diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..94e8e06
Binary files /dev/null and b/.DS_Store differ
diff --git a/.github/workflows/pytests.yml b/.github/workflows/pytests.yml
index 0b5a445..c6cb39a 100644
--- a/.github/workflows/pytests.yml
+++ b/.github/workflows/pytests.yml
@@ -10,9 +10,9 @@ jobs:
     strategy:
       max-parallel: 1
       matrix:
-        python-version: [3.6,
-                         3.7,
-                         3.8]
+        python-version: [3.7,
+                         3.8,
+                         3.9]
 
     steps:
     - uses: actions/checkout@v1
diff --git a/.gitignore b/.gitignore
index 85e7c1d..d46b8e2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
 /.idea/
+/examples/.ipynb_checkpoints/
diff --git a/examples/demo-jaynes-cummings.ipynb b/examples/demo-jaynes-cummings.ipynb
index 0af9b90..5fe1188 100644
--- a/examples/demo-jaynes-cummings.ipynb
+++ b/examples/demo-jaynes-cummings.ipynb
@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# scqubits example: Jaynes-Cummings model\n",
     "J. Koch and P. Groszkowski\n",
@@ -22,15 +26,12 @@
     },
     "init_cell": true,
     "pycharm": {
-     "is_executing": false
+     "name": "#%%\n"
     },
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import numpy as np\n",
     "\n",
     "import scqubits as scq\n",
@@ -39,7 +40,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## The Jaynes-Cummings model\n",
     "\n",
@@ -59,18 +64,22 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "# exact eigenenergies for comparing with numerics\n",
     "\n",
-    "def energies(omega_r, omega_q, g, n_cutoff):\n",
-    "    delta = omega_r - omega_q\n",
-    "    energies1 = (np.arange(1, n_cutoff) - 0.5) * omega_r\n",
+    "def energies(frequency_r, frequency_q, g, n_cutoff):\n",
+    "    delta = frequency_r - frequency_q\n",
+    "    energies1 = (np.arange(1, n_cutoff) - 0.5) * frequency_r\n",
     "    energies2 = np.sqrt(delta**2/4 + np.arange(1, n_cutoff) * g**2)\n",
     "    energies_plus = energies1 + energies2\n",
     "    energies_minus = energies1 - energies2\n",
-    "    energies_0 = np.asarray([[-0.5 * omega_q]])\n",
+    "    energies_0 = np.asarray([[-0.5 * frequency_q]])\n",
     "    all_energies = np.append(energies_0, energies_minus)\n",
     "    all_energies = np.append(all_energies, energies_plus)\n",
     "    return np.sort(all_energies)"
@@ -78,7 +87,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "### Set up the subsystems"
    ]
@@ -97,15 +110,15 @@
    },
    "outputs": [],
    "source": [
-    "omega_q = 1.0\n",
-    "omega_r = 0.8\n",
+    "frequency_q = 1.0\n",
+    "frequency_r = 0.8\n",
     "g = 0.1\n",
     "\n",
     "\n",
-    "qubit = scq.GenericQubit(E=omega_q)\n",
+    "qubit = scq.GenericQubit(E=frequency_q)\n",
     "\n",
     "osc = scq.Oscillator(\n",
-    "    E_osc=omega_r,\n",
+    "    E_osc=frequency_r,\n",
     "    truncated_dim=10  # up to 9 photons\n",
     ")"
    ]
@@ -153,6 +166,9 @@
     "ExecuteTime": {
      "end_time": "2020-03-31T11:43:54.772581Z",
      "start_time": "2020-03-31T11:43:54.765597Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
     }
    },
    "outputs": [
@@ -182,8 +198,7 @@
       "--------------------------------\n",
       "InteractionTerm----------| [Interaction_1]\n",
       "                         | g_strength: 0.1\n",
-      "                         | operator_list: [(0, array([[0., 0.],\n",
-      "       [1., 0.]])), (1, arra ...\n",
+      "                         | operator_list: [(0, <bound method GenericQubit.sm_operator of Gen ...\n",
       "                         | add_hc: True\n",
       "\n",
       "\n"
@@ -201,6 +216,9 @@
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:06.327709Z",
      "start_time": "2020-03-31T11:44:06.314743Z"
+    },
+    "pycharm": {
+     "name": "#%%\n"
     }
    },
    "outputs": [
@@ -220,13 +238,15 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "array([-0.5       ,  0.25857864,  0.54142136,  1.02679492,  1.37320508])"
-      ]
+      "text/plain": "array([-0.5       ,  0.25857864,  0.54142136,  1.02679492,  1.37320508])"
      },
      "execution_count": 7,
      "metadata": {},
@@ -234,14 +254,26 @@
     }
    ],
    "source": [
-    "energies(omega_r, omega_q, g, 3)"
+    "energies(frequency_r, frequency_q, g, 3)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
   }
  ],
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -255,7 +287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
   },
   "toc": {
    "base_numbering": 1,
@@ -309,4 +341,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/demo_cos2phi_qubit.ipynb b/examples/demo_cos2phi_qubit.ipynb
index 3e52665..1bcaece 100644
--- a/examples/demo_cos2phi_qubit.ipynb
+++ b/examples/demo_cos2phi_qubit.ipynb
@@ -77,26 +77,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac018e81e14f425ca7b0b15c6f0eed1b",
+       "model_id": "b26d8450254647faac16b41bcdb158a8",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "HBox(children=(VBox(children=(HBox(children=(Label(value='EJ [GHz]'), FloatText(value=15.0, layout=Layout(widt…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b87a86e2c9c44a2faa89b0b8c3dd1ebc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+       "Row(children=[Row(children=[ValidatedNumberField(class_='ml-2 py-0', dense=True, error=False, filled=True, lab…"
       ]
      },
      "metadata": {},
@@ -196,4263 +182,4319 @@
    "outputs": [
     {
      "data": {
+      "application/pdf": 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-      ],
-      "text/plain": [
-       "<Figure size 1200x450 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = cos2phi_qubit.plot_wavefunction(which=0, mode='real', zero_calibrate=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-03-11T02:43:39.832466Z",
      "start_time": "2021-03-11T02:43:38.391435Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
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+   "outputs": [],
    "source": [
     "fig, ax = cos2phi_qubit.plot_wavefunction(which=1, mode='real', zero_calibrate=True)"
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-       " </defs>\n",
-       "</svg>\n"
-      ],
-      "text/plain": [
-       "<Figure size 720x360 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "cos2phi_qubit.plot_matrixelements('n_phi_operator', evals_count=10);"
    ]
@@ -13152,38 +4638,16 @@
      "start_time": "2021-03-11T02:42:34.385Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e632a0d9a7e448baa09de125382b83b8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(HTML(value='Spectral data'), FloatProgress(value=0.0, max=101.0), HTML(value='')))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "flux_list = np.linspace(0.4, 0.6, 101)\n",
     "fig, ax = cos2phi_qubit.plot_matelem_vs_paramvals('n_phi_operator', 'flux', flux_list, select_elems=4);"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -13197,7 +4661,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/demo_customcircuit.ipynb b/examples/demo_customcircuit.ipynb
index 99e2759..f679264 100644
--- a/examples/demo_customcircuit.ipynb
+++ b/examples/demo_customcircuit.ipynb
@@ -3,11 +3,7 @@
   {
    "cell_type": "markdown",
    "id": "6e21ebf5",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "# scqubits examples: custom circuits\n",
     "\n",
@@ -31,11 +27,7 @@
   {
    "cell_type": "markdown",
    "id": "6f934120",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Example:   asymmetric $0$-$\\pi$ circuit"
    ]
@@ -53,9 +45,6 @@
    "execution_count": 2,
    "id": "334a9b1e",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [],
@@ -85,14 +74,11 @@
    "execution_count": 3,
    "id": "fdb44a19",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [],
    "source": [
-    "zero_pi = scq.Circuit.from_yaml(zp_yaml, from_file=False, ext_basis=\"discretized\")"
+    "zero_pi = scq.Circuit(zp_yaml, from_file=False, ext_basis=\"discretized\")"
    ]
   },
   {
@@ -118,38 +104,20 @@
    "execution_count": 4,
    "id": "eb41bc00",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/latex": [
-       "$\\displaystyle \\left(0.5 \\dot{φ_1} \\cdot \\left(6.25625 \\dot{φ_1} - 6.25 \\dot{φ_3} - 0.00625 \\dot{φ_2}\\right) + 0.5 \\dot{φ_2} \\left(6.25625 \\dot{φ_2} - 6.25 \\dot{φ_4} - 0.00625 \\dot{φ_1}\\right) + 0.5 \\dot{φ_3} \\left(6.25625 \\dot{φ_3} - 6.25 \\dot{φ_1} - 0.00625 \\dot{φ_4}\\right) + 0.5 \\dot{φ_4} \\left(6.25625 \\dot{φ_4} - 6.25 \\dot{φ_2} - 0.00625 \\dot{φ_3}\\right)\\right) + \\left(- 0.005 \\left(φ_{3} - φ_{2}\\right)^{2} - 0.004 \\left(φ_{1} - φ_{4}\\right)^{2} + EJ \\cos{\\left(φ_{1} - φ_{2} \\right)} + EJ \\cos{\\left((2πΦ_{1}) + φ_{4} - φ_{3} \\right)}\\right)$"
+       "$ \\left(0.5 \\dot{φ_1} \\cdot \\left(6.25625 \\dot{φ_1} - 6.25 \\dot{φ_3} - 0.00625 \\dot{φ_2}\\right) + 0.5 \\dot{φ_2} \\left(6.25625 \\dot{φ_2} - 6.25 \\dot{φ_4} - 0.00625 \\dot{φ_1}\\right) + 0.5 \\dot{φ_3} \\left(6.25625 \\dot{φ_3} - 6.25 \\dot{φ_1} - 0.00625 \\dot{φ_4}\\right) + 0.5 \\dot{φ_4} \\left(6.25625 \\dot{φ_4} - 6.25 \\dot{φ_2} - 0.00625 \\dot{φ_3}\\right)\\right) + \\left(- 0.005 \\left(φ_{3} - φ_{2}\\right)^{2} - 0.004 \\left(φ_{1} - φ_{4}\\right)^{2} + EJ \\cos{\\left(φ_{1} - φ_{2} \\right)} + EJ \\cos{\\left((2πΦ_{1}) + φ_{4} - φ_{3} \\right)}\\right) $"
       ],
       "text/plain": [
-       "                                                                              \n",
-       "0.5⋅\\dot{φ_1}⋅(6.25625⋅\\dot{φ_1} - 6.25⋅\\dot{φ_3} - 0.00625⋅\\dot{φ_2}) + 0.5⋅\\\n",
-       "\n",
-       "                                                                              \n",
-       "dot{φ_2}⋅(6.25625⋅\\dot{φ_2} - 6.25⋅\\dot{φ_4} - 0.00625⋅\\dot{φ_1}) + 0.5⋅\\dot{φ\n",
-       "\n",
-       "                                                                              \n",
-       "_3}⋅(6.25625⋅\\dot{φ_3} - 6.25⋅\\dot{φ_1} - 0.00625⋅\\dot{φ_4}) + 0.5⋅\\dot{φ_4}⋅(\n",
-       "\n",
-       "                                                                           2  \n",
-       "6.25625⋅\\dot{φ_4} - 6.25⋅\\dot{φ_2} - 0.00625⋅\\dot{φ_3}) + - 0.005⋅(φ₃ - φ₂)  -\n",
-       "\n",
-       "                2                                                \n",
-       " 0.004⋅(φ₁ - φ₄)  + EJ⋅cos(φ₁ - φ₂) + EJ⋅cos((2πΦ_{1}) + φ₄ - φ₃)"
+       "<IPython.core.display.Latex object>"
       ]
      },
-     "execution_count": 4,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -168,26 +136,19 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "94d2fbcc",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle \\left[ φ_{1} = 1.0 θ_{3} + 1.0 θ_{4} - 1.0 θ_{2}, \\  φ_{2} = 1.0 θ_{1} + 1.0 θ_{4} - 1.0 θ_{2}, \\  φ_{3} = 1.0 θ_{1} + 1.0 θ_{2} + 1.0 θ_{3} + 1.0 θ_{4}, \\  φ_{4} = 1.0 θ_{2} + 1.0 θ_{4}\\right]$"
+       "$ φ_{1} = 1.0 θ_{3} + 1.0 θ_{4} - 1.0 θ_{2} \\\\ φ_{2} = 1.0 θ_{1} + 1.0 θ_{4} - 1.0 θ_{2} \\\\ φ_{3} = 1.0 θ_{1} + 1.0 θ_{2} + 1.0 θ_{3} + 1.0 θ_{4} \\\\ φ_{4} = 1.0 θ_{2} + 1.0 θ_{4} $"
       ],
       "text/plain": [
-       "[φ₁ = 1.0⋅θ₃ + 1.0⋅θ₄ - θ₂, φ₂ = 1.0⋅θ₁ + 1.0⋅θ₄ - θ₂, φ₃ = 1.0⋅θ₁ + 1.0⋅θ₂ + \n",
-       "1.0⋅θ₃ + 1.0⋅θ₄, φ₄ = 1.0⋅θ₂ + 1.0⋅θ₄]"
+       "<IPython.core.display.Latex object>"
       ]
      },
-     "execution_count": 5,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -199,9 +160,6 @@
    "execution_count": 6,
    "id": "9f9f66c1",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -236,9 +194,6 @@
    "execution_count": 7,
    "id": "e892fdcf",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -273,29 +228,20 @@
    "execution_count": 8,
    "id": "caa76553",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/latex": [
-       "$\\displaystyle \\left(25.0 \\dot{θ_2}^{2} + 0.00625 \\dot{θ_3}^{2} + 6.25625 \\dot{θ_1}^{2}\\right) - \\left(0.009 θ_{3}^{2} + 0.036 θ_{2}^{2} - EJ \\cos{\\left(θ_{1} - 1.0 θ_{3} \\right)} - EJ \\cos{\\left(θ_{1} + θ_{3} - (2πΦ_{1}) \\right)} + 0.004 θ_{2} θ_{3}\\right)$"
+       "$ \\left(25.0 \\dot{θ_2}^{2} + 0.00625 \\dot{θ_3}^{2} + 6.25625 \\dot{θ_1}^{2}\\right) - \\left(0.009 θ_{3}^{2} + 0.036 θ_{2}^{2} - EJ \\cos{\\left(θ_{1} - 1.0 θ_{3} \\right)} - EJ \\cos{\\left(θ_{1} + θ_{3} - (2πΦ_{1}) \\right)} + 0.004 θ_{2} θ_{3}\\right) $"
       ],
       "text/plain": [
-       "              2                    2                    2             2       \n",
-       "25.0⋅\\dot{θ_2}  + 0.00625⋅\\dot{θ_3}  + 6.25625⋅\\dot{θ_1}  + - 0.009⋅θ₃  - 0.03\n",
-       "\n",
-       "    2                                                              \n",
-       "6⋅θ₂  + EJ⋅cos(θ₁ - θ₃) + EJ⋅cos(θ₁ + θ₃ - (2πΦ_{1})) - 0.004⋅θ₂⋅θ₃"
+       "<IPython.core.display.Latex object>"
       ]
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -323,35 +269,91 @@
    "execution_count": 9,
    "id": "79d9115a",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/latex": [
-       "$\\displaystyle \\left(40.0 Q_{3}^{2} + 0.03996 n_{1}^{2} + 0.03996 n_{g1}^{2} + 0.01 Q_{2}^{2} + 0.07992 n_{1} n_{g1}\\right) + \\left(0.009 θ_{3}^{2} + 0.036 θ_{2}^{2} - EJ \\cos{\\left(θ_{1} - 1.0 θ_{3} \\right)} - EJ \\cos{\\left(θ_{1} + θ_{3} - (2πΦ_{1}) \\right)} + 0.004 θ_{2} θ_{3}\\right)$"
+       "$ \\left(40.0 Q_{3}^{2} + 0.03996 n_{1}^{2} + 0.03996 n_{g1}^{2} + 0.01 Q_{2}^{2} + 0.07992 n_{1} n_{g1}\\right) + \\left(0.009 θ_{3}^{2} + 0.036 θ_{2}^{2} - EJ \\cos{\\left(θ_{1} - 1.0 θ_{3} \\right)} - EJ \\cos{\\left(θ_{1} + θ_{3} - (2πΦ_{1}) \\right)} + 0.004 θ_{2} θ_{3}\\right) $"
       ],
       "text/plain": [
-       "       2             2               2          2                             \n",
-       "40.0⋅Q₃  + 0.03996⋅n₁  + 0.03996⋅n_g1  + 0.01⋅Q₂  + 0.07992⋅n₁⋅n_g1 + 0.009⋅θ₃\n",
-       "\n",
-       "2           2                                                              \n",
-       "  + 0.036⋅θ₂  - EJ⋅cos(θ₁ - θ₃) - EJ⋅cos(θ₁ + θ₃ - (2πΦ_{1})) + 0.004⋅θ₂⋅θ₃"
+       "<IPython.core.display.Latex object>"
       ]
      },
-     "execution_count": 9,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
     "zero_pi.sym_hamiltonian()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1c7dceb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[ng1]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero_pi.offset_charges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "dd43c3aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero_pi.ng1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cf142928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$ n_{g1} = 1.0 q_{g3} + 1.0 q_{g4} - 1.0 q_{g2} $"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "zero_pi.offset_charge_transformation()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b5ea9066-d370-4be3-b92f-a6c0a71f9185",
@@ -365,9 +367,6 @@
    "execution_count": 10,
    "id": "41794bb0",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -401,11 +400,7 @@
   {
    "cell_type": "markdown",
    "id": "5c126ca6",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "**Eigenvalues**"
    ]
@@ -443,11 +438,7 @@
   {
    "cell_type": "markdown",
    "id": "a79d5886",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Spectrum using hierarchical diagonalization algorithm (HD) to reduce computational time"
    ]
@@ -465,9 +456,6 @@
    "execution_count": 13,
    "id": "b3007408",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -522,9 +510,6 @@
    "execution_count": 15,
    "id": "200e23a1",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [],
@@ -538,11 +523,7 @@
   {
    "cell_type": "markdown",
    "id": "2d8b5098",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "**Hamiltonian of each subsystem**"
    ]
@@ -552,9 +533,6 @@
    "execution_count": 16,
    "id": "f49c0b98",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -610,11 +588,7 @@
   {
    "cell_type": "markdown",
    "id": "f9d66d86",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "**Interaction between subsystems**"
    ]
@@ -624,9 +598,6 @@
    "execution_count": 18,
    "id": "6b99a190",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -690,11 +661,7 @@
   {
    "cell_type": "markdown",
    "id": "cb09fd8e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Set external fluxes"
    ]
@@ -704,9 +671,6 @@
    "execution_count": 20,
    "id": "fcf140cd",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -756,9 +720,6 @@
    "execution_count": 22,
    "id": "b8eb8bed",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -808,11 +769,7 @@
   {
    "cell_type": "markdown",
    "id": "f0e10ef8",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Visualization capabilities"
    ]
@@ -830,9 +787,6 @@
    "execution_count": 23,
    "id": "ad266245",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -864,9 +818,6 @@
    "execution_count": 24,
    "id": "c21bbc97",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -903,11 +854,7 @@
   {
    "cell_type": "markdown",
    "id": "f9c7bc0c",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "### Eigenstates"
    ]
@@ -917,9 +864,6 @@
    "execution_count": 29,
    "id": "edc2931d",
    "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    },
     "tags": []
    },
    "outputs": [
@@ -954,11 +898,7 @@
   {
    "cell_type": "markdown",
    "id": "9347a84a",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
+   "metadata": {},
    "source": [
     "A maximum of two variable indices can be specified for a plot:"
    ]
@@ -967,11 +907,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "id": "74d19cae",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1017,7 +953,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('sai')",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1031,7 +967,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.16"
   },
   "vscode": {
    "interpreter": {
diff --git a/examples/demo_explorer.ipynb b/examples/demo_explorer.ipynb
index dfff688..ac74432 100644
--- a/examples/demo_explorer.ipynb
+++ b/examples/demo_explorer.ipynb
@@ -1,138 +1,47 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:22.144589Z",
-     "start_time": "2020-02-13T22:51:19.760664Z"
-    },
-    "nbsphinx": "hidden"
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
+    "# scqubits example: the Explorer class\n",
+    "J. Koch and P. Groszkowski\n",
     "\n",
-    "import numpy as np\n",
+    "For further documentation of scqubits see https://scqubits.readthedocs.io/en/latest/.\n",
     "\n",
-    "import scqubits as scq"
+    "---"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Explorer\n",
-    "\n",
-    "Exploring properties of coupled quantum systems benefits from visualizing how properties change when system parameters are vaired. The `Explorer` class in scqubits provides an interactive plot with multiple panels collecting an important set of information. \n",
+    "# The Explorer Class\n",
     "\n",
-    "The workflow is to generate a `HilbertSpace` object specifying the physical system, and a `ParameterSweep` object that contains all data to be visualized. Once this data is stored, the `Explorer` class is invoked for display.\n",
+    "Sometimes, exploring the properties of coupled quantum systems benefits from visual aides, and the possibility to see how properties change when system parameters are changed. The `Explorer` class in scqubits provides an interactive plot with multiple panels. \n",
     "\n",
-    "## Example 1: fluxonium coupled to resonator\n",
-    "As a first example, we consider a system composed of a fluxonium qubit, coupled through its charge operator to the voltage inside a resonator.\n",
+    "The idea behind the `Explorer` class is simple: the user selects an external parameter that they wish to sweep, e.g., an external magnetic flux. The composite system is user-defined through the `HilbertSpace` and `InteractionTerm` classes, allowing flexibility to include different types and numbers of superconducting qubits and harmonic modes.\n",
     "\n",
-    "### HilbertSpace setup\n",
-    "The initialization of the composite Hilbert space proceeds as usual; we first define the individual two subsystems that will make up the Hilbert space:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:22.164237Z",
-     "start_time": "2020-02-13T22:51:22.149299Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "qbt = scq.Fluxonium(\n",
-    "    EJ=2.55,\n",
-    "    EC=0.72,\n",
-    "    EL=0.12,\n",
-    "    flux=0.0,\n",
-    "    cutoff=110,\n",
-    "    truncated_dim=9\n",
-    ")\n",
-    "\n",
-    "osc = scq.Oscillator(\n",
-    "    E_osc=4.0,\n",
-    "    truncated_dim=5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, the `truncated_dim` parameters are important. For the fluxonium, with `cutoff` set to 110, the internal Hilbert space dimension is 110. Once diagonalized, we will only keep a few eigenstates going forward - in the above example 9. Similarly, we keep 5 levels for the resonators, i.e., photon states n=0,1,...,4 are included in the following.\n",
+    "Once defined, the parameter sweep is computed by means of the `ParameterSweep` class, and spectral data is stored in memory to allow efficient, interactive display of data. The `Explorer` supports plots of:\n",
     "\n",
-    "Next, the two subsystems are declared as the two components of a joint Hilbert space:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:22.175209Z",
-     "start_time": "2020-02-13T22:51:22.168227Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "hilbertspace = scq.HilbertSpace([qbt, osc])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The interaction between fluxonium and resonator is of the form $H_\\text{int} = g n (a+a^\\dagger)$, where $n$ is the fluxonium's charge operator: `qbt.n_operator()`.  This structure is captured by creating an `InteractionTerm` object via `add_interaction`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:22.186179Z",
-     "start_time": "2020-02-13T22:51:22.178199Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "hilbertspace.add_interaction(\n",
-    "    g_strength=0.2,\n",
-    "    op1=qbt.n_operator,\n",
-    "    op2=osc.creation_operator,\n",
-    "    add_hc=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Parameter sweep setup\n",
-    "We consider sweeping the external flux through the fluxonium loop. To create the necessary `ParameterSweep` object, we specify:\n",
-    "1. `param_name`: the name of the sweep parameter (below expressed in LaTeX format as the flux in units of the flux quantum)\n",
-    "2. `param_vals_by_name`: a dictionary that names our parameter and associates the array of flux values with it\n",
-    "3. `subsys_update_info` (optional): a dictionary listing the particular Hilbert space subsystems that change as each parameter (here the flux) is varied\n",
-    "4. `update_hilbertspace(param_val)`: a function that shows how a change in the sweep parameter affects the Hilbert space; here only the `.flux` attributed of the fluxonium qubit needs to be changed\n",
+    "1. Bare spectra of the individual qubits\n",
+    "2. Wave functions of the bare qubits\n",
+    "3. Dressed spectrum of the composite Hilbert space\n",
+    "4. Spectrum for n-photon qubit transitions, starting from a given initial state\n",
+    "5. AC Stark shift $\\chi_{01}$ for any of the qubits\n",
+    "6. Charge matrix elements for any of the qubits, using the same initial state as in point 4.\n",
     "\n",
-    "These ingredients all make it into the initialization of the `ParameterSweep` object. Once initialized, spectral data is generated and stored. Here, we additionally generate data for dispersive shifts and charge matrix elements."
+    "## Example 1: fluxonium coupled to resonator\n",
+    "As a first example, we consider a system composed of a fluxonium qubit, coupled through its charge operator to the voltage inside a resonator."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:26.490669Z",
-     "start_time": "2020-02-13T22:51:23.156585Z"
+     "end_time": "2020-02-13T14:26:06.228141Z",
+     "start_time": "2020-02-13T14:26:06.213556Z"
     }
    },
    "outputs": [
@@ -144,7 +53,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys for subsystem Fluxonium_1 [num_cpus=4]"
+       "Bare spectra:   0%|          | 0/101 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -158,7 +67,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys for subsystem Oscillator_1 [num_cpus=4]"
+       "Bare spectra:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -172,7 +81,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute dressed eigensys [num_cpus=4]"
+       "Dressed spectrum:   0%|          | 0/101 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -180,9 +89,37 @@
     }
    ],
    "source": [
-    "param_name = \"Φext/Φ0\"\n",
+    "import numpy as np\n",
+    "import scqubits as scq\n",
+    "\n",
+    "qbt = scq.Fluxonium(\n",
+    "    EJ=2.55,\n",
+    "    EC=0.72,\n",
+    "    EL=0.12,\n",
+    "    flux=0.0,\n",
+    "    cutoff=110,\n",
+    "    truncated_dim=9\n",
+    ")\n",
+    "\n",
+    "osc = scq.Oscillator(\n",
+    "    E_osc=4.0,\n",
+    "    truncated_dim=5\n",
+    ")\n",
+    "\n",
+    "hilbertspace = scq.HilbertSpace([qbt, osc])\n",
+    "\n",
+    "hilbertspace.add_interaction(\n",
+    "    g_strength=0.2,\n",
+    "    op1=qbt.n_operator,\n",
+    "    op2=osc.creation_operator,\n",
+    "    add_hc=True\n",
+    ")\n",
+    "\n",
+    "param_name = 'Φext/Φ0'\n",
     "param_vals = np.linspace(-0.5, 0.5, 101)\n",
     "\n",
+    "subsys_update_list = [qbt]\n",
+    "\n",
     "\n",
     "def update_hilbertspace(param_val):\n",
     "    qbt.flux = param_val\n",
@@ -190,11 +127,10 @@
     "\n",
     "sweep = scq.ParameterSweep(\n",
     "    paramvals_by_name={param_name: param_vals},\n",
-    "    evals_count=10,\n",
+    "    evals_count=20,\n",
     "    hilbertspace=hilbertspace,\n",
     "    subsys_update_info={param_name: [qbt]},\n",
     "    update_hilbertspace=update_hilbertspace,\n",
-    "    num_cpus=4\n",
     ")"
    ]
   },
@@ -203,28 +139,23 @@
    "metadata": {},
    "source": [
     "### Starting the Explorer class\n",
-    "At this point, everything is prepared to start the interactive `Explorer` and play with the interactive display!"
+    "At this point, everything is prepared to start the interactive `Explorer` display!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:51:30.660521Z",
-     "start_time": "2020-02-13T22:51:27.493984Z"
-    }
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fb6290da209488caebef9d43f4c2eae",
+       "model_id": "a8abbe67076148c9818a965331f367dd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(Tab(children=(HBox(children=(HBox(children=(VBox(children=(Dropdown(layout=Layout(width='165px'…"
+       "Container(children=[Sheet(children=[Card(children=[Img(layout=None, src='data:image/png;base64,iVBORw0KGgoAAAA…"
       ]
      },
      "metadata": {},
@@ -232,7 +163,7 @@
     }
    ],
    "source": [
-    "explorer = scq.Explorer(sweep)"
+    "xp = scq.Explorer(sweep)"
    ]
   },
   {
@@ -247,13 +178,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:53:12.216513Z",
-     "start_time": "2020-02-13T22:53:12.198561Z"
-    }
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "qbt1 = scq.Transmon(\n",
@@ -305,13 +231,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:53:19.930404Z",
-     "start_time": "2020-02-13T22:53:15.996439Z"
-    }
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -371,7 +292,7 @@
     }
    ],
    "source": [
-    "param_name = \"Φext/Φ0\"\n",
+    "param_name = r'$\\Phi_{ext}/\\Phi_0$'\n",
     "param_vals = np.linspace(0.0, 1.0, 151)\n",
     "\n",
     "subsys_update_list = [qbt1, qbt2]\n",
@@ -400,23 +321,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-13T22:53:23.456005Z",
-     "start_time": "2020-02-13T22:53:21.239927Z"
-    }
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "32cbd4bda9b04cd28eb7b0389452806c",
+       "model_id": "853d55effe1743afb95b71b5f9011f65",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(Tab(children=(HBox(children=(HBox(children=(VBox(children=(Dropdown(layout=Layout(width='165px'…"
+       "Container(children=[Sheet(children=[Card(children=[Img(layout=None, src='data:image/png;base64,iVBORw0KGgoAAAA…"
       ]
      },
      "metadata": {},
@@ -426,10 +342,16 @@
    "source": [
     "explorer = scq.Explorer(sweep=sweep)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-  "celltoolbar": "Edit Metadata",
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -445,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.16"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/examples/demo_flux_qubit.ipynb b/examples/demo_flux_qubit.ipynb
index 623bb9e..a2a376d 100644
--- a/examples/demo_flux_qubit.ipynb
+++ b/examples/demo_flux_qubit.ipynb
@@ -28,9 +28,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import numpy as np\n",
     "import scqubits as scq"
    ]
@@ -13154,7 +13151,7 @@
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -13168,7 +13165,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/examples/demo_fluxonium.ipynb b/examples/demo_fluxonium.ipynb
index ecaa7b3..a9f510f 100644
--- a/examples/demo_fluxonium.ipynb
+++ b/examples/demo_fluxonium.ipynb
@@ -28,9 +28,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import numpy as np\n",
     "import scqubits as scq"
    ]
@@ -58,74 +55,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-27T12:08:37.174509Z",
      "start_time": "2020-03-27T12:08:37.105692Z"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(VBox(children=(HBox(children=(Label(value='EJ [GHz]'), FloatText(value=8.9, layout=Layout(width…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fluxonium = scq.Fluxonium.create()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-24T22:08:13.758806Z",
      "start_time": "2020-03-24T22:08:13.744843Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fluxonium-----------| [Fluxonium_1]\n",
-      "                    | EJ: 8.9\n",
-      "                    | EC: 2.5\n",
-      "                    | EL: 0.5\n",
-      "                    | flux: 0.0\n",
-      "                    | cutoff: 110\n",
-      "                    | truncated_dim: 10\n",
-      "                    |\n",
-      "                    | dim: 110\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(fluxonium)"
    ]
@@ -139,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-27T12:06:07.463676Z",
@@ -166,1129 +117,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-2.7022458 ,  6.05619946,  6.27730681,  8.29978855, 14.23074422,\n",
-       "       16.86961382])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fluxonium.eigenvals()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-24T22:08:15.008710Z",
-     "start_time": "2020-03-24T22:08:14.988765Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-2.7022458 ,  6.05619946,  6.27730681,  8.29978855, 14.23074422,\n",
-       "       16.86961382])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fluxonium.eigenvals()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Spectral data:   0%|          | 0/151 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/svg+xml": [
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-       " <metadata>\r\n",
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+   "outputs": [],
    "source": [
     "flux_list = np.linspace(-0.5, 0.5, 151)\n",
     "fluxonium.plot_evals_vs_paramvals('flux', flux_list, evals_count=8);"
@@ -1296,5007 +136,37 @@
   },
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-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
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+   "outputs": [],
    "source": [
     "fluxonium.cos_phi_operator()"
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    "cell_type": "code",
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-   "metadata": {
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-       "Spectral data:   0%|          | 0/151 [00:00<?, ?it/s]"
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-    "flux_list = np.linspace(-0.5, 0.5, 151)\n",
-    "fluxonium.plot_evals_vs_paramvals('flux', flux_list, evals_count=8);"
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+   "outputs": [],
    "source": [
     "fluxonium.plot_evals_vs_paramvals('flux', flux_list, evals_count=9, subtract_ground=True);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-13T22:55:15.087870Z",
      "start_time": "2020-02-13T22:55:14.155331Z"
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+   "outputs": [],
    "source": [
     "fluxonium.flux = 0.5\n",
     "fluxonium.plot_wavefunction(esys=None, which=range(5), mode='real');"
@@ -6304,3010 +174,9 @@
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    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
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-   ],
+   "outputs": [],
    "source": [
     "fluxonium = scq.Fluxonium(\n",
     "    EJ=5.7,\n",
@@ -9328,51 +197,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
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      "end_time": "2020-02-13T22:55:23.804641Z",
      "start_time": "2020-02-13T22:55:22.664581Z"
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-     "output_type": "stream",
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-      "   2.41344721e+00 -6.45618181e-15  2.65902027e+00  5.82901810e-15\n",
-      "  -7.06894388e-02  3.34797329e-15]\n",
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-      "   9.55042118e-16  2.65902027e+00  3.66400698e-15 -2.95543793e+00\n",
-      "  -8.25765333e-15  2.89606649e-02]\n",
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-      "   3.32236039e+00 -2.75391230e-14]\n",
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-      "   4.75438291e-14  3.82811850e+00]\n",
-      " [-2.08796925e-03 -5.82740140e-16  1.96457721e-04  2.16859244e-15\n",
-      "   5.74957935e-02  3.34797329e-15  2.89606649e-02 -2.75391230e-14\n",
-      "   3.82811850e+00  9.58022230e-15]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "phimat = fluxonium.matrixelement_table('phi_operator', evals_count=10)\n",
     "print(phimat)"
@@ -9380,13005 +212,32 @@
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+   "execution_count": null,
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-   "source": [
-    "fluxonium.plot_matelem_vs_paramvals('n_operator', 'flux', flux_list, select_elems=4);"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "fluxonium.plot_matelem_vs_paramvals('n_operator', 'flux', flux_list, select_elems=5);"
+   ]
   }
  ],
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -22392,7 +251,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.15"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/examples/demo_gui.ipynb b/examples/demo_gui.ipynb
index f189b3a..3f44ee7 100644
--- a/examples/demo_gui.ipynb
+++ b/examples/demo_gui.ipynb
@@ -7,9 +7,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import scqubits as scq"
    ]
   },
@@ -17,33 +14,17 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "84376185",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57c401d9bdab40d48b3f1b599ed2950a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(HBox(children=(ToggleButtons(description='Qubits:', layout=Layout(width='800px'), options=('Tra…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6c1cf2046ab2415e90d5bdee40bbc432",
+       "model_id": "98e57c8424b8474a8039e6fdb0525397",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(layout=Layout(width='975px'))"
+       "Container(children=[Sheet(children=[Card(children=[Img(layout=None, src='data:image/png;base64,iVBORw0KGgoAAAA…"
       ]
      },
      "metadata": {},
@@ -78,1019 +59,1083 @@
    "outputs": [
     {
      "data": {
+      "application/pdf": 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       ],
       "text/plain": [
-       "<Figure size 900x600 with 1 Axes>"
+       "<Figure size 600x480 with 1 Axes>"
       ]
      },
      "execution_count": 3,
@@ -1115,7 +1160,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1129,7 +1174,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
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diff --git a/examples/demo_hilbertspace.ipynb b/examples/demo_hilbertspace.ipynb
index 2b0a687..9175efd 100644
--- a/examples/demo_hilbertspace.ipynb
+++ b/examples/demo_hilbertspace.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -29,16 +30,11 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
-    "import scqubits as scq\n",
-    "\n",
-    "import numpy as np\n",
-    "import qutip as qt\n"
+    "import scqubits as scq"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -48,6 +44,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -55,8 +52,11 @@
     "\n",
     "As an interesting example of a coupled quantum system consider two harmonic modes coupled by an rf SQUID. The effective Hamiltonian describing this composite system, after integrating out the SQUID degrees of freedom,  is given by [https://www.nature.com/articles/s41567-019-0703-5]\n",
     "\n",
-    "$\\displaystyle H/\\hbar = \\omega_a a^\\dagger a + \\omega_b b^\\dagger b + g(a^\\dagger b + a b^\\dagger) + g_2(a^\\dagger a^\\dagger b + a a b^\\dagger) + \\chi_{ab} a^\\dagger ab^\\dagger b + \\frac{\\chi_{aa}}{2} a^\\dagger a^\\dagger a a + \\frac{\\chi_{bb}}{2} b^\\dagger b^\\dagger b b$.\n",
+    "$\\displaystyle H/\\hbar = \\omega_a a^\\dagger a + \\omega_b b^\\dagger b + g(a^\\dagger b + a b^\\dagger) + g_2(a^\\dagger a^\\dagger b + a a b^\\dagger) + \\chi_{ab} a^\\dagger ab^\\dagger b + \\frac{\\chi_{aa}}{2} a^\\dagger a^\\dagger a a + \\frac{\\chi_{bb}}{2} b^\\dagger b^\\dagger b b$,\n",
+    "\n",
+    "which can be rewritten as\n",
     "\n",
+    "$\\displaystyle H = E_{\\text{osc}_a} a^\\dagger a + E_{\\text{osc}_b} b^\\dagger b + g(a^\\dagger b + a b^\\dagger) + g_2(a^\\dagger a^\\dagger b + a a b^\\dagger) + \\chi_{ab} a^\\dagger ab^\\dagger b - K_a a^\\dagger a^\\dagger a a - K_b b^\\dagger b^\\dagger b b$.\n",
     "\n",
     "### Define Hilbert space components, initialize `HilbertSpace` object\n",
     "\n",
@@ -78,18 +78,21 @@
    "outputs": [],
    "source": [
     "# Set up the components / subspaces of our Hilbert space\n",
+    "E_osc_a = 4.284\n",
+    "K_a = 0.003\n",
+    "\n",
+    "E_osc_b = 7.073\n",
+    "K_b = 0.015\n",
     "\n",
     "osc1 = scq.KerrOscillator(\n",
-    "    E_osc = 4.284,\n",
-    "    K = 0.003,\n",
-    "    l_osc = 1,\n",
+    "    E_osc = E_osc_a,\n",
+    "    K = K_a,\n",
     "    truncated_dim = 4,\n",
     ")\n",
     "\n",
     "osc2 = scq.KerrOscillator(\n",
-    "    E_osc = 7.073,\n",
-    "    K = 0.015,\n",
-    "    l_osc = 1,\n",
+    "    E_osc = E_osc_b,\n",
+    "    K = K_b,\n",
     "    truncated_dim = 4,\n",
     ")\n",
     "\n",
@@ -117,7 +120,7 @@
       "KerrOscillator------| [KerrOscillator_1]\n",
       "                    | E_osc: 4.284\n",
       "                    | K: 0.003\n",
-      "                    | l_osc: 1\n",
+      "                    | l_osc: None\n",
       "                    | truncated_dim: 4\n",
       "                    |\n",
       "                    | dim: 4\n",
@@ -126,7 +129,7 @@
       "KerrOscillator------| [KerrOscillator_2]\n",
       "                    | E_osc: 7.073\n",
       "                    | K: 0.015\n",
-      "                    | l_osc: 1\n",
+      "                    | l_osc: None\n",
       "                    | truncated_dim: 4\n",
       "                    |\n",
       "                    | dim: 4\n",
@@ -140,6 +143,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -166,7 +170,7 @@
     {
      "data": {
       "text/latex": [
-       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 7.073 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 14.116 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 21.129 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 4.284 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 29.691 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 12.834 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 19.907 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 26.950 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 33.963\\\\\\end{array}\\right)\\end{equation*}"
+       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True $ \\\\ \\left(\\begin{matrix}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 7.073 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 14.116 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 21.129 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 4.284 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 29.691 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 12.834 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 19.907 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 26.950 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 33.963\\\\\\end{matrix}\\right)$"
       ],
       "text/plain": [
        "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True\n",
@@ -216,6 +220,35 @@
    ]
   },
   {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we have not added any interactions yet, there is no difference between the methods `bare_hamiltonian()` and `hamiltonian()` at the moment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bare_hamiltonian == hilbertspace.hamiltonian()"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -228,6 +261,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -235,10 +269,13 @@
     }
    },
    "source": [
-    "### Set up interaction terms between individual subsystems"
+    "### Set up interaction terms between individual subsystems\n",
+    "\n",
+    "Interactions are added using the `add_interaction()` method. If the interaction is of the form $g \\cdot \\text{op1} \\cdot \\text{op2}$, it can be added by specifying the coefficient $g$ and the two operators $\\text{op1}$ and $\\text{op2}$. Otherwise, the interaction is given as a Python string that is later evaluated."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -249,7 +286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:00.099411Z",
@@ -261,9 +298,8 @@
    },
    "outputs": [],
    "source": [
-    "# Term 1\n",
     "# Option A: operator product specification via callables\n",
-    "\n",
+    "# Term 1\n",
     "g = 0.1\n",
     "\n",
     "hilbertspace.add_interaction(\n",
@@ -273,34 +309,30 @@
     "    add_hc = True\n",
     ")\n",
     "\n",
-    "\n",
-    "# Term 2\n",
     "# Option B: string-based specification of interaction term\n",
-    "\n",
+    "# Term 2\n",
     "g2 = 0.035 \n",
     "\n",
     "hilbertspace.add_interaction(\n",
-    "    expr = \"g2 * (adag * adag * b)\",\n",
-    "    op1 = (\"adag\", osc1.creation_operator),\n",
+    "    expr = \"g2 * a.dag() * a.dag() * b\",\n",
+    "    op1 = (\"a\", osc1.annihilation_operator),\n",
     "    op2 = (\"b\", osc2.annihilation_operator),\n",
     "    add_hc = True\n",
     ")\n",
     "\n",
-    "\n",
     "# Term 3\n",
-    "\n",
     "chi_ab = 0.01\n",
     "\n",
     "hilbertspace.add_interaction(\n",
-    "    expr = \"chi_ab * a.dag() * a.dag() * b.dag() * b\",\n",
+    "    expr = \"chi_ab * a.dag() * a * b.dag() * b\",\n",
     "    op1 = (\"a\", osc1.annihilation_operator),\n",
     "    op2 = (\"b\", osc2.annihilation_operator),\n",
-    "    add_hc = True\n",
-    ")\n",
-    "\n"
+    "    add_hc = False\n",
+    ")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -308,12 +340,12 @@
     }
    },
    "source": [
-    "Now that the interactions are specified, the full Hamiltonian of the coupled system can be obtained via:"
+    "Now that the interactions are specified, the full Hamiltonian of the coupled system can be obtained with `hamiltonian()`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:03.627440Z",
@@ -327,78 +359,62 @@
     {
      "data": {
       "text/latex": [
-       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True\\begin{equation*}\\left(\\begin{array}{*{11}c}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 7.073 & 0.0 & 0.0 & 0.100 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 14.116 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 21.129 & 0.0 & \\cdots & 0.042 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.100 & 0.0 & 0.0 & 4.284 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.042 & 0.0 & \\cdots & 29.691 & 0.0 & 0.0 & 0.300 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 12.834 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 19.907 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.300 & 0.0 & 0.0 & 26.950 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 33.963\\\\\\end{array}\\right)\\end{equation*}"
+       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True $ \\\\ \\left(\\begin{matrix}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 7.073 & 0.0 & 0.0 & 0.100 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 14.116 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 21.129 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.100 & 0.0 & 0.0 & 4.284 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 29.751 & 0.0 & 0.0 & 0.300 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 12.834 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 19.937 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.300 & 0.0 & 0.0 & 27.010 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 34.053\\\\\\end{matrix}\\right)$"
       ],
       "text/plain": [
        "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True\n",
        "Qobj data =\n",
-       "[[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 7.07300000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  1.00000000e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  4.94974747e-02 1.41421356e-02 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 1.41160000e+01 0.00000000e+00\n",
-       "  0.00000000e+00 1.41421356e-01 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 7.00000000e-02 2.82842712e-02 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 2.11290000e+01\n",
-       "  0.00000000e+00 0.00000000e+00 1.73205081e-01 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 8.57321410e-02 4.24264069e-02\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 1.00000000e-01 0.00000000e+00 0.00000000e+00\n",
-       "  4.28400000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 1.41421356e-01 0.00000000e+00\n",
-       "  0.00000000e+00 1.13570000e+01 0.00000000e+00 0.00000000e+00\n",
-       "  1.41421356e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  8.57321410e-02 2.44948974e-02 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 1.73205081e-01\n",
-       "  0.00000000e+00 0.00000000e+00 1.84000000e+01 0.00000000e+00\n",
-       "  0.00000000e+00 2.00000000e-01 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 1.21243557e-01 4.89897949e-02 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.54130000e+01\n",
-       "  0.00000000e+00 0.00000000e+00 2.44948974e-01 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 1.48492424e-01 7.34846923e-02]\n",
-       " [0.00000000e+00 4.94974747e-02 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 1.41421356e-01 0.00000000e+00 0.00000000e+00\n",
-       "  8.56200000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 1.41421356e-02 7.00000000e-02 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 2.00000000e-01 0.00000000e+00\n",
-       "  0.00000000e+00 1.56350000e+01 0.00000000e+00 0.00000000e+00\n",
-       "  1.73205081e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 2.82842712e-02 8.57321410e-02\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.44948974e-01\n",
-       "  0.00000000e+00 0.00000000e+00 2.26780000e+01 0.00000000e+00\n",
-       "  0.00000000e+00 2.44948974e-01 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 4.24264069e-02\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.96910000e+01\n",
-       "  0.00000000e+00 0.00000000e+00 3.00000000e-01 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 8.57321410e-02 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 1.73205081e-01 0.00000000e+00 0.00000000e+00\n",
-       "  1.28340000e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 2.44948974e-02 1.21243557e-01 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 2.44948974e-01 0.00000000e+00\n",
-       "  0.00000000e+00 1.99070000e+01 0.00000000e+00 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 4.89897949e-02 1.48492424e-01\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 3.00000000e-01\n",
-       "  0.00000000e+00 0.00000000e+00 2.69500000e+01 0.00000000e+00]\n",
-       " [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 7.34846923e-02\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
-       "  0.00000000e+00 0.00000000e+00 0.00000000e+00 3.39630000e+01]]"
+       "[[ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          7.073       0.          0.          0.1         0.\n",
+       "   0.          0.          0.04949747  0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.         14.116       0.          0.          0.14142136\n",
+       "   0.          0.          0.          0.07        0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.         21.129       0.          0.\n",
+       "   0.17320508  0.          0.          0.          0.08573214  0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.1         0.          0.          4.284       0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.14142136  0.          0.         11.367\n",
+       "   0.          0.          0.14142136  0.          0.          0.\n",
+       "   0.08573214  0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.17320508  0.          0.\n",
+       "  18.42        0.          0.          0.2         0.          0.\n",
+       "   0.          0.12124356  0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.         25.443       0.          0.          0.24494897  0.\n",
+       "   0.          0.          0.14849242  0.        ]\n",
+       " [ 0.          0.04949747  0.          0.          0.          0.14142136\n",
+       "   0.          0.          8.562       0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.07        0.          0.          0.\n",
+       "   0.2         0.          0.         15.655       0.          0.\n",
+       "   0.17320508  0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.08573214  0.          0.\n",
+       "   0.          0.24494897  0.          0.         22.718       0.\n",
+       "   0.          0.24494897  0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.         29.751\n",
+       "   0.          0.          0.3         0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.08573214\n",
+       "   0.          0.          0.          0.17320508  0.          0.\n",
+       "  12.834       0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.12124356  0.          0.          0.          0.24494897  0.\n",
+       "   0.         19.937       0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.14849242  0.          0.          0.          0.3\n",
+       "   0.          0.         27.01        0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.         34.053     ]]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,15 +425,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since the composite Hamiltonian is a `qutip.Qobj`, The eigenvalues and eigenvectors can be now be obtained via the usual QuTiP routine:"
+    "Since the composite Hamiltonian is a `qutip.Qobj`, the eigenvalues and eigenvectors can be now be obtained via the usual QuTiP routine:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:06.327709Z",
@@ -429,7 +446,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.         4.28041833 7.07490698 8.55649874]\n"
+      "[0.         4.28041835 7.07493032 8.55652435]\n"
      ]
     }
    ],
@@ -439,6 +456,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -449,13 +467,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d4c04124bfbf462faa2bbf19bebb0d32",
+       "model_id": "f3f947cb87bf4b198948a37ee4bee782",
        "version_major": 2,
        "version_minor": 0
       },
@@ -469,7 +487,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2a1675a4c99143b0bc229a546d5e86d5",
+       "model_id": "2ffb1a622bac411199662b5548b04746",
        "version_major": 2,
        "version_minor": 0
       },
@@ -482,21 +500,181 @@
     }
    ],
    "source": [
-    "hilbertspace_new = scq.HilbertSpace.create()"
+    "hilbertspace_gui = scq.HilbertSpace.create()"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HilbertSpace:  subsystems\n",
+      "-------------------------\n",
+      "\n",
+      "KerrOscillator------| [KerrOscillator_1]\n",
+      "                    | E_osc: 4.284\n",
+      "                    | K: 0.003\n",
+      "                    | l_osc: None\n",
+      "                    | truncated_dim: 4\n",
+      "                    |\n",
+      "                    | dim: 4\n",
+      "\n",
+      "\n",
+      "KerrOscillator------| [KerrOscillator_2]\n",
+      "                    | E_osc: 7.073\n",
+      "                    | K: 0.015\n",
+      "                    | l_osc: None\n",
+      "                    | truncated_dim: 4\n",
+      "                    |\n",
+      "                    | dim: 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "HilbertSpace:  interaction terms\n",
+      "--------------------------------\n",
+      "InteractionTerm----------| [InteractionTerm_0]\n",
+      "                         | expr: 'g * op1 * op2'\n",
+      "                         | operator_list: [(0, 'op1', array([[0.        , 0.        , 0.     ...\n",
+      "                         | const: {}\n",
+      "                         | add_hc: True\n",
+      "\n",
+      "InteractionTerm----------| [InteractionTerm_1]\n",
+      "                         | expr: 'g2 * op1.dag() * op1.dag() * op2'\n",
+      "                         | operator_list: [(0, 'op1', array([[0.        , 1.        , 0.     ...\n",
+      "                         | const: {}\n",
+      "                         | add_hc: True\n",
+      "\n",
+      "InteractionTerm----------| [InteractionTerm_2]\n",
+      "                         | expr: 'chi_ab * op1.dag() * op1 * op2.dag() * op2'\n",
+      "                         | operator_list: [(0, 'op1', array([[0.        , 1.        , 0.     ...\n",
+      "                         | const: {}\n",
+      "                         | add_hc: False\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(hilbertspace_gui)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True $ \\\\ \\left(\\begin{matrix}0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 7.073 & 0.0 & 0.0 & 0.100 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 14.116 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 21.129 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.100 & 0.0 & 0.0 & 4.284 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 0.0\\\\\\vdots & \\vdots & \\vdots & \\vdots & \\vdots & \\ddots & \\vdots & \\vdots & \\vdots & \\vdots & \\vdots\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 29.751 & 0.0 & 0.0 & 0.300 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 12.834 & 0.0 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 19.937 & 0.0 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.300 & 0.0 & 0.0 & 27.010 & 0.0\\\\0.0 & 0.0 & 0.0 & 0.0 & 0.0 & \\cdots & 0.0 & 0.0 & 0.0 & 0.0 & 34.053\\\\\\end{matrix}\\right)$"
+      ],
+      "text/plain": [
+       "Quantum object: dims = [[4, 4], [4, 4]], shape = (16, 16), type = oper, isherm = True\n",
+       "Qobj data =\n",
+       "[[ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          7.073       0.          0.          0.1         0.\n",
+       "   0.          0.          0.04949747  0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.         14.116       0.          0.          0.14142136\n",
+       "   0.          0.          0.          0.07        0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.         21.129       0.          0.\n",
+       "   0.17320508  0.          0.          0.          0.08573214  0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.1         0.          0.          4.284       0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.14142136  0.          0.         11.367\n",
+       "   0.          0.          0.14142136  0.          0.          0.\n",
+       "   0.08573214  0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.17320508  0.          0.\n",
+       "  18.42        0.          0.          0.2         0.          0.\n",
+       "   0.          0.12124356  0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.         25.443       0.          0.          0.24494897  0.\n",
+       "   0.          0.          0.14849242  0.        ]\n",
+       " [ 0.          0.04949747  0.          0.          0.          0.14142136\n",
+       "   0.          0.          8.562       0.          0.          0.\n",
+       "   0.          0.          0.          0.        ]\n",
+       " [ 0.          0.          0.07        0.          0.          0.\n",
+       "   0.2         0.          0.         15.655       0.          0.\n",
+       "   0.17320508  0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.08573214  0.          0.\n",
+       "   0.          0.24494897  0.          0.         22.718       0.\n",
+       "   0.          0.24494897  0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.         29.751\n",
+       "   0.          0.          0.3         0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.08573214\n",
+       "   0.          0.          0.          0.17320508  0.          0.\n",
+       "  12.834       0.          0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.12124356  0.          0.          0.          0.24494897  0.\n",
+       "   0.         19.937       0.          0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.14849242  0.          0.          0.          0.3\n",
+       "   0.          0.         27.01        0.        ]\n",
+       " [ 0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.         34.053     ]]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hilbertspace_gui.hamiltonian()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that it indeed corresponds to the Hamiltonian created programatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hilbertspace_gui.hamiltonian() == dressed_hamiltonian"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Spectrum lookup and converting between bare and dressed indices\n",
     "\n",
-    "To use lookup functions for state indices, energies and states, first generate the lookup table via:"
+    "In the dispersive regime where bare states don't hybridize too much, it may be useful to convert between dressed states and their closest product bare states as physical interpretation may be easier. To use lookup functions for state indices, energies and states, first generate the lookup table via `generate_lookup()`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:10.103117Z",
@@ -509,15 +687,16 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here are the bare energies of the first Kerr oscillator:"
+    "We can first print the bare energies of the two Kerr oscillators:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:13.722890Z",
@@ -526,30 +705,30 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([ 0.   ,  4.284,  8.562, 12.834])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.     4.284  8.562 12.834]\n",
+      "[ 0.     7.073 14.116 21.129]\n"
+     ]
     }
    ],
    "source": [
-    "hilbertspace.lookup.bare_eigenenergies(osc1)"
+    "print(hilbertspace.bare_eigenvals(osc1))\n",
+    "print(hilbertspace.bare_eigenvals(osc2))"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dressed state with index j=8 corresponds to following bare product state:"
+    "Next, we can lookup the closest bare product state to the dressed state with index `8` with `bare_index()`, which corresponds to 1 excitation in the first Kerr oscillator and 2 excitations in the second."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:16.331869Z",
@@ -563,25 +742,26 @@
        "(1, 2)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hilbertspace.lookup.bare_index(8)"
+    "hilbertspace.bare_index(8)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And the bare product state (2,1) most closely matches the following dressed state:"
+    "Conversely, we can verify that the bare product state (1,2) most closely matches the dressed state with index `8`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:19.844481Z",
@@ -592,28 +772,29 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "8"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hilbertspace.lookup.dressed_index((2,1))"
+    "hilbertspace.dressed_index((1,2))"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is the eigenenergy for dressed index j=10:"
+    "Finally, we can get the eigenenergy for dressed index `8` using `energy_by_dressed_index()`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-31T11:44:52.454767Z",
@@ -624,32 +805,33 @@
     {
      "data": {
       "text/plain": [
-       "21.135009003451163"
+       "18.413688985491895"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hilbertspace.lookup.energy_dressed_index(10)"
+    "hilbertspace.energy_by_dressed_index(8)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Sweeping over an external parameter\n",
     "\n",
-    "scqubits provides the class `ParameterSweep` to facilitate computation of spectra as function of an external parameter. See the example notebook for `ParameterSweep` to explore sweeps and visualizing sweep data."
+    "Equipped with a programmatic interface to explore interactions between subsystems, scqubits provides the class `ParameterSweep` to facilitate computation of spectra as function of an external parameter. See the [example notebook](./demo_parametersweep.ipynb) for `ParameterSweep` to explore sweeps and visualizing sweep data."
    ]
   }
  ],
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -663,7 +845,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.11.3"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/demo_noise.ipynb b/examples/demo_noise.ipynb
index 209d87b..fde51a1 100644
--- a/examples/demo_noise.ipynb
+++ b/examples/demo_noise.ipynb
@@ -24091,7 +24091,7 @@
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -24105,7 +24105,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/demo_parametersweep.ipynb b/examples/demo_parametersweep.ipynb
index f7a7bf0..4d09b59 100644
--- a/examples/demo_parametersweep.ipynb
+++ b/examples/demo_parametersweep.ipynb
@@ -29,9 +29,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import scqubits as scq\n",
     "from scqubits import HilbertSpace, ParameterSweep\n",
     "\n",
@@ -53,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2016-08-23T15:16:24.535943",
@@ -86,7 +83,7 @@
     "    ng=0.0,\n",
     "    ncut=30,\n",
     "    truncated_dim=3,\n",
-    "    id_str=\"tmon2\"\n",
+    "    # id_str=\"tmon2\"\n",
     ")\n",
     "\n",
     "resonator = scq.Oscillator(\n",
@@ -110,7 +107,7 @@
     "\n",
     "hilbertspace.add_interaction(\n",
     "    g_strength = g2,\n",
-    "    op1 = tmon2.n_operator,\n",
+    "    op1 = (tmon2.n_operator(), tmon2),\n",
     "    op2 = resonator.creation_operator,\n",
     "    add_hc = True,\n",
     "    id_str=\"tmon2-resonator\"  # optional keyword argument\n",
@@ -143,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -156,7 +153,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem tmon1 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -170,7 +167,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem TunableTransmon_12 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -184,7 +181,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Oscillator_6 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -193,7 +190,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "7981871f2000413694c0f59b5b81c9b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -208,7 +205,7 @@
    "source": [
     "# Set up parameter name and values\n",
     "pname1 = 'flux'  \n",
-    "flux_vals = np.linspace(0.0, 2.0, 171)\n",
+    "flux_vals = np.linspace(0.0, 2.0, 17)\n",
     "pname2 = 'ng'\n",
     "ng_vals = np.linspace(-0.5, 0.5, 49)\n",
     "\n",
@@ -235,26 +232,14 @@
     "    update_hilbertspace=update_hilbertspace,\n",
     "    evals_count=20,\n",
     "    subsys_update_info=subsys_update_info,\n",
-    "    num_cpus=4\n",
+    "    num_cpus=4,\n",
+    "    deepcopy=True\n",
     ")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `update_hilbertspace`: use of global variables vs. accessing objects via `ParameterSweep`  \n",
-    "\n",
-    "In the above code, `update_hilbertspace` directly manipulates the two transmon instances via global variables. It is generally considered better progamming style to avoid such use of global variables. \n",
-    "\n",
-    "To facilitate this, `update_hilbertspace` may be defined with an additional first argument that takes in the `ParameterSweep` object itself. This way, all `HilbertSpace` constituents can be accessed via \n",
-    "\n",
-    "```<ParameterSweep>.hilbertspace[<id_str>]```:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +250,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem tmon1 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -279,7 +264,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem TunableTransmon_12 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -293,7 +278,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Oscillator_6 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -302,7 +287,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
+       "model_id": "ac3df467c3494930b81d6bf5dd8799d1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -315,57 +300,108 @@
     }
    ],
    "source": [
-    "def update_hilbertspace(param_sweep, flux, ng):  # function that defines how Hilbert space components are updated\n",
-    "    param_sweep.hilbertspace[\"tmon1\"].flux = flux\n",
-    "    param_sweep.hilbertspace[\"tmon2\"].flux = area_ratio * flux\n",
-    "    param_sweep.hilbertspace[\"tmon2\"].ng = ng"
+    "sweep2 = ParameterSweep(\n",
+    "    hilbertspace=hilbertspace,\n",
+    "    paramvals_by_name=paramvals_by_name,\n",
+    "    update_hilbertspace=update_hilbertspace,\n",
+    "    evals_count=20,\n",
+    "    subsys_update_info=subsys_update_info,\n",
+    "    num_cpus=4\n",
+    ")"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HilbertSpace(**{'subsystem_list': [TunableTransmon(**{'EJmax': 40.0, 'EC': 0.2, 'd': 0.1, 'flux': 0.0, 'ng': 0.3, 'ncut': 40, 'truncated_dim': 3, 'id_str': 'tmon1'}), TunableTransmon(**{'EJmax': 15.0, 'EC': 0.15, 'd': 0.2, 'flux': 0.0, 'ng': -0.5, 'ncut': 30, 'truncated_dim': 3, 'id_str': 'TunableTransmon_12'}), Oscillator(**{'E_osc': 4.5, 'l_osc': None, 'truncated_dim': 4, 'id_str': 'Oscillator_6'})], 'interaction_list': [InteractionTerm(**{'g_strength': 0.1, 'operator_list': [(0, array([[-40,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0, -39,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0, -38, ...,   0,   0,   0],\n",
+       "       ...,\n",
+       "       [  0,   0,   0, ...,  38,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,  39,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,  40]])), (2, array([[0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 1.41421356, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 1.73205081, 0.        ]]))], 'add_hc': True}), InteractionTerm(**{'g_strength': 0.2, 'operator_list': [(1, array([[-30,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0, -29,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0, -28, ...,   0,   0,   0],\n",
+       "       ...,\n",
+       "       [  0,   0,   0, ...,  28,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,  29,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,  30]])), (2, array([[0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 1.41421356, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 1.73205081, 0.        ]]))], 'add_hc': True})]})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#### State after the sweep\n",
-    "\n",
-    "By default, the `HilbertSpace` object and its constituents will be left by `ParameterSweep` in a state reached with the last evaluation involved in the sweep. (When multiprocessing, this final state may not be easy to predict.)\n",
-    "\n",
-    "Alternatively, the `deepcopy` option can be used to\n",
-    "1. disconnect all global ``HilbertSpace` constituents from the sweep,and\n",
-    "2. restore the `HilbertSpace` object stored internally with the `ParameterSweep` to the parameters prior to the sweep.\n",
-    "\n",
-    "Without use of `deepcopy`, there is a good chance that, for example, the offset charge of `tmon2` is now different than prior to the sweep:"
+    "sweep2.hilbertspace"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-0.5"
+       "HilbertSpace(**{'subsystem_list': [TunableTransmon(**{'EJmax': 40.0, 'EC': 0.2, 'd': 0.1, 'flux': 0.23, 'ng': 0.3, 'ncut': 40, 'truncated_dim': 3, 'id_str': 'tmon1'}), TunableTransmon(**{'EJmax': 15.0, 'EC': 0.15, 'd': 0.2, 'flux': 0.0, 'ng': 0.0, 'ncut': 30, 'truncated_dim': 3, 'id_str': 'TunableTransmon_12'}), Oscillator(**{'E_osc': 4.5, 'l_osc': None, 'truncated_dim': 4, 'id_str': 'Oscillator_6'})], 'interaction_list': [InteractionTerm(**{'g_strength': 0.1, 'operator_list': [(0, array([[-40,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0, -39,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0, -38, ...,   0,   0,   0],\n",
+       "       ...,\n",
+       "       [  0,   0,   0, ...,  38,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,  39,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,  40]])), (2, array([[0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 1.41421356, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 1.73205081, 0.        ]]))], 'add_hc': True}), InteractionTerm(**{'g_strength': 0.2, 'operator_list': [(1, array([[-30,   0,   0, ...,   0,   0,   0],\n",
+       "       [  0, -29,   0, ...,   0,   0,   0],\n",
+       "       [  0,   0, -28, ...,   0,   0,   0],\n",
+       "       ...,\n",
+       "       [  0,   0,   0, ...,  28,   0,   0],\n",
+       "       [  0,   0,   0, ...,   0,  29,   0],\n",
+       "       [  0,   0,   0, ...,   0,   0,  30]])), (2, array([[0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 1.41421356, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 1.73205081, 0.        ]]))], 'add_hc': True})]})"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tmon2.ng"
+    "sweep.hilbertspace"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When using `deepcopy=True`, the original state is restored. Note that `update_hilbertspace` must be based on manipulating the objects interior to `ParameterSweep` with the `deepcopy` option. (Changing the global variables will have no effect in this case.)"
+    "#### `update_hilbertspace`: use of global variables vs. accessing objects via `ParameterSweep`  \n",
+    "\n",
+    "In the above code, `update_hilbertspace` directly manipulates the two transmon instances via global variables. It is generally considered better progamming style to avoid such use of global variables. \n",
+    "\n",
+    "To facilitate this, `update_hilbertspace` may be defined with an additional first argument that takes in the `ParameterSweep` object itself. This way, all `HilbertSpace` constituents can be accessed via \n",
+    "\n",
+    "```<ParameterSweep>.hilbertspace[<id_str>]```:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +412,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Bare spectra:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Parallel compute bare eigensys [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -390,7 +426,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Bare spectra:   0%|          | 0/68 [00:00<?, ?it/s]"
+       "Parallel compute bare eigensys [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -404,7 +440,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Bare spectra:   0%|          | 0/1 [00:00<?, ?it/s]"
+       "Parallel compute bare eigensys [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -418,13 +454,79 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Dressed spectrum:   0%|          | 0/68 [00:00<?, ?it/s]"
+       "Parallel compute dressed eigensys [num_cpus=4]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "def update_hilbertspace(param_sweep, flux, ng):  # function that defines how Hilbert space components are updated\n",
+    "    param_sweep.hilbertspace[\"tmon1\"].flux = flux\n",
+    "    param_sweep.hilbertspace[\"tmon2\"].flux = area_ratio * flux\n",
+    "    param_sweep.hilbertspace[\"tmon2\"].ng = ng"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### State after the sweep\n",
+    "\n",
+    "By default, the `HilbertSpace` object and its constituents will be left by `ParameterSweep` in a state reached with the last evaluation involved in the sweep. (When multiprocessing, this final state may not be easy to predict.)\n",
+    "\n",
+    "Alternatively, the `deepcopy` option can be used to\n",
+    "1. disconnect all global ``HilbertSpace` constituents from the sweep,and\n",
+    "2. restore the `HilbertSpace` object stored internally with the `ParameterSweep` to the parameters prior to the sweep.\n",
+    "\n",
+    "Without use of `deepcopy`, there is a good chance that, for example, the offset charge of `tmon2` is now different than prior to the sweep:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.5"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tmon2.ng"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using `deepcopy=True`, the original state is restored. Note that `update_hilbertspace` must be based on manipulating the objects interior to `ParameterSweep` with the `deepcopy` option. (Changing the global variables will have no effect in this case.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'paramvals_by_name' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[3], line 64\u001b[0m\n\u001b[0;32m     58\u001b[0m     param_sweep\u001b[38;5;241m.\u001b[39mhilbertspace[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtmon2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mflux \u001b[38;5;241m=\u001b[39m area_ratio \u001b[38;5;241m*\u001b[39m flux\n\u001b[0;32m     59\u001b[0m     param_sweep\u001b[38;5;241m.\u001b[39mhilbertspace[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtmon2\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mng \u001b[38;5;241m=\u001b[39m ng\n\u001b[0;32m     62\u001b[0m sweep \u001b[38;5;241m=\u001b[39m ParameterSweep(\n\u001b[0;32m     63\u001b[0m     hilbertspace\u001b[38;5;241m=\u001b[39mhilbertspace,\n\u001b[1;32m---> 64\u001b[0m     paramvals_by_name\u001b[38;5;241m=\u001b[39m\u001b[43mparamvals_by_name\u001b[49m,\n\u001b[0;32m     65\u001b[0m     update_hilbertspace\u001b[38;5;241m=\u001b[39mupdate_hilbertspace,\n\u001b[0;32m     66\u001b[0m     evals_count\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,\n\u001b[0;32m     67\u001b[0m     subsys_update_info\u001b[38;5;241m=\u001b[39msubsys_update_info,\n\u001b[0;32m     68\u001b[0m \u001b[38;5;66;03m# -------- This time: enable deepcopy ----------------\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     deepcopy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     70\u001b[0m \u001b[38;5;66;03m# ----------------------------------------------------\u001b[39;00m\n\u001b[0;32m     71\u001b[0m )\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'paramvals_by_name' is not defined"
+     ]
+    }
+   ],
    "source": [
     "tmon1 = scq.TunableTransmon(\n",
     "    EJmax=40.0,\n",
@@ -4126,183 +4228,10262 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/pdf": 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+       " <defs>\n",
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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep[\"ng\":0.0].plot_transitions();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+    "sweep.hilbertspace"
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+   "execution_count": 9,
+   "metadata": {},
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+    {
+     "data": {
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+       "       [  0,   0,   0, ...,   0,   0,  30]])), (2, array([[0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 1.41421356, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 1.73205081, 0.        ]]))], 'add_hc': True})]})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sweep2.hilbertspace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  The generated transition plot is based on default choices:\n",
+    "\n",
+    "* The origin of each transition is the system's ground state.\n",
+    "* Only single-photon transitions are included.\n",
+    "* Transitions are generally plotted in light grey. \n",
+    "* By default, transitions among states of individual subsystems are marked separately and accompanied by a legend. This is possible in regions where the dispersive approximation holds, i.e., hybridization between subsystems remains weak.\n",
+    "* Labels in the legend are excitation levels of individual systems: ((0,0,0), (1,0,0)) denotes a transition from the ground state to the state with subsystem 1 (here: `tmon1`) in the first excited state, and subsystems 2 and 3 in their respective ground states.\n",
+    "\n",
+    "One peculiarity is nearly unavoidable when coloring transitions according to the dispersive-limit state labeling: whenever states undergo avoided crossings and the dispersive limit breaks down, coloring must discontinuously switch from one branch to another. scqubits attempts to interrupt coloring in such regions. However, if the avoided crossing is so narrow that it occurs between two adjacent parameter values, then discontinuities from connecting separate branches will remain visible.\n",
+    "\n",
+    "### Transition plot options\n",
+    "Multiple aspects of transition energy plots can be changed. The following gives a quick overview; see the API documentatation for a comprehensive discussion of options.\n",
+    "\n",
+    "#### `coloring`\n",
+    "If ordinary coloring is preferred, this can be obtained by choosing `\"plain\"` coloring:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "FutureWarning: `subsys_list` is deprecated. Use `subsystem_list` instead.\n",
+      " c:\\users\\drjen\\onedrive\\coding\\pycharmprojects\\scqubits\\scqubits\\utils\\misc.py: 195"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
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+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep[\"ng\":0.0].plot_transitions(coloring=\"plain\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `subsystems`\n",
+    "When coloring transitions according to nature of the transition, all subsystems are included by default. If transitions for a single or smaller set of subsystem(s) should be highlighted, then these are specified in list form:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
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+    "sweep[\"ng\":0.0].plot_transitions(subsystems=[tmon1]);"
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+   "metadata": {},
+   "source": [
+    "#### `initial` and `final`\n",
+    "By default, the ground state is taken as origin for all transitions. In case of thermal excitations, other states can be of interest as initial states. Specification of an alternative initial state uses dispersive labeling of states:\n",
+    "\n",
+    "e.g., (1,0,0) uses the 1st excited `tmon1` state as the initial state:"
+   ]
+  },
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+       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
+       "       <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n",
+       "       <use x=\"139.013672\" xlink:href=\"#ArialMT-53\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "    </g>\r\n",
+       "    <g id=\"xtick_3\">\r\n",
+       "     <g id=\"line2d_3\">\r\n",
+       "      <g>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"130.953778\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
+       "      </g>\r\n",
+       "     </g>\r\n",
+       "     <g id=\"text_3\">\r\n",
+       "      <!-- 0.50 -->\r\n",
+       "      <g transform=\"translate(121.22331 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-48\"/>\r\n",
        "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
        "       <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n",
@@ -4313,12 +14494,12 @@
        "    <g id=\"xtick_4\">\r\n",
        "     <g id=\"line2d_4\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"177.338295\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"168.999233\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_4\">\r\n",
        "      <!-- 0.75 -->\r\n",
-       "      <g transform=\"translate(167.607827 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(159.268764 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <defs>\r\n",
        "        <path d=\"M 4.734375 62.203125 \r\n",
        "L 4.734375 70.65625 \r\n",
@@ -4344,12 +14525,12 @@
        "    <g id=\"xtick_5\">\r\n",
        "     <g id=\"line2d_5\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"215.38375\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"207.044688\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_5\">\r\n",
        "      <!-- 1.00 -->\r\n",
-       "      <g transform=\"translate(205.653281 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(197.314219 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <defs>\r\n",
        "        <path d=\"M 37.25 0 \r\n",
        "L 28.46875 0 \r\n",
@@ -4373,12 +14554,12 @@
        "    <g id=\"xtick_6\">\r\n",
        "     <g id=\"line2d_6\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"253.429205\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"245.090142\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_6\">\r\n",
        "      <!-- 1.25 -->\r\n",
-       "      <g transform=\"translate(243.698736 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(235.359673 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-49\"/>\r\n",
        "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
        "       <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n",
@@ -4389,12 +14570,12 @@
        "    <g id=\"xtick_7\">\r\n",
        "     <g id=\"line2d_7\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"291.474659\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"283.135597\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_7\">\r\n",
        "      <!-- 1.50 -->\r\n",
-       "      <g transform=\"translate(281.74419 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(273.405128 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-49\"/>\r\n",
        "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
        "       <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n",
@@ -4405,12 +14586,12 @@
        "    <g id=\"xtick_8\">\r\n",
        "     <g id=\"line2d_8\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"329.520114\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"321.181051\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_8\">\r\n",
        "      <!-- 1.75 -->\r\n",
-       "      <g transform=\"translate(319.789645 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(311.450582 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-49\"/>\r\n",
        "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
        "       <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n",
@@ -4421,12 +14602,12 @@
        "    <g id=\"xtick_9\">\r\n",
        "     <g id=\"line2d_9\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"367.565568\" xlink:href=\"#m5d810b8682\" y=\"224.64\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"359.226506\" xlink:href=\"#m00395e2262\" y=\"224.64\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_9\">\r\n",
        "      <!-- 2.00 -->\r\n",
-       "      <g transform=\"translate(357.835099 238.797812)scale(0.1 -0.1)\">\r\n",
+       "      <g transform=\"translate(349.496037 238.797812)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-50\"/>\r\n",
        "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
        "       <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n",
@@ -4436,7 +14617,7 @@
        "    </g>\r\n",
        "    <g id=\"text_10\">\r\n",
        "     <!-- $\\Phi_{ext}/\\Phi_0$ -->\r\n",
-       "     <g transform=\"translate(195.85875 252.658906)scale(0.11 -0.11)\">\r\n",
+       "     <g transform=\"translate(187.519688 252.658906)scale(0.11 -0.11)\">\r\n",
        "      <defs>\r\n",
        "       <path d=\"M 34.46875 56.9375 \r\n",
        "Q 27.15625 55.90625 22.359375 51.65625 \r\n",
@@ -4579,147 +14760,196 @@
        "      <defs>\r\n",
        "       <path d=\"M 0 0 \r\n",
        "L -3.5 0 \r\n",
-       "\" id=\"ma694a54a2b\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
+       "\" id=\"m82264c498f\" style=\"stroke:#000000;stroke-width:0.8;\"/>\r\n",
        "      </defs>\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"214.756364\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.644688\" xlink:href=\"#m82264c498f\" y=\"214.756364\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_11\">\r\n",
-       "      <!-- 0.0 -->\r\n",
+       "      <!-- 0 -->\r\n",
        "      <g transform=\"translate(27.08375 218.33527)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-48\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
-       "       <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "    </g>\r\n",
        "    <g id=\"ytick_2\">\r\n",
        "     <g id=\"line2d_11\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"190.004091\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.644688\" xlink:href=\"#m82264c498f\" y=\"182.257799\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_12\">\r\n",
-       "      <!-- 2.5 -->\r\n",
-       "      <g transform=\"translate(27.08375 193.582998)scale(0.1 -0.1)\">\r\n",
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+       "      <g transform=\"translate(27.08375 185.836705)scale(0.1 -0.1)\">\r\n",
        "       <use xlink:href=\"#ArialMT-50\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
-       "       <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "    </g>\r\n",
        "    <g id=\"ytick_3\">\r\n",
        "     <g id=\"line2d_12\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"165.251819\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.644688\" xlink:href=\"#m82264c498f\" y=\"149.759235\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_13\">\r\n",
-       "      <!-- 5.0 -->\r\n",
-       "      <g transform=\"translate(27.08375 168.830725)scale(0.1 -0.1)\">\r\n",
-       "       <use xlink:href=\"#ArialMT-53\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
-       "       <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n",
+       "      <!-- 4 -->\r\n",
+       "      <g transform=\"translate(27.08375 153.338141)scale(0.1 -0.1)\">\r\n",
+       "       <defs>\r\n",
+       "        <path d=\"M 32.328125 0 \r\n",
+       "L 32.328125 17.140625 \r\n",
+       "L 1.265625 17.140625 \r\n",
+       "L 1.265625 25.203125 \r\n",
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+       "L 41.109375 71.578125 \r\n",
+       "L 41.109375 25.203125 \r\n",
+       "L 50.78125 25.203125 \r\n",
+       "L 50.78125 17.140625 \r\n",
+       "L 41.109375 17.140625 \r\n",
+       "L 41.109375 0 \r\n",
+       "z\r\n",
+       "M 32.328125 25.203125 \r\n",
+       "L 32.328125 57.46875 \r\n",
+       "L 9.90625 25.203125 \r\n",
+       "z\r\n",
+       "\" id=\"ArialMT-52\"/>\r\n",
+       "       </defs>\r\n",
+       "       <use xlink:href=\"#ArialMT-52\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "    </g>\r\n",
        "    <g id=\"ytick_4\">\r\n",
        "     <g id=\"line2d_13\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"140.499547\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.644688\" xlink:href=\"#m82264c498f\" y=\"117.26067\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_14\">\r\n",
-       "      <!-- 7.5 -->\r\n",
-       "      <g transform=\"translate(27.08375 144.078453)scale(0.1 -0.1)\">\r\n",
-       "       <use xlink:href=\"#ArialMT-55\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n",
-       "       <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n",
+       "      <!-- 6 -->\r\n",
+       "      <g transform=\"translate(27.08375 120.839576)scale(0.1 -0.1)\">\r\n",
+       "       <defs>\r\n",
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+       "Q 39.84375 58.546875 37.703125 60.890625 \r\n",
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+       "Q 35.0625 -1.21875 28.609375 -1.21875 \r\n",
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+       "Q 18.109375 71.875 29.4375 71.875 \r\n",
+       "Q 37.890625 71.875 43.28125 67.140625 \r\n",
+       "Q 48.6875 62.40625 49.75 54.046875 \r\n",
+       "z\r\n",
+       "M 13.875 23.1875 \r\n",
+       "Q 13.875 18.65625 15.796875 14.5 \r\n",
+       "Q 17.71875 10.359375 21.1875 8.171875 \r\n",
+       "Q 24.65625 6 28.46875 6 \r\n",
+       "Q 34.03125 6 38.03125 10.484375 \r\n",
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+       "Q 42.046875 30.125 38.078125 34.390625 \r\n",
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+       "Q 22.171875 38.671875 18.015625 34.390625 \r\n",
+       "Q 13.875 30.125 13.875 23.1875 \r\n",
+       "z\r\n",
+       "\" id=\"ArialMT-54\"/>\r\n",
+       "       </defs>\r\n",
+       "       <use xlink:href=\"#ArialMT-54\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "    </g>\r\n",
        "    <g id=\"ytick_5\">\r\n",
        "     <g id=\"line2d_14\">\r\n",
        "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"115.747275\"/>\r\n",
+       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"39.644688\" xlink:href=\"#m82264c498f\" y=\"84.762106\"/>\r\n",
        "      </g>\r\n",
        "     </g>\r\n",
        "     <g id=\"text_15\">\r\n",
-       "      <!-- 10.0 -->\r\n",
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-       "       <use xlink:href=\"#ArialMT-49\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-48\"/>\r\n",
-       "       <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n",
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-       "      </g>\r\n",
-       "     </g>\r\n",
-       "    </g>\r\n",
-       "    <g id=\"ytick_6\">\r\n",
-       "     <g id=\"line2d_15\">\r\n",
-       "      <g>\r\n",
-       "       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"47.98375\" xlink:href=\"#ma694a54a2b\" y=\"90.995002\"/>\r\n",
-       "      </g>\r\n",
-       "     </g>\r\n",
-       "     <g id=\"text_16\">\r\n",
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-       "       <use xlink:href=\"#ArialMT-49\"/>\r\n",
-       "       <use x=\"55.615234\" xlink:href=\"#ArialMT-50\"/>\r\n",
-       "       <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n",
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-       "      </g>\r\n",
-       "     </g>\r\n",
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-       "    <g id=\"ytick_7\">\r\n",
-       "     <g id=\"line2d_16\">\r\n",
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-       "      </g>\r\n",
-       "     </g>\r\n",
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-    "  The generated transition plot is based on default choices:\n",
-    "\n",
-    "* The origin of each transition is the system's ground state.\n",
-    "* Only single-photon transitions are included.\n",
-    "* Transitions are generally plotted in light grey. \n",
-    "* By default, transitions among states of individual subsystems are marked separately and accompanied by a legend. This is possible in regions where the dispersive approximation holds, i.e., hybridization between subsystems remains weak.\n",
-    "* Labels in the legend are excitation levels of individual systems: ((0,0,0), (1,0,0)) denotes a transition from the ground state to the state with subsystem 1 (here: `tmon1`) in the first excited state, and subsystems 2 and 3 in their respective ground states.\n",
-    "\n",
-    "One peculiarity is nearly unavoidable when coloring transitions according to the dispersive-limit state labeling: whenever states undergo avoided crossings and the dispersive limit breaks down, coloring must discontinuously switch from one branch to another. scqubits attempts to interrupt coloring in such regions. However, if the avoided crossing is so narrow that it occurs between two adjacent parameter values, then discontinuities from connecting separate branches will remain visible.\n",
-    "\n",
-    "### Transition plot options\n",
-    "Multiple aspects of transition energy plots can be changed. The following gives a quick overview; see the API documentatation for a comprehensive discussion of options.\n",
-    "\n",
-    "#### `coloring`\n",
-    "If ordinary coloring is preferred, this can be obtained by choosing `\"plain\"` coloring:"
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-    "#### `subsystems`\n",
-    "When coloring transitions according to nature of the transition, all subsystems are included by default. If transitions for a single or smaller set of subsystem(s) should be highlighted, then these are specified in list form:"
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-    "#### `initial` and `final`\n",
-    "By default, the ground state is taken as origin for all transitions. In case of thermal excitations, other states can be of interest as initial states. Specification of an alternative initial state uses dispersive labeling of states:\n",
-    "\n",
-    "e.g., (1,0,0) uses the 1st excited `tmon1` state as the initial state:"
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-    "sweep[\"ng\":0.0].plot_transitions(initial=(1,0,0));"
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-   "metadata": {},
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-    "Similarly `final` option may be used if only the transitions to a specific state should be highlighted:"
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-  {
-   "cell_type": "code",
-   "execution_count": 22,
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-    "#### `make_positive`\n",
-    "When considering excited as in the previous example, some of the transition energies will naturally be negative. Experimentally, these transitions are driven at the absolute frequency. By default, absolute values of transition energies are displayed. This can be disabled by setting `make_positive=False`.\n",
-    "\n",
-    "\n",
-    "#### `sidebands`\n",
-    "By default, sideband transitions are not highlighted. If set to true, sideband transitions with multiple subsystems changing excitation levels are included as well:"
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+     "metadata": {
+      "needs_background": "light"
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+     "output_type": "display_data"
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+   ],
+   "source": [
+    "sweep[\"ng\":0.0].plot_transitions(initial=(1,0,0));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly `final` option may be used if only the transitions to a specific state should be highlighted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
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      "output_type": "display_data"
     }
    ],
+   "source": [
+    "sweep[\"ng\":0.0].plot_transitions(final=(0,1,0));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `make_positive`\n",
+    "When considering excited as in the previous example, some of the transition energies will naturally be negative. Experimentally, these transitions are driven at the absolute frequency. By default, absolute values of transition energies are displayed. This can be disabled by setting `make_positive=False`.\n",
+    "\n",
+    "\n",
+    "#### `sidebands`\n",
+    "By default, sideband transitions are not highlighted. If set to true, sideband transitions with multiple subsystems changing excitation levels are included as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "     </g>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pd017b31bdb\">\n",
+       "   <rect x=\"49.792344\" y=\"7.2\" width=\"357.12\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sweep[\"ng\":0.0].plot_transitions(subsystems=[tmon1, resonator], sidebands=True);"
    ]
@@ -29710,7 +30190,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.16"
   },
   "toc": {
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@@ -29756,5 +30236,5 @@
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  "nbformat": 4,
- "nbformat_minor": 2
-}
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+ "nbformat_minor": 4
+}
diff --git a/examples/demo_qutip_fluxoniumcz.ipynb b/examples/demo_qutip_fluxoniumcz.ipynb
new file mode 100644
index 0000000..978ece5
--- /dev/null
+++ b/examples/demo_qutip_fluxoniumcz.ipynb
@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fluxonium CZ gate tutorial\n",
+    "\n",
+    "B. McDonough\n",
+    "\n",
+    "This notebook provides a tutorial on using scQubits to simulate the CZ-gate on a coupled pair of fluxonium qubits described by Nesterov et al. in [Microwave-Activated Controlled-Z Gate for Fixed-Frequency Fluxonium Qubits.](https://arxiv.org/abs/1802.03095)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Capacitive coupling of two fluxonium qubits $A$ and $B$ results in the addition of the term $g \\hat n_A \\hat n_B$ ($\\hat n$ is the charge operator) to the Hamiltonian. This coupling shifts the energy levels so that the dressed (coupled) $\\omega_{01 \\to 02}$ transition frequency is detuned from the dressed (coupled) $\\omega_{11 \\to 12}$ transition frequency. Without the capacitive coupling between two qubits, the bare (uncoupled) $\\omega_{01 \\to 02}$ transition frequency is the same as the bare (uncoupled) $\\omega_{11 \\to 12}$ transition frequency. This allows the $|11\\rangle \\to |12\\rangle$ transition to be Rabi driven without causing leakage from other states in the computational space. Following Nesterov et al., we consider a case of selective drive on only qubit $B$, which is expressed as the term $H_d = f(t)\\cos(\\omega_d t)\\hat n_B$ added to the Hamiltonian.\n",
+    "\n",
+    "Driving the $|11\\rangle\\to|12\\rangle$ transition for a full period of Rabi oscillation causes the $|11\\rangle$ state to acquire a phase of $e^{i\\pi}=-1$, without affecting the other states in the computational subspace. This results in the application of $CZ = \\operatorname{diag}(1,1,1,-1)$ up to single-qubit $Z$ gates.\n",
+    "\n",
+    "The Hamiltonian of the two coupled fluxonia and the drive is\n",
+    "$$\n",
+    "H = 4E_{CA}\\hat n_A - E_{JA}\\cos(\\hat \\phi_A-\\pi) + \\frac{1}{2} E_{LA}\\hat \\phi_A^2 + g\\hat n_A\\hat n_B + 4E_{CB}\\hat n_B - E_{JB}\\cos(\\hat \\phi_B-\\pi) + \\frac{1}{2}E_{LB}\\hat \\phi_B^2 + f(t)\\cos(w_dt)\\hat n_B\n",
+    "$$\n",
+    "\n",
+    "where all of the notations follow closely the cited paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scqubits as scq\n",
+    "import qutip as qt\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from qutip.qip.operations import rz, cz_gate\n",
+    "import cmath\n",
+    "\n",
+    "# experimental values borrowed from\n",
+    "# [https://arxiv.org/abs/1802.03095]\n",
+    "# define fluxonium A\n",
+    "qbta = scq.Fluxonium(\n",
+    "    EC=1.5,\n",
+    "    EJ=5.5,\n",
+    "    EL=1.0,\n",
+    "    flux=0.5,  # flux frustration point\n",
+    "    cutoff=110,\n",
+    "    truncated_dim=10,\n",
+    ")\n",
+    "\n",
+    "# define fluxonium B\n",
+    "qbtb = scq.Fluxonium(\n",
+    "    EC=1.2,\n",
+    "    EJ=5.7,\n",
+    "    EL=1.0,\n",
+    "    flux=0.5,\n",
+    "    cutoff=110,\n",
+    "    truncated_dim=10,\n",
+    ")\n",
+    "\n",
+    "# define the common Hilbert space\n",
+    "hilbertspace = scq.HilbertSpace([qbta, qbtb])\n",
+    "\n",
+    "# add interaction between two qubits\n",
+    "hilbertspace.add_interaction(\n",
+    "    g_strength=0.15,\n",
+    "    op1=qbta.n_operator,\n",
+    "    op2=qbtb.n_operator,\n",
+    ")\n",
+    "\n",
+    "# generate spectrum lookup table\n",
+    "hilbertspace.generate_lookup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drive simulation functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the transition frequency between two states specified by dressed indices\n",
+    "def transition_frequency(s0: int, s1: int) -> float:\n",
+    "    return (\n",
+    "        (\n",
+    "            hilbertspace.energy_by_dressed_index(s1)\n",
+    "            - hilbertspace.energy_by_dressed_index(s0)\n",
+    "        )\n",
+    "        * 2\n",
+    "        * np.pi\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Truncation\n",
+    "The efficiency of the simulation is greatly improved by truncating the operators.  The truncation happens in two steps:\n",
+    "1) The first truncation is controlled by setting the `truncated_dim` parameter in the subsystem initialization, which determines the dimension used in diagonalizing the Hamiltonian. Setting these too low can result in an unphysical simulation. Even if energy levels above the truncation are not explicitly involved, they can still drastically affect the matrix elements of the operators. \n",
+    "\n",
+    "2) Once the matrix elements and eigenvalues have been obtained, the operators can be truncated further. This truncation can greatly improve the runtime of the simulation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The matrix representations can be truncated further for the simulation\n",
+    "total_truncation = 20\n",
+    "\n",
+    "# truncate operators to desired dimension\n",
+    "def truncate(operator: qt.Qobj, dimension: int) -> qt.Qobj:\n",
+    "    return qt.Qobj(operator[:dimension, :dimension])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## States and operators "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the representation of the n_b operator in the dressed eigenbasis of the composite system\n",
+    "n_b = hilbertspace.op_in_dressed_eigenbasis(op=qbtb.n_operator)\n",
+    "# truncate the operator after expressing in the dressed basis to speed up the simulation\n",
+    "n_b = truncate(n_b, total_truncation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert the product states to the closes eigenstates of the dressed system\n",
+    "product_states = [(0, 0), (1, 0), (0,1), (1, 1), (2, 1)]\n",
+    "idxs = [hilbertspace.dressed_index((s1, s2)) for (s1, s2) in product_states]\n",
+    "states = [qt.basis(total_truncation, idx) for idx in idxs]\n",
+    "\n",
+    "# The computational subspace is spanned by the first 4 states\n",
+    "computational_subspace = states[:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Units\n",
+    "scQubits natively reports energy in GHz, units of frequency. The frequency $f$ and the radial frequency $\\omega$ are related via\n",
+    "$$\n",
+    "\\hbar \\omega = h f\n",
+    "$$\n",
+    "The constants $h$ and $\\hbar$ are related by $h = 2\\pi \\hbar$, so the angular frequency in units of radians per nanosecond is given by $\\omega = 2\\pi f$. For this reason, the energies obtained from diagonalization and the matrix elements need to be multiplied by $2\\pi$ for simulating in QuTiP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get dressed state 11 to 12 transition frequency\n",
+    "omega_1112 = transition_frequency(idxs[3], idxs[4])\n",
+    "\n",
+    "# Gaussian pulse parameters optimized by hand\n",
+    "A = 0.022 # GHz\n",
+    "tg = 100 # ns\n",
+    "\n",
+    "#Gaussian pulse envelope\n",
+    "def drive_coeff(t: float, args: dict) -> float:\n",
+    "    return A * np.exp(-8 * t * (t - tg) / tg**2) * np.cos(omega_1112 * t)\n",
+    "\n",
+    "# Hamiltonian in dressed eigenbasis\n",
+    "(evals,) = hilbertspace[\"evals\"]\n",
+    "# The factor of 2pi converts the energy to GHz so that the time is in units of ns\n",
+    "diag_dressed_hamiltonian = (\n",
+    "        2 * np.pi * qt.Qobj(np.diag(evals),\n",
+    "        dims=[hilbertspace.subsystem_dims] * 2)\n",
+    ")\n",
+    "diag_dressed_hamiltonian_trunc = truncate(diag_dressed_hamiltonian, total_truncation)\n",
+    "\n",
+    "# time-dependent drive Hamiltonian\n",
+    "H_qbt_drive = [\n",
+    "    diag_dressed_hamiltonian_trunc,\n",
+    "    [2 * np.pi * n_b, drive_coeff],  # driving through the resonator\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drive Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Text(0.5, 0, 't (ns)')"
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# array of time list\n",
+    "tlist = np.linspace(0, 120, 200)  # total time\n",
+    "\n",
+    "# This simulation is just for viewing the affect of the pulse\n",
+    "result = qt.sesolve(\n",
+    "    H_qbt_drive,\n",
+    "    qt.basis(20, hilbertspace.dressed_index(product_states[3])),\n",
+    "    tlist,\n",
+    "    e_ops=[state * state.dag() for state in states]\n",
+    ")\n",
+    "\n",
+    "for idx, res in zip(idxs, result.expect):\n",
+    "    plt.plot(tlist, res, label=r\"$|%u\\rangle$\" % (idx))\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.ylabel(\"population\")\n",
+    "plt.xlabel(\"t (ns)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Propagator calculation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the propagator at the final time step\n",
+    "prop = qt.propagator(H_qbt_drive, tlist)[-1]  \n",
+    "\n",
+    "# truncate the propagator to the computational subspace\n",
+    "Uc = qt.Qobj(\n",
+    "    [\n",
+    "        [prop.matrix_element(s1, s2) for s1 in computational_subspace]\n",
+    "        for s2 in computational_subspace\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Factor global phase so that upper-left corner of matrix is real\n",
+    "def remove_global_phase(op):\n",
+    "    return op * np.exp(-1j * cmath.phase(op[0, 0]))\n",
+    "\n",
+    "# The process for obtaining the Z rotations is taken from page 3 of Nesterov et al., at the\n",
+    "# bottom of the paragraph beginning, \"To model gate operation...\"\n",
+    "def dphi(state):\n",
+    "    return -np.angle(prop.matrix_element(state, state)) + np.angle(\n",
+    "        prop.matrix_element(states[0], states[0])\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False\nQobj data =\n[[ 9.99997558e-01+0.00000000e+00j  7.35241090e-08-9.49725529e-08j\n   3.99463511e-05+2.18320693e-05j -8.53948262e-07+2.82839250e-08j]\n [-7.01944586e-08-9.48664962e-08j  9.95520147e-01+0.00000000e+00j\n   2.17470258e-06-6.46731035e-06j -7.57443647e-05+2.00623781e-05j]\n [-3.99721921e-05+2.17851550e-05j -9.74663830e-07-3.01235561e-06j\n   9.99620205e-01+0.00000000e+00j -3.47478033e-06-6.59973136e-07j]\n [ 2.42061470e-07+1.48908800e-07j -7.02332015e-05-3.47883383e-05j\n  -3.53709345e-06-4.98170249e-08j -9.77936404e-01-2.00098574e-01j]]",
+      "text/latex": "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = False $ \\\\ \\left(\\begin{matrix}1.000 & (7.352\\times10^{-08}-9.497\\times10^{-08}j) & (3.995\\times10^{-05}+2.183\\times10^{-05}j) & (-8.539\\times10^{-07}+2.828\\times10^{-08}j)\\\\(-7.019\\times10^{-08}-9.487\\times10^{-08}j) & 0.996 & (2.175\\times10^{-06}-6.467\\times10^{-06}j) & (-7.574\\times10^{-05}+2.006\\times10^{-05}j)\\\\(-3.997\\times10^{-05}+2.179\\times10^{-05}j) & (-9.747\\times10^{-07}-3.012\\times10^{-06}j) & 1.000 & (-3.475\\times10^{-06}-6.600\\times10^{-07}j)\\\\(2.421\\times10^{-07}+1.489\\times10^{-07}j) & (-7.023\\times10^{-05}-3.479\\times10^{-05}j) & (-3.537\\times10^{-06}-4.982\\times10^{-08}j) & (-0.978-0.200j)\\\\\\end{matrix}\\right)$"
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# product of single-qubit Z-gates\n",
+    "Uz = remove_global_phase(qt.tensor(rz(dphi(states[2])), rz(dphi(states[1]))))\n",
+    "Uc_reshaped = qt.Qobj(Uc.data, dims=[[2, 2], [2, 2]])\n",
+    "Ucprime = remove_global_phase(Uz * Uc_reshaped)\n",
+    "Ucprime  # result should be close to diag(1,1,1,-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "0.9906027020232415"
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fidelity measure given on page 3 of Nesterov et al.\n",
+    "((Ucprime.dag() * Ucprime).tr() + np.abs((Ucprime.dag() * cz_gate()).tr()) ** 2) / 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f7e1e6ad09d196a93f40716548eb447094128ac28363ee78c20c7751b30b0cbc"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/demo_qutip_fluxoniumreset.ipynb b/examples/demo_qutip_fluxoniumreset.ipynb
new file mode 100644
index 0000000..139c4b2
--- /dev/null
+++ b/examples/demo_qutip_fluxoniumreset.ipynb
@@ -0,0 +1,6946 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Driven composite systems and interface with QuTiP in scQubits\n",
+    "\n",
+    "Systems of interest in quantum computing generally involve mutual coupling between multiple quantum systems.\n",
+    "By driving these individual subsystems, we can perform single- or multi-qubit gates.\n",
+    "\n",
+    "In the context of superconducting circuits, a common method of driving a circuit is to capacitively couple a voltage bias to one or multiple nodes of the circuit. \n",
+    "By a proper choice of drive frequency, amplitude and envelope, logical operations can be achieved.\n",
+    "\n",
+    "The popular Python package QuTiP can be used to simulate the time dynamics of quantum systems. \n",
+    "For the simulation of superconducting circuits, scQubits provides a transparent interface to QuTiP through the `HilbertSpace` class.\n",
+    "\n",
+    "These notebooks showcase how scQubits can be easily used to simulate dynamics of driven superconducting circuits through examples from the literature. The notebook\n",
+    "`FluxoniumRestTutorial.ipynb` provides a tutorial for using scQubits to simulate the heavy-fluxonium initialization procedure described in [Universal fast flux control of a coherent, low-frequency qubit](https://journals.aps.org/prx/pdf/10.1103/PhysRevX.11.011010) by Zhang et al. The notebook `FluxoniumCZTutorial.ipynb` contains a tutorial for using scQubits to simulate the procedure for effecting a CZ-gate on a coupled pair of fluxonium qubits described by Nesterov et al. in [Microwave-Activated Controlled-Z Gate for Fixed-Frequency Fluxonium Qubits.](https://arxiv.org/abs/1802.03095)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Heavy-fluxonium initialization tutorial\n",
+    "\n",
+    "B. McDonough\n",
+    "\n",
+    "This notebook provides a tutorial for using scQubits to simulate the heavy-fluxonium initialization procedure described in [Universal fast flux control of a coherent, low-frequency qubit](https://journals.aps.org/prx/pdf/10.1103/PhysRevX.11.011010) by Zhang et al."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a typical heavy-fluxonium qubit, the frequency of the transition between the first two energy levels is small in comparison to the temperature of the bath. Thus the qubit relaxes to a mixed state, in contrast to higher-frequency transmon qubits which typically relax to the ground state. The coupling to a low-Q resonator provides the non-unitary process needed to remove entropy from this state.\n",
+    "\n",
+    "To summarize this procedure, the qubit begins in a nearly completely mixed state between the first two energy levels, $\\rho \\approx \\frac{1}{2}(|g\\rangle \\langle g| +|e\\rangle \\langle e|)\\otimes |0\\rangle \\langle 0 |$. The first four levels of the fluxonium are labeled with $g,e,f,h$ respectively, and the resonator Fock states are labeled with numbers. The transitions $|g0\\rangle$ to $|h0\\rangle$ and $|h0\\rangle$ to $|e1\\rangle$ are driven simulataneously for a period of 15 $\\mu$ s. Throughout this process, photon loss in the resonator causes the population of $|e1\\rangle$ to transition to $|e0\\rangle$. The system eventually reaches a steady state, preparing $|e0\\rangle$ with high fidelity.\n",
+    "\n",
+    "The Hamiltonian of the coupled fluxonium-resonator system with a drive at frequency $\\omega_d$ capacitively coupled to the resonator is\n",
+    "$$\n",
+    "H = 4E_C\\hat n - E_J \\cos(\\hat \\phi-\\pi) + \\frac{1}{2}E_L \\hat \\phi^2 + g \\hat n (a+a^\\dagger) + E_{osc}a^\\dagger a + f(t)\\cos(\\omega_d t)(a+a^\\dagger)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import qutip as qt\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import scqubits as scq\n",
+    "\n",
+    "# experimental values borrowed from\n",
+    "# https://journals.aps.org/prx/pdf/10.1103/PhysRevX.11.011010\n",
+    "qbt = scq.Fluxonium(\n",
+    "    EJ=3.395,\n",
+    "    EC=0.479,\n",
+    "    EL=0.132,\n",
+    "    flux=0.5,  # flux frustration point\n",
+    "    cutoff=110,\n",
+    "    truncated_dim=10,\n",
+    "    id_str=\"qubit\"\n",
+    ")\n",
+    "\n",
+    "osc = scq.Oscillator(E_osc=5.7, truncated_dim=8, id_str=\"resonator\")\n",
+    "\n",
+    "hilbertspace = scq.HilbertSpace([qbt, osc])\n",
+    "\n",
+    "hilbertspace.add_interaction(\n",
+    "    g_strength=.4, op1=qbt.n_operator, op2=osc.creation_operator, add_hc=True\n",
+    ")\n",
+    "\n",
+    "# Precompute eigensystem to save later computation\n",
+    "hilbertspace.generate_lookup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drive simulation methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the transition frequency between two states specified by dressed indices\n",
+    "def transition_frequency(s0: int, s1: int) -> float:\n",
+    "    return (\n",
+    "        (\n",
+    "            hilbertspace.energy_by_dressed_index(s1)\n",
+    "            - hilbertspace.energy_by_dressed_index(s0)\n",
+    "        )\n",
+    "        * 2\n",
+    "        * np.pi\n",
+    "    )\n",
+    "\n",
+    "# Get the period of one Rabi cycle as a function of operator matrix element and drive strength\n",
+    "def drive_strength(s0: int, s1: int, period: float, drive_operator: qt.Qobj):\n",
+    "    # divide by 2pi for drive strength in units of GHz\n",
+    "    return abs(1 / (period * drive_operator.data.toarray()[s0][s1]))\n",
+    "\n",
+    "def rabi_period(s0: int, s1: int, Omega: float, drive_operator: qt.Qobj):\n",
+    "    # divide by 2pi for period in units of ns\n",
+    "    return abs(1 / (Omega * drive_operator.data.toarray()[s0][s1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Truncating operators\n",
+    "The efficiency of the simulation is greatly improved by truncating the operators.  The truncation happens in two steps:\n",
+    "1) The first truncation is controlled by setting the `truncated_dim` parameter in the subsystem initialization, which determines the dimension used in diagonalizing the Hamiltonian. Setting these too low can result in an unphysical simulation. Even if energy levels above the truncation are not explicitly involved, they can still drastically affect the matrix elements of the operators. \n",
+    "\n",
+    "2) Once the matrix elements and eigenvalues have been obtained, the operators can be truncated further. This truncation can greatly improve the runtime of the simulation. \n",
+    "\n",
+    "Here, a coupled system with dimensions $10$ by $8$ is simulated. These truncation levels are reasonable to obtain the operators for each individual system. However, this results in a full Hamiltonian represented by an $80 \\times 80$ matrix. After diagonalization, the Hamiltonian and subsystem operators can be safely truncated to a lower dimension, specified here by the variable `total_truncation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The matrix representations can be truncated further for the simulation\n",
+    "total_truncation = 20\n",
+    "\n",
+    "# truncate operators to desired dimension\n",
+    "def truncate(operator: qt.Qobj, dimension: int) -> qt.Qobj:\n",
+    "    return qt.Qobj(operator[:dimension, :dimension])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Drive operators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# express the resonator drive operator in the dressed eigenbasis of the system\n",
+    "phi_r = hilbertspace.op_in_dressed_eigenbasis(op=osc.annihilation_operator) \\\n",
+    "    + hilbertspace.op_in_dressed_eigenbasis(op=osc.creation_operator)\n",
+    "\n",
+    "#truncate operator for more efficient simulation\n",
+    "phi_r_trunc = truncate(phi_r, total_truncation)\n",
+    "\n",
+    "# do the same for the qubit charge operator\n",
+    "n_qbt = hilbertspace.op_in_dressed_eigenbasis(op=qbt.n_operator)\n",
+    "\n",
+    "#truncate operator\n",
+    "n_qbt_trunc = truncate(n_qbt, total_truncation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dressed eigenstates correspond closely to the bare product states when the coupling is weak. The `dressed_index` function matches a product state to the corresponding dressed eigenstate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Get energy indices most closely matching product state indices\n",
+    "g0 = hilbertspace.dressed_index((0,0))\n",
+    "e0 = hilbertspace.dressed_index((1,0))\n",
+    "h0 = hilbertspace.dressed_index((3,0))\n",
+    "e1 = hilbertspace.dressed_index((1,1))\n",
+    "\n",
+    "# Get eigenstates matching product state dressed indices\n",
+    "states = [qt.basis(total_truncation, i) for i in [g0, e0, h0, e1]]\n",
+    "period = 500\n",
+    "Omega1 = drive_strength(g0, h0, period, phi_r)\n",
+    "Omega2 = drive_strength(h0, e1, period, phi_r)\n",
+    "\n",
+    "# g0 -> h0 transition\n",
+    "omega_1 = transition_frequency(h0, g0)\n",
+    "# h0 -> e1 transition\n",
+    "omega_2 = transition_frequency(e1, h0)\n",
+    "\n",
+    "# drive amplitude as a function of time\n",
+    "def drive_coeff(t : float, args : dict) -> float:\n",
+    "    return Omega1 * np.cos(omega_1 * t) + Omega2 * np.cos(omega_2 * t)\n",
+    "\n",
+    "# get the diagonal Hamiltonian in the dressed basis\n",
+    "(evals,) = hilbertspace[\"evals\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Units\n",
+    "scQubits natively reports energy in GHz, units of frequency. The frequency $f$ and the radial frequency $\\omega$ are related via\n",
+    "$$\n",
+    "\\hbar \\omega = h f\n",
+    "$$\n",
+    "The constants $h$ and $\\hbar$ are related by $h = 2\\pi \\hbar$, so the angular frequency in units of radians per nanosecond is given by $\\omega = 2\\pi f$. For this reason, the energies obtained from diagonalization and the matrix elements need to be multiplied by $2\\pi$ for simulating in QuTiP."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The factor of 2pi converts the energy to GHz so that the time is in units of ns\n",
+    "diag_dressed_hamiltonian = qt.Qobj(\n",
+    "    2 * np.pi * np.diag(evals), dims=[hilbertspace.subsystem_dims] * 2\n",
+    ")\n",
+    "diag_dressed_hamiltonian_trunc = truncate(diag_dressed_hamiltonian, total_truncation)\n",
+    "\n",
+    "# The time-dependent driven Hamiltonian\n",
+    "H_qbt_drive = [\n",
+    "    diag_dressed_hamiltonian_trunc,\n",
+    "    #Another factor of 2pi is needed to convert energy to units where time is in ns\n",
+    "    [2 * np.pi * phi_r_trunc, drive_coeff],  # driving through the resonator\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resonator photon loss\n",
+    "The resonator photon loss is modeled through the quantum channel $\\mathcal{E}(\\rho) = \\kappa a\\rho a^\\dagger$, defined by the collapse operator $\\sqrt{\\kappa } a$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# resonator decay constant\n",
+    "kappa = 0.003\n",
+    "# annihilation operator\n",
+    "a_osc = truncate(\n",
+    "    #basis_change(osc.annihilation_operator(), osc, hilbertspace), total_truncation\n",
+    "    hilbertspace.op_in_dressed_eigenbasis(op=osc.annihilation_operator),\n",
+    "    total_truncation\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "\n",
+    "Note:  evaluating the following cell will likely require several minutes of computation time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initial mixed state\n",
+    "thermal_state = 0.5 * (states[0] * states[0].dag() + states[1] * states[1].dag())\n",
+    "\n",
+    "dt = 2 \n",
+    "total_time = 6000\n",
+    "tlist = np.linspace(0, 6000, int(total_time/dt))  # total time\n",
+    "\n",
+    "\n",
+    "result = qt.mesolve(\n",
+    "    H_qbt_drive,  # hamiltonian\n",
+    "    thermal_state,  # initial density matrix\n",
+    "    tlist,\n",
+    "    e_ops=[state * state.dag() for state in states],  # expectation values\n",
+    "    c_ops=[np.sqrt(kappa) * a_osc],  # photon loss\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'population')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/pdf": 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+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(4):\n",
+    "    plt.plot(tlist, result.expect[i], label=r\"$|%u\\rangle$\" % (i))\n",
+    "plt.legend()\n",
+    "plt.xlabel(\"t (ns)\")\n",
+    "plt.ylabel(\"population\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fidelity: 0.9773075921183019\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Fidelity:\", result.expect[1][-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9164de953f07298d5425f06b7f44c8ff0307d8bc321681348712c726632dc63f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/demo_zeropi.ipynb b/examples/demo_zeropi.ipynb
index 1c93b69..6d90425 100644
--- a/examples/demo_zeropi.ipynb
+++ b/examples/demo_zeropi.ipynb
@@ -12,9 +12,16 @@
     "---"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up Matplotlib for plotting into notebook, and import scqubits and numpy:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-27T13:04:04.721293Z",
@@ -28,30 +35,38 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import numpy as np\n",
     "import scqubits as scq"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# zero-pi qubit (decoupled from zeta-mode)"
+    "# $0$-$\\pi$ qubit (decoupled from $\\zeta$-mode)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$H=-2E_\\text{CJ}\\partial_\\phi^2+2E_{\\text{C}\\Sigma}(i\\partial_\\theta-n_g)^2-2E_\\text{J}\\cos\\theta\\cos(\\phi-\\varphi_\\text{ext}/2)+E_L\\phi^2+2E_\\text{J} +2E_{C\\Sigma}dC_J\\,\\partial_\\phi\\partial_\\theta + E_J dE_J \\sin\\theta\\sin(\\phi-\\phi_\\text{ext}/2)$"
+    "The ideal $0$-$\\pi$ circuit has an identical pair of capacitors, an identical pair of linear inductors and an identical pair of Josephson junctions (including paracitic capacitances). The circuit has three modes: a transmon-like $\\theta$-mode, a fluxonium-like $\\phi$-mode and a spurious harmonic oscillator $\\zeta$-mode. When is no disorder (asymmetry) in the capacitor pairs or the inductor pairs, the $\\zeta$-mode is decoupled from the rest two. When the small disorder in the junction capacitances and junction energies are taken into account in first order, the Hamiltonian for the $0$-$\\pi$ circuit decoupled from the $\\zeta$-mode is\n",
+    "\\begin{align*}\n",
+    "H_{\\phi,\\theta}&=-2E_\\text{CJ}\\partial_\\phi^2+2E_{\\text{C}\\Sigma}(i\\partial_\\theta-n_g)^2-2E_\\text{J}\\cos\\theta\\cos(\\phi-\\varphi_\\text{ext}/2)+E_L\\phi^2+2E_\\text{J} \\\\\n",
+    "&+2E_{C\\Sigma}dC_J\\,\\partial_\\phi\\partial_\\theta + E_J dE_J \\sin\\theta\\sin(\\phi-\\phi_\\text{ext}/2),\n",
+    "\\end{align*}\n",
+    "where $\\varphi_{\\text{ext}} = 2\\pi\\Phi_{\\text{ext}}/\\Phi_0$ is the reduced external magnetic flux, the diorder of parasitic capacitances and junction energies are defined as $dC_J = 2(C_{J1} - C_{J2})/(C_{J1}+C_{J2})$ and $dE_J = 2(E_{J1} - E_{J2})/(E_{J1}+E_{J2})$.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "**Creation via GUI** (ipywidgets needs to be installed for this to work.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-27T13:04:05.513875Z",
@@ -62,7 +77,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52b838faf6db4771a6e9f94f695622d1",
+       "model_id": "272ebf4df0a5423f8dc35aaafc756be9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -76,7 +91,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc7e8efa167241e192f053a2a75faee8",
+       "model_id": "c01e02e4e1234a458314feff3780aaae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -93,15 +108,18 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Diagonlization of zero-pi proceeds in a hybrid basis, using charge basis for $\\theta$ and discretization over a final interval for $\\phi$. (The latter explains the need for providing grid parameters.)"
+    "**Programmatic creation**\n",
+    "\n",
+    "The diagonlization of $0$-$\\pi$ proceeds in a hybrid basis, using the charge basis for $\\theta$ and discretization over an interval for $\\phi$. (The latter explains the need for providing grid parameters.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-03-27T12:55:37.604003Z",
@@ -111,7 +129,8 @@
    "outputs": [],
    "source": [
     "zero_pi = scq.ZeroPi(\n",
-    "    grid = scq.Grid1d(-6*np.pi, 6*np.pi, 200),\n",
+    "    # reminder: the parameters for generating a 1D grid are (min_value, max_value, num_grid_points)\n",
+    "    grid = scq.Grid1d(-6*np.pi, 6*np.pi, 200), \n",
     "    EJ   = 10.0,\n",
     "    EL   = 0.04,\n",
     "    ECJ  = 20.0,\n",
@@ -124,19 +143,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-03-27T13:04:09.028466Z",
-     "start_time": "2020-03-27T13:04:09.021450Z"
-    }
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ZeroPi--------------|\n",
+      "ZeroPi--------------| [ZeroPi_4]\n",
       "                    | EJ: 10.0\n",
       "                    | EL: 0.04\n",
       "                    | ECJ: 20.0\n",
@@ -157,19437 +171,70 @@
     "print(zero_pi)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like other qubits implemented in scqubits, parameters of $0$-$\\pi$ can be modified by either assigning values to the corresponding attributes, or through modifying values in the above GUI."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-07T03:22:25.523435Z",
-     "start_time": "2020-02-07T03:22:25.274049Z"
-    }
-   },
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zero_pi.flux = 0.31"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An interesting alternative way to specify capacitive energies is to provide $E_{CJ}$ and $E_{C\\Sigma}$ (the latter corresponds to the optional argument `ECS`) during the initialization. The $E_C$ value can also be changed by providing an $E_{C\\Sigma}$ value in the following way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
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-       "L 175.883504 70.146946 \r\n",
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-       "L 144.690142 83.181226 \r\n",
-       "L 144.069591 83.258906 \r\n",
-       "z\r\n",
-       "M 299.475565 81.07358 \r\n",
-       "L 297.394842 80.833646 \r\n",
-       "L 296.676478 80.709359 \r\n",
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-       "z\r\n",
-       "M 224.61728 80.709359 \r\n",
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-       "z\r\n",
-       "M 262.757342 80.709359 \r\n",
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-       "z\r\n",
-       "M 335.614585 79.616695 \r\n",
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-       "z\r\n",
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-       "L 357.34348 30.082625 \r\n",
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-       "L 357.33784 30.811067 \r\n",
-       "L 357.333977 31.175288 \r\n",
-       "L 357.329392 31.539509 \r\n",
-       "L 357.324059 31.90373 \r\n",
-       "L 357.31795 32.267951 \r\n",
-       "L 357.311028 32.632173 \r\n",
-       "L 357.303253 32.996394 \r\n",
-       "L 357.294579 33.360615 \r\n",
-       "L 357.284951 33.724836 \r\n",
-       "L 357.274309 34.089057 \r\n",
-       "L 357.26258 34.453278 \r\n",
-       "L 357.249686 34.817499 \r\n",
-       "L 357.235534 35.18172 \r\n",
-       "L 357.220018 35.545941 \r\n",
-       "L 357.203016 35.910163 \r\n",
-       "L 357.18439 36.274384 \r\n",
-       "L 357.163979 36.638605 \r\n",
-       "L 357.141595 37.002826 \r\n",
-       "L 357.117022 37.367047 \r\n",
-       "L 357.090007 37.731268 \r\n",
-       "L 357.060251 38.095489 \r\n",
-       "L 357.027403 38.45971 \r\n",
-       "L 356.991043 38.823931 \r\n",
-       "L 356.95067 39.188152 \r\n",
-       "L 356.905677 39.552374 \r\n",
-       "L 356.855326 39.916595 \r\n",
-       "L 356.798706 40.280816 \r\n",
-       "L 356.734687 40.645037 \r\n",
-       "L 356.66184 41.009258 \r\n",
-       "L 356.578342 41.373479 \r\n",
-       "L 356.481824 41.7377 \r\n",
-       "L 356.369153 42.101921 \r\n",
-       "L 356.295227 42.311393 \r\n",
-       "L 356.234386 42.466142 \r\n",
-       "L 356.070465 42.830364 \r\n",
-       "L 355.87093 43.194585 \r\n",
-       "L 355.62309 43.558806 \r\n",
-       "L 355.307405 43.923027 \r\n",
-       "L 354.892141 44.287248 \r\n",
-       "L 354.322101 44.651469 \r\n",
-       "L 354.113731 44.757423 \r\n",
-       "L 353.441992 45.01569 \r\n",
-       "L 352.010238 45.379911 \r\n",
-       "L 351.932235 45.394978 \r\n",
-       "L 349.750739 45.671289 \r\n",
-       "L 348.698016 45.744132 \r\n",
-       "L 347.569244 45.809039 \r\n",
-       "L 345.387748 45.873955 \r\n",
-       "L 343.206252 45.890837 \r\n",
-       "L 341.024757 45.868576 \r\n",
-       "L 338.843261 45.807566 \r\n",
-       "z\r\n",
-       "M 190.167744 45.379911 \r\n",
-       "L 188.320057 45.280852 \r\n",
-       "L 186.138561 45.060148 \r\n",
-       "L 185.849722 45.01569 \r\n",
-       "L 183.97745 44.651469 \r\n",
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-       "L 182.042598 43.923027 \r\n",
-       "L 181.775569 43.777929 \r\n",
-       "L 181.44839 43.558806 \r\n",
-       "L 180.99739 43.194585 \r\n",
-       "L 180.632845 42.830364 \r\n",
-       "L 180.332397 42.466142 \r\n",
-       "L 180.080792 42.101921 \r\n",
-       "L 179.867269 41.7377 \r\n",
-       "L 179.684017 41.373479 \r\n",
-       "L 179.594074 41.172621 \r\n",
-       "L 179.52811 41.009258 \r\n",
-       "L 179.395179 40.645037 \r\n",
-       "L 179.278203 40.280816 \r\n",
-       "L 179.174629 39.916595 \r\n",
-       "L 179.082427 39.552374 \r\n",
-       "L 178.999962 39.188152 \r\n",
-       "L 178.925904 38.823931 \r\n",
-       "L 178.859158 38.45971 \r\n",
-       "L 178.798819 38.095489 \r\n",
-       "L 178.744127 37.731268 \r\n",
-       "L 178.694446 37.367047 \r\n",
-       "L 178.649234 37.002826 \r\n",
-       "L 178.608031 36.638605 \r\n",
-       "L 178.570443 36.274384 \r\n",
-       "L 178.53613 35.910163 \r\n",
-       "L 178.5048 35.545941 \r\n",
-       "L 178.476197 35.18172 \r\n",
-       "L 178.450101 34.817499 \r\n",
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-       "L 178.404682 34.089057 \r\n",
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-       "L 178.351262 32.996394 \r\n",
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-       "L 178.318303 25.711972 \r\n",
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-       "L 178.520109 22.069761 \r\n",
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-       "L 178.671311 20.612876 \r\n",
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-       "L 179.969815 15.878002 \r\n",
-       "L 180.201245 15.513781 \r\n",
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-       "L 190.501552 45.395963 \r\n",
-       "z\r\n",
-       "M 265.373747 44.651469 \r\n",
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-       "L 283.290037 25.34775 \r\n",
-       "L 283.312593 25.711972 \r\n",
-       "L 283.332409 26.076193 \r\n",
-       "L 283.349592 26.440414 \r\n",
-       "L 283.364235 26.804635 \r\n",
-       "L 283.376415 27.168856 \r\n",
-       "L 283.386196 27.533077 \r\n",
-       "L 283.393629 27.897298 \r\n",
-       "L 283.39875 28.261519 \r\n",
-       "L 283.401588 28.62574 \r\n",
-       "L 283.402154 28.989962 \r\n",
-       "L 283.400453 29.354183 \r\n",
-       "L 283.396476 29.718404 \r\n",
-       "L 283.390203 30.082625 \r\n",
-       "L 283.381602 30.446846 \r\n",
-       "L 283.370628 30.811067 \r\n",
-       "L 283.357225 31.175288 \r\n",
-       "L 283.341323 31.539509 \r\n",
-       "L 283.322836 31.90373 \r\n",
-       "L 283.301665 32.267951 \r\n",
-       "L 283.277692 32.632173 \r\n",
-       "L 283.250782 32.996394 \r\n",
-       "L 283.220777 33.360615 \r\n",
-       "L 283.1875 33.724836 \r\n",
-       "L 283.150745 34.089057 \r\n",
-       "L 283.110278 34.453278 \r\n",
-       "L 283.065832 34.817499 \r\n",
-       "L 283.017103 35.18172 \r\n",
-       "L 282.963743 35.545941 \r\n",
-       "L 282.905353 35.910163 \r\n",
-       "L 282.841476 36.274384 \r\n",
-       "L 282.771586 36.638605 \r\n",
-       "L 282.695077 37.002826 \r\n",
-       "L 282.611246 37.367047 \r\n",
-       "L 282.519274 37.731268 \r\n",
-       "L 282.418206 38.095489 \r\n",
-       "L 282.306915 38.45971 \r\n",
-       "L 282.184071 38.823931 \r\n",
-       "L 282.124372 38.987285 \r\n",
-       "L 282.043438 39.188152 \r\n",
-       "L 281.883213 39.552374 \r\n",
-       "L 281.7046 39.916595 \r\n",
-       "L 281.504621 40.280816 \r\n",
-       "L 281.279606 40.645037 \r\n",
-       "L 281.024976 41.009258 \r\n",
-       "L 280.734948 41.373479 \r\n",
-       "L 280.402107 41.7377 \r\n",
-       "L 280.016799 42.101921 \r\n",
-       "L 279.942877 42.165469 \r\n",
-       "L 279.525647 42.466142 \r\n",
-       "L 278.934976 42.830364 \r\n",
-       "L 278.22597 43.194585 \r\n",
-       "L 277.761381 43.399006 \r\n",
-       "L 277.292528 43.558806 \r\n",
-       "L 276.030624 43.923027 \r\n",
-       "L 275.579885 44.034184 \r\n",
-       "L 274.112112 44.287248 \r\n",
-       "L 273.398389 44.394357 \r\n",
-       "L 271.216894 44.5974 \r\n",
-       "L 270.013338 44.651469 \r\n",
-       "L 269.035398 44.69326 \r\n",
-       "L 266.853902 44.70219 \r\n",
-       "z\r\n",
-       "M 226.83602 44.287248 \r\n",
-       "L 225.405484 44.104283 \r\n",
-       "L 224.47845 43.923027 \r\n",
-       "L 223.223988 43.640251 \r\n",
-       "L 222.960035 43.558806 \r\n",
-       "L 221.916333 43.194585 \r\n",
-       "L 221.049095 42.830364 \r\n",
-       "L 221.042492 42.827312 \r\n",
-       "L 220.41767 42.466142 \r\n",
-       "L 219.879779 42.101921 \r\n",
-       "L 219.416053 41.7377 \r\n",
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-       "L 218.860997 41.222216 \r\n",
-       "L 218.677977 41.009258 \r\n",
-       "L 218.395203 40.645037 \r\n",
-       "L 218.144164 40.280816 \r\n",
-       "L 217.920143 39.916595 \r\n",
-       "L 217.719325 39.552374 \r\n",
-       "L 217.538591 39.188152 \r\n",
-       "L 217.375369 38.823931 \r\n",
-       "L 217.227519 38.45971 \r\n",
-       "L 217.093246 38.095489 \r\n",
-       "L 216.971036 37.731268 \r\n",
-       "L 216.859602 37.367047 \r\n",
-       "L 216.757843 37.002826 \r\n",
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-       "L 216.665642 36.638605 \r\n",
-       "L 216.585311 36.274384 \r\n",
-       "L 216.511789 35.910163 \r\n",
-       "L 216.444497 35.545941 \r\n",
-       "L 216.382929 35.18172 \r\n",
-       "L 216.326645 34.817499 \r\n",
-       "L 216.275258 34.453278 \r\n",
-       "L 216.228429 34.089057 \r\n",
-       "L 216.185862 33.724836 \r\n",
-       "L 216.147294 33.360615 \r\n",
-       "L 216.112496 32.996394 \r\n",
-       "L 216.081268 32.632173 \r\n",
-       "L 216.053434 32.267951 \r\n",
-       "L 216.028841 31.90373 \r\n",
-       "L 216.007358 31.539509 \r\n",
-       "L 215.988871 31.175288 \r\n",
-       "L 215.973286 30.811067 \r\n",
-       "L 215.960521 30.446846 \r\n",
-       "L 215.950514 30.082625 \r\n",
-       "L 215.943215 29.718404 \r\n",
-       "L 215.938587 29.354183 \r\n",
-       "L 215.936608 28.989962 \r\n",
-       "L 215.937267 28.62574 \r\n",
-       "L 215.940569 28.261519 \r\n",
-       "L 215.946529 27.897298 \r\n",
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-       "L 215.980721 26.804635 \r\n",
-       "L 215.997747 26.440414 \r\n",
-       "L 216.017719 26.076193 \r\n",
-       "L 216.040741 25.711972 \r\n",
-       "L 216.066937 25.34775 \r\n",
-       "L 216.096448 24.983529 \r\n",
-       "L 216.129437 24.619308 \r\n",
-       "L 216.166093 24.255087 \r\n",
-       "L 216.20663 23.890866 \r\n",
-       "L 216.251293 23.526645 \r\n",
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-       "L 216.354152 22.798203 \r\n",
-       "L 216.413026 22.433982 \r\n",
-       "L 216.477397 22.069761 \r\n",
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-       "L 216.624586 21.341318 \r\n",
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-       "L 217.030718 19.884434 \r\n",
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-       "L 217.454934 18.791771 \r\n",
-       "L 217.626619 18.427549 \r\n",
-       "L 217.817043 18.063328 \r\n",
-       "L 218.029033 17.699107 \r\n",
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-       "L 218.833054 16.606444 \r\n",
-       "L 218.860997 16.575071 \r\n",
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-       "L 219.639601 15.878002 \r\n",
-       "L 220.138429 15.513781 \r\n",
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-       "L 221.042492 14.971028 \r\n",
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-       "L 238.494458 13.736467 \r\n",
-       "L 239.917196 14.056896 \r\n",
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-       "L 243.711514 15.878002 \r\n",
-       "L 244.100526 16.242223 \r\n",
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-       "L 244.733934 16.970665 \r\n",
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-       "L 245.211375 17.699107 \r\n",
-       "L 245.401729 18.063328 \r\n",
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-       "L 245.863866 19.155992 \r\n",
-       "L 245.989076 19.520213 \r\n",
-       "L 246.102697 19.884434 \r\n",
-       "L 246.206027 20.248655 \r\n",
-       "L 246.300167 20.612876 \r\n",
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-       "L 246.464505 21.341318 \r\n",
-       "L 246.536206 21.705539 \r\n",
-       "L 246.60176 22.069761 \r\n",
-       "L 246.66169 22.433982 \r\n",
-       "L 246.716451 22.798203 \r\n",
-       "L 246.76644 23.162424 \r\n",
-       "L 246.812006 23.526645 \r\n",
-       "L 246.853451 23.890866 \r\n",
-       "L 246.891043 24.255087 \r\n",
-       "L 246.925017 24.619308 \r\n",
-       "L 246.955575 24.983529 \r\n",
-       "L 246.982899 25.34775 \r\n",
-       "L 247.007143 25.711972 \r\n",
-       "L 247.028442 26.076193 \r\n",
-       "L 247.046913 26.440414 \r\n",
-       "L 247.062655 26.804635 \r\n",
-       "L 247.075749 27.168856 \r\n",
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-       "L 247.094256 27.897298 \r\n",
-       "L 247.099763 28.261519 \r\n",
-       "L 247.102813 28.62574 \r\n",
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-       "L 247.101594 29.354183 \r\n",
-       "L 247.097318 29.718404 \r\n",
-       "L 247.090574 30.082625 \r\n",
-       "L 247.081326 30.446846 \r\n",
-       "L 247.069528 30.811067 \r\n",
-       "L 247.05512 31.175288 \r\n",
-       "L 247.038025 31.539509 \r\n",
-       "L 247.018153 31.90373 \r\n",
-       "L 246.995397 32.267951 \r\n",
-       "L 246.969631 32.632173 \r\n",
-       "L 246.940711 32.996394 \r\n",
-       "L 246.908469 33.360615 \r\n",
-       "L 246.872713 33.724836 \r\n",
-       "L 246.833226 34.089057 \r\n",
-       "L 246.789756 34.453278 \r\n",
-       "L 246.742019 34.817499 \r\n",
-       "L 246.68969 35.18172 \r\n",
-       "L 246.632398 35.545941 \r\n",
-       "L 246.569716 35.910163 \r\n",
-       "L 246.501159 36.274384 \r\n",
-       "L 246.426166 36.638605 \r\n",
-       "L 246.34409 37.002826 \r\n",
-       "L 246.254183 37.367047 \r\n",
-       "L 246.155576 37.731268 \r\n",
-       "L 246.047249 38.095489 \r\n",
-       "L 245.92801 38.45971 \r\n",
-       "L 245.796444 38.823931 \r\n",
-       "L 245.650871 39.188152 \r\n",
-       "L 245.489276 39.552374 \r\n",
-       "L 245.309225 39.916595 \r\n",
-       "L 245.107748 40.280816 \r\n",
-       "L 245.038945 40.395304 \r\n",
-       "L 244.868219 40.645037 \r\n",
-       "L 244.590901 41.009258 \r\n",
-       "L 244.275192 41.373479 \r\n",
-       "L 243.913094 41.7377 \r\n",
-       "L 243.4942 42.101921 \r\n",
-       "L 243.004718 42.466142 \r\n",
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-       "L 242.367225 42.830364 \r\n",
-       "L 241.578214 43.194585 \r\n",
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-       "L 240.602263 43.558806 \r\n",
-       "L 239.147084 43.923027 \r\n",
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-       "L 231.949971 44.56045 \r\n",
-       "L 229.768475 44.517294 \r\n",
-       "L 227.586979 44.374755 \r\n",
-       "z\r\n",
-       "M 300.795157 44.287248 \r\n",
-       "L 299.576338 44.224073 \r\n",
-       "L 297.394842 43.982111 \r\n",
-       "L 297.061183 43.923027 \r\n",
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-       "L 289.492227 38.823931 \r\n",
-       "L 289.348725 38.45971 \r\n",
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-       "L 288.806312 36.638605 \r\n",
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-       "L 288.651244 35.910163 \r\n",
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-       "L 288.473776 34.817499 \r\n",
-       "L 288.424632 34.453278 \r\n",
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-       "L 288.339253 33.724836 \r\n",
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-       "L 288.269294 32.996394 \r\n",
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-       "L 288.189643 31.90373 \r\n",
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-       "L 318.808046 27.168856 \r\n",
-       "L 318.821017 27.533077 \r\n",
-       "L 318.830876 27.897298 \r\n",
-       "L 318.837671 28.261519 \r\n",
-       "L 318.841435 28.62574 \r\n",
-       "L 318.842187 28.989962 \r\n",
-       "L 318.83993 29.354183 \r\n",
-       "L 318.834654 29.718404 \r\n",
-       "L 318.826332 30.082625 \r\n",
-       "L 318.814924 30.446846 \r\n",
-       "L 318.800374 30.811067 \r\n",
-       "L 318.782608 31.175288 \r\n",
-       "L 318.761537 31.539509 \r\n",
-       "L 318.737053 31.90373 \r\n",
-       "L 318.709027 32.267951 \r\n",
-       "L 318.677312 32.632173 \r\n",
-       "L 318.641734 32.996394 \r\n",
-       "L 318.602095 33.360615 \r\n",
-       "L 318.558169 33.724836 \r\n",
-       "L 318.509696 34.089057 \r\n",
-       "L 318.456381 34.453278 \r\n",
-       "L 318.39789 34.817499 \r\n",
-       "L 318.333839 35.18172 \r\n",
-       "L 318.263794 35.545941 \r\n",
-       "L 318.187258 35.910163 \r\n",
-       "L 318.103662 36.274384 \r\n",
-       "L 318.012357 36.638605 \r\n",
-       "L 317.912594 37.002826 \r\n",
-       "L 317.80351 37.367047 \r\n",
-       "L 317.684105 37.731268 \r\n",
-       "L 317.553217 38.095489 \r\n",
-       "L 317.409486 38.45971 \r\n",
-       "L 317.251312 38.823931 \r\n",
-       "L 317.076805 39.188152 \r\n",
-       "L 317.028304 39.28234 \r\n",
-       "L 316.871343 39.552374 \r\n",
-       "L 316.638648 39.916595 \r\n",
-       "L 316.379331 40.280816 \r\n",
-       "L 316.08907 40.645037 \r\n",
-       "L 315.762532 41.009258 \r\n",
-       "L 315.393066 41.373479 \r\n",
-       "L 314.972267 41.7377 \r\n",
-       "L 314.846808 41.836831 \r\n",
-       "L 314.441214 42.101921 \r\n",
-       "L 313.807224 42.466142 \r\n",
-       "L 313.066302 42.830364 \r\n",
-       "L 312.665312 43.004421 \r\n",
-       "L 312.095144 43.194585 \r\n",
-       "L 310.83626 43.558806 \r\n",
-       "L 310.483817 43.649091 \r\n",
-       "L 308.953567 43.923027 \r\n",
-       "L 308.302321 44.027375 \r\n",
-       "L 306.120825 44.242421 \r\n",
-       "L 305.125673 44.287248 \r\n",
-       "L 303.939329 44.339139 \r\n",
-       "L 301.757834 44.334281 \r\n",
-       "z\r\n",
-       "M 158.383024 42.830364 \r\n",
-       "L 157.779117 42.804712 \r\n",
-       "L 155.597621 42.541453 \r\n",
-       "L 155.232012 42.466142 \r\n",
-       "L 153.606983 42.101921 \r\n",
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-       "L 152.507602 41.7377 \r\n",
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-       "L 150.267599 40.645037 \r\n",
-       "L 149.742176 40.280816 \r\n",
-       "L 149.270488 39.916595 \r\n",
-       "L 149.053134 39.73397 \r\n",
-       "L 148.874555 39.552374 \r\n",
-       "L 148.544306 39.188152 \r\n",
-       "L 148.244983 38.823931 \r\n",
-       "L 147.972966 38.45971 \r\n",
-       "L 147.725198 38.095489 \r\n",
-       "L 147.49908 37.731268 \r\n",
-       "L 147.292394 37.367047 \r\n",
-       "L 147.103231 37.002826 \r\n",
-       "L 146.929947 36.638605 \r\n",
-       "L 146.871638 36.506829 \r\n",
-       "L 146.780983 36.274384 \r\n",
-       "L 146.649679 35.910163 \r\n",
-       "L 146.529334 35.545941 \r\n",
-       "L 146.419088 35.18172 \r\n",
-       "L 146.318185 34.817499 \r\n",
-       "L 146.225964 34.453278 \r\n",
-       "L 146.141842 34.089057 \r\n",
-       "L 146.065307 33.724836 \r\n",
-       "L 145.995907 33.360615 \r\n",
-       "L 145.933247 32.996394 \r\n",
-       "L 145.876977 32.632173 \r\n",
-       "L 145.826793 32.267951 \r\n",
-       "L 145.782431 31.90373 \r\n",
-       "L 145.74366 31.539509 \r\n",
-       "L 145.710283 31.175288 \r\n",
-       "L 145.682135 30.811067 \r\n",
-       "L 145.659076 30.446846 \r\n",
-       "L 145.640994 30.082625 \r\n",
-       "L 145.627802 29.718404 \r\n",
-       "L 145.619437 29.354183 \r\n",
-       "L 145.615859 28.989962 \r\n",
-       "L 145.617051 28.62574 \r\n",
-       "L 145.623019 28.261519 \r\n",
-       "L 145.633791 27.897298 \r\n",
-       "L 145.649419 27.533077 \r\n",
-       "L 145.669977 27.168856 \r\n",
-       "L 145.695565 26.804635 \r\n",
-       "L 145.726308 26.440414 \r\n",
-       "L 145.762359 26.076193 \r\n",
-       "L 145.803899 25.711972 \r\n",
-       "L 145.851142 25.34775 \r\n",
-       "L 145.904333 24.983529 \r\n",
-       "L 145.963757 24.619308 \r\n",
-       "L 146.029741 24.255087 \r\n",
-       "L 146.102656 23.890866 \r\n",
-       "L 146.182924 23.526645 \r\n",
-       "L 146.271027 23.162424 \r\n",
-       "L 146.367512 22.798203 \r\n",
-       "L 146.472998 22.433982 \r\n",
-       "L 146.588194 22.069761 \r\n",
-       "L 146.713903 21.705539 \r\n",
-       "L 146.851046 21.341318 \r\n",
-       "L 146.871638 21.290158 \r\n",
-       "L 147.014698 20.977097 \r\n",
-       "L 147.195731 20.612876 \r\n",
-       "L 147.393434 20.248655 \r\n",
-       "L 147.609579 19.884434 \r\n",
-       "L 147.846223 19.520213 \r\n",
-       "L 148.105764 19.155992 \r\n",
-       "L 148.39102 18.791771 \r\n",
-       "L 148.705313 18.427549 \r\n",
-       "L 149.052595 18.063328 \r\n",
-       "L 149.053134 18.062797 \r\n",
-       "L 149.500095 17.699107 \r\n",
-       "L 149.997667 17.334886 \r\n",
-       "L 150.553117 16.970665 \r\n",
-       "L 151.176133 16.606444 \r\n",
-       "L 151.234629 16.57461 \r\n",
-       "L 152.031394 16.242223 \r\n",
-       "L 153.014954 15.878002 \r\n",
-       "L 153.416125 15.742546 \r\n",
-       "L 154.392928 15.513781 \r\n",
-       "L 155.597621 15.256472 \r\n",
-       "L 156.457563 15.14956 \r\n",
-       "L 157.779117 14.99349 \r\n",
-       "L 159.960612 14.901549 \r\n",
-       "L 162.142108 14.968285 \r\n",
-       "L 163.786434 15.14956 \r\n",
-       "L 164.323604 15.21407 \r\n",
-       "L 165.714696 15.513781 \r\n",
-       "L 166.505099 15.708102 \r\n",
-       "L 166.981339 15.878002 \r\n",
-       "L 167.884533 16.242223 \r\n",
-       "L 168.658959 16.606444 \r\n",
-       "L 168.686595 16.620606 \r\n",
-       "L 169.219069 16.970665 \r\n",
-       "L 169.704878 17.334886 \r\n",
-       "L 170.132813 17.699107 \r\n",
-       "L 170.512043 18.063328 \r\n",
-       "L 170.849878 18.427549 \r\n",
-       "L 170.868091 18.448646 \r\n",
-       "L 171.119431 18.791771 \r\n",
-       "L 171.359887 19.155992 \r\n",
-       "L 171.576807 19.520213 \r\n",
-       "L 171.773064 19.884434 \r\n",
-       "L 171.951063 20.248655 \r\n",
-       "L 172.112834 20.612876 \r\n",
-       "L 172.260099 20.977097 \r\n",
-       "L 172.39433 21.341318 \r\n",
-       "L 172.516786 21.705539 \r\n",
-       "L 172.628556 22.069761 \r\n",
-       "L 172.730578 22.433982 \r\n",
-       "L 172.823669 22.798203 \r\n",
-       "L 172.908538 23.162424 \r\n",
-       "L 172.985805 23.526645 \r\n",
-       "L 173.049587 23.85706 \r\n",
-       "L 173.055556 23.890866 \r\n",
-       "L 173.114685 24.255087 \r\n",
-       "L 173.168079 24.619308 \r\n",
-       "L 173.216073 24.983529 \r\n",
-       "L 173.258958 25.34775 \r\n",
-       "L 173.296988 25.711972 \r\n",
-       "L 173.330383 26.076193 \r\n",
-       "L 173.35933 26.440414 \r\n",
-       "L 173.383991 26.804635 \r\n",
-       "L 173.404498 27.168856 \r\n",
-       "L 173.420963 27.533077 \r\n",
-       "L 173.433472 27.897298 \r\n",
-       "L 173.442091 28.261519 \r\n",
-       "L 173.446865 28.62574 \r\n",
-       "L 173.447818 28.989962 \r\n",
-       "L 173.444956 29.354183 \r\n",
-       "L 173.438264 29.718404 \r\n",
-       "L 173.427707 30.082625 \r\n",
-       "L 173.41323 30.446846 \r\n",
-       "L 173.394756 30.811067 \r\n",
-       "L 173.372187 31.175288 \r\n",
-       "L 173.345402 31.539509 \r\n",
-       "L 173.314252 31.90373 \r\n",
-       "L 173.278565 32.267951 \r\n",
-       "L 173.238137 32.632173 \r\n",
-       "L 173.192731 32.996394 \r\n",
-       "L 173.142077 33.360615 \r\n",
-       "L 173.08586 33.724836 \r\n",
-       "L 173.049587 33.938798 \r\n",
-       "L 173.021759 34.089057 \r\n",
-       "L 172.948086 34.453278 \r\n",
-       "L 172.86709 34.817499 \r\n",
-       "L 172.778193 35.18172 \r\n",
-       "L 172.680731 35.545941 \r\n",
-       "L 172.573945 35.910163 \r\n",
-       "L 172.456958 36.274384 \r\n",
-       "L 172.32876 36.638605 \r\n",
-       "L 172.188182 37.002826 \r\n",
-       "L 172.033863 37.367047 \r\n",
-       "L 171.864211 37.731268 \r\n",
-       "L 171.677358 38.095489 \r\n",
-       "L 171.471097 38.45971 \r\n",
-       "L 171.242802 38.823931 \r\n",
-       "L 170.989324 39.188152 \r\n",
-       "L 170.868091 39.348306 \r\n",
-       "L 170.685737 39.552374 \r\n",
-       "L 170.328022 39.916595 \r\n",
-       "L 169.925456 40.280816 \r\n",
-       "L 169.469869 40.645037 \r\n",
-       "L 168.950939 41.009258 \r\n",
-       "L 168.686595 41.176537 \r\n",
-       "L 168.286125 41.373479 \r\n",
-       "L 167.450881 41.7377 \r\n",
-       "L 166.505099 42.090279 \r\n",
-       "L 166.460017 42.101921 \r\n",
-       "L 164.896911 42.466142 \r\n",
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-       "L 162.132946 42.830364 \r\n",
-       "L 159.960612 42.896422 \r\n",
-       "z\r\n",
-       "M 372.718818 42.101921 \r\n",
-       "L 371.565697 41.989747 \r\n",
-       "L 370.309786 41.7377 \r\n",
-       "L 369.384201 41.529107 \r\n",
-       "L 368.931751 41.373479 \r\n",
-       "L 367.984962 41.009258 \r\n",
-       "L 367.202705 40.662831 \r\n",
-       "L 367.172398 40.645037 \r\n",
-       "L 366.597846 40.280816 \r\n",
-       "L 366.091223 39.916595 \r\n",
-       "L 365.641876 39.552374 \r\n",
-       "L 365.241293 39.188152 \r\n",
-       "L 365.021209 38.968241 \r\n",
-       "L 364.901293 38.823931 \r\n",
-       "L 364.622183 38.45971 \r\n",
-       "L 364.370251 38.095489 \r\n",
-       "L 364.14222 37.731268 \r\n",
-       "L 363.935341 37.367047 \r\n",
-       "L 363.747291 37.002826 \r\n",
-       "L 363.576095 36.638605 \r\n",
-       "L 363.420067 36.274384 \r\n",
-       "L 363.27776 35.910163 \r\n",
-       "L 363.147924 35.545941 \r\n",
-       "L 363.029477 35.18172 \r\n",
-       "L 362.921483 34.817499 \r\n",
-       "L 362.839714 34.515633 \r\n",
-       "L 362.824558 34.453278 \r\n",
-       "L 362.74284 34.089057 \r\n",
-       "L 362.668695 33.724836 \r\n",
-       "L 362.601629 33.360615 \r\n",
-       "L 362.541211 32.996394 \r\n",
-       "L 362.487065 32.632173 \r\n",
-       "L 362.438862 32.267951 \r\n",
-       "L 362.396318 31.90373 \r\n",
-       "L 362.359189 31.539509 \r\n",
-       "L 362.327265 31.175288 \r\n",
-       "L 362.300369 30.811067 \r\n",
-       "L 362.278356 30.446846 \r\n",
-       "L 362.261105 30.082625 \r\n",
-       "L 362.248527 29.718404 \r\n",
-       "L 362.240554 29.354183 \r\n",
-       "L 362.237144 28.989962 \r\n",
-       "L 362.23828 28.62574 \r\n",
-       "L 362.243968 28.261519 \r\n",
-       "L 362.254237 27.897298 \r\n",
-       "L 362.269141 27.533077 \r\n",
-       "L 362.28876 27.168856 \r\n",
-       "L 362.313199 26.804635 \r\n",
-       "L 362.342588 26.440414 \r\n",
-       "L 362.377091 26.076193 \r\n",
-       "L 362.416899 25.711972 \r\n",
-       "L 362.462239 25.34775 \r\n",
-       "L 362.513376 24.983529 \r\n",
-       "L 362.570614 24.619308 \r\n",
-       "L 362.634306 24.255087 \r\n",
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-       "L 362.782719 23.526645 \r\n",
-       "L 362.839714 23.283055 \r\n",
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-       "L 362.974228 22.798203 \r\n",
-       "L 363.087338 22.433982 \r\n",
-       "L 363.211354 22.069761 \r\n",
-       "L 363.347281 21.705539 \r\n",
-       "L 363.496281 21.341318 \r\n",
-       "L 363.659697 20.977097 \r\n",
-       "L 363.839093 20.612876 \r\n",
-       "L 364.036291 20.248655 \r\n",
-       "L 364.253431 19.884434 \r\n",
-       "L 364.493039 19.520213 \r\n",
-       "L 364.758112 19.155992 \r\n",
-       "L 365.021209 18.828867 \r\n",
-       "L 365.057085 18.791771 \r\n",
-       "L 365.435952 18.427549 \r\n",
-       "L 365.859966 18.063328 \r\n",
-       "L 366.336767 17.699107 \r\n",
-       "L 366.875864 17.334886 \r\n",
-       "L 367.202705 17.135696 \r\n",
-       "L 367.560647 16.970665 \r\n",
-       "L 368.440839 16.606444 \r\n",
-       "L 369.384201 16.268182 \r\n",
-       "L 369.494124 16.242223 \r\n",
-       "L 371.199323 15.878002 \r\n",
-       "L 371.565697 15.807901 \r\n",
-       "L 373.747192 15.602833 \r\n",
-       "L 375.928688 15.595417 \r\n",
-       "L 378.110184 15.776614 \r\n",
-       "L 378.690014 15.878002 \r\n",
-       "L 380.291679 16.177758 \r\n",
-       "L 380.511427 16.242223 \r\n",
-       "L 381.663732 16.606444 \r\n",
-       "L 382.473175 16.890019 \r\n",
-       "L 382.641371 16.970665 \r\n",
-       "L 383.346576 17.334886 \r\n",
-       "L 383.979874 17.699107 \r\n",
-       "L 384.550832 18.063328 \r\n",
-       "L 384.654671 18.134387 \r\n",
-       "L 384.996488 18.427549 \r\n",
-       "L 385.384238 18.791771 \r\n",
-       "L 385.736584 19.155992 \r\n",
-       "L 386.057518 19.520213 \r\n",
-       "L 386.350427 19.884434 \r\n",
-       "L 386.618206 20.248655 \r\n",
-       "L 386.836167 20.571543 \r\n",
-       "L 386.859981 20.612876 \r\n",
-       "L 387.056798 20.977097 \r\n",
-       "L 387.237208 21.341318 \r\n",
-       "L 387.402645 21.705539 \r\n",
-       "L 387.554358 22.069761 \r\n",
-       "L 387.693438 22.433982 \r\n",
-       "L 387.820845 22.798203 \r\n",
-       "L 387.937419 23.162424 \r\n",
-       "L 388.043901 23.526645 \r\n",
-       "L 388.140941 23.890866 \r\n",
-       "L 388.229114 24.255087 \r\n",
-       "L 388.308925 24.619308 \r\n",
-       "L 388.380818 24.983529 \r\n",
-       "L 388.445182 25.34775 \r\n",
-       "L 388.502356 25.711972 \r\n",
-       "L 388.552638 26.076193 \r\n",
-       "L 388.596281 26.440414 \r\n",
-       "L 388.633503 26.804635 \r\n",
-       "L 388.664487 27.168856 \r\n",
-       "L 388.689381 27.533077 \r\n",
-       "L 388.708307 27.897298 \r\n",
-       "L 388.721352 28.261519 \r\n",
-       "L 388.72858 28.62574 \r\n",
-       "L 388.730024 28.989962 \r\n",
-       "L 388.725691 29.354183 \r\n",
-       "L 388.71556 29.718404 \r\n",
-       "L 388.699584 30.082625 \r\n",
-       "L 388.677687 30.446846 \r\n",
-       "L 388.649765 30.811067 \r\n",
-       "L 388.615683 31.175288 \r\n",
-       "L 388.575275 31.539509 \r\n",
-       "L 388.528342 31.90373 \r\n",
-       "L 388.474648 32.267951 \r\n",
-       "L 388.413918 32.632173 \r\n",
-       "L 388.345836 32.996394 \r\n",
-       "L 388.270036 33.360615 \r\n",
-       "L 388.186103 33.724836 \r\n",
-       "L 388.093564 34.089057 \r\n",
-       "L 387.991879 34.453278 \r\n",
-       "L 387.880438 34.817499 \r\n",
-       "L 387.758546 35.18172 \r\n",
-       "L 387.625414 35.545941 \r\n",
-       "L 387.480145 35.910163 \r\n",
-       "L 387.321716 36.274384 \r\n",
-       "L 387.14896 36.638605 \r\n",
-       "L 386.960538 37.002826 \r\n",
-       "L 386.836167 37.225366 \r\n",
-       "L 386.743463 37.367047 \r\n",
-       "L 386.487294 37.731268 \r\n",
-       "L 386.207284 38.095489 \r\n",
-       "L 385.900753 38.45971 \r\n",
-       "L 385.564572 38.823931 \r\n",
-       "L 385.195065 39.188152 \r\n",
-       "L 384.787892 39.552374 \r\n",
-       "L 384.654671 39.663203 \r\n",
-       "L 384.272616 39.916595 \r\n",
-       "L 383.671572 40.280816 \r\n",
-       "L 383.003636 40.645037 \r\n",
-       "L 382.473175 40.907686 \r\n",
-       "L 382.192568 41.009258 \r\n",
-       "L 381.104196 41.373479 \r\n",
-       "L 380.291679 41.619831 \r\n",
-       "L 379.681726 41.7377 \r\n",
-       "L 378.110184 42.021263 \r\n",
-       "L 377.154641 42.101921 \r\n",
-       "L 375.928688 42.202341 \r\n",
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-       "L 54.984682 82.894685 \r\n",
-       "z\r\n",
-       "M 131.519055 82.530464 \r\n",
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-       "L 120.693689 48.070103 \r\n",
-       "L 122.875185 47.947158 \r\n",
-       "L 123.323289 47.929459 \r\n",
-       "L 125.056681 47.859871 \r\n",
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-       "L 135.964159 47.824839 \r\n",
-       "L 137.327579 47.929459 \r\n",
-       "L 138.145655 48.039467 \r\n",
-       "L 139.042266 48.29368 \r\n",
-       "L 139.636589 48.657901 \r\n",
-       "L 139.945078 49.022122 \r\n",
-       "L 140.133806 49.386343 \r\n",
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-       "L 140.327151 50.002418 \r\n",
-       "L 140.352214 50.114786 \r\n",
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-       "L 140.473198 50.843228 \r\n",
-       "L 140.515684 51.207449 \r\n",
-       "L 140.55045 51.57167 \r\n",
-       "L 140.579392 51.935891 \r\n",
-       "L 140.603832 52.300112 \r\n",
-       "L 140.624718 52.664333 \r\n",
-       "L 140.64275 53.028554 \r\n",
-       "L 140.658454 53.392776 \r\n",
-       "L 140.672234 53.756997 \r\n",
-       "L 140.684404 54.121218 \r\n",
-       "L 140.695214 54.485439 \r\n",
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-       "L 140.767268 71.23961 \r\n",
-       "L 140.764112 71.603831 \r\n",
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-       "L 140.756859 72.332273 \r\n",
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-       "L 140.748142 73.060715 \r\n",
-       "L 140.743138 73.424936 \r\n",
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-       "L 140.203084 80.709359 \r\n",
-       "L 140.049597 81.07358 \r\n",
-       "L 139.811934 81.437801 \r\n",
-       "L 139.395279 81.802022 \r\n",
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-       "L 131.730656 82.530464 \r\n",
-       "L 131.601168 82.531117 \r\n",
-       "z\r\n",
-       "M 75.913529 81.437801 \r\n",
-       "L 74.882279 81.31308 \r\n",
-       "L 73.709013 81.07358 \r\n",
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-       "L 69.306557 70.875389 \r\n",
-       "L 69.293533 70.511168 \r\n",
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-       "L 79.245271 81.64601 \r\n",
-       "L 77.063775 81.545062 \r\n",
-       "z\r\n",
-       "M 155.977136 77.79559 \r\n",
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-       "L 153.664033 77.431369 \r\n",
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-       "L 157.779117 52.369577 \r\n",
-       "L 159.960612 52.442091 \r\n",
-       "L 161.643973 52.664333 \r\n",
-       "L 162.142108 52.73353 \r\n",
-       "L 163.315568 53.028554 \r\n",
-       "L 164.323604 53.306171 \r\n",
-       "L 164.538702 53.392776 \r\n",
-       "L 165.378047 53.756997 \r\n",
-       "L 166.132334 54.121218 \r\n",
-       "L 166.505099 54.317218 \r\n",
-       "L 166.749987 54.485439 \r\n",
-       "L 167.239576 54.84966 \r\n",
-       "L 167.683304 55.213881 \r\n",
-       "L 168.086541 55.578102 \r\n",
-       "L 168.453823 55.942323 \r\n",
-       "L 168.686595 56.19298 \r\n",
-       "L 168.773884 56.306544 \r\n",
-       "L 169.034922 56.670766 \r\n",
-       "L 169.273687 57.034987 \r\n",
-       "L 169.492292 57.399208 \r\n",
-       "L 169.692566 57.763429 \r\n",
-       "L 169.876101 58.12765 \r\n",
-       "L 170.04428 58.491871 \r\n",
-       "L 170.198315 58.856092 \r\n",
-       "L 170.339269 59.220313 \r\n",
-       "L 170.468074 59.584534 \r\n",
-       "L 170.585547 59.948755 \r\n",
-       "L 170.692411 60.312977 \r\n",
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-       "L 170.946288 61.40564 \r\n",
-       "L 171.009076 61.769861 \r\n",
-       "L 171.06457 62.134082 \r\n",
-       "L 171.113062 62.498303 \r\n",
-       "L 171.154801 62.862524 \r\n",
-       "L 171.189999 63.226745 \r\n",
-       "L 171.21883 63.590967 \r\n",
-       "L 171.241436 63.955188 \r\n",
-       "L 171.257928 64.319409 \r\n",
-       "L 171.268385 64.68363 \r\n",
-       "L 171.272857 65.047851 \r\n",
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-       "L 171.263907 65.776293 \r\n",
-       "L 171.25044 66.140514 \r\n",
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-       "L 171.173206 67.233178 \r\n",
-       "L 171.134762 67.597399 \r\n",
-       "L 171.089675 67.96162 \r\n",
-       "L 171.037715 68.325841 \r\n",
-       "L 170.978613 68.690062 \r\n",
-       "L 170.912057 69.054283 \r\n",
-       "L 170.868091 69.270592 \r\n",
-       "L 170.83418 69.418504 \r\n",
-       "L 170.742066 69.782725 \r\n",
-       "L 170.640264 70.146946 \r\n",
-       "L 170.528179 70.511168 \r\n",
-       "L 170.405136 70.875389 \r\n",
-       "L 170.270367 71.23961 \r\n",
-       "L 170.122996 71.603831 \r\n",
-       "L 169.96203 71.968052 \r\n",
-       "L 169.786335 72.332273 \r\n",
-       "L 169.594616 72.696494 \r\n",
-       "L 169.385389 73.060715 \r\n",
-       "L 169.156949 73.424936 \r\n",
-       "L 168.907332 73.789158 \r\n",
-       "L 168.686595 74.085187 \r\n",
-       "L 168.625208 74.153379 \r\n",
-       "L 168.274421 74.5176 \r\n",
-       "L 167.889683 74.881821 \r\n",
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-       "L 167.000887 75.610263 \r\n",
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-       "L 166.481163 75.974484 \r\n",
-       "L 165.765072 76.338705 \r\n",
-       "L 164.969795 76.702926 \r\n",
-       "L 164.323604 76.971587 \r\n",
-       "L 163.987672 77.067147 \r\n",
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-       "L 159.960612 77.836135 \r\n",
-       "L 157.779117 77.908076 \r\n",
-       "z\r\n",
-       "M 84.613758 43.923027 \r\n",
-       "L 83.608262 43.808404 \r\n",
-       "L 82.210711 43.558806 \r\n",
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-       "L 72.955781 34.817499 \r\n",
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-       "L 72.646666 32.996394 \r\n",
-       "L 72.603805 32.632173 \r\n",
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-       "L 72.53174 31.90373 \r\n",
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-       "L 72.437623 30.446846 \r\n",
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-       "L 72.407356 29.354183 \r\n",
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-       "L 72.465474 26.804635 \r\n",
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-       "L 72.516433 26.076193 \r\n",
-       "L 72.54811 25.711972 \r\n",
-       "L 72.584119 25.34775 \r\n",
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-       "L 72.721456 24.255087 \r\n",
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-       "L 76.00343 16.970665 \r\n",
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-       "L 78.178846 15.513781 \r\n",
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-       "L 90.152749 13.600979 \r\n",
-       "L 92.334245 13.691436 \r\n",
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-       "L 95.210336 14.056896 \r\n",
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-       "L 97.758122 14.785338 \r\n",
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-       "L 99.983381 16.242223 \r\n",
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-       "L 102.611133 33.360615 \r\n",
-       "L 102.579825 33.724836 \r\n",
-       "L 102.54519 34.089057 \r\n",
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-       "L 102.418776 35.18172 \r\n",
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-       "L 102.251485 36.274384 \r\n",
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-       "L 102.111016 37.002826 \r\n",
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-       "L 87.971254 44.176458 \r\n",
-       "L 85.789758 44.051662 \r\n",
-       "z\r\n",
-       "M 118.342046 43.194585 \r\n",
-       "L 116.330698 42.946173 \r\n",
-       "L 115.781712 42.830364 \r\n",
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-       "L 107.922097 32.996394 \r\n",
-       "L 107.882909 32.632173 \r\n",
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-       "L 393.077301 20.612876 \r\n",
-       "L 393.104423 20.977097 \r\n",
-       "L 393.129276 21.341318 \r\n",
-       "L 393.15206 21.705539 \r\n",
-       "L 393.172949 22.069761 \r\n",
-       "L 393.192093 22.433982 \r\n",
-       "L 393.209627 22.798203 \r\n",
-       "L 393.225667 23.162424 \r\n",
-       "L 393.240315 23.526645 \r\n",
-       "L 393.253662 23.890866 \r\n",
-       "L 393.265788 24.255087 \r\n",
-       "L 393.276762 24.619308 \r\n",
-       "L 393.286646 24.983529 \r\n",
-       "L 393.295494 25.34775 \r\n",
-       "L 393.303353 25.711972 \r\n",
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-       "L 393.321377 26.804635 \r\n",
-       "L 393.325635 27.168856 \r\n",
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-       "L 393.331656 27.897298 \r\n",
-       "L 393.333449 28.261519 \r\n",
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-       "L 393.334045 29.354183 \r\n",
-       "L 393.332653 29.718404 \r\n",
-       "L 393.330458 30.082625 \r\n",
-       "L 393.327449 30.446846 \r\n",
-       "L 393.323612 30.811067 \r\n",
-       "L 393.318929 31.175288 \r\n",
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-       "L 393.306925 31.90373 \r\n",
-       "L 393.299545 32.267951 \r\n",
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-       "L 393.271415 33.360615 \r\n",
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-       "L 393.233159 34.453278 \r\n",
-       "L 393.217827 34.817499 \r\n",
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-       "L 393.140915 36.274384 \r\n",
-       "L 393.11712 36.638605 \r\n",
-       "L 393.091159 37.002826 \r\n",
-       "L 393.062818 37.367047 \r\n",
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-       "L 392.997973 38.095489 \r\n",
-       "L 392.960849 38.45971 \r\n",
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-       "L 392.875239 39.188152 \r\n",
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-       "L 365.649076 47.565238 \r\n",
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-       "L 363.472128 47.929459 \r\n",
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-       "L 359.348316 51.935891 \r\n",
-       "L 359.266199 52.300112 \r\n",
-       "L 359.194412 52.664333 \r\n",
-       "L 359.131202 53.028554 \r\n",
-       "L 359.075197 53.392776 \r\n",
-       "L 359.025303 53.756997 \r\n",
-       "L 358.980639 54.121218 \r\n",
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-       "L 358.702526 72.332273 \r\n",
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-       "L 358.756859 73.424936 \r\n",
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-       "L 359.002365 76.338705 \r\n",
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-       "L 360.135889 80.345137 \r\n",
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-       "L 360.658218 81.001826 \r\n",
-       "L 360.733848 81.07358 \r\n",
-       "L 361.193779 81.437801 \r\n",
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-       "L 362.776696 82.166243 \r\n",
-       "L 362.839714 82.185577 \r\n",
-       "L 364.381682 82.530464 \r\n",
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-       "L 367.202705 82.87115 \r\n",
-       "L 367.495249 82.894685 \r\n",
-       "z\r\n",
-       "M 403.583338 81.437801 \r\n",
-       "L 402.106637 81.293876 \r\n",
-       "L 400.81023 81.07358 \r\n",
-       "L 399.925141 80.872926 \r\n",
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-       "L 395.327642 57.399208 \r\n",
-       "L 395.360341 57.034987 \r\n",
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-       "L 395.435824 56.306544 \r\n",
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-       "L 396.211437 52.664333 \r\n",
-       "L 396.360616 52.300112 \r\n",
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-       "L 397.302683 50.843228 \r\n",
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-       "L 424.543237 52.300112 \r\n",
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-       "L 425.15656 53.028554 \r\n",
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-       "L 425.642917 53.756997 \r\n",
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-       "L 426.035797 54.485439 \r\n",
-       "L 426.103089 54.627515 \r\n",
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-       "L 426.341012 55.213881 \r\n",
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-       "L 427.572227 68.325841 \r\n",
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-       "L 427.51825 69.054283 \r\n",
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-       "L 427.411768 70.146946 \r\n",
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-       "L 427.084453 72.332273 \r\n",
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-       "L 426.931342 73.060715 \r\n",
-       "L 426.844271 73.424936 \r\n",
-       "L 426.749263 73.789158 \r\n",
-       "L 426.645489 74.153379 \r\n",
-       "L 426.53199 74.5176 \r\n",
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-       "L 426.271156 75.246042 \r\n",
-       "L 426.120973 75.610263 \r\n",
-       "L 426.103089 75.650924 \r\n",
-       "L 425.944968 75.974484 \r\n",
-       "L 425.748873 76.338705 \r\n",
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-       "L 425.288503 77.067147 \r\n",
-       "L 425.016768 77.431369 \r\n",
-       "L 424.710896 77.79559 \r\n",
-       "L 424.364547 78.159811 \r\n",
-       "L 423.969694 78.524032 \r\n",
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-       "L 423.47242 78.888253 \r\n",
-       "L 422.891106 79.252474 \r\n",
-       "L 422.211751 79.616695 \r\n",
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-       "L 420.253498 80.345137 \r\n",
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-       "L 416.666072 81.07358 \r\n",
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-       "L 406.469628 81.581559 \r\n",
-       "L 404.288132 81.494281 \r\n",
-       "z\r\n",
-       "M 375.309467 78.524032 \r\n",
-       "L 373.747192 78.379004 \r\n",
-       "L 372.557961 78.159811 \r\n",
-       "L 371.565697 77.963076 \r\n",
-       "L 371.013914 77.79559 \r\n",
-       "L 369.918419 77.431369 \r\n",
-       "L 369.384201 77.236366 \r\n",
-       "L 369.041072 77.067147 \r\n",
-       "L 368.3642 76.702926 \r\n",
-       "L 367.757294 76.338705 \r\n",
-       "L 367.210898 75.974484 \r\n",
-       "L 367.202705 75.968662 \r\n",
-       "L 366.796767 75.610263 \r\n",
-       "L 366.423116 75.246042 \r\n",
-       "L 366.083932 74.881821 \r\n",
-       "L 365.775276 74.5176 \r\n",
-       "L 365.493814 74.153379 \r\n",
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-       "L 365.021209 73.456219 \r\n",
-       "L 365.003807 73.424936 \r\n",
-       "L 364.813705 73.060715 \r\n",
-       "L 364.639557 72.696494 \r\n",
-       "L 364.479953 72.332273 \r\n",
-       "L 364.333664 71.968052 \r\n",
-       "L 364.19962 71.603831 \r\n",
-       "L 364.07688 71.23961 \r\n",
-       "L 363.964621 70.875389 \r\n",
-       "L 363.862118 70.511168 \r\n",
-       "L 363.768734 70.146946 \r\n",
-       "L 363.68391 69.782725 \r\n",
-       "L 363.607151 69.418504 \r\n",
-       "L 363.538024 69.054283 \r\n",
-       "L 363.47615 68.690062 \r\n",
-       "L 363.421198 68.325841 \r\n",
-       "L 363.37288 67.96162 \r\n",
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-       "L 363.295189 67.233178 \r\n",
-       "L 363.265427 66.868957 \r\n",
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-       "L 363.202484 65.047851 \r\n",
-       "L 363.206645 64.68363 \r\n",
-       "L 363.216374 64.319409 \r\n",
-       "L 363.231717 63.955188 \r\n",
-       "L 363.252748 63.590967 \r\n",
-       "L 363.279568 63.226745 \r\n",
-       "L 363.312308 62.862524 \r\n",
-       "L 363.35113 62.498303 \r\n",
-       "L 363.396226 62.134082 \r\n",
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-       "L 363.571658 61.041419 \r\n",
-       "L 363.64455 60.677198 \r\n",
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-       "L 364.906665 57.034987 \r\n",
-       "L 365.021209 56.822677 \r\n",
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-       "L 365.362374 56.306544 \r\n",
-       "L 365.631334 55.942323 \r\n",
-       "L 365.926009 55.578102 \r\n",
-       "L 366.249477 55.213881 \r\n",
-       "L 366.60536 54.84966 \r\n",
-       "L 366.997945 54.485439 \r\n",
-       "L 367.202705 54.310134 \r\n",
-       "L 367.477054 54.121218 \r\n",
-       "L 368.052636 53.756997 \r\n",
-       "L 368.693223 53.392776 \r\n",
-       "L 369.384201 53.040733 \r\n",
-       "L 369.416475 53.028554 \r\n",
-       "L 370.450062 52.664333 \r\n",
-       "L 371.565697 52.314139 \r\n",
-       "L 371.634154 52.300112 \r\n",
-       "L 373.537112 51.935891 \r\n",
-       "L 373.747192 51.898419 \r\n",
-       "L 375.928688 51.698477 \r\n",
-       "L 378.110184 51.678075 \r\n",
-       "L 380.291679 51.840815 \r\n",
-       "L 380.866271 51.935891 \r\n",
-       "L 382.473175 52.23237 \r\n",
-       "L 382.704697 52.300112 \r\n",
-       "L 383.827581 52.664333 \r\n",
-       "L 384.654671 52.975205 \r\n",
-       "L 384.759618 53.028554 \r\n",
-       "L 385.41613 53.392776 \r\n",
-       "L 385.989707 53.756997 \r\n",
-       "L 386.494366 54.121218 \r\n",
-       "L 386.836167 54.396699 \r\n",
-       "L 386.926224 54.485439 \r\n",
-       "L 387.267642 54.84966 \r\n",
-       "L 387.57321 55.213881 \r\n",
-       "L 387.847761 55.578102 \r\n",
-       "L 388.095275 55.942323 \r\n",
-       "L 388.319061 56.306544 \r\n",
-       "L 388.521887 56.670766 \r\n",
-       "L 388.706085 57.034987 \r\n",
-       "L 388.873636 57.399208 \r\n",
-       "L 389.017662 57.742478 \r\n",
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-       "L 389.151814 58.12765 \r\n",
-       "L 389.267064 58.491871 \r\n",
-       "L 389.372165 58.856092 \r\n",
-       "L 389.46796 59.220313 \r\n",
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-       "L 389.706382 60.312977 \r\n",
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-       "L 390.063203 63.226745 \r\n",
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-       "L 390.1012 63.955188 \r\n",
-       "L 390.113371 64.319409 \r\n",
-       "L 390.121086 64.68363 \r\n",
-       "L 390.124385 65.047851 \r\n",
-       "L 390.123286 65.412072 \r\n",
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-       "L 390.107846 66.140514 \r\n",
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-       "L 390.074438 66.868957 \r\n",
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-       "L 389.950388 68.325841 \r\n",
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-       "L 389.856934 69.054283 \r\n",
-       "L 389.801467 69.418504 \r\n",
-       "L 389.739727 69.782725 \r\n",
-       "L 389.671317 70.146946 \r\n",
-       "L 389.595782 70.511168 \r\n",
-       "L 389.512601 70.875389 \r\n",
-       "L 389.421178 71.23961 \r\n",
-       "L 389.320828 71.603831 \r\n",
-       "L 389.210765 71.968052 \r\n",
-       "L 389.090085 72.332273 \r\n",
-       "L 389.017662 72.533843 \r\n",
-       "L 388.951706 72.696494 \r\n",
-       "L 388.79183 73.060715 \r\n",
-       "L 388.616183 73.424936 \r\n",
-       "L 388.422939 73.789158 \r\n",
-       "L 388.20995 74.153379 \r\n",
-       "L 387.974677 74.5176 \r\n",
-       "L 387.714096 74.881821 \r\n",
-       "L 387.424583 75.246042 \r\n",
-       "L 387.101756 75.610263 \r\n",
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-       "L 386.724385 75.974484 \r\n",
-       "L 386.249903 76.338705 \r\n",
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-       "L 385.099262 77.067147 \r\n",
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-       "L 383.288913 77.79559 \r\n",
-       "L 382.473175 78.045253 \r\n",
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-       "L 378.110184 78.599852 \r\n",
-       "L 375.928688 78.579586 \r\n",
-       "z\r\n",
-       "M 409.792979 43.923027 \r\n",
-       "L 408.651124 43.786048 \r\n",
-       "L 407.419677 43.558806 \r\n",
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-       "L 397.802047 29.718404 \r\n",
-       "L 397.795574 29.354183 \r\n",
-       "L 397.792805 28.989962 \r\n",
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-       "L 405.255054 14.785338 \r\n",
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-       "L 428.129933 23.526645 \r\n",
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-       "L 428.376315 27.897298 \r\n",
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-       "L 428.38357 28.62574 \r\n",
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-       "L 428.382536 29.354183 \r\n",
-       "L 428.378912 29.718404 \r\n",
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-       "L 428.355335 30.811067 \r\n",
-       "L 428.343099 31.175288 \r\n",
-       "L 428.32857 31.539509 \r\n",
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-       "L 428.292289 32.267951 \r\n",
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-       "L 428.269807 32.632173 \r\n",
-       "L 428.244225 32.996394 \r\n",
-       "L 428.215662 33.360615 \r\n",
-       "L 428.183932 33.724836 \r\n",
-       "L 428.148826 34.089057 \r\n",
-       "L 428.110101 34.453278 \r\n",
-       "L 428.067479 34.817499 \r\n",
-       "L 428.020641 35.18172 \r\n",
-       "L 427.96922 35.545941 \r\n",
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-       "L 427.850882 36.274384 \r\n",
-       "L 427.782913 36.638605 \r\n",
-       "L 427.708234 37.002826 \r\n",
-       "L 427.626077 37.367047 \r\n",
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-       "L 427.435571 38.095489 \r\n",
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-       "L 427.065074 39.188152 \r\n",
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-       "L 426.739702 39.916595 \r\n",
-       "L 426.54514 40.280816 \r\n",
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-       "L 415.195611 44.211881 \r\n",
-       "L 413.014115 44.177679 \r\n",
-       "L 410.832619 44.041583 \r\n",
-       "z\r\n",
-       "M 442.37843 42.830364 \r\n",
-       "L 441.373559 42.682435 \r\n",
-       "L 440.492894 42.466142 \r\n",
-       "L 439.243753 42.101921 \r\n",
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-       "L 434.659899 37.731268 \r\n",
-       "L 434.519855 37.367047 \r\n",
-       "L 434.392802 37.002826 \r\n",
-       "L 434.277338 36.638605 \r\n",
-       "L 434.172272 36.274384 \r\n",
-       "L 434.076583 35.910163 \r\n",
-       "L 433.989395 35.545941 \r\n",
-       "L 433.909951 35.18172 \r\n",
-       "L 433.837597 34.817499 \r\n",
-       "L 433.771765 34.453278 \r\n",
-       "L 433.711959 34.089057 \r\n",
-       "L 433.657748 33.724836 \r\n",
-       "L 433.608757 33.360615 \r\n",
-       "L 433.564657 32.996394 \r\n",
-       "L 433.525163 32.632173 \r\n",
-       "L 433.490027 32.267951 \r\n",
-       "L 433.459035 31.90373 \r\n",
-       "L 433.432 31.539509 \r\n",
-       "L 433.408765 31.175288 \r\n",
-       "L 433.389197 30.811067 \r\n",
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+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "### Potential energy for symmetric 0-$\\pi$ qubit\n",
-    "zero_pi.plot_potential();"
+    "zero_pi.set_EC_via_ECS(0.04)\n",
+    "zero_pi.EC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing and plotting eigenenergies and wavefunctions"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Eigenenergies"
+    "**Eigenenergies.** Similar to other qubits, eigenvalue computations and parameter sweeps can be performed using the following methods:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2020-02-07T03:18:47.984128Z",
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-       "       18.93031195, 19.16309846, 19.17777943, 19.92663351, 20.0898962 ])"
+       "array([17.00199548, 17.02238769, 18.12303267, 18.14028472, 18.72257217,\n",
+       "       18.93090846, 19.16555977, 19.18024366, 19.92838159, 20.0925804 ])"
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-     "execution_count": 6,
+     "execution_count": 27,
      "metadata": {},
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    "source": [
-    "zero_pi.flux = 0.31\n",
     "zero_pi.eigenvals(evals_count=10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {
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      "end_time": "2020-02-07T03:19:42.994539Z",
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-       "<Figure size 900x600 with 1 Axes>"
+       "(<Figure size 432x288 with 1 Axes>,\n",
+       " <AxesSubplot:xlabel='$\\\\Phi_{ext}/\\\\Phi_0$', ylabel='energy [GHz]'>)"
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+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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UYHpyGHDYRQxYNzy9+31zc84XJlPlHVSwm4pt+aRjfu4NMwjq69p03n6S4R46cTLR8b9iqb/+AMbaXZ4KZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDQxOSA+PgpzdHJlYW0KeJw9UltuBDEI+59TcIFK4Z2cZ6uqP3v/39rMbKXNwgQCtiGzZEmofKlKqknrkW+9tFt8b3lfGogvFVWXsCUnJSLldSEj6gh+ccakB67p7JLdUnZELaWK6VoujTqGOmxinWNfl3uPx3690M0Kb1gr8F+2JbajaNzWjRF4cRDpGBSR/cAKP4MziBf9/GGCiPEL+RniqXiLyCBIdDUgpgAW57aL1ehpsBeYG1owibWWCxBHjXDWt31dfEVPYyOu+Jr0snnN+6Cx1SwCJ8EIzRBFDTeyhpqeKeoOuCX6T+D30qTMzbHQAwhtUIWUyvrJ56Zo4SSCG4PloIyiOYDRc9+T4bWeN75tqvgBHIp2PkKPhzH4xn4cRNC3IO09tnK8WbiBEBSBFgjQeW6AhBnEVso+RJv4GvTV8uEz3PzW5T2eop86M3AwEp3l0uIiLrDeFNQWZOMAbdYMai4BJzKGIeFDxyFy+1DQtWZ6G5t5y6L1yLRm4+gBOjNs4ynPovieFA4zUpxkkxiL5pQSnmIfmaGtIwrgYto2REANq/OhSLo/f5rTpYwKZW5kc3RyZWFtCmVuZG9iagoyMyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI0OSA+PgpzdHJlYW0KeJxFUUluxDAMu/sV/EABa7X9nikGPUz/fy2ZBOghEWNLJMVUNSbS8WWGssaajW8bPLG98TssEnYKn2E5YaWnYey0bTiJ13COLINHoyeckOU1wkIg8mA1Yh3Y3DxPvsWVHuTwq3qUboR2QR3hidgcrxBXOb/4WCHOosi8KsXp9Dqhozh0d4JaujH1NN1rNm/NcDmohYitlfxe+DOS5P+o3XVL2gfVRsYk8mlIbZmNXAWnnKos1oVkPmk6i52mIJIpRfcVbzwxe2otIVvsp5JRKYtZXUkwO6NLcujHKFPVO2showJnjDMi4qrMN8Wy8Py71/gZ7z/QtlloCmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA5NCA+PgpzdHJlYW0KeJxNjUEOwCAIBO+8gie4ULT+p2k82P9fKxijF5jsLqxZ5sTQMSzdXJD5Aam48MVGAXfCAWIyQLVGvNMFHDRdf7Zpnrq7KfmP6OnUgjw/O63YUGtdVbJKG70/usEiDQplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzU4ID4+CnN0cmVhbQp4nD2SO44eMQyD+zmFLjCAqZft8/xBkCK5f7ukvZtKhG2NyE9T1TYs3F4f23Jtqxj2C08Ne9Pt34Moe6vsL9W2d8GQrBjD0LwC3D4P1qT0ZY6miGleecXn8f0tw/u+ilw/nTE5abfF4t0MCz3O1M3mI3akRuGUXjpvmLsFgtb9BJCBZIVvntgeNjW4aFbT3LBPSu8zUs38ICxqWm3O3DxmS/IMIyxpRIaTxvjtCSsoGw4eV1doavBUKmisRtHjsiz1SBU2sR2o/xXTXkGC5M3szYSdlyj7DplIS0Z80eOCwTrjtBXQbIx5N+bczBF0ueNKZOhVMYn20yLRWk9ow4Qtr2e7n+fPAw9i2dq01CCAaIPQNSH4gUt6i/k9bep1qdDOOCJi6TYYBNhncWzkiKRLuVX1vlT0Ewg428g7WsxOT1z6IfrFWX0r/x5OlcLmymgahJU4tmg0Jm9cJmA/URTr9xdNFIc6CmVuZHN0cmVhbQplbmRvYmoKMjYgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxODUgPj4Kc3RyZWFtCnicTVAxkgMxCOv9Cp5gEHjhPclkrsj9v40gk0yKtQQystiII1tOH56XBLbcdU393xBa8lzq8cOcH1lCYqsYcsage/C24PruXOyYC6p9QMXNOGN0sHnOg26nWjnJSsUvdq2o8sb2VjIEmXMfUW/UmSHbTIKqL0Ljw+iG4iwdkTWc2dqXWTqbWztCnBtQQW+W4+DhYmWDt2U8p2M6ybVYa8/ooQMrpQqvQcvetFlnmj/5XH/r8QImWEQtCmVuZHN0cmVhbQplbmRvYmoKMjcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzODYgPj4Kc3RyZWFtCnicLZJJshwxCET3dQou4AgxSjpPOxx/Yd9/6wfVq0QgyGTILFkSJr9UJdVk65Xf+mSKVsm/R/2Kbpe/j0aJuuNfoov4WXK2fB5bS+qKWQkhy9OA+0QbvniXuB3ZB7fHkYu7qLC2OE61CVwVJRjLYCwJFBmKPk8YZWMjM8WODzo6O4Jl7QnwvugtKhac64ofIwc7weocN6yk9kFzOLYPfh69MZYGWIE+p0ZXK6PAzCnWHthDYpJxpZxGaQPKyhbcbdQtsfUO1GoPQq4+ljPg3RKhrKNdy41s2oaraDgQnjuIJFXS9qwnmGij7xbclpHdPwwFnaPZOV1F2UuwsLxD483iRH120gs3+BrfHbbV2TSuB4W4T2uSws8ovzfxeX6e0IRMuY+2ktKxkLEVYb31oWJjRcT5UdyOMbJGyPy8nsVf55o2Ne6ZwZVk9Z11yzEYM4SxuBMNNt6XwgC8/CvdnY153wCXsnOwh/1azo76h3dkclptV5k+/Iuzhp/nz3+bEZHrCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxMTYgPj4Kc3RyZWFtCnicNU45DgNBDOr9Cp7g2+P3bBRtMfl/G+8oaQzCgIhIMIR7rpWhpPESeijjQ7picB+MPCwN4Qy1UcasLPBuXCRZ8GqIJTz9lHr48xkW1pOWWNOjJxX9tCyk2ni0HBkBY0augkmeMRf9Z+3fqk03vb9y0iLQCmVuZHN0cmVhbQplbmRvYmoKMjkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA0NCA+PgpzdHJlYW0KeJwzMrdQMFCwNAQShkDS0MBAIcWQC8zP5YIK5HAZorBANJTK4EoDAJdwDIQKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE0NSA+PgpzdHJlYW0KeJxNj7sRAzAIQ3tPwQggwJ95ksulcPZvI+wUKWzJ1j1xuLuo9OTlMSRd5WntvD8laUt2s4g/F6HlOt3oYhOSqgKeNMijwViEEHg/hcirTOZ1blT8Rmox9ROoXiz2OgTDqYs0jpL262BJ9TMxULRNMqZiZJy89SE+opKkC4glE51HMewktfm+u+72bq8vJuEyAQplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTQxID4+CnN0cmVhbQp4nDWPyw3DMAxD756CI+hryfOkKHJI97+WTpCLTZHGE505IYjikaooKXx0cJ5m+G2xrWu84aOmN1XMRPZC6EJawCsRETiGu8BnwFbCWmGl0FVMLB3qBQsDTSNIaOvd4OLdYCPNBSVRW2CyiSZ83CS6kvwQw3PvYp+UBSc56frqu/zx/uIa5/j+Ab33K4gKZW5kc3RyZWFtCmVuZG9iagozMiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDM1OCA+PgpzdHJlYW0KeJw9UktuBTEI2+cUXKBS+JPzTFV10Xf/bQ3p68qIiTE24x60SYs+mMl5U/KhT152ityYXsvQdDX6WbaFPIr04OlR0kyKfehZ6kqh6AjQgqTO4LMk+HY08KJI2Cnw6llczVbiCPIEeut4f4GanSAWJ8MOjRqtw5hkG50UMjES8M1260Dd4EUCnMCXcwZ7t5zKNtDAs3bQ0wxbKjhtW/ceFBV86ar3c3TZMLGgCT447afIsKieu8sEEIkE4f9MkFIxiL1YpmJvhzNknETbEppEuEHHOgrLzvJGwoayZdkLPAzmmgvJscG2d2+mJyk7DgQRybMqjtBLHlhDnO+TPusbEZ+x+roVDts2ec5QU0MzYZ4TQRSB3k5KJmqcMEkc4xFYeQMWEe6if4VEOAXy7jG2cUlQTNDJiyKTZVfZFw1Svhy1ezPD34V4pLOBVl2EuP11ds0L/uewy0wZQ1n0tth2v34Bi+iKFQplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTggPj4Kc3RyZWFtCnicMzK3UDCAwxRDrjQAHjoDVwplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjY5ID4+CnN0cmVhbQp4nDVRy23FMAy7ewqOYP3teV5R9JDufy2loEAcKtGPpCMSG3r5im0oufiS1eFx/E6w8SzbA6xTgRlc+knBZ4XhslEh6rgHwomf1R9yCpIGVR7hyWBGLyfogbnBilg9q3uM3R49XOHnDIYqMxNxrt2LOMRyLt/d4xdpDpNCekLrRe6xeP9sEiVlqUTu09yCYg8JWyG8XtyzhwFXPS0q6qJbKF1IL3NkkURxoIqMV9pFxCZSEzkHJWm6E8cg56qkBb0iOHFQm3xHTjv8JpxGOT13iyHCzK6xo01ypWg/Y9IdsRbO7YG2U8ckNZrPWt20nrVyLqV1RmhXa5Ck6E09oX29n/97ftbP+v4D7U1hSgplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjEwID4+CnN0cmVhbQp4nDWQy40DMQxD765CDRgQ5a/qSRDsIdv/NaSdnMQxx0+kh7u5YVoNT+sJG0h7osywikz7L0OKf7zLAlXA9tbsBm9XPAoivmdtSZDZU/eOOXXRl2G51dycJOwla3er/EISNaaFD0YBnXCBdEJOWqAxqFgn4bsA+KqA7C3VmmG7RR/cz0ki1xE2J5Nti3WXRwavTDqdHS7mV56YZKjDW2w1++FUtRWY7YaQoX6DJtRucWNK7FRKPVV4MMW6TwuSj6DpuHKH5rDfWz/KX3l9AGuUSpIKZW5kc3RyZWFtCmVuZG9iagozNiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDExMSA+PgpzdHJlYW0KeJxNjjEOBDEIA3te4ScEQkLynj2dtuD+357JNluAByFbHq2hwWp5bAzd+Kic+1cSipQ++OWQ1OC+YLGoAasP9ZK+D2RBpaT47C/yWX5dBu2VqbaecJ2BcFZwxITRTrmk2hDyQFlPp5Rbvn/tfCI4CmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNzUgPj4Kc3RyZWFtCnicNVFLbgUxCNvPKXyBSvxJzvOqp256/21N0ifNCBKwMU5mQRCGL1WkLLRufOvDG0/H7yThzRK/RC1kNl7PYi4bSlQFY/DcU9DeaHaa+eGyzhNfj+u98WhGhXehdrISEkRvylgo0gc7ijkrVcjNyqK6CsQ2pBkrKRS25GgOzpo4iqeyYEUMcSbKLqO+fdgSm/S+kURRpcsIawXXtT4mjOCJr8fkZpr8nbsaVfGeLGo6ppnO8P+5P4/6x7XJzPP4otxIe/DrkAq4qjlXFg47Ycw5icea6lhz28eaIQiehnDiHTdZUPl0ZFxMrsEMSVnhcEbdIYwc7n5vaEsZn41PlucJlJbn2ZO2tuCzyqz1/gOaQ2YtCmVuZHN0cmVhbQplbmRvYmoKMTUgMCBvYmoKPDwgL0Jhc2VGb250IC9HT0ZZUFkrQXJpYWxNVCAvQ2hhclByb2NzIDE2IDAgUgovRW5jb2RpbmcgPDwKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ2IC9wZXJpb2QgNDggL3plcm8gL29uZSAvdHdvIDUyIC9mb3VyIC9maXZlIC9zaXggL3NldmVuCi9laWdodCAvbmluZSA3MSAvRyAvSCA5MSAvYnJhY2tldGxlZnQgOTMgL2JyYWNrZXRyaWdodCAxMDEgL2UgMTAzIC9nIDExMCAvbgoxMTQgL3IgMTIxIC95IC96IF0KL1R5cGUgL0VuY29kaW5nID4+Ci9GaXJzdENoYXIgMCAvRm9udEJCb3ggWyAtNjY1IC0zMjUgMjAwMCAxMDA2IF0gL0ZvbnREZXNjcmlwdG9yIDE0IDAgUgovRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXSAvTGFzdENoYXIgMjU1IC9OYW1lIC9HT0ZZUFkrQXJpYWxNVAovU3VidHlwZSAvVHlwZTMgL1R5cGUgL0ZvbnQgL1dpZHRocyAxMyAwIFIgPj4KZW5kb2JqCjE0IDAgb2JqCjw8IC9Bc2NlbnQgOTA2IC9DYXBIZWlnaHQgNzE2IC9EZXNjZW50IC0yMTIgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC02NjUgLTMyNSAyMDAwIDEwMDYgXSAvRm9udE5hbWUgL0dPRllQWStBcmlhbE1UIC9JdGFsaWNBbmdsZSAwCi9NYXhXaWR0aCAxMDE1IC9TdGVtViAwIC9UeXBlIC9Gb250RGVzY3JpcHRvciAvWEhlaWdodCA1MTkgPj4KZW5kb2JqCjEzIDAgb2JqClsgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAKNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCA3NTAgNzUwIDc1MCAyNzggMjc4IDM1NSA1NTYgNTU2Cjg4OSA2NjcgMTkxIDMzMyAzMzMgMzg5IDU4NCAyNzggMzMzIDI3OCAyNzggNTU2IDU1NiA1NTYgNTU2IDU1NiA1NTYgNTU2IDU1Ngo1NTYgNTU2IDI3OCAyNzggNTg0IDU4NCA1ODQgNTU2IDEwMTUgNjY3IDY2NyA3MjIgNzIyIDY2NyA2MTEgNzc4IDcyMiAyNzgKNTAwIDY2NyA1NTYgODMzIDcyMiA3NzggNjY3IDc3OCA3MjIgNjY3IDYxMSA3MjIgNjY3IDk0NCA2NjcgNjY3IDYxMSAyNzggMjc4CjI3OCA0NjkgNTU2IDMzMyA1NTYgNTU2IDUwMCA1NTYgNTU2IDI3OCA1NTYgNTU2IDIyMiAyMjIgNTAwIDIyMiA4MzMgNTU2IDU1Ngo1NTYgNTU2IDMzMyA1MDAgMjc4IDU1NiA1MDAgNzIyIDUwMCA1MDAgNTAwIDMzNCAyNjAgMzM0IDU4NCA3NTAgNTU2IDc1MCAyMjIKNTU2IDMzMyAxMDAwIDU1NiA1NTYgMzMzIDEwMDAgNjY3IDMzMyAxMDAwIDc1MCA2MTEgNzUwIDc1MCAyMjIgMjIyIDMzMyAzMzMKMzUwIDU1NiAxMDAwIDMzMyAxMDAwIDUwMCAzMzMgOTQ0IDc1MCA1MDAgNjY3IDI3OCAzMzMgNTU2IDU1NiA1NTYgNTU2IDI2MAo1NTYgMzMzIDczNyAzNzAgNTU2IDU4NCAzMzMgNzM3IDU1MiA0MDAgNTQ5IDMzMyAzMzMgMzMzIDU3NiA1MzcgMzMzIDMzMyAzMzMKMzY1IDU1NiA4MzQgODM0IDgzNCA2MTEgNjY3IDY2NyA2NjcgNjY3IDY2NyA2NjcgMTAwMCA3MjIgNjY3IDY2NyA2NjcgNjY3CjI3OCAyNzggMjc4IDI3OCA3MjIgNzIyIDc3OCA3NzggNzc4IDc3OCA3NzggNTg0IDc3OCA3MjIgNzIyIDcyMiA3MjIgNjY3IDY2Nwo2MTEgNTU2IDU1NiA1NTYgNTU2IDU1NiA1NTYgODg5IDUwMCA1NTYgNTU2IDU1NiA1NTYgMjc4IDI3OCAyNzggMjc4IDU1NiA1NTYKNTU2IDU1NiA1NTYgNTU2IDU1NiA1NDkgNjExIDU1NiA1NTYgNTU2IDU1NiA1MDAgNTU2IDUwMCBdCmVuZG9iagoxNiAwIG9iago8PCAvRyAxNyAwIFIgL0ggMTggMCBSIC9icmFja2V0bGVmdCAxOSAwIFIgL2JyYWNrZXRyaWdodCAyMCAwIFIgL2UgMjEgMCBSCi9laWdodCAyMiAwIFIgL2ZpdmUgMjMgMCBSIC9mb3VyIDI0IDAgUiAvZyAyNSAwIFIgL24gMjYgMCBSIC9uaW5lIDI3IDAgUgovb25lIDI4IDAgUiAvcGVyaW9kIDI5IDAgUiAvciAzMCAwIFIgL3NldmVuIDMxIDAgUiAvc2l4IDMyIDAgUgovc3BhY2UgMzMgMCBSIC90d28gMzQgMCBSIC95IDM1IDAgUiAveiAzNiAwIFIgL3plcm8gMzcgMCBSID4+CmVuZG9iago0MiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMzMCA+PgpzdHJlYW0KeJw9kktuxEAIRPc+BRcYqfn15zyORllM7r/No9vJwipkoCio7prSJLq81CWHSvYmX3rFVHEz+XmiITFN3A10cMp97SiCPwGug5k7Q7RSYlBvXaKyfGTs/PX19OXB+/J2GGyUniXm1adkrMGCPB1dfIA5t7b7X+Xn+r4y6clSrHRV9LnUi2eJ+hCzP6xOhd3YfWNbD8aTqQmaTdRq6pS1towuA32R0k1s9QJ0e1Tg3K6juIr2CRDBfYIOKhNq1diSkZBoi6xfnKcJ3RMH9hXQ/dImrmNbYtM2nvEVaQxBd8OROg4zxygD1eaGtaezTZaZ4kyuu2VxrBJolNZpqS3Bs+p1UcwZzEArhWiOVmMrQ7NDX2/D+0FmeNtRjEUtVXr2vLECw/rY7yn04HkYO4I3Mdkt6MdKViGDFpswdZaud/jYeWPt+xdE834qCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxODEgPj4Kc3RyZWFtCnicPZBLEgMhCET3nqKPID/R8ySVymJy/20aJ2ah/QQKG2wpOobzcjVkVzylFYYnPs37gmfiamr9R5IKmX1Th4zFM3mKExKKR5MxIO7YauuncTLCDLssJmZgroqvRC6oUALqUfJoZqPgaqaCvn3EVpn1FhcIRQImLGcr/p8D0+sWHQwWmO03y7M89grTdCzMe4Z5D0UjfgblWm5gZa2Dn4Ydmve2aCj/5Cd7Fni1d3t9AWR8QxMKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDk1ID4+CnN0cmVhbQp4nD2MQQ7AIAgE77xiP9AEERX/0zQ92P9fu0bbC0x2YUo3KA4rnFUVxRvOJB8+kr3DWseQoplHQ5zd3BYOS40Uq1gWFp5hEaS0Ncz4vChrYEop6mln9b+75XoB/58cLAplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9CYXNlRm9udCAvR0NXWERWK0RlamFWdVNhbnMtT2JsaXF1ZSA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@@ -20734,617 +309,117 @@
    ],
    "source": [
     "flux_list = np.linspace(0, 1, 27)\n",
-    "zero_pi.plot_evals_vs_paramvals('flux', flux_list, evals_count=8);"
+    "zero_pi.plot_evals_vs_paramvals(param_name='flux', param_vals=flux_list, evals_count=8)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Matrix elements.** Similar to other qubits, matrix elements can be obtained and visualized as follows:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
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       "text/plain": [
-       "<Figure size 1200x450 with 2 Axes>"
+       "array([[-9.99999988e-02, -9.08155057e-06, -1.87089570e+00,\n",
+       "        -7.30546369e-03,  4.15189011e-06,  2.86984696e-08],\n",
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+       "        -8.42335338e-04,  7.37293731e-07, -9.99999360e-02]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero_pi.matrixelement_table(operator=\"n_theta_operator\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 720x360 with 3 Axes>, (<Axes3DSubplot:>, <AxesSubplot:>))"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 720x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "zero_pi.plot_matrixelements(operator='n_theta_operator', evals_count=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d9f0d4439a340e4b34515048437cb0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Spectral data:   0%|          | 0/27 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 432x288 with 1 Axes>,\n",
+       " <AxesSubplot:xlabel='$\\\\Phi_{ext}/\\\\Phi_0$', ylabel='matrix element'>)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/pdf": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -21354,12 +429,43 @@
     }
    ],
    "source": [
-    "fig, ax = zero_pi.plot_wavefunction(which=0, mode='real', zero_calibrate=True);"
+    "zero_pi.plot_matelem_vs_paramvals(operator='n_theta_operator', param_name='flux', param_vals=flux_list)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Plot wavefunctions.** "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/pdf": 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+      "text/plain": [
+       "<Figure size 576x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = zero_pi.plot_wavefunction(which=0, mode='real', zero_calibrate=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 33,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2016-08-14T10:44:42.893578",
@@ -21369,607 +475,63 @@
    "outputs": [
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       "text/plain": [
-       "<Figure size 1200x450 with 2 Axes>"
+       "(<Figure size 576x216 with 2 Axes>,\n",
+       " <AxesSubplot:xlabel='$\\\\phi$', ylabel='$\\\\theta$'>)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
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+      "text/plain": [
+       "<Figure size 576x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "zero_pi.plot_wavefunction(which=1, mode='real', zero_calibrate=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Visualizing potentials.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 576x96 with 2 Axes>,\n",
+       " <AxesSubplot:xlabel='$\\\\phi$', ylabel='$\\\\theta$'>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "application/pdf": 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70.146946 \nL 178.666927 70.511168 \nL 178.645505 70.875389 \nL 178.622033 71.23961 \nL 178.596355 71.603831 \nL 178.568296 71.968052 \nL 178.537654 72.332273 \nL 178.504199 72.696494 \nL 178.467669 73.060715 \nL 178.427758 73.424936 \nL 178.384117 73.789158 \nL 178.336339 74.153379 \nL 178.283951 74.5176 \nL 178.226399 74.881821 \nL 178.16303 75.246042 \nL 178.093069 75.610263 \nL 178.015595 75.974484 \nL 177.929495 76.338705 \nL 177.833422 76.702926 \nL 177.725726 77.067147 \nL 177.604365 77.431369 \nL 177.46678 77.79559 \nL 177.412578 77.926292 \nL 177.305654 78.159811 \nL 177.117882 78.524032 \nL 176.899976 78.888253 \nL 176.6444 79.252474 \nL 176.340865 79.616695 \nL 175.974946 79.980916 \nL 175.525782 80.345137 \nL 175.231082 80.54603 \nL 174.942675 80.709359 \nL 174.16472 81.07358 \nL 173.12807 81.437801 \nL 173.049587 81.46115 \nL 171.542107 81.802022 \nL 170.868091 81.922914 \nL 168.973509 82.166243 \nL 168.686595 82.197702 \nL 166.505099 82.37699 \nL 164.323604 82.501332 \nL 163.625238 82.530464 \nL 162.142108 82.590864 \nL 159.960612 82.657058 \nL 157.779117 82.706811 \nL 155.597621 82.744516 \nL 153.416125 82.77321 \nL 151.234629 82.795316 \nL 149.053134 82.813654 \nL 146.871638 82.835261 \nL 145.008467 82.894685 \nL 144.690142 82.929751 \nL 144.069591 83.258906 \nz\nM 301.889857 81.07358 \nL 301.757834 81.062404 \nL 299.576338 80.769057 \nL 299.269141 80.709359 \nL 297.653759 80.345137 \nL 297.394842 80.277377 \nL 296.535525 79.980916 \nL 295.665211 79.616695 \nL 295.213347 79.3945 \nL 294.977917 79.252474 \nL 294.445063 78.888253 \nL 293.993644 78.524032 \nL 293.60677 78.159811 \nL 293.27193 77.79559 \nL 293.031851 77.499544 \nL 292.983422 77.431369 \nL 292.745017 77.067147 \nL 292.534125 76.702926 \nL 292.346522 76.338705 \nL 292.178813 75.974484 \nL 292.028245 75.610263 \nL 291.892555 75.246042 \nL 291.769874 74.881821 \nL 291.658639 74.5176 \nL 291.557539 74.153379 \nL 291.465462 73.789158 \nL 291.381462 73.424936 \nL 291.30473 73.060715 \nL 291.234569 72.696494 \nL 291.170379 72.332273 \nL 291.111636 71.968052 \nL 291.057888 71.603831 \nL 291.008737 71.23961 \nL 290.963838 70.875389 \nL 290.922886 70.511168 \nL 290.885614 70.146946 \nL 290.85179 69.782725 \nL 290.850355 69.765842 \nL 290.822285 69.418504 \nL 290.795782 69.054283 \nL 290.772077 68.690062 \nL 290.751036 68.325841 \nL 290.732546 67.96162 \nL 290.716507 67.597399 \nL 290.702835 67.233178 \nL 290.69146 66.868957 \nL 290.682324 66.504735 \nL 290.67538 66.140514 \nL 290.670595 65.776293 \nL 290.667944 65.412072 \nL 290.667414 65.047851 \nL 290.669004 64.68363 \nL 290.672719 64.319409 \nL 290.67858 63.955188 \nL 290.686615 63.590967 \nL 290.696864 63.226745 \nL 290.70938 62.862524 \nL 290.724225 62.498303 \nL 290.741479 62.134082 \nL 290.761231 61.769861 \nL 290.783588 61.40564 \nL 290.808675 61.041419 \nL 290.836632 60.677198 \nL 290.850355 60.514718 \nL 290.868285 60.312977 \nL 290.903805 59.948755 \nL 290.942886 59.584534 \nL 290.985776 59.220313 \nL 291.032761 58.856092 \nL 291.084164 58.491871 \nL 291.140356 58.12765 \nL 291.201762 57.763429 \nL 291.268868 57.399208 \nL 291.342233 57.034987 \nL 291.422505 56.670766 \nL 291.510435 56.306544 \nL 291.606896 55.942323 \nL 291.712913 55.578102 \nL 291.829693 55.213881 \nL 291.958665 54.84966 \nL 292.101538 54.485439 \nL 292.260366 54.121218 \nL 292.437641 53.756997 \nL 292.636418 53.392776 \nL 292.860477 53.028554 \nL 293.031851 52.778477 \nL 293.120972 52.664333 \nL 293.433497 52.300112 \nL 293.793006 51.935891 \nL 294.210352 51.57167 \nL 294.700033 51.207449 \nL 295.213347 50.883039 \nL 295.290334 50.843228 \nL 296.078272 50.479007 \nL 297.04431 50.114786 \nL 297.394842 50.000522 \nL 298.411432 49.750565 \nL 299.576338 49.508874 \nL 300.427802 49.386343 \nL 301.757834 49.215648 \nL 303.939329 49.042361 \nL 304.42808 49.022122 \nL 306.120825 48.954221 \nL 308.302321 48.936408 \nL 310.483817 48.988246 \nL 311.068521 49.022122 \nL 312.665312 49.123654 \nL 314.846808 49.37834 \nL 314.893198 49.386343 \nL 316.671934 49.750565 \nL 317.028304 49.840052 \nL 317.839493 50.114786 \nL 318.695756 50.479007 \nL 319.209799 50.747912 \nL 319.357788 50.843228 \nL 319.853969 51.207449 \nL 320.264213 51.57167 \nL 320.608676 51.935891 \nL 320.901656 52.300112 \nL 321.153582 52.664333 \nL 321.372232 53.028554 \nL 321.391295 53.063073 \nL 321.553975 53.392776 \nL 321.713175 53.756997 \nL 321.854292 54.121218 \nL 321.980041 54.485439 \nL 322.092609 54.84966 \nL 322.193783 55.213881 \nL 322.285032 55.578102 \nL 322.367577 55.942323 \nL 322.442439 56.306544 \nL 322.510479 56.670766 \nL 322.572428 57.034987 \nL 322.628909 57.399208 \nL 322.680456 57.763429 \nL 322.727528 58.12765 \nL 322.770523 58.491871 \nL 322.809787 58.856092 \nL 322.84562 59.220313 \nL 322.878284 59.584534 \nL 322.908008 59.948755 \nL 322.934993 60.312977 \nL 322.959413 60.677198 \nL 322.981423 61.041419 \nL 323.001156 61.40564 \nL 323.01873 61.769861 \nL 323.034245 62.134082 \nL 323.047789 62.498303 \nL 323.059438 62.862524 \nL 323.069253 63.226745 \nL 323.077289 63.590967 \nL 323.083586 63.955188 \nL 323.088179 64.319409 \nL 323.09109 64.68363 \nL 323.092335 65.047851 \nL 323.091921 65.412072 \nL 323.089844 65.776293 \nL 323.086094 66.140514 \nL 323.080653 66.504735 \nL 323.073491 66.868957 \nL 323.064571 67.233178 \nL 323.053846 67.597399 \nL 323.041258 67.96162 \nL 323.026739 68.325841 \nL 323.010206 68.690062 \nL 322.991566 69.054283 \nL 322.970711 69.418504 \nL 322.947513 69.782725 \nL 322.921831 70.146946 \nL 322.893501 70.511168 \nL 322.862333 70.875389 \nL 322.828115 71.23961 \nL 322.790602 71.603831 \nL 322.749512 71.968052 \nL 322.704525 72.332273 \nL 322.655269 72.696494 \nL 322.601317 73.060715 \nL 322.542174 73.424936 \nL 322.477263 73.789158 \nL 322.405911 74.153379 \nL 322.327325 74.5176 \nL 322.240567 74.881821 \nL 322.144522 75.246042 \nL 322.037853 75.610263 \nL 321.91894 75.974484 \nL 321.78581 76.338705 \nL 321.636029 76.702926 \nL 321.466564 77.067147 \nL 321.391295 77.214232 \nL 321.26666 77.431369 \nL 321.032214 77.79559 \nL 320.760876 78.159811 \nL 320.443664 78.524032 \nL 320.068406 78.888253 \nL 319.618187 79.252474 \nL 319.209799 79.529912 \nL 319.053455 79.616695 \nL 318.293953 79.980916 \nL 317.321451 80.345137 \nL 317.028304 80.437765 \nL 315.855496 80.709359 \nL 314.846808 80.899199 \nL 313.423697 81.07358 \nL 312.665312 81.1542 \nL 310.483817 81.289404 \nL 308.302321 81.34138 \nL 306.120825 81.323519 \nL 303.939329 81.235258 \nz\nM 227.769105 80.709359 \nL 227.586979 80.688872 \nL 225.653405 80.345137 \nL 225.405484 80.293406 \nL 224.327931 79.980916 \nL 223.321307 79.616695 \nL 223.223988 79.576617 \nL 222.600977 79.252474 \nL 222.018214 78.888253 \nL 221.530599 78.524032 \nL 221.11707 78.159811 \nL 221.042492 78.087167 \nL 220.785601 77.79559 \nL 220.503845 77.431369 \nL 220.257751 77.067147 \nL 220.041256 76.702926 \nL 219.849606 76.338705 \nL 219.679023 75.974484 \nL 219.526466 75.610263 \nL 219.389463 75.246042 \nL 219.265982 74.881821 \nL 219.15434 74.5176 \nL 219.05313 74.153379 \nL 218.961168 73.789158 \nL 218.877451 73.424936 \nL 218.860997 73.34808 \nL 218.803766 73.060715 \nL 218.737174 72.696494 \nL 218.676347 72.332273 \nL 218.620765 71.968052 \nL 218.569978 71.603831 \nL 218.523593 71.23961 \nL 218.481268 70.875389 \nL 218.442705 70.511168 \nL 218.40764 70.146946 \nL 218.375846 69.782725 \nL 218.347121 69.418504 \nL 218.321289 69.054283 \nL 218.298198 68.690062 \nL 218.277714 68.325841 \nL 218.259721 67.96162 \nL 218.24412 67.597399 \nL 218.230826 67.233178 \nL 218.219769 66.868957 \nL 218.21089 66.504735 \nL 218.204144 66.140514 \nL 218.199495 65.776293 \nL 218.19692 65.412072 \nL 218.196405 65.047851 \nL 218.197949 64.68363 \nL 218.201559 64.319409 \nL 218.207253 63.955188 \nL 218.215061 63.590967 \nL 218.225022 63.226745 \nL 218.237189 62.862524 \nL 218.251627 62.498303 \nL 218.268413 62.134082 \nL 218.287638 61.769861 \nL 218.30941 61.40564 \nL 218.333853 61.041419 \nL 218.361111 60.677198 \nL 218.391347 60.312977 \nL 218.42475 59.948755 \nL 218.461534 59.584534 \nL 218.501943 59.220313 \nL 218.546258 58.856092 \nL 218.594798 58.491871 \nL 218.64793 58.12765 \nL 218.706074 57.763429 \nL 218.769714 57.399208 \nL 218.839409 57.034987 \nL 218.860997 56.929979 \nL 218.918335 56.670766 \nL 219.006059 56.306544 \nL 219.102509 55.942323 \nL 219.208775 55.578102 \nL 219.326145 55.213881 \nL 219.456156 54.84966 \nL 219.600657 54.485439 \nL 219.761886 54.121218 \nL 219.942582 53.756997 \nL 220.146127 53.392776 \nL 220.376755 53.028554 \nL 220.639823 52.664333 \nL 220.942221 52.300112 \nL 221.042492 52.190774 \nL 221.315638 51.935891 \nL 221.763994 51.57167 \nL 222.296087 51.207449 \nL 222.93692 50.843228 \nL 223.223988 50.701454 \nL 223.796232 50.479007 \nL 224.927051 50.114786 \nL 225.405484 49.98456 \nL 226.611359 49.750565 \nL 227.586979 49.589188 \nL 229.551269 49.386343 \nL 229.768475 49.366183 \nL 231.949971 49.255322 \nL 234.131467 49.230434 \nL 236.312962 49.285298 \nL 237.872826 49.386343 \nL 238.494458 49.429095 \nL 240.675954 49.6911 \nL 241.007517 49.750565 \nL 242.762195 50.114786 \nL 242.857449 50.137376 \nL 243.923558 50.479007 \nL 244.85136 50.843228 \nL 245.038945 50.92673 \nL 245.544762 51.207449 \nL 246.106354 51.57167 \nL 246.583467 51.935891 \nL 246.993341 52.300112 \nL 247.220441 52.527641 \nL 247.337997 52.664333 \nL 247.62257 53.028554 \nL 247.873098 53.392776 \nL 248.095025 53.756997 \nL 248.292683 54.121218 \nL 248.469563 54.485439 \nL 248.628506 54.84966 \nL 248.771847 55.213881 \nL 248.901524 55.578102 \nL 249.019159 55.942323 \nL 249.126113 56.306544 \nL 249.223545 56.670766 \nL 249.31244 57.034987 \nL 249.393641 57.399208 \nL 249.401937 57.439041 \nL 249.464758 57.763429 \nL 249.529443 58.12765 \nL 249.588609 58.491871 \nL 249.642709 58.856092 \nL 249.692139 59.220313 \nL 249.737246 59.584534 \nL 249.778333 59.948755 \nL 249.815666 60.312977 \nL 249.849478 60.677198 \nL 249.879975 61.041419 \nL 249.907335 61.40564 \nL 249.931715 61.769861 \nL 249.953251 62.134082 \nL 249.972059 62.498303 \nL 249.988242 62.862524 \nL 250.001882 63.226745 \nL 250.013052 63.590967 \nL 250.021808 63.955188 \nL 250.028195 64.319409 \nL 250.032244 64.68363 \nL 250.033975 65.047851 \nL 250.033398 65.412072 \nL 250.03051 65.776293 \nL 250.025295 66.140514 \nL 250.017729 66.504735 \nL 250.007772 66.868957 \nL 249.995375 67.233178 \nL 249.980473 67.597399 \nL 249.962989 67.96162 \nL 249.942831 68.325841 \nL 249.919889 68.690062 \nL 249.894037 69.054283 \nL 249.865129 69.418504 \nL 249.832998 69.782725 \nL 249.797453 70.146946 \nL 249.758274 70.511168 \nL 249.715213 70.875389 \nL 249.667985 71.23961 \nL 249.616266 71.603831 \nL 249.559686 71.968052 \nL 249.497821 72.332273 \nL 249.430186 72.696494 \nL 249.401937 72.837757 \nL 249.353954 73.060715 \nL 249.269004 73.424936 \nL 249.175954 73.789158 \nL 249.073894 74.153379 \nL 248.961756 74.5176 \nL 248.838286 74.881821 \nL 248.701997 75.246042 \nL 248.55112 75.610263 \nL 248.38353 75.974484 \nL 248.196652 76.338705 \nL 247.987343 76.702926 \nL 247.751719 77.067147 \nL 247.484932 77.431369 \nL 247.220441 77.750539 \nL 247.177207 77.79559 \nL 246.795922 78.159811 \nL 246.35427 78.524032 \nL 245.837417 78.888253 \nL 245.22521 79.252474 \nL 245.038945 79.351052 \nL 244.41057 79.616695 \nL 243.382813 79.980916 \nL 242.857449 80.140142 \nL 241.938238 80.345137 \nL 240.675954 80.586585 \nL 239.708777 80.709359 \nL 238.494458 80.848564 \nL 236.312962 80.992534 \nL 234.131467 81.047573 \nL 231.949971 81.022606 \nL 229.768475 80.911394 \nz\nM 265.780782 80.709359 \nL 264.672407 80.599893 \nL 263.104659 80.345137 \nL 262.490911 80.230769 \nL 261.549246 79.980916 \nL 260.413503 79.616695 \nL 260.309415 79.578953 \nL 259.610243 79.252474 \nL 258.956428 78.888253 \nL 258.405694 78.524032 \nL 258.127919 78.314797 \nL 257.955358 78.159811 \nL 257.591308 77.79559 \nL 257.274618 77.431369 \nL 256.996982 77.067147 \nL 256.75194 76.702926 \nL 256.534391 76.338705 \nL 256.340259 75.974484 \nL 256.166243 75.610263 \nL 256.009648 75.246042 \nL 255.946424 75.086482 \nL 255.872988 74.881821 \nL 255.752688 74.5176 \nL 255.643456 74.153379 \nL 255.544062 73.789158 \nL 255.453462 73.424936 \nL 255.370762 73.060715 \nL 255.295197 72.696494 \nL 255.226104 72.332273 \nL 255.162912 71.968052 \nL 255.105122 71.603831 \nL 255.052301 71.23961 \nL 255.00407 70.875389 \nL 254.960096 70.511168 \nL 254.92009 70.146946 \nL 254.883795 69.782725 \nL 254.850988 69.418504 \nL 254.821473 69.054283 \nL 254.79508 68.690062 \nL 254.771658 68.325841 \nL 254.751078 67.96162 \nL 254.733229 67.597399 \nL 254.718017 67.233178 \nL 254.705361 66.868957 \nL 254.695197 66.504735 \nL 254.687473 66.140514 \nL 254.68215 65.776293 \nL 254.679202 65.412072 \nL 254.678613 65.047851 \nL 254.680381 64.68363 \nL 254.684514 64.319409 \nL 254.691033 63.955188 \nL 254.699971 63.590967 \nL 254.711374 63.226745 \nL 254.725299 62.862524 \nL 254.741819 62.498303 \nL 254.76102 62.134082 \nL 254.783006 61.769861 \nL 254.807896 61.40564 \nL 254.83583 61.041419 \nL 254.866968 60.677198 \nL 254.901493 60.312977 \nL 254.939614 59.948755 \nL 254.98157 59.584534 \nL 255.027634 59.220313 \nL 255.078116 58.856092 \nL 255.13337 58.491871 \nL 255.193803 58.12765 \nL 255.259879 57.763429 \nL 255.332131 57.399208 \nL 255.411175 57.034987 \nL 255.497722 56.670766 \nL 255.592599 56.306544 \nL 255.69677 55.942323 \nL 255.811369 55.578102 \nL 255.937733 55.213881 \nL 255.946424 55.19041 \nL 256.085917 54.84966 \nL 256.250917 54.485439 \nL 256.43462 54.121218 \nL 256.640004 53.756997 \nL 256.870733 53.392776 \nL 257.131359 53.028554 \nL 257.427613 52.664333 \nL 257.766804 52.300112 \nL 258.127919 51.962557 \nL 258.161836 51.935891 \nL 258.669719 51.57167 \nL 259.268772 51.207449 \nL 259.984994 50.843228 \nL 260.309415 50.69913 \nL 260.950898 50.479007 \nL 262.219341 50.114786 \nL 262.490911 50.046935 \nL 264.19355 49.750565 \nL 264.672407 49.677841 \nL 266.853902 49.47146 \nL 268.797579 49.386343 \nL 269.035398 49.37642 \nL 271.216894 49.372813 \nL 271.569588 49.386343 \nL 273.398389 49.4592 \nL 275.579885 49.652008 \nL 276.262379 49.750565 \nL 277.761381 49.995135 \nL 278.26796 50.114786 \nL 279.594901 50.479007 \nL 279.942877 50.589253 \nL 280.556504 50.843228 \nL 281.306176 51.207449 \nL 281.935958 51.57167 \nL 282.124372 51.693899 \nL 282.43332 51.935891 \nL 282.843177 52.300112 \nL 283.199035 52.664333 \nL 283.5105 53.028554 \nL 283.785015 53.392776 \nL 284.028437 53.756997 \nL 284.245434 54.121218 \nL 284.305868 54.231189 \nL 284.430336 54.485439 \nL 284.592777 54.84966 \nL 284.739371 55.213881 \nL 284.872071 55.578102 \nL 284.992512 55.942323 \nL 285.102073 56.306544 \nL 285.201923 56.670766 \nL 285.293062 57.034987 \nL 285.376344 57.399208 \nL 285.452509 57.763429 \nL 285.522195 58.12765 \nL 285.585956 58.491871 \nL 285.644277 58.856092 \nL 285.697578 59.220313 \nL 285.74623 59.584534 \nL 285.790556 59.948755 \nL 285.830841 60.312977 \nL 285.867335 60.677198 \nL 285.900255 61.041419 \nL 285.929794 61.40564 \nL 285.95612 61.769861 \nL 285.979376 62.134082 \nL 285.999691 62.498303 \nL 286.01717 62.862524 \nL 286.031905 63.226745 \nL 286.043972 63.590967 \nL 286.053432 63.955188 \nL 286.060332 64.319409 \nL 286.064706 64.68363 \nL 286.066578 65.047851 \nL 286.065954 65.412072 \nL 286.062833 65.776293 \nL 286.057199 66.140514 \nL 286.049024 66.504735 \nL 286.038268 66.868957 \nL 286.024875 67.233178 \nL 286.008779 67.597399 \nL 285.989894 67.96162 \nL 285.968123 68.325841 \nL 285.943349 68.690062 \nL 285.915436 69.054283 \nL 285.884229 69.418504 \nL 285.849548 69.782725 \nL 285.811187 70.146946 \nL 285.768915 70.511168 \nL 285.722464 70.875389 \nL 285.671531 71.23961 \nL 285.615769 71.603831 \nL 285.554785 71.968052 \nL 285.488125 72.332273 \nL 285.415274 72.696494 \nL 285.335636 73.060715 \nL 285.248525 73.424936 \nL 285.153146 73.789158 \nL 285.048575 74.153379 \nL 284.933733 74.5176 \nL 284.807349 74.881821 \nL 284.667925 75.246042 \nL 284.513674 75.610263 \nL 284.342455 75.974484 \nL 284.305868 76.046848 \nL 284.139984 76.338705 \nL 283.910297 76.702926 \nL 283.651979 77.067147 \nL 283.359809 77.431369 \nL 283.027196 77.79559 \nL 282.645704 78.159811 \nL 282.204349 78.524032 \nL 282.124372 78.584133 \nL 281.634228 78.888253 \nL 280.948312 79.252474 \nL 280.125816 79.616695 \nL 279.942877 79.688667 \nL 278.967558 79.980916 \nL 277.761381 80.282787 \nL 277.403104 80.345137 \nL 275.579885 80.625821 \nL 274.675377 80.709359 \nL 273.398389 80.818561 \nL 271.216894 80.904743 \nL 269.035398 80.901125 \nL 266.853902 80.806344 \nz\nM 194.217 79.980916 \nL 192.683048 79.755538 \nL 192.058337 79.616695 \nL 190.610845 79.252474 \nL 190.501552 79.222122 \nL 189.611061 78.888253 \nL 188.775818 78.524032 \nL 188.320057 78.299235 \nL 188.093725 78.159811 \nL 187.558275 77.79559 \nL 187.08891 77.431369 \nL 186.674662 77.067147 \nL 186.306878 76.702926 \nL 186.138561 76.520273 \nL 185.995056 76.338705 \nL 185.731048 75.974484 \nL 185.493482 75.610263 \nL 185.278953 75.246042 \nL 185.08463 74.881821 \nL 184.908143 74.5176 \nL 184.74749 74.153379 \nL 184.600972 73.789158 \nL 184.467138 73.424936 \nL 184.344742 73.060715 \nL 184.23271 72.696494 \nL 184.130112 72.332273 \nL 184.036139 71.968052 \nL 183.957065 71.633919 \nL 183.950536 71.603831 \nL 183.876884 71.23961 \nL 183.809559 70.875389 \nL 183.748118 70.511168 \nL 183.692169 70.146946 \nL 183.641371 69.782725 \nL 183.595421 69.418504 \nL 183.554054 69.054283 \nL 183.51704 68.690062 \nL 183.484175 68.325841 \nL 183.455286 67.96162 \nL 183.430219 67.597399 \nL 183.408848 67.233178 \nL 183.391063 66.868957 \nL 183.376777 66.504735 \nL 183.365918 66.140514 \nL 183.358433 65.776293 \nL 183.354287 65.412072 \nL 183.353459 65.047851 \nL 183.355945 64.68363 \nL 183.361757 64.319409 \nL 183.370923 63.955188 \nL 183.383488 63.590967 \nL 183.399513 63.226745 \nL 183.419079 62.862524 \nL 183.442283 62.498303 \nL 183.469244 62.134082 \nL 183.5001 61.769861 \nL 183.535016 61.40564 \nL 183.57418 61.041419 \nL 183.617807 60.677198 \nL 183.666146 60.312977 \nL 183.71948 59.948755 \nL 183.778129 59.584534 \nL 183.84246 59.220313 \nL 183.912889 58.856092 \nL 183.957065 58.645431 \nL 183.992162 58.491871 \nL 184.082094 58.12765 \nL 184.180285 57.763429 \nL 184.287492 57.399208 \nL 184.404581 57.034987 \nL 184.532552 56.670766 \nL 184.67256 56.306544 \nL 184.82595 55.942323 \nL 184.99429 55.578102 \nL 185.179423 55.213881 \nL 185.383527 54.84966 \nL 185.609188 54.485439 \nL 185.859508 54.121218 \nL 186.138232 53.756997 \nL 186.138561 53.756594 \nL 186.485429 53.392776 \nL 186.875484 53.028554 \nL 187.31608 52.664333 \nL 187.816943 52.300112 \nL 188.320057 51.978233 \nL 188.402538 51.935891 \nL 189.177577 51.57167 \nL 190.080029 51.207449 \nL 190.501552 51.056033 \nL 191.303391 50.843228 \nL 192.683048 50.522046 \nL 192.962193 50.479007 \nL 194.864544 50.209433 \nL 196.099984 50.114786 \nL 197.046039 50.045439 \nL 199.227535 50.000186 \nL 201.409031 50.068042 \nL 201.954185 50.114786 \nL 203.590527 50.267607 \nL 204.86518 50.479007 \nL 205.772022 50.650727 \nL 206.46701 50.843228 \nL 207.587557 51.207449 \nL 207.953518 51.344217 \nL 208.420929 51.57167 \nL 209.070551 51.935891 \nL 209.623391 52.300112 \nL 210.098988 52.664333 \nL 210.135014 52.694417 \nL 210.470547 53.028554 \nL 210.792507 53.392776 \nL 211.076427 53.756997 \nL 211.328291 54.121218 \nL 211.552877 54.485439 \nL 211.754045 54.84966 \nL 211.934947 55.213881 \nL 212.098184 55.578102 \nL 212.245914 55.942323 \nL 212.316509 56.131234 \nL 212.375728 56.306544 \nL 212.489499 56.670766 \nL 212.593135 57.034987 \nL 212.687665 57.399208 \nL 212.773972 57.763429 \nL 212.852816 58.12765 \nL 212.924857 58.491871 \nL 212.990665 58.856092 \nL 213.05074 59.220313 \nL 213.105517 59.584534 \nL 213.155375 59.948755 \nL 213.200647 60.312977 \nL 213.241625 60.677198 \nL 213.278564 61.041419 \nL 213.311688 61.40564 \nL 213.341191 61.769861 \nL 213.367242 62.134082 \nL 213.389986 62.498303 \nL 213.409548 62.862524 \nL 213.426033 63.226745 \nL 213.43953 63.590967 \nL 213.450108 63.955188 \nL 213.457823 64.319409 \nL 213.462713 64.68363 \nL 213.464805 65.047851 \nL 213.464108 65.412072 \nL 213.460619 65.776293 \nL 213.454321 66.140514 \nL 213.44518 66.504735 \nL 213.433151 66.868957 \nL 213.418169 67.233178 \nL 213.400157 67.597399 \nL 213.379019 67.96162 \nL 213.354638 68.325841 \nL 213.326881 68.690062 \nL 213.295591 69.054283 \nL 213.260585 69.418504 \nL 213.221656 69.782725 \nL 213.178565 70.146946 \nL 213.131039 70.511168 \nL 213.078766 70.875389 \nL 213.021391 71.23961 \nL 212.958507 71.603831 \nL 212.889649 71.968052 \nL 212.814284 72.332273 \nL 212.731797 72.696494 \nL 212.64148 73.060715 \nL 212.542516 73.424936 \nL 212.433951 73.789158 \nL 212.316509 74.14794 \nL 212.314542 74.153379 \nL 212.173867 74.5176 \nL 212.01863 74.881821 \nL 211.846855 75.246042 \nL 211.656176 75.610263 \nL 211.443733 75.974484 \nL 211.206043 76.338705 \nL 210.938815 76.702926 \nL 210.636713 77.067147 \nL 210.293007 77.431369 \nL 210.135014 77.582887 \nL 209.869837 77.79559 \nL 209.35771 78.159811 \nL 208.759307 78.524032 \nL 208.05183 78.888253 \nL 207.953518 78.933886 \nL 207.05537 79.252474 \nL 205.813295 79.616695 \nL 205.772022 79.627501 \nL 203.780696 79.980916 \nL 203.590527 80.010492 \nL 201.409031 80.209572 \nL 199.227535 80.277714 \nL 197.046039 80.23227 \nL 194.864544 80.068407 \nz\nM 339.595444 79.616695 \nL 338.843261 79.576103 \nL 336.661765 79.293483 \nL 336.468674 79.252474 \nL 334.944948 78.888253 \nL 334.480269 78.760631 \nL 333.861213 78.524032 \nL 333.03547 78.159811 \nL 332.332333 77.79559 \nL 332.298774 77.776582 \nL 331.811922 77.431369 \nL 331.363529 77.067147 \nL 330.970431 76.702926 \nL 330.623505 76.338705 \nL 330.315548 75.974484 \nL 330.117278 75.714894 \nL 330.048269 75.610263 \nL 329.826041 75.246042 \nL 329.626042 74.881821 \nL 329.445459 74.5176 \nL 329.281948 74.153379 \nL 329.133539 73.789158 \nL 328.998572 73.424936 \nL 328.875634 73.060715 \nL 328.763515 72.696494 \nL 328.66118 72.332273 \nL 328.567734 71.968052 \nL 328.482401 71.603831 \nL 328.404509 71.23961 \nL 328.333471 70.875389 \nL 328.268777 70.511168 \nL 328.209978 70.146946 \nL 328.156684 69.782725 \nL 328.108552 69.418504 \nL 328.065282 69.054283 \nL 328.026613 68.690062 \nL 327.992319 68.325841 \nL 327.962202 67.96162 \nL 327.936094 67.597399 \nL 327.935782 67.592322 \nL 327.91507 67.233178 \nL 327.897598 66.868957 \nL 327.883571 66.504735 \nL 327.872912 66.140514 \nL 327.865568 65.776293 \nL 327.861501 65.412072 \nL 327.860688 65.047851 \nL 327.863127 64.68363 \nL 327.868829 64.319409 \nL 327.877824 63.955188 \nL 327.890159 63.590967 \nL 327.905898 63.226745 \nL 327.925125 62.862524 \nL 327.935782 62.691762 \nL 327.948657 62.498303 \nL 327.97675 62.134082 \nL 328.008932 61.769861 \nL 328.045387 61.40564 \nL 328.086326 61.041419 \nL 328.131992 60.677198 \nL 328.182666 60.312977 \nL 328.238666 59.948755 \nL 328.300361 59.584534 \nL 328.368167 59.220313 \nL 328.442563 58.856092 \nL 328.524097 58.491871 \nL 328.613397 58.12765 \nL 328.711184 57.763429 \nL 328.81829 57.399208 \nL 328.935679 57.034987 \nL 329.064469 56.670766 \nL 329.205966 56.306544 \nL 329.361701 55.942323 \nL 329.53348 55.578102 \nL 329.723451 55.213881 \nL 329.934179 54.84966 \nL 330.117278 54.562764 \nL 330.174337 54.485439 \nL 330.465045 54.121218 \nL 330.791685 53.756997 \nL 331.16069 53.392776 \nL 331.580152 53.028554 \nL 332.060391 52.664333 \nL 332.298774 52.501812 \nL 332.67029 52.300112 \nL 333.431163 51.935891 \nL 334.330137 51.57167 \nL 334.480269 51.516936 \nL 335.668244 51.207449 \nL 336.661765 50.98462 \nL 337.698673 50.843228 \nL 338.843261 50.701965 \nL 341.024757 50.58846 \nL 343.206252 50.613983 \nL 345.387748 50.77817 \nL 345.840986 50.843228 \nL 347.569244 51.110355 \nL 347.97755 51.207449 \nL 349.365172 51.57167 \nL 349.750739 51.683571 \nL 350.389049 51.935891 \nL 351.207524 52.300112 \nL 351.925528 52.664333 \nL 351.932235 52.667993 \nL 352.459737 53.028554 \nL 352.932731 53.392776 \nL 353.354599 53.756997 \nL 353.73263 54.121218 \nL 354.072769 54.485439 \nL 354.113731 54.532441 \nL 354.34972 54.84966 \nL 354.596278 55.213881 \nL 354.820136 55.578102 \nL 355.023865 55.942323 \nL 355.209649 56.306544 \nL 355.379347 56.670766 \nL 355.534558 57.034987 \nL 355.676657 57.399208 \nL 355.806834 57.763429 \nL 355.926126 58.12765 \nL 356.035433 58.491871 \nL 356.135544 58.856092 \nL 356.22715 59.220313 \nL 356.295227 59.515608 \nL 356.309691 59.584534 \nL 356.380332 59.948755 \nL 356.444588 60.312977 \nL 356.50284 60.677198 \nL 356.555426 61.041419 \nL 356.602641 61.40564 \nL 356.644742 61.769861 \nL 356.681954 62.134082 \nL 356.714472 62.498303 \nL 356.742463 62.862524 \nL 356.766067 63.226745 \nL 356.785402 63.590967 \nL 356.800563 63.955188 \nL 356.811624 64.319409 \nL 356.818637 64.68363 \nL 356.821636 65.047851 \nL 356.820637 65.412072 \nL 356.815633 65.776293 \nL 356.806602 66.140514 \nL 356.793499 66.504735 \nL 356.776262 66.868957 \nL 356.754806 67.233178 \nL 356.729024 67.597399 \nL 356.698789 67.96162 \nL 356.663946 68.325841 \nL 356.624315 68.690062 \nL 356.579688 69.054283 \nL 356.529822 69.418504 \nL 356.474442 69.782725 \nL 356.413233 70.146946 \nL 356.345836 70.511168 \nL 356.295227 70.761527 \nL 356.269956 70.875389 \nL 356.182371 71.23961 \nL 356.086593 71.603831 \nL 355.981976 71.968052 \nL 355.867782 72.332273 \nL 355.743168 72.696494 \nL 355.607168 73.060715 \nL 355.458673 73.424936 \nL 355.296403 73.789158 \nL 355.118877 74.153379 \nL 354.924372 74.5176 \nL 354.710874 74.881821 \nL 354.476015 75.246042 \nL 354.216995 75.610263 \nL 354.113731 75.744851 \nL 353.907108 75.974484 \nL 353.548695 76.338705 \nL 353.149565 76.702926 \nL 352.703142 77.067147 \nL 352.201329 77.431369 \nL 351.932235 77.609085 \nL 351.577953 77.79559 \nL 350.812049 78.159811 \nL 349.935698 78.524032 \nL 349.750739 78.594392 \nL 348.697009 78.888253 \nL 347.569244 79.167482 \nL 347.041207 79.252474 \nL 345.387748 79.499493 \nL 343.850067 79.616695 \nL 343.206252 79.66406 \nL 341.024757 79.689456 \nz\nM 338.946594 45.744132 \nL 338.843261 45.738035 \nL 336.661765 45.548581 \nL 335.346295 45.379911 \nL 334.480269 45.25189 \nL 333.325641 45.01569 \nL 332.298774 44.756991 \nL 331.976524 44.651469 \nL 331.0379 44.287248 \nL 330.300317 43.923027 \nL 330.117278 43.818299 \nL 329.739819 43.558806 \nL 329.292175 43.194585 \nL 328.918704 42.830364 \nL 328.602728 42.466142 \nL 328.332229 42.101921 \nL 328.098329 41.7377 \nL 327.935782 41.450994 \nL 327.896367 41.373479 \nL 327.725984 41.009258 \nL 327.575357 40.645037 \nL 327.441432 40.280816 \nL 327.321759 39.916595 \nL 327.214351 39.552374 \nL 327.11758 39.188152 \nL 327.0301 38.823931 \nL 326.950789 38.45971 \nL 326.878702 38.095489 \nL 326.813044 37.731268 \nL 326.753134 37.367047 \nL 326.698394 37.002826 \nL 326.648324 36.638605 \nL 326.602491 36.274384 \nL 326.560524 35.910163 \nL 326.522096 35.545941 \nL 326.486923 35.18172 \nL 326.454757 34.817499 \nL 326.42538 34.453278 \nL 326.398601 34.089057 \nL 326.374251 33.724836 \nL 326.352185 33.360615 \nL 326.33227 32.996394 \nL 326.314395 32.632173 \nL 326.29846 32.267951 \nL 326.284379 31.90373 \nL 326.272076 31.539509 \nL 326.261487 31.175288 \nL 326.25256 30.811067 \nL 326.245248 30.446846 \nL 326.239515 30.082625 \nL 326.235333 29.718404 \nL 326.232681 29.354183 \nL 326.231547 28.989962 \nL 326.231925 28.62574 \nL 326.233817 28.261519 \nL 326.237231 27.897298 \nL 326.242186 27.533077 \nL 326.248704 27.168856 \nL 326.256819 26.804635 \nL 326.266571 26.440414 \nL 326.278009 26.076193 \nL 326.291193 25.711972 \nL 326.306191 25.34775 \nL 326.323085 24.983529 \nL 326.341966 24.619308 \nL 326.362941 24.255087 \nL 326.386132 23.890866 \nL 326.411676 23.526645 \nL 326.439732 23.162424 \nL 326.470478 22.798203 \nL 326.504119 22.433982 \nL 326.540886 22.069761 \nL 326.581046 21.705539 \nL 326.624902 21.341318 \nL 326.672804 20.977097 \nL 326.725151 20.612876 \nL 326.782409 20.248655 \nL 326.845114 19.884434 \nL 326.913895 19.520213 \nL 326.989486 19.155992 \nL 327.072754 18.791771 \nL 327.164726 18.427549 \nL 327.266631 18.063328 \nL 327.379948 17.699107 \nL 327.506473 17.334886 \nL 327.648408 16.970665 \nL 327.808486 16.606444 \nL 327.935782 16.346942 \nL 327.992942 16.242223 \nL 328.211166 15.878002 \nL 328.462409 15.513781 \nL 328.754368 15.14956 \nL 329.097343 14.785338 \nL 329.505443 14.421117 \nL 329.998523 14.056896 \nL 330.117278 13.979441 \nL 330.648194 13.692675 \nL 331.477336 13.328454 \nL 332.298774 13.040787 \nL 332.580665 12.964233 \nL 334.19328 12.600012 \nL 334.480269 12.545823 \nL 336.661765 12.249077 \nL 336.802282 12.235791 \nL 338.843261 12.059925 \nL 341.024757 11.937196 \nL 342.901166 11.87157 \nL 343.206252 11.861049 \nL 345.387748 11.822093 \nL 347.569244 11.817559 \nL 349.750739 11.851577 \nL 350.315513 11.87157 \nL 351.932235 11.938949 \nL 354.113731 12.118519 \nL 354.945495 12.235791 \nL 356.295227 12.509094 \nL 356.595861 12.600012 \nL 357.488579 12.964233 \nL 358.075969 13.328454 \nL 358.476722 13.67772 \nL 358.490802 13.692675 \nL 358.78694 14.056896 \nL 359.015785 14.421117 \nL 359.197747 14.785338 \nL 359.345738 15.14956 \nL 359.468322 15.513781 \nL 359.571405 15.878002 \nL 359.659192 16.242223 \nL 359.734756 16.606444 \nL 359.800397 16.970665 \nL 359.857868 17.334886 \nL 359.908531 17.699107 \nL 359.953458 18.063328 \nL 359.993504 18.427549 \nL 360.029361 18.791771 \nL 360.061593 19.155992 \nL 360.090665 19.520213 \nL 360.116963 19.884434 \nL 360.140812 20.248655 \nL 360.162483 20.612876 \nL 360.182209 20.977097 \nL 360.200187 21.341318 \nL 360.216586 21.705539 \nL 360.231553 22.069761 \nL 360.245213 22.433982 \nL 360.257676 22.798203 \nL 360.269038 23.162424 \nL 360.279382 23.526645 \nL 360.28878 23.890866 \nL 360.297295 24.255087 \nL 360.304983 24.619308 \nL 360.311893 24.983529 \nL 360.318067 25.34775 \nL 360.323541 25.711972 \nL 360.328348 26.076193 \nL 360.332514 26.440414 \nL 360.336062 26.804635 \nL 360.339013 27.168856 \nL 360.341383 27.533077 \nL 360.343183 27.897298 \nL 360.344423 28.261519 \nL 360.34511 28.62574 \nL 360.345247 28.989962 \nL 360.344835 29.354183 \nL 360.343872 29.718404 \nL 360.342353 30.082625 \nL 360.34027 30.446846 \nL 360.337612 30.811067 \nL 360.334364 31.175288 \nL 360.330509 31.539509 \nL 360.326026 31.90373 \nL 360.320889 32.267951 \nL 360.31507 32.632173 \nL 360.308533 32.996394 \nL 360.301239 33.360615 \nL 360.293144 33.724836 \nL 360.284195 34.089057 \nL 360.274333 34.453278 \nL 360.26349 34.817499 \nL 360.251588 35.18172 \nL 360.238539 35.545941 \nL 360.22424 35.910163 \nL 360.208574 36.274384 \nL 360.191405 36.638605 \nL 360.172576 37.002826 \nL 360.151904 37.367047 \nL 360.129176 37.731268 \nL 360.104139 38.095489 \nL 360.076498 38.45971 \nL 360.045899 38.823931 \nL 360.011918 39.188152 \nL 359.974044 39.552374 \nL 359.931653 39.916595 \nL 359.883978 40.280816 \nL 359.830061 40.645037 \nL 359.768696 41.009258 \nL 359.698343 41.373479 \nL 359.616997 41.7377 \nL 359.522006 42.101921 \nL 359.409788 42.466142 \nL 359.275376 42.830364 \nL 359.111691 43.194585 \nL 358.90827 43.558806 \nL 358.64899 43.923027 \nL 358.476722 44.119781 \nL 358.300125 44.287248 \nL 357.809773 44.651469 \nL 357.093496 45.01569 \nL 356.295227 45.288707 \nL 355.913662 45.379911 \nL 354.113731 45.679332 \nL 353.441937 45.744132 \nL 351.932235 45.858837 \nL 349.750739 45.946127 \nL 347.569244 45.980191 \nL 345.387748 45.975651 \nL 343.206252 45.936642 \nL 341.024757 45.860587 \nz\nM 189.529067 45.01569 \nL 188.320057 44.841052 \nL 187.438984 44.651469 \nL 186.14504 44.287248 \nL 186.138561 44.285081 \nL 185.30621 43.923027 \nL 184.664838 43.558806 \nL 184.1587 43.194585 \nL 183.957065 43.023761 \nL 183.763854 42.830364 \nL 183.449728 42.466142 \nL 183.186512 42.101921 \nL 182.963022 41.7377 \nL 182.771134 41.373479 \nL 182.604804 41.009258 \nL 182.459442 40.645037 \nL 182.331502 40.280816 \nL 182.218202 39.916595 \nL 182.117326 39.552374 \nL 182.027092 39.188152 \nL 181.946046 38.823931 \nL 181.872995 38.45971 \nL 181.806949 38.095489 \nL 181.775569 37.907552 \nL 181.747914 37.731268 \nL 181.695117 37.367047 \nL 181.647067 37.002826 \nL 181.603274 36.638605 \nL 181.563321 36.274384 \nL 181.526847 35.910163 \nL 181.493542 35.545941 \nL 181.463134 35.18172 \nL 181.43539 34.817499 \nL 181.410106 34.453278 \nL 181.387101 34.089057 \nL 181.36622 33.724836 \nL 181.347327 33.360615 \nL 181.330301 32.996394 \nL 181.315038 32.632173 \nL 181.301447 32.267951 \nL 181.28945 31.90373 \nL 181.278977 31.539509 \nL 181.269971 31.175288 \nL 181.262382 30.811067 \nL 181.25617 30.446846 \nL 181.251302 30.082625 \nL 181.247752 29.718404 \nL 181.245501 29.354183 \nL 181.244539 28.989962 \nL 181.24486 28.62574 \nL 181.246465 28.261519 \nL 181.249363 27.897298 \nL 181.25357 27.533077 \nL 181.259106 27.168856 \nL 181.266002 26.804635 \nL 181.274294 26.440414 \nL 181.284027 26.076193 \nL 181.295254 25.711972 \nL 181.308039 25.34775 \nL 181.322455 24.983529 \nL 181.338587 24.619308 \nL 181.356533 24.255087 \nL 181.376404 23.890866 \nL 181.398328 23.526645 \nL 181.422452 23.162424 \nL 181.448943 22.798203 \nL 181.477991 22.433982 \nL 181.509816 22.069761 \nL 181.54467 21.705539 \nL 181.582842 21.341318 \nL 181.624666 20.977097 \nL 181.670531 20.612876 \nL 181.720889 20.248655 \nL 181.775569 19.888883 \nL 181.776288 19.884434 \nL 181.839152 19.520213 \nL 181.908587 19.155992 \nL 181.9855 18.791771 \nL 182.070977 18.427549 \nL 182.166331 18.063328 \nL 182.273172 17.699107 \nL 182.393483 17.334886 \nL 182.529744 16.970665 \nL 182.685089 16.606444 \nL 182.863546 16.242223 \nL 183.070367 15.878002 \nL 183.312542 15.513781 \nL 183.599572 15.14956 \nL 183.944712 14.785338 \nL 183.957065 14.773597 \nL 184.397651 14.421117 \nL 184.96549 14.056896 \nL 185.695463 13.692675 \nL 186.138561 13.51307 \nL 186.737934 13.328454 \nL 188.280508 12.964233 \nL 188.320057 12.956508 \nL 190.501552 12.665207 \nL 191.341815 12.600012 \nL 192.683048 12.508194 \nL 194.864544 12.435293 \nL 197.046039 12.425893 \nL 199.227535 12.47451 \nL 201.409031 12.586673 \nL 201.570018 12.600012 \nL 203.590527 12.781696 \nL 204.908012 12.964233 \nL 205.772022 13.100405 \nL 206.804837 13.328454 \nL 207.953518 13.634901 \nL 208.119739 13.692675 \nL 209.037512 14.056896 \nL 209.780835 14.421117 \nL 210.135014 14.623631 \nL 210.36947 14.785338 \nL 210.834033 15.14956 \nL 211.22832 15.513781 \nL 211.56676 15.878002 \nL 211.860076 16.242223 \nL 212.116401 16.606444 \nL 212.316509 16.927511 \nL 212.340473 16.970665 \nL 212.528183 17.334886 \nL 212.695296 17.699107 \nL 212.844792 18.063328 \nL 212.979093 18.427549 \nL 213.100192 18.791771 \nL 213.209737 19.155992 \nL 213.309108 19.520213 \nL 213.399463 19.884434 \nL 213.481786 20.248655 \nL 213.556912 20.612876 \nL 213.625561 20.977097 \nL 213.688349 21.341318 \nL 213.745809 21.705539 \nL 213.798405 22.069761 \nL 213.846541 22.433982 \nL 213.890568 22.798203 \nL 213.930794 23.162424 \nL 213.96749 23.526645 \nL 214.000893 23.890866 \nL 214.031211 24.255087 \nL 214.058627 24.619308 \nL 214.083302 24.983529 \nL 214.105374 25.34775 \nL 214.124968 25.711972 \nL 214.142189 26.076193 \nL 214.157128 26.440414 \nL 214.169863 26.804635 \nL 214.180459 27.168856 \nL 214.188971 27.533077 \nL 214.19544 27.897298 \nL 214.199898 28.261519 \nL 214.202368 28.62574 \nL 214.202861 28.989962 \nL 214.20138 29.354183 \nL 214.197918 29.718404 \nL 214.192458 30.082625 \nL 214.184973 30.446846 \nL 214.175425 30.811067 \nL 214.163767 31.175288 \nL 214.149938 31.539509 \nL 214.133869 31.90373 \nL 214.115474 32.267951 \nL 214.094655 32.632173 \nL 214.071298 32.996394 \nL 214.045272 33.360615 \nL 214.016426 33.724836 \nL 213.98459 34.089057 \nL 213.949568 34.453278 \nL 213.911138 34.817499 \nL 213.869048 35.18172 \nL 213.823008 35.545941 \nL 213.772689 35.910163 \nL 213.717714 36.274384 \nL 213.657652 36.638605 \nL 213.592006 37.002826 \nL 213.520201 37.367047 \nL 213.441573 37.731268 \nL 213.355347 38.095489 \nL 213.260617 38.45971 \nL 213.156317 38.823931 \nL 213.041183 39.188152 \nL 212.913708 39.552374 \nL 212.772083 39.916595 \nL 212.614113 40.280816 \nL 212.437115 40.645037 \nL 212.316509 40.869846 \nL 212.232712 41.009258 \nL 211.992433 41.373479 \nL 211.718499 41.7377 \nL 211.403779 42.101921 \nL 211.038956 42.466142 \nL 210.611625 42.830364 \nL 210.135014 43.174565 \nL 210.101699 43.194585 \nL 209.427768 43.558806 \nL 208.603993 43.923027 \nL 207.953518 44.162769 \nL 207.51614 44.287248 \nL 205.995467 44.651469 \nL 205.772022 44.697561 \nL 203.595786 45.01569 \nL 203.590527 45.016366 \nL 201.409031 45.210943 \nL 199.227535 45.323374 \nL 197.046039 45.372107 \nL 194.864544 45.362684 \nL 192.683048 45.289609 \nL 190.501552 45.132549 \nz\nM 268.111172 44.651469 \nL 266.853902 44.588192 \nL 264.672407 44.372062 \nL 264.126688 44.287248 \nL 262.490911 43.995866 \nL 262.201728 43.923027 \nL 260.944274 43.558806 \nL 260.309415 43.339366 \nL 259.983457 43.194585 \nL 259.267363 42.830364 \nL 258.668418 42.466142 \nL 258.160626 42.101921 \nL 258.127919 42.076207 \nL 257.764429 41.7377 \nL 257.424185 41.373479 \nL 257.127136 41.009258 \nL 256.865907 40.645037 \nL 256.634719 40.280816 \nL 256.428984 39.916595 \nL 256.245016 39.552374 \nL 256.079815 39.188152 \nL 255.946424 38.863166 \nL 255.931827 38.823931 \nL 255.805088 38.45971 \nL 255.690184 38.095489 \nL 255.585766 37.731268 \nL 255.490689 37.367047 \nL 255.403981 37.002826 \nL 255.324807 36.638605 \nL 255.25245 36.274384 \nL 255.186291 35.910163 \nL 255.125792 35.545941 \nL 255.070485 35.18172 \nL 255.019962 34.817499 \nL 254.973867 34.453278 \nL 254.931887 34.089057 \nL 254.893749 33.724836 \nL 254.859212 33.360615 \nL 254.828065 32.996394 \nL 254.800126 32.632173 \nL 254.775232 32.267951 \nL 254.753245 31.90373 \nL 254.734043 31.539509 \nL 254.717524 31.175288 \nL 254.7036 30.811067 \nL 254.692199 30.446846 \nL 254.683261 30.082625 \nL 254.676743 29.718404 \nL 254.672611 29.354183 \nL 254.670843 28.989962 \nL 254.671432 28.62574 \nL 254.67438 28.261519 \nL 254.679702 27.897298 \nL 254.687425 27.533077 \nL 254.697588 27.168856 \nL 254.710242 26.804635 \nL 254.725454 26.440414 \nL 254.743302 26.076193 \nL 254.763883 25.711972 \nL 254.787307 25.34775 \nL 254.813705 24.983529 \nL 254.843227 24.619308 \nL 254.876044 24.255087 \nL 254.912353 23.890866 \nL 254.95238 23.526645 \nL 254.996381 23.162424 \nL 255.044649 22.798203 \nL 255.097516 22.433982 \nL 255.155365 22.069761 \nL 255.218631 21.705539 \nL 255.287817 21.341318 \nL 255.3635 20.977097 \nL 255.446344 20.612876 \nL 255.537124 20.248655 \nL 255.63674 19.884434 \nL 255.746246 19.520213 \nL 255.866884 19.155992 \nL 255.946424 18.935355 \nL 256.003465 18.791771 \nL 256.160234 18.427549 \nL 256.334481 18.063328 \nL 256.528918 17.699107 \nL 256.746868 17.334886 \nL 256.992437 16.970665 \nL 257.270766 16.606444 \nL 257.588369 16.242223 \nL 257.953634 15.878002 \nL 258.127919 15.722071 \nL 258.40444 15.513781 \nL 258.955075 15.14956 \nL 259.608773 14.785338 \nL 260.309415 14.458173 \nL 260.410905 14.421117 \nL 261.537699 14.056896 \nL 262.490911 13.802034 \nL 263.065195 13.692675 \nL 264.672407 13.425785 \nL 265.607363 13.328454 \nL 266.853902 13.209704 \nL 269.035398 13.10218 \nL 271.216894 13.081782 \nL 273.398389 13.145347 \nL 275.579885 13.305888 \nL 275.766111 13.328454 \nL 277.761381 13.601479 \nL 278.206772 13.692675 \nL 279.722113 14.056896 \nL 279.942877 14.118389 \nL 280.761105 14.421117 \nL 281.573685 14.785338 \nL 282.124372 15.07955 \nL 282.231216 15.14956 \nL 282.731078 15.513781 \nL 283.155907 15.878002 \nL 283.520975 16.242223 \nL 283.83766 16.606444 \nL 284.114613 16.970665 \nL 284.305868 17.252989 \nL 284.354839 17.334886 \nL 284.555832 17.699107 \nL 284.734883 18.063328 \nL 284.895135 18.427549 \nL 285.039147 18.791771 \nL 285.169024 19.155992 \nL 285.286514 19.520213 \nL 285.39308 19.884434 \nL 285.489952 20.248655 \nL 285.578175 20.612876 \nL 285.658639 20.977097 \nL 285.732106 21.341318 \nL 285.799235 21.705539 \nL 285.860592 22.069761 \nL 285.916672 22.433982 \nL 285.967904 22.798203 \nL 286.014662 23.162424 \nL 286.057274 23.526645 \nL 286.096026 23.890866 \nL 286.131171 24.255087 \nL 286.162927 24.619308 \nL 286.191488 24.983529 \nL 286.217023 25.34775 \nL 286.239677 25.711972 \nL 286.259578 26.076193 \nL 286.276835 26.440414 \nL 286.291541 26.804635 \nL 286.303774 27.168856 \nL 286.313597 27.533077 \nL 286.321061 27.897298 \nL 286.326205 28.261519 \nL 286.329054 28.62574 \nL 286.329623 28.989962 \nL 286.327915 29.354183 \nL 286.323921 29.718404 \nL 286.317621 30.082625 \nL 286.308983 30.446846 \nL 286.297962 30.811067 \nL 286.284502 31.175288 \nL 286.268531 31.539509 \nL 286.249965 31.90373 \nL 286.228702 32.267951 \nL 286.204624 32.632173 \nL 286.177596 32.996394 \nL 286.14746 33.360615 \nL 286.114035 33.724836 \nL 286.077116 34.089057 \nL 286.036468 34.453278 \nL 285.991821 34.817499 \nL 285.94287 35.18172 \nL 285.889264 35.545941 \nL 285.830602 35.910163 \nL 285.766425 36.274384 \nL 285.696203 36.638605 \nL 285.619326 37.002826 \nL 285.535086 37.367047 \nL 285.442659 37.731268 \nL 285.341081 38.095489 \nL 285.22922 38.45971 \nL 285.105735 38.823931 \nL 284.96903 39.188152 \nL 284.817187 39.552374 \nL 284.647888 39.916595 \nL 284.458302 40.280816 \nL 284.305868 40.54472 \nL 284.240343 40.645037 \nL 283.980646 41.009258 \nL 283.684771 41.373479 \nL 283.345125 41.7377 \nL 282.951809 42.101921 \nL 282.491677 42.466142 \nL 282.124372 42.718122 \nL 281.924513 42.830364 \nL 281.187694 43.194585 \nL 280.287305 43.558806 \nL 279.942877 43.679294 \nL 279.011324 43.923027 \nL 277.761381 44.196323 \nL 277.145182 44.287248 \nL 275.579885 44.491679 \nL 273.417257 44.651469 \nL 273.398389 44.652761 \nL 271.216894 44.716125 \nL 269.035398 44.695791 \nz\nM 229.39152 44.287248 \nL 227.586979 44.02018 \nL 227.141998 43.923027 \nL 225.690959 43.558806 \nL 225.405484 43.476987 \nL 224.662171 43.194585 \nL 223.849176 42.830364 \nL 223.223988 42.500179 \nL 223.171592 42.466142 \nL 222.659183 42.101921 \nL 222.21763 41.7377 \nL 221.833667 41.373479 \nL 221.497154 41.009258 \nL 221.200216 40.645037 \nL 221.042492 40.431319 \nL 220.945257 40.280816 \nL 220.729231 39.916595 \nL 220.535614 39.552374 \nL 220.361387 39.188152 \nL 220.204063 38.823931 \nL 220.061574 38.45971 \nL 219.932184 38.095489 \nL 219.814431 37.731268 \nL 219.70707 37.367047 \nL 219.60904 37.002826 \nL 219.51943 36.638605 \nL 219.437453 36.274384 \nL 219.362428 35.910163 \nL 219.293763 35.545941 \nL 219.230943 35.18172 \nL 219.173517 34.817499 \nL 219.12109 34.453278 \nL 219.073316 34.089057 \nL 219.02989 33.724836 \nL 218.990545 33.360615 \nL 218.955048 32.996394 \nL 218.923192 32.632173 \nL 218.8948 32.267951 \nL 218.869714 31.90373 \nL 218.860997 31.759513 \nL 218.848386 31.539509 \nL 218.830367 31.175288 \nL 218.815175 30.811067 \nL 218.802733 30.446846 \nL 218.792979 30.082625 \nL 218.785864 29.718404 \nL 218.781353 29.354183 \nL 218.779424 28.989962 \nL 218.780067 28.62574 \nL 218.783285 28.261519 \nL 218.789094 27.897298 \nL 218.797523 27.533077 \nL 218.808614 27.168856 \nL 218.822422 26.804635 \nL 218.839018 26.440414 \nL 218.858485 26.076193 \nL 218.860997 26.035133 \nL 218.881852 25.711972 \nL 218.908573 25.34775 \nL 218.938676 24.983529 \nL 218.972329 24.619308 \nL 219.009722 24.255087 \nL 219.051077 23.890866 \nL 219.096641 23.526645 \nL 219.146702 23.162424 \nL 219.201582 22.798203 \nL 219.261652 22.433982 \nL 219.327334 22.069761 \nL 219.39911 21.705539 \nL 219.477532 21.341318 \nL 219.563235 20.977097 \nL 219.656949 20.612876 \nL 219.759522 20.248655 \nL 219.871936 19.884434 \nL 219.995339 19.520213 \nL 220.13108 19.155992 \nL 220.28075 18.791771 \nL 220.446243 18.427549 \nL 220.629824 18.063328 \nL 220.83423 17.699107 \nL 221.042492 17.365892 \nL 221.064594 17.334886 \nL 221.34416 16.970665 \nL 221.660016 16.606444 \nL 222.019145 16.242223 \nL 222.430465 15.878002 \nL 222.905547 15.513781 \nL 223.223988 15.298013 \nL 223.492626 15.14956 \nL 224.23758 14.785338 \nL 225.128111 14.421117 \nL 225.405484 14.32086 \nL 226.377988 14.056896 \nL 227.586979 13.777619 \nL 228.126422 13.692675 \nL 229.768475 13.460635 \nL 231.382551 13.328454 \nL 231.949971 13.284905 \nL 234.131467 13.21396 \nL 236.312962 13.234784 \nL 238.062249 13.328454 \nL 238.494458 13.353038 \nL 240.675954 13.596269 \nL 241.221995 13.692675 \nL 242.857449 14.029327 \nL 242.953158 14.056896 \nL 244.071895 14.421117 \nL 244.97974 14.785338 \nL 245.038945 14.811898 \nL 245.637014 15.14956 \nL 246.182638 15.513781 \nL 246.647067 15.878002 \nL 247.046684 16.242223 \nL 247.220441 16.419119 \nL 247.378287 16.606444 \nL 247.65508 16.970665 \nL 247.899129 17.334886 \nL 248.115601 17.699107 \nL 248.308617 18.063328 \nL 248.481507 18.427549 \nL 248.63699 18.791771 \nL 248.777305 19.155992 \nL 248.904313 19.520213 \nL 249.019572 19.884434 \nL 249.124399 20.248655 \nL 249.219908 20.612876 \nL 249.307053 20.977097 \nL 249.38665 21.341318 \nL 249.401937 21.416431 \nL 249.456554 21.705539 \nL 249.519779 22.069761 \nL 249.577584 22.433982 \nL 249.630409 22.798203 \nL 249.678633 23.162424 \nL 249.722593 23.526645 \nL 249.76258 23.890866 \nL 249.798852 24.255087 \nL 249.831633 24.619308 \nL 249.861121 24.983529 \nL 249.887488 25.34775 \nL 249.910885 25.711972 \nL 249.93144 26.076193 \nL 249.949266 26.440414 \nL 249.964458 26.804635 \nL 249.977097 27.168856 \nL 249.987246 27.533077 \nL 249.994959 27.897298 \nL 250.000274 28.261519 \nL 250.003218 28.62574 \nL 250.003806 28.989962 \nL 250.002041 29.354183 \nL 249.997914 29.718404 \nL 249.991404 30.082625 \nL 249.982479 30.446846 \nL 249.971092 30.811067 \nL 249.957186 31.175288 \nL 249.940688 31.539509 \nL 249.92151 31.90373 \nL 249.899549 32.267951 \nL 249.874685 32.632173 \nL 249.846777 32.996394 \nL 249.815666 33.360615 \nL 249.781165 33.724836 \nL 249.743066 34.089057 \nL 249.701127 34.453278 \nL 249.655074 34.817499 \nL 249.604594 35.18172 \nL 249.54933 35.545941 \nL 249.488874 35.910163 \nL 249.422755 36.274384 \nL 249.401937 36.381064 \nL 249.347748 36.638605 \nL 249.264472 37.002826 \nL 249.173255 37.367047 \nL 249.073216 37.731268 \nL 248.963324 38.095489 \nL 248.842368 38.45971 \nL 248.708919 38.823931 \nL 248.561275 39.188152 \nL 248.397395 39.552374 \nL 248.214816 39.916595 \nL 248.010534 40.280816 \nL 247.780847 40.645037 \nL 247.521149 41.009258 \nL 247.22563 41.373479 \nL 247.220441 41.37941 \nL 246.854132 41.7377 \nL 246.423863 42.101921 \nL 245.921207 42.466142 \nL 245.327052 42.830364 \nL 245.038945 42.985549 \nL 244.548333 43.194585 \nL 243.543148 43.558806 \nL 242.857449 43.76819 \nL 242.153376 43.923027 \nL 240.675954 44.201553 \nL 239.956837 44.287248 \nL 238.494458 44.444542 \nL 236.312962 44.563026 \nL 234.131467 44.583921 \nL 231.949971 44.512734 \nL 229.768475 44.33734 \nz\nM 305.259405 44.287248 \nL 303.939329 44.264623 \nL 301.757834 44.114708 \nL 300.303642 43.923027 \nL 299.576338 43.815303 \nL 298.443136 43.558806 \nL 297.394842 43.277005 \nL 297.168511 43.194585 \nL 296.285721 42.830364 \nL 295.553366 42.466142 \nL 295.213347 42.272159 \nL 294.970919 42.101921 \nL 294.509857 41.7377 \nL 294.112249 41.373479 \nL 293.766289 41.009258 \nL 293.462942 40.645037 \nL 293.195177 40.280816 \nL 293.031851 40.034331 \nL 292.963284 39.916595 \nL 292.767754 39.552374 \nL 292.592447 39.188152 \nL 292.434666 38.823931 \nL 292.292185 38.45971 \nL 292.163149 38.095489 \nL 292.046003 37.731268 \nL 291.939432 37.367047 \nL 291.84232 37.002826 \nL 291.753712 36.638605 \nL 291.672788 36.274384 \nL 291.59884 35.910163 \nL 291.531257 35.545941 \nL 291.469506 35.18172 \nL 291.413124 34.817499 \nL 291.361705 34.453278 \nL 291.314895 34.089057 \nL 291.272383 33.724836 \nL 291.233898 33.360615 \nL 291.199201 32.996394 \nL 291.168085 32.632173 \nL 291.140368 32.267951 \nL 291.115891 31.90373 \nL 291.09452 31.539509 \nL 291.076137 31.175288 \nL 291.060644 30.811067 \nL 291.04796 30.446846 \nL 291.038018 30.082625 \nL 291.030767 29.718404 \nL 291.026171 29.354183 \nL 291.024205 28.989962 \nL 291.02486 28.62574 \nL 291.028139 28.261519 \nL 291.034059 27.897298 \nL 291.04265 27.533077 \nL 291.053956 27.168856 \nL 291.068035 26.804635 \nL 291.084962 26.440414 \nL 291.104825 26.076193 \nL 291.127733 25.711972 \nL 291.153812 25.34775 \nL 291.183208 24.983529 \nL 291.21609 24.619308 \nL 291.252653 24.255087 \nL 291.293119 23.890866 \nL 291.337744 23.526645 \nL 291.386817 23.162424 \nL 291.440671 22.798203 \nL 291.499683 22.433982 \nL 291.564288 22.069761 \nL 291.634982 21.705539 \nL 291.712336 21.341318 \nL 291.797007 20.977097 \nL 291.889757 20.612876 \nL 291.991469 20.248655 \nL 292.103177 19.884434 \nL 292.226089 19.520213 \nL 292.361636 19.155992 \nL 292.511514 18.791771 \nL 292.677753 18.427549 \nL 292.862801 18.063328 \nL 293.031851 17.763199 \nL 293.072856 17.699107 \nL 293.324989 17.334886 \nL 293.609768 16.970665 \nL 293.933432 16.606444 \nL 294.303935 16.242223 \nL 294.731582 15.878002 \nL 295.213347 15.525107 \nL 295.232313 15.513781 \nL 295.903194 15.14956 \nL 296.705622 14.785338 \nL 297.394842 14.520865 \nL 297.744758 14.421117 \nL 299.226573 14.056896 \nL 299.576338 13.982423 \nL 301.677195 13.692675 \nL 301.757834 13.682772 \nL 303.939329 13.533447 \nL 306.120825 13.496586 \nL 308.302321 13.562137 \nL 309.926327 13.692675 \nL 310.483817 13.74002 \nL 312.613578 14.056896 \nL 312.665312 14.065375 \nL 314.137951 14.421117 \nL 314.846808 14.61934 \nL 315.291674 14.785338 \nL 316.151893 15.14956 \nL 316.884471 15.513781 \nL 317.028304 15.592739 \nL 317.450148 15.878002 \nL 317.924622 16.242223 \nL 318.339876 16.606444 \nL 318.705854 16.970665 \nL 319.030381 17.334886 \nL 319.209799 17.556814 \nL 319.309662 17.699107 \nL 319.545104 18.063328 \nL 319.756806 18.427549 \nL 319.947849 18.791771 \nL 320.120791 19.155992 \nL 320.277772 19.520213 \nL 320.420597 19.884434 \nL 320.550796 20.248655 \nL 320.669673 20.612876 \nL 320.778348 20.977097 \nL 320.877786 21.341318 \nL 320.968822 21.705539 \nL 321.05218 22.069761 \nL 321.128493 22.433982 \nL 321.198312 22.798203 \nL 321.262121 23.162424 \nL 321.320345 23.526645 \nL 321.373355 23.890866 \nL 321.391295 24.02548 \nL 321.419846 24.255087 \nL 321.461016 24.619308 \nL 321.498075 24.983529 \nL 321.531231 25.34775 \nL 321.560666 25.711972 \nL 321.58654 26.076193 \nL 321.608987 26.440414 \nL 321.628125 26.804635 \nL 321.644049 27.168856 \nL 321.656841 27.533077 \nL 321.666563 27.897298 \nL 321.673264 28.261519 \nL 321.676977 28.62574 \nL 321.677718 28.989962 \nL 321.675493 29.354183 \nL 321.670289 29.718404 \nL 321.662083 30.082625 \nL 321.650832 30.446846 \nL 321.636483 30.811067 \nL 321.618963 31.175288 \nL 321.598184 31.539509 \nL 321.574039 31.90373 \nL 321.546403 32.267951 \nL 321.515129 32.632173 \nL 321.480046 32.996394 \nL 321.440959 33.360615 \nL 321.397646 33.724836 \nL 321.391295 33.773832 \nL 321.34748 34.089057 \nL 321.291907 34.453278 \nL 321.23094 34.817499 \nL 321.164182 35.18172 \nL 321.091181 35.545941 \nL 321.011419 35.910163 \nL 320.924306 36.274384 \nL 320.829167 36.638605 \nL 320.725222 37.002826 \nL 320.611575 37.367047 \nL 320.487188 37.731268 \nL 320.350853 38.095489 \nL 320.201157 38.45971 \nL 320.03644 38.823931 \nL 319.854739 39.188152 \nL 319.653717 39.552374 \nL 319.430568 39.916595 \nL 319.209799 40.241534 \nL 319.179099 40.280816 \nL 318.872877 40.645037 \nL 318.528493 41.009258 \nL 318.138973 41.373479 \nL 317.695511 41.7377 \nL 317.186831 42.101921 \nL 317.028304 42.205 \nL 316.53235 42.466142 \nL 315.739671 42.830364 \nL 314.846808 43.17888 \nL 314.793292 43.194585 \nL 313.418966 43.558806 \nL 312.665312 43.732058 \nL 311.436937 43.923027 \nL 310.483817 44.05762 \nL 308.302321 44.235821 \nL 306.601271 44.287248 \nL 306.120825 44.30152 \nz\nM 159.82211 42.830364 \nL 157.779117 42.531111 \nL 157.501994 42.466142 \nL 156.074611 42.101921 \nL 155.597621 41.968001 \nL 155.007799 41.7377 \nL 154.169218 41.373479 \nL 153.426895 41.009258 \nL 153.416125 41.003593 \nL 152.879629 40.645037 \nL 152.392521 40.280816 \nL 151.955496 39.916595 \nL 151.561842 39.552374 \nL 151.234629 39.218461 \nL 151.209616 39.188152 \nL 150.927663 38.823931 \nL 150.671501 38.45971 \nL 150.438232 38.095489 \nL 150.225396 37.731268 \nL 150.030889 37.367047 \nL 149.852907 37.002826 \nL 149.689893 36.638605 \nL 149.540497 36.274384 \nL 149.403547 35.910163 \nL 149.278018 35.545941 \nL 149.163017 35.18172 \nL 149.057758 34.817499 \nL 149.053134 34.800264 \nL 148.9694 34.453278 \nL 148.889168 34.089057 \nL 148.816177 33.724836 \nL 148.749994 33.360615 \nL 148.690241 32.996394 \nL 148.636584 32.632173 \nL 148.588733 32.267951 \nL 148.546434 31.90373 \nL 148.509467 31.539509 \nL 148.477645 31.175288 \nL 148.450808 30.811067 \nL 148.428824 30.446846 \nL 148.411585 30.082625 \nL 148.399008 29.718404 \nL 148.391033 29.354183 \nL 148.387622 28.989962 \nL 148.388758 28.62574 \nL 148.394448 28.261519 \nL 148.404718 27.897298 \nL 148.419617 27.533077 \nL 148.439216 27.168856 \nL 148.463612 26.804635 \nL 148.492924 26.440414 \nL 148.527296 26.076193 \nL 148.566904 25.711972 \nL 148.611949 25.34775 \nL 148.662669 24.983529 \nL 148.719335 24.619308 \nL 148.782259 24.255087 \nL 148.851796 23.890866 \nL 148.92835 23.526645 \nL 149.012381 23.162424 \nL 149.053134 22.999615 \nL 149.109215 22.798203 \nL 149.219253 22.433982 \nL 149.339413 22.069761 \nL 149.470533 21.705539 \nL 149.613569 21.341318 \nL 149.769618 20.977097 \nL 149.939935 20.612876 \nL 150.12597 20.248655 \nL 150.329399 19.884434 \nL 150.552168 19.520213 \nL 150.796551 19.155992 \nL 151.065215 18.791771 \nL 151.234629 18.580129 \nL 151.37951 18.427549 \nL 151.753618 18.063328 \nL 152.168198 17.699107 \nL 152.629342 17.334886 \nL 153.144458 16.970665 \nL 153.416125 16.794993 \nL 153.787024 16.606444 \nL 154.575417 16.242223 \nL 155.468817 15.878002 \nL 155.597621 15.829488 \nL 156.764354 15.513781 \nL 157.779117 15.266881 \nL 158.5517 15.14956 \nL 159.960612 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46.03645 \nL 65.919772 46.108353 \nL 64.361549 46.472575 \nL 63.974801 46.541474 \nL 61.793305 46.830976 \nL 61.738119 46.836796 \nL 59.611809 47.031509 \nL 57.430314 47.192731 \nL 57.313507 47.201017 \nL 55.248818 47.340391 \nL 53.067322 47.492055 \nL 52.128108 47.565238 \nL 50.885827 47.664454 \nL 48.704331 47.878519 \nL 48.28526 47.929459 \nL 46.522835 48.167756 \nL 45.804846 48.29368 \nL 44.341339 48.595063 \nL 44.101877 48.657901 \nL 42.907162 49.022122 \nL 42.159844 49.304078 \nL 42.159844 49.386343 \nL 42.159844 49.750565 \nL 42.159844 50.114786 \nL 42.159844 50.479007 \nL 42.159844 50.843228 \nL 42.159844 51.207449 \nL 42.159844 51.57167 \nL 42.159844 51.935891 \nL 42.159844 52.300112 \nL 42.159844 52.664333 \nL 42.159844 53.028554 \nL 42.159844 53.392776 \nL 42.159844 53.756997 \nL 42.159844 54.121218 \nL 42.159844 54.485439 \nL 42.159844 54.84966 \nL 42.159844 55.213881 \nL 42.159844 55.578102 \nL 42.159844 55.942323 \nL 42.159844 56.306544 \nL 42.159844 56.670766 \nL 42.159844 57.034987 \nL 42.159844 57.399208 \nL 42.159844 57.763429 \nL 42.159844 58.12765 \nL 42.159844 58.491871 \nL 42.159844 58.856092 \nL 42.159844 59.220313 \nL 42.159844 59.584534 \nL 42.159844 59.948755 \nL 42.159844 60.166723 \nL 42.226865 59.948755 \nL 42.349114 59.584534 \nL 42.482901 59.220313 \nL 42.629005 58.856092 \nL 42.788307 58.491871 \nL 42.961808 58.12765 \nL 43.15064 57.763429 \nL 43.356092 57.399208 \nL 43.579633 57.034987 \nL 43.822938 56.670766 \nL 44.087931 56.306544 \nL 44.341339 55.985989 \nL 44.38327 55.942323 \nL 44.755623 55.578102 \nL 45.162108 55.213881 \nL 45.60663 54.84966 \nL 46.093748 54.485439 \nL 46.522835 54.191298 \nL 46.656393 54.121218 \nL 47.39738 53.756997 \nL 48.212746 53.392776 \nL 48.704331 53.190161 \nL 49.271111 53.028554 \nL 50.6447 52.664333 \nL 50.885827 52.604219 \nL 52.98941 52.300112 \nL 53.067322 52.289277 \nL 55.248818 52.188127 \nL 57.430314 52.290343 \nL 57.498672 52.300112 \nL 59.611809 52.636103 \nL 59.714139 52.664333 \nL 60.910926 53.028554 \nL 61.793305 53.341356 \nL 61.896878 53.392776 \nL 62.568609 53.756997 \nL 63.154965 54.121218 \nL 63.670427 54.485439 \nL 63.974801 54.724716 \nL 64.102711 54.84966 \nL 64.445383 55.213881 \nL 64.752102 55.578102 \nL 65.027672 55.942323 \nL 65.276057 56.306544 \nL 65.50055 56.670766 \nL 65.703913 57.034987 \nL 65.888472 57.399208 \nL 66.056203 57.763429 \nL 66.156297 58.000271 \nL 66.203553 58.12765 \nL 66.328628 58.491871 \nL 66.442539 58.856092 \nL 66.546241 59.220313 \nL 66.64056 59.584534 \nL 66.726214 59.948755 \nL 66.80383 60.312977 \nL 66.873953 60.677198 \nL 66.937057 61.041419 \nL 66.993558 61.40564 \nL 67.043813 61.769861 \nL 67.088135 62.134082 \nL 67.12679 62.498303 \nL 67.160007 62.862524 \nL 67.187979 63.226745 \nL 67.210864 63.590967 \nL 67.228791 63.955188 \nL 67.241859 64.319409 \nL 67.250142 64.68363 \nL 67.253683 65.047851 \nL 67.252503 65.412072 \nL 67.246595 65.776293 \nL 67.235927 66.140514 \nL 67.22044 66.504735 \nL 67.200048 66.868957 \nL 67.174638 67.233178 \nL 67.144066 67.597399 \nL 67.108156 67.96162 \nL 67.066698 68.325841 \nL 67.019446 68.690062 \nL 66.966109 69.054283 \nL 66.906355 69.418504 \nL 66.839797 69.782725 \nL 66.765991 70.146946 \nL 66.684429 70.511168 \nL 66.594526 70.875389 \nL 66.495612 71.23961 \nL 66.386916 71.603831 \nL 66.267551 71.968052 \nL 66.156297 72.27835 \nL 66.134294 72.332273 \nL 65.974329 72.696494 \nL 65.798412 73.060715 \nL 65.604718 73.424936 \nL 65.391106 73.789158 \nL 65.155043 74.153379 \nL 64.893515 74.5176 \nL 64.602914 74.881821 \nL 64.278881 75.246042 \nL 63.974801 75.553513 \nL 63.905218 75.610263 \nL 63.420786 75.974484 \nL 62.87147 76.338705 \nL 62.244472 76.702926 \nL 61.793305 76.936064 \nL 61.439687 77.067147 \nL 60.337623 77.431369 \nL 59.611809 77.641178 \nL 58.694772 77.79559 \nL 57.430314 77.986836 \nL 55.248818 78.089834 \nL 53.067322 77.98791 \nL 51.713357 77.79559 \nL 50.885827 77.673326 \nL 49.94177 77.431369 \nL 48.704331 77.088311 \nL 48.651456 77.067147 \nL 47.795166 76.702926 \nL 47.018163 76.338705 \nL 46.522835 76.086289 \nL 46.354918 75.974484 \nL 45.84455 75.610263 \nL 45.379346 75.246042 \nL 44.954369 74.881821 \nL 44.565402 74.5176 \nL 44.341339 74.291098 \nL 44.229239 74.153379 \nL 43.952593 73.789158 \nL 43.698701 73.424936 \nL 43.465502 73.060715 \nL 43.251201 72.696494 \nL 43.054231 72.332273 \nL 42.873215 71.968052 \nL 42.706947 71.603831 \nL 42.554361 71.23961 \nL 42.414519 70.875389 \nL 42.286592 70.511168 \nL 42.169848 70.146946 \nL 42.159844 70.11303 \nL 42.159844 70.146946 \nL 42.159844 70.511168 \nL 42.159844 70.875389 \nL 42.159844 71.23961 \nL 42.159844 71.603831 \nL 42.159844 71.968052 \nL 42.159844 72.332273 \nL 42.159844 72.696494 \nL 42.159844 73.060715 \nL 42.159844 73.424936 \nL 42.159844 73.789158 \nL 42.159844 74.153379 \nL 42.159844 74.5176 \nL 42.159844 74.881821 \nL 42.159844 75.246042 \nL 42.159844 75.610263 \nL 42.159844 75.974484 \nL 42.159844 76.338705 \nL 42.159844 76.702926 \nL 42.159844 77.067147 \nL 42.159844 77.431369 \nL 42.159844 77.79559 \nL 42.159844 78.159811 \nL 42.159844 78.524032 \nL 42.159844 78.888253 \nL 42.159844 79.252474 \nL 42.159844 79.616695 \nL 42.159844 79.980916 \nL 42.159844 80.345137 \nL 42.159844 80.709359 \nL 42.159844 80.973695 \nL 42.409416 81.07358 \nL 43.467214 81.437801 \nL 44.341339 81.682678 \nL 44.882303 81.802022 \nL 46.522835 82.110102 \nL 46.911091 82.166243 \nL 48.704331 82.399237 \nL 50.008312 82.530464 \nL 50.885827 82.613377 \nL 53.067322 82.785735 \nL 54.639158 82.894685 \nz\nM 127.296527 82.530464 \nL 127.238177 82.52861 \nL 125.056681 82.432161 \nL 122.875185 82.301289 \nL 121.235359 82.166243 \nL 120.693689 82.118364 \nL 118.512194 81.851043 \nL 118.21444 81.802022 \nL 116.348092 81.437801 \nL 116.330698 81.433852 \nL 115.108423 81.07358 \nL 114.149202 80.70978 \nL 114.148305 80.709359 \nL 113.45536 80.345137 \nL 112.892574 79.980916 \nL 112.426896 79.616695 \nL 112.0356 79.252474 \nL 111.967707 79.182373 \nL 111.719773 78.888253 \nL 111.452061 78.524032 \nL 111.219752 78.159811 \nL 111.016507 77.79559 \nL 110.837423 77.431369 \nL 110.678647 77.067147 \nL 110.537109 76.702926 \nL 110.410335 76.338705 \nL 110.296309 75.974484 \nL 110.19337 75.610263 \nL 110.100142 75.246042 \nL 110.015472 74.881821 \nL 109.938388 74.5176 \nL 109.868067 74.153379 \nL 109.803805 73.789158 \nL 109.786211 73.682205 \nL 109.746395 73.424936 \nL 109.694352 73.060715 \nL 109.646647 72.696494 \nL 109.602901 72.332273 \nL 109.562783 71.968052 \nL 109.526004 71.603831 \nL 109.492312 71.23961 \nL 109.461485 70.875389 \nL 109.433326 70.511168 \nL 109.407663 70.146946 \nL 109.384345 69.782725 \nL 109.363239 69.418504 \nL 109.344226 69.054283 \nL 109.327204 68.690062 \nL 109.312084 68.325841 \nL 109.298786 67.96162 \nL 109.287244 67.597399 \nL 109.2774 67.233178 \nL 109.269206 66.868957 \nL 109.262622 66.504735 \nL 109.257617 66.140514 \nL 109.254166 65.776293 \nL 109.252255 65.412072 \nL 109.251873 65.047851 \nL 109.253019 64.68363 \nL 109.255698 64.319409 \nL 109.259923 63.955188 \nL 109.265715 63.590967 \nL 109.273099 63.226745 \nL 109.282113 62.862524 \nL 109.292799 62.498303 \nL 109.305211 62.134082 \nL 109.319412 61.769861 \nL 109.335472 61.40564 \nL 109.353477 61.041419 \nL 109.373523 60.677198 \nL 109.39572 60.312977 \nL 109.420192 59.948755 \nL 109.447083 59.584534 \nL 109.476553 59.220313 \nL 109.508787 58.856092 \nL 109.543993 58.491871 \nL 109.582407 58.12765 \nL 109.624301 57.763429 \nL 109.669983 57.399208 \nL 109.719806 57.034987 \nL 109.774176 56.670766 \nL 109.786211 56.595325 \nL 109.835218 56.306544 \nL 109.902429 55.942323 \nL 109.976036 55.578102 \nL 110.056802 55.213881 \nL 110.145619 54.84966 \nL 110.243545 54.485439 \nL 110.35184 54.121218 \nL 110.472012 53.756997 \nL 110.60589 53.392776 \nL 110.755704 53.028554 \nL 110.924207 52.664333 \nL 111.114831 52.300112 \nL 111.331916 51.935891 \nL 111.581026 51.57167 \nL 111.869412 51.207449 \nL 111.967707 51.095551 \nL 112.223082 50.843228 \nL 112.649206 50.479007 \nL 113.160064 50.114786 \nL 113.782942 49.750565 \nL 114.149202 49.568354 \nL 114.599759 49.386343 \nL 115.685611 49.022122 \nL 116.330698 48.844179 \nL 117.212794 48.657901 \nL 118.512194 48.42686 \nL 119.536025 48.29368 \nL 120.693689 48.159508 \nL 122.875185 47.976475 \nL 123.643168 47.929459 \nL 125.056681 47.845632 \nL 127.238177 47.749292 \nL 129.419672 47.677033 \nL 131.601168 47.622492 \nL 133.782664 47.581682 \nL 135.105947 47.565238 \nL 135.964159 47.552309 \nL 138.145655 47.533761 \nL 140.327151 47.529329 \nL 142.331925 47.565238 \nL 142.508647 47.573588 \nL 143.550038 47.929459 \nL 143.668964 48.29368 \nL 143.714115 48.657901 \nL 143.73787 49.022122 \nL 143.752512 49.386343 \nL 143.762432 49.750565 \nL 143.76959 50.114786 \nL 143.774994 50.479007 \nL 143.779214 50.843228 \nL 143.782597 51.207449 \nL 143.785367 51.57167 \nL 143.787674 51.935891 \nL 143.789622 52.300112 \nL 143.791288 52.664333 \nL 143.792726 53.028554 \nL 143.793979 53.392776 \nL 143.795078 53.756997 \nL 143.79605 54.121218 \nL 143.796913 54.485439 \nL 143.797683 54.84966 \nL 143.798373 55.213881 \nL 143.798995 55.578102 \nL 143.799556 55.942323 \nL 143.800064 56.306544 \nL 143.800525 56.670766 \nL 143.800944 57.034987 \nL 143.801325 57.399208 \nL 143.801673 57.763429 \nL 143.80199 58.12765 \nL 143.80228 58.491871 \nL 143.802544 58.856092 \nL 143.802784 59.220313 \nL 143.803004 59.584534 \nL 143.803203 59.948755 \nL 143.803384 60.312977 \nL 143.803548 60.677198 \nL 143.803695 61.041419 \nL 143.803827 61.40564 \nL 143.803945 61.769861 \nL 143.804049 62.134082 \nL 143.804139 62.498303 \nL 143.804217 62.862524 \nL 143.804283 63.226745 \nL 143.804336 63.590967 \nL 143.804378 63.955188 \nL 143.804409 64.319409 \nL 143.804429 64.68363 \nL 143.804437 65.047851 \nL 143.804434 65.412072 \nL 143.80442 65.776293 \nL 143.804395 66.140514 \nL 143.804359 66.504735 \nL 143.804311 66.868957 \nL 143.804251 67.233178 \nL 143.80418 67.597399 \nL 143.804096 67.96162 \nL 143.803998 68.325841 \nL 143.803888 68.690062 \nL 143.803763 69.054283 \nL 143.803623 69.418504 \nL 143.803468 69.782725 \nL 143.803296 70.146946 \nL 143.803106 70.511168 \nL 143.802897 70.875389 \nL 143.802667 71.23961 \nL 143.802415 71.603831 \nL 143.802138 71.968052 \nL 143.801835 72.332273 \nL 143.801503 72.696494 \nL 143.801139 73.060715 \nL 143.800739 73.424936 \nL 143.8003 73.789158 \nL 143.799816 74.153379 \nL 143.799282 74.5176 \nL 143.798692 74.881821 \nL 143.798037 75.246042 \nL 143.797308 75.610263 \nL 143.796494 75.974484 \nL 143.795579 76.338705 \nL 143.794546 76.702926 \nL 143.793373 77.067147 \nL 143.792032 77.431369 \nL 143.790486 77.79559 \nL 143.788687 78.159811 \nL 143.786571 78.524032 \nL 143.784048 78.888253 \nL 143.780994 79.252474 \nL 143.777227 79.616695 \nL 143.772469 79.980916 \nL 143.766279 80.345137 \nL 143.757907 80.709359 \nL 143.745969 81.07358 \nL 143.727591 81.437801 \nL 143.695686 81.802022 \nL 143.626739 82.166243 \nL 143.367582 82.530464 \nL 142.508647 82.704164 \nL 140.327151 82.748437 \nL 138.145655 82.744003 \nL 135.964159 82.725443 \nL 133.782664 82.696077 \nL 131.601168 82.655302 \nL 129.419672 82.600809 \nz\nM 82.85557 81.437801 \nL 81.426767 81.383108 \nL 79.245271 81.192125 \nL 78.471855 81.07358 \nL 77.063775 80.783 \nL 76.821175 80.709359 \nL 75.873018 80.345137 \nL 75.204545 79.980916 \nL 74.882279 79.755612 \nL 74.720309 79.616695 \nL 74.363325 79.252474 \nL 74.079311 78.888253 \nL 73.848223 78.524032 \nL 73.656749 78.159811 \nL 73.4957 77.79559 \nL 73.358531 77.431369 \nL 73.240454 77.067147 \nL 73.137885 76.702926 \nL 73.048089 76.338705 \nL 72.968943 75.974484 \nL 72.898775 75.610263 \nL 72.836248 75.246042 \nL 72.780283 74.881821 \nL 72.73 74.5176 \nL 72.700784 74.285836 \nL 72.685013 74.153379 \nL 72.644903 73.789158 \nL 72.608557 73.424936 \nL 72.575558 73.060715 \nL 72.545552 72.696494 \nL 72.518239 72.332273 \nL 72.493359 71.968052 \nL 72.470691 71.603831 \nL 72.450041 71.23961 \nL 72.431244 70.875389 \nL 72.414155 70.511168 \nL 72.398647 70.146946 \nL 72.38461 69.782725 \nL 72.371949 69.418504 \nL 72.36058 69.054283 \nL 72.350431 68.690062 \nL 72.341438 68.325841 \nL 72.333546 67.96162 \nL 72.32671 67.597399 \nL 72.320889 67.233178 \nL 72.316051 66.868957 \nL 72.312168 66.504735 \nL 72.309219 66.140514 \nL 72.307187 65.776293 \nL 72.306062 65.412072 \nL 72.305837 65.047851 \nL 72.306512 64.68363 \nL 72.308089 64.319409 \nL 72.310578 63.955188 \nL 72.313992 63.590967 \nL 72.318349 63.226745 \nL 72.323675 62.862524 \nL 72.329999 62.498303 \nL 72.337357 62.134082 \nL 72.345793 61.769861 \nL 72.355357 61.40564 \nL 72.366108 61.041419 \nL 72.378113 60.677198 \nL 72.391451 60.312977 \nL 72.40621 59.948755 \nL 72.422494 59.584534 \nL 72.440421 59.220313 \nL 72.460124 58.856092 \nL 72.481761 58.491871 \nL 72.505509 58.12765 \nL 72.531576 57.763429 \nL 72.560201 57.399208 \nL 72.591662 57.034987 \nL 72.626287 56.670766 \nL 72.664459 56.306544 \nL 72.700784 55.991334 \nL 72.706757 55.942323 \nL 72.75448 55.578102 \nL 72.807504 55.213881 \nL 72.866629 54.84966 \nL 72.932827 54.485439 \nL 73.007299 54.121218 \nL 73.091535 53.756997 \nL 73.187419 53.392776 \nL 73.297354 53.028554 \nL 73.424464 52.664333 \nL 73.57288 52.300112 \nL 73.748184 51.935891 \nL 73.958103 51.57167 \nL 74.213649 51.207449 \nL 74.53107 50.843228 \nL 74.882279 50.521971 \nL 74.939344 50.479007 \nL 75.512125 50.114786 \nL 76.302269 49.750565 \nL 77.063775 49.494884 \nL 77.524762 49.386343 \nL 79.245271 49.085619 \nL 79.876852 49.022122 \nL 81.426767 48.894789 \nL 83.608262 48.815089 \nL 85.789758 48.808774 \nL 87.971254 48.863288 \nL 90.152749 48.979311 \nL 90.656792 49.022122 \nL 92.334245 49.169476 \nL 93.988775 49.386343 \nL 94.515741 49.46065 \nL 95.986472 49.750565 \nL 96.697237 49.908222 \nL 97.404273 50.114786 \nL 98.48566 50.479007 \nL 98.878732 50.628955 \nL 99.329689 50.843228 \nL 100.003599 51.207449 \nL 100.583092 51.57167 \nL 101.060228 51.916009 \nL 101.083581 51.935891 \nL 101.480061 52.300112 \nL 101.828831 52.664333 \nL 102.137609 53.028554 \nL 102.412522 53.392776 \nL 102.658496 53.756997 \nL 102.879533 54.121218 \nL 103.07892 54.485439 \nL 103.241724 54.812823 \nL 103.25821 54.84966 \nL 103.411112 55.213881 \nL 103.550184 55.578102 \nL 103.676959 55.942323 \nL 103.792738 56.306544 \nL 103.898638 56.670766 \nL 103.995617 57.034987 \nL 104.084504 57.399208 \nL 104.16602 57.763429 \nL 104.24079 58.12765 \nL 104.309362 58.491871 \nL 104.372216 58.856092 \nL 104.429772 59.220313 \nL 104.482401 59.584534 \nL 104.530428 59.948755 \nL 104.574142 60.312977 \nL 104.613794 60.677198 \nL 104.649607 61.041419 \nL 104.681777 61.40564 \nL 104.710475 61.769861 \nL 104.73585 62.134082 \nL 104.758031 62.498303 \nL 104.777129 62.862524 \nL 104.793239 63.226745 \nL 104.806437 63.590967 \nL 104.816787 63.955188 \nL 104.824339 64.319409 \nL 104.829128 64.68363 \nL 104.831177 65.047851 \nL 104.830494 65.412072 \nL 104.827077 65.776293 \nL 104.820911 66.140514 \nL 104.811965 66.504735 \nL 104.800197 66.868957 \nL 104.785552 67.233178 \nL 104.767959 67.597399 \nL 104.747332 67.96162 \nL 104.723569 68.325841 \nL 104.69655 68.690062 \nL 104.666136 69.054283 \nL 104.632167 69.418504 \nL 104.594461 69.782725 \nL 104.552808 70.146946 \nL 104.506971 70.511168 \nL 104.456681 70.875389 \nL 104.401632 71.23961 \nL 104.341476 71.603831 \nL 104.275819 71.968052 \nL 104.204212 72.332273 \nL 104.126142 72.696494 \nL 104.041025 73.060715 \nL 103.948188 73.424936 \nL 103.84686 73.789158 \nL 103.736149 74.153379 \nL 103.615023 74.5176 \nL 103.482275 74.881821 \nL 103.336495 75.246042 \nL 103.241724 75.464295 \nL 103.171365 75.610263 \nL 102.981751 75.974484 \nL 102.771911 76.338705 \nL 102.538857 76.702926 \nL 102.278966 77.067147 \nL 101.987804 77.431369 \nL 101.659885 77.79559 \nL 101.288343 78.159811 \nL 101.060228 78.361291 \nL 100.843229 78.524032 \nL 100.303907 78.888253 \nL 99.679782 79.252474 \nL 98.950107 79.616695 \nL 98.878732 79.649163 \nL 97.9686 79.980916 \nL 96.786069 80.345137 \nL 96.697237 80.369864 \nL 95.089624 80.709359 \nL 94.515741 80.817117 \nL 92.61291 81.07358 \nL 92.334245 81.10851 \nL 90.152749 81.298362 \nL 87.971254 81.414693 \nL 87.024995 81.437801 \nL 85.789758 81.469192 \nL 83.608262 81.462893 \nz\nM 157.225957 77.431369 \nL 155.688804 77.067147 \nL 155.597621 77.043539 \nL 154.732463 76.702926 \nL 153.918444 76.338705 \nL 153.416125 76.087832 \nL 153.244529 75.974484 \nL 152.737386 75.610263 \nL 152.284342 75.246042 \nL 151.877939 74.881821 \nL 151.512062 74.5176 \nL 151.234629 74.213512 \nL 151.189106 74.153379 \nL 150.931966 73.789158 \nL 150.698497 73.424936 \nL 150.48615 73.060715 \nL 150.292755 72.696494 \nL 150.116453 72.332273 \nL 149.955646 71.968052 \nL 149.808952 71.603831 \nL 149.675175 71.23961 \nL 149.553274 70.875389 \nL 149.442345 70.511168 \nL 149.341597 70.146946 \nL 149.250339 69.782725 \nL 149.167968 69.418504 \nL 149.093957 69.054283 \nL 149.053134 68.830332 \nL 149.030172 68.690062 \nL 148.97695 68.325841 \nL 148.930226 67.96162 \nL 148.889732 67.597399 \nL 148.855241 67.233178 \nL 148.826561 66.868957 \nL 148.803539 66.504735 \nL 148.786049 66.140514 \nL 148.773999 65.776293 \nL 148.767325 65.412072 \nL 148.765992 65.047851 \nL 148.769993 64.68363 \nL 148.779349 64.319409 \nL 148.794109 63.955188 \nL 148.814352 63.590967 \nL 148.840185 63.226745 \nL 148.871749 62.862524 \nL 148.909215 62.498303 \nL 148.952794 62.134082 \nL 149.00273 61.769861 \nL 149.053134 61.445055 \nL 149.05994 61.40564 \nL 149.129947 61.041419 \nL 149.208077 60.677198 \nL 149.294821 60.312977 \nL 149.390744 59.948755 \nL 149.496491 59.584534 \nL 149.612799 59.220313 \nL 149.740517 58.856092 \nL 149.880613 58.491871 \nL 150.034204 58.12765 \nL 150.202573 57.763429 \nL 150.387206 57.399208 \nL 150.589827 57.034987 \nL 150.812441 56.670766 \nL 151.057401 56.306544 \nL 151.234629 56.0648 \nL 151.342703 55.942323 \nL 151.690273 55.578102 \nL 152.07572 55.213881 \nL 152.504603 54.84966 \nL 152.983667 54.485439 \nL 153.416125 54.189737 \nL 153.548369 54.121218 \nL 154.312407 53.756997 \nL 155.181108 53.392776 \nL 155.597621 53.234904 \nL 156.428616 53.028554 \nL 157.779117 52.728333 \nL 158.378212 52.664333 \nL 159.960612 52.506416 \nL 162.142108 52.507098 \nL 163.740084 52.664333 \nL 164.323604 52.724024 \nL 165.755036 53.028554 \nL 166.505099 53.201059 \nL 167.04533 53.392776 \nL 167.985334 53.756997 \nL 168.686595 54.056972 \nL 168.797925 54.121218 \nL 169.386671 54.485439 \nL 169.918772 54.84966 \nL 170.401147 55.213881 \nL 170.839603 55.578102 \nL 170.868091 55.603337 \nL 171.177848 55.942323 \nL 171.482064 56.306544 \nL 171.759978 56.670766 \nL 172.014196 57.034987 \nL 172.246963 57.399208 \nL 172.460224 57.763429 \nL 172.655669 58.12765 \nL 172.834771 58.491871 \nL 172.998817 58.856092 \nL 173.049587 58.977835 \nL 173.137291 59.220313 \nL 173.258374 59.584534 \nL 173.368788 59.948755 \nL 173.469214 60.312977 \nL 173.560253 60.677198 \nL 173.642433 61.041419 \nL 173.716214 61.40564 \nL 173.782003 61.769861 \nL 173.84015 62.134082 \nL 173.890961 62.498303 \nL 173.934696 62.862524 \nL 173.971576 63.226745 \nL 174.001786 63.590967 \nL 174.025473 63.955188 \nL 174.042754 64.319409 \nL 174.053711 64.68363 \nL 174.058397 65.047851 \nL 174.056836 65.412072 \nL 174.049019 65.776293 \nL 174.034908 66.140514 \nL 174.014437 66.504735 \nL 173.987505 66.868957 \nL 173.953981 67.233178 \nL 173.913698 67.597399 \nL 173.866455 67.96162 \nL 173.812012 68.325841 \nL 173.750085 68.690062 \nL 173.680347 69.054283 \nL 173.60242 69.418504 \nL 173.515873 69.782725 \nL 173.42021 70.146946 \nL 173.31487 70.511168 \nL 173.199213 70.875389 \nL 173.072511 71.23961 \nL 173.049587 71.300793 \nL 172.918602 71.603831 \nL 172.747178 71.968052 \nL 172.560077 72.332273 \nL 172.355921 72.696494 \nL 172.133133 73.060715 \nL 171.889901 73.424936 \nL 171.624137 73.789158 \nL 171.333423 74.153379 \nL 171.014947 74.5176 \nL 170.868091 74.673526 \nL 170.62554 74.881821 \nL 170.165789 75.246042 \nL 169.659341 75.610263 \nL 169.099862 75.974484 \nL 168.686595 76.220533 \nL 168.419023 76.338705 \nL 167.52803 76.702926 \nL 166.535266 77.067147 \nL 166.505099 77.077513 \nL 164.91621 77.431369 \nL 164.323604 77.553532 \nL 162.142108 77.771251 \nL 159.960612 77.771939 \nL 157.779117 77.54926 \nz\nM 89.62495 44.287248 \nL 87.971254 44.167984 \nL 86.028424 43.923027 \nL 85.789758 43.891279 \nL 84.125422 43.558806 \nL 83.608262 43.445203 \nL 82.773499 43.194585 \nL 81.704111 42.830364 \nL 81.426767 42.726352 \nL 80.882621 42.466142 \nL 80.205923 42.101921 \nL 79.611389 41.7377 \nL 79.245271 41.488128 \nL 79.105321 41.373479 \nL 78.695961 41.009258 \nL 78.33063 40.645037 \nL 78.003062 40.280816 \nL 77.70814 39.916595 \nL 77.441648 39.552374 \nL 77.200077 39.188152 \nL 77.063775 38.964994 \nL 76.987789 38.823931 \nL 76.805315 38.45971 \nL 76.638794 38.095489 \nL 76.486564 37.731268 \nL 76.347197 37.367047 \nL 76.219463 37.002826 \nL 76.102296 36.638605 \nL 75.994772 36.274384 \nL 75.896082 35.910163 \nL 75.805518 35.545941 \nL 75.722462 35.18172 \nL 75.646369 34.817499 \nL 75.576758 34.453278 \nL 75.513207 34.089057 \nL 75.455342 33.724836 \nL 75.402836 33.360615 \nL 75.355397 32.996394 \nL 75.312773 32.632173 \nL 75.27474 32.267951 \nL 75.241103 31.90373 \nL 75.211694 31.539509 \nL 75.186368 31.175288 \nL 75.165003 30.811067 \nL 75.147496 30.446846 \nL 75.133765 30.082625 \nL 75.123746 29.718404 \nL 75.117392 29.354183 \nL 75.114675 28.989962 \nL 75.11558 28.62574 \nL 75.120113 28.261519 \nL 75.128295 27.897298 \nL 75.140163 27.533077 \nL 75.155772 27.168856 \nL 75.175197 26.804635 \nL 75.198529 26.440414 \nL 75.225879 26.076193 \nL 75.257383 25.711972 \nL 75.293195 25.34775 \nL 75.333498 24.983529 \nL 75.3785 24.619308 \nL 75.428438 24.255087 \nL 75.483585 23.890866 \nL 75.544249 23.526645 \nL 75.610781 23.162424 \nL 75.683577 22.798203 \nL 75.763088 22.433982 \nL 75.849825 22.069761 \nL 75.944369 21.705539 \nL 76.047382 21.341318 \nL 76.159619 20.977097 \nL 76.281946 20.612876 \nL 76.415353 20.248655 \nL 76.560987 19.884434 \nL 76.72017 19.520213 \nL 76.894443 19.155992 \nL 77.063775 18.832157 \nL 77.0877 18.791771 \nL 77.317943 18.427549 \nL 77.571574 18.063328 \nL 77.851802 17.699107 \nL 78.162469 17.334886 \nL 78.508214 16.970665 \nL 78.894692 16.606444 \nL 79.245271 16.309501 \nL 79.340612 16.242223 \nL 79.899301 15.878002 \nL 80.533027 15.513781 \nL 81.256982 15.14956 \nL 81.426767 15.071369 \nL 82.219544 14.785338 \nL 83.370279 14.421117 \nL 83.608262 14.352486 \nL 85.008338 14.056896 \nL 85.789758 13.906787 \nL 87.45488 13.692675 \nL 87.971254 13.629706 \nL 90.152749 13.473301 \nL 92.334245 13.416556 \nL 94.515741 13.45891 \nL 96.697237 13.620716 \nL 97.225249 13.692675 \nL 98.878732 13.96074 \nL 99.265955 14.056896 \nL 100.487966 14.421117 \nL 101.060228 14.632434 \nL 101.375634 14.785338 \nL 102.01713 15.14956 \nL 102.540521 15.513781 \nL 102.975173 15.878002 \nL 103.241724 16.137591 \nL 103.332893 16.242223 \nL 103.618876 16.606444 \nL 103.865796 16.970665 \nL 104.080843 17.334886 \nL 104.269538 17.699107 \nL 104.436185 18.063328 \nL 104.58419 18.427549 \nL 104.716285 18.791771 \nL 104.834682 19.155992 \nL 104.941193 19.520213 \nL 105.037316 19.884434 \nL 105.1243 20.248655 \nL 105.20319 20.612876 \nL 105.274873 20.977097 \nL 105.340099 21.341318 \nL 105.399512 21.705539 \nL 105.423219 21.862886 \nL 105.45245 22.069761 \nL 105.499855 22.433982 \nL 105.543065 22.798203 \nL 105.582422 23.162424 \nL 105.618223 23.526645 \nL 105.650727 23.890866 \nL 105.680159 24.255087 \nL 105.706718 24.619308 \nL 105.730575 24.983529 \nL 105.75188 25.34775 \nL 105.770763 25.711972 \nL 105.787336 26.076193 \nL 105.801697 26.440414 \nL 105.813927 26.804635 \nL 105.824094 27.168856 \nL 105.832255 27.533077 \nL 105.838454 27.897298 \nL 105.842725 28.261519 \nL 105.84509 28.62574 \nL 105.845562 28.989962 \nL 105.844144 29.354183 \nL 105.840829 29.718404 \nL 105.835597 30.082625 \nL 105.828422 30.446846 \nL 105.819265 30.811067 \nL 105.808074 31.175288 \nL 105.794788 31.539509 \nL 105.779332 31.90373 \nL 105.761617 32.267951 \nL 105.741538 32.632173 \nL 105.718974 32.996394 \nL 105.693787 33.360615 \nL 105.665814 33.724836 \nL 105.634872 34.089057 \nL 105.60075 34.453278 \nL 105.563206 34.817499 \nL 105.521962 35.18172 \nL 105.4767 35.545941 \nL 105.427055 35.910163 \nL 105.423219 35.936401 \nL 105.370496 36.274384 \nL 105.30825 36.638605 \nL 105.239883 37.002826 \nL 105.164698 37.367047 \nL 105.081881 37.731268 \nL 104.99047 38.095489 \nL 104.889323 38.45971 \nL 104.777074 38.823931 \nL 104.652077 39.188152 \nL 104.512333 39.552374 \nL 104.355386 39.916595 \nL 104.178191 40.280816 \nL 103.976926 40.645037 \nL 103.746724 41.009258 \nL 103.481299 41.373479 \nL 103.241724 41.660318 \nL 103.16584 41.7377 \nL 102.767501 42.101921 \nL 102.291492 42.466142 \nL 101.713461 42.830364 \nL 101.060228 43.165713 \nL 100.987038 43.194585 \nL 99.919404 43.558806 \nL 98.878732 43.837084 \nL 98.395824 43.923027 \nL 96.697237 44.17701 \nL 95.270358 44.287248 \nL 94.515741 44.339059 \nL 92.334245 44.381257 \nL 90.152749 44.32472 \nz\nM 119.712029 42.830364 \nL 118.512194 42.62057 \nL 117.944285 42.466142 \nL 116.783869 42.101921 \nL 116.330698 41.938171 \nL 115.915717 41.7377 \nL 115.253363 41.373479 \nL 114.685624 41.009258 \nL 114.194244 40.645037 \nL 114.149202 40.608659 \nL 113.813413 40.280816 \nL 113.483175 39.916595 \nL 113.190722 39.552374 \nL 112.930362 39.188152 \nL 112.69751 38.823931 \nL 112.488429 38.45971 \nL 112.300046 38.095489 \nL 112.129813 37.731268 \nL 111.975598 37.367047 \nL 111.967707 37.347087 \nL 111.845729 37.002826 \nL 111.728164 36.638605 \nL 111.621106 36.274384 \nL 111.523539 35.910163 \nL 111.434586 35.545941 \nL 111.353489 35.18172 \nL 111.279593 34.817499 \nL 111.212325 34.453278 \nL 111.15119 34.089057 \nL 111.095754 33.724836 \nL 111.045638 33.360615 \nL 111.000512 32.996394 \nL 110.960089 32.632173 \nL 110.924116 32.267951 \nL 110.892378 31.90373 \nL 110.864688 31.539509 \nL 110.840887 31.175288 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361.379243 67.597399 \nL 361.382831 67.96162 \nL 361.386965 68.325841 \nL 361.391667 68.690062 \nL 361.396963 69.054283 \nL 361.40288 69.418504 \nL 361.409451 69.782725 \nL 361.416714 70.146946 \nL 361.424711 70.511168 \nL 361.433491 70.875389 \nL 361.443108 71.23961 \nL 361.453626 71.603831 \nL 361.465116 71.968052 \nL 361.477659 72.332273 \nL 361.491348 72.696494 \nL 361.506289 73.060715 \nL 361.522605 73.424936 \nL 361.540438 73.789158 \nL 361.559949 74.153379 \nL 361.581331 74.5176 \nL 361.604805 74.881821 \nL 361.630633 75.246042 \nL 361.659124 75.610263 \nL 361.690648 75.974484 \nL 361.725646 76.338705 \nL 361.764656 76.702926 \nL 361.808331 77.067147 \nL 361.857481 77.431369 \nL 361.913115 77.79559 \nL 361.97651 78.159811 \nL 362.049305 78.524032 \nL 362.133641 78.888253 \nL 362.232362 79.252474 \nL 362.349337 79.616695 \nL 362.489962 79.980916 \nL 362.661999 80.345137 \nL 362.839714 80.651876 \nL 362.877972 80.709359 \nL 363.160718 81.07358 \nL 363.535654 81.437801 \nL 364.056002 81.802022 \nL 364.825723 82.166243 \nL 365.021209 82.237329 \nL 366.11774 82.530464 \nL 367.202705 82.725751 \nL 368.597138 82.894685 \nz\nM 405.144244 81.07358 \nL 404.288132 80.939183 \nL 403.334288 80.709359 \nL 402.226291 80.345137 \nL 402.106637 80.296933 \nL 401.503201 79.980916 \nL 400.965148 79.616695 \nL 400.543602 79.252474 \nL 400.204779 78.888253 \nL 399.926811 78.524032 \nL 399.925141 78.521608 \nL 399.707689 78.159811 \nL 399.522171 77.79559 \nL 399.363219 77.431369 \nL 399.225691 77.067147 \nL 399.105697 76.702926 \nL 399.000239 76.338705 \nL 398.906973 75.974484 \nL 398.824037 75.610263 \nL 398.749935 75.246042 \nL 398.683451 74.881821 \nL 398.623588 74.5176 \nL 398.56952 74.153379 \nL 398.520557 73.789158 \nL 398.476121 73.424936 \nL 398.435721 73.060715 \nL 398.39894 72.696494 \nL 398.36542 72.332273 \nL 398.334855 71.968052 \nL 398.30698 71.603831 \nL 398.281566 71.23961 \nL 398.258414 70.875389 \nL 398.237349 70.511168 \nL 398.218221 70.146946 \nL 398.200898 69.782725 \nL 398.185264 69.418504 \nL 398.171219 69.054283 \nL 398.158675 68.690062 \nL 398.147555 68.325841 \nL 398.137795 67.96162 \nL 398.129338 67.597399 \nL 398.122134 67.233178 \nL 398.116146 66.868957 \nL 398.111339 66.504735 \nL 398.107687 66.140514 \nL 398.105171 65.776293 \nL 398.103778 65.412072 \nL 398.1035 65.047851 \nL 398.104335 64.68363 \nL 398.106288 64.319409 \nL 398.10937 63.955188 \nL 398.113596 63.590967 \nL 398.11899 63.226745 \nL 398.125582 62.862524 \nL 398.133407 62.498303 \nL 398.142509 62.134082 \nL 398.152941 61.769861 \nL 398.164764 61.40564 \nL 398.178049 61.041419 \nL 398.192876 60.677198 \nL 398.209341 60.312977 \nL 398.227552 59.948755 \nL 398.24763 59.584534 \nL 398.269719 59.220313 \nL 398.293979 58.856092 \nL 398.320597 58.491871 \nL 398.349786 58.12765 \nL 398.381792 57.763429 \nL 398.416902 57.399208 \nL 398.455444 57.034987 \nL 398.497806 56.670766 \nL 398.544439 56.306544 \nL 398.595875 55.942323 \nL 398.652747 55.578102 \nL 398.715808 55.213881 \nL 398.785964 54.84966 \nL 398.864315 54.485439 \nL 398.952209 54.121218 \nL 399.051311 53.756997 \nL 399.163707 53.392776 \nL 399.292045 53.028554 \nL 399.43973 52.664333 \nL 399.611226 52.300112 \nL 399.81249 51.935891 \nL 399.925141 51.756813 \nL 400.05924 51.57167 \nL 400.365426 51.207449 \nL 400.742282 50.843228 \nL 401.216826 50.479007 \nL 401.83188 50.114786 \nL 402.106637 49.981048 \nL 402.731044 49.750565 \nL 404.067747 49.386343 \nL 404.288132 49.338481 \nL 406.469628 49.037406 \nL 406.672114 49.022122 \nL 408.651124 48.893898 \nL 410.832619 48.844691 \nL 413.014115 48.866453 \nL 415.195611 48.953906 \nL 416.133263 49.022122 \nL 417.377107 49.114861 \nL 419.558602 49.371023 \nL 419.651589 49.386343 \nL 421.64998 49.750565 \nL 421.740098 49.768758 \nL 423.012547 50.114786 \nL 423.921594 50.404555 \nL 424.105354 50.479007 \nL 424.915356 50.843228 \nL 425.605735 51.207449 \nL 426.103089 51.508661 \nL 426.189817 51.57167 \nL 426.649587 51.935891 \nL 427.051671 52.300112 \nL 427.405841 52.664333 \nL 427.719768 53.028554 \nL 427.999557 53.392776 \nL 428.250126 53.756997 \nL 428.284585 53.810773 \nL 428.460888 54.121218 \nL 428.648668 54.485439 \nL 428.818646 54.84966 \nL 428.972957 55.213881 \nL 429.113399 55.578102 \nL 429.241493 55.942323 \nL 429.358537 56.306544 \nL 429.465643 56.670766 \nL 429.563769 57.034987 \nL 429.653743 57.399208 \nL 429.736285 57.763429 \nL 429.812021 58.12765 \nL 429.8815 58.491871 \nL 429.945203 58.856092 \nL 430.003551 59.220313 \nL 430.056917 59.584534 \nL 430.105627 59.948755 \nL 430.149971 60.312977 \nL 430.190201 60.677198 \nL 430.226543 61.041419 \nL 430.259192 61.40564 \nL 430.288321 61.769861 \nL 430.314081 62.134082 \nL 430.3366 62.498303 \nL 430.355991 62.862524 \nL 430.372348 63.226745 \nL 430.385751 63.590967 \nL 430.396262 63.955188 \nL 430.403932 64.319409 \nL 430.408795 64.68363 \nL 430.410876 65.047851 \nL 430.410182 65.412072 \nL 430.406712 65.776293 \nL 430.400449 66.140514 \nL 430.391364 66.504735 \nL 430.379415 66.868957 \nL 430.364544 67.233178 \nL 430.34668 67.597399 \nL 430.325738 67.96162 \nL 430.301614 68.325841 \nL 430.274187 68.690062 \nL 430.243318 69.054283 \nL 430.208845 69.418504 \nL 430.170585 69.782725 \nL 430.128328 70.146946 \nL 430.081835 70.511168 \nL 430.030835 70.875389 \nL 429.975022 71.23961 \nL 429.914046 71.603831 \nL 429.847511 71.968052 \nL 429.774967 72.332273 \nL 429.695902 72.696494 \nL 429.609728 73.060715 \nL 429.515774 73.424936 \nL 429.413269 73.789158 \nL 429.301323 74.153379 \nL 429.178903 74.5176 \nL 429.044811 74.881821 \nL 428.89764 75.246042 \nL 428.73574 75.610263 \nL 428.557152 75.974484 \nL 428.359543 76.338705 \nL 428.284585 76.466658 \nL 428.128225 76.702926 \nL 427.863598 77.067147 \nL 427.567422 77.431369 \nL 427.234224 77.79559 \nL 426.857172 78.159811 \nL 426.427624 78.524032 \nL 426.103089 78.76896 \nL 425.913831 78.888253 \nL 425.27383 79.252474 \nL 424.527112 79.616695 \nL 423.921594 79.873167 \nL 423.598896 79.980916 \nL 422.371198 80.345137 \nL 421.740098 80.50878 \nL 420.697663 80.709359 \nL 419.558602 80.906539 \nL 418.168831 81.07358 \nL 417.377107 81.162967 \nL 415.195611 81.323834 \nL 413.014115 81.411519 \nL 410.832619 81.433339 \nL 408.651124 81.384001 \nL 406.469628 81.240198 \nz\nM 376.921365 78.524032 \nL 375.928688 78.402039 \nL 374.80944 78.159811 \nL 373.747192 77.909246 \nL 373.416278 77.79559 \nL 372.441284 77.431369 \nL 371.573328 77.067147 \nL 371.565697 77.063723 \nL 370.951856 76.702926 \nL 370.396256 76.338705 \nL 369.89626 75.974484 \nL 369.444671 75.610263 \nL 369.384201 75.55806 \nL 369.086753 75.246042 \nL 368.769939 74.881821 \nL 368.481678 74.5176 \nL 368.218845 74.153379 \nL 367.978774 73.789158 \nL 367.759184 73.424936 \nL 367.558109 73.060715 \nL 367.373846 72.696494 \nL 367.204921 72.332273 \nL 367.202705 72.327167 \nL 367.06589 71.968052 \nL 366.938708 71.603831 \nL 366.822254 71.23961 \nL 366.715747 70.875389 \nL 366.618497 70.511168 \nL 366.529901 70.146946 \nL 366.449427 69.782725 \nL 366.376605 69.418504 \nL 366.311025 69.054283 \nL 366.252327 68.690062 \nL 366.200195 68.325841 \nL 366.154357 67.96162 \nL 366.114577 67.597399 \nL 366.080655 67.233178 \nL 366.052422 66.868957 \nL 366.029739 66.504735 \nL 366.012496 66.140514 \nL 366.000611 65.776293 \nL 365.994026 65.412072 \nL 365.992711 65.047851 \nL 365.996658 64.68363 \nL 366.005888 64.319409 \nL 366.020443 63.955188 \nL 366.040394 63.590967 \nL 366.065837 63.226745 \nL 366.096895 62.862524 \nL 366.133723 62.498303 \nL 366.176505 62.134082 \nL 366.225458 61.769861 \nL 366.280836 61.40564 \nL 366.342933 61.041419 \nL 366.412086 60.677198 \nL 366.48868 60.312977 \nL 366.573153 59.948755 \nL 366.666004 59.584534 \nL 366.767802 59.220313 \nL 366.879191 58.856092 \nL 367.000905 58.491871 \nL 367.133778 58.12765 \nL 367.202705 57.952531 \nL 367.287551 57.763429 \nL 367.463973 57.399208 \nL 367.656444 57.034987 \nL 367.866549 56.670766 \nL 368.096115 56.306544 \nL 368.347259 55.942323 \nL 368.622446 55.578102 \nL 368.924561 55.213881 \nL 369.256996 54.84966 \nL 369.384201 54.720226 \nL 369.664814 54.485439 \nL 370.139789 54.121218 \nL 370.6666 53.756997 \nL 371.253167 53.392776 \nL 371.565697 53.214912 \nL 371.99498 53.028554 \nL 372.914289 52.664333 \nL 373.747192 52.368397 \nL 374.02706 52.300112 \nL 375.640276 51.935891 \nL 375.928688 51.875546 \nL 378.110184 51.613953 \nL 379.25414 51.57167 \nL 380.291679 51.533673 \nL 381.221524 51.57167 \nL 382.473175 51.625638 \nL 384.654671 51.917582 \nL 384.734244 51.935891 \nL 386.165614 52.300112 \nL 386.836167 52.496049 \nL 387.242959 52.664333 \nL 388.027688 53.028554 \nL 388.7068 53.392776 \nL 389.017662 53.578726 \nL 389.25375 53.756997 \nL 389.690778 54.121218 \nL 390.077935 54.485439 \nL 390.422708 54.84966 \nL 390.731138 55.213881 \nL 391.008147 55.578102 \nL 391.199158 55.854387 \nL 391.251509 55.942323 \nL 391.453044 56.306544 \nL 391.635732 56.670766 \nL 391.801668 57.034987 \nL 391.952627 57.399208 \nL 392.090125 57.763429 \nL 392.21546 58.12765 \nL 392.329752 58.491871 \nL 392.433968 58.856092 \nL 392.528947 59.220313 \nL 392.615417 59.584534 \nL 392.694015 59.948755 \nL 392.765295 60.312977 \nL 392.829742 60.677198 \nL 392.887777 61.041419 \nL 392.939769 61.40564 \nL 392.98604 61.769861 \nL 393.026867 62.134082 \nL 393.062489 62.498303 \nL 393.093111 62.862524 \nL 393.118904 63.226745 \nL 393.140013 63.590967 \nL 393.156552 63.955188 \nL 393.168611 64.319409 \nL 393.176254 64.68363 \nL 393.179522 65.047851 \nL 393.178433 65.412072 \nL 393.172981 65.776293 \nL 393.163137 66.140514 \nL 393.148847 66.504735 \nL 393.130036 66.868957 \nL 393.106601 67.233178 \nL 393.078413 67.597399 \nL 393.045315 67.96162 \nL 393.007118 68.325841 \nL 392.963602 68.690062 \nL 392.914507 69.054283 \nL 392.859536 69.418504 \nL 392.798345 69.782725 \nL 392.730538 70.146946 \nL 392.655664 70.511168 \nL 392.573204 70.875389 \nL 392.482564 71.23961 \nL 392.383065 71.603831 \nL 392.273923 71.968052 \nL 392.154239 72.332273 \nL 392.022973 72.696494 \nL 391.878919 73.060715 \nL 391.720676 73.424936 \nL 391.546605 73.789158 \nL 391.354777 74.153379 \nL 391.199158 74.423367 \nL 391.136164 74.5176 \nL 390.873299 74.881821 \nL 390.581135 75.246042 \nL 390.255212 75.610263 \nL 389.890084 75.974484 \nL 389.479034 76.338705 \nL 389.017662 76.699974 \nL 389.012915 76.702926 \nL 388.379131 77.067147 \nL 387.649936 77.431369 \nL 386.836167 77.782391 \nL 386.793046 77.79559 \nL 385.481901 78.159811 \nL 384.654671 78.359707 \nL 383.478728 78.524032 \nL 382.473175 78.651937 \nL 380.291679 78.743786 \nL 378.110184 78.663545 \nz\nM 414.959145 44.287248 \nL 413.014115 44.13409 \nL 411.422933 43.923027 \nL 410.832619 43.839617 \nL 409.482853 43.558806 \nL 408.651124 43.36641 \nL 408.100475 43.194585 \nL 407.061046 42.830364 \nL 406.469628 42.595766 \nL 406.210553 42.466142 \nL 405.550905 42.101921 \nL 404.973973 41.7377 \nL 404.465753 41.373479 \nL 404.288132 41.23421 \nL 404.046379 41.009258 \nL 403.691122 40.645037 \nL 403.373429 40.280816 \nL 403.088077 39.916595 \nL 402.830781 39.552374 \nL 402.597998 39.188152 \nL 402.38677 38.823931 \nL 402.194608 38.45971 \nL 402.106637 38.279629 \nL 402.026246 38.095489 \nL 401.87881 37.731268 \nL 401.743992 37.367047 \nL 401.620563 37.002826 \nL 401.507458 36.638605 \nL 401.403755 36.274384 \nL 401.308652 35.910163 \nL 401.221448 35.545941 \nL 401.14153 35.18172 \nL 401.068358 34.817499 \nL 401.001459 34.453278 \nL 400.940417 34.089057 \nL 400.884865 33.724836 \nL 400.834479 33.360615 \nL 400.788975 32.996394 \nL 400.748103 32.632173 \nL 400.711646 32.267951 \nL 400.679412 31.90373 \nL 400.651237 31.539509 \nL 400.62698 31.175288 \nL 400.606519 30.811067 \nL 400.589757 30.446846 \nL 400.576612 30.082625 \nL 400.567021 29.718404 \nL 400.560939 29.354183 \nL 400.558337 28.989962 \nL 400.559204 28.62574 \nL 400.563543 28.261519 \nL 400.571375 27.897298 \nL 400.582736 27.533077 \nL 400.597681 27.168856 \nL 400.616282 26.804635 \nL 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+      "text/plain": [
+       "<Figure size 576x96 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -21979,25 +541,44 @@
     }
    ],
    "source": [
-    "zero_pi.plot_wavefunction(which=1, mode='real', zero_calibrate=True);"
+    "zero_pi.plot_potential()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Full zero-pi qubit (incl. zeta-mode)"
+    "# Full $0$-$\\pi$ qubit (incl. $\\zeta$-mode)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When there is disorder in the capacitor pairs and/or the inductor pairs, the $\\zeta$-mode couples to $\\theta$ and/or $\\phi$-mode. To accurately obtain spectral properties of such circuit, all three modes need to be taken into account. Taking all the disorder into account in the first order, the Hamiltonian for the full $0$-$\\pi$ circuit is:\n",
+    "\\begin{align*}\n",
+    "H&= H_{\\theta,\\phi} + H_{\\zeta} + H_{\\text{int}},\\\\\n",
+    "H_{\\zeta}&= \\omega_\\zeta a^\\dagger a,\\\\\n",
+    "H_{\\text{int}}&= 2E_{\\text C \\Sigma } dC \\partial_\\theta \\partial_\\eta + E_{\\text L} dE_{\\text L} \\phi\\zeta,\n",
+    "\\end{align*}\n",
+    "where disorder in the capacitors and the inductors are defined as $dC = 2(C_{1} - C_{2})/(C_{1}+C_{2})$ and $dL = 2(L_{1} - L_{2})/(L_{1}+L_{2})$.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "**Creation via GUI** (ipywidgets needs to be installed for this to work.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c44000aa3d9a4e83af11eb126ffd5a21",
+       "model_id": "5db50ddfdbe149deabcfc988abb80e24",
        "version_major": 2,
        "version_minor": 0
       },
@@ -22011,7 +592,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b117b0aa347499b90ca6508b162d060",
+       "model_id": "7dbef60d269244d79a3d544064252638",
        "version_major": 2,
        "version_minor": 0
       },
@@ -22029,14 +610,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "FullZeroPi----------|\n",
+      "FullZeroPi----------| [FullZeroPi_1]\n",
       "                    | EJ: 10.0\n",
       "                    | EL: 0.04\n",
       "                    | ECJ: 20.0\n",
@@ -22062,11 +643,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Notice that four basis cutoffs need to be specified: `ncut`, `zeropi_cutoff`, `zeta_cutoff` and `grid`. The full circuit is treated as a composite system made by subsystems with Hamiltonians $H_{\\theta, \\phi}$ and $H_\\zeta$, and their interactions $H_{\\text{int}}$. The diagonalization of the full $0$-$\\pi$ is carried out using hierarchical diagonalization with the following steps: \n",
+    "1. The symmetric $0$-$\\pi$ Hamiltonian $H_{\\theta,\\phi}$ is diagonalized using the hybrid basis; the basis is specified using `ncut` and `grid`.\n",
+    "2. The $\\zeta$-mode Hamiltonian $H_{\\zeta}$ is a harmonic oscillator Hamiltonian; its spectrum is obtained directly without performing numerical diagonalization.\n",
+    "3. From the resulting lowest energy eigenstates of $H_{\\theta,\\phi}$ and $H_\\zeta$, the lowest `zeropi_cutoff` and `zeta_cutoff` energy eigenstates of the respective subsystems are kept. A basis for the full circuit is obtained by taking tensor products of these subsystem eigenstates. \n",
+    "4. The full circuit Hamiltonian $H$ is represented under the above basis and diagonalized."
+   ]
   }
  ],
  "metadata": {
@@ -22086,7 +672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.6"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/examples/scqubits_paper_source_code.ipynb b/examples/scqubits_paper_source_code.ipynb
index 8ddaac0..5f25143 100644
--- a/examples/scqubits_paper_source_code.ipynb
+++ b/examples/scqubits_paper_source_code.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:02:21.367859Z",
@@ -23,9 +23,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'svg'\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import scqubits as scq"
@@ -40,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:02:29.283958Z",
@@ -66,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:02:30.343579Z",
@@ -77,12 +74,26 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33be25d712c841acbc5e91ce7b81fab2",
+       "model_id": "70ff215ecb2a4bff82509ca1bacfef3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(ToggleButtons(description='Qubits:', options=('Transmon', 'TunableTransmon', 'Fl…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c9a5a92cde244a8b2a35797a014529f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "VBox(children=(HBox(children=(ToggleButtons(description='Qubits:', layout=Layout(width='800px'), options=('Tra…"
+       "Tab(children=(HBox(children=(VBox(children=(SelectMultiple(description='States', index=(0, 1, 2, 3, 4), layout…"
       ]
      },
      "metadata": {},
@@ -91,12 +102,26 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd29c8a15f7c4788b86419994a431ec9",
+       "model_id": "44c12a77e4d94967965594dde0286a64",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output(layout=Layout(width='975px'))"
+       "HBox(children=(HBox(children=(Checkbox(value=False, description='Manual Update', indent=False, layout=Layout(w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3eb3caedbad44e1f8b67e0d35b555616",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output(layout=Layout(width='100%'))"
       ]
      },
      "metadata": {},
@@ -106,7 +131,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 54,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:02:30.973822Z",
@@ -139,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:02:31.566655Z",
@@ -155,7 +180,7 @@
        "        39.72096178,  59.76406786,  61.19239859,  85.87012806])"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:10:36.438704Z",
@@ -180,363 +205,409 @@
        "Text(-0.15, 0.97, '(a)')"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
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       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
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   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:10:50.492907Z",
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+     "execution_count": 9,
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       "text/plain": [
-       "<Figure size 720x360 with 3 Axes>"
+       "<Figure size 1000x500 with 3 Axes>"
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      },
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@@ -10549,7 +11051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 11,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-10T07:11:10.274025Z",
@@ -10559,1397 +11061,403 @@
    "outputs": [
     {
      "data": {
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-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
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@@ -12801,7 +12611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 12,
    "metadata": {
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@@ -12834,7 +12644,7 @@
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   {
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-   "execution_count": 65,
+   "execution_count": 13,
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@@ -12848,7 +12658,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 66,
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    "metadata": {
     "ExecuteTime": {
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@@ -12887,7 +12697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 15,
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@@ -12910,7 +12720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 16,
    "metadata": {
     "ExecuteTime": {
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@@ -12946,7 +12756,7 @@
   },
   {
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@@ -12962,7 +12772,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Transmon_2 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -12976,7 +12786,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Transmon_3 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -12990,7 +12800,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Oscillator_1 [num_cpus=4]"
       ]
      },
      "metadata": {},
@@ -13054,7 +12864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 18,
    "metadata": {},
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@@ -13115,7 +12925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
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    "metadata": {},
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@@ -13126,7 +12936,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem TunableTransmon_2 [num_cpus=4]"
       ]
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      "metadata": {},
@@ -13140,7 +12950,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem TunableTransmon_3 [num_cpus=4]"
       ]
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      "metadata": {},
@@ -13154,7 +12964,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Parallel compute bare eigensys [num_cpus=4]"
+       "Parallel compute bare eigensys for subsystem Oscillator_2 [num_cpus=4]"
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@@ -13213,422 +13023,441 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "FutureWarning: `subsys_list` is deprecated. Use `subsystem_list` instead.\n",
+      " c:\\users\\drjen\\onedrive\\coding\\pycharmprojects\\scqubits\\scqubits\\utils\\misc.py: 195"
+     ]
+    },
     {
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@@ -15184,7 +15021,7 @@
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@@ -15203,7 +15040,7 @@
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@@ -15214,7 +15051,7 @@
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@@ -15222,25 +15059,13 @@
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "UserWarning: By default all methods that involve calculations of the t1 coherence times/rates, return a sum of upward (i.e., excitation), and downward (i.e., relaxation) rates. To change this behavior, parameter total=False can be passed to any t1-related coherence methods. With total=False, only a one-directional transition between levels i and j is used to calculate the required t1 time or rate.\n",
-      "See documentation for details.\n",
-      "This warning can be disabled by executing:\n",
-      "scqubits.settings.T1_DEFAULT_WARNING=False\n",
-      "\n",
-      " /home/pgroszko/Dropbox/1_research/libs/scqubits/scqubits/core/noise.py: 1187"
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    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-04T22:34:31.517919Z",
@@ -15320,7 +15145,7 @@
        "0.25580751799518137"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15335,7 +15160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-04T22:34:34.029431Z",
@@ -15349,7 +15174,7 @@
        "33726.40283983535"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -15366,7 +15191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-04T22:34:58.196288Z",
@@ -15376,3482 +15201,3781 @@
    "outputs": [
     {
      "data": {
+      "application/pdf": 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        "</svg>\n"
       ],
       "text/plain": [
-       "<Figure size 1200x1350 with 6 Axes>"
+       "<Figure size 800x900 with 6 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -18863,7 +18987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2021-07-04T22:35:11.262358Z",
@@ -18873,1610 +18997,1867 @@
    "outputs": [
     {
      "data": {
+      "application/pdf": 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        "</svg>\n"
       ],
       "text/plain": [
-       "<Figure size 1200x450 with 2 Axes>"
+       "<Figure size 800x300 with 2 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
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    ],
@@ -20504,7 +20885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20522,7 +20903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20531,7 +20912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20545,7 +20926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -20611,32 +20992,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d30e4e7c309d4480a4c21548ca8f5eec",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "HBox(children=(VBox(children=(FloatSlider(value=0.0, continuous_update=False, description='$\\\\Phi_{ext}/\\\\Phi_…"
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-     "metadata": {},
-     "output_type": "display_data"
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-       "model_id": "5d07293275b34180bb82be71dca44c9c",
+       "model_id": "e43af49059ea4c0c90abf2a4d7b5f0c9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Output()"
+       "VBox(children=(Tab(children=(HBox(children=(HBox(children=(VBox(children=(Dropdown(layout=Layout(width='165px'…"
       ]
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      "metadata": {},
@@ -20644,11 +21011,7 @@
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    ],
    "source": [
-    "explorer = scq.Explorer(\n",
-    "\tsweep=sweep,\n",
-    "\tevals_count=10\n",
-    ")\n",
-    "explorer.interact()"
+    "explorer = scq.Explorer(sweep=sweep)"
    ]
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@@ -20660,7 +21023,7 @@
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-   "execution_count": 36,
+   "execution_count": 35,
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@@ -20674,7 +21037,7 @@
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+   "execution_count": 36,
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@@ -20696,7 +21059,7 @@
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@@ -20718,7 +21081,7 @@
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+   "execution_count": 38,
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@@ -20742,7 +21105,7 @@
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+   "execution_count": 39,
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@@ -20783,7 +21146,7 @@
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+   "execution_count": 40,
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@@ -20816,7 +21179,7 @@
   },
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-   "execution_count": 43,
+   "execution_count": 41,
    "metadata": {
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      "end_time": "2021-07-04T22:49:25.665940Z",
@@ -20852,7 +21215,7 @@
  "metadata": {
   "celltoolbar": "Initialisation Cell",
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
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@@ -20866,7 +21229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.9.15"
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