k * a_j_k
             new_variables[
                 f"Sum of x-averaged {domain} electrode {phase_name}"
                 "electrolyte reaction source terms [A.m-3]"
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index 0f46615724..dab48b5f79 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -199,9 +199,7 @@ def _get_distribution_variables(self, R):
         f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R)  # [m-1]
 
         # Number-based particle-size distribution
-        f_num_dist = (f_a_dist / R**2) / pybamm.Integral(
-            f_a_dist / R**2, R
-        )  # [m-1]
+        f_num_dist = (f_a_dist / R**2) / pybamm.Integral(f_a_dist / R**2, R)  # [m-1]
 
         # True mean radii and standard deviations, calculated from the f_a_dist that
         # was given, all have units [m]
diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py
index 65ab913e97..c53f313ab4 100644
--- a/pybamm/models/submodels/particle/msmr_diffusion.py
+++ b/pybamm/models/submodels/particle/msmr_diffusion.py
@@ -262,9 +262,7 @@ def get_coupled_variables(self, variables):
         N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U)
         variables.update(
             {
-                f"{Domain} {phase_name}particle rhs [V.s-1]": -(
-                    1 / (R_broad_nondim**2)
-                )
+                f"{Domain} {phase_name}particle rhs [V.s-1]": -(1 / (R_broad_nondim**2))
                 * pybamm.div(N_s)
                 / c_max
                 / dxdU,
diff --git a/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py b/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py
index 45497c4c54..8b4b7ffe7c 100644
--- a/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py
+++ b/pybamm/models/submodels/particle/x_averaged_polynomial_profile.py
@@ -203,8 +203,7 @@ def get_coupled_variables(self, variables):
             # The flux may be computed directly from the polynomial for c
             N_s_xav = -D_eff_xav * (
                 (-70 * c_s_surf_xav + 20 * q_s_av * R + 70 * c_s_av) * r / R**2
-                + (105 * c_s_surf_xav - 28 * q_s_av * R - 105 * c_s_av)
-                * (r**3 / R**4)
+                + (105 * c_s_surf_xav - 28 * q_s_av * R - 105 * c_s_av) * (r**3 / R**4)
             )
 
         N_s = pybamm.SecondaryBroadcast(N_s_xav, [f"{domain} electrode"])
diff --git a/pybamm/models/submodels/particle_mechanics/base_mechanics.py b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
index 35adadf47d..4e25becbab 100644
--- a/pybamm/models/submodels/particle_mechanics/base_mechanics.py
+++ b/pybamm/models/submodels/particle_mechanics/base_mechanics.py
@@ -50,7 +50,7 @@ def _get_mechanical_results(self, variables):
         )
         eps_s = variables[f"{Domain} electrode active material volume fraction"]
 
-        #use a tangential approximation for omega
+        # use a tangential approximation for omega
         sto = variables[f"{Domain} particle concentration"]
         Omega = pybamm.r_average(domain_param.Omega(sto, T))
         R0 = domain_param.prim.R
diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index 27e52d6638..b2a79c52d5 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -117,7 +117,7 @@ def _get_standard_coupled_variables(self, variables):
         # Total Ohmic heating
         Q_ohm = Q_ohm_s + Q_ohm_e
 
-        num_phases = int(getattr(self.options, 'positive')["particle phases"])
+        num_phases = int(getattr(self.options, "positive")["particle phases"])
         phase_names = [""]
         if num_phases > 1:
             phase_names = ["primary ", "secondary "]
@@ -135,8 +135,7 @@ def _get_standard_coupled_variables(self, variables):
             dUdT_p = variables[f"Positive electrode {phase}entropic change [V.K-1]"]
             Q_rev_p += a_j_p * T_p * dUdT_p
 
-
-        num_phases = int(getattr(self.options, 'negative')["particle phases"])
+        num_phases = int(getattr(self.options, "negative")["particle phases"])
         phase_names = [""]
         if num_phases > 1:
             phase_names = ["primary", "secondary"]
@@ -156,7 +155,9 @@ def _get_standard_coupled_variables(self, variables):
                 a_j_n = variables[
                     f"Negative electrode {phase}volumetric interfacial current density [A.m-3]"
                 ]
-                eta_r_n = variables[f"Negative electrode {phase}reaction overpotential [V]"]
+                eta_r_n = variables[
+                    f"Negative electrode {phase}reaction overpotential [V]"
+                ]
                 # Irreversible electrochemical heating
                 Q_rxn_n += a_j_n * eta_r_n
 
diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py
index 8efd26cd57..a97288b062 100644
--- a/pybamm/parameters/bpx.py
+++ b/pybamm/parameters/bpx.py
@@ -261,12 +261,12 @@ def _positive_electrode_entropic_change(sto, c_s_max):
         "Maximum concentration in " + negative_electrode.pre_name.lower() + "[mol.m-3]"
     ]
     k_n_norm = pybamm_dict[
-        negative_electrode.pre_name
-        + "reaction rate constant [mol.m-2.s-1]"
+        negative_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]"
     ]
     Ea_k_n = pybamm_dict.get(
         negative_electrode.pre_name
-        + "reaction rate constant activation energy [J.mol-1]", 0.0
+        + "reaction rate constant activation energy [J.mol-1]",
+        0.0,
     )
     # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...),
     # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...)
@@ -292,12 +292,12 @@ def _negative_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T):
         "Maximum concentration in " + positive_electrode.pre_name.lower() + "[mol.m-3]"
     ]
     k_p_norm = pybamm_dict[
-        positive_electrode.pre_name
-        + "reaction rate constant [mol.m-2.s-1]"
+        positive_electrode.pre_name + "reaction rate constant [mol.m-2.s-1]"
     ]
     Ea_k_p = pybamm_dict.get(
         positive_electrode.pre_name
-        + "reaction rate constant activation energy [J.mol-1]", 0.0
+        + "reaction rate constant activation energy [J.mol-1]",
+        0.0,
     )
     # Note that in BPX j = 2*F*k_norm*sqrt((ce/ce0)*(c/c_max)*(1-c/c_max))*sinh(...),
     # and in PyBaMM j = 2*k*sqrt(ce*c*(c_max - c))*sinh(...)
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index be842a7bca..5dcb3c950a 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -295,8 +295,7 @@ def set_initial_stoichiometry_half_cell(
             {
                 "Initial concentration in {} electrode [mol.m-3]".format(
                     options["working electrode"]
-                ): x
-                * c_max
+                ): x * c_max
             }
         )
         return parameter_values
@@ -392,9 +391,7 @@ def process_model(self, unprocessed_model, inplace=True):
             `model.variables = {}`)
 
         """
-        pybamm.logger.info(
-            f"Start setting parameters for {unprocessed_model.name}"
-        )
+        pybamm.logger.info(f"Start setting parameters for {unprocessed_model.name}")
 
         # set up inplace vs not inplace
         if inplace:
@@ -414,18 +411,14 @@ def process_model(self, unprocessed_model, inplace=True):
 
         new_rhs = {}
         for variable, equation in unprocessed_model.rhs.items():
-            pybamm.logger.verbose(
-                f"Processing parameters for {variable!r} (rhs)"
-            )
+            pybamm.logger.verbose(f"Processing parameters for {variable!r} (rhs)")
             new_variable = self.process_symbol(variable)
             new_rhs[new_variable] = self.process_symbol(equation)
         model.rhs = new_rhs
 
         new_algebraic = {}
         for variable, equation in unprocessed_model.algebraic.items():
-            pybamm.logger.verbose(
-                f"Processing parameters for {variable!r} (algebraic)"
-            )
+            pybamm.logger.verbose(f"Processing parameters for {variable!r} (algebraic)")
             new_variable = self.process_symbol(variable)
             new_algebraic[new_variable] = self.process_symbol(equation)
         model.algebraic = new_algebraic
@@ -443,17 +436,13 @@ def process_model(self, unprocessed_model, inplace=True):
 
         new_variables = {}
         for variable, equation in unprocessed_model.variables.items():
-            pybamm.logger.verbose(
-                f"Processing parameters for {variable!r} (variables)"
-            )
+            pybamm.logger.verbose(f"Processing parameters for {variable!r} (variables)")
             new_variables[variable] = self.process_symbol(equation)
         model.variables = new_variables
 
         new_events = []
         for event in unprocessed_model.events:
-            pybamm.logger.verbose(
-                f"Processing parameters for event '{event.name}''"
-            )
+            pybamm.logger.verbose(f"Processing parameters for event '{event.name}''")
             new_events.append(
                 pybamm.Event(
                     event.name, self.process_symbol(event.expression), event.event_type
@@ -462,9 +451,7 @@ def process_model(self, unprocessed_model, inplace=True):
 
         interpolant_events = self._get_interpolant_events(model)
         for event in interpolant_events:
-            pybamm.logger.verbose(
-                f"Processing parameters for event '{event.name}''"
-            )
+            pybamm.logger.verbose(f"Processing parameters for event '{event.name}''")
             new_events.append(
                 pybamm.Event(
                     event.name, self.process_symbol(event.expression), event.event_type
diff --git a/pybamm/parameters/process_parameter_data.py b/pybamm/parameters/process_parameter_data.py
index 8998c6e583..03b3c2b54d 100644
--- a/pybamm/parameters/process_parameter_data.py
+++ b/pybamm/parameters/process_parameter_data.py
@@ -35,7 +35,7 @@ def process_1D_data(name, path=None):
     """
     filename, name = _process_name(name, path, ".csv")
 
-    data = np.genfromtxt(filename, delimiter=',', skip_header=1)
+    data = np.genfromtxt(filename, delimiter=",", skip_header=1)
     x = data[:, 0]
     y = data[:, 1]
 
@@ -88,7 +88,7 @@ def process_2D_data_csv(name, path=None):
 
     filename, name = _process_name(name, path, ".csv")
 
-    data = np.genfromtxt(filename, delimiter=',',skip_header=1)
+    data = np.genfromtxt(filename, delimiter=",", skip_header=1)
 
     x1 = np.unique(data[:, 0])
     x2 = np.unique(data[:, 1])
@@ -135,7 +135,7 @@ def process_3D_data_csv(name, path=None):
 
     filename, name = _process_name(name, path, ".csv")
 
-    data = np.genfromtxt(filename, delimiter=',',skip_header=1)
+    data = np.genfromtxt(filename, delimiter=",", skip_header=1)
 
     x1 = np.unique(data[:, 0])
     x2 = np.unique(data[:, 1])
diff --git a/pybamm/plotting/plot2D.py b/pybamm/plotting/plot2D.py
index d4f6d31e3a..3a69bab803 100644
--- a/pybamm/plotting/plot2D.py
+++ b/pybamm/plotting/plot2D.py
@@ -54,7 +54,7 @@ def plot2D(x, y, z, ax=None, testing=False, **kwargs):
         z.entries,
         vmin=ax_min(z.entries),
         vmax=ax_max(z.entries),
-        **kwargs
+        **kwargs,
     )
     plt.colorbar(plot, ax=ax)
 
diff --git a/pybamm/plotting/plot_voltage_components.py b/pybamm/plotting/plot_voltage_components.py
index a681094bea..ef95f7016f 100644
--- a/pybamm/plotting/plot_voltage_components.py
+++ b/pybamm/plotting/plot_voltage_components.py
@@ -12,7 +12,7 @@ def plot_voltage_components(
     show_legend=True,
     split_by_electrode=False,
     testing=False,
-    **kwargs_fill
+    **kwargs_fill,
 ):
     """
     Generate a plot showing the component overpotentials that make up the voltage
@@ -105,14 +105,14 @@ def plot_voltage_components(
             initial_ocv - delta_ocp_n,
             initial_ocv,
             **kwargs_fill,
-            label="Negative open-circuit potential"
+            label="Negative open-circuit potential",
         )
         ax.fill_between(
             time,
             initial_ocv - delta_ocp_n + delta_ocp_p,
             initial_ocv - delta_ocp_n,
             **kwargs_fill,
-            label="Positive open-circuit potential"
+            label="Positive open-circuit potential",
         )
         ocv = initial_ocv - delta_ocp_n + delta_ocp_p
     top = ocv
@@ -138,8 +138,9 @@ def plot_voltage_components(
     ax.set_xlim([time[0], time[-1]])
     ax.set_xlabel("Time [h]")
 
-    y_min, y_max = 0.98 * min(np.nanmin(V), np.nanmin(ocv)), 1.02 * (
-        max(np.nanmax(V), np.nanmax(ocv))
+    y_min, y_max = (
+        0.98 * min(np.nanmin(V), np.nanmin(ocv)),
+        1.02 * (max(np.nanmax(V), np.nanmax(ocv))),
     )
     ax.set_ylim([y_min, y_max])
 
diff --git a/pybamm/settings.py b/pybamm/settings.py
index 30b4c3aa0a..2ccd9bcd13 100644
--- a/pybamm/settings.py
+++ b/pybamm/settings.py
@@ -66,9 +66,7 @@ def min_max_mode(self):
     @min_max_mode.setter
     def min_max_mode(self, mode):
         if mode not in ["exact", "soft", "smooth"]:
-            raise ValueError(
-                "Smoothing mode must be 'exact', 'soft', or 'smooth'"
-            )
+            raise ValueError("Smoothing mode must be 'exact', 'soft', or 'smooth'")
         self._min_max_mode = mode
 
     @property
@@ -78,13 +76,9 @@ def min_max_smoothing(self):
     @min_max_smoothing.setter
     def min_max_smoothing(self, k):
         if self._min_max_mode == "soft" and k <= 0:
-            raise ValueError(
-                "Smoothing parameter must be a strictly positive number"
-            )
+            raise ValueError("Smoothing parameter must be a strictly positive number")
         if self._min_max_mode == "smooth" and k < 1:
-            raise ValueError(
-                "Smoothing parameter must be greater than 1"
-            )
+            raise ValueError("Smoothing parameter must be greater than 1")
         self._min_max_smoothing = k
 
     @property
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 5c2cf0bff1..c95ab3039c 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -577,9 +577,7 @@ def solve(
                             capture the input. Try refining t_eval. Alternatively,
                             passing t_eval = None automatically sets t_eval to be the
                             points in the data.
-                            """.format(
-                                dt_eval_max, dt_data_min
-                            ),
+                            """.format(dt_eval_max, dt_data_min),
                             pybamm.SolverWarning,
                         )
 
diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py
index 76cf3e9367..69de3be968 100644
--- a/pybamm/solvers/base_solver.py
+++ b/pybamm/solvers/base_solver.py
@@ -684,9 +684,7 @@ def calculate_consistent_state(self, model, time=0, inputs=None):
         try:
             root_sol = self.root_method._integrate(model, np.array([time]), inputs)
         except pybamm.SolverError as e:
-            raise pybamm.SolverError(
-                f"Could not find consistent states: {e.args[0]}"
-            )
+            raise pybamm.SolverError(f"Could not find consistent states: {e.args[0]}")
         pybamm.logger.debug("Found consistent states")
 
         self.check_extrapolation(root_sol, model.events)
@@ -1044,9 +1042,7 @@ def _get_discontinuity_start_end_indices(self, model, inputs, t_eval):
         # remove any discontinuities after end of t_eval
         discontinuities = [v for v in discontinuities if v < t_eval[-1]]
 
-        pybamm.logger.verbose(
-            f"Discontinuity events found at t = {discontinuities}"
-        )
+        pybamm.logger.verbose(f"Discontinuity events found at t = {discontinuities}")
         if isinstance(inputs, list):
             raise pybamm.SolverError(
                 "Cannot solve for a list of input parameters"
@@ -1205,9 +1201,7 @@ def step(
             isinstance(old_solution, pybamm.EmptySolution)
             and old_solution.termination is None
         ):
-            pybamm.logger.verbose(
-                f"Start stepping {model.name} with {self.name}"
-            )
+            pybamm.logger.verbose(f"Start stepping {model.name} with {self.name}")
 
         if isinstance(old_solution, pybamm.EmptySolution):
             if not first_step_this_model:
@@ -1236,9 +1230,7 @@ def step(
         self._check_events_with_initial_conditions(t_eval, model, model_inputs)
 
         # Step
-        pybamm.logger.verbose(
-            f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}"
-        )
+        pybamm.logger.verbose(f"Stepping for {t_start_shifted:.0f} < t < {t_end:.0f}")
         timer.reset()
         solution = self._integrate(model, t_eval, model_inputs)
         solution.solve_time = timer.time()
@@ -1475,10 +1467,8 @@ def report(string):
         jacp = None
         if model.calculate_sensitivities:
             report(
-
-                    f"Calculating sensitivities for {name} with respect "
-                    f"to parameters {model.calculate_sensitivities} using jax"
-
+                f"Calculating sensitivities for {name} with respect "
+                f"to parameters {model.calculate_sensitivities} using jax"
             )
             jacp = func.get_sensitivities()
         if use_jacobian:
@@ -1496,10 +1486,8 @@ def report(string):
         # to python evaluator
         if model.calculate_sensitivities:
             report(
-
-                    f"Calculating sensitivities for {name} with respect "
-                    f"to parameters {model.calculate_sensitivities}"
-
+                f"Calculating sensitivities for {name} with respect "
+                f"to parameters {model.calculate_sensitivities}"
             )
             jacp_dict = {
                 p: symbol.diff(pybamm.InputParameter(p))
@@ -1602,11 +1590,9 @@ def jacp(*args, **kwargs):
                     casadi_expression = casadi.vertcat(x0, Sx_0, z0, Sz_0)
         elif model.calculate_sensitivities:
             report(
-
-                    f"Calculating sensitivities for {name} with respect "
-                    f"to parameters {model.calculate_sensitivities} using "
-                    "CasADi"
-
+                f"Calculating sensitivities for {name} with respect "
+                f"to parameters {model.calculate_sensitivities} using "
+                "CasADi"
             )
             # Compute derivate wrt p-stacked (can be passed to solver to
             # compute sensitivities online)
diff --git a/pybamm/solvers/casadi_algebraic_solver.py b/pybamm/solvers/casadi_algebraic_solver.py
index cdde5bb99c..ec7305906a 100644
--- a/pybamm/solvers/casadi_algebraic_solver.py
+++ b/pybamm/solvers/casadi_algebraic_solver.py
@@ -153,9 +153,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
                     Could not find acceptable solution: solver terminated
                     successfully, but maximum solution error ({})
                     above tolerance ({})
-                    """.format(
-                        casadi.mmax(casadi.fabs(fun)), self.tol
-                    )
+                    """.format(casadi.mmax(casadi.fabs(fun)), self.tol)
                 )
 
         # Concatenate differential part
diff --git a/pybamm/solvers/casadi_solver.py b/pybamm/solvers/casadi_solver.py
index 6ee8758de3..02ff4a2cd9 100644
--- a/pybamm/solvers/casadi_solver.py
+++ b/pybamm/solvers/casadi_solver.py
@@ -183,9 +183,7 @@ def _integrate(self, model, t_eval, inputs_dict=None):
             t = t_eval[0]
             t_f = t_eval[-1]
 
-            pybamm.logger.debug(
-                f"Start solving {model.name} with {self.name}"
-            )
+            pybamm.logger.debug(f"Start solving {model.name} with {self.name}")
 
             if self.mode == "safe without grid":
                 # in "safe without grid" mode,
diff --git a/pybamm/solvers/idaklu_solver.py b/pybamm/solvers/idaklu_solver.py
index d9819f1608..6c81bf91e7 100644
--- a/pybamm/solvers/idaklu_solver.py
+++ b/pybamm/solvers/idaklu_solver.py
@@ -281,10 +281,14 @@ def resfn(t, y, inputs, ydot):
                 # Convert derivative functions for sensitivities
                 if (len(inputs) > 0) and (model.calculate_sensitivities):
                     self.dvar_dy_idaklu_fcns.append(
-                        idaklu.generate_function(self.computed_dvar_dy_fcns[key].serialize())
+                        idaklu.generate_function(
+                            self.computed_dvar_dy_fcns[key].serialize()
+                        )
                     )
                     self.dvar_dp_idaklu_fcns.append(
-                        idaklu.generate_function(self.computed_dvar_dp_fcns[key].serialize())
+                        idaklu.generate_function(
+                            self.computed_dvar_dp_fcns[key].serialize()
+                        )
                     )
 
         else:
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 8f5b8ed817..b193b945e7 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -217,9 +217,7 @@ def _bdf_init(fun, jac, mass, t0, y0, h0, rtol, atol):
         state["rtol"] = rtol
         state["M"] = mass
         EPS = jnp.finfo(y0.dtype).eps
-        state["newton_tol"] = jnp.maximum(
-            10 * EPS / rtol, jnp.minimum(0.03, rtol**0.5)
-        )
+        state["newton_tol"] = jnp.maximum(10 * EPS / rtol, jnp.minimum(0.03, rtol**0.5))
 
         scale_y0 = atol + rtol * jnp.abs(y0)
         y0, not_converged = _select_initial_conditions(
@@ -645,7 +643,8 @@ def while_body(while_state):
             # try again
             (state, updated_jacobian) = tree_map(
                 partial(
-                    jnp.where, not_converged * (updated_jacobian == False)  # noqa: E712
+                    jnp.where,
+                    not_converged * (updated_jacobian == False),  # noqa: E712
                 ),
                 (_update_jacobian(state, jac), True),
                 (state, False + updated_jacobian),
@@ -883,7 +882,12 @@ def arg_dicts_to_values(args):
             """
             return sum((tuple(b.values()) for b in args if isinstance(b, dict)), ())
 
-        aug_mass = (mass, mass, onp.array(1.0), *arg_dicts_to_values(tree_map(arg_to_identity, args)))
+        aug_mass = (
+            mass,
+            mass,
+            onp.array(1.0),
+            *arg_dicts_to_values(tree_map(arg_to_identity, args)),
+        )
 
         def scan_fun(carry, i):
             y_bar, t0_bar, args_bar = carry
diff --git a/pybamm/solvers/scipy_solver.py b/pybamm/solvers/scipy_solver.py
index e0065cf4ec..fb320f558d 100644
--- a/pybamm/solvers/scipy_solver.py
+++ b/pybamm/solvers/scipy_solver.py
@@ -123,7 +123,7 @@ def event_fn(t, y):
             t_eval=t_eval,
             method=self.method,
             dense_output=True,
-            **extra_options
+            **extra_options,
         )
         integration_time = timer.time()
 
diff --git a/pybamm/spatial_methods/finite_volume.py b/pybamm/spatial_methods/finite_volume.py
index 84f76a2bbd..11313a1450 100644
--- a/pybamm/spatial_methods/finite_volume.py
+++ b/pybamm/spatial_methods/finite_volume.py
@@ -1395,8 +1395,7 @@ def upwind_or_downwind(self, symbol, discretised_symbol, bcs, direction):
 
         if symbol not in bcs:
             raise pybamm.ModelError(
-                "Boundary conditions must be provided for "
-                f"{direction}ing '{symbol}'"
+                "Boundary conditions must be provided for " f"{direction}ing '{symbol}'"
             )
 
         if direction == "upwind":
diff --git a/pybamm/spatial_methods/spectral_volume.py b/pybamm/spatial_methods/spectral_volume.py
index a10422813f..50e1cadf25 100644
--- a/pybamm/spatial_methods/spectral_volume.py
+++ b/pybamm/spatial_methods/spectral_volume.py
@@ -527,8 +527,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
             lbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, "
-                f"not '{lbc_type}'"
+                "boundary condition must be Dirichlet or Neumann, " f"not '{lbc_type}'"
             )
 
         if rbc_type == "Dirichlet":
@@ -543,8 +542,7 @@ def replace_dirichlet_values(self, symbol, discretised_symbol, bcs):
             rbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, "
-                f"not '{rbc_type}'"
+                "boundary condition must be Dirichlet or Neumann, " f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
@@ -621,8 +619,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
             lbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, "
-                f"not '{lbc_type}'"
+                "boundary condition must be Dirichlet or Neumann, " f"not '{lbc_type}'"
             )
 
         if rbc_type == "Neumann":
@@ -637,8 +634,7 @@ def replace_neumann_values(self, symbol, discretised_gradient, bcs):
             rbc_vector = pybamm.Vector(np.zeros(n * second_dim_repeats))
         else:
             raise ValueError(
-                "boundary condition must be Dirichlet or Neumann, "
-                f"not '{rbc_type}'"
+                "boundary condition must be Dirichlet or Neumann, " f"not '{rbc_type}'"
             )
 
         bcs_vector = lbc_vector + rbc_vector
diff --git a/pybamm/util.py b/pybamm/util.py
index 71883e3d27..8f76566171 100644
--- a/pybamm/util.py
+++ b/pybamm/util.py
@@ -271,10 +271,9 @@ def have_jax():
 
 def is_jax_compatible():
     """Check if the available version of jax and jaxlib are compatible with PyBaMM"""
-    return (
-        importlib.metadata.distribution("jax").version.startswith(JAX_VERSION)
-        and importlib.metadata.distribution("jaxlib").version.startswith(JAXLIB_VERSION)
-    )
+    return importlib.metadata.distribution("jax").version.startswith(
+        JAX_VERSION
+    ) and importlib.metadata.distribution("jaxlib").version.startswith(JAXLIB_VERSION)
 
 
 def is_constant_and_can_evaluate(symbol):
@@ -346,6 +345,7 @@ def install_jax(arguments=None):  # pragma: no cover
         ]
     )
 
+
 # https://docs.pybamm.org/en/latest/source/user_guide/contributing.html#managing-optional-dependencies-and-their-imports
 def have_optional_dependency(module_name, attribute=None):
     err_msg = f"Optional dependency {module_name} is not available. See https://docs.pybamm.org/en/latest/source/user_guide/installation/index.html#optional-dependencies for more details."
@@ -360,7 +360,7 @@ def have_optional_dependency(module_name, attribute=None):
                 return imported_attribute  # Return the imported attribute
             else:
                 # Raise an ModuleNotFoundError if the attribute is not available
-                raise ModuleNotFoundError(err_msg)      # pragma: no cover
+                raise ModuleNotFoundError(err_msg)  # pragma: no cover
         else:
             # Return the entire module if no attribute is specified
             return module
diff --git a/scripts/fix_casadi_rpath_mac.py b/scripts/fix_casadi_rpath_mac.py
index 3f7f71e834..32f98d2945 100644
--- a/scripts/fix_casadi_rpath_mac.py
+++ b/scripts/fix_casadi_rpath_mac.py
@@ -57,15 +57,17 @@
 # This is needed for the casadi python bindings to work while repairing the wheel
 
 subprocess.run(
-    ["cp",
-    os.path.join(casadi_dir, libcasadi_37_name),
-    os.path.join(os.getenv("HOME"),".local/lib")
+    [
+        "cp",
+        os.path.join(casadi_dir, libcasadi_37_name),
+        os.path.join(os.getenv("HOME"), ".local/lib"),
     ]
 )
 
 subprocess.run(
-    ["cp",
-    os.path.join(casadi_dir, libcpp_name),
-    os.path.join(os.getenv("HOME"),".local/lib")
+    [
+        "cp",
+        os.path.join(casadi_dir, libcpp_name),
+        os.path.join(os.getenv("HOME"), ".local/lib"),
     ]
 )
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 8cbc51f1ee..30d2240e9c 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -19,7 +19,6 @@ def update_version():
     release_version = os.getenv("VERSION")[1:]
     last_day_of_month = date.today() + relativedelta(day=31)
 
-
     # pybamm/version.py
     with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file:
         output = file.read()
@@ -33,9 +32,7 @@ def update_version():
     # pyproject.toml
     with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file:
         output = file.read()
-        replace_version = re.sub(
-            '(?<=version = ")(.+)(?=")', release_version, output
-        )
+        replace_version = re.sub('(?<=version = ")(.+)(?=")', release_version, output)
         file.truncate(0)
         file.seek(0)
         file.write(replace_version)
diff --git a/setup.py b/setup.py
index 2c89603b74..b53ed50c39 100644
--- a/setup.py
+++ b/setup.py
@@ -17,27 +17,30 @@
 
 # ---------- set environment variables for vcpkg on Windows ----------------------------
 
+
 def set_vcpkg_environment_variables():
     if not os.getenv("VCPKG_ROOT_DIR"):
         raise OSError("Environment variable 'VCPKG_ROOT_DIR' is undefined.")
     if not os.getenv("VCPKG_DEFAULT_TRIPLET"):
-        raise OSError(
-            "Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined."
-        )
+        raise OSError("Environment variable 'VCPKG_DEFAULT_TRIPLET' is undefined.")
     if not os.getenv("VCPKG_FEATURE_FLAGS"):
-        raise OSError(
-            "Environment variable 'VCPKG_FEATURE_FLAGS' is undefined."
-        )
+        raise OSError("Environment variable 'VCPKG_FEATURE_FLAGS' is undefined.")
     return (
         os.getenv("VCPKG_ROOT_DIR"),
         os.getenv("VCPKG_DEFAULT_TRIPLET"),
         os.getenv("VCPKG_FEATURE_FLAGS"),
     )
 
+
 # ---------- CMakeBuild class (custom build_ext for IDAKLU target) ---------------------
 
+
 class CMakeBuild(build_ext):
-    user_options = [*build_ext.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
+    user_options = [
+        *build_ext.user_options,
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
 
     def initialize_options(self):
         build_ext.initialize_options(self)
@@ -95,9 +98,7 @@ def run(self):
                 f"-DSuiteSparse_ROOT={os.path.abspath(self.suitesparse_root)}"
             )
         if self.sundials_root:
-            cmake_args.append(
-                f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}"
-            )
+            cmake_args.append(f"-DSUNDIALS_ROOT={os.path.abspath(self.sundials_root)}")
 
         build_dir = self.get_build_directory()
         if not os.path.exists(build_dir):
@@ -110,7 +111,7 @@ def run(self):
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             os.remove(os.path.join(build_dir, "CMakeError.log"))
 
-# ---------- configuration for vcpkg on Windows ----------------------------------------
+        # ---------- configuration for vcpkg on Windows ----------------------------------------
 
         build_env = os.environ
         if os.getenv("PYBAMM_USE_VCPKG"):
@@ -123,13 +124,16 @@ def run(self):
             build_env["vcpkg_default_triplet"] = vcpkg_default_triplet
             build_env["vcpkg_feature_flags"] = vcpkg_feature_flags
 
-# ---------- Run CMake and build IDAKLU module -----------------------------------------
+        # ---------- Run CMake and build IDAKLU module -----------------------------------------
 
         cmake_list_dir = os.path.abspath(os.path.dirname(__file__))
         print("-" * 10, "Running CMake for IDAKLU solver", "-" * 40)
         subprocess.run(
-            ["cmake", cmake_list_dir, *cmake_args], cwd=build_dir, env=build_env
-        , check=True)
+            ["cmake", cmake_list_dir, *cmake_args],
+            cwd=build_dir,
+            env=build_env,
+            check=True,
+        )
 
         if os.path.isfile(os.path.join(build_dir, "CMakeError.log")):
             msg = (
@@ -193,7 +197,11 @@ def move_output(self, ext):
 class CustomInstall(install):
     """A custom install command to add 2 build options"""
 
-    user_options = [*install.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
+    user_options = [
+        *install.user_options,
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
 
     def initialize_options(self):
         install.initialize_options(self)
@@ -217,7 +225,11 @@ def run(self):
 class bdist_wheel(orig.bdist_wheel):
     """A custom install command to add 2 build options"""
 
-    user_options = [*orig.bdist_wheel.user_options, ("suitesparse-root=", None, "suitesparse source location"), ("sundials-root=", None, "sundials source location")]
+    user_options = [
+        *orig.bdist_wheel.user_options,
+        ("suitesparse-root=", None, "suitesparse source location"),
+        ("sundials-root=", None, "sundials source location"),
+    ]
 
     def initialize_options(self):
         orig.bdist_wheel.initialize_options(self)
@@ -270,6 +282,7 @@ def compile_KLU():
 
     return CMakeFound and PyBind11Found
 
+
 idaklu_ext = Extension(
     name="pybamm.solvers.idaklu",
     sources=[
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6e3beeb1fc..6694248b5d 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -272,10 +272,9 @@ def test_negative_cracking(self):
             "r_n": 26,  # negative particle
             "r_p": 26,  # positive particle
         }
-        self.run_basic_processing_test(options,
-                                       parameter_values=parameter_values,
-                                       var_pts=var_pts
-                                       )
+        self.run_basic_processing_test(
+            options, parameter_values=parameter_values, var_pts=var_pts
+        )
 
     def test_positive_cracking(self):
         options = {"particle mechanics": ("none", "swelling and cracking")}
@@ -287,10 +286,9 @@ def test_positive_cracking(self):
             "r_n": 26,  # negative particle
             "r_p": 26,  # positive particle
         }
-        self.run_basic_processing_test(options,
-                                       parameter_values=parameter_values,
-                                       var_pts=var_pts
-                                       )
+        self.run_basic_processing_test(
+            options, parameter_values=parameter_values, var_pts=var_pts
+        )
 
     def test_both_cracking(self):
         options = {"particle mechanics": "swelling and cracking"}
@@ -302,10 +300,9 @@ def test_both_cracking(self):
             "r_n": 26,  # negative particle
             "r_p": 26,  # positive particle
         }
-        self.run_basic_processing_test(options,
-                                       parameter_values=parameter_values,
-                                       var_pts=var_pts
-                                       )
+        self.run_basic_processing_test(
+            options, parameter_values=parameter_values, var_pts=var_pts
+        )
 
     def test_both_swelling_only(self):
         options = {"particle mechanics": "swelling only"}
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
index 18d773bed2..e217a11d75 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
@@ -56,10 +56,12 @@ def test_current_sigmoid_ocp(self):
         parameter_values = pybamm.get_size_distribution_parameters(parameter_values)
         parameter_values.update(
             {
-                "Negative electrode lithiation OCP [V]"
-                "": parameter_values["Negative electrode OCP [V]"],
-                "Negative electrode delithiation OCP [V]"
-                "": parameter_values["Negative electrode OCP [V]"],
+                "Negative electrode lithiation OCP [V]" "": parameter_values[
+                    "Negative electrode OCP [V]"
+                ],
+                "Negative electrode delithiation OCP [V]" "": parameter_values[
+                    "Negative electrode OCP [V]"
+                ],
             },
             check_already_exists=False,
         )
diff --git a/tests/unit/test_experiments/test_experiment.py b/tests/unit/test_experiments/test_experiment.py
index ec1a1cbeae..78ca39e6e8 100644
--- a/tests/unit/test_experiments/test_experiment.py
+++ b/tests/unit/test_experiments/test_experiment.py
@@ -227,6 +227,7 @@ def test_set_next_start_time(self):
         # TODO: once #3176 is completed, the test should pass for
         # operating_conditions_steps (or equivalent) as well
 
+
 if __name__ == "__main__":
     print("Add -v for more debug output")
     import sys
diff --git a/tests/unit/test_expression_tree/test_binary_operators.py b/tests/unit/test_expression_tree/test_binary_operators.py
index ab582ade12..b6cbe093eb 100644
--- a/tests/unit/test_expression_tree/test_binary_operators.py
+++ b/tests/unit/test_expression_tree/test_binary_operators.py
@@ -104,9 +104,7 @@ def test_diff(self):
         self.assertEqual((a**b).diff(b).evaluate(y=y), 5**3 * np.log(5))
         self.assertEqual((a**b).diff(a).evaluate(y=y), 3 * 5**2)
         self.assertEqual((a**b).diff(a**b).evaluate(), 1)
-        self.assertEqual(
-            (a**a).diff(a).evaluate(y=y), 5**5 * np.log(5) + 5 * 5**4
-        )
+        self.assertEqual((a**a).diff(a).evaluate(y=y), 5**5 * np.log(5) + 5 * 5**4)
         self.assertEqual((a**a).diff(b).evaluate(y=y), 0)
 
         # addition
diff --git a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
index 426e7811f6..552e79bc7e 100644
--- a/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
+++ b/tests/unit/test_expression_tree/test_operations/test_evaluate_python.py
@@ -45,9 +45,7 @@ def test_find_symbols(self):
 
         var_a = pybamm.id_to_python_variable(a.id)
         var_b = pybamm.id_to_python_variable(b.id)
-        self.assertEqual(
-            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
-        )
+        self.assertEqual(list(variable_symbols.values())[2], f"{var_a} + {var_b}")
 
         # test identical subtree
         constant_symbols = OrderedDict()
@@ -65,14 +63,10 @@ def test_find_symbols(self):
         # test values of variable_symbols
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         self.assertEqual(list(variable_symbols.values())[1], "y[1:2]")
-        self.assertEqual(
-            list(variable_symbols.values())[2], f"{var_a} + {var_b}"
-        )
+        self.assertEqual(list(variable_symbols.values())[2], f"{var_a} + {var_b}")
 
         var_child = pybamm.id_to_python_variable(expr.children[0].id)
-        self.assertEqual(
-            list(variable_symbols.values())[3], f"{var_child} + {var_b}"
-        )
+        self.assertEqual(list(variable_symbols.values())[3], f"{var_child} + {var_b}")
 
         # test unary op
         constant_symbols = OrderedDict()
@@ -107,9 +101,7 @@ def test_find_symbols(self):
         self.assertEqual(list(variable_symbols.keys())[1], expr.id)
         self.assertEqual(next(iter(variable_symbols.values())), "y[0:1]")
         var_funct = pybamm.id_to_python_variable(expr.id, True)
-        self.assertEqual(
-            list(variable_symbols.values())[1], f"{var_funct}({var_a})"
-        )
+        self.assertEqual(list(variable_symbols.values())[1], f"{var_funct}({var_a})")
 
         # test matrix
         constant_symbols = OrderedDict()
diff --git a/tests/unit/test_expression_tree/test_operations/test_jac.py b/tests/unit/test_expression_tree/test_operations/test_jac.py
index 503e7321ea..d3572cafdc 100644
--- a/tests/unit/test_expression_tree/test_operations/test_jac.py
+++ b/tests/unit/test_expression_tree/test_operations/test_jac.py
@@ -77,9 +77,7 @@ def test_nonlinear(self):
         np.testing.assert_array_equal(jacobian, dfunc_dy.toarray())
 
         func = 2**v
-        jacobian = np.array(
-            [[0, 0, 2**3 * np.log(2), 0], [0, 0, 0, 2**4 * np.log(2)]]
-        )
+        jacobian = np.array([[0, 0, 2**3 * np.log(2), 0], [0, 0, 0, 2**4 * np.log(2)]])
         dfunc_dy = func.jac(y).evaluate(y=y0)
         np.testing.assert_array_equal(jacobian, dfunc_dy.toarray())
 
diff --git a/tests/unit/test_meshes/test_scikit_fem_submesh.py b/tests/unit/test_meshes/test_scikit_fem_submesh.py
index 1e0839250e..83c0192d30 100644
--- a/tests/unit/test_meshes/test_scikit_fem_submesh.py
+++ b/tests/unit/test_meshes/test_scikit_fem_submesh.py
@@ -280,7 +280,7 @@ def test_to_json(self):
 
         new_submesh = pybamm.ScikitUniform2DSubMesh._from_json(submesh)
 
-        for x, y in zip(mesh['current collector'].edges, new_submesh.edges):
+        for x, y in zip(mesh["current collector"].edges, new_submesh.edges):
             np.testing.assert_array_equal(x, y)
 
 
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 34c4b8b969..c56cd2304c 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -361,10 +361,7 @@ def test_options(self):
         # thermal half-cell
         with self.assertRaisesRegex(pybamm.OptionError, "X-full"):
             pybamm.BaseBatteryModel(
-                {
-                    "thermal": "x-full",
-                    "working electrode": "positive"
-                }
+                {"thermal": "x-full", "working electrode": "positive"}
             )
         with self.assertRaisesRegex(pybamm.OptionError, "X-lumped"):
             pybamm.BaseBatteryModel(
@@ -451,9 +448,7 @@ def test_option_type(self):
         self.assertEqual(model.options, options)
 
     def test_save_load_model(self):
-        model = (
-            pybamm.lithium_ion.SPM()
-        )
+        model = pybamm.lithium_ion.SPM()
         geometry = model.default_geometry
         param = model.default_parameter_values
         param.process_model(model)
@@ -463,13 +458,15 @@ def test_save_load_model(self):
         disc.process_model(model)
 
         # save model
-        model.save_model(filename="test_base_battery_model", mesh=mesh,
-                         variables=model.variables)
+        model.save_model(
+            filename="test_base_battery_model", mesh=mesh, variables=model.variables
+        )
 
         # raises error if variables are saved without mesh
         with self.assertRaises(ValueError):
-            model.save_model(filename="test_base_battery_model",
-                             variables=model.variables)
+            model.save_model(
+                filename="test_base_battery_model", variables=model.variables
+            )
 
         os.remove("test_base_battery_model.json")
 
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
index 442817e354..88049c0c63 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
@@ -21,9 +21,7 @@ def test_default_parameter_values(self):
         # check default parameters are added correctly
         model = pybamm.lithium_ion.MPM()
         self.assertEqual(
-            model.default_parameter_values[
-                "Negative minimum particle radius [m]"
-            ],
+            model.default_parameter_values["Negative minimum particle radius [m]"],
             0.0,
         )
 
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py
index ebd19ba614..77d51f6cf7 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm_half_cell.py
@@ -21,9 +21,7 @@ def test_default_parameter_values(self):
         # check default parameters are added correctly
         model = pybamm.lithium_ion.MPM({"working electrode": "positive"})
         self.assertEqual(
-            model.default_parameter_values[
-                "Positive minimum particle radius [m]"
-            ],
+            model.default_parameter_values["Positive minimum particle radius [m]"],
             0.0,
         )
 
diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py
index 2559641d7e..e131e906c4 100644
--- a/tests/unit/test_parameters/test_bpx.py
+++ b/tests/unit/test_parameters/test_bpx.py
@@ -9,6 +9,7 @@
 import pybamm
 import copy
 
+
 class TestBPX(TestCase):
     def setUp(self):
         self.base = {
@@ -180,7 +181,6 @@ def check_constant_output(func):
             check_constant_output(kappa)
             check_constant_output(De)
 
-
     def test_table_data(self):
         bpx_obj = copy.copy(self.base)
         data = {"x": [0, 1], "y": [0, 1]}
@@ -255,7 +255,6 @@ def test_bpx_arrhenius(self):
 
             pv = pybamm.ParameterValues.create_from_bpx(tmp.name)
 
-
         def arrhenius_assertion(pv, param_key, Ea_key):
             sto = 0.5
             T = 300
@@ -269,11 +268,10 @@ def arrhenius_assertion(pv, param_key, Ea_key):
                 eval_ratio = (
                     pv[param_key](c_e, c_s_surf, c_s_max, T).value
                     / pv[param_key](c_e, c_s_surf, c_s_max, T_ref).value
-                    )
+                )
             else:
                 eval_ratio = (
-                    pv[param_key](sto, T).value
-                    / pv[param_key](sto, T_ref).value
+                    pv[param_key](sto, T).value / pv[param_key](sto, T_ref).value
                 )
 
             calc_ratio = pybamm.exp(Ea / pybamm.constants.R * (1 / T_ref - 1 / T)).value
@@ -301,6 +299,7 @@ def arrhenius_assertion(pv, param_key, Ea_key):
         for param_key, Ea_key in zip(param_keys, Ea_keys):
             arrhenius_assertion(pv, param_key, Ea_key)
 
+
 if __name__ == "__main__":
     print("Add -v for more debug output")
     import sys
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
index a537afc93d..894213f92d 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py
@@ -29,7 +29,7 @@ def test_functions(self):
             "Positive electrode OCP [V]": ([sto], 3.9478),
             # Electrolyte
             "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10),
-            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738)
+            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738),
         }
 
         for name, value in fun_test.items():
diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
index 6dde10cd9c..f548030f26 100644
--- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
+++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py
@@ -22,7 +22,7 @@ def test_functions(self):
             "Positive electrode OCP [V]": ([sto], 0.124),
             # Electrolyte
             "Electrolyte diffusivity [m2.s-1]": ([1000, T], 2.593e-10),
-            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738)
+            "Electrolyte conductivity [S.m-1]": ([1000, T], 0.9738),
         }
 
         for name, value in fun_test.items():
diff --git a/tests/unit/test_parameters/test_size_distribution_parameters.py b/tests/unit/test_parameters/test_size_distribution_parameters.py
index e633b2764a..5deeaa62be 100644
--- a/tests/unit/test_parameters/test_size_distribution_parameters.py
+++ b/tests/unit/test_parameters/test_size_distribution_parameters.py
@@ -37,7 +37,6 @@ def test_parameter_values(self):
         np.testing.assert_almost_equal(values.evaluate(param.n.prim.R_max), 2.5e-5, 3)
         np.testing.assert_almost_equal(values.evaluate(param.p.prim.R_max), 2.5e-5, 3)
 
-
         # check function parameters (size distributions) evaluate
         R_test = pybamm.Scalar(1.0)
         values.evaluate(param.n.prim.f_a_dist(R_test))
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index 4375e745ad..ef055fdc97 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -208,14 +208,14 @@ def test_solve_with_initial_soc(self):
         options = {"working electrode": "positive"}
         model = pybamm.lithium_ion.DFN(options)
         sim = pybamm.Simulation(model)
-        sim.solve([0,1], initial_soc = 0.9)
+        sim.solve([0, 1], initial_soc=0.9)
         self.assertEqual(sim._built_initial_soc, 0.9)
 
         # Test whether initial_soc works with half cell (build)
         options = {"working electrode": "positive"}
         model = pybamm.lithium_ion.DFN(options)
         sim = pybamm.Simulation(model)
-        sim.build(initial_soc = 0.9)
+        sim.build(initial_soc=0.9)
         self.assertEqual(sim._built_initial_soc, 0.9)
 
         # Test whether initial_soc works with half cell when it is a voltage
@@ -227,7 +227,7 @@ def test_solve_with_initial_soc(self):
         options = {"working electrode": "positive"}
         parameter_values["Current function [A]"] = 0.0
         sim = pybamm.Simulation(model, parameter_values=parameter_values)
-        sol = sim.solve([0,1], initial_soc = f"{ucv} V")
+        sol = sim.solve([0, 1], initial_soc=f"{ucv} V")
         voltage = sol["Terminal voltage [V]"].entries
         self.assertAlmostEqual(voltage[0], ucv, places=5)
 
@@ -302,12 +302,10 @@ def test_save_load(self):
             sim = pybamm.Simulation(model)
             sim.solve([0, 600])
             with self.assertRaisesRegex(
-                    NotImplementedError,
-                    "Cannot save simulation if model format is python"
+                NotImplementedError, "Cannot save simulation if model format is python"
             ):
                 sim.save(test_name)
 
-
     def test_load_param(self):
         # Test load_sim for parameters imports
         filename = f"{uuid.uuid4()}.p"
diff --git a/tests/unit/test_solvers/test_idaklu_solver.py b/tests/unit/test_solvers/test_idaklu_solver.py
index cc54f3dfd5..604f559049 100644
--- a/tests/unit/test_solvers/test_idaklu_solver.py
+++ b/tests/unit/test_solvers/test_idaklu_solver.py
@@ -87,7 +87,7 @@ def test_model_events(self):
 
             # Check invalid atol type raises an error
             with self.assertRaises(pybamm.SolverError):
-                solver._check_atol_type({'key': 'value'}, [])
+                solver._check_atol_type({"key": "value"}, [])
 
             # enforce events that won't be triggered
             model.events = [pybamm.Event("an event", var + 1)]
@@ -566,9 +566,9 @@ def test_with_output_variables(self):
         t_eval = np.linspace(0, 3600, 100)
 
         options = {
-            'linear_solver': 'SUNLinSol_KLU',
-            'jacobian': 'sparse',
-            'num_threads': 4,
+            "linear_solver": "SUNLinSol_KLU",
+            "jacobian": "sparse",
+            "num_threads": 4,
         }
 
         # Use a selection of variables of different types
@@ -587,7 +587,8 @@ def test_with_output_variables(self):
 
         # Use the full model as comparison (tested separately)
         solver_all = pybamm.IDAKLUSolver(
-            atol=1e-8, rtol=1e-8,
+            atol=1e-8,
+            rtol=1e-8,
             options=options,
         )
         sol_all = solver_all.solve(
@@ -599,7 +600,8 @@ def test_with_output_variables(self):
 
         # Solve for a subset of variables and compare results
         solver = pybamm.IDAKLUSolver(
-            atol=1e-8, rtol=1e-8,
+            atol=1e-8,
+            rtol=1e-8,
             options=options,
             output_variables=output_variables,
         )
@@ -640,9 +642,9 @@ def test_with_output_variables_and_sensitivities(self):
         t_eval = np.linspace(0, 3600, 100)
 
         options = {
-            'linear_solver': 'SUNLinSol_KLU',
-            'jacobian': 'sparse',
-            'num_threads': 4,
+            "linear_solver": "SUNLinSol_KLU",
+            "jacobian": "sparse",
+            "num_threads": 4,
         }
 
         # Use a selection of variables of different types
@@ -656,7 +658,8 @@ def test_with_output_variables_and_sensitivities(self):
 
         # Use the full model as comparison (tested separately)
         solver_all = pybamm.IDAKLUSolver(
-            atol=1e-8, rtol=1e-8,
+            atol=1e-8,
+            rtol=1e-8,
             options=options,
         )
         sol_all = solver_all.solve(
@@ -668,7 +671,8 @@ def test_with_output_variables_and_sensitivities(self):
 
         # Solve for a subset of variables and compare results
         solver = pybamm.IDAKLUSolver(
-            atol=1e-8, rtol=1e-8,
+            atol=1e-8,
+            rtol=1e-8,
             options=options,
             output_variables=output_variables,
         )
diff --git a/tests/unit/test_solvers/test_processed_variable_computed.py b/tests/unit/test_solvers/test_processed_variable_computed.py
index c8b1f2597d..b5f105b34b 100644
--- a/tests/unit/test_solvers/test_processed_variable_computed.py
+++ b/tests/unit/test_solvers/test_processed_variable_computed.py
@@ -169,11 +169,15 @@ def test_processed_variable_1D(self):
         np.testing.assert_array_equal(processed_var.unroll(), y_sol)
 
         # Check no error when data dimension is transposed vs node/edge
-        processed_var.mesh.nodes, processed_var.mesh.edges = \
-            processed_var.mesh.edges, processed_var.mesh.nodes
+        processed_var.mesh.nodes, processed_var.mesh.edges = (
+            processed_var.mesh.edges,
+            processed_var.mesh.nodes,
+        )
         processed_var.initialise_1D()
-        processed_var.mesh.nodes, processed_var.mesh.edges = \
-            processed_var.mesh.edges, processed_var.mesh.nodes
+        processed_var.mesh.nodes, processed_var.mesh.edges = (
+            processed_var.mesh.edges,
+            processed_var.mesh.nodes,
+        )
 
         # Check that there are no errors with domain-specific attributes
         #  (see ProcessedVariableComputed.initialise_1D() for details)
diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py
index 9fc93dfb26..c7dfb716de 100644
--- a/tests/unit/test_solvers/test_solution.py
+++ b/tests/unit/test_solvers/test_solution.py
@@ -279,15 +279,16 @@ def test_save(self):
                 solution.save_data(f"{test_stub}.mat", to_format="matlab")
             # Works if providing alternative name
             solution.save_data(
-                f"{test_stub}.mat", to_format="matlab",
-                short_names={"c + d": "c_plus_d"}
+                f"{test_stub}.mat",
+                to_format="matlab",
+                short_names={"c + d": "c_plus_d"},
             )
             data_load = loadmat(f"{test_stub}.mat")
             np.testing.assert_array_equal(solution.data["c + d"], data_load["c_plus_d"])
 
             # to csv
             with self.assertRaisesRegex(
-                    ValueError, "only 0D variables can be saved to csv"
+                ValueError, "only 0D variables can be saved to csv"
             ):
                 solution.save_data(f"{test_stub}.csv", to_format="csv")
             # only save "c" and "2c"
@@ -319,19 +320,23 @@ def test_save(self):
             np.testing.assert_array_almost_equal(json_data["d"], solution.data["d"])
 
             # raise error if format is unknown
-            with self.assertRaisesRegex(ValueError,
-                                        "format 'wrong_format' not recognised"):
+            with self.assertRaisesRegex(
+                ValueError, "format 'wrong_format' not recognised"
+            ):
                 solution.save_data(f"{test_stub}.csv", to_format="wrong_format")
 
             # test save whole solution
             solution.save(f"{test_stub}.pickle")
             solution_load = pybamm.load(f"{test_stub}.pickle")
-            self.assertEqual(solution.all_models[0].name,
-                             solution_load.all_models[0].name)
-            np.testing.assert_array_equal(solution["c"].entries,
-                                          solution_load["c"].entries)
-            np.testing.assert_array_equal(solution["d"].entries,
-                                          solution_load["d"].entries)
+            self.assertEqual(
+                solution.all_models[0].name, solution_load.all_models[0].name
+            )
+            np.testing.assert_array_equal(
+                solution["c"].entries, solution_load["c"].entries
+            )
+            np.testing.assert_array_equal(
+                solution["d"].entries, solution_load["d"].entries
+            )
 
     def test_get_data_cycles_steps(self):
         model = pybamm.BaseModel()
diff --git a/tests/unit/test_spatial_methods/test_scikit_finite_element.py b/tests/unit/test_spatial_methods/test_scikit_finite_element.py
index 657d896dfd..05b424e053 100644
--- a/tests/unit/test_spatial_methods/test_scikit_finite_element.py
+++ b/tests/unit/test_spatial_methods/test_scikit_finite_element.py
@@ -203,7 +203,7 @@ def test_manufactured_solution(self):
         u = np.sin(np.pi * z_vertices)
         mass = pybamm.Mass(var)
         mass_disc = disc.process_symbol(mass)
-        soln = -np.pi**2 * u
+        soln = -(np.pi**2) * u
         np.testing.assert_array_almost_equal(
             eqn_zz_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=3
         )
@@ -226,7 +226,7 @@ def test_manufactured_solution(self):
         u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices)
         mass = pybamm.Mass(var)
         mass_disc = disc.process_symbol(mass)
-        soln = -np.pi**2 * u
+        soln = -(np.pi**2) * u
         np.testing.assert_array_almost_equal(
             laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=2
         )
@@ -287,7 +287,7 @@ def test_manufactured_solution_cheb_grid(self):
         u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices)
         mass = pybamm.Mass(var)
         mass_disc = disc.process_symbol(mass)
-        soln = -np.pi**2 * u
+        soln = -(np.pi**2) * u
         np.testing.assert_array_almost_equal(
             laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=1
         )
@@ -350,7 +350,7 @@ def test_manufactured_solution_exponential_grid(self):
         u = np.cos(np.pi * y_vertices) * np.sin(np.pi * z_vertices)
         mass = pybamm.Mass(var)
         mass_disc = disc.process_symbol(mass)
-        soln = -np.pi**2 * u
+        soln = -(np.pi**2) * u
         np.testing.assert_array_almost_equal(
             laplace_eqn_disc.evaluate(None, u), mass_disc.entries @ soln, decimal=1
         )
diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py
index 730e4cc08d..d0ac5337bf 100644
--- a/tests/unit/test_util.py
+++ b/tests/unit/test_util.py
@@ -12,7 +12,8 @@
 from io import StringIO
 from tempfile import TemporaryDirectory
 
-anytree = sys.modules['anytree']
+anytree = sys.modules["anytree"]
+
 
 class TestUtil(TestCase):
     """
@@ -31,7 +32,7 @@ def test_rmse(self):
             pybamm.rmse(np.ones(5), np.zeros(3))
 
     def test_is_constant_and_can_evaluate(self):
-        sys.modules['anytree'] = anytree
+        sys.modules["anytree"] = anytree
         symbol = pybamm.PrimaryBroadcast(0, "negative electrode")
         self.assertEqual(False, pybamm.is_constant_and_can_evaluate(symbol))
         symbol = pybamm.StateVector(slice(0, 1))
@@ -92,13 +93,17 @@ def test_git_commit_info(self):
         self.assertEqual(git_commit_info[:2], "v2")
 
     def test_have_optional_dependency(self):
-        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency pybtex is not available."):
-            pybtex = sys.modules['pybtex']
-            sys.modules['pybtex'] = None
+        with self.assertRaisesRegex(
+            ModuleNotFoundError, "Optional dependency pybtex is not available."
+        ):
+            pybtex = sys.modules["pybtex"]
+            sys.modules["pybtex"] = None
             pybamm.print_citations()
-        with self.assertRaisesRegex(ModuleNotFoundError, "Optional dependency anytree is not available."):
+        with self.assertRaisesRegex(
+            ModuleNotFoundError, "Optional dependency anytree is not available."
+        ):
             with TemporaryDirectory() as dir_name:
-                sys.modules['anytree'] = None
+                sys.modules["anytree"] = None
                 test_stub = os.path.join(dir_name, "test_visualize")
                 test_name = f"{test_stub}.png"
                 c = pybamm.Variable("c", "negative electrode")
@@ -106,7 +111,7 @@ def test_have_optional_dependency(self):
                 sym = pybamm.div(c * pybamm.grad(c)) + (c / d + c - d) ** 5
                 sym.visualise(test_name)
 
-        sys.modules['pybtex'] = pybtex
+        sys.modules["pybtex"] = pybtex
         pybamm.util.have_optional_dependency("pybtex")
         pybamm.print_citations()
 

From d8eedf13d2e461644afe3f91254a552c9c86d061 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Sat, 23 Dec 2023 21:41:52 +0530
Subject: [PATCH 575/615] Migrate to ruff-format

---
 .git-blame-ignore-revs | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs
index ec0f52cbfd..b38e6697cb 100644
--- a/.git-blame-ignore-revs
+++ b/.git-blame-ignore-revs
@@ -4,7 +4,9 @@
 a63e49ece0f9336d1f5c2562f7459e555c6e6693
 # activated standard pre-commits - https://github.com/pybamm-team/PyBaMM/pull/3192
 5273214b585c5a4286609aed40e0b092d0e05f42
-# migrate config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
+# migrated config to pyproject.toml - https://github.com/pybamm-team/PyBaMM/pull/3557
 12c5d77203bd93542785d237bac00bad5ed5469a
 # activated pyupgrade - https://github.com/pybamm-team/PyBaMM/pull/3579
 ff6d81c01331c7d269303b4a8321d9881bdf98fa
+# migrated to ruff-format - https://github.com/pybamm-team/PyBaMM/pull/3655
+60ebd4148059a95428a496f4f55c1175ead362d3

From da51644e8d2d8512bbcf929e60bd43d7ca2872ed Mon Sep 17 00:00:00 2001
From: Shubham Bhardwaj <rockstar24061996@gmail.com>
Date: Sat, 23 Dec 2023 23:01:27 +0530
Subject: [PATCH 576/615] review

---
 run-tests.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/run-tests.py b/run-tests.py
index 55a02a7297..b5dad91518 100755
--- a/run-tests.py
+++ b/run-tests.py
@@ -86,7 +86,7 @@ def run_doc_tests():
         # Regardless of whether the doctests pass or fail, attempt to remove the built files.
         print("Deleting built files.")
         try:
-            shutil.rmtree("docs/build")
+            shutil.rmtree("docs/build/html/.doctrees/")
         except Exception as e:
             print(f"Error deleting built files: {e}")
 

From 34d3e6bd6bfddc741bfaa2af6b756e1bdabfd540 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 00:22:41 +0530
Subject: [PATCH 577/615] Fix title underline

---
 docs/source/user_guide/installation/gnu-linux-mac.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index 0e765a37a3..ddd58e963e 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,5 +1,5 @@
 gnu-linux-mac & MacOS
-=================
+=====================
 
 .. contents::
 

From e766cca98c05d2617517670625ee1408095bc4df Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 00:39:10 +0530
Subject: [PATCH 578/615] Fix table malformation

---
 docs/source/user_guide/installation/index.rst | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/source/user_guide/installation/index.rst b/docs/source/user_guide/installation/index.rst
index 5f1b5eaab8..f0c12d46fc 100644
--- a/docs/source/user_guide/installation/index.rst
+++ b/docs/source/user_guide/installation/index.rst
@@ -233,11 +233,11 @@ odes dependencies
 
 Installable with ``pip install "pybamm[odes]"``
 
-================================================================================================================================ ================== ================== =============================
-Dependency                                                                                                                       Minimum Version    pip extra          Notes
-================================================================================================================================ ================== ================== =============================
-`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__  \-                 odes               For scikits ODE & DAE solvers
-================================================================================================================================ ================== ================== =============================
+======================================================================================================================================= ================== ================== =============================
+Dependency                                                                                                                              Minimum Version    pip extra          Notes
+======================================================================================================================================= ================== ================== =============================
+`scikits.odes <https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html#optional-scikits-odes-solver>`__     \-                 odes               For scikits ODE & DAE solvers
+======================================================================================================================================= ================== ================== =============================
 
 .. note::
 

From 96d63deb8b4716f95c524313edffeaa46c9fcaa5 Mon Sep 17 00:00:00 2001
From: "arjxn.py" <arjxn.py@gmail.com>
Date: Sun, 24 Dec 2023 02:07:51 +0530
Subject: [PATCH 579/615] Add non-fixable link to `.lycheeignore`

---
 .lycheeignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.lycheeignore b/.lycheeignore
index 399827d27c..fd332a54ff 100644
--- a/.lycheeignore
+++ b/.lycheeignore
@@ -1,4 +1,5 @@
 # a list of links/files to be ignored by lychee link checker (see workflow file)
+https://github.com/LLNL/sundials/releases/download/v%7BSUNDIALS_VERSION%7D/sundials-%7BSUNDIALS_VERSION%7D.tar.gz
 
 # Errors in docs/source/user_guide/getting_started.md
 file:///home/runner/work/PyBaMM/PyBaMM/docs/source/user_guide/api_docs

From 430c86fd92f7b0ae6dfe85ab2837758f845f202c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sun, 24 Dec 2023 15:25:33 +0000
Subject: [PATCH 580/615] style: pre-commit fixes

---
 pybamm/install_odes.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 44773fa5c6..b1c1a069b1 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,10 +20,12 @@
 try:
     # wget module is required to download SUNDIALS or SuiteSparse.
     import wget
+
     NO_WGET = False
 except ModuleNotFoundError:
     NO_WGET = True
 
+
 def download_extract_library(url, directory):
     # Download and extract archive at url
     if NO_WGET:
@@ -36,6 +38,7 @@ def download_extract_library(url, directory):
     tar = tarfile.open(archive)
     tar.extractall(directory)
 
+
 def install_sundials(download_dir, install_dir):
     # Download the SUNDIALS library and compile it.
     logger = logging.getLogger("scikits.odes setup")
@@ -45,9 +48,7 @@ def install_sundials(download_dir, install_dir):
     except OSError:
         raise RuntimeError("CMake must be installed to build SUNDIALS.")
 
-    url = (
-        f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
-    )
+    url = f"https://github.com/LLNL/sundials/releases/download/v{SUNDIALS_VERSION}/sundials-{SUNDIALS_VERSION}.tar.gz"
     logger.info("Downloading sundials")
     download_extract_library(url, download_dir)
 
@@ -77,6 +78,7 @@ def install_sundials(download_dir, install_dir):
     make_cmd = ["make", "install"]
     subprocess.run(make_cmd, cwd=build_directory, check=True)
 
+
 def update_LD_LIBRARY_PATH(install_dir):
     # Look for the current python virtual env and add an export statement
     # for LD_LIBRARY_PATH in the activate script. If no virtual env is found,
@@ -97,12 +99,16 @@ def update_LD_LIBRARY_PATH(install_dir):
         elif os.path.exists(zshrc_path):
             script_path = os.path.join(os.environ.get("HOME"), ".zshrc")
         elif os.path.exists(bashrc_path) and os.path.exists(zshrc_path):
-            print("Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path")
+            print(
+                "Both .bashrc and .zshrc found in the home directory. Setting .bashrc as path"
+            )
             script_path = os.path.join(os.environ.get("HOME"), ".bashrc")
         else:
             print("Neither .bashrc nor .zshrc found in the home directory.")
 
-    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv("LD_LIBRARY_PATH"):
+    if os.getenv("LD_LIBRARY_PATH") and f"{install_dir}/lib" in os.getenv(
+        "LD_LIBRARY_PATH"
+    ):
         print(f"{install_dir}/lib was found in LD_LIBRARY_PATH.")
         if os.path.exists(bashrc_path):
             print("--> Not updating venv activate or .bashrc scripts")
@@ -117,6 +123,7 @@ def update_LD_LIBRARY_PATH(install_dir):
                     f"Adding {install_dir}/lib to LD_LIBRARY_PATH" f" in {script_path}"
                 )
 
+
 def main(arguments=None):
     log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
     logger = logging.getLogger("scikits.odes setup")
@@ -182,5 +189,6 @@ def main(arguments=None):
     env = os.environ.copy()
     subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
 
+
 if __name__ == "__main__":
     main(sys.argv[1:])

From 6aa6685b7b9d705169b9fdb8993235ad9d294e96 Mon Sep 17 00:00:00 2001
From: Arjun <arjunverma.oc@gmail.com>
Date: Sun, 24 Dec 2023 20:56:41 +0530
Subject: [PATCH 581/615] Apply suggestions from code review

Co-authored-by: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
---
 .github/workflows/run_periodic_tests.yml          | 15 +++++++++------
 .../user_guide/installation/gnu-linux-mac.rst     |  4 ++--
 2 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index f247176e40..1178b2ec96 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -155,8 +155,7 @@ jobs:
           pyenv uninstall -f $( python --version )
 
   test_install_odes:
-    needs: style
-    runs-on: macos-latest
+    runs-on: ${{ matrix.os }}
     strategy:
         matrix:
             os: [ubuntu-latest, macos-latest]
@@ -168,7 +167,13 @@ jobs:
       - name: Check out PyBaMM repository
         uses: actions/checkout@v4
 
+      - name: Install Linux system dependencies
+        if: matrix.os == 'ubuntu-latest'
+        run: |
+          sudo apt-get update
+          sudo apt-get install gfortran gcc libopenblas-dev
       - name: Install macOS system dependencies
+        if: matrix.os == 'macos-latest'
         env:
           # Homebrew environment variables
           HOMEBREW_NO_INSTALL_CLEANUP: 1
@@ -187,10 +192,8 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install PyBaMM dependencies
-        run: |
-          pip install --upgrade pip wheel setuptools nox
-          pip install -e .[all]
+      - name: Install PyBaMM
+        run: pip install -e .
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index ddd58e963e..3e93587cde 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -1,5 +1,5 @@
-gnu-linux-mac & MacOS
-=====================
+GNU/Linux & macOS
+=================
 
 .. contents::
 

From 9fd0cfd49bfe3466e883699cab325bf0f976c2f9 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 26 Dec 2023 02:31:58 +0530
Subject: [PATCH 582/615] chore: update pre-commit hooks (#3663)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.8 → v0.1.9](https://github.com/astral-sh/ruff-pre-commit/compare/v0.1.8...v0.1.9)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---
 .pre-commit-config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5cfbdf4710..3998ad1076 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.1.8"
+    rev: "v0.1.9"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

From ee64eafc7f945fb6684ce22a60169f97f5a0f235 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 27 Dec 2023 13:17:57 +0530
Subject: [PATCH 583/615] ignore internal nbmake warnings - pytest (notebook
 tests) (#3665)

---
 pyproject.toml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 28b43e7729..96c4e97950 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -245,7 +245,12 @@ testpaths = [
 ]
 console_output_style = "progress"
 xfail_strict = true
-filterwarnings = ["error"]
+filterwarnings = [
+    "error",
+    # ignore internal nbmake warnings
+    'ignore:unclosed \<socket.socket:ResourceWarning',
+    'ignore:unclosed event loop \<:ResourceWarning',
+]
 
 # Logging configuration
 log_cli = "true"

From 7110cac8502b46ff738f671ed0f03f57b636794e Mon Sep 17 00:00:00 2001
From: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
Date: Sun, 31 Dec 2023 16:56:56 +0530
Subject: [PATCH 584/615] import jax.extended

Signed-off-by: Pradyot Ranjan <99216956+pradyotRanjan@users.noreply.github.com>
---
 pybamm/solvers/jax_bdf_solver.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 8f5b8ed817..313059cbe5 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -10,7 +10,7 @@
     import jax
     import jax.numpy as jnp
     from jax import core, dtypes
-    from jax import linear_util as lu
+    from jax.extend import linear_util as lu
     from jax.api_util import flatten_fun_nokwargs
     from jax.config import config
     from jax.flatten_util import ravel_pytree

From 995d7e3b57b413917d0a057cfe2bb91a5678e616 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 1 Jan 2024 23:49:52 +0530
Subject: [PATCH 585/615] Use distinct names for macOS and Linux wheel
 artifacts (#3677)

---
 .github/workflows/publish_pypi.yml | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 90b67e9f87..17cfc6b5cb 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -109,10 +109,19 @@ jobs:
             delocate-wheel -v -w {dest_dir} {wheel}
           CIBW_SKIP: "pp* *musllinux*"
 
-      - name: Upload wheels
+      - name: Upload wheels for Linux
         uses: actions/upload-artifact@v4
+        if: matrix.os == 'ubuntu-latest'
         with:
-          name: wheels
+          name: linux_wheels
+          path: ./wheelhouse/*.whl
+          if-no-files-found: error
+
+      - name: Upload wheels for macOS
+        uses: actions/upload-artifact@v4
+        if: matrix.os == 'macos-latest'
+        with:
+          name: macos_wheels
           path: ./wheelhouse/*.whl
           if-no-files-found: error
 
@@ -151,7 +160,7 @@ jobs:
       - name: Move all package files to files/
         run: |
           mkdir files
-          mv windows_wheels/* wheels/* sdist/* files/
+          mv windows_wheels/* linux_wheels/* macos_wheels/* sdist/* files/
 
       - name: Publish on PyPI
         if: github.event.inputs.target == 'pypi' || github.event_name == 'release'

From c24383c7df0bdc69b67d97d07fe5776ea82eb6f4 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Mon, 1 Jan 2024 23:50:27 +0530
Subject: [PATCH 586/615] Update license copyright year(s) (#3673)

* docs(license): update copyright year(s)

* docs(license): update copyright year(s)

---------

Co-authored-by: github-actions <github-actions@github.com>
---
 LICENSE.txt  | 2 +-
 docs/conf.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/LICENSE.txt b/LICENSE.txt
index 0f57d1e706..9937766a58 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,4 +1,4 @@
-Copyright (c) 2018-2023, the PyBaMM team.
+Copyright (c) 2018-2024, the PyBaMM team.
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
diff --git a/docs/conf.py b/docs/conf.py
index 35edadb249..928fc76cc6 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -25,7 +25,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "PyBaMM"
-copyright = "2018-2023, The PyBaMM Team"
+copyright = "2018-2024, The PyBaMM Team"
 author = "The PyBaMM Team"
 
 # The short X.Y version

From be2c34821fda1c7d54f92d0d93c9345e2ab54050 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 10:36:14 +0000
Subject: [PATCH 587/615] docs: update README.md [skip ci]

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d5050cfe55..0d4fd2eeaf 100644
--- a/README.md
+++ b/README.md
@@ -271,7 +271,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Agnik7"><img src="https://avatars.githubusercontent.com/u/77234005?v=4?s=100" width="100px;" alt="Agnik Bakshi"/><br /><sub><b>Agnik Bakshi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=Agnik7" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/RuiheLi"><img src="https://avatars.githubusercontent.com/u/84007676?v=4?s=100" width="100px;" alt="RuiheLi"/><br /><sub><b>RuiheLi</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=RuiheLi" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/chmabaur"><img src="https://avatars.githubusercontent.com/u/127507466?v=4?s=100" width="100px;" alt="chmabaur"/><br /><sub><b>chmabaur</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3Achmabaur" title="Bug reports">🐛</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=chmabaur" title="Code">💻</a></td>
-      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AbhishekChaudharii"><img src="https://avatars.githubusercontent.com/u/91185083?v=4?s=100" width="100px;" alt="Abhishek Chaudhari"/><br /><sub><b>Abhishek Chaudhari</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/AbhishekChaudharii"><img src="https://avatars.githubusercontent.com/u/91185083?v=4?s=100" width="100px;" alt="Abhishek Chaudhari"/><br /><sub><b>Abhishek Chaudhari</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Documentation">📖</a> <a href="https://github.com/pybamm-team/PyBaMM/commits?author=AbhishekChaudharii" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/shubhambhar007"><img src="https://avatars.githubusercontent.com/u/32607282?v=4?s=100" width="100px;" alt="Shubham Bhardwaj"/><br /><sub><b>Shubham Bhardwaj</b></sub></a><br /><a href="#infra-shubhambhar007" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/jlauber18"><img src="https://avatars.githubusercontent.com/u/28939653?v=4?s=100" width="100px;" alt="Jonathan Lauber"/><br /><sub><b>Jonathan Lauber</b></sub></a><br /><a href="#infra-jlauber18" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
     </tr>

From 7ca862ae2d711bcb6476b937bf21853b0c3a6f01 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 10:36:15 +0000
Subject: [PATCH 588/615] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 1cc25d48f8..7035c3bf42 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -744,7 +744,8 @@
       "avatar_url": "https://avatars.githubusercontent.com/u/91185083?v=4",
       "profile": "https://github.com/AbhishekChaudharii",
       "contributions": [
-        "doc"
+        "doc",
+        "code"
       ]
     },
     {

From c2b19ede55b7f663f689aad32c5e8e60724a172c Mon Sep 17 00:00:00 2001
From: kratman <kratmandu@gmail.com>
Date: Wed, 3 Jan 2024 10:15:30 -0500
Subject: [PATCH 589/615] Fix formatting

---
 pybamm/experiment/experiment.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pybamm/experiment/experiment.py b/pybamm/experiment/experiment.py
index 58bd4fa724..b04281d78d 100644
--- a/pybamm/experiment/experiment.py
+++ b/pybamm/experiment/experiment.py
@@ -50,7 +50,6 @@ def __init__(
         drive_cycles=None,
         cccv_handling=None,
     ):
-
         if cccv_handling is not None:
             raise ValueError(
                 "cccv_handling has been deprecated, use "

From 0218ac4c60fca54878688be70c74bd1fe834406e Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 15:35:28 +0000
Subject: [PATCH 590/615] style: pre-commit fixes

---
 pybamm/install_odes.py          |  1 +
 scripts/install_KLU_Sundials.py | 12 +++++++-----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index d1c38a61af..caf36f226e 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -20,6 +20,7 @@
 # Build in parallel wherever possible
 os.environ["CMAKE_BUILD_PARALLEL_LEVEL"] = str(cpu_count())
 
+
 def download_extract_library(url, directory):
     # Download and extract archive at url
     if NO_WGET:
diff --git a/scripts/install_KLU_Sundials.py b/scripts/install_KLU_Sundials.py
index 0bfa02cefa..2aa8394ac4 100755
--- a/scripts/install_KLU_Sundials.py
+++ b/scripts/install_KLU_Sundials.py
@@ -95,11 +95,13 @@ def download_extract_library(url, download_dir):
     if libdir == "SuiteSparse_config":
         env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir}"
     else:
-    # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
-    # INSTALL RPATH in order to ensure that the dynamic libraries are found
-    # at runtime just once. Otherwise, delocate complains about multiple
-    # references to the SuiteSparse_config dynamic library (auditwheel does not).
-        env["CMAKE_OPTIONS"] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
+        # For AMD, COLAMD, BTF and KLU; do not set a BUILD RPATH but use an
+        # INSTALL RPATH in order to ensure that the dynamic libraries are found
+        # at runtime just once. Otherwise, delocate complains about multiple
+        # references to the SuiteSparse_config dynamic library (auditwheel does not).
+        env[
+            "CMAKE_OPTIONS"
+        ] = f"-DCMAKE_INSTALL_PREFIX={install_dir} -DCMAKE_INSTALL_RPATH={install_dir}/lib -DCMAKE_INSTALL_RPATH_USE_LINK_PATH=FALSE -DCMAKE_BUILD_WITH_INSTALL_RPATH=FALSE"
     subprocess.run(make_cmd, cwd=build_dir, env=env, shell=True, check=True)
     subprocess.run(install_cmd, cwd=build_dir, check=True)
 

From ca743587841bf8f3b0bd13dee3d4abc73bd76501 Mon Sep 17 00:00:00 2001
From: cringeyburger <medaankit18@gmail.com>
Date: Thu, 4 Jan 2024 03:40:09 +0530
Subject: [PATCH 591/615] Update JAX Imports

---
 pybamm/expression_tree/operations/evaluate_python.py | 3 +--
 pybamm/solvers/jax_bdf_solver.py                     | 3 +--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/pybamm/expression_tree/operations/evaluate_python.py b/pybamm/expression_tree/operations/evaluate_python.py
index 9c6f734553..7977443b83 100644
--- a/pybamm/expression_tree/operations/evaluate_python.py
+++ b/pybamm/expression_tree/operations/evaluate_python.py
@@ -11,11 +11,10 @@
 
 if pybamm.have_jax():
     import jax
-    from jax.config import config
 
     platform = jax.lib.xla_bridge.get_backend().platform.casefold()
     if platform != "metal":
-        config.update("jax_enable_x64", True)
+        jax.config.update("jax_enable_x64", True)
 
 
 class JaxCooMatrix:
diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index dc4cb89906..988de0c9c6 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -12,7 +12,6 @@
     from jax import core, dtypes
     from jax.extend import linear_util as lu
     from jax.api_util import flatten_fun_nokwargs
-    from jax.config import config
     from jax.flatten_util import ravel_pytree
     from jax.interpreters import partial_eval as pe
     from jax.tree_util import tree_flatten, tree_map, tree_unflatten
@@ -20,7 +19,7 @@
 
     platform = jax.lib.xla_bridge.get_backend().platform.casefold()
     if platform != "metal":
-        config.update("jax_enable_x64", True)
+        jax.config.update("jax_enable_x64", True)
 
     MAX_ORDER = 5
     NEWTON_MAXITER = 4

From c02b8ebf909793adb9b48fce5f0493c945529b29 Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 23:53:59 +0000
Subject: [PATCH 592/615] docs: update README.md [skip ci]

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0d4fd2eeaf..f173906454 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@
 [![code style](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-72-orange.svg)](#-contributors)
+[![All Contributors](https://img.shields.io/badge/all_contributors-73-orange.svg)](#-contributors)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 </div>
@@ -278,6 +278,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
     <tr>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/prady0t"><img src="https://avatars.githubusercontent.com/u/99216956?v=4?s=100" width="100px;" alt="Pradyot Ranjan"/><br /><sub><b>Pradyot Ranjan</b></sub></a><br /><a href="#infra-prady0t" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/XuboGU"><img src="https://avatars.githubusercontent.com/u/53944452?v=4?s=100" width="100px;" alt="XuboGU"/><br /><sub><b>XuboGU</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=XuboGU" title="Code">💻</a> <a href="https://github.com/pybamm-team/PyBaMM/issues?q=author%3AXuboGU" title="Bug reports">🐛</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/cringeyburger"><img src="https://avatars.githubusercontent.com/u/121183876?v=4?s=100" width="100px;" alt="Ankit Meda"/><br /><sub><b>Ankit Meda</b></sub></a><br /><a href="https://github.com/pybamm-team/PyBaMM/commits?author=cringeyburger" title="Code">💻</a></td>
     </tr>
   </tbody>
 </table>

From ccc72a6c54c5d511b23604d71a9b7fd99d317e1b Mon Sep 17 00:00:00 2001
From: "allcontributors[bot]"
 <46447321+allcontributors[bot]@users.noreply.github.com>
Date: Wed, 3 Jan 2024 23:54:00 +0000
Subject: [PATCH 593/615] docs: update .all-contributorsrc [skip ci]

---
 .all-contributorsrc | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/.all-contributorsrc b/.all-contributorsrc
index 7035c3bf42..5b003ed874 100644
--- a/.all-contributorsrc
+++ b/.all-contributorsrc
@@ -784,6 +784,15 @@
         "code",
         "bug"
       ]
+    },
+    {
+      "login": "cringeyburger",
+      "name": "Ankit Meda",
+      "avatar_url": "https://avatars.githubusercontent.com/u/121183876?v=4",
+      "profile": "https://github.com/cringeyburger",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

From 3bf9084113b1e9191c322dca1b6445a7666219af Mon Sep 17 00:00:00 2001
From: Simon O'Kane <42972513+DrSOKane@users.noreply.github.com>
Date: Fri, 5 Jan 2024 18:42:02 +0000
Subject: [PATCH 594/615] Degradation example update (#3691)

* fixed tests

* Added graphite half-cell parameter files

* Revert "Added graphite half-cell parameter files"

This reverts commit 78001e81eecc38919364190940e095e0e51fab76.

* Revert "fixed tests"

This reverts commit cf53ff1d9e74eda7e68bc65b5dea5c18f7fcf872.

* Restored original experiment protocol to coupled degradation example notebook

* changelog

* changelog
---
 CHANGELOG.md                                    |  1 +
 .../notebooks/models/coupled-degradation.ipynb  | 17 ++++++++---------
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index ef2f5c2bab..599e1fc696 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,7 @@
 
 ## Bug fixes
 
+- Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691))
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
 - Fixed bug in calculation of theoretical energy that made it very slow ([#3506](https://github.com/pybamm-team/PyBaMM/pull/3506))
diff --git a/docs/source/examples/notebooks/models/coupled-degradation.ipynb b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
index 1551a79a64..8c083d986a 100644
--- a/docs/source/examples/notebooks/models/coupled-degradation.ipynb
+++ b/docs/source/examples/notebooks/models/coupled-degradation.ipynb
@@ -105,22 +105,21 @@
     "cycle_number = 10\n",
     "exp = pybamm.Experiment(\n",
     "    [\n",
-    "        \"Hold at 4.2 V until C/100\",\n",
-    "        \"Rest for 4 hours\",\n",
-    "        \"Discharge at 0.1C until 2.5 V\",  # initial capacity check\n",
-    "        \"Charge at 0.3C until 4.2 V\",\n",
-    "        \"Hold at 4.2 V until C/100\",\n",
+    "        \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
+    "        \"Rest for 4 hours (5 minute period)\",\n",
+    "        \"Discharge at 0.1C until 2.5 V (5 minute period)\",  # initial capacity check\n",
+    "        \"Charge at 0.3C until 4.2 V (5 minute period)\",\n",
+    "        \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
     "    ]\n",
     "    + [\n",
     "        (\n",
     "            \"Discharge at 1C until 2.5 V\",  # ageing cycles\n",
-    "            \"Charge at 0.3C until 4.2 V\",\n",
-    "            \"Hold at 4.2 V until C/100\",\n",
+    "            \"Charge at 0.3C until 4.2 V (5 minute period)\",\n",
+    "            \"Hold at 4.2 V until C/100 (5 minute period)\",\n",
     "        )\n",
     "    ]\n",
     "    * cycle_number\n",
-    "    + [\"Discharge at 0.1C until 2.5 V\"],  # final capacity check\n",
-    "    period=\"5 minutes\",\n",
+    "    + [\"Discharge at 0.1C until 2.5 V (5 minute period)\"],  # final capacity check\n",
     ")\n",
     "sim = pybamm.Simulation(model, parameter_values=param, experiment=exp, var_pts=var_pts)\n",
     "sol = sim.solve()"

From 738cd5797cc94bd675219f013323bc5544894957 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 03:56:30 +0530
Subject: [PATCH 595/615] Use `python -m pip` invocation instead

Co-authored-by: Saransh Chopra <saransh0701@gmail.com>
---
 .github/workflows/run_periodic_tests.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 1178b2ec96..446ad9a9fb 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -193,10 +193,10 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install PyBaMM
-        run: pip install -e .
+        run: python -m pip install -e .
 
       - name: Test pybamm_install_odes on ${{ matrix.os }}
         run: |
-          pip cache purge
-          pip install wget cmake
+          python -m pip cache purge
+          python -m pip install wget cmake
           pybamm_install_odes

From 9017c21bdc69c7d36461f7235fef6951c227f86e Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 15:38:55 +0530
Subject: [PATCH 596/615] #3646 set CMake parallelism for Windows wheels

---
 .github/workflows/publish_pypi.yml | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 10b318b9ed..556ffd1a1f 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -37,6 +37,16 @@ jobs:
         with:
           python-version: 3.8
 
+      - name: Get number of cores on Windows
+        id: get_num_cores
+        shell: python
+        run: |
+          from os import environ, sched_getaffinity
+          num_cpus = len(sched_getaffinity(0))
+          output_file = environ['GITHUB_OUTPUT']
+          with open(output_file, "a", encoding="utf-8") as output_stream:
+              output_stream.write(f"count={num_cpus}\n")
+
       - name: Clone pybind11 repo (no history)
         run: git clone --depth 1 --branch v2.11.1 https://github.com/pybind/pybind11.git
 
@@ -64,7 +74,14 @@ jobs:
       - name: Build 64-bit wheels on Windows
         run: pipx run cibuildwheel --output-dir wheelhouse
         env:
-          CIBW_ENVIRONMENT: 'PYBAMM_USE_VCPKG=ON VCPKG_ROOT_DIR=C:\vcpkg VCPKG_DEFAULT_TRIPLET=x64-windows-static-md VCPKG_FEATURE_FLAGS=manifests,registries CMAKE_GENERATOR="Visual Studio 17 2022" CMAKE_GENERATOR_PLATFORM=x64'
+          CIBW_ENVIRONMENT: >
+            PYBAMM_USE_VCPKG=ON
+            VCPKG_ROOT_DIR=C:\vcpkg
+            VCPKG_DEFAULT_TRIPLET=x64-windows-static-md
+            VCPKG_FEATURE_FLAGS=manifests,registries
+            CMAKE_GENERATOR="Visual Studio 17 2022"
+            CMAKE_GENERATOR_PLATFORM=x64'
+            CMAKE_BUILD_PARALLEL_LEVEL: ${{ steps.get_num_cores.outputs.num_cpus }}
           CIBW_ARCHS: "AMD64"
           CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"

From 632bcecc40a8e22044354818fbb303a255b36542 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Sat, 6 Jan 2024 15:47:39 +0530
Subject: [PATCH 597/615] #3646 Use `os.cpu_count` rather than processor
 affinity

---
 .github/workflows/publish_pypi.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 556ffd1a1f..8a8126b0e4 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -41,8 +41,8 @@ jobs:
         id: get_num_cores
         shell: python
         run: |
-          from os import environ, sched_getaffinity
-          num_cpus = len(sched_getaffinity(0))
+          from os import environ, cpu_count
+          num_cpus = cpu_count()
           output_file = environ['GITHUB_OUTPUT']
           with open(output_file, "a", encoding="utf-8") as output_stream:
               output_stream.write(f"count={num_cpus}\n")
@@ -80,9 +80,9 @@ jobs:
             VCPKG_DEFAULT_TRIPLET=x64-windows-static-md
             VCPKG_FEATURE_FLAGS=manifests,registries
             CMAKE_GENERATOR="Visual Studio 17 2022"
-            CMAKE_GENERATOR_PLATFORM=x64'
-            CMAKE_BUILD_PARALLEL_LEVEL: ${{ steps.get_num_cores.outputs.num_cpus }}
-          CIBW_ARCHS: "AMD64"
+            CMAKE_GENERATOR_PLATFORM=x64
+            CMAKE_BUILD_PARALLEL_LEVEL=${{ steps.get_num_cores.outputs.count }}
+          CIBW_ARCHS: AMD64
           CIBW_BEFORE_BUILD: python -m pip install setuptools wheel # skip CasADi and CMake
           CIBW_TEST_COMMAND: python -c "import pybamm; pybamm.IDAKLUSolver()"
 

From c602d7cfbfe7b94f24b93f26cc10ebb58843a22c Mon Sep 17 00:00:00 2001
From: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
Date: Mon, 1 Jan 2024 10:10:07 +0000
Subject: [PATCH 598/615] Bump to v24.1rc0

---
 CHANGELOG.md             | 2 ++
 CITATION.cff             | 2 +-
 pybamm/version.py        | 2 +-
 pyproject.toml           | 4 ++--
 vcpkg-configuration.json | 2 +-
 vcpkg.json               | 2 +-
 6 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index eda34bcdd1..17adc3a31f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,7 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+# [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
+
 ## Features
 
 - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
diff --git a/CITATION.cff b/CITATION.cff
index 44f1c5d407..494f226a89 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "23.9"
+version: "24.1rc0"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index 970be77f66..b2305df5cb 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "23.9"
+__version__ = "24.1rc0"
diff --git a/pyproject.toml b/pyproject.toml
index d01e4f8fc3..6e01e80812 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "23.9"
+version = "24.1rc0"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
@@ -230,7 +230,7 @@ ignore = [
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]
-minversion = "6"
+minversion = "24.1rc0"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [
diff --git a/vcpkg-configuration.json b/vcpkg-configuration.json
index f33d9205b0..d97bc3c617 100644
--- a/vcpkg-configuration.json
+++ b/vcpkg-configuration.json
@@ -7,7 +7,7 @@
     {
       "kind": "git",
       "repository": "https://github.com/pybamm-team/sundials-vcpkg-registry.git",
-      "baseline": "af9f5e4bc730bf2361c47f809dcfb733e7951faa",
+      "baseline": "13d432fcf5da8591bb6cb2d46be9d6acf39cd02b",
       "packages": ["sundials"]
     },
     {
diff --git a/vcpkg.json b/vcpkg.json
index f62c18ddd2..911703e7cf 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "23.9",
+  "version-string": "24.1rc0",
   "dependencies": [
     "casadi",
     {

From 82f04dcf8890011990da7ce21e23f4bd1a6c1244 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Mon, 1 Jan 2024 16:09:12 +0530
Subject: [PATCH 599/615] Fix up `pytest` minversion

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 6e01e80812..a39a37ecc4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -230,7 +230,7 @@ ignore = [
 
 # NOTE: currently used only for notebook tests with the nbmake plugin.
 [tool.pytest.ini_options]
-minversion = "24.1rc0"
+minversion = "6"
 # Use pytest-xdist to run tests in parallel by default, exit with
 # error if not installed
 required_plugins = [

From 0182ab1c6fc69bdb6d43b2fe38aef874e5117e5e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:03:42 +0530
Subject: [PATCH 600/615] Fix regex for version in pyproject.toml

---
 scripts/update_version.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/update_version.py b/scripts/update_version.py
index 30d2240e9c..1d2d64ce41 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -32,7 +32,9 @@ def update_version():
     # pyproject.toml
     with open(os.path.join(pybamm.root_dir(), "pyproject.toml"), "r+") as file:
         output = file.read()
-        replace_version = re.sub('(?<=version = ")(.+)(?=")', release_version, output)
+        replace_version = re.sub(
+            r'(?<=\bversion = ")(.+)(?=")', release_version, output
+        )
         file.truncate(0)
         file.seek(0)
         file.write(replace_version)

From 09632a291f3e24f64f1227cb284af0fa6710eeec Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:06:50 +0530
Subject: [PATCH 601/615] Fix release issue tag

---
 .github/release_reminder.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 94066e80c8..4b7b361b28 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,6 +1,6 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
-labels: priority:high
+labels: priority: high
 ---
 Quarterly reminder to create a -
 

From d978f6f1e6520a90d79e14ff2ebbc0d073fa5701 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:12 +0530
Subject: [PATCH 602/615] Use quotes

---
 .github/release_reminder.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/release_reminder.md b/.github/release_reminder.md
index 4b7b361b28..09c524fbec 100644
--- a/.github/release_reminder.md
+++ b/.github/release_reminder.md
@@ -1,6 +1,6 @@
 ---
 title: Create {{ date | date('YY.MM') }} (final or rc0) release
-labels: priority: high
+labels: "priority: high"
 ---
 Quarterly reminder to create a -
 

From 17a4494cc2b70882195a5bee0dda1c88d57210f7 Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:40 +0530
Subject: [PATCH 603/615] Update wheel_failure.md

---
 .github/wheel_failure.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
index 107b4dd6d6..d2a8a74ce9 100644
--- a/.github/wheel_failure.md
+++ b/.github/wheel_failure.md
@@ -1,6 +1,6 @@
 ---
 title: Fortnightly build for wheels failed
-labels: priority:high, bug
+labels: "priority: high", bug
 ---
 
 The build is failing with the following logs - {{ env.LOGS }}

From 0580f06e57df074235912d317331dd5b2db88b5a Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Tue, 2 Jan 2024 00:07:54 +0530
Subject: [PATCH 604/615] Fix YAML

---
 .github/wheel_failure.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/wheel_failure.md b/.github/wheel_failure.md
index d2a8a74ce9..2bbe659358 100644
--- a/.github/wheel_failure.md
+++ b/.github/wheel_failure.md
@@ -1,6 +1,6 @@
 ---
 title: Fortnightly build for wheels failed
-labels: "priority: high", bug
+labels: "priority: high, bug"
 ---
 
 The build is failing with the following logs - {{ env.LOGS }}

From 89b9420154fd6bacda6fb7aab0802c1f9fa65f23 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 12 Jan 2024 23:36:34 +0530
Subject: [PATCH 605/615] Fix docs about Jax solver compatibility with Python
 versions (#3702)

* Ensure correct Python versions for Jax solver compatibility

* Simplify array of Python versions

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>

* Use different conjunction

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>

---------

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 docs/source/user_guide/installation/gnu-linux-mac.rst | 2 +-
 docs/source/user_guide/installation/windows.rst       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index 3e93587cde..c73f549299 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -161,7 +161,7 @@ Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
 
 .. note::
 
-   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
+   The Jax solver is only supported for Python versions 3.9 through 3.12.
 
 .. code:: bash
 
diff --git a/docs/source/user_guide/installation/windows.rst b/docs/source/user_guide/installation/windows.rst
index 6e815b33c8..d99d1f2eb2 100644
--- a/docs/source/user_guide/installation/windows.rst
+++ b/docs/source/user_guide/installation/windows.rst
@@ -73,7 +73,7 @@ Users can install ``jax`` and ``jaxlib`` to use the Jax solver.
 
 .. note::
 
-   The Jax solver is not supported on Python 3.8. It is supported on Python 3.9, 3.10, and 3.11.
+   The Jax solver is only supported for Python versions 3.9 through 3.12.
 
 .. code:: bash
 

From 6ddd47eab6bba78593c92fecdbb82e526f67b111 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Fri, 12 Jan 2024 23:39:59 +0530
Subject: [PATCH 606/615] Merge pull request #3706 from
 agriyakhetarpal/fix-pybamm-install-odes

Make `pybamm_install_odes` a bit more robust
---
 CHANGELOG.md                                          | 2 +-
 docs/source/user_guide/installation/gnu-linux-mac.rst | 4 +++-
 pybamm/install_odes.py                                | 7 ++++++-
 3 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 17adc3a31f..965a2aa7b4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,7 +4,7 @@
 
 ## Features
 
-- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417))
+- The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706]))
 - Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531))
 - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
 - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596))
diff --git a/docs/source/user_guide/installation/gnu-linux-mac.rst b/docs/source/user_guide/installation/gnu-linux-mac.rst
index c73f549299..d774285556 100644
--- a/docs/source/user_guide/installation/gnu-linux-mac.rst
+++ b/docs/source/user_guide/installation/gnu-linux-mac.rst
@@ -119,7 +119,8 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
 
    .. code:: bash
 
-      apt install libopenblas-dev
+      apt-get install libopenblas-dev
+      pip install wget cmake
       pybamm_install_odes
 
    system (under ``~/.local``), before installing ``scikits.odes``. (Alternatively, one can install SUNDIALS without this script and run ``pip install pybamm[odes]`` to install ``pybamm`` with ``scikits.odes``.)
@@ -131,6 +132,7 @@ Users can install `scikits.odes <https://github.com/bmcage/odes>`__ to utilize i
    .. code:: bash
 
       brew install openblas gcc gfortran
+      pip install wget cmake
       pybamm_install_odes
 
 The ``pybamm_install_odes`` command, installed with PyBaMM, automatically downloads and installs the SUNDIALS library on your
diff --git a/pybamm/install_odes.py b/pybamm/install_odes.py
index 128d3ca396..3809d763f2 100644
--- a/pybamm/install_odes.py
+++ b/pybamm/install_odes.py
@@ -190,7 +190,12 @@ def main(arguments=None):
     # see https://scikits-odes.readthedocs.io/en/latest/installation.html#id1
     os.environ["SUNDIALS_INST"] = SUNDIALS_LIB_DIR
     env = os.environ.copy()
-    subprocess.run(["pip", "install", "scikits.odes"], env=env, check=True)
+    logger.info("Installing scikits.odes via pip")
+    subprocess.run(
+        [f"{sys.executable}", "-m", "pip", "install", "scikits.odes", "--verbose"],
+        env=env,
+        check=True,
+    )
 
 
 if __name__ == "__main__":

From f22f54703081098bfd368ba553006c4f357f8698 Mon Sep 17 00:00:00 2001
From: Robert Timms <43040151+rtimms@users.noreply.github.com>
Date: Mon, 15 Jan 2024 16:14:25 +0000
Subject: [PATCH 607/615] #3690 fix issue with skipped steps (#3708)

* #3690 fix issue with skipped steps

* #3690 changelog

* #3690 add test
---
 CHANGELOG.md                                  |  3 +++
 pybamm/simulation.py                          | 15 ++++++++++++++-
 .../test_simulation_with_experiment.py        | 19 +++++++++++++++++++
 3 files changed, 36 insertions(+), 1 deletion(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 965a2aa7b4..00ad65f9a0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
+## Bug Fixes
+
+- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index c95ab3039c..8a6150cc4e 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -839,7 +839,20 @@ def solve(
 
                     steps.append(step_solution)
 
-                    cycle_solution = cycle_solution + step_solution
+                    # If there haven't been any successful steps yet in this cycle, then
+                    # carry the solution over from the previous cycle (but
+                    # `step_solution` should still be an EmptySolution so that in the
+                    # list of returned step solutions we can see which steps were
+                    # skipped)
+                    if (
+                        cycle_solution is None
+                        and isinstance(step_solution, pybamm.EmptySolution)
+                        and not isinstance(current_solution, pybamm.EmptySolution)
+                    ):
+                        cycle_solution = current_solution.last_state
+                    else:
+                        cycle_solution = cycle_solution + step_solution
+
                     current_solution = cycle_solution
 
                     callbacks.on_step_end(logs)
diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py
index cc04177ba2..36475081c3 100644
--- a/tests/unit/test_experiments/test_simulation_with_experiment.py
+++ b/tests/unit/test_experiments/test_simulation_with_experiment.py
@@ -519,6 +519,25 @@ def test_run_experiment_skip_steps(self):
                 decimal=5,
             )
 
+    def test_skipped_step_continuous(self):
+        model = pybamm.lithium_ion.SPM({"SEI": "solvent-diffusion limited"})
+        experiment = pybamm.Experiment(
+            [
+                ("Rest for 24 hours (1 hour period)",),
+                (
+                    "Charge at C/3 until 4.1 V",
+                    "Hold at 4.1V until C/20",
+                    "Discharge at C/3 until 2.5 V",
+                ),
+            ]
+        )
+        sim = pybamm.Simulation(model, experiment=experiment)
+        sim.solve(initial_soc=1)
+        np.testing.assert_array_almost_equal(
+            sim.solution.cycles[0].last_state.y.full(),
+            sim.solution.cycles[1].steps[-1].first_state.y.full(),
+        )
+
     def test_all_empty_solution_errors(self):
         model = pybamm.lithium_ion.SPM()
         parameter_values = pybamm.ParameterValues("Chen2020")

From adda3365307349d14fa6775bd9a6744293f70a97 Mon Sep 17 00:00:00 2001
From: Robert Timms <43040151+rtimms@users.noreply.github.com>
Date: Tue, 16 Jan 2024 17:00:50 +0000
Subject: [PATCH 608/615] #3611 use actual cell volume for average total
 heating (#3707)

* #3611 use actual cell volume for average total heating

* #3611 changelog

* #3611 account for number of electrode pairs

* #3611 update variable names
---
 CHANGELOG.md                                  |   7 +-
 .../notebooks/models/jelly-roll-model.ipynb   |  19 +-
 .../notebooks/models/pouch-cell-model.ipynb   |  31 +-
 .../notebooks/models/thermal-models.ipynb     | 950 +++++++++---------
 examples/scripts/thermal_lithium_ion.py       |  34 +-
 .../full_battery_models/base_battery_model.py |  19 -
 .../models/submodels/thermal/base_thermal.py  |  52 +-
 pybamm/parameters/geometric_parameters.py     |  15 +-
 8 files changed, 606 insertions(+), 521 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 00ad65f9a0..0692d152ca 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,8 +1,13 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-## Bug Fixes
+## Bug fixes
 
 - Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
+- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
+
+## Breaking changes
+- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
+
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index 43e65fbe7d..86dd684b64 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -46,10 +46,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
-      "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
-      "\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -154,7 +152,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.base_model.BaseModel at 0x7f98ac190a90>"
+       "<pybamm.models.base_model.BaseModel at 0x280b0ed90>"
       ]
      },
      "execution_count": 4,
@@ -356,7 +354,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -421,7 +419,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -435,7 +433,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.11.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
index 69cfbfec40..a11046d967 100644
--- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb
@@ -49,10 +49,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'cite'\u001b[0m\u001b[33m\n",
-      "\u001b[0m\u001b[33mWARNING: pybamm 23.5 does not provide the extra 'plot'\u001b[0m\u001b[33m\n",
-      "\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
       "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
@@ -81,16 +79,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robertwtimms/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:910: OptionWarning: The 'lumped' thermal option with 'dimensionality' 0 now uses the parameters 'Cell cooling surface area [m2]', 'Cell volume [m3]' and 'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell cooling term, regardless of the value of the the 'cell geometry' option. Please update your parameters accordingly.\n",
-      "  options = BatteryModelOptions(extra_options)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cc_model = pybamm.current_collector.EffectiveResistance({\"dimensionality\": 1})\n",
     "dfn_av = pybamm.lithium_ion.DFN({\"thermal\": \"lumped\"}, name=\"Average DFN\")\n",
@@ -579,7 +568,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -623,7 +612,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -687,7 +676,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -740,7 +729,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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WrVqFDRs2YMqUKXj99dexceNG9e9xoPX2IG1NmTIFU6ZMUX+/fPky1q9fb5Uw2L17t/oUP08RQmj62GqqGSqe9+eeew4DBw5Uf2/QoIHNOg0bNsSzzz6LCRMm2HxJ4ej7n7+8TzLRQ1RL7dixA4DnkygJCQlWb7LuqK6Op0+fxsCBA7F69WpceeWVePfdd9GlSxePxLbUoUMHLFmyBD169MAdd9yBtWvX2nxbSkRENZspmbFz507cdNNNNst37txpVc6TrrnmGkiShL1791Za5tprrwUA7NmzBx07drRZvmfPHrRo0cJmfv369XHXXXdh8ODBKC4uRvfu3XHx4kW7Ma644gr0798f/fv3xyuvvIJrr70Wc+fOxcSJE3HttddaffteMbZlHcl9ERERKCws9Elcb3nsscdw//33q7/369cPvXv3xj333KPOs3dLjLv27NnjF7eP1kSma9Dyunjuuefw9NNP2wyHcurUKQCwerr18OHDMWTIEOh0Oquypp42lmU91T4wqXjeGzRogKuvvrra9UaNGoV3330X7777rtX8hg0bIiYmpsr3acD8Prh37160bdvWhZpro8bfukVE3rF9+3bodDq7HxpriurqOHz4cCQkJOD06dOYPn067r//fpw7d84rdenSpQs+++wzHDx4EN26dbPq7k5ERDVf586d0bhxY0yZMsVmfAZFUTB16lSkpqaic+fOHo8dGxuLzMxMzJkzB0VFRTbL8/Pz0bVrV8TGxuLNN9+0Wf7dd9/hwIEDeOCBB+xu/+GHH8bq1asxYMAAmwZXZerVq4fExES1Pn379sWBAwfw/fff25R98803Ub9+fdxxxx0ObZuqJ0kSIiMjNZ+82fMlNjYWV199tTqFh4cjLi7Oap6nx1JdtWoVduzYgd69e3t0u7WFvesiJCQEkZGRCA0NtVvW8tbX4OBgREZGIiwszKGynuLOea9Tpw7Gjx+PyZMnWyXFZVlG37598emnn+LEiRM26xUWFqK8vBxt2rRBixYt8Oabb9od6yc/P9/pOnkDEz1EtdT27dvVP8I1VVV1LCwsxJIlSzBx4kRERETg3//+N1q1aoVvv/3Wa/W5++678eGHH2Lr1q3497//bTPwHBER1Vw6nQ5vvvkmli5dil69eiEnJwcXL15ETk4OevXqhaVLl+KNN95wOFHirDlz5kCv16NDhw74+uuvceDAAezZswezZ89Geno6IiMj8f777+Pbb7/F0KFDsX37dhw+fBjz5s3DwIEDce+991r1lrDUrVs3nD59GpMmTbK7/P3338ewYcPw888/46+//sKuXbswevRo7Nq1Sx18uW/fvrj77ruRnZ2NefPm4fDhw9i+fTseffRRfPfdd/i///s/q4Gk9Xo9tm3bZjVV1iOI/FdhYaF6fgHg0KFD2LZtm9U4LVrFKCkpQW5uLo4fP46tW7diypQp6NmzJ+666y4MGDDAY/WhmsUb533o0KGIjo7GwoULreZPnjwZycnJSEtLwyeffILdu3fjwIED+Oijj9C2bVsUFhZCkiTMnz8f+/fvR+fOnbFs2TL8/fff2L59OyZPnoyePXt6Yrfdxlu3iGqh8vJy7Nmzp8a8EdlTXR0PHDiAOnXq4Morr1TntWrVCrt27fJqvQYNGoRz587h2WefxX333YfFixfzSX9ERH7innvuwVdffYVnnnnG6vao1NRUfPXVV1a3mHhakyZNsHXrVkyePBnPPPMMTp48iYYNG6Jdu3Z47733ABgeLPLrr79i8uTJ6Ny5M4qLi3HNNdfgxRdfxNNPP11pbwxJkuyOR2HSoUMH/P7773jsscdw4sQJ1KlTBy1btsSSJUtw6623qtv44osvMGvWLMycOROPP/44wsLCkJ6ejtWrV+Pmm2+22mZhYaHNbQtXXXUVDh486M5hohpm8+bNuP3229XfR40aBQDIzs722OC6jsZYvnw5EhMTERQUhHr16qF169aYPXs2srOz7T79iAKDN857cHAwXnnlFTz44INW82NjY7F+/Xq89tprePXVV3HkyBHUq1cPrVq1wvTp0xEdHQ3A8J66efNmTJ48GUOGDMGZM2eQmJiIjh07YtasWe7uskdIwl9GEyIisrB27Vr079/farT9F198EWfPnsXcuXMd3s7AgQOxatUqbN26FUFBQS49ErG4uBiFhYWYNm0apk+fjtOnT1f5gZuIiFxTXFyMQ4cOITU11eZWAWfo9XqsXbsWJ0+eRGJiIjp37uy1njxERESA5/6GOYJfQxORX6pTp47NODkFBQWoU6eO09s6duwYGjZsiJYtW6qDcTpj7ty5GDlypNPrERGRb+h0Otx2222+rgYREZFXMNFDRH7pmmuuQWFhIY4fP44rrrgCgOGJKc7eq/v888/joYceAgCXkkQA0Lt3b6vH9Zq6dRIREREREWmNt24Rkd+67777EB0djbfffhsrV65EdnY2Dhw4gNjYWF9XjYiIvEDLbu9ERESexFu3iIgc8O677yI7Oxv169fHlVdeic8//5xJHiIiIiIiqtWY6CEiv9WwYUMsW7bM19UgIiIiIiKqMfgcOiIiIiLyKxx5gIiI/I2Wf7uY6CEiIiIivxAcHAwAuHTpko9rQkRE5JzS0lIAhic/ehtv3SIiIiIiv6DT6RATE4NTp04BACIiIiBJko9rRUREVDVFUXD69GlEREQgKMj7aRgmeoiIiIjIbyQkJACAmuwhIiLyB7IsIyUlRZMvKPh4dSIiIiLyO3q9HmVlZb6uBhERkUNCQkIgy9qMnsNEDxERERERERFRgOBgzEREREREREREAYKJHiIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREREREREFCCZ6iIiIiIiIiIgCBBM95LY5c+agcePGCAsLQ1paGjZu3Fhp2Q8//BCdO3dGvXr1UK9ePWRkZFRZPlA4c4wsLVq0CJIkoVevXt6toI85e3zy8/MxfPhwJCYmIjQ0FNdeey2WLVumUW19w9ljNGvWLDRt2hTh4eFITk7GyJEjUVxcrFFttbdmzRpkZWUhKSkJkiRhyZIl1a6zevVq3HDDDQgNDcXVV1+NBQsWeL2evuLs8fnmm29wxx13oGHDhoiKikJ6ejp++uknbSpLRERERG5hoofc8vnnn2PUqFF46aWXsHXrVrRu3RqZmZk4deqU3fKrV6/GAw88gF9//RU5OTlITk5G165dcfz4cY1rrh1nj5HJ4cOH8eyzz6Jz584a1dQ3nD0+paWluOOOO3D48GF89dVX2LdvHz788ENcccUVGtdcO84eo4ULF2LMmDF46aWXsGfPHsybNw+ff/45XnjhBY1rrp2ioiK0bt0ac+bMcaj8oUOH0KNHD9x+++3Ytm0bnn76aTzyyCMBm8xw9visWbMGd9xxB5YtW4YtW7bg9ttvR1ZWFv78808v15SIiIiI3CUJIYSvK0H+Ky0tDTfeeCPeeecdAICiKEhOTsYTTzyBMWPGVLu+Xq9HvXr18M4772DAgAHerq5PuHKM9Ho9brnlFjz88MNYu3Yt8vPzHeqh4I+cPT5z587F9OnTsXfvXgQHB2tdXZ9w9hiNGDECe/bswcqVK9V5zzzzDDZs2IDff/9ds3r7iiRJWLx4cZU94UaPHo0ffvgBO3fuVOf17dsX+fn5WL58uQa19B1Hjo89LVu2RJ8+fTBhwgTvVIyIiIiIPII9eshlpaWl2LJlCzIyMtR5siwjIyMDOTk5Dm3j0qVLKCsrQ2xsrLeq6VOuHqNJkyYhLi4OgwcP1qKaPuPK8fnuu++Qnp6O4cOHIz4+Htdddx2mTJkCvV6vVbU15cox6tixI7Zs2aLe3vX3339j2bJluPPOOzWpsz/IycmxOqYAkJmZ6fB7V22jKAouXrwYsO/VRERERIEkyNcVIP915swZ6PV6xMfHW82Pj4/H3r17HdrG6NGjkZSUZNPgChSuHKPff/8d8+bNw7Zt2zSooW+5cnz+/vtvrFq1Cv369cOyZctw8OBBPP744ygrK8NLL72kRbU15coxevDBB3HmzBl06tQJQgiUl5fjscceC+hbt5yVm5tr95gWFBTg8uXLCA8P91HNaqY33ngDhYWFuP/++31dFSIiIiKqBnv0kM+89tprWLRoERYvXoywsDBfV6dGuHjxIvr3748PP/wQDRo08HV1aiRFURAXF4cPPvgA7dq1Q58+ffDiiy9i7ty5vq5ajbF69WpMmTIF7777LrZu3YpvvvkGP/zwA1555RVfV4380MKFCzFx4kR88cUXiIuL83V1iIiIiKga7NFDLmvQoAF0Oh3y8vKs5ufl5SEhIaHKdd944w289tpr+OWXX3D99dd7s5o+5ewx+uuvv3D48GFkZWWp8xRFAQAEBQVh3759uOqqq7xbaQ25cg0lJiYiODgYOp1Onde8eXPk5uaitLQUISEhXq2z1lw5RuPHj0f//v3xyCOPAABatWqFoqIiDB06FC+++CJkmTn+hIQEu8c0KiqKvXksLFq0CI888gi+/PLLgO15SURERBRo+GmfXBYSEoJ27dpZDfiqKApWrlyJ9PT0StebNm0aXnnlFSxfvhzt27fXoqo+4+wxatasGXbs2IFt27ap07///W/1yUDJyclaVt/rXLmGbr75Zhw8eFBNgAHA/v37kZiYGHBJHsC1Y3Tp0iWbZI4pMcbx9w3S09OtjikArFixosr3rtrms88+w6BBg/DZZ5+hR48evq4OERERETmIPXrILaNGjUJ2djbat2+PDh06YNasWSgqKsKgQYMAAAMGDMAVV1yBqVOnAgBef/11TJgwAQsXLkTjxo2Rm5sLAKhTpw7q1Knjs/3wJmeOUVhYGK677jqr9WNiYgDAZn6gcPYaGjZsGN555x089dRTeOKJJ3DgwAFMmTIFTz75pC93w6ucPUZZWVmYMWMG2rZti7S0NBw8eBDjx49HVlaWVU+oQFJYWIiDBw+qvx86dAjbtm1DbGwsUlJSMHbsWBw/fhyffPIJAOCxxx7DO++8g+effx4PP/wwVq1ahS+++AI//PCDr3bBq5w9PgsXLkR2djbeeustpKWlqe/V4eHhiI6O9sk+EBEREZGDBJGb3n77bZGSkiJCQkJEhw4dxPr169Vlt956q8jOzlZ/b9SokQBgM7300kvaV1xDzhyjirKzs0XPnj29X0kfcvb4rFu3TqSlpYnQ0FDRpEkTMXnyZFFeXq5xrbXlzDEqKysTL7/8srjqqqtEWFiYSE5OFo8//rg4f/689hXXyK+//mr3vcV0XLKzs8Wtt95qs06bNm1ESEiIaNKkiZg/f77m9daKs8fn1ltvrbI8EREREdVckhDsx09ERERE/kWv16OsrMzX1SAiInJISEiIZmNl8tYtIiIiIvIbQgjk5uYiPz/f11UhIiJymCzLSE1N1WRcUfboISIiIiK/cfLkSeTn5yMuLg4RERGQJMnXVSIiIqqSoig4ceIEgoODkZKS4vW/XezRQ0RERER+Qa/Xq0me+vXr+7o6REREDmvYsCFOnDiB8vJyBAcHezUWH69ORERERH7BNCZPRESEj2tCRETkHNMtW3q93uuxmOghIiIiIr/C27WIiMjfaPm3i4keIiIiIiIiIqIAwUQPeVVJSQlefvlllJSU+LoqNRaPUfV4jKrG41M9HqPq8RgRec/UqVNx4403om7duoiLi0OvXr2wb98+qzLFxcUYPnw46tevjzp16qB3797Iy8uzKnP06FH06NEDERERiIuLw3PPPYfy8nItd4UC2PHjx/HQQw+hfv36CA8PR6tWrbB582Z1uRACEyZMQGJiIsLDw5GRkYEDBw5YbePcuXPo168foqKiEBMTg8GDB6OwsFDrXaEAs2bNGmRlZSEpKQmSJGHJkiU2ZTx1fW7fvh2dO3dGWFgYkpOTMW3aNG/umtcw0UNeVVJSgokTJ7LhUAUeo+rxGFWNx6d6PEbV4zEi8p7ffvsNw4cPx/r167FixQqUlZWha9euKCoqUsuMHDkS33//Pb788kv89ttvOHHiBO655x51uV6vR48ePVBaWop169bh448/xoIFCzBhwgRf7BIFmPPnz+Pmm29GcHAwfvzxR+zevRtvvvkm6tWrp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouL1TL9+vXDrl27sGLFCixduhRr1qzB0KFDfbFLFECKiorQunVrzJkzp9Iynrg+CwoK0LVrVzRq1AhbtmzB9OnT8fLLL+ODDz7w6v55hSDyogsXLggA4sKFC76uSo3FY1Q9HqOq8fhUj8eoejxG5A8uX74sdu/eLS5fvuzrqrjl1KlTAoD47bffhBBC5Ofni+DgYPHll1+qZfbs2SMAiJycHCGEEMuWLROyLIvc3Fy1zHvvvSeioqJESUmJ3TglJSVi+PDhIiEhQYSGhoqUlBQxZcoUL+4Z+avRo0eLTp06VbpcURSRkJAgpk+frs7Lz88XoaGh4rPPPhNCCLF7924BQGzatEkt8+OPPwpJksTx48cr3e5LL70kkpOTRUhIiEhMTBRPPPGEh/aKAhEAsXjxYqt5nro+3333XVGvXj2r99TRo0eLpk2bVlqfc+fOiQcffFA0aNBAhIWFiauvvlp89NFHdstq+TeMj1cnIiIiIr8lhMClS5c0jxsREeHywJoXLlwAAMTGxgIAtmzZgrKyMmRkZKhlmjVrhpSUFOTk5OCmm25CTk4OWrVqhfj4eLVMZmYmhg0bhl27dqFt27Y2cWbPno3vvvsOX3zxBVJSUnDs2DEcO3bMpTqTa4QQKL9c6pPYQeEhDl+j3333HTIzM3Hffffht99+wxVXXIHHH38cQ4YMAQAcOnQIubm5VtdodHQ00tLSkJOTg759+yInJwcxMTFo3769WiYjIwOyLGPDhg24++67beJ+/fXXmDlzJhYtWoSWLVsiNzcX//vf/9zcc3KUEALQa//+CQDQuf4eWpGnrs+cnBzccsst6tOxAMP77Ouvv47z589b9XAzGT9+PHbv3o0ff/wRDRo0wMGDB3H58mWP7Jc7mOhxU3FxMUpLffPm7Q8KCgqs/iVbPEbV4zGqGo9P9XiMqsdjQ/7q0qVLqFMnRvO4hYX5iIyMdHo9RVHw9NNP4+abb8Z1110HAMjNzUVISAhiYmKsysbHxyM3N1ctY5nkMS03LbPn6NGjuOaaa9CpUydIkoRGjRo5XV9yT/nlUrzf9imfxH70z7cQHBHqUNm///4b7733HkaNGoUXXngBmzZtwpNPPomQkBBkZ2er15i9a9DyGo2Li7NaHhQUhNjY2Cqv0YSEBGRkZCA4OBgpKSno0KGDs7tKrtJfgvJFXPXlvEC+/xQQ5Px7qD2euj5zc3ORmppqsw3TMnuJnqNHj6Jt27ZqAqlx48bu75AHMNHjhuLiYoSHh/u6Gn4hOTnZ11Wo8XiMqsdjVDUen+rxGFWtTp06hm/3iMhrhg8fjp07d+L333/3eqyBAwfijjvuQNOmTdGtWzfcdddd6Nq1q9fjkv9RFAXt27fHlClTAABt27bFzp07MXfuXGRnZ3st7n333YdZs2ahSZMm6NatG+68805kZWUhKIjNVPIPw4YNQ+/evbF161Z07doVvXr1QseOHX1dLSZ63GHuyaMDYOp2JsE0xrVkGutaMo15LavzJHWeBEnSWZWXJNtylmVMXdwk6NQyMmzL2WzL+K8s6cw/w1xe3YZxnmzcJxmyOZZpPSFbbKPCv0KGee9kdVuyMG1fUv81x5CslsmS5TKYy6v7bpwnmctY/mzYhuX2oK5n+lndrmT+17Rd2eJf03LLbZh+r7gNWbKsm51tVFFetlteWAU1xBR2ti8q7Luw2a5kWq/a8qbtm8uo8yzLq/Mq1EcS6jy5inmSJCyOh1DLGWYIi/001UPYlLOsg029Leto8a/5Z9v9tbf9ituQJaXSZZAt62FRzvRWULE+srAqZ7NducK2ZGEVS10mW++TJAug4nZl03qKw/PU9dVlsCkPy22Yfq5YR1lYzLOsI6zmWb54JcsXsulf8w6a58mmn2Xb8hWWCVkGjO+RtuvJhuUVl8mG8kI9iTrzPDWW6Xed9c/GZep2pYrLgizKB5nrIQWZlxv/VZdDZ7NMqlBekoIA4zxJnadTl8mSnXmyjIKCy2ic/JTHulETaSUiIgKFhfk+ieusESNGqAOAXnnller8hIQElJaWIj8/36pXT15eHhISEtQyGzdutNqe6alcpjIV3XDDDTh06BB+/PFH/PLLL7j//vuRkZGBr776yum6k2uCwkPw6J9v+Sy2oxITE9GiRQurec2bN8fXX38NwHyN5eXlITExUS2Tl5eHNm3aqGVOnTpltY3y8nKcO3eu0ms0OTkZ+/btwy+//IIVK1bg8ccfx/Tp0/Hbb78hODjY4fqTi3QRhp41PortKZ66PhMSEmyedljd+2z37t1x5MgRLFu2DCtWrECXLl0wfPhwvPHGGx7ZN1cx0eMhpoSCoSEi2ZlnmmNcJplbOGoyp8pEj7mM+WednfIVEjKSnSSNw4kecwLH/rxKEj3GpVYxjRFMP5uOhquJHnOCwJVET8VtWJaHVXl3Ej32EzcVy9smZOwleiy373Kix6KMJxM9tnWsLtFj3n5liR7JXqKnkkSMoV6OJXoqm2f6vcpEj1x9okeqNNFjm3AylVcTt3LlMS3L2CZ6JNvty8KcULFJ4Dg+T11frrhMgsVbmHme+cVqvQ3ZspzFPGcTPeaL2Xaezb+y/USPvWSO8V9zAqaKRI9VMsdiHqpK9OhsywGVJHp0VSd67CwzJ3qCjYfHXqLH/K/9RI+xTkR+SJIkl26h0pIQAk888QQWL16M1atX29wa0K5dOwQHB2PlypXo3bs3AGDfvn04evQo0tPTAQDp6emYPHkyTp06pd5+sGLFCkRFRdk00C1FRUWhT58+6NOnD+69915069YN586dU8cHIu+SJMnh26d86eabb8a+ffus5u3fv1+93S81NRUJCQlYuXKl2nAuKCjAhg0bMGzYMACGazQ/Px9btmxBu3btAACrVq2CoihIS0urNHZ4eDiysrKQlZWF4cOHo1mzZtixYwduuOEGL+wpWZIkyWO3T/mSp67P9PR0vPjiiygrK1MTjStWrEDTpk3t3rZl0rBhQ2RnZyM7OxudO3fGc889x0QPEREREVEgGz58OBYuXIhvv/0WdevWVceDiI6ORnh4OKKjozF48GCMGjUKsbGxiIqKwhNPPIH09HTcdNNNAICuXbuiRYsW6N+/P6ZNm4bc3FyMGzcOw4cPR2io/UTCjBkzkJiYiLZt20KWZXz55ZdISEiwGQuIaOTIkejYsSOmTJmC+++/Hxs3bsQHH3ygPlZakiQ8/fTTePXVV3HNNdcgNTUV48ePR1JSEnr16gXA0AOoW7duGDJkCObOnYuysjKMGDECffv2RVJSkt24CxYsgF6vR1paGiIiIvDf//4X4eHhHE+KrBQWFuLgwYPq74cOHcK2bdsQGxuLlJQUj12fDz74ICZOnIjBgwdj9OjR2LlzJ9566y3MnDmz0rpNmDAB7dq1Q8uWLVFSUoKlS5eiefPmXj0ejmCih4iIiIjIi9577z0AwG233WY1f/78+Rg4cCAAYObMmZBlGb1790ZJSQkyMzPx7rvvqmV1Oh2WLl2KYcOGIT09HZGRkcjOzsakSZMqjVu3bl1MmzYNBw4cgE6nw4033ohly5ZBNvVIJDK68cYbsXjxYowdOxaTJk1CamoqZs2ahX79+qllnn/+eRQVFWHo0KHIz89Hp06dsHz5coSFhallPv30U4wYMQJdunRRr+fZs2dXGjcmJgavvfYaRo0aBb1ej1atWuH7779H/fr1vbq/5F82b96M22+/Xf191KhRAIDs7GwsWLAAgGeuz+joaPz8888YPnw42rVrhwYNGmDChAkYOnRopXULCQnB2LFjcfjwYYSHh6Nz585YtGiRh4+A8yTBURddVlBQgOjoaADmW6EMtxQ4M0aPbHO7VfVj9NjeulXZGD1eu3Wr2jF67Ny6JXjrFm/d4q1bFctbjr9TWczqxuixf+uWB8fosbl1y4UxeireEubKGD3qxezIGD2V3bpV1Rg9tfvWrYKCS4iNHooLFy4gKioKRDVRcXExDh06hNTUVKsP70RERDWdln/DmM4nIiIiIiIiIgoQTPQQEREREREREQUIJnqIiIiIiIiIiAIEEz1ERERERERERAGCiR4iIiIiIiIiogDBRA8RERERERERUYBgooeIiIiIiIiIKEAw0UNEREREREREFCCY6CEiIiIiIiIiChBM9BARERERERERBQgmeoiIiIiINPLaa69BkiQ8/fTTVvOLi4sxfPhw1K9fH3Xq1EHv3r2Rl5dnVebo0aPo0aMHIiIiEBcXh+eeew7l5eUa1p4ClV6vx/jx45Gamorw8HBcddVVeOWVVyCEUMsIITBhwgQkJiYiPDwcGRkZOHDggNV2zp07h379+iEqKgoxMTEYPHgwCgsLtd4dolqPiR4iIiIiIg1s2rQJ77//Pq6//nqbZSNHjsT333+PL7/8Er/99htOnDiBe+65R12u1+vRo0cPlJaWYt26dfj444+xYMECTJgwQctdoAD1+uuv47333sM777yDPXv24PXXX8e0adPw9ttvq2WmTZuG2bNnY+7cudiwYQMiIyORmZmJ4uJitUy/fv2wa9curFixAkuXLsWaNWswdOhQX+wSUa3GRA8RERERkZcVFhaiX79++PDDD1GvXj2rZRcuXMC8efMwY8YM/Otf/0K7du0wf/58rFu3DuvXrwcA/Pzzz9i9ezf++9//ok2bNujevTteeeUVzJkzB6WlpXZjlpaWYsSIEUhMTERYWBgaNWqEqVOnen1fyf+sW7cOPXv2RI8ePdC4cWPce++96Nq1KzZu3AjA0Jtn1qxZGDduHHr27Inrr78en3zyCU6cOIElS5YAAPbs2YPly5fj//7v/5CWloZOnTrh7bffxqJFi3DixAm7cYUQePnll5GSkoLQ0FAkJSXhySef1Gq3iQIWEz1ERERE5LeEELhcVKL5ZHlLiyOGDx+OHj16ICMjw2bZli1bUFZWZrWsWbNmSElJQU5ODgAgJycHrVq1Qnx8vFomMzMTBQUF2LVrl92Ys2fPxnfffYcvvvgC+/btw6efforGjRs7VW9yjxACSvFln0zOXKMdO3bEypUrsX//fgDA//73P/z+++/o3r07AODQoUPIzc21ukajo6ORlpZmdY3GxMSgffv2apmMjAzIsowNGzbYjfv1119j5syZeP/993HgwAEsWbIErVq1cvo4E5G1IF9XgIiIiIjIVcWXSnFX3NOax116ahbCI0MdKrto0SJs3boVmzZtsrs8NzcXISEhiImJsZofHx+P3NxctYxlkse03LTMnqNHj+Kaa65Bp06dIEkSGjVq5FB9yXNESTEOP2ib3NNC44W/QAoLd6jsmDFjUFBQgGbNmkGn00Gv12Py5Mno168fAPM1Zu8atLxG4+LirJYHBQUhNja2yms0ISEBGRkZCA4ORkpKCjp06ODUfhKRLSZ6PERAmH4w/a+SfyvOUwBIDkSwLFMxOy/M8SuUUSAgGX+W1A5cwupn8zLZ+LNpW6aYMiQoxtKGMkLI6jZExX+FbLEF0zIJQsjGOknGZRJk48+yxTzT7+ZlsCkvWcwzlZEqlLc3T6qw3HpbFnssLLZhnClLFY6KZLENizJSxXkVfq6svGy3vLAKaogp7GxfWO+7JGy2a74Oqitv2r65jDrPsrw6r0J9JPM1J1cxT5KExfEQajnDDGGxn6Z6CJtylnWwqbdlHS3+Nf9su7/2tl9xG7KkVLoMsmU9LMrJ1vup/isLq3I225UrbEsWVrHUZbL1PkmyACpuV72oFYfnqeury6DGrlgPyMK8bsU6ysJinmUdYTXP8l/Jzjzzi8tiW3LFepjKKzbbFfZejBb/Cov9U/81ratem5bbU6zKC1lY/2xcppYznhOr9WSlQnk9hGRabv5XXQd6tZzpX0mynidJQYBk+PMuycZ/JZ26TJbszJNlFBRcBhF53rFjx/DUU09hxYoVCAsL0zT2wIEDcccdd6Bp06bo1q0b7rrrLnTt2lXTOpB/+OKLL/Dpp59i4cKFaNmyJbZt24ann34aSUlJyM7O9lrc++67D7NmzUKTJk3QrVs33HnnncjKykJQEJupRO7gK8gNISEhSEhIqDRDLWx+sD+PiIioJkhISEBISIivq0HklLCIECw9NcsncR2xZcsWnDp1CjfccIM6T6/XY82aNXjnnXdQUlKChIQElJaWIj8/36pXT15eHhISEgAYXp+m8VIsl5uW2XPDDTfg0KFD+PHHH/HLL7/g/vvvR0ZGBr766itndpXcIIWGofHCX3wW21HPPfccxowZg759+wIAWrVqhSNHjmDq1KnIzs5Wr7G8vDwkJiaq6+Xl5aFNmzYADNfhqVOnrLZbXl6Oc+fOVXqNJicnY9++ffjll1+wYsUKPP7445g+fTp+++03BAcHO7O7RGSBiR43hIWF4dChQ5UOgEdERORPQkJCNO9xQOQuSZIcvoXKF7p06YIdO3ZYzRs0aBCaNWuG0aNHQ6fToV27dggODsbKlSvRu3dvAMC+fftw9OhRpKenAwDS09MxefJknDp1Sr09ZsWKFYiKikKLFi0qjR8VFYU+ffqgT58+uPfee9GtWzecO3cOsbGxXtpjsiRJksO3T/nSpUuXIMvWw7fqdDooiqFHaWpqKhISErBy5Uo1sVNQUIANGzZg2LBhAAzXaH5+PrZs2YJ27doBAFatWgVFUZCWllZp7PDwcGRlZSErKwvDhw9Hs2bNsGPHDqvkKBE5h4keN4WFhfFDMRERERHZVbduXVx33XVW8yIjI1G/fn11fnR0NAYPHoxRo0YhNjYWUVFReOKJJ5Ceno6bbroJANC1a1e0aNEC/fv3x7Rp05Cbm4tx48Zh+PDhCA21n+iaMWMGEhMT0bZtW8iyjC+//BIJCQk2YwERZWVlYfLkyUhJSUHLli3x559/YsaMGXj44YcBGBJWTz/9NF599VVcc801SE1Nxfjx45GUlIRevXoBAJo3b45u3bphyJAhmDt3LsrKyjBixAj07dsXSUlJduMuWLAAer0eaWlpiIiIwH//+1+Eh4dzPCkiNzHRQ0RERETkYzNnzoQsy+jduzdKSkqQmZmJd999V12u0+mwdOlSDBs2DOnp6YiMjER2djYmTZpU6Tbr1q2LadOm4cCBA9DpdLjxxhuxbNkym54bRG+//TbGjx+Pxx9/HKdOnUJSUhIeffRRTJgwQS3z/PPPo6ioCEOHDkV+fj46deqE5cuXW33p/emnn2LEiBHo0qWLej3Pnj270rgxMTF47bXXMGrUKOj1erRq1Qrff/896tev79X9JQp0knD22ZBERERERD5QXFyMQ4cOITU1lT2qiYjIr2j5N4zpfCIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREZFf4UNjiYjI32j5t4uJHiIiIiLyC8HBwQCAS5cu+bgmREREziktLQUA6HQ6r8cK8noEIiIiIiIP0Ol0iImJwalTpwAAERERkCTJx7UiIiKqmqIoOH36NCIiIhAU5P00DBM9REREROQ3EhISAEBN9hAREfkDWZaRkpKiyRcUkuBNzkRERETkZ/R6PcrKynxdDSIiIoeEhIRAlrUZPYeJHiIiIiIiIiKiAMHBmImIiIiIiIiIAgQTPUREREREREREAYKJHiIiIiIiIiKiAMFEDxERERERERFRgGCih4iIiIiIiIgoQDDRQ0REREREREQUIJjoISIiIiIiIiIKEEz0EBEREREREREFCCZ6iIiIiIiIiIgCRI1M9KxZswZZWVlISkqCJElYsmSJuqysrAyjR49Gq1atEBkZiaSkJAwYMAAnTpyw2sa5c+fQr18/REVFISYmBoMHD0ZhYaFVme3bt6Nz584ICwtDcnIypk2bpsXuERERERERERF5RY1M9BQVFaF169aYM2eOzbJLly5h69atGD9+PLZu3YpvvvkG+/btw7///W+rcv369cOuXbuwYsUKLF26FGvWrMHQoUPV5QUFBejatSsaNWqELVu2YPr06Xj55ZfxwQcfeH3/iIiIiIiIiIi8QRJCCF9XoiqSJGHx4sXo1atXpWU2bdqEDh064MiRI0hJScGePXvQokULbNq0Ce3btwcALF++HHfeeSf++ecfJCUl4b333sOLL76I3NxchISEAADGjBmDJUuWYO/evVrsGhERERERERGRR9XIHj3OunDhAiRJQkxMDAAgJycHMTExapIHADIyMiDLMjZs2KCWueWWW9QkDwBkZmZi3759OH/+vKb1JyIiIiIiIiLyhCBfV8BdxcXFGD16NB544AFERUUBAHJzcxEXF2dVLigoCLGxscjNzVXLpKamWpWJj49Xl9WrV88mVklJCUpKStTfFUXBuXPnUL9+fUiS5NH9IiIi8jYhBC5evIikpCTIckB890MBTlEUnDhxAnXr1uVnLyIi8itafu7y60RPWVkZ7r//fggh8N5773k93tSpUzFx4kSvxyEiItLSsWPHcOWVV/q6GkTVOnHiBJKTk31dDSIiIpdp8bnLbxM9piTPkSNHsGrVKrU3DwAkJCTg1KlTVuXLy8tx7tw5JCQkqGXy8vKsyph+N5WpaOzYsRg1apT6+4ULF5CSkoJjx45ZxSciIvIHBQUFSE5ORt26dX1dFSKHmK5VfvYiIiJ/o+XnLr9M9JiSPAcOHMCvv/6K+vXrWy1PT09Hfn4+tmzZgnbt2gEAVq1aBUVRkJaWppZ58cUXUVZWhuDgYADAihUr0LRpU7u3bQFAaGgoQkNDbeZHRUXxwwYREfkt3gJD/sJ0rfKzFxER+SstPnfVyERPYWEhDh48qP5+6NAhbNu2DbGxsUhMTMS9996LrVu3YunSpdDr9eq4O7GxsQgJCUHz5s3RrVs3DBkyBHPnzkVZWRlGjBiBvn37IikpCQDw4IMPYuLEiRg8eDBGjx6NnTt34q233sLMmTN9ss9ERERERBS4hKIHTv8BcTkXUngC0PBmSLLOb+NoGSvQ4mgZi3Fqpxr5ePXVq1fj9ttvt5mfnZ2Nl19+2WYQZZNff/0Vt912GwDg3LlzGDFiBL7//nvIsozevXtj9uzZqFOnjlp++/btGD58ODZt2oQGDRrgiSeewOjRox2uZ0FBAaKjo3HhwgV+q0RERH6Hf8fI3/Ca9T02hF2McexbKFvHAkVHzDMjG0G+YSqk5J5+F0fLWIEWR8tYjONmPA+/N2j5N6xGPmLjtttugxDCZlqwYAEaN25sd5kQQk3yAIbePQsXLsTFixdx4cIFfPTRR1ZJHgC4/vrrsXbtWhQXF+Off/5xKslDREREFMjmzJmDxo0bIywsDGlpadi4cWOV5WfNmoWmTZsiPDwcycnJGDlyJIqLi9XlL7/8MiRJspqaNWvm7d3wOaHoIfLWQDn8BUTeGkPDwU/jiGPfQvm+FZSV3SHWDYKysjuU71tBHPvWb2NpEUcc+xbK2n5ATEvIXX+FfF8e5K6/AjEtoazt57FYWsXRMlagxdEyFuN4IJ5G73feUCN79PgLU0Zu+86vEBkZASEUCKEAAlAUQEAAQoGAgKLA+DMgBKAIBbIkQQIgAMiybPjQAwCSBFm2+CAECZJsmAcI6BUFOlkHRZGgKAokCChCgRAShKgYU0BAGGMCsiSMMWRIEqqJCeNj3xTjunKVMRVhiGOKqSgChh0UkCXJJqZk2mfTPOMky4Ci6CFJQZAkw7FUFKHGVBQBQDIeb+uYhv3UQ5ZkSBCApKsmJiBJMmQZhnMHXeUxBdQYQggIRQEk8/nUSRIEBGRjlleWJUBUjGmIJxmPt16vhywHQZKEGtN0nSiKYtwvmGMKvXHfAb2iR5AsQxhOH2RJB0kGAEmNZzjuFWIq5dDJIWqC1BRTEcZjYHn+IFnEFMb9lI3XrPk6EUIyXsOmmJbn07CfOl2wMZ5iPGemmJbXrLA4l5LxdWK8jCBZxYSQIOtk87GGpD6mUCdL0CsKZDkIiiKsX5vG/TYfZ2H4MCwZjquiCENM47k1bVOWJeN+69RzaYopAZB1kuGagM7w+jfFNMUwvR8Y6wDjtWs4FHrDdQIAEFbvB0IyH1tJki2uWcnw2tQbrjnDa1NvvH7NCXCgspjC8Bo3vv4tXyfVxRSKgCTpjNesYrx+LWOafjfsj+m8q+8Hsmy8Tq1fhxXfC0zvB3pFgSzJgCQZjq0iIEmKGh8wHW/j9QQYr1fDuTUdT+uYlU+G9wMBAVk9nlAEhGSIrRgbT6b3HzW26T1I0UOnk42vf8l4/RiuI+tYgATZcB4k4/mTg9SYQrF4XzO8oRv+M773KRbHV69XEBSkM5xcyfZvivkYC2NMyRgzEtdcfQd7R5CNzz//HAMGDMDcuXORlpaGWbNm4csvv8S+ffsQFxdnU37hwoV4+OGH8dFHH6Fjx47Yv38/Bg4ciL59+2LGjBkADImer776Cr/88ou6XlBQEBo0aOBwvTz5bSh7cbgQY20/4IrukFs+B0S3AC7shrJrOnD8R8idP/W7WFrEEYoeyvetDI3TWz6HJJm/ZxdCgbKmD5C/G3LWdreuP63iBOI+8dgxjk08L703aNmjp0aO0eNvLl7cAUWEmhMO6gd+UyMAgGWCwPihvaS4HKZrVLYYkMnUCICxgWuZmIAERESEWiQdTAkI07ZNDYEKSQlTzJJymEKZYpobAsYGOoR1MkSWEB4WAuNeGBsfBoaEh6lhZRtTX65HaZne3Cg2xq4upqyTEBZaSUzF0IAzBRUW+w8BlJXpUVZeTUyr42r4OShIh5CQIPN+CfsxKx5zCKC0tBx6vWI8Z5J6jCXDgTaeR1NDzxwzNDQIOmPjzCqmsREnbGKaEltAcUkphAKL/TFfP+Y4xrpYNPjCw0MMSQthPleAgDEPYo6pCKvrFpBw6VKJ+fp0MKYkG2JKkjkmTOfUtN+GWRaJAlNDHSguLjM2iG1jGk8xJPUYG8sYY9o7X8Ii4QEBKLA8toZ6FReXGeovUOH4mV+HMiSr3yUAERGmmObjaJVgsdpf83uEogiUlJRZHDPz/kKyeK1K5pim/Y+ICFGTgebEoLA41rB4jVrGNLwfVB3TdC6tE3jhYcGGc6juh52YwvLYm5IRepSW6G2Om3n7xjpYvCdAkhAkSwgJCzLmiSpeNxbXrnq+Ta8lCWVlepQb34OsX4/m14R6PZkSeJKEkCAdgkJ05vpbvR6sX5um9yDT8S8tLYNeL9R9M4S1uH7VJI+hgOn6DQnVGZM1wiKRYz6Oivr+bn0+AQnFxWUQAhavD8vr1vxaBIzJPWNMftdDlZkxYwaGDBmCQYMGAQDmzp2LH374AR999BHGjBljU37dunW4+eab8eCDDwIAGjdujAceeAAbNmywKhcUFFTp0021VDExIgDvJkZuXmDVUFDW9vNOssJLcYSiNxyvK7pbN7IadIB8y+dQ1vSBsvUFyFfc5ZmGsAaxNNun038ARUcg37zAqnEKGL9EafEslBX/MpSLv6Xmx9EyVqDF0TIW47hMy/c7b2KixwNSUsoQFWU8yepnZsn4i2Q5E+a75SRUffgt1pEqzJbKKilaU2JKFhuQAQR7OKY9noipuBEztJrClcXUA5LiYswwx+LZxCy3PdZVxgPM11C4azGVMkC2uFbsxRQW149kGVNn3rblepKdmGpNBCRRal0HB6psPr5hFRZaXtv2z6UQgITSSo5txdeknQ0gpIpllcQEIIkSc+u+spiV7mcI1HNiE1pNc1osFxBQIImqbgewsz2VDPW8qnWyyE7YnmDjPAVANbcg2N1HU12CKqmXsJilWMdU9NVcP3a2p9ZBrjij8gobsi3GWabXZmWBRYVDZDqPAobXSWVlLM+ldfyCC5eqqCPVVqWlpdiyZQvGjh2rzpNlGRkZGcjJybG7TseOHfHf//4XGzduRIcOHfD3339j2bJl6N+/v1W5AwcOICkpCWFhYUhPT8fUqVORkpJSaV1KSkpQUlKi/l5QUODm3gVWYiTgkhVaxtIojrhseGgMolvYLxDTQi1X3ceWmhBHy1iBFkfLWIzjBi3f77yIiR4PkM7vh1ReMbHgyLek1bW2K16m1ZW3bpDZritgvM+mknWrilnZ/ngrpqPfMtuLCUPjxtmYQlTevqo2pmTdYHM4pmLdSHconr1tOVLGSFEAubLhuao67pUlEh2Iaepq4PAxrqx6lgmPShJGar8UPayHIbOTLKnyMrNMklR/fKVKN1jxeFV2PVRTDzvXjwRRybF19DVUIVNV5aqSxf8B+/uEKo+t4eZGR7KM9o5Nde959spIdnanQhlhJ55kem1WVsXKj63pOhBVHQhhXVqlKFC7IDoYz7wVyViqkvdhe/EASAUlNvOIzpw5A71ej/j4eKv58fHx2Lt3r911HnzwQZw5cwadOnWCEALl5eV47LHH8MILL6hl0tLSsGDBAjRt2hQnT57ExIkT0blzZ+zcuRN169a1u92pU6di4sSJHtu3gEuMBFiyQstYWsWRwhMM78IXdgMNOtgWyN+tlnOHVnG0jBVocbSMxTiu0zSp5EVM9HhAUP5xBJW7cCitvsV24DKxbGebVquW5Qf8it/sOhjT3uacjulE49blmKYVXYxped+Dt2Oq57CamMLeL1KFtm0VlbVXDaWyHhSVxTclz5xLfFjNUoRjnRzUAqZrx3Kek8dWgfYxnXp9eui1aUrwVHv9VFUHYadsFefTlMZwNB9rtXIgvB/YO14VN6BUUdZOYgmwTvw6nGByNHFW4boy/hhcUF7JOkTOWb16NaZMmYJ3330XaWlpOHjwIJ566im88sorGD9+PACge/fuavnrr78eaWlpaNSoEb744gsMHjzY7nbHjh2LUaNGqb8XFBQgOTnZ9YoGWGIk0JIVWsbSbJ8a3gxENoKya7r9sUV2vwFENjaU84c4WsYKtDhaxmIcl2n5fudNTPR4gK7oPHSo5FsfJzpamMtbNsQrK1RZz4lKVqk2ZjVlNI9ZTWPXH2I6clyFAkiV9a7xVcxKjoPVLGH7Y1XXbHU9eRxOGAirf6pcWVT3OgJsG9z2NlshprdS91Y9lDwU06FzaW8dOHDsqlnfJpz1wbU+hU5ljBwiAbC8bckmhNvvB7bLpQrbE06/ThR1nKlKq+QQe68T+3SF3nn6D/m3Bg0aQKfTIS8vz2p+Xl5epePrjB8/Hv3798cjjzwCAGjVqhWKioowdOhQvPjii+qA5JZiYmJw7bXX4uDBg5XWJTQ0FKGhoW7sjbVAS4wEXLJCy1gaxZFkHeQbpkJZ2w/Kmj6QWzxruM7ydxtimAZ2dXOsD63iBOQ+STKktpMhfn8Iym/3QWo+CohpBuTvgdgzAzjxE6SbPwZEGYTeOMyD+sHC8o+t5d/9ypdL14+HyHkEyup7IDV9whhrL8S+t4GTKyClzwP0l2F4HorlF5GmL18rzJMqLDfOM+/T/YYBhf34HGl5fWv6fudFfOqWG0yjZp/9+gpERVbScLb7KaGajw5V9tCQqm8UeSNmtWVciFnTOJQYcGGb1anqnHr6XJqWy5XdTueFmJIE6PWATqdtTKWy2+K8kLGxHIHY09eQI7G1vG5N2Qyn3w8cjVlFYqrKXi6OBHXg9WiVbPPGubSsQ4Xt2z2X1dXZvfeDgiI96vc5xadukY20tDR06NABb7/9NgDDAO4pKSkYMWKE3cGY27Vrh4yMDLz++uvqvM8++wyDBw/GxYsXodPZfvguLCxESkoKXn75ZTz55JMO1cvdJ5aIvDVQVnaH3PVXSHYSI+L0Bigr/gW5y4+Q3OnRE4BPorEa26iyRpY3BrL2Yizl6BKI3/sBSZmQmj0JRDUF8ndD7J0N5P4CKe1dSIl3GL4kE3rjZPEzFIt5lZQx/ixOr4M4uAAoOW2uQGhDSI37ALFtDOWgWGzLYtsV41RRTlzYC+T9BpRZjGcVVBdoeBOkOo0r34b6uzBsp5L5VuUvnwIuHgQUi9uA5VAgMhkIiTGWE9b/qj8L22U2ZYz/ll8GyguMv6uBgKAwQAq23p7TP9dSUhAgBwGQDV8AS7LFz8ZEks182Tzfcp4kA2WFwOVcQFiMdyqHAnVSgfA4QNIZYkpBgGz4WVJ/1pnro5bTqeXMvwcBBfshTiwHSvPNcULqQ0rtC6lhOiCHAHKw8V/jpAux/l0Otp4nBdl80eat9yAtn7rFRI8bTCfq3H/rISrC2QZBZfdaeIqT3+Lb3YQDK1SbdLLTkHElTqDGrKqR7miSSOuYrqwTKAkQqnlcumZdXliJ6q4zb7zXeyZmQZFAbPYFJnrIxueff47s7Gy8//776NChA2bNmoUvvvgCe/fuRXx8PAYMGIArrrgCU6dOBWB4dPqMGTPwwQcfqLduDRs2DO3atcPnn38OAHj22WeRlZWFRo0a4cSJE3jppZewbds27N69Gw0bNnSoXm4negIwMeJoHMMT/MoND0gw/auUG3+uMF+UGwalF6Yyhn/Fqd8h/vrEOlkRUh9Syt1AveuM61hsUygWP+sr/Gwqq7eohx7CNP/SP8CFPYC+2BzLlEQIjrbYRsXESoVtC8VYLztTbW7oE5GRVCEpZEwUKaVA8Vnr5FVkY8g3THH5vZuPV/c3JaWAjg1KckFtSYAwZuDS6ruCWvBZXAhR9a1b3lCiVF+GaqU+ffrg9OnTmDBhAnJzc9GmTRssX75cHaD56NGjVrdjjRs3DpIkYdy4cTh+/DgaNmyIrKwsTJ48WS3zzz//4IEHHsDZs2fRsGFDdOrUCevXr3c4yeMJ9rr/izqNgAt7Ifa+A5xYDrnzp4DQQ5QXG77p1ZlvHRPlRYYfdOEAJEAphSgvNHybrZQb/gToSwClBCI4Gmj5LHBggWHcH5PgWKDJAIhLJyH2vm3cxmVDrwhhfLKhUgahLwH0lw3bhYBQSg1JGOM8cwKmDAhLBI7/BOX4Mou9lQEpGMrvA40NFS+9kZaehTj4f97ZdkVKiaEHiWYkYw8DnfXPsuU8qULvCJ3xs4Bs7oWg9oCQLObpYH5giWyYZ9qGKbasgyQHw9R7Qphuv5eDIRnLqmdVMpY1bleYxn2TjGUl2fgnWzGU1YXC1DvDkFyTAF2wGk8AxmSYDCkoTO3NIUQ5TA1jQzzZ8PdLlBu3G67uvxBlhv2TgyHJIYBkfHCAUg5IEiRdhLmsYrxG5VDzdiEAfamhbFCk2tNEmJKIcjAkXYixXgCUYgASpOBIQx0hGbYr9MayYYZ5EMYEogQERRgTvpKhvooekIPUsgAgyi8ZYuvCjX+nJfPrUQo2HEvJVLbIsJ4u3LxdpcyQNFC3azi/lu8npr//Ql9quM7VcwRACEMdoAC6UEiSoZe8UMqNZSXDdo2fyQxlheEcSYan1wqlzPDeJAGSbHxasFAMZUW5sVeLbJgnyoDyEkAShvNm7FlleJ8qNxxL44MrhDBuVyiGc2HqwaYvNpw7SIbDqJQb63DZUFaS1YSsul0JkIQwrK+UGc6nUm7c33LjNozHXeiN74vG+Ppi4/uh3nCslRJDfKXEnLzWlxrrWlrhdS6M5UqAyoYubNgJ8vUvAg1vrtGPVLfEHj1uUHv0zA1BVLh2H86dv6HAA3XzRdvV6ZhV3aZTPddu1KgJMZ17Cbv74CtXeOsmmBoX0+vH1vZcu7efHrrdKQD56pp153C7cjYLLgvUH1bKHj3kNzz1bag49i2UTSOBYotxiORwoF4LIKQeUHAQKDoMhMQC4fFq8gaXjhvKSsHW3/L6MznYsD9KiaHhFRwFBNUxzFfKgMsnDN9uR7cwJiOCgQt7gdLzhnmRKYbkQnkRkLca0IVBSr5bTWaI3N+AokNAXGdIsW0BOQiitAA4+H+GRn2rsertGuLIV8C5LcCVPSEldQVknaHs1ucBSQf55k/UZItycB5wYjnQ+AHIVw0wxCq/DLHa8E271PU3YwNcB2XvbODv/wBXDYJ83WhDWaGH+LaZoey/90AKjQXkICg7pwG7XgeuGQLdjbPUw6T/LBoQ5ZB7HYAUkQQAUHbPhNg2DlJqP8jpH5jLfpkElF2AfNf/IEVdbSi7/32IzaOA5Luh6/xfc9nFVwOXT0Luvg5SvdaGsn//B2L9Y0BSJnS3fWMu+931QOFfkO/4xXCbCgBx9Bsov/cH4jpDl7HcXHbZTUD+Dsi3fwcpsYuh7PHlUH7rDcTeAF23teayP3cBzqyH3HkRpOQsQ1njbY6Ibg5dj83msqvuAnJ/hZQ+D3JqX0PZs1ug/HQLEJkCXc895rJr+gD/LIXU4R3IVw8ylM3fDWXZjUBoA+h6H1HLKn8MgjjyBaQbXofcbIShbOERKN+1AHQR0PUx9yJTNgyH+GsBpOtfgnzd84ayxaehfNMYAKB7sMhcdstzEPvehdTyOcitXzaULS+C8kWc4fK//5QhiQRA+d/LELumQ2r6OOR20837sdCwXL7nMKQwQ2Ja2TkNYvtESFcNhJw2x1z284aA/hLkf++GVKeRoezedyC2jobU6H7IN883l/26EVByBvKdmyAZxwdTDs6H2DgCuPIu6G753Fz22+ZA0VHImWsg1W9nKHtoEUTOYCDhduj+tdRc9of2wIU9VregimPfQ1nbF2hwE3RdV5rLLu8MnNsK+davIV3RzVD25Eoov/4biGkF3Z3rzWV/6QacWgu5038gpdxjKHs6B8qKDKDOVdD9e7u57Op7DGMb3TQXcpP+hrLn/wflx45AeCJ0d5sTtvq1DwHHFkNqPwPytY8ayhYchLK0NRAcDd19J8znM2coxKFPIbV5FXKLkYayl05AWXINIAVB98AFc9lNIyEOfADpuhcMSRoAojQfyldXAACke49DMiYTlR2vAgc/Apr0h9z0cUOyvawIYpXhIQJy9xxI9a6Hu9ijx8+IMgHhxpF0+nO90y0Q+41DZ7nb6HE2picaWb5oomodszbsI2N6NxrfD7zHqZiWHcJcviPNyRVLa2cij0hK7gmp+AzEJouxgZTLwNkt1gVLzxmmiuwmeSRDgkQXakiMXM4FoBjGfQmONtwSUHwaKNgHhMVDirvZ0BNEFwJxdAlQXgik3Acp8kpADoG4sA/4ZwlQ92pI1z6mji0htr8CXD4BqdV4SLFtDPPObjLMj7nOkHCQggE5GMrv/YD8XZDSPzSMNSMHQ5z6A2LN/ZU34tLm2DbiIpKh6/6HuaypEdf8SdtGXEg9yB3NPXz0ax8Cig5BSrnbuhF38P8AXZhhkFjTKcjfCXFuC6QGN0K+eqCh7KUTULY+D0CClNLLfLRPrjB8JK6Tah5PqTTfPHJbbGtj7xRACo01lA2JghRpfGqbUmYuGxIFKbiO4WdJZ/yoXct66BLVMlJQpPk9Iriu4XUfGmtO6Fi+R3ggyaM19uhxgzoY84zgGt6jx8f8pLK1ptcJYzJmxQ2QDZ/1CLMM6ui5cSM5dOGyQINnytijh/yGJ78NFef+hDj2PYQkG5IoQXUMt6jIIRBSkOF2gaBwSEF1AF0YoAuFUPSGsiHRkILCATnUsD4ASb01xLh9q9syDGXUWzgkXYWyplstwtTbAtTbMiAbYrlU9jIAxXg7TJCxrN54i4szZSVIQRHmsvpi4+0wIWpDyamyQjHcwgGovSkMZUuMt5GYbslxtqwA9JeMxz3C+nYYUeZk2cpv21PPpzNlnTr3nrhOTOfT3eukwvl09zqp9Hy6e52Yz6f710ll59PF66TS88n3iErL1rD3CMvtuoODMfsJ04k6M02jRI9k0fjQsgWipjI1jul3LWbGrDExfXXNMqb3YvrkPqoAjmdUcFmgwfNM9JD/0PJDMhERkSfx1i1/o0iAXptWiAQ73/oGqlqwj7Uh58KYgRdTawKApHEipEb06NEipl7beERERETkfUz0eIAolyDKtf10rmWbx+0GT6C3Qt1U48cN8cOYpmuWMb0XU2uaX7Mi8F8nAAAmeoiIiIgCDhM9HiAUGUIJ7GyG9nfeiErieaoWzjy9iDH9LaYp+VEbYlbOX2NWdv1on0wP9LvFAEAovHubiIiIKNAw0eMJelmzW7d80frwRa8BU1ytCR/k62pFrwHGDBg1qkePlw92oJ9LAIC+VuwlERERUa3CRI8HCL0MoVWix0d80hSwGZTDG8e4ut4K3o1pf0wOT8e03sdaEdN4243vY3r5mrWbdfFuTMkHMe3z/mtT+wFztA0HAIKJHiIiIqKAI/u6AvasWbMGWVlZSEpKgiRJWLJkidVyIQQmTJiAxMREhIeHIyMjAwcOHLAqc+7cOfTr1w9RUVGIiYnB4MGDUVhYaFVm+/bt6Ny5M8LCwpCcnIxp06a5VmFF1nQSGseDIgNC60myUw/JC5P1cbWdJKcmxaFJVichtIgp14yYNpPk1ORUTLvxfBHTuXiuxLQ9vgEY00evTXffX5yto7D7vqfBREREREQBpUb26CkqKkLr1q3x8MMP45577rFZPm3aNMyePRsff/wxUlNTMX78eGRmZmL37t0ICwsDAPTr1w8nT57EihUrUFZWhkGDBmHo0KFYuHAhAMOjzbp27YqMjAzMnTsXO3bswMMPP4yYmBgMHTrUqfoKxcUePa58WWx8/IzF9+tOr+50aeM3+OZI3vyW2+LZzV7vGVExpnOjkXg+prd7D1S2nxrEFLwtjjH9JZ75QvXJ+4H6ozbvB0LxchgiIiIi0lyNTPR0794d3bt3t7tMCIFZs2Zh3Lhx6NmzJwDgk08+QXx8PJYsWYK+fftiz549WL58OTZt2oT27dsDAN5++23ceeedeOONN5CUlIRPP/0UpaWl+OijjxASEoKWLVti27ZtmDFjhguJHmg8GLNWsSTDnQuaDsrhi1vgDDG1HXuktsQ08Mljo1E7HnVeGx6v7guB/9o0xmSmh4iIiCjg1MhET1UOHTqE3NxcZGRkqPOio6ORlpaGnJwc9O3bFzk5OYiJiVGTPACQkZEBWZaxYcMG3H333cjJycEtt9yCkJAQtUxmZiZef/11nD9/HvXq1bOJXVJSgpKSEvX3goICww+m2400o2F/DFOoWjKMgz/2jHBpfTdbk8IHB8qlmL7YTz+L6eq5dCdx50pMdxOFfnMu3eRsyEB/YiQRERFRbeR3iZ7c3FwAQHx8vNX8+Ph4dVlubi7i4uKslgcFBSE2NtaqTGpqqs02TMvsJXqmTp2KiRMn2sw3DMbsmURPtR/Sjbdu1YqP5rViJ93hRivSzWPr7OrCA10VanxM42DM/hbT6VU98Bg+l6rrZtKkxl8/xpia36LGDj1EREREAcfvEj2+NHbsWIwaNUr9vaCgAMnJyepAmlowtD0kzzQGHNyI2t7xxT0ptYKrO+r6CfHHnkuMWbNi1ga14bgKDsZMREREFHD8LtGTkJAAAMjLy0NiYqI6Py8vD23atFHLnDp1ymq98vJynDt3Tl0/ISEBeXl5VmVMv5vKVBQaGorQ0FCb+aanwDjPtWaEO40PV25/kNz8ltmVmEK4mVdyNaZPxpHRNqhPxpGpOLZ2gOIYPYHDZ+dS6+ySwi49RERERIHG7xI9qampSEhIwMqVK9XETkFBATZs2IBhw4YBANLT05Gfn48tW7agXbt2AIBVq1ZBURSkpaWpZV588UWUlZUhODgYALBixQo0bdrU7m1bVXE90eMq11sC/tTbQOvkEuDZMTUc2pSHW5O1JabDIfmkL3KDT86lxkGZ5yEiIiIKPDUy0VNYWIiDBw+qvx86dAjbtm1DbGwsUlJS8PTTT+PVV1/FNddcoz5ePSkpCb169QIANG/eHN26dcOQIUMwd+5clJWVYcSIEejbty+SkpIAAA8++CAmTpyIwYMHY/To0di5cyfeeustzJw50/kKK5Jh0ojQvA8IfNBlwL3Wji8GQXWVW3U1nRcnt+FXMd3oEea1R7rb2Rev3+KodcxKzpcvYsJb8RhT079dRERERKSNGpno2bx5M26//Xb1d9O4ONnZ2ViwYAGef/55FBUVYejQocjPz0enTp2wfPlyhIWFqet8+umnGDFiBLp06QJZltG7d2/Mnj1bXR4dHY2ff/4Zw4cPR7t27dCgQQNMmDDB6UerA57v0VNle1iLnhH2tu9HiRPX1Yqd9DMWA487cd2bbjfUskePr2K6OzCyP8QEfBDPBzF9cvuo17KhREREROQrkhD+1PehZikoKEB0dDSOPJqCqBAXEj0VP187cCbczvM4s7KwiOnttoC9fQ/EmA5VoiqVVdCbL2NfxPQMl09nhRWrTb4ypksxHXrKoLvxanpMTx1XF2MWlCho9P4xXLhwAVFRUe5EJ9KE6bMXr1kiIvI3Wv4Nq5E9evyN1mP0eHSEHkdaFb4a7bU2fIMPyUNJNMc34rleA05sxEfXkMun043rINBjut2hx8UV3ep44kpMdx917up++iAmEREREQUWJno8wLHHqzvaStGiZ4bzMQTgcnbA1Uah1u06uJkAcTxmxZLez4B49KpysLqWMSWHW82+eJ0wprPx3B8oveoteLqfqSOva4/HdKCMOaSz7wGeu36q/9tFRERERP6GiR5PEBKE8FSPHkdaJI4V8yzXv972yVO3HJ5ZIaabjT3HVq9wf4Wb51LzL/Fd6gXi6QvWF43TmhrTFwko1wmN4tjG1FbN6FxT/XFmooeIiIgo8DDR4wGO9ejxdFBtw2nfyBWe711TzTHzzUCo8Mljx7Wm9W5qOpaVX3B2h9y9Slw5gLUhpiPxfPEmRERERESBhIkeT1Bkw6QlDdsChkaz1o0BySftD4+Of+Tgar7okaP5g3Y0jul2As2v2r7eOLA1tfeSv8eseRlE9ughIiIiCjxM9HiAEJJHHlHraNvSgSEuPK7qcF5qUfsgGeGxxxdpEtNFvui5BONjuTUiwdhDS7uQ5sCaq+rA+iLbxZg1L2YV8fh4dSIiIqKAw0SPB3jy1i1HHv0rJF80YL0VsYpHd/ugV4XmORcfJCM83VOqRo5/4qMEmm9iav3a9CbG1DqeUPyq+xoREREROYCJHg/wVI8exwP6xwC8bvPDR7o7vbqvBmP24PmskYkehwt5NmaN69CjeUBPHAFndshTR7z2xuQQPURERESBh4keDxCKDKHlGD0B2Jq03SXtd9K9cV1cOz7aPOWrZtCyrqaBtbU+PrWj0ezt16ak+a2F9vbJ+4OzOx7Tc9eVnZjs0UNEREQUcJjo8QAhKn4Q93TrQFT5q3caXhWDeLfx5cpTsrzB9QZVVQen8o0KwCtjZNTIppvmDxOydxS8/Nq0y7sxtUmSVthPL59Lw6nz/VWsdeJOCGi425IxJsfoISIiIgo0Gj8qKjAJIVeYJI9OhtNknoSQK8yTvDBVjOfZfdJiUlyYvFOXiteHeYIXrhchJEPyqJLJZ+dEqQkTPDrZHFvF3jGHhyeLbSsSFKXisYYLk6hmsiir7r/r50FxaJKtJuHmVHF7jkxax6zqvcLRSXF4Mr/nEVVmzpw5aNy4McLCwpCWloaNGzdWWX7WrFlo2rQpwsPDkZycjJEjR6K4uNitbRIREZHz2KPHU7z4Lazlt8qmsVe1/KbZEMvPGgOuPu3cz3bTXwiICj3CvHWgzSdeCO/fAlTxdah1RxT1FjW3YzpxoPzwdsPaENPVeEz0UGU+//xzjBo1CnPnzkVaWhpmzZqFzMxM7Nu3D3FxcTblFy5ciDFjxuCjjz5Cx44dsX//fgwcOBCSJGHGjBkubZOIiIhcIwn79zeQAwoKChAdHY0997VC3WCdJjG9P25EzYjpMwG8n6Zd8+qDmiohRMVEjx9x8B1SHeJJ23HZffOkOH89l2TjYqkezb7YiQsXLiAqKsrX1aEaJC0tDTfeeCPeeecdAICiKEhOTsYTTzyBMWPG2JQfMWIE9uzZg5UrV6rznnnmGWzYsAG///67S9u0x/TZi9csERH5Gy3/hrFHjwcId7q/O5lmU3sM+CDZo7Xa0JbUMgFieQq1P5+S5r3QfJGMEBI078qh/UtTGHpLaRrRR0+urxUxa8M7LTmrtLQUW7ZswdixY9V5siwjIyMDOTk5dtfp2LEj/vvf/2Ljxo3o0KED/v77byxbtgz9+/d3eZsAUFJSgpKSEvX3goICd3ePiIgo4DHR4wFaP3VL68akoZeCL5qT/hTQ1ZW1TYDUJlofV0kyjF0T6D3uTLfE+cutSe4E1PyuJp/EZKKHbJ05cwZ6vR7x8fFW8+Pj47F371676zz44IM4c+YMOnXqBCEEysvL8dhjj+GFF15weZsAMHXqVEycONHNPSIiIqpdmOjxCNMAxhrwwdNoFAFIHmtJOlN3X3TJcHVFN+rqg910NaSrPQ5801NBy9vFhJpY8kXPJU1jqv/z1MYc5IlzWRsG63EyplC8Vw2qXVavXo0pU6bg3XffRVpaGg4ePIinnnoKr7zyCsaPH+/ydseOHYtRo0apvxcUFCA5OdkTVSYiIgpYTPR4gO3j1b0bS9ubJjy9b/z22Jo7N0641rXLvdMpQbi4BffiunCPo6TlQLOSb8bPUv+ncVxfvIx9cfuoOzGlCv86QNvHqxtj1oDH2FPN06BBA+h0OuTl5VnNz8vLQ0JCgt11xo8fj/79++ORRx4BALRq1QpFRUUYOnQoXnzxRZe2CQChoaEIDQ11c4+IiIhqF798vLper8f48eORmpqK8PBwXHXVVXjllVdgOa60EAITJkxAYmIiwsPDkZGRgQMHDlht59y5c+jXrx+ioqIQExODwYMHo7Cw0On6aPmIal885tzdmN55/HvgTK4fW9euPfcesa7t9W6enHzMNEw/B/brRPLVdSu0nuCTybVH1RsnxYXJR/tJVFFISAjatWtnNbCyoihYuXIl0tPT7a5z6dIlyLL1x0qdzvCgCiGES9skIiIi1/hlj57XX38d7733Hj7++GO0bNkSmzdvxqBBgxAdHY0nn3wSADBt2jTMnj0bH3/8MVJTUzF+/HhkZmZi9+7dCAsLAwD069cPJ0+exIoVK1BWVoZBgwZh6NChWLhwoXMVMjVGnOXsKsL8j/YDzbreGuAYNFXR/ET6jC96u2h/dLV9nfhk0GkvHtjKD4H3+jFWddh9EdNbKoupuPK3i2qFUaNGITs7G+3bt0eHDh0wa9YsFBUVYdCgQQCAAQMG4IorrsDUqVMBAFlZWZgxYwbatm2r3ro1fvx4ZGVlqQmf6rZJREREnuGXiZ5169ahZ8+e6NGjBwCgcePG+Oyzz7Bx40YAhm+OZs2ahXHjxqFnz54AgE8++QTx8fFYsmQJ+vbtiz179mD58uXYtGkT2rdvDwB4++23ceedd+KNN95AUlKSw/Vx+dYtNxp2midP/Ox5yi7XVutkhC8a6j4hNL1m1deJdiGNfJBaqklZAwdUdb374qVQk2IKLybRKt9srXgDIhf06dMHp0+fxoQJE5Cbm4s2bdpg+fLl6mDKR48eterBM27cOEiShHHjxuH48eNo2LAhsrKyMHnyZIe3SURERJ4hCeF//S2mTJmCDz74AD///DOuvfZa/O9//0PXrl0xY8YM9OvXD3///Teuuuoq/Pnnn2jTpo263q233oo2bdrgrbfewkcffYRnnnkG58+fV5eXl5cjLCwMX375Je6+++5q61FQUIDo6Gj8L6sd6gZrlTMztAQCPzngd5dljVTZUdT88vHR9apJwk9Y/xj4r03An1+fzvzF89S5rMkxL5bpcf23W3HhwgVERUV5JjiRF5k+e/GaJSIif6Pl3zC/7NEzZswYFBQUoFmzZtDpdNDr9Zg8eTL69esHAMjNzQUAu4/wNC3Lzc1FXFyc1fKgoCDExsaqZSoqKSlBSUmJ+ntBQYHxJ9PYFVqQ4KveEdryv9ayK8fI28e2xhxFrfMCAoDkxjCzftXbTuN4bgb1dW87XyTiHI3pzGDMnrvMasy7BBERERF5iF8mer744gt8+umnWLhwIVq2bIlt27bh6aefRlJSErKzs70Wd+rUqZg4caLNfG8/davipiVILn82d3Q1q5i1ph3g3kl09Rrwvz51/kAAQtueb6Zbxfxo+CzX47nxOCotk2/ururOufSXO+r4/kNEREQUePwy0fPcc89hzJgx6Nu3LwDDIzyPHDmCqVOnIjs7W31MZ15eHhITE9X18vLy1Fu5EhIScOrUKavtlpeX49y5c5U+5nPs2LEYNWqU+ntBQQGSk5Ph7R49drfs0odzF3s4CNfGmq6wCadpn19yM6Irq/ugt5RvBinWmu96vtWKdrMbbwg+6VGjfUjX3g4cfHF69LquFRcsERERUe3il4meyh7hqSgKACA1NRUJCQlYuXKlmtgpKCjAhg0bMGzYMABAeno68vPzsWXLFrRr1w4AsGrVKiiKgrS0NLtxQ0NDERoaajNfEZJ7Ty5x8oO26w2l6ns4VNaAcLeh5Pz6jhwUFx9b5i3C5gfHVnOzWr5qpzkT1zeJJe/2fLPZfx/16KkNTxfzFGdDe+K4Or277r4fOLk+e/QQERERBR6/TPSYnuKQkpKCli1b4s8//8SMGTPw8MMPAwAkScLTTz+NV199Fddcc436ePWkpCT06tULANC8eXN069YNQ4YMwdy5c1FWVoYRI0agb9++Tj1xy8DNHj1OrCos/u8Klx7hDLfu1LDdmEO8cdtN1Rv0XIPZua24u59Op7s8tKPObcJHrUmNe77BQz16an7b2z8HhHdxjG3tYnrgten0efHHE0lEREREVfLLRM/bb7+N8ePH4/HHH8epU6eQlJSERx99FBMmTFDLPP/88ygqKsLQoUORn5+PTp06Yfny5QgLC1PLfPrppxgxYgS6dOkCWZbRu3dvzJ492/kKeXmMHttwkqbf4Evq/zy1Mcdo39h1Y/Bed6J6LIlWk/vY+KhPj0thXe8G5KlkoW+SaNr2RHN2Gx57GpVnNuNcTB8EdTSmULxbD/KctWvXonPnzvjjjz9w8803+7o6REREVIP55ePVawrT49G2dr8JdTR6vLphHBBtB2gwPxlK48a6l8JVtlmfPBq7dgyYA62b1x4/rA5UX43pg5cJO2WQqy6WlaPN0o18VLUfeOGFF5CVlYXvv/8eU6ZMsVvm/Pnz+Pnnn3H8+HEAQFJSEjIzM1GvXj0tq+pVfLw6ERH5Ky3/hsnVF6GaxJxw8dQkVztJkulnT8atboKh5eyFSVQyQRi+3fbcJKqfhKi0Pt6cvHVsKz/mWl47krHPmwcnqfpJkiTNkzymQ6wINybFuUlv+tmdmC7UwRevk9oyUc03ceJElJeX41//+hf0ej0mTZpkU2bevHlIT0/Hhg0boCgKFEXBhg0b0LFjR8ybN88HtSYiIiJfYY8eN5gyclu6ad2jR5NQNthrIFDUope8F69Ze5sW4OskcPjqdaLtBXSxrBw3/LCBvSP8wIcffogLFy4gJiYGjzzyiM3ypk2bYuvWrYiMjLSaX1hYiBtuuAH79+/XqqpexR49RETkr7T8G+aXY/TUPBa9ULwdSQK0boCYkktMCXqH9omBwM9EGK5Z4dWXSsVN++x14qvELwMSaaq8vBzPPvss3n//fbvLJUnCxYsXbRI9Fy9eNPQ4JCIiolqDiR4P8Hb394qfz4TQ9ok3tefzoW8yWbUlgab9Navtheuz14nG148pieZeWBfWFrXkjajibnr7/HIwZr8xbNgwAMCjjz5qd/kbb7yBW2+9Fddddx2uuOIKAMA///yDXbt24c0339SsnkREROR7LiV6vvvuO6fXueOOOxAeHu5KuFrPMhFQm3rXsKeLtzCh5U2Bnhj1TBItwA+SOzR/nfBc+JtffvkFXbp0semlc9ddd6F79+7YuHEjTpw4AcAwGHOHDh2g0+l8UVUiIiLyEZcSPb169XKqvCRJOHDgAJo0aeJKOD+g9a1b2hIAJF887DyAEwOm8+ibMZdqS8Mu8G9x5JhdRLVPZmYmTp48ibi4OJtlOp0O6enpPqgVERER1SQu37qVm5tr90OGPXXr1nU1jF/wxZNLNL0NxuL/gU+bE1mxl1ZtEOgJLV8kPjhmF7mL59E/vPbaa3jssccQExMDPkODiIiIquNSoic7O9up27AeeughPhnBQ2pLrwHf9RaoDQmtWnDrlqEbmqZMr0smtMi/8IT6gylTpuD+++9HTEyMr6tCREREfsClRM/8+fOdKv/ee++5EsaPBPatW77qNeArWh7j2pAY8AkfXLOK4rueLky+EAW2ir143nvvPXTq1Ak33HAD6tWr56NaERERUU3l9lO3Ll++DCEEIiIiAABHjhzB4sWL0aJFC3Tt2tXtCpItJge8xfBBWuuGem1IDPigg43mEX05flag9/DzFc1fJz44trwJyD+98847mDhxIiRJQnJyMm644QarKSEhwddVJCIiIh9yO9HTs2dP3HPPPXjssceQn5+PtLQ0BAcH48yZM5gxY4b6ONBApggJitOP/nX947WEwB4TyHdqxU4CGicGzFG1F+jXrS/Gzwr0Y2rmo9eJh2I6uh3Bx6v7hRdeeAGxsbHq77t27UJ5eTn+/PNPbN26FVu3bsWHH36IY8eOQZIkJCQk4Pjx4z6sMREREfmS24merVu3YubMmQCAr776CvHx8fjzzz/x9ddfY8KECbUi0eM8y1u9nPtU714bwLW1BQDJWw2eKrbrToPS2XVNjaLa0Yj1xU7WgnGBjGrHNVQb+PeJdPQ6rPiIbqqZxo4dq/5sOmdJSUlISkpCjx491GVnz57Fli1bsG3bNq2rSERERDWI24meS5cuqU/V+vnnn3HPPfdAlmXcdNNNOHLkiNsV9A/ujNHj3HrufSR3Z20vtZirqJLrEd34Jt5H9zEEflsr4HfQqPbcRhX41yxRzVTVU7fq16+Prl278tZ5IiKiWs7tRM/VV1+NJUuW4O6778ZPP/2EkSNHAgBOnTpVe560JeBagsCZhlLF7fPx6lVwta7CreSSO7yWHKhku1r1lrLcr9qRGPDFbVS15/HqgT5QOsfoIUcsX74c0dHRvq4GERER1WBuJ3omTJiABx98ECNHjkSXLl2Qnp4OwNC7p23btm5X0B8IuNjocbWlJAFwekwg86raruhP3NlJ15NLXlVJtWpLbykml7wQzSfH1P8GSveX8X04Ro//YW8dIiIiqo7biZ57770XnTp1wsmTJ9G6dWt1fpcuXXD33Xe7u/kAYq91VHFe9Z/ShXD3Bix3Wh8aNyi1D+kDNX28nIr1c/6adSWqu2sBvhm0HKgtySWt+d9B9cV14EpMjtFDREREFHjcTvQAQEJCgs2jPDt06OCJTfsJd8boqbidakq4Hcb5DRiSS+62mJ1fX0iS5r1ANE8u+eS5454M6O3Ku7Z9YfF/V9d2GZNLRERERETkQ7IrK23fvh2K4nh/b9NjQD3p+PHjeOihh1C/fn2Eh4ejVatW2Lx5s7pcCIEJEyYgMTER4eHhyMjIwIEDB6y2ce7cOfTr1w9RUVGIiYnB4MGDUVhY6NF6BgJJzX64M8lOTpJ57CO3JuHUJIThVgb3JuH4JIR7+1cdO+v4opeL1szjSmlxrRomYbxuhUuTa6dfMf0r3JwU56bacA0R+ZP9+/d7/HMWERER+S+XEj1t27bF2bNnHS6fnp6Oo0ePuhLKrvPnz+Pmm29GcHAwfvzxR+zevRtvvvkm6tWrp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouL1TL9+vXDrl27sGLFCixduhRr1qzB0KFDPVZPcoe7iSVXGu2e6hbhXB2dzEVZT9Ull4TtZMj2eGhXHVULEgO+SC6Zr1tXpgqVd3ASkoeSS04mmGpDcskn4/poH5K8oHnz5vj77799XQ0iIiKqIVy6dUsIgfHjxyMiIsKh8qWlpa6EqdTrr7+O5ORkzJ8/X52XmppqVb9Zs2Zh3Lhx6NmzJwDgk08+QXx8PJYsWYK+fftiz549WL58OTZt2oT27dsDAN5++23ceeedeOONN5CUlORwfUyNbvc4twGOq+ANvjimnmpmSY5vy5Tn8UhoxzfCa9bzJIv/u7q2azx03UqOb8fUQc9tTmzDF5es4qm3BEffDjgYc0Co6pHrREREVPu4lOi55ZZbsG/fPofLp6enIzw83JVQdn333XfIzMzEfffdh99++w1XXHEFHn/8cQwZMgQAcOjQIeTm5iIjI0NdJzo6GmlpacjJyUHfvn2Rk5ODmJgYNckDABkZGZBlGRs2bPDBQNKONdRry6ONaw9fjJfjyYvH8QSTLxqUkkt9Fqkq7iWX7G9NU06E9Oums6P7yRcJERERUcBxKdGzevVqD1fDOX///Tfee+89jBo1Ci+88AI2bdqEJ598EiEhIcjOzkZubi4AID4+3mq9+Ph4dVlubi7i4uKslgcFBSE2NlYtU1FJSQlKSkrU3wsKCow/2bkVwiVaNdRdW1+4+Eh3VzGx5C2ePrCObE/ba1ZNiCoaX0QSr1siIiIiIvItv/wqT1EU3HDDDZgyZQratm2LoUOHYsiQIZg7d65X406dOhXR0dHqlJyc7NV49vhiYGQh3B2/xrWhZt0au6aayW4t/frr+5pO22vW/TGXXLtmvX7d2pl43RKRt8yZMweNGzdGWFgY0tLSsHHjxkrL3nbbbZAkyWbq0aOHWmbgwIE2y7t166bFrhAREdUqfpnoSUxMRIsWLazmNW/eXB3w2fSo97y8PKsyeXl56rKEhAScOnXKanl5eTnOnTtn86h4k7Fjx+LChQvqdOzYMY/sT01XeQ8FTzbSqxks1mNMjfFKBimmgKDNNWt93QphMVKxRydzpofXLRFp5fPPP8eoUaPw0ksvYevWrWjdujUyMzNtPjuZfPPNNzh58qQ67dy5EzqdDvfdd59VuW7dulmV++yzz7TYHSIiolrFLxM9N998s80YQfv370ejRo0AGAZmTkhIwMqVK9XlBQUF2LBhA9LT0wEYxg3Kz8/Hli1b1DKrVq2CoihIS0uzGzc0NBRRUVFWU+3h7USMN7Ztr9VczRpe7I1hf2Ij3Xu0SB56evvOXbPqQ9QUjSdetkQBb8aMGRgyZAgGDRqEFi1aYO7cuYiIiMBHH31kt3xsbCwSEhLUacWKFYiIiLBJ9ISGhlqVs3xiKhEREXmGXyZ6Ro4cifXr12PKlCk4ePAgFi5ciA8++ADDhw8HYHi6z9NPP41XX30V3333HXbs2IEBAwYgKSkJvXr1AmDoAdStWzcMGTIEGzduxB9//IERI0agb9++Tj1xi7zF3Vt9XLndx5uDq1TWXYMChS9uq/TudVtdVyMiClSlpaXYsmWL1UMtZFlGRkYGcnJyHNrGvHnz0LdvX0RGRlrNX716NeLi4tC0aVMMGzYMZ8+erXI7JSUlKCgosJqIiIioai4NxuxrN954IxYvXoyxY8di0qRJSE1NxaxZs9CvXz+1zPPPP4+ioiIMHToU+fn56NSpE5YvX46wsDC1zKeffooRI0agS5cukGUZvXv3xuzZs32xSxRQnGsE8wlqVDPUvOtWqvQXIrI0evRo1K9f32PbO3PmDPR6vd2HWuzdu7fa9Tdu3IidO3di3rx5VvO7deuGe+65B6mpqfjrr7/wwgsvoHv37sjJyYFOp7O7ralTp2LixImu7wwREVEtJAneO+KygoICREdHI6fLragT5Jc5M/I5T7z8nN+GxExPADGdfy3OqVCfaObudpwlSRKTPV5QWFaOtBVrceHChVp2OzJV5cSJE7jiiiuwbt069ZZ3wPAl2m+//YYNGzZUuf6jjz6KnJwcbN++vcpyf//9N6666ir88ssv6NKli90y9p54mpyczGuWiIj8jil/oMXfMKdv3Vq7di0A4I8//vB4ZYjIFd4cvJpqPi3Pv6dieXvQdSJyR4MGDaDT6ap8qEVlioqKsGjRIgwePLjaOE2aNEGDBg1w8ODBSsvU7vERiYiIXON0oufHH39ETk4OfvjhB2/Uh4hcwo555G841g9RTRUSEoJ27dpZPdRCURSsXLnSqoePPV9++SVKSkrw0EMPVRvnn3/+wdmzZ5GYmOh2nYmIiMjMqUTPxIkTUV5ejn/961/Q6/WYNGmSt+pFRA5hY5n8Da9ZIn8watQofPjhh/j444+xZ88eDBs2DEVFRRg0aBAAYMCAARg7dqzNevPmzUOvXr1sxgwqLCzEc889h/Xr1+Pw4cNYuXIlevbsiauvvhqZmZma7BMREVFt4dTAMi+99BI+/PBDvPLKK4iJicEjjzzirXoR+YA/Nj4tb33xx/pT7VPxdi1et0Q1UZ8+fXD69GlMmDABubm5aNOmDZYvX64O0Hz06FHIsvX3hfv27cPvv/+On3/+2WZ7Op0O27dvx8cff4z8/HwkJSWha9eueOWVVxAaGqrJPhEREdUWTo8gXF5ejmeffRbvv/++N+pDZORu489XY3/4cmBkjndS2xgGRtZ2QG/z5eru9eZATF7SRD41YsQIjBgxwu6y1atX28xr2rQpKnvGR3h4OH766SdPVo+IiIgq4fQYPcOGDQNgeKICUc0l7ExKNZMAJEND1vVJcnqiwGBo29i77rw9eYKTgyNLgCS7O0nVT5avE75UqJbjwzCIiIjIUXwmuF8S0LLV47leA4BzDVNP7aMTvRU8FJF8y/1r1pkrwRu3Hjm2TcmYm5GcqW9lRUV1BYjIl3788UcEBQXhhx9+wM033+zr6hAREVEN5nSPnoo6duyIgoICT9SFHGDuEe3qt//V9WqporeL67WGK70P3OtZ41ovG/IWf+vp4uxrQxgTS0Lza1aCZN0hp7qpMs5sgy8VIk3xYRhEROQqodfj8s6tKFy7Apd3boXQ6xmnFnC7R8/69etRXFyMqKgoq/kFBQWYPHkyXn/9dXdD+AFXG5aujOei/uRCPNeYYrqeB2GrsHLa9s4yx/Q3zicJnerpYlOswgxfdEQjIjLiwzCIiLQh9HoU7/kf9OfPQlevPsKat4ak0/ltrKL1q3F2wTsoP3VSnRcUl4j6A0cg8qbbGKcaWl4PnuZyoufee+9F+/btIUkSTp06hbi4OKvlRUVFeOONN2pFosf0LbwLa3q6KjU0JlXN3cSLK+trMJBuhTDu9Zjy8XXLl42P+WNy0l/w2PoLPgyDiGoirRrCgZQU0SpW0frVyJs+DhHtOiJu5MsISWmC0qN/I//rT5A3fRzin3vVI7ECLY5lPC2TSp4micoej1CNUaNGYePGjVi3bh0kSUL9+vXRunVrtG7dGm3atMG+ffswf/58/PPPP56uc41RUFCA6OhorM+4BXWCONyRZ/lr46M2JF2ochqPnwVA0vi14ouYWnL27DnzUnLtr62D27b5wTGFZeVI++UPXLhwwaZnLtVMv/zyC7p06VJr38dNn714zZI/CqSkCBBYvTgskwgxvQdYJREubVnn0SSCFrGEXo9jw/sgJKUJ4se8Bkk2j9giFAV5r41B6bFDSH5nkVvXRqDFMfHWOdLyb5jLiR6TkJAQ/PHHHzhx4gT+/PNPbNu2DTt27ICiKJg8eTIefPBBT9W1xvFdosdfB2N2dhue2EfnHuGsjndCAcAXSRfFgZiebO1rt48Vo9TSNmbAKSwrx40/M9HjT3Q6HU6ePGnTk7q2YKKHvCGQeotoGUeLxEggJUW0jHV551acnPAEkqa+j7Cm19ksL963EyfGPorESW8j/LobGMeCN8+Rln/D3M5OFBUVITg4GADQs2dPtyvkn1wdo6eqlpL97RmSLpUvd44TT/Yx/ORmPKn6mFKFH91uTLI1WjP4qteJdnHNV5r3Y0oVfmLShaj2cPP7OSK3cQwT12IE0q0tQq/H2QXvIKJdR6uGcFjT6xA/5jXkvTYGZz+eg4gbO7vdi0OLOMV7/ofyUycRN/Jlq0Y9AEiyjJjeA3Bi7KMo3vM/t5MIWsXSnz8LAAhJaWJ3eUhKqlU5xjHT8nrwJrcTPaYkD7mi4oe16j+8ufQ45cq35tXiXtwIuaWmNRI8Xx/14VC83IgoALz22mt47LHHEBMT4+uqkAsC6XYdjmHivEBLigDaNYQDLSmiZSxdvfoAgNKjf9vtAVN69JBVOcYx0/J68Ca3H69O7nLwcdAVH2vs7KOQPTGRF/jbY8ddiylVMckS3Jp4uRJRoJsyZQrOnTun/v7ee+9h5cqVOH/+vA9r5f+0eERv0frVODa8D05OeAKnZr6MkxOewLHhfVC0frXfxTElRUJSmiBp6vto/OkKJE19HyEpTZA3fZzfxaqYGAlreh3k8Ag1MRLRriPOfjzH7evClKyI6T2g0mRFed4JFO/5n1/EAQKvF4dlEsEeTyYRtIoV1rw1guISkf/1JxCKYrVMKAryv/4EQfFJCGvemnEq0PJ68CYmejxAkgBJdnWSHJsk64nIG0mX6ifXc4Smp9NVnNxV2Xb5MiF31LR+b1S7Vbxd65133sEdd9yBBg0aoHHjxrjnnnvw6quvYtmyZcjNzfVRLf1LICVGAikpomUsrRIjgZYUAbRrCAdaUkTLWJJOh/oDR+DSlnXIe20MivfthHK5CMX7diLvtTG4tGUd6mcPr7R3lxACQlEg9OUQZWVQSkugFF+GcrkISlEh9IUF0F+8AKWwADH3DcSlzX/g5KSRKNqwBqXHDqFwwxqcnDgSlzb/gZi7+0F/7jTKT+ei/ExeFdMp+9PZ09Dnn0NM7/7mOBvXovTEURRtXofcV5/Bpc1/oN4Dj0CUG+pb8dg6Q8vrwZv4qCiiWoqJEPIf2qddas/Lgyktf7Rr1y6Ul5fjzz//xNatW7F161Z8+OGHOHbsGCRJQkJCAo4fP+7ratZYvF3HeRzDxHWBeGuLZUPY3mC13ujF4c04pqRI3rQXkTvleUT/uy+CE69A6dFDuPD95yjevhn1h4xC2cljgF5vSCYoeqC83JAI0euBCv+K8nKLeXqLcnqENm2JorW/4NgTDyDs2paQI+tCn38OJQf3oPzUSYRf3x6n33vdsJ6iN/+rKMZt6QHFPE+NYVqulKvz5agYXNqag0ub/7DYYQlSSChOvTMFmP2qYbtCQAgFUAQgDL87q3j7ZhRv32wz/8zc6e6cHofjnJ41CacxyXqmTgdJ1gE6HSDLhp9N/+p0hmvK6l8dYCxzafMfODLwTgQ1TEDEjZ0Q0fYmq0HAvTU2macw0UNERFQJLROinhhj19ltcFxf//DCCy8gNjYWANRevUlJSUhKSkKPHj3UcmfPnsWWLVuwbds2X1TTLwRaYiTQkiJaxtIqMRJoSRHAIjEyfRzyXhuDmN4DEHxlCkoP/YX8xf/B5T83oMHjY6A/d9qQ4CjXQ5SXGRIf5eXmJEh5OUR5GYS+3JCgKDP9bC4X1qwVCtf8bE6KRESakyKn8xDWsg1OvTXJIulSbvi53OJndb4x4aImYazrAwCXt+bg8tYcm30+++EMt49bReUn/0HhyX9s5l+2k8TwKCEgSoo9tz319hbjmArCME8y/a7GrapKlS20M18Y/6c4kJAyJcbKqq2CXUrhRZQWXkTpoQPI/2I+guKTPDaoubcFRKLntddew9ixY/HUU09h1qxZAIDi4mI888wzWLRoEUpKSpCZmYl3330X8fHx6npHjx7FsGHD8Ouvv6JOnTrIzs7G1KlTEaTpo9KJfEPrBh57EJHrHHhin5f4WyKksteZv+0HWRs7dqz6c1VP3apfvz66du2Krl27alEtvxRoiZFAS4poGUvz3iIWSZGQlFSUHj1k1TsAsmydnDAmQES5HlB/LjP8rv5skRTRlyPixk4o+OFLHH9mIMJb3wg5Kgb6s6dw+X+bUXbiKCJuuhXn/vueYT3LGHo9UFZmnQQxbt82KWOsT3k5pKBgXNr8h3VvEaMz777m1nGrqLKkSPGubR6NY0Ong6QLUv+VgoIAWWf41zRPpwMs/w0y9gwJCjL/W6GcpAsCZBn6/HMQZaWQwiMQHJcEKdi4fVPPE53OqleK1XxTLxTjtqpdR9YZB7iUAVmCZErOyLLh+rf83fivIWEjW6wnW/9eA4YVMfdkUgCl3Pp3U+8nvbH3k6IH9IpVryfzv5brGMqK8jKUHv0bkqxDWIvWXn3KoKf5fUZj06ZNeP/993H99ddbzR85ciR++OEHfPnll4iOjsaIESNwzz334I8/DG9Eer0ePXr0QEJCAtatW4eTJ09iwIABCA4OxpQpU5yqgxDe/xBdMRnKRjO5enOJe5eqa2vzciX38SpyR2V/M3z94Yyct3z5ckRHR/u6Gn4r0BIjgZYUcSeWMN4eo/bgKC+334PEmMSAXo86t2Yi/8sFOD56CCI7dIIuJhblp3JxafMfKD3yF+rcficufLfIvI7ltivpQWI3rr4cutiGuPTneptbaBAUhLwZLwHl5W4fO5PSI3+h9MhfNvMvrf/NYzEcEhQMKciQHDEkS4IgBQcbGspBwWrixJA0Mf0cbE6gBAeb58s66C+chygrhRxZB0HxVxi3VWF9Ndli2JakCwKCdNblrNbRGWNWqGeQ4RYe/p2s+STZmIACAIR6PkDHf3l+mxrw60RPYWEh+vXrhw8//BCvvvqqOv/ChQuYN28eFi5ciH/9y3Bi5s+fj+bNm2P9+vW46aab8PPPP2P37t345ZdfEB8fjzZt2uCVV17B6NGj8fLLLyMkJMS5yrjcenZsRatSkvo/8nu+OI/af7Uv1P9pi3+biSjQsLeOe+wlRpTiywAAKTRMTYzIdaOhFF82fPMebP5MqJYNCVUTEKaeDpBlyCGGRkZY89bQNYzH+S/mI37s65CNvcVFeTmU0hKc/3KBVbJCKSkGhIAUHKJ+W2waBBWSDDnU3HixLGtKipz/6mNDLyVdkFpWKArOfzkfurgEhF7bUl1f6PUQZaVW2xVCQLlUBFFaYvgWXwigvBxKWQmUy5cAvYKobnfj3Cfv4sSLjyMy/TYE1auPstMnUZTzG0r/2ou6d96Liyu+MyQ3ZEm9VUYpuWw4PsaxP9TBUstKIPTlkISxTuVlUEpLgfIyICTUMD7GgO6QomIgyRKUkhKIixcgSksgR0Ti8MA71YQLFL1b37qW/rUXpX/ttZlf+Osyl7fpECGAsrLKl0uyIdmhJiCMvTeCgiAHh6jzTPPlkBC1F4m+6CJEeTnkiEgENYiHHBQMYey5IQUHQwoJMydZBAzJjdBQyMHBkExlIRkSIWERalIFQoGQdJDDww3Xuy7I8HgfAUMdIuuqSRJRVgooiiHhYnoNKIrhOgMgh4Wru6qUljhcVpSVQuj15mQQjIMHG29HcqasFBqmJnNMt45JsmTYV1PdLN4jbMq6+B7hdFkX3yOqL1sCCMV8fmH/PaL6shLk0LCqz6czZZ059y5eJ/bPZ/XXlL/w60TP8OHD0aNHD2RkZFglerZs2YKysjJkZGSo85o1a4aUlBTk5OTgpptuQk5ODlq1amV1K1dmZiaGDRuGXbt2oW3btjbxSkpKUFJSov5eUFDggb1w8MZFUwlhvInBF41mPqMtQLiT/XB1XeFinsf1C91nrxMml4iIaix7vUUOP2j4vJgy7zu1t0jJ/l3InTQSdTOy0PDxMer6RwbdBVFSjOS5XyE4LhEAUPDj1zg7fzbqdL4DcSNfNhSUJCiFF3H5z/U4OX44Yu5+CEHxibjww1co/OV7AEC9fkNRvOd/EOXlOPXWJCgXzqPeg0MR1CAeorwMxXu2o/DXZQhKvBJ1bzclNcpQsOJbKBcLEH7jzQiKqoegBvG4vGUdjjzUFVJYBEKvbgqlqAjlp05CKboIADg2vC+kkBCgrAxK8SUohRfVJIIwJUocVLJvB0r27bCZf3HZV7joykmpgnKpELhUaGd+kWMbMDYchb4cKCuDFBYOXVSMYZ6sQ/k/hwEAIVc3N9yOpChQCgugP3cGQQlXIOyaFmqS5eLKpYAQqNu1J3R1ooCgYJTs34XL2zYg5OpmqHv7ncbbc4Jw5sM3IUpLUP+RUQiKS4CkC8KlLetQsOwrhLW6AfUHjDD0IgnS4eS44dBfOI+El99CaJNrIemCUPj7Lzjz3usIb5uGxHFvqLtzdHgflJ/8B0mT30NYc8PdDIXrVuHUG+MR1rItEl9+Sy37z6hslB4+iIQJMxHRpgMA4NLmdcid8hxCr2qGK6bPU8seH/sYSvbtQPzoqYhMuwUAcHnnVpyc8ASCkxsj+a1P1bInX34Kl7dvRsOnJqDurZkAgOIDe3Bi9CMIapiAlPe/VsvmzXgJlzauRYNhoxF1x78BAGX/HMY/T/eHHBWDxgt+UMuemTMVhWtXoP6gJxGd1QcAUH4mD8ceuxdSaBhSP1tpLvvhDFz85XvUe3Ao6t2bbbgmCvJxZNBdAIAm35h7TZ395F0U/PAlYnoPQGy/RwEAoqRYfd03XvgLJGMj/vwXHyH/608Q1eM+NBj8tLoNU9lG85dCF10PAJD/7UKcX/iB6+8RAI4+di+UgnxcOes/ai/Di78uw5n3XkdEh85IGGO+/e2fJ/uh/HQukl7/P4Rd09xw7n9fidNvTUL49e2tzv3x5wej7NhhJE56W70F9dLmdch7fSxCm7bCFVPnms/nuMdR8tdeJLwwHRHtOxrO/Y4tyJ00EiGNr8aVMz5Wy+a++gyKd/2JuGdfQR1jL5eS/btw4sVhCEq8EilzPjef+2kv4vLWHDQc8QLq/sswllzp0b9w/JlB0MU2QKP/+1Yte/qtV1CU8yvqDxmF6O69Dec+9ziOjegLOaIOGv/3J3PZudNQ+OuPiB3wOGJ69QNg6IF5dEgvQKdDky/XmM/9/LdRsPwbxNz/MGL7DgZgeE850r8bACD1i98AY2Lp3ML3ceHbzxDd8wHUzx5h2IBer557y2vKX/htomfRokXYunUrNm3aZLMsNzcXISEhiImJsZofHx+vPnY0NzfXKsljWm5aZs/UqVMxceJEO0ssHyLtDke34YnWq+PbEMLQeBWKti1YyVOHlWoIV06mO7enufo68a/kEhNLRESOsxwv5eRLTyAktam67PjYR6E/dRIR7Tri0uZ1AAyN3bw3xhu+GS4rgygtBQDkTnnesFJ5GfQX8gEAhTm/omjT74ZvrC0e/V1ifJxxRec//cB23kLbeeUn/7E7//Im24aHKL6E4p1/2szXnz1lMw/C/C24XUGGcT9QWgrIMnQx9dVbW8rPnoIoLYEupj6C4g3jiojSUpTs3wUpOAQRN3Yy3p4ThOJd21B+6iTCWrZF6NXNIOmCoL98CRd//BpScAjqPfSo8VaaYFxc/SNK9u5AZOc7UCf9dkCWUXJgN/K//gSQZCROftfQkyUoCOe//g+Kfl+BqLvuR8zdD0EKCoJSXIxjj94DwNCIM/UOOPvxO7jw7WeIyuylNuJEeTkO3X8rACDxpZnQRdYFAJxbNA/5X3yEiDZpaDD0GfVwXPx1GaDXo959gxBUvyEAIH/Jp4ZET3Kq2kAFgLPzZ0OUliCiTQcEJyUDAMpyDePL6OpEI/Qq83UHY48LXd0oQwIJ8JsxQIio5pJEVaP61VDHjh1D+/btsWLFCnVsnttuuw1t2rTBrFmzsHDhQgwaNMiq9w0AdOjQAbfffjtef/11DB06FEeOHMFPP5kzhJcuXUJkZCSWLVuG7t2728S116MnOTkZ6zNuQZ0AHsDZkOhx5zJxdV3tW7BaJ5dMSTTyBm3f2gTcuXTcSy75Aq/bwFBYVo4bf/4DFy5cQFRUlK+rQ1StgoICREdHu33NFq1fjdPvvQ7loid6ZzvAOCCq0Okgh4QBwcHqLTKmwV0RFAzJYr4wriMFBUEODVPHLoEkGRIuIaGGWzuCgtXbrcrP5AFlpZCjYhDS6GpIkCB0EuTQcIuykuG2jqAQyBERavJGKIrhdpGwcMOtJJJUa27LcPmWHE/cvmPvNhuP3L5jus3G3dt3KpxPd2/f8Ydbtyo7n7x1i7duuclTf8Mc4ZfZiS1btuDUqVO44Qbz0xD0ej3WrFmDd955Bz/99BNKS0uRn59v1asnLy8PCQkJAICEhARs3LjRart5eXnqMntCQ0MRanGx1xaS22MCubKuD5+w45HQDm5EAoRw/di6emRrRyNd4x5oPli7NvVcAgL/unUvWUhEzoi86TZIYRHI/+Y/gF4PKSICQQ0SIIeEGp56ExRiaMwYe48Y/jUkYkz/mhIztsss1jGV89M3MMPTgWwbOJYNUJfKyrJ6u4zV/OAQSMFulJWkSsoGq403V8oC9ht6TpU1jYdTsaxFg9elssbbxWzL2jtHzpSt5Hw6U9aJc+/2dVLp+XTvOgG8eO7dvU4qPZ/uXideOvcB8h7hL/wy0dOlSxfs2GF9b/CgQYPQrFkzjB49GsnJyQgODsbKlSvRu7ehG+W+fftw9OhRpKenAwDS09MxefJknDp1CnFxcQCAFStWICoqCi1atNB2h8gOfxqk2N44Sw7W383GsqsNfeGr3lLkUZLF/11d21nuJZest+L0Whp3LPTFNat4Y6NV7LvilYBE/iGiTQd17BIiIqJA4peJnrp16+K666wfIRkZGYn69eur8wcPHoxRo0YhNjYWUVFReOKJJ5Ceno6bbroJgOGpFS1atED//v0xbdo05ObmYty4cRg+fLjTvXY8+3h1RzfkWguEje2qaDnOkic5G1N4uMOUo0+OY3IpELiXXLLeiqZcCOl39zVXpqp95yj7RERERAHHLxM9jpg5cyZkWUbv3r1RUlKCzMxMvPvuu+pynU6HpUuXYtiwYUhPT0dkZCSys7MxadIkL9fM3iduUc1yi5JujulinZBythnjg8GYyQs8fWAd2Z6nkksOJpVMA4i7cVucSyQ3jq4L+TqX1yUiIiIiooDll4Mx1xSmwZRyutzKwZir3oKL67EHCLlD48GY3R5Y28X6ujGGlmZJKaqxCsvKkbZiLQdjJr+h5UCWREREnsTBmKlG8c/BmF0dA8TbLVh79WJCyzt80QPNmeuuYv0q/l79toRwby+FK+M7CU+8JzjPrWvW0XWF9Y+av044GjMREREReQATPR7g6hg9taOx7So/HAPEIc435qvnSmOdySXv8OROVr8t94+pqxvQviOo+wMx+8Gg5R576p8TIdmnl4iIiCjgMNHjQ+YP2I5/0jZ/4cuGemDwxQDQgZxcsq6Xvz7OtibzRW8ez6jNozFXxR/PJRERERFVhYkej5Cg1a1NhpK+GXvEn775Zfu+Kv6aXHKE8RXi0WtWm0HLXb1mebcPERERERFZYqLHL/nR2Bguc6+F7vrTxdhTyjtq4ng5jtbJwdvt3My4uHLNqgktXrdERERERGQk+7oCRPZJHpxkByfftFxNYzxpOvlkT7VW3XXh4e1K2l+zksevW1HtZHhQo/DNdeuFqdIj4YMXSXUxtT4GRHPmzEHjxo0RFhaGtLQ0bNy4sdKyt912GyRJspl69OihlhFCYMKECUhMTER4eDgyMjJw4MABLXaFiIioVmGixwMUoe3ED+be4smGuqOTj3i0oSgcmnywi4FPHS9HuwSTJFWVXKqprwlR6VT59ap9MgtwLyFT+V5WPRHZ8/nnn2PUqFF46aWXsHXrVrRu3RqZmZk4deqU3fLffPMNTp48qU47d+6ETqfDfffdp5aZNm0aZs+ejblz52LDhg2IjIxEZmYmiouLtdotIiKiWoG3bvkZIQBIvkn2eC0tUcmGa8edIYGwl47sg/DwNVv1xoQAb2nyKm/upDe2XStOikt7KdWSY0POmzFjBoYMGYJBgwYBAObOnYsffvgBH330EcaMGWNTPjY21ur3RYsWISIiQk30CCEwa9YsjBs3Dj179gQAfPLJJ4iPj8eSJUvQt29fL+8REVHNotcr2PHHQZzLvYDYhGi0uvlq6HSe74fBOP4Ry9OY6PEI7Xpn+KIRaWg0Cw9881vJFirdsA92VqotTUKtefqoVr09x8bocYTj21CTS3x0PRH5udLSUmzZsgVjx45V58myjIyMDOTk5Di0jXnz5qFv376IjIwEABw6dAi5ubnIyMhQy0RHRyMtLQ05OTmVJnpKSkpQUlKi/l5QUODKLhGRnwu0JMLab//E3LFfI/fIWXVeQqP6eGxqb3Tu2ZZxfBxH61jewEQPVctzj1N2ZhteTCxVuYqk+a0MksbJJWOnsFpA22vWc8klx5mTS5pGBQA+up4ogJ05cwZ6vR7x8fFW8+Pj47F3795q19+4cSN27tyJefPmqfNyc3PVbVTcpmmZPVOnTsXEiROdqT4RaUiLxEigJRHWfvsnJvb7EDd1vw4vLngYqS2ScGj3CSycvhwT+32Ilz4d4pF4jOMfsbzFP/odUS2kzXgjNWUwZu+M5yTsThDC5bE83JlqB2+M41T55Itki6HHkuSlcWqqGudJ2/FyfHF7rC/20Rf7SYFv3rx5aNWqFTp06OD2tsaOHYsLFy6o07FjxzxQQyLf0OsVbFuzH6u+2IRta/ZDr1f8Os7ab//EgFYT8Ez3mZg86CM8030mBrSagLXf/unRGBP7fYjUlkl4+9fnsDRvJt7+9TmktkzCxH4feiyWVnH0egVzx36Nm7pfh0mfP4YWHZogvE4YWnRogkmfP4abul+H91/42u1zZhnn5YVDESXKcGz1DkSJMry8cCjj1JBY3sQePR6g/YdlwW/TvSLQjqn9/REQHsq8OL4RCdBmvBzLEKK23NKk7U5695jWpBPm6XGltONMvf11H8m7GjRoAJ1Oh7y8PKv5eXl5SEhIqHLdoqIiLFq0CJMmTbKab1ovLy8PiYmJVtts06ZNpdsLDQ1FaGiok3tAgSKQbtdhrxTnVUyKyLLhnJiSIhP6zMX7L/w/e/cdH0Xx9wH8czU9Ib0QSpAOgdB7R4EHqYIoKEVFxYA0ARui/gRERECliUpREEUpUkSQ3nuvoYWWQCCkJ1fn+ePIkktCSHKXu+Tyefs6yc3O7szu7d3Nfm9m9i80f76uRa9X9gv72GNXTBf2/p74dPmb+LT/D1YpBwBO772M2OgH+Gjxa4AAbh28iLS4JLj6eyKkYRW8/F5nvNt+Ok7vvYyI1lUtLmdoZEss6zQJybcfnw8eZX3RsXcr/G/jaZZj57KKEgM9JYxpqIbMPpMxF6drMLKAtV7I/G/H8oF4+Vw/SzabBZey4dvEUZTcV7Ign9X80YByo1ar0aBBA2zduhU9e/YEABiNRmzduhXDhw/Pc92VK1dCo9HglVdeMUsPCwtDUFAQtm7dKgV2kpKScPDgQQwbNqwodoOKCOcwKVwZjjS0xVYBGEcLigBAfGwiAEAWG4dfnv0pR1mNRnY3y2dJOSHOAme+W4uKbcPR6ZvX4VMlBPFRd3Bk/iac+X4tQpwFy7FzWUWJgR6rsPVkzLaN8thnHhDbB5YESvKlXXFn6ZEt2Pqmu9NZq+cSkN8N2TS4lFlMqem5RES2NGbMGAwaNAgNGzZE48aNMWvWLKSmpkp34Ro4cCDKli2LqVOnmq33008/oWfPnvD19TVLl8lkGDVqFL744gtUqVIFYWFhmDhxIkJCQqRgkqNir5TCleMogRH2Sil8YMTRgiIA4BPkhRBnga3jF6Fi2zo5yto2YRFCnE35LOEd4IHaZQC/iEroOvdtyB6dd0GPnq/oPwO1D1+Bd4AHy7FjWUWJgZ4SyR5DNWzfhcg6gaUCbsTGV8yCF+lFwnoTiEtbfGoOKbhksYL1XpIhc94c2+J5S+TY+vXrh7i4OHzyySeIjY1FREQENm3aJE2mfOPGDemCNdPFixexZ88ebN68Oddtjh8/HqmpqXjzzTeRkJCAli1bYtOmTXB2di7y/cmNowVGHKVXij0CI+yVUjC2CsA4WlAEAGo1rYS6fnJkuLuhy/dvQqFUSmV1+f5NzGz6AeqKVNRqWsmicnydADclcDEp54/ZAsClJMBXacqXFyEEjAYjDAYjjAbT30bjo38NRqjTUuGmBE7H6dE6+gEgAKPR1Eg1Ggw4fVeHECVgiHuIGxdjpW2a/pUKyTVdSH8AmttxcFMCJ2I0qH/yJmQy02skl8sAOXDqrhZllQAeJCA2+gHkChnkCjnkcjnk8kd/K+RQKB7/bVqec85Lax07e2Ogh/KppF7VFaTels7JUdiVS+qxpazscXc6y4NLhVvXHsElBpaIbG/48OFPHKq1Y8eOHGnVqlV73DDPhUwmw+eff55j/h57cKTAiCP1SgFsHxhhr5SCs1UApjgGRYQQMOiN0OsMpodWD73eAMOj5zrto7/1j5Zl5nv0SLhwE84wYseVZLzd8ks0aF8D3v4eiLudgKPbzyPlajLaBABLRv8K+JaBQW8wlffoX4PetB2j3hR8yXxuWpaZzwDXpCSUBbB31xV0Dx4DD283KBRyaDJ0SElIg1GjRbeywNT+8xGjV0qBG2NmQMdo+jevz3QACHURaOQLHD4YjVdrf5JjuVImEFIW+G7YEtxKL3xjLrOcY0dv4XDLL3Mtp2xZYNZbiwtcTmYgSCaXQaGQI8TZiLqupmP3f74j4eLhhDa96uPZ/k3x29ebcGT3VTwfAmQ8SC70/tgCAz1EEtsOLzKx5oSvBZgcmVfNDsHy4FLB17VezyWgQIEmO/Ragsy2YVhhyfjRQr4knIyZSiNHCozYariOrYIigG0DI7Yoh71S8mYwGKHX6qHTmgIjuszgiVaP2GNXpKDIqOe+QdsXGsIv2At3rt3HztVH8eCKKSiyZvIaKIN8pW1kBlnMnxug0+qh12YJwjxKc05MQDAeB0Xcy7hCBkCTrkNqcjqg06NbWWBSr29xI9myL87MgEWSDnh4+jaunr5ttlz5qB2w+/cDFgVG/JwEyvoDnirgYYoG6Skas+U+atO/8YkZSNYUvpyMRzee8naW436G+bQJMpkMvh5KADrIXJ3g4azMXJD1H+m6RPZ4xcdpjxLVcj2AVAT5OCMmQQuj4XFBcrkM/h4qAFoIJxWcIIPRIB71Qnr6nbGMRgGj0QAA0AFIMgjA9dGx0+qhe6DHuh93Y92PuxFc0RejJnfH5Tlr4ervWbCDZWMlMtAzdepUrFq1ChcuXICLiwuaN2+OadOmoVq1alKejIwMjB07FitWrIBGo0GnTp0wd+5cqcsxYOp2PGzYMGzfvh3u7u4YNGgQpk6dCqWyYIfFHreo5XU65VTAniBWl/dGGVxyDNYdFpff7Vg+nXehWHOapwKUacttMtBDpQ2H6xSOrYIigO0CI47aK+VCEh716tA/CnboodXocOaeHsFKQBtzH5eORz8OrGgN0Ov0ZoEQnU5vHijRZVmm1cMQe18KwPSvMRGhVQLh5KJCcnwqbl+5B0VyCtoEABNafI5EocwSvDFt2/Co54teq5eG+eTGLChy8BrOHbxmtjwzKLJ+zn8WB0WC8xEUSdMakVvbRS6XQaFSQKVWQqFSQKlUQKlSQKl+9K9S8Wi5Au4GDXDvDpo0qwiDpwdSE9Nh0Bvg7OYM32AvqNNSgaOn0aBrXTQO8IVSpYDi0foKpRwKpdy0PaXpuVKlgFwph0KheJTXlEehkOHslOXoFe6HasO6I/pCDFIT0uDl74Eqdcvh9OzVSLkZh6krI6FQKx8NazL1apGGOWXr6fJ4yJNMygsB/PLsRLzariw6f/cmzuy/Kg2Hrd2sEjaN+AHxUXfw8+bPIbfgM9VoMOKXZyfixap5l7M4l3JyHX4mPX+UJrKka/XY/Pos9GsUgIj3+uDS8RvQpusQVrusVJZnqB9CGlYp9P7YQokM9OzcuRORkZFo1KgR9Ho9PvzwQzz33HM4d+4c3NzcAACjR4/Ghg0bsHLlSnh5eWH48OHo3bs39u7dCwAwGAzo2rUrgoKCsG/fPsTExGDgwIFQqVSYMmWKPXcvT5k/+Ba2cc5r7eLGHi9IUV3Z5b0vvFMcFR5fSIvkdfj4JqFShsN1CsdWQRHAdoGR/JZTo3FFaDN0ZsEOs2E5uQQvsvYgSbp0SwqKvNlsCiJaV4Onrxvibifg1O5LSL9p6pUy942F0Ht4PC5Dp5eGAemyB1uylafTGeAvNKjrBuzbdQWdvd/Nsb9KmUC3ssDcEb9aFBQBzAMwhjsJuH8nIVtZpn8fXLtX4LIUSjlUaqUpSKIGgDRUKu+FdLULDDoDhBBwclXDy9cdrnoNcCsalZs8g8q+3lAoTcEUpVoJlVrx6LlpWyq10my5Um0KwJjSZbg4/Xf0rO2HasO64ealu0hNTIN3oCeqNaiIk1+vRNKNOMzePBZqZ7UpeJMlkJN9vrK8ZAYsKj7jadbzCgCE0YgN78xHfKgfXvn5rVwDFsJghFFvgPHRcLHMv02Px8uMOgOcBrXHgZlrcW3B36jcpQHcw4KReOs+Tk/7HXFno1F3UHtor96GMJoCHOLRw2gw5pImIAwGGI1GCL3R9O+jvN5hgbi+/RR+fXYifKuUhdrDGTcS03D84ztIvZuA4AaVsWXcz6Yf0B71kHh8XfDo76zp0nLzZc5erri+/RQWt34fnqF+ULs64UG6Fkffv4/0B8kIrFMR/45eCJlMBplC/vjfrH/LHgWvsqWZ8skgk5n+DWlYGZc3HsHBD35GaNNqqNakGtw8lNg04gdc33EaXb5906LAlS3IxNMG3pUAcXFxCAgIwM6dO9G6dWskJibC398fy5cvR58+fQAAFy5cQI0aNbB//340bdoU//zzD55//nncuXNH6uUzf/58TJgwAXFxcVCr1U8tNykpCV5eXtjVpgPcC9gLqNCsOmyiYHg9QIVnh6E+AOwRIOD7hEqSFL0eLbdvR2JiIjw9i3cXZCLgcdursOfstj8OY/KQn/H9b4NxaNbfuQZgRvRfjPcXDkLLHvWgUMqhdlJJedJTTb/0O7mopIu7zAtwhUIOtbMp74ldlzCj1zdo4g9UbBuOhm93gU+VEMSdv4WjCzbhxu4zOBgHjF09BhGtqyIjTQshBNTOKqknkUFvgFajh1wug5PL43Zp1ryndl/C5tdnoXzDZ9Djx+FQKBVSXmE0YvlLX+PW0avo8MO7aNihpmm7BiO0Gboc29Wka2E0CqidTBfEWfPqdQYsaPIe4OGG0QemwqA3DYtQqZWQyQRmNv0ASE7FmwemQ+2kgrPr4+1qM3TQ6w2mnr1CQK8zQKvRIS1ZA73OAKVS/mj+EiPSUzMQd+o6zs/6CzvuAYHhFdG8a114+rohNvoBDm85i4cXb6FNAOD5fEvA1xsGoxEwCugeBVY06VrodQYIY+ZcKqYeLTqNaR4To9E0VEiVkoKwhLvYcQ9IU6jh5uliOr7pWmSkalBGYUSbAGB3HHDfgqEtmUGRv28DhlyGIWcGYA4/gMW9Ulr5AzvuAQ+1Obfj7yZHS28DjmldkKp0hspJaQqCqB73DFE5q6BWK02BEJWpx4ZKpYSTq1rKC7kMivgE6HcdhWf3NnAK9cPd6w+QlpIBT193VKlbDrq78bg6by2eebsbvKuXh5OrCs6uTlCqlFAo5RBGAaVaAXcvVykIYzQaIZPL4PQokAIAep0evz73CXwqh6DbgnekoIhWo4Neq8fW937Cw8uxeGXz55DJZchI0wIAXNwez5Kr1ehg0BtN+6c2Xa8JIZCeqoFRb4BKKYdRZ8DV/05g+8e/IrhhFVTv3Qze5f0Rf+0uLqzah9jjV1H39WcRUv8ZCJ0BBp0e2nQt9Bo9hNEAmQCMOj0MWgM0aabtyoSA0Jvy6jV66DU6KWiSEhuP++dvwcnTFS5+HlAoldClaZAWnwx9mgZOZdwgVyogsgRxDHoDhP7pw5CoaHmG+qHFhBfwzHOFG95r6XdYQZTIHj3ZJSaafqXw8fEBABw9ehQ6nQ4dO3aU8lSvXh3ly5eXAj379+9HeHi42VCuTp06YdiwYTh79izq1bPenRCsyg4Xkfa6vbqt58egomTboT6mc9bGY2AkPGuJiIqr3HqmDGz5FTxVwIAWgVLPlJO7o/Dl0CX4v8EtMHbOK9L6fSqOR0aaFsvOfYGgCqZbyK9dsANzJ/yJDi82woeLXgNg6i1SuwwQkwZ0ieyOoPByAIATp2Mxe/kZtApRoq6fUeqV8lqDz3D3Rjzm7JqA6g0qAgC2/3kUU19fhPrtqmP6+pEATIGXYS2n4sbFWHz221vwkunhpgT2XUrCj0FjUCm8LEZ/218KpmzcfRPNPIGjK/cjKT4Veq0Bl0/fwl/fbYVvsBf6DO9gCrLoDfhnyV7cu/kQTTrVRlBFX+i1BsTdfohDm8+irJcSjT1MvVL6PvMBdBo90pIz4BPkiYxULZw16WgTAAx9Ziwe6OTw9HGTerxoMnQF+p3GbLjO0WhcOBpttjyzt8jWJXssntw1LLNXilYHTbrObHnSo2tq51x+tFc7q6BUKWAwGKBJ08HZVQ3fYC/T8ByVEtfO3YbRIFC9UUX4Ko3AzWjUqO6PM+fvo4y/B8pXC4KbpwsCynnj4G/7AGhRr3M4nqtbCUqlApdP3cK2Pw4jrFYI+o16DgqVqZfLd2N+x4PYREROfxFV65WHUq3AyV1R+OHjVfCrXRGuskQMaB6ENl8OxrvtpyPm+gMM/aIX+gxvj00jfsCdU9cRfTIJ1eoHYu7u96X9ebfDdJw9cBWfrXgLLbtFADAFK8d2mYkKNYLx85HHk+yOe342jm0/jxdru8Mn+SG6juiHi8dvILL1NASW98GwKb2x4Z358Cjri317r+PQxHWI/Kov2r/QAEadHjfO38HEPnPh6e2KL1dFIl2nh0Grx+9fb8LpvVHo9HIT1G9bDUadHg9jE3E2Kh7lb8fj1y6fIrBOGJw8nHFi8xloYh7AVQkE138Gm0b+AE2qBse2nYdcBtRuEgbjo4BM/O2HSE1Mh5uHE5yclDBq9TDo9DDqDLmeFzFHohBzJCpH+smftuDkT1sKdpI9hSYpDZqktJzpCan53oYQAOQyqF3UkCnkkCsVSEpIg15nhE+wF1w8XCBTypESn4L42CSonFWoWKc8ZEo55AoFLhy9jpTEDFRrVBG+QV6QKeRIuJ+KE7svwb2MK5o/H2Hq6SKXY9/GU4i7k4gmnWujfLVgyJRy3I9JxL/LDsDD2w0vDO+A5Jh46NI0OLH3Cq5ei0fbPg1Ro1FFADLcj0nA77O2wM3TBa9N6m6aiwfAhiX7EHXiBtr1bYiI1tUgkwEP41Lw86droXZR491v+j2atwfYuHgvTu+/glbtq+KZGkFQuztD7uWBHz78CwqFDO9+8xKE0QhhFNi56ijO7r+CBu2ro37b6oDRiIxUDX6b8S9kAF4e8xxkMhmEwYiTuy7i0vEbqBpRDrWaVDIN4dIbsGnpPrgogHavtkDVbo0R0rBKse/Jk6nEB3qMRiNGjRqFFi1aoHbt2gCA2NhYqNVqlClTxixvYGAgYmNjpTxZgzyZyzOX5Uaj0UCjeTxmMykpyVq7UUA2vtsNAHvcXt3WLJkHlYoXy3vVFPx8t1tAFOxFRESUX7kN1zEIGR5qgeaf9cfSntNQV6TC3ccVAJCSmI5rZ29D92i+ksyJPY/tuAB3LxfotQacP3IdAHAz6i7+mP0fdFodMm7chZsSOBwPfNDre1SrXxHObk64fPImABnO3tejTQDwWadpSFY64cGjoVX/G/gjFEoF9Fo9UhPTAZguuDv7jDAN08gyn8mklxdIQZGLUXEAZLh6+jZGtJsu5VHKBOAJ7PvzMP745YjZsXgQk4gFH63KcYwO/nsmR5pMawqCJOkAw/0UKT0+1tQWzuxA4iwHhFEgMUuep3Ev42Ka00StREpiOjL0GQCAmjUCoHVxRWpSBm5F3QUANOlUGy46DXDpEjyCvIFrCahSrzxqNakEpUoBo8GIVXO3AwBem9QdamcVVGol9m04haPbzqNxp1roNKAZlCoFUq7exuV56+CpAsateAd3bzxESmIazh+6hn0bTqHD/9UCTp7Fh8vfRmjTauhV7j0Y9EasiJoK/5AyAIDfZ27GDx+vRute9THhh0HSPnUPGYPUxHR88OMQhIT54ZdnJyLcSYUz5wXqtKyCSb8OBWDqdfVw/R6k6oHeY59HlXoVAACbftmPbX8cRkCoD57t30Ta7sKJqwEAVeuVR62mlWDUGXDnUixUMgFXtRyN3+iCHZOWY8d7P6Gs2gCdWsD94UOsHjADd09eQ2D7CJS9dAJe6Sk4+8duGHQGGLR6eCc9RHUPgZh/DmLPucsw6gy4F/0A9b0FPNIf4p93F8CgNQVOAm9Fo7U/AJkM17efwoL6oyB3UqFLsIDSGI+5tYdDPHqfBN5+gG5lgRuzV2Lx7JXSfnQIAoA0/PXy43PVFUATXyBh80Fs23xQSq9gmpUDidfvIfH6PQCAAoDro6vYmGNXpLyBzqZ/754wn8vHTQkgXQNNuvncO1nJ5DIImQxanRFqtRJObmoo1Eqo3V0QffEujACq1K8AtasaCqUCMTce4kbUXQRW9EPNJs9ArlJArlRgw5J90OkMeH5oG3j6uUOhVODs4evYu+EUqtSvgK6vtTblVSkwY/gyyNMz0H1ICwRVDYJ/zfI4uOUcln/9L+q0roYR3/SDXGna7rsdv8a92wmYtn4kqjUIg1ylwPa/juLLoUvMgsEA8FrDzxF9PgYzFr0uDUHds+4EJr20ALWalsfY38ZJed9p9SUuXo3G80O7omnncADAka3n8ePaKDxT1hcdpg6U8q47dBenzyeh1/PN0KJ3AwDAmf1XcGHeQZT1d0Ojd/5Pyruz9xxcPv0QvZrWQsSrzQAAl0/exNUv/oOvhxPqvNJOyrvqnyhcT70J9zqVUbtfKwDArcv3ED3+b7iplKjZp4WU9+9NUbiZdhXuTWuj7ejnAABxdxIQPWoVFEo5wvu3kfLuOH4Xl7dcRfPaldHwrc4AgJSENEyatBkA0GRUD6n32JnEv3B6x03UiKiONpNeAGDqrTl19n4AwP++ePWJ505xVeKHbg0bNgz//PMP9uzZg9DQUADA8uXLMWTIELOgDAA0btwY7dq1w7Rp0/Dmm28iOjoa//77r7Q8LS0Nbm5u2LhxI7p06ZKjrE8//RSfffZZjnSbDt0qNex0WrIXERWSdXoRFfJ254z0UCFx6BaVNJZ2e7918CLWDJyJnfdk8K5WFreuxJluT6w1/brvoxYlariOv4sMLX2N2JuoRFyKwezHBqVKgQqBrqiDJNz0C4He08M0TEelgFwug9JJCScn1aNhOgrI5YBcoYCTiwoqJxVUagVkcjnkchmM9x7g4ZpdCB3cBa4VAnHzUixSkzLgE+iJKvUqQHM7Dqe+XIHw9/rCv04YXD2cpe0ahRFymRzOrmo4uzlBqVJAJge0GXoAOYfZ6DR6/NnzC/hWLYuuc9+GAKTeNs4uKtMcJlF38OK6iRACOYbkZA7fcXZVS9+PmXPcZM1r0Bvwy7MT4VM5BM/PHwa5QiHVQZumwX/v/YzEq7F4YcV4CKMRaYnpMOr0UChkpjlKdKZhYrp0LWA0QgZIvUgyUjJg1Bkgh4BRb8T9CzcRteEIPEL94F+rPJw9XJD2IAlx524i9W4C/GtXgIuP+6OhPqbgi16jg1FngNFgeDQsyPQwlWFKK0lkCrlpQmG1EjKl6V+lWmWaCFilhFypgEypgNJJCaWTynSeqpSAXA65Wgm1swrp8SkwaPVQuTvDI9QPKhc11C5qKFRKyFQKGAWgUCng7O4MhUoJhVoJIwDI5VC7qqF2dYJCpYRcpYBOb4RcpYCblysUahXkCrl0njxpyGZu51Rhh3cWNG9hh3c+LW9eQzYLklcmk+UYspk5vDMzmFKQvEajUXrfP20oXkHyFuQzImverNu1BIdu5dPw4cOxfv167Nq1SwryAEBQUBC0Wi0SEhLMevXcvXsXQUFBUp5Dhw6Zbe/u3bvSstx88MEHGDNmjPQ8KSkJ5cqVs9buUCnEXkRkDexFRESUP2lxph4okQuGYPbYP6BJ00IhE1DIAIMw9VgBABcF4OKshMJJCaVKCZWTCkq1Ak5qOZQq04Wo0klpms9EpYBKJTcFR5zVpjlHUlKAgyfRq39DqEP8EX83CdoMHTw8nVG2cgBEfAJil/+HPuP+D17VQiGHgFKphNpNLU32KpfLIJcBaiclnDwye70oIDMaoVDK4eTuDLlcjl+enYj+bULQcfpgnDt8HYnxadKdaDa+Mx8PL8dg8qYPoHx0MWo0GGHQ6CCTy6B0fnyxpc/QQhiF6cL70UVcZl6jUWDF4bNQRt/GsxO6mSbFNRhNF+gKGTa8sxMeZX3R9KXmkCsVUGW5ONSla2DQ6AAhA7Q6aFLTYdDooE3VwKjXI1WhkAIX2rQMGLR6VO7SAMd/3Iw/+nyJCm1qw9nLDUl3HuDWvguIj7qDqt0a48TCTTBo9KZeVkYBw6NgiClAYrqbkxQ4ydCZhuvoTfXOzKtL0+DGrjOYFz4Cikc9grIP6Vnc+n1YS/Kt+0i+dT9HetyZ6FxyF5zCyRQ4UagUMBoFZDIZFE4qOHu5Sq+rTGEKsqicHwdZZAoZFMpH57uzKXAiVz6aj0ethNrVCXK1EgqV6dJRrlJA6aKGysUJMrkcD6JuISMhDa5+nghtUg3KR9sWRgG5Qg6lqzNUj4IWwmiEPsP0RlO5Pr54zpzDRq4y1T+vvAatDka9EfJHASPAdFGuT9cWOK/S+fHFvhymoYHybG0cZS5tnqflzZose5Seve1UkLyKIswrl1meN/sFjfzR+hbnzUbxKK+8kHnxhLzS65mPvCVFiQz0CCEwYsQIrF69Gjt27EBYWJjZ8gYNGkClUmHr1q144QVT16uLFy/ixo0baNbM1HWsWbNmmDx5Mu7du4eAgAAAwJYtW+Dp6YmaNWvmWq6TkxOcnKwTzaOnsdPdqOxwO2XBXkQOwTq3HS/Y+tabi6gw2+BZS0Qlj6u/6RfU6lX9sPjEpzi85RwuTPoZANBn/afQ3E/EusEz8eqbzXH+z32o2bcx2mfpsj8/4l3oE7QYuPULeIb6AQBOLN6KPVNXourzjfDcjMEATAGSebUjYdh5BD3+/hh+1Uw/SJ79Yze2T1wGFz9PeIb6oet7z0OukGNJ+w+RfDsefVe+j8A6FQEAF/8+iE3jFqFc8+rosWgUAFMbeHnXz/DwSiz+b+4wBNapiAZvdsKOScvxU+Ox8Krgj3afv4KH12Ox+pU1uHviKgDg8JwN8K1WFkadAffP38SJxVvhFlAGdQe3N/Ua0Rtw7s+9SIl5iAqta8OjrA+MOgOS7ybg5u6zULo6wadSEK5vP4Wfmo0z3fEpNQMuvh7QZ2ihS9XAydMFCxuOBmQyqN2cpLsCZQ7jKYy4szcQd/ZGjvRL6w7lkrvwhMEIfR71lKuUUg8aZx93qJzVkKsU0KZkIP1BMpy93eBdKVjqsXJz33kY9UZUbFcHLt7ukKsUeHgtFncORcE92BsBtSvCycsVZSoG4OiCTdAmp6PR8OdRpoI/FGol7hy5jFO/bEdgnYpo+X4fU/BDrcT6YXORciceXb5/C8H1n4FCpcT1HaexZdwihDSugt6/jJXqvKLHF7h/4Ra6//wuyrcwXdtc33Ea69+ag4DaFdBz0QdS3j9f+gqxx6/i/+a8jUodIwA87v3mUzkY/TdMkvKuHTILN/ddwLPTh6BS+7oAAJWLGiv7fgmPsj4If6m1lHfDO/NwbetJtPvfANR60TQkJ/5KLH57/nM4e7vjjQNfS3m3fbgUl9YfRssP+iJicAcAQPKdeCzt8DGULmq8feJbKe/Oz1fg3Mq9aDqqOxoOMw0XyniYgp+amYYkDb84X8q7b/pqnFy6DQ3e7oxmo3sCAPTpWiyoZxrq9Nbx2VJg6NCc9Tg6fxPqDmyPVh+9KG0jM+/r+6fDxccDAHD8p804MOtv1Ozbwuwz4qfm46BPN/+MOL1sR5bPiNelvEvaf4SMhyl4ef0n8K0SAgC4sHoftk9chrAOddF17jAp7/Kun+b4jIjaeARbsn1GAMDKPlMRfzkGPZeORmiTatJrvzFyPoLqVUKfFeOlvKsGzMC9M9F4fkEkKrY1Dd26deAC/n7tW/hVD8VLaz+W8v499DvcORSFzrOGonIX09Ctuyeu4q/+X8Orgj9e3fw/Ke8/IxYgeucZdJg6EDV6NwcAPLh0G7/3nAy3AC8M2T1Nyrtl3CJc+fcYWn/yEuoMaAsASLwRh187TYLawwVvHpkp5d3+yTJcWH0Azcf1Rv03TEO3UuMSsbj1B5Ar5Xjn7Fwp756pf+L08p1oNLwrmozoBgDQJqdjYSNTx41hZ+ZIQcUDM9fi+M9bUO+1Z9Figil+YNQbpdc+6zlVUpTIQE9kZCSWL1+OtWvXwsPDQ5pTx8vLCy4uLvDy8sLrr7+OMWPGwMfHB56enhgxYgSaNWuGpk2bAgCee+451KxZE6+++iq++uorxMbG4uOPP0ZkZCSDOURUyshQGubhIiIKaVgFHmV9cWT+JnSd+zY6vtRYCvR4lHHG4el/wiPER7qY06Zk4OHVWNOwGZ0BwmgKBtw+FIX7F26Z5jJ51BsjIfoeTi7ZKuWVKRQQRj3+Hvo9gupUhMrVGXFnTXnT7yfBvVZ5rBv6HQw6PVIf9TTaPPYnyJUKGHR6aZLWWwcvYX7Eu6ZeKlnuurPxnXk59i8xOg5rBs3MkX50waYcaan3ErDvq5xz9ETvyjlHjz5NI+1nRpaJYtMfJEt/a5JMcwpBCGhTMnJsQyKTQf7oTkeAKfiW2YMk/WEytMkZcAssA48QH8iVCmQkpiL+0h0AwDOd60OpVkGuUuDuyWuIvxyDwIgwhDapBrlKAWEQODJvIwCg1cf9pIBM1D9HEb3jNCo9G4HaL5nmSJEBWD3QdKx6/zYOSbfuQ5uYhttHo3Dln2Oo+WJLtPnkZciVptsvz631Dox6I15a8xHcA70BAMd+3Ix901ehYttwdPxysLSLPzQcDW1yOlq+/wLKVDTNAXpq2Q7cORSFwDph6PLtm1LeU0u3QZucjkod68K/hmmkgC7t0VAhb3cEN6gs5c28KHX18YCrr+ejtBJ5OUdkkStXruLibysQHByMelVr27s6xVKJnKPnSfNRLFq0CIMHDwYAZGRkYOzYsfjtt9+g0WjQqVMnzJ0712xYVnR0NIYNG4YdO3bAzc0NgwYNwpdffgllPufbscvt1akIlbi3QuGxFxEVknXvaJb/7XAeoqLBOXqopLHG/AZXNh/HP+/+AL9qZZF06z6MBiP0Gh1gLIHtAJns0ZAcuTTcSiaXQaFWQe3uArlSLs11olCb5j2RKeWQyeWmOVKcVNKcJQAgVyqhdFFC6aR+NMzHFOSQqxRQuzqbAiQKOR5eiYEmRQNXPw8E1an4aI4TGcSjbTh5OEvzrgijgFwpg9LZCSpXJ8gV8oINySnk8B2ly+MhOQatadhWwfLKoVA/niMlM/iidFZJt/kuUN5Hw9NkCrk0jA6AaY4fIaBwUkl38ylIXqPeNKfPE4fiFSRvLsP2IJOZDcUrUN4CDMey29Ctgrz2hT1PnvB6ZiSnYd++/Yi9fw8hZUPQqlVLwAiLX/vseQ0GA3bt2IWYW3cQFByENh3aQvFoTqq8Xs+CnidytQK7d+9BTEwMAv0C0LxpU6icnaTX0xrnyV9//IX3J3yIa9evw2CagQkVK1bE9KlT0KN7d6t/RmhS0rFv337cjY9DcHAwWrVqKR27wrDlHD0lMtBTXDDQQ5bjpNNUslge6OGE08UJAz1U0lirkXxl83Fsm/hrnrcxlivl0pCZzIld5SqFFDCRm6Vn5lNke266IEqNS4RBq4eTlyu8wwKlXimZQRZp2yqldLEj/at8lK7Oubyk3OaXyFIGg0EKIljjgtve5axatRpjx47H9evXpbSKFStixoyv0Lt3L5bzhDL69OmH55/vig8/nIDatWvjzJkzmDJlGtav34A///y92O8TAz0lROYLtbN1EQV6nnRdIzgRquMoPW8/nrNUeEXxPnn6Nm0dXLLHt3GKXo9WOxjooZLDmo3ktPgURO84BU1SOtwCvRDcoApULmrp12oGUYiezhaBEUcKVmSWY4uAhSOVYzAYULlydYSH18aaNX9JdykDTHfd6tnzBZw5cxZRUeetcv4V1T4x0FNCZL5QO4oq0PMEVpnztTBlkoMoPW95BpccSek5by1R0G/0FL0erXcy0EMlhy0byUTWxl4phSvDUYIVgO0CFo5Wzo4dO9GuXUfs379bmnM3q/3796N589bYvv0/tG3bptDlAEW7T7b8DuNPFVYhs9lDwDT+WQjbP8hR2O58ffywrczT1dbvCb5PipI9ztuS95DJCv4gIirtDAYDduzYid9+W4EdO3bCYDA8faUCWrVqNSpXro527Tqif/9X0a5dR1SuXB2rVq0useX06dMP4eG1sX//biQnP8T+/bsRHl4bffr0s0p5BoMBY8eOx/PPd8WaNX+hadOmcHd3R9OmTbFmzV94/vmueO+9CRa/XrYqBwB2796D69ev48MPJ5gFEABALpfjgw/G49q1a9i9ew/LySImJgYAULt27hMvZ6Zn5rOErfapqDHQQ08lHt1y3Chs+7BHMMvW1+mlJy5g44tdYZuL16znTuYcngzCEhGRI7NFUMSWZdkiMGKLoIgty7FVYMTRghWA7QIWjlZOcHAwAODMmZx3BcyanpnPErYMKhUlBnpKGJn0f9v+Qmz7X6ZNQRCbPxw1mJVtH23N5kXKMv9nw/cJbN8zIvO1tGVQyZbnLRGRI7BVYMRRgiK2LIu9UgrH0Xpx2PLC3lYBC0crp1WrlqhYsSKmTJkGo9FotsxoNGLq1K8QFhZmunOZhWwZVCpKvFWUFZSWixLb9vAvLcMJhG2CINkKKQ3nKwA7nEa2DffYY9SNTGT+r+hZ6zwt7GZKw6dQqfksIMoFJ5EteBmZc5j89tsvZnOY9OnTz6p3vLFFWdkDI5kBi8zASM+eL+C99yagR4/uFp0XmUGR33775YlBkebNW2P37j0WzS1iq3IA+/TiyG1elqIIVhRlOYB5wCK3+V+sFbB4WjlTpkxDxYoV0bhxI6SlpcFgMJg99Hp9jrTc8iiVSgQFBWHMmPH4/PNJEELAaDRCCAGDwYDPP/8CgYGBSEpKxvr1GyCEQOYUwZl/5zetZ8/umDXrWzRs2BTdu3dFaGg53L59G+vWrcfRo8cwfvx72LBhI+RyORQKBRQKRba/ZTnSc8sbGloWoaGhmDjxUyxe/BOUSiVcXV3h4eFh9aBSUeJkzBbInExpe6uOcFeq7F2dIiMELLydMhUXMlnm62nvmtiCbc9Zex5Xh345ZWb/2Iw9Xk8B2++naTLmrZzYlkoMa01kyUlkC8aWd7xxtMldf/ttBfr3fxXJyQ/h7u6eY3lycjI8PX2wfPkvePnll4p9OYDtjl1xmlD49OkzOH36OIxGI7RaLXQ6XR7/6nKkZ/378OGj+Omnn1GjRnW0bNkCPj4+iImJwd69+3DlylV07twJFSqUh06ng16vh06nf/SvLktazn+z5tPr9UhOTsbDhw+hUqmgUilhNAro9aY8VDAvvdQP774bialTv+Jdt0qDzBdqW0vbBXpKz0W6fdj8wk6IUnExWTqYjqwdOhHZlGkv+bWRL4U5TJa+ngUsM0WvR9vdDPRQyWGNRrIjBUYcLShiy7JsFRix1f6UxLsSZQZNMh8ajcbsuVarxebN/+HDDz9G06ZN8MILvRAcHIwrV65i9eq1OHHiBIYOfQO1atWAVqvLsW7Ox5Pz3L//ALdu3YKLiwtcXV1gNApoNBnIyNDkGCpUGmTt6ZL5UCqVOdIyH2lpaYiLi4NOp5O2oVarERpaFt7e3gCQ680gCpMGAAkJCdBotFCrVVJPG4PBkONf098il7Tc8pmn6fU66PXmQx3DwsLw9dfTCv0dYctAD4duWYEQMhhtOPmrPS7SS0dgwPYXr8KC88aiEK3NA1qlIUD5eG4pm5UoA4TRtsFC0/5ZUGAJGUdllx49dvh8F6Xk050oE4frFI4t5zBxtGFB9hiqs2rVShgMBimIkZGRgYkTP0VoaCh8fHxw7NixJwY+cgZbdLkur1z5Gaxbtx7ly4ehSpUqcHFxQXx8PC5fvoIHDx7gmWeeQf36jczWz77tgswXdODAQRw4cDBH+sKFP1p03LJLT09Henr6U/MpFAqoVCqo1eon/Kt66nKlUon79x9Aq9XC09MTFSpUkNZTKpWPeuKooFQqzNKy/5szTWm2TCaT4/jxE3jw4AGCg4PQrFlTqNXqJwZv5HJ5oe7KaYvhsLaWuU937txBSEhIidonBnqswGiUwWgs+JuhMBcRdplI124X6bbeWbkdDrCslMyOXPgyc5x7+dhOZhZbn7e2DvWYTh05bN0x0y79eez02VdoWc+9Amyn0EUWcsVS+CMllXKOFhhxtKCILcsqSABGCNOQl+xDcHJ/rsuxvEePbpg9+ztERDREx47t4e/vhxs3bmL79h24ePESevTojrFjxz2190n2crIvT09Px/Xr16FSuTxxv+vWrW/Rccvu9u07uH37To70K1euFHhbSqUSarUaarUaTk5O0t+mhwoajal3jZubG/z9/bPlUWXLn/3xtOWmPAqFEmfOnMXDh/EIDg5B8+ZN4eLiIgVpMgM12T8/iruaNWsUeRkKhcLi3mLFTUneJwZ6rMBY2B49hb3wLWkXPIVWDPsuFaZKTzt2FkQjnrRmnkXmY+yW9Y984U8gS869UnPe5nEOWbs2wg7njz3KtDiyVNjVbXzO2qo3KlFxkVtgJDU1FQDg6uoqpd+8eROpqalQKpVwcnLKkdfFxUW60Mu8+FYoFHB2dgbwOAhx+PBhtGnTJkfeo0ePmuVLS0uDEALOzs7Sr8V6vR4ajQZyuRwuLo8v3LPmzVz/xIkTqFevXo68meUEBARIaQaDARkZGTnypqenw2g0wsnJCUql0ixvgwb1zYIimb0xMnsFTJ36FSpWrIj69eshLS0Nrq6u0nYzMjKkvEqlEjqdDhqNBomJidDp9FCplNLcIykpKVAoFAgKCsLYsRPw+eeToNVqkZqaCq1WC5lMjq+++hp+fn44efIUDh8+DKPRNPlr5nbT0tKg0+lgNBql4ExGRgY0Gq00gWxmXqVSgXXr1sPLyxe+vj6QyeRITU1BYmIStFotnJ2d4OLiYTYcxRKnT5/G6dOnc6SvXfu3VbafF7VaDWdn5xyBFCcntdT7w8nJGc7OTlIwQ6FQwMlJDVdXV2k9mUwGlUoFFxcXKQBy7do1JCcnw8/PD/Xr15PKkclMk9+6ublK21AqlTAajVCr1fD29pa2azAYIISQygZMAbfM3jVubm7Svmg0Guj1ein4ApimQ0hLSytwXldXV6n3SmYwLSKibq7v+9zyFvYzoqB5C/sZ8bS8eb3vC5JXJpM98X2f+XoWJG9BXvvCnie5vZ75OadKCs7RY4HMMXb/NP4/uNl0Mma+ZJQ/uV3/22dIih3mIioVw8Usk6/Dk0smHlbHkaLTodPBTZyjh0oMS+c3yG0eE5nM1Ia7d+8OLl++jObNW+P114fgp58W4Y03XsPChQuk9d3cvJCWloZr16JQsWJFAMCsWbMxevR76N//JSxb9gsAPLpocYXRaMSpU8cRHm4KIC1c+CPefHMYAgMD4erqKs1hUrFiZURHR+PQoX1o1KgRAGDZsuV45ZVB6NixA7Zs2STVoVatOjh37jw2bdqARo0aIiKiIby9vXHq1CnUqxeBpUsXQafTISNDg2ef7YzU1FRMmvQJGjSoB51OhyNHjmLq1GkoVy4UH374vjR57Jw583Dt2jX07t0LlSs/A51Oh+joaKxatQYeHh6oV68udu3ag6CgQGRkaJCQkIDy5cshOTkFDx8+RHBwMGJiYh7dtSZUunBKSEiAXq+HXC53qLlO1Go1hBDQ6XRwdnaGv7+/dJF46dIlGI1GRETUhZeXF9RqNe7cuYOzZ8/B398PVatWhaenJ8qXL4dff12O1NRUvP76EJQrVw5qtRonTpzEH3+sRO3atTB69EgpGDJq1BjExMRi5swZqF+/HtRqFXbu3I333/8QjRs3wu+/L5eCb82atcS1a9cxbdoUjB07BgqFAhs2bMTzz/dAw4YNcPjwAWlfWrRojX379mP16j/Rs2cPAI/fKzVr1sTZsyelvM8+2xn//bcVv/66BAMG9AdgCmg2btwcFSpUwPXrl6W8PXu+gLVr/8YPP8zD0KFvAADOnj2L2rUj4Ofnh7i4xz3NBgx4FcuXr8DMmV9j1KiRAIDr168jLKwKXF1dkZqaKOUdOvQt/Pjjz/jii8/x0UcfAADi4uIQEBACABDicWBu1KgxmD37O3z44fuYPPl/AEwBFnf3MgCAlJQE6SL+o48mYsqULzFy5AjMmvWNtI2snxH+/v4AgMmTp+Ljjz8p9GcEAPj7B+P+/fs4c+YEatWqBeDxZ0SPHt2xZs1fUt6CfUbUxblz58zmY1qzZi169eqD5s2bYe/eXVLeRo2a4siRo1i/fi26dv0/AMCWLf/huee6oG7dOjhx4qiUt23bDti5cxf++OM39O3bBwCwd+9etGzZFpUrV0ZU1Hkpb9eu3bFx4z9YtOhHDB48CEBmQLoRQkJCcPt2tJS3b9+X8Oeff+H772cjMvIdAEBUVBSqVq0JLy8vJCTcl/IOHvwaliz5BV999SXGjRsLALh9+zZCQys+CiQ/HnYXGTkCc+fOx6RJE/Hpp58AMM3t4+1teg212jQpsDRu3AR8/fU3eO+9MZg+fRoAU9BNrTYFpLKeU5bgHD0ljN4oh95oy+57DPSUNla/sLb5lbrI12mb32rl+x1gjzLzoVSXWdBzzxoVKmSZhQ2EFUpB9tOKZdr2u4vI/vIarmMwGPDFF1NRoUIF6WIuOTkFly5dkuYXyQxU7Nq1B2fOnIVWq8WxY8cBAJcvX8GcOXMfzUNi+mVYo9Gga9duaNq0CTw9PXHo0GEAwN27d9GkSWP06NEbOp0OsbGxAIBBg16DWq2GVqvDgwcPHpW1G76+gVLgRKPRAAA6d+4q1f3mzZsAgOPHTyA8vF6O/f7ss89zpN28eQvDhg3Pkb5q1eocacnJydi1aw8AIDb2rpR+48ZN6e/M3lIGgwHR0dHI7klBnszhOiqV6lHPGw28vb3h7OyM+/fvm/WmcXZ2RrVq1VCuXCjOnj2Ha9euoU6dcDRs2ABqtRpGoxE//GCas+Wjjz6QepZs2rQJ27fvxHPPdcTLL78k9Tbp3/9VAMCff/6OmzdvITExEYcPH8GGDRvRv/9L+N//PpOCLGXLVoBer8e1a1GoUKECZDIZpk+fgfHj30e/fn2xePHPUj3LlPFDYmIi/vjjN1SpUgUAMGfOXAwfPhJt2rTBypUrpLzr1m1Aamoqhg9/BxEREQCAxYuX4I8/VqJ8+fJ47bUhUt6PPjJdrDZq1AAtWrQAYHodAVOvkMzAAgDpIrJevXolZk4RIrIe9uixQGZEblX9HnBTOO7t1akoPf3tZ/E1XS5z3GTv6WLtD4EcRdprYttSUKYtAr/sGeW4UvQ69Dy6jj16qMSw5l236tatg6tXr0lDGgoyMWxxk1tvGaVSCT8/v0fDY1RwcnKSJmtVKJRQq1VZ5h5RSUNynJ2dpWE9mROzqlQquLu7SetHRV1GcnIyAgICUK9eBJydnaWgmVqtgqenpzQRrBACcrkcbm5u0vAdhUIBg8EApVJpdser7EMtDAYDdu7chejoaAQFBeG5556VghbWHJZR2CE51hi+k9swG2sM38kcZmPp8J3sw2wsHb7zpGE2lg7fsebQrSe9nhy6xaFbluLt1UuIzBdqRZ3ecM1PoCevq6WiehnyukArUJEFyJxlP2XZ1yyi/cx7Zvi8y5RlyVWQOW/yupG2yGUNy6+Vn7aF/B3bgt1eXfaUc6gIyrTSOWuPMgtEZv6n+fskl+zFKthire4nNji4NiuzZEoz6ND3xBoGeqjEsFYjedWq1XjzzWFSr5ncKBSKXCeFNT1X5Vj2+G/zZUqlCnfv3oVGk4EyZcqgatWqcHZ2MpvcNesdebLelefJyx6vp1QqIZfLHfKON0REjoRDt0qYDIMScqjyvHwo1KS5T1nXtnhxlMmS1zLXlfLxAlvlHMgS0coaNHhSvS0Z6lN6e/TYfi4iWynoeV+Uh8EeZRZEjsBdfhUsCmuVMtP1hSuPqKTr3bsXWrduhY0b/0F8/EOULWu6ba6Li4sUQClpQZKSfHcYIiKyLgZ6rCBNp4QwOvahzO3aI78XFVl7zBRk+7mxdZmF6bdQkDIFRG4jq5645tNfh6f3JMrtWvKpZea5Qi5lZh8alu/hYk/uJ1WQ/RSP/mfrMvP/PsmrP9jT1y7sWtYLhOQsQbqlfS4l5l3u0/cx//EP632SlJYy0/SOMzEqUUH5+flh4MBX7V0NIiIiq3Ps6EQ+zZkzB9OnT0dsbCzq1q2L7777Do0bN873+sl6NXRGdZH2ecn8tVYG84tmW5WZVVGXmVlGjgv3Qmwn67p5XRplHle5LPeyCtrzKnv+3MqWATAKUegyn/a6PLnMwu9nYcs0CEBhwzIBy17PwpYJC96bhSpT9rjM/OynJYGekvx5UKgy89nzraSXmWooLv2gqDgqaPsoISEBH330EVatWoX4+HhUqFABs2bNwv/9n+lOLp9++ik+++wzs3WqVauGCxcuFOl+EBERlTalPtDz+++/Y8yYMZg/fz6aNGmCWbNmoVOnTrh48SICAgLytY0HGiWc5E86lE9vROe8gMm7ef/0X32tX+bTlfwy8/dr+pNzZN63I2sJTypTCgY8CkZkW+HpZWa5As693rlN8vK4TJktysx6wWoEZPInZ32irMPN8rNaXl1srNkdLD9lFrQ7WCGnvbFgtI8FChoqy3vJE98n+aiJLJe/sm/tyS9J4T6DnlZm3qddHp9BufRCy3kMnl5mzuVPLjPDwB49lLuCto+0Wi2effZZBAQE4M8//0TZsmURHR2NMmXKmOWrVasW/vvvP+l55oSeREREZD2l/tv1m2++wdChQzFkyBAAwPz587Fhwwb8/PPPeP/99/O1jbvpCqjleY/jzq2ZLc8l/WkXNgK5BAcKUKb0y3+Whda8SMxPmUVdXmHLzO+cLtYs02jBawnZ455IWZKKtMysAaqClGnJsS1smfmNgORVZh7xskLJsTlZLn8WMOBj0edBLulF/nmQLd0mnweFKNPiz4NClGnR50Ehy2Sgh56koO2jn3/+GfHx8di3b59015Sst3rOpFQqERQUVKR1JyIiKu1KdaBHq9Xi6NGj+OCDD6Q0uVyOjh07Yv/+/fnezs10HZS5XBHk/KU1exNcZMv35Ga5WS+RPC4G8lumedrT5b/XQG7bL3iZBeul8LQy87clIwTkhZz7QgYZhE3LzC0SYYsyC3v+iDzP76Io0whAkcvauW/J1u+T0lKm/T8PnvbefFJMTyDrZ3Le0bfsdctvB63sNTVN5i17tH7Bysyv7GVqjLpCbokcWWHaR3///TeaNWuGyMhIrF27Fv7+/ujfvz8mTJhgNqlxVFQUQkJC4OzsjGbNmmHq1KkoX778E+ui0Wig0Wik50lJSVbYQyIiIsdWqgM99+/fh8FgQGBgoFl6YGBgruPFszc2EhMTAQDnxAXIHx3Kx43z7M1wkeWvzMuHnJcDskfLs6dl3Y4RgFxkTXn6gAHx6HInt35E+Skz5yiY/JQph8xU21zKlOV5uZf7JMXZy8y5hcf7+aRLrSfvpxHGHAGQgpT55IvRvMoUkJtnzleZmftXmDIFjGZBl9wvgnM7fzJfs5yvzdMGmOSvTPNyM8vMzF+wMoV05uUss3DvzdwH7GTmFo/+y+3MflIANnPfMtdRZFs3M495XR6/QpnngCzbdrPXWeRIz1znSfXN+/wxr3/+y5Q/Ok6FKTPnJ1BRl2mEEbJcB2TmVablnwcykVdwSQZTCDN7WuE/D3RIN5UmciuPSquCto8A4OrVq9i2bRsGDBiAjRs34vLly3jnnXeg0+kwadIkAECTJk2wePFiVKtWDTExMfjss8/QqlUrnDlzBh4eHrlud+rUqTnm9QEY8CEiopIn87vLFu2uUh3oKagnNTZiM/bZoTZERETWkZycDC8vL3tXg0owo9GIgIAA/PDDD1AoFGjQoAFu376N6dOnS4GeLl26SPnr1KmDJk2aoEKFCvjjjz/w+uuv57rdDz74AGPGjJGe3759GzVr1kS5cuWKdoeIiIiKiC3aXaU60OPn5weFQoG7d++apd+9ezfX8ePZGxtGoxHx8fHw9fWFLD+TOZQASUlJKFeuHG7evAlPT097V8fueDwe47Ewx+PxGI+FuZJ0PIQQSE5ORkhIiL2rQsVIQdtHABAcHAyVSmU2TKtGjRqIjY2FVquFWq3OsU6ZMmVQtWpVXL58+Yl1cXJygpOTk/Tc3d0dN2/ehIeHR462V0l671kL97l07DNQOveb+1w69hkoPftty3ZXqQ70qNVqNGjQAFu3bkXPnj0BmII3W7duxfDhw3Pkz97YAJDjbhKOwtPT06HfZAXF4/EYj4U5Ho/HeCzMlZTjwZ48lF1B20cA0KJFCyxfvhxGoxFyuWnY46VLlxAcHJxrkAcAUlJScOXKFbz66qv5rptcLkdoaGieeUrKe8+auM+lR2ncb+5z6VEa9ttW7a7s04OUOmPGjMHChQuxZMkSnD9/HsOGDUNqaqp0lwkiIiKi0uZp7aOBAweaTdY8bNgwxMfHY+TIkbh06RI2bNiAKVOmIDIyUsrz3nvvYefOnbh+/Tr27duHXr16QaFQ4OWXX7b5/hERETmyUt2jBwD69euHuLg4fPLJJ4iNjUVERAQ2bdqUYwJCIiIiotLiae2jGzduSD13AKBcuXL4999/MXr0aNSpUwdly5bFyJEjMWHCBCnPrVu38PLLL+PBgwfw9/dHy5YtceDAAfj7+9t8/4iIiBxZqQ/0AMDw4cOf2BW5tHFycsKkSZNyDFErrXg8HuOxMMfj8RiPhTkeD3IUebWPduzYkSOtWbNmOHDgwBO3t2LFCmtVLVel8b3HfS49SuN+c59Lj9K630VJJnhPVSIiIiIiIiIih1Dq5+ghIiIiIiIiInIUDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgZ5SaurUqWjUqBE8PDwQEBCAnj174uLFi2Z5MjIyEBkZCV9fX7i7u+OFF17A3bt37VRj2/nyyy8hk8kwatQoKa00HYvbt2/jlVdega+vL1xcXBAeHo4jR45Iy4UQ+OSTTxAcHAwXFxd07NgRUVFRdqxx0TEYDJg4cSLCwsLg4uKCZ555Bv/73/+QdQ57Rz4eu3btQrdu3RASEgKZTIY1a9aYLc/PvsfHx2PAgAHw9PREmTJl8PrrryMlJcWGe2EdeR0LnU6HCRMmIDw8HG5ubggJCcHAgQNx584ds204yrEgKo7mzJmDihUrwtnZGU2aNMGhQ4fsXSWrYZutdLXNSls7rLS0tUpjm4ptJ/tioKeU2rlzJyIjI3HgwAFs2bIFOp0Ozz33HFJTU6U8o0ePxrp167By5Urs3LkTd+7cQe/eve1Y66J3+PBhLFiwAHXq1DFLLy3H4uHDh2jRogVUKhX++ecfnDt3DjNmzIC3t7eU56uvvsK3336L+fPn4+DBg3Bzc0OnTp2QkZFhx5oXjWnTpmHevHn4/vvvcf78eUybNg1fffUVvvvuOymPIx+P1NRU1K1bF3PmzMl1eX72fcCAATh79iy2bNmC9evXY9euXXjzzTdttQtWk9exSEtLw7FjxzBx4kQcO3YMq1atwsWLF9G9e3ezfI5yLIiKm99//x1jxozBpEmTcOzYMdStWxedOnXCvXv37F01qyjtbbbS1DYrje2w0tLWKo1tKrad7EwQCSHu3bsnAIidO3cKIYRISEgQKpVKrFy5Uspz/vx5AUDs37/fXtUsUsnJyaJKlSpiy5Ytok2bNmLkyJFCiNJ1LCZMmCBatmz5xOVGo1EEBQWJ6dOnS2kJCQnCyclJ/Pbbb7aook117dpVvPbaa2ZpvXv3FgMGDBBClK7jAUCsXr1aep6ffT937pwAIA4fPizl+eeff4RMJhO3b9+2Wd2tLfuxyM2hQ4cEABEdHS2EcNxjQVQcNG7cWERGRkrPDQaDCAkJEVOnTrVjrYpOaWqzlba2WWlsh5XGtlZpbFOx7WR77NFDAIDExEQAgI+PDwDg6NGj0Ol06Nixo5SnevXqKF++PPbv32+XOha1yMhIdO3a1WyfgdJ1LP7++280bNgQffv2RUBAAOrVq4eFCxdKy69du4bY2FizY+Hl5YUmTZo43LEAgObNm2Pr1q24dOkSAODkyZPYs2cPunTpAqD0HY+s8rPv+/fvR5kyZdCwYUMpT8eOHSGXy3Hw4EGb19mWEhMTIZPJUKZMGQCl+1gQFSWtVoujR4+afRbJ5XJ07NjRYT+HS1ObrbS1zUpjO4xtLbapMrHtZF1Ke1eA7M9oNGLUqFFo0aIFateuDQCIjY2FWq2W3miZAgMDERsba4daFq0VK1bg2LFjOHz4cI5lpelYXL16FfPmzcOYMWPw4Ycf4vDhw3j33XehVqsxaNAgaX8DAwPN1nPEYwEA77//PpKSklC9enUoFAoYDAZMnjwZAwYMAIBSdzyyys++x8bGIiAgwGy5UqmEj4+PQx+fjIwMTJgwAS+//DI8PT0BlN5jQVTU7t+/D4PBkOtn0YULF+xUq6JTmtpspbFtVhrbYWxrsU0FsO1UFBjoIURGRuLMmTPYs2ePvatiFzdv3sTIkSOxZcsWODs727s6dmU0GtGwYUNMmTIFAFCvXj2cOXMG8+fPx6BBg+xcO9v7448/sGzZMixfvhy1atXCiRMnMGrUKISEhJTK40FPp9Pp8OKLL0IIgXnz5tm7OkTkYEpLm620ts1KYzuMbS1i26locOhWKTd8+HCsX78e27dvR2hoqJQeFBQErVaLhIQEs/x3795FUFCQjWtZtI4ePYp79+6hfv36UCqVUCqV2LlzJ7799lsolUoEBgaWmmMRHByMmjVrmqXVqFEDN27cAABpf7Pf1cIRjwUAjBs3Du+//z5eeuklhIeH49VXX8Xo0aMxdepUAKXveGSVn30PCgrKMRmqXq9HfHy8Qx6fzIZKdHQ0tmzZIv0iBZS+Y0FkK35+flAoFKXic7g0tdlKa9usNLbD2NYq3W0qtp2KDgM9pZQQAsOHD8fq1auxbds2hIWFmS1v0KABVCoVtm7dKqVdvHgRN27cQLNmzWxd3SLVoUMHnD59GidOnJAeDRs2xIABA6S/S8uxaNGiRY5btl66dAkVKlQAAISFhSEoKMjsWCQlJeHgwYMOdywA0x0B5HLzj0mFQgGj0Qig9B2PrPKz782aNUNCQgKOHj0q5dm2bRuMRiOaNGli8zoXpcyGSlRUFP777z/4+vqaLS9Nx4LIltRqNRo0aGD2WWQ0GrF161aH+RwujW220to2K43tMLa1Sm+bim2nImbfuaDJXoYNGya8vLzEjh07RExMjPRIS0uT8rz99tuifPnyYtu2beLIkSOiWbNmolmzZnaste1kvbODEKXnWBw6dEgolUoxefJkERUVJZYtWyZcXV3Fr7/+KuX58ssvRZkyZcTatWvFqVOnRI8ePURYWJhIT0+3Y82LxqBBg0TZsmXF+vXrxbVr18SqVauEn5+fGD9+vJTHkY9HcnKyOH78uDh+/LgAIL755htx/Phx6W4I+dn3zp07i3r16omDBw+KPXv2iCpVqoiXX37ZXrtUaHkdC61WK7p37y5CQ0PFiRMnzD5TNRqNtA1HORZExc2KFSuEk5OTWLx4sTh37px48803RZkyZURsbKy9q2YVbLOZlIa2WWlsh5WWtlZpbFOx7WRfDPSUUgByfSxatEjKk56eLt555x3h7e0tXF1dRa9evURMTIz9Km1D2RsTpelYrFu3TtSuXVs4OTmJ6tWrix9++MFsudFoFBMnThSBgYHCyclJdOjQQVy8eNFOtS1aSUlJYuTIkaJ8+fLC2dlZVKpUSXz00UdmX0COfDy2b9+e6+fEoEGDhBD52/cHDx6Il19+Wbi7uwtPT08xZMgQkZycbIe9sUxex+LatWtP/Ezdvn27tA1HORZExdF3330nypcvL9RqtWjcuLE4cOCAvatkNWyzmZSWtllpa4eVlrZWaWxTse1kXzIhhLB+PyEiIiIiIiIiIrI1ztFDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CAY6CEiqxJCAAA+/fRTs+dEREREZH1sexFRdjLBTwIisqK5c+dCqVQiKioKCoUCXbp0QZs2bexdLSIiIiKHxLYXEWXHHj1EZFXvvPMOEhMT8e2336Jbt275ami0bdsWMpkMMpkMJ06cKPpKZjN48GCp/DVr1ti8fCIiIqLCYtuLiLJjoIeIrGr+/Pnw8vLCu+++i3Xr1mH37t35Wm/o0KGIiYlB7dq1i7iGOc2ePRsxMTE2L5eIiIjIUmx7EVF2SntXgIgcy1tvvQWZTIZPP/0Un376ab7Hibu6uiIoKKiIa5c7Ly8veHl52aVsIiIiIkuw7UVE2bFHDxEVyJQpU6Sutlkfs2bNAgDIZDIAjycEzHxeUG3btsWIESMwatQoeHt7IzAwEAsXLkRqaiqGDBkCDw8PVK5cGf/8849V1iMiIiIqjtj2IqKCYqCHiApkxIgRiImJkR5Dhw5FhQoV0KdPH6uXtWTJEvj5+eHQoUMYMWIEhg0bhr59+6J58+Y4duwYnnvuObz66qtIS0uzynpERERExQ3bXkRUULzrFhEV2sSJE/HLL79gx44dqFixYqG307ZtW0REREi/TGWmGQwGaZy5wWCAl5cXevfujaVLlwIAYmNjERwcjP3796Np06YWrQeYfgFbvXo1evbsWeh9ISIiIioqbHsRUX6wRw8RFconn3xilYZGXurUqSP9rVAo4Ovri/DwcCktMDAQAHDv3j2rrEdERERUXLHtRUT5xUAPERXYpEmTsHTp0iJtaACASqUyey6TyczSMsegG41Gq6xHREREVByx7UVEBcFADxEVyKRJk7BkyZIib2gQEREREdteRFRwvL06EeXbF198gXnz5uHvv/+Gs7MzYmNjAQDe3t5wcnKyc+2IiIiIHAvbXkRUGAz0EFG+CCEwffp0JCUloVmzZmbLDh06hEaNGtmpZkRERESOh20vIiosBnqIKF9kMhkSExNtVt6OHTtypF2/fj1HWvYbBxZ2PSIiIqLihG0vIiosztFDRMXC3Llz4e7ujtOnT9u87Lfffhvu7u42L5eIiIjIXtj2InJcMsHQKhHZ2e3bt5Geng4AKF++PNRqtU3Lv3fvHpKSkgAAwcHBcHNzs2n5RERERLbEtheRY2Ogh4iIiIiIiIjIQXDoFhERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0RERERERETkIBjoISIiIiIiIiJyEAz0EBERERERERE5CAZ6iIiIiIiIiIgcBAM9REREREREREQOgoEeIiIiIiIiIiIHwUAPEREREREREZGDYKCHiIiIiIiIiMhBMNBDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CAY6CEiIiIiIiIichAM9BAREREREREROQgGeoiIiIiIiIiIHAQDPUREREREREREDoKBHiIiIiIiIiIiB8FADxERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0RERERERETkIBjoISIiIiIiIiJyEAz0EBERERERERE5CAZ6iIiIiIiIiIgcBAM9REREREREREQOgoEeIiIiIiIiIiIHwUAPEREREREREZGDYKCHiIiIiIiIiMhBMNBDREREREREROQgGOghIiIiIiIiInIQDPQQERERERERETkIBnqIiIiIiIiIiBwEAz1ERERERERERA6CgR4iIiIiIiIiIgfBQA8RERERERERkYNgoIeIiIiIiIiIyEEw0ENERERERERE5CCKdaDnwYMHCAgIwPXr15+a9/3338eIESOKvlJEREREDuppba8dO3ZAJpMhISEBALBp0yZERETAaDTarpJERESUp2Id6Jk8eTJ69OiBihUrPjXve++9hyVLluDq1atFXzEiIiIiB1SQthcAdO7cGSqVCsuWLSvaihEREVG+Ke1dgSdJS0vDTz/9hH///Tdf+f38/NCpUyfMmzcP06dPL+LaEVFxYDAYoNPp7F0NohJJpVJBoVDYuxpUjBS07ZVp8ODB+Pbbb/Hqq68WUc2IqDhgu4vIMmq1GnK5bfraFNtAz8aNG+Hk5ISmTZtKaWfPnsWECROwa9cuCCEQERGBxYsX45lnngEAdOvWDR999BEDPUQOTgiB2NhYaegAERVOmTJlEBQUBJlMZu+qUDGQW9tr48aNGDVqFG7evImmTZti0KBBOdbr1q0bhg8fjitXrkhtMiJyHGx3EVmHXC5HWFgY1Gp1kZdVbAM9u3fvRoMGDaTnt2/fRuvWrdG2bVts27YNnp6e2Lt3L/R6vZSncePGuHXrFq5fv57vLsdEVPJkNjYCAgLg6urKi1SiAhJCIC0tDffu3QMABAcH27lGVBxkb3vdvHkTvXv3RmRkJN58800cOXIEY8eOzbFe+fLlERgYiN27dzPQQ+SA2O4ispzRaMSdO3cQExOD8uXLF/n7qNgGeqKjoxESEiI9nzNnDry8vLBixQqoVCoAQNWqVc3WycwfHR3NQA+RgzIYDFJjw9fX197VISqxXFxcAAD37t1DQEAAh3FRjrbXvHnz8Mwzz2DGjBkAgGrVquH06dOYNm1ajnVDQkIQHR1ts7oSkW2w3UVkPf7+/rhz5w70er0U0ygqxXYy5vT0dDg7O0vPT5w4gVatWuV5QDIbrWlpaUVePyKyj8yx4a6urnauCVHJl/k+4pwLBORse50/fx5NmjQxy9OsWbNc13VxcWH7i8gBsd1FZD2ZQ7YMBkORl1VsAz1+fn54+PCh9DwziJOX+Ph4AKZIGRE5NnYbJrIc30eUVfa2V0HEx8ez/UXkwPh9QWQ5W76Pim2gp169ejh37pz0vE6dOti9e3eevzqeOXMGKpUKtWrVskUViYiIiBxG9rZXjRo1cOjQIbM8Bw4cyLFeRkYGrly5gnr16hV5HYmIiOjpim2gp1OnTjh79qz0y9Lw4cORlJSEl156CUeOHEFUVBR++eUXXLx4UVpn9+7daNWqVb56/xAR2dquXbvQrVs3hISEQCaTYc2aNXYpY/DgwZDJZJDJZFCpVAgMDMSzzz6Ln3/+GUaj0ep1ciT5PXYVK1aU8mU+QkNDcyzPftE8atQotG3b1iwtKSkJH330EapXrw5nZ2cEBQWhY8eOWLVqFYQQUr7Lly9jyJAhCA0NhZOTE8LCwvDyyy/jyJEjRXMwyOFkb3u9/fbbiIqKwrhx43Dx4kUsX74cixcvzrHegQMH4OTk9MRhXURE9sK2V8nGdlfhFdtAT3h4OOrXr48//vgDAODr64tt27YhJSUFbdq0QYMGDbBw4UKzOXtWrFiBoUOH2qvKRER5Sk1NRd26dTFnzpwCr9u2bdtcL7AKW0bnzp0RExOD69ev459//kG7du0wcuRIPP/882Z3M6Sc8nvsPv/8c8TExEiP48ePm23H2dkZEyZMyLOshIQENG/eHEuXLsUHH3yAY8eOYdeuXejXrx/Gjx+PxMREAMCRI0fQoEEDXLp0CQsWLMC5c+ewevVqVK9ePde7JBHlJnvbq3z58vjrr7+wZs0a1K1bF/Pnz8eUKVNyrPfbb79hwIABnMODiIodtr1KPra7CkkUY+vXrxc1atQQBoPhqXk3btwoatSoIXQ6nQ1qRkT2kp6eLs6dOyfS09PtXRWLABCrV6/Od/42bdqIRYsWWaWMQYMGiR49euRI37p1qwAgFi5cWKBySpP8HrsKFSqImTNnPnE7FSpUEO+++65Qq9Viw4YNUvrIkSNFmzZtpOfDhg0Tbm5u4vbt2zm2kZycLHQ6nTAajaJWrVqiQYMGuX5fPnz48In1cJT3E1lPQdpeQggRFxcnfHx8xNWrV4u4ZkRkD470PcG2V8nDdlfhFdvbqwNA165dERUVhdu3b6NcuXJ55k1NTcWiRYugVBbrXSIiKxNC2O1OL66urg41OWH79u1Rt25drFq1Cm+88YZd6pCamgrA/NhqtVrodDoolUo4OTnlyOvi4gK53NRBVafTQavVQqFQmN09KLe81lSYYxcWFoa3334bH3zwATp37pyjXkajEStWrMCAAQPMbnmdyd3dHQBw/PhxnD17FsuXL89138qUKVPwHaJSqyBtLwC4fv065s6di7CwMBvUjoiKA7a9rMfebS9btrt0Op3VbinOdtfTFduhW5lGjRqVr4ZGnz59ctwClIgcX1paGtzd3e3ycMRbCVevXh3Xr1+3W/mZx/b+/ftS2vTp0+Hu7o7hw4eb5Q0ICIC7uztu3Lghpc2ZMwfu7u54/fXXzfJWrFgR7u7uOH/+fJHVPfuxmzBhgtn58u233+ZY5+OPP8a1a9ewbNmyHMvu37+Phw8fonr16nmWGxUVJZVPZA35bXsBQMOGDdGvX78irhERFSdse1mXPdtetmx35WcYXEGw3ZW3Yh/oISIqjaZMmWL2ZbV79268/fbbZmlZv2itRQjhUL+U2VL2Yzdu3DicOHFCegwcODDHOv7+/njvvffwySefQKvV5thefsslIiIiy7DtVbKw3ZU3jnMiohLN1dUVKSkpdiu7qLz99tt48cUXpecDBgzACy+8gN69e0tpuXUrtdT58+ftOgQj87XMemzHjRuHUaNG5Riae+/ePQAwu9NiZGQkhg4dCoVCYZY38xeforwrY/Zj5+fnh8qVKz91vTFjxmDu3LmYO3euWbq/vz/KlCmDCxcu5Ll+1apVAQAXLlzg7a2JiKjIse1lXfZse9my3TV48GBrVp3trqdgoIeISjSZTAY3Nzd7V8PqfHx84OPjIz13cXFBQEBAvr7ACmvbtm04ffo0Ro8eXWRlPE1ur6VarYZarc5XXpVKlev476I+Ryw5du7u7pg4cSI+/fRTdO/eXUqXy+V46aWX8Msvv2DSpEk5GpcpKSlwdnZGREQEatasiRkzZqBfv345xosnJCQUm/HiRERU8rHtZT32bnvZst1lrfl5ALa78oNDt4iIbCQlJUXqTgoA165dw4kTJ6zaDTi/ZWg0GsTGxuL27ds4duwYpkyZgh49euD555/PtasrPVYUx+7NN9+El5cXli9fbpY+efJklCtXDk2aNMHSpUtx7tw5REVF4eeff0a9evWQkpICmUyGRYsW4dKlS2jVqhU2btyIq1ev4tSpU5g8eTJ69Ohhjd0mIiIqcdj2KvnY7ioc9ughIrKRI0eOoF27dtLzMWPGAAAGDRpktQnq8lvGpk2bEBwcDKVSCW9vb9StWxfffvstBg0aVCR3pXIkRXHsVCoV/ve//6F///5m6T4+Pjhw4AC+/PJLfPHFF4iOjoa3tzfCw8Mxffp0eHl5AQAaN26MI0eOYPLkyRg6dCju37+P4OBgNG/eHLNmzbJ0l4mIiEoktr1KPra7CkcmSspsQkREADIyMnDt2jWEhYWZ3caRiAqO7yciIsoLvyeIrMeW7yeGDomIiIiIiIiIHAQDPUREREREREREDoKBHiIiIiIiIiIiB8FADxERERERERGRg2Cgh4iIiIiIiIjIQTDQQ0QlEm8YSGQ5vo+IiCg/+H1BZDlbvo8Y6CGiEkWlUgEA0tLS7FwTopIv832U+b4iIiLKiu0uIuvRarUAAIVCUeRlKYu8BCIiK1IoFChTpgzu3bsHAHB1dYVMJrNzrYhKFiEE0tLScO/ePZQpU8YmDQ4iIip52O4isg6j0Yi4uDi4urpCqSz6MAwDPURU4gQFBQGA1OggosIpU6aM9H4iIiLKDdtdRNYhl8tRvnx5mwRLZYIDLomohDIYDNDpdPauBlGJpFKp2JOHiIjyje0uIsuo1WrI5baZPYeBHiIiIiIiIiIiB8HJmK1k165d6NatG0JCQiCTybBmzZoiLW/q1Klo1KgRPDw8EBAQgJ49e+LixYtFWiYRERERERERFW8M9FhJamoq6tatizlz5tikvJ07dyIyMhIHDhzAli1boNPp8NxzzyE1NdUm5RMRERERERFR8cOhW0VAJpNh9erV6Nmzp5Sm0Wjw0Ucf4bfffkNCQgJq166NadOmoW3btlYpMy4uDgEBAdi5cydat25tlW0SERERERERUcnCHj02Mnz4cOzfvx8rVqzAqVOn0LdvX3Tu3BlRUVFW2X5iYiIAwMfHxyrbIyIiIiIiIqKShz16ikD2Hj03btxApUqVcOPGDYSEhEj5OnbsiMaNG2PKlCkWlWc0GtG9e3ckJCRgz549Fm2LiIiIiIiIiEou9uixgdOnT8NgMKBq1apwd3eXHjt37sSVK1cAABcuXIBMJsvz8f777+e6/cjISJw5cwYrVqyw5W4RERERERERUTGjtHcFSoOUlBQoFAocPXoUCoXCbJm7uzsAoFKlSjh//nye2/H19c2RNnz4cKxfvx67du1CaGio9SpNRERERERERCUOAz02UK9ePRgMBty7dw+tWrXKNY9arUb16tXzvU0hBEaMGIHVq1djx44dCAsLs1Z1iYiIiIiIiKiEYqDHSlJSUnD58mXp+bVr13DixAn4+PigatWqGDBgAAYOHIgZM2agXr16iIuLw9atW1GnTh107dq1wOVFRkZi+fLlWLt2LTw8PBAbGwsA8PLygouLi9X2i4iIiIiIiIhKDk7GbCU7duxAu3btcqQPGjQIixcvhk6nwxdffIGlS5fi9u3b8PPzQ9OmTfHZZ58hPDy8wOXJZLJc0xctWoTBgwcXeHtEREREREREVPIx0ENERERERERE5CB41y0iIiIiIiIiIgfBQA8RERERERERkYPgZMwWMBqNuHPnDjw8PJ44Zw4REVFxJYRAcnIyQkJCIJfztx8q/tj2IiKiksqW7S4Geixw584dlCtXzt7VICIissjNmzcRGhpq72oQPRXbXkREVNLZot3FQI8FPDw8AJheKE9PTzvXhoiIqGCSkpJQrlw56fuMqLhj24uIiEoqW7a7GOixQGaXYU9PTzY2iIioxOIQGCop2PYiIqKSzhbtLg7IJyIiIiIiIiJyEAz0EBEREZFN3Lx5E23btkXNmjVRp04drFy50t5VIiIicjgcukVERERENqFUKjFr1ixEREQgNjYWDRo0wP/93//Bzc3N3lUjIiJyGOzRQ0REDic9PR3Lli3D119/jcTERCn94cOHiImJgVartWPtiEqv4OBgREREAACCgoLg5+eH+Ph4+1aKiIgoG41Gg5UrV+Lw4cP2rkqhMNBDREQl2vr16zFw4EAsWrRIShNC4JVXXsG4ceOg0Wik9Pnz5yMkJARvvfWW2TZ69+6N/v374969e1La5cuXsXnzZly+fLnod4KomNi1axe6deuGkJAQyGQyrFmzJkeeOXPmoGLFinB2dkaTJk1w6NChQpV19OhRGAwG3i6diIhsRgiB+/fvw2g0SmmbNm3C4MGD8eOPP5rlfemll7BkyRJbV9EqGOghIqJiKTU1FXFxcdJznU6H1q1bIygoCA8fPpTSz5w5g19++QVbt26V0lxdXdGnTx/0798fPj4+Unp6ejrkcjl8fX2lNL1ej9WrV+O3334zuwvCypUr0alTJ0yePNmsXjVq1EB4eDhu3LghpR05cgTbt283azQQlUSpqamoW7cu5syZk+vy33//HWPGjMGkSZNw7Ngx1K1bF506dTILkkZERKB27do5Hnfu3JHyxMfHY+DAgfjhhx+KfJ+IiKhkMhqNEEJIz+Pi4nDy5ElER0eb5fv111+xYMECpKamSmlbt27F66+/joULF0ppQgh4eXnB398fd+/eldLPnTuHJUuWYMuWLVKak5MTevTogQoVKhTFrhU5BnqIiMiuzp49izVr1iAtLU1KmzVrFtzd3fHee+9JaSqVClFRUbh7965ZL5uOHTti8uTJeO2118y2u3LlSixbtgxK5ePp6D7//HPodDqz4I0QAosXL8aMGTPg7e0tpXt7e6NOnTp45plnpDSdTocLFy7gzJkzcHV1ldK3bNmC9u3bo3///hYeDSL76tKlC7744gv06tUr1+XffPMNhg4diiFDhqBmzZqYP38+XF1d8fPPP0t5Tpw4gTNnzuR4hISEADB1h+/Zsyfef/99NG/ePM/6aDQaJCUlmT2IiMg6tFotkpOTzXo/6/V6XLt2LUeP5mvXrmHfvn1mP3RpNBr8+eef+O2338wCMjt27MD06dOxc+dOs7LeffddDB06FDqdTkpfsGABmjRpghkzZkhpBoMBzs7OUCgUZsN7582bh4iICEyZMsWsbkOHDsXbb7+N+/fvS2mnTp3Czz//jO3bt0tpMplMar/FxMRI6W3atMHkyZPxxhtvADC1De/cuYM33ngD6enpWLdu3dMOZbHDQA8REdmEXq/H7t27zb5wAaBdu3bo1asXLly4IKWVLVsWABAbG2uW95dffsGRI0cQHh4upTVs2BAffvgh2rdvn696yOVyODk5Sc9VKhUGDRqEMWPGmAWF3n77bZw8eRIff/yx2bpHjx7F5s2bzYJCZcuWhYeHBzp37iylabVa/PPPPzAYDPmqF1Fxp9VqcfToUXTs2FFKk8vl6NixI/bv35+vbQghMHjwYLRv3x6vvvrqU/NPnToVXl5e0oPDvIjIGoQQ0Ov1Zj1xNRoNHjx4gISEBLO8N2/eRFRUlNkPUvHx8di3bx9OnjxplnfHjh34888/zYIIN2/exLx587BixQqzvIsXL8Ynn3yCs2fPSmmXL1/GsGHDMHHiRLO8kydPRs+ePbFt2zYp7dKlS2jRogW6detmlnf48OGoVKkSfv31VyktKioK7u7uCA4ONsv7xhtvwNPTE99//72UFhMTg0qVKqF27dpmeadNm4YWLVpg8eLFUlpqair69u2L/v37m7V31q9fj/Hjx2Pjxo1SmhAC3333HX788UezY3nnzh0cOnQIV65ckdIUCoW0vfT0dCndx8cHQUFBOSbw79q1K3r27AmVSiWlNWvWDJMnT8aAAQPM8h49ehQZGRmoX78+ANNrmZGRAV9fX6xduxZt2rSBn58fypYti65du2LSpEn4448/UOIIKrTExEQBQCQmJtq7KkRExd4PP/wgAIgWLVqYpXfp0kU0bNhQ7Nu3T0pLTU0VDx48sHUVLZKSkiIyMjKk5ytXrhQARJMmTexYq7zxe4zyAkCsXr1aen779m0BwOy9KoQQ48aNE40bN87XNnfv3i1kMpmoW7eu9Dh16tQT82dkZIjExETpcfPmzVJ/zmo0GpGcnGz2eaPT6UR0dLS4fv26Wd7o6Ghx5MgRcefOHSktPT1dbN68Wfz7779meY8cOSKWLVsmTpw4IaVlZGSI77//Xnz33XdCp9NJ6Xv27BGzZ88Wu3btktIMBoOYOXOmmDlzpkhLS5PS9+/fL2bMmCG2bNliVt7MmTPF9OnTzV7LQ4cOiWnTpol169aZ5Z01a5aYOnWquH//vpR29OhRMXnyZPHnn3+a5Z09e7b43//+Z7bPJ0+eFJ999pn49ddfzfJ+9913YtKkSWbH7cyZM2LixIli4cKFZnnnzJkjPvroI3Hx4kUp7cKFC2L8+PFi9uzZObY7atQocfLkSSktKipKvPPOO+Kzzz7LsW9vvPGG2fvq2rVrYtCgQWLUqFFmeb/88kvRo0cPsWnTJint+vXrokOHDqJnz55meSdPnizatm0rfv/9dyktNjZWtG7dWnTs2NEs71dffSVatGghFi1aJKUlJCSIpk2biqZNmwqDwSClz5w5UzRu3Fh8//33UlpGRoZo0KCBaNCggUhJSTE7ZvXq1RPTpk0zKy8iIkLUqVNHxMXFSWkLFy4UtWrVEh9//LFZ3oYNG4oaNWqIGzduSGlLliwRVatWzXF8GjZsKMqVKycuXLhgltfT01P069fPLG+FChUEAHHw4EEpbdmyZQKA6NChg1nemjVrCgBi+/btUtrq1asFANGsWTOzvI0aNRIAxPr166W0zZs3CwCiTp06Znnbtm0rAJi9Rnv37hUAxDPPPGOWt2vXrgKA+Omnn6S0EydOCAAiKCjILO+LL74oAIhvv/1WSrt8+bIAIDw8PMzyDhkyRAAQX375pZQWExMj3NzchI+Pj1neSZMmicqVK5ud76mpqaJVq1aiffv2Zp9Jv/32mxg4cKD47bffpDSDwSA+/PBD8cUXX5idJ+fPnxd///23OHPmjFl50dHR4u7du0Kv14vCSk1NFVevqUgQqQAAY0VJREFUXhUHDhwQf//9t1iwYIGYMGGC6Nu3r6hfv74oU6aMAJDrQy6Xi+rVq4tXXnklx2dHYdmy3cVAjwXYQCYiyt3cuXNF48aNxdq1a6W06Oho4evrK1599VWzRqOjWrBggfD29hYffvihWfqWLVuEVqu1U63M8XuM8lIUgR5LlaRzNi4uThw6dMgsOHHp0iUxdOjQHBeoY8aMEfXr1xdr1qyR0k6dOiU8PDxEhQoVzPL27ds3x0VcVFRUrhdxgwcPznERd+fOHekiJqvhw4cLAGLixIlSWubxBiDS09Ol9AkTJggAYsyYMVKaXq+X8mbd588//1wAEG+99ZZZeU5OTgKA2QX8119/LQCIV155xSyvj4+PACDOnz8vpc2bN08AEL179zbLGxoaKgCII0eOSGlLly4VAESnTp3M8larVk0AMAtY/fnnnwKAaNmypVne+vXrCwDin3/+kdI2btwoAIh69eqZ5W3ZsqUAYBaE2rlzpwAgqlWrZpb3ueeeEwDEkiVLpLQjR44IACI0NNQsb69evQQAMXfuXCnt/PnzAkCOi/JXXnlFABAzZsyQ0qKjowUA4eTkZJb3rbfeEgDE559/LqXdv39fej2zfmePHj1aABATJkyQ0tLT06W8SUlJUvrHH38sAIgRI0aYlSeTyQQAERMTI6VNnTpVABCvvfaaWV53d3cBQFy5ckVKmz17tgCQI3gTGBgoAJgF2H766ScBQHTt2tUsb6VKlXJ8nq1YsUIAEG3btjXL26RJE+Hp6WkW6Nm6dauoXLmy6NOnj1neoUOHilatWondu3dLaadOnRK9e/c2e78IYQryRUZGmp2rN2/eFJ9++qlZIE0I07m2YMECsyBWQkKCWLVqVY6g7YULF8T+/ftFbGyslKbVasWVK1dEdHS0Wd709HSRlpZmUTDFmoxGo0hNTRWxsbEiKipKHD9+XOzatUts2LBB/P777+LHH38UM2fOFP/73//E+PHjxbBhw8Qrr7wievbsKTp06CAaN24satasKcqVKyfc3NyeGMTJ/ggNDRVdunQR48aNE0uXLhXHjh0zC1hbiy2/wx73USciIiqEhIQE7N6926zr8Pnz53Ho0CGsX78e3bt3BwCUL18e9+7dg1xeOkYNv/nmmxg4cKDZuPeTJ0/i2WefRbly5RAVFWU2hIyouPPz84NCoTCbwBIA7t69i6CgIDvVyv4SExOxc+dOJCcnmw0R6NmzJ/bu3Yvff/8dL774IgDT5+XChQtRoUIFzJw5U8p75coVHDt2zOzYKhQKJCcnw9nZ2ay8zCGmWYdJKJVKODk55fhM8fPzQ2hoqNkwBycnJ9SpUwcKhQJCCGkS+urVq6NDhw4ICwuT8qrVavTp0wcymczss7tu3bro168fIiIipDSZTIaXX35ZWi9TeHg4XnnlFTRu3NisbgMGDIBerzeb76xWrVoYPHgwmjVrliNvamoqvLy8pLQaNWrgjTfeQL169XLkffjwIfz8/KS0KlWq4O2330bNmjXN8vbv3x937941G87yzDPPYPjw4WbzswGmu++0bNkS5cuXl9LCwsIwZswYhIaGmuV95ZVX0LJlS1SrVk1Kq1ChAiZOnAh/f3+zvIMGDUKrVq3MjmXZsmUxbdo0s/0FgLfeegudOnVCq1atpLSQkBD8+uuvOc6TyMhIPP/882bb9fPzw8qVK3N8D2dut0aNGlKah4eHdNe9rDcqeP3119G+fXuz46NSqbBhwwYAgIuLi5Q+cODAHMcMADZv3gwAZkOgX3rpJTRu3DjH0KKNGzfCaDSapb/wwguoV6+e2WucNW/lypXN8rZq1Qru7u5meQ8dOgSZTAZPT08prW/fvujTp0+O43PgwAFk1759e0RFReVIz22C+fDwcPz111850keOHJkjLTQ0FJMmTcqR3qVLlxxpXl5euc6nlvW8y6RSqVCpUqUc6dnPm6yEEDAYDNBqtdBoNLk+MjIynpqW+TwjIwMpKSlISUlBcnKy9Mj6PCUlxeo3tnByckJgYCACAgIQEBCAsLAwPPPMM9IjLCzM7HPIUciEyDJrEhVIUlISvLy8kJiYaPYhQURUWqSnp8PX1xfp6em4dOkSqlSpAgA4duwYTpw4gc6dO0sTsBLw999/480330Tr1q3Nxnvv27cP9evXz7PBVRT4PUZ5kclkWL16NXr27CmlNWnSBI0bN8Z3330HwHRHlPLly2P48OF4//33i7xOxeWcNRqN0sXg9u3b0b59ewQHB5vdWWzgwIHYtm0bvvzyS7zyyisATHeMmT9/PoKDg6VJPwHg8OHDiIuLQ3h4uDQPkVarxY0bN+Di4iLNWwYAGRkZMBqNUKvVZvOKEVHJIoSATqeDRqOBVquVAir2/Dt7mj1DBe7u7vDw8JD+zf7305aVKVMGgYGBcHd3NwtW2pMtv8MY6LFAcWlsEBHZwsWLFzFr1izI5XKzWy+3b98esbGxWLBggdmvjJQ7nU6Hhw8fIiAgAIBpEsCQkBC4ubnh1KlTZhd0RY3fY5RdSkqKdKeVevXq4ZtvvkG7du3g4+OD8uXL4/fff8egQYOwYMECNG7cGLNmzcIff/yBCxcuIDAwsMjrZ+9zdv369Zg0aRJee+01REZGAjAFXpo3b47w8HAsXLhQ6s2StbcMET1ZZs+R7A+j0Zhruj3yZgY/8hsgyc/fWe88VVI4OztLvQczHwVJyy14k1vAxs3NzSF7gNvyO4w/AxARUa4uX75s9ktySkoK5s+fD3d3d3zzzTfSEIH169c7ZJfXoqJSqaQgD2C6E4a/vz98fX3Nej+dPHkSlStXznFnCaKidOTIEbRr1056PmbMGACm4SWLFy9Gv379EBcXh08++QSxsbGIiIjApk2bbBLkKQ7Onz+PY8eOwdnZWQr0ODs749ixYzny2irIk9krICMjA1qtFjqdLt+PzLsOCSGkf7P/ndeyvNbJrFv2R1Gm26PMokg3Go3Q6/Vmj8zXK/vfAKShdTKZLMff1ngOoEiDLOx3YKJQKKBWq6FWq+Hk5FRs/s78V6lUMnhdgrBHjwXs/asSEVFRGTlyJL799lt89NFH+OKLLwCYhipMmDAB7dq1w7PPPmt2C0uyjMFgwJ07d6QhGwaDAWFhYUhMTMR///2HRo0aFUm5/B6jksbW56xer4dGo5ECrqmpqZg9ezbefPPNHPOD5CU1NRX3799HXFwc4uLi8PDhQ2RkZCA9PR3p6em5/p3f5ZlDuYgclUwmg0KhMHvI5fIcaU97WLKOLQIrCoXC3oeaihh79BARkU0IIXDkyBEsWbIE06ZNky5mGjRoAKVSifj4eCmvXC7H9OnT7VVVh6ZQKKQgDwDcvHlTavSFh4dL6VFRUQgODs4xqSQRWV96ejp69+4NpVKJNWvWQKFQwM3NDR9++OET8x8/fhynTp3C5cuXpce1a9eQlpZms3rL5XKoVKp8PZRKJeRyudV7gGTtCVKQdFutU1y3lXVZ1tco85H9eeYcTfnpYZXfnli5LQNQ5IGUvB7sRUJUcAz0EBGVcv3798fly5fRsGFDDB48GADQp08f9OzZk7087KRixYq4dOkSrl69ajZBc2RkJH755RcGeohs4OjRo9i2bRuUSiXOnDmDunXrmi3PyMjAjh07sGHDBuzbtw+nTp2ShtLkxsnJCf7+/vD394e3tzdcXV3h7OwMFxcXuLi4SH8XNs3Z2Rlqtdoh57UgIqKCYaCHiKiU0Ov12LRpEzZt2oTvvvtO+vVw2LBhOHbsGGrXri3l5Zw79ieXy81uEZuamoqIiIhSMxcKkb21bNkSGzduhFqtNgvyHD9+HAsWLMDy5cuRnJxstk5gYCAaNmyIqlWronLlyqhcuTIqVapU7O78QkREjo1z9FiAcxsQUUmSnJyMoKAgpKWlYe/evWjevLm9q0R2xu8xKmnsec6ePn0aH330EdatWyellS1bFs8//zw6dOiAJk2aoFy5cgzmEBFRrjhHDxERWSQlJQV//PEHrly5gsmTJwMAPDw8MGzYMBiNRgQHB9u5hkRExY9Wq8Ubb7yByZMnS/NmaTQaTJo0CdOnT4fRaIRCoUDfvn3x1ltvoU2bNgzsEBFRscNADxGRA7p16xZef/11KBQKDB8+XArsfP3113auGRFR8bVo0SL88ssvOHr0KM6cOYO4uDj06NEDBw4cAAC88MILmDx5MqpVq2bnmhIRET0ZAz1ERCXc3bt3sXjxYiiVSowdOxYAUL16dbz66quoWbMmnJyc7FxDIqKSoWHDhujSpQu6dOmC2NhYtG7dGpcvX4a3tzd+/vln9OzZ095VJCIieiqbz9GTlJRU4HWK67wBnNuAiIqD9evXo1u3bvD398etW7egVqvtXSUqIfg9VjI5UluqoGx1ziYlJaFt27Y4fvw4KlasiE2bNrEXDxERWcSh5+gpU6ZMgcYyy2QyXLp0CZUqVSrCWhERlQwXLlzAwoULUa9ePbzyyisAgM6dO6N79+7o0aMHOL8+keNjW6roRUZG4vjx4wgICMB///2HZ555xt5VIiIiyje7DN36888/4ePj89R8Qgj83//9X762uWvXLkyfPh1Hjx5FTEwMVq9e/dTutTt27MCYMWNw9uxZlCtXDh9//DEGDx6cr/KIiOxh48aN+Oabb9CoUSMp0KNUKrF27Vo714yIbKko2lKlWUJCAtasWYMuXbpg//79+PXXXyGXy7F69WoGeYiIqMSxeaCnQoUKaN26NXx9ffOVv1KlSlCpVE/Nl5qairp16+K1115D7969n5r/2rVr6Nq1K95++20sW7YMW7duxRtvvIHg4GB06tQpX3UjIipKZ8+exbfffovBgwejWbNmAIBXX30Ve/fuxZAhQyCE4N1eiEqhompLlWabNm3CkCFDULNmTSQmJgIAxo8fj+bNm9u5ZkRERAVn80DPtWvXCpT/zJkz+cqXOXFefs2fPx9hYWGYMWMGAKBGjRrYs2cPZs6cyUAPERULM2fOxE8//YSHDx9KgR5/f3/89ddfdq4ZEdlTUbWlSjODwYCGDRvC398f//zzD0JDQzFp0iR7V4uIiKhQ5PYodNeuXU/NM2LEiCKtw/79+9GxY0eztE6dOmH//v1FWi4RUW5SU1Mxb9483LlzR0obOXIkevXqhXfffdeONSOi4qg4tKUcyYABA7Bv3z6cP38egKk3j7Ozs51rRUREVDh2CfR0794dJ06ceOLyESNGYMmSJUVah9jYWAQGBpqlBQYGIikpCenp6bmuo9FokJSUZPYgIrKGF198Ee+88w7mzp0rpYWHh2PVqlVo2bKlHWtGRMVRcWhLOZq1a9fi+vXrCAgIwBtvvGHv6hARERWaXQI9b7zxBjp37ozLly/nWDZy5EgsWrQI69ats0PN8jZ16lR4eXlJj3Llytm7SkRUAgkhsG/fPmg0GinttddeQ+XKlVG5cmU71oyIbMXb2xs+Pj75euSmpLaliqPMuxX++uuvAEyfxy4uLvasEhERkUXsctetr7/+GvHx8ejYsSP27duHkJAQAMCoUaPw448/Yv369WjTpk2R1iEoKAh37941S7t79y48PT2f+OX+wQcfYMyYMdLzpKQkBnuIqMB69+6NNWvWYOnSpXj11VcBAL169UKvXr0gl9sl/k5ENjZr1izp7wcPHuCLL75Ap06dpPm49u/fj3///RcTJ07Mdf3i0JZyFLNnz8a0adNw7949AKZhXERERCVZgQM9WQMdT/PNN988cdmPP/6IPn36oGPHjti9ezcmT56MH374AevWrUO7du0KWq0Ca9asGTZu3GiWtmXLFqmBlRsnJyc4OTkVddWIyME8fPgQ3t7e0vPGjRvjn3/+we3bt6U0BniISpdBgwZJf7/wwgv4/PPPMXz4cCnt3Xffxffff4///vsPo0ePznUb9m5LOYpLly4hNjYWABAWFoZatWrZuUZERESWkYnM/qr5lL3hcOzYMej1elSrVg2A6ctSoVCgQYMG2LZtW57b0mq16Nq1K06ePInU1FSsXbs2xwTJ+ZWSkiJ1X65Xrx6++eYbtGvXDj4+Pihfvjw++OAD3L59G0uXLgVgumNF7dq1ERkZiddeew3btm3Du+++iw0bNuT7rltJSUnw8vJCYmIiPD09C1VvInJcQgiMGDECP/30E7Zv346mTZsCABITE6HVauHv72/nGlJpx++x4sHd3R0nTpzIMXTz8uXLiIiIQEpKyhPXtWZbqiQoinM2MTERr7/+Ov766y+88847mDNnjlW2S0RElJUt210F7tGzfft26e9vvvkGHh4eWLJkifRr9cOHDzFkyBC0atXqidv49ttvpb/btm2L3bt3o1OnTjh37hzOnTsnLSvInWaOHDliFoTK7Hk0aNAgLF68GDExMbhx44a0PCwsDBs2bMDo0aMxe/ZshIaG4scff+St1YnIamQyGVJSUpCRkYG1a9dKgR4vLy8714yIihNfX1+sXbsWY8eONUtfu3YtfH19c12nKNpSpZWXlxeuXLkCAGjfvr2da0NERGS5Avfoyaps2bLYvHlzji6uZ86cwXPPPWd2m+CswsLCnl4xmQxXr14tbNVsgr+EElEmo9GIefPmYcGCBdi8eTOCgoIAmHo53rt3Dy1atIBMJrNzLYnM8XuseFi8eDHeeOMNdOnSBU2aNAEAHDx4EJs2bcLChQsxePDgHOs4SluqoIrinE1LS4OnpycMBgNu3ryJ0NBQq2yXiIgoq2LdoyerpKQkxMXF5UiPi4tDcnLyE9e7du2aJcUSERU7crkcv/76K06fPo0ffvgBn3zyCQCgatWqqFq1qp1rR0TF2eDBg1GjRg18++23WLVqFQCgRo0a2LNnjxT4yY5tKetISEjAJ598AoPBgODgYJQtW9beVSIiIrKYRYGeXr16YciQIZgxYwYaN24MwPQL1Lhx49C7d2+rVJCIqDg6cOAAfvrpJ8ydOxcqlQoAMGnSJFy+fNlsklUiovxo0qQJli1bZu9qlDrXr1/Hd999BwCoX78+e14SEZFDsOg2L/Pnz0eXLl3Qv39/VKhQARUqVED//v3RuXNnzJ07N9d1vv32W2RkZBSojLx6BxER2ZpGo0GPHj3w448/4s8//5TSO3fujOHDh8PDw8OOtSOikujKlSv4+OOP0b9/f+k23//88w/Onj2bIy/bUtbj4uIi3VCEvS+JiMhRWBTocXV1xdy5c/HgwQMcP34cx48fR3x8PObOnQs3N7dc1xk9enSBGhvjx4/PdXgYEZGt6HQ6bN68WXru5OSE9957D0OGDEG9evXsWDMicgQ7d+5EeHg4Dh48iL/++ku6y9bJkycxadKkHPnZlrKeatWqSQGeKlWq2Lk2RERE1mHR0K1MMTExiImJQevWreHi4gIhxBO7vgoh0KFDByiV+Ss6PT3dGlUkIioUjUaD2rVr4/Llyzh8+DAaNmwIABg3bpyda0ZEjuL999/HF198gTFjxpj1CGzfvj2+//77HPnZlrKuqKgoAAz0EBGR47Ao0PPgwQO8+OKL2L59O2QyGaKiolCpUiW8/vrr8Pb2xowZM3Ksk9svU3np0aMHfHx8LKkmEVGBaDQaODk5ATD13mnatCmSkpJw8+ZNKdBDRGQtp0+fxvLly3OkBwQE4P79+znS2ZayHqPRKN2ZrHLlynauDRERkXVYFOgZPXo0VCoVbty4gRo1akjp/fr1w5gxY6wS6CEishWNRoPx48dj+fLlOHfuHPz9/QEAM2bMgIeHB1xcXOxcQyJyRGXKlEFMTEyOW6YfP34817tAsS1lPUOHDoVWqwUA3nGLiIgchkVz9GzevBnTpk1DaGioWXqVKlUQHR1tUcWIiGxNrVZj3759uH//PlasWCGlBwQEMMhDREXmpZdewoQJExAbGwuZTAaj0Yi9e/fivffew8CBA+1dPYd28+ZNAKZJmTPvoEhERFTSWRToSU1Nhaura470+Ph4adgDEVFxJITArl278Prrr0Ov1wMAZDIZZsyYgS1btmD48OF2riERlRZTpkxB9erVUa5cOaSkpKBmzZpo3bo1mjdvjo8//tje1XNob7zxBgAgKCjIzjUhIiKyHouGbrVq1QpLly7F//73PwCQfoX66quv0K5dO6tUkIioKGg0Grzwwgu4f/8+OnbsiJdffhkA0Lp1azvXjIhKG7VajYULF2LixIk4c+YMUlJSUK9ePU4ObAOZvXgY6CEiIkdiUaDnq6++QocOHXDkyBFotVqMHz8eZ8+eRXx8PPbu3WutOhIRWUyr1WLr1q3o0qULAMDZ2Rnjxo3DlStX0LhxYzvXjogIKF++PMqXL2/vapQqmZNd+/n52bkmRERE1mNRoKd27dq4dOkSvv/+e3h4eCAlJQW9e/dGZGQkgoOD81xXp9OhevXqWL9+vdlEzkRE1paeno4aNWogOjoax44dQ7169QAA48ePt3PNiIiAMWPG5Jouk8ng7OyMypUr53rnLLalLLdnzx4A4JQDRETkUCwK9ACAl5cXPvroowKvp1KpkJGRYWnxRES5Sk9PlyZQdnFxQYsWLaDVanH79m0p0ENEVBwcP34cx44dg8FgQLVq1QAAly5dgkKhQPXq1TF37lyMHTsWe/bsQc2aNaX12Jay3Pbt2wGAx5GIiByKRZMxnzp1KtfH6dOnERUVBY1Gk+f6kZGRmDZtmjQRKhGRpdLT0zFs2DCUK1cODx48kNJnzZqFa9eu4fnnn7dj7YiIcurRowc6duyIO3fu4OjRozh69Chu3bqFZ599Fi+//DJu376N1q1bY/To0TnWZVvKMl5eXgCQ4w6yREREJZlFPXoiIiIgk8kAmO5gA0B6Dph+aerXrx8WLFgAZ2fnHOsfPnwYW7duxebNmxEeHg43Nzez5atWrbKkekRUCjk7O+PgwYN48OABVq5cibfffhsA4O/vb+eaERHlbvr06diyZQs8PT2lNC8vL3z66ad47rnnMHLkSHzyySd47rnncqzLtpRlKlSogDNnzqBhw4b2rgoREZHVWBToWb16NSZMmIBx48ZJk5keOnQIM2bMwKRJk6DX6/H+++/j448/xtdff51j/TJlyuCFF16wpApEVModOHAAixcvxpw5c6BQKCCTyTBz5kwAvIMWEZUMiYmJuHfvntmwLACIi4tDUlISAFObSavV5liXbSnLGAwGAI/vvkVEROQILAr0TJ48GbNnz0anTp2ktPDwcISGhmLixIk4dOgQ3NzcMHbs2FwDPYsWLbKkeCIq5dLT09G1a1fEx8ejXbt26NevHwCgTZs2dq4ZEVH+9ejRA6+99hpmzJiBRo0aATD11HnvvffQs2dPAKYf0qpWrZpjXbalLGM0GgGY90gnIiIq6SwK9Jw+fRoVKlTIkV6hQgWcPn0agGl4V0xMTJ7biYuLw8WLFwEA1apV4xALIsqV0WjE/v370aJFCwCmSZbfe+89REVFoX79+nauHRFR4SxYsACjR4/GSy+9JM21o1QqMWjQIKmHYvXq1fHjjz8+cRtsSxXOiRMnAABnzpyxb0WIiIisyKJAT/Xq1fHll1/ihx9+gFqtBmC61eeXX36J6tWrAwBu376NwMDAXNdPTU3FiBEjsHTpUukXFYVCgYEDB+K7776Dq6urJdUjIgei0WjQqFEjnD59GidOnEDdunUBAB988IGda0ZEZBl3d3csXLgQM2fOxNWrVwEAlSpVgru7u5QnIiIi13XZlrJM5o1DdDqdnWtCRERkPRbddWvOnDlYv349QkND0bFjR3Ts2BGhoaFYv3495s2bBwC4evUq3nnnnVzXHzNmDHbu3Il169YhISEBCQkJWLt2LXbu3ImxY8daUjUicgCZk7wDgJOTE2rVqgVPT0/pV2siIkfi7u6OOnXqoE6dOmZBnrywLWWZzNvZZ58fiYiIqCSTiaxXUoWQnJyMZcuW4dKlSwBMX5j9+/eHh4fHU9f18/PDn3/+ibZt25qlb9++HS+++CLi4uIsqVqRS0pKgpeXFxITE83ulEFEltHpdJg1axYWLlyIAwcOwMfHB4Cph6C7u7t0O1wisgy/x4qPI0eO4I8//sCNGzdyTLqc152zSnpbqqCsfc62a9cOO3bswO+//44XX3zRCjUkIiLKnS3bXRYN3QIADw8P6fbFBZWWlpbrsK6AgACkpaVZWjUiKqGUSiV+/fVXREVF4aeffsK4ceMAAGXLlrVzzYiIrG/FihUYOHAgOnXqhM2bN+O5557DpUuXcPfuXfTq1SvPddmWsgwnYyYiIkdkcaAHAM6dO5frL1Ddu3fPc71mzZph0qRJWLp0KZydnQGY7qLz2WefoVmzZtaoGhGVALdv38aiRYvwwQcfSLdInzZtGu7evYv+/fvbu3pEREVqypQpmDlzJiIjI+Hh4YHZs2cjLCwMb731FoKDg/Ncl20pyzx48ACA6Rb3REREjsKiQM/Vq1fRq1cvnD59GjKZTJpPI/NXEYPBkOf6s2bNQufOnREaGipNrHry5Ek4Ozvj33//taRqRFRC6HQ6NGjQAHfv3kX16tXRp08fAEDnzp3tXDMiItu4cuUKunbtCgBQq9VITU2FTCbD6NGj0b59e3z22WdPXLektqXS0tJQo0YN9O3bF19//bXd6nH9+nUAQHR0tN3qQEREZG0WTcY8cuRIhIWF4d69e3B1dcXZs2exa9cuNGzYEDt27Hjq+uHh4YiKisLUqVMRERGBiIgIfPnll4iKikKtWrUsqRoRFWOZDWsAUKlUeOutt9C6deun/nJNROSIvL29kZycDMA0RDXzVt8JCQlPHX5VUttSkydPRtOmTe1dDWnS6/zMLUlERFRSWNSjZ//+/di2bRv8/Pwgl8shl8vRsmVLTJ06Fe+++y6OHz/+xHV1Oh2qV6+O9evXY+jQoZZUg4hKCL1ejxdeeAHr1q3DyZMnER4eDgCYOHFinr9YExE5statW2PLli0IDw9H3759MXLkSGzbtg1btmxBhw4dnrheSW1LRUVF4cKFC+jWrZsU1LKXsLAwqUcpERGRo7CoR4/BYJB+AfHz88OdO3cAABUqVHjq7Y9VKhUyMjIsKZ6IShilUgm1Wg0A2Llzp1k6EVFp9f333+Oll14CAHz00UcYM2YM7t69ixdeeAE//fTTE9crirbUrl270K1bN4SEhEAmk2HNmjU58syZMwcVK1aEs7MzmjRpgkOHDhWojPfeew9Tp061Uo0tw8mYiYjIEVkU6KlduzZOnjwJAGjSpAm++uor7N27F59//jkqVar01PUjIyMxbdo06PV6S6pBRMWQ0WjE6tWr0b59eyQkJEjpU6dOxYULFzB8+HD7VY6IqJjQ6/VYv349FAoFAEAul+P999/H33//jRkzZsDb2zvP9a3dlkpNTUXdunUxZ86cXJf//vvvGDNmDCZNmoRjx46hbt266NSpE+7duyfliYiIQO3atXM87ty5g7Vr16Jq1aqoWrWqVeprqcxAj1xuUZOYiIioWJGJzBmUC+Hff/9FamoqevfujcuXL+P555/HpUuX4Ovri99//x3t27fPc/1evXph69atcHd3R3h4ONzc3MyWr1q1qrBVs4mkpCR4eXkhMTERnp6e9q4OUbFiNBpRt25dnDlzBl988QX+v737Doviet8Gfi8dLCAiRQTBXkBECEiMLRIVE40xxRhUrNFojIpGxYLBhtFfCHYSolETe9dEjQa7YkOxRbGAYgEsSLNRdt4/fJkvK0jZHVgY7s917eXu2TkzzxkX5uHsmXOmTJmi7ZCI6A28jpUPJiYmuHr1KurWrVviuqWZSykUCmzbtg09e/YUyzw9PfHOO+9g8eLFAF7/rrezs8OoUaMwadKkIvcZEBCAP//8E7q6usjIyEBWVhbGjRuHwMDAArd/9eoVXr16Jb5OS0uDnZ2dZJ9ZMzMzpKam4ueff8aYMWM03h8REdHblGXepdH9El26dBGfN2jQANeuXUNycjJq1KhRrCGwZmZm+PTTTzUJgYjKiZcvX2Ljxo3o27evOGdXUFAQzp07h6+//lrb4RERlVseHh6Ijo5Wq6OnLHOpzMxMREVFISAgQCzT0dGBt7c3IiMji7WP4OBg8batlStX4vLly2/t5MndvjTncMu99S0zM7PUjkFERFTW1O7oycrKgrGxMaKjo+Hk5CSWm5ubF6t+dnY2OnbsiM6dO8Pa2lrdMIioHFAqlXB1dcW1a9dQrVo1fPLJJwCAXr16oVevXlqOjoiofBsxYgT8/f1x9+5duLm55RuV06JFiwLrlXUu9fjxY+Tk5MDKykql3MrKCteuXSuVYwYEBMDf3198nTuiRyp2dna4efMm6tevL9k+iYiItE3tjh59fX3Y29sjJydHvQPr6WH48OG4evWquiEQkRZlZGSIy9Lq6Ojg008/xerVq9X+nUBEVFnlTsT83XffiWUKhQKCIEChULz192pFz6UGDBhQ5DaGhoYwNDQstRhMTEwAgLcuEhGRrGg089yUKVMwefJkJCcnq1Xfw8Oj0CXYS6qkq0CEhoaicePGMDY2hp2dHcaOHcuVwIiKIAgCxo8fDxsbG/z3339ieUBAAG7evInPPvtMi9EREVU8cXFx+R6xsbHiv4WROpcqjIWFBXR1dZGUlKRSnpSUVGFHZ3MyZiIikiON5uhZvHgxbt68idq1a6Nu3br5hhqfO3eu0PojRozAuHHjcO/evRINVS5I7ioQYWFh8PT0RGhoKLp06YKYmBhYWlrm237t2rWYNGkSVqxYgXfffRfXr1/HgAEDoFAoEBISUuzjElU2CoUCsbGxyMjIwNq1azFr1iwAyPfzS0RExaPO3Dy5pMylimJgYAA3NzdERESIEzQrlUpERERU2JUU09PTAbxebYyIiEguNOroybsKgzrUHapckJCQEAwdOhQDBw4EAISFheHvv//GihUrClwF4sSJE2jTpg2++uorAICDgwP69OmDU6dOadIkItm5fPkyQkJC8PPPP8PU1BQAMGPGDHz99dcqE7ITEZH6/vjjD4SFhSEuLg6RkZGoW7cuQkND4ejoiI8//vit9aTMpYDXt+XevHlTfB0XF4fo6GiYm5vD3t4e/v7+8PPzg7u7Ozw8PBAaGopnz56J+VdF8+DBAwDAvXv3tBwJERGRdDTq6Jk+fbpGB4+Li9Oofi51VoF499138eeff+L06dPw8PBAbGwsdu/ejX79+r31OAUt8UkkZ4Ig4Msvv8SVK1fQqFEjsdPUyclJZRJ2IiJS37JlyxAYGIgxY8Zg9uzZYueMmZkZQkNDC+3okSqXynX27Fl07NhRfJ07EbKfnx9WrlyJ3r1749GjRwgMDERiYiJatmyJvXv35puguaIwNDREVlYWR6USEZGsaNTRAwApKSnYvHkzbt26he+//x7m5uY4d+4crKysYGtrW2hdTYYq56XOKhBfffUVHj9+jPfeew+CICA7OxvDhw/H5MmT33qc0l7ik0jbBEHAsWPH0KZNG+jo6EChUCAgIAA7duxA165dtR0eEZEsLVq0COHh4ejZsyfmzp0rlru7u2P8+PGF1pUql8rVoUMHCIJQ6Dbffvtthb1V6021a9fG9evXUa9ePW2HQkREJBmNZp67ePEiGjVqhB9//BH/93//h5SUFADA1q1bVUbXFOaPP/5AmzZtULt2bdy5cwfA60mSd+zYoUloRTp06BDmzJmDpUuX4ty5c9i6dSv+/vtvzJw58611AgICkJqaKj7u3r1bqjESlSVBENC1a1e0a9cOu3btEst9fX2xceNGtGzZUnvBERHJWFxcHFxdXfOVGxoaFmvuGG3lUnLAyZiJiEiONLqq+fv7Y8CAAbhx4waMjIzE8m7duuHIkSNF1l+2bBn8/f3RrVs3pKSk5BuqXFzqrAIxbdo09OvXD0OGDIGzszM++eQTzJkzB8HBweJF/02GhoaoXr26yoOoIsv7WVcoFGjVqhWMjIxw+/Zt7QVFRFTJODo6Ijo6Ol/53r170bRp00LrSpVLVVa5o5fY0UNERHKi0VXtzJkzGDZsWL5yW1tbJCYmFlk/d6jylClToKurK5a7u7vj0qVLxY4j7yoQuXJXgfDy8iqwzvPnz/Nd1HNjKGrIMpEc/Prrr2jcuLHK7Y3ff/89bt++jdGjR2sxMiKiysXf3x8jR47Ehg0bIAgCTp8+jdmzZyMgIAATJkwotK5UuVRllTsZM0dpExGRnGg0R4+hoWGBExJfv34dtWrVKrK+pkOV8ypqFYj+/fvD1tYWwcHBAIDu3bsjJCQErq6u8PT0xM2bNzFt2jR0795dJVEikqvdu3fj5s2bWLRoEZYsWQIAMDc313JURESVz5AhQ2BsbIypU6fi+fPn+Oqrr1C7dm0sWLBAXFXrbaTMpSqjzMxMlX+JiIjkQKOOnh49emDGjBnYuHEjgNe3fsTHx2PixIn49NNPi6yfO1T5zYkEizNU+U1FrQIRHx+vMoJn6tSpUCgUmDp1Ku7fv49atWqhe/fumD17domOS1QRPH78GEuXLsWYMWPEWw6nTZuG999/H0OGDNFydERE5OvrC19fXzx//hwZGRmwtLQsVj0pc6nKyNzcHI8ePSpyAREiIqKKRKOOnp9++gmfffYZLC0t8eLFC7Rv3x6JiYnw8vIqVodJ7lDlly9fikOV161bh+DgYPz2228ljqewVSAOHTqk8lpPTw/Tp0/XeIl4ooqga9euiIqKgpGRkXgbgJubG9zc3LQcGRERzZo1C76+vnB0dISJiQlMTEyKXVfqXKqyMTQ0BADOu0hERLKiUUePqakp9u/fj2PHjuHixYvIyMhAq1at4O3tXaz6mgxVJqK3i42NhaOjIxQKBYDXnaCLFi2Ck5OTliMjIqI3bdq0CdOnT4enpyf69u2LL774AhYWFsWqy1xKM5yMmYiI5EghaDDz8N27d2FnZydJICUdqlwepKWlwdTUFKmpqfwmiMqNoUOHYsWKFdixYwc++ugjAK8nJ1coFGLHDxERwOtYeXLlyhWsWbMG69evx7179/DBBx/A19cXPXv2LPYIn4qYS5WU1J/ZmjVrIjk5GSdOnHjrAh5ERERSKMu8S6OvLxwcHNC+fXuEh4fj6dOnGgViYmIi68SEqKzUqFEDSqUSx48fF8t0dHTYyUNEVI41b94cc+bMQWxsLA4ePAgHBweMGTMG1tbWxd4Hc6mSy81fHz58qOVIiIiIpKNRR8/Zs2fh4eGBGTNmwMbGBj179sTmzZvx6tUrqeIjokLs2bMH7du3x/Xr18Wy8ePHIzo6WlxhjoiIKpYqVarA2NgYBgYGyMrK0nY4sqan93oWg9y5eoiIiORAo44eV1dXzJ8/H/Hx8dizZw9q1aqFr7/+GlZWVhg0aJBUMRLRWyxduhRHjhzB/PnzxTJLS0u4uLhoMSoiIiqpuLg4zJ49G82bN4e7uzvOnz+PoKAgJCYmajs0WTM1NQUAyaYiICIiKg8kmXlOoVCgY8eOCA8Px7///gtHR0esWrVKil0T0f+XmZmJFStWICMjQyybMmUKxo0bh6CgIC1GRkREmmjdujUaNGiAzZs3Y+DAgbhz5w4iIiIwePBgsSOCSgcnYyYiIjnSaNWtXPfu3cPatWuxdu1aXL58GV5eXliyZEmJ9vHy5UsYGRlJEQ6RLPn4+ODAgQNISUmBv78/gNd/HLRu3VrLkRERkSY6deqEFStWoFmzZhrth7lUySmVSgDgPHZERCQrGn198csvv6B9+/ZwcHDA6tWr0bt3b9y6dQtHjx7F8OHDi6yvVCoxc+ZM2NraomrVqoiNjQUATJs2DcuXL9ckNKIKLyUlBXkXxfvqq69gY2MDMzMz7QVFRESSmz17ttqdPMylNJOeng4ASE5O1nIkRERE0tGoo2fWrFnw9PREVFQULl++jICAANStW7dE9VeuXIl58+bBwMBALHdycsJvv/2mSWhEFVpQUBDs7Oywd+9esax///6Ii4vj/FdERDJ07949LF26FJMmTYK/v7/KozDMpTSTnZ0NAJz0moiIZEWjW7fi4+M1Guq6evVq/Prrr+jUqZPKCCAXFxdcu3ZNk9CIKrT09HRkZGRg8+bN8PHxAQDo6+trOSoiIioNERER6NGjB+rVq4dr167ByckJt2/fhiAIaNWqVaF1mUtpxsjICC9fvoS5ubm2QyEiIpKMRiN6cjt5nj9/jmvXruHixYsqj6Lcv38fDRo0yFeuVCr5zQpVGleuXEH//v1x8+ZNsWzcuHHYtWsXv40lIqoEAgICMH78eFy6dAlGRkbYsmUL7t69i/bt2+Pzzz8vtC5zKc3kTsJctWpVLUdCREQkHY1G9Dx69AgDBgxQub0kr5ycnELrN2vWDEePHs13u9fmzZvh6uqqSWhEFcb333+PPXv2wNjYGL/88gsAwMbGBh999JGWIyMiorJw9epVrFu3DgCgp6eHFy9eoGrVqpgxYwY+/vhjfPPNN2+ty1xKM7lz4XEyZiIikhONOnrGjBmD1NRUnDp1Ch06dMC2bduQlJSEWbNm4aeffiqyfmBgIPz8/HD//n0olUps3boVMTExWL16Nf766y9NQiMqlwRBwKFDh+Dh4YEqVaoAACZPngwTExN8/fXXWo6OiIi0oUqVKsjMzATwuqP/1q1baN68OQDg8ePHhdZlLqWZ3Dl6cv8lIiKSA41u3Tpw4ABCQkLg7u4OHR0d1K1bF3379sW8efMQHBxcZP2PP/4Yu3btwr///osqVaogMDAQV69exa5du/DBBx9oEhpRueTr64v3338f4eHhYtl7772HzZs3w83NTYuRERGRtrRu3RrHjh0DAHTr1g3jxo3D7NmzMWjQILRu3brQusylNJN7e1taWpqWIyEiIpKORiN6nj17BktLSwBAjRo18OjRIzRq1AjOzs44d+5csfbRtm1b7N+/X5MwiMqt7Oxs6OrqikPCc0e+5S7nSkREFBISgoyMDACvV13MyMjAhg0b0LBhQ4SEhBRZn7mU5vKuWEZERFTRaTSip3HjxoiJiQHwenWHX375Bffv30dYWBhsbGyKrF+vXj08efIkX3lKSgrq1aunSWhEWrdq1So0btxYJfn28/PD7du3MW3aNC1GRkRE5Um9evXQokULAK9v4woLC8PFixexZcuWfHPvFFSXuZT6cidjrlmzppYjISIiko5GHT2jR49GQkICAGD69OnYs2cP7O3tsXDhQsyZM6fI+rdv3y5wwuZXr17h/v37moRGpHXR0dGIjY3F0qVLxTJDQ0NYWVlpMSoiIirPRowYUeS8PHkxl9IMJ2MmIiI50ujWrb59+4rP3dzccOfOHVy7dg329vawsLB4a72dO3eKz//55x+YmpqKr3NychAREQEHBwdNQiMqU8nJyVi0aBH69esnfoM6btw4ODg4YMiQIVqOjoiIKoo///wT48ePLzSPAphLSSW3oyd3ZA8REZEcaNTRk9fx48fh7u6OVq1aFbltz549Abz+9sTPz0/lPX19fTg4OBRr1S6i8mLQoEHYsWMHEhMTsWzZMgBAnTp1MHr0aC1HRkREFUlux0NRmEtpLnelMwB4/vy5FiMhIiKSlmQdPT4+PoiOji7W/eBKpRIA4OjoiDNnzhT5rRVReRMbGwtra2uYmJgAAMaOHYvbt29zhRMiIioTzKU0l7ejh4iISE4kG6da3G+g8oqLi2NiQhXOpEmT0LBhQyxfvlwsa9euHc6fP49evXppMTIiIqro0tPTSzSJMnMp9enq6orPc7+4ISIikgPJRvSoY8aMGYW+HxgYWEaREBWfg4MDlEoloqOjxTJO4khERJq4desWfv/9d8TGxiI0NBSWlpbiIhfNmzd/az3mUurL29HD5dWJiEhOFII6Q3EKsHbtWnz88ceoUqVKseu4urqqvM7KykJcXBz09PRQv359nDt3TorQSk1aWhpMTU2RmpqK6tWrazscKgVnzpzBjBkzMHbsWLz//vsAgJcvX+LKlStwc3PTcnRERJrhdax8OHz4MHx8fNCmTRscOXIEV69eRb169TB37lycPXsWmzdvfmvdip5LlZSUn9mXL1/C2NgYAPgzQEREpa4s8y5JRvTcvHkTNWvWFFcsEAShWCMczp8/n68sLS0NAwYMwCeffCJFaEQaWb16Nf766y88f/5c7OgxMjJiJw8REUlm0qRJmDVrFvz9/VGtWjWx/P3338fixYsLrctcSn3Z2dnic4m+9yQiIioXNJqj58mTJ/D29kajRo3QrVs3JCQkAAAGDx6McePGqbXP6tWrIygoCNOmTdMkNKISEwQBBw4cwL1798SyCRMmYNCgQQgLC9NiZEREJGeXLl0qsFPG0tISjx8/LvH+mEsVT2pqqvg8d3JrIiIiOdCoo2fs2LHQ09NDfHy8yiR2vXv3xt69e9Xeb2pqqsrFl6gsjB07Fp06dcLcuXPFMjs7OyxfvhwNGzbUYmRERCRnZmZm4pdleZ0/fx62trZq7ZO5VNHyjuLJO18PERFRRafRrVv79u3DP//8gzp16qiUN2zYEHfu3Cmy/sKFC1VeC4KAhIQE/PHHH/Dx8dEkNKIiKZVKKJVK6Om9/jH4+OOPERYWJt6vT0REVBa+/PJLTJw4EZs2bYJCoYBSqcTx48cxfvx49O/fv9C6zKXUl3d+BE7GTEREcqJRR8+zZ88KXI4yOTkZhoaGRdb/+eefVV7r6OigVq1a8PPzQ0BAgCahERVq9+7dCAgIwPDhw/HNN98AADp06IC7d++iVq1aWo6OiIgqkzlz5mDkyJGws7NDTk4OmjVrhpycHHz11VeYOnVqoXWZS6kv7+1aufNMEhERyYFGHT1t27bF6tWrMXPmTAAQv4WaN28eOnbsWGT9uLg4TQ5PpLbY2FhcvHgRS5cuxfDhw6FQKKBQKNjJQ0REZc7AwADh4eEIDAzEpUuXkJGRAVdX12LdNsxcSn15b90qziIiREREFYVGHT3z5s1Dp06dcPbsWWRmZmLChAm4cuUKkpOTcfz4caliJNJIVlYW/vjjDzRv3hyenp4AXk8Ynp6ejmHDhjG5IyKicsHOzg52dnbaDqPSePr0qficI3qIiEhONOrocXJywvXr17F48WJUq1YNGRkZ6NWrF0aOHAkbG5sC6/Tq1avY+9+6dasm4REBAAIDAzF37lx4e3tj//79AABjY2MOaScionLh008/hYeHByZOnKhSPm/ePJw5cwabNm1SKWcuJY3nz5+Lz9nRQ0REcqJRRw8AmJqaYsqUKSXavrQsWbIE8+fPR2JiIlxcXLBo0SJ4eHi8dfuUlBRMmTIFW7duRXJyMurWrYvQ0FB069at1GKk0vfixQu8evUKZmZmAIDhw4djzZo16Nq1K5RKJZM5IiIqV44cOYIffvghX7mPjw9++umnfOWlmUtVJlWqVBGfc3QvERHJicYdPS9fvsTFixfx8OFDlUntAKBHjx75tv/99981PWSBNmzYAH9/f4SFhcHT0xOhoaHo0qULYmJiYGlpmW/7zMxMfPDBB7C0tMTmzZtha2uLO3fuiJ0DVDFt2rQJo0aNQu/evbFgwQIAQN26dREXF8elU4mIqFzKyMgocNUnfX19pKWl5SsvrVyqssldZZOdPEREJDcadfTs3bsX/fv3x+PHj/O9p1AokJOTU6z9PHr0CDExMQCAxo0bqzUhbkhICIYOHYqBAwcCAMLCwvD3339jxYoVmDRpUr7tV6xYgeTkZJw4cQL6+voAAAcHhxIfl8qXGjVqICkpCfv370d2dra4dDo7eYiIqLxydnbGhg0bEBgYqFK+fv16NGvWrFj7kCKXqmxyJ2NmRw8REcmNRvewjBo1Cp9//jkSEhKgVCpVHsXp5Hn27BkGDRoEGxsbtGvXDu3atUPt2rUxePBglfumi5KZmYmoqCh4e3uLZTo6OvD29kZkZGSBdXbu3AkvLy+MHDkSVlZWcHJywpw5cwqN+9WrV0hLS1N5kPa8ePECP/30EzZu3CiWderUCdu2bcOFCxfETh4iIqLybNq0aZg5cyb8/PywatUqrFq1Cv3798fs2bMxbdq0QutKlUtVRq9evQLAjh4iIpIfjTp6kpKS4O/vDysrK7Xq+/v74/Dhw9i1axdSUlKQkpKCHTt24PDhwxg3blyx9/P48WPk5OTki8PKygqJiYkF1omNjcXmzZuRk5OD3bt3Y9q0afjpp58wa9astx4nODgYpqam4oMrY2jXb7/9hvHjx2PChAkqyVrPnj3FUVpERETlXffu3bF9+3bcvHkTI0aMwLhx43Dv3j38+++/6NmzZ6F1pcqlKqP79+8DQLFHoBMREVUUCiF33KoaBg0ahDZt2mDw4MFq1bewsMDmzZvRoUMHlfKDBw/iiy++wKNHj4q1nwcPHsDW1hYnTpyAl5eXWD5hwgQcPnwYp06dylenUaNGePnypcrcLSEhIZg/fz4SEhIKPM6rV6/EDgUASEtLg52dHVJTU1G9evVixUrqy87OxtOnT8Xh6C9evECnTp0wZMgQ9O/fnyN4iIhKKC0tDaampryOVWBS5VIVhZSf2ePHj+O9994D8L/buIiIiEpLWeZdGv1lvHjxYnz++ec4evQonJ2d842i+O677wqt//z58wJHA1laWpZouLGFhQV0dXWRlJSkUp6UlARra+sC69jY2EBfX19l7pamTZsiMTERmZmZBU6KaGhoCENDw2LHRdKJjIzEwIED4eDggL179wJ4PYniiRMntBwZERGR9kiVS5WluLg4DBo0CElJSdDV1cXJkydVVsAqK7a2tgD+NykzERGRXGjU0bNu3Trs27cPRkZGOHTokMo9zgqFosiOHi8vL0yfPh2rV6+GkZERgNejNIKCglRG5hTFwMAAbm5uiIiIEIc4K5VKRERE4Ntvvy2wTps2bbB27VqV5bavX78OGxubAjt5SLssLS1x69YtPH78GImJiW/twCMiIqpocnJy8PPPP2Pjxo2Ij49HZmamyvvJyclvrStVLlWWBgwYgFmzZqFt27ZITk7W2pdouavFco4eIiKSG406eqZMmYKgoCBMmjRJ7CwpiQULFqBLly6oU6cOXFxcAAAXLlyAkZER/vnnnxLty9/fH35+fnB3d4eHhwdCQ0Px7NkzcRWu/v37w9bWFsHBwQCAb775BosXL8bo0aMxatQo3LhxA3PmzCmyc4pKnyAIiIiIwK1btzBs2DAAQP369bFt2za0a9dO67cXCIIgPgp7LdW2uc81KZNqP1LvW1vvFfWvunXf9lyd999Wp6jXJdm2qLrFJcUtD29+UVDQc3W309HRgUKhEB+l9VpHRwe6urql8q8611iqOIKCgvDbb79h3LhxmDp1KqZMmYLbt29j+/bt+VbiepOUuVRZuHLlCvT19dG2bVsAgLm5udZiyf3dxZ8vIiKSG406ejIzM9G7d2+1L5BOTk64ceMG1qxZg2vXrgEA+vTpA19f3xIPo+3duzcePXqEwMBAJCYmomXLlti7d684nDk+Pl4lTjs7O/zzzz8YO3YsWrRoAVtbW4wePRoTJ05Uqy2aevHiBRYsWJBv9bKCVjMrapuSbJ/7R21pPy/qAfzvD+wXL14gMTERCoUCM2bMgI6OTon3URplRETalNvxY2JigpSUFG2HQxJas2YNwsPD8eGHH+KHH35Anz59UL9+fbRo0QInT54s9EsoKXMpADhy5Ajmz5+PqKgoJCQkYNu2bfkmhF6yZAnmz5+PxMREuLi4YNGiRfDw8CjW/m/cuIGqVauie/fuuH//Pj777DNMnjy5xHFKIXfBjjdHUBEREVV0Gk3GPHbsWNSqVUtrF2htk3IypZSUFNSoUUOiyEjuckcr5I4oUPe5Ou9LVacs3tPGvyV9T53ti3pdkm2LqlsWpByFVNDrgjqeS+P1mx3sOTk5Ks/f9m9JL8MmJiZ49uxZieq8DSdjLh+qVKmCq1evwt7eHjY2Nvj777/RqlUrxMbGwtXVFampqWUWy549e3D8+HG4ubmhV69e+Tp6NmzYgP79+yMsLAyenp4IDQ3Fpk2bEBMTA0tLSwBAy5YtkZ2dnW/f+/btw4kTJzBs2DBER0fD0tISXbt2xeTJk/HBBx8UKz4pP7N79uxBt27doFAoxNu4iIiISkuFmYw5JycH8+bNwz///IMWLVrkm4w5JCSk0PqrVq2ChYUFPvzwQwCvV8n69ddf0axZM6xbtw5169bVJLwKxdDQEIMGDRKH6Bf1yDucX51t37z9oLSfv+3x5MkT/Pzzz4iNjcWaNWugr68PhUIBQRCgq6tbZP03HwDKvCzva02eF2c7IpKfvB1FBXUEvVnGP0jlp06dOkhISIC9vT3q16+Pffv2oVWrVjhz5kyR89dInUv5+PjAx8fnre+HhIRg6NCh4q3xYWFh+Pvvv7FixQpMmjQJABAdHf3W+ra2tnB3d4ednR0AoFu3boiOjn5rR09BK55KxczMDADEuY2IiIjkQqOOnkuXLsHV1RUAcPnyZZX3ivNH6Zw5c7Bs2TIAr1dVWrx4MUJDQ/HXX39h7Nix2Lp1qybhVSjGxsZYvny5tsMocxkZGRg6dCgeP36M7Oxs8Z59IqLKQqFQQFdXF7q6uvm+MKHK4ZNPPkFERAQ8PT0xatQo9O3bF8uXL0d8fDzGjh1baN2yzKUyMzMRFRWFgIAAsUxHRwfe3t6IjIws1j7eeecdPHz4EE+fPoWpqSmOHDkizsdXkODgYAQFBWkce0FMTU0BcNUtIiKSH406eg4ePKjRwe/evYsGDRoAALZv347PPvsMX3/9Ndq0aYMOHTpotG8qn+7evYudO3di5MiRAICqVavi999/h4ODA5o3b67l6IiIiMre3Llzxee9e/eGvb09IiMj0bBhQ3Tv3r3QumWZSz1+/Bg5OTn5lnO3srIS5wcqip6eHubMmYN27dpBEAR07twZH3300Vu3DwgIgL+/v/g6LS1NHA2kKU7GTEREcqVRR4+mqlatiidPnsDe3h779u0TL+RGRkZ48eKFNkOjUpCcnIwmTZrg+fPneOedd8SJG3OHmxMREdHrJdOLuzR6Rcyliro9LC9DQ8NSW3497y1hREREclLijp5evXph5cqVqF69Onr16lXotkUNF/7ggw8wZMgQuLq64vr16+jWrRuA10tvOjg4lDQ0KoeysrLEWxHMzc3xxRdfIC4uDgYGBlqOjIiIqPyIiYnBokWLcPXqVQBA06ZNMWrUKDRu3LjQemWZS1lYWEBXVxdJSUkq5UlJSbC2tpb0WGXhxo0bAF5/EUVERCQnJR6rampqKs6/Y2pqWuijKEuWLIGXlxcePXqELVu2oGbNmgCAqKgo9OnTp6ShUTmSlZWFefPmoV69enj06JFYvmzZMhw8eBAtW7bUXnBERETlyJYtW+Dk5ISoqCi4uLjAxcUF586dg5OTE7Zs2VJo3bLMpQwMDODm5oaIiAixTKlUIiIiotgjkMoTTmxORERypdby6jNmzMD48eNhYmJSGjFVGFyW9u1ycnLg5uaGCxcu4Mcff8SECRO0HRIREb2B17HyoX79+vD19cWMGTNUyqdPn44///wTt27dKrNYMjIycPPmTQCAq6srQkJC0LFjR5ibm8Pe3h4bNmyAn58ffvnlF3h4eCA0NBQbN27EtWvX8s3dUxqk/MyeO3cObm5uqF27Nu7fvy9RhERERAUry7xLrY4eXV1dJCQkwNLSUuMAnj59iuXLl6sMVR40aBDMzc013ndpY4Ks6vr162jQoIE4qeHx48dx8+ZN9OvXjxMdEhGVQ7yOlQ8mJia4ePGiOKlyrhs3bsDFxQXPnz8vtL6UudShQ4fQsWPHfOV+fn5YuXIlAGDx4sWYP38+EhMT0bJlSyxcuBCenp4lPpY6pPzMRkVFwd3dHXXq1MHdu3clipCIiKhgZZl3qfXXtxp9QwU6cuQIHBwcsHDhQjx9+hRPnz7FokWL4OjoiCNHjkhyDCobAQEBaNasGdasWSOWtWnTBn5+fuzkISIiKkSHDh1w9OjRfOXHjh1D27ZtC60rdS7VoUMHCIKQ75HbyQMA3377Le7cuYNXr17h1KlTZdbJI7XcW7eYpxARkdyovepW7jw9mhg5ciR69+6NZcuWQVdXF8DrW35GjBiBkSNH4tKlSxofg8qGmZkZcnJyEBkZiX79+mk7HCIiogqjR48emDhxIqKiotC6dWsAwMmTJ7Fp0yYEBQVh586dKtvmxVxKfbm3a6Wmpmo5EiIiImmpdeuWjo6OyqTMb1PUKgbGxsaIjo7Ot6JETEwMWrZsWW6XBc1VmYe879+/H3Z2dmjSpAmA10uUnjx5Eu3bt9dyZEREVFyV+TpWnhR3RIlCoUBOTo5KWUXPpUpKys/sr7/+imHDhkFPTw9ZWVkSRUhERFSwssy71B7RExQUVKyVtQrTqlUrXL16NV9ycvXqVbi4uGi0byo9P/74IyZNmoTOnTtj7969UCgUMDQ0ZCcPERGRGjRZ/Ym5lPosLCwAQON8loiIqLxRu6Pnyy+/VGsy5osXL4rPv/vuO4wePRo3b95UGaq8ZMkSzJ07V93QqJR99tlnmDlzJpo2bYrs7Gzo6+trOyQiIqIKJzIyEk+ePMFHH30klq1evRrTp0/Hs2fP0LNnTyxatAiGhoYq9ZhLSSN3lbAaNWpoORIiIiJplfmqWzo6OlAoFEVO6FzQ8OTypjIMeVcqlVi5ciXS0tIwZswYsTw5OblCrIxGRERvVxmuY+WZj48POnTogIkTJwIALl26hFatWmHAgAFo2rQp5s+fj2HDhuGHH35QqSenXKqkpPzMHj16FO3atUOjRo0QExMjUYREREQFK/e3bmmy6lZcXJzadans7du3D4MHD4aRkRF69eoFe3t7AGAnDxERkYaio6Mxc+ZM8fX69evh6emJ8PBwAICdnR2mT5+er6OHuZQ0cuflkWo1WSIiovJCrY4eTe4lr1u3rtp1qWzk5OSIK3d06dIFPXr0QLt27WBtba3lyIiIiOTj6dOn4u1DAHD48GH4+PiIr9955x3cvXs3Xz3mUtKIjo4GAMTHx2s3ECIiIompPUePlP777z/Ex8cjMzNTpfzNJUSpdL148QLz5s3Dzp07cfLkSejr60OhUGD79u1FrrBGREREJWNlZYW4uDjY2dkhMzMT586dQ1BQkPh+enp6sefBYy5Vcpp8cUlERFSeabWjJzY2Fp988gkuXbqkcq95bqeC3O4rL+8yMzOxZMkSPHr0CJs2bcJXX30FAOzkISIiKgXdunXDpEmT8OOPP2L79u0wMTFB27ZtxfcvXryI+vXrF7oP5lLqy12VrKhzTEREVNHoaPPgo0ePhqOjIx4+fAgTExNcuXIFR44cgbu7Ow4dOqTN0CqNO3fuiM9NTU2xePFibNiwAX369NFiVERERPI3c+ZM6OnpoX379ggPD0d4eDgMDAzE91esWIHOnTsXug/mUprT0ysXA9yJiIgko9UrW2RkJA4cOAALCwvo6OhAR0cH7733HoKDg/Hdd9/h/Pnz2gxP1pRKJYYPH47ly5fj2LFj8PLyAgB88cUXWo6MiIiocrCwsMCRI0eQmpqKqlWrivPj5dq0aROqVq1a6D6YS6kv99YtjlwmIiK50eqInpycHFSrVg3A62TnwYMHAF5PMshlLkuXjo4OcnJyoFQq8e+//2o7HCIiokrL1NQ0XycP8HqFy7wjfArCXEp9uZMwP3nyRMuREBERSUurI3qcnJxw4cIFODo6wtPTE/PmzYOBgQF+/fVX1KtXT5uhyVJcXBxq1aolfjs4e/ZsDB48GO+++66WIyMiIiJ1MJdS3/379wEAycnJWo6EiIhIWlod0TN16lRx2OyMGTMQFxeHtm3bYvfu3Vi4cKE2Q5OddevWwdnZGRMmTBDLrK2t2clDRERUgTGXUl/u0vbm5uZajoSIiEhaWh3R06VLF/F5gwYNcO3aNSQnJ6NGjRq8X1pilpaWePbsGf777z9kZmYWORSciIiIyj/mUuqrU6cOAKB27dpajoSIiEhaWh3RUxBzc3MmJhIQBEG8Tx8AOnXqhIiICBw4cICdPERERDLGXKp4OBkzERHJVbnr6CHNPXnyBD179oSHhwdSUlLE8vfffx86OvwvJyIiIsrJyQHAjh4iIpIf/tUvQ0ZGRvjvv//w8OFDHD9+XNvhEBEREZU7kZGRAIArV65oORIiIiJpaXWOHpJORkaGuJpWlSpVsG7dOhgaGsLZ2VnLkRERERGVP4IgAOCIHiIikh+O6JGBnTt3on79+ti+fbtY5u7uzk4eIiIiordwc3MD8HqJeiIiIjlhR48MnDhxAg8fPsTChQvFb6eIiIiI6O1y5y3U19fXciRERETS4q1bFVR2djb09F7/9wUFBcHCwgLffvsthx8TERERFUPul2NcqIKIiORGdle2JUuWwMHBAUZGRvD09MTp06eLVW/9+vVQKBTo2bNn6QaoofT0dAwbNgxffvmlmKAYGhpi/PjxMDIy0nJ0RERERBXD7du3AQBJSUnaDYSIiEhisuro2bBhA/z9/TF9+nScO3cOLi4u6NKlCx4+fFhovdu3b2P8+PFo27ZtGUWqvtjYWKxYsQJbtmzBuXPntB0OERERUYUUHx8PAEhMTNRyJERERNKSVUdPSEgIhg4dioEDB6JZs2YICwuDiYkJVqxY8dY6OTk58PX1RVBQEOrVq1eG0RZf3nl3XFxcsHDhQhw8eFCcRJCIiIiISsbS0hIAUKtWLS1HQkREJC3ZdPRkZmYiKioK3t7eYpmOjg68vb0RGRn51nozZsyApaUlBg8eXOQxXr16hbS0NJVHaYuMjETr1q1x//59seybb75Bhw4dSv3YRERERHLl6OgIAHBwcNBuIERERBKTTUfP48ePkZOTAysrK5VyKyurtw7JPXbsGJYvX47w8PBiHSM4OBimpqbiw87OTuO4CyMIAsaNG4fTp09j8uTJpXosIiIiosqEkzETEZFcVdorW3p6Ovr164fw8HBYWFgUq05AQABSU1PFx927d0s1RoVCgRUrVmDQoEFYsGBBqR6LiIiIqDJRKpUAwBVLiYhIdmSzvLqFhQV0dXXzrZyQlJQEa2vrfNvfunULt2/fRvfu3cWy3Au+np4eYmJiUL9+fZU6hoaGMDQ0LIXoX8vKysLs2bNRp04dDBkyBADQpEkTLF++vNSOSURERFQZHT16FAC4uAUREcmObEb0GBgYwM3NDREREWKZUqlEREQEvLy88m3fpEkTXLp0CdHR0eKjR48e6NixI6Kjo0v9tqyCrFu3DkFBQRgzZgyX+iQiIiIqRTk5OdoOgYiIqFTIZkQPAPj7+8PPzw/u7u7w8PBAaGgonj17hoEDBwIA+vfvD1tbWwQHB8PIyAhOTk4q9c3MzAAgX3lZ6du3L3bs2IEvvvgi31xDRERERCQdNzc3/PHHH2jZsqW2QyEiIpKUrDp6evfujUePHiEwMBCJiYlo2bIl9u7dK3aaxMfHl+sJ93R0dLBlyxZth0FEREQke35+fujatSuqVKmi7VCIiIgkpRBylxygEktLS4OpqSlSU1NRvXp1bYdDRERUIryOUUXDzywREVVUZXkNK7/DW4iIiIiIiIiIqETY0UNEREREREREJBPs6CEiIiIiIiIikgl29BARERERERERyYSsVt0qa7nzWKelpWk5EiIiopLLvX5xXQaqKJh7ERFRRVWWeRc7ejSQnp4OALCzs9NyJEREROpLT0+HqamptsMgKhJzLyIiqujKIu/i8uoaUCqVePDgAapVqwaFQqHyXlpaGuzs7HD37t1Ks/xnZWwzUDnbzTZXjjYDlbPdlanNgiAgPT0dtWvXho4O7+am8q+w3EsdlennvTh4PvLjOcmP50QVz0d+PCeqcs9HfHw8FApFmeRdHNGjAR0dHdSpU6fQbapXr17pPtyVsc1A5Ww321x5VMZ2V5Y2cyQPVSTFyb3UUVl+3ouL5yM/npP8eE5U8Xzkx3OiytTUtMzOB7++IyIiIiIiIiKSCXb0EBERERERERHJBDt6SomhoSGmT58OQ0NDbYdSZipjm4HK2W62ufKojO2ujG0mqqz4866K5yM/npP8eE5U8Xzkx3OiShvng5MxExERERERERHJBEf0EBERERERERHJBDt6iIiIiIiIiIhkgh09REREREREREQywY4eIiIiIiIiIiKZYEdPKViyZAkcHBxgZGQET09PnD59WtshSSY4OBjvvPMOqlWrBktLS/Ts2RMxMTEq27x8+RIjR45EzZo1UbVqVXz66adISkrSUsTSmzt3LhQKBcaMGSOWybXN9+/fR9++fVGzZk0YGxvD2dkZZ8+eFd8XBAGBgYGwsbGBsbExvL29cePGDS1GrJmcnBxMmzYNjo6OMDY2Rv369TFz5kzknbNeDm0+cuQIunfvjtq1a0OhUGD79u0q7xenjcnJyfD19UX16tVhZmaGwYMHIyMjowxbUTKFtTkrKwsTJ06Es7MzqlSpgtq1a6N///548OCByj4qWpuJqHByztfykip3i4+Px4cffggTExNYWlri+++/R3Z2dlk2pVSom9fJ7XxIkfPJ6TopVU5Ykc9JWeWLFy9eRNu2bWFkZAQ7OzvMmzevtJumlrLKJSU7HwJJav369YKBgYGwYsUK4cqVK8LQoUMFMzMzISkpSduhSaJLly7C77//Lly+fFmIjo4WunXrJtjb2wsZGRniNsOHDxfs7OyEiIgI4ezZs0Lr1q2Fd999V4tRS+f06dOCg4OD0KJFC2H06NFiuRzbnJycLNStW1cYMGCAcOrUKSE2Nlb4559/hJs3b4rbzJ07VzA1NRW2b98uXLhwQejRo4fg6OgovHjxQouRq2/27NlCzZo1hb/++kuIi4sTNm3aJFStWlVYsGCBuI0c2rx7925hypQpwtatWwUAwrZt21TeL04bu3btKri4uAgnT54Ujh49KjRo0EDo06dPGbek+Aprc0pKiuDt7S1s2LBBuHbtmhAZGSl4eHgIbm5uKvuoaG0moreTe76WlxS5W3Z2tuDk5CR4e3sL58+fF3bv3i1YWFgIAQEB2miSZNTN6+R2PqTK+eR0nZQqJ6zI56Qs8sXU1FTByspK8PX1FS5fviysW7dOMDY2Fn755ZeyamaxlUUuKeX5YEePxDw8PISRI0eKr3NycoTatWsLwcHBWoyq9Dx8+FAAIBw+fFgQhNcfcn19fWHTpk3iNlevXhUACJGRkdoKUxLp6elCw4YNhf379wvt27cXEwK5tnnixInCe++999b3lUqlYG1tLcyfP18sS0lJEQwNDYV169aVRYiS+/DDD4VBgwaplPXq1Uvw9fUVBEGebX7zQlWcNv73338CAOHMmTPiNnv27BEUCoVw//79MotdXQUlK286ffq0AEC4c+eOIAgVv81EpKqy5Wt5qZO77d69W9DR0RESExPFbZYtWyZUr15dePXqVdk2QCKa5HVyOx9S5Hxyu05KkRPK6ZyUVr64dOlSoUaNGio/NxMnThQaN25cyi3STGnlklKeD966JaHMzExERUXB29tbLNPR0YG3tzciIyO1GFnpSU1NBQCYm5sDAKKiopCVlaVyDpo0aQJ7e/sKfw5GjhyJDz/8UKVtgHzbvHPnTri7u+Pzzz+HpaUlXF1dER4eLr4fFxeHxMRElXabmprC09Ozwrb73XffRUREBK5fvw4AuHDhAo4dOwYfHx8A8mzzm4rTxsjISJiZmcHd3V3cxtvbGzo6Ojh16lSZx1waUlNToVAoYGZmBqBytJmosqiM+Vpe6uRukZGRcHZ2hpWVlbhNly5dkJaWhitXrpRh9NLRJK+T2/mQIueT23VSipxQbuckL6naHxkZiXbt2sHAwEDcpkuXLoiJicHTp0/LqDWlQ51cUsrzoad5EyjX48ePkZOTo/JLHwCsrKxw7do1LUVVepRKJcaMGYM2bdrAyckJAJCYmAgDAwPxA53LysoKiYmJWohSGuvXr8e5c+dw5syZfO/Jtc2xsbFYtmwZ/P39MXnyZJw5cwbfffcdDAwM4OfnJ7atoM97RW33pEmTkJaWhiZNmkBXVxc5OTmYPXs2fH19AUCWbX5TcdqYmJgIS0tLlff19PRgbm4ui/Pw8uVLTJw4EX369EH16tUByL/NRJVJZcvX8lI3d0tMTCzwfOW+V9FomtfJ7XxIkfPJ7TopRU4ot3OSl1TtT0xMhKOjY7595L5Xo0aNUom/tKmbS0p5PtjRQ2obOXIkLl++jGPHjmk7lFJ19+5djB49Gvv374eRkZG2wykzSqUS7u7umDNnDgDA1dUVly9fRlhYGPz8/LQcXenYuHEj1qxZg7Vr16J58+aIjo7GmDFjULt2bdm2mVRlZWXhiy++gCAIWLZsmbbDISKSVGXJ3QpTWfO6wlTGnK8ozAlJXeUll+StWxKysLCArq5uvln5k5KSYG1traWoSse3336Lv/76CwcPHkSdOnXEcmtra2RmZiIlJUVl+4p8DqKiovDw4UO0atUKenp60NPTw+HDh7Fw4ULo6enByspKdm0GABsbGzRr1kylrGnTpoiPjwcAsW1y+rx///33mDRpEr788ks4OzujX79+GDt2LIKDgwHIs81vKk4bra2t8fDhQ5X3s7OzkZycXKHPQ+6F+c6dO9i/f7/4DQwg3zYTVUaVKV/LS5PczdrausDzlfteRSJFXien8wFIk/PJ7TopRU4ot3OSl1Ttl9vPkqa5pJTngx09EjIwMICbmxsiIiLEMqVSiYiICHh5eWkxMukIgoBvv/0W27Ztw4EDB/INLXNzc4O+vr7KOYiJiUF8fHyFPQedOnXCpUuXEB0dLT7c3d3h6+srPpdbmwGgTZs2+ZZfvX79OurWrQsAcHR0hLW1tUq709LScOrUqQrb7ufPn0NHR/XXoq6uLpRKJQB5tvlNxWmjl5cXUlJSEBUVJW5z4MABKJVKeHp6lnnMUsi9MN+4cQP//vsvatasqfK+HNtMVFlVhnwtLylyNy8vL1y6dEnlj5TcP2Le7CAo76TI6+R0PgBpcj65XSelyAnldk7ykqr9Xl5eOHLkCLKyssRt9u/fj8aNG1e427akyCUlPR8lnr6ZCrV+/XrB0NBQWLlypfDff/8JX3/9tWBmZqYyK39F9s033wimpqbCoUOHhISEBPHx/PlzcZvhw4cL9vb2woEDB4SzZ88KXl5egpeXlxajll7e1RkEQZ5tPn36tKCnpyfMnj1buHHjhrBmzRrBxMRE+PPPP8Vt5s6dK5iZmQk7duwQLl68KHz88ccVbqnxvPz8/ARbW1txKc2tW7cKFhYWwoQJE8Rt5NDm9PR04fz588L58+cFAEJISIhw/vx5cVWA4rSxa9eugqurq3Dq1Cnh2LFjQsOGDcv1cqGFtTkzM1Po0aOHUKdOHSE6Olrld1veVQ8qWpuJ6O3knq/lJUXulruceOfOnYXo6Ghh7969Qq1atSrscuJvKmleJ7fzIVXOJ6frpFQ5YUU+J2WRL6akpAhWVlZCv379hMuXLwvr168XTExMyuXy6mWRS0p5PtjRUwoWLVok2NvbCwYGBoKHh4dw8uRJbYckGQAFPn7//XdxmxcvXggjRowQatSoIZiYmAiffPKJkJCQoL2gS8GbCYFc27xr1y7ByclJMDQ0FJo0aSL8+uuvKu8rlUph2rRpgpWVlWBoaCh06tRJiImJ0VK0mktLSxNGjx4t2NvbC0ZGRkK9evWEKVOmqPyClkObDx48WODPsZ+fnyAIxWvjkydPhD59+ghVq1YVqlevLgwcOFBIT0/XQmuKp7A2x8XFvfV328GDB8V9VLQ2E1Hh5Jyv5SVV7nb79m3Bx8dHMDY2FiwsLIRx48YJWVlZZdya0qFOXie38yFFzien66RUOWFFPidllS9euHBBeO+99wRDQ0PB1tZWmDt3blk1sUTKKpeU6nwoBEEQSjYGiIiIiIiIiIiIyiPO0UNEREREREREJBPs6CEiIiIiIiIikgl29BARERERERERyQQ7eoiIiIiIiIiIZIIdPUREREREREREMsGOHiIiIiIiIiIimWBHDxERERERERGRTLCjh4iIiIiIiIhIJtjRQ0REREREREQkE+zoISJJCYIAAPjhhx9UXhMRERGRdjA/I6pcFAJ/yolIQkuXLoWenh5u3LgBXV1d+Pj4oH379toOi4iIiKjSYn5GVLlwRA8RSWrEiBFITU3FwoUL0b1792IlER06dIBCoYBCoUB0dHTpB/mGAQMGiMffvn17mR+fiIiIqDSVND9TJzdjPkVUfrCjh4gkFRYWBlNTU3z33XfYtWsXjh49Wqx6Q4cORUJCApycnEo5wvwWLFiAhISEMj8uERERkZTGjh2LXr165StXJz8raW7GfIqo/NDTdgBEJC/Dhg2DQqHADz/8gB9++KHY94CbmJjA2tq6lKMrmKmpKUxNTbVybCIiIiKpnD59Gh9++GG+cnXys5LmZsyniMoPjughohKZM2eOOCw37yM0NBQAoFAoAPxvsr/c1yXVoUMHjBo1CmPGjEGNGjVgZWWF8PBwPHv2DAMHDkS1atXQoEED7NmzR5J6RERERBVVZmYm9PX1ceLECUyZMgUKhQKtW7cW35cqP9u8eTOcnZ1hbGyMmjVrwtvbG8+ePdM4fiKSFjt6iKhERo0ahYSEBPExdOhQ1K1bF5999pnkx1q1ahUsLCxw+vRpjBo1Ct988w0+//xzvPvuuzh37hw6d+6Mfv364fnz55LUIyIiIqqI9PT0cPz4cQBAdHQ0EhISsHfvXkmPkZCQgD59+mDQoEG4evUqDh06hF69enEFL6JyiB09RFQi1apVg7W1NaytrbFkyRLs27cPhw4dQp06dSQ/louLC6ZOnYqGDRsiICAARkZGsLCwwNChQ9GwYUMEBgbiyZMnuHjxoiT1iIiIiCoiHR0dPHjwADVr1oSLiwusra1hZmYm6TESEhKQnZ2NXr16wcHBAc7OzhgxYgSqVq0q6XGISHPs6CEitQQGBuKPP/7AoUOH4ODgUCrHaNGihfhcV1cXNWvWhLOzs1hmZWUFAHj48KEk9YiIiIgqqvPnz8PFxaXU9u/i4oJOnTrB2dkZn3/+OcLDw/H06dNSOx4RqY8dPURUYtOnT8fq1atLtZMHAPT19VVeKxQKlbLc+8uVSqUk9YiIiIgqqujo6FLt6NHV1cX+/fuxZ88eNGvWDIsWLULjxo0RFxdXasckIvWwo4eISmT69OlYtWpVqXfyEBEREVHxXbp0CS1btizVYygUCrRp0wZBQUE4f/48DAwMsG3btlI9JhGVHJdXJ6JimzVrFpYtW4adO3fCyMgIiYmJAIAaNWrA0NBQy9ERERERVV5KpRIxMTF48OABqlSpIvlS56dOnUJERAQ6d+4MS0tLnDp1Co8ePULTpk0lPQ4RaY4jeoioWARBwPz58/Ho0SN4eXnBxsZGfHBSYyIiIiLtmjVrFlauXAlbW1vMmjVL8v1Xr14dR44cQbdu3dCoUSNMnToVP/30E3x8fCQ/FhFphiN6iKhYFAoFUlNTy+x4hw4dyld2+/btfGVvLumpbj0iIiKiiqxv377o27dvqe2/adOmki/ZTkSlgyN6iKhcWLp0KapWrYpLly6V+bGHDx/OpUGJiIiI8ihpbsZ8iqj8UAj8WpuItOz+/ft48eIFAMDe3h4GBgZlevyHDx8iLS0NAGBjY4MqVaqU6fGJiIiIyhN1cjPmU0TlBzt6iIiIiIiIiIhkgrduERERERERERHJBDt6iIiIiIiIiIhkgh09REREREREREQywY4eIiIiIiIiIiKZYEcPEREREREREZFMsKOHiIiIiIiIiEgm2NFDRERERERERCQT7OghIiIiIiIiIpIJdvQQEREREREREckEO3qIiIiIiIiIiGSCHT1ERERERERERDLx/wAIMM/+6K3WzAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1300x700 with 5 Axes>"
       ]
@@ -839,7 +828,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev",
+   "display_name": "venv",
    "language": "python",
    "name": "python3"
   },
@@ -853,7 +842,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,
@@ -870,7 +859,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
    }
   }
  },
diff --git a/docs/source/examples/notebooks/models/thermal-models.ipynb b/docs/source/examples/notebooks/models/thermal-models.ipynb
index 8bcc504af0..599a362b4b 100644
--- a/docs/source/examples/notebooks/models/thermal-models.ipynb
+++ b/docs/source/examples/notebooks/models/thermal-models.ipynb
@@ -1,473 +1,507 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Thermal models\n",
-        "\n",
-        "There are a number of thermal submodels available in PyBaMM. In this notebook we give details of each of the models, and highlight any relevant parameters. At present PyBaMM includes an isothermal and a lumped thermal model, both of which can be used with any cell geometry, as well as a 1D thermal model which accounts for the through-cell variation in temperature in a pouch cell, and \"1+1D\" and \"2+1D\" pouch cell models which assumed the temperature is uniform through the thickness of the pouch, but accounts for variations in temperature in the remaining dimensions. Here we give the governing equations for each model (except the isothermal model, which just sets the temperature to be equal to to the parameter \"Ambient temperature [K]\"). \n",
-        "\n",
-        "A more comprehensive review of the pouch cell models, including how to properly compute the effective cooling terms, can be found in references [4] and [6] at the end of this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "zsh:1: no matches found: pybamm[plot,cite]\n",
-            "Note: you may need to restart the kernel to use updated packages.\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
-        "import pybamm"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Lumped model\n",
-        "\n",
-        "The lumped thermal model solves the following ordinary differential equation for the average temperature, given here in dimensional terms,\n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\bar{Q} - \\frac{hA}{V}(T-T_{\\infty}),\n",
-        "$$\n",
-        "\n",
-        "where $\\rho_{eff}$ is effective volumetric heat capacity, $T$ is the temperature, $t$ is time, $\\bar{Q}$ is the averaged heat source term, $h$ is the heat transfer coefficient, $A$ is the surface area (available for cooling), $V$ is the cell volume, and $T_{\\infty}$ is the ambient temperature. An initial temperature $T_0$ must be prescribed.\n",
-        "\n",
-        "\n",
-        "The effective volumetric heat capacity is computed as \n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} = \\frac{\\sum_k \\rho_k c_{p,k} L_k}{\\sum_k L_k},\n",
-        "$$\n",
-        "\n",
-        "where $\\rho_k$ is the density, $c_{p,k}$ is the specific heat, and $L_k$ is the thickness of each component. The subscript $k \\in \\{cn, n, s, p, cp\\}$ is used to refer to the components negative current collector, negative electrode, separator, positive electrode, and positive current collector.\n",
-        "\n",
-        "The heat source term accounts for Ohmic heating $Q_{Ohm,k}$ due to resistance in the solid and electrolyte, irreverisble heating due to electrochemical reactions $Q_{rxn,k}$, and reversible heating due to entropic changes in the the electrode $Q_{rev,k}$:\n",
-        "\n",
-        "$$\n",
-        "Q = Q_{Ohm,k}+Q_{rxn,k}+Q_{rev,k},\n",
-        "$$\n",
-        "\n",
-        "with\n",
-        "\n",
-        "$$ \n",
-        "Q_{Ohm,k} = -i_k \\nabla \\phi_k, \\quad Q_{rxn,k} = a_k j_k \\eta_k, \\quad Q_{rev,k} = a_k j_k T_k \\frac{\\partial U}{\\partial T} \\bigg|_{T=T_{\\infty}}.\n",
-        "$$\n",
-        "\n",
-        "Here $i_k$ is the current, $\\phi_k$ the potential, $a_k$ the surface area to volume ratio, $j_k$ the interfacial current density, $\\eta_k$ the overpotential, and $U$ the open-circuit potential. The averaged heat source term $\\bar{Q}$ is computed by taking the volume-average of $Q$.\n"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The relevant parameters to specify the cooling conditions are:  \n",
-        "\n",
-        "\"Total heat transfer coefficient [W.m-2.K-1]\"  \n",
-        "\"Cell cooling surface area [m2]\"  \n",
-        "\"Cell volume [m3]\"\n",
-        "\n",
-        "which correspond directly to the parameters $h$, $A$ and $V$ in the governing equation.\n",
-        "\n",
-        "The lumped thermal option can be selected as follows\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\"thermal\": \"lumped\"}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Pouch cell models"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### 1D (through-cell) model\n",
-        "\n",
-        "The 1D model solves for $T(x,t)$, capturing variations through the thickness of the cell, but ignoring variations in the other dimensions. The temperature is found as the solution of a partial differential equation, given here in dimensional terms\n",
-        "\n",
-        "$$\\rho_k c_{p,k} \\frac{\\partial T}{\\partial t} = \\lambda_k \\nabla^2 T + Q(x,t) - Q_{cool}(x,t)$$\n",
-        "\n",
-        "with boundary conditions \n",
-        "\n",
-        "$$ -\\lambda_{cn} \\frac{\\partial T}{\\partial x}\\bigg|_{x=0} = h_{cn}(T_{\\infty} - T) \\quad -\\lambda_{cp} \\frac{\\partial T}{\\partial x}\\bigg|_{x=1} = h_{cp}(T-T_{\\infty}),$$\n",
-        "\n",
-        "and initial condition\n",
-        "\n",
-        "$$ T\\big|_{t=0} = T_0.$$\n",
-        "\n",
-        "Here $\\lambda_k$ is the thermal conductivity of component $k$, and the heat transfer coefficients $h_{cn}$ and $h_{cp}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, respectively. The heat source term $Q$ is as described in the section on lumped models. The term $Q_cool$ accounts for additional heat losses due to heat transfer at the sides of the pouch, as well as the tabs. This term is computed automatically by PyBaMM based on the cell geometry and heat transfer coefficients on the edges and tabs of the cell.\n",
-        "\n",
-        "The relevant heat transfer parameters are:\n",
-        "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\n",
-        "The 1D model is termed \"x-full\" (since it fully accounts for variation in the x direction) and can be selected as follows\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\"thermal\": \"x-full\"}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Higher dimensional pouch cell models\n",
-        "\n",
-        "These pouch cell thermal models ignore any variation in temperature through the thickness of the cell (x direction), and solve for $T(y,z,t)$. It is therefore referred to as an \"x-lumped\" model. The temperature is found as the solution of a partial differential equation, given here in dimensional terms,\n",
-        "\n",
-        "$$\n",
-        "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\lambda_{eff} \\nabla_\\perp^2T + \\bar{Q} - \\frac{(h_{cn}+h_{cp})A}{V}(T-T_{\\infty}),\n",
-        "$$\n",
-        "\n",
-        "along with boundary conditions\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{L_{cn}h_{cn} + (L_n+L_s+L_p+L_{cp})h_{edge}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "at the negative tab,\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{(L_{cn}+L_n+L_s+L_p)h_{edge}+L_{cp}h_{cp}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "at the positive tab, and\n",
-        "\n",
-        "$$\n",
-        "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = h_{edge}(T-T_\\infty),\n",
-        "$$\n",
-        "\n",
-        "elsewhere. Again, an initial temperature $T_0$ must be prescribed.\n",
-        "\n",
-        "Here the heat source term is averaged in the x direction so that $\\bar{Q}=\\bar{Q}(y,z)$. The parameter $\\lambda_{eff}$ is the effective thermal conductivity, computed as \n",
-        "\n",
-        "$$\n",
-        "\\lambda_{eff} = \\frac{\\sum_k \\lambda_k L_k}{\\sum_k L_k}.\n",
-        "$$\n",
-        "\n",
-        "The heat transfer coefficients $h_{cn}$, $h_{cp}$ and $h_{egde}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, and  heat transfer at the remaining, respectively.\n",
-        "\n",
-        "The relevant heat transfer parameters are:\n",
-        "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
-        "\n",
-        "The \"2+1D\" model can be selected as follows"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "options = {\n",
-        "    \"current collector\": \"potential pair\",\n",
-        "    \"dimensionality\": 2,\n",
-        "    \"thermal\": \"x-lumped\",\n",
-        "}\n",
-        "model = pybamm.lithium_ion.DFN(options)"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Model usage"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Here we compare the \"full\" one-dimensional model with the lumped model for a pouch cell. We first set up our models, passing the relevant options, and then show how to adjust the parameters to so that the lumped and full models give the same behaviour"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "full_thermal_model = pybamm.lithium_ion.SPMe(\n",
-        "    {\"thermal\": \"x-full\"}, name=\"full thermal model\"\n",
-        ")\n",
-        "lumped_thermal_model = pybamm.lithium_ion.SPMe(\n",
-        "    {\"thermal\": \"lumped\"}, name=\"lumped thermal model\"\n",
-        ")\n",
-        "models = [full_thermal_model, lumped_thermal_model]"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We then pick our parameter set"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "parameter_values = pybamm.ParameterValues(\"Marquis2019\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For the \"full\" model we use a heat transfer coefficient of $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ on the large surfaces of the pouch and zero heat transfer coefficient on the tabs and edges"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "full_params = parameter_values.copy()\n",
-        "full_params.update(\n",
-        "    {\n",
-        "        \"Negative current collector\"\n",
-        "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Positive current collector\"\n",
-        "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 0,\n",
-        "    }\n",
-        ")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "For the lumped model we set the \"Total heat transfer coefficient [W.m-2.K-1]\"\n",
-        "parameter as well as the \"Cell cooling surface area [m2]\" parameter. Since the \"full\"\n",
-        "model only accounts for cooling from the large surfaces of the pouch, we set the\n",
-        "\"Surface area for cooling\" parameter to the area of the large surfaces of the pouch,\n",
-        "and the total heat transfer coefficient to $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ "
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thermal models\n",
+    "\n",
+    "There are a number of thermal submodels available in PyBaMM. In this notebook we give details of each of the models, and highlight any relevant parameters. At present PyBaMM includes an isothermal and a lumped thermal model, both of which can be used with any cell geometry, as well as a 1D thermal model which accounts for the through-cell variation in temperature in a pouch cell, and \"1+1D\" and \"2+1D\" pouch cell models which assumed the temperature is uniform through the thickness of the pouch, but accounts for variations in temperature in the remaining dimensions. Here we give the governing equations for each model (except the isothermal model, which just sets the temperature to be equal to to the parameter \"Ambient temperature [K]\"). \n",
+    "\n",
+    "A more comprehensive review of the pouch cell models, including how to properly compute the effective cooling terms, can be found in references [4] and [6] at the end of this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "A = parameter_values[\"Electrode width [m]\"] * parameter_values[\"Electrode height [m]\"]\n",
-        "lumped_params = parameter_values.copy()\n",
-        "lumped_params.update(\n",
-        "    {\n",
-        "        \"Total heat transfer coefficient [W.m-2.K-1]\": 5,\n",
-        "        \"Cell cooling surface area [m2]\": 2 * A,\n",
-        "    }\n",
-        ")"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
+    "import pybamm"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lumped model\n",
+    "\n",
+    "The lumped thermal model solves the following ordinary differential equation for the average temperature, given here in dimensional terms,\n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\bar{Q} - \\frac{hA}{V}(T-T_{\\infty}),\n",
+    "$$\n",
+    "\n",
+    "where $\\rho_{eff}$ is effective volumetric heat capacity, $T$ is the temperature, $t$ is time, $\\bar{Q}$ is the averaged heat source term, $h$ is the heat transfer coefficient, $A$ is the surface area (available for cooling), $V$ is the cell volume, and $T_{\\infty}$ is the ambient temperature. An initial temperature $T_0$ must be prescribed.\n",
+    "\n",
+    "\n",
+    "The effective volumetric heat capacity is computed as \n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} = \\frac{\\sum_k \\rho_k c_{p,k} L_k}{\\sum_k L_k},\n",
+    "$$\n",
+    "\n",
+    "where $\\rho_k$ is the density, $c_{p,k}$ is the specific heat, and $L_k$ is the thickness of each component. The subscript $k \\in \\{cn, n, s, p, cp\\}$ is used to refer to the components negative current collector, negative electrode, separator, positive electrode, and positive current collector.\n",
+    "\n",
+    "The heat source term accounts for Ohmic heating $Q_{Ohm,k}$ due to resistance in the solid and electrolyte, irreverisble heating due to electrochemical reactions $Q_{rxn,k}$, and reversible heating due to entropic changes in the the electrode $Q_{rev,k}$:\n",
+    "\n",
+    "$$\n",
+    "Q = Q_{Ohm,k}+Q_{rxn,k}+Q_{rev,k},\n",
+    "$$\n",
+    "\n",
+    "with\n",
+    "\n",
+    "$$ \n",
+    "Q_{Ohm,k} = -i_k \\nabla \\phi_k, \\quad Q_{rxn,k} = a_k j_k \\eta_k, \\quad Q_{rev,k} = a_k j_k T_k \\frac{\\partial U}{\\partial T} \\bigg|_{T=T_{\\infty}}.\n",
+    "$$\n",
+    "\n",
+    "Here $i_k$ is the current, $\\phi_k$ the potential, $a_k$ the surface area to volume ratio, $j_k$ the interfacial current density, $\\eta_k$ the overpotential, and $U$ the open-circuit potential. The averaged heat source term $\\bar{Q}$ is computed by taking the volume-average of $Q$.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using the option `{\"cell geometry\": \"arbitrary\"}` the relevant parameters to specify the cooling conditions are:  \n",
+    "\n",
+    "\"Total heat transfer coefficient [W.m-2.K-1]\"  \n",
+    "\"Cell cooling surface area [m2]\"  \n",
+    "\"Cell volume [m3]\"\n",
+    "\n",
+    "which correspond directly to the parameters $h$, $A$ and $V$ in the governing equation.\n",
+    "\n",
+    "When using the option `{\"cell geometry\": \"pouch\"}` the parameter $A$ and $V$ are computed automatically from the pouch dimensions, assuming a single-layer pouch cell, i.e. $A$ is the total surface area of a single-layer pouch cell and $V$ is the volume. The parameter $h$ is still set by the \"Total heat transfer coefficient [W.m-2.K-1]\" parameter.\n",
+    "\n",
+    "The lumped thermal option can be selected as follows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\"cell geometry\": \"arbitrary\", \"thermal\": \"lumped\"}\n",
+    "arbitrary_lumped_model = pybamm.lithium_ion.DFN(options)\n",
+    "# OR\n",
+    "options = {\"cell geometry\": \"pouch\", \"thermal\": \"lumped\"}\n",
+    "pouch_lumped_model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If no cell geometry is specified, the \"arbitrary\" cell geometry is used by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's run simulations with both options and compare the results. For demonstration purposes we'll increase the current to amplify the thermal effects"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cell geometry: arbitrary\n"
+     ]
+    }
+   ],
+   "source": [
+    "options = {\"thermal\": \"lumped\"}\n",
+    "model = pybamm.lithium_ion.DFN(options)\n",
+    "print(\"Cell geometry:\", model.options[\"cell geometry\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pouch cell models"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1D (through-cell) model\n",
+    "\n",
+    "The 1D model solves for $T(x,t)$, capturing variations through the thickness of the cell, but ignoring variations in the other dimensions. The temperature is found as the solution of a partial differential equation, given here in dimensional terms\n",
+    "\n",
+    "$$\\rho_k c_{p,k} \\frac{\\partial T}{\\partial t} = \\lambda_k \\nabla^2 T + Q(x,t) - Q_{cool}(x,t)$$\n",
+    "\n",
+    "with boundary conditions \n",
+    "\n",
+    "$$ -\\lambda_{cn} \\frac{\\partial T}{\\partial x}\\bigg|_{x=0} = h_{cn}(T_{\\infty} - T) \\quad -\\lambda_{cp} \\frac{\\partial T}{\\partial x}\\bigg|_{x=1} = h_{cp}(T-T_{\\infty}),$$\n",
+    "\n",
+    "and initial condition\n",
+    "\n",
+    "$$ T\\big|_{t=0} = T_0.$$\n",
+    "\n",
+    "Here $\\lambda_k$ is the thermal conductivity of component $k$, and the heat transfer coefficients $h_{cn}$ and $h_{cp}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, respectively. The heat source term $Q$ is as described in the section on lumped models. The term $Q_cool$ accounts for additional heat losses due to heat transfer at the sides of the pouch, as well as the tabs. This term is computed automatically by PyBaMM based on the cell geometry and heat transfer coefficients on the edges and tabs of the cell.\n",
+    "\n",
+    "The relevant heat transfer parameters are:\n",
+    "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\n",
+    "The 1D model is termed \"x-full\" (since it fully accounts for variation in the x direction) and can be selected as follows\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\"thermal\": \"x-full\"}\n",
+    "model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Higher dimensional pouch cell models\n",
+    "\n",
+    "These pouch cell thermal models ignore any variation in temperature through the thickness of the cell (x direction), and solve for $T(y,z,t)$. It is therefore referred to as an \"x-lumped\" model. The temperature is found as the solution of a partial differential equation, given here in dimensional terms,\n",
+    "\n",
+    "$$\n",
+    "\\rho_{eff} \\frac{\\partial T}{\\partial t} = \\lambda_{eff} \\nabla_\\perp^2T + \\bar{Q} - \\frac{(h_{cn}+h_{cp})A}{V}(T-T_{\\infty}),\n",
+    "$$\n",
+    "\n",
+    "along with boundary conditions\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{L_{cn}h_{cn} + (L_n+L_s+L_p+L_{cp})h_{edge}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "at the negative tab,\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = \\frac{(L_{cn}+L_n+L_s+L_p)h_{edge}+L_{cp}h_{cp}}{L_{cn}+L_n+L_s+L_p+L_{cp}}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "at the positive tab, and\n",
+    "\n",
+    "$$\n",
+    "-\\lambda_{eff} \\nabla_\\perp T \\cdot \\boldsymbol{n} = h_{edge}(T-T_\\infty),\n",
+    "$$\n",
+    "\n",
+    "elsewhere. Again, an initial temperature $T_0$ must be prescribed.\n",
+    "\n",
+    "Here the heat source term is averaged in the x direction so that $\\bar{Q}=\\bar{Q}(y,z)$. The parameter $\\lambda_{eff}$ is the effective thermal conductivity, computed as \n",
+    "\n",
+    "$$\n",
+    "\\lambda_{eff} = \\frac{\\sum_k \\lambda_k L_k}{\\sum_k L_k}.\n",
+    "$$\n",
+    "\n",
+    "The heat transfer coefficients $h_{cn}$, $h_{cp}$ and $h_{egde}$ correspond to heat transfer at the large surface of the pouch on the side of the negative current collector, heat transfer at the large surface of the pouch on the side of the positive current collector, and  heat transfer at the remaining, respectively.\n",
+    "\n",
+    "The relevant heat transfer parameters are:\n",
+    "\"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Negative tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Positive tab heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\"Edge heat transfer coefficient [W.m-2.K-1]\"\n",
+    "\n",
+    "The \"2+1D\" model can be selected as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "options = {\n",
+    "    \"current collector\": \"potential pair\",\n",
+    "    \"dimensionality\": 2,\n",
+    "    \"thermal\": \"x-lumped\",\n",
+    "}\n",
+    "model = pybamm.lithium_ion.DFN(options)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model usage"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we compare the \"full\" one-dimensional model with the lumped model for a pouch cell. We first set up our models, passing the relevant options, and then show how to adjust the parameters to so that the lumped and full models give the same behaviour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_thermal_model = pybamm.lithium_ion.SPMe(\n",
+    "    {\"thermal\": \"x-full\"}, name=\"full thermal model\"\n",
+    ")\n",
+    "lumped_thermal_model = pybamm.lithium_ion.SPMe(\n",
+    "    {\"thermal\": \"lumped\"}, name=\"lumped thermal model\"\n",
+    ")\n",
+    "models = [full_thermal_model, lumped_thermal_model]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We then pick our parameter set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_values = pybamm.ParameterValues(\"Marquis2019\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the \"full\" model we use a heat transfer coefficient of $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ on the large surfaces of the pouch and zero heat transfer coefficient on the tabs and edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_params = parameter_values.copy()\n",
+    "full_params.update(\n",
+    "    {\n",
+    "        \"Negative current collector\"\n",
+    "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Positive current collector\"\n",
+    "        + \" surface heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "        \"Positive tab heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "        \"Edge heat transfer coefficient [W.m-2.K-1]\": 0,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the lumped model we set the \"Total heat transfer coefficient [W.m-2.K-1]\"\n",
+    "parameter as well as the \"Cell cooling surface area [m2]\" parameter. Since the \"full\"\n",
+    "model only accounts for cooling from the large surfaces of the pouch, we set the\n",
+    "\"Surface area for cooling\" parameter to the area of the large surfaces of the pouch,\n",
+    "and the total heat transfer coefficient to $5\\,  \\text{Wm}^{-2}\\text{K}^{-1}$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = parameter_values[\"Electrode width [m]\"] * parameter_values[\"Electrode height [m]\"]\n",
+    "lumped_params = parameter_values.copy()\n",
+    "lumped_params.update(\n",
+    "    {\n",
+    "        \"Total heat transfer coefficient [W.m-2.K-1]\": 5,\n",
+    "        \"Cell cooling surface area [m2]\": 2 * A,\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's run simulations with both options and compare the results. For demonstration purposes we'll increase the current to amplify the thermal effects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "97a1370f6f8745b0a4b2a7bb4df5b477",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7667871396477, step=11.547667871396477)…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "fb646d540c774a10af2ee25e79251283",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7667871396477, step=11.547667871396477)…"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "text/plain": [
-              "<pybamm.plotting.quick_plot.QuickPlot at 0x14e1456d0>"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "params = [full_params, lumped_params]\n",
-        "# loop over the models and solve\n",
-        "sols = []\n",
-        "for model, param in zip(models, params):\n",
-        "    param[\"Current function [A]\"] = 3 * 0.68\n",
-        "    sim = pybamm.Simulation(model, parameter_values=param)\n",
-        "    sim.solve([0, 3600])\n",
-        "    sols.append(sim.solution)\n",
-        "\n",
-        "\n",
-        "# plot\n",
-        "output_variables = [\n",
-        "    \"Voltage [V]\",\n",
-        "    \"X-averaged cell temperature [K]\",\n",
-        "    \"Cell temperature [K]\",\n",
-        "]\n",
-        "pybamm.dynamic_plot(sols, output_variables)\n",
-        "\n",
-        "# plot the results\n",
-        "pybamm.dynamic_plot(\n",
-        "    sols,\n",
-        "    [\n",
-        "        \"Volume-averaged cell temperature [K]\",\n",
-        "        \"Volume-averaged total heating [W.m-3]\",\n",
-        "        \"Current [A]\",\n",
-        "        \"Voltage [V]\",\n",
-        "    ],\n",
-        ")"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b29f3527b0cb47b888bf748ff800f359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7660708378553, step=11.547660708378553)…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## References\n",
-        "\n",
-        "The relevant papers for this notebook are:"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de290974a0c4649b7edddae4562bf90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1154.7660708378553, step=11.547660708378553)…"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-            "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
-            "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-            "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-            "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-            "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "pybamm.print_citations()"
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x17f501e90>"
       ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "dev",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.16"
-    },
-    "toc": {
-      "base_numbering": 1,
-      "nav_menu": {},
-      "number_sections": true,
-      "sideBar": true,
-      "skip_h1_title": false,
-      "title_cell": "Table of Contents",
-      "title_sidebar": "Contents",
-      "toc_cell": false,
-      "toc_position": {},
-      "toc_section_display": true,
-      "toc_window_display": true
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
-      }
+   ],
+   "source": [
+    "params = [full_params, lumped_params]\n",
+    "# loop over the models and solve\n",
+    "sols = []\n",
+    "for model, param in zip(models, params):\n",
+    "    param[\"Current function [A]\"] = 3 * 0.68\n",
+    "    sim = pybamm.Simulation(model, parameter_values=param)\n",
+    "    sim.solve([0, 3600])\n",
+    "    sols.append(sim.solution)\n",
+    "\n",
+    "\n",
+    "# plot\n",
+    "output_variables = [\n",
+    "    \"Voltage [V]\",\n",
+    "    \"X-averaged cell temperature [K]\",\n",
+    "    \"Cell temperature [K]\",\n",
+    "]\n",
+    "pybamm.dynamic_plot(sols, output_variables)\n",
+    "\n",
+    "# plot the results\n",
+    "pybamm.dynamic_plot(\n",
+    "    sols,\n",
+    "    [\n",
+    "        \"Volume-averaged cell temperature [K]\",\n",
+    "        \"Volume-averaged total heating [W.m-3]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Voltage [V]\",\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[6] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
+      "\n"
+     ]
     }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  "vscode": {
+   "interpreter": {
+    "hash": "9ff3d0c7e37de5f5aa47f4f719e4c84fc6cba7b39c571a05173422444e82fa58"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/examples/scripts/thermal_lithium_ion.py b/examples/scripts/thermal_lithium_ion.py
index 68b6cabdf0..c6aa978c99 100644
--- a/examples/scripts/thermal_lithium_ion.py
+++ b/examples/scripts/thermal_lithium_ion.py
@@ -6,11 +6,15 @@
 pybamm.set_logging_level("INFO")
 
 # load models
+# for the full model we use the "x-full" thermal submodel, which means that we solve
+# the thermal model in the x-direction for a single-layer pouch cell
+# for the lumped model we use the "arbitrary" cell geometry, which means that we can
+# specify the surface area for cooling and total heat transfer coefficient
 full_thermal_model = pybamm.lithium_ion.SPMe(
     {"thermal": "x-full"}, name="full thermal model"
 )
 lumped_thermal_model = pybamm.lithium_ion.SPMe(
-    {"thermal": "lumped"}, name="lumped thermal model"
+    {"cell geometry": "arbitrary", "thermal": "lumped"}, name="lumped thermal model"
 )
 models = [full_thermal_model, lumped_thermal_model]
 
@@ -31,27 +35,43 @@
     }
 )
 # for the lumped model we set the "Total heat transfer coefficient [W.m-2.K-1]"
-# parameter as well as the "Cell cooling surface area [m2]" parameter. Since the "full"
-# model only accounts for cooling from the large surfaces of the pouch, we set the
-# "Surface area for cooling" parameter to the area of the large surfaces of the pouch,
-# and the total heat transfer coefficient to 5 W.m-2.K-1
+# parameter as well as the "Cell cooling surface area [m2]" and "Cell volume [m3]
+# parameters. Since the "full" model only accounts for cooling from the large surfaces
+# of the pouch, we set the "Surface area for cooling [m2]" parameter to the area of the
+# large surfaces of the pouch, and the total heat transfer coefficient to 5 W.m-2.K-1
 A = parameter_values["Electrode width [m]"] * parameter_values["Electrode height [m]"]
+contributing_layers = [
+    "Negative current collector",
+    "Negative electrode",
+    "Separator",
+    "Positive electrode",
+    "Positive current collector",
+]
+total_thickness = sum(
+    [parameter_values[layer + " thickness [m]"] for layer in contributing_layers]
+)
+electrode_volume = (
+    total_thickness
+    * parameter_values["Electrode height [m]"]
+    * parameter_values["Electrode width [m]"]
+)
 lumped_params = parameter_values.copy()
 lumped_params.update(
     {
         "Total heat transfer coefficient [W.m-2.K-1]": 5,
         "Cell cooling surface area [m2]": 2 * A,
+        "Cell volume [m3]": electrode_volume,
     }
 )
-params = [full_params, lumped_params]
+
 # loop over the models and solve
+params = [full_params, lumped_params]
 sols = []
 for model, param in zip(models, params):
     sim = pybamm.Simulation(model, parameter_values=param)
     sim.solve([0, 3600])
     sols.append(sim.solution)
 
-
 # plot
 output_variables = [
     "Voltage [V]",
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 4886251e0a..cbc270653b 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -4,7 +4,6 @@
 
 import pybamm
 from functools import cached_property
-import warnings
 
 from pybamm.expression_tree.operations.serialise import Serialise
 
@@ -683,24 +682,6 @@ def __init__(self, extra_options):
                             f"Possible values are {self.possible_options[option]}"
                         )
 
-        # Issue a warning to let users know that the 'lumped' thermal option (or
-        # equivalently 'x-lumped' with 0D current collectors) now uses the total heat
-        # transfer coefficient, surface area for cooling, and cell volume parameters,
-        # regardless of the 'cell geometry option' chosen.
-        thermal_option = options["thermal"]
-        dimensionality_option = options["dimensionality"]
-        if thermal_option == "lumped" or (
-            thermal_option == "x-lumped" and dimensionality_option == 0
-        ):
-            message = (
-                f"The '{thermal_option}' thermal option with "
-                f"'dimensionality' {dimensionality_option} now uses the parameters "
-                "'Cell cooling surface area [m2]', 'Cell volume [m3]' and "
-                "'Total heat transfer coefficient [W.m-2.K-1]' to compute the cell "
-                "cooling term, regardless of the value of the the 'cell geometry' "
-                "option. Please update your parameters accordingly."
-            )
-            warnings.warn(message, pybamm.OptionWarning, stacklevel=2)
         super().__init__(options.items())
 
     @property
diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index b2a79c52d5..808cdefc67 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -179,32 +179,74 @@ def _get_standard_coupled_variables(self, variables):
         Q = Q_ohm + Q_rxn + Q_rev
 
         # Compute the X-average over the entire cell, including current collectors
+        # Note: this can still be a function of y and z for higher-dimensional pouch
+        # cell models
         Q_ohm_av = self._x_average(Q_ohm, Q_ohm_s_cn, Q_ohm_s_cp)
         Q_rxn_av = self._x_average(Q_rxn, 0, 0)
         Q_rev_av = self._x_average(Q_rev, 0, 0)
         Q_av = self._x_average(Q, Q_ohm_s_cn, Q_ohm_s_cp)
 
-        # Compute volume-averaged heat source terms
-        Q_ohm_vol_av = self._yz_average(Q_ohm_av)
-        Q_rxn_vol_av = self._yz_average(Q_rxn_av)
-        Q_rev_vol_av = self._yz_average(Q_rev_av)
-        Q_vol_av = self._yz_average(Q_av)
+        # Compute the integrated heat source per unit simulated electrode-pair area
+        # in W.m-2. Note: this can still be a function of y and z for
+        # higher-dimensional pouch cell models
+        Q_ohm_Wm2 = Q_ohm_av * param.L
+        Q_rxn_Wm2 = Q_rxn_av * param.L
+        Q_rev_Wm2 = Q_rev_av * param.L
+        Q_Wm2 = Q_av * param.L
+        # Now average over the electrode height and width
+        Q_ohm_Wm2_av = self._yz_average(Q_ohm_Wm2)
+        Q_rxn_Wm2_av = self._yz_average(Q_rxn_Wm2)
+        Q_rev_Wm2_av = self._yz_average(Q_rev_Wm2)
+        Q_Wm2_av = self._yz_average(Q_Wm2)
+
+        # Compute total heat source terms (in W) over the *entire cell volume*, not
+        # the product of electrode height * electrode width * electrode stack thickness
+        # Note: we multiply by the number of electrode pairs, since the Q_xx_Wm2_av
+        # variables are per electrode pair
+        n_elec = param.n_electrodes_parallel
+        A = param.L_y * param.L_z  # *modelled* electrode area
+        Q_ohm_W = Q_ohm_Wm2_av * n_elec * A
+        Q_rxn_W = Q_rxn_Wm2_av * n_elec * A
+        Q_rev_W = Q_rev_Wm2_av * n_elec * A
+        Q_W = Q_Wm2_av * n_elec * A
+
+        # Compute volume-averaged heat source terms over the *entire cell volume*, not
+        # the product of electrode height * electrode width * electrode stack thickness
+        V = param.V_cell  # *actual* cell volume
+        Q_ohm_vol_av = Q_ohm_W / V
+        Q_rxn_vol_av = Q_rxn_W / V
+        Q_rev_vol_av = Q_rev_W / V
+        Q_vol_av = Q_W / V
 
         variables.update(
             {
+                # Ohmic
                 "Ohmic heating [W.m-3]": Q_ohm,
                 "X-averaged Ohmic heating [W.m-3]": Q_ohm_av,
                 "Volume-averaged Ohmic heating [W.m-3]": Q_ohm_vol_av,
+                "Ohmic heating per unit electrode-pair area [W.m-2]": Q_ohm_Wm2,
+                "Ohmic heating [W]": Q_ohm_W,
+                # Irreversible
                 "Irreversible electrochemical heating [W.m-3]": Q_rxn,
                 "X-averaged irreversible electrochemical heating [W.m-3]": Q_rxn_av,
                 "Volume-averaged irreversible electrochemical heating "
                 + "[W.m-3]": Q_rxn_vol_av,
+                "Irreversible electrochemical heating per unit "
+                + "electrode-pair area [W.m-2]": Q_rxn_Wm2,
+                "Irreversible electrochemical heating [W]": Q_rxn_W,
+                # Reversible
                 "Reversible heating [W.m-3]": Q_rev,
                 "X-averaged reversible heating [W.m-3]": Q_rev_av,
                 "Volume-averaged reversible heating [W.m-3]": Q_rev_vol_av,
+                "Reversible heating per unit electrode-pair area " "[W.m-2]": Q_rev_Wm2,
+                "Reversible heating [W]": Q_rev_W,
+                # Total
                 "Total heating [W.m-3]": Q,
                 "X-averaged total heating [W.m-3]": Q_av,
                 "Volume-averaged total heating [W.m-3]": Q_vol_av,
+                "Total heating per unit electrode-pair area [W.m-2]": Q_Wm2,
+                "Total heating [W]": Q_W,
+                # Current collector
                 "Negative current collector Ohmic heating [W.m-3]": Q_ohm_s_cn,
                 "Positive current collector Ohmic heating [W.m-3]": Q_ohm_s_cp,
             }
diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py
index 8ef6add863..ecc52e30f1 100644
--- a/pybamm/parameters/geometric_parameters.py
+++ b/pybamm/parameters/geometric_parameters.py
@@ -42,8 +42,19 @@ def _set_parameters(self):
         self.r_inner = pybamm.Parameter("Inner cell radius [m]")
         self.r_outer = pybamm.Parameter("Outer cell radius [m]")
         self.A_cc = self.L_y * self.L_z  # Current collector cross sectional area
-        self.A_cooling = pybamm.Parameter("Cell cooling surface area [m2]")
-        self.V_cell = pybamm.Parameter("Cell volume [m3]")
+
+        # Cell surface area and volume (for thermal models only)
+        cell_geometry = self.options.get("cell geometry", None)
+        if cell_geometry == "pouch":
+            # assuming a single-layer pouch cell for now, see
+            # https://github.com/pybamm-team/PyBaMM/issues/1777
+            self.A_cooling = 2 * (
+                self.L_y * self.L_z + self.L_z * self.L + self.L_y * self.L
+            )
+            self.V_cell = self.L_y * self.L_z * self.L
+        else:
+            self.A_cooling = pybamm.Parameter("Cell cooling surface area [m2]")
+            self.V_cell = pybamm.Parameter("Cell volume [m3]")
 
 
 class DomainGeometricParameters(BaseParameters):

From a4be1a3a484d2288e64c69e010c0168559cc791e Mon Sep 17 00:00:00 2001
From: Saransh Chopra <saransh0701@gmail.com>
Date: Wed, 17 Jan 2024 11:01:49 +0100
Subject: [PATCH 609/615] Improve the release workflow (#3737)

* Try fixing the release workflow

* Turn off safety

* Fix CHANGELOG

* Add OS

* Use regex for better matches

* Update instructions, add safety checks

* checkout to the version branch for the final release
---
 .github/release_workflow.md          |  8 +++---
 .github/workflows/publish_pypi.yml   |  2 +-
 .github/workflows/update_version.yml | 42 +++++++++++++++++++++++-----
 CHANGELOG.md                         | 11 ++------
 scripts/update_version.py            | 15 ++++++++--
 5 files changed, 55 insertions(+), 23 deletions(-)

diff --git a/.github/release_workflow.md b/.github/release_workflow.md
index 690f7fa407..89a22e7d38 100644
--- a/.github/release_workflow.md
+++ b/.github/release_workflow.md
@@ -21,9 +21,9 @@ This file contains the workflow required to make a `PyBaMM` release on GitHub, P
 
 ## rcX releases (manual)
 
-If a new release candidate is required after the release of `rc0` -
+If a new release candidate is required after the release of `rc{X-1}` -
 
-1. Fix a bug in `vYY.MM` (no new features should be added to `vYY.MM` once `rc0` is released) and `develop` individually.
+1. Cherry-pick the bug fix (no new features should be added to `vYY.MM` once `rc{X-1}` is released) commit to `vYY.MM` branch once the fix is merged into `develop`. The CHANGELOG entry for such fixes should go under the `rc{X-1}` heading in `CHANGELOG.md`
 
 2. Run `update_version.yml` manually while using `append_to_tag` to specify the release candidate version number (`rc1`, `rc2`, ...).
 
@@ -36,7 +36,7 @@ If a new release candidate is required after the release of `rc0` -
    - `vcpkg.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR will be created to update version strings in `develop`.
 
 4. Create a new GitHub _pre-release_ with the same tag (`vYY.MMrcX`) from the `vYY.MM` branch and a description copied from `CHANGELOG.md`.
 
@@ -57,7 +57,7 @@ Once satisfied with the release candidates -
    - `vcpkg.json`
    - `CHANGELOG.md`
 
-      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR from `vvYY.MM` to `develop` will be created (to sync the branches).
+      These changes will be automatically pushed to the existing `vYY.MM` branch and a PR will be created to update version strings in `develop`.
 
 3. Next, a PR from `vYY.MM` to `main` will be generated that should be merged once all the tests pass.
 
diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml
index 8a8126b0e4..ce930733db 100644
--- a/.github/workflows/publish_pypi.yml
+++ b/.github/workflows/publish_pypi.yml
@@ -213,7 +213,7 @@ jobs:
   open_failure_issue:
     needs: [build_windows_wheels, build_macos_and_linux_wheels, build_sdist]
     name: Open an issue if build fails
-    if: ${{ always() && contains(needs.*.result, 'failure') }}
+    if: ${{ always() && contains(needs.*.result, 'failure') && github.repository_owner == 'pybamm-team'}}
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
diff --git a/.github/workflows/update_version.yml b/.github/workflows/update_version.yml
index a6c35c0333..f04b033272 100644
--- a/.github/workflows/update_version.yml
+++ b/.github/workflows/update_version.yml
@@ -29,11 +29,13 @@ jobs:
           echo "VERSION=$(date +'v%y.%-m')${{ github.event.inputs.append_to_tag }}" >> $GITHUB_ENV
           echo "NON_RC_VERSION=$(date +'v%y.%-m')" >> $GITHUB_ENV
 
+      # the schedule workflow is for rc0 release
       - uses: actions/checkout@v4
         if: github.event_name == 'schedule'
         with:
           ref: 'develop'
 
+      # the dispatch workflow is for rcX and final releases
       - uses: actions/checkout@v4
         if: github.event_name == 'workflow_dispatch'
         with:
@@ -49,29 +51,55 @@ jobs:
           pip install wheel
           pip install --editable ".[all]"
 
+      # update all the version strings and add CHANGELOG headings
       - name: Update version
         run: python scripts/update_version.py
 
+      # create a new version branch for rc0 release and commit
       - uses: EndBug/add-and-commit@v9
         if: github.event_name == 'schedule'
         with:
           message: 'Bump to ${{ env.VERSION }}'
           new_branch: '${{ env.NON_RC_VERSION }}'
 
+      # use the already created release branch for rcX + final releases
+      # and commit
       - uses: EndBug/add-and-commit@v9
         if: github.event_name == 'workflow_dispatch'
         with:
           message: 'Bump to ${{ env.VERSION }}'
 
-      - name: Make a PR from ${{ env.NON_RC_VERSION }} to develop
-        uses: repo-sync/pull-request@v2
+      # checkout to develop for updating versions in the same
+      - uses: actions/checkout@v4
         with:
-          source_branch: '${{ env.NON_RC_VERSION }}'
-          destination_branch: "develop"
-          pr_title: "Sync ${{ env.NON_RC_VERSION }} and develop"
-          pr_body: "**Merge as soon as possible to avoid potential conflicts.**"
-          github_token: ${{ secrets.GITHUB_TOKEN }}
+          ref: 'develop'
+
+      # update all the version strings
+      - name: Update version
+        if: github.event_name == 'workflow_dispatch'
+        run: python scripts/update_version.py
+
+      # create a pull request updating versions in develop
+      - name: Create Pull Request
+        id: version_pr
+        uses: peter-evans/create-pull-request@v3
+        with:
+          delete-branch: true
+          branch-suffix: short-commit-hash
+          base: develop
+          commit-message: Update version to ${{ env.VERSION }}
+          title: Bump to ${{ env.VERSION }}
+          body: |
+            - [x] Update to ${{ env.VERSION }}
+            - [ ] Check the [release workflow](https://github.com/pybamm-team/PyBaMM/blob/develop/.github/release_workflow.md)
+
+      # checkout to the version branch for the final release
+      - uses: actions/checkout@v4
+        if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
+        with:
+          ref: '${{ env.NON_RC_VERSION }}'
 
+      # for final releases, create a PR from version branch to main
       - name: Make a PR from ${{ env.NON_RC_VERSION }} to main
         id: release_pr
         if: github.event_name == 'workflow_dispatch' && !startsWith(github.event.inputs.append_to_tag, 'rc')
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0692d152ca..20559a11d4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,13 +1,5 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-## Bug fixes
-
-- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
-- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
-
-## Breaking changes
-- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
-
 # [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
 
 ## Features
@@ -26,6 +18,8 @@
 
 ## Bug fixes
 
+- Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
+- Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
 - Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691))
 - Fixed a bug where simulations using the CasADi-based solvers would fail randomly with the half-cell model ([#3494](https://github.com/pybamm-team/PyBaMM/pull/3494))
 - Fixed bug that made identical Experiment steps with different end times crash ([#3516](https://github.com/pybamm-team/PyBaMM/pull/3516))
@@ -38,6 +32,7 @@
 
 ## Breaking changes
 
+- The parameters `GeometricParameters.A_cooling` and `GeometricParameters.V_cell` are now automatically computed from the electrode heights, widths and thicknesses if the "cell geometry" option is "pouch" and from the parameters "Cell cooling surface area [m2]" and "Cell volume [m3]", respectively, otherwise. When using the lumped thermal model we recommend using the "arbitrary" cell geometry and specifying the parameters "Cell cooling surface area [m2]", "Cell volume [m3]" and "Total heat transfer coefficient [W.m-2.K-1]" directly. ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
 - Dropped support for the `[jax]` extra, i.e., the Jax solver when running on Python 3.8. The Jax solver is now available on Python 3.9 and above ([#3550](https://github.com/pybamm-team/PyBaMM/pull/3550))
 
 # [v23.9](https://github.com/pybamm-team/PyBaMM/tree/v23.9) - 2023-10-31
diff --git a/scripts/update_version.py b/scripts/update_version.py
index 1d2d64ce41..dfc6b7f32e 100644
--- a/scripts/update_version.py
+++ b/scripts/update_version.py
@@ -17,7 +17,11 @@ def update_version():
     Opens file and updates the version number
     """
     release_version = os.getenv("VERSION")[1:]
-    last_day_of_month = date.today() + relativedelta(day=31)
+    release_date = (
+        date.today()
+        if "rc" in release_version
+        else date.today() + relativedelta(day=31)
+    )
 
     # pybamm/version.py
     with open(os.path.join(pybamm.root_dir(), "pybamm", "version.py"), "r+") as file:
@@ -72,16 +76,21 @@ def update_version():
         file.write(replace_commit_id)
 
     changelog_line1 = "# [Unreleased](https://github.com/pybamm-team/PyBaMM/)\n"
-    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {last_day_of_month}\n\n"
+    changelog_line2 = f"# [v{release_version}](https://github.com/pybamm-team/PyBaMM/tree/v{release_version}) - {release_date}\n\n"
 
     # CHANGELOG.md
     with open(os.path.join(pybamm.root_dir(), "CHANGELOG.md"), "r+") as file:
         output_list = file.readlines()
         output_list[0] = changelog_line1
+        # add a new heading for rc0 releases
         if "rc0" in release_version:
             output_list.insert(2, changelog_line2)
         else:
-            output_list[2] = changelog_line2
+            # for rcX and final releases, update the already existing rc
+            # release heading
+            for i in range(0, len(output_list)):
+                if re.search("[v]\d\d\.\drc\d", output_list[i]):
+                    output_list[i] = changelog_line2[:-1]
         file.truncate(0)
         file.seek(0)
         file.writelines(output_list)

From b84f7ad46993071f36b3811feedb263da2340ca5 Mon Sep 17 00:00:00 2001
From: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
Date: Wed, 17 Jan 2024 10:05:48 +0000
Subject: [PATCH 610/615] Bump to v24.1rc1

---
 CHANGELOG.md      | 2 +-
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 pyproject.toml    | 2 +-
 vcpkg.json        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 20559a11d4..9cfcc2f3fe 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-# [v24.1rc0](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc0) - 2024-01-31
+# [v24.1rc1](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc1) - 2024-01-17
 
 ## Features
 
diff --git a/CITATION.cff b/CITATION.cff
index 494f226a89..1512a57965 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "24.1rc0"
+version: "24.1rc1"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index b2305df5cb..96e7fef1e7 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "24.1rc0"
+__version__ = "24.1rc1"
diff --git a/pyproject.toml b/pyproject.toml
index a39a37ecc4..6bd016bb56 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "24.1rc0"
+version = "24.1rc1"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
diff --git a/vcpkg.json b/vcpkg.json
index 911703e7cf..959964dc7c 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "24.1rc0",
+  "version-string": "24.1rc1",
   "dependencies": [
     "casadi",
     {

From cf43cfd0d98ed94dc4184157fd8a5bbf8abd3b8c Mon Sep 17 00:00:00 2001
From: Robert Timms <43040151+rtimms@users.noreply.github.com>
Date: Wed, 24 Jan 2024 13:00:52 +0000
Subject: [PATCH 611/615] #3630 fix interpolant shape error (#3761)

* #3630 fix interpolant shape error

* #3630 changelog
---
 CHANGELOG.md                                  |  1 +
 pybamm/models/submodels/thermal/isothermal.py | 18 +++++++++++++--
 .../base_lithium_ion_tests.py                 | 22 +++++++++++++++++++
 3 files changed, 39 insertions(+), 2 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9cfcc2f3fe..f38e9823a0 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -18,6 +18,7 @@
 
 ## Bug fixes
 
+- Fixed a bug that lead to a `ShapeError` when specifying "Ambient temperature [K]" as an `Interpolant` with an isothermal model ([#3761](https://github.com/pybamm-team/PyBaMM/pull/3761))
 - Fixed a bug where if the first step(s) in a cycle are skipped then the cycle solution started from the model's initial conditions instead of from the last state of the previous cycle ([#3708](https://github.com/pybamm-team/PyBaMM/pull/3708))
 - Fixed a bug where the lumped thermal model conflates cell volume with electrode volume  ([#3707](https://github.com/pybamm-team/PyBaMM/pull/3707))
 - Reverted a change to the coupled degradation example notebook that caused it to be unstable for large numbers of cycles ([#3691](https://github.com/pybamm-team/PyBaMM/pull/3691))
diff --git a/pybamm/models/submodels/thermal/isothermal.py b/pybamm/models/submodels/thermal/isothermal.py
index d8070f6eba..52b5277986 100644
--- a/pybamm/models/submodels/thermal/isothermal.py
+++ b/pybamm/models/submodels/thermal/isothermal.py
@@ -26,13 +26,17 @@ def get_fundamental_variables(self):
         # specified as a function of space (y, z) only and time
         y = pybamm.standard_spatial_vars.y
         z = pybamm.standard_spatial_vars.z
-        T_x_av = self.param.T_amb(y, z, pybamm.t)
+        # Broadcast t to be the same size as y and z (to catch cases where the ambient
+        # temperature is a function of time only)
+        t_broadcast = pybamm.PrimaryBroadcast(pybamm.t, "current collector")
+        T_x_av = self.param.T_amb(y, z, t_broadcast)
+        T_vol_av = self._yz_average(T_x_av)
 
         T_dict = {
             "negative current collector": T_x_av,
             "positive current collector": T_x_av,
             "x-averaged cell": T_x_av,
-            "volume-averaged cell": T_x_av,
+            "volume-averaged cell": T_vol_av,
         }
         for domain in ["negative electrode", "separator", "positive electrode"]:
             T_dict[domain] = pybamm.PrimaryBroadcast(T_x_av, domain)
@@ -50,15 +54,25 @@ def get_coupled_variables(self, variables):
                 "Ohmic heating [W.m-3]",
                 "X-averaged Ohmic heating [W.m-3]",
                 "Volume-averaged Ohmic heating [W.m-3]",
+                "Ohmic heating per unit electrode-pair area [W.m-2]",
+                "Ohmic heating [W]",
                 "Irreversible electrochemical heating [W.m-3]",
                 "X-averaged irreversible electrochemical heating [W.m-3]",
                 "Volume-averaged irreversible electrochemical heating [W.m-3]",
+                "Irreversible electrochemical heating per unit electrode-pair area [W.m-2]",
+                "Irreversible electrochemical heating [W]",
                 "Reversible heating [W.m-3]",
                 "X-averaged reversible heating [W.m-3]",
                 "Volume-averaged reversible heating [W.m-3]",
+                "Reversible heating per unit electrode-pair area [W.m-2]",
+                "Reversible heating [W]",
                 "Total heating [W.m-3]",
                 "X-averaged total heating [W.m-3]",
                 "Volume-averaged total heating [W.m-3]",
+                "Total heating per unit electrode-pair area [W.m-2]",
+                "Total heating [W]",
+                "Negative current collector Ohmic heating [W.m-3]",
+                "Positive current collector Ohmic heating [W.m-3]",
             ]:
                 # All variables are zero
                 variables.update({var: zero})
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 6694248b5d..4db5ddea61 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -347,3 +347,25 @@ def test_basic_processing_msmr(self):
         model = self.model(options)
         modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
         modeltest.test_all(skip_output_tests=True)
+
+    def test_basic_processing_temperature_interpolant(self):
+        times = np.arange(0, 4000, 10)
+        tmax = max(times)
+
+        def temp_drive_cycle(y, z, t):
+            return pybamm.Interpolant(
+                times,
+                298.15 + 20 * (times / tmax),
+                t,
+            )
+
+        parameter_values = pybamm.ParameterValues("Chen2020")
+        parameter_values.update(
+            {
+                "Initial temperature [K]": 298.15,
+                "Ambient temperature [K]": temp_drive_cycle,
+            }
+        )
+        model = self.model()
+        modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
+        modeltest.test_all(skip_output_tests=True)

From 7d83dc1e58d3a7ec16ec793f1e8cbb5c0c93bfb9 Mon Sep 17 00:00:00 2001
From: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
Date: Wed, 24 Jan 2024 13:05:09 +0000
Subject: [PATCH 612/615] Bump to v24.1rc2

---
 CHANGELOG.md      | 2 +-
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 pyproject.toml    | 2 +-
 vcpkg.json        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f38e9823a0..76b61edf8b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-# [v24.1rc1](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc1) - 2024-01-17
+# [v24.1rc2](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc2) - 2024-01-24
 
 ## Features
 
diff --git a/CITATION.cff b/CITATION.cff
index 1512a57965..c2710cc8e0 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "24.1rc1"
+version: "24.1rc2"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index 96e7fef1e7..fedcaaf5e2 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "24.1rc1"
+__version__ = "24.1rc2"
diff --git a/pyproject.toml b/pyproject.toml
index 6bd016bb56..a16f7819c6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "24.1rc1"
+version = "24.1rc2"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
diff --git a/vcpkg.json b/vcpkg.json
index 959964dc7c..95411fcb96 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "24.1rc1",
+  "version-string": "24.1rc2",
   "dependencies": [
     "casadi",
     {

From 7cdb5ef1589c5802cb0d179c5f7a2c9df4794776 Mon Sep 17 00:00:00 2001
From: Saransh-cpp <Saransh-cpp@users.noreply.github.com>
Date: Mon, 29 Jan 2024 22:43:00 +0000
Subject: [PATCH 613/615] Bump to v24.1

---
 CHANGELOG.md      | 2 +-
 CITATION.cff      | 2 +-
 pybamm/version.py | 2 +-
 pyproject.toml    | 2 +-
 vcpkg.json        | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 76b61edf8b..6f713d828d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
-# [v24.1rc2](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc2) - 2024-01-24
+# [v24.1](https://github.com/pybamm-team/PyBaMM/tree/v24.1) - 2024-01-31
 
 ## Features
 
diff --git a/CITATION.cff b/CITATION.cff
index c2710cc8e0..10e942667c 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -24,6 +24,6 @@ keywords:
   - "expression tree"
   - "python"
   - "symbolic differentiation"
-version: "24.1rc2"
+version: "24.1"
 repository-code: "https://github.com/pybamm-team/PyBaMM"
 title: "Python Battery Mathematical Modelling (PyBaMM)"
diff --git a/pybamm/version.py b/pybamm/version.py
index fedcaaf5e2..61641b1fbe 100644
--- a/pybamm/version.py
+++ b/pybamm/version.py
@@ -1 +1 @@
-__version__ = "24.1rc2"
+__version__ = "24.1"
diff --git a/pyproject.toml b/pyproject.toml
index a16f7819c6..fca4de17ac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pybamm"
-version = "24.1rc2"
+version = "24.1"
 license = { file = "LICENSE.txt" }
 description = "Python Battery Mathematical Modelling"
 authors = [{name = "The PyBaMM Team", email = "pybamm@pybamm.org"}]
diff --git a/vcpkg.json b/vcpkg.json
index 95411fcb96..23cdcb3f58 100644
--- a/vcpkg.json
+++ b/vcpkg.json
@@ -1,6 +1,6 @@
 {
   "name": "pybamm",
-  "version-string": "24.1rc2",
+  "version-string": "24.1",
   "dependencies": [
     "casadi",
     {

From e0ac58a8d19142b917cd898c218fd27fa92b87a2 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 30 Jan 2024 20:51:12 +0530
Subject: [PATCH 614/615] Fix doctests failures in scheduled tests (#3784)

Closes #3781
---
 .github/workflows/run_periodic_tests.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 446ad9a9fb..0e64c2a63d 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -49,7 +49,11 @@ jobs:
         python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
-      - uses: actions/checkout@v4
+      - name: Check out PyBaMM repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:

From e29dcc0c66841dcdddb1a7bf04d6cf3c43af2408 Mon Sep 17 00:00:00 2001
From: Agriya Khetarpal <74401230+agriyakhetarpal@users.noreply.github.com>
Date: Tue, 30 Jan 2024 23:42:48 +0530
Subject: [PATCH 615/615] Resolve broken `scikits.odes` installation on
 self-hosted M-series runner (#3785)

* Try fixing M-series runner tests

This is being done by adding SuiteSparse and SUNDIALS installations which might have been missing on the runner, which broke `scikits.odes`.

* Don't use Homebrew SUNDIALS, use LD_LIBRARY_PATH

* Don't use Homebrew to install SUNDIALS

* Force remove pip cache for `scikits.odes`

---------

Co-authored-by: Eric G. Kratz <kratman@users.noreply.github.com>
---
 .github/workflows/run_periodic_tests.yml | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/run_periodic_tests.yml b/.github/workflows/run_periodic_tests.yml
index 0e64c2a63d..6d21393599 100644
--- a/.github/workflows/run_periodic_tests.yml
+++ b/.github/workflows/run_periodic_tests.yml
@@ -115,13 +115,14 @@ jobs:
         if: matrix.os == 'ubuntu-latest'
         run: python -m nox -s scripts
 
-  #M-series Mac Mini
+  # M-series Mac Mini
   build-apple-mseries:
     if: github.repository_owner == 'pybamm-team'
     needs: style
     runs-on: [self-hosted, macOS, ARM64]
     env:
       GITHUB_PATH: ${PYENV_ROOT/bin:$PATH}
+      LD_LIBRARY_PATH: $HOME/.local/lib
     strategy:
       fail-fast: false
       matrix:
@@ -129,28 +130,39 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - name: Install python & create virtualenv
+      - name: Install Python & create virtualenv
         shell: bash
         run: |
           eval "$(pyenv init -)"
           pyenv install ${{ matrix.python-version }} -s
           pyenv virtualenv ${{ matrix.python-version }} pybamm-${{ matrix.python-version }}
 
-      - name: Install dependencies & run unit tests for Windows and MacOS
+      - name: Install build-time dependencies & run unit tests for M-series macOS runner
         shell: bash
+        env:
+          # Point scikits.odes to the correct SUNDIALS installation
+          SUNDIALS_INST: $HOME/.local/lib
+          # Homebrew environment variables
+          HOMEBREW_NO_INSTALL_CLEANUP: 1
+          NONINTERACTIVE: 1
         run: |
           eval "$(pyenv init -)"
           pyenv activate pybamm-${{ matrix.python-version }}
-          python -m pip install --upgrade pip wheel setuptools nox
+          python -m pip install --upgrade pip nox
+          # Don't use Homebrew to install SUNDIALS because scikits.odes looks for
+          # in Homebrew folders instead, which we don't want
+          brew uninstall sundials --force
+          pip cache remove scikits.odes
+          python -m nox -s pybamm-requires -- --force
           python -m nox -s unit
 
-      - name: Run integration tests for Windows and MacOS
+      - name: Run integration tests for M-series macOS runner
         run: |
           eval "$(pyenv init -)"
           pyenv activate pybamm-${{ matrix.python-version }}
           python -m nox -s integration
 
-      - name: Uninstall pyenv-virtualenv & python
+      - name: Uninstall pyenv-virtualenv & Python
         if: always()
         shell: bash
         run: |