diff --git a/.github/workflows/binder.yml b/.github/workflows/binder.yml
index 02f04b44..b2555dcd 100644
--- a/.github/workflows/binder.yml
+++ b/.github/workflows/binder.yml
@@ -6,7 +6,6 @@ on:
       - master
       - release/*
   workflow_dispatch:
-  pull_request:
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -16,21 +15,26 @@ jobs:
   build:
     runs-on: ubuntu-latest
     name: Run repo2docker
+
+    permissions:
+      contents: read
+      packages: write
+      
     steps:
 
-    - name: checkout files in repo
-      uses: actions/checkout@v3
+      - name: checkout files in repo
+        uses: actions/checkout@v3
 
-    - name: downcase repository name
-      run: |
-        echo "REPO=${GITHUB_REPOSITORY,,}" >>${GITHUB_ENV}
+      - name: downcase repository name
+        run: |
+          echo "REPO=${GITHUB_REPOSITORY,,}" >>${GITHUB_ENV}
 
-    - name: update jupyter dependencies with repo2docker
-      uses: jupyterhub/repo2docker-action@master
-      with:
-        DOCKER_USERNAME: ${{ github.repository_owner }}
-        DOCKER_PASSWORD: ${{ secrets.GITHUB_TOKEN }}
-        DOCKER_REGISTRY: ghcr.io
-        MYBINDERORG_TAG: ${{ github.event.ref }}
-        IMAGE_NAME: ${{ env.REPO }}
-        PUBLIC_REGISTRY_CHECK: true
+      - name: update jupyter dependencies with repo2docker
+        uses: jupyterhub/repo2docker-action@master
+        with:
+          DOCKER_USERNAME: ${{ github.repository_owner }}
+          DOCKER_PASSWORD: ${{ secrets.GITHUB_TOKEN }}
+          DOCKER_REGISTRY: ghcr.io
+          MYBINDERORG_TAG: ${{ github.event.ref }}
+          IMAGE_NAME: ${{ env.REPO }}
+          PUBLIC_REGISTRY_CHECK: true
diff --git a/.github/workflows/build-test.yml b/.github/workflows/build-test.yml
index 34d6764a..a2249cd1 100644
--- a/.github/workflows/build-test.yml
+++ b/.github/workflows/build-test.yml
@@ -28,13 +28,11 @@ jobs:
         os: [ubuntu-latest, macos-latest]
         python-version:
           - "3.8"
-          - "3.9"
-          - "3.10"
           - "3.11"
 
     steps:
     - uses: actions/checkout@v3
-    
+
     - name: Cache conda
       uses: actions/cache@v3
       env:
@@ -52,7 +50,7 @@ jobs:
           environment-file: environment.yml
           channel-priority: true
           python-version: ${{ matrix.python-version }}
-  
+
     - name: Configure
       shell: bash -l {0}
       env:
diff --git a/.github/workflows/pip-install.yml b/.github/workflows/pip-install.yml
index c34fa481..c99a817d 100644
--- a/.github/workflows/pip-install.yml
+++ b/.github/workflows/pip-install.yml
@@ -24,8 +24,6 @@ jobs:
       matrix:
         python-version:
           - "3.8"
-          - "3.9"
-          - "3.10"
           - "3.11"
 
     steps:
diff --git a/.gitignore b/.gitignore
index 9a6054df..c001453d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,12 +3,14 @@ install*
 debug*
 .idea
 .ipynb_checkpoints
-*.ipynb
 *.swp
 cmake-build-debug
 .direnv
 .pytest_cache
 
+# Editor junk
+.vscode
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -131,3 +133,6 @@ GTAGS
 # Validated input files produced during testing
 validated*.json
 .ccls-cache
+
+
+_skbuild/
diff --git a/.readthedocs.yml b/.readthedocs.yml
index aa422207..aac9b4f5 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "miniconda3-4.7"
+    python: "mambaforge-22.9"
 
 conda:
   environment: environment.yml
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 216ceaab..496fbe2c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,6 +16,10 @@ The format is based on [Keep a Changelog], and this project adheres to [Semantic
 
 ## [Unreleased]
 
+## [1.0rc1]
+
+Release candidate, added time evolution operator and docs.
+
 ## [1.0rc0]
 
 Third release candidate, closing in on major version.
diff --git a/README.md b/README.md
index 5cb21fc5..c5541342 100644
--- a/README.md
+++ b/README.md
@@ -66,3 +66,34 @@ Note that the conda-forge package is _always_ built with OpenMP support enabled
 in the MRCPP backend.
 
 The VAMPyR module is now available whenever you have activated the `myenv` environment.
+
+### Creating a Conda environment from a .yml file
+
+You can also create a Conda environment from a .yml file that already specifies VAMPyR and
+other useful packages such as numpy, and matplotlib. Here's how:
+
+1. Write an `environment.yml` file, for example:
+
+    ```yaml
+    name: myenv
+    channels:
+      - conda-forge
+    dependencies:
+      - vampyr
+      - numpy
+      - matplotlib
+      - jupyterlab
+    ```
+
+2. Create the environment from the `environment.yml` file:
+
+    ```sh
+    $ conda env create -f environment.yml
+    ```
+3. Activate the environment:
+
+    ```sh
+    $ conda activate myenv
+    ```
+The VAMPyR module, along with numpy and matplotlib, is now available whenever
+you have activated the myenv environment.
\ No newline at end of file
diff --git a/VERSION b/VERSION
index f4fb83ce..0f82de4c 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-1.0rc0
+1.0rc1
diff --git a/cmake/custom/fetch_mrcpp.cmake b/cmake/custom/fetch_mrcpp.cmake
index a883a076..517e320c 100644
--- a/cmake/custom/fetch_mrcpp.cmake
+++ b/cmake/custom/fetch_mrcpp.cmake
@@ -18,7 +18,7 @@ else()
     GIT_REPOSITORY
       https://github.com/MRChemSoft/mrcpp.git
     GIT_TAG
-      v1.5.0
+      8107aabe28d6e75f04d66c95a94c157731484eae
     )
 
   set(CMAKE_CXX_COMPILER ${CMAKE_CXX_COMPILER})
diff --git a/cmake/custom/fetch_pybind11.cmake b/cmake/custom/fetch_pybind11.cmake
index d0d00b23..b27a0ea4 100644
--- a/cmake/custom/fetch_pybind11.cmake
+++ b/cmake/custom/fetch_pybind11.cmake
@@ -1,4 +1,4 @@
-find_package(pybind11 2.6 CONFIG QUIET)
+find_package(pybind11 2.9 CONFIG QUIET)
 
 set(PYBIND11_CPP_STANDARD "-std=c++${CMAKE_CXX_STANDARD}")
 
@@ -10,9 +10,8 @@ else()
   FetchContent_Declare(pybind11
     QUIET
     URL
-      https://github.com/pybind/pybind11/archive/v2.7.1.tar.gz
+      https://github.com/pybind/pybind11/archive/v2.10.0.tar.gz
     )
   set(PYBIND11_TEST OFF CACHE BOOL "")
   FetchContent_MakeAvailable(pybind11)
 endif()
-
diff --git a/docs/bibliography.bib b/docs/bibliography.bib
index a0ec3886..cc5a78ad 100644
--- a/docs/bibliography.bib
+++ b/docs/bibliography.bib
@@ -35,3 +35,17 @@ @ARTICLE{Anderson2019-bx
   issn    = "2590-0552",
   doi     = "10.1016/j.jcpx.2019.100033"
 }
+
+@article{Tomasi2005,
+author = {Tomasi, Jacopo and Mennucci, Benedetta and Cammi, Roberto},
+title = {Quantum Mechanical Continuum Solvation Models},
+journal = {Chemical Reviews},
+volume = {105},
+number = {8},
+pages = {2999-3094},
+year = {2005},
+doi = {10.1021/cr9904009},
+note ={PMID: 16092826},
+URL = {  https://doi.org/10.1021/cr9904009},
+eprint = { https://doi.org/10.1021/cr9904009 }
+}
diff --git a/docs/conf.py b/docs/conf.py
index 68777b0f..19b45ab6 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -108,9 +108,4 @@ def version_from_file(f):
         "notebook_interface": "jupyterlab",
     },
 }
-html_sidebars = {
-    "**": [
-        "sidebar-logo.html",
-        "sbt-sidebar-nav.html",
-    ]
-}
+
diff --git a/docs/index.rst b/docs/index.rst
index cfbe8559..d7a37096 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -19,8 +19,16 @@ Check out the :doc:`usage` section for further information, including how to :re
    :caption: Contents
 
    usage
-   notebooks/vampyr_intro
+   notebooks/introduction
+   notebooks/function_representations
+   notebooks/multiwavelets
+   notebooks/poisson_equation
+   notebooks/helmholtz_equation
+   notebooks/hydrogen_atom
+   notebooks/helium_atom
+   notebooks/beryllium_atom
    notebooks/PCMSolvent
+   notebooks/harmonic_oscillator_evolution
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/notebooks/PCMSolvent.ipynb b/docs/notebooks/PCMSolvent.ipynb
index 0bfa41b4..adadf404 100644
--- a/docs/notebooks/PCMSolvent.ipynb
+++ b/docs/notebooks/PCMSolvent.ipynb
@@ -3,37 +3,122 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "cbb20271-50cc-4ad7-92fe-0889e06c44b4",
+   "id": "ea98d79d",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Polarizable continuum model (PCM).\"\"\"\n",
+    "\n",
+    "__author__    = \"Gabriel Gerez\"\n",
+    "__credit__    = [\"Gabriel Gerez\"]\n",
+    "\n",
+    "__date__      = \"2023-08-28\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2178d62d",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "687f3715",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Import necessary packages\n",
     "from vampyr import vampyr3d as vp\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.special import erf\n",
-    "from matplotlib import cm\n",
-    "%matplotlib inline"
+    "from scipy.special import erf"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2d14d0c2-61ec-425b-a3ae-3f96229288fa",
+   "id": "110905f6",
    "metadata": {},
    "source": [
-    "# initialize classes and functions used in the notebook"
+    "# Polarizable continuum model (PCM)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb876dd",
+   "metadata": {},
+   "source": [
+    "Polarizable continuum models have been used for almost half a century to describe solvation effects in quantum chemistry {cite}`Tomasi2005`.\n",
+    "This method has proved itself an effective way to reduce the degrees of freedom necessary to compute solvent effects, as no \n",
+    "solvent molecules are explicitly included in the calculation.\n",
+    "\n",
+    "PCM treats the solvent as an isotropic continuum characterized by its permittivity, obtained from the \n",
+    "solvent's bulk properties. The solute is described at the quantum level, and their interaction is \n",
+    "represented through an electrostatic potential supported on the so-called cavity of the solute. Below, we \n",
+    "detail the mathematical formulation of this model and provide a practical example of how it can be \n",
+    "implemented.\n",
+    "\n",
+    "The cavity is the area where the solute is located and we define it using an interlocking spheres \n",
+    "model where the spheres are centered in the atoms of the solute and the radii of the spheres is usually the \n",
+    "vdW-radii.\n",
+    "\n",
+    "In our case we define a cavity function as \n",
+    "\\begin{align}\n",
+    "C(\\vec{r}) = \\begin{cases}0 & \\mathbf{r}\\text{ inside cavity} \\\\\n",
+    "1 & \\mathbf{r} \\text{ outside cavity}\\end{cases}\n",
+    "\\end{align}\n",
+    "In our implementation we defined the boundary of the cavity to be smooth by use of the error function.\n",
+    "Below we outline the mathemathical details of our implementation.\n",
+    "\n",
+    "Each i-th sphere is defined by a center $\\mathbf{r}_i$ and a radius $R_i$ and the cavity function is defined as\n",
+    "\\begin{equation}\n",
+    "C_i(\\mathbf{r}) = \\frac{1}{2} \\left( 1 + \\text{erf} \\left( \\frac{|\\mathbf{r} - \\mathbf{r}_i| - R_i}{ \\sigma} \\right) \\right),\n",
+    "\\end{equation}\n",
+    "where erf is the error function and $\\sigma$ is a parameter that controls the width of the boundary.\n",
+    "\n",
+    "The total cavity function is then defined as\n",
+    "\\begin{equation}\n",
+    "C(\\mathbf{r}) = 1 - \\prod_i \\left(1-C_i(\\mathbf{r})\\right).\n",
+    "\\end{equation}\n",
+    "\n",
+    "This has been implemented as a class below"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "7c2661ef-a8a1-4a08-9c6e-7e87f6a06216",
+   "execution_count": 3,
+   "id": "a74a3d77",
    "metadata": {},
    "outputs": [],
    "source": [
-    "class Cavity(object):\n",
+    "class Cavity():\n",
+    "    \n",
     "    def __init__(self, cav_coords, radii, width):\n",
-    "        self.r_list = cav_coords # list of cavity centers. Normally, but not always, nucleus coordinates. \n",
-    "        self.R_list = radii  # list of cavity radii. Normally, but not always, nucleus radii.\n",
+    "        self.centers = cav_coords # list of cavity centers. Normally, but not always, nucleus coordinates. \n",
+    "        self.radii = radii  # list of cavity radii. Normally, but not always, nucleus radii.\n",
     "        self.sigma = width  # width of the cavity boundary\n",
     "   \n",
     "\n",
@@ -43,446 +128,419 @@
     "        \"\"\"\n",
     "        r_vec = np.array(r)\n",
     "        C = 1.0\n",
-    "        for i, r_i in enumerate(self.r_list):\n",
+    "        for i, r_i in enumerate(self.centers):\n",
     "            r_vec_i = np.array(r_i)\n",
-    "            s_i = np.linalg.norm(r_vec_i - r_vec) - self.R_list[i]\n",
+    "            s_i = np.linalg.norm(r_vec_i - r_vec) - self.radii[i]\n",
     "            O_i = (1.0/2.0)*(1 + erf(s_i/self.sigma))\n",
     "            C_i = 1 - O_i\n",
     "            C *= 1 - C_i\n",
     "        C = 1.0 - C\n",
-    "        return C\n",
+    "        return C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9998102",
+   "metadata": {},
+   "source": [
     "\n",
-    "    \n",
-    "class lin_permittivity():\n",
-    "    def __init__(self, cavity, e_0=1.0, e_inf=2.0):\n",
-    "        \n",
-    "        self.C = cavity\n",
-    "        self.eps_0 = e_0 # permittivity of free space\n",
-    "        self.eps_inf = e_inf # permittivity of solvent\n",
-    "    \n",
-    "    \n",
-    "    def __call__(self, r):\n",
-    "        C_eval = self.C(r)\n",
-    "        permittivity = C_eval*(self.eps_0 - self.eps_inf) + self.eps_inf\n",
-    "    \n",
-    "        return permittivity\n",
     "\n",
+    "The electrostatic potential $V(\\mathbf{r})$ satisfies the generalized Poisson equation (GPE)\n",
+    "\\begin{equation}\n",
+    "\\nabla \\cdot (\\varepsilon(\\mathbf{r}) \\nabla V(\\mathbf{r})) = -4\\pi \\rho(\\mathbf{r}),\n",
+    "\\end{equation}\n",
+    "where $\\rho(\\mathbf{r}$ is the total total density of the solute including both electronic, $\\rho_{el}$ and nuclear $\\rho_{nuc}$ densities,\n",
+    "\\begin{equation}\n",
+    "\\rho(\\mathbf{r}) = \\rho_{el}(\\mathbf{r}) +  \\rho_{nuc}(\\mathbf{r}),\n",
+    "\\end{equation}\n",
+    "and $\\varepsilon(\\mathbf{r})$ is the position dependent permittivity parametrized using the cavity of the solute where\n",
+    "\\begin{align}\n",
+    "\\varepsilon(\\mathbf{r}) = \\begin{cases}\\varepsilon_0 & \\mathbf{r}~\\text{inside}~\\text{cavity} \\\\\n",
+    "\\varepsilon_r & \\mathbf{r}~\\text{outside}~ \\text{cavity}.\\end{cases}\n",
+    "\\end{align}\n",
+    "where $\\varepsilon_0$ is the permittivity of free space and $\\varepsilon_r$ is the relative permittivity of the solvent.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf5a3237",
+   "metadata": {},
+   "source": [
+    "We have defined the permittivity function as follows\n",
+    "\\begin{equation}\n",
+    "\\varepsilon(\\mathbf{r}) = \\varepsilon_0  \\exp\\left(\\log\\left[\\frac{\\varepsilon_r}{ \\varepsilon_0}\\right]\\left(1 - C(\\mathbf{r})\\right)\\right).\n",
+    "\\end{equation}\n",
     "\n",
-    "class exp_permittivity():\n",
-    "    def __init__(self, cavity, e_0=1.0, e_inf=2.0):\n",
+    "This has been implemented as a class below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7036a9eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Permittivity():\n",
+    "    def __init__(self, cavity, inside=1.0, outside=2.0):\n",
     "        \n",
-    "        self.C = cavity\n",
-    "        self.eps_0 = e_0 # permittivity of free space\n",
-    "        self.eps_inf = e_inf # permittivity of solvent\n",
-    "    \n",
+    "        self.C = cavity # instance of cavity class\n",
+    "        self.inside = inside # permittivity of free space\n",
+    "        self.outside = outside # permittivity of solvent\n",
     "    \n",
     "    def __call__(self, r):\n",
     "        C_eval = self.C(r)\n",
-    "        permittivity = self.eps_0*np.exp((np.log((self.eps_inf/self.eps_0)))*(1.0 - C_eval))\n",
+    "        permittivity = self.inside*np.exp((np.log((self.outside/self.inside)))*(1.0 - C_eval))\n",
     "    \n",
-    "        return permittivity\n",
-    "\n",
-    "# Analytic nuclear potential\n",
-    "def f_nuc(r):\n",
-    "    R = np.sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2])\n",
-    "    return -1.0 / R\n",
+    "        return permittivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fc4d4e6",
+   "metadata": {},
+   "source": [
     "\n",
-    "# Analytic guess for solution\n",
-    "def f_phi(r):\n",
-    "    \n",
-    "    R = np.sqrt(r[0]*r[0] + r[1]*r[1] + r[2]*r[2])\n",
-    "    return np.exp(-R*R)\n",
     "\n",
-    "def dot (A, B):\n",
-    "    out_vec = []\n",
-    "    for A_i, B_i in zip(A, B):\n",
-    "        out_vec.append(A_i*B_i)\n",
-    "    out_tree = vp.sum(out_vec)\n",
-    "    return out_tree"
+    "The electrostatic potential can be divided into two contributions, the solute-solvent interaction reaction potential $V_R$ (also normally simply called reaction potential), and the vacuum potential $V_{vac}$,\n",
+    "\\begin{equation}\n",
+    "V(\\mathbf{r}) = V_R(\\mathbf{r}) + V_{vac}(\\mathbf{r}),\n",
+    "\\end{equation}\n",
+    "where \n",
+    "\\begin{equation}\n",
+    "\\nabla^2 V_{vac}(\\mathbf{r}) = -4\\pi \\rho(\\mathbf{r}).\n",
+    "\\end{equation}\n",
+    "\n",
+    "The energy contribution from the reaction potential, $E_R$ is given as\n",
+    "\\begin{equation}\n",
+    "E_R =\\frac{1}{2} \\int d\\mathbf{r}V_R(\\mathbf{r})\\rho(\\mathbf{r}).\n",
+    "\\end{equation}\n",
+    "Since this energy is only dependent on the reaction potential, we want to directly solve for it.\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "53f22d33-1370-4cdd-9d5f-178090733f54",
+   "cell_type": "markdown",
+   "id": "3e6e67f2",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "r_x = np.linspace(-10.0, 10.0, 1000) # create an evenly spaced set of points between -0.99 and 0.99\n",
-    "r_y = np.zeros(1000)\n",
-    "r_z = np.zeros(1000)\n",
-    "r = [r_x, r_y, r_z]"
+    "Resolving the chain rule, substituting in equations 8 and 9, and collecting terms gives us the following expression for the reaction potential\n",
+    "\\begin{equation}\n",
+    "\\nabla^2  V_R(\\mathbf{r})  = -4\\pi\\left( \\frac{\\rho(\\mathbf{r})}{\\varepsilon(\\mathbf{r})} -\\rho(\\mathbf{r})\\right) - \\frac{\\nabla \\varepsilon(\\mathbf{r}) \\cdot \\nabla V(\\mathbf{r})}{\\varepsilon(\\mathbf{r})}\n",
+    "\\end{equation}\n",
+    "We solve directly for the reaction potential by applying the Poisson operator $P(\\mathbf{r})$  \n",
+    "\\begin{equation}\n",
+    "V_R(\\mathbf{r})  = P(\\mathbf{r}) \\star\\left[ \\rho_{eff}(\\mathbf{r}) + \\gamma_s(\\mathbf{r}) \\right]\n",
+    "\\end{equation}\n",
+    "where we did the following substitutions\n",
+    "\\begin{align}\n",
+    "\\rho_{eff}(\\mathbf{r}) = 4\\pi \\left(\\frac{\\rho(\\mathbf{r})}{\\varepsilon(\\mathbf{r})} - \\rho(\\mathbf{r})\\right) \\\\\n",
+    "~\\\\\n",
+    "\\gamma_s(\\mathbf{r}) = \\frac{\\nabla \\varepsilon(\\mathbf{r}) \\cdot \\nabla V(\\mathbf{r})}{\\varepsilon(\\mathbf{r})}\n",
+    "\\end{align}\n",
+    "which we call respectively the effective molecular density $\\rho_{eff}$ and the surface charge distribution $\\gamma_s$.\n",
+    "\n",
+    "Note that the Reaction potential must be solved iteratively since it appears on both sides of equation 12 through the total electrostatic potential $V$ which is part of $\\gamma_s$ as shown in equation 13."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b63f60f3-0b08-4622-8e20-0668f4491af0",
+   "id": "1463bb1f",
    "metadata": {},
    "source": [
-    "# initialize static functions"
+    "Below is an implementation of the self consistent procedure for the reaction potential for a single positive charge inside a sphere cavity of unit radius.\n",
+    "We start by defining several functions to compute the objects needed for the reaction potential"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "8a2cfeae-46d9-4890-9072-6135bb7929cd",
+   "execution_count": 5,
+   "id": "77a9524b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Global parameters\n",
-    "k = 7                        # Polynomial order\n",
-    "L = [-20,20]                 # Simulation box size\n",
-    "epsilon = 1.0e-5             # Relative precision\n",
-    "\n",
-    "# Define MRA and multiwavelet projector\n",
-    "MRA = vp.MultiResolutionAnalysis(order=k, box=L)\n",
-    "P_eps = vp.ScalingProjector(mra=MRA, prec=epsilon)\n",
-    "D_abgv = vp.ABGVDerivative(mra=MRA, a=0.0, b=0.0)\n",
-    "Poissop = vp.PoissonOperator(mra=MRA, prec=epsilon)\n",
-    "\n",
-    "coords = [[0.0000000000,    0.0000000000,    0.000000000]] #centered in \n",
-    "radii = [2.0786985874]  \n",
-    "\n",
-    "width = 0.2\n",
-    "\n",
-    "C = Cavity(coords, radii, width)\n",
-    "lin_perm = lin_permittivity(C, e_0=1.0, e_inf=80.0)\n",
-    "exp_perm = exp_permittivity(C, e_0=1.0, e_inf=80.0)\n",
-    "\n",
-    "lin_perm_tree = P_eps(lin_perm)\n",
-    "exp_perm_tree = P_eps(exp_perm)\n",
-    "\n",
-    "grad_perm =vp.gradient(oper=D_abgv, inp=lin_perm_tree)\n",
-    "# nuclear density and total molecular density to compute the vacuum potential\n",
-    "alpha = 10000\n",
-    "beta = (alpha / np.pi)**(3.0/2.0)\n",
-    "nuc_dens = P_eps(vp.GaussFunc(coef=beta*1.0, exp=alpha))"
+    "def constructChargeDensity(positions, charges, width_parameter):\n",
+    "    \"\"\"Computes the charge density of a set of point charges using a Gaussian\n",
+    "    function. The Gaussian function is centered at the position of the charge\n",
+    "    and the width parameter is the standard deviation of the Gaussian.\n",
+    "\n",
+    "    Args:\n",
+    "        positions (list of lists of floats): list of positions of the charges\n",
+    "        charges (list of floats): list of charges\n",
+    "        width_parameter (int, optional): Standard deviation of the Gaussian. Defaults to 1000.\n",
+    "\n",
+    "    Returns:\n",
+    "        function: function that computes the charge density at a given position\n",
+    "    \"\"\"\n",
+    "    charge_density = vp.GaussExp()\n",
+    "    for (pos, charge) in zip(positions, charges):\n",
+    "        beta = width_parameter\n",
+    "        alpha = (beta / np.pi)**(3.0/2.0)\n",
+    "        charge_density.append(vp.GaussFunc(beta=beta, alpha=alpha*charge, position=pos, poly_exponent=[0,0,0]))\n",
+    "    return charge_density\n",
+    "\n",
+    "\n",
+    "def computeGamma(Derivative_op, V, permittivity, epsilon):\n",
+    "    \"\"\"Computes gamma_s for a given permittivity and potential.\n",
+    "\n",
+    "    Args:\n",
+    "        Derivative_op (vp.ABGVDerivative): Derivative operator\n",
+    "        V (vp.FunctionTree): Potential used to compute gamma_s\n",
+    "        permittivity (vp.FunctionTree): permittivity function of the solvent\n",
+    "        epsilon (float): crop precision\n",
+    "\n",
+    "    Returns:\n",
+    "        _type_: _description_\n",
+    "    \"\"\"\n",
+    "    gamma =  vp.dot(vp.gradient(Derivative_op,V), vp.gradient(Derivative_op, permittivity)) * ( permittivity**(-1))\n",
+    "    gamma = gamma.crop(epsilon)\n",
+    "    return gamma.deepCopy()\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "1b58d6c6-640c-441c-b8ba-b8157766df74",
+   "id": "f7f6f209",
    "metadata": {},
    "source": [
-    "# solve the SCF"
+    "Initialize the Multiresolution analysis"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "fa3eea68-97dc-41b1-9494-f568fe183724",
+   "execution_count": 6,
+   "id": "5996cb89",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.9540438618528291"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================\n",
+      "                    MultiResolution Analysis                    \n",
+      "----------------------------------------------------------------\n",
+      " polynomial order      : 5\n",
+      " polynomial type       : Interpolating\n",
+      "----------------------------------------------------------------\n",
+      " total boxes           : 8\n",
+      " boxes                 : [          2           2           2 ]\n",
+      " unit lengths          : [  10.000000   10.000000   10.000000 ]\n",
+      " scaling factor        : [   1.250000    1.250000    1.250000 ]\n",
+      " lower bounds          : [ -10.000000  -10.000000  -10.000000 ]\n",
+      " upper bounds          : [  10.000000   10.000000   10.000000 ]\n",
+      " total length          : [  20.000000   20.000000   20.000000 ]\n",
+      "================================================================\n",
+      "\n"
+     ]
     }
    ],
    "source": [
-    "# Project analytic nuclear potential\n",
-    "V_nuc = P_eps(f_nuc)\n",
-    "\n",
-    "# Initial guess for energy and orbital\n",
-    "E_n = -0.5\n",
-    "phi_n = P_eps(f_phi)\n",
-    "phi_n.normalize()\n",
-    "phi_n *= -1.0\n",
-    "\n",
-    "\n",
-    "## Here is the zeroth iteration of the reaction potential solver\n",
-    "\n",
-    "\n",
-    "rho =  phi_n**2 + nuc_dens\n",
-    "U_vac = Poissop(rho)\n",
-    "\n",
-    "#construct gamma_0\n",
-    "\n",
-    "grad_U_vac = vp.gradient(oper=D_abgv, inp=U_vac)\n",
-    "\n",
-    "dot_tree = dot(grad_perm, grad_U_vac)\n",
-    "gamma_n = (1/(4*np.pi))*(lin_perm_tree**(-1.0))*dot_tree # zero-th gamma\n",
-    "\n",
-    "#construct rho_eff\n",
-    "rho_eff = rho*(lin_perm_tree**(-1.0))\n",
-    "poiss_tree = rho_eff \n",
-    "poiss_tree += gamma_n\n",
-    "\n",
-    "#solve the poisson equation for the zeroth order potential\n",
-    "U_n = Poissop(poiss_tree) #zero-th  total potential\n",
-    "\n",
-    "# extract the reaction potential for convergence criteria\n",
-    "U_r_n = U_n\n",
-    "U_r_n -= U_vac\n",
-    "\n",
-    "diff = U_r_n.norm()  # this is U_r_n - U_r_nm1, but since this started as zero we should just not think about it too much\n",
-    "diff"
+    "k = 5\n",
+    "L = [-10, 10]\n",
+    "MRA = vp.MultiResolutionAnalysis(order=k, box=L)\n",
+    "print(MRA)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "11b302f2-b802-47bd-ab5c-6d7d37a830f3",
+   "id": "d2163897",
    "metadata": {},
    "source": [
-    "## Minimization loop"
+    "Initialize all operators that will be used"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "52d81dcd-1b56-475d-81f1-365ae685f930",
+   "execution_count": 7,
+   "id": "898a27c8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def SCRF(update):\n",
-    "    global U_n\n",
-    "    global U_r_n\n",
-    "    global k\n",
-    "    global axes\n",
-    "    global diff\n",
-    "    j = 0\n",
-    "    print(\"Running SCRF\")\n",
-    "    while((j < 20) and (diff > update)) :   #set a precision threshold\n",
-    "        # construct new gamma from U_n and lin_perm_tree (which is already differentiated)'\n",
-    "        grad_U_nm1 = vp.gradient(oper=D_abgv , inp=U_n) # take gradient from previous iteration\n",
-    "\n",
-    "        dot_tree = dot(grad_perm, grad_U_nm1)\n",
-    "\n",
-    "        gamma_n = (1/(4*np.pi))*(lin_perm_tree**(-1.0))*dot_tree\n",
-    "\n",
-    "        poiss_tree = rho_eff + gamma_n\n",
-    "\n",
-    "        # solve poisson equation\n",
-    "        U_n = Poissop(poiss_tree)\n",
-    "\n",
-    "        # extract reaction potential for convergence check\n",
-    "        diff_U_r_n = (U_n - U_vac) - U_r_n\n",
-    "        diff = diff_U_r_n.norm()\n",
-    "        U_r_n += diff_U_r_n\n",
-    "        E_r_n = vp.dot(U_r_n, phi_n)\n",
-    "        U_r_plt = [U_r_n([x, 0.0, 0.0]) for x in r_x]\n",
-    "        U_plt = [U_n([x, 0.0, 0.0]) for x in r_x]\n",
-    "        axes[0][1].plot(r_x, U_r_plt, label=\"U_r_n \"+str(k))\n",
-    "        axes[1][1].plot(r_x, U_plt, label=\"U_n \"+str(k))\n",
-    "        axes[0][1].legend()\n",
-    "        axes[1][1].legend()\n",
-    "        print(\"micro-iteration: {}   Reaction-Energy: {}  Update: {}\".format(j, E_r_n, diff))\n",
-    "        j += 1\n",
-    "        k +=1"
+    "epsilon = 1e-5\n",
+    "Projection_op = vp.ScalingProjector(mra=MRA, prec=epsilon)\n",
+    "Derivative_op = vp.ABGVDerivative(mra=MRA, a=0.0, b=0.0)\n",
+    "Poisson_op = vp.PoissonOperator(mra=MRA, prec=epsilon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2044a0c",
+   "metadata": {},
+   "source": [
+    "Compute the density of the solute"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "6ed3119f-3249-4fa8-8f2d-cd13154d02f5",
+   "execution_count": 8,
+   "id": "980ddc95",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "iteration: 0   Energy: -0.385174418796303   Norm: 1.0397877110533356   Update: 0.3746381858760064\n",
-      "Running SCRF\n",
-      "micro-iteration: 0   Reaction-Energy: 0.17704447420162425  Update: 0.1663349061161934\n",
-      "iteration: 1   Energy: -0.4824046298946165   Norm: 1.2457112558689492   Update: 0.3416477154092891\n",
-      "iteration: 2   Energy: -0.5196137732435839   Norm: 1.0678429514098169   Update: 0.0846230255927735\n",
-      "Running SCRF\n",
-      "micro-iteration: 0   Reaction-Energy: 0.5314461156740161  Update: 0.9642247194179855\n",
-      "micro-iteration: 1   Reaction-Energy: 0.48206878470520115  Update: 0.16609876179450891\n",
-      "micro-iteration: 2   Reaction-Energy: 0.48772043972317664  Update: 0.019205008692094943\n",
-      "iteration: 3   Energy: -0.5456361525056247   Norm: 1.0350832284293747   Update: 0.03800553880305254\n",
-      "iteration: 4   Energy: -0.5465063079249487   Norm: 0.9993958688207177   Update: 0.0034314454689965516\n",
-      "Running SCRF\n",
-      "micro-iteration: 0   Reaction-Energy: 0.4904394200196914  Update: 0.0016692221137804527\n",
-      "iteration: 5   Energy: -0.5464713393786428   Norm: 0.9990412089878934   Update: 0.0021272806876957762\n",
-      "iteration: 6   Energy: -0.5464744172721099   Norm: 0.9995383999760492   Update: 0.0010225075582359889\n",
-      "Running SCRF\n",
-      "micro-iteration: 0   Reaction-Energy: 0.4887964476105912  Update: 0.00011617836051528584\n",
-      "iteration: 7   Energy: -0.5464782993118219   Norm: 0.9997666027597615   Update: 0.0005092474962139957\n",
-      "iteration: 8   Energy: -0.5464785004941621   Norm: 0.9998784102638317   Update: 0.0002585274662661521\n",
-      "iteration: 9   Energy: -0.5464785532035065   Norm: 0.9999378880654322   Update: 0.00013075808106129122\n",
-      "iteration: 10   Energy: -0.5464785669148633   Norm: 0.9999682959339049   Update: 6.63154998863379e-05\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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LxPIAAAAlbuLEiZFKpXrczjnnnGKXBgBALzJzoAwZMqTIley4Mue2P/PN6EQBAAAocYsXL46Wlpb2x3/+85/juOOOi5NPPrmIVQEA0BeG8MqfXJxbIQoAAECJ22233bo8vvTSS2OfffaJY445pkgVAQDAjkGIAgAAsANpbGyMG264Ic4777xev3nX0NAQDQ0N7Y/XrVtXqPIAAKCkmBMFAABgB7JgwYJYu3ZtzJ49u9d1LrnkkhgxYkT7bfz48YUrEAAASogQBQAAYAcyb968OOGEE2LcuHG9rnPBBRdEfX19+2358uUFrBAAgFI2Y8aMmDNnTo/lCxYsyPv8Lk888UQcc8wxMXjw4Nhjjz3i4osvjiRJ8npMw3kBAADsIF566aW466674te//vUW16uuro7q6uoCVQUAAP23bt26OO644+LYY4+NxYsXx7PPPhuzZ8+OoUOHxvnnn5+34+pEAQAA2EFce+21sfvuu8eJJ55Y7FIAANhGSZLExsbmotzy3c3RXV1dXRx88MFx/fXXx8SJE2PEiBFx2mmnxfr163vd5sYbb4zNmzfH/PnzY9q0afHRj340vva1r8UPfvCDvNavEwUAAGAHkE6n49prr41Zs2ZFRYVLPQCAUrOpqSUO+H93FOXYT118fAypKuzfkEuXLo0FCxbErbfeGmvWrIlTTjklLr300pg7d27W9R988ME45phjunRUH3/88XHBBRfEiy++GJMmTcpLnTpRAAAAdgB33XVXLFu2LD796U8XuxQAANiqdDrd3lVy9NFHx+mnnx533313r+uvWrUqxowZ02VZ5vGqVavyVqevJwEAAOwAZs6cWfBhGAAAyJ3BleXx1MXHF+3YhTZx4sQYPnx4++Pa2tpYvXr1FrfpPnF95u/ffE5oL0QBAAAAAIAiS6VSBR9Sa3vU1NREfX19j+Vr166NmpqaPu+nsrKyy+NUKhXpdLrX9ceOHduj4yQTunTvUMklw3kBAAAAAAB9MmXKlHj44Yd7LF+8eHFMnjw5b8c94ogj4r777ovGxsb2ZXfeeWeMGzcuJk6cmLfjClEAAAAAAIA+Ofvss2Pp0qVxzjnnxOOPPx7PPvts/OhHP4p58+bFl770pbwd9xOf+ERUV1fH7Nmz489//nPccsst8e1vfzvOO+88w3kBAAAAAADFN3HixLj//vvjwgsvjJkzZ8bmzZtj//33j/nz58fJJ5+ct+OOGDEiFi5cGOecc04ceuihMXLkyDjvvPPivPPOy9sxIyJSyU4w8+C6detixIgRUV9fv01jsgEAQKnyNzDbwusFAKDwNm/eHC+88EJMmjQpBg0aVOxydkhbOsd9/RvYcF4AAAAAAABZCFEAAAAAAICcmTp1agwbNizr7cYbbyx2edukZOZE+fGPfxzf/e53Y+XKlTF16tS4/PLL4+ijjy52WQAAAAAAQCe33XZbNDU1ZX1uzJgxBa6mf0oiRPnFL34Rc+bMiR//+Mdx1FFHxU9/+tM44YQT4qmnnoq99tqr2OUBAAAAAABtJkyYUOwScqYkhvP6wQ9+EJ/5zGfis5/9bLztbW+Lyy+/PMaPHx9XXXVV1vUbGhpi3bp1XW4AAAAAAADbYsB3ojQ2NsYjjzwSX/3qV7ssnzlzZvzhD3/Ius0ll1wS3/zmNwtR3lZtbGyOd37rroiISKVSkYqI1v9p/ZFKpSLV/XFE+7KI1uczD1vv99wmOj/fyzqddtllu1SW/Ua3bTrvt6OO1jtda8u+367H7bafbo8j6z661pTqdCJTnfbbdsba95t53Pvv3O13bVtQ1va4LJXZVyrK2vZZlkpFWdt+yrotz/z3LGv7ncpS0WlZz8dZjxERZWWp9po6HyPVduzWdaJLHZ2PEW3H7ry86zGy11ZWloryVCrKy1qPU16WivK243Rd1ul+KhVlZZFlWacXE7DTS5IkWtJJpJOIdJJEOvM43fq4pW1ZOh2t99Ntj5OIlnTSun3bNknbso79tO2zbf8d++o4ZtK2ryQ6HieZx+mIJFr3EW31ZR4nmXUjIp3OLO+2fRKRtG2X+V07L0uiY/3O+24/VhLttSVZa93K9tG2TnTU2vo/nffZ7XF0LItOy6LTProvi/Zlne53f9x27O7L2uvqsl7bOp32215np+2iy3ad1sn6+3atP9s56P47but5ic7bdfodMne6n7PPvGtSnDdzcgAAZPObx1dE3W+ejHfsNTKu+dT0Lp/xADAwDPgQ5fXXX4+WlpYe46SNGTMmVq1alXWbCy64IM4777z2x+vWrYvx48fntc7eJEnEhsaWohwbiq2iLNUtmIkeIUzXYKZrGFPRtryyvCwqy8uiojwVFWVlUVmeiory1p+VZa3LK8vLoqKs0/K29TPPV5SXRWVZp+Vt61dWlEV1RVlUV5RHdUVZDKps/Vld2bpsUGVZVJWX+UOWokink9jc3BKNzenWW0vrz6aWpO1xSzQ2J9HYko6mtuebWtLR0Nz6M7Nd+/2WJFrS6WhuCxea00k0t3R93NKSRHPndVpafzal01t83Jxu3a6lpVvY0XYfdlaNLV7/AEDv/ufxFfHmhsa46+lXY93m5hgxuLLYJQHQzYAPUTK6f4CZJEmvH2pWV1dHdXV1IcraqsGV5XHfl47t8g3HiO7fgOz+bc22dTp9c7LzNzoj2zrR9duQve23+zrt36lMei7Ltt8e3y7dlmP3st/o9Hxv30DtfOyu30DN9g3Ybt92zfLt1619szXzzeCIzt88bv1GcCQd36TOLM98G7jLN4+7fZO58zeX29ft9q3jzt9Ybv1mdNdvIXccs+u+Ov/MbNPjG9RJx7et2/fV+fdIt30TPN36De/MN8EzH4B2eb6PH4o2p9t+8R1AdVvYMqiyvD1g6Ry6DKkqjyFVFTG0uiKGVpW3/qxu+9nL8hGDK2NwZbmAZgfQkk5iQ2NzvLW5OdZvbo63GprafnYsW9/QHJubWmJTY0tsamqJzW23Te3L0j2WNTSni/2rFVSmg64sE6q23+8IWjsHsJmOvPK2rrr25Z3XL+t4vks3XlnnzsVOnYCd1u38uKytfbGji7Cjmy+zn877jvaOv45tenQilnVf3tEpGD06B3vvZIzo6Ezs0RXaa+do9k7XHt2y0blLtPeu1R7doJ2WRZdl3TpC+9Q12rW7NjK1dvt9U11q7zhu99+n998lezdr9/PUqZKudXWrPfN8zSAfhAAAvdvlhQfj3GV/ijVVgyJiZrHLASCLAR+ijB49OsrLy3t0naxevbpHd8pAVFaWir12HVLsMiAvuoQsScfQPM3pdJfAJTOkTs9gJrJs3/F85tvtTS3paG5p+9n2zfnGlo5v0Hd+vqntW/RNbc83taSjKfNt+5bWb+w3d1on8w39zc0t0dDU+g3+huaW2NzU9cPr1uXpWLe5OafnsKq8LEYMqYxdBlfGyCFV7fd3GVIZuwypipFDqmL34dWxe011jKkZFLsOrYqK8pKYzqpkJUkS6xuaY1X95li9riHe3NgYazY0xpsbGmPNxs4/m2LNhsZYv7mpYB2HVRVlUV1eFpUVrR1SVRWtnVdVFeVRVZ5qe9y6vKptveryjmVdurbKUlGe6dQq69T91da1VV6Wiory1nW29rii7X77Psu2HGp0Dz46hxwAALAzGf+Xx6IpvTp22RTx5ivLY8S+exe7JAC6GfAhSlVVVUyfPj0WLlwYH/nIR9qXL1y4MD70oQ8VsTKgrCwVZZGKyvJiV5J7SdIauDQ0p9vCldZgpaG5JeuyjY0tsaGhOTY0tMTGxtYOhI2NLW0/m+OthpbY2NDcuk7bus3p1mO8tr4hXlvf0Ke6ylIRuw6rjt2HV8fYmkExftSQmLDrkNir7eeeI4fEoB3xP0gOJUkSr73VEC+9sTFefH1DvPTGxlixdlOsrN8cr67bHKvWbY6N2xmKVJanYvigyhg+qCKGVbfeMveHtt0GVZTFoKryGFzZdqsqj+qK1p8dy8ral2W6nSra5koCAAB2HB1jZ0Q0rH0tIoQoAAPNgA9RIiLOO++8OP300+PQQw+NI444Iq6++upYtmxZnHXWWcUuDdhBpVKptiG7yiMG5X7/SZLExsaWWLOxMdZubIr6TU2xdmNTrN3U8TjT/bB6fUOsXr85XlvfEOkk2kOXJ1esy7rvPXYZHJPHDo/JY4fHlLHD4221NbHPbsOivGzn+wC+fmNTPLVyXTy9cl08tXJd/GXVunj+tQ19CklGDK6MMTXVMWpoVYwa2toV1OXn0KoYOaQyRgyubA1MBlW0vl4AAAC2QzptTl2gNMyYMSMOPvjguPzyy7ssX7BgQXzkIx/pMpVCLm3evDnOOuuseOSRR+Lpp5+OD3zgA7FgwYK8HKuzkghRTj311HjjjTfi4osvjpUrV8a0adPitttuiwkTJhS7NIDtkkql2jsT9hzZt21a0km8saEhVq9rDVVWrN0cy9/cGMve3BgvvdH6862G5nhl7aZ4Ze2m+P1fVrdvO7y6Ig6ZMDIOnTAyDp04Mg6dMCqqKna8YcFeXrMx/u+FN1tvL74Zz7+2Iet6ZamIcbsMjom7Dm3v4KkdMSjG1AyKsSMGxdiaQTG4SiACAAAAMFC0tLTE4MGD49xzz42bb765YMctiRAlIuLss8+Os88+u9hlABRNeVkqdh8+KHYfPigiRvR4PkmSWLOxKZ5b/VY8s2pdPL1qfTyzan08vXJdrG9ojvuefS3ue/a1iIgYVl0RR+83Ot4zZfeYecDYGDGkNCc+TpIk/vRyfdz51Kq488lX46+r3+qxzp4jB8cBtTXxtrbbfmOGxZ4jB+saAQAAAAaWJIlo2licY1cOiSjgMOJ1dXWxYMGCOP/88+Mb3/hGrFmzJk444YS45pprYvjw4Vm3GTp0aFx11VUREfG///u/sXbt2oLUWjIhCgBblkqlYtTQqvibSaPibyaNal/e3JKOv6xaH4+8tCYefmlNPLj0jXj9rYb43Z9Xxe/+vCourPhzzDxgTJxy6Pg4er/RJTHvxmvrG+JXj7wcNy1eFi+90fHHRUVZKqbtMSIOazsHh04YVbIBEQAAsHNJWpqLXQJQbE0bI749rjjH/tqKiKqhBT3k0qVLY8GCBXHrrbfGmjVr4pRTTolLL7005s6dW9A6tkaIArCDqygvi2l7jIhpe4yIWUdOjHQ6iSdeqY/f/2V13P7nVfHMq+vj1j+tjFv/tDLeVlsT/zRjnzjxwNoBOYfKirWb4kf3PBe/fHh5NLW0jq85tKo8ZkzZPWYeMCZmTN49RgwWmgAAAAAMdOl0OubPn9/eeXL66afH3XffLUQBoLjKylLx9vG7xNvH7xJz3rdfPLliXfzXw8vjvx55OZ5euS7O/flj8eN7notvfnBqHLb3rsUuNyIi3mpojn9d+Gxc/+BL0diSjoiIg8fvEp/4m73iA2+vjSFV3s4AAIDSlk6ni10CUGyVQ1o7Qop17AKbOHFil6G7amtrY/Xq1VvYojh86gSwE0ulUu1dKl84bv/42YMvxbwHXoi/rFofp179x/j43+wV/+8DBxR1kvX7//pafOVXf4oV9ZsjIuLwvUfFnPftH4cPkIAHAAAAICdSqYIPqbU9ampqor6+vsfytWvXRk1NTZ/3U1nZdTSRVCo1IAPlsmIXAMDAsMuQqjj3vfvFoi/OiE8etlekUhE//79l8aEfPRBLX+s5YXu+JUkSP7rnufjUf/xfrKjfHHuOHBzz/+GdcdOZRwhQAACAHU46nRS7BIA+mTJlSjz88MM9li9evDgmT55chIryS4gCQBcjh1bF3I8cGDd85rDYbXh1PPvqW/Gxq/4Qjy5bU7AamlvScd4vH4/v3vFMJEnEx/9mfNz5hXfHjMm7F6wGAACAgkqEKEBpOPvss2Pp0qVxzjnnxOOPPx7PPvts/OhHP4p58+bFl770pbwe+6mnnoolS5bEm2++GfX19bFkyZJYsmRJXo9pOC8Asjpq39Fx27lHx2d/9nA8vnxtzJr3f/HzMw+PaXuMyOtx0+kkzvvl4/Gbx1dERVkqLv7QtPjEYXvl9ZgAAADFlghRgBIxceLEuP/+++PCCy+MmTNnxubNm2P//feP+fPnx8knn5zXY//t3/5tvPTSS+2PDznkkIjI7/+H6kQBoFe7Da+On59xWBw2aVSsb2iOWf/xf7Fi7aa8HvO7dz4Tv3l8RVSWp+LHn3yHAAUAANgpJMnAmwcAoDfTp0+P22+/PV599dWor6+PxYsXx2mnndbn7evq6np0kMyZMydefPHFLW734osvRpIkPW75JEQBYIuGVFXEv886NA6orYk3NjTGOf/5aDQ25+eP+9v/vDKuWrQ0IiK++7G3x8ypY/NyHAAAAADoCyEKAFs1fFBl/PT06VEzqCIeW7Y2fnTPczk/xmvrG+Jrt/w5IiLOfPfe8eFD9sj5MQAAAAaqdItOFGDHMXXq1Bg2bFjW24033ljs8raJOVEA6JPxo4bE3I8cGP/888fiqkVL48OH7BGTRg/N2f6/fdvT8eaGxnhbbU18cebknO0XAAAAgMK67bbboqmpKetzY8aMKXA1/SNEAaDPPnBQbfzy4eVx/19fj7m/fSr+fdY7c7LfP79SH7c89kpERFz2dwdGVYVGSQAAYGfQMY5/S9JSxDoAcmvChAnFLiFnfEoFQJ+lUqmo++DUKEtF3PX06nhqxbqc7Pc7dzwTEREfOnhcHLTnLjnZJwAAQEnJ88TIAGwfIQoA22Sf3YbF3x5YGxERP1rU/7lRnlu9Pu579rUoS0Wcf5xhvAAAgJ1UWogCMBAJUQDYZuccu29ERPzuiZWxet3mfu3rZw++FBER73vbmNhr1yH9rg0AAKAUpYpdAABZCVEA2GZvq62J6RNGRjqJ+O8lK7Z7PxsamuPmR16OiIhZR07MUXUAAAAAkBtCFAC2y0cO2SMiIn7dNiH89lj0zGuxobElJuw6JI7cZ9dclQYAAFBy0km62CUAkIUQBYDt8oGDaqOqvCyeXrku/rJq+yaYv/OpVRER8f5pYyOV0rwOAAAAwMAiRAFgu+wypCqO3m90RETc/fTqbd6+sTkdv/9L63YzDxib09oAAABKTdKiEwUoDTNmzIg5c+b0WL5gwYK8fkl20aJF8aEPfShqa2tj6NChcfDBB8eNN96Yt+NlCFEA2G7HTtk9IqI9DNkWD73wRqzf3Byjh1XHIeN3yXFlAAAAAOxI/vCHP8RBBx0UN998c/zpT3+KT3/60/GpT30q/ud//ievx63I694B2KEds/9uERGxZPna2NDQHEOr+/628oelb0RExLGTd4uyMkN5AQAAO7ckrRMFdnZJksSm5k1FOfbgisEFHWq9rq4uFixYEOeff3584xvfiDVr1sQJJ5wQ11xzTQwfPjzrNl/72te6PD733HPjjjvuiFtuuSVOOumkvNUqRAFgu40fNST22GVwvLJ2Uzy6bE0cvd9ufd72sWVrIiLi0Ikj81UeAABACUmKXQBQZJuaN8Vh/3lYUY790CceiiGVQwp6zKVLl8aCBQvi1ltvjTVr1sQpp5wSl156acydO7fP+6ivr4+3ve1teazScF4A9NNhk0ZFRMT/vfBmn7dpbknH48vrIyLikL2EKAAAAAA7m3Q6HfPnz49p06bF0UcfHaeffnrcfffdfd7+V7/6VSxevDj+4R/+IY9V6kQBoJ8OmTAyfv3YK/H4y/V93uaZV9fHpqaWGF5dEfvuNiyP1QEAAAxgnZpP0iaWh53e4IrB8dAnHirasQtt4sSJXYbuqq2tjdWr+zbv7qJFi2L27NlxzTXXxNSpU/NVYkQIUQDopwP3GBEREX9+pT6SJOnT+JmPLVsbEREH77WL+VAAAAAAIiKVShV8SK3tUVNTE/X1Pb9Mu3bt2qipqenzfiorK7s8TqVSke7D/FD33ntvnHTSSfGDH/wgPvWpT/X5eNvLcF4A9MuUscOjoiwVb25ojFfW9m3ys0yIcsj4XfJXGAAAQAlJJzpRgNIwZcqUePjhh3ssX7x4cUyePDmvx160aFGceOKJcemll8aZZ56Z12NlCFEA6JdBleWx7+6tQ3I9++r6Pm3zl1XrIiJiWlsXCwAAAACl4eyzz46lS5fGOeecE48//ng8++yz8aMf/SjmzZsXX/rSl/J23EyAcu6558bf/d3fxapVq2LVqlXx5pt9n6d3ewhRAOi3TIjy11ff2uq6Lekknlvdut5+Y4ZvZW0AAICdg04UoFRMnDgx7r///li6dGnMnDkz3vnOd8b8+fNj/vz5cfLJJ+ftuPPnz4+NGzfGJZdcErW1te23j370o3k7ZoQQBYAc2G/31jAkE45syStrNkVDczqqKspi/MjCT1oGAAAwICXJ1tcBGCCmT58et99+e7z66qtRX18fixcvjtNOO63P29fV1cWSJUu6LJszZ068+OKLvW4zf/78SJKkx23RokXb90v0kRAFgH5r70TpQ4jy3GutQ37tPXpoVJR7GwIAAABg4PLpFQD9Nmn00IiIeOmNDVtd98XXN3bZBgAAYOfV0X2STutEAXYcU6dOjWHDhmW93XjjjcUub5tUFLsAAErf+FGtw3Kt2dgUbzU0x7Dq3t9eXl6zqW2bIQWpDQAAAIDCuu2226KpqSnrc2PGjClwNf0jRAGg34YPqoxdhlTG2o1N8fKajTFlbE2v6y5f09qJYj4UAACATtImlgd2HBMmTCh2CTljOC8AcmLPtlBk+Zubtrje8jc3tq2vEwUAAACAgU2IAkBOjG8LRTIhSW9eaR/OSycKAABARqIRBWBAEqIAkBO1I1pDkVfXbe51nbcammN9Q3OX9QEAAIjoPMk8AAOHEAWAnBhTUx0REavXN/S6zuq2gGVYdUUM3cLk8wAAAAAwEAhRAMiJ3dtClC11ory6rjVg2X14dUFqAgAAKBWJieUBBiQhCgA5sfvwQRGxlU6U9a0By25CFAAAAABKgBAFgJzIdJes3kInymttAcvuNYMKUhMAAMCAluq4m063FK8OgG0wY8aMmDNnTo/lCxYsiFQq1XODHHnmmWfi2GOPjTFjxsSgQYNi7733jq9//evR1NSUt2NGRBiQHoCcyHSirNvcHJubWmJQZXmPdTJdKmN0ogAAAACwDSorK+NTn/pUvOMd74hddtklHn/88TjjjDMinU7Ht7/97bwdV4gCQE7UDK6IqoqyaGxOx2vrG2L8qCE91sl0ohjOCwAAICKSTneT3lcDdg5JkkSyaVNRjp0aPDivXSTd1dXVxYIFC+L888+Pb3zjG7FmzZo44YQT4pprronhw4dn3WbvvfeOvffeu/3xhAkTYtGiRXH//ffntVYhCgA5kUqlYuSQynh1XUPUb2qK8VnWWbOxMSIiRg6tKmxxAAAAA50UBXZ6yaZN8cw7phfl2JMffSRSQ3p+ITafli5dGgsWLIhbb7011qxZE6ecckpceumlMXfu3D5t/9xzz8Xtt98eH/3oR/NapzlRAMiZXQa3hiOZsKS7tRub2tarLFhNAAAApSAJIQqwc0mn0zF//vyYNm1aHH300XH66afH3XffvdXtjjzyyBg0aFDst99+cfTRR8fFF1+c1zp1ogCQM7sMaQ1HMmFJd2t1ogAAALTrEpsk6WKVAQwQqcGDY/KjjxTt2IU2ceLELkN31dbWxurVq7e63S9+8YtYv359PP744/GlL30pvve978WXv/zlvNUpRAEgZ9pDlE29hCibdKIAAAAAZJNKpQo+pNb2qKmpifr6+h7L165dGzU1NX3eT2Vl18+HUqlUpNNbD5THj28dRP6AAw6IlpaWOPPMM+P888+P8vLyPh97WxjOC4CcGTmktcNk7Yaew3ml00nUt4UoI4YIUQAAAMwsD5SiKVOmxMMPP9xj+eLFi2Py5MkFrSVJkmhqaookj/8fqhMFgJwZsYVOlHWbm9qvCTJzpwAAAABQWs4+++y48sor45xzzokzzzwzBg8eHAsXLox58+bF9ddfn7fj3njjjVFZWRkHHnhgVFdXxyOPPBIXXHBBnHrqqVFRkb+oQ4gCQM5kOlGyTSyfmSdlaFV5VFVohAQAAOgsndaJApSGiRMnxv333x8XXnhhzJw5MzZv3hz7779/zJ8/P04++eS8HbeioiIuu+yyePbZZyNJkpgwYUKcc8458YUvfCFvx4wQogCQQ5m5TuqzTCyfCVZ2GaILBQAAAKCUTZ8+PW6//fbt3r6uri7q6uq6LJszZ07MmTOn121OPfXUOPXUU7f7mNvLV4EByJnMxPL1WYbzyiyrMak8AABAD0kfJlMGoPCEKADkzNDq1gbHtxqaezy3oaElIiKGV2uCBAAAANiRTZ06NYYNG5b1duONNxa7vG3ikywAciYTomxozBaiNLetU17QmgAAAAAorNtuuy2amnqOVBIRMWbMmAJX0z9CFAByZmhVW4jS1nXSWaY7ZYhOFAAAgJ4SE8sDO44JEyYUu4ScMZwXADmT6TLJNpzXxrbulGFVQhQAAAAASoMQBYCcGdbWZdLYnI6mlq6TIr7V1p0yVCcKAABAD0laJwrAQCREASBnhnTqMtnYbUivzJwow8yJAgAAkIUQBWAgEqIAkDNVFWVRVd761vJWt8nlM5PNmxMFAACgp8ScKAADkhAFgJzKzIuyodu8KJnHhvMCAAAAoFQIUQDIqUxI0n1y+Q1tw3sZzgsAAACAUiFEASCnMpPLd58TJROqDK3SiQIAANCqYwivJJ0uYh0AfTdjxoyYM2dOj+ULFiyIVCpVkBqee+65GD58eOyyyy55P5YQBYCcGlLV2mnSvRNlY6PhvAAAAADon6ampvj4xz8eRx99dEGOJ0QBIKcyIUnPOVFaujwPAABAB50oQJIk0dTQUpRbkiRbLzCH6urq4uCDD47rr78+Jk6cGCNGjIjTTjst1q9fv9Vtv/71r8eUKVPilFNOKUClET7JAiCnMsN1ZTpPMjKPM50qAAAAAHRobkzH1Z+/tyjHPvPfjonKAs9ju3Tp0liwYEHceuutsWbNmjjllFPi0ksvjblz5/a6ze9///v4r//6r1iyZEn8+te/LkidOlEAyKnqyta3lobmrt+iyjweVCFEAQAA6K7Q3wIHKLZ0Oh3z58+PadOmxdFHHx2nn3563H333b2u/8Ybb8Ts2bNj/vz5UVNTU7A6daIAkFPVFVsOUTIhCwCQW6+88kp85Stfid/97nexadOm2H///WPevHkxffr0YpcGQF/IUGCnV1FVFmf+2zFFO3ahTZw4MYYPH97+uLa2NlavXt3r+meccUZ84hOfiHe/+92FKK+dEAWAnBpU2dpp0jlEaW5JR0u69YpAJwoA5N6aNWviqKOOimOPPTZ+97vfxe677x5Lly6NXXbZpdilAQDQR6lUquBDam2PmpqaqK+v77F87dq129QhUllZ2eVxKpWK9Bbmh/r9738fv/nNb+J73/teRLR28KXT6aioqIirr746Pv3pT/f52NtCiAJATnV0orS0L+scqOhEAYDcu+yyy2L8+PFx7bXXti+bOHFir+s3NDREQ0ND++N169blszwA+sBwXkCpmDJlSvzud7/rsXzx4sUxefLkvB33wQcfjJaWjs+b/vu//zsuu+yy+MMf/hB77LFH3o7rkywAcqq6rdOkoakjONnc1PEGV1XurQcAcu03v/lNHHrooXHyySfH7rvvHoccckhcc801va5/ySWXxIgRI9pv48ePL2C1AACUsrPPPjuWLl0a55xzTjz++OPx7LPPxo9+9KOYN29efOlLX8rbcd/2trfFtGnT2m977LFHlJWVxbRp02LkyJF5O65PsgDIqS11olSVl0VZWaoodQHAjuz555+Pq666Kvbbb7+444474qyzzopzzz03fvazn2Vd/4ILLoj6+vr22/LlywtcMQDdJUnvQ9gADCQTJ06M+++/P5YuXRozZ86Md77znTF//vyYP39+nHzyycUuL+cM5wVATmWG6+rcidI+qXyF7B4A8iGdTsehhx4a3/72tyMi4pBDDoknn3wyrrrqqvjUpz7VY/3q6uqorq4udJkAAOwgpk+fHrfffvt2b19XVxd1dXVdls2ZMyfmzJnT533Mnj07Zs+evd019JVPswDIqfbhvJo7hyitXSnmQwGA/KitrY0DDjigy7K3ve1tsWzZsiJVBMC2MiUKwMDk0ywAcirrcF5NmU6U8qLUBAA7uqOOOiqeeeaZLsueffbZmDBhQpEqAmCbSVGAHcjUqVNj2LBhWW833nhjscvbJobzAiCn2ofzajacFwAUyhe+8IU48sgj49vf/naccsop8X//939x9dVXx9VXX13s0gAA2Anddttt0dTUlPW5MWPGFLia/hGiAJBTgzLDeTVlG85LJwoA5MM73/nOuOWWW+KCCy6Iiy++OCZNmhSXX355fPKTnyx2aQBsQRId3SeJThRgB7IjdUQLUQDIqY5OlGzDeelEAYB8+cAHPhAf+MAHil0GAADsUHyaBUBOZZ9YXogCAACwRTpRAAYkn2YBkFMdE8sbzgsAAACA0iZEASCnMp0om5s6hvPabDgvAACALTInCsDA5NMsAHKqY06ULJ0oQhQAAIDsZCgAA5JPswDIqfbhvDp1onTMiWI4LwAAgGxkKAADkxAFgJzKOrF8ZjivSm87AAAAWRnOCygRM2bMiDlz5vRYvmDBgkilUnk77osvvhipVKrH7fbbb8/bMSMiKvK6dwB2OplOlOZ0Es0t6agoLzOcFwAAAAA5cdddd8XUqVPbH48aNSqvxxOiAJBTnbtNGttDFMN5AQAA9NTRfZIk6S2sB+wMkiSJ5oaGohy7oro6r10k3dXV1cWCBQvi/PPPj2984xuxZs2aOOGEE+Kaa66J4cOHb3HbXXfdNcaOHVugSoUoAORYRVlHiNLU0npB0NzSejFQWV64N2MAAACAUtLc0BA/nPWxohz73Ot+FZWDBhX0mEuXLo0FCxbErbfeGmvWrIlTTjklLr300pg7d+4Wt/vgBz8Ymzdvjv322y++8IUvxMc+lt9zZlwVAHKqoqwjKGlJt4UobT/Ly4QoAAAAWZkSBdjJpNPpmD9/fkybNi2OPvroOP300+Puu+/udf1hw4bFD37wg/jVr34Vt912W7z3ve+NU089NW644Ya81qkTBYCcKitLRVkqIp10dKA0t3WkVJbL7gEAALKTosDOrqK6Os697ldFO3ahTZw4scvQXbW1tbF69epe1x89enR84QtfaH986KGHxpo1a+I73/lO/P3f/33e6hSiAJBzFWVl0diSbu9A0YkCAACwZYkMBXZ6qVSq4ENqbY+ampqor6/vsXzt2rVRU1PT5/1UVlZ2eZxKpSKd3rb5oQ4//PD493//923aZlv5SjAAOVfRNvdJpgOlue0NsEKIAgAA0AspClAapkyZEg8//HCP5YsXL47JkycXtJbHHnssamtr83oMnSgA5Fym4yQTnmQ6UYQoAAAAAKXt7LPPjiuvvDLOOeecOPPMM2Pw4MGxcOHCmDdvXlx//fV5O+51110XlZWVccghh0RZWVn8z//8T/zwhz+Myy67LG/HjBCiAJAHmblP2ofzapsbpcKcKAAAAFkZzgsoFRMnToz7778/Lrzwwpg5c2Zs3rw59t9//5g/f36cfPLJeT32t771rXjppZeivLw89t9///iP//iPvM6HEiFEASAP2jtR2obzatGJAgAAALDDmD59etx+++3bvX1dXV3U1dV1WTZnzpyYM2dOr9vMmjUrZs2atd3H3F6+EgxAzlV2G86rqS1M0YkCAADQWaf2E60oAAOST7MAyLnyzMTyaZ0oAAAAADubqVOnxrBhw7LebrzxxmKXt00M5wVAzlWWtc2J0taB0tQ+J4oQBQAAIJskSRe7BICcue2226KpqSnrc2PGjClwNf0jRAEg58q7DeelEwUAAABg5zFhwoRil5AzhvMCIOcyc5+0d6K0hyjedgAAAAA6S8yJlDe5OLc+zQIg5zIdJ5kOlOa24bzKDecFAACQnQ9RYadTWVkZEREbN24sciU7rsy5zZzr7WE4LwByLjP3SWYulEyYUqkTBQAAACAiIsrLy2OXXXaJ1atXR0TEkCFDIpXyBdRcSJIkNm7cGKtXr45ddtklysvLt3tfQhQAcq57J0omTCk3JwoAAEBWhvOBndPYsWMjItqDFHJrl112aT/H20uIAkDOZeY+ycyF0t6JYjgvAAAAgHapVCpqa2tj9913j6ampmKXs0OprKzsVwdKhhAFgJzLDOfVkm7tQGlqm2BeJwoAAEBnHd0nOlFg51ZeXp6TD/zJPYPTA5BzmeG8MuFJRyeKtx0AAAAASodPswDIufK24bwy4Ulz2pwoAAAAAJQeIQoAOZeZ+6S5bUL5ZnOiAAAAbJnRvAAGJCEKADmX6TjJhCfN7XOieNsBAAAAoHT4NAuAnMvMfZIJTzLDeVUYzgsAACCrJEkXuwQAshCiAJBzvXWiVBjOCwAAAIASIkQBIOc6z4mSJEl7mFJhOC8AAIB2Sa8PABgofJoFQM5lwpLmdBIt6Y4rARPLAwAA9EaKAjAQCVEAyLmO4bzS7V0onZcDAAAAQCkQogCQc+3DeaWTLiFKZsJ5AAAAIjp3nyQaUQAGJJ9mAZBz5ZnhvFqSaGnRiQIAAABAaRKiAJBzmU6UlnQSTel0+/IKIQoAAEB2WlEABiQhCgA5l+k4aWpJR3NbJ0p5WSpSKSEKAAAAAKVDiAJAzmXmPmlJJ9Hc1omiCwUAAGALNKIADEhFC1FefPHF+MxnPhOTJk2KwYMHxz777BMXXXRRNDY2dllv2bJlcdJJJ8XQoUNj9OjRce655/ZYB4CBpaMTJWnvRBGiAAAAAFBqKop14L/85S+RTqfjpz/9aey7777x5z//Oc4444zYsGFDfO9734uIiJaWljjxxBNjt912iwceeCDeeOONmDVrViRJEldccUWxSgdgKzKBSUs6Hc3pthClXPMjAABAbxKtKAADUtFClPe///3x/ve/v/3x3nvvHc8880xcddVV7SHKnXfeGU899VQsX748xo0bFxER3//+92P27Nkxd+7cqKmpKUrtAGxZJkRpMpwXAADAFiRZ7wIwcAyorwXX19fHqFGj2h8/+OCDMW3atPYAJSLi+OOPj4aGhnjkkUd63U9DQ0OsW7euyw2Awsl0nTR3m1geAAAAAErJgAlRli5dGldccUWcddZZ7ctWrVoVY8aM6bLeyJEjo6qqKlatWtXrvi655JIYMWJE+238+PF5qxuAnjKBSTqJSCfmRAEAANgaw3kBDEw5D1Hq6uoilUpt8fbwww932WbFihXx/ve/P04++eT47Gc/2+W5VKrnh25JkmRdnnHBBRdEfX19+2358uW5+eUA6JNMXpIkSbRNibLF/98GAAAAgIEo53OifO5zn4vTTjtti+tMnDix/f6KFSvi2GOPjSOOOCKuvvrqLuuNHTs2HnrooS7L1qxZE01NTT06VDqrrq6O6urqbS8egJzIBCadO1HKBkzvIwAAwACkEQVgQMp5iDJ69OgYPXp0n9Z95ZVX4thjj43p06fHtddeG2XdPmE74ogjYu7cubFy5cqora2NiNbJ5qurq2P69Om5Lh2AHMn0nKSTJJK2ECUVOlEAAAAAKC05D1H6asWKFTFjxozYa6+94nvf+1689tpr7c+NHTs2IiJmzpwZBxxwQJx++unx3e9+N95888344he/GGeccUbU1NQUq3QAtqKsSydKZlkRCwIAABiQOtpPMl9AA2BgKVqIcuedd8Zzzz0Xzz33XOy5555dnsu8aZSXl8dvf/vbOPvss+Ooo46KwYMHxyc+8Yn43ve+V4ySAeijTGNhkiSRtIcoUhQAAIBeyVAABqSihSizZ8+O2bNnb3W9vfbaK2699db8FwRAzmQCk6TTnCgyFAAAAABKjWl+Aci5jonlk46J5aUoAAAAvTOcF8CAJEQBIOcy85+kDecFAAAAQAkTogCQc10nljecFwAAwNboQwEYmIQoAORcphMlSZJI60QBAADYKldMAAOTEAWAnEtl6UQp844DAADQTaf+E3OiAAxIPtICIOfKOk0sn5hYHgAAAIASJUQBIOc6JpaPSKdb76eEKAAAAACUGCEKADmX6TppnRMl04lSzIoAAAAGNoN5AQxMQhQAci7V3oliYnkAAAAASpcQBYCca58TJR2d5kQpZkUAAAADnFYUgAFJiAJAznWeWD7TiWJOFAAAAABKjRAFgJzL5CVJEu1zoohQAAAAuks63dOKAjAQCVEAyLn2EKXTZYA5UQAAAAAoNUIUAHKuYzivTnOieMcBAAAAoMT4SAuAnOs6J0rSZRkAAABZJIbzAhiIhCgA5FxZ5zlR0q33TSwPAAAAQKkRogCQc6msnSjFrAgAAGDg6dx8ohEFYGASogCQc5nAJJ0k7RcChvMCAAAAoNQIUQDIufY5UdKhEwUAAKBXWlEABjohCgA5lwlRkiSJdNt1gDlRAAAAACg1QhQAci7VPpyXThQAAAAASpcQBYCcK+s0sXzSHqJIUQAAAHpnOC+AgUiIAkDOlbW9u7R2orQtE6IAAAAAUGKEKADkXNc5UVpTFBkKAADAFmhEARiQhCgA5FxZ+5woiU4UAAAAAEqWEAWAPMjMiRLtc6LIUAAAALrraD9JdKIADEhCFAByrmsnionlAQAAtk6KAjAQCVEAyLn2wCTp+DaVDAUAAACAUiNEASDnMiGKOVEAAAAAKGVCFAByLtU+nFd0Gs6riAUBAAAAwHYQogCQc2VlHZ0oiTlRAAAAtsrE8gADkxAFgJzLdJ0kSbQP55USogBA3tTV1UUqlepyGzt2bLHLAgCAkldR7AIA2PF0nRPFcF4AUAhTp06Nu+66q/1xeXl5EasBoG+SXu4DMFAIUQDIuY45UUwsDwCFUlFRofsEAAByzHBeAORcRydKdJoTpZgVAcCO769//WuMGzcuJk2aFKeddlo8//zzva7b0NAQ69at63IDoMg0ogAMSEIUAHKuc9dJS1srijlRACB/DjvssPjZz34Wd9xxR1xzzTWxatWqOPLII+ONN97Iuv4ll1wSI0aMaL+NHz++wBUDAEBpEKIAkHOdu04yIYrhvAAgf0444YT4u7/7uzjwwAPjfe97X/z2t7+NiIjrrrsu6/oXXHBB1NfXt9+WL19eyHIBAKBkmBMFgJzr3HXSnDacFwAU2tChQ+PAAw+Mv/71r1mfr66ujurq6gJXBUBPxvACGOh0ogCQc1k7UaQoAFAwDQ0N8fTTT0dtbW2xSwEAgJImRAEg58q6dKKkIyLCaF4AkD9f/OIX4957740XXnghHnroofjYxz4W69ati1mzZhW7NAD6KEl0pQAMRIbzAiDnUl06UdqWhRQFAPLl5Zdfjo9//OPx+uuvx2677RaHH354/PGPf4wJEyYUuzQAAChpQhQAcq5zJ0ranCgAkHc33XRTsUsAoL80ogAMSIbzAiDnOneidEwsL0UBAAAAoLQIUQDIuc6BSUvbnCg6UQAAAAAoNUIUAHKu68TyrZ0oKZ0oAAAA3RjDC2CgE6IAkHNlXSaWN5wXAAAAAKVJiAJAzqWydKIYzgsAAGALEl0pAAOREAWAvMiEJu2dKFIUAAAAAEqMEAWAvMgM39UxJ0oxqwEAABiIdJ8ADHRCFADyIhOitKTTXR4DAAAAQKkQogCQF5nMpLnFnCgAAAAAlCYhCgB50dGJknR5DAAAQE+JieUBBiQhCgB5kek86ZgTRYgCAAAAQGkRogCQFz07UYpZDQAAAABsOyEKAPnRvROliKUAAAAAwPYQogCQF5lOlHSmE0UrCgAAQDed50FxzQQwEAlRAMiLTGbSkpgTBQAAYOtMLA8wEAlRAMgLc6IAAAAAUOqEKADkRabzpDmdjoiOUAUAAAAASoUQBYC8aB/Oq0UnCgAAAAClSYgCQF6UtXeimBMFAAAguyTrXQAGDiEKAHnR3onSPieKEAUAAACA0iJEASAvUt06UQznBQAA0Lsk0YoCMBAJUQDIi7K2dxidKAAAAACUKiEKAHnRMSdKOiIiZCgAAAAddJ4AlAYhCgB5kQlRdKIAAABsnSsmgIFJiAJAXmQyk2YhCgAAAAAlSogCQF5kQpNMh7qJ5QEAADo0NTZ1eWxwL4CBSYgCQF50D01SOlEAAAAAKDFCFADyItVtRF8ZCgAAQId0S0vXBVpRAAYkIQoAedE9NDEnCgAAQGfdUxMpCsBAJEQBIC+6hybmRAEAAOjQ3L0TJVw0AQxEQhQA8qKs2zuMThQAAIBOEp0oAKVAiAJAXnQPTWQoAAAAAJQaIQoAeZHqMZyXFAUAACAj3dLcdUHimglgIBKiAJAX3edAEaIAAAAAUGqEKADkhYnlAQAAepdOp7stMScKwEAkRAEgL3qEJkIUAACADjITgJIgRAEgL1JhThQAAIDetHSfEwWAAUmIAgAAAAAFpxUFoBQIUQAAAAAAALIQogCQH6ktPgQAANippZu7DueV6EwBGJCEKAAAAAAAAFkIUQDIi+6dJykTywMAALRLp1u6LXHNBDAQCVEAAAAAAACyEKIAAAAAQIG1tKS7LjAlCsCAJEQBIC+6j95lNC8AAIBOEqkJQCkQogAAAAAAAGQhRAEAAACAAku6TyyvMQVgQBKiAJAXqUh1ewwAAAAApUWIAgAAAAAFlm5p7rZEKwrAQCREASAvTCwPAAAAQKkTogAAAABAgbWkdZ4AlAIhCgAAAAAUnBAFoBQIUQDIi57DdxnPCwAAAIDSIkQBAAAAgAJrae46sXyiMQVgQBKiAJAXqW6dJyaWBwAAAKDUCFEAAAAAoMCSdLrYJQDQB0IUAAAAAACALIQoAORF9+G7jOYFAADQIa0TBaAkCFEAAAAAAACyEKIAUBApM8sDAAAAUGKEKAAAAABQYOnmlm5LkqLUAcCWCVEAAAAAAACyEKIAkBfdh+8ymBcAAECHdLp7J4qrJoCBSIgCAAAAAACQhRAFAAAAAAos6dGJAsBAJEQBIC+6N6KndKYDAAB0SEwkD1AKhCgAAAAAUGDpdLrbEqEKwEAkRAEgL7p3nqRMkggAAABAiRGiAAAAAAAAZCFEAQAAAIACS7d0G87LaF4AA5IQBYC8MLE8AAAAAKVOiAIAAAAABZZOt3R5nOhEARiQhCgAAAAAAABZCFEAyIuU8bsAAAB6lW7RegJQCoQoAAAAAFBwQhSAUjAgQpSGhoY4+OCDI5VKxZIlS7o8t2zZsjjppJNi6NChMXr06Dj33HOjsbGxOIUC0GcmlgcAAACg1FUUu4CIiC9/+csxbty4ePzxx7ssb2lpiRNPPDF22223eOCBB+KNN96IWbNmRZIkccUVVxSpWgAAAADon5Z0c7FLAKAPit6J8rvf/S7uvPPO+N73vtfjuTvvvDOeeuqpuOGGG+KQQw6J973vffH9738/rrnmmli3bl0RqgVge6V69KYAAAAAwMBW1BDl1VdfjTPOOCOuv/76GDJkSI/nH3zwwZg2bVqMGzeufdnxxx8fDQ0N8cgjj/S634aGhli3bl2XGwCFZfguAACA3qXS5kQBKAVFC1GSJInZs2fHWWedFYceemjWdVatWhVjxozpsmzkyJFRVVUVq1at6nXfl1xySYwYMaL9Nn78+JzWDgAAAAAA7PhyHqLU1dVFKpXa4u3hhx+OK664ItatWxcXXHDBFveXyvJV5iRJsi7PuOCCC6K+vr79tnz58n7/XgBsq67/P60zBQAAoEM6SRe7BAD6IOcTy3/uc5+L0047bYvrTJw4Mb71rW/FH//4x6iuru7y3KGHHhqf/OQn47rrrouxY8fGQw891OX5NWvWRFNTU48Olc6qq6t77BcAAAAAAGBb5DxEGT16dIwePXqr6/3whz+Mb33rW+2PV6xYEccff3z84he/iMMOOywiIo444oiYO3durFy5MmprayOidbL56urqmD59eq5LBwAAAAAAaFe0OVH22muvmDZtWvtt//3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\n",
-      "text/plain": [
-       "<Figure size 2000x2000 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "thrs = 1.0e-4\n",
-    "update = 1.0\n",
-    "i = 0\n",
-    "k = 0\n",
-    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=[20, 20])\n",
-    "while (update > thrs and i < 100):\n",
-    "    if (i != 0 and (diff > update)):\n",
-    "        SCRF(update)    \n",
-    "    # Prepare Helmholtz operator\n",
-    "    mu = np.sqrt(-2*E_n)\n",
-    "    H = vp.HelmholtzOperator(mra=MRA, exp=mu, prec=epsilon)\n",
-    "    \n",
-    "    # Apply Helmholtz operator\n",
-    "    Vphi = (V_nuc + U_r_n)* phi_n #add the solvent reaction potential to the equation\n",
-    "    phi_np1 = -2*H(Vphi)\n",
-    "    norm = phi_np1.norm()\n",
-    "    \n",
-    "    # Compute orbital and energy updates\n",
-    "    dPhi_n = phi_np1 - phi_n\n",
-    "    dE_n = vp.dot(Vphi, dPhi_n)/phi_np1.squaredNorm()\n",
-    "    update = dPhi_n.norm()\n",
-    "    \n",
-    "    # Prepare for next iteration\n",
-    "    E_n += dE_n\n",
-    "    phi_n += dPhi_n\n",
-    "    phi_n.normalize()\n",
-    "    phi_n.crop(epsilon)\n",
-    "    E_r_n = vp.dot(U_r_n, phi_n)\n",
-    "\n",
-    "    rho =  phi_n**2 +  nuc_dens\n",
-    "\n",
-    "    phi_n_plt = [phi_n([x, 0.0, 0.0]) for x in r_x]\n",
-    "    axes[0][0].plot(r_x, phi_n_plt, label=str(i))\n",
-    "    axes[0][0].legend()\n",
-    "    \n",
-    "    print(\"iteration: {}   Energy: {}   Norm: {}   Update: {}\".format(i, E_n, norm, update))\n",
-    "    i += 1\n",
-    "V_nuc_eval = [V_nuc([x, 0.0, 0.0]) for x in r_x]\n",
-    "axes[1][0].plot(r_x, V_nuc_eval, label=\"nuclear potential\")\n",
-    "axes[1][0].legend()\n",
-    "plt.legend()\n",
-    "plt.show()"
+    "charge_coords = [[0.0000000000,    0.0000000000,    0.000000000]]\n",
+    "charges = [1.0]\n",
+    "charge_width = 1000.0\n",
+    "dens = Projection_op(constructChargeDensity(charge_coords, charges, width_parameter=charge_width))\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b3c6deb6-fac9-4f1c-97c6-de36d520e693",
-   "metadata": {
-    "tags": []
-   },
+   "id": "e8b9f770",
+   "metadata": {},
    "source": [
-    "# water single iteration"
+    "Project the permittivity of the solvent"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "5d3728a0-06b4-4012-8e6e-b7d9dea290a6",
+   "execution_count": 9,
+   "id": "84cee3c8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Global parameters\n",
-    "k = 7                        # Polynomial order\n",
-    "L = [-20,20]                 # Simulation box size\n",
-    "epsilon = 1.0e-5             # Relative precision\n",
+    "cav_coords = [[0.0000000000,    0.0000000000,    0.000000000]] # list of cavity centers. Normally, but not always, nucleus coordinates.\n",
+    "cav_radii = [1.0] # list of cavity radii. Normally, but not always, nucleus radii.\n",
+    "boundary_width = 0.2 # width of the cavity boundary\n",
     "\n",
-    "# Define MRA and multiwavelet projector\n",
-    "MRA = vp.MultiResolutionAnalysis(order=k, box=L)\n",
-    "P_eps = vp.ScalingProjector(mra=MRA, prec=epsilon)\n",
-    "D_abgv = vp.ABGVDerivative(mra=MRA, a=0.0, b=0.0)\n",
-    "Poissop = vp.PoissonOperator(mra=MRA, prec=epsilon)"
+    "C = Cavity(cav_coords, cav_radii, boundary_width)\n",
+    "perm = Projection_op(Permittivity(C, inside=1.0, outside=2.0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdfc058a",
+   "metadata": {},
+   "source": [
+    "Construct the effective solute density as defined above and compute the vacuum potential $V_{vac}$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "6527e6dd-e600-40ee-8da2-84eecf64c22b",
+   "execution_count": 10,
+   "id": "c04749f8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "wat_coords = [[0.0000000000,    0.0000000000,    0.2199848921],\n",
-    "          [0.0000000000,    1.4389053978,   -0.879941458],\n",
-    "          [0.0000000000,   -1.4389053978,   -0.879941458]] #centered in \n",
-    "wat_radii = [2.87236857024075, 2.0598013276, 2.0598013276]  \n",
-    "width = 0.2\n",
-    "\n",
-    "C_wat = Cavity(wat_coords, wat_radii, width)\n",
-    "wat_lin_perm = lin_permittivity(C_wat, e_0=1.0, e_inf=80.0)\n",
-    "wat_exp_perm = exp_permittivity(C_wat, e_0=1.0, e_inf=80.0)\n",
-    "\n",
-    "wat_lin_perm_tree = P_eps(wat_lin_perm)\n",
-    "wat_exp_perm_tree = P_eps(wat_exp_perm)\n",
-    "\n",
-    "wat_grad_perm =vp.gradient(oper=D_abgv, inp=wat_lin_perm_tree)\n",
-    "\n",
-    "\n",
-    "O_R = 2.872383502672\n",
-    "H_R = 2.0786985874\n",
-    "O_alpha = 3.0/(2.0*O_R)\n",
-    "H_alpha = 3.0/(2.0*H_R)\n",
-    "O_beta = (O_alpha / np.pi)**(3.0/2.0)\n",
-    "H_beta = (O_alpha / np.pi)**(3.0/2.0)\n",
-    "\n",
-    "H_1 =vp.GaussFunc(coef=H_beta*1.0, exp=alpha, pos=[1.4389053978,   -0.879941458, 0.0000000000])\n",
-    "H_2 = vp.GaussFunc(coef=H_beta*1.0, exp=alpha, pos=[-1.4389053978,   -0.879941458, 0.0000000000])\n",
-    "O_1 = vp.GaussFunc(coef=O_beta*(-2.0), exp=alpha, pos=[0.0000000000,    0.2199848921, 0.0000000000])\n",
-    "\n",
-    "\n",
-    "wat_dens = vp.GaussExp()\n",
-    "wat_dens.append(H_1)\n",
-    "wat_dens.append(H_2)\n",
-    "wat_dens.append(O_1)\n",
-    "water_tree = P_eps(wat_dens)"
+    "rho_eff = (4*np.pi)*((dens * (perm)**(-1))  - (dens))\n",
+    "V_vac =Poisson_op((4*np.pi)*(dens))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "421894bc",
+   "metadata": {},
+   "source": [
+    "Compute the zero-th reaction potential before the iterative procedure"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "ceaeb0da-24bc-4d09-abc9-393dd965a888",
+   "execution_count": 11,
+   "id": "624ae35d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma_0 = computeGamma(Derivative_op, V_vac, perm, epsilon)\n",
+    "V_R = Poisson_op(rho_eff + gamma_0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f46917d",
+   "metadata": {},
+   "source": [
+    "Iterate throught the application of the Poisson operator to compute the reaction potential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "71337e6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter.  Norm            Update          Energy (a.u.)   Energy update (a.u.)\n",
+      "---------------------------------------------------------------------------\n",
+      "0       5.4607414e+00   2.8853494e+00  -2.4138496e-01  -2.4138496e-01\n",
+      "1       6.1262685e+00   6.6570352e-01  -2.6637517e-01  -2.4990213e-02\n",
+      "2       6.0111386e+00   1.1516806e-01  -2.6215285e-01   4.2223193e-03\n",
+      "3       6.0270817e+00   1.5949632e-02  -2.6272795e-01  -5.7509706e-04\n",
+      "4       6.0252390e+00   1.8435902e-03  -2.6266231e-01   6.5638875e-05\n",
+      "5       6.0254219e+00   1.8305719e-04  -2.6266876e-01  -6.4520209e-06\n",
+      "6       6.0254060e+00   1.5950934e-05  -2.6266820e-01   5.5755347e-07\n",
+      "7       6.0254072e+00   1.2399247e-06  -2.6266825e-01  -4.3037920e-08\n"
+     ]
+    }
+   ],
+   "source": [
+    "update = 1.0\n",
+    "E_r_old = 0.0\n",
+    "max_iter = 100\n",
+    "print(f\"Iter.{' '*2}Norm{' '*12}Update{' '*10}Energy (a.u.){' '*3}Energy update (a.u.)\")\n",
+    "print(f\"{'-'*75}\")\n",
+    "\n",
+    "for i in range(max_iter):\n",
+    "    V_tot = V_vac + V_R\n",
+    "                \n",
+    "    # compute the surface charge distribution \n",
+    "    gamma_s = computeGamma(Derivative_op, V_tot, perm, epsilon)\n",
+    "\n",
+    "    # solve the generalized poisson equation for V_R \n",
+    "    V_R_np1 = Poisson_op((rho_eff) + (gamma_s)) \n",
+    "    dV_R = V_R_np1 - V_R\n",
+    "    \n",
+    "    update = dV_R.norm()\n",
+    "    \n",
+    "    V_R =  V_R + dV_R\n",
+    "    \n",
+    "    E_r = (1/2)*vp.dot(V_R, dens) # computing energy\n",
+    "    \n",
+    "    dE_r = E_r - E_r_old\n",
+    "    E_r_old = E_r\n",
+    "    \n",
+    "    print(f\"{i}{' '*6}{V_R.norm():14.7e}  {update:14.7e}  {E_r:14.7e}  {dE_r:14.7e}\") \n",
+    "    \n",
+    "    if (update < epsilon):\n",
+    "        break\n",
+    "else:\n",
+    "    print(\"WARNING: SCRF did not converge\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac887cf1",
+   "metadata": {},
+   "source": [
+    "The energy can then be computed as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "50f238b2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "-0.2626682475802428"
       ]
      },
+     "execution_count": 13,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
-    "\n",
-    "x = np.linspace(-0.5, 0.5, 1000)\n",
-    "y = np.linspace(0.2, 0.5, 1000)\n",
-    "X, Y = np.meshgrid(x, y)\n",
-    "Z = []\n",
-    "\n",
-    "for x_i in x:\n",
-    "    z = []\n",
-    "    for y_i in y:\n",
-    "        z.append(water_tree([x_i, y_i, 0.0]))\n",
-    "    Z.append(z)\n",
-    "Z = np.array(Z)\n",
-    "\n",
-    "surf = ax.plot_surface(X, Y, Z, cmap=cm.PRGn, linewidth=0, antialiased=False)"
+    "(1/2)*vp.dot(V_R, dens)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2914158c-b338-416b-bab0-86c884fe409f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.11.4 ('MRPyCM-env')",
    "language": "python",
    "name": "python3"
   },
@@ -496,7 +554,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "487dd71aba060d73bfc278ebf788be90cc4e85b80399312c78d8b97665a3c999"
+   }
   }
  },
  "nbformat": 4,
diff --git a/docs/notebooks/beryllium_atom.ipynb b/docs/notebooks/beryllium_atom.ipynb
new file mode 100644
index 00000000..d1ae00dc
--- /dev/null
+++ b/docs/notebooks/beryllium_atom.ipynb
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Beryllium atom\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\", \"Stig Rune Jensen\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Beryllium atom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We solve the Hartree-Fock equations for a closed-shell many-electron system\n",
+    "\\begin{equation*}\n",
+    "\\left[\\hat{T} + \\hat{V}\\right]\\Phi = \\Phi F\n",
+    "\\end{equation*}\n",
+    "\n",
+    "In the equation above $\\Phi$ is a row vector of doubly occupied orbitals,\n",
+    "$F = \\langle\\Phi|\\hat{T}+\\hat{V}|\\Phi\\rangle$ is the Fock matrix, and\n",
+    "$\\hat{V} = \\hat{V}_{nuc} + 2\\hat{J} - \\hat{K}$ is the potential operator.\n",
+    "We here define the nuclear attraction potential\n",
+    "\\begin{align*}\n",
+    "\\hat{V}_{nuc}(r) = \\sum_I \\frac{-Z_I}{|R_I - r|}\n",
+    "\\end{align*}\n",
+    "with $Z_I$ and $R_I$ being the nuclear charges and positions,\n",
+    "and the Coulomb and Exchange operators\n",
+    "\\begin{align*}\n",
+    "\\hat{J} \\varphi_i &= \\sum_j \\hat{P}[\\varphi_j \\varphi_j^\\dagger] \\varphi_i \\\\\n",
+    "\\hat{K} \\varphi_i &= \\sum_j \\hat{P}[\\varphi_i \\varphi_j^\\dagger] \\varphi_j\n",
+    "\\end{align*}\n",
+    "where $\\hat{P}$ is the convolution operator with the Poisson kernel $P=1/r$.\n",
+    "We iterate the Hartree-Fock equations in their integral form\n",
+    "\\begin{equation*}\n",
+    "\\tilde{\\Phi}^{n+1} = -2 \\hat{G}^{\\vec{\\mu}^n}\\left[\\hat{V} \\Phi^n + \\Phi^n\\left(\\Lambda^n - F^n\\right)\\right]\n",
+    "\\end{equation*}\n",
+    "where $\\hat{G}^{\\vec{\\mu}}$ is a vector of convolution operators with the\n",
+    "bound-state Helmholtz kernel $G^{\\mu} = e^{-\\mu r}/r$ and $\\mu_i^n = \\sqrt{-2\\lambda_i^n}$\n",
+    "is a positive real number. $\\Lambda_{ij}^n = \\lambda_i^n\\delta_{ij}$ is a diagonal matrix containing\n",
+    "the same parameters as used in the construction of the Helmholz vector. In practice we choose\n",
+    "$\\lambda_i^n = F_{ii}^n$ such that diagonal elements vanish in $\\left(\\Lambda^n - F^n\\right)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vampyr import vampyr3d as vp\n",
+    "import numpy as np\n",
+    "import numpy.linalg as LA\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class HelmholtzOperator():\n",
+    "    \"\"\"\n",
+    "    Vectorized Helmholtz operator\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mra : The multiresolution analysis we work on\n",
+    "    lamb : vector of lambda parameters, mu_i = sqrt(-2*lambda_i)\n",
+    "    prec : Precision requirement\n",
+    "\n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    operators : list containing HelmholtzOperators for each orbital\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, mra, lamb, prec):\n",
+    "        self.mra = mra\n",
+    "        self.lamb = lamb\n",
+    "        self.prec = prec\n",
+    "        self.operators = []\n",
+    "        self.setup()\n",
+    "\n",
+    "    def setup(self):\n",
+    "        mu = [np.sqrt(-2.0*l) if l < 0 else 1.0 for l in self.lamb]\n",
+    "        for m in mu:\n",
+    "            self.operators.append(vp.HelmholtzOperator(mra=self.mra, exp=m, prec=self.prec))\n",
+    "\n",
+    "    def __call__(self, Psi):\n",
+    "        \"\"\"Operate the Helmholtz operator onto an orbital vector\"\"\"\n",
+    "        return np.array([self.operators[i](Psi[i]) for i in range(len(Psi))])\n",
+    "\n",
+    "class ExchangeOperator():\n",
+    "    \"\"\"\n",
+    "    Vectorized Exchange operator\n",
+    "\n",
+    "    K(phi) = \\sum_i P[phi * psi_i] * psi_i\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mra : The multiresolution analysis we work on\n",
+    "    Psi : Orbital vector defining the operator\n",
+    "    prec : Precision requirement\n",
+    "\n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    poisson : Poisson Operator\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, mra, Psi, prec):\n",
+    "        self.mra = mra\n",
+    "        self.Psi = Psi\n",
+    "        self.prec = prec\n",
+    "        self.poisson = vp.PoissonOperator(mra=mra, prec=self.prec)\n",
+    "\n",
+    "    def __call__(self, Phi):\n",
+    "        \"\"\"Apply the exchange operator onto an orbital vector Phi\"\"\"\n",
+    "\n",
+    "        Phi_out = []\n",
+    "        for j in range(len(Phi)):\n",
+    "            V_j0 = self.poisson(Phi[j] * self.Psi[0])\n",
+    "            tmp = (self.Psi[0] * V_j0).crop(self.prec)\n",
+    "            for i in range(1, len(self.Psi)):\n",
+    "                V_ji = self.poisson(Phi[j] * self.Psi[i])\n",
+    "                tmp += (self.Psi[i] * V_ji).crop(self.prec)\n",
+    "            tmp *= 4.0*np.pi\n",
+    "            Phi_out.append(tmp)\n",
+    "        return np.array([phi.crop(self.prec) for phi in Phi_out])\n",
+    "\n",
+    "class KineticEnergyOperator():\n",
+    "    def __init__(self, mra, prec):\n",
+    "        self.prec = prec\n",
+    "        self.mra = mra\n",
+    "        self.derivative = vp.ABGVDerivative(mra, 0.5, 0.5)\n",
+    "\n",
+    "\n",
+    "    def laplace_operator(self, phi):\n",
+    "        return self.derivative(self.derivative(phi, 0), 0) + self.derivative(self.derivative(phi, 1), 1) + self.derivative(self.derivative(phi, 2), 2)\n",
+    "\n",
+    "    def __call__(self, Phi):\n",
+    "        return np.array([(-0.5*self.laplace_operator(phi)).crop(self.prec) for phi in Phi])\n",
+    "\n",
+    "class CouloumbOperator():\n",
+    "    \"\"\"\n",
+    "    Vectorized Couloumb operator\n",
+    "\n",
+    "    J(phi) = \\sum_i P[psi_i * psi_i] * phi\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mra : The multiresolution analysis we work on\n",
+    "    Psi : Orbital vector defining the operator\n",
+    "    prec : Precision requirement\n",
+    "\n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    poisson : Poisson operator\n",
+    "    potential : Coulomb potential\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, mra, Psi, prec):\n",
+    "        self.mra = mra\n",
+    "        self.Psi = Psi\n",
+    "        self.prec = prec\n",
+    "        self.poisson = vp.PoissonOperator(mra=mra, prec=self.prec)\n",
+    "        self.potential = None\n",
+    "        self.setup()\n",
+    "\n",
+    "    def setup(self):\n",
+    "        rho = self.Psi[0]**2\n",
+    "        for i in range(1, len(self.Psi)):\n",
+    "            rho += self.Psi[i]**2\n",
+    "        rho.crop(self.prec)\n",
+    "        self.potential = (4.0*np.pi)*self.poisson(rho).crop(self.prec)\n",
+    "\n",
+    "    def __call__(self, Phi):\n",
+    "        \"\"\"Apply Couloumb operator onto an orbital vector Phi\"\"\"\n",
+    "        return np.array([(self.potential*phi).crop(self.prec) for phi in Phi])\n",
+    "\n",
+    "\n",
+    "\n",
+    "class NuclearOperator():\n",
+    "    \"\"\"\n",
+    "    Vectorized Nuclear potential operator\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mra : The multiresolution analysis we work on\n",
+    "    atoms : List of dicts containing charge and coordinates of the atoms\n",
+    "    prec : Precision requirement\n",
+    "\n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    potential : Nuclear potential\n",
+    "\n",
+    "    \"\"\"\n",
+    "    def __init__(self, mra, atoms, prec):\n",
+    "        self.mra = mra\n",
+    "        self.prec= prec\n",
+    "        self.atoms = atoms\n",
+    "        self.potential = None\n",
+    "        self.setup()\n",
+    "\n",
+    "    def setup(self):\n",
+    "        # Project analytic function onto MRA basis\n",
+    "        P_mra = vp.ScalingProjector(mra=self.mra, prec=self.prec)\n",
+    "        f_nuc = NuclearFunction(self.atoms)\n",
+    "        self.potential = P_mra(f_nuc)\n",
+    "\n",
+    "    def __call__(self, Phi):\n",
+    "        \"\"\"Apply nuclear potential operator onto an orbital vector\"\"\"\n",
+    "        return np.array([(self.potential*phi).crop(self.prec) for phi in Phi])\n",
+    "\n",
+    "class NuclearFunction():\n",
+    "    \"\"\"\n",
+    "    Vectorized Nuclear potential operator\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "\n",
+    "    atoms : List of dicts containing charge and coordinates of the atoms\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "\n",
+    "    def __init__(self, atoms):\n",
+    "        self.atoms = atoms\n",
+    "\n",
+    "    def __call__(self, r):\n",
+    "        \"Returns the nuclear potential value in R\"\n",
+    "        tmp = 0.0\n",
+    "        for atom in self.atoms:\n",
+    "            Z = atom[\"Z\"]\n",
+    "            R = atom[\"R\"]\n",
+    "            R2 = (R[0]-r[0])**2 + (R[1]-r[1])**2 + (R[2]-r[2])**2\n",
+    "            tmp += -Z / np.sqrt(R2)\n",
+    "        return tmp\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scf_solver(mra, atoms, Phi_n, F_n, precision, threshold, max_iter=30):\n",
+    "    \"\"\"Kinetric-free Hartree-Fock SCF solver\n",
+    "\n",
+    "    Parameters:\n",
+    "    mra : The Multiresolution analysis to work on\n",
+    "    atoms : List of dicts containing charge and coordinates of the atoms\n",
+    "    Phi_n : Starting guess orbitals\n",
+    "    F_n : Starting guess for Fock matrix\n",
+    "    precision : Precision requirement\n",
+    "    threshold : Usually set to the same as precision, set to -1 to limit iterations by max_iter\n",
+    "    max_iter : Set maximum iterations\n",
+    "\n",
+    "    Returns:\n",
+    "    Updates : Vector of orbital residual norms at each iteration\n",
+    "    Energies : List of energy contributions at each iteration\n",
+    "    Phi_n : Converged orbital vector\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Setup nuclear potential\n",
+    "    V_nuc = NuclearOperator(mra, atoms, precision)\n",
+    "\n",
+    "    # Loop parameters\n",
+    "    iteration = 0                # Iteration counter\n",
+    "    update = np.ones(len(Phi_n)) # Initialize error measure (norm of orbital updates)\n",
+    "    updates = []                 # Will capture wavefunction updates for visusualization\n",
+    "    energies = []                # Will capture energies for visualization\n",
+    "\n",
+    "    # SCF loop\n",
+    "    while (max(update) > threshold):\n",
+    "        if iteration > max_iter-1:\n",
+    "            break\n",
+    "\n",
+    "        # Initialize operators for first iteration\n",
+    "        J_n = CouloumbOperator(mra, Phi_n, precision)\n",
+    "        K_n = ExchangeOperator(mra, Phi_n, precision)\n",
+    "\n",
+    "        # Initialize vector of Helmholtz operators based on Fock matrix diagonal\n",
+    "        Lambda_n = np.diag(np.diag(F_n))\n",
+    "        G = HelmholtzOperator(mra, np.diag(Lambda_n), precision)\n",
+    "\n",
+    "        # Apply potential operator to all orbitals\n",
+    "        VPhi = V_nuc(Phi_n) + 2*J_n(Phi_n) - K_n(Phi_n)\n",
+    "        KinOpr = KineticEnergyOperator(mra, precision)\n",
+    "\n",
+    "        # Apply Helmholtz operators to all orbitals\n",
+    "        Phi_np1 = -2*G(VPhi + (Lambda_n - F_n) @ Phi_n)\n",
+    "        dPhi_n = Phi_np1 - Phi_n\n",
+    "        update = np.array([phi.norm() for phi in dPhi_n])\n",
+    "\n",
+    "        # Compute overlap matrices\n",
+    "        S_tilde = calc_overlap(Phi_np1, Phi_np1)\n",
+    "\n",
+    "        # Löwdin orthonormalization S^{-1/2} = U * Sigma^{-1/2} * U^T\n",
+    "        sigma, U = LA.eig(S_tilde)\n",
+    "        Sm5 = U @ np.diag(sigma**(-0.5)) @ U.transpose()\n",
+    "        Phi_bar = Sm5 @ Phi_np1\n",
+    "\n",
+    "        # Initialize n+1 operators\n",
+    "        J_np1 = CouloumbOperator(mra, Phi_bar, precision)\n",
+    "        K_np1 = ExchangeOperator(mra, Phi_bar, precision)\n",
+    "\n",
+    "        # Prepare for next iteration\n",
+    "        Phi_n = Phi_bar\n",
+    "        VPhikin = V_nuc(Phi_n) + 2*J_np1(Phi_n) - K_np1(Phi_n) + KinOpr(Phi_n)\n",
+    "        F_n = calc_overlap(Phi_n, VPhikin)\n",
+    "\n",
+    "        # Compute energy contributions\n",
+    "        energy = calc_energies(F_n, Phi_n, V_nuc, J_np1, K_np1)\n",
+    "\n",
+    "        # Collect output\n",
+    "        updates.append(update)\n",
+    "        energies.append(energy)\n",
+    "        print(iteration, \" |  E_tot:\", energy[\"$E_{tot}$\"], \" |  dPhi:\", max(update))\n",
+    "        iteration += 1\n",
+    "\n",
+    "\n",
+    "    return np.array(updates), energies, Phi_n\n",
+    "\n",
+    "\n",
+    "def calc_energies(F_mat, Phi, V, J, K):\n",
+    "    \"\"\"\"Calcuate all energy contributions\"\"\"\n",
+    "\n",
+    "    V_mat = calc_overlap(Phi, V(Phi))\n",
+    "    J_mat = calc_overlap(Phi, J(Phi))\n",
+    "    K_mat = calc_overlap(Phi, K(Phi))\n",
+    "\n",
+    "    E_orb  = 2.0*F_mat.trace()\n",
+    "    E_en   = 2.0*V_mat.trace()\n",
+    "    E_coul = 2.0*J_mat.trace()\n",
+    "    E_ex   = -K_mat.trace()\n",
+    "    E_tot  = E_orb - E_coul - E_ex\n",
+    "    E_kin  = E_tot - E_en - E_coul - E_ex\n",
+    "\n",
+    "    return {\n",
+    "        \"$E_{orb}$\": E_orb,\n",
+    "        \"$E_{en}$\": E_en,\n",
+    "        \"$E_{coul}$\": E_coul,\n",
+    "        \"$E_{ex}$\": E_ex,\n",
+    "        \"$E_{kin}$\": E_kin,\n",
+    "        \"$E_{tot}$\": E_tot\n",
+    "    }\n",
+    "\n",
+    "\n",
+    "def calc_overlap(Bra, Ket):\n",
+    "    \"\"\"Calculate the overlap matrix between the orbitals <Bra| and |Ket>\n",
+    "\n",
+    "    Parameters:\n",
+    "    Bra : bra vector <Phi|\n",
+    "    Ket : ket vector |Phi>\n",
+    "\n",
+    "    Returns:\n",
+    "    Overlap matrix\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    S = np.empty((len(Bra), len(Ket)))\n",
+    "    for i in range(len(Bra)):\n",
+    "        for j in range(len(Ket)):\n",
+    "            S[i, j] = vp.dot(Bra[i], Ket[j])\n",
+    "    return S\n",
+    "\n",
+    "\n",
+    "def starting_guess(mra, atom, n_orbs, prec):\n",
+    "    \"\"\"Primitive starting guess, works for Be\"\"\"\n",
+    "\n",
+    "    # Define projector onto the MRA basis\n",
+    "    P_mra = vp.ScalingProjector(mra=mra, prec=prec)\n",
+    "\n",
+    "    Phi = []\n",
+    "    for i in range(1, n_orbs+1):\n",
+    "        R0 = atom[\"R\"]\n",
+    "        def f_gauss(r):\n",
+    "            R2 = (r[0]-R0[0])**2 + (r[1]-R0[1])**2 + (r[2]-R0[2])**2\n",
+    "            return np.exp(-R2/i)\n",
+    "\n",
+    "        phi = P_mra(f_gauss)\n",
+    "        phi.normalize()\n",
+    "        Phi.append(phi)\n",
+    "    Phi = np.array(Phi)\n",
+    "\n",
+    "    # Löwdin orthonormalization S^{-1/2} = U * Sigma^{-1/2} * U^T\n",
+    "    S = calc_overlap(Phi, Phi)\n",
+    "    sigma, U = LA.eig(S)\n",
+    "    Sm5 = U @ np.diag(sigma**(-0.5)) @ U.transpose()\n",
+    "    return Sm5 @ Phi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define molecule\n",
+    "mol = [\n",
+    "    {\"Z\": 4.0, \"R\": [0.0, 0.0, 0.0]}\n",
+    "]\n",
+    "\n",
+    "# Set target precision\n",
+    "precision = 1.0e-3\n",
+    "\n",
+    "# Define MRA basis and computational domain\n",
+    "k = 5                       # Polynomial order of MRA basis\n",
+    "world = [-20, 20]           # Computational domain [-L,L]^3 (a.u.)\n",
+    "MRA = vp.MultiResolutionAnalysis(order=k, box=world)\n",
+    "\n",
+    "# Setup guess for the orbitals and the Fock matrix\n",
+    "Phi_n = starting_guess(mra=MRA, atom=mol[0], n_orbs=2, prec=precision)\n",
+    "F_n = np.array([\n",
+    "    [-1.0, 0.0],\n",
+    "    [ 0.0,-0.5]\n",
+    "]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0  |  E_tot: -12.158806160497663  |  dPhi: 0.9554261103233852\n",
+      "1  |  E_tot: -13.910545962062471  |  dPhi: 0.6030032083595198\n",
+      "2  |  E_tot: -14.425154911621195  |  dPhi: 0.11248971009686566\n",
+      "3  |  E_tot: -14.550559502300937  |  dPhi: 0.056367744794410625\n",
+      "4  |  E_tot: -14.573091352701868  |  dPhi: 0.024464066721782796\n",
+      "5  |  E_tot: -14.576656010668405  |  dPhi: 0.009607118621517596\n",
+      "6  |  E_tot: -14.577184437878596  |  dPhi: 0.0036909439005734903\n",
+      "7  |  E_tot: -14.577255599761454  |  dPhi: 0.0017015854922230098\n",
+      "8  |  E_tot: -14.577262672068981  |  dPhi: 0.0009093846224262461\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run SCF solver to optimize the orbitals\n",
+    "orbital_updates, energies, Phi_n = scf_solver(MRA, mol, Phi_n, F_n, precision, precision, max_iter=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "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": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/function_representations.ipynb b/docs/notebooks/function_representations.ipynb
new file mode 100644
index 00000000..d8efba58
--- /dev/null
+++ b/docs/notebooks/function_representations.ipynb
@@ -0,0 +1,383 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Function representations\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\", \"Stig Rune Jensen\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Function representations\n",
+    "\n",
+    "Functions $f(x)$ can be projected onto a scaling basis $\\{\\phi_{i,l}^n\\}$ by using a \n",
+    "scaling projector $P^n_k$. \n",
+    "\n",
+    "\\begin{align}\n",
+    "    f^n(x) = P^n_k [f(x)] = \\sum_{l=0}^{2^n-1} \\sum_{j=0}^k s_{j, l}^{n,f} \\phi_{j,l}^n(x) \n",
+    "\\end{align}\n",
+    "where the scaling coefficients can be obtained by the projection integral\n",
+    "\\begin{align}\n",
+    "    s^{n,f}_{j, l} = \\int f(x) \\phi_{i,l}^n(x) \\text{ d}x\n",
+    "\\end{align}\n",
+    "Next we'll make the projector $P^n_k$ then project the function, $f$\n",
+    "\\begin{align}\n",
+    "    f(x) = \n",
+    "    \\begin{cases}\n",
+    "        e^{-a(x - b)} && x \\geq b \\\\ \n",
+    "        e^{a(x - b)} && x < b \n",
+    "    \\end{cases}\n",
+    "\\end{align}\n",
+    "\n",
+    "onto a scaling basis at different scales.\n",
+    "\n",
+    "![image.png](attachment:image-2.png)\n",
+    "\n",
+    "Then we'll plot the various function representaions $f^n$ together with the basis they are projected onto. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vampyr import vampyr1d as vp1\n",
+    "from vampyr import plotter\n",
+    "from numpy import exp, arange\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "# vampyr assumes the argument is a vector\n",
+    "def f(x):\n",
+    "    a = 10.0 # Increase a to make the function sharper\n",
+    "    b = 1.5 # Modify b to shift the function\n",
+    "    if x[0] >= b:\n",
+    "        return exp(-a*(x[0] -b))\n",
+    "    return exp(a*(x[0] - b))\n",
+    "\n",
+    "# Make the MRA\n",
+    "# Note: This can be done in 1 step, but in this case we need a bit more control.\n",
+    "b_box = vp1.BoundingBox(scale=0, corner=[0], nboxes=[1], scaling=[4.0])\n",
+    "mra = vp1.MultiResolutionAnalysis(box=b_box, order=3)\n",
+    "\n",
+    "# Make the scaling projectors P^n_k\n",
+    "P0 = vp1.ScalingProjector(mra, 0) # P^0_k\n",
+    "P1 = vp1.ScalingProjector(mra, 1) # P^1_k\n",
+    "P2 = vp1.ScalingProjector(mra, 2) # P^2_k\n",
+    "P3 = vp1.ScalingProjector(mra, 3) # P^3_k\n",
+    "P4 = vp1.ScalingProjector(mra, 4) # P^4_k\n",
+    "P5 = vp1.ScalingProjector(mra, 5) # P^4_k\n",
+    "P6 = vp1.ScalingProjector(mra, 6) # P^4_k\n",
+    "\n",
+    "# Project f onto the basis\n",
+    "f0 = P0(f) #f^0 = P^0_k[f]\n",
+    "f1 = P1(f) #f^1 = P^1_k[f]\n",
+    "f2 = P2(f) #f^2 = P^2_k[f]\n",
+    "f3 = P3(f) #f^3 = P^3_k[f]\n",
+    "f4 = P4(f) #f^4 = P^4_k[f]\n",
+    "f5 = P5(f) #f^5 = P^5_k[f]\n",
+    "f6 = P6(f) #f^6 = P^6_k[f]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.representation_vs_basis(f2, type = \"scaling\") # Vary between f0 and f4 and see how the function and corresponding basis changes\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wavelet projection\n",
+    "\n",
+    "Functions $f(x)$ can also be projected onto the corresponding wavelet basis $\\{\\psi_{i,l}^n\\}$ by using a \n",
+    "wavelet projector $Q^n_k$. \n",
+    "\n",
+    "\\begin{align*}\n",
+    "    df^n(x) = Q^n_k [f(x)] = \\sum_{l=0}^{2^n-1} \\sum_{j=0}^k w_{j, l}^{n,f} \\psi_{j,l}^n(x) \n",
+    "\\end{align*}\n",
+    "where the scaling coefficients can be obtained by the projection integral\n",
+    "\\begin{align*}\n",
+    "    w^{n,f}_{j, l} = \\int f(x) \\psi_{i,l}^n(x) \\text{ d}x\n",
+    "\\end{align*}\n",
+    "where we write the function projection onto the wavelet basis as $df^n$. We'll explain why farther down.\n",
+    "\n",
+    "Next, we use vampyr to project $f$ onto the wavelet basis, then as we did for the projection onto the \n",
+    "scaling basis, we plot the $df^n$ next to it's corresponding basis. We'll also print the square norm\n",
+    "of the representation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make the wavelet projectors Q^n_k\n",
+    "Q0 = vp1.WaveletProjector(mra, 0) # P^0_k\n",
+    "Q1 = vp1.WaveletProjector(mra, 1) # P^1_k\n",
+    "Q2 = vp1.WaveletProjector(mra, 2) # P^2_k\n",
+    "Q3 = vp1.WaveletProjector(mra, 3) # P^3_k\n",
+    "Q4 = vp1.WaveletProjector(mra, 4) # P^4_k\n",
+    "Q5 = vp1.WaveletProjector(mra, 5) # P^4_k\n",
+    "\n",
+    "# Project f onto the basis\n",
+    "df0 = Q0(f) #f^0 = P^0_k[f]\n",
+    "df1 = Q1(f) #f^1 = P^1_k[f]\n",
+    "df2 = Q2(f) #f^2 = P^2_k[f]\n",
+    "df3 = Q3(f) #f^3 = P^3_k[f]\n",
+    "df4 = Q4(f) #f^4 = P^4_k[f]\n",
+    "df5 = Q5(f) #f^4 = P^4_k[f]\n",
+    "\n",
+    "# This does not behave as expected\n",
+    "# print(\"Square norm df0\", df0.norm())\n",
+    "# print(\"Square norm df1\", df1.norm())\n",
+    "# print(\"Square norm df2\", df2.norm())\n",
+    "# print(\"Square norm df3\", df3.norm())\n",
+    "# print(\"Square norm df4\", df4.norm())\n",
+    "# print(\"Square norm df5\", df5.norm())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.representation_vs_basis(df0, \"wavelet\") \n",
+    "# Vary between df0 and df5 and see how the\n",
+    "# function and corresponding basis changes                                                \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we projected the function onto the scaling basis. Then the function representation\n",
+    "improved when we increased the scale. However, when we increase the scale of the wavelet\n",
+    "representation we see that the magnitude of the function representation decreases. That's\n",
+    "because the wavelet basis is constructed such that $df^n$ represent the difference \n",
+    "between the $f$ projected onto the scaling basis at scale $n+1$ and $n$, i.e.,\n",
+    "\\begin{align}\n",
+    "    df^n(x) = f^{n+1}(x) - f^n(x)\n",
+    "\\end{align}\n",
+    "\n",
+    "As an example let's plot both $df^n$ and $f^{n+1} - f^{n}$ and see how simular they are.\n",
+    "Note they look beter the higher scale we use, this has to do with the fact that the scaling\n",
+    "and wavelet factors are approximated numerically. The difference beween the figures are larger\n",
+    "for lower scales. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0.0, 4.0, 0.001)\n",
+    "fnp1_fn_plot = [(f4-f3)([_]) for _ in x]\n",
+    "df_plot = [df3([_]) for _ in x]\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].plot(x, fnp1_fn_plot)\n",
+    "ax[0].title.set_text(\"$f^{n+1}(x) - f^n(x)$\")\n",
+    "ax[1].plot(x, df_plot)\n",
+    "ax[1].title.set_text(\"$df^{n}(x)$\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This means $f^n$ can be written in, what we call a multiresolution (or multiwavelet) representation, as \n",
+    "\\begin{align*}\n",
+    "    f^n(x) = f^0(x) + \\sum_{n'=0}^n df^{n'}(x)\n",
+    "\\end{align*}\n",
+    "This implies that we can start with projecting the function onto the cheapest zeroth scale, scaling basis, then\n",
+    "improve the function representation one scale at a time. \n",
+    "\n",
+    "If we look at the first figure where we compared the function representation with its basis. \n",
+    "There if we plot the function projection onto the scaling basis with polynomial order $k=3$. We see that the cusp\n",
+    "of the slater function takes a while to converge but farther away from the cusp it looks converged at scale\n",
+    "$n=2$. From an eyeballing perspective this means at scale $n=4$, we use enough basis functions to represent the \n",
+    "cusp accurately but we use way to many basis functions to represent the other edges, which were fine at scale\n",
+    "$n=2$. Meaning, wasted work to represent the entire function. This is when multiwavelets start to shine.\n",
+    "\n",
+    "Keeping in mind that we work on computers with a finite precision available. With multiwavelets we can\n",
+    "project functions onto the multiwavelet basis, with a specified precision $\\epsilon$ du to the relation\n",
+    "\\begin{align*}\n",
+    "    ||w_l^n|| < 2^{-n/2} \\epsilon ||f|| \n",
+    "\\end{align*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epsilon = 1.0e-2\n",
+    "P_eps = vp1.ScalingProjector(mra, epsilon)\n",
+    "\n",
+    "f_eps = P_eps(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotter.representation_vs_basis(f_eps)\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that to represent the cusp to a relative precision of \n",
+    "$\\epsilon = 1.0e-2$.\n",
+    "we need to go down to a scale $n=5$. But as the basis plot shows us is that we reach a relative\n",
+    "precision of $\\epsilon = 1.0e-2$ a lot earler."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "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": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/harmonic_oscillator_evolution.ipynb b/docs/notebooks/harmonic_oscillator_evolution.ipynb
new file mode 100644
index 00000000..b6c308e2
--- /dev/null
+++ b/docs/notebooks/harmonic_oscillator_evolution.ipynb
@@ -0,0 +1,560 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "26d935ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"Schrödinger equation\"\"\"\n",
+    "\n",
+    "__author__    = \"Evgueni Dinvay\"\n",
+    "__credit__    = [\"Evgueni Dinvay\"]\n",
+    "\n",
+    "__date__      = \"2023-11-24\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "559d1557",
+   "metadata": {},
+   "source": [
+    "# The Schrödinger equation\n",
+    "\n",
+    "\n",
+    "\n",
+    "Here we demonstrate how the multiwavelets machinery can be exploited for time evolution simulations\n",
+    "of the following one dimensional equation\n",
+    "\\begin{equation}\n",
+    "    i \\partial_t \\Psi\n",
+    "    =\n",
+    "    \\left( - \\partial_x^2 + V(x) \\right) \\Psi\n",
+    "    ,\n",
+    "\\end{equation}\n",
+    "where we restrict ourselves to consideration of a time independent potential $V$.\n",
+    "This equation is complemented by a given initial wave function $\\Psi(x, 0) = \\Psi_0(x)$.\n",
+    "As an example, we take a harmonic potential and a gaussian initial wave packet.\n",
+    "\n",
+    "\n",
+    "A common way of numerical treatment of this equation\n",
+    "is to choose a small time step $t > 0$ and construct the time evolution operator\n",
+    "\\begin{equation}\n",
+    "    \\Psi(t)\n",
+    "    =\n",
+    "    \\exp\n",
+    "    \\left(\n",
+    "        i t \\partial_x^2 -i t V\n",
+    "    \\right)\n",
+    "    \\Psi_0\n",
+    "\\end{equation}\n",
+    "on interval $[0, t]$.\n",
+    "Then applying it iteratively one can arrive to the solution\n",
+    "at some given finite time moment,\n",
+    "thanks to the semigroup property of the propagator.\n",
+    "As we normally do not have an access to the full collection of eigenfunctions and eigenvalues\n",
+    "for the Hamiltonian, we have to split the propagator on kinetic\n",
+    "$\n",
+    "    \\exp\n",
+    "    \\left(\n",
+    "        i t \\partial_x^2\n",
+    "    \\right)\n",
+    "$\n",
+    "and potential \n",
+    "$\n",
+    "    \\exp\n",
+    "    \\left(\n",
+    "        -i t V\n",
+    "    \\right)\n",
+    "$\n",
+    "parts.\n",
+    "Note that the latter is a multiplication operator in the physical space.\n",
+    "\n",
+    "\n",
+    "We will make use of the following fourth order scheme\n",
+    "\\begin{equation}\n",
+    "    \\exp ( At + Bt )\n",
+    "    =\n",
+    "    \\exp \\left(  \\frac t6  B \\right)\n",
+    "    \\exp \\left(  \\frac t2  A \\right)\n",
+    "    \\exp \\left(  \\frac {2t}3  \\widetilde B \\right)\n",
+    "    \\exp \\left(  \\frac t2  A \\right)\n",
+    "    \\exp \\left(  \\frac t6  B \\right)\n",
+    "    +\n",
+    "    \\mathcal O \\left( t^5 \\right)\n",
+    "    ,\n",
+    "\\end{equation}\n",
+    "where\n",
+    "\\begin{equation}\n",
+    "    \\widetilde B\n",
+    "    =\n",
+    "    B\n",
+    "    +\n",
+    "    \\frac{t^2}{48}\n",
+    "    [ B, [A, B]]\n",
+    "    .\n",
+    "\\end{equation}\n",
+    "In the case of $A = i \\partial_x^2$ and $B = -i V$\n",
+    "this\n",
+    "$\n",
+    "    \\widetilde B\n",
+    "$\n",
+    "turns out to be a multiplication operator containing potential gradient $\\partial_x V(x)$.\n",
+    "Remarkably,\n",
+    "this high order scheme requires only two applications\n",
+    "of the free-particle semigroup operator\n",
+    "$\n",
+    "    \\exp \\left(  it  \\partial_x^2 / 2 \\right)\n",
+    "$\n",
+    "per time step.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3cf82f2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ChinChenA(object):\n",
+    "    def __init__(self, expA, expB, exp_tildeB):\n",
+    "        self.expA = expA\n",
+    "        self.expB = expB\n",
+    "        self.exp_tildeB = exp_tildeB\n",
+    "    def __call__(self, u):\n",
+    "        u = self.expB(u)        # 1/6*dt\n",
+    "        u = self.expA(u)        # 1/2*dt\n",
+    "        u = self.exp_tildeB(u)  # 2/3*dt\n",
+    "        u = self.expA(u)        # 1/2*dt\n",
+    "        u = self.expB(u)        # 1/6*dt\n",
+    "        return u    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4f5c5cf",
+   "metadata": {},
+   "source": [
+    "`expA`: stands for operator $\\exp \\left(  \\frac t2  A \\right) = \\exp \\left(  i \\frac t2  \\partial_x^2 \\right)$\n",
+    "\n",
+    "\n",
+    "`expB`: stands for operator $\\exp \\left(  \\frac t6  B \\right) = \\exp \\left(  -i \\frac t6  V \\right)$\n",
+    "\n",
+    "\n",
+    "`exp_tildeB`: stands for operator\n",
+    "$\n",
+    "    \\exp \\left(  \\frac {2t}3  \\widetilde B \\right) = \\exp \\left(  -i \\frac {2t}3  \\widetilde V \\right)\n",
+    "$\n",
+    "with\n",
+    "$\n",
+    "    \\widetilde V\n",
+    "    =\n",
+    "    V\n",
+    "    -\n",
+    "    \\frac{t^2}{24}\n",
+    "    ( \\partial_x V )^2\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "613c15a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from vampyr import vampyr1d as vp1\n",
+    "from vampyr import LegendreBasis\n",
+    "\n",
+    "# Define parameters, final time moment and time step\n",
+    "x0 = 0.3\n",
+    "sigma = 0.04\n",
+    "x1 = 0.5\n",
+    "V0 = 25000\n",
+    "N = 10\n",
+    "t_period = np.pi / np.sqrt(V0)\n",
+    "time_step = 0.5 * t_period / N\n",
+    "\n",
+    "# Set the precision and make the MRA\n",
+    "precision = 1.0e-5\n",
+    "finest_scale = 9\n",
+    "mra = vp1.MultiResolutionAnalysis(vp1.BoundingBox(0), LegendreBasis(5))\n",
+    "\n",
+    "# Make the scaling projector\n",
+    "P = vp1.ScalingProjector(mra, prec = precision)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "487dcca8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MultiplicationOperator(object):\n",
+    "    def __init__(self, function):\n",
+    "        self.function = function\n",
+    "    def __call__(self, function):\n",
+    "        return self.function * function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a47b2264",
+   "metadata": {},
+   "source": [
+    "\\begin{equation*}\n",
+    "    U\n",
+    "    =\n",
+    "    U \\left( \\widehat H, t \\right)\n",
+    "    =\n",
+    "    \\exp \\left( -i \\widehat H t \\right)\n",
+    "    =\n",
+    "    \\begin{pmatrix}\n",
+    "        \\cos \\widehat H t\n",
+    "        &\n",
+    "        \\sin \\widehat H t\n",
+    "        \\\\\n",
+    "        - \\sin \\widehat H t\n",
+    "        &\n",
+    "        \\cos \\widehat H t\n",
+    "    \\end{pmatrix}\n",
+    "    =\n",
+    "    \\begin{pmatrix}\n",
+    "        \\Re U\n",
+    "        &\n",
+    "        -\\Im U\n",
+    "        \\\\\n",
+    "        \\Im U\n",
+    "        &\n",
+    "        \\Re U\n",
+    "    \\end{pmatrix}\n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "04f44589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class UnitaryExponentGroup(object):\n",
+    "    def __init__(self, real, imag):\n",
+    "        self.real = real\n",
+    "        self.imag = imag\n",
+    "    def __call__(self, psi):\n",
+    "        u = psi[0]\n",
+    "        v = psi[1]\n",
+    "        res0 = self.real(u) - self.imag(v)\n",
+    "        res1 = self.imag(u) + self.real(v)\n",
+    "        return np.array([ res0, res1 ])     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c709cd",
+   "metadata": {},
+   "source": [
+    "The unitary potential semigroup operator\n",
+    "\\begin{equation*}\n",
+    "    U\n",
+    "    =\n",
+    "    U \\left( V, t \\right)\n",
+    "    =\n",
+    "    \\exp \\left( -i V t \\right)\n",
+    "    \\approx\n",
+    "    \\begin{pmatrix}\n",
+    "        P \\cos (V t)\n",
+    "        &\n",
+    "        P \\sin (V t)\n",
+    "        \\\\\n",
+    "        - P \\sin (V t)\n",
+    "        &\n",
+    "        P \\cos (V t)\n",
+    "    \\end{pmatrix}\n",
+    "    =\n",
+    "    \\begin{pmatrix}\n",
+    "        P \\Re \\exp \\left( -i V t \\right)\n",
+    "        &\n",
+    "        - P \\Im \\exp \\left( -i V t \\right)\n",
+    "        \\\\\n",
+    "        P \\Im \\exp \\left( -i V t \\right)\n",
+    "        &\n",
+    "        P \\Re \\exp \\left( -i V t \\right)\n",
+    "    \\end{pmatrix}\n",
+    "\\end{equation*}\n",
+    "which should be understood as a multiplication operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "defcebb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_unitary_potential_operator(P, V, t):\n",
+    "    def real(x):\n",
+    "        return np.cos(V(x) * t)\n",
+    "    real = P(real)\n",
+    "    def imag(x):\n",
+    "        return - np.sin(V(x) * t)\n",
+    "    imag = P(imag)\n",
+    "    real = MultiplicationOperator(real)\n",
+    "    imag = MultiplicationOperator(imag)\n",
+    "    return UnitaryExponentGroup(real, imag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f40d5135",
+   "metadata": {},
+   "source": [
+    "The Schrodinger free-particle semigroup operator\n",
+    "\\begin{equation*}\n",
+    "    U\n",
+    "    =\n",
+    "    U \\left( \\widehat T, t \\right)\n",
+    "    =\n",
+    "    U \\left( - \\partial_x^2, t \\right)\n",
+    "    =\n",
+    "    \\exp \\left( it \\partial_x^2 \\right)\n",
+    "    =\n",
+    "    \\begin{pmatrix}\n",
+    "        \\Re \\exp \\left( it \\partial_x^2 \\right)\n",
+    "        &\n",
+    "        - \\Im \\exp \\left( it \\partial_x^2 \\right)\n",
+    "        \\\\\n",
+    "        \\Im \\exp \\left( it \\partial_x^2 \\right)\n",
+    "        &\n",
+    "        \\Re \\exp \\left( it \\partial_x^2 \\right)\n",
+    "    \\end{pmatrix}\n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "50644dd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_unitary_kinetic_operator(mra, precision, time, finest_scale):\n",
+    "    real = vp1.TimeEvolutionOperator(mra, precision, time, finest_scale, False)\n",
+    "    imag = vp1.TimeEvolutionOperator(mra, precision, time, finest_scale, True)\n",
+    "    return UnitaryExponentGroup(real, imag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11845e68",
+   "metadata": {},
+   "source": [
+    "It is well known that in the harmonic potential\n",
+    "$\n",
+    "    V(x) = V_0 (x - x_1)^2\n",
+    "$\n",
+    "the density\n",
+    "$\n",
+    "    \\left| \\Psi(t) \\right|^2\n",
+    "$\n",
+    "oscillates with the period\n",
+    "$\n",
+    "    t_{\\text{period}} = \\pi / \\sqrt{V_0}\n",
+    "    .\n",
+    "$\n",
+    "More precisely,\n",
+    "$\n",
+    "    \\Psi \\left( t_{\\text{period}} \\right) = - \\Psi_0\n",
+    "    .\n",
+    "$\n",
+    "This can immediately be seen taking into account that\n",
+    "the eigenvalues for the Hamiltonian are\n",
+    "$\n",
+    "    \\sqrt{V_0} ( 2n + 1)\n",
+    "    .\n",
+    "$\n",
+    "\n",
+    "For the time step $t$ the scheme modification potential\n",
+    "$\n",
+    "    \\widetilde V\n",
+    "    =\n",
+    "    V\n",
+    "    -\n",
+    "    \\frac{t^2}{24}\n",
+    "    ( \\partial_x V )^2\n",
+    "    =\n",
+    "    \\widetilde V_0 (x - x_0)^2\n",
+    "$\n",
+    "where\n",
+    "$\n",
+    "    \\widetilde V_0\n",
+    "    =\n",
+    "    V_0\n",
+    "    -\n",
+    "    \\frac{(tV_0)^2}6\n",
+    "    .\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c6d5c201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the harmonic potential with its scheme modification\n",
+    "def V(x):\n",
+    "    return V0 * (x[0] - x1)**2\n",
+    "def tilde_V(x):\n",
+    "    A = V0 - ( time_step * V0 )**2 / 6.0\n",
+    "    return A * (x[0] - x1)**2\n",
+    "\n",
+    "# Define the iteration procedure\n",
+    "iteratorA = ChinChenA(\n",
+    "    expA = create_unitary_kinetic_operator(mra, precision, time_step / 2, finest_scale),\n",
+    "    expB = create_unitary_potential_operator(P, V, time_step / 6),\n",
+    "    exp_tildeB = create_unitary_potential_operator(P, tilde_V, 2 * time_step / 3)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e1bd7d5",
+   "metadata": {},
+   "source": [
+    "Let an initial condition be of the form\n",
+    "\\begin{equation*}\n",
+    "    \\Psi_0(x)\n",
+    "    =\n",
+    "    \\left(\n",
+    "        \\frac 1{2 \\pi \\sigma^2}\n",
+    "    \\right)^{1/4}\n",
+    "    \\exp\n",
+    "    \\left(\n",
+    "        - \\frac {(x - x_0)^2}{4 \\sigma^2}\n",
+    "    \\right)\n",
+    "\\end{equation*}\n",
+    "with $x_0 = 0.3$ and $\\sigma = 0.04$.\n",
+    "All the parameters are chosen in a way that the solution\n",
+    "stays localized mainly on the space interval $[0, 1]$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5caca45d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Define the initial wave function\n",
+    "psi0 = vp1.GaussFunc(\n",
+    "    beta = 1.0 / (4 * sigma**2),alpha = (2 * np.pi * sigma**2)**(-1/4), position = [x0]\n",
+    ")\n",
+    "psi0 = np.array([ P(psi0), vp1.FunctionTree(mra).setZero() ])\n",
+    "\n",
+    "# Plot the initial density\n",
+    "x_vec = np.linspace(0, 0.9999, 1000)\n",
+    "real_psi0_plt = np.array([( psi0[0] * psi0[0] + psi0[1] * psi0[1] )([x]) for x in x_vec ])\n",
+    "plt.plot(x_vec, real_psi0_plt, '--', color='black')\n",
+    "\n",
+    "# Solve the initial value problem and plot the result\n",
+    "psiA = psi0\n",
+    "for n in range(N):\n",
+    "    psiA = iteratorA(psiA)\n",
+    "    densityA = psiA[0] * psiA[0] + psiA[1] * psiA[1]\n",
+    "    densityA_plt = np.array([densityA([x]) for x in x_vec ])\n",
+    "    plt.plot(x_vec, densityA_plt , \"black\")\n",
+    "    \n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel(r'$| \\Psi(x, t) |^2$')\n",
+    "plt.title(r'Density time evolution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4ebf7549",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L2-norm of real part error:      0.00011595685155302059\n",
+      "L2-norm of imaginary part error: 9.475768012015029e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Continue solving until t = period \n",
+    "for n in range(N):\n",
+    "    psiA = iteratorA(psiA)\n",
+    "\n",
+    "# Find error at t = period\n",
+    "per_errorA = psiA + psi0\n",
+    "\n",
+    "print( f\"L2-norm of real part error:      {per_errorA[0].norm()}\" )\n",
+    "print( f\"L2-norm of imaginary part error: {per_errorA[1].norm()}\" )\n",
+    "\n",
+    "real_psi_plt = np.array([per_errorA[0]([x]) for x in x_vec ])\n",
+    "plt.plot(x_vec, real_psi_plt , \"blue\", label=r'Re$( \\Psi(x, t_{ \\text{period} }) + \\Psi_0(x) )$')\n",
+    "\n",
+    "imag_psi_plt = np.array([per_errorA[1]([x]) for x in x_vec ])\n",
+    "plt.plot(x_vec, imag_psi_plt , \"orange\", label=r'Im$( \\Psi(x, t_{ \\text{period} }) + \\Psi_0(x) )$')\n",
+    "\n",
+    "plt.xlabel('x')\n",
+    "plt.title(r'Difference with the exact solution at $t = t_{ \\text{period} }$')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/helium_atom.ipynb b/docs/notebooks/helium_atom.ipynb
new file mode 100644
index 00000000..5ac3bcd7
--- /dev/null
+++ b/docs/notebooks/helium_atom.ipynb
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Helium atom\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Helium atom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have a solver for the Hydrogen Atom (1 atom), the next level off difficulty is the Helium atom (2 atoms). Here we'll show how\n",
+    "to make and SCF for the Helium atom within the framework of Hartree&ndash;Fock."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vampyr import vampyr3d as vp\n",
+    "import numpy as np\n",
+    "\n",
+    "def laplace_operator(D, f_tree):\n",
+    "    return D(D(f_tree, 0), 0) + D(D(f_tree, 1), 1) + D(D(f_tree, 2), 2)\n",
+    "\n",
+    "def calculate_energy(phi_tree, V_tree):\n",
+    "    mra = phi_tree.MRA()\n",
+    "    d_oper = vp.ABGVDerivative(mra, 0.5, 0.5)\n",
+    "    return -0.5*vp.dot(laplace_operator(d_oper, phi_tree), phi_tree) + vp.dot(phi_tree, V_tree*phi_tree)\n",
+    "\n",
+    "def couloumb_potential(prec, phi_tree):\n",
+    "    mra = phi_tree.MRA()\n",
+    "    P = vp.PoissonOperator(mra, prec)\n",
+    "    return P(4.0*np.pi*phi_tree*phi_tree)\n",
+    "\n",
+    "# Analytic nuclear potential: f_nuc(r) = Z/|r|\n",
+    "def f_nuc(r):\n",
+    "    Z = 2.0                 # Nuclear charge\n",
+    "    R = np.sqrt(r[0]**2 + r[1]**2 + r[2]**2)\n",
+    "    return -Z / R\n",
+    "\n",
+    "# Analytic guess for wavefunction: f_phi(r) = exp(-r^2)\n",
+    "def f_phi(r):\n",
+    "    R2 = r[0]**2 + r[1]**2 + r[2]**2\n",
+    "    return np.exp(-R2)\n",
+    "\n",
+    "# Set target precision\n",
+    "precision = 1.0e-5\n",
+    "\n",
+    "# Define MRA basis and computational domain\n",
+    "k = 5                       # Polynomial order of MRA basis\n",
+    "world = [-20, 20]           # Computational domain [-L,L]^3 (a.u.)\n",
+    "MRA = vp.MultiResolutionAnalysis(order=k, box=world)\n",
+    "\n",
+    "# Define projector onto the MRA basis\n",
+    "P_mra = vp.ScalingProjector(mra=MRA, prec=precision)\n",
+    "\n",
+    "# Initialize the calculation\n",
+    "phi_n = P_mra(f_phi)           # Project analytic guess for wavefunction onto MRA\n",
+    "phi_n.normalize()              # Normalize the wavefunction guess\n",
+    "V_n = P_mra(f_nuc)               # Project analytic nuclear potential onto MRA\n",
+    "J = couloumb_potential(precision, phi_n)\n",
+    "E_n = calculate_energy(phi_n, V_n + J)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loop parameters\n",
+    "iteration = 0                  # Iteration counter\n",
+    "max_iter = 30                  # Maximum iterations \n",
+    "thrs = precision # -1           # Convergence requirement. Set to -1 if you wish to limit using max_iter\n",
+    "update = 1.0                   # Initialize error measure (norm of wavefunction update)\n",
+    "# Minimization loop\n",
+    "while (update > thrs):\n",
+    "    if iteration > max_iter-1:\n",
+    "        break\n",
+    "    # Build Helmholtz operator from current energy\n",
+    "    mu = np.sqrt(-2*E_n)\n",
+    "    G = vp.HelmholtzOperator(mra=MRA, exp=mu, prec=precision)\n",
+    "    \n",
+    "    # Apply Helmholtz operator\n",
+    "    phi_np1 = -2*G((V_n + J)*phi_n)\n",
+    "    \n",
+    "    # Compute wavefunction and energy update\n",
+    "    d_phi_n = phi_np1 - phi_n\n",
+    "    update = d_phi_n.norm()\n",
+    "\n",
+    "    # Prepare for next iteration\n",
+    "    phi_n = phi_np1\n",
+    "    phi_n.normalize()\n",
+    "    J = couloumb_potential(precision, phi_n)\n",
+    "    E_n = calculate_energy(phi_n, V_n + J)\n",
+    "\n",
+    "    # Collect output\n",
+    "    print(iteration, \" |  E:\", E_n, \" |  d_phi:\", update)\n",
+    "    iteration += 1\n",
+    "E_tot = 2.0*E_n - vp.dot(phi_n*phi_n, couloumb_potential(precision, phi_n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "documentation",
+   "language": "python",
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diff --git a/docs/notebooks/helmholtz_equation.ipynb b/docs/notebooks/helmholtz_equation.ipynb
new file mode 100644
index 00000000..40f9dd0b
--- /dev/null
+++ b/docs/notebooks/helmholtz_equation.ipynb
@@ -0,0 +1,208 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Helmholtz equation\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Helmholtz equation\n",
+    "\n",
+    "\n",
+    "An equation that appears in multiple setting when doing quantum chemistry is the The Helmholtz equation. For instance\n",
+    "it hides within the Scrodinger equation, Kohn--Sham equations and the Hartree--Fock equations.\n",
+    "It is usually written in its differentail form\n",
+    "\n",
+    "\\begin{align*}\n",
+    "    (\\nabla^2 - \\mu^2)g(\\mathbf{x}) = -f(\\mathbf{x})\n",
+    "\\end{align*}\n",
+    "\n",
+    "with $\\mu > 0$ is a non-negative real number, $\\nabla^2 = d^2/dx^2 + d^2/dy^2 + d^2/dz^2$ is the [Laplace Operator](https://en.wikipedia.org/wiki/Laplace_operator).\n",
+    "The solution can be written in terms of the convolution integral\n",
+    "\n",
+    "\\begin{align*}\n",
+    "    g(\\mathbf{x}) = \\int H(\\mathbf{x} - \\mathbf{y}) \\, d  \\mathbf{y}  = \\hat{H}[f](\\mathbf{x})\n",
+    "\\end{align*}\n",
+    "where $H$ is the Hemholtz kernel\n",
+    "\\begin{align*}\n",
+    "    H(\\mathbf{x}) = \\frac{e^{-\\mu ||\\mathbf{x} - \\mathbf{y}||}}{4 \\pi ||\\mathbf{x} - \\mathbf{y}||}\n",
+    "\\end{align*}\n",
+    "and $\\hat{H}$ is the corresponding Helmholtz operator.\n",
+    "\n",
+    "To show how we do this in vampyr we make a source test. We start by picking the solution $g$ as,\n",
+    "\\begin{align*}\n",
+    "    g(\\mathbf{x}) = \\exp(-x^2 - y^2 - z^2)\n",
+    "\\end{align*}\n",
+    "then based upon our pick $g$ we calcualte $f$, *the source*, to be\n",
+    "\\begin{align*}\n",
+    "    f(\\mathbf{x}) = (\\nabla^2 - \\mu^2) g(\\mathbf{x}) = - (4x^2 + 4y^2 + 4z^2 + 6 + \\mu^2) \\exp(-x^2 - y^2 - z^2)\n",
+    "\\end{align*}\n",
+    "next we'll project $f$ onto an MRA in vampyr, then we'll apply the Helmholtz operator onto it to caluclate an approximation to $g$,\n",
+    "then we'll compare our approximation of $g$ to the exact representation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Helmholtz operator is a 3 dimensional operator so we use vampyr3d\n",
+    "from vampyr import vampyr3d as vp3\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set the precision and make the MRA\n",
+    "prec = 1.0e-3\n",
+    "mra = vp3.MultiResolutionAnalysis(box=[-20, 20], order=5)\n",
+    "\n",
+    "# Make the scaling projector\n",
+    "P_eps = vp3.ScalingProjector(mra, prec)\n",
+    "\n",
+    "# Define mu and make the Helmholtz operator \n",
+    "mu = 1.0\n",
+    "helmholtz_operator = vp3.HelmholtzOperator(mra, mu, prec)\n",
+    "\n",
+    "\n",
+    "# Define the analytical source term f\n",
+    "def f(x):\n",
+    "    return -2.0*(2.0*x[0]**2 + 2.0*x[1]**2 + 2.0*x[2]**2 - 3.0 - mu**2/2) * np.exp(-x[0]**2 - x[1]**2 - x[2]**2)\n",
+    "\n",
+    "# Define the analytical solution reference g\n",
+    "def g(x):\n",
+    "    return np.exp(-x[0]**2 - x[1]**2 - x[2]**2)\n",
+    "\n",
+    "# Use the projector to project the function onto a function_tree\n",
+    "f_tree = P_eps(f)\n",
+    "\n",
+    "# Apply the helmholtz operator onto f_tree\n",
+    "g_tree = helmholtz_operator(f_tree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# One way to compare g_tree to the analytical reference is to plot them together\n",
+    "\n",
+    "x = np.arange(-20, 20, 0.001)\n",
+    "y = [g_tree([_, 0.0, 0.0]) for _ in x]\n",
+    "y1= [g([_, 0.0, 0.0]) for _ in x]\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "plt.plot(x, y1)\n",
+    "plt.show()\n",
+    "\n",
+    "# An exercise is to modify the code and plot the relative difference along the same line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g_tree - g_tree_reference square norm: 0.00044356533974258583\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A problem with that comparrison is that we only see a line in 3D space. Another\n",
+    "# purhaps better way to compare the accuray of g_tree and g is to project g onto\n",
+    "# a function_tree g_tree_reference. Then calcualte the norm of g_tree - g_tree_reference\n",
+    "\n",
+    "# An exercise is to see how the square norm decreases when the precision is increased.\n",
+    "\n",
+    "g_tree_reference = P_eps(g)\n",
+    "\n",
+    "print(\"g_tree - g_tree_reference square norm:\", (g_tree - g_tree_reference).norm())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.4 ('vp-env')",
+   "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": "3.11.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "1b6ee7d088ac15f9e4e30b8ec349c151b4a28c1392dbe2e7155438b22bc99b82"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/hydrogen_atom.ipynb b/docs/notebooks/hydrogen_atom.ipynb
new file mode 100644
index 00000000..69bb624d
--- /dev/null
+++ b/docs/notebooks/hydrogen_atom.ipynb
@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Hydrogen atom\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hydrogen atom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "One of the simplest problems to solve, if not the simplest, is the Schrödinger equation for the Hydrogen atom. It's a\n",
+    "good starting point to get familiar with `vampyr`. Since, we know the analytic solution, so we have a solution we \n",
+    "can refer to.\n",
+    "\n",
+    "\\begin{equation*}\n",
+    "\\left[\\hat{T} + \\hat{V}\\right]\\phi = E\\phi\n",
+    "\\end{equation*}\n",
+    "Here $\\hat{T}$ is the Kinetic energy operator\n",
+    "\\begin{equation*}\n",
+    "\\hat{T} = -\\frac{1}{2}\\nabla^2\n",
+    "\\end{equation*}\n",
+    "and $\\hat{V}$ is the potentail operator, which in this case only \n",
+    "contain the nulear attraction\n",
+    "\\begin{equation*}\n",
+    "\\hat{V}(r) = \\frac{-Z}{|R-r|}\n",
+    "\\end{equation*}\n",
+    "where $Z=1$ and $R=(0,0,0)$ are the nuclear charge and position, respectively. We iterate the Schrödinger equation in its integral form\n",
+    "\\begin{equation*}\n",
+    "\\tilde{\\phi}^{n+1} = -2 \\hat{G}^{\\mu^n}[\\hat{V} \\phi^n]\n",
+    "\\end{equation*}\n",
+    "where $\\hat{G}^\\mu$ is the integral convolution operator with the bound-state Helmholtz kernel $G^{\\mu} = e^{-\\mu r}/r$ and $\\mu^n = \\sqrt{-2E^n}$ is a positive real number.\n",
+    "\n",
+    "\n",
+    "The analytic solution is know to be\n",
+    "\\begin{align}\n",
+    "    \\phi = e^{-|r|}\n",
+    "\\end{align}\n",
+    "and energy $E = -0.5$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vampyr import vampyr3d as vp\n",
+    "import numpy as np\n",
+    "\n",
+    "def laplace_operator(D, f_tree):\n",
+    "    return D(D(f_tree, 0), 0) + D(D(f_tree, 1), 1) + D(D(f_tree, 2), 2)\n",
+    "\n",
+    "def calculate_energy(phi_tree, V_tree):\n",
+    "    mra = phi_tree.MRA()\n",
+    "    d_oper = vp.ABGVDerivative(mra, 0.5, 0.5)\n",
+    "    return -0.5*vp.dot(laplace_operator(d_oper, phi_tree), phi_tree) + vp.dot(phi_tree, V_tree*phi_tree)\n",
+    "\n",
+    "# Analytic nuclear potential: f_nuc(r) = Z/|r|\n",
+    "def f_nuc(r):\n",
+    "    Z = 1.0                 # Nuclear charge\n",
+    "    R = np.sqrt(r[0]**2 + r[1]**2 + r[2]**2)\n",
+    "    return -Z / R\n",
+    "\n",
+    "# Analytic guess for wavefunction: f_phi(r) = exp(-r^2)\n",
+    "def f_phi(r):\n",
+    "    R2 = r[0]**2 + r[1]**2 + r[2]**2\n",
+    "    return np.exp(-R2)\n",
+    "\n",
+    "# Analytic exact wavefunction for comparison\n",
+    "def f_phi_exact(r):\n",
+    "    return np.exp(-np.sqrt(r[0]**2 + r[1]**2 + r[2]**2))\n",
+    "\n",
+    "# Set target precision\n",
+    "precision = 1.0e-3\n",
+    "\n",
+    "# Define MRA basis and computational domain\n",
+    "k = 5                       # Polynomial order of MRA basis\n",
+    "world = [-20, 20]           # Computational domain [-L,L]^3 (a.u.)\n",
+    "MRA = vp.MultiResolutionAnalysis(order=k, box=world)\n",
+    "\n",
+    "# Define projector onto the MRA basis\n",
+    "P_mra = vp.ScalingProjector(mra=MRA, prec=precision)\n",
+    "\n",
+    "# Initialize the calculation\n",
+    "V = P_mra(f_nuc)               # Project analytic nuclear potential onto MRA\n",
+    "phi_n = P_mra(f_phi)           # Project analytic guess for wavefunction onto MRA\n",
+    "phi_n.normalize()              # Normalize the wavefunction guess\n",
+    "phi_exact = P_mra(f_phi_exact) # Project exact wavefunction onto MRA\n",
+    "phi_exact.normalize()          # Normalize the exact wavefunction\n",
+    "E_n = calculate_energy(phi_n, V)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loop parameters\n",
+    "iteration = 0                  # Iteration counter\n",
+    "max_iter = 30                  # Maximum iterations \n",
+    "thrs = precision # -1           # Convergence requirement. Set to -1 if you wish to limit using max_iter\n",
+    "update = 1.0                   # Initialize error measure (norm of wavefunction update)\n",
+    "# Minimization loop\n",
+    "while (update > thrs):\n",
+    "    if iteration > max_iter-1:\n",
+    "        break\n",
+    "    # Build Helmholtz operator from current energy\n",
+    "    mu = np.sqrt(-2*E_n)\n",
+    "    G = vp.HelmholtzOperator(mra=MRA, exp=mu, prec=precision)\n",
+    "    \n",
+    "    # Apply Helmholtz operator\n",
+    "    phi_np1 = -2*G(V*phi_n)\n",
+    "    \n",
+    "    # Compute wavefunction and energy update\n",
+    "    d_phi_n = phi_np1 - phi_n\n",
+    "    update = d_phi_n.norm()\n",
+    "\n",
+    "    # Prepare for next iteration\n",
+    "    phi_n = phi_np1\n",
+    "    phi_n.normalize()\n",
+    "    E_n = calculate_energy(phi_n, V)\n",
+    "\n",
+    "    # Collect output\n",
+    "    print(iteration, \" |  E:\", E_n, \" |  d_phi:\", update)\n",
+    "    iteration += 1"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.6 64-bit",
+   "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": "3.9.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/introduction.ipynb b/docs/notebooks/introduction.ipynb
new file mode 100644
index 00000000..80f400a8
--- /dev/null
+++ b/docs/notebooks/introduction.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4169f162",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Introduction\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "492590ec",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "VAMPyR is a python overlay to the MRCPP library. It is implemented using [pybind11](https://pybind11.readthedocs.io/en/stable/).\n",
+    "\n",
+    "In addition it offers so much more:\n",
+    "    \n",
+    "    - Reduced barrier of entry for new students\n",
+    "    - Easier to prototype new ideas\n",
+    "    - Easily combined with the superpowers of Python\n",
+    "\n",
+    "The structure of the code:\n",
+    "\n",
+    "```\n",
+    "vampyr \n",
+    "        vampyr1d\n",
+    "        vampyr2d\n",
+    "        vampyr3d\n",
+    "```\n",
+    "\n",
+    "Each module can be imported as\n",
+    "```Python\n",
+    "    from vampyr import vampyr1d as vp1 \n",
+    "    from vampyr import vampyr2d as vp2 \n",
+    "    from vampyr import vampyr3d as vp3 \n",
+    "```\n",
+    "\n",
+    "## Making the MRA\n",
+    "\n",
+    "Once we have imported vampyr is the next step to make a MultiResolutionAnalysis (MRA), the MRA can be thought of as the physical space our functions and operators exist and work.\n",
+    "\n",
+    "To make the MRA do we call upon the `MultiResolutionAnalysis` class in vampyr\n",
+    "\n",
+    "```Python\n",
+    "    mra = vp1.MultiresolutionAnalysis(box=[-20, 20], order=7)\n",
+    "```\n",
+    "Here we made a 1-dimensional MRA, where we specified the size of our box and the number of scaling/wavelet functions in the basis. The procedure is the same for 2- and 3-dimensional cells.\n",
+    "\n",
+    "## Projecting a function\n",
+    "\n",
+    "You can project analytical functions onto the MRA. As an example let's project the Gaussian functions\n",
+    "\n",
+    "\\begin{align}\n",
+    "    g(x) = \\exp(-\\sum_{i=0}^d x_i^2)\n",
+    "\\end{align}\n",
+    "\n",
+    "onto the MRA in 1-,2- and 3-dimensions\n",
+    "\n",
+    "Note: Vampyr assumes the argument to be a vector, such as $x = (x_0, x_1, ..., x_{d-1})$\n",
+    "\n",
+    "This Gaussian can be written as a standard Pyhon function:\n",
+    "\n",
+    "```Python\n",
+    "    # A 1D Gaussian\n",
+    "    def f(x):\n",
+    "        from numpy import exp\n",
+    "        return exp(-x[0]**2)\n",
+    "\n",
+    "```\n",
+    "```Python\n",
+    "    # A 2D Gaussian\n",
+    "    def f(x):\n",
+    "        from numpy import exp\n",
+    "        return exp(-(x[0]**2 + x[1]**2))\n",
+    "\n",
+    "```\n",
+    "```Python\n",
+    "    # A 3D Gaussian\n",
+    "    def f(x):\n",
+    "        from numpy import exp\n",
+    "        return exp(-(x[0]**2 + x[1]**2 + x[2]**2))\n",
+    "\n",
+    "```\n",
+    "\n",
+    "We can make a `vp.ScalingProjector` object which we use to project functions onto the MRA with a set precision.\n",
+    "\n",
+    "```\n",
+    "projector = vp1.ScalingProjector(mra, prec)\n",
+    "```\n",
+    "\n",
+    "Now let's project a function onto a `FunctionTree` object.\n",
+    "\n",
+    "```Python\n",
+    "    f_tree = projector(f)\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94c8df55-6c14-44c9-bf05-97fe546ca399",
+   "metadata": {},
+   "source": [
+    "# Making the MRA\n",
+    "\n",
+    "\n",
+    "The Multiresolution analysis is the numerical space we work on, it has a size and an order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1967defc-f567-4676-8309-2607aabb1659",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================\n",
+      "                    MultiResolution Analysis                    \n",
+      "----------------------------------------------------------------\n",
+      " polynomial order      : 7\n",
+      " polynomial type       : Interpolating\n",
+      "----------------------------------------------------------------\n",
+      " total boxes           : 8\n",
+      " boxes                 : [          2           2           2 ]\n",
+      " unit lengths          : [  20.000000   20.000000   20.000000 ]\n",
+      " scaling factor        : [   1.250000    1.250000    1.250000 ]\n",
+      " lower bounds          : [ -20.000000  -20.000000  -20.000000 ]\n",
+      " upper bounds          : [  20.000000   20.000000   20.000000 ]\n",
+      " total length          : [  40.000000   40.000000   40.000000 ]\n",
+      "================================================================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mra = vp.MultiResolutionAnalysis(box=[-20,20], order=7)\n",
+    "print(mra)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0f267db-c351-4bac-ae1c-e70cd7074867",
+   "metadata": {},
+   "source": [
+    "# Projecting a function onto the MRA\n",
+    "\n",
+    "\n",
+    "Projection a function onto the MRA is done through a projection operator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aabd45d4",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a63fb7a6-48d2-4bd2-b5b0-3aaa3aa6221e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "*FunctionTree: nn\n",
+      "  genNodes: 0\n",
+      "  square norm: 1.9687\n",
+      "  root scale: -4\n",
+      "  order: 7\n",
+      "  nodes: 264\n",
+      "  endNodes: 232\n",
+      "  nodes per scale: \n",
+      "    scale=-4  nodes=8\n",
+      "    scale=-3  nodes=64\n",
+      "    scale=-2  nodes=64\n",
+      "    scale=-1  nodes=64\n",
+      "    scale=0  nodes=64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "prec = 1.0e-4\n",
+    "projection_operator = vp.ScalingProjector(mra, prec)\n",
+    "\n",
+    "def gauss(x):\n",
+    "    return exp(-x[0]**2 -x[1]**2 - x[2]**2)\n",
+    "\n",
+    "\n",
+    "gauss_tree = projection_operator(gauss)\n",
+    "print(gauss_tree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "104a7da8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/multiwavelets.ipynb b/docs/notebooks/multiwavelets.ipynb
new file mode 100644
index 00000000..d23f6c43
--- /dev/null
+++ b/docs/notebooks/multiwavelets.ipynb
@@ -0,0 +1,623 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ed5412c9",
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"An introduction to scaling and wavelet functions\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c729631a",
+   "metadata": {},
+   "source": [
+    "# An introduction to scaling and wavelet functions\n",
+    "\n",
+    "We build our $k+1$ scaling functions $\\{\\phi_i\\}_{i=0}^k$ from either [Legendre Polynomials](https://en.wikipedia.org/wiki/Legendre_polynomials) or [Interpolating Polynomials](https://en.wikipedia.org/wiki/Polynomial_interpolation).\n",
+    "\n",
+    "Where the Legendre polynomials are defined as\n",
+    "\\begin{align*}\n",
+    "        L_0(x) &= 1 \\\\\n",
+    "        L_1(x) &= x \\\\\n",
+    "        L_2(x) &= \\frac{1}{2} \\left( 3x^2 - 1\\right)\\\\\n",
+    "        L_3(x) &= \\frac{1}{2} \\left( 5x^5 - 3x\\right)  \\\\\n",
+    "        L_{j+1}(x) &= \\frac{2j+1}{j+1} x L_j(x) - \\frac{j}{j+1}L_{j-1}(x) \n",
+    "\\end{align*}\n",
+    "\n",
+    "and they are orthonormal with the respect to the norm $L^2([-1, 1])$\n",
+    "\\begin{align*}\n",
+    "    \\int L_i(x) L_j(x) = \\delta_{i, j}\n",
+    "\\end{align*}\n",
+    "\n",
+    "for (mostly) historical reasons do we define our basis on the interval $[0,1]$ so we shift\n",
+    "it and normalize it. So we define our Legendre scaling functions $\\{\\phi_i^L\\}_{i=0}^k$ as \n",
+    "\n",
+    "\\begin{align*}\n",
+    "    \\phi_i^L  (x) = \\sqrt{2j + 1}L_j(2x - 1), \\qquad x \\in [0, 1]\n",
+    "\\end{align*}\n",
+    "\n",
+    "An alternative to the Legendre Scaling functions are the Interpolating scaling functions $\\{\\phi_i^I\\}_{i=0}^k$.\n",
+    "They are based upon the Legendre scaling functions and it's roots $\\{x_j\\}_{j=0}^k$ and weights $\\{w_j\\}_{j=0}^k$ of the [Gauss&ndash;Legendre quadrature](https://en.wikipedia.org/wiki/Gauss%E2%80%93Legendre_quadrature) of order $k+1$ and they are constructed as the linear combinations\n",
+    "\\begin{align*}\n",
+    "\\phi_j^I (x) = \\sqrt{w_j} \\sum_{i=0}^k \\phi_i^L (x_j) \\phi_i^L (x) && x \\in [0, 1]  \n",
+    "\\end{align*}\n",
+    "\n",
+    "When you use VAMPyR you choose to either use the Legendre or Interpolating basis as the scaling\n",
+    "function, and in practice developers never need to touch the basis. But for educational purposes\n",
+    "it is possible to get access to the scaling functions in VAMPyR.\n",
+    "\n",
+    "For that purpose let's plot the Legendre Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3af6bbee",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2d6c7768-22c1-49d0-953c-cc4260aca2da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from vampyr import vampyr3d as vp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Begin with import the LegendreBasis\n",
+    "# Note: Change the import to InterpolatingBasis if you want to do the\n",
+    "# same analysis using the InterpolatingBasis\n",
+    "from vampyr import LegendreBasis\n",
+    "k = 5\n",
+    "basis_set = LegendreBasis(order=k) # Set the highest polynomial order, k.\n",
+    "scaling = basis_set.scaling # Get the scaling basis\n",
+    "\n",
+    "# Plot the scaling basis\n",
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "y = [scaling(i=2)([_]) for _ in x] # Vary the order i up to k + 1\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "b6a62fdb-b382-4e9c-ad0e-f362217a368f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we show multiple scaling functions in one figure\n",
+    "\n",
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "for i in range(k+1):\n",
+    "    y = [scaling(i=i)([_]) for _ in x]\n",
+    "    plt.plot(x, y)\n",
+    "plt.show()    "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "450a6092-c8d3-40c2-acfc-cf9a053f5506",
+   "metadata": {},
+   "source": [
+    "The scaling functions can also be compressed and dilated. These are defined as\n",
+    "\n",
+    "\\begin{align}\n",
+    "    \\phi_{i, l}^n(x) &= 2^{n/2} \\phi_i(2^nx - l)\n",
+    "\\end{align}\n",
+    "here $i$ is the polynomial order, $n$ is the scale and $l$ is the translation index.\n",
+    "\n",
+    "Below do we have an example of a scaling function plotted, play with the order of\n",
+    "the polynomial $i$, the scale $n$ and the translation index to see how it behaves.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "a10e24ab-9691-4c9a-8704-29c03f23d99f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Vary i, l and n to see how the function changes\n",
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "y = [scaling(i=2, l=2, n=4)([_]) for _ in x]\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ed3582e2-1401-4f9b-afc5-b48342961d86",
+   "metadata": {},
+   "source": [
+    "Next let's introduce our wavelet function $\\psi_{i, l}^n$ are polynomials functions designed following Alperts [cite  me]\n",
+    "construction. They are connected to the scaling functions through the two-scale difference relation:\n",
+    "\\begin{align}\n",
+    "\\phi_i(x) = \\sqrt{2} \\sum_j h^{(0)}_{ij} \\phi_j(2x) + h^{(1)}_{ij} \\phi_j(2x-1) \\\\\n",
+    "\\psi_i(x) = \\sqrt{2} \\sum_j g^{(0)}_{ij} \\phi_j(2x) + g^{(1)}_{ij} \\phi_j(2x-1) \\\\\n",
+    "\\end{align}\n",
+    "where $h^{(0)}_{ij}, h^{(1)}_{ij}$ and $h^{(0)}_{ij}, h^{(1)}_{ij}$ and $g^{(0)}_{ij}, g^{(1)}_{ij}$\n",
+    "are components of the filter matrices $H^{(0)}, H^{(1)}, G^{(0)}$ and $G^{(1)}$.\n",
+    "In the following do we have the same figures as above only for the wavelet functions.\n",
+    "\n",
+    "Properties to note is the symmetric/asymmetric nature by varying $i$, also note that the\n",
+    "wavelet functions change depending on the number of scaling/wavelet functions $k+1$. And that\n",
+    "they stay the same if we change between Legendre and Interpolating polynomials. The reason for the\n",
+    "latter is that the wavelets are the same, what changes are the filter matrices. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "62d5f493-4807-4cba-9b17-785a4b5ce1ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wavelet = basis_set.wavelet\n",
+    "\n",
+    "# Plot the scaling basis\n",
+    "x = np.arange(0.001, 1.0, 0.01)\n",
+    "y = [wavelet(i=2)([_]) for _ in x] # Vary the order i up to k + 1\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4f6be0df-c413-4ad0-9a44-74cadf739764",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we show multiple wavelet functions in one figure\n",
+    "\n",
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "for i in range(k+1):\n",
+    "    y = [wavelet(i=i, n=0)([_]) for _ in x]\n",
+    "    plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "032a6587-7082-40c5-9ff6-e91b5289f583",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IAMDVG9RA5KkjI7ZdV6lSRfaIyFNdK92sCnDNux2yx3cZihB5GQMRD3rqyAiiiTR6I2FsXNqc9zE71nQAAJ4/Ma73XbhFVrNqzk0sqjVBhgKV/2jLjMgYd4lYhhkRIpIYiHjQz185CwB406bugifUru5sxJKWEOKpNJ47cd7Oy1tQsRXvZmZEzjMjYpl4kfIaEXkLAxEPeuboGADgirWF17grioLLVqlZEdcFIsVKM0l56F0VGRGWZiwXM2TZGIcQeRsDEY85PxPHq2enAQCXrGwr+tjX9bcCAH57ctziqyqPsVk19y4my0ihQBUZEa55t1y8yJp+IvIWBiIes1fLbqzpakRHU6joYy+WgcjguKtuFlmbVXMiEdkEyYyIu8kSGhERAxGP2XtcLctcuqp9wcde2NuCgE/ByHQcg+fds+49qzRTICMSriIjIgOR87Mc37VKLMVmVSJSMRDxmGe1QGT7ioUDkXDQj4096lTNi6cmLL2uciTThW9iUe037VBVGZEgAGZErCKEyD5rhpEIkacxEPGQaCKlBxSXrFw4EAGAjUvVrauvnHHHhlUhRNZm1VyxpHk9IhNziex+FDJFPOc55Yp3Im9jIOIhh85MIZESaGsIor+9vqSPkXtGDrtk1XvuMTPzSzPVj+9G6oOQU83jcyzPmC335F0XtR8RkQMYiHjIAS0bsnlZpOD+kFwbtEDkkEsyIokFfpvOTM1U/qMd8PsQqVfLM9wlYr4YAxEiMmAg4iGyLLNlWaTkj9mwRA1Ejo/OuGLDau7Ju8abmBDCMDVTeUYEMGxXZSBiunkZEZZmiDyNgYiHvDCoBiIX9ZUeiHQ1h9DWEERaAK9p+0eclLtV1fgn42/a1YzvAobtqtwlYjpmRIjIiIGIR0QTKb3PY3MZGRFFUfTyjBsaVo3nzADIuosZd1NU06wKZBpWx3gCr+nmZ0SIyMsYiHjEoTNTSKbVRtVlraU1qkqyPHPozKQVl1aW3NKMkZyY8SlA0F9aD0whcoSXGRHz5QYiRORtDEQ84uBpNYgop1FVWqcFIkfOzZh+XeWa36yaYZyYKfffmEuWZkanGYiYTQaMEkszRN7GQMQj5NSLzG6UY1VnIwDg+Ijzgci8HhHDH6PJ6idmpHaeN2OZ+RkRRiJEXsZAxCP0QGRp+YHISi0QGRibdXzBV3Le1Mz8HpFqJ2aATEaEUzPmY7MqERkxEPEI2ahaSSDS0xJGKOBDMi1watzZM2eS6SKlGS0jYkYgwoyIdeYFIg5dBxG5AwMRDxiZjmF0Jg5FAdZ2N5X98T6fghUdDQCAYw6XZ4qWZkxYZia1NzEjYpX5PSIMRYi8jIGIBxzWyjLL2xvQUBeo6HOs7HBHn0huacYoph94Z2JGhIGI6Ti+S0RGDEQ84JUqGlUlvWF1dNaUa6pUbo9K3tKMCRkR2SMyE0+5YqNstYQQ+M3xMTz4mwEMOPw9ZI8IERlV9usx1ZRq+kMk2bDqdGkm9+RdY1o/amJGpCUcgN+nIJUWGJ9NYGmk+s/plHRa4M9/8AK+/9wgACDgU/C5d1+E39va58j15AZ2jEOIvI0ZEQ84pAUi66vIiOilmVGnA5HCUzsxEzMiiqIYtqvWdnnmm08exfefG4Tfp+DC3hYk0wJ/8f0D+tlDdpMZkTq/+n1ijwiRtzEQWeSEEHqPSDUZEVmaGTw/VzQYsNq8qZmsZlXzxneBxbFddWh8Dl949DAA4H/97mb8349fgWs3LUE8lcadP37RkSBAZkSqPQ+IiBYHvhIsYHQ6hg//61689X8/gZ+/Muz05ZTt1PgcZuIpBP2KHkxUors5hDq/D6m0wJmJqIlXWJ54MvfQO2NpxrypGQCLIiPy1V+8hngqjUtXteP3L+2Hoij42/9vM0IBH54fGMcTr47Yfk3y+1RfV7vlLiIyDwORBfzVj17Ef788jEPDU/jIvz+Pk2PONvqVS65lX9HRiKC/8m+3z6dgWZt6Rs3geed2ieRmRIxkyt+8jEht7xKZmEvofSG3XbteX3vf3RzG+y9bDgD4t18ft/265mQgon2fWJkh8jYGIkUMjM7ipy+eAQC0NQQRS6bx9SeOOHxV5Tl6bhoAsLqKbIgkD8tzcqnZvLNmDDexmMkp/1rfrvqT3w4hlkxj/ZImXL66PevvPnDZCgDALw6dw7mpmK3XlVtCE2xXJfI0BiJF/NeB0wCAK9Z24qsfeD0A4Mf7hmrq9NCjWkZkdVf5i8xy9ekZEeeyQol5pZmMTGnGpIxIje8Seeh5NRvynu398w4BXNvdhK3LW5FKC/znb4dsva7c0gwzIkTeZmkg8sQTT+DGG29Eb28vFEXBj370Iyu/nOmeOHwOAHDdhUtw+aoOdDWHMBVL4pljow5fWemOjmgZka7qMyJ9LijNJIo0q2ZKMyZnRGYTpnw+O41Mx7Dv5DgA4MaLe/M+5u0Xqe//75ft7X2SGZEGBiJEBIsDkZmZGVx88cX4p3/6Jyu/jCXSaYEXBscBAJet6oDPp+DaTd0AgP8+WDtNqzIjssaEQET2iJxyMhCZt5VzfrOq2VMzYzP2li7M8PNXzkIIYMuyCJa0hPM+Rv48P3NsDBM2BltyzLqepRkigsULzW644QbccMMNVn4JyxwbncFMPIVw0KffxN+8cQm+8+xJRyYNKjEbT+K0NuGyutOM0ox63szguIOlmaJnzWgLzUyfmqm9jIgMlt+sBRv5rOhoxLruJrx6dhpPvnZOz5BYbS6eHTAyI0Lkba7qEYnFYpicnMx6c4pc9nRBTwsC2rTJJSvVhr9jIzMYmXb/b8kyG9LWENTLDNWQpZnT41Gkipz5YqXc0oyR/E3bjM2qANDRGAJQez0i0UQKv3xNDZav3bSk6GOvWNcJAHj6qH3lxui8jAgReZmrApHdu3cjEonob/39/Y5diwxENi+L6O+LNASxfomaWXjuxHlHrqscch27GY2qgDr2GfApSKYFhied2SWS26xqZPZCszZZmpmN19T2z/0nxzEbT6GrOYQLe1uKPvayVR0AgGePjdlxaQAy3yfuESEiwGWByK5duzAxMaG/nTx50rFrkQfF5b6Qb1uhZkVqIRDRJ2ZMGN0FAL9PQW+rsw2r8zerGnpEkuYuNJN7ROLJNGbjtXPwnQwqLlvVPm9aJtelq9Sf58PD07aNKUdz9ogwJULkba4KREKhEFpaWrLenHJCO6FUnrEivX55KwD1t063y0zMmJMRAYy7RJzpE4kX3SNibkakPujXg5pa2iViDEQW0t5Yp2f5nrVpGox7RIjIyFWBiFskUml9adeKnEDkAi1D8vLpSden6zM7RMzJiABAT0SdwDgz4UyPTNE9IiYeegeoB9/V2nbVRCqtZ+su1couC5HlmaeP2lOe4R4RIjKyNBCZnp7G/v37sX//fgDAsWPHsH//fgwMDFj5Zas2ND6HVFogFPChuzmU9Xdru5sQ8CmYiiYx5OCZKwsRQpi6VVVaqgcibinNZP6/zIiY1awK1N55My+emsBcIoVIfRDrukvLhG1f2QYA+t4Rq+WWZhiHEHmbpYHI3r17sXXrVmzduhUAcPvtt2Pr1q248847rfyyVRvQzpPpb2+Az5ddYw8F/FirvcC/POTcVM9Czk7FMBNPwacAyzsaTPu8eiDiVLNqkZN/5dSMmae61lpG5DfH1azGJSvb5v3sFnJxXysANctn9cnKyVQaSW3iKnPWDEMRIi+zNBC5+uqrIYSY9/btb3/byi9bNdkfsqI9/w18U0+mPONWR7RsSH97g2krzwFgaYvMiDgViBQ+fVdmROqqONwvV+a8mdrYJfLbQXXa6/Ur2kr+mBUdDWgOBxBPpnF4eMqqSwMARA0L6cJ1zIgQEXtE8pIZkUKZhI1LmwFkJmvcSB/dNbEsA7gvI5JVmtH+rs6kHhEAaG9QR3hrZZfIAS0QuWhZa8kfoygKtmhj6nJs3SqyLANkenmYECHyNgYieQxpjapyQiRXLWRE9KxOh8mBiJYROTcVQ9LiNH4+8wIR+b9C6IcRmhmIZM6bcX8gcn4mrgfRWwz7b0ohH//CoLWBiNyqGgr44NNGixmHEHkbA5E8zk6qEyGFzuiQgcix0Rn9hdVtToyqGZEVJvaHAEBHUwgBn4K0AM45sF1WlmaCfq3/Qft1OmnY9Brym1eK0ntEaiAjckDLZqzsaEBEy+SUakufPRmRTB+PH/qKE6ZEiDyNgUgew1Nq2aFQINLVHEJrQxBCZHZ1uE0mI2JuIOL3KfokkRN9IjIjIvtA5C0sbug9MDUjUkNTMzIQ2aI1n5ZDZkRePj2V9VyaLbNDxIcFdq0RkUcwEMkhhDBkREIFH7dGWxImd3W4iRBCT9GbXZoBMn0iTqx5l4FIMCfYsCoQqaWpGXla9MV95ZVlAGB5ewOaQwHEU2m9v8gK87aqgqUZIq9jIJJjKpbEnPZi2d2cPyMCZJpA3RiIjEzHMRtPQVEyB9WZSQYipx3JiMjSTHajo9y46vcp8Jc4tlqKWsqIvHhK7VnaXGZ/CKA2rK7XmrAPWTg5Y9yqqkDrEWEkQuRpDERynNV+y28JB4oeyrVG2yUix2TdZGBMDY56I/Wmju5KS1vU4MaJyZn5pRn1LqY3qpo4ugsAHU0yI5JA2qETh0sxGU3o24A3La3saIT1S9RA5LCF02AyIxIK+gHZ5sOcCJGnMRDJMbxAo6qkZ0Rc2CMi+0OWF9iDUq2lEed6RJJaRqQuZ/QzZsHEDAC0ak2fqbTAVDRp6uc2kwwelraEy25UlTZoZ85YOZYus43hgA/sVSUigIHIPLLvYcFAxNAj4rbNkFY1qkpLHFxqtlCzqtmBSCjgR1MoAMDdI7wyeNjY01zx59igZVKsXGomMyLq1AxLM0TEQGSes1NqRiT3jJlcKzoaEPApmI2nHFvuVchCC9mq1RNRSzNONKvG9WbV7JtYPGVNaQYA2hrVDIOb+0QOaYHIhqWVByLyFN6BsVnMxq3J/sjNqvVBfyYjYslXIqJawUAkx4gWiHQuEIgE/T699OG2htXjcodIu/kTM0Bmqdnpiajt2SC9NOPPPzVjdkYEANob3L9LRAYiG6sIRDqaQuhsUn/uDw9bU3KMJTLnASn6KhiGIkRexkAkh0y/y7HNYlZ3yckZd/WJDFhcmunWxppjyTQm5uw9g0Uf37WpWRVw/3ZVIQRePqNOzGxYUlmjqiQDGasaVrNKM+AiESJiIDKP/K1X/hZcjNzRIXsy3GA6lsSo9m+wqjQTDvr1QM3uslQi9zwZUeD9JnJ7RuT0RBRT0SQCPgVruqvLgq21eBpsLpFnsyoReRoDkRxjs+pv+G0lZERkxuHEmHsCEbnavb2xDi3hyqYnStGlpfBHpuy9OSdySjMyqW/V1Azg/oyILMus7mqselx7lT4NZk25Ue4RCQU5NUNEKgYiOfSMSOPCN3HZIzLgoozIgMWju1KX1kNzbtrZjIjsL7CyWdXt5828ojeqVleWAawvN+qlmYBxsyojESIvYyCSQ95s2koozeiByNisaxruToxZ2x8i6YHIlL0H3yULbVa1MiOib1e1tx+mVHLctppGVUmOpQ+MzVpyurLMiNTXGRaaueM/HSJyCAMRg3gyjamYOrZYSrNqX1sDfIpa93biJNp89B0idmVEbAxEhBCZ8V07p2a07Jhbz5uR/Rzy/KNq9LSEEQ76kEgJnDw/V/XnyxVNGheaaSPYpn8VIqolDEQMxrUbjU9BSf0VdQGfvlPDLeUZud59uQWH3RnJHpGzNgYiScOK9bpA9k0srt3grMyIuLE0I4TAkbNqILK2ykZVAPD5FKzqlMv6zC/PROPzm1Xdkk0kImcwEDGQzYhtDXXwlXhwmizPuGVyxuqtqpITGRFZlgEMzao5C81CFvaIuLFZdXgyhpl4Cn6fguUm7Y3J9ImY37CqZ0S40IyINAxEDOTmzFImZiR5wx9wweRMPJnGkHbw2WIszcQNPQt6s2rOHpHcko0ZZCAyMZewpG+iGjJrsby9wbRs0BoLz1Gai89f8c5IhMjbGIgYnNeaEUvZISItd1EgMnh+Fmmhrs/uWmAzbLUyUzP2BSIJQxAQsLFZNVIfhKKoX2vc5gVuC8n0h5hXilulfa4jFmREZrVApKHOUJox/asQUS1hIGIgU++tZZxeKteoy/0dTjJOzCgWb4uSZ/GMzyYQ09LtVstMzCjIrZzFLFxoFvD7EKnXGlZd1icig4XVJjSqSqs7Mwc6mk0uNGuoM5Rm2CNC5GkMRAwmtd925U2nFJkRXvMnDMpl1w4RQH2Ogn71VjI6bc/NWWZEAj7fvPXgiaS26MyCQATIZMncdvCdFRkR2SMyMh3DZNTcDFAmIxLgZlUiAsBAJIt80S0rENFKMyPTMczErDmxtFR2NaoCgKIo+uSMXX0imXNmMnewzEIzbWrGgh4RINM35LYRXpm1MGN0V2oOB/XS2zGTsyKz2n8jDXXGhWZE5GUMRAwm59QXyZYyApFIfVAv5TjdJ2LX6K5kd8NqwrDMLLe/wMoeEcCdS81m40mc0pqTzSzNAMBKC44vEEJg1lCakRvNWJkh8jYGIgayNNMSDpT1cW4Z4bVrmZlkd8Oq8eTd3HNKZCASsqo048KlZse082DaGoIlLeArhxwFHjCx9ymWTOvfr/qsZlVGIkRexkDEQJZmysmIAJlA5KSDGZF0WugZGTtKM4ATGREtEAkokHcxfXzXwmZVwHDwnYt6RI5YUJaRrAiuZX8IoPWIaP+fGREib2MgYpDJiJQXiGRO4XVucubsVAyxZBp+n4Le1npbvqb9PSJaacZXOCNiVY9Iuwu3q8odIqtNbFSVrNiPMxtXS591AR/8PkWf7GIgQuRtDEQMJqPqC2WkjPFdwDjC61xGRI4P97XVW7LUKx/HMiKGHhEpZnWPiAu3q1qaEbEkEFEzIo1aoyqHZogIYCCSZaLCjEhfu5qBGLTgkLBSyaZCO0Z3JRmInJ2K2vL1skozmtxmVauCMDdmROQZM1YEIrLP6MxkFNGEOXtijKO7AHjWDBEBYCCiE0JkSjP1lTWrDp6fRSrtzIuqzIjY1R8CONGsqj63xj0iuWfNeCUjIoTAce17vsqC0kx7Yx2aQgEIof5cm0GWZur1jAhP3yUiBiK6uURKP9213IxIT6QeAZ+CREpgeNKe7ECuzMSMPaO7ANDVFAZgX2lGnvNSl1WayT5rxrKFZnKPiEvGd0em45iNp6AoajnObIqioN/khtW5uHF0F1xoRkQAGIjo5A4Rv0/JWrZUCr9PwTLtZuDULhH5dZfbmBHpaFJvztFEWv9t10oy6xHwK/OaVRMWnr4LZEoz07GkbSvti5EZsN5IPUKB8n5eS7Wi3dw+EVmaqQ9mXy8rM0TexkBEo4/uhgMVndOy3OQX7XLZuVVVaqjz63s77FjznnehmQ2H3gFquc6vHXAzPut8VuSEDev89WkwkzIiMlhtDGWXPrlHhMjbGIhoJio4Z8ZIprEHHQhEJmYT+vXb2ayqKAo6tRHeURuaOJNZUzPZwaLVgYiiKIbtqs73iZywYWeM2ZMzekYkpzTDjAiRtzEQ0chG1eYy+0Ok/jbnMiJyf0lXc0ifSLCLLM+M2tCwmvesGZsWmgGG7aouCEQG9OZk63qCzD5ZWp+aCbJZlYgyGIhoprXDuJrLXO8uOVmasXu1u5Fs4rSjNBM3lGYk+dt0zOKFZkDmvBk7sj8LsSUjIjcGn59D2oRpsELNqsyIEHkbAxGNDERy69elMr5o282JRlWpo9Gp0oz6PrsOvQMMkzMuGOEdsKFHpLc1jIBPQTyZxrAJu2IypZnsPSLMiRB5GwMRzbS2VbWpwkCkX1tqdm4qpv/mZxd9h4iNo7uSU6UZ4x4RIYQtpRm3nDczHUvqgZ+VGZGA36dPg5nRsKo3q+buEWEcQuRpDEQ0M7HqApFIfVAv65w0aQFUqeRNYmWnExkR+27OeadmIJBMC/1mFvJbM8oKuGe7qgw82xvrKu5pKpVecjQlECnQrFr1ZyaiWsZARDMd087BqDAQURTFsVN4BxxY7y7JcsWILYHI/D0iQKYsA2SvfzdbZruqs+O7dozuSvopvCYc6Jg7vpvZBcNQhMjLGIhopmNyaqbyqRMnJmeiiRROT6j1eysnKArRx3dtLM1kNaSK7EDEymZVt0zN2LkzJhNcV9/7NJVT/uRmVSICbApEvva1r2HVqlUIh8PYtm0bnnzySTu+bFlmYtkng1bCihNLFyKzL82hANrKPDXYDLJHxLnSTCZA8SlqX4NV3LJHZGBM9gTZGIiYUG6ciecvfzIfQuRtlgciDz74ID75yU/i05/+NPbt24crr7wSN9xwAwYGBqz+0mWpdmoGyCw1M+O3x1LpafqOhoo2wlbLOL5rdYo9uzQjGx1FZnTXwkZVwD1TM5mMiPUZsH4Ty40z88qfbFYlIhsCkS9+8Yu4+eab8eEPfxibNm3CP/7jP6K/vx/33nuv1V+6LNXuEQGAfm3CwM4eETv2SRQjx3fjqTSmYtaeNyNLMLkZkXi+ko0FjBkRJ/sa7CzNyEBkZDquN3RXqlBphj0iRN5m6RrOeDyO5557DnfccUfW+6+77jo89dRT8x4fi8UQi2V6DSYnJ628vCwzJmREjEvNhBC2ZCjkhs3lDozuAuoEREOdH7PxFMam42WfXFwOGXCEAtkLzTI7RKybmAEyGZFYMo25RMr2LbaA+m89PaFm3OzYGxOpDyJSH8TEXAKD5+ewYWlzxZ8rdzJN/tcRTaRxz3++VO2lElGF1nQ14X9cvsKxr2/pK+nIyAhSqRSWLFmS9f4lS5bgzJkz8x6/e/du3HPPPVZeUkFmlGaWtdVDUYC5RAqjM3G9kdNKTmdEALVPZHZsDqMzMazstC4gShh2hch+ESATiIQsLs3IQ/5iyTTGZuKOBCKD52eRFuq1dNnw8wWoO3ImTiUwMDZbcSCSSgvMJWRpRg0Ym7TsYzyVxn2/Om7KtRJR+d64vmvxBiJSbmagULZg165duP322/U/T05Oor+/3/LrAwylmSoCkVDAj56WMIYmohgYm7UlEBlwcL271NEYwsmxOcvXvMcNa9wTKfWmllWasTgQURQF7Y11OD0RxfmZBPraLP1yeZ0wjGrb1RO0vL0BL56arKoJe9pQ1pEBSHdzGP/0/q14+bR9mU8imm+lAxOXRpYGIp2dnfD7/fOyH2fPnp2XJQGAUCiEUMie3/JymVGaAYC+9gYMTURxcmwWr19u7Z0qlRb6NIMT690ludTM6jXvxqZUuRxLCJEVoFitrUENRMYcali1Y7V7LjMaVuV/X0G/gpChhPb2i3rx9ot6q7tAIqpplr5y19XVYdu2bdizZ0/W+/fs2YOdO3da+aXLEkum9FR/tYGInUvNTk/MIZESCPoV9ETqLf96hdi15t14nky+ZlUrl5lJ+uSMQyO8x/VTd20MRNrMC0Sq/e+LiBYfy18Vbr/9dtx0003Yvn07duzYgW984xsYGBjALbfcYvWXLpk8ZwaofMW7ZOcpvPK34/62Bvh9zm2Harfp4DvjdIzxvLSEYZrGak6fNzNg4+iuZMbP9FSVRygQ0eJl+avCe9/7XoyOjuJv/uZvcPr0aWzevBkPP/wwVqxwrjEml9xvUB/0V31Dl4ff2RGInHDw1F2jzqbMLhErZWdEtB0U2lkzgD2BSLu2NM6pQMSJ5mTjUrNKp8GqPcuJiBYvW14VPvrRj+KjH/2oHV+qImZMzEhmrsReyAkXNKoCmXKF1TdnYy+I8V5oPJXXapnzZuwPRNJpoQe4dp603NuqToNFE2mcm46huzlc9udgaYaICuFZMzAexlX9HgrZ2Hd6Yi7rDBQryFNYlzvc8dyhTQeNWN0jkmc6xrhHxJaMiIM9IsNTUcSTaQR8Cnpbyw8GKlUX8KE3Ut2yvtxlZkREEgMRGI4nD1YfiHQ1hRAO+pAWwNC4tVkRmRFZ6XBpxq6pmYSxNKO9TwjopZmAz56pGcCZ0oz8fi9rq7f0TJ18ZMmx0kwfSzNEVAgDEWQCkYYqDryTFEXJTBmYcFBYIUIY0vROByJNmSxBOm3duu6sjIihRySz6MzGqRkHSjNOjO5K1Z4sPRPPXmZGRCQxEAEwl1B/WzNrU6YdkzNjM3G9t6WvzR09Ism0wGQ0YdnXiSXnT80IkX0qr9UczYiM2T+6K1U7lp4pzdh/QjQRuRsDEQBzcfUGV29CRgTI9IlYGYgcG1FvSsta6xE2oaRUjVDAr2+ktbI8U2iPiH4qrw2lma5mtR9mbCaOlIXZn3yOj9rfqCrJyaxKf6anY2qA2sSMCBHlYCCCTLOqGT0iQCYQGbRwcuaoFoissvBsl3J0WDzCK4TIKs0omU0iht4Re0ozPgVIC2B0xtrm3FwDNp66m6uvyqVmk3Pqf2Mt9cyIEFE2BiIA5kzsEQHsKc0cd1kg0mZx70QyLSBPiw/5M98nIYCEjXtE/D5FL0WNTNlbnjmhb1V1ICMip8EmoxVNg8mSHQMRIsrFQATArHYqqHmlGeuXmh1zWSDS3mDtWKvx5mcszcDQrGpHaQaAfpjhOYvHlY3GZ+OY1PosnGhW7WyqQ33QDyGAUxVMg03OaYFImIEIEWVjIALzMyJywmBiLoGJOWuaN/VApMsdgUhrg7WLvoyBSNCvZI/v2njWDJDpExmZsi8QkaO73c0h0wLmciiKUlWALYOolnqO7xJRNgYiyAQiZvWINIYC+tpzKw6/S6eFHoisdktGpFH9TdeyjIgWbPgUIODPbVbVSjM2ZUS6bFrgZuTEavdc1UzOyIxIhKUZIsrBQATG0ox5v61V29xXzOnJKGLJNIJ+BctanTt11yhzGJw1GSDjxAwAvVnV2MRqR48IAHRqGZFzNmZEBuQWXQcmZqT+KgKRCZZmiKgABiIA5uJyj4h5KW/jQWFmO3ZO3pQabN+wWYjsERm3qjRjOHkXAAxDM7aXZmS2y9aMiIMTM1KlS82iiZS+A4bNqkSUyx13MYfNJcztEQGsnZw5NjINAFjV2WT6566U1YfBZTIi2d8jR0ozzfY3q7qqNFNmcC2XmSkK9H0zREQSAxFkVrybuRgsE4iYv0tE7hBZ7ZJGVcD6w+BkIBLSSzMqIWAozdiVEZHNqvaN7zo5uivpS81GywtE5OhuUygAn8+e7xER1Q4GIjB/agYA+rQJg0FLMiLuGt0FrF99nnvyrqKfNWMszSzOjEg0kcLwpPq1Vjgwuiv1tak/05PRJCZmS+8F4uguERXDQATmHnonyYzI4Pk501eBuzMQUW8yk9GkvtfDTPFkdo9IJiMibC/NyIzI+dm4HgRZSZb3msMBtDY4dzNvqAvo//ZyyjOZ0V0GIkQ0HwMRZHpE6oPm1a97IvUI+hXEU2mcnjCvPBNPpvWpBbeM7gLqWKYcqR0v47flUslARDakKoYMf8LmZtW2BnXNuxD2HH5nbFRVFGdLG5XsEsmM7rI/hIjmYyACa0ozfp+iZ0WOalMuZhgYm0VaAI11fr1E4AYBv0/fEWHFmvdYbkYkTyBi12ZVv09Bh5YZOGvDCK/eH+Lg6K5USRO2vt6dpRkiysPzgYgQInPonckbK1d3qVMtR89Nm/Y5ZVlmRUej478d52q3sE8kkdMjIglhmJqxcZS508alZvKmv9zBiRmpkqVm+g4RlmaIKA/PByKxZBqyhcP8QET9DVZOuZjh1bNTAIB1S9wzuivJEV4rdonkju/qC80g9D4NO07fleQuETuWmumlGQcbVaVKdonISSo5WUVEZOT5QCSq9YcAQIOJ47sAsEbb83HMxEDktWE1u7Ku24WBSIN121VzF5rpK94FENcyInaVZgDDeTPTdvSIaAvsXJARqWS7qvx5kD8fRERGng9E5MRMnd9n+pZSPSNiYo/Iq2fVQGRtd7Npn9Ms+nkzFmZEQnlLM/aueAfsO28mmUpj8Lza7OyGKSkZDJ0aL30aTP48yJ8PIiIjBiL6MjPznwrZI3JqfE5viK1GOi3wmhaIrHdxacaKHpF5Z80ozpZmumw6b2ZoPIpkWqAu4MOS5rClX6sUS1vCCPoVJFICZyajJX2M/HlgRoSI8vF8IJKZmDF/tLC9sU7f+2BGeebU+BzmEinU+X1606CbyBuNFdtV55VmDH+XcLA0c3aqtJtxpU6MyYmZBldsJfX7MgctllqeyWREGIgQ0XwMRCw4Z8ZIptOPjlQ/OSMbVVd3NbrmsDsjfWrGyvFdl5RmlrSo2Ymzk9ZmRI674LC7XP1ljvCOTTMQIaLC3Hc3s5lVo7vS6k45wlt9RuSwbFRd4r7+ECBTmrEkI5LMDjb0ZlUYAxH7MgYyEDkzGYUQ5m7ONRpwwRkzucppWI0n05iKqf+NMRAhonw8H4jI0ky9yRMzUqZh1YSMiIsnZgBjs6p1m1X1HhFkIhEn9ogsaVFLM7PxFKa1G60VZEZkpYsyIuXsEpGj3D6FC82IKD/PByKyWdWqjMgaE3eJvCZ3iLg0ELGyRyR3oVkmIyIMK97t+3FuqAugOaz2FQ2X2LRZCXnS7XIXZUTK2a4qy3RtDXWu6HEhIvfxfCBidY/IGm1y5rWz00hXcfhdOi300V23lmZk6n0qltQzGGbJHd/NHHpnKM3YfKOT5Zlhi/pE0mmhN6u6MSMiF60Vo0/MsCxDRAUwELFwagYAVnY2IuhXMBtP4dR45YffnRqfw2w8haBfcVXjolFLOAiffvCduVmRQgvNUkLom3HtLM0A6igrYF1G5OxUDNFEGn6fgl5tUsUNVmoN2KMzcUwsUIY7ry0za+foLhEV4PlAJLNHxJqMSNDv07Mih4enKv48L5+eBKAuMrP7hlsqn09Bq0WTM7k9IpLMhgD2lmYAoFvrEyl1n0a55EbVvrZ6V33Pm0IBPQg7ssA02DltvLmjiYEIEeXnnlc3h8wm1EZDq0ozALBhqVpKeeVMNYGI+rEX9LSYck1WadP2ppi91Gz++K6aEkkkM+WugM2lmaUWj/DqZ8y4qD9EKnVr8LC28E2WsYiIcnk+EIlaPDUDAOu1no5qMiIHT08AAC7odXcg0q6P8Jo7OVOoNBM3ZkRszhroI7wTFmVEDMvM3EZm+Y4sMA0my1Yye0RElIuBSEK9kVmx4l3aqGVEDlWRETmolWbcnxHRAhHTSzNqwBjMaVaVJRufom79tJPerGrRdlU3LjOTSh1Llyvw3bCenojcyfOBSCxpbY8IkMmIHD03k9XTUKqJuQROjqmNrm4PRNotWmqm94joGRE16Ig7sFVVkrtEhi3KiAy4ujQjMyILlGa0jAhLM0RUiOcDEZkRyT3V1UzLWuvRWOdHPJXG8Qr2ibyiZUOWtdYj0uDupVD6wXcWTc3kju8mcko2dloa0XpEpmJVjWbnI4TA8VH3je5Kcj/OidEZ/dDBfORo8xKWZoioAM8HIjIjErIwI+LzKdioZTJeGpos++Plx7i9PwTIjGmanRGRTam5UzMyUxKwcb271NkUgqIAybTAqMn/3vOzCUxFk1CUzEp1N+mN1CMc9CGREhg8n38sPZpIYWJO7RXqZkaEiArwfCBiR0YEALYsiwAADpyaKPtjXxgcBwBs7o2YeUmWyGRELGpWzdms6sSBd1LQ70Nnk1aeMXmEV47uLm0JW1o2rJTPp+jnKBVqwpbTRKGADy1ha/b0EFHt83wgYkePCABsloHIYPmByP6T4wCA1y1vNfGKrCHHd63vEVHf78Q5M0Z6n4jpgYh7G1WljT1q75McLc8lm3iXtIT1nh4iolyeD0Tsyohc1KcGIi8NTSBVRj/B+Zm4Pj3xur5WKy7NVHpGxOI9Ivqhdxo7T941ktMgZq95l/0hK9rd16gqycZpuWwv1+B59ee2J8KyDBEV5vlAxK6MyJquJtQH/ZiJp3BsgW2URvu1sszqzkbXN6oCmR4R01e8a98nvUckJ+5wLCMSkbtEKl/fn49cFCbHZN1o41I1EHnlTP5AZGBUfU7cnNUhIud5PhCxKyPi9ym4UGs2faGM8sz+gXEAwOv6Wy24KvPJjMhMPIWodqCgGeYtNMv5+4BDgcgy7QyYIZNHeI9qwaock3WjTVpp5sTYLGZiyXl/ry9kc+H4MRG5h+cDEZnyt6MhUPaJlBWI1FB/CAC0hAP6YrFxkxpWhRALfp/qHCrN9LaqGZGhKg40zCWEwLEayIh0NIXQ3RyCEPmPL5B7UJa7cOqHiNzD0kDks5/9LHbu3ImGhga0trZa+aUqFkvYU5oBgNevaAMA/Ob4WEmPT6bSeP7EeQDA1v42y67LTIqi6NtVzeoTiafSEFpbTSiYvdBMcqo00xtRMyLVnKyca3gyhpl4Cn6f4vqb+KYifSIDY+5vuCUi51n66h2Px/Hud78bH/nIR6z8MlWRv2lbXZoBgMtXtQNQ17XL/QrFvDQ0ialYEi3hQE3sEJHaG7XJGZP6ROT3CJi/0ExyYo8IACxrUwOR0+NR05aaybXpy9sbXHXqbj5yciZ3P85cPIWz2np3twdTROQsS1/l7rnnHtx2223YsmWLlV+mYqm00HsP7MiIdLeEsaqzEUIAe0vIivzqyAgA4PLVHbafo1KNVi0jYtaSr5jWx6Mo88d3JefGd8PwKWrWZmTGnMmZI9r23dWd7i3LSFu13qV9A+ez3i+nflrCAf3ngYgoH1f9uhWLxTA5OZn1ZqV4nt+0rXbpSjUr8syxhQORXx8ZBQDsXNNh6TWZzezJGdn0Ggr49JLM/PFdZ36Ug36ffo7K0Lg5DasyI+Lm/hBp2wr15/nQ8FRWlu9FbXHfJpefjUREznNVILJ7925EIhH9rb+/39KvZ5zqsCsQuWy1FogcHS36uFgypfeS7Fzbafl1malNP/jOnGbVTPksk7WanxFxLmPUq03OnCqw6rxcmdFd907MSF3NIazoaIAQmcZqIFOqkQ3aRESFlH33vfvuu6EoStG3vXv3VnQxu3btwsTEhP528uTJij5PqeQNLuBTbBv/vHy1mt04cGqi6PbRvcfPI5pIo7MphHXd7r8hGZndIxLVG4oLf4+c7KXQR3hNaljVR3droDQDANu0JuynDcG1PMpgCwMRIlpA2QdA3HrrrXjf+95X9DErV66s6GJCoRBCIftO6YzaODEj9bbWY+PSZrxyZgqPHT6L39val/dxP3vpDADgzRu7a249ttlTM3kzIjmPcTIQ0TMiJgQi0URKP0SuFjIiAHDV+i788PlT+P9fHsZfXL8RqbTAQT0jwtIMERVXdiDS2dmJzs7aKhUUYufEjNG1m5bglTNT+O+D+QMRIQQefWkYAPDWzUtsvTYzyEDEvKmZTI+IzkWlmWXaLhEzApETo7MQAmgOB9DZVBtNnlev70bAp+Dw8DROjM5gdCaOuUQKzeEAVnXWRjBFRM6x9A48MDCA/fv3Y2BgAKlUCvv378f+/fsxPV36inMrOZERAYC3XKAGFz9/5Sym82ykfPbYGM5MRtFY58fONbUX9LU3mhyIJOZPNrmlWRXIjPCa0SOSaVRtqplMWKQhiEu10fQf7x/Sg+g3ruuqqWkvInKGpWdz33nnnfjXf/1X/c9bt24FAPziF7/A1VdfbeWXLolTGZGL+iJY3dWIo+dm8PALp/GeS7Kbch/8jdobc+PFva48An4h5jerzs+IuGV8FwCWtap7Mk6en4UQoqoA4qg2urumRvpDpHdv78NTR0bxlZ+/qi+fu/HiHmcviohqgqWv3t/+9rchhJj35oYgBDCMhdp8s1cUBe/appZk/v2ZExAiswjr7FQU/3XgNADMC1BqRbtVPSKGZtX5PSLO/eYtF3ZNRZNVr7U/UkOju0Zvv6gX67qbkEgJJNMCa7oa8eZNtVdWJCL7uWp8125OZUQA4D3b+1Ef9OOFwQn84tBZ/f1f+8URxJJpvK6/VV8WVWtatamZuUQKc/HqD77TS2iBwgGjU4feAUB9nR/dzWqT9QltrXmljpx1/2F3+QT9PnzzD7bjynWduHJdJ75+0zbXb4UlInfw9CtFKWOhVulsCuGmHSsAAH/9o5cwPhvHs8fG8G9PnwAA3P6W9TXTI5CrORRAQOsNMKNPJG9GxCVnzUjyPJUT2kbRSqTTAoeH1UBkw9JmU67LTis7G/FvN1+Gf7v5Mqztrr3rJyJneDoQyTcWaqdb37QWKzoacGp8Dm/70pP40H3PIpUW+L2ty3DlutprUpUURcn0iZgRiCQWXmjm1Om70vJ2tZQiT5ytxOD5OcwlUqgL+LCC57MQkUd4OhBxMiMCAC3hIP75f2xDd3MIQxNRzMZTuHx1Oz7zzs01mw2RZJ+IGQ2r+b5P8w+9c0lGpIrSzCtn1N0ba7uaHP/3EBHZxdKpGbdzOiMCqGdx/PzPrsYvXz2HlnAQl6/ugG8RjDy2Nqh9ImNmlmaKZEQCDj9nMhCpJiNyeHgKQG2WZYiIKuXpQMTpjIjUFArg+s2La9RR3yViwuRM3oVmLtojAmQmZ06MVd4jcqiG+0OIiCrl6fyvGzIii5WZPSJR2SNSZMza6cVZKzrUHpHhyVjWYYrlOKSVZjYsYSBCRN7h7UDEJRmRxSjTI2JNRsRNp+8CQFtDEM0hNcE4UEGfSDyZ1k/dXc+MCBF5iKfvwMyIWEdmRMaqXPAF5N/3Mq9Z1efsj7KiKFilLSGTa9rLcWxkBsm0QFMogN5I2OzLIyJyLU8HIm7pEVmM2rRmVTMyIvnOBMqdKgo4nBEBgLXd6hKyV4fLD0TkxMz6JbVzxgwRkRk8fQdmRsQ6pu4RqYGMCACs05Z4vXq2/EDk0BlOzBCRNzn/6u0gZkSsY2qPSJ5m1Xnjuy7IiKyTGZEKApEXh9SMyAW9EVOviYjI7Tx9B2ZGxDrteo+ICaWZpDxrpvCPq9N7RIBMaebouWmk0mKBR2cIIXBgcBwAsGUZAxEi8hZPByKZ03c9/TRYQi40iybSVR98lzcjgtweEee/h/3tDagL+BBLpjF4vvTJmVPjczg/m0DAp2AjSzNE5DHOv3o7iBkR6zSFAvpIbbVZkZLGd12QEfH7FKzpKr9h9cVTEwCAdUuasxpyiYi8wNOBCHtErKMoCtpM6hORC83qXbzQTKqkT+SAFohsWdZiyTUREbmZp+/AzIhYq92kyRkZMNbXFWtWdceP8volaiDy8unJkj/mwCn1sVv6Wq24JCIiV3PHq7dDmBGxln7wXZUZkTn5fQoU7hFxerOqJIMJWW5ZSDot8AIbVYnIwzx9B2ZGxFpmHHwnhMgEInWFf1zdUpqRwcTRkRlMRhfeKnvk3DTGZxMIB324oIelGSLyHm8HIpyasZTeI1LFmvdYMg2hTcLWF9kj4vTpu1J7Yx362uoBlJYVefb4GABga38b6oqMJxMRLVaefuWLp9SMSJ1LbmKLjRk9IsaTbMPFFpq5JCMCABf1qVmRA4MLByK/OaYGIpesbLP0moiI3MrTd+B4ntXhZB6ZEammR0SWZYJ+JSvrMW+PiAtWvEtblrUCAF4oISPym+PnAQCXrGq38pKIiFzLPa/eNkum0pDLL5kSt0Zbo3bwXRUZEbkMLXe/hhtXvEsyI/Lbk+NFHzc0PodT43Pw+xS8fjkzIkTkTZ69A8uyDMBAxCqZjEjlPSIyI5K7Q2TeoXcuC0T8PgWD5+eKblj95WsjAIDNyyJoDAXsujwiIlfx7B1YlmUA9ohYRfaIjJvQI2LcIZKPm0ozzeGgnhX59ZHRgo97/NA5AMBV67tsuS4iIjdyz6u3zWQg4lPcswxrsTH2iAhR+iFwRoW2qrq5NAMAO1Z3ACgciCRTaTz5KgMRIiLP3oHlDhGWZazTpmVEYsm0XmIpl+wRCc1b756z0MxFGREAeMPaTgDAE6+ey3sS7zPHxjAZTaKtIYjX9bfafHVERO7hrldvG3F013qNdX79+a10cibTI5L9fcrNiLhloZl0ycp2tIQDGJmOY6+2K8ToJ/uHAADXb+5x3bUTEdnJs3fhODMillMURZ+cGa9wqVmpzapuWfEu1QV8eMsFSwEAP33xTNbfRRMp/PTF0wCAd1zca/u1ERG5iWfvwnogwoyIpardJVKoWVUxpET8PiXrz27x9ot6AAA/fH4Qs/Gk/v4fPD+IyWgSfW31uJT7Q4jI4zx7F9ZLM8yIWCqz5r3C0kyBPSJGbi1tXLW+Cys7GjAZTeI/fnMSABBLpvDNJ44CAP7wDatce+1ERHbx7F2YpRl7yBHe6ntECpdmgi69mft8Cm6+cjUA4At7DuPU+Bz+955XcXx0Fp1NdXjvJf0OXyERkfM8u0WJgYg9MttVTe4RMcQebs4q/P4l/fjBc4PYf3Ic13zuMT0T9ze/uxlNXGJGROTdjEiMPSK2aJelmUp7ROIFekQMORG3nLybT8Dvwz+9fyvWL2lCPJWG36fgU2/dgLdt6XH60oiIXMGzv5Il2CNiC7lLZKzSHpHEwmfNuG2ZWa6+tgY8/KdX4pUzU+huCaG7Oez0JRERuYZnA5FMaab46nCqTlu1GRFts2qxZlU3rXcvJOD3YfOyiNOXQUTkOu5/BbcIF5rZo82iZlUjt2dEiIioMM/ehWVGJMTSjKVkj0ilC80ye0QKb1YNuLhZlYiIivPsXZhTM/aQUzNjs5UdfDcbLzQ1kwk+aqE0Q0RE+Xn2FZylGXvIHpF4Mq0HFeWQH9NQl93OZMyBsDRDRFS7PHsX5um79mio8+vPcSV9InI1emOo2NQMv4dERLXKs6/gLM3YQ1GUqvpEZmKFMiLG0gwzIkREtcqyu/Dx48dx8803Y9WqVaivr8eaNWtw1113IR6vbHrCbAxE7FPNLhE9I1JXeNKcgQgRUe2ybI/IK6+8gnQ6ja9//etYu3YtXnzxRfzxH/8xZmZm8PnPf96qL1uyeEr9TZs9ItZra9DWvJdZmkmnRaZHpEhpxs2bVYmIqDjLApHrr78e119/vf7n1atX49ChQ7j33nvdEYgwI2IbmREZLTMQiSYzza25GRFjDsTNZ80QEVFxtt6FJyYm0N7ebueXLCjOs2Zs06kvNYuV9XGyP0RRgHAw5/uUlRFhIEJEVKtsW/F+5MgRfOUrX8EXvvCFgo+JxWKIxTI3q8nJScuuJ86zZmzT0RQCAIxOl5cRkf0hDUF/1t4QILdZld9DIqJaVfYr+N133w1FUYq+7d27N+tjhoaGcP311+Pd7343PvzhDxf83Lt370YkEtHf+vv7y/8XlYilGft0NKkZkZEyAxF9YiY0P142xiV+ZkSIiGpW2RmRW2+9Fe973/uKPmblypX6/x8aGsI111yDHTt24Bvf+EbRj9u1axduv/12/c+Tk5OWBSMxlmZs09GoZkTKLc1kJmaKH0wYZI8IEVHNKjsQ6ezsRGdnZ0mPPXXqFK655hps27YN9913H3wLpNBDoRBCoVC5l1SRBEsztulsqqxZtdBWVSC7WdXHQISIqGZZ1iMyNDSEq6++GsuXL8fnP/95nDt3Tv+7pUuXWvVlS8bSjH3a5dRMhT0iuVtVgdyzZhiIEBHVKssCkUcffRSvvfYaXnvtNfT19WX9XSWHn5mNzar2kc2q07EkookUwsHipRZJ9ojUL5AR8bNZlYioZln2Cv6hD30IQoi8b24gMyIh9ohYriUc0EdsyynPFOsRyWpW5beQiKhmefYlnKUZ+yiKojesjk6X3rA6U7RHhOO7RESLgWdfwRmI2EuO8JbTJyKbVfP1iBhxsyoRUe3y7F2YPSL2kn0iI2VkRGZj2kKzfAfeGWIPNqsSEdUuz96FuUfEXp0VnDcjSzML94gwECEiqlWevQuzNGOvTGmmjIyI1qxany8QMfx/BiJERLXLk3dhIQRLMzar5LyZ6agaiDSH8614zwQfDESIiGqXJ+/CybSAnCJmacYeHVppZqSM0syUFog0hYLz/s4YerBHhIiodnnyLizLMgAzInbpbCp/fHcqVjgjYsSFZkREtcuTr+BZgQgzIraoZM37VDQBoFBpJvP/mREhIqpdnrwLy/4QnwIEGIjYQm9WnYmVvF13ukhGxLjQjD0iRES1y5N3YU7M2E9uVk2khF5yKUYIofeINIfz9IhwfJeIaFHw5J1Yn5hhNsQ29XV+fR9IKeWZuUQKqbSaOWkKLdQjwkCEiKhWefJOnMmIlHYKLJmjnO2qcnTXpwANCyw0Y48IEVHt8nQgEmJpxlZdzWogcm5q4UBkUh/dDWTtDMmHGREiotrlyTsxl5k5o1sLRM5ORhd8bKZRdX5/CJBz+q6fgQgRUa3y5J04znNmHKEHIiVkRIqN7gK5zar8PhIR1SpPvoJzasYZ3S1hAKUGIsWXmWWdNbNA6YaIiNyr+DjCInXFuk7s++u3oLRtFmSWrqbSMyLTRUZ3AZ41Q0S0WHgyEAn6fWjTNn2SfbpaSu8RmdRKM4VGd3nWDBHR4sDaBNmmu4ypmWJbVXP52axKRFSzGIiQbbqb1R6Rsdk4Eql00cfqJ++W0KzKjAgRUe1iIEK26Wisg9+nQIiFt6uOz6qlmdb6/CU09ogQES0ODETINj6fgk7t8LuzU8X7RMZn1UClrSF/s6oRp2aIiGoXAxGylSzPnJ0s3icyPqdlREoIRLjQjIiodjEQIVuVutTsvJYRaW1YeLqJC82IiGoXX8HJVl16ILJQaUbNiLSVEIis6Wqs/sKIiMgRntwjQs6R21WHi5Rm0mmh94gUK80cuPs6xJPpgkvPiIjI/RiIkK16ImogcmZiruBjpmJJpLW1t5H6wkEGAxAiotrH0gzZqre1HgAwNF64NDOhlWXqg36Eg35brouIiJzBQIRs1atlRIaKZETOlzG6S0REtY2BCNmqR8uITEWTmNLOk8klR3cjJTSqEhFRbWMgQrZqCgXQoq1tPz2RvzxTzjIzIiKqbQxEyHayT+TUeP7yTDmju0REVNsYiJDtlmmByOkCDauj0+pob1sjMyJERIsdAxGyXU+r1rBaICMit67KdfBERLR4MRAh2+kjvAUmZ87pgUjItmsiIiJnMBAh2/VG5C6R4hmRLgYiRESLHgMRsp3MiBSampHn0LA0Q0S0+DEQIdv1GnpEUnKXuyadFhiZVsd3u1uYESEiWuwYiJDteiL1qPP7kEiJeeWZsdk4UmkBRQE6Gjm+S0S02DEQIdv5fQr629XyzPHRmay/O6udytvRWIeAnz+eRESLHV/pyRGrOhsBAMdHZ7Pef25aNqqyP4SIyAsYiJAjVnRogchIdkZkeEI2qrI/hIjICywNRN7xjndg+fLlCIfD6OnpwU033YShoSErvyTViJVaRuRETmnmxJj65+XtDbZfExER2c/SQOSaa67Bf/zHf+DQoUP4wQ9+gCNHjuBd73qXlV+SasTKDjXQOJqTEZGlmhUdDESIiLwgYOUnv+222/T/v2LFCtxxxx145zvfiUQigWCQ54h42folzQDU0kw0kUI46AcADGiBCDMiRETeYFuPyNjYGO6//37s3LmzYBASi8UwOTmZ9UaLU3dzCO2NdUgL4NCZKQCAEEKfopGlGyIiWtwsD0T+4i/+Ao2Njejo6MDAwAB+/OMfF3zs7t27EYlE9Lf+/n6rL48coigKNvWoWZFXzqgB5/nZBKaiSQDMiBAReUXZgcjdd98NRVGKvu3du1d//Kc+9Sns27cPjz76KPx+P/7gD/4AQoi8n3vXrl2YmJjQ306ePFn5v4xcb9PSFgDAy6fVjIhsXF3SEtJLNUREtLiV3SNy66234n3ve1/Rx6xcuVL//52dnejs7MT69euxadMm9Pf34+mnn8aOHTvmfVwoFEIoxLFNr9jUowYiLwyOAwBe0Uo0a7ubnLokIiKyWdmBiAwsKiEzIbFYrKKPp8Xl0lXtAIDfDk5gKprAgVMTAIDNyyJOXhYREdnIsqmZZ599Fs8++yyuuOIKtLW14ejRo7jzzjuxZs2avNkQ8p7+9gas7GjA8dFZ/PrIKPYeHwMAXNzX6uyFERGRbSxrVq2vr8cPf/hDvPnNb8aGDRvwR3/0R9i8eTMef/xxll9Id+W6LgDAA88O4PDwNBQF2LG6w+GrIiIiu1iWEdmyZQt+/vOfW/XpaZF464VL8W9Pn8Bjh84BAC5b1Y42nrpLROQZPGuGHPWGtR3YvqJN//NHrl7r4NUQEZHdLN2sSrQQRVHwrQ9dgv/4zUlc2NuCnWsra4QmIqLaxECEHBepD+KP37ja6csgIiIHsDRDREREjmEgQkRERI5hIEJERESOYSBCREREjmEgQkRERI5hIEJERESOYSBCREREjmEgQkRERI5hIEJERESOYSBCREREjmEgQkRERI5hIEJERESOYSBCREREjnH16btCCADA5OSkw1dCREREpZL3bXkfL8bVgcjU1BQAoL+/3+ErISIionJNTU0hEokUfYwiSglXHJJOpzE0NITm5mYoimLq556cnER/fz9OnjyJlpYWUz83ZfB5tgefZ/vwubYHn2d7WPU8CyEwNTWF3t5e+HzFu0BcnRHx+Xzo6+uz9Gu0tLTwh9wGfJ7twefZPnyu7cHn2R5WPM8LZUIkNqsSERGRYxiIEBERkWM8G4iEQiHcddddCIVCTl/Kosbn2R58nu3D59oefJ7t4Ybn2dXNqkRERLS4eTYjQkRERM5jIEJERESOYSBCREREjmEgQkRERI5Z1IHI1772NaxatQrhcBjbtm3Dk08+WfTxjz/+OLZt24ZwOIzVq1fjn//5n2260tpWzvP8wx/+EG95y1vQ1dWFlpYW7NixAz/72c9svNraVe7Ps/SrX/0KgUAAr3vd66y9wEWi3Oc5Fovh05/+NFasWIFQKIQ1a9bgX/7lX2y62tpW7nN9//334+KLL0ZDQwN6enrwh3/4hxgdHbXpamvTE088gRtvvBG9vb1QFAU/+tGPFvwY2++FYpH67ne/K4LBoPjmN78pDh48KD7xiU+IxsZGceLEibyPP3r0qGhoaBCf+MQnxMGDB8U3v/lNEQwGxfe//32br7y2lPs8f+ITnxB///d/L5599llx+PBhsWvXLhEMBsXzzz9v85XXlnKfZ2l8fFysXr1aXHfddeLiiy+252JrWCXP8zve8Q5x2WWXiT179ohjx46JZ555RvzqV7+y8aprU7nP9ZNPPil8Pp/40pe+JI4ePSqefPJJceGFF4p3vvOdNl95bXn44YfFpz/9afGDH/xAABAPPfRQ0cc7cS9ctIHIpZdeKm655Zas923cuFHccccdeR//53/+52Ljxo1Z7/uTP/kTcfnll1t2jYtBuc9zPhdccIG45557zL60RaXS5/m9732v+Ku/+itx1113MRApQbnP809/+lMRiUTE6OioHZe3qJT7XH/uc58Tq1evznrfl7/8ZdHX12fZNS42pQQiTtwLF2VpJh6P47nnnsN1112X9f7rrrsOTz31VN6P+fWvfz3v8W9961uxd+9eJBIJy661llXyPOdKp9OYmppCe3u7FZe4KFT6PN933304cuQI7rrrLqsvcVGo5Hn+yU9+gu3bt+Mf/uEfsGzZMqxfvx5/9md/hrm5OTsuuWZV8lzv3LkTg4ODePjhhyGEwPDwML7//e/jd37nd+y4ZM9w4l7o6kPvKjUyMoJUKoUlS5ZkvX/JkiU4c+ZM3o85c+ZM3scnk0mMjIygp6fHsuutVZU8z7m+8IUvYGZmBu95z3usuMRFoZLn+dVXX8Udd9yBJ598EoHAovzP3HSVPM9Hjx7FL3/5S4TDYTz00EMYGRnBRz/6UYyNjbFPpIhKnuudO3fi/vvvx3vf+15Eo1Ekk0m84x3vwFe+8hU7LtkznLgXLsqMiKQoStafhRDz3rfQ4/O9n7KV+zxL3/nOd3D33XfjwQcfRHd3t1WXt2iU+jynUim8//3vxz333IP169fbdXmLRjk/z+l0Goqi4P7778ell16Kt73tbfjiF7+Ib3/728yKlKCc5/rgwYP40z/9U9x555147rnn8Mgjj+DYsWO45ZZb7LhUT7H7Xrgof1Xq7OyE3++fF1mfPXt2XqQnLV26NO/jA4EAOjo6LLvWWlbJ8yw9+OCDuPnmm/G9730P1157rZWXWfPKfZ6npqawd+9e7Nu3D7feeisA9YYphEAgEMCjjz6KN73pTbZcey2p5Oe5p6cHy5YtyzrufNOmTRBCYHBwEOvWrbP0mmtVJc/17t278YY3vAGf+tSnAAAXXXQRGhsbceWVV+Izn/kMs9YmceJeuCgzInV1ddi2bRv27NmT9f49e/Zg586deT9mx44d8x7/6KOPYvv27QgGg5Zday2r5HkG1EzIhz70ITzwwAOs75ag3Oe5paUFBw4cwP79+/W3W265BRs2bMD+/ftx2WWX2XXpNaWSn+c3vOENGBoawvT0tP6+w4cPw+fzoa+vz9LrrWWVPNezs7Pw+bJvWX6/H0DmN3aqniP3QsvaYB0mR8O+9a1viYMHD4pPfvKTorGxURw/flwIIcQdd9whbrrpJv3xcmTptttuEwcPHhTf+ta3OL5bgnKf5wceeEAEAgHx1a9+VZw+fVp/Gx8fd+qfUBPKfZ5zcWqmNOU+z1NTU6Kvr0+8613vEi+99JJ4/PHHxbp168SHP/xhp/4JNaPc5/q+++4TgUBAfO1rXxNHjhwRv/zlL8X27dvFpZde6tQ/oSZMTU2Jffv2iX379gkA4otf/KLYt2+fPibthnvhog1EhBDiq1/9qlixYoWoq6sTr3/968Xjjz+u/90HP/hBcdVVV2U9/rHHHhNbt24VdXV1YuXKleLee++1+YprUznP81VXXSUAzHv74Ac/aP+F15hyf56NGIiUrtzn+eWXXxbXXnutqK+vF319feL2228Xs7OzNl91bSr3uf7yl78sLrjgAlFfXy96enrEBz7wATE4OGjzVdeWX/ziF0Vfc91wL1SEYE6LiIiInLEoe0SIiIioNjAQISIiIscwECEiIiLHMBAhIiIixzAQISIiIscwECEiIiLHMBAhIiIixzAQISIiIscwECEiIiLHMBAhIiIixzAQISIiIscwECEiIiLH/D8uoYS5/RNHMQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "y = [wavelet(i=3, l=0, n=1)([_]) for _ in x]\n",
+    "plt.plot(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a474fe5-5dde-4c33-a3b7-dd3234ff5068",
+   "metadata": {},
+   "source": [
+    "# Scaling Projectors\n",
+    "\n",
+    "\n",
+    "Let's introduce the Scaling Projectior $P^n_k$. This operator projects the function $f$ onto the\n",
+    "scaling basis $\\{\\phi_{j,l}^n\\}$. I.e.,\n",
+    "\n",
+    "\\begin{align}\n",
+    "f^n(x) = P^n_k [f(x)] = \\sum_{l=0}^{2^n-1} \\sum_{j=0}^k s_{j, l}^{n,f} \\phi_{j,l}^n(x) \n",
+    "\\end{align}\n",
+    "\n",
+    "\n",
+    "Let's use it and plot some functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2301c0e0-c3e0-4049-b958-d2ce0c906ffa",
+   "metadata": {},
+   "source": [
+    "We begin with making an analytical gaussian function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e770d05b-eae3-4fad-9e15-a88806ecd9a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from vampyr import vampyr3d as vp\n",
+    "\n",
+    "r0 = [0.8, 0.8, 0.8]\n",
+    "beta = 100.0 # Do 100 and 1000000\n",
+    "alpha = (beta / np.pi) ** (3 / 2.0)\n",
+    "f = vp.GaussFunc(alpha=alpha, beta=beta, position=r0)\n",
+    "\n",
+    "# PS: They will show a better way to do analytical functions during the exercises."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "75ba2a7d-09bc-4cab-ba03-b91f117139d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mra = vp.MultiResolutionAnalysis(box=[0, 2], order=5)\n",
+    "\n",
+    "P0 = vp.ScalingProjector(mra, 0) # P^0_k\n",
+    "P1 = vp.ScalingProjector(mra, 1) # P^1_k\n",
+    "P2 = vp.ScalingProjector(mra, 2) # P^2_k\n",
+    "P3 = vp.ScalingProjector(mra, 3) # P^3_k\n",
+    "P4 = vp.ScalingProjector(mra, 4) # P^4_k\n",
+    "\n",
+    "f0 = P0(f) #f^0 = P^0_k[f]\n",
+    "f1 = P1(f) #f^1 = P^1_k[f]\n",
+    "f2 = P2(f) #f^2 = P^2_k[f]\n",
+    "f3 = P3(f) #f^3 = P^3_k[f]\n",
+    "f4 = P4(f) #f^4 = P^4_k[f]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d26bd59a-8e93-4e77-8054-aa6dcd85a848",
+   "metadata": {},
+   "source": [
+    "Next let's show you what the basis we project onto looks like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc5530b9-0163-455f-8b84-8e1f97cf4a11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n = 0\n",
+    "x = np.arange(0.0, 1.0, 0.001)\n",
+    "for l in range(2**n):\n",
+    "    for i in range(6):\n",
+    "        y = [scaling(i=i, l=l, n=n)([x]) for x in x]\n",
+    "        plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16c3806c-37df-4a32-8084-6d3603f01be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plot_diff import plot_f_fn\n",
+    "\n",
+    "plot_f_fn(0, f, f0, alpha*1.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4992f74f-5902-4a47-94a3-2d1898bf74d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_f_fn(1, f, f1, alpha*1.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a434fe1a-8b98-474a-b109-8076795872b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_f_fn(2, f, f2, alpha*1.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12d40122-1291-4fdd-a9fd-85859e1d60b3",
+   "metadata": {},
+   "source": [
+    "# Wavelet Projectors\n",
+    "\n",
+    "\n",
+    "Next let's introduce the Wavelet Projector $Q^n_k$. This operator projects the function $f$ onto the\n",
+    "wavelet basis $\\{\\psi_{j,l}^n\\}$. I.e.,\n",
+    "\n",
+    "\\begin{align}\n",
+    "df^n(x) = Q^n_k [f(x)] = \\sum_{l=0}^{2^n-1} \\sum_{j=0}^k w_{j, l}^{n,f} \\psi_{j,l}^n(x) \n",
+    "\\end{align}\n",
+    "\n",
+    "\n",
+    "The Wavelet Projector $Q^n_k$ is related to the Scaling Projector $P^n_k$ through the relation \n",
+    "\n",
+    "\\begin{align}\n",
+    "P^{n+1}_k = P^n_k + Q^n_k\n",
+    "\\end{align}\n",
+    "\n",
+    "This leads to the following relation\n",
+    "\n",
+    "\\begin{align}\n",
+    "P^{n+1}_k = P^0_k + Q^n_k + Q^{n-1}_k +... + Q^0_k\n",
+    "\\end{align}\n",
+    "\n",
+    "\n",
+    "Which means we can write the function projection $f^N$ as \n",
+    "\n",
+    "\n",
+    "\\begin{align}\n",
+    "f^N(x) = P^0_k [f(x)] + \\sum_{n=0}^N Q^n_k [f(x)]\n",
+    "\\end{align}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9589c8e-e935-4a05-ae42-bbb3010b393e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q0 = vp.WaveletProjector(mra, 0) # Q^0_k\n",
+    "Q1 = vp.WaveletProjector(mra, 1) # Q^1_k\n",
+    "Q2 = vp.WaveletProjector(mra, 2) # Q^2_k\n",
+    "Q3 = vp.WaveletProjector(mra, 3) # Q^3_k\n",
+    "Q4 = vp.WaveletProjector(mra, 4) # Q^4_k\n",
+    "\n",
+    "df0 = Q0(f) #df^0 = Q^0_k[f]\n",
+    "df1 = Q1(f) #df^0 = Q^0_k[f]\n",
+    "df2 = Q2(f) #df^0 = Q^0_k[f]\n",
+    "df3 = Q3(f) #df^0 = Q^0_k[f]\n",
+    "df4 = Q4(f) #df^0 = Q^0_k[f]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf64c9a9-e39b-4cef-9829-9f351d8e5bf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plot_diff import plot_fn_df\n",
+    "plot_fn_df(0, f0, df0, alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59d10f23-2be7-48c8-bddc-95ce8f7c7df1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_fn_df(1, f1, df1, alpha)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9004f59-ad72-4f75-90a5-acd03bb9a89b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_fn_df(2, f2, df2, alpha)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c42ce5d2-fe07-450c-a2cc-13444da1c51c",
+   "metadata": {},
+   "source": [
+    "Next, we can try to look at some Adaptivity. Luca mentioned that we have very good error control,\n",
+    "this can be employed to drastically reduce the work we need to do on our functions, while at the same time have high precision\n",
+    "\n",
+    "This is what happens when we apply adaptivity to our projector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7327e81d-6ef5-4be4-b002-863409a9abbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r0 = [0.8, 0.8, 0.8]\n",
+    "beta = 500.0 # Do 500 then compare with P4\n",
+    "alpha = (beta / np.pi) ** (3 / 2.0)\n",
+    "f = vp.GaussFunc(coef=alpha, exp=beta, pos=r0)\n",
+    "\n",
+    "P_eps = vp.ScalingProjector(mra, prec=1.0e-3)\n",
+    "P2 = vp.ScalingProjector(mra, 2)\n",
+    "P4 = vp.ScalingProjector(mra, 4)\n",
+    "f_eps = P_eps(f)\n",
+    "f2 = P2(f)\n",
+    "f4 = P4(f)\n",
+    "\n",
+    "x = np.arange(0.0, 2.0, 0.001)\n",
+    "y = [f2([x, 0.8, 0.8]) for x in x]\n",
+    "plt.plot(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c381c611-0162-4f32-85e6-1ebf9a321a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efecc8bb-b35a-4efd-a55b-9fa7bb49916c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f_eps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3229e112-b355-4916-98dd-bc11f3c3fc8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from grid_plotter import grid_plotter\n",
+    "\n",
+    "# Uniform grid (DO f2 not f4, you'll blow up your laptop)\n",
+    "grid_plotter(f2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "537d1ce0-54ee-4a47-8731-f77e743854aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grid_plotter(f_eps)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.4 ('MRPyCM-env')",
+   "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": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "487dd71aba060d73bfc278ebf788be90cc4e85b80399312c78d8b97665a3c999"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/notebooks/poisson_equation.ipynb b/docs/notebooks/poisson_equation.ipynb
new file mode 100644
index 00000000..b5319401
--- /dev/null
+++ b/docs/notebooks/poisson_equation.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "hide-cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"The Poisson equation\"\"\"\n",
+    "\n",
+    "__author__    = \"Magnar Bjørgve\"\n",
+    "__credit__    = [\"Magnar Bjørgve\"]\n",
+    "\n",
+    "__date__      = \"2021-02-16\""
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Poisson equation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "A useful equation to solve is the Poisson equation, which among other places appear\n",
+    "when we wish to figure out the electrostatic potentail $V$ from the electronic density $\\rho$.\n",
+    "\n",
+    "\n",
+    "The Poisson equation is usually written in its differential form \n",
+    "\\begin{align}\n",
+    "    \\nabla^2 g(r) = -f(r)\n",
+    "\\end{align}\n",
+    "\n",
+    "The solution can be written in terms of the convolution integral \n",
+    "\n",
+    "\\begin{align}\n",
+    "    g(r) = \\int P(r - r') \\, d r'= \\hat{P}[f](r)\n",
+    "\\end{align}\n",
+    "where $P(r)$ is the Poisson kernel \n",
+    "\\begin{align}\n",
+    "    P(r) = \\frac{1}{4 \\pi r}\n",
+    "\\end{align}\n",
+    "and $\\hat{P}$ is the Poisson Operator.\n",
+    "\n",
+    "\n",
+    "Next we'll use VAMPyR to sovle the Poisson equation. We'll do this through what is called a source test.\n",
+    "That is, we'll start by defining an analytic funtion $g$, or solution, then fabricate $f$ from this.\n",
+    "We then use VAMPyR and solve the Poisson equation by applying poisson operator onto $f$\n",
+    "\n",
+    "Assume\n",
+    "\\begin{align}\n",
+    "    g(r) = \\exp(-x^2 - y^2 - z^2)\n",
+    "\\end{align}\n",
+    "we then apply the laplace operator $\\nabla^2$ onto $g$ and we get $f$ to be \n",
+    "\\begin{align}\n",
+    "    f(r) = -\\nabla^2 g(r) = - 2(2x^2 + 2y^2 + 2z^2 + 3) \\exp(-x^2 - y^2 - z^2)\n",
+    "\\end{align}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": [
+     "remove_cell"
+    ]
+   },
+   "source": [
+    "<div style=\"background: #efffed;\n",
+    "            border: 1px solid grey\n",
+    "            margin: 8px 0 8px 0;\n",
+    "            text-align: center;\n",
+    "            padding: 8px;\">\n",
+    "    <i class=\"fa-play fa\"\n",
+    "        style=\"font-size: 40px;\n",
+    "              line-height: 40px;\n",
+    "              margin: 8px;\n",
+    "              color: #444;\" >\n",
+    "    </i>\n",
+    "    <div>\n",
+    "    To run the selected code cell, hit <pre style=\"background: #efffed\">Shift + Enter</pre>\n",
+    "    </div>\n",
+    "<div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The Poisson operator is a 3 dimensional operator so we use vampyr3d\n",
+    "from vampyr import vampyr3d as vp3\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set the precision and make the MRA\n",
+    "prec = 1.0e-3\n",
+    "mra = vp3.MultiResolutionAnalysis(box=[-20, 20], order=5)\n",
+    "\n",
+    "# Make the scaling projector\n",
+    "P_eps = vp3.ScalingProjector(mra, prec)\n",
+    "\n",
+    "# Make the Poisson operator\n",
+    "poisson_operator = vp3.PoissonOperator(mra, prec)\n",
+    "\n",
+    "# Define the analytical source term f\n",
+    "def f(x):\n",
+    "    return -2.0*(2.0*x[0]**2 + 2.0*x[1]**2 + 2.0*x[2]**2 - 3.0) * np.exp(-x[0]**2 - x[1]**2 - x[2]**2)\n",
+    "\n",
+    "# Define the analytical solution reference g\n",
+    "def g(x):\n",
+    "    return np.exp(-x[0]**2 - x[1]**2 - x[2]**2)\n",
+    "\n",
+    "f_tree = P_eps(f)\n",
+    "g_tree_analytic = P_eps(g)\n",
+    "\n",
+    "# Apply the PoissonOperator onto f_tree\n",
+    "g_tree = poisson_operator(f_tree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# One way to compare g_tree to the analytical reference is to plot them together\n",
+    "\n",
+    "x = np.arange(-20, 20, 0.001)\n",
+    "y = [g_tree([_, 0.0, 0.0]) for _ in x]\n",
+    "y1= [g([_, 0.0, 0.0]) for _ in x]\n",
+    "\n",
+    "plt.plot(x, y)\n",
+    "plt.plot(x, y1)\n",
+    "plt.show()\n",
+    "\n",
+    "# An exercise is to modify the code and plot the relative difference along the same line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g_tree - g_tree_reference square norm: 0.0004611916258279639\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A problem with that comparrison is that we only see a line in 3D space. Another\n",
+    "# purhaps better way to compare the accuray of g_tree and g is to project g onto\n",
+    "# a function_tree g_tree_reference. Then calcualte the norm of g_tree - g_tree_reference\n",
+    "\n",
+    "# An exercise is to see how the square norm decreases when the precision is increased.\n",
+    "\n",
+    "g_tree_reference = P_eps(g)\n",
+    "\n",
+    "print(\"g_tree - g_tree_reference square norm:\", (g_tree - g_tree_reference).norm())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.4 ('vp-env')",
+   "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": "3.11.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "1b6ee7d088ac15f9e4e30b8ec349c151b4a28c1392dbe2e7155438b22bc99b82"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/vampyr_intro.ipynb b/docs/notebooks/vampyr_intro.ipynb
deleted file mode 100644
index 7a933d89..00000000
--- a/docs/notebooks/vampyr_intro.ipynb
+++ /dev/null
@@ -1,87 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0594f25e-2756-46d0-b061-cb850158f97f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from vampyr import vampyr3d as vp\n",
-    "from numpy import exp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94c8df55-6c14-44c9-bf05-97fe546ca399",
-   "metadata": {},
-   "source": [
-    "# Making the MRA\n",
-    "\n",
-    "\n",
-    "The Multiresolution analysis is the numerical space we work on, it has a size and an order"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1967defc-f567-4676-8309-2607aabb1659",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mra = vp.MultiResolutionAnalysis(box=[-20,20], order=7)\n",
-    "print(mra)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0f267db-c351-4bac-ae1c-e70cd7074867",
-   "metadata": {},
-   "source": [
-    "# Projecting a function onto the MRA\n",
-    "\n",
-    "\n",
-    "Projection a function onto the MRA is done through a projection operator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a63fb7a6-48d2-4bd2-b5b0-3aaa3aa6221e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prec = 1.0e-4\n",
-    "projection_operator = vp.ScalingProjector(mra, prec)\n",
-    "\n",
-    "def gauss(x):\n",
-    "    return exp(-x[0]**2 -x[1]**2 - x[2]**2)\n",
-    "\n",
-    "\n",
-    "gauss_tree = projection_operator(gauss)\n",
-    "print(gauss_tree)"
-   ]
-  }
- ],
- "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": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index a772a6eb..39e41183 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,7 +22,7 @@ requires = [
   "wheel",
   "scikit-build>=0.13",
   "cmake>=3.17",
-  "ninja; platform_system!='Windows'",
+  "ninja",
 ]
 
 build-backend = "setuptools.build_meta"
diff --git a/requirements.txt b/requirements.txt
index efa72f94..902584f3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
 numpy
+matplotlib
 pytest
 pybind11-global
diff --git a/setup.cfg b/setup.cfg
index dc077b7c..7dc38513 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -29,10 +29,11 @@ setup_requires =
     wheel
     scikit-build >=0.13
     cmake >= 3.17
-    ninja; platform_system!='Windows'
+    ninja
 
 install_requires =
     numpy >= 1.15.0
+    matplotlib
 
 test_suite =
     tests
diff --git a/src/vampyr/core/bases.h b/src/vampyr/core/bases.h
index 7fbe5572..9d9b5693 100644
--- a/src/vampyr/core/bases.h
+++ b/src/vampyr/core/bases.h
@@ -29,8 +29,9 @@ void bases(pybind11::module &m) {
 
     py::class_<PyWaveletFunction, RepresentableFunction<1>>(m, "WaveletFunction");
 
-    py::class_<ScalingBasis>(m, "ScalingBasis",
-    R"mydelimiter(
+    py::class_<ScalingBasis>(m,
+                             "ScalingBasis",
+                             R"mydelimiter(
         Filler text
     )mydelimiter")
         .def(py::init<int, int>(), "order"_a, "type"_a)
diff --git a/src/vampyr/core/filter.h b/src/vampyr/core/filter.h
new file mode 100644
index 00000000..a99c381c
--- /dev/null
+++ b/src/vampyr/core/filter.h
@@ -0,0 +1,36 @@
+/*
+ *  \date Oct 30, 2023
+ *  \author Evgueni Dinvay <evgueni.dinvay@uit.no> \n
+ *          Hylleraas Centre for Quantum Molecular Sciences \n
+ *          UiT - The Arctic University of Norway
+ */
+
+#include <pybind11/operators.h>
+#include <pybind11/pybind11.h>
+
+#include <MRCPP/core/MWFilter.h>
+
+namespace vampyr {
+
+void filter(pybind11::module &m)
+{
+    using namespace mrcpp;
+    namespace py = pybind11;
+    using namespace pybind11::literals;
+
+
+    py::class_<MWFilter>(m, "Filter")
+        .def(py::init<int, int>(),
+             "order"_a,
+             "type"_a)
+        .def(
+            "getFilter",
+            &MWFilter::getFilter, py::return_value_policy::reference_internal)
+        .def(
+            "getReconstructionSubFilter",
+            &MWFilter::getReconstructionSubFilter,
+            py::return_value_policy::reference_internal,
+            py::arg("i"));
+}
+
+} // namespace vampyr
diff --git a/src/vampyr/export_vampyr.cpp b/src/vampyr/export_vampyr.cpp
index 96b391c7..e43b8123 100644
--- a/src/vampyr/export_vampyr.cpp
+++ b/src/vampyr/export_vampyr.cpp
@@ -13,6 +13,7 @@
 #include <MRCPP/version.h>
 
 #include "core/bases.h"
+#include "core/filter.h"
 #include "functions/functions.h"
 #include "operators/convolutions.h"
 #include "operators/derivatives.h"
@@ -94,5 +95,6 @@ PYBIND11_MODULE(_vampyr, m) {
     bind_vampyr<2>(m);
     bind_vampyr<3>(m);
     bases(m);
+    filter(m);
 }
 } // namespace vampyr
diff --git a/src/vampyr/functions/functions.h b/src/vampyr/functions/functions.h
index 1bd50355..0a11101f 100644
--- a/src/vampyr/functions/functions.h
+++ b/src/vampyr/functions/functions.h
@@ -12,8 +12,9 @@ template <int D> void functions(pybind11::module &m) {
     using namespace pybind11::literals;
 
     // RepresentableFunction class
-    py::class_<RepresentableFunction<D>, PyRepresentableFunction<D>>(m, "RepresentableFunction",
-    R"mydelimiter(
+    py::class_<RepresentableFunction<D>, PyRepresentableFunction<D>>(m,
+                                                                     "RepresentableFunction",
+                                                                     R"mydelimiter(
        Filler text
     )mydelimiter")
         .def(py::init<const std::vector<double> &, const std::vector<double> &>())
diff --git a/src/vampyr/functions/gaussians.h b/src/vampyr/functions/gaussians.h
index aae799f3..be81226d 100644
--- a/src/vampyr/functions/gaussians.h
+++ b/src/vampyr/functions/gaussians.h
@@ -15,14 +15,12 @@ template <int D> void gaussians(pybind11::module &m) {
     using namespace pybind11::literals;
 
     // Gaussian class
-    py::class_<Gaussian<D>, PyGaussian<D>, RepresentableFunction<D>>(m, "Gaussian",
-    R"mydelimiter(
+    py::class_<Gaussian<D>, PyGaussian<D>, RepresentableFunction<D>>(m,
+                                                                     "Gaussian",
+                                                                     R"mydelimiter(
+        Parent class to the GaussFunc class.
 
-        Parameters
-        ----------
-
-        Returns
-        -------
+        Note: This is only a template. Do not use it directly.
 
     )mydelimiter")
         .def(py::init<double, double, const Coord<D> &, const std::array<int, D> &>())
@@ -39,8 +37,9 @@ template <int D> void gaussians(pybind11::module &m) {
         });
 
     // GaussFunc class
-    py::class_<GaussFunc<D>, PyGaussian<D, GaussFunc<D>>, Gaussian<D>>(m, "GaussFunc",
-    R"mydelimiter(
+    py::class_<GaussFunc<D>, PyGaussian<D, GaussFunc<D>>, Gaussian<D>>(m,
+                                                                       "GaussFunc",
+                                                                       R"mydelimiter(
         An analytic Gaussian function in d dimensions:
 
         .. math::
@@ -73,9 +72,11 @@ template <int D> void gaussians(pybind11::module &m) {
         .def(
             "differentiate",
             [](const GaussFunc<D> &gauss, int dir) { return gauss.differentiate(dir).asGaussExp(); },
-            "dir"_a, "Differentiates the Gaussian along the specified axis.")
+            "dir"_a,
+            "Differentiates the Gaussian along the specified axis.")
         .def("squaredNorm", &GaussFunc<D>::calcSquareNorm)
-        .def("calcCoulombEnergy", &GaussFunc<D>::calcCoulombEnergy,
+        .def("calcCoulombEnergy",
+             &GaussFunc<D>::calcCoulombEnergy,
              R"mydelimiter(
              Calculate energy interaction between this Gaussian and an input Gaussian.
              Warning: power has to be a zero vector)mydelimiter");
@@ -83,23 +84,23 @@ template <int D> void gaussians(pybind11::module &m) {
     // GaussExp class
     py::class_<GaussExp<D>, RepresentableFunction<D>>(m, "GaussExp")
         .def(py::init())
-        .def("size",
-            py::overload_cast<>(&GaussExp<D>::size, py::const_),
-            "Number of Gaussians in the GaussExp")
+        .def("size", py::overload_cast<>(&GaussExp<D>::size, py::const_), "Number of Gaussians in the GaussExp")
         .def("func",
-            py::overload_cast<int>(&GaussExp<D>::getFunc),
-            "term"_a,
-            py::return_value_policy::reference_internal)
+             py::overload_cast<int>(&GaussExp<D>::getFunc),
+             "term"_a,
+             py::return_value_policy::reference_internal)
         .def("append",
-            py::overload_cast<const Gaussian<D> &>(&GaussExp<D>::append),
-            "Append Gaussians to the end of the GaussExp")
-        .def("periodify", &GaussExp<D>::periodify,
-            "period"_a,
-            "std_dev"_a = 4.0,
-            "Make copies of the Gaussian to simulate periodicity, then append it to the GaussExp")
+             py::overload_cast<const Gaussian<D> &>(&GaussExp<D>::append),
+             "Append Gaussians to the end of the GaussExp")
+        .def("periodify",
+             &GaussExp<D>::periodify,
+             "period"_a,
+             "std_dev"_a = 4.0,
+             "Make copies of the Gaussian to simulate periodicity, then append it to the GaussExp")
         .def("differentiate",
-            &GaussExp<D>::differentiate,
-            "dir"_a, "Differentiate all Gaussians in GaussExp along the specified axis")
+             &GaussExp<D>::differentiate,
+             "dir"_a,
+             "Differentiate all Gaussians in GaussExp along the specified axis")
         .def("squaredNorm", &GaussExp<D>::calcSquareNorm)
         .def("calcCoulombEnergy", &GaussExp<D>::calcCoulombEnergy)
         .def("__str__", [](const GaussExp<D> &func) {
diff --git a/src/vampyr/operators/convolutions.h b/src/vampyr/operators/convolutions.h
index f7500f7c..72d8bb6a 100644
--- a/src/vampyr/operators/convolutions.h
+++ b/src/vampyr/operators/convolutions.h
@@ -2,10 +2,11 @@
 
 #include <pybind11/pybind11.h>
 
+#include <MRCPP/operators/CartesianConvolution.h>
 #include <MRCPP/operators/HelmholtzOperator.h>
 #include <MRCPP/operators/IdentityConvolution.h>
 #include <MRCPP/operators/PoissonOperator.h>
-#include <MRCPP/operators/CartesianConvolution.h>
+#include <MRCPP/operators/TimeEvolutionOperator.h>
 #include <MRCPP/treebuilders/apply.h>
 
 namespace vampyr {
@@ -13,6 +14,7 @@ namespace vampyr {
 void cartesian_convolution(pybind11::module &);
 void helmholtz_operator(pybind11::module &);
 void poisson_operator(pybind11::module &);
+void time_evolution_operator(pybind11::module &m);
 
 template <int D> void convolutions(pybind11::module &m) {
     namespace py = pybind11;
@@ -20,15 +22,13 @@ template <int D> void convolutions(pybind11::module &m) {
     using namespace pybind11::literals;
 
     py::class_<ConvolutionOperator<D>>(m, "ConvolutionOperator")
-        .def(py::init<const MultiResolutionAnalysis<D> &, GaussExp<1> &, double>(),
-             "mra"_a,
-             "kernel"_a,
-             "prec"_a)
+        .def(py::init<const MultiResolutionAnalysis<D> &, GaussExp<1> &, double>(), "mra"_a, "kernel"_a, "prec"_a)
+        .def(py::init<const MultiResolutionAnalysis<D> &, GaussExp<1> &, double, int, int>())
         .def(
             "__call__",
-            [](ConvolutionOperator<D> &O, FunctionTree<D> *inp) {
+            [](ConvolutionOperator<D> &C, FunctionTree<D> *inp) {
                 auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                apply<D>(O.getBuildPrec(), *out, O, *inp);
+                apply<D>(C.getBuildPrec(), *out, C, *inp);
                 return out;
             },
             "inp"_a);
@@ -52,6 +52,7 @@ template <int D> void convolutions(pybind11::module &m) {
     if constexpr (D == 3) cartesian_convolution(m);
     if constexpr (D == 3) helmholtz_operator(m);
     if constexpr (D == 3) poisson_operator(m);
+    if constexpr (D == 1) time_evolution_operator(m);
 }
 
 void cartesian_convolution(pybind11::module &m) {
@@ -60,10 +61,7 @@ void cartesian_convolution(pybind11::module &m) {
     using namespace pybind11::literals;
 
     py::class_<CartesianConvolution, ConvolutionOperator<3>>(m, "CartesianConvolution")
-        .def(py::init<const MultiResolutionAnalysis<3> &, GaussExp<1> &, double>(),
-             "mra"_a,
-             "kernel"_a,
-             "prec"_a)
+        .def(py::init<const MultiResolutionAnalysis<3> &, GaussExp<1> &, double>(), "mra"_a, "kernel"_a, "prec"_a)
         .def(
             "__call__",
             [](CartesianConvolution &O, FunctionTree<3> *inp) {
@@ -122,4 +120,29 @@ void helmholtz_operator(pybind11::module &m) {
             "inp"_a);
 }
 
+
+void time_evolution_operator(pybind11::module &m)
+{
+    namespace py = pybind11;
+    using namespace mrcpp;
+    using namespace pybind11::literals;
+
+    py::class_<TimeEvolutionOperator<1>, ConvolutionOperator<1>>(m, "TimeEvolutionOperator")
+        .def(py::init<const MultiResolutionAnalysis<1> &, double, double, int, bool, int>(),
+             "mra"_a,
+             "prec"_a,
+             "time"_a,
+             "finest_scale"_a,
+             "imaginary"_a,
+             "max_Jpower"_a = 20)
+        .def(
+            "__call__",
+            [](TimeEvolutionOperator<1> &T, FunctionTree<1> *inp) {
+                auto out = std::make_unique<FunctionTree<1>>(inp->getMRA());
+                apply<1>(T.getBuildPrec(), *out, T, *inp);
+                return out;
+            },
+            "inp"_a);
+}
+
 } // namespace vampyr
diff --git a/src/vampyr/operators/derivatives.h b/src/vampyr/operators/derivatives.h
index 76c1cedc..cd34e47f 100644
--- a/src/vampyr/operators/derivatives.h
+++ b/src/vampyr/operators/derivatives.h
@@ -20,6 +20,7 @@ template <int D> void derivatives(pybind11::module &m) {
         An abstract base class for derivative operators
     )mydelimiter")
         // clang-format on
+        .def(py::init<const MultiResolutionAnalysis<D> &, int, int>(), "mra"_a, "root"_a, "reach"_a)
         .def("getOrder", &DerivativeOperator<D>::getOrder)
         .def(
             "__call__",
diff --git a/src/vampyr/plotter.py b/src/vampyr/plotter.py
new file mode 100644
index 00000000..1624f8d4
--- /dev/null
+++ b/src/vampyr/plotter.py
@@ -0,0 +1,123 @@
+import numpy as np
+
+try:
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+except ImportError:
+    from warnings import warn
+
+    warn(
+        "Please install matplotlib to use plotting functionality!",
+        UserWarning,
+        stacklevel=2,
+    )
+
+
+def plot_surface_xy(x=0.0, y=0.0, z=0.0, length=1.0):
+    rx = [x, x + length]
+    ry = [y, y + length]
+    X, Y = np.meshgrid(rx, ry)
+    r_1 = [z, z]
+    r_2 = [z + length, z + length]
+    p1, _ = np.meshgrid(r_1, r_1)
+    p2, _ = np.meshgrid(r_2, r_2)
+    return (X, Y, p1), (X, Y, p2)
+
+
+def plot_surface_yz(x=0.0, y=0.0, z=0.0, length=1.0):
+    ry = [y, y + length]
+    rz = [z, z + length]
+    Y, Z = np.meshgrid(ry, rz)
+    r_1 = [x, x]
+    r_2 = [x + length, x + length]
+    p1, _ = np.meshgrid(r_1, r_1)
+    p2, _ = np.meshgrid(r_2, r_2)
+    return (p1, Y, Z), (p2, Y, Z)
+
+
+def plot_surface_xz(x=0.0, y=0.0, z=0.0, length=1.0):
+    rx = [x, x + length]
+    rz = [z, z + length]
+    X, Z = np.meshgrid(rx, rz)
+    r_1 = [y, y]
+    r_2 = [y + length, y + length]
+    p1, _ = np.meshgrid(r_1, r_1)
+    p2, _ = np.meshgrid(r_2, r_2)
+
+    return (X, p1, Z), (X, p2, Z)
+
+
+def plot_cube(corner, length):
+    x, y, z = corner[0], corner[1], corner[2]
+    a, b = plot_surface_xy(x, y, z, length)
+    c, d = plot_surface_yz(x, y, z, length)
+    e, f = plot_surface_xz(x, y, z, length)
+    return (a, b, c, d, e, f)
+
+
+def grid_plotter(tree=None, dpi=150, lw=0.08, color=(1, 0, 0, 0.01)):
+    assert (
+        len(tree.MRA().world().upperBounds()) == 3
+    ), "basis plotter only works for 3D FunctionTrees"
+    fig, ax = plt.subplots(figsize=(6, 6), dpi=150, subplot_kw={"projection": "3d"})
+    ax.grid(False)
+    ax.axis("off")
+
+    corners = [tree.fetchEndNode(i).lowerBounds() for i in range(tree.nEndNodes())]
+    lengths = [
+        tree.fetchEndNode(i).upperBounds()[0] - tree.fetchEndNode(i).lowerBounds()[0]
+        for i in range(tree.nEndNodes())
+    ]
+
+    for i in range(tree.nEndNodes()):
+        data = plot_cube(corners[i], lengths[i])
+        for d in data:
+            ax.plot_surface(d[0], d[1], d[2], color=color, edgecolor="black", lw=lw)
+
+    return fig, ax
+
+
+def representation_vs_basis(tree, type="scaling"):
+    assert (
+        len(tree.MRA().world().upperBounds()) == 1
+    ), "basis plotter only works for 1D FunctionTrees"
+    mra = tree.MRA()
+    k = mra.basis().scalingOrder()
+    n = tree.depth() - 1
+
+    upper_bound = mra.world().upperBound(0)
+    lower_bound = mra.world().lowerBound(0)
+    x = np.arange(lower_bound, upper_bound, 0.001)
+    y = [
+        tree([_]) for _ in x
+    ]  # Plot f1 to f4 to see how the function representation improves.
+
+    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
+    ax[1].title.set_text(f"Basis")
+    ax[0].plot(x, y)
+    ax[0].set_xlim(lower_bound, upper_bound)
+    ax[1].set_xlim(lower_bound, upper_bound)
+    basis_polys = None
+    if type.lower() == "scaling":
+        ax[0].title.set_text(f"Function tree: $f^{n}(x)$")
+        basis_polys = mra.basis().scaling
+    elif type.lower() == "wavelet":
+        ax[0].title.set_text(f"Function tree: $df^{n}(x)$")
+        basis_polys = mra.basis().wavelet
+    else:
+        raise Exception(f"{type} : type not recogniced")
+    for _ in range(tree.nEndNodes()):
+        idx = tree.fetchEndNode(_).index()
+        n = idx.scale()
+        l = idx.translation()[0]
+
+        for i in range(k + 1):
+            y = [
+                basis_polys(i=i, l=l, n=n)([x])
+                if basis_polys(i=i, l=l, n=n)([x]) != 0.0
+                else np.nan
+                for x in x
+            ]
+            ax[1].plot(x, y)
+
+    return fig, ax
diff --git a/src/vampyr/tests/test_convolutions_3d.py b/src/vampyr/tests/test_convolutions_3d.py
index 4aefed2d..95023aa9 100644
--- a/src/vampyr/tests/test_convolutions_3d.py
+++ b/src/vampyr/tests/test_convolutions_3d.py
@@ -1,8 +1,8 @@
 import numpy as np
 import pytest
 
-from vampyr import vampyr3d as vp
 from vampyr import vampyr1d as vp1
+from vampyr import vampyr3d as vp
 
 epsilon = 1.0e-3
 mu = epsilon / 10
@@ -23,6 +23,7 @@
 vp.advanced.build_grid(out=ftree, inp=ffunc)
 vp.advanced.project(prec=epsilon, out=ftree, inp=ffunc)
 
+
 def test_GaussKernel():
     b = 1.0e4
     a = (b / np.pi) ** (D / 2.0)
diff --git a/src/vampyr/tests/test_projector1d.py b/src/vampyr/tests/test_projector1d.py
new file mode 100644
index 00000000..bf43b081
--- /dev/null
+++ b/src/vampyr/tests/test_projector1d.py
@@ -0,0 +1,18 @@
+import pytest
+
+from vampyr import vampyr1d as vp
+
+
+def test_ScalingProjector():
+    def f(x):
+        return x
+
+    mra = vp.MultiResolutionAnalysis(box=[0, 1], order=7)
+    P_scaling = vp.ScalingProjector(mra, 2)
+    P_wavelet = vp.WaveletProjector(mra, 2)
+
+    with pytest.raises(Exception):
+        P_scaling(f)
+
+    with pytest.raises(Exception):
+        P_wavelet(f)
diff --git a/src/vampyr/tests/test_projector3d.py b/src/vampyr/tests/test_projector3d.py
new file mode 100644
index 00000000..25faa0a8
--- /dev/null
+++ b/src/vampyr/tests/test_projector3d.py
@@ -0,0 +1,18 @@
+import pytest
+
+from vampyr import vampyr3d as vp
+
+
+def test_ScalingProjector():
+    def f(x):
+        return x
+
+    mra = vp.MultiResolutionAnalysis(box=[0, 1], order=7)
+    P_scaling = vp.ScalingProjector(mra, 2)
+    P_wavelet = vp.WaveletProjector(mra, 2)
+
+    with pytest.raises(Exception):
+        P_scaling(f)
+
+    with pytest.raises(Exception):
+        P_wavelet(f)
diff --git a/src/vampyr/tests/test_time_evolution_1d.py b/src/vampyr/tests/test_time_evolution_1d.py
new file mode 100644
index 00000000..0ba50c44
--- /dev/null
+++ b/src/vampyr/tests/test_time_evolution_1d.py
@@ -0,0 +1,65 @@
+from vampyr import vampyr1d as vp1
+import numpy as np
+import pytest
+
+#Analytical solution parameters
+x0 = 0.5
+sigma = 0.001
+
+#Time step
+time = 0.002
+
+# Set the precision and make the MRA
+order = 5
+precision = 1.0e-6
+finest_scale = 8
+
+
+from vampyr import LegendreBasis
+lbasis = LegendreBasis(order=order)
+box = vp1.BoundingBox(0)
+mra = vp1.MultiResolutionAnalysis(box, lbasis) 
+
+# Make the scaling projector
+P = vp1.ScalingProjector(mra, prec = precision)
+
+# Make the Schrodinger free-particle semigroup operator
+imaginary = False
+real_semigroup_operator = vp1.TimeEvolutionOperator(mra, precision, time, 
+finest_scale, imaginary)
+imaginary = True
+imag_semigroup_operator = vp1.TimeEvolutionOperator(mra, precision, time, 
+finest_scale, imaginary)
+
+def free_particle_analytical_solution(x, x0, t, sigma):
+    denominator = 4.0j * t + sigma
+    sqrt_denom = np.sqrt(denominator)
+    exponent = -((x - x0) ** 2) / denominator
+    return np.sqrt(sigma) / sqrt_denom * np.exp(exponent)
+
+def f(x):
+    return np.exp( - (x[0] - x0)**2 / sigma )
+f = P(f)
+
+def Re_g(x):
+    return free_particle_analytical_solution(x[0], x0, time, sigma).real
+Re_g = P(Re_g)
+
+def Im_g(x):
+    return free_particle_analytical_solution(x[0], x0, time, sigma).imag
+Im_g = P(Im_g)
+
+Re_out = real_semigroup_operator(f)
+Im_out = imag_semigroup_operator(f)
+
+Re_difference = Re_out - Re_g           #2.1e-14
+Im_difference = Im_out - Im_g           #2.2e-14
+
+#print(Re_difference.squaredNorm())
+#print(Im_difference.squaredNorm())
+
+epsilon = 2.5e-14
+
+def test_time_evolution():
+    assert Re_difference.squaredNorm() == pytest.approx(0.0, abs = epsilon)
+    assert Im_difference.squaredNorm() == pytest.approx(0.0, abs = epsilon)
\ No newline at end of file
diff --git a/src/vampyr/treebuilders/applys.h b/src/vampyr/treebuilders/applys.h
index d2c0fa27..f17aabec 100644
--- a/src/vampyr/treebuilders/applys.h
+++ b/src/vampyr/treebuilders/applys.h
@@ -3,6 +3,7 @@
 #include <pybind11/pybind11.h>
 
 #include <MRCPP/treebuilders/apply.h>
+//#include <MRCPP/treebuilders/complex_apply.h>
 
 namespace vampyr {
 template <int D> void applys(pybind11::module &m) {
diff --git a/src/vampyr/treebuilders/project.h b/src/vampyr/treebuilders/project.h
index 3b168994..20bf279a 100644
--- a/src/vampyr/treebuilders/project.h
+++ b/src/vampyr/treebuilders/project.h
@@ -10,9 +10,9 @@ template <int D> void project(pybind11::module &m) {
     namespace py = pybind11;
     using namespace pybind11::literals;
 
-    m.def("ZeroTree",
-        [](const MultiResolutionAnalysis<D> &mra,
-           const std::string &name) {
+    m.def(
+        "ZeroTree",
+        [](const MultiResolutionAnalysis<D> &mra, const std::string &name) {
             auto out = std::make_unique<FunctionTree<D>>(mra, name);
             out->setZero();
             return out;
@@ -28,6 +28,17 @@ template <int D> void project(pybind11::module &m) {
         .def(
             "__call__",
             [](PyScalingProjector<D> &P, std::function<double(const Coord<D> &r)> func) {
+                try {
+                    // When the analytic function func is badly defined, it kills the kernel
+                    // of Notebooks. This evaluates func in a point, and if it is not successful
+                    // it throws an error instead of killing the kernel.
+                    auto arr = std::array<double, D>();
+                    arr.fill(111111.111); // A number which hopefully does not divide by zero
+                    func(arr);
+                } catch (py::cast_error &e) {
+                    py::print("Error: Invalid definition of analytic function");
+                    throw;
+                }
                 auto old_threads = mrcpp_get_num_threads();
                 set_max_threads(1);
                 auto out = P(func);
@@ -43,6 +54,15 @@ template <int D> void project(pybind11::module &m) {
         .def(
             "__call__",
             [](PyWaveletProjector<D> &P, std::function<double(const Coord<D> &r)> func) {
+                try {
+                    auto arr = std::array<double, D>();
+                    arr.fill(111111.111); // A number which hopefully does not divide by zero
+                    func(arr);
+                } catch (py::cast_error &e) {
+                    py::print("Error: Invalid definition of analytic function");
+                    throw;
+                }
+
                 auto old_threads = mrcpp_get_num_threads();
                 set_max_threads(1);
                 auto out = P(func);
diff --git a/src/vampyr/trees/trees.h b/src/vampyr/trees/trees.h
index 9c5ffee4..8c38ca13 100644
--- a/src/vampyr/trees/trees.h
+++ b/src/vampyr/trees/trees.h
@@ -2,6 +2,7 @@
 
 #include <filesystem>
 
+#include <pybind11/eigen.h>
 #include <pybind11/stl/filesystem.h>
 
 #include <MRCPP/trees/FunctionNode.h>
@@ -11,6 +12,95 @@
 #include <MRCPP/trees/TreeIterator.h>
 
 namespace vampyr {
+template <int D>
+auto impl__add__(mrcpp::FunctionTree<D> *inp_a, mrcpp::FunctionTree<D> *inp_b)
+    -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({1.0, inp_a});
+    vec.push_back({1.0, inp_b});
+    build_grid(*out, vec);
+    add(-1.0, *out, vec);
+    return out;
+};
+
+template <int D>
+auto impl__sub__(mrcpp::FunctionTree<D> *inp_a, mrcpp::FunctionTree<D> *inp_b)
+    -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({1.0, inp_a});
+    vec.push_back({-1.0, inp_b});
+    build_grid(*out, vec);
+    add(-1.0, *out, vec);
+    return out;
+};
+
+template <int D>
+auto impl__mul__(mrcpp::FunctionTree<D> *inp_a, mrcpp::FunctionTree<D> *inp_b)
+    -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({1.0, inp_a});
+    vec.push_back({1.0, inp_b});
+    build_grid(*out, vec);
+    build_grid(*out, 1);
+    multiply(-1, *out, vec);
+    return out;
+};
+
+template <int D> auto impl__mul__(mrcpp::FunctionTree<D> *inp_a, double c) -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({c, inp_a});
+    build_grid(*out, vec);
+    add(-1.0, *out, vec);
+    return out;
+};
+
+template <int D> auto impl__pos__(mrcpp::FunctionTree<D> *inp) -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
+    copy_grid(*out, *inp);
+    copy_func(*out, *inp);
+    return out;
+};
+
+template <int D> auto impl__neg__(mrcpp::FunctionTree<D> *inp) -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({-1.0, inp});
+    build_grid(*out, vec);
+    add(-1.0, *out, vec);
+    return out;
+};
+
+template <int D>
+auto impl__truediv__(mrcpp::FunctionTree<D> *inp, double c) -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
+    FunctionTreeVector<D> vec;
+    vec.push_back({1.0 / c, inp});
+    build_grid(*out, vec);
+    add(-1.0, *out, vec);
+    return out;
+};
+
+template <int D> auto impl__pow__(mrcpp::FunctionTree<D> *inp, double c) -> std::unique_ptr<mrcpp::FunctionTree<D>> {
+    using namespace mrcpp;
+    auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
+    copy_grid(*out, *inp);
+    copy_func(*out, *inp);
+    refine_grid(*out, 1);
+    out->power(c);
+    return out;
+};
+
 template <int D> void trees(pybind11::module &m) {
     using namespace mrcpp;
     namespace py = pybind11;
@@ -29,12 +119,11 @@ template <int D> void trees(pybind11::module &m) {
              py::return_value_policy::reference_internal)
         .def("rootScale", &MWTree<D>::getRootScale)
         .def("depth", &MWTree<D>::getDepth)
-        .def(
-            "setZero",
-            [](MWTree<D> *out) {
-                out->setZero();
-                return out;
-            })
+        .def("setZero",
+             [](MWTree<D> *out) {
+                 out->setZero();
+                 return out;
+             })
         .def("clear", &MWTree<D>::clear)
         .def("setName", &MWTree<D>::setName)
         .def("name", &MWTree<D>::getName)
@@ -58,12 +147,11 @@ template <int D> void trees(pybind11::module &m) {
         .def("nGenNodes", &FunctionTree<D>::getNGenNodes)
         .def("deleteGenerated", &FunctionTree<D>::deleteGenerated)
         .def("integrate", &FunctionTree<D>::integrate)
-        .def(
-            "normalize",
-            [](FunctionTree<D> *out) {
-                out->normalize();
-                return out;
-            })
+        .def("normalize",
+             [](FunctionTree<D> *out) {
+                 out->normalize();
+                 return out;
+             })
         .def(
             "saveTree",
             [](FunctionTree<D> &obj, const std::string &filename) {
@@ -88,155 +176,47 @@ template <int D> void trees(pybind11::module &m) {
                  copy_func(*out, *inp);
                  return out;
              })
+        .def("quadrature",
+             [](FunctionTree<D> *tree) {
+                 if constexpr (D != 1) { throw std::runtime_error("quadrature only implemented for 1D"); }
+
+                 // Current implementation only makes sense in 1D
+
+                 std::vector<double> vec_pts;
+                 // Iterate over all end nodes
+                 for (int i = 0; i < tree->getNEndNodes(); i++) {
+                     MWNode<D> &node = tree->getEndMWNode(i);
+
+                     Eigen::MatrixXd pts;
+                     node.getPrimitiveQuadPts(pts);
+
+                     // Flatten the MatrixXd and add the points from this node to the vector
+                     vec_pts.insert(vec_pts.end(), pts.data(), pts.data() + pts.size());
+                 }
+
+                 // Now we need to create an Eigen vector from our std::vector
+                 Eigen::VectorXd final_pts =
+                     Eigen::Map<Eigen::VectorXd, Eigen::Unaligned>(vec_pts.data(), vec_pts.size());
+
+                 // Now final_pts holds all the points from all nodes
+                 return final_pts;
+             })
         .def("__call__", [](FunctionTree<D> &func, const Coord<D> &r) { return func.evalf_precise(r); })
-        .def(
-            "__pos__",
-            [](FunctionTree<D> *inp) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                copy_grid(*out, *inp);
-                copy_func(*out, *inp);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__neg__",
-            [](FunctionTree<D> *inp) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({-1.0, inp});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__add__",
-            [](FunctionTree<D> *inp_a, FunctionTree<D> *inp_b) {
-                auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({1.0, inp_a});
-                vec.push_back({1.0, inp_b});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__iadd__",
-            [](FunctionTree<D> *out, FunctionTree<D> *inp) {
-                refine_grid(*out, *inp);
-                out->add(1.0, *inp);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__sub__",
-            [](FunctionTree<D> *inp_a, FunctionTree<D> *inp_b) {
-                auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({1.0, inp_a});
-                vec.push_back({-1.0, inp_b});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__isub__",
-            [](FunctionTree<D> *out, FunctionTree<D> *inp) {
-                refine_grid(*out, *inp);
-                out->add(-1.0, *inp);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__mul__",
-            [](FunctionTree<D> *inp, double c) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({c, inp});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__rmul__",
-            [](FunctionTree<D> *inp, double c) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({c, inp});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__imul__",
-            [](FunctionTree<D> *out, double c) {
-                out->rescale(c);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__mul__",
-            [](FunctionTree<D> *inp_a, FunctionTree<D> *inp_b) {
-                auto out = std::make_unique<FunctionTree<D>>(inp_a->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({1.0, inp_a});
-                vec.push_back({1.0, inp_b});
-                build_grid(*out, vec);
-                build_grid(*out, 1);
-                multiply(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__imul__",
-            [](FunctionTree<D> *out, FunctionTree<D> *inp) {
-                refine_grid(*out, *inp);
-                refine_grid(*out, 1);
-                out->multiply(1.0, *inp);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__truediv__",
-            [](FunctionTree<D> *inp, double c) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                FunctionTreeVector<D> vec;
-                vec.push_back({1.0 / c, inp});
-                build_grid(*out, vec);
-                add(-1.0, *out, vec);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__itruediv__",
-            [](FunctionTree<D> *out, double c) {
-                out->rescale(1.0 / c);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__pow__",
-            [](FunctionTree<D> *inp, double c) {
-                auto out = std::make_unique<FunctionTree<D>>(inp->getMRA());
-                copy_grid(*out, *inp);
-                copy_func(*out, *inp);
-                refine_grid(*out, 1);
-                out->power(c);
-                return out;
-            },
-            py::is_operator())
-        .def(
-            "__ipow__",
-            [](FunctionTree<D> *out, double c) {
-                refine_grid(*out, 1);
-                out->power(c);
-                return out;
-            },
-            py::is_operator());
+        .def("__pos__", &impl__pos__<D>, py::is_operator())
+        .def("__neg__", &impl__neg__<D>, py::is_operator())
+        .def("__add__", &impl__add__<D>, py::is_operator())
+        .def("__iadd__", &impl__add__<D>, py::is_operator())
+        .def("__sub__", &impl__sub__<D>, py::is_operator())
+        .def("__isub__", &impl__sub__<D>, py::is_operator())
+        .def("__mul__", py::overload_cast<FunctionTree<D> *, FunctionTree<D> *>(&impl__mul__<D>), py::is_operator())
+        .def("__mul__", py::overload_cast<FunctionTree<D> *, double>(&impl__mul__<D>), py::is_operator())
+        .def("__imul__", py::overload_cast<FunctionTree<D> *, FunctionTree<D> *>(&impl__mul__<D>), py::is_operator())
+        .def("__imul__", py::overload_cast<FunctionTree<D> *, double>(&impl__mul__<D>), py::is_operator())
+        .def("__rmul__", py::overload_cast<FunctionTree<D> *, double>(&impl__mul__<D>), py::is_operator())
+        .def("__truediv__", &impl__truediv__<D>, py::is_operator())
+        .def("__itruediv__", &impl__truediv__<D>, py::is_operator())
+        .def("__pow__", &impl__pow__<D>, py::is_operator())
+        .def("__ipow__", &impl__pow__<D>, py::is_operator());
 
     py::class_<MWNode<D>>(m, "MWNode")
         .def("depth", &MWNode<D>::getDepth)
@@ -263,6 +243,12 @@ template <int D> void trees(pybind11::module &m) {
         .def("isGenNode", &MWNode<D>::isGenNode)
         .def("hasParent", &MWNode<D>::hasParent)
         .def("hasCoefs", &MWNode<D>::hasCoefs)
+        .def("quadrature",
+             [](MWNode<D> &node) {
+                 Eigen::MatrixXd pts;
+                 node.getPrimitiveQuadPts(pts);
+                 return pts;
+             })
         .def("center", &MWNode<D>::getCenter)
         .def("upperBounds", &MWNode<D>::getUpperBounds)
         .def("lowerBounds", &MWNode<D>::getLowerBounds)