diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 1e11f0d..289b860 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -16,7 +16,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v4
diff --git a/doc_source/notebooks/CodingFiguresFern.ipynb b/Extra_Notebooks/CodingFiguresFern.ipynb
similarity index 100%
rename from doc_source/notebooks/CodingFiguresFern.ipynb
rename to Extra_Notebooks/CodingFiguresFern.ipynb
diff --git a/doc_source/notebooks/Matisse/Matisse_ECT.ipynb b/Extra_Notebooks/Matisse_ECT.ipynb
similarity index 100%
rename from doc_source/notebooks/Matisse/Matisse_ECT.ipynb
rename to Extra_Notebooks/Matisse_ECT.ipynb
diff --git a/Extra_Notebooks/Mesh.ipynb b/Extra_Notebooks/Mesh.ipynb
new file mode 100644
index 0000000..6d045a5
--- /dev/null
+++ b/Extra_Notebooks/Mesh.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 0\n",
+      "4 5\n",
+      "9 8\n",
+      "10 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from CGAL.CGAL_Kernel import Point_2\n",
+    "from CGAL.CGAL_Triangulation_2 import Triangulation_2\n",
+    "from CGAL.CGAL_Triangulation_2 import Triangulation_2_Vertex_circulator\n",
+    "from CGAL.CGAL_Triangulation_2 import Triangulation_2_Vertex_handle\n",
+    "\n",
+    "points=[]\n",
+    "points.append( Point_2(1,0) )\n",
+    "points.append( Point_2(3,2) )\n",
+    "points.append( Point_2(4,5) )\n",
+    "points.append( Point_2(9,8) )\n",
+    "points.append( Point_2(7,4) )\n",
+    "points.append( Point_2(5,2) )\n",
+    "points.append( Point_2(6,3) )\n",
+    "points.append( Point_2(10,1) )\n",
+    "  \n",
+    "t=Triangulation_2()\n",
+    "t.insert(points)\n",
+    "\n",
+    "vc = t.incident_vertices(t.infinite_vertex())\n",
+    "\n",
+    "if vc.hasNext():\n",
+    "  done = vc.next();\n",
+    "  iter=Triangulation_2_Vertex_handle()\n",
+    "  while(1):\n",
+    "    iter=vc.next()\n",
+    "    print (iter.point())\n",
+    "    if iter == done:\n",
+    "      break\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Extra_Notebooks/Playing_with_Spirals.ipynb b/Extra_Notebooks/Playing_with_Spirals.ipynb
new file mode 100644
index 0000000..1aa05a2
--- /dev/null
+++ b/Extra_Notebooks/Playing_with_Spirals.ipynb
@@ -0,0 +1,1891 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/liz/Library/CloudStorage/Dropbox/Math/Code/ect/ect/ect/ect_graph.py:10: SyntaxWarning: invalid escape sequence '\\c'\n",
+      "  \"\"\"\n",
+      "/Users/liz/Library/CloudStorage/Dropbox/Math/Code/ect/ect/ect/ect_graph.py:165: SyntaxWarning: invalid escape sequence '\\o'\n",
+      "  \"\"\"\n",
+      "/Users/liz/Library/CloudStorage/Dropbox/Math/Code/ect/ect/ect/ect_graph.py:222: SyntaxWarning: invalid escape sequence '\\p'\n",
+      "  \"\"\"\n",
+      "/Users/liz/Library/CloudStorage/Dropbox/Math/Code/ect/ect/ect/embed_graph.py:445: SyntaxWarning: invalid escape sequence '\\i'\n",
+      "  \"\"\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Circle\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Recreating the sprial example from Arya \n",
+    "\n",
+    "# Create the graph\n",
+    "Spiral = EmbeddedGraph()\n",
+    "Spiral.add_node('a', .5, 0)\n",
+    "Spiral.add_node('b', 1, 1)\n",
+    "Spiral.add_node('c', -1.5, -.5)\n",
+    "Spiral.add_node('d', 3, -2.5)\n",
+    "Spiral.add_node('e',1.5,2)\n",
+    "Spiral.add_node('f',-1.5,2)\n",
+    "Spiral.add_node('g', -3,-3)\n",
+    "\n",
+    "nodelist = list(Spiral.nodes())\n",
+    "nodelist.sort()\n",
+    "for i in range(len(nodelist)-1):\n",
+    "    Spiral.add_edge(nodelist[i], nodelist[i+1])\n",
+    "\n",
+    "fix, ax = plt.subplots()\n",
+    "Spiral.plot(ax = ax)\n",
+    "Spiral.plot_angle_circle(ax = ax, edges_only=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fix, ax = plt.subplots()\n",
+    "Spiral.plot(ax = ax)\n",
+    "Spiral.plot_angle_circle(ax = ax, edges_only=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Angle</th>\n",
+       "      <th>Swapped Verts</th>\n",
+       "      <th>Sorted list</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>gcfabed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.32</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>gcfabed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.52</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>gcfadbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.79</td>\n",
+       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
+       "      <td>gcdafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.15</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>gcdafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>gdcafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.57</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>gdcabfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.65</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>gdcabef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.82</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>dgacbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.11</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>dgacbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.27</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>dgabecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2.28</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>dagbecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.36</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>dabgecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.41</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>dabegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.60</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>dabegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.68</td>\n",
+       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
+       "      <td>debacgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.85</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>debacgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>3.14</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>debacfg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>3.46</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>debafcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>3.66</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>edbafcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3.93</td>\n",
+       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
+       "      <td>ebfadcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>4.29</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>ebfadcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.33</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>ebfacdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>4.71</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>efbacdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>4.79</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>febacdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>4.96</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>febcagd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5.25</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>febcagd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.41</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>fcebagd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5.42</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>fcebagd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>fcegbad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>5.55</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>fcegbad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>5.74</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>fcgebad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>5.82</td>\n",
+       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
+       "      <td>fgcabed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>5.99</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>fgcabed</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Angle             Swapped Verts Sorted list\n",
+       "0    0.00                  [(c, f)]     gcfabed\n",
+       "1    0.32                  [(d, e)]     gcfabed\n",
+       "2    0.52                  [(b, d)]     gcfadbe\n",
+       "3    0.79  [(a, d), (f, a), (f, d)]     gcdafbe\n",
+       "4    1.15                  [(c, d)]     gcdafbe\n",
+       "5    1.19                  [(b, f)]     gdcafbe\n",
+       "6    1.57                  [(e, f)]     gdcabfe\n",
+       "7    1.65                  [(d, g)]     gdcabef\n",
+       "8    1.82                  [(a, c)]     dgacbef\n",
+       "9    2.11                  [(b, c)]     dgacbef\n",
+       "10   2.27                  [(c, e)]     dgabecf\n",
+       "11   2.28                  [(a, g)]     dagbecf\n",
+       "12   2.36                  [(b, g)]     dabgecf\n",
+       "13   2.41                  [(e, g)]     dabegcf\n",
+       "14   2.60                  [(c, g)]     dabegcf\n",
+       "15   2.68  [(a, b), (a, e), (b, e)]     debacgf\n",
+       "16   2.85                  [(f, g)]     debacgf\n",
+       "17   3.14                  [(c, f)]     debacfg\n",
+       "18   3.46                  [(d, e)]     debafcg\n",
+       "19   3.66                  [(b, d)]     edbafcg\n",
+       "20   3.93  [(a, d), (f, a), (f, d)]     ebfadcg\n",
+       "21   4.29                  [(c, d)]     ebfadcg\n",
+       "22   4.33                  [(b, f)]     ebfacdg\n",
+       "23   4.71                  [(e, f)]     efbacdg\n",
+       "24   4.79                  [(d, g)]     febacdg\n",
+       "25   4.96                  [(a, c)]     febcagd\n",
+       "26   5.25                  [(b, c)]     febcagd\n",
+       "27   5.41                  [(c, e)]     fcebagd\n",
+       "28   5.42                  [(a, g)]     fcebagd\n",
+       "29   5.50                  [(b, g)]     fcegbad\n",
+       "30   5.55                  [(e, g)]     fcegbad\n",
+       "31   5.74                  [(c, g)]     fcgebad\n",
+       "32   5.82  [(a, b), (a, e), (b, e)]     fgcabed\n",
+       "33   5.99                  [(f, g)]     fgcabed"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles = Spiral.get_normals_dict(num_rounding_digits=2, opposites = True)\n",
+    "keys = list(All_Angles.keys())\n",
+    "keys.sort()\n",
+    "\n",
+    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
+    "pd_table['Angle'] = keys\n",
+    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
+    "\n",
+    "\n",
+    "\n",
+    "pd_table.sort_values(by='Angle', inplace=True)\n",
+    "pd_table['Sorted list'] = [''.join(Spiral.sort_vertices(key)) for key in keys]\n",
+    "pd_table.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys([5.82, 1.82, 3.93, 2.28, 2.11, 3.66, 1.19, 2.36, 4.29, 5.41, 0.0, 2.6, 0.32, 1.65, 1.57, 2.41, 2.85, 2.68, 4.96, 0.79, 5.42, 5.25, 0.52, 4.33, 5.5, 1.15, 2.27, 3.14, 5.74, 3.46, 4.79, 4.71, 5.55, 5.99])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>angle</th>\n",
+       "      <th>vertex_pair(s)_swapped</th>\n",
+       "      <th>sorted_order_at_next_midpoint</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>[g, c, f, a, b, e, d]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.32</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>[g, c, f, a, d, b, e]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.04</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>[g, c, d, a, f, b, e]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>[g, d, c, a, b, f, e]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.57</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>[g, d, c, a, b, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.65</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>[d, g, c, a, b, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.73</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>[d, g, c, a, b, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.82</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>[d, g, a, c, b, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.11</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>[d, g, a, b, c, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.24</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>[d, g, a, b, c, e, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.28</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>[d, a, g, b, e, c, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2.36</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>[d, a, b, g, e, c, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.41</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>[d, a, b, e, g, c, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.49</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>[d, a, b, e, g, c, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.52</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>[d, a, b, e, g, c, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.60</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>[d, e, b, a, c, g, f]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.85</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>[d, e, b, a, c, f, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>3.02</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>[d, e, b, a, f, c, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>3.37</td>\n",
+       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
+       "      <td>[e, d, b, a, f, c, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>3.59</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>[e, d, b, a, f, c, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3.66</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>[e, b, d, a, f, c, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3.93</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>[e, b, f, a, d, c, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.29</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>[e, f, b, a, c, d, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>4.47</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>[e, f, b, a, c, d, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>4.50</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>[e, f, b, a, c, d, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>4.59</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>[e, f, b, a, c, d, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>4.72</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>[f, e, b, a, c, d, g]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>4.87</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>[f, e, b, c, a, g, d]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5.41</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>[f, c, e, b, g, a, d]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5.47</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>[f, c, g, e, b, a, d]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>5.82</td>\n",
+       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
+       "      <td>[g, f, c, a, b, e, d]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>6.26</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>[g, f, c, a, b, e, d]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    angle    vertex_pair(s)_swapped sorted_order_at_next_midpoint\n",
+       "0    0.00                  [(c, f)]         [g, c, f, a, b, e, d]\n",
+       "1    0.32                  [(d, e)]         [g, c, f, a, d, b, e]\n",
+       "2    1.04                  [(b, f)]         [g, c, d, a, f, b, e]\n",
+       "3    1.19                  [(b, f)]         [g, d, c, a, b, f, e]\n",
+       "4    1.57                  [(e, f)]         [g, d, c, a, b, e, f]\n",
+       "5    1.65                  [(d, g)]         [d, g, c, a, b, e, f]\n",
+       "6    1.73                  [(c, e)]         [d, g, c, a, b, e, f]\n",
+       "7    1.82                  [(a, c)]         [d, g, a, c, b, e, f]\n",
+       "8    2.11                  [(b, c)]         [d, g, a, b, c, e, f]\n",
+       "9    2.24                  [(e, f)]         [d, g, a, b, c, e, f]\n",
+       "10   2.28                  [(a, g)]         [d, a, g, b, e, c, f]\n",
+       "11   2.36                  [(b, g)]         [d, a, b, g, e, c, f]\n",
+       "12   2.41                  [(e, g)]         [d, a, b, e, g, c, f]\n",
+       "13   2.49                  [(d, g)]         [d, a, b, e, g, c, f]\n",
+       "14   2.52                  [(b, d)]         [d, a, b, e, g, c, f]\n",
+       "15   2.60                  [(c, g)]         [d, e, b, a, c, g, f]\n",
+       "16   2.85                  [(f, g)]         [d, e, b, a, c, f, g]\n",
+       "17   3.02                  [(a, c)]         [d, e, b, a, f, c, g]\n",
+       "18   3.37  [(a, d), (f, a), (f, d)]         [e, d, b, a, f, c, g]\n",
+       "19   3.59                  [(c, f)]         [e, d, b, a, f, c, g]\n",
+       "20   3.66                  [(b, d)]         [e, b, d, a, f, c, g]\n",
+       "21   3.93                  [(b, c)]         [e, b, f, a, d, c, g]\n",
+       "22   4.29                  [(c, d)]         [e, f, b, a, c, d, g]\n",
+       "23   4.47                  [(a, g)]         [e, f, b, a, c, d, g]\n",
+       "24   4.50                  [(c, d)]         [e, f, b, a, c, d, g]\n",
+       "25   4.59                  [(d, e)]         [e, f, b, a, c, d, g]\n",
+       "26   4.72                  [(b, g)]         [f, e, b, a, c, d, g]\n",
+       "27   4.87                  [(e, g)]         [f, e, b, c, a, g, d]\n",
+       "28   5.41                  [(c, e)]         [f, c, e, b, g, a, d]\n",
+       "29   5.47                  [(c, g)]         [f, c, g, e, b, a, d]\n",
+       "30   5.82  [(a, b), (a, e), (b, e)]         [g, f, c, a, b, e, d]\n",
+       "31   6.26                  [(f, g)]         [g, f, c, a, b, e, d]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles = Spiral.get_normals_dict( num_rounding_digits=2)\n",
+    "\n",
+    "# add pi + angle for all angles \n",
+    "All_Angles_2pi = {}\n",
+    "for key in All_Angles.keys():\n",
+    "    All_Angles_2pi[round(key,2)] = All_Angles[key]\n",
+    "    other_key = (key + np.pi) % 2*np.pi\n",
+    "    other_key = round(other_key, 2)\n",
+    "    All_Angles_2pi[other_key] = All_Angles[key]\n",
+    "\n",
+    "keys = list(All_Angles_2pi.keys())\n",
+    "keys.sort()\n",
+    "\n",
+    "pd_table = pd.DataFrame(columns=['angle', 'vertex_pair(s)_swapped', 'sorted_order_at_next_midpoint'])\n",
+    "pd_table['angle'] = keys\n",
+    "pd_table['vertex_pair(s)_swapped'] = [All_Angles_2pi[key] for key in keys]\n",
+    "\n",
+    "# sort the vertex pairs at the midpoint\n",
+    "\n",
+    "for i in range(len(keys)-1):\n",
+    "    angle = np.average([keys[i], keys[i+1]])\n",
+    "    sorted_order = Spiral.sort_vertices(angle)\n",
+    "    pd_table.at[i, 'sorted_order_at_next_midpoint'] = sorted_order\n",
+    "\n",
+    "angle = np.average([keys[-1], keys[0]+2*np.pi])\n",
+    "sorted_order = Spiral.sort_vertices(angle)\n",
+    "pd_table.at[len(keys)-1, 'sorted_order_at_next_midpoint'] = sorted_order\n",
+    "\n",
+    "pd_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'd', 'e', 'f', 'g']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nodelist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modified version breaking the spiral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Modified version with vertex added between C and D\n",
+    "\n",
+    "# Create the graph\n",
+    "G = EmbeddedGraph()\n",
+    "G.add_node('a', .5, 0)\n",
+    "G.add_node('b', 1, 1)\n",
+    "G.add_node('c', -1.5, -.5)\n",
+    "G.add_node('X', 1.5, -1)\n",
+    "G.add_node('d', 3, -2.5)\n",
+    "G.add_node('e',1.5,2)\n",
+    "G.add_node('f',-1.5,2)\n",
+    "G.add_node('g', -3,-3)\n",
+    "\n",
+    "nodelist = ['a', 'b', 'c','X',  'd', 'e', 'f', 'g']\n",
+    "for i in range(len(nodelist)-1):\n",
+    "    G.add_edge(nodelist[i], nodelist[i+1])\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "G.plot(ax = ax)\n",
+    "G.plot_angle_circle(ax = ax, edges_only=True)\n",
+    "# remove axes \n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x150709d30>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(G.get_all_normals_matrix()[0])\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Angle</th>\n",
+       "      <th>Swapped Verts</th>\n",
+       "      <th>Sorted list</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>[(c, f), (X, e)]</td>\n",
+       "      <td>gcfabXed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>[(b, X)]</td>\n",
+       "      <td>gcfaXbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.32</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>gcfaXbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.52</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>gcfaXdbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.79</td>\n",
+       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
+       "      <td>gcdXafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.15</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>gcdXafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>gdcXafbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.41</td>\n",
+       "      <td>[(c, X)]</td>\n",
+       "      <td>gdXcabfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.57</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>gdXcabfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.65</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>gdXcabef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.82</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>dgXacbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.99</td>\n",
+       "      <td>[(X, g)]</td>\n",
+       "      <td>dXgacbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.11</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>dXgacbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.27</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>dXgabecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.28</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>dXagbecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.36</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>dXabgecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.41</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>dXabegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>2.60</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>dXabegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2.68</td>\n",
+       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
+       "      <td>dXebacgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>2.85</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>dXebacgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3.14</td>\n",
+       "      <td>[(c, f), (X, e)]</td>\n",
+       "      <td>dXebacfg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3.39</td>\n",
+       "      <td>[(b, X)]</td>\n",
+       "      <td>debXafcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>3.46</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>debXafcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>3.66</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>edbXafcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>3.93</td>\n",
+       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
+       "      <td>ebfaXdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>4.29</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>ebfaXdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>4.33</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>ebfaXcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>4.55</td>\n",
+       "      <td>[(c, X)]</td>\n",
+       "      <td>efbacXdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>4.71</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>efbacXdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>4.79</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>febacXdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>4.96</td>\n",
+       "      <td>[(a, c)]</td>\n",
+       "      <td>febcaXgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>5.13</td>\n",
+       "      <td>[(X, g)]</td>\n",
+       "      <td>febcaXgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>5.25</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>febcagXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>5.41</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>fcebagXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>5.42</td>\n",
+       "      <td>[(a, g)]</td>\n",
+       "      <td>fcebagXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>fcegbaXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>5.55</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>fcegbaXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>5.74</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>fcgebaXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>5.82</td>\n",
+       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
+       "      <td>fgcabeXd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>5.99</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>fgcabeXd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Angle                                     Swapped Verts Sorted list\n",
+       "0    0.00                                  [(c, f), (X, e)]    gcfabXed\n",
+       "1    0.25                                          [(b, X)]    gcfaXbed\n",
+       "2    0.32                                          [(d, e)]    gcfaXbed\n",
+       "3    0.52                                          [(b, d)]    gcfaXdbe\n",
+       "4    0.79  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    gcdXafbe\n",
+       "5    1.15                                          [(c, d)]    gcdXafbe\n",
+       "6    1.19                                          [(b, f)]    gdcXafbe\n",
+       "7    1.41                                          [(c, X)]    gdXcabfe\n",
+       "8    1.57                                          [(e, f)]    gdXcabfe\n",
+       "9    1.65                                          [(d, g)]    gdXcabef\n",
+       "10   1.82                                          [(a, c)]    dgXacbef\n",
+       "11   1.99                                          [(X, g)]    dXgacbef\n",
+       "12   2.11                                          [(b, c)]    dXgacbef\n",
+       "13   2.27                                          [(c, e)]    dXgabecf\n",
+       "14   2.28                                          [(a, g)]    dXagbecf\n",
+       "15   2.36                                          [(b, g)]    dXabgecf\n",
+       "16   2.41                                          [(e, g)]    dXabegcf\n",
+       "17   2.60                                          [(c, g)]    dXabegcf\n",
+       "18   2.68                          [(a, b), (a, e), (b, e)]    dXebacgf\n",
+       "19   2.85                                          [(f, g)]    dXebacgf\n",
+       "20   3.14                                  [(c, f), (X, e)]    dXebacfg\n",
+       "21   3.39                                          [(b, X)]    debXafcg\n",
+       "22   3.46                                          [(d, e)]    debXafcg\n",
+       "23   3.66                                          [(b, d)]    edbXafcg\n",
+       "24   3.93  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    ebfaXdcg\n",
+       "25   4.29                                          [(c, d)]    ebfaXdcg\n",
+       "26   4.33                                          [(b, f)]    ebfaXcdg\n",
+       "27   4.55                                          [(c, X)]    efbacXdg\n",
+       "28   4.71                                          [(e, f)]    efbacXdg\n",
+       "29   4.79                                          [(d, g)]    febacXdg\n",
+       "30   4.96                                          [(a, c)]    febcaXgd\n",
+       "31   5.13                                          [(X, g)]    febcaXgd\n",
+       "32   5.25                                          [(b, c)]    febcagXd\n",
+       "33   5.41                                          [(c, e)]    fcebagXd\n",
+       "34   5.42                                          [(a, g)]    fcebagXd\n",
+       "35   5.50                                          [(b, g)]    fcegbaXd\n",
+       "36   5.55                                          [(e, g)]    fcegbaXd\n",
+       "37   5.74                                          [(c, g)]    fcgebaXd\n",
+       "38   5.82                          [(a, b), (a, e), (b, e)]    fgcabeXd\n",
+       "39   5.99                                          [(f, g)]    fgcabeXd"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles = G.get_normals_dict(num_rounding_digits=2, opposites = True)\n",
+    "keys = list(All_Angles.keys())\n",
+    "keys.sort()\n",
+    "\n",
+    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
+    "pd_table['Angle'] = keys\n",
+    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
+    "\n",
+    "\n",
+    "\n",
+    "pd_table.sort_values(by='Angle', inplace=True)\n",
+    "pd_table['Sorted list'] = [''.join(G.sort_vertices(key)) for key in keys]\n",
+    "pd_table.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simplified version from Arya paper "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Recreating the sprial example from Arya \n",
+    "\n",
+    "# Create the graph\n",
+    "Spiral2 = EmbeddedGraph()\n",
+    "# Spiral2.add_node('a', .5, 0)\n",
+    "Spiral2.add_node('b', 1, 1)\n",
+    "Spiral2.add_node('c', -1.5, -.5)\n",
+    "Spiral2.add_node('d', 3, -2.5)\n",
+    "Spiral2.add_node('e',1.5,2)\n",
+    "Spiral2.add_node('f',-1.5,2)\n",
+    "Spiral2.add_node('g', -3,-3)\n",
+    "\n",
+    "nodelist = list(Spiral2.nodes())\n",
+    "nodelist.sort()\n",
+    "for i in range(len(nodelist)-1):\n",
+    "    Spiral2.add_edge(nodelist[i], nodelist[i+1])\n",
+    "\n",
+    "fig,ax = plt.subplots()\n",
+    "Spiral2.plot(ax = ax)\n",
+    "Spiral2.plot_angle_circle(ax = ax, edges_only=True)\n",
+    "# remove axes \n",
+    "ax.set_axis_off()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Angle</th>\n",
+       "      <th>Swapped Verts</th>\n",
+       "      <th>Sorted list</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>gcfbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.32</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>gcfbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.52</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>gcfdbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.79</td>\n",
+       "      <td>[(d, f)]</td>\n",
+       "      <td>gcdfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.15</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>gcdfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>gdcfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.57</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>gdcbfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.65</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>gdcbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.11</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>dgcbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2.27</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>dgbecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2.36</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>dbgecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2.41</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>dbegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.60</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>dbegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.68</td>\n",
+       "      <td>[(b, e)]</td>\n",
+       "      <td>debcgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.85</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>debcgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>3.14</td>\n",
+       "      <td>[(c, f)]</td>\n",
+       "      <td>debcfg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>3.46</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>debfcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>3.66</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>edbfcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>3.93</td>\n",
+       "      <td>[(d, f)]</td>\n",
+       "      <td>ebfdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>4.29</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>ebfdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>4.33</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>ebfcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>4.71</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>efbcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>4.79</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>febcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>5.25</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>febcgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>5.41</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>fcebgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>fcegbd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5.55</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>fcegbd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>5.74</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>fcgebd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5.82</td>\n",
+       "      <td>[(b, e)]</td>\n",
+       "      <td>fgcbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5.99</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>fgcbed</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Angle Swapped Verts Sorted list\n",
+       "0    0.00      [(c, f)]      gcfbed\n",
+       "1    0.32      [(d, e)]      gcfbed\n",
+       "2    0.52      [(b, d)]      gcfdbe\n",
+       "3    0.79      [(d, f)]      gcdfbe\n",
+       "4    1.15      [(c, d)]      gcdfbe\n",
+       "5    1.19      [(b, f)]      gdcfbe\n",
+       "6    1.57      [(e, f)]      gdcbfe\n",
+       "7    1.65      [(d, g)]      gdcbef\n",
+       "8    2.11      [(b, c)]      dgcbef\n",
+       "9    2.27      [(c, e)]      dgbecf\n",
+       "10   2.36      [(b, g)]      dbgecf\n",
+       "11   2.41      [(e, g)]      dbegcf\n",
+       "12   2.60      [(c, g)]      dbegcf\n",
+       "13   2.68      [(b, e)]      debcgf\n",
+       "14   2.85      [(f, g)]      debcgf\n",
+       "15   3.14      [(c, f)]      debcfg\n",
+       "16   3.46      [(d, e)]      debfcg\n",
+       "17   3.66      [(b, d)]      edbfcg\n",
+       "18   3.93      [(d, f)]      ebfdcg\n",
+       "19   4.29      [(c, d)]      ebfdcg\n",
+       "20   4.33      [(b, f)]      ebfcdg\n",
+       "21   4.71      [(e, f)]      efbcdg\n",
+       "22   4.79      [(d, g)]      febcdg\n",
+       "23   5.25      [(b, c)]      febcgd\n",
+       "24   5.41      [(c, e)]      fcebgd\n",
+       "25   5.50      [(b, g)]      fcegbd\n",
+       "26   5.55      [(e, g)]      fcegbd\n",
+       "27   5.74      [(c, g)]      fcgebd\n",
+       "28   5.82      [(b, e)]      fgcbed\n",
+       "29   5.99      [(f, g)]      fgcbed"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles = Spiral2.get_normals_dict(num_rounding_digits=2, opposites = True)\n",
+    "keys = list(All_Angles.keys())\n",
+    "keys.sort()\n",
+    "\n",
+    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
+    "pd_table['Angle'] = keys\n",
+    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
+    "\n",
+    "\n",
+    "\n",
+    "pd_table.sort_values(by='Angle', inplace=True)\n",
+    "pd_table['Sorted list'] = [''.join(Spiral2.sort_vertices(key)) for key in keys]\n",
+    "pd_table.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Modified version with vertex added between C and D\n",
+    "\n",
+    "# Create the graph\n",
+    "G = EmbeddedGraph()\n",
+    "# G.add_node('a', .5, 0)\n",
+    "G.add_node('b', 1, 1)\n",
+    "G.add_node('c', -1.5, -.5)\n",
+    "G.add_node('X', 1.5, -1)\n",
+    "G.add_node('d', 3, -2.5)\n",
+    "G.add_node('e',1.5,2)\n",
+    "G.add_node('f',-1.5,2)\n",
+    "G.add_node('g', -3,-3)\n",
+    "\n",
+    "nodelist = ['b', 'c','X',  'd', 'e', 'f', 'g']\n",
+    "for i in range(len(nodelist)-1):\n",
+    "    G.add_edge(nodelist[i], nodelist[i+1])\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "G.plot(ax = ax)\n",
+    "G.plot_angle_circle(ax = ax, edges_only=True)\n",
+    "# remove axes \n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Angle</th>\n",
+       "      <th>Swapped Verts</th>\n",
+       "      <th>Sorted list</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "      <td>[(c, f), (X, e)]</td>\n",
+       "      <td>gcfbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.25</td>\n",
+       "      <td>[(b, X)]</td>\n",
+       "      <td>gcfbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.32</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>gcfbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.52</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>gcfdbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.79</td>\n",
+       "      <td>[(X, d), (f, X), (f, d)]</td>\n",
+       "      <td>gcdfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.15</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>gcdfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.19</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>gdcfbe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.41</td>\n",
+       "      <td>[(c, X)]</td>\n",
+       "      <td>gdcbfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.57</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>gdcbfe</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.65</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>gdcbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.99</td>\n",
+       "      <td>[(X, g)]</td>\n",
+       "      <td>dgcbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2.11</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>dgcbef</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2.27</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>dgbecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2.36</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>dbgecf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2.41</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>dbegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>2.60</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>dbegcf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.68</td>\n",
+       "      <td>[(b, e)]</td>\n",
+       "      <td>debcgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>2.85</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>debcgf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>3.14</td>\n",
+       "      <td>[(c, f), (X, e)]</td>\n",
+       "      <td>debcfg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>3.39</td>\n",
+       "      <td>[(b, X)]</td>\n",
+       "      <td>debfcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>3.46</td>\n",
+       "      <td>[(d, e)]</td>\n",
+       "      <td>debfcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>3.66</td>\n",
+       "      <td>[(b, d)]</td>\n",
+       "      <td>edbfcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>3.93</td>\n",
+       "      <td>[(X, d), (f, X), (f, d)]</td>\n",
+       "      <td>ebfdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>4.29</td>\n",
+       "      <td>[(c, d)]</td>\n",
+       "      <td>ebfdcg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>4.33</td>\n",
+       "      <td>[(b, f)]</td>\n",
+       "      <td>ebfcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>4.55</td>\n",
+       "      <td>[(c, X)]</td>\n",
+       "      <td>efbcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>4.71</td>\n",
+       "      <td>[(e, f)]</td>\n",
+       "      <td>efbcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>4.79</td>\n",
+       "      <td>[(d, g)]</td>\n",
+       "      <td>febcdg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>5.13</td>\n",
+       "      <td>[(X, g)]</td>\n",
+       "      <td>febcgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>5.25</td>\n",
+       "      <td>[(b, c)]</td>\n",
+       "      <td>febcgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>5.41</td>\n",
+       "      <td>[(c, e)]</td>\n",
+       "      <td>fcebgd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>5.50</td>\n",
+       "      <td>[(b, g)]</td>\n",
+       "      <td>fcegbd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>5.55</td>\n",
+       "      <td>[(e, g)]</td>\n",
+       "      <td>fcegbd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>5.74</td>\n",
+       "      <td>[(c, g)]</td>\n",
+       "      <td>fcgebd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>5.82</td>\n",
+       "      <td>[(b, e)]</td>\n",
+       "      <td>fgcbed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>5.99</td>\n",
+       "      <td>[(f, g)]</td>\n",
+       "      <td>fgcbed</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Angle             Swapped Verts Sorted list\n",
+       "0    0.00          [(c, f), (X, e)]      gcfbed\n",
+       "1    0.25                  [(b, X)]      gcfbed\n",
+       "2    0.32                  [(d, e)]      gcfbed\n",
+       "3    0.52                  [(b, d)]      gcfdbe\n",
+       "4    0.79  [(X, d), (f, X), (f, d)]      gcdfbe\n",
+       "5    1.15                  [(c, d)]      gcdfbe\n",
+       "6    1.19                  [(b, f)]      gdcfbe\n",
+       "7    1.41                  [(c, X)]      gdcbfe\n",
+       "8    1.57                  [(e, f)]      gdcbfe\n",
+       "9    1.65                  [(d, g)]      gdcbef\n",
+       "10   1.99                  [(X, g)]      dgcbef\n",
+       "11   2.11                  [(b, c)]      dgcbef\n",
+       "12   2.27                  [(c, e)]      dgbecf\n",
+       "13   2.36                  [(b, g)]      dbgecf\n",
+       "14   2.41                  [(e, g)]      dbegcf\n",
+       "15   2.60                  [(c, g)]      dbegcf\n",
+       "16   2.68                  [(b, e)]      debcgf\n",
+       "17   2.85                  [(f, g)]      debcgf\n",
+       "18   3.14          [(c, f), (X, e)]      debcfg\n",
+       "19   3.39                  [(b, X)]      debfcg\n",
+       "20   3.46                  [(d, e)]      debfcg\n",
+       "21   3.66                  [(b, d)]      edbfcg\n",
+       "22   3.93  [(X, d), (f, X), (f, d)]      ebfdcg\n",
+       "23   4.29                  [(c, d)]      ebfdcg\n",
+       "24   4.33                  [(b, f)]      ebfcdg\n",
+       "25   4.55                  [(c, X)]      efbcdg\n",
+       "26   4.71                  [(e, f)]      efbcdg\n",
+       "27   4.79                  [(d, g)]      febcdg\n",
+       "28   5.13                  [(X, g)]      febcgd\n",
+       "29   5.25                  [(b, c)]      febcgd\n",
+       "30   5.41                  [(c, e)]      fcebgd\n",
+       "31   5.50                  [(b, g)]      fcegbd\n",
+       "32   5.55                  [(e, g)]      fcegbd\n",
+       "33   5.74                  [(c, g)]      fcgebd\n",
+       "34   5.82                  [(b, e)]      fgcbed\n",
+       "35   5.99                  [(f, g)]      fgcbed"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "All_Angles = G.get_normals_dict(num_rounding_digits=2, opposites = True)\n",
+    "keys = list(All_Angles.keys())\n",
+    "keys.sort()\n",
+    "\n",
+    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
+    "pd_table['Angle'] = keys\n",
+    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
+    "\n",
+    "\n",
+    "\n",
+    "pd_table.sort_values(by='Angle', inplace=True)\n",
+    "pd_table['Sorted list'] = [''.join(Spiral2.sort_vertices(key)) for key in keys]\n",
+    "pd_table.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Makefile b/Makefile
index 9f159fe..8ed1292 100644
--- a/Makefile
+++ b/Makefile
@@ -13,7 +13,7 @@ reqs:
 
 clean:
 	# Running autopep8
-	@autopep8 -r --in-place ect/
+	@autopep8 -r --in-place src/ect/
 
 tests:
 	# Running unittests
@@ -41,4 +41,9 @@ all:
 	@make html
 	
 	# Running unittests
-	@make tests
\ No newline at end of file
+	@make tests
+
+
+
+develop:
+	pip install -e .
\ No newline at end of file
diff --git a/benchmarks/benchmark_cw.py b/benchmarks/benchmark_cw.py
index 78a2b53..ae29e67 100644
--- a/benchmarks/benchmark_cw.py
+++ b/benchmarks/benchmark_cw.py
@@ -1,7 +1,8 @@
 """Benchmarks for CW complex computations"""
 import numpy as np
 import time
-from ect import ECT, EmbeddedCW, create_example_cw
+from ect import ECT
+from ect.utils.examples import create_example_cw
 import json
 from pathlib import Path
 
@@ -11,9 +12,9 @@ def benchmark_cw_ect(num_runs=5):
     results = {}
 
     configs = [
-        (8, 10),    # Small
-        (36, 36),   # Medium
-        (360, 360),  # Large
+        (8, 10),    
+        (36, 36),   
+        (360, 360),  
     ]
 
     for num_dir, num_thresh in configs:
@@ -26,7 +27,7 @@ def benchmark_cw_ect(num_runs=5):
             start_time = time.time()
 
             myect = ECT(num_dirs=num_dir, num_thresh=num_thresh)
-            myect.calculateECT(K)
+            myect.calculate(K)
 
             execution_time = time.time() - start_time
             times.append(execution_time)
@@ -45,7 +46,7 @@ def benchmark_cw_ect(num_runs=5):
     print("Running CW complex benchmarks...")
     results = benchmark_cw_ect()
 
-    # Save results
+    
     output_dir = Path("benchmark_results")
     output_dir.mkdir(exist_ok=True)
 
diff --git a/benchmarks/benchmark_graph.py b/benchmarks/benchmark_graph.py
index cdfad0d..46339eb 100644
--- a/benchmarks/benchmark_graph.py
+++ b/benchmarks/benchmark_graph.py
@@ -21,12 +21,20 @@ def benchmark_graph_ect(num_runs=5):
     """Benchmark ECT computation on graphs"""
     results = {}
 
+    # Warmup run to trigger JIT compilation
+    warmup_shape = create_test_shape(100, 1)
+    G_warmup = EmbeddedGraph()
+    G_warmup.add_cycle(warmup_shape)
+    myect = ECT(num_dirs=360, num_thresh=360)
+    myect.calculate(G_warmup)  # Warmup run
+
     configs = [
         (100, 1),
         (1000, 1),
         (100, 3),
         (1000, 3),
         (10000, 3),
+        (100000, 5),
     ]
 
     for points, complexity in configs:
@@ -41,7 +49,7 @@ def benchmark_graph_ect(num_runs=5):
         for _ in range(num_runs):
             start_time = time.time()
             myect = ECT(num_dirs=360, num_thresh=360)
-            myect.calculateECT(G)
+            myect.calculate(G)
             times.append(time.time() - start_time)
 
         results[f'points_{points}_complexity_{complexity}'] = {
@@ -50,30 +58,9 @@ def benchmark_graph_ect(num_runs=5):
             'min_time': float(np.min(times)),
             'max_time': float(np.max(times))
         }
+    print(results)
 
     return results
 
 
-def benchmark_g_omega(num_runs=5):
-    """Benchmark g_omega computation"""
-    results = {}
-
-    sizes = [100, 500, 1000]
-    for size in sizes:
-        shape = create_test_shape(size)
-        G = EmbeddedGraph()
-        G.add_cycle(shape)
-
-        times = []
-        for _ in range(num_runs):
-            start_time = time.time()
-            for theta in np.linspace(0, 2*np.pi, 360):
-                G.g_omega(theta)
-            times.append(time.time() - start_time)
-
-        results[f'size_{size}'] = {
-            'mean_time': float(np.mean(times)),
-            'std_time': float(np.std(times))
-        }
 
-    return results
diff --git a/benchmarks/run_benchmarks.py b/benchmarks/run_benchmarks.py
index 6f30bbb..6cab6f6 100644
--- a/benchmarks/run_benchmarks.py
+++ b/benchmarks/run_benchmarks.py
@@ -3,7 +3,7 @@
 import time
 from pathlib import Path
 import json
-from benchmark_graph import benchmark_graph_ect, benchmark_g_omega
+from benchmark_graph import benchmark_graph_ect
 from benchmark_cw import benchmark_cw_ect
 import platform
 
@@ -26,8 +26,7 @@ def run_all_benchmarks(num_runs=5):
     print("\nRunning CW complex benchmarks...")
     results['benchmarks']['cw_ect'] = benchmark_cw_ect(num_runs=num_runs)
 
-    print("\nRunning g_omega benchmarks...")
-    results['benchmarks']['g_omega'] = benchmark_g_omega(num_runs=num_runs)
+
 
     return results
 
diff --git a/doc_source/notebooks/Matisse/example_matisse.ipynb b/doc_source/notebooks/Matisse/example_matisse.ipynb
new file mode 100644
index 0000000..6f0c4f8
--- /dev/null
+++ b/doc_source/notebooks/Matisse/example_matisse.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ECT on Matisse's \"The Parakeet and the Mermaid\"\n",
+    "\n",
+    "\n",
+    "\n",
+    " Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312).\n",
+    "\n",
+    "\n",
+    "\n",
+    " ![matisse.jpg](matisse.jpg)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 150 files in the directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -----------------\n",
+    "# Standard imports\n",
+    "# -----------------\n",
+    "import numpy as np  # for arrays\n",
+    "import matplotlib.pyplot as plt  # for plotting\n",
+    "from sklearn.decomposition import PCA  # for PCA for normalization\n",
+    "from scipy.spatial import distance_matrix\n",
+    "\n",
+    "from os import listdir  # for retrieving files from directory\n",
+    "from os.path import isfile, join  # for retrieving files from directory\n",
+    "from sklearn.manifold import MDS  # for MDS\n",
+    "import pandas as pd  # for loading in colors csv\n",
+    "import os\n",
+    "import zipfile\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ---------------------------\n",
+    "# The ECT packages we'll use\n",
+    "# ---------------------------\n",
+    "from ect import ECT, EmbeddedGraph  # for calculating ECTs\n",
+    "\n",
+    "# Simple data paths\n",
+    "data_dir = \"outlines/\"\n",
+    "colors_path = \"colors.csv\"\n",
+    "\n",
+    "file_names = [\n",
+    "    f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n",
+    "]\n",
+    "file_names.sort()\n",
+    "print(f\"There are {len(file_names)} files in the directory\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
+    "\n",
+    "\n",
+    "\n",
+    " <img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 3\n",
+    "shape = np.loadtxt(data_dir + file_names[i])\n",
+    "# shape = normalize(shape)\n",
+    "G = EmbeddedGraph()\n",
+    "G.add_cycle(shape)\n",
+    "G.plot(with_labels=False, node_size=10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "G.transform_coordinates(projection_type=\"pca\")  # project with PCA\n",
+    "G.scale_coordinates(1)  # scale to radius 1\n",
+    "G.plot(with_labels=False, node_size=10, bounding_circle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And then we can compute the ECT of this leaf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh);  # intiate ECT\n",
+    "result = myect.calculate(G);  # calculate ECT on embedded graph\n",
+    "\n",
+    "result.plot();  # plot ECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Let's just make a data loader with all of this for ease in a bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def matisse_ect(filename, ect):\n",
+    "    shape = np.loadtxt(data_dir + filename)\n",
+    "    G = EmbeddedGraph()\n",
+    "    G.add_cycle(shape)\n",
+    "    G.transform_coordinates(projection_type=\"pca\")\n",
+    "    G.scale_coordinates(1)\n",
+    "    result = ect.calculate(G)\n",
+    "    return result\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And now we can load in all the outlines, compute their ECT and store it in a 3D array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "ect_arr = np.zeros((len(file_names), num_dirs, num_thresh))\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh, bound_radius=1)\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    ect_arr[i, :, :] = matisse_ect(file_names[i], myect)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Here, we are just going to compute the distance between two ECTs using $L_2$ distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x32185cbd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs * num_thresh)\n",
+    "D = distance_matrix(flattened_ect, flattened_ect)\n",
+    "plt.matshow(D)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_components = 2  # select number of components\n",
+    "mds = MDS(\n",
+    "    n_components=n_components,  # initialize MDS\n",
+    "    dissimilarity=\"precomputed\",  # we have precomputed the distance matrix\n",
+    "    normalized_stress=\"auto\",\n",
+    "    random_state=5,  # select random state for reproducibility\n",
+    ")\n",
+    "MDS_scores = mds.fit_transform(D)  # get MDS scores\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# read in color hexcodes\n",
+    "col_df = pd.read_csv(colors_path, header=None)\n",
+    "\n",
+    "scale_val = 6  # set scale value\n",
+    "plt.figure(figsize=(5, 5))  # set figure dimensions\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    shape = np.loadtxt(data_dir + file_names[i])  # get the current shape\n",
+    "    shape = shape - np.mean(shape, axis=0)  # zero center shape\n",
+    "    shape = (\n",
+    "        scale_val * shape / max(np.linalg.norm(shape, axis=1))\n",
+    "    )  # scale to radius 1 then mult by scale_val\n",
+    "\n",
+    "    trans_sh = shape + MDS_scores[i]  # translate shape to MDS position\n",
+    "\n",
+    "    plt.fill(trans_sh[:, 0], trans_sh[:, 1], c=col_df[0][i], lw=0)  # plot shape\n",
+    "    plt.gca().set_aspect(\"equal\")\n",
+    "\n",
+    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
+    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "\n",
+    "\n",
+    " This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
deleted file mode 100644
index c19d638..0000000
--- a/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial: ECT for CW complexes\n",
-    "\n",
-    "This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedCW, create_example_cw\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = EmbeddedCW()\n",
-    "\n",
-    "K.add_node('A', 0,0)\n",
-    "K.add_node('B', 1,0)\n",
-    "K.add_node('C', 1,1)\n",
-    "K.add_node('D', 0,1)\n",
-    "\n",
-    "K.add_edges_from((('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')))\n",
-    "\n",
-    "K.add_face(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "K.set_centered_coordinates()\n",
-    "K.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = create_example_cw(centered = True)\n",
-    "K.plot(bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also color the nodes based on a function value for a given direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = np.pi/7\n",
-    "K.plot(bounding_circle=True,color_nodes_theta=theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function value for any direction can be computed and returned for vertices, edges, or faces. The function value for an edge or face $\\sigma$ is given by $g_\\omega(\\sigma) = \\max_{v \\in \\sigma}\\{f(v)\\}$. Here we show the function values for the triangle `KDC` and its faces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "K: 0.18\n",
-      "D: 0.65\n",
-      "C: 2.89\n",
-      "('D', 'K'): 0.65\n",
-      "('C', 'D'): 2.89\n",
-      "('C', 'K'): 2.89\n",
-      "('K', 'D', 'C'): 2.89\n"
-     ]
-    }
-   ],
-   "source": [
-    "vert_g = K.g_omega(theta)\n",
-    "edge_g = K.g_omega_edges(theta)\n",
-    "face_g = K.g_omega_faces(theta)\n",
-    "\n",
-    "for v in ['K','D','C']:\n",
-    "    print(f'{v}: {round(vert_g[v],2)}')\n",
-    "\n",
-    "for edge in [('D','K'),('C','D'),('C','K')]:\n",
-    "    print(f'{edge}: {round(edge_g[edge],2)}')\n",
-    "\n",
-    "for face in [('K','D','C')]:\n",
-    "    print(f'{face}: {round(face_g[face],2)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myect = ECT(num_dirs = 100,num_thresh = 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,\n",
-       "        3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  1,\n",
-       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r = K.get_bounding_radius()\n",
-    "myect.calculateECC(K,0,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(K,theta,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       ...,\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2*r)\n",
-    "myect.calculateECT(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once it's been computed, we can use the internal plotting function to take a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.841874542459709\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECT()\n",
-    "print(myect.bound_radius)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly we can take a look at the SECT. This one was computed when we asked to compute the ECT. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotSECT()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/doc_source/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb b/doc_source/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
deleted file mode 100644
index 0ba510f..0000000
--- a/doc_source/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
+++ /dev/null
@@ -1,623 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial : ECT for embedded graphs \n",
-    "\n",
-    "This jupyter notebook will walk you through using the `ect` package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see [this paper](https://arxiv.org/abs/2310.10395) for a more thorough treatment of details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing the embedded graph\n",
-    "\n",
-    "We assume our input is an undirected graph $G$ with an embedding in 2D given by a map on the vertices $f: V(G) \\to \\mathbb{R}^2$. A graph can be constructed as follows. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Construct an example graph \n",
-    "# Note that this is the same graph that is returned by:\n",
-    "# G = create_example_graph()\n",
-    "\n",
-    "G = EmbeddedGraph()\n",
-    "\n",
-    "G.add_node('A', 1, 2)\n",
-    "G.add_node('B', 3, 4)\n",
-    "G.add_node('C', 5, 7)\n",
-    "G.add_node('D', 3, 6)\n",
-    "G.add_node('E', 4, 3)\n",
-    "G.add_node('F', 4, 5)\n",
-    "\n",
-    "G.add_edge('A', 'B')\n",
-    "G.add_edge('B', 'C')\n",
-    "G.add_edge('B', 'D')\n",
-    "G.add_edge('B', 'E')\n",
-    "G.add_edge('C', 'D')\n",
-    "G.add_edge('E', 'F')\n",
-    "\n",
-    "G.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The coordinates of all vertices, given as a dictionary, can be accessed using the `coordinates` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': (1, 2), 'B': (3, 4), 'C': (5, 7), 'D': (3, 6), 'E': (4, 3), 'F': (4, 5)}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G.coordinates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the `set_mean_centered_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'A': (-2.0, -2.5), 'B': (0.0, -0.5), 'C': (2.0, 2.5), 'D': (0.0, 1.5), 'E': (1.0, -1.5), 'F': (1.0, 0.5)}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_centered_coordinates(type = 'min_max')\n",
-    "print(G.coordinates)\n",
-    "G.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a bounding radius we can use the `get_bounding_radius` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This is actually getting the radius \n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n",
-    "\n",
-    "# plotting the graph with it's bounding circle of radius r.\n",
-    "G.plot(bounding_circle=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also rescale our graph to have unit radius using `rescale_to_unit_disk`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.rescale_to_unit_disk(preserve_center=False)\n",
-    "G.plot(bounding_circle=True)\n",
-    "\n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Directions are given in the code by specifying $\\theta \\in [0,2\\pi]$. We often convert this to $\\omega \\in \\mathbb{S}^1$ by defining the unit vector $\\omega = (\\cos(\\theta), \\sin(\\theta))$. Then the function $g_\\omega$ is defined on the vertices of $G$ by taking the dot product of the embedding coordinates with the unit vector, specifically\n",
-    "$$\n",
-    "g_\\omega(v) = \\langle f(v), \\omega\\rangle.\n",
-    "$$\n",
-    "This is done in the code using the `g_omega` method as shown. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': 0.11043152607484681,\n",
-       " 'B': 0.11043152607484658,\n",
-       " 'C': -0.11043152607484681,\n",
-       " 'D': -0.3312945782245397,\n",
-       " 'E': 0.5521576303742327,\n",
-       " 'F': 0.11043152607484646}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This gives the function value of the nodes as a dictionary\n",
-    "G.g_omega(theta = 7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This input to the plotting function will color the nodes by function value.\n",
-    "G.plot(color_nodes_theta=7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Here's an example in another direction.\n",
-    "G.plot(color_nodes_theta=np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Computing the ECT of $G$\n",
-    "\n",
-    "Now we can set up the ECT for the embedded graph. The ECT is defined as \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{ECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ a \\mapsto \\chi(g_\\omega^{-1}(-\\infty,a]) \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "When we initialize the `ECT` object, we specify the number of directions in $\\mathbb{S}^1$ to use with `num_dir`, and the number of thresholds at which to compute the Euler characteristic by `num_thresh`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Directions chosen are: [0.         0.39269908 0.78539816 1.17809725 1.57079633 1.96349541\n",
-      " 2.35619449 2.74889357 3.14159265 3.53429174 3.92699082 4.3196899\n",
-      " 4.71238898 5.10508806 5.49778714 5.89048623]\n",
-      "Thresholds chosen are: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect = ECT(num_dirs = 16, num_thresh=20)\n",
-    "\n",
-    "# The ECT object will automatically choose the directions. \n",
-    "print(f'Directions chosen are: {myect.thetas}')\n",
-    "\n",
-    "# However, because a bounding radius hasn't been chosen yet, the thresholds are not yet set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can set the bounding radius as follows. Note that some methods will automatically set `bound_radius` to be the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Internally set radius is: 1.2\n",
-      "Thresholds chosen are: [-1.2        -1.07368421 -0.94736842 -0.82105263 -0.69473684 -0.56842105\n",
-      " -0.44210526 -0.31578947 -0.18947368 -0.06315789  0.06315789  0.18947368\n",
-      "  0.31578947  0.44210526  0.56842105  0.69473684  0.82105263  0.94736842\n",
-      "  1.07368421  1.2       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2 * G.get_bounding_radius())\n",
-    "\n",
-    "print(f'Internally set radius is: {myect.bound_radius}')\n",
-    "\n",
-    "# Now the thresholds are set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want the Euler characteristic curve for a fixed direction, we use the `calculateECC` function. This outputs a list of integers where each corresponds to $\\chi(f_\\omega^{-1}(-\\infty,a])$ for the $a$ values in `myect.threshes`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.calculateECC(G, np.pi/2,bound_radius=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This can also be calculated on the fly and plotted using the `plotECC` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(G, -np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To calculate the full ECT, we call the `calculateECT` method. This matrix is size `myect.num_dirs x myect.num_thresh`. Once computed, it is stored in the `ECT` class as `ECT.matrix`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n",
-      "16 20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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5MLetIZkBAMBALDPVYpkJAAAYjcoMAAAGqlIHVXlQk6hqxli8jWQGAAADWR7umbHa0Z4ZlpkAAIDRqMwAAGAgNgDXIpkBAMBAVVYHVVke7Jnh2UzekZOTo+nTp6tnz55yOBzatGlTk3O2b9+uyMhI+fv765JLLtHTTz/d8oECANDCquVQtTp40NpPZcaoZObkyZMaOnSoVq5c6dL4w4cPa+rUqRo3bpzy8vJ077336vbbb1dGRkYLRwoAAFqLUctMcXFxiouLc3n8008/rd69eyslJUWSFBERod27d+vRRx/VNddc00JRAgDQ8tgzU8uoZMZdubm5io2NdeqbPHmy1q9fr9OnT6tTp0515pSXl6u8vNx+XVZW1uJxtqRPU6K8HcJZ6bd4p7dDAIA2zfM9M+1n04xRy0zuKikpUXBwsFNfcHCwKisrdfTo0XrnJCcnKzAw0G5hYWGtESoAADhL7TqZkSSHw7mMZv2Qif60v0ZSUpJKS0vtVlhY2OIxAgDgrjMbgD1r7UW7XmYKCQlRSUmJU9+RI0fUsWNHXXjhhfXO8fPzk5+fX2uEBwDAWav28HEG1WKZyQjR0dHKyspy6tu2bZtGjhxZ734ZAABgHqOSmRMnTig/P1/5+fmSzlx6nZ+fr4KCAklnlohmz55tj1+wYIE+//xzJSYmav/+/Xr22We1fv163XXXXd4IHwCAZlOzAdiT5g537/U2Z84cORyOOm3QoEH2mNTU1HrHnDp1yq3YjEpmdu/ereHDh2v48OGSpMTERA0fPlwPPvigJKm4uNhObCSpb9++2rJli7KzszVs2DD9/ve/14oVK7gsGwBgPM9umHemucPde709+eSTKi4utlthYaGCgoJ07bXXOo0LCAhwGldcXCx/f3+3YjNqz8yECRPsDbz1SU1NrdN35ZVX6n//939bMCoAANo/d+/1VnNVcI1Nmzbpm2++0Q033OA0zuFwKCQkxKPYjKrMAACAM6osh8dNOnM/tR+3H99rrTmtX79eV199tcLDw536T5w4ofDwcPXq1UvTpk1TXl6e28cmmQEAwEBVP1zN5EmTpLCwMKf7qyUnJzd7rMXFxXrjjTc0b948p/4BAwYoNTVVmzdvVlpamvz9/TV27FgdPHjQreMbtcwEAADOqLY6qNqDOwBX/7Bto7CwUAEBAXZ/S9yeJDU1Veeff75mzpzp1B8VFaWoqNo71Y8dO1YjRozQU089pRUrVrh8fJIZAADOYQEBAU7JTHOzLEvPPvusEhIS5Ovr2+jYDh06aNSoUVRmAAA4F/x4qejs5rfOTfO2b9+uQ4cOae7cuU2OtSxL+fn5uvzyy906B8kMAAAGqpbsTbxnO98dJ06c0KFDh+zXNfd6CwoKUu/evZWUlKSioiJt2LDBad769es1ZswYDR48uM4xly1bpqioKF166aUqKyvTihUrlJ+fr1WrVrkVG8kMAABo0u7du3XVVVfZrxMTEyVJ119/vVJTU+vc602SSktLlZGRoSeffLLeY3777be66aabVFJSosDAQA0fPlw5OTkaPXq0W7GRzAAAYKCzufHdT+e742zu9RYYGKjvvvuuwTlPPPGEnnjiCbfiqA/JDAAABjqbRxL8dH570X7eCQAAOCdRmfGyT1Oimh4EAMBPVMuhanmyAfjs57Y1JDMAABiIZaZa7eedAACAcxKVGQAADOT5TfPaTz2DZAYAAANVWw5Ve3LTPA/mtjUkMwAAGKjaw8qMJ/eoaWvazzsBAADnJCozAAAYqNrqoGoPrkjyZG5bQzIDAICBquRQlQf3ivFkblvTftIyAABwTqIyAwCAgVhmqkUyAwCAgark2VJRVfOF4nXtJy0DAADnJCozAAAYiGWmWiQzAAAYiAdN1mo/7wQAAJyTqMwAAGAgSw5Ve7AB2GpH95khmQEAwEAsM9UimQEAwEA8NbsWyQzapE9Tolr0+P0W72zR4wMAWg/JDAAABqpSB1V5cB2PJ3PbGpIZAAAMxDJTrfaTlgEAgHMSlRkAAAxUrQ6q9qAm4cnctoZkBgAAA1VZDlV5sFTkydy2pv2kZQAA4JxEZQYAAAOxAbgWyQwAAAayPHxqttWO7gDcft4JAAA4J1GZAQDAQFVyqMqDh0V6MretIZkBAMBA1ZZn+16qrWYMxstIZgAAMFC1h3tmPJnb1rSfdwIAAM5JJDMAABioWg6PmztycnI0ffp09ezZUw6HQ5s2bWp0fHZ2thwOR5328ccfO43LyMjQwIED5efnp4EDB2rjxo3ufhQkMwAAmKjmDsCeNHecPHlSQ4cO1cqVK92ad+DAARUXF9vt0ksvtf+Wm5ur+Ph4JSQkaO/evUpISNCsWbP0/vvvu3UO9swAAIAmxcXFKS4uzu15PXr00Pnnn1/v31JSUjRp0iQlJSVJkpKSkrR9+3alpKQoLS3N5XNQmQEAwEA1G4A9aZJUVlbm1MrLy5s1zuHDhys0NFQTJ07UO++84/S33NxcxcbGOvVNnjxZO3bscOscVGZcdHj5KHXw9/d2GAAAD32aEtVix64+dUq6J7PFju90Lnn4OIMf9syEhYU59S9ZskRLly71JDRJUmhoqNauXavIyEiVl5fr+eef18SJE5Wdna3x48dLkkpKShQcHOw0Lzg4WCUlJW6di2QGAIBzWGFhoQICAuzXfn5+zXLc/v37q3///vbr6OhoFRYW6tFHH7WTGUlyOJwTMsuy6vQ1hWUmAAAMZHl4JZP1Q2UmICDAqTVXMlOfqKgoHTx40H4dEhJSpwpz5MiROtWappDMAABgoJqnZnvSWlteXp5CQ0Pt19HR0crKynIas23bNsXExLh1XJaZAABAk06cOKFDhw7Zrw8fPqz8/HwFBQWpd+/eSkpKUlFRkTZs2CDpzJVKffr00aBBg1RRUaEXXnhBGRkZysjIsI+xaNEijR8/XsuXL9eMGTOUmZmpt956S++9955bsZHMAABgoNZ+nMHu3bt11VVX2a8TExMlSddff71SU1NVXFysgoIC++8VFRW66667VFRUpM6dO2vQoEF6/fXXNXXqVHtMTEyMXnrpJd1///164IEH1K9fP6Wnp2vMmDFuxUYyAwCAgTxdKnJ37oQJE2RZDT+dMjU11en13XffrbvvvrvJ4/7617/Wr3/9a7di+SmSGQAADHQ2jyT46fz2gg3AAADAaFRmAAAwUGsvM7VlJDMAABiIZKYWy0wAAMBoVGYAADAQlZlaJDMAABiIZKYWy0wAAMBoxiUzq1evVt++feXv76/IyEi9++67DY7Nzs6Ww+Go0z7++ONWjBgAgOZnSR4+aLL9MGqZKT09XYsXL9bq1as1duxYPfPMM4qLi9NHH32k3r17NzjvwIEDTo83v+iii1ojXAAAWgzLTLWMqsw8/vjjmjt3rubNm6eIiAilpKQoLCxMa9asaXRejx49FBISYjcfH59WihgAALQ0Y5KZiooK7dmzR7GxsU79sbGx2rFjR6Nzhw8frtDQUE2cOFHvvPNOo2PLy8tVVlbm1AAAaGtqKjOetPbCmGWmo0ePqqqqSsHBwU79wcHBKikpqXdOaGio1q5dq8jISJWXl+v555/XxIkTlZ2drfHjx9c7Jzk5WcuWLWv2+NG2fJoS1WLH7rd4Z4sdG+1PS34X0b6xzFTLmGSmhsPh/OFbllWnr0b//v3Vv39/+3V0dLQKCwv16KOPNpjMJCUl2Y81l6SysjKFhYU1Q+QAADQfkplaxiwzde/eXT4+PnWqMEeOHKlTrWlMVFSUDh482ODf/fz8FBAQ4NQAAEDbZUwy4+vrq8jISGVlZTn1Z2VlKSYmxuXj5OXlKTQ0tLnDAwCgVVmWw+PWXhi1zJSYmKiEhASNHDlS0dHRWrt2rQoKCrRgwQJJZ5aIioqKtGHDBklSSkqK+vTpo0GDBqmiokIvvPCCMjIylJGR4c23AQCAx2ruF+PJ/PbCqGQmPj5ex44d00MPPaTi4mINHjxYW7ZsUXh4uCSpuLhYBQUF9viKigrdddddKioqUufOnTVo0CC9/vrrmjp1qrfeAgAAaGZGJTOStHDhQi1cuLDev6Wmpjq9vvvuu3X33Xe3QlQAALQuNgDXMi6ZAQAA8njfS3vaM2PMBmAAAID6UJkBAMBALDPVIpkBAMBALDPVYpkJAAAYjcoMAAAGsjxcZmpPlRmSGQAADGRJsizP5rcXJDMAABioWg45uAOwJPbMAAAAw1GZAQDAQFzNVItkBgAAA1VbDjm4z4wkkhmg2X2aEtVix+63eGeLHRsATEUyAwCAgSzLw6uZ2tHlTCQzAAAYiD0ztbiaCQAAGI3KDAAABqIyU4vKDAAABqp5arYnzR05OTmaPn26evbsKYfDoU2bNjU6/tVXX9WkSZN00UUXKSAgQNHR0XrzzTedxqSmpsrhcNRpp06dcis2khkAAAxUswHYk+aOkydPaujQoVq5cqVL43NycjRp0iRt2bJFe/bs0VVXXaXp06crLy/PaVxAQICKi4udmr+/v1uxscwEAACaFBcXp7i4OJfHp6SkOL1+5JFHlJmZqddee03Dhw+3+x0Oh0JCQjyKjcoMAAAGOlNdcXjQzhynrKzMqZWXl7dIvNXV1Tp+/LiCgoKc+k+cOKHw8HD16tVL06ZNq1O5cQXJDAAABvIskandPBwWFqbAwEC7JScnt0i8jz32mE6ePKlZs2bZfQMGDFBqaqo2b96stLQ0+fv7a+zYsTp48KBbx2aZCQCAc1hhYaECAgLs135+fs1+jrS0NC1dulSZmZnq0aOH3R8VFaWoqNq7po8dO1YjRozQU089pRUrVrh8fJIZAAAMZP3QPJkvndmA++Nkprmlp6dr7ty5euWVV3T11Vc3OrZDhw4aNWqU25UZlpkAADBQcy0ztaS0tDTNmTNHL774on7+85+78J4s5efnKzQ01K3zUJkBAABNOnHihA4dOmS/Pnz4sPLz8xUUFKTevXsrKSlJRUVF2rBhg6Qziczs2bP15JNPKioqSiUlJZKkzp07KzAwUJK0bNkyRUVF6dJLL1VZWZlWrFih/Px8rVq1yq3YqMwAAGAiqxmaG3bv3q3hw4fbl1UnJiZq+PDhevDBByVJxcXFKigosMc/88wzqqys1K233qrQ0FC7LVq0yB7z7bff6qabblJERIRiY2NVVFSknJwcjR492q3YqMwAAGAiT5eK3Jw7YcIEWY3caS81NdXpdXZ2dpPHfOKJJ/TEE0+4FUd9qMwAAACjUZkBAMBAZ/NIgp/Oby9IZgAAMBBPza5FMgMATfg0JarpQUBrsxxu73upM7+dYM8MAAAwGpUZAAAMxJ6ZWiQzAACYqLmeZ9AOsMwEAACMRmUGAAADcTVTLZIZAABM1Y6WijzBMhMAADAalRkAAAzEMlMtkhkAAEzE1Uw2lpkAAIDRqMwAAGAkxw/Nk/ntA8kMAAAmYpnJRjIDAICJSGZs7JkBAABGozIDAICJLMeZ5sn8doJkBgAAA/HU7FosMwEAAKNRmQEM8mlKVIsev9/inS16/Jby5pd7W/gMLX18M/3Hyzd7O4RzGxuAbSQzAACYiD0zNpaZAACA0ajMAABgIId1pnkyv70gmQEAwETsmbGxzAQAAIxGZQYAABOxAdhGMgMAgIlYZrKRzAAAYCKSGRt7ZgAAgNGozAAAYCIqMzaSGQAATMQGYBvLTAAAwGhUZgAAMBB3AK5FMgMAgInYM2Mzbplp9erV6tu3r/z9/RUZGal333230fHbt29XZGSk/P39dckll+jpp59upUgBAGg/cnJyNH36dPXs2VMOh0ObNm1qco4rv8EZGRkaOHCg/Pz8NHDgQG3cuNHt2NxOZqqqqvTMM8/ojjvu0GOPPaa///3vOnbsmNsnPhvp6elavHix7rvvPuXl5WncuHGKi4tTQUFBveMPHz6sqVOnaty4ccrLy9O9996r22+/XRkZGa0SLwAA7cXJkyc1dOhQrVy50qXxrvwG5+bmKj4+XgkJCdq7d68SEhI0a9Ysvf/++27F5rAsy61C08KFC/W3v/1NkyZN0iuvvKIOHTro9OnTuvjiizVs2DBt3rzZrQDcMWbMGI0YMUJr1qyx+yIiIjRz5kwlJyfXGX/PPfdo8+bN2r9/v923YMEC7d27V7m5uS6ds6ysTIGBgQpf/j/q4O/v+ZsA2rB+i3d6O4Sz8uaXe70dwjnpP16+2dshtDnVp07p83vuV2lpqQICAlrkHM31u+RJrA6HQxs3btTMmTMbHOPKb3B8fLzKysr0xhtv2GOmTJmiCy64QGlpaS7H43Zl5tVXX9Xzzz+vv/71r/Lz89Pu3bu1YsUKnTp1SuHh4e4ezmUVFRXas2ePYmNjnfpjY2O1Y8eOeufk5ubWGT958mTt3r1bp0+frndOeXm5ysrKnBoAAO3VT3/zysvLm+W4rvwGNzSmod/1hri9AfjEiRMaOHCgJKlTp07y8fHRrbfeqoqKCn355ZfuHs5lR48eVVVVlYKDg536g4ODVVJSUu+ckpKSesdXVlbq6NGjCg0NrTMnOTlZy5Yta77AgWZ0aNYzLXuCWS17eLQukysnLfldN/lzcdJM95kJCwtz6l6yZImWLl3qQWBnuPIb3NCYhn7XG+J2ZeaSSy6xk5aLL75YRUVFkqTp06frhRdecPdwbnM4nP/FWZZVp6+p8fX110hKSlJpaandCgsLPYwYAIAWYDVDk1RYWOj0u5eUlNRsIbryG+zu73p93E5mrr32Wm3dulWSNGHCBD377LOSpI8++kjff/+9u4dzWffu3eXj41MnWzty5EidrK5GSEhIveM7duyoCy+8sN45fn5+CggIcGoAALQ5zZTM/PQ3z8/Pr1nCc+U3uKExDf2uN8TtZOaBBx7QkiVLJEm/+93v9Pbbb+uiiy7Stddeq+uuu87dw7nM19dXkZGRysrKcurPyspSTExMvXOio6PrjN+2bZtGjhypTp06tVisAACc61z5DW5oTEO/6w3x6KZ5vXv31r/+9S9t2bJFQUFB+vnPf+7J4ZqUmJiohIQEjRw5UtHR0Vq7dq0KCgq0YMECSWeWiIqKirRhwwZJZ3ZNr1y5UomJiZo/f75yc3O1fv16t3ZIAwDQFrX2HYBPnDihQ4cO2a8PHz6s/Px8BQUFqXfv3mf1G7xo0SKNHz9ey5cv14wZM5SZmam33npL7733nluxeXwH4AsvvFAJCQmeHsYl8fHxOnbsmB566CEVFxdr8ODB2rJli30VVXFxsdM9Z/r27astW7bojjvu0KpVq9SzZ0+tWLFC11xzTavECwBAi2nlOwDv3r1bV111lf06MTFRknT99dcrNTX1rH6DY2Ji9NJLL+n+++/XAw88oH79+ik9PV1jxoxxKza37zNzruE+M2hLWvxqJrQrJl+1Y+rVTK15n5k+//Owx/eZ+ez++1o01tbCs5kAADARz2aykcwAAGAgnppdy7gHTQIAAPwYlRkAAEzUTHcAbg9IZgAAMBF7ZmwsMwEAAKNRmQEAwEBsAK5FMgMAgIlYZrKRzAAAYCIPKzPtKZlhzwwAADAalRkAAEzEMpONZAZnjecE1c/k5+Gg9fF9qR+fiwtIZmwsMwEAAKNRmQEAwEBcml2LygwAADAayQwAADAay0wAAJiIDcA2khkAAAzEnplaLDMBAACjUZkBAMBU7ai64gmSGQAATMSeGRvJDAAABmLPTC32zAAAAKNRmQEAwEQsM9lIZgAAMBDLTLVYZgIAAEajMgMAgIlYZrKRzAAAYCKSGRvLTAAAwGhUZgAAMBAbgGuRzOCs/cfLN7fYsQ/NeqbFjm2ylvzMW5rJ/05N/txN1W/xzhY79qcpUS127FbFMpONZSYAAGA0KjMAAJiIyoyNZAYAAAOxZ6YWyQwAACaiMmNjzwwAAHDZ6tWr1bdvX/n7+ysyMlLvvvtug2PnzJkjh8NRpw0aNMgek5qaWu+YU6dOuRwTyQwAAAaqWWbypLkrPT1dixcv1n333ae8vDyNGzdOcXFxKigoqHf8k08+qeLiYrsVFhYqKChI1157rdO4gIAAp3HFxcXy9/d3OS6SGQAATGQ1Q3PT448/rrlz52revHmKiIhQSkqKwsLCtGbNmnrHBwYGKiQkxG67d+/WN998oxtuuMFpnMPhcBoXEhLiVlwkMwAAnMPKysqcWnl5eb3jKioqtGfPHsXGxjr1x8bGaseOHS6da/369br66qsVHh7u1H/ixAmFh4erV69emjZtmvLy8tx6DyQzAACYqJkqM2FhYQoMDLRbcnJyvac7evSoqqqqFBwc7NQfHByskpKSJsMtLi7WG2+8oXnz5jn1DxgwQKmpqdq8ebPS0tLk7++vsWPH6uDBg659DuJqJgAAjOT4oXkyX5IKCwsVEBBg9/v5+TU+z+F8Vsuy6vTVJzU1Veeff75mzpzp1B8VFaWoqNq7Mo8dO1YjRozQU089pRUrVjR5XIlkBgCAc1pAQIBTMtOQ7t27y8fHp04V5siRI3WqNT9lWZaeffZZJSQkyNfXt9GxHTp00KhRo9yqzLDMBACAiVp5A7Cvr68iIyOVlZXl1J+VlaWYmJhG527fvl2HDh3S3LlzmzyPZVnKz89XaGioy7FRmQEAwEDeuANwYmKiEhISNHLkSEVHR2vt2rUqKCjQggULJElJSUkqKirShg0bnOatX79eY8aM0eDBg+scc9myZYqKitKll16qsrIyrVixQvn5+Vq1apXLcZHMAAAAl8THx+vYsWN66KGHVFxcrMGDB2vLli321UnFxcV17jlTWlqqjIwMPfnkk/Ue89tvv9VNN92kkpISBQYGavjw4crJydHo0aNdjotkBgAAE3npcQYLFy7UwoUL6/1bampqnb7AwEB99913DR7viSee0BNPPHF2wfyAZAYAAFO1o+creYJkBgAAA/HU7FokMwBaxX+8fLO3Q0Az67d4p7dDACSRzAAAYCYv7Zlpi0hmAAAwEMtMtbhpHgAAMBqVGQAATMQyk41kBgAAA7HMVItlJgAAYDQqMwAAmIhlJhvJDAAAJiKZsbHMBAAAjEZlBgAAA7EBuBbJDAAAJmKZyUYyAwCAgRyWJYd19hmJJ3PbGvbMAAAAoxmTzHzzzTdKSEhQYGCgAgMDlZCQoG+//bbROXPmzJHD4XBqUVFRrRMwAAAtyWqG1k4Ys8x03XXX6YsvvtDWrVslSTfddJMSEhL02muvNTpvypQpeu655+zXvr6+LRonAACtgQ3AtYxIZvbv36+tW7dq586dGjNmjCRp3bp1io6O1oEDB9S/f/8G5/r5+SkkJKS1QgUAAK3MiGWm3NxcBQYG2omMJEVFRSkwMFA7duxodG52drZ69Oihyy67TPPnz9eRI0caHV9eXq6ysjKnBgBAm8Myk82IZKakpEQ9evSo09+jRw+VlJQ0OC8uLk5//etf9fbbb+uxxx7Trl279LOf/Uzl5eUNzklOTrb35QQGBiosLKxZ3gMAAM2pZpnJk9ZeeHWZaenSpVq2bFmjY3bt2iVJcjgcdf5mWVa9/TXi4+Ptfx48eLBGjhyp8PBwvf766/rVr35V75ykpCQlJibar8vKykhovOA/Xr65RY/fb/HOlju2Wu7YLe3TFDbItyct+T0H2hKvJjO33XabfvOb3zQ6pk+fPtq3b5+++uqrOn/7+uuvFRwc7PL5QkNDFR4eroMHDzY4xs/PT35+fi4fEwAAr+CmeTavJjPdu3dX9+7dmxwXHR2t0tJS/fOf/9To0aMlSe+//75KS0sVExPj8vmOHTumwsJChYaGnnXMAAC0BVzNVMuIPTMRERGaMmWK5s+fr507d2rnzp2aP3++pk2b5nQl04ABA7Rx40ZJ0okTJ3TXXXcpNzdXn332mbKzszV9+nR1795dv/zlL731VgAAQDMzIpmRpL/+9a+6/PLLFRsbq9jYWA0ZMkTPP/+805gDBw6otLRUkuTj46MPPvhAM2bM0GWXXabrr79el112mXJzc9WtWzdvvAUAAJoPVzPZjLjPjCQFBQXphRdeaHSM9aPnTHTu3FlvvvlmS4cFAIDXtKelIk8Yk8wAAIAfsawzzZP57YQxy0wAAAD1oTIDAICBuJqpFskMAAAm4j4zNpaZAACA0ajMAABgIEf1mebJ/PaCZAYAABOxzGRjmQkAABiNZAYAAAPVXM3kSTsbq1evVt++feXv76/IyEi9++67DY7Nzs6Ww+Go0z7++GOncRkZGRo4cKD8/Pw0cOBA+9FEriKZAQDARDU3zfOkuSk9PV2LFy/Wfffdp7y8PI0bN05xcXEqKChodN6BAwdUXFxst0svvdT+W25uruLj45WQkKC9e/cqISFBs2bN0vvvv+9yXCQzAADAJY8//rjmzp2refPmKSIiQikpKQoLC9OaNWsandejRw+FhITYzcfHx/5bSkqKJk2apKSkJA0YMEBJSUmaOHGiUlJSXI6LZAYAAAM11zJTWVmZUysvL6/3fBUVFdqzZ49iY2Od+mNjY7Vjx45GYx0+fLhCQ0M1ceJEvfPOO05/y83NrXPMyZMnN3nMH+NqJhf1vWeXOjo6NftxP02JavZjAmer3+KdLXZsvutoK1rye15pndbnLXb0n2imq5nCwsKcupcsWaKlS5fWGX706FFVVVUpODjYqT84OFglJSX1niI0NFRr165VZGSkysvL9fzzz2vixInKzs7W+PHjJUklJSVuHbM+JDMAABiouR5nUFhYqICAALvfz8+v8XkOh9Nry7Lq9NXo37+/+vfvb7+Ojo5WYWGhHn30UTuZcfeY9WGZCQCAc1hAQIBTayiZ6d69u3x8fOpUTI4cOVKnstKYqKgoHTx40H4dEhLi8TFJZgAAMFErX83k6+uryMhIZWVlOfVnZWUpJibG5ePk5eUpNDTUfh0dHV3nmNu2bXPrmCwzAQBgIG88NTsxMVEJCQkaOXKkoqOjtXbtWhUUFGjBggWSpKSkJBUVFWnDhg2Szlyp1KdPHw0aNEgVFRV64YUXlJGRoYyMDPuYixYt0vjx47V8+XLNmDFDmZmZeuutt/Tee++5HBfJDAAAcEl8fLyOHTumhx56SMXFxRo8eLC2bNmi8PBwSVJxcbHTPWcqKip01113qaioSJ07d9agQYP0+uuva+rUqfaYmJgYvfTSS7r//vv1wAMPqF+/fkpPT9eYMWNcjsthWWdx15xzSFlZmQIDAzVBM7iaqR1pyasZUD++662P73nrq7ROK1uZKi0tddpU25xqfpeipzykjp38z/o4ladPKXfrgy0aa2uhMgMAgIG8sczUVrEBGAAAGI3KDAAAJqq2zjRP5rcTJDMAAJiome4A3B6wzAQAAIxGZQYAAAM55OEG4GaLxPtIZgAAMNFZ3MW3zvx2gmQGAAADcWl2LfbMAAAAo1GZAQDARFzNZCOZAQDAQA7LksODfS+ezG1rSGbQJvFMGZwr+K4DniOZAQDARNU/NE/mtxMkMwAAGIhlplpczQQAAIxGZQYAABNxNZONZAYAABNxB2Aby0wAAMBoVGYAADAQjzOoRTIDAICJWGaykcwAAGAgR/WZ5sn89oI9MwAAwGhUZgAAMBHLTDaSGQAATMR9ZmwsMwEAAKNRmQEAwEA8m6kWyQwAACZiz4yNZSYAAGA0KjMAAJjIkuTJvWLaT2GGZAYAABOxZ6YWyYyX9Vu8s0WP/2lKVIseH3BVS37X+Z4D5zaSGQAATGTJww3AzRaJ15HMAABgIq5mspHMAABgompJDg/ntxNcmg0AAIxGMgMAgIFqrmbypJ2N1atXq2/fvvL391dkZKTefffdBse++uqrmjRpki666CIFBAQoOjpab775ptOY1NRUORyOOu3UqVMux0QyAwCAiWr2zHjS3JSenq7FixfrvvvuU15ensaNG6e4uDgVFBTUOz4nJ0eTJk3Sli1btGfPHl111VWaPn268vLynMYFBASouLjYqfn7+7scF3tmAACASx5//HHNnTtX8+bNkySlpKTozTff1Jo1a5ScnFxnfEpKitPrRx55RJmZmXrttdc0fPhwu9/hcCgkJOSs46IyAwCAiZqpMlNWVubUysvL6z1dRUWF9uzZo9jYWKf+2NhY7dixw6WQq6urdfz4cQUFBTn1nzhxQuHh4erVq5emTZtWp3LTFJIZAABM1EzJTFhYmAIDA+1WX4VFko4ePaqqqioFBwc79QcHB6ukpMSlkB977DGdPHlSs2bNsvsGDBig1NRUbd68WWlpafL399fYsWN18OBBlz8KlpkAADiHFRYWKiAgwH7t5+fX6HiHw/l6cMuy6vTVJy0tTUuXLlVmZqZ69Ohh90dFRSkqqvYu3mPHjtWIESP01FNPacWKFS69B5IZAABM1Ez3mQkICHBKZhrSvXt3+fj41KnCHDlypE615qfS09M1d+5cvfLKK7r66qsbHduhQweNGjXKrcoMy0wAABiotS/N9vX1VWRkpLKyspz6s7KyFBMT0+C8tLQ0zZkzRy+++KJ+/vOfN3key7KUn5+v0NBQl2MzJpl5+OGHFRMToy5duuj88893aY5lWVq6dKl69uypzp07a8KECfrXv/7VsoECANBOJSYm6s9//rOeffZZ7d+/X3fccYcKCgq0YMECSVJSUpJmz55tj09LS9Ps2bP12GOPKSoqSiUlJSopKVFpaak9ZtmyZXrzzTf173//W/n5+Zo7d67y8/PtY7rCmGSmoqJC1157rW655RaX5/zxj3/U448/rpUrV2rXrl0KCQnRpEmTdPz48RaMFACAVuCF+8zEx8crJSVFDz30kIYNG6acnBxt2bJF4eHhkqTi4mKne84888wzqqys1K233qrQ0FC7LVq0yB7z7bff6qabblJERIRiY2NVVFSknJwcjR492uW4HJZl1pOmUlNTtXjxYn377beNjrMsSz179tTixYt1zz33SJLKy8sVHBys5cuX6+abb3bpfGVlZQoMDNQEzVBHRydPw291n6ZENT2oDeq3eKe3Q4BBTP2eS3zX25tK67SylanS0lKX9qGcjZrfpav7LVZHn8Y36zamsqpcb32a0qKxthZjKjPuOnz4sEpKSpyuh/fz89OVV17Z6PXw5eXlda65BwCgzfFCZaatarfJTM1ua3evh09OTna63j4sLKxF4wQAAJ7x6qXZS5cu1bJlyxods2vXLo0cOfKsz+Hu9fBJSUlKTEy0X5eVlRmd0FDCxrmA7znOTZ5WV9pPZcarycxtt92m3/zmN42O6dOnz1kdu+YZDyUlJU6XdzV1Pbyfn1+TNwwCAMDrPF0qakfLTF5NZrp3767u3bu3yLH79u2rkJAQZWVl2Q+zqqio0Pbt27V8+fIWOScAAGh9xuyZKSgoUH5+vgoKClRVVaX8/Hzl5+frxIkT9pgBAwZo48aNks4sLy1evFiPPPKINm7cqA8//FBz5sxRly5ddN1113nrbQAA0DyqLc9bO2HM4wwefPBB/eUvf7Ff11Rb3nnnHU2YMEGSdODAAacb8dx99936/vvvtXDhQn3zzTcaM2aMtm3bpm7durVq7AAANDur+kzzZH47Ydx9Zlqb6feZAQC0nla9z0zvherYwYP7zFSX662C1e3iPjPGVGYAAMCPsAHYRjIDAICJqi15dHl1O9ozY8wGYAAAgPpQmQEAwEQsM9lIZgAAMJElD5OZZovE60hmAAAwEZUZG3tmAACA0ajMAABgoupqSR7c+K66/dw0j2QGAAATscxkY5kJAAAYjcoMAAAmojJjI5kBAMBE3AHYxjITAAAwGpUZAAAMZFnVsqyzvyLJk7ltDckMAAAmsizPlora0Z4ZlpkAAIDRqMwAAGAiy8MNwO2oMkMyAwCAiaqrJYcH+17YMwMAALyKyoyNPTMAAMBoVGYAADCQVV0ty4NlJi7NBgAA3sUyk41lJgAAYDQqMwAAmKjakhxUZiSSGQAAzGRZkjy5NLv9JDMsMwEAAKNRmQEAwEBWtSXLg2Umi8oMAADwKqva83YWVq9erb59+8rf31+RkZF69913Gx2/fft2RUZGyt/fX5dccomefvrpOmMyMjI0cOBA+fn5aeDAgdq4caNbMZHMAABgIKva8ri5Kz09XYsXL9Z9992nvLw8jRs3TnFxcSooKKh3/OHDhzV16lSNGzdOeXl5uvfee3X77bcrIyPDHpObm6v4+HglJCRo7969SkhI0KxZs/T++++7HJfDak91phZQVlamwMBATdAMdXR08nY4AIA2rNI6rWxlqrS0VAEBAS1yDvt3yfFLj36XKq3TyrY2uhXrmDFjNGLECK1Zs8bui4iI0MyZM5WcnFxn/D333KPNmzdr//79dt+CBQu0d+9e5ebmSpLi4+NVVlamN954wx4zZcoUXXDBBUpLS3MpLvbMNKEm16vUaY/uTQQAaP8qdVpS6+xHqbTKPXpYZE2sZWVlTv1+fn7y8/OrM76iokJ79uzR//t//8+pPzY2Vjt27Kj3HLm5uYqNjXXqmzx5stavX6/Tp0+rU6dOys3N1R133FFnTEpKisvvhWSmCcePH5ckvactXo4EAGCK48ePKzAwsEWO7evrq5CQEL1X4vnvUteuXRUWFubUt2TJEi1durTO2KNHj6qqqkrBwcFO/cHBwSopKan3+CUlJfWOr6ys1NGjRxUaGtrgmIaOWR+SmSb07NlThYWF6tatmxwOR5Pjy8rKFBYWpsLCwhYrMbYEU+OWiN0bTI1bInZvMDVuyf3YLcvS8ePH1bNnzxaLyd/fX4cPH1ZFRYXHx7Isq85vW31VmR/76fj6jtHU+J/2u3vMnyKZaUKHDh3Uq1cvt+cFBAQY9z9aydy4JWL3BlPjlojdG0yNW3Iv9paqyPyYv7+//P39W/w8P9a9e3f5+PjUqZgcOXKkTmWlRkhISL3jO3bsqAsvvLDRMQ0dsz5czQQAAJrk6+uryMhIZWVlOfVnZWUpJiam3jnR0dF1xm/btk0jR45Up06dGh3T0DHrQ2UGAAC4JDExUQkJCRo5cqSio6O1du1aFRQUaMGCBZKkpKQkFRUVacOGDZLOXLm0cuVKJSYmav78+crNzdX69eudrlJatGiRxo8fr+XLl2vGjBnKzMzUW2+9pffee8/luEhmmpmfn5+WLFnS5JpjW2Nq3BKxe4OpcUvE7g2mxi2ZHXtLiI+P17Fjx/TQQw+puLhYgwcP1pYtWxQeHi5JKi4udrrnTN++fbVlyxbdcccdWrVqlXr27KkVK1bommuuscfExMTopZde0v33368HHnhA/fr1U3p6usaMGeNyXNxnBgAAGI09MwAAwGgkMwAAwGgkMwAAwGgkMwAAwGgkMwAAwGgkM81o9erV6tu3r/z9/RUZGal3333X2yE1af/+/QoKClK/fv00bNgwde3aVRMmTPB2WC4xLfbjx49r1KhRGjZsmC6//HKtW7fO2yGdEzp27Khhw4Zp2LBhmjdvnrfDcZmp35cDBw7Yn/ewYcPUuXNnbdq0ydthNSo5OVmjRo1St27d1KNHD82cOVMHDhzwdlhwh4Vm8dJLL1mdOnWy1q1bZ3300UfWokWLrPPOO8/6/PPPvR1ak2JjY619+/ZZlmVZ/fr1s77//nsvR+Q6k2KvrKy0Tp48aVmWZZ08edLq27evdfToUS9H1f5deOGF3g7hrLSH78vx48etCy+80Dpx4oS3Q2nU5MmTreeee8768MMPrfz8fOvnP/+51bt37zYfN2pRmWkmjz/+uObOnat58+YpIiJCKSkpCgsL05o1a7wdWpM++eQT9e/fX8ePH5ePj0+rP+/DEybF7uPjoy5dukiSTp06paqqKlmWZVSFacCAAXI4HPW2FStWeDs8l5nwmbeH78vmzZs1ceJEnXfeeW067q1bt2rOnDkaNGiQhg4dqueee04FBQXas2ePpPbzvW/XvJ1NtQfl5eWWj4+P9eqrrzr133777db48eO9FJVrSktLrYiICMuyLGvHjh3WzJkzvRyR60yM/ZtvvrGGDBlide7c2Vq5cqXdb0qF6aOPPrIkWX//+9+t4uJiq6CgwOrYsaP1yiuvWKdOnfJ2ePXq1KmTNWLECGvs2LFWdna23W/CZ27692XGjBlWRkaG/dqUuA8ePGhJsj744APLssz83p9rqMw0g6NHj6qqqqrOEz6Dg4PrPAm0rfnXv/6lgQMHSjrz/1Yvu+wyL0fkOhNjP//887V3714dPnxYL774or766itJ5lSYSkpK1LFjR40dO1YhISE6duyYKisrNW7cuDZ7u/fPPvtMe/bs0dNPP63Zs2errKxMkhmfucnfl7KyMv3jH//Q1KlT7T4T4rYsS4mJibriiis0ePBgSWZ+7881JDPNyOFwOL22LKtOX1vz0UcfadCgQZKk8847T2+88Yb+7//+z8tRucbk2IODgzVkyBDl5OSorKxMnTt3lq+vrz788EM7QWuLPvjgA1122WX2f8Dz8/N10UUX1Unk25KePXtKkgYPHqyBAwfqk08+Meozl8z8vmRmZmry5Ml2wmJK3Lfddpv27dvn9CBEE7/35xoeNNkMunfvLh8fnzpVmCNHjrT5L/vcuXPtf46Pj1d8fLwXo3GPabF/9dVX6ty5swICAlRWVqacnBzdcsstRlWY9u3bp8svv9x+nZ+fryFDhngxosZ988036tKli/z8/PTFF1/oo48+0iWXXGLEZ2769+Xll1/WTTfdZL82Ie7//u//1ubNm5WTk6NevXrZ/aZ9789FVGaaga+vryIjI5WVleXUn5WVpZiYGC9Fhbbmiy++0Pjx4zV06FBdccUVuu222zRkyBCjKkz79u1z+o94W/+P+v79+zVy5EgNHTpU06ZN05NPPqmgoCAjPnOTvy+lpaX65z//qcmTJ9t9bTluy7J022236dVXX9Xbb7+tvn37Ov3dtO/9uYinZjeT9PR0JSQk6Omnn1Z0dLTWrl2rdevW6V//+pf9aPS2qKllsLb89TA5dhNVV1erW7duSk9P17Rp0yRJvXv31p133qlFixZ5OTrg7C1cuFAvvviiMjMz1b9/f7s/MDBQfn5+fO9N4MXNx+3OqlWrrPDwcMvX19caMWKEtX37dm+H5LKTJ09avXv3tu68805vh+KygoIC68orr7QiIiKsyy+/3Hr55Ze9HVK79sknn1iSnO6dNG3aNOv888836rsO/JSkettzzz3H994QVGYgSbrvvvt08OBB9e7dW48++qi3w3FJcXGxvvrqKw0bNkxHjhzRiBEjdODAAZ133nneDq1eZ7sZnP+Jnj2TP3NTYzc1bpiNPTPQwYMH9fHHHztdQmmC0NBQDRs2TJLUo0cPBQUFtZk1+PpYllWnnTx50i5Z1/d3/gPvGZM/8x/HU1BQoCuvvFIRERG6/PLL9fLLL7fZ2E3+zGEukhnorrvuUnJysrfD8Mju3btVXV2tsLAwb4filocfflhjxozxdhjnFBM/844dOyolJUUfffSR3nrrLd1xxx06efKkt8NymYmfOcxCMnOOy8zM1GWXXdYmL5N01bFjxzR79mytXbvW26G4xdSKmMlM/cxNq0L+mKmfOcxCMnOO27lzp1566SX16dNHd911l9atW6eHHnrI22G5rLy8XL/85S+VlJRk3GXw7aEiZpr28JmbVoVsD5852j6SmXNccnKyCgsL9dlnn+nRRx/V/Pnz9eCDD3o7LJdYlqU5c+boZz/7mRISErwdjlvaQ0XMNO3hMzetCtkePnOYgTsAw1j/+Mc/lJ6eriFDhmjTpk2SpOeff97pTp1tVU1F7JVXXtGJEyd0+vRpBQQEGJNImsj0z9zEKqTpnznMwaXZgJelpqbqww8/NOaS+PbAtM/csixdd9116t+/v5YuXertcM6KaZ85zMIyEwC0cTVVyE2bNmnYsGEaNmyYPvjgA2+HBbQZVGYAAIDRqMwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAAACjkcwAcNltt92mK664ot6/9enTRw8//HArRwQAUkdvBwDADB999JHWrFmjnJycev8eERGh/Pz81g0KAERlBoCL/vSnP2nUqFEaO3ZsvX8PCgrSV1991cpRAQDJDAAXVFZWKiMjQ9dcc43dd/PNN2v9+vX26+PHj+u8887zRngAznEkMwCa9Omnn+r48eO6/PLLJUnV1dV65ZVX1LVrV3vMvn37FBER4a0QAZzDSGYANOnbb7+VJDt5efPNN/XNN9/I19dXkvTPf/5Tn3/+uWbOnOmlCAGcy9gADKBJ4eHhcjgcSktL03nnnac777xTU6dOVWZmpvr06aObb75ZP/vZzzR+/HhvhwrgHOSwLMvydhAA2r7k5GT94Q9/UOfOnfU///M/Gj16tGbMmKEjR45o+vTpWr16tYKCgrwdJoBzEMkMAAAwGntmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0UhmAACA0f4/9zDEH7PSZZMAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.calculateECT(G)\n",
-    "\n",
-    "# The matrix is passed as an output above but is also saved internally. Get the saved matrix\n",
-    "M = myect.get_ECT()\n",
-    "\n",
-    "print(M.shape)\n",
-    "print(myect.num_dirs, myect.num_thresh)\n",
-    "\n",
-    "# We can use the built in command to plot the matrix. Unlike the plotECC function, this command does not calculate the ECT when called so it must have been run earlier. An equivalent command is myect.plotECT()\n",
-    "myect.plot('ECT')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SECT \n",
-    "\n",
-    "The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
-    "$$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
-    "Then the SECT is defined by \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    " defined from the \n",
-    "TODO: Write intro "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can calculate the SECT directly using the `calculateSECT` method. Note that this both returns the function and sets the SECT internally to the `SECT_matrix` attribute. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n"
-     ]
-    }
-   ],
-   "source": [
-    "M_SECT = myect.calculateSECT()\n",
-    "print(M_SECT.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However this is not technically necessary since, as a default, the SECT was also set internally when the ECT matrix was computed. If you don't want to calculate the SECT at the same time as calculating the ECT, you can use the following command instead.\n",
-    "```python\n",
-    "myect.calculateECT(G, compute_SECT=False)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the internally set plotting functions to easily visualize it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plot('SECT')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/doc_source/notebooks/tutorial_cw.ipynb b/doc_source/notebooks/tutorial_cw.ipynb
new file mode 100644
index 0000000..575387a
--- /dev/null
+++ b/doc_source/notebooks/tutorial_cw.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for CW complexes\n",
+    "\n",
+    "\n",
+    "\n",
+    " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedCW\n",
+    "from ect.utils.examples import create_example_cw\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = EmbeddedCW()\n",
+    "\n",
+    "# Add vertices with coordinates\n",
+    "K.add_node(\"A\", [0, 0])\n",
+    "K.add_node(\"B\", [1, 0])\n",
+    "K.add_node(\"C\", [1, 1])\n",
+    "K.add_node(\"D\", [0, 1])\n",
+    "\n",
+    "# Add edges to form a square\n",
+    "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n",
+    "\n",
+    "# Add the square face\n",
+    "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n",
+    "\n",
+    "K.center_coordinates()\n",
+    "K.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = create_example_cw()\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n",
+      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
+      "\n",
+      "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n",
+      "\u001b[1m\n",
+      "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n",
+      "\u001b[1m    @njit(parallel=True, fastmath=True)\n",
+      "\u001b[1m    def shape_descriptor(simplex_counts_list):\n",
+      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\n",
+      "  result[i, j] = shape_descriptor(simplex_counts_list)\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "override_bound_radius = 1.2 * K.get_bounding_radius()\n",
+    "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n",
+    "result.plot();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n",
+    "result.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also look at the Smooth ECT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate SECT and plot\n",
+    "smooth = result.smooth()\n",
+    "smooth.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also compute the ECT in 3D."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = [\n",
+    "    (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n",
+    "]\n",
+    "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n",
+    "faces = [\n",
+    "    (\"a\", \"b\", \"c\"),\n",
+    "    (\"a\", \"b\", \"d\"),\n",
+    "    (\"a\", \"c\", \"d\"),\n",
+    "    (\"b\", \"c\", \"d\"),\n",
+    "    (\"a\", \"b\", \"c\", \"d\"),\n",
+    "]\n",
+    "K = EmbeddedCW()\n",
+    "K.add_nodes_from(vertices)\n",
+    "K.add_edges_from(edges)\n",
+    "\n",
+    "K.add_faces_from(faces)\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n",
+    "result = ect.calculate(K)\n",
+    "result.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc_source/notebooks/tutorial_cw.py b/doc_source/notebooks/tutorial_cw.py
new file mode 100644
index 0000000..4d5a4b7
--- /dev/null
+++ b/doc_source/notebooks/tutorial_cw.py
@@ -0,0 +1,74 @@
+# %% [markdown]
+# # Tutorial: ECT for CW complexes
+#
+# This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform
+
+# %%
+from ect import ECT, EmbeddedCW
+from ect.utils.examples import create_example_cw
+import numpy as np
+
+# %% [markdown]
+# The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!
+
+# %%
+K = EmbeddedCW()
+
+# Add vertices with coordinates
+K.add_node("A", [0, 0])
+K.add_node("B", [1, 0])
+K.add_node("C", [1, 1])
+K.add_node("D", [0, 1])
+
+# Add edges to form a square
+K.add_edges_from([("A", "B"), ("B", "C"), ("C", "D"), ("D", "A")])
+
+# Add the square face
+K.add_face(["A", "B", "C", "D"])
+
+K.center_coordinates()
+K.plot()
+
+# %% [markdown]
+# Just to have something a bit more interesting, let's make a more complicated example that's built into the class.
+
+# %%
+K = create_example_cw()
+K.plot(bounding_circle=True)
+
+
+# %% [markdown]
+# As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use.
+
+# %%
+ect = ECT(num_dirs=100, num_thresh=80)
+
+# %% [markdown]
+# Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex.
+
+# %%
+result = ect.calculate(K, theta=0)
+result.plot()
+
+# %% [markdown]
+# But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it.
+
+# %%
+# Set bounding radius to control threshold range
+radius = K.get_bounding_radius()
+ect.set_bounding_radius(1.2 * radius)
+
+# %% [markdown]
+# Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot.
+
+# %%
+result = ect.calculate(K)
+result.plot()
+
+# %% [markdown]
+# We can also look at the Smooth ECT:
+
+# %%
+# Calculate SECT and plot
+smooth = result.smooth()
+smooth.plot()
diff --git a/doc_source/notebooks/tutorial_graph.ipynb b/doc_source/notebooks/tutorial_graph.ipynb
new file mode 100644
index 0000000..7988362
--- /dev/null
+++ b/doc_source/notebooks/tutorial_graph.ipynb
@@ -0,0 +1,791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for Embedded Graphs\n",
+    "\n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates how to use the `ect` package..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedGraph\n",
+    "\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n",
+    "\n",
+    "\n",
+    "\n",
+    "First, let's create a simple graph\"\"\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Construct an example graph\n",
+    "# Note that this is the same graph that is returned by:\n",
+    "# G = create_example_graph()\n",
+    "\n",
+    "G = EmbeddedGraph()\n",
+    "\n",
+    "G.add_node(\"A\", [1, 2])\n",
+    "G.add_node(\"B\", [3, 4])\n",
+    "G.add_node(\"C\", [5, 7])\n",
+    "G.add_node(\"D\", [3, 6])\n",
+    "G.add_node(\"E\", [4, 3])\n",
+    "G.add_node(\"F\", [4, 5])\n",
+    "\n",
+    "G.add_edge(\"A\", \"B\")\n",
+    "G.add_edge(\"B\", \"C\")\n",
+    "G.add_edge(\"B\", \"D\")\n",
+    "G.add_edge(\"B\", \"E\")\n",
+    "G.add_edge(\"C\", \"D\")\n",
+    "G.add_edge(\"E\", \"F\")\n",
+    "\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n",
+    "\n",
+    " The coordinates of all vertices can be accessed using the `coord_matrix` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2.],\n",
+       "       [3., 4.],\n",
+       "       [5., 7.],\n",
+       "       [3., 6.],\n",
+       "       [4., 3.],\n",
+       "       [4., 5.]])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.coord_matrix\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.33333333 -2.5       ]\n",
+      " [-0.33333333 -0.5       ]\n",
+      " [ 1.66666667  2.5       ]\n",
+      " [-0.33333333  1.5       ]\n",
+      " [ 0.66666667 -1.5       ]\n",
+      " [ 0.66666667  0.5       ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.center_coordinates(center_type=\"mean\")\n",
+    "print(G.coord_matrix)\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To get a bounding radius we can use the `get_bounding_radius` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This is actually getting the radius\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n",
+    "# plotting the graph with it's bounding circle of radius r.\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also rescale our graph to have unit radius using `scale_coordinates`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 0.9362075413977773\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.scale_coordinates(radius=1)\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(num_dirs=16, num_thresh=20)\n",
+    "\n",
+    "# The ECT object will automatically create directions when needed\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n",
+      " -0.41390228 -0.29564449 -0.17738669 -0.0591289   0.0591289   0.17738669\n",
+      "  0.29564449  0.41390228  0.53216008  0.65041787  0.76867567  0.88693346\n",
+      "  1.00519125  1.12344905]\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_bound_radius = 1.2 * G.get_bounding_radius()\n",
+    "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n",
+    "\n",
+    "print(f\"Thresholds chosen are: {myect.thresholds}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G, theta=np.pi / 2)\n",
+    "print(f\"ECT values for direction pi/2: {result[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT matrix shape: (16, 20)\n",
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G)\n",
+    "\n",
+    "print(f\"ECT matrix shape: {result.shape}\")\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n",
+    "\n",
+    "# We can plot the result matrix\n",
+    "result.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## SECT\n",
+    "\n",
+    "\n",
+    "\n",
+    " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
+    "\n",
+    " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
+    "\n",
+    " Then the SECT is defined by\n",
+    "\n",
+    " $$\n",
+    "\n",
+    " \\begin{matrix}\n",
+    "\n",
+    " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
+    "\n",
+    " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
+    "\n",
+    " \\end{matrix}\n",
+    "\n",
+    " $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The SECT can be computed from the ECT result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sect = result.smooth()\n",
+    "\n",
+    "sect.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## ECT for higher dimensions\n",
+    "\n",
+    "\n",
+    "\n",
+    " The `ECT` class can also be used for higher dimensional graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# generate a 3d graph\n",
+    "list_3d = [\n",
+    "    (\"A\", [0, 0, 0]),\n",
+    "    (\"B\", [1, 0, 0]),\n",
+    "    (\"C\", [0, 1, 0]),\n",
+    "    (\"D\", [1, 1, 0]),\n",
+    "    (\"E\", [0, 0, 1]),\n",
+    "    (\"F\", [1, 0, 1]),\n",
+    "    (\"G\", [0, 1, 1]),\n",
+    "    (\"H\", [1, 1, 1]),\n",
+    "]\n",
+    "graph_3d = EmbeddedGraph()\n",
+    "graph_3d.add_nodes_from(list_3d)\n",
+    "\n",
+    "# add edges\n",
+    "graph_3d.add_edge(\"A\", \"B\")\n",
+    "graph_3d.add_edge(\"B\", \"C\")\n",
+    "graph_3d.add_edge(\"C\", \"D\")\n",
+    "graph_3d.add_edge(\"D\", \"E\")\n",
+    "graph_3d.add_edge(\"E\", \"F\")\n",
+    "graph_3d.add_edge(\"F\", \"G\")\n",
+    "graph_3d.add_edge(\"G\", \"H\")\n",
+    "graph_3d.add_edge(\"H\", \"A\")\n",
+    "\n",
+    "# plot the graph\n",
+    "graph_3d.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " lets center the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph_3d.center_coordinates(center_type=\"mean\")\n",
+    "graph_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Now we can compute the ECT of the 3d graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect_3d = ECT(num_dirs=16, num_thresh=20)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n",
+    "\n",
+    " ECT results inherit from ndarrays but they store the associated directions and thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.48609512, -0.71244342, -0.50609872],\n",
+       "       [ 0.00364975, -0.82190818,  0.56960831],\n",
+       "       [-0.77710663, -0.23260984,  0.5848059 ],\n",
+       "       [ 0.75362655, -0.61431121, -0.23381352],\n",
+       "       [-0.18133219, -0.98270096, -0.03764916],\n",
+       "       [-0.92728701,  0.26549447,  0.26391569],\n",
+       "       [-0.81397529,  0.14802792,  0.56172232],\n",
+       "       [ 0.54073592, -0.29337918,  0.78837385],\n",
+       "       [ 0.48363696,  0.74722911, -0.45578937],\n",
+       "       [ 0.99631892,  0.01735462,  0.08394891],\n",
+       "       [ 0.74179171, -0.62822234, -0.23469503],\n",
+       "       [ 0.09906268, -0.01998319, -0.99488052],\n",
+       "       [ 0.57301878,  0.71110718, -0.40740159],\n",
+       "       [ 0.93645869, -0.11437729, -0.33160663],\n",
+       "       [-0.19170715,  0.53609831, -0.82209913],\n",
+       "       [ 0.69801901,  0.38101835, -0.6062957 ]])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.directions.vectors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n",
+       "       -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n",
+       "        0.04558028,  0.13674085,  0.22790142,  0.31906199,  0.41022256,\n",
+       "        0.50138313,  0.5925437 ,  0.68370427,  0.77486483,  0.8660254 ])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.thresholds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ect import Directions\n",
+    "\n",
+    "directions = Directions.random(16, dim=3)\n",
+    "thresholds = np.cos(np.linspace(0, np.pi, 20))\n",
+    "ect_3d = ECT(directions=directions, thresholds=thresholds)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of direction vectors: 10000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# another example sampling from the half sphere\n",
+    "sample_size = 100\n",
+    "theta = np.linspace(0, np.pi / 2, sample_size)  # Only go up to pi/2 for half sphere\n",
+    "phi = np.linspace(0, 2 * np.pi, sample_size)\n",
+    "theta, phi = np.meshgrid(theta, phi)\n",
+    "\n",
+    "# Flatten the meshgrid arrays and create vectors\n",
+    "half_sphere_vectors = np.column_stack(\n",
+    "    [\n",
+    "        np.sin(theta.flatten()) * np.cos(phi.flatten()),\n",
+    "        np.sin(theta.flatten()) * np.sin(phi.flatten()),\n",
+    "        np.cos(theta.flatten()),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Normalize the vectors\n",
+    "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n",
+    "    half_sphere_vectors, axis=1, keepdims=True\n",
+    ")\n",
+    "\n",
+    "directions = Directions.from_vectors(half_sphere_vectors)\n",
+    "print(f\"Number of direction vectors: {len(directions)}\")\n",
+    "ect_3d = ECT(directions=directions, num_thresh=20)  # Reduced number of thresholds\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/doc_source/notebooks/tutorial_graph.py b/doc_source/notebooks/tutorial_graph.py
new file mode 100644
index 0000000..9d8faf8
--- /dev/null
+++ b/doc_source/notebooks/tutorial_graph.py
@@ -0,0 +1,136 @@
+# %% [markdown]
+# # Tutorial : ECT for embedded graphs
+#
+# This jupyter notebook will walk you through using the `ect` package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see [this paper](https://arxiv.org/abs/2310.10395) for a more thorough treatment of details.
+
+# %%
+from ect import ECT, EmbeddedGraph
+from ect.utils.examples import create_example_graph
+
+import matplotlib.pyplot as plt
+import numpy as np
+import networkx as nx
+
+# %% [markdown]
+# ## Constructing the embedded graph
+#
+# We assume our input is an undirected graph $G$ with an embedding in 2D given by a map on the vertices $f: V(G) \to \mathbb{R}^2$. A graph can be constructed as follows.
+#
+
+# %%
+# Construct an example graph
+# Note that this is the same graph that is returned by:
+# G = create_example_graph()
+
+G = EmbeddedGraph()
+
+G.add_node("A", [1, 2])
+G.add_node("B", [3, 4])
+G.add_node("C", [5, 7])
+G.add_node("D", [3, 6])
+G.add_node("E", [4, 3])
+G.add_node("F", [4, 5])
+
+G.add_edge("A", "B")
+G.add_edge("B", "C")
+G.add_edge("B", "D")
+G.add_edge("B", "E")
+G.add_edge("C", "D")
+G.add_edge("E", "F")
+
+G.plot()
+
+
+# %% [markdown]
+# The coordinates of all vertices, given as a dictionary, can be accessed using the `coord_matrix` attribute.
+
+# %%
+G.coord_matrix
+
+# %% [markdown]
+# Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points.
+
+# %%
+G.center_coordinates(center_type="mean")
+print(G.coord_matrix)
+G.plot()
+
+# %% [markdown]
+# To get a bounding radius we can use the `get_bounding_radius` method.
+
+# %%
+# This is actually getting the radius
+r = G.get_bounding_radius()
+print(f"The radius of bounding circle centered at the origin is {r}")
+
+# plotting the graph with it's bounding circle of radius r.
+G.plot(bounding_circle=True)
+
+
+# %% [markdown]
+# We can also rescale our graph to have unit radius using `scale_coordinates`
+
+# %%
+G.scale_coordinates(radius=1)
+G.plot(bounding_circle=True)
+
+r = G.get_bounding_radius()
+print(f"The radius of bounding circle centered at the origin is {r}")
+
+
+# %%
+myect = ECT(num_dirs=16, num_thresh=20)
+
+# The ECT object will automatically create directions when needed
+print(f"Number of directions: {myect.num_dirs}")
+print(f"Number of thresholds: {myect.num_thresh}")
+
+# %% [markdown]
+# We can set the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures.
+
+# %%
+myect.set_bounding_radius(1.2 * G.get_bounding_radius())
+
+print(f"Internally set radius is: {myect.bound_radius}")
+print(f"Thresholds chosen are: {myect.thresholds}")
+
+# %% [markdown]
+# If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values.
+
+# %%
+result = myect.calculate(G, theta=np.pi / 2)
+print(f"ECT values for direction pi/2: {result[0]}")
+
+# %% [markdown]
+# To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata.
+
+# %%
+result = myect.calculate(G)
+
+print(f"ECT matrix shape: {result.shape}")
+print(f"Number of directions: {myect.num_dirs}")
+print(f"Number of thresholds: {myect.num_thresh}")
+
+# We can plot the result matrix
+result.plot()
+
+# %% [markdown]
+# ## SECT
+#
+# The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\omega$ defined on function values $[-R,R]$ is given by
+# $$\overline{\text{ECT}_\omega} = \frac{1}{2R} \int_{t = -R}^{R} \chi(g_\omega^{-1}(-\infty,t]) \; dt. $$
+# Then the SECT is defined by
+# $$
+# \begin{matrix}
+# \text{SECT}(G): & \mathbb{S}^1 & \to & \text{Func}(\mathbb{R}, \mathbb{Z})\\
+# & \omega & \mapsto & \{ t \mapsto \int_{-R}^t \left( \chi(g_\omega^{-1}(-\infty,a]) -\overline{\text{ECT}_\omega}\right)\:da \}
+# \end{matrix}
+# $$
+
+# %% [markdown]
+# The SECT can be computed from the ECT result:
+
+# %%
+sect = result.smooth()
+
+sect.plot()
diff --git a/doc_source/tutorials.rst b/doc_source/tutorials.rst
index f7ffaab..5a5d1b1 100644
--- a/doc_source/tutorials.rst
+++ b/doc_source/tutorials.rst
@@ -5,8 +5,8 @@ Tutorials
    :maxdepth: 2
    :caption: Contents:
 
-   notebooks/Tutorial-ECT_for_embedded_graphs
-   notebooks/Tutorial-ECT_for_CW_Complexes
-   notebooks/Matisse/Matisse_ECT
-   notebooks/Tutorial-ExactECT
+   notebooks/tutorial_graph
+   notebooks/tutorial_cw
+   notebooks/Matisse/example_matisse
+   .. notebooks/Tutorial-ExactECT
 
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+++ b/docs/.buildinfo
@@ -1,4 +1,4 @@
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+config: 4fa06ba1eba33764a2433ecca7acac59
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--- a/docs/_modules/ect/ect_graph.html
+++ b/docs/_modules/ect/ect_graph.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>ect.ect_graph &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=e59714d7" />
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+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=59fadc99"></script>
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@@ -54,12 +56,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -69,18 +69,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">4. Contributing</a><ul>
@@ -125,11 +122,13 @@
              
   <h1>Source code for ect.ect_graph</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">compress</span><span class="p">,</span> <span class="n">combinations</span>
-<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">jit</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">ect.embed_cw</span> <span class="kn">import</span> <span class="n">EmbeddedCW</span>
-<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">numba</span> <span class="kn">import</span> <span class="n">prange</span><span class="p">,</span> <span class="n">njit</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
+
+<span class="kn">from</span> <span class="nn">.embed_cw</span> <span class="kn">import</span> <span class="n">EmbeddedCW</span>
+<span class="kn">from</span> <span class="nn">.embed_graph</span> <span class="kn">import</span> <span class="n">EmbeddedGraph</span>
+<span class="kn">from</span> <span class="nn">.directions</span> <span class="kn">import</span> <span class="n">Directions</span>
+<span class="kn">from</span> <span class="nn">.results</span> <span class="kn">import</span> <span class="n">ECTResult</span>
 
 
 <div class="viewcode-block" id="ECT">
@@ -138,384 +137,221 @@ <h1>Source code for ect.ect_graph</h1><div class="highlight"><pre>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedGraph` or :any:`EmbeddedCW`.</span>
 
-<span class="sd">    The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.threshes``, and :math:`\omega_j` is the ith entry in ``self.thetas``.</span>
+<span class="sd">    The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.thresholds``, and :math:`\omega_j` is the ith entry in ``self.thetas``.</span>
 
 <span class="sd">    Attributes</span>
 <span class="sd">        num_dirs (int):</span>
 <span class="sd">            The number of directions to consider in the matrix.</span>
 <span class="sd">        num_thresh (int):</span>
 <span class="sd">            The number of thresholds to consider in the matrix.</span>
-<span class="sd">        bound_radius (int):</span>
+<span class="sd">        directions (Directions):</span>
+<span class="sd">            The directions to consider for projection.</span>
+<span class="sd">        bound_radius (float):</span>
 <span class="sd">            Either ``None``, or a positive radius of the bounding circle.</span>
-<span class="sd">        ECT_matrix (np.array):</span>
-<span class="sd">            The matrix to store the ECT.</span>
-<span class="sd">        SECT_matrix (np.array):</span>
-<span class="sd">            The matrix to store the SECT.</span>
-
 <span class="sd">    &quot;&quot;&quot;</span>
 
 <div class="viewcode-block" id="ECT.__init__">
 <a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.__init__">[docs]</a>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">,</span> <span class="n">bound_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Constructs all the necessary attributes for the ECT object.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            num_dirs (int):</span>
-<span class="sd">                The number of directions to consider in the matrix.</span>
-<span class="sd">            num_thresh (int):</span>
-<span class="sd">                The number of thresholds to consider in the matrix.</span>
-<span class="sd">            bound_radius (int):</span>
-<span class="sd">                Either None, or a positive radius of the bounding circle.</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">directions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Directions</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">num_dirs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">num_thresh</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">bound_radius</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">thresholds</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialize ECT calculator with either a Directions object or sampling parameters</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            directions: Optional pre-configured Directions object</span>
+<span class="sd">            num_dirs: Number of directions to sample (ignored if directions provided)</span>
+<span class="sd">            num_thresh: Number of threshold values (required if directions not provided)</span>
+<span class="sd">            bound_radius: Optional radius for bounding circle</span>
+<span class="sd">            thresholds: Optional array of thresholds</span>
+<span class="sd">            dtype: Data type for output array</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="o">=</span> <span class="n">directions</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">num_dirs</span> <span class="o">=</span> <span class="n">num_dirs</span>
-
-        <span class="c1"># Note: This version doesn&#39;t include 2pi since its the same as the 0 direction.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">thetas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
         <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span> <span class="o">=</span> <span class="n">num_thresh</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="n">bound_radius</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">SECT_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="ECT.set_bounding_radius">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.set_bounding_radius">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_bounding_radius</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Manually sets the radius of the bounding circle centered at the origin for the ECT object.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            bound_radius (int): </span>
-<span class="sd">                Either None, or a positive radius of the bounding circle.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span> <span class="o">=</span> <span class="n">bound_radius</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">threshes</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">threshes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">bound_radius</span><span class="p">,</span>
-                                        <span class="n">bound_radius</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">)</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">thresholds</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">dtype</span></div>
 
 
-<div class="viewcode-block" id="ECT.get_radius_and_thresh">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.get_radius_and_thresh">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_radius_and_thresh</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        An internally used function to get the bounding radius and thresholds for the ECT calculation.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            G (EmbeddedGraph / EmbeddedCW):</span>
-<span class="sd">                The input graph to calculate the ECT for.</span>
-<span class="sd">            bound_radius (float):</span>
-<span class="sd">                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            float, np.array</span>
-<span class="sd">                The bounding radius and the thresholds for the ECT calculation.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-       <span class="c1"># Either use the global radius and the set self.threshes; or use the tight bounding box and calculate</span>
-        <span class="c1"># the thresholds from that.</span>
-        <span class="k">if</span> <span class="n">bound_radius</span> <span class="o">==</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># First try to get the internally stored bounding radius</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span>
-                <span class="n">r_threshes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshes</span>
-
-            <span class="c1"># If the bounding radius is not set, use the global bounding radius</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">r</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
-                <span class="n">r_threshes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">r</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">)</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># The user wants to use a different bounding radius</span>
-            <span class="k">if</span> <span class="n">bound_radius</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">_ensure_directions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph_dim</span><span class="p">,</span> <span class="n">theta</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Ensures directions is a valid Directions object of correct dimension&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_dirs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s1">&#39;Bounding radius given was </span><span class="si">{</span><span class="n">bound_radius</span><span class="si">}</span><span class="s1">, but must be a positive number.&#39;</span><span class="p">)</span>
-            <span class="n">r</span> <span class="o">=</span> <span class="n">bound_radius</span>
-            <span class="n">r_threshes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">r</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">r</span><span class="p">,</span> <span class="n">r_threshes</span></div>
-
-
-<div class="viewcode-block" id="ECT.get_ECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.get_ECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_ECT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the ECT matrix.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span></div>
-
-
-<div class="viewcode-block" id="ECT.get_SECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.get_SECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_SECT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the SECT matrix.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">SECT_matrix</span></div>
-
-
-<div class="viewcode-block" id="ECT.calculateECC">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.calculateECC">[docs]</a>
-    <span class="k">def</span> <span class="nf">calculateECC</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">G</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">bound_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to compute the Euler Characteristic Curve (ECC) of an `EmbeddedGraph`.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            G (nx.Graph): The graph to compute the ECC for.</span>
-<span class="sd">            theta (float): The angle (in radians) for the direction function.</span>
-<span class="sd">            bound_radius (float, optional): Radius for threshold range. Default is None.</span>
-<span class="sd">            return_counts (bool, optional): Whether to return vertex, edge, and face counts. Default is False.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            numpy.ndarray: ECC values at each threshold.</span>
-<span class="sd">            (Optional) Tuple of counts: (ecc, vertex_count, edge_count, face_count)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">r</span><span class="p">,</span> <span class="n">r_threshes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_radius_and_thresh</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">)</span>
-
-        <span class="n">r_threshes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">)</span>
-
-        <span class="c1"># Sort vertices and edges based on projection</span>
-        <span class="n">v_list</span><span class="p">,</span> <span class="n">g</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">sort_vertices</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">g_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">g</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">v_list</span><span class="p">])</span>
-        <span class="n">sorted_g_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">g_list</span><span class="p">)</span>
-
-        <span class="n">e_list</span><span class="p">,</span> <span class="n">g_e</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">sort_edges</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">g_e_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">g_e</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">e_list</span><span class="p">])</span>
-        <span class="n">sorted_g_e_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">g_e_list</span><span class="p">)</span>
-
-        <span class="n">vertex_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">sorted_g_list</span><span class="p">,</span> <span class="n">r_threshes</span><span class="p">,</span> <span class="n">side</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-        <span class="n">edge_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">sorted_g_e_list</span><span class="p">,</span> <span class="n">r_threshes</span><span class="p">,</span> <span class="n">side</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">):</span>
-            <span class="n">f_list</span><span class="p">,</span> <span class="n">g_f</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">sort_faces</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="n">g_f_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">g_f</span><span class="p">[</span><span class="n">f</span><span class="p">]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">f_list</span><span class="p">])</span>
-            <span class="n">sorted_g_f_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">g_f_list</span><span class="p">)</span>
-            <span class="n">face_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span>
-                <span class="n">sorted_g_f_list</span><span class="p">,</span> <span class="n">r_threshes</span><span class="p">,</span> <span class="n">side</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">face_count</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
-        <span class="n">ecc</span> <span class="o">=</span> <span class="n">vertex_count</span> <span class="o">-</span> <span class="n">edge_count</span> <span class="o">+</span> <span class="n">face_count</span>
+                    <span class="s2">&quot;Either &#39;directions&#39; or &#39;num_dirs&#39; must be provided.&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="o">=</span> <span class="n">Directions</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">graph_dim</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">directions</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="c1"># if list of vectors, convert to Directions object</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">directions</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="o">=</span> <span class="n">Directions</span><span class="o">.</span><span class="n">from_vectors</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">directions</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">directions</span><span class="p">,</span> <span class="n">Directions</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;directions must be a Directions object, ndarray, or list of vectors.&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">theta</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">graph_dim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Theta must be provided for 2D graphs. &quot;</span>
+                <span class="s2">&quot;Use &#39;directions&#39; or &#39;num_dirs&#39; to specify directions.&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">directions</span><span class="o">.</span><span class="n">dim</span> <span class="o">!=</span> <span class="n">graph_dim</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Dimension mismatch: directions dimension does not match graph dimension.&quot;</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_ensure_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">override_bound_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Ensures thresholds is a valid 1-dimensional ndarray.&quot;&quot;&quot;</span>
+
+        <span class="c1"># determine if we need to generate thresholds</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">override_bound_radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Either &#39;thresholds&#39; or &#39;num_thresh&#39; must be provided.&quot;</span>
+                <span class="p">)</span>
+            <span class="c1"># determine the radius based on priority</span>
+            <span class="k">if</span> <span class="n">override_bound_radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">radius</span> <span class="o">=</span> <span class="n">override_bound_radius</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">radius</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bound_radius</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">radius</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
 
-        <span class="k">if</span> <span class="n">return_counts</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">ecc</span><span class="p">,</span> <span class="n">vertex_count</span><span class="p">,</span> <span class="n">edge_count</span><span class="p">,</span> <span class="n">face_count</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">radius</span><span class="p">,</span> <span class="n">radius</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">ecc</span></div>
-
-
-<div class="viewcode-block" id="ECT.calculateECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.calculateECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">calculateECT</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">bound_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compute_SECT</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Calculates the ECT from an input either `EmbeddedGraph` or `EmbeddedCW`. The entry ``M[i,j]`` is :math:`\\chi(K_{a_j})` for the direction :math:`\omega_i` where :math:`a_j` is the jth entry in ``self.threshes``, and :math:`\omega_i` is the ith entry in ``self.thetas``.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            graph (EmbeddedGraph/EmbeddedCW):</span>
-<span class="sd">                The input graph to calculate the ECT from.</span>
-<span class="sd">            bound_radius (float):</span>
-<span class="sd">                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.</span>
-<span class="sd">            compute_SECT (bool):</span>
-<span class="sd">                Whether to compute the SECT after the ECT is computed. Default is True. Sets the SECT_matrix attribute, but doesn&#39;t return it. Can be returned with the get_SECT method.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            np.array</span>
-<span class="sd">                The matrix representing the ECT of size (num_dirs,num_thresh).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">r</span><span class="p">,</span> <span class="n">r_threshes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_radius_and_thresh</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">)</span>
-
-        <span class="c1"># Note... this overwrites the self.threshes if it&#39;s not set.</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
-
-        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">num_dirs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">))</span>
-
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">theta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">thetas</span><span class="p">):</span>
-            <span class="n">M</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculateECC</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span> <span class="o">=</span> <span class="n">M</span>
-
-        <span class="k">if</span> <span class="n">compute_SECT</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">SECT_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculateSECT</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span></div>
-
-
-<div class="viewcode-block" id="ECT.calculateSECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.calculateSECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">calculateSECT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to calculate the Smooth Euler Characteristic Transform (SECT) from the ECT matrix. </span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            np.array</span>
-<span class="sd">                The matrix representing the SECT of size (num_dirs,num_thresh).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Calculate the SECT</span>
-        <span class="n">M</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span>
-
-        <span class="c1"># Get average of each row, corresponds to each direction</span>
-        <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="c1"># Subtract the average from each row</span>
-        <span class="n">M_normalized</span> <span class="o">=</span> <span class="n">M</span> <span class="o">-</span> <span class="n">A</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-
-        <span class="c1"># Take the cumulative sum of each row to get the SECT</span>
-        <span class="n">M_SECT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">M_normalized</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">M_SECT</span></div>
-
-
-<div class="viewcode-block" id="ECT.plotECC">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.plotECC">[docs]</a>
-    <span class="k">def</span> <span class="nf">plotECC</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">bound_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">draw_counts</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the Euler Characteristic Curve (ECC) for a specific direction theta. Note that this calculates the ECC for the input graph and then plots it.</span>
-
-<span class="sd">        Parameters:</span>
+            <span class="c1"># validate existing thresholds are valid</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;thresholds must be a numpy ndarray&quot;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;thresholds must be a 1-dimensional array&quot;</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ECT.calculate">
+<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.calculate">[docs]</a>
+    <span class="k">def</span> <span class="nf">calculate</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">graph</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">EmbeddedGraph</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">],</span>
+        <span class="n">theta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">override_bound_radius</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta</span>
+
+<span class="sd">        Args:</span>
 <span class="sd">            graph (EmbeddedGraph/EmbeddedCW):</span>
-<span class="sd">                The input graph or CW complex.</span>
+<span class="sd">                The input graph to calculate the ECT for.</span>
 <span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2\pi]` for the direction to plot the ECC.</span>
-<span class="sd">            bound_radius (float):</span>
-<span class="sd">                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None. </span>
-<span class="sd">            draw_counts (bool):</span>
-<span class="sd">                Whether to draw the counts of vertices, edges, and faces varying across thresholds. Default is False.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">r</span><span class="p">,</span> <span class="n">r_threshes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_radius_and_thresh</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">draw_counts</span><span class="p">:</span>
-            <span class="n">ECC</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculateECC</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ECC</span><span class="p">,</span> <span class="n">vertex_count</span><span class="p">,</span> <span class="n">edge_count</span><span class="p">,</span> <span class="n">face_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calculateECC</span><span class="p">(</span>
-                <span class="n">graph</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="c1"># if self.threshes is None:</span>
-        <span class="c1">#     self.set_bounding_radius(graph.get_bounding_radius())</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">,</span> <span class="n">ECC</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;ECC&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">draw_counts</span><span class="p">:</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">,</span> <span class="n">vertex_count</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Vertices&#39;</span><span class="p">)</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">,</span> <span class="n">edge_count</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Edges&#39;</span><span class="p">)</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">r_threshes</span><span class="p">,</span> <span class="n">face_count</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Faces&#39;</span><span class="p">)</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-
-        <span class="n">theta_round</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECC for $\omega = &#39;</span> <span class="o">+</span> <span class="n">theta_round</span> <span class="o">+</span> <span class="s1">&#39;$&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\chi(K_a)$&#39;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ECT.plotECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.plotECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">plotECT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the Euler Characteristic Transform (ECT) matrix. Note that the ECT matrix must be calculated before calling this function.</span>
-
-<span class="sd">        The resulting plot will have the angle on the x-axis and the threshold on the y-axis.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="c1"># Make meshgrid.</span>
-        <span class="c1"># Add back the 2pi to thetas for the pcolormesh</span>
-        <span class="n">thetas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">thetas</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">]))</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">thetas</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshes</span><span class="p">)</span>
-        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-        <span class="c1"># Transpose to get the correct orientation</span>
-        <span class="n">M</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ECT_matrix</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">M</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">M</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>  <span class="c1"># Add the 2pi direction to the 0 direction</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-
-        <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-                  <span class="p">]</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECT of Input Graph&#39;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ECT.plotSECT">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.plotSECT">[docs]</a>
-    <span class="k">def</span> <span class="nf">plotSECT</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the Smooth Euler Characteristic Transform (SECT) matrix. Note that the SECT matrix must be calculated before calling this function.</span>
-
-<span class="sd">        The resulting plot will have the angle on the x-axis and the threshold on the y-axis.</span>
+<span class="sd">                The angle in :math:`[0,2\pi]` for the direction to calculate the ECT.</span>
+<span class="sd">            override_bound_radius (float):</span>
+<span class="sd">                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a positive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_directions</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">theta</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_thresholds</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">override_bound_radius</span><span class="p">)</span>
+
+        <span class="c1"># override with theta if provided</span>
+        <span class="n">directions</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">directions</span> <span class="k">if</span> <span class="n">theta</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">Directions</span><span class="o">.</span><span class="n">from_angles</span><span class="p">([</span>
+                                                                         <span class="n">theta</span><span class="p">])</span>
+        <span class="p">)</span>
+
+        <span class="n">simplex_projections</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_simplex_projections</span><span class="p">(</span>
+            <span class="n">graph</span><span class="p">,</span> <span class="n">directions</span><span class="p">)</span>
+
+        <span class="n">ect_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_directional_transform</span><span class="p">(</span>
+            <span class="n">simplex_projections</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_descriptor</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ECTResult</span><span class="p">(</span><span class="n">ect_matrix</span><span class="p">,</span> <span class="n">directions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_compute_node_projections</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coords</span><span class="p">,</span> <span class="n">directions</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute inner products of coordinates with directions&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">directions</span><span class="o">.</span><span class="n">vectors</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_compute_simplex_projections</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">graph</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">EmbeddedGraph</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">],</span> <span class="n">directions</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute projections of each simplex (vertices, edges, faces)&quot;&quot;&quot;</span>
+        <span class="n">simplex_projections</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">node_projections</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_node_projections</span><span class="p">(</span>
+            <span class="n">graph</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">,</span> <span class="n">directions</span>
+        <span class="p">)</span>
+        <span class="n">edge_maxes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span>
+            <span class="n">node_projections</span><span class="p">[</span><span class="n">graph</span><span class="o">.</span><span class="n">edge_indices</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]],</span>
+            <span class="n">node_projections</span><span class="p">[</span><span class="n">graph</span><span class="o">.</span><span class="n">edge_indices</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]],</span>
+        <span class="p">)</span>
+
+        <span class="n">simplex_projections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">node_projections</span><span class="p">)</span>
+        <span class="n">simplex_projections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">edge_maxes</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">graph</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">faces</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">node_to_index</span> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">graph</span><span class="o">.</span><span class="n">node_list</span><span class="p">)}</span>
+            <span class="n">face_indices</span> <span class="o">=</span> <span class="p">[[</span><span class="n">node_to_index</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">face</span><span class="p">]</span>
+                            <span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="n">graph</span><span class="o">.</span><span class="n">faces</span><span class="p">]</span>
+            <span class="n">face_maxes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">node_projections</span><span class="p">[</span><span class="n">face</span><span class="p">,</span> <span class="p">:],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                 <span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="n">face_indices</span><span class="p">]</span>
+            <span class="p">)</span>
+            <span class="n">simplex_projections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">face_maxes</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">simplex_projections</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">_compute_directional_transform</span><span class="p">(</span>
+        <span class="n">simplex_projections_list</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">shape_descriptor</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int32</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Compute ECT by counting simplices below each threshold</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            simplex_projections_list: List of arrays containing projections for each simplex type</span>
+<span class="sd">                [vertex_projections, edge_projections, face_projections]</span>
+<span class="sd">            thresholds: Array of threshold values to compute ECT at</span>
+<span class="sd">            dtype: Data type for output array (default: np.int32)</span>
 
-        <span class="c1"># Make meshgrid.</span>
-        <span class="c1"># Add back the 2pi to thetas for the pcolormesh</span>
-        <span class="n">thetas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">thetas</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">]))</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">thetas</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshes</span><span class="p">)</span>
-        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-        <span class="c1"># Transpose to get the correct orientation</span>
-        <span class="n">M</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">SECT_matrix</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">M</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">M</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>  <span class="c1"># Add the 2pi direction to the 0 direction</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
-
-        <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-                  <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-                  <span class="p">]</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$t$&#39;</span><span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;SECT of Input Graph&#39;</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ECT.plot">
-<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.plot">[docs]</a>
-    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plot_type</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the ECT or SECT matrix. The type parameter should be either &#39;ECT&#39; or &#39;SECT&#39;.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            plot_type : str</span>
-<span class="sd">                The type of plot to make. Either &#39;ECT&#39; or &#39;SECT&#39;.</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            Array of shape (num_directions, num_thresholds) containing Euler characteristics</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s1">&#39;ECT&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">plotECT</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">plot_type</span> <span class="o">==</span> <span class="s1">&#39;SECT&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">plotSECT</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;plot_type must be either &quot;ECT&quot; or &quot;SECT&quot;.&#39;</span><span class="p">)</span></div>
+        <span class="n">num_dir</span> <span class="o">=</span> <span class="n">simplex_projections_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">num_thresh</span> <span class="o">=</span> <span class="n">thresholds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">num_dir</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span>
+
+        <span class="n">sorted_projections</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">proj</span> <span class="ow">in</span> <span class="n">simplex_projections_list</span><span class="p">:</span>
+            <span class="n">sorted_proj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">proj</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">num_dir</span><span class="p">):</span>
+                <span class="n">sorted_proj</span><span class="p">[:,</span> <span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">proj</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+            <span class="n">sorted_projections</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sorted_proj</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">prange</span><span class="p">(</span><span class="n">num_thresh</span><span class="p">):</span>
+            <span class="n">thresh</span> <span class="o">=</span> <span class="n">thresholds</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_dir</span><span class="p">):</span>
+                <span class="n">simplex_counts_list</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sorted_projections</span><span class="p">)):</span>
+                    <span class="n">projs</span> <span class="o">=</span> <span class="n">sorted_projections</span><span class="p">[</span><span class="n">k</span><span class="p">][:,</span> <span class="n">i</span><span class="p">]</span>
+                    <span class="n">simplex_counts_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                        <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">projs</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">side</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">)</span>
+                    <span class="p">)</span>
+                <span class="n">result</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">shape_descriptor</span><span class="p">(</span><span class="n">simplex_counts_list</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">result</span>
+
+<div class="viewcode-block" id="ECT.shape_descriptor">
+<a class="viewcode-back" href="../../ect_on_graphs.html#ect.ect_graph.ECT.shape_descriptor">[docs]</a>
+    <span class="nd">@staticmethod</span>
+    <span class="nd">@njit</span><span class="p">(</span><span class="n">parallel</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fastmath</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">shape_descriptor</span><span class="p">(</span><span class="n">simplex_counts_list</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate shape descriptor from simplex counts (Euler characteristic)&quot;&quot;&quot;</span>
+        <span class="n">chi</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">simplex_counts_list</span><span class="p">)):</span>
+            <span class="n">chi</span> <span class="o">+=</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="n">k</span> <span class="o">*</span> <span class="n">simplex_counts_list</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">chi</span></div>
 </div>
 
 </pre></div>
diff --git a/docs/_modules/ect/embed_cw.html b/docs/_modules/ect/embed_cw.html
index e612836..f901a23 100644
--- a/docs/_modules/ect/embed_cw.html
+++ b/docs/_modules/ect/embed_cw.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>ect.embed_cw &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="../../_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="../../_static/ECT_Logo_128x128.png"/>
-      <script src="../../_static/jquery.js?v=5d32c60e"></script>
-      <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=59fadc99"></script>
-      <script src="../../_static/doctools.js?v=9a2dae69"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=59fadc99"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" /> 
@@ -54,12 +56,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -69,18 +69,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">4. Contributing</a><ul>
@@ -125,18 +122,18 @@
              
   <h1>Source code for ect.embed_cw</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">compress</span><span class="p">,</span> <span class="n">combinations</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-<span class="kn">from</span> <span class="nn">ect.embed_graph</span> <span class="kn">import</span> <span class="n">EmbeddedGraph</span><span class="p">,</span> <span class="n">create_example_graph</span>
-<span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">linprog</span>
+<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d.art3d</span> <span class="kn">import</span> <span class="n">Poly3DCollection</span>
+<span class="kn">from</span> <span class="nn">.embed_graph</span> <span class="kn">import</span> <span class="n">EmbeddedGraph</span>
+<span class="kn">from</span> <span class="nn">.utils.face_check</span> <span class="kn">import</span> <span class="n">point_in_polygon</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
 
 
 <div class="viewcode-block" id="EmbeddedCW">
 <a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW">[docs]</a>
 <span class="k">class</span> <span class="nc">EmbeddedCW</span><span class="p">(</span><span class="n">EmbeddedGraph</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges. </span>
+<span class="sd">    A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges.</span>
 
 <span class="sd">    Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane.</span>
 
@@ -149,7 +146,6 @@ <h1>Source code for ect.embed_cw</h1><div class="highlight"><pre>
 <span class="sd">        Initializes an empty EmbeddedCW object.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
 
-        <span class="c1"># The super class initializes the graph and the coordinates dictionary.</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">faces</span> <span class="o">=</span> <span class="p">[]</span></div>
 
@@ -161,116 +157,61 @@ <h1>Source code for ect.embed_cw</h1><div class="highlight"><pre>
 <span class="sd">        Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.</span>
 
 <span class="sd">        Parameters:</span>
-<span class="sd">            embedded_graph (EmbeddedGraph):</span>
+<span class="sd">            G (EmbeddedGraph):</span>
 <span class="sd">                The EmbeddedGraph object to add from.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">(),</span> <span class="n">G</span><span class="o">.</span><span class="n">coordinates</span><span class="p">)</span>
+        <span class="n">nodes_with_coords</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">(</span><span class="n">node</span><span class="p">,</span> <span class="n">G</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">[</span><span class="n">G</span><span class="o">.</span><span class="n">node_to_index</span><span class="p">[</span><span class="n">node</span><span class="p">]])</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">()</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">nodes_with_coords</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">())</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedCW.add_face">
-<a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW.add_face">[docs]</a>
-    <span class="k">def</span> <span class="nf">add_face</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">face</span><span class="p">,</span> <span class="n">check</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="nd">@EmbeddedGraph</span><span class="o">.</span><span class="n">_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">add_face</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">face</span><span class="p">,</span> <span class="n">check</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Adds a face to the list of faces.</span>
 
-<span class="sd">        TODO: Do we want a check to make sure the face is legit? (i.e. is a cycle in the graph, and bounds a region in the plane)</span>
-
 <span class="sd">        Parameters:</span>
 <span class="sd">            face (list):</span>
 <span class="sd">                A list of vertices that make up the face.</span>
 <span class="sd">            check (bool):</span>
 <span class="sd">                Whether to check that the face is a valid addition to the cw complex.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">check</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">face</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Face must have at least 3 vertices&quot;</span><span class="p">)</span>
 
-            <span class="c1"># Make sure all edges are in the graph</span>
+        <span class="k">if</span> <span class="n">check</span><span class="p">:</span>
             <span class="n">edges</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">face</span><span class="p">,</span> <span class="n">face</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="p">[</span><span class="n">face</span><span class="p">[</span><span class="mi">0</span><span class="p">]]))</span>
-            <span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">edge</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Edge </span><span class="si">{</span><span class="n">edge</span><span class="si">}</span><span class="s2"> not in graph.&quot;</span><span class="p">)</span>
-
-            <span class="c1"># TODO: The goal here is to check that none of the other vertices are in the polygon defined by the face.</span>
-            <span class="c1"># Problem is that the face could be concave, so we can&#39;t just check if the point is in the convex hull of the face.</span>
-            <span class="c1"># This is a bit of a tricky problem, so I&#39;m going to leave it for now.</span>
-
-            <span class="c1"># def in_hull(points, x):</span>
-            <span class="c1">#     # Solution for checking if a point is in a convex hull</span>
-            <span class="c1">#     # from Nils answer here:</span>
-            <span class="c1">#     # https://stackoverflow.com/questions/16750618/whats-an-efficient-way-to-find-if-a-point-lies-in-the-convex-hull-of-a-point-cl</span>
-            <span class="c1">#     n_points = len(points)</span>
-            <span class="c1">#     n_dim = len(x)</span>
-            <span class="c1">#     c = np.zeros(n_points)</span>
-            <span class="c1">#     A = np.r_[points.T,np.ones((1,n_points))]</span>
-            <span class="c1">#     b = np.r_[x, np.ones(1)]</span>
-            <span class="c1">#     lp = linprog(c, A_eq=A, b_eq=b)</span>
-            <span class="c1">#     return lp.success</span>
-
-            <span class="c1"># points = np.array([self.coordinates[v] for v in face])</span>
-            <span class="c1"># if not in_hull(points.T, self.coordinates[face[0]]):</span>
-            <span class="c1">#     raise ValueError(f&quot;Face {face} does not bound an empty region in the plane.&quot;)</span>
-
-        <span class="c1"># Note: faces need to be tuples to make</span>
-        <span class="c1"># the face hashable so it can be used as a key in a dictionary</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">faces</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">face</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedCW.g_omega_faces">
-<a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW.g_omega_faces">[docs]</a>
-    <span class="k">def</span> <span class="nf">g_omega_faces</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">edges</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_edge</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Edge (</span><span class="si">{</span><span class="n">u</span><span class="si">}</span><span class="s2">,</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s2">) missing&quot;</span><span class="p">)</span>
+
+            <span class="n">polygon</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">v</span><span class="p">]]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">face</span><span class="p">]</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">face</span><span class="p">:</span>
+                    <span class="k">continue</span>
+                <span class="k">if</span> <span class="n">point_in_polygon</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">node</span><span class="p">]],</span> <span class="n">polygon</span>
+                <span class="p">):</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Node </span><span class="si">{</span><span class="n">node</span><span class="si">}</span><span class="s2"> inside face </span><span class="si">{</span><span class="n">face</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">faces</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">face</span><span class="p">))</span>
+
+<div class="viewcode-block" id="EmbeddedCW.add_faces_from">
+<a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW.add_faces_from">[docs]</a>
+    <span class="k">def</span> <span class="nf">add_faces_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">faces</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Calculates the function value of the faces of the graph by making the value equal to the max vertex value </span>
+<span class="sd">        Adds a list of faces to the graph.</span>
 
 <span class="sd">        Parameters:</span>
-
-<span class="sd">            theta (float): </span>
-<span class="sd">                The direction of the function to be calculated.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            dict</span>
-<span class="sd">                A dictionary of the function values of the faces.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">g</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-        <span class="n">g_faces</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">faces</span><span class="p">:</span>
-            <span class="n">g_faces</span><span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">face</span><span class="p">)]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">g</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">face</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">g_faces</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedCW.sort_faces">
-<a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW.sort_faces">[docs]</a>
-    <span class="k">def</span> <span class="nf">sort_faces</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to sort the faces of the graph according to the function</span>
-
-<span class="sd">        .. math ::</span>
-
-<span class="sd">            g_\\omega(\\sigma) = \\max \\{ g_\\omega(v) \\mid  v \\in \\sigma \\}</span>
-
-<span class="sd">        in the direction of :math:`\\theta \\in [0,2\\pi]` .</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.</span>
-<span class="sd">            return_g (bool):</span>
-<span class="sd">                Whether to return the :math:`g(v)` values along with the sorted edges.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            A list of edges sorted in increasing order of the :math:`g(v)` values. </span>
-<span class="sd">            If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. </span>
-
+<span class="sd">            faces (list):</span>
+<span class="sd">                A list of faces to add.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">g_f</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_omega_faces</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-        <span class="n">f_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">faces</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">face</span><span class="p">:</span> <span class="n">g_f</span><span class="p">[</span><span class="n">face</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="n">return_g</span><span class="p">:</span>
-            <span class="c1"># g_sorted = [g[v] for v in v_list]</span>
-            <span class="k">return</span> <span class="n">f_list</span><span class="p">,</span> <span class="n">g_f</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">f_list</span></div>
+        <span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">add_face</span><span class="p">(</span><span class="n">face</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="EmbeddedCW.plot_faces">
@@ -281,7 +222,7 @@ <h1>Source code for ect.embed_cw</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters:</span>
 <span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.</span>
+<span class="sd">                The angle in :math:`[0,2\pi]` for the direction to sort the edges.</span>
 <span class="sd">            ax (matplotlib.axes.Axes):</span>
 <span class="sd">                The axes to plot the graph on. If None, a new figure is created.</span>
 <span class="sd">            **kwargs:</span>
@@ -297,64 +238,58 @@ <h1>Source code for ect.embed_cw</h1><div class="highlight"><pre>
             <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">faces</span><span class="p">:</span>
-            <span class="n">face_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">face</span><span class="p">])</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">face_coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">face_coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">face_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">node_to_index</span><span class="p">[</span><span class="n">v</span><span class="p">]]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">face</span><span class="p">]</span>
+            <span class="p">)</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">face_coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">face_coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>  <span class="c1"># 3D case</span>
+                <span class="n">verts</span> <span class="o">=</span> <span class="p">[</span><span class="n">face_coords</span><span class="p">]</span>
+                <span class="n">collection</span> <span class="o">=</span> <span class="n">Poly3DCollection</span><span class="p">(</span><span class="n">verts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">add_collection3d</span><span class="p">(</span><span class="n">collection</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">ax</span></div>
 
 
 <div class="viewcode-block" id="EmbeddedCW.plot">
 <a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW.plot">[docs]</a>
-    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bounding_circle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">color_nodes_theta</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">bounding_circle</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">bounding_center_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">,</span>
+        <span class="n">color_nodes_theta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">ax</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">with_labels</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">node_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">300</span><span class="p">,</span>
+        <span class="n">edge_color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gray&quot;</span><span class="p">,</span>
+        <span class="n">elev</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">25</span><span class="p">,</span>
+        <span class="n">azim</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span>
+        <span class="n">face_color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;lightblue&quot;</span><span class="p">,</span>
+        <span class="n">face_alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Plots the graph with the faces filled in.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            matplotlib.axes.Axes</span>
-<span class="sd">                The axes object with the plot.</span>
+<span class="sd">        Visualize the embedded CW complex in 2D or 3D, including faces</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
-
-        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">plot_faces</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;lightblue&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="n">bounding_circle</span><span class="p">,</span>
-                          <span class="n">color_nodes_theta</span><span class="o">=</span><span class="n">color_nodes_theta</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ax</span></div>
+        <span class="c1"># plot faces then use parent class</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">plot_faces</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="n">face_color</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">face_alpha</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+            <span class="n">bounding_circle</span><span class="o">=</span><span class="n">bounding_circle</span><span class="p">,</span>
+            <span class="n">bounding_center_type</span><span class="o">=</span><span class="n">bounding_center_type</span><span class="p">,</span>
+            <span class="n">color_nodes_theta</span><span class="o">=</span><span class="n">color_nodes_theta</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">with_labels</span><span class="o">=</span><span class="n">with_labels</span><span class="p">,</span>
+            <span class="n">node_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
+            <span class="n">edge_color</span><span class="o">=</span><span class="n">edge_color</span><span class="p">,</span>
+            <span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span>
+            <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span></div>
 </div>
 
-
-
-<div class="viewcode-block" id="create_example_cw">
-<a class="viewcode-back" href="../../embed_cw.html#ect.embed_cw.create_example_cw">[docs]</a>
-<span class="k">def</span> <span class="nf">create_example_cw</span><span class="p">(</span><span class="n">centered</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates an example EmbeddedCW object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be ``mean``, ``min_max`` or ``origin``.</span>
-
-
-<span class="sd">    Returns:</span>
-<span class="sd">        EmbeddedCW</span>
-<span class="sd">            The example EmbeddedCW object.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">G</span> <span class="o">=</span> <span class="n">create_example_graph</span><span class="p">(</span><span class="n">centered</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">K</span> <span class="o">=</span> <span class="n">EmbeddedCW</span><span class="p">()</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_from_embedded_graph</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;I&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;J&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">([(</span><span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;I&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">),</span>
-                     <span class="p">(</span><span class="s1">&#39;I&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;J&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)])</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_face</span><span class="p">([</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;G&#39;</span><span class="p">,</span> <span class="s1">&#39;H&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">])</span>
-    <span class="n">K</span><span class="o">.</span><span class="n">add_face</span><span class="p">([</span><span class="s1">&#39;K&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">])</span>
-
-    <span class="k">if</span> <span class="n">centered</span><span class="p">:</span>
-        <span class="n">K</span><span class="o">.</span><span class="n">set_centered_coordinates</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">center_type</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">K</span></div>
-
 </pre></div>
 
            </div>
diff --git a/docs/_modules/ect/embed_graph.html b/docs/_modules/ect/embed_graph.html
index 5e6c0c7..55ae7c5 100644
--- a/docs/_modules/ect/embed_graph.html
+++ b/docs/_modules/ect/embed_graph.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>ect.embed_graph &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="../../_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="../../_static/ECT_Logo_128x128.png"/>
-      <script src="../../_static/jquery.js?v=5d32c60e"></script>
-      <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../../_static/documentation_options.js?v=59fadc99"></script>
-      <script src="../../_static/doctools.js?v=9a2dae69"></script>
-      <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../_static/documentation_options.js?v=59fadc99"></script>
+        <script src="../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" /> 
@@ -54,12 +56,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -69,18 +69,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../../notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../../notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">4. Contributing</a><ul>
@@ -124,881 +121,741 @@
            <div itemprop="articleBody">
              
   <h1>Source code for ect.embed_graph</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
+<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span>
+
+<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>  <span class="c1"># for PCA for normalization</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
+
+<span class="kn">from</span> <span class="nn">.utils.naming</span> <span class="kn">import</span> <span class="n">next_vert_name</span>
+
+
+<span class="n">CENTER_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">,</span> <span class="s2">&quot;origin&quot;</span><span class="p">]</span>
+<span class="n">TRANSFORM_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;pca&quot;</span><span class="p">]</span>
 
 
 <div class="viewcode-block" id="EmbeddedGraph">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph">[docs]</a>
 <span class="k">class</span> <span class="nc">EmbeddedGraph</span><span class="p">(</span><span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A class to represent a graph with 2D embedded coordinates for each vertex.</span>
+<span class="sd">    A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations.</span>
 
 <span class="sd">    Attributes</span>
 <span class="sd">        graph : nx.Graph</span>
 <span class="sd">            a NetworkX graph object</span>
-<span class="sd">        coordinates : dict</span>
-<span class="sd">            a dictionary mapping vertices to their (x, y) coordinates</span>
+<span class="sd">        coord_matrix : np.ndarray</span>
+<span class="sd">            a matrix of embedded coordinates for each vertex</span>
+<span class="sd">        node_list : list</span>
+<span class="sd">            a list of node names</span>
+<span class="sd">        node_to_index : dict</span>
+<span class="sd">            a dictionary mapping node ids to their index in the coord_matrix</span>
+<span class="sd">        dim : int</span>
+<span class="sd">            the dimension of the embedded coordinates</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
 
 <div class="viewcode-block" id="EmbeddedGraph.__init__">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.__init__">[docs]</a>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Initializes an empty EmbeddedGraph object.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span> <span class="o">=</span> <span class="p">{}</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">coord_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the N x D coordinate matrix&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">dim</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the dimension of the embedded coordinates&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">node_list</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return ordered list of node names.&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">node_to_index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a dictionary mapping node ids to their index in the coord_matrix&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">position_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a dictionary mapping node ids to their coordinates&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">node</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">)}</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">edge_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return edges as array of index pairs&quot;&quot;&quot;</span>
+        <span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+            <span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">u</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">v</span><span class="p">])</span>
+             <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">()],</span>
+            <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">edges</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+
+    <span class="c1"># ======================================</span>
+    <span class="c1"># Node Management</span>
+    <span class="c1"># ======================================</span>
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_validate_coords</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Validates if coordinates are nonempty and have valid dimension&quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span>
+                <span class="p">(</span><span class="n">arg</span> <span class="k">for</span> <span class="n">arg</span> <span class="ow">in</span> <span class="n">args</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))),</span> <span class="kc">None</span>
+            <span class="p">)</span>
+            <span class="k">if</span> <span class="n">coords</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">coords</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Coordinates must be a 1D array&quot;</span><span class="p">)</span>
+
+                <span class="c1"># Skip dimension check for first node</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">coords</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                            <span class="sa">f</span><span class="s2">&quot;Coordinates must have dimension </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+                        <span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">wrapper</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Validates if nodes exist or not already&quot;&quot;&quot;</span>
+
+        <span class="k">def</span> <span class="nf">decorator</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
+            <span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+                <span class="c1"># Handle both positional and keyword arguments</span>
+                <span class="k">if</span> <span class="n">args</span><span class="p">:</span>
+                    <span class="n">nodes</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span>
+                        <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span> <span class="k">else</span> <span class="p">[</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">node_id</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;node_id&quot;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;node_id1&quot;</span><span class="p">)</span>
+                    <span class="n">nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">node_id</span><span class="p">]</span> <span class="k">if</span> <span class="n">node_id</span> <span class="k">else</span> <span class="p">[]</span>
 
+                <span class="k">for</span> <span class="n">node_id</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">:</span>
+                    <span class="n">node_exists</span> <span class="o">=</span> <span class="n">node_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span>
+                    <span class="k">if</span> <span class="n">exists</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">node_exists</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Node </span><span class="si">{</span><span class="n">node_id</span><span class="si">}</span><span class="s2"> does not exist&quot;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="ow">not</span> <span class="n">exists</span> <span class="ow">and</span> <span class="n">node_exists</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Node </span><span class="si">{</span><span class="n">node_id</span><span class="si">}</span><span class="s2"> already exists&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">wrapper</span>
+
+        <span class="k">return</span> <span class="n">decorator</span>
 
 <div class="viewcode-block" id="EmbeddedGraph.add_node">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_node">[docs]</a>
-    <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertex</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add a vertex to the graph. </span>
-<span class="sd">        If the vertex name is given as None, it will be assigned via the ``next_vert_name`` method.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            vertex (hashable like int or str, or None) : The name of the vertex to add.</span>
-<span class="sd">            x, y (floats) : The function value of the vertex being added.</span>
-<span class="sd">            reset_pos (bool, optional) </span>
-<span class="sd">                If True, will reset the positions of the nodes based on the function values.</span>
+    <span class="nd">@_validate_coords</span>
+    <span class="nd">@_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">add_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_id</span><span class="p">,</span> <span class="n">coord</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add a vertex to the graph.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            node_id: Identifier for the node</span>
+<span class="sd">            coord: Array-like coordinates for the node</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">vertex</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s1">&#39;The vertex name </span><span class="si">{</span><span class="n">vertex</span><span class="si">}</span><span class="s1"> is already used in the graph.&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">vertex</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">vertex</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">vertex</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_vert_name</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">))</span>
+        <span class="n">coord</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">coord</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
 
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">vertex</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">vertex</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">=</span> <span class="n">coord</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">coord_reshaped</span> <span class="o">=</span> <span class="n">coord</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span>
+                <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">,</span> <span class="n">coord_reshaped</span><span class="p">])</span>
 
+        <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">node_id</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">node_id</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">node_id</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="EmbeddedGraph.add_nodes_from">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_nodes_from">[docs]</a>
-    <span class="k">def</span> <span class="nf">add_nodes_from</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Adds multiple vertices to the graph and assigns them the given coordinates.</span>
 
-<span class="sd">        Parameters:</span>
-<span class="sd">            nodes (list):</span>
-<span class="sd">                A list of vertices to be added.</span>
-<span class="sd">            coordinates (dict):</span>
-<span class="sd">                A dictionary mapping vertices to their coordinates.</span>
+<div class="viewcode-block" id="EmbeddedGraph.add_nodes_from_dict">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_nodes_from_dict">[docs]</a>
+    <span class="k">def</span> <span class="nf">add_nodes_from_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nodes_with_coords</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">node_id</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="n">nodes_with_coords</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">node_id</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">)</span></div>
 
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span></div>
 
+<div class="viewcode-block" id="EmbeddedGraph.add_nodes_from">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_nodes_from">[docs]</a>
+    <span class="k">def</span> <span class="nf">add_nodes_from</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">nodes_with_coords</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]]</span>
+    <span class="p">):</span>
+        <span class="k">for</span> <span class="n">node_id</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="n">nodes_with_coords</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">node_id</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="EmbeddedGraph.next_vert_name">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.next_vert_name">[docs]</a>
-    <span class="k">def</span> <span class="nf">next_vert_name</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">num_verts</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot; </span>
-<span class="sd">        Making a simple name generator for vertices. </span>
-<span class="sd">        If you&#39;re using integers, it will just up the count by one. </span>
-<span class="sd">        Letters will be incremented in the alphabet. If you reach ``Z``, it will return ``AA``. If you reach ``ZZ``, it will return ``AAA``, etc.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            s (str or int): The name of the vertex to increment.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            str or int</span>
-<span class="sd">                The next name in the sequence.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
 
-        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">int</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">num_verts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">return</span> <span class="p">[</span><span class="n">s</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_verts</span><span class="p">)]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">s</span><span class="o">+</span><span class="mi">1</span>
-        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">s</span> <span class="o">==</span> <span class="s1">&#39;Z&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">num_verts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="p">[</span><span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_verts</span><span class="p">)]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">num_verts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="p">[</span><span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="s1">&#39;AA&#39;</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_verts</span><span class="p">)]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="s1">&#39;AA&#39;</span>
-        <span class="k">elif</span> <span class="nb">type</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">str</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;Z&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">num_verts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="p">[</span><span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_verts</span><span class="p">)]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="s1">&#39;A&#39;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">num_verts</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="p">[</span><span class="n">s</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_verts</span><span class="p">)]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="nb">chr</span><span class="p">(</span><span class="nb">ord</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Input must be a string or an integer&#39;</span><span class="p">)</span></div>
+    <span class="c1"># ======================================</span>
+    <span class="c1"># Coordinate Access</span>
+    <span class="c1"># ======================================</span>
 
+<div class="viewcode-block" id="EmbeddedGraph.get_coord">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_coord">[docs]</a>
+    <span class="nd">@_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">get_coord</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_id</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the coordinates of a node&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">node_id</span><span class="p">]]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div>
 
-<div class="viewcode-block" id="EmbeddedGraph.add_edge">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_edge">[docs]</a>
-    <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Adds an edge between the vertices u and v if they exist.</span>
 
-<span class="sd">        Parameters:</span>
-<span class="sd">            u (str):</span>
-<span class="sd">                The first vertex of the edge.</span>
-<span class="sd">            v (str):</span>
-<span class="sd">                The second vertex of the edge.</span>
+<div class="viewcode-block" id="EmbeddedGraph.set_coord">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.set_coord">[docs]</a>
+    <span class="nd">@_validate_coords</span>
+    <span class="nd">@_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">set_coord</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_id</span><span class="p">,</span> <span class="n">new_coords</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the coordinates of a node&quot;&quot;&quot;</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="p">[</span><span class="n">node_id</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_coords</span></div>
 
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_node</span><span class="p">(</span><span class="n">u</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_node</span><span class="p">(</span><span class="n">v</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;One or both vertices do not exist in the graph.&quot;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span></div>
 
+    <span class="c1"># ======================================</span>
+    <span class="c1"># Graph Operations</span>
+    <span class="c1"># ======================================</span>
 
 <div class="viewcode-block" id="EmbeddedGraph.add_cycle">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_cycle">[docs]</a>
     <span class="k">def</span> <span class="nf">add_cycle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coord_matrix</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Add nodes and edges from a cycle of coordinates. </span>
-<span class="sd">        Specifically, will add a node for each row and the edges connecting the nodes in the order they appear in the matrix as a closed cycle.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            coord_matrix : numpy array</span>
-<span class="sd">                An (n x 2) matrix of coordinates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coord_matrix</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">last_name</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">last_name</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">)</span>
-
-        <span class="n">nodes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_vert_name</span><span class="p">(</span><span class="n">last_name</span><span class="p">,</span> <span class="n">num_verts</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
-        <span class="n">coords</span> <span class="o">=</span> <span class="p">{</span><span class="n">nodes</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">coord_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">coords</span><span class="p">)</span>
-        <span class="n">edges</span> <span class="o">=</span> <span class="p">[(</span><span class="n">nodes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nodes</span><span class="p">[(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">n</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.get_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertex</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the coordinates of the given vertex.</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add nodes in a cyclic pattern from coordinate matrix&quot;&quot;&quot;</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="n">coord_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">new_names</span> <span class="o">=</span> <span class="n">next_vert_name</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">new_names</span><span class="p">,</span> <span class="n">coord_matrix</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
+            <span class="p">[(</span><span class="n">new_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">new_names</span><span class="p">[(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">n</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span></div>
 
-<span class="sd">        Parameters:</span>
-<span class="sd">            vertex (str):</span>
-<span class="sd">                The vertex whose coordinates are to be returned.</span>
 
-<span class="sd">        Returns:</span>
-<span class="sd">            tuple: The coordinates of the vertex.</span>
+    <span class="c1"># ======================================</span>
+    <span class="c1"># Geometric Calculations</span>
+    <span class="c1"># ======================================</span>
 
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">vertex</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.set_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.set_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vertex</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Sets the coordinates of the given vertex.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            vertex (str):</span>
-<span class="sd">                The vertex whose coordinates are to be set.</span>
-<span class="sd">            x (float):</span>
-<span class="sd">                The new x-coordinate of the vertex.</span>
-<span class="sd">            y (float): </span>
-<span class="sd">                The new y-coordinate of the vertex.</span>
-
-<span class="sd">        Raises:</span>
-<span class="sd">            ValueError: If the vertex does not exist in the graph.</span>
+<div class="viewcode-block" id="EmbeddedGraph.get_center">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_center">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_center</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate center of coordinates&quot;&quot;&quot;</span>
 
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">vertex</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">vertex</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Vertex does not exist in the graph.&quot;</span><span class="p">)</span></div>
+        <span class="n">coords</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span>
+        <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
+        <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;origin&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown center method: </span><span class="si">{</span><span class="n">method</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="EmbeddedGraph.get_bounding_box">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_bounding_box">[docs]</a>
     <span class="k">def</span> <span class="nf">get_bounding_box</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to find a bounding box of the vertex coordinates in the graph.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            list: A list of tuples representing the minimum and maximum :math:`x` and :math:`y` coordinates.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-        <span class="k">return</span> <span class="p">[(</span><span class="nb">min</span><span class="p">(</span><span class="n">x_coords</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">x_coords</span><span class="p">)),</span> <span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">y_coords</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">y_coords</span><span class="p">))]</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.get_center">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_center">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_center</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;origin&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Calculate and return the center of the graph. This can be done by either returning the average of the coordiantes (``mean``), the center of the bounding box (``min_max``), or the origin (``origin``).</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            type (str): The type of center to calculate. Options are ``mean``, ``min_max``, or ``origin``.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            numpy.ndarray: The :math:`(x, y)` coordinates of the center.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
-
-        <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;origin&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
-        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
-            <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;min_max&#39;</span><span class="p">:</span>
-            <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-            <span class="n">min_x</span><span class="p">,</span> <span class="n">max_x</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">x_coords</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">x_coords</span><span class="p">)</span>
-            <span class="n">min_y</span><span class="p">,</span> <span class="n">max_y</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">y_coords</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">y_coords</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([(</span><span class="n">max_x</span><span class="o">+</span><span class="n">min_x</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="n">max_y</span><span class="o">+</span><span class="n">min_y</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span><span class="p">])</span></div>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get (min, max) for each dimension&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="p">[(</span><span class="n">dim</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">dim</span><span class="o">.</span><span class="n">max</span><span class="p">())</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">]</span></div>
 
 
 <div class="viewcode-block" id="EmbeddedGraph.get_bounding_radius">
 <a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_bounding_radius">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_bounding_radius</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;origin&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to find the radius of the bounding circle of the vertex coordinates in the graph. </span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            type (str): The type of center to calculate the radius relative to. Options are ``mean``, ``min_max``, or ``origin``.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            float: The radius of the bounding circle.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">0</span>
-
-        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="nb">type</span><span class="p">)</span>
-        <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-        <span class="n">distances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">coords</span> <span class="o">-</span> <span class="n">center</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">distances</span><span class="p">)</span></div>
-
-
-    <span class="c1"># ------</span>
-    <span class="c1"># Methods for normalizing the coordinates in various ways</span>
-    <span class="c1"># ------</span>
-
-<div class="viewcode-block" id="EmbeddedGraph.get_centered_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_centered_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_centered_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to find the centered coordinates of the vertices in the graph.</span>
-
-<span class="sd">        If type is ``min_max``, the coordinates are centered at the mean of the min and max values of the :math:`x` and :math:`y` coordinates.</span>
-<span class="sd">        If type is ``mean``, the coordinates are centered at the mean of the :math:`x` and :math:`y` coordinates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="nb">type</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">y</span> <span class="o">-</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span></div>
+    <span class="k">def</span> <span class="nf">get_bounding_radius</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Get radius of minimal bounding sphere&quot;&quot;&quot;</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
+        <span class="n">coords</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">coords</span> <span class="o">-</span> <span class="n">center</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedGraph.set_centered_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.set_centered_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_centered_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">):</span>
+<div class="viewcode-block" id="EmbeddedGraph.get_normal_angle_matrix">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normal_angle_matrix">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_normal_angle_matrix</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">edges_only</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">decimals</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to set the centered coordinates of the vertices in the graph. Warning: This overwrites the original coordinates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_centered_coordinates</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="nb">type</span><span class="p">)</span></div>
+<span class="sd">        Optimized angle matrix computation using vectorized operations.</span>
 
-
-<div class="viewcode-block" id="EmbeddedGraph.get_scaled_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_scaled_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_scaled_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to find the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by ``radius``.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            radius (float):</span>
-<span class="sd">                The radius of the bounding disk.</span>
+<span class="sd">        Args:</span>
+<span class="sd">            edges_only: Only compute angles between connected vertices</span>
+<span class="sd">            decimals: Round angles to specified decimal places</span>
 
 <span class="sd">        Returns:</span>
-<span class="sd">            dict: A dictionary mapping vertices to their scaled coordinates.</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-        <span class="n">max_norm</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
-                       <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span><span class="p">))</span>
-        <span class="n">x_coords</span> <span class="o">=</span> <span class="n">x_coords</span> <span class="o">*</span> <span class="n">radius</span> <span class="o">/</span> <span class="n">max_norm</span>
-        <span class="n">y_coords</span> <span class="o">=</span> <span class="n">y_coords</span> <span class="o">*</span> <span class="n">radius</span> <span class="o">/</span> <span class="n">max_norm</span>
-
-        <span class="k">return</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span><span class="p">)}</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.set_scaled_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.set_scaled_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_scaled_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to set the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by ``radius``. Warning: This overwrites the original coordinates</span>
-
+<span class="sd">            angle_matrix: NaN-filled matrix with pair angles</span>
+<span class="sd">            vertex_labels: Ordered node identifiers</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">coords</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span>
+        <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_scaled_coordinates</span><span class="p">(</span><span class="n">radius</span><span class="p">)</span></div>
+        <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">n</span><span class="p">,</span> <span class="n">n</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
 
+        <span class="k">if</span> <span class="n">edges_only</span><span class="p">:</span>
+            <span class="n">edges</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">()))</span>
+            <span class="k">if</span> <span class="n">edges</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">angle_matrix</span><span class="p">,</span> <span class="n">vertices</span>
 
-<div class="viewcode-block" id="EmbeddedGraph.rescale_to_unit_disk">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.rescale_to_unit_disk">[docs]</a>
-    <span class="k">def</span> <span class="nf">rescale_to_unit_disk</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preserve_center</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s1">&#39;origin&#39;</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Rescales the graph coordinates to fit within a radius 1 disk.</span>
+            <span class="n">u_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="o">.</span><span class="n">get</span><span class="p">)(</span><span class="n">edges</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span>
+            <span class="n">v_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_to_index</span><span class="o">.</span><span class="n">get</span><span class="p">)(</span><span class="n">edges</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
 
-<span class="sd">        Parameters:</span>
-<span class="sd">            preserve_center (bool): If True, maintains the current center point of type ``center_type``.</span>
-<span class="sd">                                    If False, centers the graph at (0, 0).</span>
+            <span class="n">dx</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[</span><span class="n">v_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[</span><span class="n">u_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">dy</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[</span><span class="n">v_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[</span><span class="n">u_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
 
-<span class="sd">        Returns:</span>
-<span class="sd">            self: Returns the instance for method chaining.</span>
+            <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">dx</span><span class="p">,</span> <span class="o">-</span><span class="n">dy</span><span class="p">)</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+            <span class="n">rev_angles</span> <span class="o">=</span> <span class="p">(</span><span class="n">angles</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
 
-<span class="sd">        Raises:</span>
-<span class="sd">            ValueError: If the graph has no coordinates or all coordinates are identical.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Graph has no coordinates to rescale.&quot;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">decimals</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">decimals</span><span class="p">)</span>
+                <span class="n">rev_angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">rev_angles</span><span class="p">,</span> <span class="n">decimals</span><span class="p">)</span>
 
-        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
-        <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+            <span class="n">angle_matrix</span><span class="p">[</span><span class="n">u_indices</span><span class="p">,</span> <span class="n">v_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">angles</span>
+            <span class="n">angle_matrix</span><span class="p">[</span><span class="n">v_indices</span><span class="p">,</span> <span class="n">u_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">rev_angles</span>
 
-        <span class="n">coords_centered</span> <span class="o">=</span> <span class="n">coords</span> <span class="o">-</span> <span class="n">center</span>
-
-        <span class="n">max_distance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">coords_centered</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
 
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">max_distance</span><span class="p">,</span> <span class="mi">0</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;All coordinates are identical. Cannot rescale.&quot;</span><span class="p">)</span>
+            <span class="c1"># compute all pairwise differences</span>
+            <span class="n">dx</span> <span class="o">=</span> <span class="n">x</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">-</span> <span class="n">x</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
+            <span class="n">dy</span> <span class="o">=</span> <span class="n">y</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">-</span> <span class="n">y</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span>
 
-        <span class="n">scale_factor</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">max_distance</span>
+            <span class="c1"># Compute angles and mask invalid pairs</span>
+            <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">dx</span><span class="p">,</span> <span class="o">-</span><span class="n">dy</span><span class="p">)</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+            <span class="n">angle_matrix</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">dx</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">dy</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>  <span class="c1"># Zero vectors</span>
 
-        <span class="n">new_coords</span> <span class="o">=</span> <span class="p">(</span><span class="n">coords_centered</span> <span class="o">*</span> <span class="n">scale_factor</span><span class="p">)</span> <span class="o">+</span> \
-            <span class="p">(</span><span class="n">center</span> <span class="k">if</span> <span class="n">preserve_center</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">decimals</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">angle_matrix</span><span class="p">,</span> <span class="n">decimals</span><span class="p">)</span>
 
-        <span class="k">for</span> <span class="n">vertex</span><span class="p">,</span> <span class="n">new_coord</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">new_coords</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">vertex</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">new_coord</span><span class="p">)</span>
+            <span class="c1"># mask diagonal since we don&#39;t want</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">angle_matrix</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="bp">self</span></div>
+        <span class="k">return</span> <span class="n">angle_matrix</span><span class="p">,</span> <span class="n">vertices</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedGraph.get_PCA_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_PCA_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_PCA_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<div class="viewcode-block" id="EmbeddedGraph.get_normal_angles">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normal_angles">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_normal_angles</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">edges_only</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">decimals</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">6</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to find the PCA coordinates of the vertices in the graph.</span>
+<span class="sd">        Optimized angle dictionary construction using NumPy grouping.</span>
 
-<span class="sd">        Returns:</span>
-<span class="sd">            dict: A dictionary mapping vertices to their PCA normalized coordinates.</span>
+<span class="sd">        Args:</span>
+<span class="sd">            edges_only: Only include edge-connected pairs</span>
+<span class="sd">            decimals: Round angles to specified decimal places</span>
 
+<span class="sd">        Returns:</span>
+<span class="sd">            Dictionary mapping rounded angles to vertex pairs</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">angle_matrix</span><span class="p">,</span> <span class="n">vertices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_angle_matrix</span><span class="p">(</span><span class="n">edges_only</span><span class="p">,</span> <span class="n">decimals</span><span class="p">)</span>
+        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+
+        <span class="c1"># Extract upper triangle indices</span>
+        <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">triu_indices</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">angles</span> <span class="o">=</span> <span class="n">angle_matrix</span><span class="p">[</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">]</span>
+        <span class="n">valid_mask</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">angles</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">valid_mask</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+            <span class="k">return</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+
+        <span class="c1"># Filter valid pairs</span>
+        <span class="n">valid_rows</span> <span class="o">=</span> <span class="n">rows</span><span class="p">[</span><span class="n">valid_mask</span><span class="p">]</span>
+        <span class="n">valid_cols</span> <span class="o">=</span> <span class="n">cols</span><span class="p">[</span><span class="n">valid_mask</span><span class="p">]</span>
+        <span class="n">valid_angles</span> <span class="o">=</span> <span class="n">angles</span><span class="p">[</span><span class="n">valid_mask</span><span class="p">]</span>
+
+        <span class="c1"># Group pairs by rounded angle</span>
+        <span class="n">angle_dict</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+        <span class="n">unique_angles</span><span class="p">,</span> <span class="n">inverse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">valid_angles</span><span class="p">,</span> <span class="n">return_inverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">angle</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unique_angles</span><span class="p">):</span>
+            <span class="n">mask</span> <span class="o">=</span> <span class="n">inverse</span> <span class="o">==</span> <span class="n">idx</span>
+            <span class="n">pairs</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="p">(</span><span class="n">vertices</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">vertices</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">valid_rows</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span> <span class="n">valid_cols</span><span class="p">[</span><span class="n">mask</span><span class="p">])</span>
+            <span class="p">]</span>
+            <span class="n">angle_dict</span><span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">angle</span><span class="p">)]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">pairs</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-        <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">x_coords</span><span class="p">,</span> <span class="n">y_coords</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-
-        <span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># initiate PCA</span>
-        <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>  <span class="c1"># fit PCA to coordinates to find longest axis</span>
-        <span class="n">pca_scores</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>  <span class="c1"># retrieve PCA coordinates</span>
+        <span class="k">return</span> <span class="n">angle_dict</span></div>
 
-        <span class="n">nodes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="p">{</span><span class="n">nodes</span><span class="p">[</span><span class="n">i</span><span class="p">]:</span> <span class="n">pca_scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)}</span>
 
-        <span class="k">return</span> <span class="n">out</span></div>
+    <span class="c1"># ============================</span>
+    <span class="c1"># Coordinate transformations</span>
+    <span class="c1"># ============================</span>
 
+<div class="viewcode-block" id="EmbeddedGraph.transform_coordinates">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.transform_coordinates">[docs]</a>
+    <span class="k">def</span> <span class="nf">transform_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s2">&quot;bounding_box&quot;</span><span class="p">,</span> <span class="n">projection_type</span><span class="o">=</span><span class="s2">&quot;pca&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform coordinates center and orientation&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">projection_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">TRANSFORM_TYPES</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown transform type: </span><span class="si">{</span><span class="n">projection_type</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">project_coordinates</span><span class="p">(</span><span class="n">projection_type</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">center_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">CENTER_TYPES</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown center method: </span><span class="si">{</span><span class="n">center_type</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">center_coordinates</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="EmbeddedGraph.set_PCA_coordinates">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.set_PCA_coordinates">[docs]</a>
-    <span class="k">def</span> <span class="nf">set_PCA_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scale_radius</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Method to set the PCA coordinates of the vertices in the graph which is helpful for coarse alignment. </span>
-<span class="sd">        If you also want to center at zero, the options for ``center_type`` are ``mean`` or ``min_max``.</span>
-<span class="sd">        Set ``scale_radius`` to a value to scale to a specific radius.</span>
-<span class="sd">        Warning: This overwrites the original coordinates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_PCA_coordinates</span><span class="p">()</span>
 
-        <span class="k">if</span> <span class="n">center_type</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">set_centered_coordinates</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">scale_radius</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">set_scaled_coordinates</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="n">scale_radius</span><span class="p">)</span></div>
+<div class="viewcode-block" id="EmbeddedGraph.center_coordinates">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.center_coordinates">[docs]</a>
+    <span class="k">def</span> <span class="nf">center_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">center_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">CENTER_TYPES</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown center method: </span><span class="si">{</span><span class="n">center_type</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
+        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">-=</span> <span class="n">center</span></div>
 
-    <span class="c1"># ================</span>
-    <span class="c1"># Functions for computing the function g(v) for vertices and edges</span>
-    <span class="c1"># ================</span>
 
-<div class="viewcode-block" id="EmbeddedGraph.g_omega">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.g_omega">[docs]</a>
-    <span class="k">def</span> <span class="nf">g_omega</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to compute the function :math:`g_\\omega(v)` for all vertices :math:`v` in the graph in the direction of :math:`\\theta \\in [0,2\\pi]` . This function is defined by :math:`g_\\omega(v) = \\langle \\texttt{pos}(v), \\omega \\rangle` .</span>
+<div class="viewcode-block" id="EmbeddedGraph.scale_coordinates">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.scale_coordinates">[docs]</a>
+    <span class="k">def</span> <span class="nf">scale_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Scale coordinates to fit within given radius&quot;&quot;&quot;</span>
+        <span class="n">current_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">current_max</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">*=</span> <span class="n">radius</span> <span class="o">/</span> <span class="n">current_max</span></div>
 
-<span class="sd">        Parameters:</span>
 
-<span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2\\pi]` for the direction to compute the :math:`g(v)` values.</span>
+<div class="viewcode-block" id="EmbeddedGraph.project_coordinates">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.project_coordinates">[docs]</a>
+    <span class="k">def</span> <span class="nf">project_coordinates</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">projection_type</span><span class="o">=</span><span class="s2">&quot;pca&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Project coordinates using a function&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">projection_type</span> <span class="o">==</span> <span class="s2">&quot;pca&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pca_projection</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown projection type: </span><span class="si">{</span><span class="n">projection_type</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
 
-<span class="sd">        Returns:</span>
 
-<span class="sd">            dict: A dictionary mapping vertices to their :math:`g(v)` values.</span>
+<div class="viewcode-block" id="EmbeddedGraph.pca_projection">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.pca_projection">[docs]</a>
+    <span class="k">def</span> <span class="nf">pca_projection</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_dim</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Dimensionality reduction using PCA&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">&lt;=</span> <span class="n">target_dim</span><span class="p">:</span>
+            <span class="k">return</span>
 
-<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">target_dim</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">target_dim</span></div>
 
-        <span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
 
-        <span class="n">g</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
-            <span class="n">g</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">v</span><span class="p">],</span> <span class="n">omega</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">g</span></div>
+<div class="viewcode-block" id="EmbeddedGraph.add_edge">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.add_edge">[docs]</a>
+    <span class="nd">@_validate_node</span><span class="p">(</span><span class="n">exists</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">add_edge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_id1</span><span class="p">,</span> <span class="n">node_id2</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add an edge between two nodes&quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">node_id1</span><span class="p">,</span> <span class="n">node_id2</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedGraph.g_omega_edges">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.g_omega_edges">[docs]</a>
-    <span class="k">def</span> <span class="nf">g_omega_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Calculates the function value of the edges of the graph by making the value equal to the max vertex value </span>
+    <span class="c1"># ===================</span>
+    <span class="c1"># Visualization</span>
+    <span class="c1"># ===================</span>
+<div class="viewcode-block" id="EmbeddedGraph.validate_plot_parameters">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.validate_plot_parameters">[docs]</a>
+    <span class="k">def</span> <span class="nf">validate_plot_parameters</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Decorator to validate plot method parameters&quot;&quot;&quot;</span>
 
-<span class="sd">        Parameters:</span>
+        <span class="k">def</span> <span class="nf">wrapper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+            <span class="n">bounding_center_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span>
+                <span class="s2">&quot;bounding_center_type&quot;</span><span class="p">,</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">)</span>
 
-<span class="sd">            theta (float): </span>
-<span class="sd">                The direction of the function to be calculated.</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;At least 2D or 3D coordinates required for plotting&quot;</span><span class="p">)</span>
 
-<span class="sd">        Returns:</span>
-<span class="sd">            dict</span>
-<span class="sd">                A dictionary of the function values of the edges.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">g</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">bounding_center_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">CENTER_TYPES</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Invalid center type: </span><span class="si">{</span><span class="n">bounding_center_type</span><span class="si">}</span><span class="s2">. &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;Valid options: </span><span class="si">{</span><span class="n">CENTER_TYPES</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
 
-        <span class="n">g_edges</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">:</span>
-            <span class="n">g_edges</span><span class="p">[</span><span class="n">e</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">g</span><span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">g</span><span class="p">[</span><span class="n">e</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
+            <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">g_edges</span></div>
+        <span class="k">return</span> <span class="n">wrapper</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedGraph.sort_vertices">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.sort_vertices">[docs]</a>
-    <span class="k">def</span> <span class="nf">sort_vertices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="EmbeddedGraph.plot">
+<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.plot">[docs]</a>
+    <span class="nd">@validate_plot_parameters</span>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">bounding_circle</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">bounding_center_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bounding_box&quot;</span><span class="p">,</span>
+        <span class="n">color_nodes_theta</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">ax</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">with_labels</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">node_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">300</span><span class="p">,</span>
+        <span class="n">edge_color</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gray&quot;</span><span class="p">,</span>
+        <span class="n">elev</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">25</span><span class="p">,</span>
+        <span class="n">azim</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mi">60</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to sort the vertices of the graph according to the function `g_omega(v)` in the direction of :math:`\\theta \\in [0,2\\pi]`.</span>
-
-<span class="sd">        TODO: eventually, do we want this to return a sorted list of g values as well? Since we&#39;re already doing the sorting work, it might be helpful.</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2 \\pi]` for the direction to sort the vertices.</span>
-<span class="sd">            return_g (bool):</span>
-<span class="sd">                Whether to return the :math:`g(v)` values along with the sorted vertices.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            list</span>
-<span class="sd">                A list of vertices sorted in increasing order of the :math:`g(v)` values. </span>
-<span class="sd">                If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. </span>
-
+<span class="sd">        Visualize the embedded graph in 2D or 3D</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">g</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
 
-        <span class="n">v_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">v</span><span class="p">:</span> <span class="n">g</span><span class="p">[</span><span class="n">v</span><span class="p">])</span>
+        <span class="n">pos</span> <span class="o">=</span> <span class="p">{</span><span class="n">node</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+               <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_node_list</span><span class="p">)}</span>
 
-        <span class="k">if</span> <span class="n">return_g</span><span class="p">:</span>
-            <span class="c1"># g_sorted = [g[v] for v in v_list]</span>
-            <span class="k">return</span> <span class="n">v_list</span><span class="p">,</span> <span class="n">g</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_draw_2d</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">with_labels</span><span class="p">,</span>
+                          <span class="n">node_size</span><span class="p">,</span> <span class="n">edge_color</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">v_list</span></div>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_draw_3d</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="p">,</span> <span class="n">edge_color</span><span class="p">,</span> <span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
+        <span class="k">if</span> <span class="n">color_nodes_theta</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="c1"># calculate directional projection values</span>
+            <span class="n">direction</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">color_nodes_theta</span><span class="p">),</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">color_nodes_theta</span><span class="p">)]</span>
+            <span class="p">)</span>
+            <span class="n">node_colors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coord_matrix</span><span class="p">,</span> <span class="n">direction</span><span class="p">)</span>
 
-<div class="viewcode-block" id="EmbeddedGraph.sort_edges">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.sort_edges">[docs]</a>
-    <span class="k">def</span> <span class="nf">sort_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">return_g</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to sort the edges of the graph according to the function</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_add_node_coloring</span><span class="p">(</span>
+                <span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_colors</span><span class="p">,</span> <span class="n">node_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
-<span class="sd">        .. math ::</span>
-
-<span class="sd">            g_\\omega(e) = \\max \\{ g_\\omega(v) \\mid  v \in e \\}</span>
-
-<span class="sd">        in the direction of :math:`\\theta \\in [0,2\\pi]` .</span>
-
-<span class="sd">        Parameters:</span>
-<span class="sd">            theta (float):</span>
-<span class="sd">                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.</span>
-<span class="sd">            return_g (bool):</span>
-<span class="sd">                Whether to return the :math:`g(v)` values along with the sorted edges.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            A list of edges sorted in increasing order of the :math:`g(v)` values. </span>
-<span class="sd">            If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. </span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">g_e</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_omega_edges</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">bounding_circle</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_add_bounding_shape</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">bounding_center_type</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
 
-        <span class="n">e_list</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="n">g_e</span><span class="p">[</span><span class="n">e</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_configure_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">return_g</span><span class="p">:</span>
-            <span class="c1"># g_sorted = [g[v] for v in v_list]</span>
-            <span class="k">return</span> <span class="n">e_list</span><span class="p">,</span> <span class="n">g_e</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">e_list</span></div>
+        <span class="k">return</span> <span class="n">ax</span></div>
 
 
-<div class="viewcode-block" id="EmbeddedGraph.lower_edges">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.lower_edges">[docs]</a>
-    <span class="k">def</span> <span class="nf">lower_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">omega</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to compute the number of lower edges of a vertex `v` for a specific direction (included by the use of sorted `v_list`).</span>
+    <span class="k">def</span> <span class="nf">_create_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create appropriate axes if not provided</span>
 
 <span class="sd">        Parameters:</span>
-<span class="sd">            v (str):</span>
-<span class="sd">                The vertex to compute the number of lower edges for.</span>
-<span class="sd">            omega (tuple): </span>
-<span class="sd">                The direction vector to consider given as an angle in [0, 2pi].</span>
+<span class="sd">            ax (matplotlib.axes.Axes, optional):</span>
+<span class="sd">                The axes to use. If None, creates new axes</span>
+<span class="sd">            dim (int, optional):</span>
+<span class="sd">                Dimension of the plot. If None, uses self.dim</span>
 
 <span class="sd">        Returns:</span>
-<span class="sd">            int: The number of lower edges of the vertex v.</span>
-
+<span class="sd">            matplotlib.axes.Axes:</span>
+<span class="sd">                The configured axes object</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">L</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">neighbors</span><span class="p">(</span><span class="n">v</span><span class="p">)]</span>
-        <span class="n">gv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">v</span><span class="p">],</span> <span class="n">omega</span><span class="p">)</span>
-        <span class="n">Lg</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">[</span><span class="n">v</span><span class="p">],</span> <span class="n">omega</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">L</span><span class="p">]</span>
-        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">n</span> <span class="o">&gt;=</span> <span class="n">gv</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">Lg</span><span class="p">)</span>  <span class="c1"># includes possible duplicate counts</span></div>
-
-
-    <span class="c1"># ================</span>
-    <span class="c1"># Functions for getting the angles where vertices switch order</span>
-    <span class="c1"># ================</span>
+        <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span>
 
-<div class="viewcode-block" id="EmbeddedGraph.get_all_normals_matrix">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_all_normals_matrix">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_all_normals_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to get all angles of normals to any line between vertices in the graph, returned as a matrix. Note this includes both adjacent vertices and non-adjacent. This function is useful for knowing the angle of the circle where two vertices switch in order. </span>
+        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s2">&quot;3d&quot;</span> <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">3</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="s2">&quot;zaxis&quot;</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;For 3D plots, provide axes with 3D projection&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span>
+
+    <span class="k">def</span> <span class="nf">_draw_2d</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">with_labels</span><span class="p">,</span> <span class="n">node_size</span><span class="p">,</span> <span class="n">edge_color</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;2D visualization components&quot;&quot;&quot;</span>
+        <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edges</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">edge_color</span><span class="o">=</span><span class="n">edge_color</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+        <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span>
+            <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
+            <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+            <span class="n">node_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
+            <span class="n">node_color</span><span class="o">=</span><span class="s2">&quot;lightblue&quot;</span><span class="p">,</span>
+            <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+            <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">with_labels</span><span class="p">:</span>
+            <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_labels</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">font_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">font_color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_draw_3d</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_size</span><span class="p">,</span> <span class="n">edge_color</span><span class="p">,</span> <span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;3D visualization components&quot;&quot;&quot;</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">view_init</span><span class="p">(</span><span class="n">elev</span><span class="o">=</span><span class="n">elev</span><span class="p">,</span> <span class="n">azim</span><span class="o">=</span><span class="n">azim</span><span class="p">)</span>
+
+        <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">pos</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span>
+            <span class="n">coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+            <span class="n">coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+            <span class="n">coords</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span>
+            <span class="n">s</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
+            <span class="n">c</span><span class="o">=</span><span class="s2">&quot;lightblue&quot;</span><span class="p">,</span>
+            <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+            <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">u</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">edges</span><span class="p">():</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">pos</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">pos</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">pos</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">pos</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">1</span><span class="p">]]</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="p">[</span><span class="n">pos</span><span class="p">[</span><span class="n">u</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">pos</span><span class="p">[</span><span class="n">v</span><span class="p">][</span><span class="mi">2</span><span class="p">]]</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot3D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">edge_color</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_add_node_coloring</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_colors</span><span class="p">,</span> <span class="n">node_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add node coloring based on provided values</span>
 
 <span class="sd">        Parameters:</span>
-<span class="sd">            num_rounding_digits (int):</span>
-<span class="sd">                The number of digits to round the angles in the matrix. If `None`, no rounding is done. </span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            A tuple consissting of a matrix of angles, and the sorted label list for the rows/columns.</span>
+<span class="sd">            ax (matplotlib.axes.Axes):</span>
+<span class="sd">                The axes to add coloring to</span>
+<span class="sd">            pos (dict):</span>
+<span class="sd">                Dictionary of node positions</span>
+<span class="sd">            node_colors (array-like):</span>
+<span class="sd">                Values to use for coloring nodes</span>
+<span class="sd">            node_size (int):</span>
+<span class="sd">                Size of nodes in visualization</span>
+<span class="sd">            dim (int, optional):</span>
+<span class="sd">                Dimension of the plot. If None, uses self.dim</span>
+<span class="sd">            **kwargs:</span>
+<span class="sd">                Additional keyword arguments for plotting</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-        <span class="n">labels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-        <span class="c1"># Make rows of repeated copies of first column of P</span>
-        <span class="n">X_Cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">P</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">X_diff</span> <span class="o">=</span> <span class="n">X_Cols</span> <span class="o">-</span> <span class="n">X_Cols</span><span class="o">.</span><span class="n">T</span>  <span class="c1"># The x value of the vector from A to B</span>
-
-        <span class="c1"># Make rows of repeated copies of second column of P</span>
-        <span class="n">Y_Cols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">P</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">Y_diff</span> <span class="o">=</span> <span class="n">Y_Cols</span> <span class="o">-</span> <span class="n">Y_Cols</span><span class="o">.</span><span class="n">T</span>
-
-        <span class="c1"># Convert to float to allow NaN assignment</span>
-        <span class="n">X_diff</span> <span class="o">=</span> <span class="n">X_diff</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">Y_diff</span> <span class="o">=</span> <span class="n">Y_diff</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-
-        <span class="c1"># Set diagonals to nan</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">X_diff</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">fill_diagonal</span><span class="p">(</span><span class="n">Y_diff</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-
-        <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">X_diff</span><span class="p">,</span> <span class="o">-</span><span class="n">Y_diff</span><span class="p">)</span>
-        <span class="c1"># Puts all entries between 0 and 2pi</span>
-        <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">angle_matrix</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">num_rounding_digits</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">angle_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">angle_matrix</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">angle_matrix</span><span class="p">,</span> <span class="n">labels</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.get_normals_dict">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normals_dict">[docs]</a>
-    <span class="k">def</span> <span class="nf">get_normals_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                         <span class="n">num_rounding_digits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                         <span class="n">edges_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                         <span class="n">opposites</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to get all angles of normals to any line between vertices in the graph, returned as a dictionary of angles with ``dict[theta]`` returning the list of pairs of vertices with vector normal to :math:`\\overrightarrow{AB}` at angle ``theta``. Note this includes both adjacent vertices and non-adjacent unless ``edges_only`` is set to ``True``. This function is useful for knowing the angle of the circle where two vertices switch in order, especially when we want to sort the events around the circle. All angles are rounded to the number of digits given by ``num_rounding_digits``, and are returned in the range :math:`[0, 2\\pi]` .</span>
+        <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span>
+
+        <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">nodes</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span>
+                <span class="bp">self</span><span class="p">,</span>
+                <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span>
+                <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+                <span class="n">node_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
+                <span class="n">node_color</span><span class="o">=</span><span class="n">node_colors</span><span class="p">,</span>
+                <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">,</span>
+                <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+                <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">pos</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
+            <span class="n">nodes</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">scatter3D</span><span class="p">(</span>
+                <span class="n">coords</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+                <span class="n">coords</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span>
+                <span class="n">coords</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">],</span>
+                <span class="n">s</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
+                <span class="n">c</span><span class="o">=</span><span class="n">node_colors</span><span class="p">,</span>
+                <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">,</span>
+                <span class="n">edgecolors</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+                <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="n">norm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">node_colors</span><span class="p">),</span> <span class="n">vmax</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">node_colors</span><span class="p">))</span>
+        <span class="n">sm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span><span class="p">)</span>
+        <span class="n">sm</span><span class="o">.</span><span class="n">set_array</span><span class="p">([])</span>
+        <span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">sm</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;vertical&quot;</span><span class="p">,</span> <span class="n">shrink</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+        <span class="n">cbar</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s2">&quot;Node Values&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_add_bounding_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s2">&quot;bounding_box&quot;</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Add bounding circle/sphere visualization</span>
 
 <span class="sd">        Parameters:</span>
-<span class="sd">            num_rounding_digits (int):</span>
-<span class="sd">                The number of digits to round the angles in the dictionary. If None, no rounding is done.</span>
-<span class="sd">            edges_only (bool):</span>
-<span class="sd">                If True, the dictionary version only returns the angle of the normals to the lines between vertices sharing an edge. The matrix version is unchanged.</span>
-<span class="sd">            opposites (bool):</span>
-<span class="sd">                If True, will also include both :math:`\\theta` and :math:`\\theta + \\pi` in the dictionary keys.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            dict: A dictionary of angles with `dict[theta]` returning the list of pairs of vertices with vector normal to :math:`\\overrightarrow\\{AB\\}` at angle `theta`, e.g. ``dict[theta] = [(&#39;A&#39;,&#39;B&#39;), (&#39;C&#39;, &#39;D&#39;)]``.</span>
+<span class="sd">            ax (matplotlib.axes.Axes):</span>
+<span class="sd">                The axes to add the bounding shape to</span>
+<span class="sd">            center_type (str, optional):</span>
+<span class="sd">                Method to compute center (&quot;mean&quot;, &quot;bounding_box&quot;, or &quot;origin&quot;)</span>
+<span class="sd">            dim (int, optional):</span>
+<span class="sd">                Dimension of the plot. If None, uses self.dim</span>
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span>
 
-        <span class="n">angle_matrix</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_normals_matrix</span><span class="p">(</span>
-            <span class="n">num_rounding_digits</span><span class="o">=</span><span class="n">num_rounding_digits</span><span class="p">)</span>
-
-        <span class="n">angle_dict</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)):</span>
-                <span class="c1"># Skip this edge if edges_only is True and the vertices are not connected</span>
-                <span class="k">if</span> <span class="n">edges_only</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_edge</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">j</span><span class="p">]):</span>
-                    <span class="k">continue</span>
-
-                <span class="k">if</span> <span class="n">angle_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="ow">in</span> <span class="n">angle_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-                    <span class="n">angle_dict</span><span class="p">[</span><span class="n">angle_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                        <span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">j</span><span class="p">]))</span>
-                <span class="k">elif</span> <span class="n">angle_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span> <span class="ow">in</span> <span class="n">angle_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-                    <span class="n">angle_dict</span><span class="p">[</span><span class="n">angle_matrix</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                        <span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">angle_dict</span><span class="p">[</span><span class="n">angle_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>
-                        <span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">j</span><span class="p">])]</span>
-
-        <span class="k">if</span> <span class="n">opposites</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">angle_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
-                <span class="n">other_key</span> <span class="o">=</span> <span class="n">key</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">num_rounding_digits</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">other_key</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">other_key</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="p">)</span>
-                <span class="n">angle_dict</span><span class="p">[</span><span class="n">other_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">angle_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-
-        <span class="c1"># Make sure all keys are in the range [0, 2pi]</span>
-        <span class="n">angle_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">):</span> <span class="n">value</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span>
-                      <span class="n">value</span> <span class="ow">in</span> <span class="n">angle_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="c1"># Make sure all keys are rounded to the correct number of digits</span>
-        <span class="k">if</span> <span class="n">num_rounding_digits</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">angle_dict</span> <span class="o">=</span> <span class="p">{</span><span class="nb">round</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="p">)</span>                          <span class="p">:</span> <span class="n">value</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">angle_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="k">return</span> <span class="n">angle_dict</span></div>
-
-
-    <span class="c1"># ================</span>
-    <span class="c1"># Plotting functions</span>
-    <span class="c1"># ================</span>
-<div class="viewcode-block" id="EmbeddedGraph.plot">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.plot">[docs]</a>
-    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-             <span class="n">bounding_circle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-             <span class="n">color_nodes_theta</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-             <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-             <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-             <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the graph with the embedded coordinates.</span>
-
-<span class="sd">        If ``bounding_circle`` is True, a bounding circle is drawn around the graph.</span>
-
-<span class="sd">        If ``color_nodes_theta`` is not ``None``, it should be given as a :math:`theta` in :math:`[0,2\\pi]`. Then the nodes are colored according to the :math:`g(v)` values in the direction of :math:`\\theta`.</span>
-
-<span class="sd">        If ``with_labels`` is ``True``, the nodes are labeled with their names.</span>
-
-<span class="sd">        If ``ax`` is not ``None``, the plot is drawn on the given axis.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+        <span class="n">center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
+        <span class="n">radius</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">circle</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Circle</span><span class="p">(</span>
+                <span class="n">center</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span>
+                <span class="n">radius</span><span class="p">,</span>
+                <span class="n">fill</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+                <span class="n">color</span><span class="o">=</span><span class="s2">&quot;darkred&quot;</span><span class="p">,</span>
+                <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.2</span><span class="p">,</span>
+                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">circle</span><span class="p">)</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="mf">0.1</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">radius</span> <span class="o">-</span> <span class="n">padding</span><span class="p">,</span>
+                        <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">radius</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">radius</span> <span class="o">-</span> <span class="n">padding</span><span class="p">,</span>
+                        <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">radius</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
-
-        <span class="n">pos</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span>
-        <span class="c1"># center = self.get_center(type = &#39;min_max&#39;)</span>
-        <span class="c1"># r = self.get_bounding_radius(type = &#39;min_max&#39;)</span>
-
-        <span class="k">if</span> <span class="n">color_nodes_theta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="n">with_labels</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="c1"># sphere wireframe</span>
+            <span class="n">u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+            <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">u</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">v</span><span class="p">))</span> <span class="o">+</span> <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">u</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">v</span><span class="p">))</span> <span class="o">+</span> <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">u</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">v</span><span class="p">))</span> <span class="o">+</span> <span class="n">center</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot_wireframe</span><span class="p">(</span>
+                <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;darkred&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">rstride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">cstride</span><span class="o">=</span><span class="mi">2</span>
+            <span class="p">)</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="mf">0.1</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim3d</span><span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">radius</span> <span class="o">-</span> <span class="n">padding</span><span class="p">,</span>
+                          <span class="n">center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">radius</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim3d</span><span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">radius</span> <span class="o">-</span> <span class="n">padding</span><span class="p">,</span>
+                          <span class="n">center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">radius</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_zlim3d</span><span class="p">(</span><span class="n">center</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">radius</span> <span class="o">-</span> <span class="n">padding</span><span class="p">,</span>
+                          <span class="n">center</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">radius</span> <span class="o">+</span> <span class="n">padding</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_configure_axes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Finalize plot appearance&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="s2">&quot;zaxis&quot;</span><span class="p">):</span>
+            <span class="c1"># 3D plot configuration</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_pane_color</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_pane_color</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">zaxis</span><span class="o">.</span><span class="n">set_pane_color</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">))</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;X&quot;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Y&quot;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s2">&quot;Z&quot;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">g</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">color_nodes_theta</span><span class="p">)</span>
-            <span class="n">color_map</span> <span class="o">=</span> <span class="p">[</span><span class="n">g</span><span class="p">[</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="p">]</span>
-            <span class="c1"># Some weird plotting to make the colorbar work.</span>
-            <span class="n">pathcollection</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_nodes</span><span class="p">(</span>
-                <span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">node_color</span><span class="o">=</span><span class="n">color_map</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-            <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">font_color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-            <span class="n">nx</span><span class="o">.</span><span class="n">draw_networkx_edges</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pos</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-            <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pathcollection</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-        <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;on&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                       <span class="n">labelleft</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">labelbottom</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="c1"># 2D plot configuration</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;:&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
 
-        <span class="k">if</span> <span class="n">bounding_circle</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">plot_bounding_circle</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+            <span class="n">xlim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_xlim</span><span class="p">()</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
 
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">,</span> <span class="s1">&#39;box&#39;</span><span class="p">)</span>
+            <span class="n">x_interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nice_interval</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">y_interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_nice_interval</span><span class="p">(</span><span class="n">ylim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">ylim</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 
-        <span class="k">return</span> <span class="n">ax</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.plot_bounding_circle">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.plot_bounding_circle">[docs]</a>
-    <span class="k">def</span> <span class="nf">plot_bounding_circle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bounding_center_type</span><span class="o">=</span><span class="s1">&#39;origin&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the bounding circle of the graph. </span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="n">x_interval</span><span class="p">))</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="n">y_interval</span><span class="p">))</span>
 
-<span class="sd">        If ``ax`` is not None, the plot is drawn on the given axis.</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="n">x_interval</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">plt</span><span class="o">.</span><span class="n">MultipleLocator</span><span class="p">(</span><span class="n">y_interval</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
 
-<span class="sd">        If ``bounding_center_type`` is &#39;origin&#39;, the bounding circle is centered at the origin. If it is ``min_max``, the bounding circle is centered at the mean of the min and max values of the :math:`x` and :math`y` coordinates.</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span>
+            <span class="n">axis</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span>
+            <span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span>
+            <span class="n">bottom</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">labelbottom</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">labelleft</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="p">)</span>
 
+    <span class="k">def</span> <span class="nf">_get_nice_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">range_size</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Calculate a nice interval for tick spacing</span>
 
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
-
-        <span class="n">circle_center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="n">bounding_center_type</span><span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="n">bounding_center_type</span><span class="p">)</span>
-        <span class="n">circle</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Circle</span><span class="p">(</span><span class="n">circle_center</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                            <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">circle</span><span class="p">)</span>
-
-        <span class="c1"># Always adjust the plot limits to show the full graph</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">,</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">,</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">r</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="EmbeddedGraph.plot_angle_circle">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph.plot_angle_circle">[docs]</a>
-    <span class="k">def</span> <span class="nf">plot_angle_circle</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">edges_only</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Function to plot the circle of angles for the graph. </span>
-
-<span class="sd">        Example Usage: </span>
-
-<span class="sd">        .. code-block:: python</span>
-
-<span class="sd">            fig, ax = plt.subplots()</span>
-<span class="sd">            G.plot(ax = ax)</span>
-<span class="sd">            G.plot_angle_circle(ax = ax)</span>
+<span class="sd">        Args:</span>
+<span class="sd">            range_size: Size of the axis range</span>
 
+<span class="sd">        Returns:</span>
+<span class="sd">            float: Nice interval value for tick spacing</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+        <span class="c1"># Calculate rough interval size (aim for ~5 major ticks)</span>
+        <span class="n">rough_interval</span> <span class="o">=</span> <span class="n">range_size</span> <span class="o">/</span> <span class="mi">5</span>
+
+        <span class="c1"># Get magnitude</span>
+        <span class="n">magnitude</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">rough_interval</span><span class="p">))</span>
+
+        <span class="c1"># Normalize rough interval to between 1 and 10</span>
+        <span class="n">normalized</span> <span class="o">=</span> <span class="n">rough_interval</span> <span class="o">/</span> <span class="n">magnitude</span>
+
+        <span class="c1"># Choose nice interval</span>
+        <span class="k">if</span> <span class="n">normalized</span> <span class="o">&lt;</span> <span class="mf">1.5</span><span class="p">:</span>
+            <span class="n">nice_interval</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">elif</span> <span class="n">normalized</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">nice_interval</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="k">elif</span> <span class="n">normalized</span> <span class="o">&lt;</span> <span class="mi">7</span><span class="p">:</span>
+            <span class="n">nice_interval</span> <span class="o">=</span> <span class="mi">5</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">fig</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_figure</span><span class="p">()</span>
-
-        <span class="n">circle_center</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_center</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="mf">1.3</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">)</span>
-        <span class="n">circle</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Circle</span><span class="p">(</span><span class="n">circle_center</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                            <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">add_patch</span><span class="p">(</span><span class="n">circle</span><span class="p">)</span>
-
-        <span class="c1"># Get all the angles with the labels to be drawn</span>
-        <span class="n">angles_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_normals_dict</span><span class="p">(</span>
-            <span class="n">edges_only</span><span class="o">=</span><span class="n">edges_only</span><span class="p">,</span> <span class="n">opposites</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">angles_dict_labels</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-            <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}{</span><span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">value</span><span class="p">])</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">angles_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-
-        <span class="c1"># Draw hash marks on the circle</span>
-        <span class="n">hash_length</span> <span class="o">=</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">r</span>  <span class="c1"># Length of the hash marks</span>
-        <span class="k">for</span> <span class="n">angle</span> <span class="ow">in</span> <span class="n">angles_dict_labels</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="n">x_start</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span><span class="o">-</span><span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">y_start</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span><span class="o">-</span><span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">x_end</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">y_end</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x_start</span><span class="p">,</span> <span class="n">x_end</span><span class="p">],</span> <span class="p">[</span><span class="n">y_start</span><span class="p">,</span> <span class="n">y_end</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">)</span>
-
-            <span class="c1"># Add labels near the hash marks</span>
-            <span class="n">scaling</span> <span class="o">=</span> <span class="mi">3</span>
-            <span class="n">label_x</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="n">scaling</span> <span class="o">*</span> <span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">label_y</span> <span class="o">=</span> <span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> \
-                <span class="p">(</span><span class="n">r</span> <span class="o">+</span> <span class="n">scaling</span> <span class="o">*</span> <span class="n">hash_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-            <span class="n">text_angle</span> <span class="o">=</span> <span class="n">angle</span> <span class="k">if</span> <span class="n">angle</span> <span class="o">&lt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span> <span class="ow">or</span> <span class="n">angle</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span> <span class="k">else</span> <span class="n">angle</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">label_x</span><span class="p">,</span> <span class="n">label_y</span><span class="p">,</span>
-                    <span class="n">angles_dict_labels</span><span class="p">[</span><span class="n">angle</span><span class="p">],</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">degrees</span><span class="p">(</span><span class="n">text_angle</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
-        <span class="c1"># Always adjust the plot limits to show the full graph</span>
-        <span class="n">scale_factor</span> <span class="o">=</span> <span class="mf">1.5</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">scale_factor</span><span class="o">*</span><span class="n">r</span><span class="p">,</span>
-                    <span class="n">circle_center</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale_factor</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">scale_factor</span><span class="o">*</span><span class="n">r</span><span class="p">,</span>
-                    <span class="n">circle_center</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale_factor</span><span class="o">*</span><span class="n">r</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="create_example_graph">
-<a class="viewcode-back" href="../../embed_graph.html#ect.embed_graph.create_example_graph">[docs]</a>
-<span class="k">def</span> <span class="nf">create_example_graph</span><span class="p">(</span><span class="n">centered</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">center_type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Function to create an example ``EmbeddedGraph`` object. Helpful for testing. If ``centered`` is True, the coordinates are centered using the center type given by ``center_type``, either ``mean`` or ``min_max``.</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        EmbeddedGraph: An example ``EmbeddedGraph`` object.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
-
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;F&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">centered</span><span class="p">:</span>
-        <span class="n">graph</span><span class="o">.</span><span class="n">set_centered_coordinates</span><span class="p">(</span><span class="n">center_type</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">graph</span></div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
-    <span class="c1"># Example usage of the EmbeddedGraph class</span>
-
-    <span class="c1"># Create an instance of the EmbeddedGraph class</span>
-    <span class="n">graph</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
-
-    <span class="c1"># Add vertices with their coordinates</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
-
-    <span class="c1"># Add edges between vertices</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">)</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Get coordinates of a vertex</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">get_coordinates</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Coordinates of A: </span><span class="si">{</span><span class="n">coords</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">nice_interval</span> <span class="o">=</span> <span class="mi">10</span>
 
-    <span class="c1"># Set new coordinates for a vertex</span>
-    <span class="n">graph</span><span class="o">.</span><span class="n">set_coordinates</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
-    <span class="n">coords</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">get_coordinates</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;New coordinates of A: </span><span class="si">{</span><span class="n">coords</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">nice_interval</span> <span class="o">*</span> <span class="n">magnitude</span></div>
 
-    <span class="c1"># Get the bounding box of the vertex coordinates</span>
-    <span class="n">bbox</span> <span class="o">=</span> <span class="n">graph</span><span class="o">.</span><span class="n">get_bounding_box</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Bounding box: </span><span class="si">{</span><span class="n">bbox</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
 </pre></div>
 
            </div>
diff --git a/docs/_modules/index.html b/docs/_modules/index.html
index b82492b..5720205 100644
--- a/docs/_modules/index.html
+++ b/docs/_modules/index.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Overview: module code &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="../_static/ECT_Logo_128x128.png"/>
-      <script src="../_static/jquery.js?v=5d32c60e"></script>
-      <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="../_static/documentation_options.js?v=59fadc99"></script>
-      <script src="../_static/doctools.js?v=9a2dae69"></script>
-      <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=59fadc99"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
@@ -54,12 +56,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -69,18 +69,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contributing.html">4. Contributing</a><ul>
diff --git a/docs/_sources/notebooks/CodingFiguresFern.ipynb.txt b/docs/_sources/notebooks/CodingFiguresFern.ipynb.txt
deleted file mode 100644
index 42c35ca..0000000
--- a/docs/_sources/notebooks/CodingFiguresFern.ipynb.txt
+++ /dev/null
@@ -1,1924 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ec2a3c4f",
-   "metadata": {},
-   "source": [
-    "# Figure generation for *Invitation to the ECT*\n",
-    "\n",
-    "This notebook was used to generate the figures for the paper \n",
-    "\n",
-    "> Elizabeth Munch. An Invitation to the Euler Characteristic Transform. [arXiv:2310.10395](https://arxiv.org/abs/2310.10395), 2023. To appear: MAA Monthly, 2024."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7dde292",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "# import teaspoon \n",
-    "\n",
-    "plt.rcParams.update({\n",
-    "    \"text.usetex\": True,\n",
-    "    # \"font.family\": \"Helvetica\",\n",
-    "    \"font.family\": \"serif\",\n",
-    "    \"font.size\": 8\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e26c7194",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4640fcce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'3.8.4'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib\n",
-    "matplotlib.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4188598",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to set colormaps to deal with the transparency issue \n",
-    "\n",
-    "cmap_viridis = matplotlib.cm.viridis\n",
-    "cmap_viridis.set_bad('white',1.)\n",
-    "\n",
-    "\n",
-    "cmap_binary = matplotlib.cm.tab20b\n",
-    "cmap_binary.set_bad('white',1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5192867b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "F = plt.imread('figures/leafbw.tif')\n",
-    "F = F.T\n",
-    "F = F/255.\n",
-    "\n",
-    "F_finite = np.copy(F)\n",
-    "F_finite[F==0] = 2000\n",
-    "\n",
-    "# G = np.ones_like(F)\n",
-    "# G[F==0] = 1\n",
-    "# print(G.shape)\n",
-    "\n",
-    "F[F==0] = np.nan\n",
-    "\n",
-    "\n",
-    "plt.matshow(F, origin = 'lower',cmap = cmap_binary)\n",
-    "# plt.matshow(G, origin = 'lower')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.title('Testing')\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c62385",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d9a8a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "F"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c5e8ecb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPic(F, theta, plot = True):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    DT = np.multiply(F,fMask)\n",
-    "    \n",
-    "    return DT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f80ebea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XtL0g7Z+fyMaO9g1Bak0JFigtxq9SNmxIJ9fR0VGYmpqC4eFhAChf5m1ubjZnsQTV94hkl7FCakOtkwiNTlt4SYeWr0uXKLmFKa4IEhRzRkx67XBSPqrEDMOw+bGuCsVi0Xx4KZvNQktLi/mzPvQrN83NzeYjzqL9dszMzMARRxwB+/fvd/XSMKKXsbc6hMe6Pjrhqp6fsDaxdtDH6WNO9XTZwxJEnJxsQBAZvIzNpG7P45dC0wf/WLre7O/fg5Fz7o9kPpBOrkGByTV83PvWEuGxyz66W7qsqL5qHbfyZW31wyZWN4vfMUAQr+hIrt2PdcHhCsn10O/fg9FPj0UyH+AKTYiQe978xPxf4vselTIE53sklx/7S7Nuub6/91VKxgKLTmIDb+UiUo4kpGr/nCEynLjnzU+YZVlbLj/2l650u7EDQYLCz5+cCxuYXBEhbh6Dv+LYZ+D7b57pWK5cxj/5Vxz7jPk38eP7b54JV8wnHJF+IpvUdxMDVoa4zIKqv+10y/rupBtBakXJUHsCuBSq66pqYHJFhKguUPCV4/5dqt533vhLUHlCmJVPEkdZjj20LjrhXHHsM7b1K8fU7aRlEJtFNtnFgo0jKUvLZH3g6UOQsOB24f4ogskVEaKyQMHVxz8BAAA7Xj8b/L7MS+DZR+ygy1htq5TzY2m1q49/wqKHh6xeUbkdr59t+kTKsH4jSBgpKa4trFI2bGByRbh8+/VPg5sk6eeKKmWbaMq6rjn+MW7Za45/DOZgAVVvgeW4ri8BrH67GKjodZYDpizymRcLBAkLjfQqTnTn3IivvGcc5rhdc/xj5t/feu18AADLPj+3a45/zNRFIDYQ+7/12vlKPrjdvvXa+VW6yeYmriplWV8QBAkHOHNFuMjc6/iHV5NAfz/7h1eT8PUTc77fJ/n6iTmL/vL/AF8/8VGzzN+f8Ki53w7WBy+2VPRVx4QgGxtVu2gdCBJW8J5rBNj2aif0npittRl1i9tH4Le92glufte098TsfF37MjTERrKfrt97Yha+fmLOUaaTHtLPWNk0sj7zyvH8lpWH/R+JGiVQXFsY77kGy5ZXLgSABfP/l7n+pIdqZ1AdUYlpcN8YSduRZEG3Kw9y/PqT2PLVNrv55rvllQurbBLJIX1RRl7viVmub6L9dmB/R6KInz+WHjYimVx5s6rhV5ZB/0m7amBNfaEyY+0/aRcMv7LMkz7SZlY5McsxAinTP59YKnWsNtP13M7AK7p2Vcmh+5qqfFH5OY7PvNg2Wh+XjQGe/9EAfxUnxAy9fDHgc1j+oTLT89oW6ZMfEMohx+x0iWwtlyXo6SsiXaozYyc5Qy9fbPreiMmC+F9pQ/t+QYjyvblGAu+5hpRbXvoseHmpH3FGdeEIFW4+5afzbVim8rdYJylz8ykPcPb73xfsbHRjAx1fazwq++kYkXL1xC0vfbaqL1Q4rCquMv772W8RfTTSzDVSXwvmjAW2G+Kdm0/5qWOc3W4Azm1Ilx148fMwZyzgDq5u7NTpm4ovdN90IyPqDLz4eRh48fPm5zljgdm2uvyvty8g9QpZREJliyqR+VWcm168REnOrR/7sV7DGgzVeANUx9yNDFYeTwbR40W+DhlB6wtzn77pxUu0xtSNr7QNiH50/CrOZx76Kvzxnx4uXe+9/z4EP7vwn/FXcfzk1o/9GNa8cKl0ebrsxlPv98Okuua9kvfLbGSgk2m3jafeb5Yjf5c/i+1Q7RM0lXrV8un+4la+CBJXNzrWvHBpaPoybQvdTk5t5oQX/3T0WcRf8LJwSNl46v0wBwvMjf0s2la/0FVr00PL6he6zI1GNrb0JsKuDtGz+oUu87NdHXbwVbVR1Q/dMlkfiN9O28ZT7w9NYgWoLM1I2y/ri2zburUJCS8kuapsUSUyM1fC5lPHuJ9XPb/cth45vvW0+/wxLKLQnZeO4dbT7oPNp445xlUGIocX+7L8mOUY2cej2h5/Tz5dJ3fF7hjjA19+mPopfe7QfgDoi48Of6M8EDcKjTRzjVxyFbH1tPtg5XMrHMutfG4FbP/4zgAsigaizkviRAY9p9jSMeWXrdZTLle9P0wnlBtbtn98p1RftKsfJugY0H+L2k8W3X6WjBie3yHHALVVl0L1QJAidZNcAcon63WTlzuWo8vc0X6PnyaFHruYWfcfZsaKV54tS7ij/R64bvJyS5xJ2Tva+YNgmF6rENlC/BLBxtUudmyZWlJpG9q/w6hj4WkbljD1G4RPI81c6+4mxR3t9ygNUjLJuN7xMqh7ifd1k5f7Gn+3fnm1ifaLxMdvX6OEap9RBeMcXvCeax1wR/s9cM3EFVJlSblvd3zfT5NCzfsS3/qvmbgCvt3xfUvZSoyr65N4vm8cZlvOjS0ylHW6k8XzVVY23Y9U/A6yH/J00T55iV0tiJq9jUojzVzrNrkClAeOq3d/Ubo8Kbtjyff8Mim0kEFWJl4yP2BMYliWp3aCqLabXzjZvmPJ94R2Xr37i7Bjyfdc/dhzEP2QtctNO8kSxPkU5R/VbiQwudYR5MS+avxK6Tp02btOv1uvQSFHJl5O632SmJVluLvzUOs1Ra0+iNmx5HtmmbtOvxuuGr/S9NuL/7Ru3X2QZ5ef8b5q/Erfz6Na9xdEDsOIgaGQMFXKho2G6ZF3nX637XV90fEv//LLtTa9Joji9OVfflnqHgkpJxN3Ff1BbTJ2sGXI33edfre56bDly7/8snQ/lCmn6qebjfbf78TK65ONet4i4UFp+cNMJgOFQgEmJiagq6sLkskkAAB0dXVBOp0GAICdO3fC8PCwWR4AoFAoQCKRMMvb4WWJLRn+5tmrhMd+cMZdjuXoMo0ILy68uMnEUoSXujr5wRl3SfWDv3n2KrOsqH948UO2z/Fir9sWGrv4yNihG9qWRj9P/UDH8oef/Le/hz/60ybpeu//9yw8/flvRXL5Q+nkOjk5Cfl8HlKpFBSLRVi8eDHs27cPAAA6Ojogn8/DkiVLYGxsDOLxOOTzeRgeHoaRkREAAOjs7IRsNuuox+/kSrj8mauFx+45c4djWbZMo0BioRIju1iL6tjpDgLdtrOwcu85cwdc/szVrvsVLa8WMQzD+cCLKaIPHcn1jB9/TTm5PnvJN6V16pzoeUX6snChUDCTYzweh+bmZpicnAQAgHQ6Dfv27YNsNgvxeBwAAHK5nPk3qZPL5arkzs7OwszMjGULgnvO3OF4uc+u7GVPdwdiZ9goGbGqQeuyp7vN/fRGH5PdLnu629x4uokeUX2i20mPUzlWp4w8Oh4iH0S+AHhLBjJ9U7UtVNut1jidx0jtIfdcVTYV8vk8LF26FPr7+80km8/nIZvNQiqVgu7ubjPh+o30A03JZNKS7QuFArS3twMAwPj4uLkPAKC7uxumpqagpaXFLN/c3AzFYrFK7tDQEKxbt86V8V6595OjAACQeuoay37yOXPWt6vK0sfpenTZeuZ95sGR1FPXQOasUUHpctzY+MrCtgPdBiK5lTqj3P3Vn/nfL1k5dj4Q+5xk0rC+yGDX39h24SFTxo5qP62U+0JtzoOyTRX/GuV8jBqqX3xUvySl02lIpVKWfaKJnt+zV1dPC/f09MCOHZVv2fQ3gba2Nli+nL8eLUm+NOl0Gnp7e83PMzMz0Nra6sYs7YgGi8xZ35ZKyPUOHR/RgOs1Hmx9mUQdZBvw+gJNrb6AySY6p/ZjZYaNMCZ6RIzbp4XZK5pNTU3Q1FR9ednLRE83ysk1k8lAZ2en+e0gk8nA+Pi4mWDJ/da2tjaLA+RaN4soSEFCTsJLnry26hjZ9+NP3cmtw9aj/2brRJ2ybwsoH61/s/z4U3eadbzppJGT5bYegW2790sLzP2XPHmt5XjmrG9z+47IJpV+YZUr9oHYR2B1iNrBrv2cCEP/Zv0mhME2pBpDceZKkis74RoYGIDBwcGq8l4merpRSq5kep1MJmFychLi8TgkEgnLlLtYLEJ7ezvE43Ho7+839+fz+UBuInuBnJCfe+I65Xq8Op974jr4ydl3aLEtDLh5l7BW7x/q0Gtt0wVmWxLZ7HEVuTL9oizfWS6vHK3DTs5Pzr7D4gdtl9N5EIb+je+3RgsDAOTfT6ks3D89PW15oIk3IfM60dON9NPC+XweOjo6zM/FYhFIVfIk1vj4OPT09JiG009oNTc3V10L5xHU08JO/K8n/p67/3+f/S1XdWXqhRniE/FDFJ+w4JedOuU69QknHf/77G/ZlnE67pVa92mZ+CD60PG08KmZVXDYn8hfqZz7wyy8kNoqpXNychIKhYI5iWtra4OpqSnI5/PQ398PY2Plnyft6OiAiYkJJfvdID1zTSQS5qs3LCRpsslTJpmGFdGli4sf/xo8cM43beuSk/rix79mqQcAjnXDSNn2mMX2qDyNqcPOB875pqUtdcm160sk5k7Y2SErQ5aw9V0738NmK1LGzxWa2tvbIZPJmDNY8nZLIpGAFStWmOs0kKeI/UZpEYkgCMvMlXDBL74hPPbwubd7kqNSvxYQm3l22sWlnnj43NsD8ZWOsaw+r7YRnSoywtRnRXaHycZ6QsfM9ZT7VivPXF9avjk0+UCFul9b2CvkRO38+cqqY50/XwnZ87YryaFlsTJlZfkNsSt73u3c/WXcz4iIn7yYBgkbb9qe7Hnbzc+8wVq37aQvleX6v/Yq7R9pZxmf2BjVEpHvFb9qY1+t9YcZw1C85xqqqZ8a+DSAJNnztkMJYlXb0p/3Olf2UZZulv68F0oQ4w4M2fO2C21X2Qhe5eiyg7Une952Mw50XOj28cse2bKkfdzqIv7R/U61fWuNTHyChPQREkekGr8XkQgTOHNV4JHztgEAwKcfud6yn3x+bOkWZVmsPPpvFXleqfiwzaFk5fWHx5ZuqYqFqi5efbdyVeDFlvj16UeuN+NQscP6PfSR8/i2e6EsT+77rsgugmoMrWUXCPue3+0igndeiF7DCRKrXc7nTqPTSL+Kg8nVBY8t3QLn5FZX7T8ntxoeT252JY/UZ+UBgCuZslR0yCdy0uHL/vJjIa+bf/Kwg7tbHfa6WWKcMuKTW9QPguLx5GahftZ20odk7eX1Zad4+Ak7yIps8fNcqdYPXBsQMSUjBjEfV2gKE/hAkwc+le3j7n+yc1OoZdPyVeWJ7NIJzyZVvU92bnJtq2xdXW1BE0R8VSF+srb54b8IN+0flA1BxqGW6Hig6bgfrlF+oOmNL2yMRD5gwZmrB57s3ARnPdxftZ/se+oC9wtEkxOWla9DdllGzJWMIL5J8nx0o1fUPjLI6KNle2kPqzw1P5+6YNi1j7JU5NduFlHrGYyobXS0eyNRfqBJ5bKwj8b4DCZXj5CT64yH1lQdO+OhNfDshRu1yGd10H/L6LCWdz8gzAU4yNHxe+qCYW6MnbBrHxY6jqp+Evm0DNX2dxtbt7EhONnoRbYuvProlorO6F6eDBONdM+19k8E1AmiAUrngGCnw06PaiKuR2S/gNQqkejuJyrtLNt37Mqwm05U5Kn67lavnZ5a9iMkPOA9V80sefAG2+O7L9oQqD7denn6dl+0QdoOt6jo4JUl+0gc7GTRsfLql1PcdcgnMnS0g07fWXlucWOHV71Bn8dRQMc917bvp+GwP/mAdL25PxyEqSuGIpkP8LKwZnZftAE6HlgbqD4AsNU5cfF6rfp4uvz2uyxb7hIRryy9T0WW35elvMonvkxcvF7JLxm82Karz+n2SU4fCHU6+dXxwFqt51u90UiXhTG5+gA5uT7+s+pkQ/Y99xm9JyB9QrN6deucuHg91zfR/jDx8Z+thec+42wnGzO2vIwMWp9Ivgw8G1j9XuPO2uhFns6+XVK8ruZGt64+G/a+HwoMqPzUjWz5iIKXhX3m1J/eZHv8hc/eGrh+XTpP/elNQln0MWID+5mHTBk3Zb3U0VmX4NZmu3h7wYtvdH0ZWB0i3ao2ue3XKn1Npr7f53St0HFZOHH3WligcFm49IeDkL9yfSTzAc5cfcbpFYKP/eRmePFzt/im/4XP3gof+8nNVToBwLNeO9/oAYYdbIhNdvp5druxg6UiU/1yE7G5ZMRM22VtZHFrM9t2XvwR6RDx4udusegX+S5qW54Oqx+xqvIvfu4WYV+ws0EFr6/50PX9PJfrgUZaWxhnrgFxisQg8JJPJybR/dLnbhHa4ZduHcjErlbQcbOzU7acrD4dMVGxSdR3WBkq9umKiZ1cEbL6nGTRcsJ8DulAx8x10XduVJ657vnKbZHMBzhzDQhy4p38bwOB66YfCqAHANqWk/9tAF7+/LpA7ZIlKg81yNhZjrk3f3TIYHGyndZJ+gnbl+m+Jepn1TIJevyx68N0H5ftU3bnhR/tUPcYsfKmUj6i4My1Rpz4o0HhsVf/SnzMqz6RbJ49ftjhB7RvdnGtJSSWXuzTIYMnz0kmT6+bvuF329jZxNouY4usPJny9YCOmesx/3yT8sx171dvjWQ+wEUkaoTqies3r/7VYJVNYU1UPFQGtqgMgsROtm102a/SvmzZE380aG61htd37XCy2UleIyZWbRgutoiCM9cQcPz94ns1r1+q717U8fffoiSPZ5dOe3RB20nsE8WUtt8u7rp5/dKbpfSRcnblZWXJ6CIQeU7xE9VXwavtXvuwFz0y/UoF1XOyluiYuR49erPyzPXX3bdEMh/gPdcQYPf15rjMLfBGSs/Jp/o1ipz0x2UqA4pOe3RB/KLtkvE1yK+VdAydMAz78iqyVHT6JZvtM17jztpo1x/d6uLJ1B0bP2IdCUI1nfMPTK4hgJzIx47x3487duxWeLPL23uMAOWHONzIogeaY8durbJTh21uKdtS/dCD6IEVq+3BPSxBYiRqYwLx582umxzL6oTES9ZOlTZn+53uB9SIraxNor7hBM83O1lu+j/dzo0ErtCE1ARyon10521Vx8i+t1bc6Ek+T7aqDNYuWqYX+1Qp641V6ST7WXtUfCf16Lh7jR0AgFFyHiwquvQMLCLbP7rzNtNP2i5Wt5c2pWURfTIxUIXfzhqXghTYrBob2rYgz5XQ0EArNGFyDSFvrbgR2u71Z31S3d8EyQBB7KXtnrrMv+XgynpiXB10/KxxlPOdlkkPgCR2U5etNeWSsk7tVSlvjZedXl19gMTKTh/xjS6ro/3Y/iayxQu0nary7Xx003ecoPtQYxIDtVhGd+aq9EBTJpOBQqEAExMT0NXVBclk0twPAFAoFCCRSDjut6MRH2iyIyEYYPMuT04iz219VT0EXfpk7BfFzA0qdjvppWXpLCsDT17+srWQuHd9lY+8fSrQ9WVtJ7bIIrLPS6x4cXBrB4tf50OQ6HigqfWfBmHB/6PwQNP/PQjTfzsYyXwgPXOdnJwEAIDu7m4oFouwePFi2LdvH+TzechmszAyMgIAAJ2dnZBMJoX7ETXyl62FxL9W/7wV2Zf/a8Wf43Ixc6X1y+pj7XZtb5UdMWcZNbpPI2orAKvfxA9pNPiT+NcNFRuMcgzL+6oHeU+JlfUt6AUDdMbKRlYQ511dgpeFqykUCpDNZiGVSkE8Hofm5maYnJyE3bt3QzweN8vF43HI5XKQz+e5+zHBqkNO5MU/rB64yb63vyB3shulSj27OjxdqrAD0OIfbqiSK2u3rJ867FbVK6NTVObtL9xg265+YJT8k0/3LxXclqfbxa1PbNvqjo+fbRk5GmiFJulFJJLJpDkLBSgn2/b2dpiamoKWlhZzf3NzMxSLReF+ltnZWZiZmbFsCB8dyfDtL9wgndB0w9PNS7h29euJMPijEv8wUgvbVdotyrFFvOHqgaaenh7YsWOH8HihUJDePzQ0BOvWhXNN2zDy9hdugEXfH+IeY/fvuSJtU47/jbAio/q4SJ4q7OC06PtDXJ/2XJE29++5Qm5As4uPFyp2VMfArU67WPuFXdu7kcXGw+uTwESeSjx1xZH44+QDz2+xXVZZus6hqNJIv4qjvPxhJpOBzs5OSKVSAADQ1tZmOU4eXhLtZ0mn07B//35zm56eVjWp4dhzRbpyecVuE2HEyonrexu5x3ibn4OCyJ9F39uofhnJRp6OjRszFzr9tFGkzxJPHW3LaxeP/pvxDTA2tO2iGKnGSyin0Wmg5Q+VZq65XA7i8Tgkk0mYnJw0/+7v7zfL5PN584Em3n6WpqYmaGpq8uBCY7Lni2sAAOCY7/IHezsMQ1yP901x75fWmH876aPLqkD8oaF1kb9l5fv5jVdkC+uDXazoY3u/tMZVO4ogdrF28mLsFiL7mO9utMRBNu6ysWFhfVOB13doP1TritDZlnWH6peMCH8hkU6u+Xweurq6zM/FYhHIWzwrVqwwX9NJp8vf6hKJBHc/ope9X1oDx9w9rK2O3bHy/uA6uyWpz9tE/t97ZT+3Dl1XNS7aURgYdNlriYsRMz8fc/ewY8yUoHyzyPZ5MKzESF0PHd+9V/ab/Vl7vxbEQCX+2tsrJMSM8qZSPqrgwv11xDHfqR6c936Ff4KSsuT4Md8Zhr1f6efKcINIrw5ENvJ0sn46yVDFzk9ZHTrjTsukbeDpYMvYyWER9TXdftRCh5N+Hk42qZwPoj5ba7S853r7LervuX7j5kjmA1yhqY4wB8u7NjkXZr5dmyfy/P69V/WZx6TkMbB1aHleqUqU87ponUSfaIDiJmJFP+18KsuKmWXsZJOyuqFtYHVU9oGtbl5MAUA8Q1WZrbvoY7piZfXfvlxQNtHtofN8CRV4WRiJNDLXIkRlDIC9X51PCP+snlRF0LKIfF2YCYzSQf5W0cWTIyxrI5db38X1IWUdbD3D+rdsfSnbrurz7KfIBp7fnu3lxdLBVpm+o+sc0XmuhRpcRAKJMnu/2gfHjG6Gvd2rHcvQlD/HqP3UTKd7dVV5txA5dva5Yv4VClouz2YnvdzBfT6edjGo+NXH2e/iHqFNnHjtV20vZYfLV2REseL5RHR67SfcvuvxFR9uLB1ksn5wYyFhl1N/Y2Op/bwIEz4nVzdL7vqF8qs4SDRQPUGdBkSnZO0WXQkboOIzLXNv92pzo3Wq6iX1WVm0TKe6uuEmXc4XANEXDJEvdvJljumAtJGbtnKSS/734oMOm3T7Fgl8fBWHLLmbSqWgu7sbhodr+0AjzlwblGNGNlv+V6njlw17ezQM2DYnIzuYivxRsYOWIarnNW528TG/UIxshr09q83/nfTS5VwnGUGs93av5uom9tUKS/zmbefZKtX+hly7qvYJLedAmPHxnit5VZRQ6yV3MbnWIcd8e34wvsbmRA3hvYxjvr3Z3mYZNFxyIvEDsI+hbLlAYm0w/weh106+zT19P9l7zWrHdqGP82zSdf7UvE+EELev4rDL4vLWR5BdcjcoMLlGnEV3bqnaF5u/f0OO7bn2+uoyPvxgtQ5of3h2O0EGtEV3brGtv+jOLWacWOh6vPgSSH0nO/des9pWjixO7bnozi2w99qK/zL+0XLtyvDg9SESd9ExkU26YHWw/YA+TvwW2WTXF2XOH14ftNPppr9HDpf3XFtbWy27BwYGYHBw0LG6aCneIMDkGlF0DNZ7rr1eixy/cEqQftcHkE+0QSLzxUFWjlsdUcKLL15iEMa+E1Wmp6ct77nyVvVra2uzzFRFS+4GBSbXCLHoDncn6KI7tsCe6663fDYpieuROqzePdddb9lHf2aPeYW1nbaH3W/Bxi8n+1jfzL8FAy1PXpVtNva4gY2LxTYHXbLt4xTnPdfy21pn+3tF1O/ZPquMIMY8WTLxcGVDBImB4mXh+f8XLlzouIiEaCneWoErNEWMxd/c6rru219bZanPfuaV5+ml9weNrL10WZEfqsj6LaODJ0unbSJZpJyqLjvfebLc6vGKyE6v/rJ9SaWNVfpsmNGxQtMxG9fDgg8orNB08CDsXbNWWif9Kk5zc7P5AzO1AGeuEcPLoGWXIGUSbdADpVc7SNlaDvAqunXUo/fpak83CSAMfYVGtf8u/uZWT4k6SkkzUHx+z7WWyZQFZ651wOLb3Q1kb39jlVmf/ps9Hjbs/GVtpn1zqquC29jI6neST+TYlVNtSzZWKniJK6/N2OOq8kV+yMjh1XWqx+tjTnaH9fwSoWXmusHFzPUG+ZlrmMCZax1ATtLEdvGJnF+5qup4YvtWyK9cBTHD+rcTtJz8yuAHCBl/yXH2mUxdv7JBdKv6T/Tz6tH+OPkWk9AtGye2vCq8OMsgsv/tb6yqiq+udrOTYxdPJ/3k/KHL2sWlFucNEiw4c60TEtv4A2i+d5XtcV75xLatZj0n+awevyF2EDvtoH1XjYMqqv7zYixbT6cdXtERT57NbuXa+a9yDni1Q0VHVNAxc120Xn3mumctzlyRGpDYum3+r+rvyPlVvZUPCiudVCXWrdu48oOGtoO1ifhaiQcF7XtYfmXDpR3cRET5TP9taX8fIG1gG3tJOVW2qvRXBz/tzhFHOQI76LLEfhn//W6T0IML9yNhpW2z9QR2GoJIeZWhvG3zNphaXRkEYjavdtDl/MbODp6fxI/8ql7zbzsZXmBj5gRtE08WD5F80YAtkiMr1462zdsgz9QjPrlBpc/x6gLw/WjbvI3b92V9FtlB+0nioKtvqfalSIHJFQkjqgOX24FuarX8IEmX83NAcOtLkNgN8rrkq8imy7LtRH+OQmxlqFVSkomfrF1ElpPMqCZft8sfRhG85xoBPropuMHvrb5eoc63+nodbSH1daDLb9onng92PrPlVGySiQWRJ2uDimyeLq/tI7LPTq5qO9Ky3PQBmfqycVDp73RZkXy/zmWd550IHfdcF6/boHzP9e2BGyKZD3DmGgHMgXejf0n2rTXMycm5xOWnfr90WfwqMf+70PVWX690+Y9u3FYdVxbKFhU76LKOOkg5hwGYp5+VbZtENbSZSJ+MbLouKU/2uYmXnU5Whox8nfER9S3W79CBl4WRMMIbKHQTZALVbcNba5wTn64BTnWw9xPVAZU3MIv8UZFNx8Vu4Fe1TVa/XZ1aJxvd55Vs+4WNRrosjMk1Yhy7gf+Ahi68PpRx7IZt8OYNveb/qnUBvD2XbGf/W2usdhF9spDyb97QqzRYs3po/W/Nx0pHu9L22UHiQOOkW+SDjGxSX6TDzl7VNiJ4iSXR+Zagn9D2kv5k1x9IO9vpcoKNkV37qZ53gYIzVyRsHLs+oG+mGjozsfXY9dvgzbUKJ7qOE8lJhsffO6X9kfGP127svjdv6NXavkSWrW0eY23nO/GHHHfyjT7OyvSaKHi6RXaL7KTb58211W0l03Zsm6i2d1W8Be2ndL7VAsWZKyZXxFeOu83f2SpNLS7DHHeb9xkr4c21vaY8HjGjrO+NG3tNX9+4sddihx10mRjzmcihyzr5RNvyxo32tqtCZPPQ0c7EVp4OEmcAtXZl/afbhtbjFCdSlk02x922TSjLzk66XxGZdvEV9QtSh7ZL5AvbL9m+JyofanDmWk2xWITR0VEAAOjr6zP3d3V1QTqdBgCAnTt3wvDwMABYf50gkUjU9Kd/Ik+QCx9o1nXcrdvNv9+4aWUgOm3lUceqBiOPdhBfiZ9v3Nhr8d++XkyLDTzZ3Lhr1MP6DSDvu7zsXmafvf2ifkfbVSkjGQsqZsQGUXyF/huxatuc2kKirYTnVthooOQq/SpOJpOB8fFxaGlpsSTXjo4OyOfzsGTJEhgbG4N4PA75fB6Gh4dhZGQEAAA6Ozshm81KGYSv4vA5fp33gUqG1wdW+qbr9YHqAUBVF20fTx4rV1Tm+HXbq44FFWM72PjraA+Rn360Na1Lp2yvtrpta5V6XnS4OQ/c2uUWHa/itN2wAQ5TeBVn7uBBmNoQzVdxFsgWTKVS0NbWVrU/nU7Dvn37IJvNQjweBwCAXC5n/g0AEI/HIZfLeTa2UTl+cHvlG5+Pm3kS+qWDhxf7JOJ1/OB2635arwdb/NqOH5xP+rSPHtqzahBmYmPR5bHvVA3iuuV6kFGFH/UYZGMrc35XyZrn+MHtzvWRmuD5nuv4+DgAlC//AgB0d3fD1NQUtLS0mGWam5uhWCxy68/OzsLs7Kz5eWZmxqtJdcUJA9sDud/62rrKQMK7H/faupVwwsB2s9wJA9uVdZA6tAwn32i7CK8Pir+J82QSvTHq79dt5NJ+sjJUbFWNEYkx7YMoRry42EHHgN3vpX+J7PAiVyRT9T6xW9vsYmtnA9u/Acp9ys25wrOH1W3nh2r/CAzVhB/hLwfSM1cRw8PDkEqloLu7G4aHh4VJlCRflqGhITjiiCPMrbW11atJ9YWGb/8y2wk3VwaA19ZZvyWTE9VywnrRJynD1QChYAPtM81r61ZWH2Ptmv+f3lzbQsfFqI6/ltlIAP0GoPz5hJu36+kfjFwVGXZ9xym+pg8u48jWl2pPiRizfY0nV9gXQwJ5z1VliypKyx+Ojo5CsVg077mS+7DkIaaOjg7YsWMH7N6921Kuq6sLenp6uA818Waura2tkbzG7jcn3rgdXr1tpfm3W2gZr9620iKLPcazwW94emXwaptqbEV2qtbntYMX/V5sCgu0b25t99o+bm1xahed/ohkuT2H7NBxz/WjazbAYU0K91xnD8JbG6N5z9XTZeFEImG5t1osFqG9vR3i8Tj09/eb+/P5vPBp4aamJmhqavJiRl1z4trttp9VeXX9SlMG/bedvlfXr9Si284mr7ixjfjv1j+2vOmHw9dV1t9Xb7NvEzv90rGL2AxAR1+jY2pBIRZu6gr1Epm3ybcxK5fXd1i9SDiQnrnmcjkYGRmBYrEIPT09kEqlAKDyys34+Dj09PRAIpGw7C8UCtDc3GyWdwKfFq5Q65PF76TK6vGKqp2s3iDiTevk6VNJrjyZPGrdj8KAU9xl6umq70aODplezzMtM9d+FzPX4WjOXPFXcULMSTfUdlB8ZcPKQOwgetwia98rG1ZaygblH6vPSSexk7VXVraIWvenWsGLjZtYyLafnV6vNjjJ1mGXCB3J9dg+9eT65qZoJlfPDzQh/vHKBu+vSbyyYaV7OQSPNjhtJ6XdDTInpbeX6yr4Y4lFQP5V6XPSCZX/pdtOhqD81LCRfqvjHNAWC8X6Zv8U4MlHEbr6il/oar8IgDPXkHJyv/ikjCovD5e/MfN8I8fcIBsrOx1+x9vOd6eysnVr6Z8f0P7osN+rPLf1Vfq2ql2sbL/sAtAzcz3uevWZ6xtbojlzxbWFQ8jJfbUfCF/etNI3OyyD1LyOk/u2w8ub1E52r/aR+i9vWsm1yRdUvsrOl5W1xzaGofoKLYfFH932u5Dnqz3zeO6HYW9n1dlo2P2xAWeuIeaU1f4n2Zc2rwxEj0g3zSmrt1ftE6FiM5EpqsMe59lgp89OPs9HFXu9tI9dLFXtqDVu4+A1BrLynGTJ9muv0H1YpY1l0DJz7XUxc90WzZkr3nMNKadcv93TfaaXNivcq/NS38N2yvXWk1/lRFeyzyGeph2Cb9WObUHgxZBFtk2Ask1TfFXtqIoL4xvZ/O4nXuLgNQaO7QlyttnaoRGzPcDenqDtIuAiEjWk0WeuH+uV7+wvbltplqf/VuHFbSs96dUFsUMGVVtl6xC/dPlH+ySrX6W8GzvcyJZpG7ofquiQjbWONhH5QctVsYdXX1W334hsc2OPjpnr8d9Qn7m+fns0Z66YXEPIx1ZKDkzbqRNcso6KDHJMdr9qGSd77FCNkUx5VrfbmLrVr9Meuzjq8KsWOojMF7ev1NLfWble5Lg5Z4OGttGNHVqS69ddJNd/iGZyxcvCIcTN5RKZ8i9uX2krgz0uks3uZ+vRJ64fl4D+4hvbpfwkdjiVZ3X/xTe2u7LdTqabusQO1p4Xt680N57PTgOnDr+IfcQ2P3UQiI9e5OuwlRdf3rklUy9I6H5TK3S1WRTA5BoiTv3adjj1a9shVgKp7dSvVQYemfIAAL+6faX5+Ve3V59kbHmebMKvbl9pyiByWZmyvvB8EmFXn7aHxFRWNy2fjZXbTbVNefXp/9n4sm0gg4xeFd+Jj17aXVY+299UNx5031WxR9Vnmb5d9xgutoiCyTVEvPBN58GRLvPCN1dyT3IZGawuJzmkDs9Gu7oyPvHkuYGny43+MBPmATrMthFEfbXe+klYaaSZK95zDRGnXuc8OL1wB5MUbeqQsnQZtj7PBpEON7qdcCNDVIdXXjWmxH82DnQMZGTqxK1OuzZwK8+pHq/P6YbXLl7l0PJU4q2jz0UFHfdcT/w79Xuur/5jNO+54iISISLm4nsOOUlP+7tttmXo4/Tfz/9jr2V/zKE+XZdXltVByxfZZleft5+nV6RHJaa0bLZezDBMHbTNp/3dNnj+H3tt468DN33DD3lsX2Ih8XAq5xa2nd3qEfUX0raycnn9XHdb1RWql3ojHEpMriHhtGudT+Tn76QSoUN5uiwAM5hQHVZGL13n+X/sla/DyGdtskOYlDknm61ciZOT649R3k9kE3tYf8h+py8RKjGj5but6yhboR1V7SBxU+0rsnZU4dcALClXdK750W5RJwb2X8p55XXT1dUF6XQaAAB27txp/h45/UtuiURC+DOpsmByDQlO9xae+yfrCezlXoSbuvQA4lb3x/+2PNiwvqjUp082GTlubKX1EJtNeUw5WVtoO577p94quTK20Hrs6svG9/k75e0AcBdLnffMPv6327i++XVfzqvcKN8v9I0QzFzz+TwsXboUlixZAmNjY+a+bDYLIyMjAADQ2dmJybUeaO/hX+oEAJgc6a0qC+D8ja69Z1tVXcJz/9RrylGpS/aLnrqUhdYtspEH0StTRzZOIiZHei1xsIsXe5xnX6xk3S8TQ7ZfEJsAAJ5TiJsddnbQ9tr1UTuc+poqRJZqLHlynPoRSeQybc+TRX8RYGXI6K9HVB9S8uMLSjqdrvp98VwuB/F43Pwcj8chl8t5SrD4tHBEaO/Zpm2Q0jnYBY3sgKRj4HIrg9dWugdSnf2BRvSlShVeHZ0xcPsFzU0dlS9ziD/MzMxYttnZWdeyxsfHIZPJwOjoKIyOjgIAwNTUFLS0tJhlmpuboVgserIZZ641pv1q/kk5uaPXsYyoTvvV2yz1q7CbrezoFesrMf9rgOiytXcelQFRNmYyciZ39Lry2bYdOPLo9hPC1JPxU6Yv0GUmR5g+4LK9if+WdnMpy6l/VNksaZu0fgn5djLNKyCUDFUb6gKXl4VbW1stuwcGBmBwcNCVCeQeKwBAW1sbLF++nFuuUCi4kk/A5Fpj2EtaE3dZT7aOq/iX49hyNE4nLO8yGi2PV5+2Y3JHL3RcVRkkJu6q/szWdYKUsfNLBa+XrgEqPndctQ0mGR9VZABU+yVqd1F7i+p5RZgMqDZ2uhys0maq9vspW7XPkVjZ9YOOq7ZJnZtEhlP5usTFpd7p6WnLqzhNTU3ccps2bYJ33323an9LSwv09fVBJpOB8fFxM8HG43HI5/PQ1tZmmamSh5q8gMk1RLhJSjJ4lUPXJ4MBSaiszbyBgk4cMrpqPdiwXxZ4+0Vl3OqLMmFoMwC9beK3nqBsDRtu77kuXLhQ6j3Xvr4+2+OJRMJyb7VYLEJ7ezvE43Ho7+839+fzec8PNOEiEjVkyZfLJ9fuf+nl7iewx1Xl64DYsOTL27h/67bLrc8qOmTY/S+9vseR4KTHrS1B9R9ZPX62v4rNXvuYnT4V2W7Po6DRsYjEKVdvgMMOV1hE4tBBeGmH3kUkyCs34+Pj0NPTY85Q6Vdxmpubqx56UgVnrjXi9Cv5l//I/vG7vZ9sMt8Qx+/uhdOvdPc6htsBQcau06/c5ikGOp8yJH6qxElWJo2TzaI+I8JrH1KNoWyb7f4XtT6ngorNxIZa97MoJFZdhOFpYZI02eTpNZmyYHKtEaITWkdSNZHsmON398LpX7If7E7/0jYY/24vgEH9zSljkcspQ/T5DuP7+HedfbSD+KwiQ+S/EIWBxKs/Uvh5Tcsn2aQvq8RH1J9l9REZumTWNSF4zzUo8FWcOuQTX9wKn/jiVogZhuNGkC07/t1eiBkGfOKLW6t0suWJHbWAtoMMcjI+2sVHVYYXm2Vk69ZPI9t/3Oq0k+E1Kam2t44kSM4LXfGvVxpp4X7pmWuxWDTfCaJvGouWjNK9lBQij8pTk2f8TTn5KS1JVgJ49gerzLpO9Um5Z3+wSkGLN0gMaJ1enrQldX/5PavfdpzxN1uVfFa1z8kWVf1ebKF10oj0s7b70Td0P1ntxC+/V/aB+FWLfh96cOZaTS6Xq3rEmSwZlUqloLu723y8WbQf8Z8zvrDV3W8mymyE+b+f/cEqpfpnfGFr2b4gMDiDmkefTdsVfVax2VVbCDZPg7qTXB3+8/zRiR9t5EYvUsHLORgxpGeuqVQKCoWC5V0g0ZJR+Xxe+1JSiBzP/nAVnHm5eLB45p7KgGtXzo6YUa77zD2rXF22IXppW3Tz7A+rZavYSmwjtpKZ+ZmXb4VnFWNIYuWEin2sXc/cY213r7F16keyttrZwWsjnRD5OttIRa8fsqNOGB5oCgpPDzSJloxSWUpqdnbWspTVzMyMF5Mamk9eZn+J9+l7V5ll7MrZyX/63lWWy21eLr0RW56+1/+B55OXbVXylxdLEj9RDIkf9HFanpOfJJZ2cuzsfUZzHEVtS8eStTWItlRFpo/6aTdJrDJ9oO5RnY02anLlIVoySrR/aGgI1q1bp9uMxkTmIQoPD1o8vfP6ahlReXBDl50ScugB9JMrtlj+NmMoI1vRZlqXaYudPick9BOfwpw0nr53FTc2NI5to8mORsfNg29RxVNytVsySnYpqXQ6Db29laf1ZmZmqtaRRJw5a/kW4czsqfuuh7OWlweXp3dW/nbiqfuqB5uzlm+Bp+f32+lU4azlW7i6dKLr8pKdHBJX2heVAZsuqzO2BNUYE3uc+osXHUHh1O/Danfd0UAzV0+v4iSTSRgfHzc/kyWjRPt5NDU1mUtbyS5xhXBweCCADB5ndW1x/SCBWZfS+dR917t7SIHZzuraUpbvFxpslJWjxRcd9nLs8tsW39vRA3Z9Naw21xv4Kg6HXC4H2WwWisUiJBIJSKVSkEgkYMWKFZDJZKBQKJi/7i7aj/gH777Sv99f+Tb+l5eWBw+V2RCpY+rg6PzLS7fAU/dfX1U2bJD4/LtHW4N6vYPV49Vuwl9eusXSL9zYImMT2a+qy2+eGgt/X61rGmjmimsL1wGiwYI3sOkaWFQGe2KHTHm/BmM6qbiJgdvk5tYfWpcXu0W4scut/rAlWAC+L2G0M0zoWFu4/a/XK68tPPmvayOZD3D5w4jzqb/aXDWjfPJHq7nlANSfELaDHoyIfB6f+qvN8OSPVgsHL3LcT2jdsZL1+yTR7eSDztg5QWyk48La7QU3MXfS/+SPVtvGMEzwfAmiHzY8DTRzxeQacehLdk/8m3hg8PtyppP8sz+/WWhf0AMasZW1J+gVfeyIlfyzz66f2CGjn7Tl2Z8Pd5J98kerQ28jEm0wuUad+W92T/xEPGCe/Tn/BpGzP7e5rFviGyZth529fiNMLj58SzbjowjXxlp/i3fQz/azWraxFBx/3LYXIkcjLSKBC/dHmHM+u8nxvTG6jM6N4GZB+5hhwDmf3eR7fFTxI046fdXddn7rD2Mb0zzxk9Va44NI4PYp/QiCyTWinPOZTfD4T/vMTUhJ3/b4T/vMv8/5zCbTBvK/qjwi45zPhGQQlowB7au03wHZpxJ3VVjfZXWFGa4/iK80wms4AHhZOLI8/jObhEph10Efe6APPn2x/OD36Ys3cR/qITpENsno+PTFm+CxB+R88gvih11cyP4Y9fnxebvt/CTHvPgYMyr1ZWJK61JpZztEfYDVqUtfEDz+M7XzAPGAYYDSymMRvpKAM9c65tMXDVc6M2dzOi61wXwyuoj/y0dEx2MP9DnKEskIDGLn/N+yG7Fbxkev9ln+lrDLjOm8b/TmBtpHnr+mTrpMBKB9qXk/rGNwEQmkLojN8XvmLx5eA+desFG7Lp5MMss594KN5t+/eHiNpQxd79wLNlYdD4rHHuw3/xbFTgTxgZ7V6fbDrX0k9nScvdimYkct21MV2pco2R0pGuhVHJy5Nhh+JFZatgznXrCxaqCntyhhZ69fcdZBmG2rFVHre1EkVlLfogom1zrlvORGSwelE9cvHl7jqpPbdfxfPLymSqfTdl5yI5yXDOcgr+J/LXwkbeql7XTYJWNDmNuZhfYH8QF8WhiJMucvHbJ9vYB3XMdrHTHDgJ/n1gT2aoifqNheCx9/nlsjrfvnuTXCdtFBlNuZB4nV+UuHam1K3YH3XJFoM98hH320+gcTzj/fnwGDlvvoI2klPeefP8S1taYwMeT58+ij1X7y9pH6vvgoMfiI2kKHPU7tHLp2lUS1DyOSqD7YF6EvZSw4c61DYoYhHNR0zVgffTRt0UH2Lz1vg+Xzo4+mpeQtPW+DWTcMsLMt2VkrAJix4fnol51uNh32BKGjVkT1i0GYwZkrEmke+fkN4oOCpzsfeXwtAAAsPWe9s/z5sqLyS89ZD488vhaWnrNeSl4YqYohEzfiHwtvn6isFubtcquD1KHbVFW/U98h/QFBGulpYUyuDUTy7PWWV0VyT1QPeE7fFEmd5NnlgZRdUCD3xFpInr2+SpeKjTy7ag0bFxX/SFk//KKTluy3fLoNvdpE9IviEca2RGpHI60tjMm1kaAuc+aevNFyKPmp21zL4h3LPXmjtEzWllCi4d5P8lO3+euroo1aE59At+8+I9EC77ki9UbnWbdCrFROerzBLlYybDe2nqgcQDlZdp51K+SevNFRbqxkQOdZt0LnWbcGFgs30DGQ8cnOVz9tlNn8wC4uYW9bJDga6Z4rJtcGIvvUTVrqyQ6WqoNqmAdhEgMdNobZT79oRJ8RDg30niteFm4QnBKraBm7h5+9WbosAMAFZ9xSLsOpT45FGdVlEVl48dQFaWOnOJPjum1R6UNIY9JI91xx5oqUIfdCqI03KF7wiXXKC/sTHn72ZsfyF3xiXVAeu4MXI5fxCMrGwGyJapsiiA9gckXKsEnjlwNS5SzleQPr6YOWTabuBacPBum5GozdIp+EiYbEIUAb7dpGJyI9wr6ENB4lQ32LKHhZGIELOgblCwvWXFWSweHhcW/1A6ME8PDEoOVz2Hh4fBAu6BiUspO0m6WsF0IYDyREqN5HjW5uxeSKAMRK5RHxoeec74mSsjQPPXcLXPhx9/fV6LoyNtQSNgk9PDHo6HstfGLt5LUbDc8HN3bz9Fz48ZtD365IMMRA8Z6rDzYUi0UYHR0FAIC+vsrvDWcyGQAAKBQKkEgkIJlM2u53Qkty7erqgnS6vFTYzp07YXh42JNRSMAYBjz0vPPTnBeeZvNQVITfR/OMg+8kbjIx9o2A2ueh526p6ic19RsJFyF4zzWXy8G7774LLS0t5r58Pg/ZbBZGRkYAAKCzsxOSyaRwvwxa7rnm83lYunQp9Pf3m0mWGJVKpaC7u9tMuEj4kB38Hnr+1vJlP2p76Plb4cK/uKlqv9vtwr9w97pQTVHwjWxBQ9ruoedv5bajnb1udZkbgswThvdcU6kUtLW1WfblcjmIx+Pm53g8DrlcTrhfBi3JNZ1Ow759+yCbzZqGeDEKCSfLTlkLsVLJ3B76VTkp0/vstod+datlE5Vbdkq0lsyz8yVMPpL2ApBvM2Krir1sP0EQE8PFBgAzMzOWbXZ2VqtZU1NTlplsc3MzFItF4X4ZtFwWHh8fB4Dy5V8AgO7ubmmjZmdnLYGamZnRYRLiA7teWg/LTi4vaL/rZerXTiQv3ZC6FpkvR/dXUywoXr6qtd+7Xqosss9rF6+ydctE6gPV3/YlZVtbWy37BwYGYHBwUKdpVZB8JrufRUtypS/5trW1wfLly6WNGhoagnXr8D24qMBNCg6Py+96tfy7mMtOrP4JL3ofKRdFdr28getfFKDb1M4HckyqnSL8CgXiI6q3CubLTk9Pw8KFC83dTU1N3OKbNm2Cd999t2p/S0uL5eEllra2NsvkjzwnBADC/U54Tq6ZTAbGx8fNBBuPxyGfz9saS5NOp6G3t9f8PDMzU/UtBQk5st9EHcotO2EN7HptowaDaoP5JeKENY5lw+qrig8ivNRF6hu3M9eFCxdakqsIuwRqRzKZhP7+fvNzPp83H2ji7ZfBc3JNJBKWe6vFYhHa29shHo9LGdXU1CT8FoJEBNn7ahLllh3XB7ve2OTRoBojGY8w+0on/mXHWQcs8llk+67XNlbVQRAACMV7rrlcDrLZLBSLRUgkEpBKpSCRSMCKFSsgk8lAoVAwH8wV7ZfBc3Jtb2+HTCZjzmCz2axno5D6Z9cbmxp+AA5rYtVJI/iIKBCCV3GSySR3opdKpbjlRfud0HLPlShnjXBrFBItYnPlmdqDU1vgorbrq46TfTFm366pLZbPovpRwykeBOJz2KETJO2Pnf0kBghCgwv3I4gK9LdRZl3ZB6e2CNfZvSixytzIIB2FZOOE6TOA41rDFyVW1dZYRR6c2mJpU6H9Qf5QARIdVH7kIuJ9CJc/RDzzYH5r5QNzMly0uPKw2oNvb7N85pV78O1t+g2sAWZMIjw42EG3Obft6tRvxBuxUnlTKR9VcOaK6GWuBDBXggff3lYebOc/w1wJLjr6G5bj9DFLmTqCF4d689n0kYb4hyA0DTRzxeSK6MUw4MFf3275zL0c2vr1crk6OplsqdMBREi9+oV4w+UKTVEELwsjWnlw+h+sOxxeS6kqX6802jKApRI8+M63am0FEjLcvucaRXDmiviL3cM8R11Xa+sC48F3vlVONo0SiwgPigiiA0yuiG8s+7NrwZgrmduDv73D8tmYK8GyP7sWlv3ZtbU2NTB4MSBbPfHgb++otQlIGMF7rgiil13/eadtEm2kBLvrP+/k7m+kGCANigFqP0MZ3dyKyRXxh2X/owegNFdOJKU58zO9kWNkW/Y/emptdmCwvjdiDJDGg9xzVdmiCiZXxBd2/Z8R2PV/RsofSgZ/my/H7msUqnxvwBggDYYBipeFa22we/BpYcRXljV/1fHYrsI/B2VO6Nj17igAWOO0rPmrDR0TpI5RvY+KM1cEESCatVLbsvhVtbay9uDsFWkEVO63qv72a8jA5Ir4yq7iXbCreBcYc3O224UfurLWptYUNk6NHg+kPsF7rgiikQs/+CWp+ysXfvBLtTa15jx04G6MB1K/NNCrOHjPFfEfh9WJHvrD9wMyJBo89PvvwoV/cgUAAFz4J1dgfJD6oYHuuWJyRXyn3hZICAKSUC9o+kKNLUEQjTRQcsXLwoj/GCXb7YLD/7rWFoaWh2d/iPFB6ocGeqAJZ66I7xgOT79m3783IEuiycOH/rXWJiCIFhpp4X5Mroj/GNavn9m5+2pkCIIgNaWBLgtjckV8Jzt3H3Qu6IJsaazWpiAIUktKBkBMIWFG+J1vTK5IIGBiRRAEZ64IgiAIoh3Vd1ejm1zxaWEEQRAE0YyvM9dMJgMAAIVCARKJBCSTST/VIQiCIGEGLwt7J5/PQzabhZGR8s+OdXZ2YnJFEARpZEoGKF3qjfADTb5dFs7lchCPx83P8XgccrmcX+oQBEGQsOOwoAx3iyi+zVynpqagpaXF/Nzc3AzFYrGq3OzsLMzOzpqf9+/fDwAAMzMzfpmGIAiCKELGZMPLpVq8LOwPhUKhat/Q0BCsW7euan9ra2sQJiEIgiAKHDhwAI444gh3lRvosrBvybWtrc0yUyUPNbGk02no7e01P5dKJdi7dy+cdtppMD09DQsXLvTLxNAyMzMDra2t6D/635D+A2AMwui/YRhw4MABOOqoo7wIwZmrV5LJJPT395uf8/k894GmpqYmaGpqsuxbsKB8K3jhwoWh6Vi1AP1H/xvZfwCMQdj8dz1jJRigmFy9qaslviXXRCIBK1asgEwmA4VCAdLptF+qEARBkCiAM1c9pFIpP8UjCIIgUaKk+DtyJXxaWCtNTU0wMDBQdbm4UUD/0f9G9h8AY1C3/odg5losFmF0dBQAAPr6+sz9XV1d5hXWnTt3wvDwMAC4XwwpZnh6rhpBEARB7JmZmYEjjjgCkv/vV+CPFhwuXe/90iHI/X/fgf3792u795zJZGB8fBxaWlosybWjowPy+TwsWbIExsbGIB6PQz6fh+HhYctiSNlsVkoPri2MIAiCBEPJUN80k0qloK2trWp/Op2Gffv2QTabNRdA8rIYUigvCyMIgiD1h2GUwFBYdYmUZRcV4r1l4pXx8XEAqKzH0N3dLb0YEg9MrgiCIEgwGIqz0fm7luyiQgMDAzA4OKjRMDDvsQKU12lYvnw5txxvMSQemFwRBEGQYDAUV2iaT67sYhqiWeumTZvg3XffrdrP3l9lIfdhSYIl91tlF0PigckVQRAECYZSCSCm8HrN/GVh2cU07BKoHYlEwnJvtVgsQnt7O8TjcanFkHhgckUQBEGCweXMVSe5XA6y2SwUi0VIJBKQSqWgvb0dMpmMOYMlTwR7WQwJX8VBEARBfIW8irP0g5fDH8UUXsUxDsEjv79H66s4QYEzVwRBECQQjFIJDIXLwipPFocNTK4IgiBIMITgsnBQYHJFEARBgqFkAMQwuSIIgiCIPgwDlBbux+SKIAiCIPYYJQMMhZlrlJ+3xeSKIAiCBIOh+JNz+EATgiAIgtiDM1cEQRAE0cz7xqzSbPR9eM9Ha/wFkyuCIAjiK4cffjgceeSR8OTvHlCue+SRR8Lhh8svPBEWcIUmBEEQxHcOHjwIhw4dUq53+OGHwwc+8AEfLPIXTK4IgiAIopkFtTYAQRAEQeoNTK4IgiAIohlMrgiCIAiiGUyuCIIgCKIZTK4IgiAIohlMrgiCIAiiGUyuCIIgCKKZ/x+TCj0XFoHNbgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cmap = matplotlib.cm.viridis\n",
-    "cmap.set_bad('white',1.)\n",
-    "\n",
-    "plt.matshow(makeFernPic(F,np.pi/2), origin = 'lower', cmap = cmap_viridis)\n",
-    "plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77423552",
-   "metadata": {},
-   "source": [
-    "# Trying Erik's stuff \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "844cd027",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import demeter.euler as euler\n",
-    "import demeter.directions as dirs\n",
-    "import demeter.misc as misc\n",
-    "import time\n",
-    "import tifffile as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e2ec851",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "leaf_file = 'figures/leafbw.tif'\n",
-    "leaf_img = tf.imread(leaf_file)\n",
-    "leaf_img[leaf_img > 0] = 1\n",
-    "\n",
-    "fig, ax = plt.subplots(1,2,figsize=(20,6))\n",
-    "ax[0].imshow(leaf_img, cmap='gray');\n",
-    "ax[0].plot([200,200,250,250,200], [280,310,310,280,280], c='red', lw=4);\n",
-    "ax[1].imshow(leaf_img[280:310, 200:250], cmap='gray');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37c4e19d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Complexify in 1.0271 seconds.\n",
-      "\n",
-      "Cubical complex made of:\n",
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    }
-   ],
-   "source": [
-    "tic = time.perf_counter()\n",
-    "leaf = euler.CubicalComplex(leaf_img).complexify()\n",
-    "toc = time.perf_counter()\n",
-    "\n",
-    "print(\"Complexify in {:.4f} seconds.\\n\\nCubical complex made of:\".format(toc-tic))\n",
-    "\n",
-    "leaf.summary();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b86a4b9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e32dfd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[-271.57746322,   89.89517387],\n",
-       "        [-271.57746322,   90.89517387],\n",
-       "        [-270.57746322,   88.89517387],\n",
-       "        ...,\n",
-       "        [ 259.42253678,  -44.10482613],\n",
-       "        [ 259.42253678,  -43.10482613],\n",
-       "        [ 260.42253678,  -45.10482613]]),\n",
-       " array([[    0,     1],\n",
-       "        [    0,     3],\n",
-       "        [    1,     4],\n",
-       "        ...,\n",
-       "        [53828, 53829],\n",
-       "        [53828, 53831],\n",
-       "        [53829, 53830]]),\n",
-       " array([[    0,     1,     3,     4],\n",
-       "        [    2,     3,     7,     8],\n",
-       "        [    3,     4,     8,     9],\n",
-       "        ...,\n",
-       "        [53821, 53822, 53826, 53827],\n",
-       "        [53825, 53826, 53828, 53829],\n",
-       "        [53826, 53827, 53829, 53830]])]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a52e965",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = np.pi/4\n",
-    "omega = (np.cos(theta),np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4f5d741",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-128.46877882, -127.76167204, -128.46877882, ...,  152.25261331,\n",
-       "        152.9597201 ,  152.25261331])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "heights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75ae5f49",
-   "metadata": {},
-   "source": [
-    "## Making pictures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db2a598d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = 6*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a048e87d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f180e96250>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(leaf.cells[0][:,0],leaf.cells[0][:,1],  \n",
-    "                         s=0.1, c=heights, cmap='magma', label='T = {}'.format(32))\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d438bea4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "177.0\n",
-      "279.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Figuring out centering \n",
-    "size = np.shape(F)\n",
-    "print(np.floor(size[0]/2))\n",
-    "print(np.floor(size[1]/2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c184035d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f16b1855d0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -150, vmax = 150)\n",
-    "ax = plt.gca()\n",
-    "midx = 279\n",
-    "ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "midy = 177\n",
-    "ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "plt.colorbar()\n",
-    "\n",
-    "# plt.savefig('figures/Fern' + str(int(theta*1000)) + '.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec2ad8d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.356194490192345\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "4.71238898038469\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "5.497787143782138\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Ef2WPNmm95ndbASBB9GYRnXv++FMm+gO3/SKnok+FgrL4PHwLkD+cST6p41uA/Nfb6Rdj8bU3vKU3cm/MojfzH+9g232ytR5X7uhIKXpjJPr++xsx+1ctTPSAVoagqCNQVYJP3ZqLZ2TpFYXAoRC8d5dYonzPH3+KImWEiZ5n7dHvY9/iX6X9PppRUCULmWDl0/PwySlAdG8yhZYh2HFvhBi/xUQ2vE6z9oY+varV3pQ922rLveHj9HZEf9dbj+LgHT8TLD3lvsP3oUgdzonoAenq2Iauka1esElbRGICdWUobwY2MNEvWZFeGHDRg+3xY+vcm0xETxTNvTETPahbMmrpk4n+1MotlqIv4sKYd731KIrUEUvRFyvGYc+dJ5an9T6mQ8FbfIq+70063ObeC6Jq9fTJWPRgO449o1llYbkgJ2KzjKy2DQFRgNOPeTDX127Lp+d9+f4HvCj7Tx8UleDs98X+nDz6kKWR6P/o1L74VPRG7g0v+qerXko4z84Ty7HpuleTvm/JkK5Omtx183ahypKHdkOg8G399NCVU3w3BAovegCWGVl99IYyb3d7fD5gw6fnRU8tvRV23JsidQQOdQRv3qm1LuR9+iJ1BCoIXr6lSxB9kTqM/1j0W+FcO08sh0MZQdO1r1lekx1kHD9N+AauNCPLr5Gl8MsFjaDdEACg6oF2YdLKix4wn8hS92b+5g6c2q6J/8qduoUpKYqeWvrZv2oxtPRX9mzHp+7NpqI/XN2MWw82MtEXKSMJlp6KvkgdThC9A6Kbk03Rp4K0+BbQjCzfqps2e6J9b/77wHrc+u29LGRpp2vxDT/uYGUIFKOMrN6n/2RzfB/q3lhlZI1WTpX9pw/9PzS39tSnt4re8KJXRp8biZ6KPZmlL1aGse6a14XXXjz9TXxv3p+Svpd6xqXFn2hNpehyQR4WrzcQPVHFKkva90YPFT0fp9dnZPmQJfXphesY9enL29rNM7KcpS97zseiN7NfaoaiEiiKln3lQ5aqSiyTU7cH149b0adC3ix+JBKBz+cTmkoFAoGk+4233pnJOpwZFZzZ5ZqNHaY+PWWurx1nGuPHtEpOARBClv0/bGSi/+zfNrI4Pb9yior++IrtqHrtsbQt/fM3PY+HQmuYe1OkaiLfff0BAEDzyXtQrAyjWBnGI1drPZae+/S20bG/Y/Xcoyl8KnHGncU3ayqlt/pDQ0MYGhpiz8eyoRStp3/7tXjhVqqlxYsebGev6d0byrUbOhIWkQjRGxU4vdFjW/RQ4wVn1KfnRU8XhutFD0CoskxH9C/d/CwAjInoUyFvcXy7TaWSNZTKNUudLVjqaknsewNtEYl+5RRRtYksURUcecGjuTcmVZbXPxJfFXXd+vhjfo2sXvSfbvLg9EZN7LTgrLxdi6JE6huSuzdAgugVhWDu/h1suSAtQwCA4yu2w6ES3NTblJalBzTRP131EhwYQceibuy5wc9ED2DMRQ+McVTHqKnUWDeUMlsYTldOAfG+N0A8enPkhfg123FviKJZer4M4dSO+gRLP7dFiwhRS08zsgyLheF89ObsmiaU/3Y3FIWwbgg0ZPnRv24V9rOy9HxG1si9AYAidRj1x1ahSB3Bxj+vRLEyjG0Lfi9Y+mLl7+x89PklynDS9y1b5E34Vs2meKwaSuWTt1/zCnF6mpy6ZXWbUCSm73tj16c/2VrPfHoqegCC6PmQZcXedhCVIOIRo0ZnH16Hsmf2GK6RpRlZ2uwp8h0xK3tq5ZaEfQBYih6A6USWwrs3VPQABNEXK8OCpb9EGcbKimO23rtskDfhmzWbGm8YtQChotd3LTZq9mQHaun5kCXFSPT8l2HOE23ayikOXvR89MYqTm+WkT1c3YxDy1oMozcr3vkxXlnylGn0BgCz9LxPDwB7P1omiJ5GdIzcm7fOzsMloF+SESycdc7eG5sCeY3jp9NUKt9RHTv19Hyrbl70Rskpnmu9HaZx+k+21psnp0ZFHw9vaj782R/FSyPMCs4UBQmWnmfhq5vgUAlC9+wSxpe+0WDq0/OlxUZlCBv/vFIQPQA88bGTiX7tVe/gxdPfFETfG7mGfRl40TtAUKyMYP7ML5N+doAsWUibZP3paRmCkej5X4oPnxQjOGaiP7W9nnVDoAjJKbo+9tEGzH6yTet7M0pZ1x7D2hte9Po4PQ8Vvd3SYjsrp3ifnvLEx048cnUQ+z5ZIlj6S+gvAIZRXf5RgqV3gGDBrC8QPnc5ihXgEkXB179h/QUYd+HMCUMS0RNVSSp6WmUpdC02sfSAmJziQ5bl7W1s29lPtgEqwexfjPa9qVsv1N4kE72qElzzu63CRNYoI0stfWBph2HtzZpDDyQkp4x8eqOJrJWlNxJ9sTLCRD/riq+y+jFL4XPYTU7xa2T7ujSR0uWCfJWlURmCUXKKj97QPjt0nFp66t5Q0RutnDr7/Q2Y85vdTPRGvSwXvLI5aRlCYKkWarVKTgHAT499x9ZE9qH5b7HzU0v/fyMLLC09dW8+/+JyzLriK/z1LzNQrKi4dMYXWfmspavDYXWjNRqn5wvOrESvLzijySk983Zzvx5JfHpm6ZGYkQUgiD4V9ybd2hu96IuVYVZazPv01L353rw/4UB4EXNvHMoIXHNOsWv74POZTPTFIAmW/m9fluH/zOi3/AzlQpQ0SGbp9VWWQHZFH/Z64iFLA9FD0TKyZc+2snGanDISPXVveHIpeocygr0fLQMAPHJ1MCF6cyC8CCsrjjHRF+vi9nrRX6Io+OtfZrDXixUHRv7fPPaXCVL4oxiFLM3KEOhfMtHTMoT5j3cYnlNv6QEg4mkQC8440TOfnvPl9RnZ8OrHmOhpafGCV+JWP1ui71jUnSB6+pidy2Aiy49fgmEc6S9j2y+Y9QXmz/wSFTO/wqwrvoIKoFhR8bcvtW1KLo8I718m4peuzij6KksavQESLb1+Imtm6emxWHJKBx+9Ca/zYM7P2tgxz/6kgU1kjUTf/wMtRm/k05stIuFDlvo4PU1OAcC3369L2dKb+fQvn1mcEL0BgPfOlidEb+zCC179+mnhNenqpIhZyJKKnnYtPvaMBx887WEhS170J/bUM0vPR2+u2ppo8fUhSwCCe2MlemrpAQjuzZU92wXRn7x3my3R0+TUynd/hG+/X4f9t3Ti5Vu6BNHT2hs+ObVtwe8TRE8zshRe9A5ure2tsyPCRDYV1K+fZn/pIoUPsQzBLE6vXy7IsrRqvJ6eZmTZ60r81+DKnWJ7Qt7S0zKEhOSUMnonEp3ooWhRHFp7w/v0vKVf+OomfLB8Z0JPy2TuzZpDDwCAYOn5jGyxMoydJ5aj+eQ9aLr2NcOM7MtnFgNAgk//3tlydh0LZ51jPv3nX1yenQ/TJtLVgdjL0kj0RtEbq16WfHLKjIq97ULBmT5kSUVf9myrlpx6oYW5Nzy0ypIX/cl7tyVNThktDF/9fq0g9n2Lf2UYsqQCp8sFnz51R9plCKkkp+wgM7c2SSZ6vgxB3w3heJsmbKPam7gLpJUW8+hFD4z2p08ykaWWXlEJIlyM3sin/2D5TtzU22SrrZ+R6Kl7o7f0Zj69vuCsWPk7/qX8uHC+I/1lpmUIf/3LDFyWR+EXfALLTPRGVZZ0Wz4jSxeRXP1Yh2Dpr9reAdulxYCl6Pt/sCEevVE1cc/z74CiEGFhOG/pAc331y8Mp8kpAHhlyVMAYGjpKR2LulkZAi96/XJBozKEwGfzhTj9jWX9OP75FYJ7Q+P0l33jS1tx+mxR0Bb/5u+2Cd0QzNDfPJl3b6jozZYLChlZ1Vj0tqM3o6JXqHVXiVBanCw5xYuex6gMIZU1shR9cqpYGcYds40noJ9/cXnW3Bse6epkCbNmT5YdzrjlgnYo69wjliGYuDdWogeQdI0sH7LkMWoBUqSOYM8N8TvJ0NobM9FT+CpLM9FTsl2GAMhwZtbQ+/RmcfpT2+sTFpHM9bVr95xKAi1DSObTn1m1iYmedkPgSZacMsMsTr/l+L1sm6ZrX0sqegCoLv/IMDllxGXf+BKXzvgCQ1+VW26XC6TFt8BOhzOjheH6jKyhT29A2fO+tCw95ebXNyTNyOq7FlP46A1v6dOBT07lYhGJFdLiZ4Fklt5oYbhRwdmcn2kNXJNiQ/SqSnDdK5sNdz+0rCVpGcLq92sN9+VFv+X4vSxOnw58curUuRnJdxgDMorqxGIxdHV1AQC83ngLDrPGUROpoRTf7AlAUp/+000eWxNZK2j0hvann9u9M0H0qjoCB5ec0kN7WVq19TOCRm+2HL+XVVnSgrN0WDjrHE6dm4FijE+HIiPhB4NBnD9/XmgbEolEEAgEhMZRTqfTdHy8IiwiQfKJLACc2eCJ30d21NLztTd2sCN6RSGsBYg+Tk8buFoVnJndlEFfZamvnkwVWk8/HsnI1XG73aioqBDGzBpHmY3rGRoawoULF4S/sYZ2QzATPT+RDXs9CK/z6LZN3eopKsHpmscTRP/hv+xIevsdwHwRidVNGailN2oBki760uLxQtZ9fLPGUROloZQRfPTGaiIb9npQ3t4mWHqoBGcfTv3GyadrtA5nektPQ5YOdQSHlrWY7v/Kkqdw4LZfGFZZ/vTYdwz3oYtIzArO0uHr3/gSxcr4m0rm5YqMGkeZjTc1NWFwcJD9nTuX36iAHpqcOrWjXquytIjeADBcRFLWmd4dUwAIoqcFZ4erm+FQCG4PJvbS0WNWWmwFX3B2/5Xvpn3tlGzG6bOFbeHX1NTA5XLB5XIhEomYbqd3fehE1mxcT0lJCaZMmSL8jSVCRtYkekNXTtHojdnKqXQ4sWK74NM71BH8U8Br+07iL938rGGcfuOfV5ru89D8twxXSE0mbE9u/X57sV2zxlGRSGRCNJQyg28Bwkdv6O136BeBNnvSlyGkcsdwPdSnd4xafyp6elMGs4wsxSoja76PuFxwspFRAisYDKKzsxOxWAx1dXWsQZRZ46iJ0FDKDvp7Tul9elpPz/e9gUrQf1/6d0i85WBjguityhD06Kss+b43RvArp/RVluMZWauTQ2j0hmJnjWwmouehfW8c6ghUhaBIGWFVlnagcXq+tJh2LebhC87ocsGJgMzc5hBe9ACSih4qwewXre+jaxcqej5OnwpGIct9nyxJ2I6unJpIok8FKfxsMBqytFpEcvZ7TVk5VVz0wyxOnwo0ZLnumteFFiAvnv5m4rlSXAs7kZDCzwIsTs+Jvv8Br1B7M+c3u7Nyrt5vPYFXlzypuTm6NbLpsPaqd3D/le+yiSzPZI7qSOFnE87Sl73QIhSc6e8Ynin65FSmrJ57FCsrjqE3cg0buwRS+BIb9K/1soksFf3ZNU1QVIK53YkFZZnCtwB5KLQmK8fkffrJbPFlVCdH8GtkIwbNW7MJvdGafrlgISKjOmMILS3mF4bnEpqcWv9hTU7PM5ko+C4LueDsmiaUv7yLLSKhBWe5glp6qzIEiYi0+DmCXzk1/8C2vJxz9/UHsPPE8ryca6IjhZ8j7KyRzQWZrpwqFKTwc8RYiJ5ipxtCoSN9/ByhvxOJZHwhLb6kIJHClxQkUviSgkQKX1KQZDS57enpQTQaRSgUQk1NTdLGUROpoZRkkkPSJBQKEb/fTwghZGBggJSWlhJCCAmHw6S2tpZt53Q6LceTMTg4SACQwcHBdC9VUkDY1UvaFj8ajSIQCMDtdqO0tBTTpk1DX18fjh49atg4KhKJGI7rrf7Q0BCGhobY88HBQQAYF42lJOMfqhOSpPYybeE7nU5BtNFoFJWVleju7s64odS2bYkp/vHQWEoycbh48SKmTp1q+npWElh1dXXYt2+f6eupNpTyeOJrWkdGRhCNRjF9+nQoipLxtV64cAEzZ87EuXPnclbmLM8xducghODixYuYMcO6baFt4dfU1DAL3dnZyZpB9fT0wOVysVYhFRUVgiXnG0eZjfOUlJSgpKREGONdpGyRj2ZV8hxjcw4rS0/JqKEUbQTrdDrR19fHHk/GhlKSyUXark4kEkFNTXzhQywWYxOKVatWsVBnU5PWXaC8vNxwXCIZC9IWfnl5OQYGBgxfM+uQZqdzWq4pKSnBli1bEtwpeY7Jew4jxv2aW4kkF8iSBUlBIoUvKUik8CUFiRS+pCCZ1EsP81E9Oha3PM1WlWu+rn1cVvHmvl5ubMhX9ajf7yder5f4fD42lu1z8GTjGJR8XHu+PodUmbSuDq0eBSBUj2Z6O1I9ubjlqRXZOAYlH9eer88hVSat8J1OJ7uZNBCvHs30dqR2yOU5snmdqRw/3fOO5edgxaQVPk82q0fTJZfnyOZ1pnL8VM87Hj4HyqSZ3OajetTsHHoyrVC1wurY2SBX157tKt6MycnMYZwQCARIIBAghGiTrHA4TMLhMHG73WybyspKQggxHbdDZ2dnwgQx2+dIdux0yce15+tzSIVJW6sTiURQVVXFnvPVo9m8HWk+bnmqJxvHyNe15+tzSJVJK3yJxIqCmNxKJHqk8CUFiRS+pCCRwpcUJFL4koJECl9SkEjhSwoSKXxJQSKFLylIpPAlBcn/Ap2i0urP3cEPAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Version without colorbars \n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    im = plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    # plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.8)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "    height_fig = fig.get_size_inches()[0]\n",
-    "    print(height_fig)\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4e09dc67",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "2.356194490192345\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "4.71238898038469\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "5.497787143782138\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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xQQlSK8pxauEiFnigkAIMRiCCBx6oETGlxppOWBKwraxL9JMzAg9sP46IVBK5rIuVcrHHrnp/GSbWFmLy7gK06ZoQuRs/XISgCCgQtFEzEsuP5WDDC1f/OSafqSI6tFGP6y1S4k0XovrntbGx0aLcAwcOREtLS8h1Ny5cuIBz585ZbsnGiWV5EDXvvHiBSiLnKzdFzfjXcQNMcaMETfPNAv3T9xZi6CvFGPbXjQjcvcwQN6vI8dvhO4rYfTCR+ubX61E/a4OoiNClx/ixbryW3//1p4+J6KpOiaV2VZFYtOoe1xsAx/fywoULEZ0zGrrQWbrdf2hubu7QenFxMQYMGCBugwcPdjznRu8m3DBzE27ILHE5Q/xz2foKZrVxS44CjUvyLFab/BiRrLimh5ZYRK7pD/loejAfQ14oZQuUuaugBKd+xxpeyqImF+hTSjDqzTU4dPsaHLxtLSiA8UaEVS7Q142+YOxc7LjuRp+4/96UP1lSSHZMrsQbk6ti9nm6UXdqFD48NRKfnk5r/8kKQRAsHUi+BQ2ZGDx4sOW7WVzs0gAiQjqqC50lqlHUtLQ0iwLzjUMAIdftFBYWIi8vTxyfO3fOInLTpxezvSadWIrMEwpKwK+eGPmzw0t4VJWHD8zAw6mFizDsqXJWj/o0q1sFYakkQ6o2oyl7iSlyxj4cr10FoaLiQU4hoWDJwJoGIxmYPaJpOsa/s0oEHiDqWBlygX4bZXtuMz9+AhoheGfq1lh/kgCs/eQ06LiEBDEr9ajjeV82DcWkIadjf4EJRJvuAdE9jjUAOHPmDPr37y/W+/XrF9E5o6ELnSWqAuf1elFQYNZLBgIBsZnotu5Gv379wn5whFIYiRGATnDDzE2ARvBxbUHI18QbTNQIi6RqVIgcwPTp5BIm8KnlFSA6xbAnyy0pI5yhz5QBmtFmCUBTNrfkQqeQnLp7OVLf2OAQOV2HpQuJpUDfuD+xthAeDWJPjhAzsKDDjK7unPpUt3xuFUdmwAOKvqQNGqHwQIeH6KKfHP8AZ6V+6/p6e3RQ4YRvM9jXAKB///4WgYuUaOhCZyGUdq5VgN/vR2VlJVpaWpCTk4OsrCwA1rDvwIED211vj3PnzmHAgAE4e/as+HBvmrYR1MOsGEoIEweNgGoEe3bmhz9hDzF6hQ+UAEfX5QIARq7xGddv5r1RDQABThaYFmxqeQWrIzUe43lypx9djKHbysRxUw4LAgx5nltxxuaeRo3oKmU1rEb0FoSVVp2cuwIjqtdB0ygI4TdA03QjhURnkVcegSUU+zKZa8JEjpdoUXiIjt22hOBosP7grw0hC0IjegiRC+LeEV+GPc+np9MwZWhj1K8vVrh9F6J97l/t+gP6/pdLLI+1/p+L+Oebn4/ofWOlC5HSaYGLFe39p06/qdgqcoaLFo9Cx0UOBDi61hC6tT6LyJ1YxsRteGkFz51F42K2xi25UwvZZKyhT5dZRE80gSCU+aEuIgfCrDl+HLhruS2FJDKR8xiP9dF0UagfLXgXEi6arAsJdYicZhw/NHJPROf98NRI9CVtCStysRC4m9/PdhW4XbdUdcv7djcJb7N/+EEhPvIvZZvyOjWijhTTZpVi6q9Le/ryrEhR0VGrfBi12ifWiE5wYlkeRmysYHlyOtCYnydaszF31RQ3ABGlkDTNL2DuqhFtBWX5dfw49Y0NEaWQHPjVBhFd1Y3OwHIXkq7yUP29AIDHD9xtqV3lHX91SPephiBYKkqk4gawNkCJPCEqFgSNVB9LkKEDeXDxRsILHOcj/1IjAkktKRjxJHJHNuSaIgc40j4uW19hfRzMAEvbXCFSSGQiSSEZ8kKpSCWRRU5M6wqTQgJApJBMeHcl60JiPAdgezOTd3d+7/P+fQ+I+0FKRIG+EDdJ5IKUFfcDEOJm71vWHsFOvKa30aZrrrdEJXGv3IWP6iRLTlhzTOSm3LYZU26PrivVGYTIGWkg1n+JIdLAyaV5zJKTrD5Z+AD+Ohcrjj9P6kLCLTp7FxK3FJJjv11ls+aMEXIibcSaQjJ5dwGu3V3gqHgIx117s0V+1fx980ViME81sXchaaUeiwW3eMwu5I2u69Bnr4MJpSI0ySZwCb8HFwoeXZUDD1NvLQXV2H7dZ/8U+Zexuxm1yieCCMdW5+Ky9RWgBDixPE+UdfGAhPmvWdZlL9B37MmJxyIPPLhh1q6ae3LmfYq9M93zEjP3LETttC2WtTv35jh6yFnvs+Onrnzdsie38fI3O/05v9U4AZeQIDToyEw93Onz9BSx2IO77u1H0ee/WLMY2v7PBXx++9aE3INLWoED3EUOAKbcvlkIymdv9rzQjVrps0RXj61m90dsqLBEV0OJHACRJyeLHM+TAyBErunBfAx5qcQicqfvLTRmPECIHAjFyTtXsuszupB0hhkf5QoB5CJ3x2ePsGJ9TY9I5DzQ4buy651I3my8EpoRdb019fsuny/WxELgrnnrcVeB++qOpxJS4BLX9oyAj3cvFe7qtFmlmDaL7cd9+vYSZsFR4PrZm3FdVhmunVPWztm6j6PrcjFqpY8dUCZyI9f6cGJ5nsWNdbir8p6c3UXllQ7ZS9CUvUS4q2JPTg48GK/nxzolOHnnSgzfsQ4jqteJiofR/1QU0c9zrbQv18b3z6RrlZtmvjG5ylK7Kt9vox6xF5d7YG4nPlmT105MNPbxlJsajqCuud4SlaQPKX28m02huiGzBJQA02aVCmuOu6nXz94MEMJEjgBf7Fgc8nzRZswyliZCCLPk5AaYI9f6RDtzezIwb7XE8+T4nyqiG6kyRkKwo2mmrQuJOa2LlXUNe22jmQxMiCUZGABG/1MRjvymyPVnYdO6KDzS98EUNrbIB9mwXzyzIqJm8rMAjGldojumDsCDZ9Nf6ezHKwjCTAaePfxAl8+XrOgugqYnsMAltYtqh4scjLy5T95z5spxkaME2Lu9e4VubIEPh0qYOzp6uc/igsquqVsyMHhWiGa6qw5X1pjzYBE5IGyeXCh31S1P7vAdReJnueK9FfAYj9nz5AC2V8fz2nhicB/JDTWPmYsazUL9545NETl0bC9Px13D90ft/LEiFi7qhJpF8PyN1UUN/ucFfJtVrlzUeOfj2oJ2U0i+qF6ML3YsBqHA5LvKu+1axub7zGgnJ0wKidU1Ne7Lr+ERWNvzhj5dFjKFhP/bdP9S4Z4OfaXYNYUEcKaQjH2b7ctNeJft1dlTSCjCdyF5Z8pWvHX9NkcXkmiXVPFi8aDh7iaiuMWKZHNRE/fKO8nHtQXYszPfkkLixhc7FmPvG4sw+a5yTLq7HJPmRUfsxueyvbZDpbk4VMqst7FLfWyfDQiZQnJ8RZ5IIXETMtGFRH4sRApJU/YSNP0hn91/wNgvM1JETt9b6JpCwpODeeoI/xeAawrJVzdvEl1I2qSUkjZq3s/csxCzPnkcb12/DW9e94xlkM28Lxfgnq8e7NJnXXFkBgDgoZF7cJH2MfbfCF45MalL5z1x5r936fXxDKXut0SlV7modqbNKrW4hJ+8G768a9K8csNdBPa9sijsc924fKEP323JdayPzfcBGnBoUy5zVQFrxFST/6Vi3VLWZYuu2mtXQ6WQNGUvMbqQGFFWo6/c0FeKLSkkvE6Vu6vcVbXUsCJ0Cgl3Vwmh2HNTmSW6Krurb12/DQDLk9MIxWuTnuvw58wpO3yzo3a1I5UPMg1NvzQiuhTjf/k/On1NXSEWLuro1wtcXdQjd5ckpIvaqwUO6LjIAcA1vysXArLv5faF7oo/VuCbp/NCPj5uiVGjqkEIKICIRW54SYVrCgnVgFNPLHJNIYkkT+70fUvb/dnMVkvO2tUDv9qAibWsfbq8J0ckUXMTOTmFpCt95YoPzQpZoL9g5KcRnePT02mi7rWnWy3FQuAu++tSV4E7/rtNCSlwSR9FbQ8eUZ02qxRUY8nA7YncV381RW3ifeWgRn82qgH7X2RClp7NRKehMi+suAEQ+2l8PgMb3gypNxxEFNXeaglg3UeGl1SYzwVEPzkAOPW4JHIGkUzrYtHVpRj2142ieaYM60JCQrZaApi7SgjFvpklov25PJJQA8XuGyrEOW/79FHw4dKasSE578sFIjfupYkvhf8sDdYevBWasQPTF/wD7IOFoyJriV0bGAMP0eFBH4C04VqbuH3/L//QY5Zcd6LrBMQRRSUhnh3/9HoLToaLHBMrgk/f7lgS8NW/rwDVTEuq/vl2hE1i3GIWRT24ORdjl/ps1pjTkutqdNU1Gbid6Kod3oUkXKulb369Hhk7l7tGV2Xr7YMbmcjN+uTxqERXV35/R8guJAtHhxe5txonsNeANdGcMczZPLMniIUFl/ryMnj+5lLLY8H/PI/AfRsT0oLrdUGGcOzZmS9t7FNMuX0zrv9N5PWrX/85D/tfzBMb/Rl/qGj3NRyRzAuIduaHN+aCUODI+lwzmGBL+uXBA75+Mj+PeZ7SIBv+2KnHFzkG2Qx9pkwEHnjNatMDBY5kYHmQDQBLFxJdN4MOh+8oEl1Ivvn1elzx3goxyEbuQsIDD0FdE+I28+MnpME11sE2oWY8PFR/Lx5tmAcAlmTgUF1Iwonbaycm4o2TV6GV9mGvgcdV3L5sGtref2fiohsBJekGZcF1H7G04DhTf1UqrDgQdNiSA4CrHqiwWF0NVZFbcx3hsnU+c09OA04UhtiT06hlr1Hek7Psz8EWeDAsOmK4t3KenLDaxB6cGXg4fEcRxr2zCh5iWnf2wIOHByBc9uTMMi5mvb153TO4a2+22JObv2++sOqeu+plAKzVErPUKDZPqMay72ajLwlizfi3UXxoFjSio2BMbcjP8pUTk+CBbml/fkfat5bn8D25a4cGovVfGDGxsOCGvbQcms2C0//zPE7N36AsuGThk3/Oxyfv5bM9LIoOW3IAhCVX/3weGqrykJ5dgfScyC26SDm+MtfMgdOBEcUVXUohAVgL9KYH85klx7uQ6Fwp0W4KyeE7ijDmraJ2u5B8NqMUn3g3u6aQtOka3pmyFTrV8OZ1zwCAJeAg58wt2H+faLXUapR3Lfl2jjhe/f3twoKzs+3oDQCAF49fJ/LkeOcS3fb1qDs1Cq20T4+IW6yguuZ6S1R6fZAhHJ+8l4+pv2YdSIgOXP+bzR3rQiLZxtyCS8+pEJHSb7ZFx6o7vtKaejJiY4WIrloCDzoB4XtyMAMN/L5szYnAAyEsg1djZVtskA0RMx642cfeh+LYb42pXFJ5Fwh1nfFwza6l+OrmTY7Ag3XGg/uX6y8TX7SUdbXCCCZARys88FDdmK7DjgFg1bh3HefhDTAv0j64hLRBNz6secP3WZ63MzCOBS1EwmJyQnV2s68lKkrg2oGXc029tRQAYZYciawLCdFZ4OHrP5tC1lBp3r/yYR6UIPh2a67bKTqHIaz26GrAaH2eWlHuKnKWYwK298KjqzoTO7dBNoA5rcsMPLDoqqYxS89tkE39rA3ickOJnL3Nksyr17wQUuRANIiSVqpjzeVvW15rppAQbDniRV9CcBF94IHz28y6kHhwCf8gw5Do0VVukdvXEpXEtT1jzCfvMpeVb9hfP7t9l/Xrv+SF/UIceIalkHy7NRcTHvVhwmM+XL7Q16XrvGydj1ljYJbcyYI84aamba5AWlmFJfDg1oXE0v5cbpppCzzIFQ+8C4l97qou3Fcz8MDd1YydywEwt/WLmSWWioe6G30hB9jM/vxh3Lk3h12eSxcSHqAIQhNNM5d++1vx+rUHb3W0P2+lfcRrHrjsc/FcuQvJReoJ2Ufuy6ah+LppSEKLGwBHgEEEGhIUZcF1AJ4fx/vJXT97c7uWHIkwhiNbcJcv9IngwPe+yC27kUU+HC8yn89nPIhOI4DoQmKfu+rGkMrNDkuu6QHWT45bcqd+V4hhf92IwLxlSH19o2k02bqQACxPjjXNZJZc/a94CgnBxNpC7GtnkPesTx7HzqlPiT26rC8eQh/CoivMejMtOQ8I+iAIEA2+CWYvuWXfzYaHeNCXBEVGNc+TWzjqY8v7vXj8OstIwlBdSFjgQcOkZNibk2qPLWsJioqidhK5aSYlBJ//Y+jOIxPvK3dUPGT8oUJEPmW31c74XJ+IxB4sCy12ctNMzmXrfB3Kk5OFbui2Mpx+hP1MkeTJgcAyrau9LiRsuDTLk4sUPnc1XJ6c3DTTQyj6kCC2pr8mzrH029+GndaVN7oOfzo6XXQekbuQzBuxz3FNdadGiSqJ7p7WFYso6uBtRdD+P1sU9f+ex5lHiuLuOxgJyoLrJDx15PrfbAYBxXW/LQPVWDcSO3Zxu+oBlsbBqxfCIVtwPBmYEohCfd5P7ugGp/jx6Kmlnxy34HSnJTdsS7kQudOPmHNXwcWRmgEHuyUHUObKGMeyJQeY+3HcHKJgrumEd1eCEBqR0PHOJGyPDhD95Iw9O7slx/vJPdowT5Ro9dGMH55XOdgtORhuL/jPzNbuvcw5c/XdwHj0NQIP0xN0FKEdN5c0kV1UtQfXRXhnYELZkBu3zsDX/M7sRHL1/dZBMg3PRR5JPViWi4ObmZCNzfdh7FIfQI1BNi4cW8XWLekiMO5DWpNbLclQ2w3suU0P5ndpWhdPIaFgohVO3ORBNnKrJZYmwlJI5FZLr17zgiWFJGj8G8m0rsVjdqHs8M1iahd/biiSskMwT+y13xIU5aJGCR5dpcbwadmS411IeA3r1fdXWJpZWorpCXDg2Y6lj4wpZENr3IRu5Bo+0IY63itcgb6dIc9utiQDQ6No+kM+hrxYYiYDG+dw60Jycu4KjKhehxNzVnboZ7t2d4EYLn3jh4siKtCPZMZDH00Xg2x4Ym9fEgSAiMq6qk9mhEwG5nzZNDSqBfoxcVGfXOvuoj6xKu6/g24oFzVKWNqfa9a/eF++tkj0k5t4bzm+NlotXf17FgCggFlMD+DKh8xhM+0W6gM4XGwKm9wZ+Oi6XDHARmbExgpr+3Pxr1mgLzP06TI0/ZH9fHLgYcjzpQAhaPq91V11SyEZvn09Ts5l4jbyH9eCEIqjs1djzFtFrl1IODyFBGDWnpxCAjD3dOeUrbjjs0dEqgnfZLQHHkKlkPA8OfYqdtU88LDliNchcqziwWN+gC58ejoNU4aedn8wnnGz2BLYgoupwNXU1AAAmpubkZqaCq/XG8u3jwmfvbnEPYXE0A2eJzrxvnKWMGt80ewix4+v+GNFh/LkuBU3eoWPDbIxhPKYLbpqJ21zhRA5mWFPleP044tZZ2ACMSNCjq6ysi7WGZjD+8kx8SQ4eddyQ+RWiOjqqDfXsDxily4kHL7GLDniyJMDeIE+23PbMblSvHbelwvaFbnNV1YDYNFV8R8k7cnljdklzvfssWlG4MEj/pPmDK93fJYs8JCgtoMOQHdZ6wDx9D2P2f9CIBBAXV0dKivZL+CMGTOSUuAA9yRgbhkRauzJacy2YVYTwoocNIoJjzKx+vZPEQrd+lyMXuHDESmyOnKNad1RjbKpXQaNS5yiN+zJcuGunv6jIXIInQw85MUS0SGYdx+RU0gCd6/A8O3rWd6wTnBizirxXmPeKrLMeODolIjKh0+8mzHVv8TSaskaaDBHEjL3tf0UktwDc9FH07F5wptY9t1s6CCWwAOHJwNDShtxs9544GFGAs5dBdDlNJF4+57HTOD8fj9SUlLEcUpKCvx+f1KJ3NRfm0X69gJ9QoHJc8sAjYhuHzzVwlXkpO8SFzkKggmPmUOiv3uyHbGzeZvcXR251gfoxHXu6sn8PKSVVaBxcZ5zL06ImYvIERZdlUUOYBG4oa8W4/Q9TORAiBRdZbC5q0XuPwJlVhsMN1WueGDwaGnHoqvPZpipI7kH5hrDpXnIGA6RC4IAtA806OKvj5wQXH0yw3idFtJtTQSIbnoZ8lqkxNv3PGbhn8bGRgwaNEgcDxw4EC0tLY7nXbhwAefOnbPc4pkbZm7CDZklmHZLiaPVEue635aJQTa8gP+rvy4Cb3NEKJXuw4xuysf25+nA5U/4xIwHN45skFqgG4xa7RPn5xULJ5fm4WRBHk7mG1acDqSVVbCRhBKnH1tkabVkqXgwWi2BMpETUILT9xRi6KvFIeeu6nrouatyqyXA3JPjrZZ4NNTaWsnZasle8bBg/30AgEcb5lkqHvj5WqkHK8a9J66DVzzoxmNydPWVE5OMNVYRkYhDpTkirVG+GY/Zv5cXLlxwvD7S73ms6NH4dnNzs2OtuLgYAwYMELfBgwf3wJVFxo3eTYYQ0ZDTsACY1QzSLw1bZ2v7Xl4kxGv/i3mofyEP9c+bfeUOPJtniorxvG+fysV3T+aKPLnxi3wYt9jH5juEYNQqn+P6CHU+rzE/D42L8xBYlIfU8gqk+sw0F35Npx9Z7JpCwlNHuMjxtuen7ym0pJCkSf3k7CkkfFoXwEq7+LSuibWFEU3r4ikkbtO6+H3eYkmnGp668nVLCsnGy9/EuvFvAWBlXQCEuMkpJIC1C0m0p4H1CGHSRAYPHmz5bhYXF7dzMobb9zxWxMxFTUtLsyg534C0U1hYiLw8cz/o3LlzcStyH/nZl/dG7yZjNw1G1w3DirvNKMw33FVCIR4DWHSV4zbbQe4I3F7qyPfluRi/yAfo5oyHQ6W5ltQR4fryfT6dudOXrTd719kDEIFFtvc1LLehW8tw+lH36o0hz7PGBG7uKtEoAvOs7c+H71jn2JOzvCUlli4k3GPSKbEU6H90YzlmfvyEaHfO3VU5utqqeyyDbFqphkfq74FGiOhCsuTbOdg8gQUfglTD2oO3Gq6n+cHxsi7ehYRnTtu7kCQa4VzUM2fOWNJE+vWzzm4AIv+ex4qYCZzX60VBgfnLHggEXP3yfv36uX5w8QwXOgC4IbPEInIgZlkXIcDnby6xJANPurtc2vjv3LQuDhc5nvYBQLQ/P1ycKxJ8LcEMQ+SIsSvGU0hOLnUX1FMLF2HYlnJXcRMpJGB7dU0PFlged2t7DgAn7wyfG+fWakkHoBGKNqNQn7P7hicx8+MnAFj35KwpJCa8akGOrsq1q63UTCHpy56BvmAjCVmJFutCIlotJTphoqj9+/dvNw8u0u95rIiZwKWmpmLu3LmoqalBc3MzCgvdf9kTjZtu3GgOnSEE0IA9tew/eOqv2MxVYjQZ4wX6XxhR1mvvLAMhXGDQ4XC8DC/QBwEO+nIxbrEP45b4cMiY8SAIJ3IUoqxr+CYWgDhZ4BQ6t+L8odvKRApJU44zijzkpZIOTevisOjqWgDAFe+tsIhckBKRQiKLnAhMwCpycgoJANZqyRJpBbamvyoe54EHez+54kOzLINs+qIND4z8HMmAvIUir0VKvH3PVSVDF/BO2WC4dgTUY4oc1cCsOCPh122QzXVZZZYqAt4eXa54iASeJ2evhpCPeXnXmEIzTUR2XUcWmeLIU0jk17uJnJxCMnRrmXg9jNe7iRyHT+uyw1JIrHNXj8429+N4gT5vf24fZPPFzBJM+2Ax9txUhps+ymOVBraKBz53FWh/WhdPIbFXPMgF+ouNPLltR28QVRJygb7c/pwPsgnVcqk9YtKyvGgjtEttlQznz+NU0bK4/A62R4JmI8YJ1MjcBwUJAtTDjqATsSfH9ryIYSlRi7sKQwyZ9UZNSy5Crnyogp1HSjWx59Dxfe+x+T4cLs3FmEIfCKwVD8QQs2Orc0UKiVygP7ykwiFyfF9m2JZyZrkZ18EeI8Jdbcp2Cp2buPEUksDcFa4/K6t4sLqrGswUEtOSY+7/BzdW4KaP8iCnjOycstVyztcmPYd5Xy7ASyEmdQVhVjxsurxarK89eCtWjN0pjpm7ak0hYR+EeT8IDXekWZOCPzw1EtOHHXN9756iq2ki8Yay4LrAjGvXGZYXCW/JSWIm6lWlVkuy5cUtOTkAYUdutSRba9ySA2FRVt5q6fsKZq2NzffhkCFyzjpUdju6Jjfk+w4vrQAFi7KmllegvZGE8nBpN6Fj7c/Zc0K1WpJrV+Vk4CveY0LoMSy9/bdstJx7qn8JPJouWiOFs+TC8fiBu+GBDt+V27H0299i04R/tDxefGgWCiWxc2u1dO8IsxOJPJKwo5ZcTMYGrtgIj82CC54/j8B6ZcH1KmZesxbw8DwwyZKj7AjEiJrqFISLnJRKYlpblKVLwNACI9JqL9C3wPfRpP00tiZiuQDMqK0dQoFDxUaOHD+HcSLurlLNOuuB78kRYpR1EeOnptRqsMhvKD0wpGqzQ+TY9RPxQ7PPwPwsKKilQP/wHSyF5NDta0Apmy+hU0ADSyHhLuvemSX4xGvmIc74KDdk+/M79+ZAA8Ubk6tw/74HXIr1CWDMhZDFbfX3t2PN+LdROHYnSg5nwgOjBx00PDTqQwBmCol1WhfL3r41LT5z5ZQFF2Pi2YKbec1aZrEBVkvOQwzLTNqD0wg+5sEHY5CNZd9MWHq2+y4iZx9JKEdhv9mWhwmP+kTt6uVP+EJWPPAuJIB5Dus1ASAUx1fkYURxhcXSs1h/HRhJyLuQcIb8ZVPYLiR2S27MW0XmuELAsicnjyQksA6XdutC8o7hsnKRs3cheeHqPwOA6CcnjyQEmMh5iI5V4961iRxrgKkRiktIm7H/RoXIyfWrtYExEVtysbDg0grdLbjGYmXB9RpuTl/N3EEPYXtvhmfILDICGqQgHpjlWMae3A2ZJUzkKEQKCcACD7wLiZxeYt+Tm3hfuYjUyvtscr4cYLY/n/CYD9CYyFHCmmeOW+wTnYF5tYRohCmd054nx4XHtQuJy7SuU48bAQijaebph820EtaFhF1A0wNLmcgh9CCbE3ea+3KRTOvae/MmAGarJbcC/TYpx92tC8mrE819OZbAqzvy5NaMfxurv79d5MnxFBI5unoRffDQyD148fh14kOTWy1dEmJqWE9BqIsFF9cmUHiUwHWCXQ1rAAAzM4oAjyFqOnUVOe68cpEDTNeRT+yacvtmJlpiPJ818MDdVV6gD2PEHzRg/wvu+WpX/LECvFsJDzyMz/WBEAiRO7TJSCHRWZ7c6OU+HF3vtPYuW+cDv3o5cMFFLrCY7cnxzsAyvO25BVuBfqhpXbwzMO9CMqJ6HTSN4PAdq0XTTPu0rvpMM7ueIrJpXXzGgyxy93z1IF41gg+tVHO2WjJYM/5tACzwYG+1xEXu2WPT8JCURvLaiYkAQs946FHkyhR5LUFRLmonuPnylWz/jRBhUUHTDBEiFnf1g0+WY/r0YkfggburABtJaAk82FzUzqSQyCMJ7a4sDzzwigdLsML279G1uWbTTMDRhWR4aYXtNe4zHmTEjAfhvhr/SoEG2V21Bx7M+Q782NpPzi2FRHZXeeCBT+267dNHXWc88IqHB7/+vWvTTN+V2y0/Fxc5ecaD7K4+NHKPeO5rJyY63NX2iIWLOmLJRnj62VzUC+dxYnNiuqjxZR8nCIRSIEhZmgilRva3btal6vwx4KapG0CCzGQj1Bg7KKW8T5tVKlxWuVhf3OfurEsCZijScypEIIPXrn63JVec4/uKXIzP85nntBf5S//y+lXeCODE8jyM2FCBERsrWOCBslZLjYt57aw5knDYlnL3C+Q/h05EgT4rMLWOJKTSsdtIQiqOme0UNIrw62dtELWpQeMxeSQhL6jnvDNlq2uB/rwvF+Cerx7EC1f/WRTrt1FPyFbmq8a9Cx3EdSRhK/Xg2WPTxHPnjdiHVupB9ckMVJ/MwFuNE/BuYHxk/8HdCA8y2G+JihK4DpI5utAUtghFDpQ6RI53IQENJWzm/b3bFwuR452Bw+LWhQRM5ETAge+1yCInH1PrOUDZjIeRa32iiwhfB5glB92oXW1H5EJ1IbGLHCjCipwpdFaRAxBW5HgXEpYnxwjXhQSAuB+kRIjc4wfudvxsq8a9ixXj3nOIHO9CIotcEJqlC0mQaj0ucskmcMpF7QSZowsBj+biorq4q4RYc+GkPDkQgHrc8+QAgHokt5YAX+xg+1mT5pW75slZoquS63ng2TxLxYOcJ8e7kQh3VXJnD29kTTMpgRhJaG+aCWIt0E8rrXAdSQgCEXjgmLWr7LnQwAbaSGVdHR1JyN1VzXBheZ6c7K5yV1ROJeEjCZmLGrS4q29MrrJc94L994mRhBqheOrK1zvw22Py4vHr8MBln+O1ExMt7qyH6K4tl2Lhoo5c6O6iHtuiXNReQebIAhg9fqzWG6XYXV+E3V8XmZYbuIsoWXKGywjAsFjCtFrilpyxdu2dLEs/VBLw/hfz3F1N6Vzy+flj4xf5nK+hEHlylnmrttdzcRu+qQLDSypELp9wV6WfadhTNmvO8rOaVl3T/AI03b8Up+9bygr0jee1N60LgGVaF9jHKx6zt1qa9oEZAKmdtsVsr2RrtXTX3mzxvPn75jumdXUVudWSDq1H+8klmwWnoqgdhRu8QZ1ZcQB2fc/G3t185SoYZgWLrkopJHVfsFSHm6ZuAPEAH/oLMf2mYktZlz1NBDBLvPhj195ZBqoR7H3DKXJX31/hSCExUzcgEot5Z+DvePtzIzhIYbzWOOYpJKOX+0TtaqhWS5GkkFgGSz/NivMtuXJikA2E5dY0v8DShWTYaxvBzT72PlSkjpizV4GDt68B4CzQ12F2Iemj6aJ2FXCfuyqiqwYvTXwJ8/fNh1yg/2jDPMtw6UjhHYF5pQOPrvYkRP6jKK0lKkrgOsAtqYtY3aUhbFzkMsctR+3BDWxPjs8jCQK761cDAGZMWivOQShAg8ZvDGWC89Hu0B0XRO2qnEKiU0yeW4a9260pGMyaNBRVSiFJzzZqVnlenmZWPFy+kKWOUN0UqVAix13NUK2W+Lpbcb4D6UvD25835SxhEVZNyKNwV8VAG2EAm3lybiLHp3UdvG19yC4kdvHiUdXMPQsdInfn3hzRjYSljbAiVbPVUteZNyIOesnp6PLQmXhC7cF1gFuG5QkLjRp7cLXHWJuezHHLxZ7cru/WiWTg3fVFlnN4r18P/2dm4ur06cX48EMmcDfM3GTpQrJnZz7s2LuQyCI38V7eW47g67+YIpPxB+cc1gPPOCse3Koj+H5auxUPMC2xUHNX7c0z3bqQyBUP9hSSpvuXimldHUkhOXjbWlzx3oqwKSTyftzMj59wnbv65nXPiOewVkvWuavbMsxWS91BLPbgxjzsvgd3+JnE3INTFlyE3DJ4oeF6sm8zCep4v7GM7ckJt1QT30+eDAwAM68uEoEHYpuZSoJU5MkRYu1CMm1WKahmJgQDwOc17l10J80rFxbY1y/bLCgqu6jMArry4QpeYmlxV8fn+kB0s0BfZswy1kzz6PpcjFrps1hyIMBxI4XEkQxMAbeEfaI7u5BYLDlCjRmzzDId8pdNaLq/0DGSkNlSVksOAI78ZrV4r3BdSGRxA0I3zZz9+cNC5F695gUmcpIllwyoWtQYEw8W3C2/eBSGOQAQgvf/5Um2nrZYWHKyyNUe3GB5/cyMIkt0te7LVeKxm6ZuQEe6kNindXEm3V2OL1932Zf7PU/4hcMq49bWN0/nsbIu49huofF+chy51ZKbxWZPBg7HsKfKTZEjrPLBLboq78lF0oVEjq4e+U0RALOfHABHdHWv1DBThkdXQ1lyAAs8cEuOz3roDmJhwY3L3gjPJTYL7uJ5HKxKTAtORVEjgAZ1UF0HdN0Ut2F5LCoa1EGCuoiq2sUN4HtjMPPkLI8BH+xZ5siTI0Ep8irlycnTumTcxI1dPNqd1nXFHytEpJLYHgNlKST2a+bnOro21/yrb6wRYyQhTwYevqkCw0srWAqJy/WdfmwRTv9xsRmdDZMnN+SlEkt0NVSeHKUER2evFuIGAN/ftlYkA8vTunjunBs8uirnyc3+/GHLc16a+JIlZy6RSbYoauL/j8QCqgM6xfv/808ADHfVSOQFpXj/5GbUHitB7dFNIV5vppPs/mqV4zHLc4zDj+qW4uPaAtcUklAi54acGhKqWgHUKNCXju3PsUzrMq7piFy3Kr2Wva/0euNxq3POkCOrYri07VxNC/LR9GA+mh4oQNN8ac4DTy0x/ivkFBK5j5wMhXsKyTW7QrdRFykkCJ0awkXuwa9/H/I8CQENcUtQlIvaDpmDsgGNoPZ/swgad1e52Lm+ZtRSkQgskoE9bvet7mo4ps0qtbRTAkFId1VGDjyEamneUJmHKx6pEM8LFWjg7urYpT4c2sTEze6uWmpXCXW8V3sR1lCBB7eGmYDZNFMOPATuXub6XAAY+/Zq11ZL9bOclrcdu7saadPMaBELF/Xy+e4u6ncvJaaLqoIM7VD7gzWL/f1/3RrimRKUWvLkCAAaBHZ9t5ZFV4MQXUgA1hm47gvT4uCDbHh0FXCPqEaCpbGmlEIip3Wk57A0ElHxAGsXku/KrXtwXNzMn9cMXoxa5QM01izT3oXEPpLQ/XpZoMF+7NYwE4BommlttRSaQ7evESkkcuAhY+dyeDQd+zJDz/rkHUhmffJ40n5xki3IoFzUKHNL2mLhuiKos5vhft58+UrTFQ1SQNdZZ2Cj+N57/Xp4p7B8Oh5dnX5TZMN1Q0GoMYqQmu6q2z6bqLzQrc8DZSkkIc/Pf/mp7Zz8MUrEntqIjS57cBLDniw3Xm+6nvLxkKrNZicSg6b7lwr39NTvluHUvNDWG+fwHUWOAn2dIqy4yeyc+hTaqIbbPn00oucnEsSoo7bfEhUlcNHGCDyIoMORYhZ4CFOgz93UUAX6028qxo3eEPt77cDLuuQi/lAil55TgQPP5OGbp/NcRW58rlPoiP1cxr+8C8nxlbk4vjIXRCftWnCnnlhkETUReJBFzwVe1tUR3ETuyn9eHvHrd059Cu9M2Zp0IpdsQYZktbR7DmNLkwR1UI/mmidnuqgsCggYMx4MD1I0zYR1WteN3k2WIdMdgUdP5UoGYiSQcRey/vk8pOeYpVfQKL7ZykTp8oU+1yABdFimdVGY7irRzRkPhLDGmfKcB9frlHLohj1VDhCC0491fhh2OHgXEtld7SjvTIlgyyKBUC6qIiS3DF4oXNL3G8tQe7zUYs3J7uquA2uxq2ENCKUsT45bctwl0J2WHKFM5G6Y2QlrzuZCullyANxTSGBrtSRxqJSthWu1JD922TofRmwI7aoG8haZbZiM1wz9UzmGPl3G2p9HkSO/KXJttdSrSbIoqvofjSa6IW6n2Bf4ltRFprjZbpnjV1j25HZ/XYSZE9dE1IWkM8XPhDJ3VU4NcaSQwClQoKbIhcV4zeGNuZZjft+RQhL2XGYXklOPW6+5O0QOgOhCMv6dyKLayUqyuahK4KJE5n97hInbmS3mIhcpFyvOvicHALv3rTYtNqlp5gefLMcHHy3Dhx8WgugUH9V13E3d+8YiTL6rXAiPHHjgFtNVD1Q4Ag9yU8twEAox48ESeNDd/jUG2YQgsCgP0JkLzQMPpx9d3G0id/iOIrPtUlTPnIBQlwBDfGeShaVLe3AtLS2oqmJpFPn5ZhpDTU0NAKC5uRmpqanwer1h15MCquP9fzXzom4ZvJCVdvGaTGNPDoBrCgnApnWJVkswB9l4r18PEAL/p8stqSMdhegUe3cslo7RbgoJAQAduOKRCnyzLXSQQJRz6bB0IZFTSOTa1WOrwwccCIWxT8haLQ3bUg5CiKNpZrTgNazUJYm3NxGrPbhYaUeXLDi/348ffvjBshYIBFBXV4esrCxkZ2ejpKQk7HqyUPvvz1oXjNIui8Vmt+SCOmq/Xy9SSHY1rGHdR4wUEhYYMP+KeqdsYLWrneSLHfb2Ss7oqqVpps2Su/Lh9l3VQ6W5Yh8vZAoJNToDh8FtxgMoCzwM3RpdCw4Ajs5ebbRAZ62Weisk6H6LNrHSji4JXFZWFtLS0hwXnpKSIo5TUlLg9/tDrictbPfa6ZbKIkepdcYDWNNM3hl491erHCJHKLokcjJfvrYIX/11kUXkrr6/QgQe6p/PcwQPIsEucvba1WNFuTi2Onw0FYDrjAfhrnYDR2evFjMeeiuxyoOLlXZEPU2ksbERgwYNEscDBw5ES0tLyHU7Fy5cwIULF8TxuXPnon2J3U7mf30IIBqIR8P7Z7aY0VWp1ZJwVwHLYwhS0RlYDHnWNFCdwv+Fe31lV5h0d7lrM0yiG3tyxEwh6QiEQjTNpDDdVD7jgZd1haMxP888l81dDTWSsKscnb0ao95c0y3nTgTCdfS1fxf79euHfv36Re29u6odbsQkyNDc3BzxenFxMQYMGCBugwcP7u7Liz6UGntyRo5UCEvO4bIC2PXdOuw6sNZwUamlC4ncGThaWEYSCouLOlJIMv5QgfQFEURTDQ5uzjU7l0hW4JH1udZk4EgQ1yFZct1Ibxa5cFHUwYMHW76bxcVdq7KJhI5ohxsRW3Bz5swRqllZWYnU1FTX56WlpVnUlW8KAgi5LlNYWIi8PNNaOHfuXEKJXGbKg4DHg9ofKs1F7q5qGivLkv+s2Pwh0f5cB2AUhVOdGPeJo261q3Cxka2sfS8vwsT7ykFBsP/FPDaty3i8Q0gnJcZ7jF7ORI0AOLIuAjeVn0IEKwhOPdExa7IzHJ29utvfIx5xc0n58ZkzZyzF9qGst9LSUsf+GgAMGjTIElCw01XtcCNigauuro7oeV6vFwUFZkubQCAAr9eLQCDgum4n2mZvrKltecFyLLur0PmuuzO6WnusRHQhyRy3HIQQ1B5Yh5uvXGWtfgg6i/O7Ag88TL6rXOjRNb8rN4bXUMcgm45AKHCwNJeNJHSJrtpHEobiZH4ehpeaIptaXuFof66IDuGiqP3794+om0g4EQtHV7XDjS7twfn9ftTV1aGlpQWpqanIyspCamoq5s6di5qaGjQ3N6OwkKU1hFpPZnirJcBMIbnl7x8zHjVFrvZ4KTIvy2cpIkEdtUc3IXPccmSOXyGljcAyyCba8ElebIgMhBDxFBKRMtIBvuddSLj1JYnc4eLIrDeBnG7SwetQdIAgFS3kLWtRJlba0SWB83q9rkqalZXl+vxQ68mKvdUSANNdBQBqtj8PFXiw167OzCgCPIQlBUcTCuzdsRiT55aFFDkQFnjY/2IHAw5STp0QuU5cH9A5S1IROYS6uKjdEFaOlXaoSoYYkvlfHwLltapntri2Pwel5nBpI09u13frrF1IgpQNsokiX1QzV5WldpiBB7c8uY7yfUWuNd1EB8YujTDAYGCvT1V0DzyKar8lKqqbSCyh7NstVzwACJtCkjm6kFlyhIimmd2J2JMLYcldfX+FZSRhpMhTuvie3NilbA8uEnf1xLI8jNhYgSQYexDXhAsyJCJK4GJJMIja5ued6/YOwEEdYvYqYHFXb758JeAhLJWkG+EJxrLIgQD7Xo3C5r68J0eAMYU+8LbnRzaEETtquruK7oEE2bQx+1qion5VYoiruAHWLiRSlQN3Wd2aZt58RfSTfmW+2LHY4a6CAtfcU46J95V36dwHy3LNPDmLG2ymkbjBm14mcneLuMdo1eW4JSjKgosH5C4kLnlyommmy8zV7r0uI2LpkicXDXiB/th8n8iTA2Ei52bJHV+Zi8vWV6ggQzeSbC6qsuDiAMsgG123jCSsPV5qqXbIHLsMmeMib63dFQg1LDnRfJOtXfO7rllwdg6V5uLQplxLgb6bJTdyjQ+gJOKh0oqOQ4x5IPZboqIELt6wdyEBXPvJxULkPq8xitqp9Je9G6Nqhzbl4nBxbkiRO7Y6V7mn3Q0NcUtQlIsab8hlXUGdpZAQYomu1h7aGNNL+qJ6Ma6dU2YEG1jgYdK8cjHQJtrIUVWHu5rAX7ZEgOg6iK471hIVZcHFGe//61Yhcu//y5OurZYyRxa0e55oI7uo3JKLBVzcRq8walgT97uWEBDdxUVN4D04Ndk+jrnl7x9jlpwRYOCzHnhZV+2x2DcNvXZOmUjpoBrBl693jxWncBKLyfbTJxSgj8daC94WvIAPvy1JyO+gsuDiGMr344y9t1uG5eGW1EWoPV7aI+IGGBUPkiU36e7oBhwUPYsKMihiB4+mcpGLkwEgFpHr+ctRRBP+R9V+S1CUwMUxtf9rm6hdha7j/SYfoFMWeOhhhMgl8P6MwoUQYy4TFSVwcUzm/58DUF2I3C1//xgTulORd9btTrjIXXtn9IfAKHoG5aIqYkbt/64Eb38uW3LxhBK5JMOY9ua4JSgqDy7eCQYBjweADhpkbmu8wVstXXtnmWM0oSLBoC57bjRxBU5ZcPGOTpnIGZZc5n97pKevKCRK3JIAZcEpYol9xoNC0a3oQQBBl7XERAmcQqEw0V2KTxM4Uq4ETqFQmOhGT3nHWmKiBE6hUJgEg8aUIwnloioUiqRA1+G04FSQQaFQJAE0GAS1WXBUWXAKhSIp0HVn3lsC58EpgVMoFCa67my6pwROoVAkAzQYBCU2F9UedEgguiRwNTU1aG5uRn19PebMmQOv1yvWAaC5uRmpqantrisUijghGBsLLlba0WmBa2hoAABkZ2ejpaUFw4YNw48//ohAIIC6ujpUVlYCAGbMmAGv1xtyXaFQxA80qLtYcNEVuFhqR6cFrrm5GXV1dcjKykJKSgoGDhyIhoYG7N+/HykpKeJ5KSkp8Pv9CAQCruv2C71w4QIuXLggjs+ePQuAtVRWKHoz/DvQnVMGWoPnQW2lWm1otbw/p1+/fujXz9rePBK6Szvc6LTAeb1eyxs0NzcjPT0d27dvx6BBg8T6wIED0dLSgsbGRtd1O8XFxVizZo1jffDgwZ29VIUiqfjpp58wYMCAqJ7zkksuwd/93d/hs397z/Xxn/3sZ47v4OrVq1FUVNTh9+ou7XAjKkGGnJwcPPfccyEfb25ujni9sLAQeXlmx1pd19Hc3IxBgwaBENL1i22Hc+fOYfDgwThz5kyPD9hQ16KuRYZSip9++gm/+MUvon7uSy+9FKdOncLFixdDvrf9+9cZ681ONLXDjYgFbs6cOUI1KysrkZqaCoBt/s2YMQNZWVkAgLS0NIu68k1BACHXZdzMXtk8jRX9+/fv8S8PR12LO73xWqJtuclceumluPTSS7t8ntLSUvzwww+O9UGDBiE/P18cR1s7XKFdoK6ujtbV1VFKKa2vr6eNjY20sbGRZmVlieekp6dTSmnI9Xjj7NmzFAA9e/ZsT1+KuhZ1LUlLrLSj03NRA4EAMjIyxHFLS4vY/JRDugMHDhQKHWo9noinOazqWtS1JCMx1Y4oiHFScf78ebp69Wp6/vz5nr4UdS3qWhRdJO4n2ysUCkVnUTMZFApF0qIETqFQJC1K4BQKRdLSq7uJxFOzgJaWFlRVVQGAI1co1tdiJ9ZNEuLts4in3xNFB+npKEdPUV9fT6urqymllP744480JSWFUspybrKzs8XzvF5v2PVoUV1dTfPz82lJSYlY66lrkYnle3Hi6bOIt98TRcfotS4qL/gFYCn49fv9roW9odajRVZWFtLS0ixrPXUtkVxDdxJPn0W8/Z4oOkavFTiv1yvarwBmwW+owt6uFPx2lni4lp74uTtyHd19fYnwe6IITa8VOJnuLviNJvFwLT3xc7sR688ikX5PFIxeE2SIVbOArlyLnVhcS3uEu4ZY0tOfRU/8niiiQE9vAvYk8dYsoLKy0rGx3tONC3qqSUI8fRbx9nuiiJxeW6oVb80C/H4/Kisr0dLSgpycnHbfM5aNC2LdJCGePot4+z1RdIxeK3AKhSL5UUEGhUKRtCiBUygUSYsSOIVCkbQogVMoFEmLEjiFQpG0KIFTKBRJixI4hUKRtCiBUygUSYsSOIVCkbQogVMoFEmLEjiFQpG0/D8DReFlyoGHagAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    plt.colorbar(fraction=0.03, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    # fig.set_figwidth(2.4)\n",
-    "    print(height_fig)\n",
-    "    fig.set_figheight(height_fig)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1db8d134",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 80x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots()\n",
-    "plt.colorbar(im,ax=ax)\n",
-    "\n",
-    "ax.remove()\n",
-    "fig.set_figwidth(0.8)\n",
-    "plt.savefig('figures/OnlyColorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "plt.savefig('figures/OnlyColorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17c0ccc0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28dbfb28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'5497'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(int(theta*1000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d379223a",
-   "metadata": {},
-   "source": [
-    "## Thresholded Figure "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08121a32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPicThreshold(F, theta, thresh, both = False):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    \n",
-    "    fMaskBelow = fMask.copy()\n",
-    "    fMaskBelow[fMaskBelow <= thresh] = np.nan\n",
-    "    \n",
-    "    fMaskAbove = fMask.copy()\n",
-    "    fMaskAbove[fMaskAbove > thresh] = np.nan\n",
-    "    \n",
-    "    \n",
-    "    DTdown = np.multiply(F,fMaskAbove)\n",
-    "    DTup = np.multiply(F,fMaskBelow)\n",
-    "    \n",
-    "    # Why the fork is this backwards!?!?!?!?!\n",
-    "    # Must have something to do with meshgrid but i just can't fight it right now\n",
-    "    if both:\n",
-    "    \n",
-    "        return DTdown,DTup\n",
-    "    else:\n",
-    "        return DTdown\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "639dcbdd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "for thresh in [-150,-100,-50,0,50,100,150]:\n",
-    "\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    plt.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "    ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "    \n",
-    "    plt.title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.25)\n",
-    "\n",
-    "    thresh_size_fig = fig.get_size_inches()\n",
-    "    print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d32f8502",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "fig,axes = plt.subplots(1,5,sharey = True, figsize = (6,2))\n",
-    "\n",
-    "for i, thresh in enumerate([-150,-50,0,50,150]):\n",
-    "\n",
-    "    ax = axes[i]\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    ax.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    # midy = 177\n",
-    "    # ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    # ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    ax.set_xticks([])   \n",
-    "    ax.set_yticks([]) \n",
-    "    ax.set_title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    # fig = plt.gcf()\n",
-    "    # fig.set_figwidth(1.25)\n",
-    "\n",
-    "    # thresh_size_fig = fig.get_size_inches()\n",
-    "    # print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.png', bbox_inches = 'tight',dpi = 300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.eps', bbox_inches = 'tight',dpi = 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd8361dd",
-   "metadata": {},
-   "source": [
-    "## ECC in one direction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a416a675",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "numThresh = 64\n",
-    "\n",
-    "threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "\n",
-    "outECC = leaf.ECC(heights, numThresh)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "positions = [-150, -50, 0, 50, 150]\n",
-    "for pos in positions:\n",
-    "    ax.axvline(x=pos, color='grey', linestyle='--')\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.step(threshes,outECC)\n",
-    "plt.title(r'ECC for $\\omega = \\frac{3 \\pi}{4}$')\n",
-    "plt.xlabel('$a$')\n",
-    "plt.ylabel(r'$\\chi(K_a)$')\n",
-    "\n",
-    "# ax.set_xticks([-200,-150,-100,-50,0,50,100,150,200])\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='y', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(5,2)\n",
-    "# fig.set_size_inches(thresh_size_fig)\n",
-    "# print(thresh_size_fig)\n",
-    "# print(fig.get_size_inches())\n",
-    "\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.png', bbox_inches = 'tight',dpi=300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f9c12e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-197\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(outECC[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7017e7c9",
-   "metadata": {},
-   "source": [
-    "## ECT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81210cb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.     0.0625 0.125  0.1875 0.25   0.3125 0.375  0.4375 0.5    0.5625\n",
-      " 0.625  0.6875 0.75   0.8125 0.875  0.9375 1.     1.0625 1.125  1.1875\n",
-      " 1.25   1.3125 1.375  1.4375 1.5    1.5625 1.625  1.6875 1.75   1.8125\n",
-      " 1.875  1.9375]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get all the directions around the circle\n",
-    "numCircleDirs = 32\n",
-    "circledirs =  np.linspace(0, 2*np.pi, num=numCircleDirs, endpoint=False)\n",
-    "circledirs\n",
-    "\n",
-    "print(circledirs/np.pi)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13cdd2ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose number of thresholds for the ECC to stop at \n",
-    "numThresh = 100 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b57d67",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-285.68893688291087 285.68893688291087\n"
-     ]
-    }
-   ],
-   "source": [
-    "ECT_preprocess = {}\n",
-    "\n",
-    "overallmin = 0\n",
-    "overallmax = 0\n",
-    "\n",
-    "for i, omega in enumerate(circledirs):\n",
-    "    \n",
-    "    omegavec = np.array((np.cos(omega),np.sin(omega)))\n",
-    "    \n",
-    "    # Function values of all the pixels\n",
-    "    heights = np.sum(leaf.cells[0]*omegavec, axis=1)\n",
-    "\n",
-    "    # Thresholds implicitly calculated by Erik's code\n",
-    "    threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "    \n",
-    "    if heights.min()<overallmin:\n",
-    "        overallmin = heights.min()\n",
-    "    if heights.max()>overallmax:\n",
-    "        overallmax= heights.max()\n",
-    "            \n",
-    "\n",
-    "    outECC = leaf.ECC(heights, numThresh)\n",
-    "    \n",
-    "    ECT_preprocess[i] = (threshes,outECC)\n",
-    "    \n",
-    "print(overallmin,overallmax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17128b08",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x73f16b12de10>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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RFkRen747M5sCzA8BBUjCwsKC7cVAkyUeACA5AgoAkBwBBQBIjoACACRHQAEAkiOgAADJEVAAgORoOgAkr/V8HmfzwHwQUIDktXaUdTYPzAdLPECSep3Pc/DiOI7+fRqvT39rvmVZlsMVAuO0kE3pT3a9Xo/V1dV4+fJlrKys5H05wBhkWdY8n6ff2TxmVWB6DPr6bQYFSFbjfJ4rS3+K9T8vOfEY5ogaFGAqOPEY5ouAAkwNJx7D/LDEAwAkx58iwEzQKwVmi4ACzAS9UmC2WOIBpla/Xil29cB0M4MCTK3OnT129cDsEFCAqWZnD8wmSzwAQHIEFAAgOeZFgZnUuu044uzW49ZzfnrdB8iPgALMpM5i2datx1mWxc6jJ/HsxXHP+wD5ssQDzIxe244j2rcev3n77kw46bwPkC8zKMDMuMiBggf3ixFxdsYFyJeAAsyUYbcdX1laHOPVABcloABzo1E421lA2+0+DQpnIR8CCjA3BlnG6VdcC0yOIllgpvUrnN2+djUuX1ocuLgWmBwzKMBM61Y429C6fDNscS0wXgIKMPMGKZztd5/WuhQ1KTAZAgrAOVpnUtSkwGSoQQHoolddipoUmAwzKABddNauqEmByRJQAHoYtukbMDqWeACA5AgoAEByBBQAIDkCCgCQHNVfAENyoCCMn4ACMCQHCsL4WeIBGIADBWGyzKAADKDboYOat8H4CCgAA9K4DSbHEg8AkBx/CgCMQOvOHrt64MMJKAAj0FqLYlcPfDhLPAAX1Gtnj1098OHMoABcUOfOHrt6YHQEFIAPYGcPjIclHgAgOWI/wBh0ntfTyU4f6E9AARiD82pR7PSB/izxAIxIv/N6OtnpA/2ZQQEYkW7n9XSy0wcGI6AAjJBdPTAalngAgOQIKABAcsxDAiQgy7KutSu2IzOvBBSAnDR6pWRZxM1HT+KnX+tn7mM7MvNKQAHIySC7eRrbkRXeMm98xwNMUKNXysGL4zPv++wvK/+ZLbEdGQQUgAnq1ytFvQn8QUABmLBhe6U414d5NPKAUqvV4vvvv4+IiLt37zbHb968Gbu7uxER8eOPP8Z3330XERGlUikiIqrVamxubkaxWBz1JQFMNef6MI9GHlDK5XIcHR3F+vp623ilUokvv/wytre34/Hjx82x/f392Nvbi4iIGzduCCgA0b9WpZNCWmbRyL+bd3Z2olqtRq1Waxvf3d2NnZ2dtrFyuRyFQqF5u1AoRLlcFlKAuedcH+bdxOL206dPI+L9Uk5ExO3bt+Pw8LBtpmVtbe1MsGk4OTmJk5OT5u16/Wy/AIBZ4lwf5tnEvvMbNScREVtbW/HVV191vV8jwHR68OBBfPvtt2O5NgAgLUMFlIcPH8bR0dGZ8fX19baC2E6lUimePn3aDCmFQiEqlUpsbW21zZg0CmW72d3djTt37jRv1+v12NjYGObyAYApMVRA6RdC+tnc3GyrNanVanH9+vUoFApx79695nilUulZf7K8vBzLy8sX+vwAwHQZyy6e/f39qNVqsbm5GTs7O3H9+vUolUrNmZT9/f2IeB9cbt26FaVSKarVanMbMgAw3xayLMvyvoiLqNfrsbq6Gi9fvoyVlZW8Lwdg4l6f/haf/f1fERFxcL8YV5YWI0LjNtI26Ou38nCAGdC63VjjNmbBR3lfAAAX02jm1qnRuA2mmRkUgCnV2cxN4zZmiYACMMU0c2NWWeIBAJIjoAAAyRFQAIDkWLgEmEGvT9t38eiNwrQRUABmUOduHr1RmDaWeABmRK++KBF6ozB9zKAAzIjOvigReqMwvQQUgBmiLwqzwhIPAJAcAQUASI6AAgAkR0ABAJIjoAAAyRFQAIDkCCgAQHIEFAAgOQIKAJAcAQUASI5+yABz4vXpH2f0XL606GRjkiagAMyJ1kMDt69djcfffNEMKVmWnTntWIghTwIKwAy7fGkxtq9djYMXx23jBy+O483bd3Fl6U+RZVnsPHoSzzru0xliYJIEFIAZtrCwEI+/+aI5O/L69F3bTEpExJu3786Ek4j2EAOT5rsOYMYtLCwMHDIO7hcjIs6EGJg0AQVgTjWKZluLZ68sLeZ1OdBGQAGYU2ZJSJk+KABzpFE02832tatx+ZIZFNJgBgVgjnQWzbbqtq24dfmn131gHAQUgDkzTNFs5zKQrcdMiiUeANr0WwZqbD2GcTODAkCbbstA3fqnwDgJKACcMcwyEIyDJR4AIDkCCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEBydOEBYCidBwh2cqAgoyCgADCU81reO1CQUbDEA8C5+h0g2MmBgoyCGRQAztXtAMFODhRklAQUAAbiAEEmyXcaALnLsuzM7Ixi2/kmoAAwcp07ffqFjSzLYufRk3j24rhtXLHtfBNQABi5zlqUfmHjzdt3Z8JJxB/FtpaV5pNdPACMRL+dPoPu7Dm4X4yD+8VRXxpTSCwFYCS67fRp3dnTuuzTa8nnytJi2+1hloqYLQIKACPTb6dP67LPoPUlwywVMVss8QAwNr2Wffot+YxiqYjpZwYFgLHpXPYZpJnbeUtFzAcBBYCx6rXs06gv6Xb44CibwumxMp0EFABycdEZkUGKbRv0WJleAgoAE9OoLzno0vdk+9rVuHxpscujOu43RLGtHivTyzMDwMT0O3Sw32xIr2Bz8OI4jv592rY9udfHafRX6bbt+bzPz+QJKABM1EXqS/oV2w66Fbmzx8qwW5jVskyWgALAVGgNNv2Wivot31z0cWpZJk9AAWDqXHQr8kUfp5Zl8nxFAZhKF92K/KFbmDtrWRgPAQWAmdOvx8ogj+s33lnLwngIKADMnIvOblzkcXYDjYeAAsBMuGiPlX6P6/VxWmtYHGg4HgIKADPhoj1W+j2u18c5bzdQZ2+WYa5nlLptjZ7k5/8QC1mWZXlfxEXU6/VYXV2Nly9fxsrKSt6XA8Cc6XzxH+ZAw3HNsrReU5ZF3Hz0JH76tX7mfp/9ZeU/n7//xxtHkBn09dsMCgBcQOduoGGWisaxPblXr5Zufvq1Hv/1j3+df79//i23LdQCCgCMwCBLRa2zLB9aXNttBqdbOGmdLek3q5KakQeUUqkU1Wo1nj17Fjdv3oxisdgcj4ioVquxubl57jgATJtheqx8SHHtebMlB/eLzRqYzuDzf//z3wPV2zQem5eRBpTnz59HRMTt27ejVqvFX//61zg+Po5KpRL7+/uxt7cXERE3btyIYrHYcxwAZtGHFtc29JotiXgfdNb/vNS3KHgaOt+O9Aqr1Wrs7+/Hzs5OFAqFWFtbi+fPn8fBwUEUCoXm/QqFQpTL5ahUKl3HhRQAZtF5rfYv0oeldbYkYjp26AxipAGlWCy2hYtqtRrXr1+PH3/8MdbX15vja2trUavV4vDwsOt4NycnJ3FyctK8Xa+nv34GAJ0+pLi203mzJdNsbHM8X3/9dfzwww8931+tVocaf/DgQXz77bcjuTYASMUwfVg6zcpsSTdDBZSHDx/G0dHRmfH19fW4e/du83apVIobN27Ezs5ORERsbW21zYw0CmIjoud4p93d3bhz507zdr1ej42NjWEuHwCSNC11IZM01FejNYT0Ui6Xo1AoRLFYjOfPnzf/fe/eveZ9KpVKs0i223g3y8vLsby8PMzlAgBTaqSdZCuVSnz++efN27VaLRofvnU78draWnN2pdf4eXSSBYDpM+jrt1b3AMDEDPr6/dEErwkAYCACCgCQHAEFAEiOgAIAJEdAAQCSI6AAAMkRUACA5AgoAEByBBQAIDlTezJRowFuvV7P+UoAgEE1XrfPa2Q/tQHl1atXERFONAaAKfTq1atYXV3t+f6pPYvn999/j19++SU+/vjjWFhYyPty2tTr9djY2Iiff/7ZOUEJ8byky3OTLs9Nmqb5ecmyLF69ehWffPJJfPRR70qTqZ1B+eijj+LTTz/N+zL6WllZmbpvnHngeUmX5yZdnps0Tevz0m/mpEGRLACQHAEFAEiOgDIGy8vL8Y9//COWl5fzvhRaeF7S5blJl+cmTfPwvExtkSwAMLvMoAAAyRFQAIDkCCgAQHKmtg9KikqlUlSr1Xj27FncvHkzisViczwiolqtxubm5rnjjFatVovvv/8+IiLu3r3bHPe8pMHXO19+PtI1968pGSPx7Nmz7PHjx1mWZdnx8XFWKBSyLMuyw8PD7Pbt2837FYvFvuOM3uPHj7O7d+9m3333XXPM85IGX+/8+flIk9eULLPEMyLVajX29/cjIqJQKMTa2lo8f/48yuVyFAqF5v0KhUKUy+We44zezs5ObG1ttY15XtLg650/Px9p8pqiBmVkisVi7O3tNW9Xq9W4fv16HB4exvr6enN8bW0tarVaz3Emw/OSBl/vNPn5yJ/XFAFlLL7++uv44Ycfer6/Wq0ONc5keF7S4OudJj8f+ZnX1xRFsgN6+PBhHB0dnRlfX18/U1h248aN2NnZiYiIra2tthTbKF6KiJ7jDG7Q56WT5yUN/Z4H8uPnIx1z/ZqSdxHMLNnf38/29/ezLHtf4HR4eJgdHh5mOzs7zftcv349y7Ks5zjjsbe3d6YI0POSP1/vNPj5SNO8v6ZodT8ilUolPv/88+btWq0WjS9t69avtbW1ZhLuNc5olcvl2Nvbi1qtFl9//fW5X3/Py2T5eufLz0eavKY4iwcASJAiWQAgOQIKAJAcAQUASI6AAgAkR0ABAJIjoAAAyRFQAIDkCCgAQHIEFAAgOQIKAJAcAQUASM7/Ay1cDo3nsTiJAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 2\n",
-    "\n",
-    "plt.step( ECT_preprocess[i][0],  ECT_preprocess[i][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ec3eefe",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_57600/1470381530.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  M[i,j] = ECT_preprocess[omegai][1][index]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Making a matrix of size numThreshes x numCircleDirs\n",
-    "# M[i,j] = \n",
-    "\n",
-    "\n",
-    "# Make meshgrid. The +1 is to draw 2pi on both ends\n",
-    "X,Y = np.meshgrid(range(numCircleDirs+1),np.linspace(-250,250,numThresh))\n",
-    "M = np.ones_like(X)\n",
-    "\n",
-    "for i in range(X.shape[0]):\n",
-    "    for j in range(X.shape[1]):\n",
-    "        omegai = X[i,j]\n",
-    "        \n",
-    "        # Deal with redrawing the 2pi\n",
-    "        omegai = np.mod(omegai,numCircleDirs)\n",
-    "        \n",
-    "        \n",
-    "        omega = i*np.pi/numCircleDirs\n",
-    "        a = Y[i,j]\n",
-    "        threshes = ECT_preprocess[omegai][0]\n",
-    "#         print(threshes)\n",
-    "        try: \n",
-    "            index = np.where(threshes>=a)[0][0]   \n",
-    "        except:\n",
-    "            index = [-1]\n",
-    "\n",
-    "        M[i,j] = ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eac07698",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern_ECT.png', bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern_ECT.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "930cb35f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  1,  1,  1,  6,  1,  1,  1,  1,  2,  1,  4,  1,  1,  3,  3,  2,\n",
-       "        1,  2, 10,  3,  3,  1,  1,  3,  3,  1,  2,  1,  1,  1,  1,  1])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M[0,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5afc592b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4., 11., 19.])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bigvals = np.where(M[0,:] >3)[0]\n",
-    "\n",
-    "\n",
-    "circledirs[bigvals]/np.pi*16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad5f2a1d",
-   "metadata": {},
-   "source": [
-    "## Let's draw a cylinder!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "203aece0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# pip install ipympl\n",
-    "\n",
-    "# %matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b2acd4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "grid = M[:,:-1]\n",
-    "print(grid.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9ab03be",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "color_dimension = grid # change to desired fourth dimension\n",
-    "minn, maxx = color_dimension.min(), color_dimension.max()\n",
-    "norm = matplotlib.colors.Normalize(minn, maxx)\n",
-    "m = plt.cm.ScalarMappable(norm=norm, cmap='viridis')\n",
-    "m.set_array([])\n",
-    "fcolors = m.to_rgba(color_dimension)\n",
-    "\n",
-    "print(fcolors.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7131aa3f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.27318197 0.54636394 0.81954591 1.09272788 1.36590985\n",
-      " 1.63909182 1.91227379 2.18545576 2.45863773 2.7318197  3.00500167\n",
-      " 3.27818364 3.55136561 3.82454758 4.09772955 4.37091152 4.64409349\n",
-      " 4.91727546 5.19045743 5.4636394  5.73682137 6.01000334 6.28318531]\n"
-     ]
-    }
-   ],
-   "source": [
-    "theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "print(theta)\n",
-    "# theta = circledirs[:-1]\n",
-    "# print(theta, theta.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c071d904",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "equation for a circle\n",
-    "\n",
-    "x  =  h + r cosθ\n",
-    "y  =  k + r sinθ\n",
-    "\n",
-    "where h and k are the co-ordinates of the center\n",
-    "\n",
-    "0 <= θ <= 360\n",
-    "\"\"\"\n",
-    "\n",
-    "fig = plt.figure(figsize=(4,3))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "\n",
-    "# theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "radius = 1\n",
-    "\n",
-    "\n",
-    "x = np.linspace(0, numThresh, numThresh)\n",
-    "\n",
-    "# theta = circledirs\n",
-    "theta = np.linspace(0, 2 * np.pi, 32)\n",
-    "\n",
-    "thetas, xs = np.meshgrid(theta, x)\n",
-    "\n",
-    "y = radius * np.cos(thetas)\n",
-    "z = radius * np.sin(thetas)\n",
-    "\n",
-    "#ax.plot_surface(xs, y, z, color='orange')\n",
-    "#ax.plot_surface(xs, y, z, facecolors=fcolors)\n",
-    "surf = ax.plot_surface(xs, y, z, facecolors = fcolors,linewidth = 0.1)\n",
-    "surf.set_edgecolor('grey')\n",
-    "\n",
-    "ax.set_axis_off()\n",
-    "\n",
-    "plt.savefig('figures/fern_ECT_cylinder.png', bbox_inches = 'tight',dpi = 900)\n",
-    "plt.savefig('figures/fern_ECT_cylinder.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b03f0104",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-193.98518858 -190.40235323 -186.81951788 -183.23668253 -179.65384718\n",
-      " -176.07101183 -172.48817648 -168.90534113 -165.32250578 -161.73967042\n",
-      " -158.15683507 -154.57399972 -150.99116437 -147.40832902 -143.82549367\n",
-      " -140.24265832 -136.65982297 -133.07698762 -129.49415227 -125.91131692\n",
-      " -122.32848157 -118.74564621 -115.16281086 -111.57997551 -107.99714016\n",
-      " -104.41430481 -100.83146946  -97.24863411  -93.66579876  -90.08296341\n",
-      "  -86.50012806  -82.91729271  -79.33445735  -75.751622    -72.16878665\n",
-      "  -68.5859513   -65.00311595  -61.4202806   -57.83744525  -54.2546099\n",
-      "  -50.67177455  -47.0889392   -43.50610385  -39.92326849  -36.34043314\n",
-      "  -32.75759779  -29.17476244  -25.59192709  -22.00909174  -18.42625639\n",
-      "  -14.84342104  -11.26058569   -7.67775034   -4.09491499   -0.51207963\n",
-      "    3.07075572    6.65359107   10.23642642   13.81926177   17.40209712\n",
-      "   20.98493247   24.56776782   28.15060317   31.73343852   35.31627387\n",
-      "   38.89910923   42.48194458   46.06477993   49.64761528   53.23045063\n",
-      "   56.81328598   60.39612133   63.97895668   67.56179203   71.14462738\n",
-      "   74.72746273   78.31029808   81.89313344   85.47596879   89.05880414\n",
-      "   92.64163949   96.22447484   99.80731019  103.39014554  106.97298089\n",
-      "  110.55581624  114.13865159  117.72148694  121.3043223   124.88715765\n",
-      "  128.469993    132.05282835  135.6356637   139.21849905  142.8013344\n",
-      "  146.38416975  149.9670051   153.54984045  157.1326758   160.71551116]\n",
-      "59\n",
-      "17.402097119801482\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-83"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "omegai = 5\n",
-    "omega = i*np.pi/numCircleDirs\n",
-    "a = 17\n",
-    "threshes = ECT_preprocess[omegai][0]\n",
-    "print(threshes)\n",
-    "\n",
-    "index = np.where(threshes>=a)[0][0]\n",
-    "print(index)\n",
-    "print(threshes[index])\n",
-    "ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "554bc00c",
-   "metadata": {},
-   "source": [
-    "## Erik's version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e5a2a91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# t = 32\n",
-    "# tic = time.perf_counter()\n",
-    "# ect = leaf.ECT(circle_dirs, T=t)\n",
-    "# toc = time.perf_counter()\n",
-    "# print(\"Complex with {} vertices\\n\".format(len(leaf.cells[0])))\n",
-    "# print(\"ECT with {} directions in {:.4f} seconds.\\n{:.4f}s per direction\".format(len(circle_dirs), toc-tic, (toc-tic)/len(circle_dirs)))\n",
-    "\n",
-    "# plt.figure(figsize=(15,2))\n",
-    "# plt.plot(ect);\n",
-    "# plt.title('ECT for {} directions and {} thresholds each'.format(len(circle_dirs),t), fontsize=24);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "719235ff",
-   "metadata": {},
-   "source": [
-    "# SECT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96766900",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'ECT of Fern Leaf')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# THe original ECT\n",
-    "\n",
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "731fe26b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 33)\n",
-      "[-100.72  -99.01  -96.97  -95.    -93.24  -92.33  -91.34  -90.83  -90.69\n",
-      "  -89.07  -87.98  -87.07  -87.2   -87.64  -88.43  -89.47  -90.97  -90.94\n",
-      "  -91.8   -91.22  -93.    -94.51  -96.85  -98.61  -99.98 -100.05 -100.44\n",
-      " -100.47 -101.04 -101.12 -101.03 -100.91 -100.72]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Following Crawford2019 for the moment. Is this different in the Meng22 version?\n",
-    "print(M.shape)\n",
-    "\n",
-    "# Averages for a fixed direction \n",
-    "AvgOverOmega = np.average(M,axis = 0)\n",
-    "print(AvgOverOmega)\n",
-    "\n",
-    "# Mean centered \n",
-    "\n",
-    "Mcentered = M-AvgOverOmega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33144af2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mcentered, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf with value centered')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern-ECT-centered.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cfbf9ef1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.09992015e-11,  1.23066002e-11, -1.07718279e-11,  0.00000000e+00,\n",
-       "       -1.27613475e-11, -5.48538992e-12,  6.96331881e-12, -5.03064257e-12,\n",
-       "        4.71800377e-12,  7.78754838e-12,  1.33582034e-12,  7.78754838e-12,\n",
-       "       -9.12336873e-12, -5.42854650e-12, -7.56017471e-12, -1.09992015e-11,\n",
-       "       -1.09992015e-11,  4.71800377e-12,  9.35074240e-12, -1.09992015e-11,\n",
-       "        0.00000000e+00,  1.25339739e-11, -6.56541488e-12,  5.54223334e-12,\n",
-       "        1.33582034e-12,  9.40758582e-12,  4.66116035e-12, -1.09992015e-11,\n",
-       "       -2.87059265e-12, -8.29913915e-12,  1.07718279e-11, -7.53175300e-12,\n",
-       "       -1.09992015e-11])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mcentered.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "101671c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [4 5 6]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [5, 7, 9]])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[1,2,3], [4,5,6]])\n",
-    "print(a)\n",
-    "np.cumsum(a,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f047f4f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Mint = np.cumsum(Mcentered,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a6cc372d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mint, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$t$')\n",
-    "\n",
-    "plt.title(r'SECT of Fern Leaf')\n",
-    "\n",
-    "plt.savefig('figures/Fern-SECT.png',bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern-SECT.eps',bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4478b3c",
-   "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/_sources/notebooks/Matisse/Matisse_ECT.ipynb.txt b/docs/_sources/notebooks/Matisse/Matisse_ECT.ipynb.txt
deleted file mode 100644
index 5b66a5e..0000000
--- a/docs/_sources/notebooks/Matisse/Matisse_ECT.ipynb.txt
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23bd9d9b",
-   "metadata": {},
-   "source": [
-    "# ECT on Matisse's \"The Parakeet and the Mermaid\" \n",
-    "\n",
-    "Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312). \n",
-    "\n",
-    "![matisse.jpg](matisse.jpg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b73a120d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-----------------\n",
-    "# Standard imports \n",
-    "#-----------------\n",
-    "import numpy as np # for arrays\n",
-    "import matplotlib.pyplot as plt # for plotting\n",
-    "from sklearn.decomposition import PCA # for PCA for normalization\n",
-    "from scipy.spatial import distance_matrix\n",
-    "\n",
-    "from os import listdir # for retrieving files from directory\n",
-    "from os.path import isfile, join # for retrieving files from directory\n",
-    "from sklearn.manifold import MDS # for MDS\n",
-    "import pandas as pd # for loading in colors csv\n",
-    "\n",
-    "#---------------------------\n",
-    "# The ECT packages we'll use\n",
-    "#---------------------------\n",
-    "from ect import ECT, EmbeddedGraph # for calculating ECTs\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c149a147",
-   "metadata": {},
-   "source": [
-    "We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
-    "\n",
-    "<img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f7b7fe4c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 150 files in the directory\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_dir = \"doc_source/notebooks/Matisse/outlines/\" # set data directory\n",
-    "file_names = [f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == '.txt'] # create a list of file names\n",
-    "file_names.sort() # sort the list of file names\n",
-    "print(f\"There are {len(file_names)} files in the directory\") # print number of files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b082bf2",
-   "metadata": {},
-   "source": [
-    "Here we have an example of one of these leaves loaded in as an `EmbeddedGraph` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3cb77aa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 3\n",
-    "shape = np.loadtxt(data_dir + file_names[i])\n",
-    "# shape = normalize(shape)\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_cycle(shape)\n",
-    "G.plot(with_labels = False, node_size = 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aef7ef04",
-   "metadata": {},
-   "source": [
-    "We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3d930346",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "G.plot(with_labels = False, node_size = 10, bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49bc6a0c",
-   "metadata": {},
-   "source": [
-    "And then we can compute the ECT of this leaf. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e0e07987",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) # intiate ECT\n",
-    "myect.set_bounding_radius(1) # set bounding radius\n",
-    "myect.calculateECT(G) # calculate ECT on embedded graph\n",
-    "\n",
-    "myect.plotECT() # plot ECT\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebc64b1d",
-   "metadata": {},
-   "source": [
-    "Let's just make a data loader with all of this for ease in a bit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dcf72cc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def matisse_ect(filename, num_dirs, num_thresh):\n",
-    "    shape = np.loadtxt(data_dir + filename)\n",
-    "    G = EmbeddedGraph()\n",
-    "    G.add_cycle(shape)\n",
-    "    G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "    myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) \n",
-    "    myect.set_bounding_radius(1) \n",
-    "    M = myect.calculateECT(G) \n",
-    "    return M"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91cb74c1",
-   "metadata": {},
-   "source": [
-    "And now we can load in all the outlines, compute their ECT and store it in a 3D array. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2dfe86ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "ect_arr = np.zeros((len(file_names),num_dirs,num_thresh))\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    ect_arr[i,:,:] = matisse_ect(file_names[i], num_dirs, num_thresh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be2f273",
-   "metadata": {},
-   "source": [
-    "Here, we are just going to compute the distance between two ECTs using $L_2$ distance. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e1efac55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x3199f6190>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs*num_thresh)\n",
-    "D = distance_matrix(flattened_ect, flattened_ect)\n",
-    "plt.matshow(D)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce93bb3f",
-   "metadata": {},
-   "source": [
-    "For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0984d176",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_components = 2 # select number of components\n",
-    "mds = MDS(n_components=n_components, # initialize MDS\n",
-    "          dissimilarity=\"precomputed\", # we have precomputed the distance matrix\n",
-    "          normalized_stress='auto',\n",
-    "          random_state=5 # select random state for reproducibility\n",
-    "         )\n",
-    "MDS_scores = mds.fit_transform(D) # get MDS scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "551aa2fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# read in color hexcodes\n",
-    "col_df = pd.read_csv(\"doc_source/notebooks/Matisse/colors.csv\", header=None) \n",
-    "\n",
-    "scale_val = 6 # set scale value\n",
-    "plt.figure(figsize=(5,5)) # set figure dimensions\n",
-    "\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    shape = np.loadtxt(data_dir+file_names[i]) # get the current shape\n",
-    "    shape = shape - np.mean(shape, axis=0) # zero center shape\n",
-    "    shape = scale_val * shape / max(np.linalg.norm(shape, axis = 1)) # scale to radius 1 then mult by scale_val\n",
-    "\n",
-    "    trans_sh = (shape + MDS_scores[i]) # translate shape to MDS position\n",
-    "\n",
-    "    plt.fill(trans_sh[:,0], trans_sh[:,1], c=col_df[0][i], lw=0) # plot shape\n",
-    "    plt.gca().set_aspect(\"equal\")\n",
-    "\n",
-    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
-    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3c3d0e7",
-   "metadata": {},
-   "source": [
-    "## Acknowledgements\n",
-    "\n",
-    "This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood). "
-   ]
-  }
- ],
- "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt b/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt
new file mode 100644
index 0000000..6f0c4f8
--- /dev/null
+++ b/docs/_sources/notebooks/Matisse/example_matisse.ipynb.txt
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ECT on Matisse's \"The Parakeet and the Mermaid\"\n",
+    "\n",
+    "\n",
+    "\n",
+    " Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312).\n",
+    "\n",
+    "\n",
+    "\n",
+    " ![matisse.jpg](matisse.jpg)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 150 files in the directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -----------------\n",
+    "# Standard imports\n",
+    "# -----------------\n",
+    "import numpy as np  # for arrays\n",
+    "import matplotlib.pyplot as plt  # for plotting\n",
+    "from sklearn.decomposition import PCA  # for PCA for normalization\n",
+    "from scipy.spatial import distance_matrix\n",
+    "\n",
+    "from os import listdir  # for retrieving files from directory\n",
+    "from os.path import isfile, join  # for retrieving files from directory\n",
+    "from sklearn.manifold import MDS  # for MDS\n",
+    "import pandas as pd  # for loading in colors csv\n",
+    "import os\n",
+    "import zipfile\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ---------------------------\n",
+    "# The ECT packages we'll use\n",
+    "# ---------------------------\n",
+    "from ect import ECT, EmbeddedGraph  # for calculating ECTs\n",
+    "\n",
+    "# Simple data paths\n",
+    "data_dir = \"outlines/\"\n",
+    "colors_path = \"colors.csv\"\n",
+    "\n",
+    "file_names = [\n",
+    "    f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n",
+    "]\n",
+    "file_names.sort()\n",
+    "print(f\"There are {len(file_names)} files in the directory\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
+    "\n",
+    "\n",
+    "\n",
+    " <img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 3\n",
+    "shape = np.loadtxt(data_dir + file_names[i])\n",
+    "# shape = normalize(shape)\n",
+    "G = EmbeddedGraph()\n",
+    "G.add_cycle(shape)\n",
+    "G.plot(with_labels=False, node_size=10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "G.transform_coordinates(projection_type=\"pca\")  # project with PCA\n",
+    "G.scale_coordinates(1)  # scale to radius 1\n",
+    "G.plot(with_labels=False, node_size=10, bounding_circle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And then we can compute the ECT of this leaf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh);  # intiate ECT\n",
+    "result = myect.calculate(G);  # calculate ECT on embedded graph\n",
+    "\n",
+    "result.plot();  # plot ECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Let's just make a data loader with all of this for ease in a bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def matisse_ect(filename, ect):\n",
+    "    shape = np.loadtxt(data_dir + filename)\n",
+    "    G = EmbeddedGraph()\n",
+    "    G.add_cycle(shape)\n",
+    "    G.transform_coordinates(projection_type=\"pca\")\n",
+    "    G.scale_coordinates(1)\n",
+    "    result = ect.calculate(G)\n",
+    "    return result\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And now we can load in all the outlines, compute their ECT and store it in a 3D array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "ect_arr = np.zeros((len(file_names), num_dirs, num_thresh))\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh, bound_radius=1)\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    ect_arr[i, :, :] = matisse_ect(file_names[i], myect)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Here, we are just going to compute the distance between two ECTs using $L_2$ distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x32185cbd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs * num_thresh)\n",
+    "D = distance_matrix(flattened_ect, flattened_ect)\n",
+    "plt.matshow(D)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_components = 2  # select number of components\n",
+    "mds = MDS(\n",
+    "    n_components=n_components,  # initialize MDS\n",
+    "    dissimilarity=\"precomputed\",  # we have precomputed the distance matrix\n",
+    "    normalized_stress=\"auto\",\n",
+    "    random_state=5,  # select random state for reproducibility\n",
+    ")\n",
+    "MDS_scores = mds.fit_transform(D)  # get MDS scores\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# read in color hexcodes\n",
+    "col_df = pd.read_csv(colors_path, header=None)\n",
+    "\n",
+    "scale_val = 6  # set scale value\n",
+    "plt.figure(figsize=(5, 5))  # set figure dimensions\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    shape = np.loadtxt(data_dir + file_names[i])  # get the current shape\n",
+    "    shape = shape - np.mean(shape, axis=0)  # zero center shape\n",
+    "    shape = (\n",
+    "        scale_val * shape / max(np.linalg.norm(shape, axis=1))\n",
+    "    )  # scale to radius 1 then mult by scale_val\n",
+    "\n",
+    "    trans_sh = shape + MDS_scores[i]  # translate shape to MDS position\n",
+    "\n",
+    "    plt.fill(trans_sh[:, 0], trans_sh[:, 1], c=col_df[0][i], lw=0)  # plot shape\n",
+    "    plt.gca().set_aspect(\"equal\")\n",
+    "\n",
+    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
+    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "\n",
+    "\n",
+    " This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_sources/notebooks/Playing_with_Spirals.ipynb.txt b/docs/_sources/notebooks/Playing_with_Spirals.ipynb.txt
deleted file mode 100644
index 3b1339f..0000000
--- a/docs/_sources/notebooks/Playing_with_Spirals.ipynb.txt
+++ /dev/null
@@ -1,1204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.patches import Circle\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Recreating the sprial example from Arya \n",
-    "\n",
-    "# Create the graph\n",
-    "Spiral = EmbeddedGraph()\n",
-    "Spiral.add_node('a', .5, 0)\n",
-    "Spiral.add_node('b', 1, 1)\n",
-    "Spiral.add_node('c', -1.5, -.5)\n",
-    "Spiral.add_node('d', 3, -2.5)\n",
-    "Spiral.add_node('e',1.5,2)\n",
-    "Spiral.add_node('f',-1.5,2)\n",
-    "Spiral.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = list(Spiral.nodes())\n",
-    "nodelist.sort()\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    Spiral.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "Spiral.plot(angle_labels_circle=True, edges_only=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Spiral.plot(angle_labels_circle=True, edges_only=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfadbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>debacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle             Swapped Verts Sorted list\n",
-       "0    0.00                  [(c, f)]     gcfabed\n",
-       "1    0.32                  [(d, e)]     gcfabed\n",
-       "2    0.52                  [(b, d)]     gcfadbe\n",
-       "3    0.79  [(a, d), (f, a), (f, d)]     gcdafbe\n",
-       "4    1.15                  [(c, d)]     gcdafbe\n",
-       "5    1.19                  [(b, f)]     gdcafbe\n",
-       "6    1.57                  [(e, f)]     gdcabfe\n",
-       "7    1.65                  [(d, g)]     gdcabef\n",
-       "8    1.82                  [(a, c)]     dgacbef\n",
-       "9    2.11                  [(b, c)]     dgacbef\n",
-       "10   2.27                  [(c, e)]     dgabecf\n",
-       "11   2.28                  [(a, g)]     dagbecf\n",
-       "12   2.36                  [(b, g)]     dabgecf\n",
-       "13   2.41                  [(e, g)]     dabegcf\n",
-       "14   2.60                  [(c, g)]     dabegcf\n",
-       "15   2.68  [(a, b), (a, e), (b, e)]     debacgf\n",
-       "16   2.85                  [(f, g)]     debacgf\n",
-       "17   3.14                  [(c, f)]     debacfg\n",
-       "18   3.46                  [(d, e)]     debafcg\n",
-       "19   3.66                  [(b, d)]     edbafcg\n",
-       "20   3.93  [(a, d), (f, a), (f, d)]     ebfadcg\n",
-       "21   4.29                  [(c, d)]     ebfadcg\n",
-       "22   4.33                  [(b, f)]     ebfacdg\n",
-       "23   4.71                  [(e, f)]     efbacdg\n",
-       "24   4.79                  [(d, g)]     febacdg\n",
-       "25   4.96                  [(a, c)]     febcagd\n",
-       "26   5.25                  [(b, c)]     febcagd\n",
-       "27   5.41                  [(c, e)]     fcebagd\n",
-       "28   5.42                  [(a, g)]     fcebagd\n",
-       "29   5.50                  [(b, g)]     fcegbad\n",
-       "30   5.55                  [(e, g)]     fcegbad\n",
-       "31   5.74                  [(c, g)]     fcgebad\n",
-       "32   5.82  [(a, b), (a, e), (b, e)]     fgcabed\n",
-       "33   5.99                  [(f, g)]     fgcabed"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(Spiral.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys([5.82, 1.82, 3.93, 2.28, 2.11, 3.66, 1.19, 2.36, 4.29, 5.41, 0.0, 2.6, 0.32, 1.65, 1.57, 2.41, 2.85, 2.68, 4.96, 0.79, 5.42, 5.25, 0.52, 4.33, 5.5, 1.15, 2.27, 3.14, 5.74, 3.46, 4.79, 4.71, 5.55, 5.99])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>angle</th>\n",
-       "      <th>vertex_pair(s)_swapped</th>\n",
-       "      <th>sorted_order_at_next_midpoint</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[g, c, f, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[g, c, f, a, d, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.04</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, c, d, a, f, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, d, c, a, b, f, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[g, d, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.73</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, g, a, c, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.24</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[d, a, g, b, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[d, a, b, g, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.49</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[d, e, b, a, c, g, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[d, e, b, a, c, f, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.02</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, e, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.37</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.59</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[e, b, d, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[e, b, f, a, d, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.47</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.50</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.59</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.72</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[f, e, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.87</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[f, e, b, c, a, g, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[f, c, e, b, g, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.47</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[f, c, g, e, b, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>6.26</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    angle    vertex_pair(s)_swapped sorted_order_at_next_midpoint\n",
-       "0    0.00                  [(c, f)]         [g, c, f, a, b, e, d]\n",
-       "1    0.32                  [(d, e)]         [g, c, f, a, d, b, e]\n",
-       "2    1.04                  [(b, f)]         [g, c, d, a, f, b, e]\n",
-       "3    1.19                  [(b, f)]         [g, d, c, a, b, f, e]\n",
-       "4    1.57                  [(e, f)]         [g, d, c, a, b, e, f]\n",
-       "5    1.65                  [(d, g)]         [d, g, c, a, b, e, f]\n",
-       "6    1.73                  [(c, e)]         [d, g, c, a, b, e, f]\n",
-       "7    1.82                  [(a, c)]         [d, g, a, c, b, e, f]\n",
-       "8    2.11                  [(b, c)]         [d, g, a, b, c, e, f]\n",
-       "9    2.24                  [(e, f)]         [d, g, a, b, c, e, f]\n",
-       "10   2.28                  [(a, g)]         [d, a, g, b, e, c, f]\n",
-       "11   2.36                  [(b, g)]         [d, a, b, g, e, c, f]\n",
-       "12   2.41                  [(e, g)]         [d, a, b, e, g, c, f]\n",
-       "13   2.49                  [(d, g)]         [d, a, b, e, g, c, f]\n",
-       "14   2.52                  [(b, d)]         [d, a, b, e, g, c, f]\n",
-       "15   2.60                  [(c, g)]         [d, e, b, a, c, g, f]\n",
-       "16   2.85                  [(f, g)]         [d, e, b, a, c, f, g]\n",
-       "17   3.02                  [(a, c)]         [d, e, b, a, f, c, g]\n",
-       "18   3.37  [(a, d), (f, a), (f, d)]         [e, d, b, a, f, c, g]\n",
-       "19   3.59                  [(c, f)]         [e, d, b, a, f, c, g]\n",
-       "20   3.66                  [(b, d)]         [e, b, d, a, f, c, g]\n",
-       "21   3.93                  [(b, c)]         [e, b, f, a, d, c, g]\n",
-       "22   4.29                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "23   4.47                  [(a, g)]         [e, f, b, a, c, d, g]\n",
-       "24   4.50                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "25   4.59                  [(d, e)]         [e, f, b, a, c, d, g]\n",
-       "26   4.72                  [(b, g)]         [f, e, b, a, c, d, g]\n",
-       "27   4.87                  [(e, g)]         [f, e, b, c, a, g, d]\n",
-       "28   5.41                  [(c, e)]         [f, c, e, b, g, a, d]\n",
-       "29   5.47                  [(c, g)]         [f, c, g, e, b, a, d]\n",
-       "30   5.82  [(a, b), (a, e), (b, e)]         [g, f, c, a, b, e, d]\n",
-       "31   6.26                  [(f, g)]         [g, f, c, a, b, e, d]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2)\n",
-    "\n",
-    "# add pi + angle for all angles \n",
-    "All_Angles_2pi = {}\n",
-    "for key in All_Angles.keys():\n",
-    "    All_Angles_2pi[round(key,2)] = All_Angles[key]\n",
-    "    other_key = (key + np.pi) % 2*np.pi\n",
-    "    other_key = round(other_key, 2)\n",
-    "    All_Angles_2pi[other_key] = All_Angles[key]\n",
-    "\n",
-    "keys = list(All_Angles_2pi.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns=['angle', 'vertex_pair(s)_swapped', 'sorted_order_at_next_midpoint'])\n",
-    "pd_table['angle'] = keys\n",
-    "pd_table['vertex_pair(s)_swapped'] = [All_Angles_2pi[key] for key in keys]\n",
-    "\n",
-    "# sort the vertex pairs at the midpoint\n",
-    "\n",
-    "for i in range(len(keys)-1):\n",
-    "    angle = np.average([keys[i], keys[i+1]])\n",
-    "    sorted_order = Spiral.sort_vertices(angle)\n",
-    "    pd_table.at[i, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "angle = np.average([keys[-1], keys[0]+2*np.pi])\n",
-    "sorted_order = Spiral.sort_vertices(angle)\n",
-    "pd_table.at[len(keys)-1, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "pd_table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f', 'g']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nodelist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modified version breaking the spiral"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Modified version with vertex added between C and D\n",
-    "\n",
-    "# Create the graph\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_node('a', .5, 0)\n",
-    "G.add_node('b', 1, 1)\n",
-    "G.add_node('c', -1.5, -.5)\n",
-    "G.add_node('X', 1.5, -1)\n",
-    "G.add_node('d', 3, -2.5)\n",
-    "G.add_node('e',1.5,2)\n",
-    "G.add_node('f',-1.5,2)\n",
-    "G.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = ['a', 'b', 'c','X',  'd', 'e', 'f', 'g']\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    G.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "G.plot(angle_labels_circle=True, edges_only=True, ax = ax)\n",
-    "# remove axes \n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x30be90c90>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(G.get_all_angles()[0])\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>gcfabXed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.25</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfaXdbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.41</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdXcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgXacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1.99</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dXgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dXagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dXabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>dXebacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.39</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfaXcdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.55</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.13</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle                                     Swapped Verts Sorted list\n",
-       "0    0.00                                  [(c, f), (X, e)]    gcfabXed\n",
-       "1    0.25                                          [(b, X)]    gcfaXbed\n",
-       "2    0.32                                          [(d, e)]    gcfaXbed\n",
-       "3    0.52                                          [(b, d)]    gcfaXdbe\n",
-       "4    0.79  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    gcdXafbe\n",
-       "5    1.15                                          [(c, d)]    gcdXafbe\n",
-       "6    1.19                                          [(b, f)]    gdcXafbe\n",
-       "7    1.41                                          [(c, X)]    gdXcabfe\n",
-       "8    1.57                                          [(e, f)]    gdXcabfe\n",
-       "9    1.65                                          [(d, g)]    gdXcabef\n",
-       "10   1.82                                          [(a, c)]    dgXacbef\n",
-       "11   1.99                                          [(X, g)]    dXgacbef\n",
-       "12   2.11                                          [(b, c)]    dXgacbef\n",
-       "13   2.27                                          [(c, e)]    dXgabecf\n",
-       "14   2.28                                          [(a, g)]    dXagbecf\n",
-       "15   2.36                                          [(b, g)]    dXabgecf\n",
-       "16   2.41                                          [(e, g)]    dXabegcf\n",
-       "17   2.60                                          [(c, g)]    dXabegcf\n",
-       "18   2.68                          [(a, b), (a, e), (b, e)]    dXebacgf\n",
-       "19   2.85                                          [(f, g)]    dXebacgf\n",
-       "20   3.14                                  [(c, f), (X, e)]    dXebacfg\n",
-       "21   3.39                                          [(b, X)]    debXafcg\n",
-       "22   3.46                                          [(d, e)]    debXafcg\n",
-       "23   3.66                                          [(b, d)]    edbXafcg\n",
-       "24   3.93  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    ebfaXdcg\n",
-       "25   4.29                                          [(c, d)]    ebfaXdcg\n",
-       "26   4.33                                          [(b, f)]    ebfaXcdg\n",
-       "27   4.55                                          [(c, X)]    efbacXdg\n",
-       "28   4.71                                          [(e, f)]    efbacXdg\n",
-       "29   4.79                                          [(d, g)]    febacXdg\n",
-       "30   4.96                                          [(a, c)]    febcaXgd\n",
-       "31   5.13                                          [(X, g)]    febcaXgd\n",
-       "32   5.25                                          [(b, c)]    febcagXd\n",
-       "33   5.41                                          [(c, e)]    fcebagXd\n",
-       "34   5.42                                          [(a, g)]    fcebagXd\n",
-       "35   5.50                                          [(b, g)]    fcegbaXd\n",
-       "36   5.55                                          [(e, g)]    fcegbaXd\n",
-       "37   5.74                                          [(c, g)]    fcgebaXd\n",
-       "38   5.82                          [(a, b), (a, e), (b, e)]    fgcabeXd\n",
-       "39   5.99                                          [(f, g)]    fgcabeXd"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = G.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(G.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt b/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt
deleted file mode 100644
index c19d638..0000000
--- a/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial: ECT for CW complexes\n",
-    "\n",
-    "This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedCW, create_example_cw\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = EmbeddedCW()\n",
-    "\n",
-    "K.add_node('A', 0,0)\n",
-    "K.add_node('B', 1,0)\n",
-    "K.add_node('C', 1,1)\n",
-    "K.add_node('D', 0,1)\n",
-    "\n",
-    "K.add_edges_from((('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')))\n",
-    "\n",
-    "K.add_face(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "K.set_centered_coordinates()\n",
-    "K.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = create_example_cw(centered = True)\n",
-    "K.plot(bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also color the nodes based on a function value for a given direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = np.pi/7\n",
-    "K.plot(bounding_circle=True,color_nodes_theta=theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function value for any direction can be computed and returned for vertices, edges, or faces. The function value for an edge or face $\\sigma$ is given by $g_\\omega(\\sigma) = \\max_{v \\in \\sigma}\\{f(v)\\}$. Here we show the function values for the triangle `KDC` and its faces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "K: 0.18\n",
-      "D: 0.65\n",
-      "C: 2.89\n",
-      "('D', 'K'): 0.65\n",
-      "('C', 'D'): 2.89\n",
-      "('C', 'K'): 2.89\n",
-      "('K', 'D', 'C'): 2.89\n"
-     ]
-    }
-   ],
-   "source": [
-    "vert_g = K.g_omega(theta)\n",
-    "edge_g = K.g_omega_edges(theta)\n",
-    "face_g = K.g_omega_faces(theta)\n",
-    "\n",
-    "for v in ['K','D','C']:\n",
-    "    print(f'{v}: {round(vert_g[v],2)}')\n",
-    "\n",
-    "for edge in [('D','K'),('C','D'),('C','K')]:\n",
-    "    print(f'{edge}: {round(edge_g[edge],2)}')\n",
-    "\n",
-    "for face in [('K','D','C')]:\n",
-    "    print(f'{face}: {round(face_g[face],2)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myect = ECT(num_dirs = 100,num_thresh = 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,\n",
-       "        3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  1,\n",
-       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r = K.get_bounding_radius()\n",
-    "myect.calculateECC(K,0,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(K,theta,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       ...,\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2*r)\n",
-    "myect.calculateECT(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once it's been computed, we can use the internal plotting function to take a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.841874542459709\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECT()\n",
-    "print(myect.bound_radius)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly we can take a look at the SECT. This one was computed when we asked to compute the ECT. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotSECT()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb.txt b/docs/_sources/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb.txt
deleted file mode 100644
index 0ba510f..0000000
--- a/docs/_sources/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb.txt
+++ /dev/null
@@ -1,623 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial : ECT for embedded graphs \n",
-    "\n",
-    "This jupyter notebook will walk you through using the `ect` package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see [this paper](https://arxiv.org/abs/2310.10395) for a more thorough treatment of details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing the embedded graph\n",
-    "\n",
-    "We assume our input is an undirected graph $G$ with an embedding in 2D given by a map on the vertices $f: V(G) \\to \\mathbb{R}^2$. A graph can be constructed as follows. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Construct an example graph \n",
-    "# Note that this is the same graph that is returned by:\n",
-    "# G = create_example_graph()\n",
-    "\n",
-    "G = EmbeddedGraph()\n",
-    "\n",
-    "G.add_node('A', 1, 2)\n",
-    "G.add_node('B', 3, 4)\n",
-    "G.add_node('C', 5, 7)\n",
-    "G.add_node('D', 3, 6)\n",
-    "G.add_node('E', 4, 3)\n",
-    "G.add_node('F', 4, 5)\n",
-    "\n",
-    "G.add_edge('A', 'B')\n",
-    "G.add_edge('B', 'C')\n",
-    "G.add_edge('B', 'D')\n",
-    "G.add_edge('B', 'E')\n",
-    "G.add_edge('C', 'D')\n",
-    "G.add_edge('E', 'F')\n",
-    "\n",
-    "G.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The coordinates of all vertices, given as a dictionary, can be accessed using the `coordinates` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': (1, 2), 'B': (3, 4), 'C': (5, 7), 'D': (3, 6), 'E': (4, 3), 'F': (4, 5)}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G.coordinates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the `set_mean_centered_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'A': (-2.0, -2.5), 'B': (0.0, -0.5), 'C': (2.0, 2.5), 'D': (0.0, 1.5), 'E': (1.0, -1.5), 'F': (1.0, 0.5)}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_centered_coordinates(type = 'min_max')\n",
-    "print(G.coordinates)\n",
-    "G.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a bounding radius we can use the `get_bounding_radius` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This is actually getting the radius \n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n",
-    "\n",
-    "# plotting the graph with it's bounding circle of radius r.\n",
-    "G.plot(bounding_circle=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also rescale our graph to have unit radius using `rescale_to_unit_disk`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.rescale_to_unit_disk(preserve_center=False)\n",
-    "G.plot(bounding_circle=True)\n",
-    "\n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Directions are given in the code by specifying $\\theta \\in [0,2\\pi]$. We often convert this to $\\omega \\in \\mathbb{S}^1$ by defining the unit vector $\\omega = (\\cos(\\theta), \\sin(\\theta))$. Then the function $g_\\omega$ is defined on the vertices of $G$ by taking the dot product of the embedding coordinates with the unit vector, specifically\n",
-    "$$\n",
-    "g_\\omega(v) = \\langle f(v), \\omega\\rangle.\n",
-    "$$\n",
-    "This is done in the code using the `g_omega` method as shown. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': 0.11043152607484681,\n",
-       " 'B': 0.11043152607484658,\n",
-       " 'C': -0.11043152607484681,\n",
-       " 'D': -0.3312945782245397,\n",
-       " 'E': 0.5521576303742327,\n",
-       " 'F': 0.11043152607484646}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This gives the function value of the nodes as a dictionary\n",
-    "G.g_omega(theta = 7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This input to the plotting function will color the nodes by function value.\n",
-    "G.plot(color_nodes_theta=7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Here's an example in another direction.\n",
-    "G.plot(color_nodes_theta=np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Computing the ECT of $G$\n",
-    "\n",
-    "Now we can set up the ECT for the embedded graph. The ECT is defined as \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{ECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ a \\mapsto \\chi(g_\\omega^{-1}(-\\infty,a]) \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "When we initialize the `ECT` object, we specify the number of directions in $\\mathbb{S}^1$ to use with `num_dir`, and the number of thresholds at which to compute the Euler characteristic by `num_thresh`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Directions chosen are: [0.         0.39269908 0.78539816 1.17809725 1.57079633 1.96349541\n",
-      " 2.35619449 2.74889357 3.14159265 3.53429174 3.92699082 4.3196899\n",
-      " 4.71238898 5.10508806 5.49778714 5.89048623]\n",
-      "Thresholds chosen are: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect = ECT(num_dirs = 16, num_thresh=20)\n",
-    "\n",
-    "# The ECT object will automatically choose the directions. \n",
-    "print(f'Directions chosen are: {myect.thetas}')\n",
-    "\n",
-    "# However, because a bounding radius hasn't been chosen yet, the thresholds are not yet set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can set the bounding radius as follows. Note that some methods will automatically set `bound_radius` to be the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Internally set radius is: 1.2\n",
-      "Thresholds chosen are: [-1.2        -1.07368421 -0.94736842 -0.82105263 -0.69473684 -0.56842105\n",
-      " -0.44210526 -0.31578947 -0.18947368 -0.06315789  0.06315789  0.18947368\n",
-      "  0.31578947  0.44210526  0.56842105  0.69473684  0.82105263  0.94736842\n",
-      "  1.07368421  1.2       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2 * G.get_bounding_radius())\n",
-    "\n",
-    "print(f'Internally set radius is: {myect.bound_radius}')\n",
-    "\n",
-    "# Now the thresholds are set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want the Euler characteristic curve for a fixed direction, we use the `calculateECC` function. This outputs a list of integers where each corresponds to $\\chi(f_\\omega^{-1}(-\\infty,a])$ for the $a$ values in `myect.threshes`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.calculateECC(G, np.pi/2,bound_radius=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This can also be calculated on the fly and plotted using the `plotECC` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(G, -np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To calculate the full ECT, we call the `calculateECT` method. This matrix is size `myect.num_dirs x myect.num_thresh`. Once computed, it is stored in the `ECT` class as `ECT.matrix`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n",
-      "16 20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.calculateECT(G)\n",
-    "\n",
-    "# The matrix is passed as an output above but is also saved internally. Get the saved matrix\n",
-    "M = myect.get_ECT()\n",
-    "\n",
-    "print(M.shape)\n",
-    "print(myect.num_dirs, myect.num_thresh)\n",
-    "\n",
-    "# We can use the built in command to plot the matrix. Unlike the plotECC function, this command does not calculate the ECT when called so it must have been run earlier. An equivalent command is myect.plotECT()\n",
-    "myect.plot('ECT')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SECT \n",
-    "\n",
-    "The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
-    "$$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
-    "Then the SECT is defined by \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    " defined from the \n",
-    "TODO: Write intro "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can calculate the SECT directly using the `calculateSECT` method. Note that this both returns the function and sets the SECT internally to the `SECT_matrix` attribute. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n"
-     ]
-    }
-   ],
-   "source": [
-    "M_SECT = myect.calculateSECT()\n",
-    "print(M_SECT.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However this is not technically necessary since, as a default, the SECT was also set internally when the ECT matrix was computed. If you don't want to calculate the SECT at the same time as calculating the ECT, you can use the following command instead.\n",
-    "```python\n",
-    "myect.calculateECT(G, compute_SECT=False)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the internally set plotting functions to easily visualize it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plot('SECT')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/_sources/notebooks/tutorial_cw.ipynb.txt b/docs/_sources/notebooks/tutorial_cw.ipynb.txt
new file mode 100644
index 0000000..575387a
--- /dev/null
+++ b/docs/_sources/notebooks/tutorial_cw.ipynb.txt
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for CW complexes\n",
+    "\n",
+    "\n",
+    "\n",
+    " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedCW\n",
+    "from ect.utils.examples import create_example_cw\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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1sn4ndzTd3d0oLi7mRjcURzY6k8mE3Fzlr4nv6urCy6+8ive6duPQ4cPIys5GcWkpCk0mTJ16Kn4wd668lxAMQTweQ06OrucA9IOdacDOyvLP93fjjddfx//+7x5YzGZkZWejvKwMxUWFaDntNMw/71zZLiEYilgsBkEQuNENxZGNThAEVdewEkUxuQrBwiU3yTbWKx3cdhvKq+SZ8ZkpsDMN2FkdolERv394FQBg+fLlyM9Xt45ZLBaYTCZZG52uT0ZR6ju68YypSN51nDIBdqYBO9NAibqt60YXDoe1jqA6ohjROoLqsDMN2JkGStRtXTe6bBnXecsUsrOytI6gOuxMA3amgRJ1W9edQI0TUcYbOexMAnamAUVnJeq2rhudIMg7vDQTCMu8YGEmwM40YGcaKFG3dd3o5FyGPlMoKinVOoLqsDMN2JkGStRtXTc6u53eyCCvy6F1BNVhZxqwMw2UqNu6bnRKTkUZr1CbHAGwMxXYmQZK1G1dN7ru7m6tI6iO09qjdQTVYWcasDMNlKjbum50dXX0/hoqr6rROoLqsDMN2JkGStRtXTc6i8WidQTV4aVMaMDONKDorETd1nWjKy8v1zqC6hSV0jtLi51pwM40UKJuq9LoOjs70dTUBKPRiFmzZmHnzp0j2m/dunXIysrCBRdcMKrHDQaDo9ovkwlTvHaQnUnAzjRQom4r3ujWr1+PpUuXYsWKFdi9ezdOPfVUzJkzBzabbcj9Dh48iFtuuQVnnnnmqB9b7anb44HcvDytI6gOO9OAnWmgRN1WvNE99NBDWLx4MRYuXIjm5masWbMGJpMJTz311KD7xONxLFiwAHfffTcmTpyodERdkQV6s/HYmQbszIwWRRudKIro6upCa2vr1w+YnY3W1lZs37590P1++ctforq6GosWLRr2MSKRCHw+X8rtyJ9RI0pw2jk704CdaaBE3Va00TkcDsTjcdTUpJ4iW1NTM+iZNe+88w6efPJJrF27dkSP0dHRgdLS0uStsbERQO9SD4IgIJFIJK/L6O7uhiiKsNlsCAQC8Hg8cLlcCIVCsFgsiMViKdtGo1FYLBYIggCXywW3241gMAir1YpoNJqybTweh8ViQV5eHgoLCyEEAggFA/C6nIiKYvJ6GKe1B5IkwWW1ICpG4HM5EQoEIAT88LldECNhuGxWJBKJlH1i0Sg8TgfCQhBBnxd+rweRUAhuuw3xeCy5bTgkIB6Pwe2wIRIKIeD1IODzIiwE4XHaEYtGU46bSCTgslkhRsLwu90Q/H6EggH4krktkCQpmdtptSAqir25gwEIfj/8bvcwue0IC0EEfF4E+nI7UnM7rT29ue29uf1eD4LJ3I4hc8fjMQgBP0KBvtwRuI7I3bdPVBThTeb2we9xQwwPkjvWl1tIye1x2BGPHZ073ps7HILf40bQ50NYCMLrdCAWFQfIbYEYicDndkEI+CGMOHcQwa9y5+Tlwm23IRGPp2aJxeBx2BEOfZ07HBIGzJ04Orffh1Bw4NxSIgGX9ejcfc/Zr3L3+92L8DodKbkj4dDwufues0flLigqSskthsOpuUfwWhNSXmsRuGyWAZ6zvbl7X2upueOD5E59rQm9r7XYIM/Zr3ILft+wNSI3P1/2GpF8rQ1WI2LR5Biunp7e3GazGaFQCE6nE16vF36/H3a7HZFIBN3d3ZAkKeW/kUgEdrsdfr8fXq8XTqcToVAIZrO5X02ORqOwWq0IBAJwu91IJBKyz7tUdIVxs9mMhoYGbNu2DbNnz07ef9ttt+Htt9/Gjh07Urb3+/2YOnUqHnvsMcybNw8AcPXVV8Pj8eDFF18c8DEikUjKXwA+nw+NjY3JX4aa01HGwwrjLqsFFTXqrao+HmBnGrCzOmi9wnh3dzeKi4tlXWFc0TUgKisrkZOTA6vVmnK/1WpFbW3/X95nn32GgwcP4vzzz0/el0gkeoPm5uKTTz7BCSeckLKPwWCAwWAY8PHr6+vHqpBxlFfTu8CUnWnAzjSor6+H3++X9ZiKfnSZn5+PlpYWbN68OXlfIpHA5s2bU97h9TFp0iR88MEH2LNnT/L2wx/+EP/+7/+OPXv2JD+WHClms3nMDpmGy0bvInl2pgE700CJuq34qn5Lly7FVVddhRkzZuD000/H6tWrEQwGsXDhQgDAlVdeiYaGBnR0dMBoNGLKlCkp+/d9Vnz0/SOBhzrTgJ1pwM40aGhoSDmpUA4Uv7zgkksuwcqVK3HnnXdi2rRp2LNnDzZt2pQ8QeXQoUPo6VFmcCkPdaYBO9OAnWmgRN1WZZ329vZ2tLe3D/izrVu3Drnv008/PerHraysHPW+mUpJ+QStI6gOO9OAnWlQWVkp+yUGup51Kffb30xACMj7JW4mwM40YGcaKFG3dd3ojEaj1hFUJ3+QM1D1DDvTgJ1poETd1nWj67s0gRISO5OAnWlA0VmJuq3rRheLxbSOoDrxeFzrCKrDzjRgZxooUbd13ehMJpPWEVTHYCzQOoLqsDMN2JkGStRtXTc6t9utdQTVCfi8WkdQHXamATvTQIm6retGd/QwaQqUVVZpHUF12JkG7EwDJeq2rhvdYCsk6Bm33Tr8RjqDnWnAzjRQom7rutHxCDAasDMN2JkGStRtXTc6HgFGA3amATvTQIm6retGV11drXUE1SmdQG/sGTvTgJ1poETd1nWjc7lcWkdQnYDXo3UE1WFnGrAzDZSo27pudEVFRVpHUB1jQaHWEVSHnWnAzjRQom7rutGJoqh1BNWJxaJaR1AddqYBO9NAibqt60YnSZLWEdSHnWnAzjQg6KxE3dZ1o6O4ekFePr1p5+xMA3amAa9ekCYU16MLBQNaR1AddqYBO9OA16NLkwkT6K3OW1xWrnUE1WFnGrAzDZSo27pudDabTesIquNx2rWOoDrsTAN2poESdVvXjY5HgNGAnWnAzjTgEWBpwiPAaMDONGBnGvAIsDShuUwPvbFn7EwDdqYBL9OTJg6HQ+sIquNzObWOoDrsTAN2poESdVvXja60tFTrCKpjKi7WOoLqsDMN2JkGStRt3TS6zs5ONDc3Y+bMmcn7QqGQhom0QQyHtY6gOuxMA3amgRJ1WzeNrq2tDR9//DF27dqVvC87Wzd6IyY7O0frCKrDzjRgZxooUbd13Qlyc3O1jqA6Obn0XhjsTAN2poESdVvXjU4QBK0jqE6E4Me17EwDdqaBEnVb142urKxM6wiqU1hSonUE1WFnGrAzDZSo27pudHY7vfE5XoKnI7MzDdiZBkrUbV03Oh4BRgN2pgE704BHgKUJjwCjATvTgJ1pwCPA0qSujt5fQ+VV9MaesTMN2JkGStRtXTc6i8WidQTV8TgILk3EziRgZxooUbd13egqKiq0jqA6RaX0FmpkZxqwMw2UqNu6bnSBAL1l6MNCUOsIqsPONGBnGihRt3Xd6PLz87WOoDq5eXlaR1AddqYBO9NAibqt60ZHkyytA2gAO9OAnZnRoetGF4lEtI6gOjGRnSnAzjSg6KxE3dZ1oyshOD6noKhI6wiqw840YGcaKFG3dd3onE5643P8brfWEVSHnWnAzjRQom7rutGRvGC8mt4FpuxMA3amAV8wniZms1nrCKrjstG7SJ6dacDONFCibuu60fFQZxqwMw3YmQY81DlNeKgzDdiZBuxMAx7qnCaVlZVaR1CdkvIJWkdQHXamATvTQIm6retG5/V6tY6gOkLAr3UE1WFnGrAzDZSo27pudAUFBVpHUJ18g1HrCKrDzjRgZxooUbd13egSiYTWEVQnkYhrHUF12JkG7EwDJeq2rhtdLBbTOoLqJOIEXxjsTAJ2poESdVuVRtfZ2YmmpiYYjUbMmjULO3fuHHTbtWvX4swzz0R5eTnKy8vR2to65PZDYTKZRhs5YzEY6X1cy840YGcaKFG3FW9069evx9KlS7FixQrs3r0bp556KubMmQObbeCVc7du3YrLLrsMb731FrZv347GxkacffbZozrl1OPxjDF95hH0+7SOoDrsTAN2poESdVvxRvfQQw9h8eLFWLhwIZqbm7FmzRqYTCY89dRTA27/pz/9CTfccAOmTZuGSZMm4YknnkAikcDmzZvTfuzq6uqxxs84SivonY7MzjRgZxooUbcVbXSiKKKrqwutra1fP2B2NlpbW7F9+/YRHUMQBESj0UGXV49EIvD5fCm3PiwWeuNz3I6B3ynrGXamATvTQIm6rWijczgciMfjqKlJHUxaU1MzYpnbb78d9fX1Kc3ySDo6OlBaWpq8NTY2AgDC4TCysrKQSCSSH3t2d3dDFEXYbDYEAgF4PB64XC6EQiFYLBbEYrGUbaPRKCwWCwRBgMvlgtvtRjAYhNVqRTQaTdk2Ho/DYrEgLy8PhYWFEAIBhIIBeF1OREUxOeHAae2BJElwWS2IihH4XE6EAgEIAT98bhfESBgumxWJRCJln1g0Co/TgbAQRNDnhd/rQSQUgttuQzweS5mgEI/H4HbYEAmFEPB6EPB5ERaC8DjtiEWjKcdNJBJw2awQI2H43W4Ifj9CwQB8ydwWSJKUzO20WhAVxd7cwQAEvx9+t3uY3HaEhSACPi8Cfbkdqbmd1p7e3Pbe3H6vB8FkbseQufPyDRACfoQCfbkjcB2Ru2+fqCjCm8ztg9/jhhgeJHesL7eQktvjsCMeOzp3vDd3OAS/x42gz4ewEITX6UAsKg6Q2wIxEoHP7YIQ8EMYce4ggl/lLi4th9tuQyIeT80Si8HjsCMc+jp3OCQMmDtxdG6/D6HgwLmlRAIu69G5+56zX+Xu97sX4XU6UnJHwqHhc/c9Z4/KPaGmLiW3GA6n5h7Ba01Iea1F4LJZBnjO9ubufa2l5o4Pkjv1tSb0vtZigzxnv8ot+H3D1oiS8grZa0TytTZYjYhFUVZWBgDo6enNbTabEQqF4HQ64fV64ff7YbfbEYlE0N3dDUmSUv4biURgt9vh9/vh9XrhdDoRCoVgNpv71eRoNAqr1YpAIAC3242CggIIgjBgvR8tWZIkSbIe8QjMZjMaGhqwbds2zJ49O3n/bbfdhrfffhs7duwYcv/7778fv/nNb7B161ZMnTp1wG0ikUjKQn0+nw+NjY3JX4aa8y5FUURHRwcAYOGSm5CXJ/+S8MPRVxAowc40YGd1iEZF/P7hVQCA5cuXIz9f3TrW3d2N4uJilJaWwuv1yrI+Xa4MuQalsrISOTk5sFqtKfdbrVbU1tYOue/KlStx//3348033xy0yQGAwWCAwWAY8GcUv6Mrm1CldQTVYWcasDMNqqurEQqFZD2moh9d5ufno6WlJeVEkr4TS458h3c0v/nNb3DPPfdg06ZNmDFjxqgf3+VyjXrfTMXvobdQIzvTgJ1poETdVvQdHQAsXboUV111FWbMmIHTTz8dq1evRjAYxMKFCwEAV155JRoaGpIf+f3617/GnXfeiWeffRZNTU3J7/KKiopQlOay8ulurweMhYVaR1AddqYBO9OgqKgIcZkvlFe80V1yySWw2+248847YbFYMG3aNGzatCl5gsqhQ4eQnf31G8vHH38coiji4osvTjnOihUrcNddd6X12KIoopDYEyUWjQLErjFlZxqwMw1EUUROTo6sx1S80QFAe3s72tvbB/zZ1q1bU/598OBB5QPpGeXOLRq/sDMN2JkZJbqedan22ULjgVx2JgE704CisxJ1W9eNLhAIaB1BdcLBoNYRVIedacDONFCibuu60Q02TUXPFJeVax1BddiZBuxMAyXqtq4b3WCDo/WMx2nXOoLqsDMN2JkGStRtXTc6NaeijBeoTY4A2JkK7EwDJeq2rhvdaJb2yXSOnHlJBXamATvTQIm6retGN9yYMT1SXklv7Bk704CdaaBE3dZ1o6P4HZ3X5dQ6guqwMw3YmQb8HV2a9C01QQlTcbHWEVSHnWnAzjRQom7rutHJPQE7ExDDYa0jqA4704CdaaBE3dZ1oztyhiYVsmWeEZcJsDMN2JkGStRtXXcCuQeDZgLZ2exMAXamAUVnJeq2rhsdzY8u2ZkC7EwDis780WWaUDwZpVCGZeczDXamATvTgE9GSRO7nd74HIqnI7MzDdiZBkrUbV03Oh4BRgN2pgE704BHgKUJjwCjATvTgJ1pwCPA0qSujt5fQxVVNVpHUB12pgE700CJuq3rRmexWLSOoDpuO72xZ+xMA3amgRJ1W9eNjuLCq0UEF2pkZxqwMw144dU0UWJJ9vFOWAhqHUF12JkG7EwDJeq2rhtdfn6+1hFUJzcvT+sIqsPONGBnGihRt3Xd6BiGYRhG141OFEWtI6hOLBrVOoLqsDMN2JkGStRt3TS6zs5ONDc3Y+bMmcn7ioqKNEykDUZTodYRVIedacDONFCibuum0bW1teHjjz/Grl27kve5XC4NE2lDwOPWOoLqsDMN2JkGStRt3TS6gaitrdU6guqUV1VrHUF12JkG7EwDJeq2rhtdTw+98Tkuu1XrCKrDzjRgZxooUbd13eh4qDMN2JkG7EwDHuqcJjzUmQbsTAN2pgEPdU6TqqoqrSOoTmnFBK0jqA4704CdaaBE3dZ1o/N4PFpHUJ2gz6d1BNVhZxqwMw2UqNu6bnQFBQVaR1CdfCM7U4CdaUDRWYm6retGF4/HtY6gOokEO1OAnWlA0VmJuq3rRpdIJLSOoDoJis2dnUnAzjRQom7rutHR/OjSqHUE1WFnGrAzDfijyzSheDKK4PdrHUF12JkG7EwDPhklTaqr6Y3PoXg6MjvTgJ1poETd1nWjs1gsWkdQHbfDpnUE1WFnGrAzDZSo27pudDwCjAbsTAN2pgGPAEsTHgFGA3amATvTgEeApQnF7+jKJtAbe8bONGBnGvB3dGlCceFVP8GFGtmZBuxMA154NU2UWJJ9vGMsLNQ6guqwMw3YmQZK1G1dNzpRFLWOoDoxdiYBO9OAorMSdVvXjY4kWVlaJ1AfdqYBOzOjRNeNLj8/X+sIqpObl6d1BNVhZxqwMw2UqNu6bnSBQEDrCKoTDga1jqA67EwDdqaBEnVb142uoqJC6wiqU1xWrnUE1WFnGrAzDZSo27pudDYbvfE5Hqdd6wiqw840YGcaKFG3VWl0nZ2daGpqgtFoxKxZs7Bz584ht3/uuecwadIkGI1GnHLKKXj11VdH9bg8AowG7EwDdqZBRo4AW79+PZYuXYoVK1Zg9+7dOPXUUzFnzpxBu/a2bdtw2WWXYdGiRXj//fdxwQUX4IILLsCHH36Y9mPzCDAasDMN2JkGGTkC7KGHHsLixYuxcOFCNDc3Y82aNTCZTHjqqacG3P63v/0t5s6di1tvvRWTJ0/GPffcg+nTp+PRRx9N+7Fra2vHGj/jKK+kN/aMnWnAzjRQom4r2uhEUURXVxdaW1u/fsDsbLS2tmL79u0D7rN9+/aU7QFgzpw5g24fiUTg8/lSbn1Q/I7O63JqHUF12JkG7EyDjPuOzuFwIB6Po6amJuX+mpqaQdccslgsaW3f0dGB0tLS5K2xsREAEA6HEYlEkEgkkm+Fu7u7IYoibDYbAoEAPB4PXC4XQqEQLBYLYrFYyrbRaBQWiwWCIMDlcsHtdiMYDMJqtSIajaZsG4/HYbFYkJeXh8LCQgiBAELBALwuJ6KimPwIwmntgSRJcFktiIoR+FxOhAIBCAE/fG4XxEgYLpsViUQiZZ9YNAqP04GwEETQ54Xf60EkFILbbkM8HktuK4bDiMdjcDtsiIRCCHg9CPi8CAtBeJx2xKLRlOMmEgm4bFaIkTD8bjcEvx+hYAC+ZG4LJElK5nZaLYiKYm/uYACC3w+/2z1MbjvCQhABnxeBvtyO1NxOa09vbntvbr/Xg2Ayt2PI3FIiASHgRyjQlzsC1xG5+/aJiiK8ydw++D1uiOFBcsf6cgspuT0OO+Kxo3PHe3OHQ/B73Aj6fAgLQXidDsSi4gC5LRAjEfjcLggBP4QR5w4i+FXuvHwD3HYbEvF4apZYDB6HHeHQ17nDIWHA3Imjc/t9CAUHzi0lEnBZj87d95z9Kne/370Ir9ORkjsSDg2fu+85e1TuwuKSlNxiOJyaewSvNSHltRaBy2YZ4Dnbm7v3tZaaOz5I7tTXmtD7WosN8pz9Krfg9w1bI/INRtlrRPK1NliNiEVRVlYGAOjp6c1tNpsRCoXgdDrh9Xrh9/tht9sRiUTQ3d0NSZJS/huJRGC32+H3++H1euF0OhEKhWA2m/vV5Gg0CqvVikAgALe7d7anIAgD1vvRkiVJkiTrEY/AbDajoaEB27Ztw+zZs5P333bbbXj77bexY8eOfvvk5+fjmWeewWWXXZa877HHHsPdd98Nq9Xab/tIJIJIJJL8t8/nQ2NjI7xeL6LRKCZMUG+FXlEU0dHRAQBYuOQm5OWpf8G63+Mmd0oyO9OAndUhGhXx+4dXAQCWL1+u+uANp9OJvLw8lJaWwuv1oqSkZMzHzJUh16BUVlYiJyenX4OyWq2Dfg5bW1ub1vYGgwEGg2HAn+XmKqo3LsnOydE6guqwMw3YmQZK1G1FP7rMz89HS0sLNm/enLwvkUhg8+bNKe/wjmT27Nkp2wPAG2+8Mej2Q5GdrevLBAckO5veC4OdacDONFCibiveCZYuXYq1a9fimWeewd69e3H99dcjGAxi4cKFAIArr7wSy5cvT27/05/+FJs2bcKDDz6Iffv24a677sJ7772H9vb2tB87FArJ5pEpiJGw1hFUh51pwM40UKJuK/7Z3iWXXAK73Y4777wTFosF06ZNw6ZNm5InnBw6dCilg59xxhl49tlncccdd+BnP/sZTjzxRLz44ouYMmVK2o9dWloqm0emYCoq1jqC6rAzDdiZBqWlpSnnXciBKl9itbe3D/qObOvWrf3u+/GPf4wf//jHY35ch8NBbjqKz+0kN02BnWnAzjRwOBwoLpa3wev6SyxqTQ6gOTKInWnAzjTIyBFgWsIjwGjAzjRgZxpk5AgwLamvr9c6gupUVNMbe8bONGBnGihRt3Xd6Hp66P015Lb1v6he77AzDdiZBkrUbV03OjWnoowXistpTY4A2JkK7EwDJeq2rhvdkQOeqRBSYBn68Q4704CdaaBE3dZ1oxtsNJieyc1nZwqwMw0oOitRt3Xd6Gii2IzucQw704CdmdGh60YniqLWEVQnFo1qHUF12JkG7EwDJeq2rhtdUVGR1hFUx2gq1DqC6rAzDdiZBkrUbV03OpfLpXUE1Ql43VpHUB12pgE700CJuq3rRjfYGnZ6pqyyWusIqsPONGBnGihRt3Xd6EheMG6nd4EpO9OAnWnAF4ynCQ91pgE704CdacBDndOEhzrTgJ1pwM404KHOaVJVVaV1BNUpraA39oydacDONFCibuu60Xk8Hq0jqE6Q4NgzdqYBO9NAibqt60ZnMpm0jqA6hoICrSOoDjvTgJ1poETd1nWji8ViWkdQnXgsrnUE1WFnGrAzDZSo27pudIlEQusIqpNI0HthsDMN2JkGStRtXTe6AoJv+/ONRq0jqA4704CdaaBE3dZ1o/N6vVpHUB3B79c6guqwMw3YmQZK1G1dN7rKykqtI6hOCcHTkdmZBuxMAyXqtq4bndVKb3yOx2HTOoLqsDMN2JkGStRt3TS6zs5ONDc3Y+bMmcn7eAQYDdiZBuxMAx4BNgRtbW34+OOPsWvXruR9PAKMBuxMA3amAY8AS5PqanpLXJRNoDf2jJ1pwM40UKJu67rROZ1OrSOojt9Db6FGdqYBO9NAibqt60ZXUlKidQTVKSiUfxn68Q4704CdaaBE3dZ1owuHw1pHUJ2oGNE6guqwMw3YmQZK1G1dN7qsrCytI6gPO9OAnWlA0FmJuq3rRpefn691BNXJzc3TOoLqsDMN2JkGStRtXTe6QCCgdQTVCYeCWkdQHXamATvTQIm6retGV1FRoXUE1SkqLdM6guqwMw3YmQZK1G1dNzqbjd74HK/ToXUE1WFnGrAzDZSo27pudDwCjAbsTAN2pgGPAEsTHgFGA3amATvTgEeApUltba3WEVSnvKpG6wiqw840YGcaKFG3dd3oaC7TY9c6guqwMw3YmQa8TE+alJeXax1BdYpKSrWOoDrsTAN2poESdVvXjU4QBK0jqE4kHNI6guqwMw3YmQZK1G1dN7rc3FytI6hOTk6O1hFUh51pwM40UKJu67rRZWfrWm9AstiZBOxMA4rOStRtXf9fpLh6gRihN+2cnWnAzjTg1QvShOJ6dKaiYq0jqA4704CdacDr0aWJw0FvfI7PTW9VdXamATvTQIm6retGxyPAaMDONGBnGvAIsDThEWA0YGcasDMNeARYmtTX12sdQXUqqumNPWNnGrAzDZSo27pudGazWesIquO20Rt7xs40YGcaKFG3FW10LpcLCxYsQElJCcrKyrBo0aIhV491uVy48cYbcfLJJ6OgoADHHnsslixZAq/XO6rHr6ysHG30jKWY4NgzdqYBO9NAibqtaKNbsGABPvroI7zxxhvYuHEj/va3v+Haa68ddHuz2Qyz2YyVK1fiww8/xNNPP41NmzZh0aJFo3p8n8832ugZS0iBZejHO+xMA3amgRJ1W7EZWXv37sWmTZuwa9cuzJgxAwDwyCOP4JxzzsHKlSsH/Bx2ypQpeP7555P/PuGEE3DvvffiP//zPxGLxdIeDWMwGMYmkYHk5bMzBdiZBhSdlajbir2j2759O8rKypJNDgBaW1uRnZ2NHTt2jPg4Xq8XJSUlJOdWjgYJktYRVIedacDOzGhRrHtYLBZUV1enPlhuLioqKmCxWEZ0DIfDgXvuuWfIjzsjkQgiR4zJOfJtryiKaabOfGLRqNYRVIedacDONBBFEfn5+bIeM+13dMuWLUNWVtaQt3379o05mM/nw7nnnovm5mbcddddg27X0dGB0tLS5K2xsRFA77y0QCCARCKRvC6ju7sboijCZrMhEAjA4/HA5XIhFArBYrEgFoulbBuNRmGxWCAIAlwuF9xuN4LBIKxWK6LRaMq28XgcFosFeXl5KCwshBAIIBQMwOtyIiqKyethnNYeSJIEl9WCqBiBz+VEKBCAEPDD53ZBjIThslmRSCRS9olFo/A4HQgLQQR9Xvi9HkRCIbjtNsTjseS2oWAA8XgMbocNkVAIAa8HAZ8XYSEIj9OOWDSactxEIgGXzQoxEobf7Ybg9yMUDMCXzG2BJEnJ3E6rBVFR7M0dDEDw++F3u4fJbUdYCCLg8yLQl9uRmttp7enNbe/N7fd6EEzmdgyZOxYVIQT8CAX6ckfgOiJ33z5RUYQ3mdsHv8cNMTxI7lhfbiElt8dhRzx2dO54b+5wCH6PG0GfD2EhCK/TgVhUHCC3BWIkAp/bBSHghzDi3EEEv8qdnZMDt92GRDyemiUWg8dhRzj0de5wSBgwd+Lo3H4fQsGBc0uJBFzWo3P3PWe/yt3vdy/C63Sk5I6EQ8Pn7nvOHpXbaDKl5BbD4dTcI3itCSmvtQhcNssAz9ne3L2vtdTc8UFyp77WhN7XWmyQ5+xXuQW/b9gakZObK3uNSL7WBqsRsSjKysoAAD09vbnNZjNCoRCcTie8Xi/8fj/sdjsikQi6u7shSVLKfyORCOx2O/x+P7xeL5xOJ0KhEMxmc7+aHI1GYbVaEQgE4Ha7EY1GZV+qJ0uSpLTeG9vtdjidQ4+lmThxIv74xz/i5ptvhtvtTt4fi8VgNBrx3HPP4Uc/+tGg+/v9fsyZMwcmkwkbN26E0WgcdNuB3tE1NjbC6/UiEAioei2dKIro6OgAACxcchPy8uT9q2QkuGxWVFTXqP64WsLONGBndYhGRfz+4VUAgOXLl8v+7mo4zGYzioqKUFpamvzqaqyk/dFlVVUVqqqqht1u9uzZ8Hg86OrqQktLCwBgy5YtSCQSmDVr1qD7+Xw+zJkzBwaDAS+//PKQTQ7o/eJysC8va2vpXWxZVjn870ZvsDMN2JkGtbW1Q16GNhoUOxll8uTJmDt3LhYvXoydO3fi3XffRXt7Oy699NLku6zu7m5MmjQJO3fuBNDb5M4++2wEg0E8+eST8Pl8sFgssFgsiMfjaWfo6aE3Psdtp3eBKTvTgJ1poETdVvRUxj/96U9ob2/HWWedhezsbFx00UV4+OGHkz+PRqP45JNPkp/H7t69O3lG5je+8Y2UY33++edoampK6/F5qDMN2JkG7EyDhoYG2a+lU7TRVVRU4Nlnnx30501NTTjyK8Lvfe97SPMrwyHp7u4m1+yc1h5yLw52pgE706C7uxvFxfKuw6frWZcj+S5Rb5RW0Bt7xs40YGcaKFG3dd3oPB6P1hFUJ+Ab3VzQTIadacDONFCibuu60ZlMJq0jqI6xoEDrCKrDzjRgZxooUbd13ehisZjWEVQnzs4kYGcaUHRWom7rutElEgmtI6hOQsaTeTIFdqYBO9NAibqt60Y33MXmeiSf4LRzdqYBO9NAibqt60ZHcT06IeDXOoLqsDMN2JkGStRtXTc6iiuMl1RUaB1BddiZBuxMg4xbYVxrrFZ643M8DrvWEVSHnWnAzjRQom7rutFRm4oC0BwZxM40YGcaKFG3dd3o+tY8okTfmlOUYGcasDMNlKjbum50NTW01q4CaC7rwc40YGcaKFG3dd3oHA6H1hFUx+d2aR1BddiZBuxMAyXqtq4bnRwr02YapkJ5p35nAuxMA3amgRJ1W9eNLhwOax1BdaJiROsIqsPONGBnGihRt3Xd6LKysrSOoD7sTAN2pgFBZyXqtq4bXV5entYRVCc3V9G1dMcl7EwDdqaBEnVb140uGAxqHUF1wiFB6wiqw840YGcaKFG3dd3oKgiOzykqKdM6guqwMw3YmQZK1G1dNzqbzaZ1BNXxuuhdUsHONGBnGihRt3Xd6HgEGA3YmQbsTAMeAZYmPAKMBuxMA3amAY8AS5O6Onp/DZVX0Rt7xs40YGcaKFG3dd3oLBaL1hFUh+KyHuxMA3amgRJ1WzeNrrOzE83NzZg5c2byvvLycg0TaUNRaanWEVSHnWnAzjRQom7rptG1tbXh448/xq5du5L3CQK9a1AiQkjrCKrDzjRgZxooUbd10+gGguJUgRx2JgE704CisxJ1W9eNLjtb13oDkpVNcDYeO5OAnWmgRN3WdScguXpBhN60c3amATvTgFcvSBOK69EVFNFbv4qdacDONOD16NKEVxinATvTgJ1pwCuMp0l9fb3WEVSnopreBabsTAN2poESdVvXjc5sNmsdQXVcNnoXybMzDdiZBkrUbV03Oprv6Gq1jqA67EwDdqYBv6NLE5rv6KxaR1AddqYBO9OA39GlSWVlpdYRVKeknN5is+xMA3amgRJ1W9eNzufzaR1BdUIBv9YRVIedacDONFCibuu60RmNRq0jqE6ewaB1BNVhZxqwMw2UqNu6bnSJRELrCKojJSStI6gOO9OAnWmgRN3WdaOLxWJaR1CdODuTgJ1pQNFZibqt60ZnMpm0jqA6BlOB1hFUh51pwM40UKJu67rRud1urSOoTsDr1TqC6rAzDdiZBkrUbV03utpaehdbllVWaR1BddiZBuxMAyXqtq4bXU9Pj9YRVMdtp3eBKTvTgJ1poETd1nWja2ho0DqC6kyoqdM6guqwMw3YmQZK1G1dN7ru7m6tI6iO00rvXSw704CdaaBE3dZ1o6uurtY6guqUVtAbe8bONGBnGihRt3Xd6FwueosWBnwerSOoDjvTgJ1poETd1nWjKyws1DqC6hgL6F07yM40YGcaKFG3dd3ootGo1hFUh+I0GHamATvTQIm6rWijc7lcWLBgAUpKSlBWVoZFixYhEAiMaF9JkjBv3jxkZWXhxRdfHNXjSxK9OXFgZxqwMw0IOitRtxVtdAsWLMBHH32EN954Axs3bsTf/vY3XHvttSPad/Xq1cjKyhrT45NcvSCf3rRzdqYBO9Mgo1Yv2Lt3LzZt2oQnnngCs2bNwre//W088sgjWLdu3bAryO7ZswcPPvggnnrqqTFloLgenRCkt34VO9OAnWmQUevRbd++HWVlZZgxY0byvtbWVmRnZ2PHjh2D7icIAi6//HJ0dnaOeRQMrzBOA3amATvTIKNWGLdYLP2uh8jNzUVFRQUsFsug+910000444wzMH/+/BE9TiQSgc/nS7n1YbXSG5/jcdi1jqA67EwDdqaBEnU77Ua3bNkyZGVlDXnbt2/fqMK8/PLL2LJlC1avXj3ifTo6OlBaWpq8NTY2AgDC4TCysrKQSCSSV9p3d3dDFEXYbDYEAgF4PB64XC6EQiFYLBbEYrGUbaPRKCwWCwRBgMvlgtvtRjAYhNVqRTQaTdk2Ho/DYrEgLy8PhYWFEAIBhIIBeF1OREUxOeHAae2BJElwWS2IihH4XE6EAgEIAT98bhfESBgumxWJRCJln1g0Co/TgbAQRNDnhd/rQSQUgttuQzweS5mgEI/H4HbYEAmFEPB6EPB5ERaC8DjtiEWjKcdNJBJw2awQI2H43W4Ifj9CwQB8ydwWSJKUzO20WhAVxd7cwQAEvx9+t3uY3HaEhSACPi8Cfbkdqbmd1p7e3Pbe3H6vB8FkbseQufPyDRACfoQCfbkjcB2Ru2+fqCjCm8ztg9/jhhgeJHesL7eQktvjsCMeOzp3vDd3OAS/x42gz4ewEITX6UAsKg6Q2wIxEoHP7YIQ8EMYce4ggl/lLi4th9tuQyIeT80Si8HjsCMc+jp3OCQMmDtxdG6/D6HgwLmlRAIu69G5+56zX+Xu97sX4XU6UnJHwqHhc/c9Z4/KPaGmLiW3GA6n5h7Ba01Iea1F4LJZBnjO9ubufa2l5o4Pkjv1tSb0vtZigzxnv8ot+H3D1oiS8grZa0TytTZYjYhFUVZWBqB37mQikYDZbEYoFILT6YTX64Xf74fdbkckEkF3dzckSUr5byQSgd1uh9/vh9frhdPpRCgUgtls7leTo9EorFYrAoEA3G43CgoKIAjCCKr/yMmS0jzFxW63w+l0DrnNxIkT8cc//hE333xzypILsVgMRqMRzz33HH70ox/12++//uu/8PDDDyM7++v+G4/HkZ2djTPPPBNbt27tt08kEkEkEkn+2+fzobGxMfnLUHPepSiK6OjoAAAsXHIT8vLyVXvsPvoKAiXYmQbsrA7RqIjfP7wKALB8+XLk56tbx7q7u1FcXIzS0lJ4vV6UlJSM+Zi56e5QVVWFqqrhl46YPXs2PB4Purq60NLSAgDYsmULEokEZs2aNeA+y5YtwzXXXJNy3ymnnIJVq1bh/PPPH3Afg8EAg2HgM5NqamqGzak3KC7rwc40YGca1NTUyP6OTrHv6CZPnoy5c+di8eLF2LlzJ9599120t7fj0ksvRX19PYDezj1p0iTs3LkTQO86RFOmTEm5AcCxxx6L448/Pu0MDodDPqEMwUdw7Bk704CdaaBE3Vb0Oro//elPmDRpEs466yycc845+Pa3v43f/e53yZ9Ho1F88sknsnfvPuR4y5tpmIqKtY6gOuxMA3amgRJ1O+2PLtOhoqICzz777KA/b2pqGvYq+LFcJR8Oh2Ey0ZoVJ4oRGAoKtI6hKuxMA3amQTgcRm6uvK1J17MujzyphQrZY5wmk4mwMw3YmQZK1G1ddwK5/yrIBHLYmQTsTAOKzkrUbV03OqW++xvPhEMhrSOoDjvTgJ1poETd1nWj67vokRJFJaVaR1AddqYBO9NAibqt60Znt9Mbn+N10bukgp1pwM40UKJu67rRqTkVZbxAbXIEwM5UYGcaKFG3dd3o+uapUeLImZdUYGcasDMNlKjbum50dXX0/hoqr6I39oydacDONFCibuu60Q21HJBeobisBzvTgJ1poETd1nWjKy8v1zqC6hSV0jtLi51pwM40UKJu67rRBYNBrSOoTpjitYPsTAJ2poESdVvXjU7tdZTGA7l5eVpHUB12pgE700CJuk1vvowCdHV14aWNr2BX124c7u5GVnY2/vr3d1FYUIBTTj0VZ8+Zi6mnTVclSxbozcZjZxqws7L88/3d+Ovrm/C/e/bA2tODrOxsbHlnG4oKTWg57TTMP+/c5NqimUbaK4yPd3w+X3Jl2kgkMqJFYkfLhuf/gocfexzVTSdgeus8NE2aAuMRqyWEBQEH932I9954DZbP92PhNdfgvPkXKJYHAHwuJ0oqJij6GOMNdqYBOyvDxpdexO+feAK1x38DM34weB3b/eZrsB38DEtuuB4XX3ShYnnsdjsMBoOsK4zrutENtfr4WLDb7biu/UYYqxtw3qIbYCgYfimgSEjAy090ImA5jI4HHsQEhRpwVIwgL19+5/EMO9OAneXFabdj+a03o6j2GPzwmrYR17GNTz6GsK0bax59RJE3EpFIBJFIRNZGp+vv6JRYqfbgwYM4/8KLMfuShbio/ZYRPTkAwFBgwo9vvBXfvXwRFl6xAIcPfSF7NgDwu92KHHc8w840YGf5+PKLL7DwigX47uWL8OMbb02rjl3UfgtmX7IQ5194Mb74Qv46pkTd1vU7uuLiYmTJuJ6T3W7H+RdejKvvXonqhmNGfRxb92E8eefN+P0fn8WEykrZ8gG9C9XK6ZwJsDMN2FkenHY7Fl6xAIt++eCY69jTK27BxheeR6WMdUySJPj9fn5HN1LMZrOsx7uu/UZcfNPPxvTkAIDqhmNwydKf42e33ixTsq9x2ehdJM/ONGBneVh+6824ZOnPZaljF9/0M1zXfqNMyXqRu24DOm90cg4H3fD8X2CsbsDEb06V5XgTvzkVpuoGbHzpRVmO1wfFIbDsTAN2HjsbX3oRRbXHyFrH8ivrsOH5v8hyPICHOqeNnMNBH37scZy36IZhtwsFAlgw/Rvo/PnSYbedv7gNv3/yCTniJaE4BJadacDOY+f3TzyBH17TNujPr/v+6bhx7rdx8wWtWHLOd7Dq5huGvWj9/Gva8PDjj8uWkYc6p4lcnxt3dXWhuumEEX1h++5rL2Fi81TseOM1hIa5wt9QYELNcSfgn3velyUnAJSU0zr9GmBnKrDz2Pjn+7tRe/w3hq1jS1etwYMvvonVG7dCCPjx1gvrh9zeUGBC1XEnYPfu3bLklPP7vj503eh8Pp8sx3lp4yuY3jpvRNtu3rAOFyxuQ/OMWXj3tZeG3X7m2efgjU2bxhoxiRDwy3asTIGdacDOY+Ovr2/CjB+MrI4BQCwqIhIKjWiV85bWeXhp4ytjiZdErrp9JLqejGI0GmU5zu49e7Bg/oJht/ty/6dwWMyY9u3vIRGP4YXfPYrWiy8fcp+mSVPw1rNPIRoVZcmanZMt27EyBXamATuPjX/+7//i/1x81bDbPXTTdcg3GmHrPowTvjkVZ8z74bD7NE2agmf//IwcMWE0GiH3xQC6bnSJREKW4wSCwog+tty84f/he/MvRk5ODqZ/5yysWXE7Dn/2LxxzwomD7mM0mWAxm/H7h1fJktVoNCIcDstyrEyBnWnAzmPDYjaPqI4tXbUGx0+egngshjUrbsN/r7wXVy9bMXROkwmBoDwDqBOJhOyXVOjmo8vOzk40Nzdj5syZyftisZgsx87KHv5/Uywaxdsvb8DWF5/Ddd8/HW1nnwExFMLmDc/KcvyRkpOTI9uxMgV2pgE7j41060xObi6+dfa5eP+dtxQ5/mDIVbePRDfv6Nra2tDW1pa8YBwATKaRXe0/HNII3hnu2vJX1DQeh/vXb0zed/izf+HOKy/CgqU/G3IKeXlZGZYvXy5L1nA4LNtHtpkCO9OAncfGlne2pb3Ph/94Bw3HnzCibUdSJ0eCyWRCNBqV5Vh96KbRDYTb7UZBQcGYj1NUaEJYEFIGnR7N5uf/H75z3o9S7jvmhBNRUVOL9956A986+5wB9wsLAoqLCmVbmsLhcMgySSCTYGcasPPYKC4qHLaOAV9/RxePx1FVfwx+ctf9wx47LAgoKpTnjYXb7UZRUZEsx+pD142upqZGluNMnzYNB/d9iEnTTx90mzt+98cB71/5l78OeeyD+z5Ey2mnjSnfkcjlnEmwMw3YeWyMpI6t2bJzVMeWs47V1NTIvviqbr6jGwiLRZ7xOfPPOxe733xNlmMdTdebr2H+eefKdjy5nDMJdqYBO4+NTKljSvyedd3o5Bol09LSAtvBzxAJybusfVgQYP/iM0yfLt+irEqMzxnvsDMN2HlsZEod4xFgaSLnKJklN1yPjU8+JtvxAGDjk5346Q3DjxVLByXG54x32JkG7Dx2MqGO8QiwNKmurpbtWBdfdCHCtm4c+OifshzvwEf/hOjowUUX/mj4jdNATudMgZ1pwM5jJxPqmBK/Z103OpfLJevx1jz6CDasug+27sNjOo7t8JfYsOo+rHn0EZmSfY3czpkAO9OAneVhvNcxJZx13ejkPkW1qqoK6/77GTy94pZR/0V04KN/4um7bsX6P/5BkeGlcjtnAuxMA3aWh/Fex5Rw1nWjE0X55+I1NTXhf/6yAdvX/x4bHnlgxF/sRkICNjzyAP7x56ex8YXncdxxx8meDVDGebzDzjRgZ/kYz3VMCWddX0cn92DQPqqqqvD8+nXY8Pxf8Mgt16PyuIloaZ2HpklTUi7GDAsCDu77EF1vvgbHFwew5IbrZf9O7miUch7PsDMN2FlexmsdU8I5S9LZs6dvBJjX60Vubq5sY8CGoqurCy+/8iq63n8fgaCArOxsSIkEigpNaDntNMw/71xZLyEYCkEQVHEeT7AzDdhZWcZLHRMEAbFYLFnH5ZgMo+t3dD6fT5UnSUtLC1paWhR/nJGglvN4gp1pwM7KMl7qmBLOuv6ObsIEeisSszMN2JkG7CwPum50NptN6wiqw840YGcasLM86Po7OmqTzhmGYfSA3HVc1+/oeGQQDdiZBuxMAyWcdf2OzmQyITdX1+fb9CMWi7EzAdiZBlSdBUHgd3QjxeFwaB1BddiZBuxMA3aWB103utLSUq0jqA4704CdacDO8qDrRhcKhbSOoDrsTAN2pgE7y4OuG112tq71BoSdacDONGBnmY4p+xHHEdS+xAXYmQrsTAN2lgddNzpBkHfJ+EyAnWnAzjRgZ3nQdaMrKyvTOoLqsDMN2JkG7CwPum50drtd6wiqw840YGcasLM8KNboXC4XFixYgJKSEpSVlWHRokUIBALD7rd9+3Z8//vfR2FhIUpKSvCd73xn1GfhNDQ0jGq/TIadacDONGBneVCs0S1YsAAfffQR3njjDWzcuBF/+9vfcO211w65z/bt2zF37lycffbZ2LlzJ3bt2oX29vZRn4XD43NowM40YGcaZMwIsL1796K5uRm7du3CjBkzAACbNm3COeecg8OHD6O+vn7A/b71rW/hBz/4Ae65555RP/aRI8CKiorInZ6bSCTYmQDsTAOqzoFAYPyPANu+fTvKysqSTQ4AWltbkZ2djR07dgy4j81mw44dO1BdXY0zzjgDNTU1+O53v4t33nlnyMeKRCLw+Xwptz4sFos8QhkEO9OAnWnAzvKgyEUaFosF1dXVqQ+Um4uKiopBJQ4cOAAAuOuuu7By5UpMmzYNf/jDH3DWWWfhww8/xIknnjjgfh0dHbj77rv73W+325PL0FssFtTX18NsNqOyshJerxcFBQWIxWJIJBIoKCiAz+fDhAkTYLPZkttWVVXB5XKhuLgY4XAYWVlZyM/PRzAYRHl5Oex2e3Lbmpoa2Gw2lJWVIRQKIScnB9nZ2YhEIiguLobT6UxuW1dXh56eHkyYMAF+vx/5+fkAgGg0CpPJBI/Hg5qammFzG41G+P3+lNwOhwNGoxFOpzMld15eHgRB6Je7trYWVqt1yNx9eY/ObTAYkEgkEI/HUVBQMGDuqqoquN1umEwmRKNRSJI0YG6z2Yzq6uqU3NnZ2cjNzUUoFEJpaSkcDseAuf1+Pw4fPgwAEEWxX+6+fYbKXV1dDavV2i93YWEhRFFMyV1RUZHy/7Avd0lJCUKh0Ihyl5eXIxgMIi8vLyW3y+VCbW1tv9w+nw9GozGZOzs7G59++imqqqqGzA1gyOes3W5HaWlpMndOTg7C4fCIc0ejURQWFsLtdg/6nDUajYjH48nXmtfrHVXuiooK7Nu3r1/uvudsOBxGSUlJv9eaxWJBRUVFv9facLnHQ40oLCzEgQMHZK0RA73WxlONEEURwWAQACDbB45SGtx+++0SgCFve/fule69917ppJNO6rd/VVWV9Nhjjw147HfffVcCIC1fvjzl/lNOOUVatmzZoJnC4bDk9XqTt48//njYjHzjG9/4xrfxf/vyyy/TaVGDktY7uptvvhlXX331kNtMnDgRtbW1/VaJjcViyb9WB6Kurg4A0NzcnHL/5MmTcejQoUEfz2AwwGAwJP9dVFSEL7/8EpIk4dhjj8WXX36p+gKsM2fOxK5du1R9TKD3+8nGxkZNnAFtvNmZndWAndWhz/nQoUPIysoa9HyOdEmr0VVVVaGqqmrY7WbPng2Px4Ouri60tLQAALZs2YJEIoFZs2YNuE9TUxPq6+vxySefpNz/6aefYt68eSPOmJ2djWOOOSb5XV1JSYnqT5KcnBxNVzfXwhnQ1pud1YOd1YWic2lpqayPrcjJKJMnT8bcuXOxePFi7Ny5E++++y7a29tx6aWXJjt0d3c3Jk2ahJ07dwIAsrKycOutt+Lhhx/Ghg0bsH//fvziF7/Avn37sGjRIiViKkZbW5vWETSBojc704CdMxxZPgAdAKfTKV122WVSUVGRVFJSIi1cuFDy+/3Jn3/++ecSAOmtt95K2a+jo0M65phjJJPJJM2ePVv6+9//PqrH93q9EgDJ6/WORSOjYGcasDMN2Fk+FBuNXVFRgWeffXbQnzc1NQ14Rs2yZcuwbNmyMT++wWDAihUrUr6/0zvsTAN2pgE7y4ciF4wzDMMwzHiB1iX3DMMwDDm40TEMwzC6hhsdwzAMo2u40TEMwzC6RleNbjysgac2o3UGAEmSMG/ePGRlZeHFF19UNqiMpOvscrlw44034uSTT0ZBQQGOPfZYLFmyBF6vV8XU6dHZ2YmmpiYYjUbMmjUreb3pYDz33HOYNGkSjEYjTjnlFLz66qsqJZWPdJzXrl2LM888E+Xl5SgvL0dra+uw/4/GI+n+nvtYt24dsrKycMEFFygbUAHSdfZ4PGhra0NdXR0MBgNOOumk9J/fsl6soDFz586VTj31VOkf//iH9Pe//136xje+IV122WVD7rNt2zappKRE6ujokD788ENp37590vr166VwOKxS6rExGuc+HnroIWnevHkSAOmFF15QNqiMpOv8wQcfSBdeeKH08ssvS/v375c2b94snXjiidJFF12kYuqRs27dOik/P1966qmnpI8++khavHixVFZWJlmt1gG3f/fdd6WcnBzpN7/5jfTxxx9Ld9xxh5SXlyd98MEHKicfPek6X3755VJnZ6f0/vvvS3v37pWuvvpqqbS0VDp8+LDKyUdPus59fP7551JDQ4N05plnSvPnz1cnrEyk6xyJRKQZM2ZI55xzjvTOO+9In3/+ubR161Zpz549aT2ubhpd3zDnXbt2Je977bXXpKysLKm7u3vQ/WbNmiXdcccdakSUndE6S5Ikvf/++1JDQ4PU09OTUY1uLM5H8uc//1nKz8+XotGoEjHHxOmnny61tbUl/x2Px6X6+nqpo6NjwO3/4z/+Qzr33HNT7ps1a5b0k5/8RNGccpKu89HEYjGpuLhYeuaZZ5SKKDujcY7FYtIZZ5whPfHEE9JVV12VcY0uXefHH39cmjhxoiSK4pgeVzcfXaq5Bt54YTTOACAIAi6//HJ0dnYOOmR7vDJa56PpW9AxN1exmQmjQhRFdHV1obW1NXlfdnY2WltbsX379gH32b59e8r2ADBnzpxBtx9vjMb5aARBQDQaRUVFhVIxZWW0zr/85S9RXV2dcWMRgdE5v/zyy5g9ezba2tpQU1ODKVOm4L777kM8Hk/rsXXT6Ma6Bt7ixYuxadMmTJ8+HWeddRb+9a9/KZ55rIzGGQBuuukmnHHGGZg/f77SEWVntM5H4nA4cM899+Daa69VIuKYcDgciMfjqKmpSbm/bw2vgbBYLGltP94YjfPR3H777aivr+/X8Mcro3F+55138OSTT2Lt2rVqRJSd0TgfOHAAGzZsQDwex6uvvopf/OIXePDBB/GrX/0qrcce941u2bJlyMrKGvK2b9++UR07kUgAAH7yk59g4cKFOO2007Bq1SqcfPLJeOqpp+TUSAslnV9++WVs2bIFq1evljf0GFHS+Uh8Ph/OPfdcNDc346677hp7cEZz7r//fqxbtw4vvPACjEaj1nEUwe/344orrsDatWtRWVmpdRzVSCQSqK6uxu9+9zu0tLTgkksuwc9//nOsWbMmreOMr89tBmA8roGnNEo6b9myBZ999hnKyspS7r/oootw5plnYuvWrWNIPnqUdO7D7/dj7ty5KC4uxgsvvJBcJXs8UVlZiZycHFit1pT7rVbroH59K0CPdPvxxmic+1i5ciXuv/9+vPnmm5g6daqSMWUlXefPPvsMBw8exPnnn5+8r+8P9dzcXHzyySc44YQTlA09Rkbze66rq0NeXh5ycnKS902ePBkWiwWiKCZXXh+WMX3DN47oO0nhvffeS973+uuvD3mSQiKRkOrr6/udjDJt2rR+K52PR0bj3NPTI33wwQcpNwDSb3/7W+nAgQNqRR81o3GWpN6p6N/61rek7373u1IwGFQj6qg5/fTTpfb29uS/4/G41NDQMOTJKOedd17KfbNnz864k1HScZYkSfr1r38tlZSUSNu3b1cjouyk4xwKhfq9bufPny99//vflz744AMpEomoGX3UpPt7Xr58uXTcccdJ8Xg8ed/q1aulurq6tB5XN41OknpPOz/ttNOkHTt2SO+884504oknppx2fvjwYenkk0+WduzYkbxv1apVUklJifTcc89J//rXv6Q77rhDMhqN0v79+7VQSJvROB8NMuisS0lK39nr9UqzZs2STjnlFGn//v1ST09P8haLxbTSGJR169ZJBoNBevrpp6WPP/5Yuvbaa6WysjLJYrFIkiRJV1xxhbRs2bLk9u+++66Um5srrVy5Utq7d6+0YsWKjLy8IB3n+++/X8rPz5c2bNiQ8vs8cimw8U66zkeTiWddput86NAhqbi4WGpvb5c++eQTaePGjVJ1dbX0q1/9Kq3H1VWj03oNPC0YrfORZFqjS9f5rbfekgAMePv888+1kRiGRx55RDr22GOl/Px86fTTT5f+8Y9/JH/23e9+V7rqqqtStv/zn/8snXTSSVJ+fr70zW9+U3rllVdUTjx20nE+7rjjBvx9rlixQv3gYyDd3/ORZGKjk6T0nbdt2ybNmjVLMhgM0sSJE6V777037T9QeZkehmEYRteM+7MuGYZhGGYscKNjGIZhdA03OoZhGEbXcKNjGIZhdA03OoZhGEbXcKNjGIZhdA03OoZhGEbXcKNjGIZhdA03OoZhGEbXcKNjGIZhdA03OoZhGEbXcKNjGIZhdM3/B+s9uZ8vgJl+AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = EmbeddedCW()\n",
+    "\n",
+    "# Add vertices with coordinates\n",
+    "K.add_node(\"A\", [0, 0])\n",
+    "K.add_node(\"B\", [1, 0])\n",
+    "K.add_node(\"C\", [1, 1])\n",
+    "K.add_node(\"D\", [0, 1])\n",
+    "\n",
+    "# Add edges to form a square\n",
+    "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n",
+    "\n",
+    "# Add the square face\n",
+    "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n",
+    "\n",
+    "K.center_coordinates()\n",
+    "K.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = create_example_cw()\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n",
+      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
+      "\n",
+      "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n",
+      "\u001b[1m\n",
+      "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n",
+      "\u001b[1m    @njit(parallel=True, fastmath=True)\n",
+      "\u001b[1m    def shape_descriptor(simplex_counts_list):\n",
+      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\n",
+      "  result[i, j] = shape_descriptor(simplex_counts_list)\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "override_bound_radius = 1.2 * K.get_bounding_radius()\n",
+    "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n",
+    "result.plot();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n",
+    "result.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also look at the Smooth ECT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate SECT and plot\n",
+    "smooth = result.smooth()\n",
+    "smooth.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also compute the ECT in 3D."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = [\n",
+    "    (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n",
+    "]\n",
+    "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n",
+    "faces = [\n",
+    "    (\"a\", \"b\", \"c\"),\n",
+    "    (\"a\", \"b\", \"d\"),\n",
+    "    (\"a\", \"c\", \"d\"),\n",
+    "    (\"b\", \"c\", \"d\"),\n",
+    "    (\"a\", \"b\", \"c\", \"d\"),\n",
+    "]\n",
+    "K = EmbeddedCW()\n",
+    "K.add_nodes_from(vertices)\n",
+    "K.add_edges_from(edges)\n",
+    "\n",
+    "K.add_faces_from(faces)\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n",
+    "result = ect.calculate(K)\n",
+    "result.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_sources/notebooks/tutorial_graph.ipynb.txt b/docs/_sources/notebooks/tutorial_graph.ipynb.txt
new file mode 100644
index 0000000..7988362
--- /dev/null
+++ b/docs/_sources/notebooks/tutorial_graph.ipynb.txt
@@ -0,0 +1,791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for Embedded Graphs\n",
+    "\n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates how to use the `ect` package..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedGraph\n",
+    "\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n",
+    "\n",
+    "\n",
+    "\n",
+    "First, let's create a simple graph\"\"\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Construct an example graph\n",
+    "# Note that this is the same graph that is returned by:\n",
+    "# G = create_example_graph()\n",
+    "\n",
+    "G = EmbeddedGraph()\n",
+    "\n",
+    "G.add_node(\"A\", [1, 2])\n",
+    "G.add_node(\"B\", [3, 4])\n",
+    "G.add_node(\"C\", [5, 7])\n",
+    "G.add_node(\"D\", [3, 6])\n",
+    "G.add_node(\"E\", [4, 3])\n",
+    "G.add_node(\"F\", [4, 5])\n",
+    "\n",
+    "G.add_edge(\"A\", \"B\")\n",
+    "G.add_edge(\"B\", \"C\")\n",
+    "G.add_edge(\"B\", \"D\")\n",
+    "G.add_edge(\"B\", \"E\")\n",
+    "G.add_edge(\"C\", \"D\")\n",
+    "G.add_edge(\"E\", \"F\")\n",
+    "\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n",
+    "\n",
+    " The coordinates of all vertices can be accessed using the `coord_matrix` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2.],\n",
+       "       [3., 4.],\n",
+       "       [5., 7.],\n",
+       "       [3., 6.],\n",
+       "       [4., 3.],\n",
+       "       [4., 5.]])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.coord_matrix\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.33333333 -2.5       ]\n",
+      " [-0.33333333 -0.5       ]\n",
+      " [ 1.66666667  2.5       ]\n",
+      " [-0.33333333  1.5       ]\n",
+      " [ 0.66666667 -1.5       ]\n",
+      " [ 0.66666667  0.5       ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.center_coordinates(center_type=\"mean\")\n",
+    "print(G.coord_matrix)\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To get a bounding radius we can use the `get_bounding_radius` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This is actually getting the radius\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n",
+    "# plotting the graph with it's bounding circle of radius r.\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also rescale our graph to have unit radius using `scale_coordinates`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 0.9362075413977773\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.scale_coordinates(radius=1)\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(num_dirs=16, num_thresh=20)\n",
+    "\n",
+    "# The ECT object will automatically create directions when needed\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n",
+      " -0.41390228 -0.29564449 -0.17738669 -0.0591289   0.0591289   0.17738669\n",
+      "  0.29564449  0.41390228  0.53216008  0.65041787  0.76867567  0.88693346\n",
+      "  1.00519125  1.12344905]\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_bound_radius = 1.2 * G.get_bounding_radius()\n",
+    "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n",
+    "\n",
+    "print(f\"Thresholds chosen are: {myect.thresholds}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G, theta=np.pi / 2)\n",
+    "print(f\"ECT values for direction pi/2: {result[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT matrix shape: (16, 20)\n",
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G)\n",
+    "\n",
+    "print(f\"ECT matrix shape: {result.shape}\")\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n",
+    "\n",
+    "# We can plot the result matrix\n",
+    "result.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## SECT\n",
+    "\n",
+    "\n",
+    "\n",
+    " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
+    "\n",
+    " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
+    "\n",
+    " Then the SECT is defined by\n",
+    "\n",
+    " $$\n",
+    "\n",
+    " \\begin{matrix}\n",
+    "\n",
+    " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
+    "\n",
+    " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
+    "\n",
+    " \\end{matrix}\n",
+    "\n",
+    " $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The SECT can be computed from the ECT result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sect = result.smooth()\n",
+    "\n",
+    "sect.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## ECT for higher dimensions\n",
+    "\n",
+    "\n",
+    "\n",
+    " The `ECT` class can also be used for higher dimensional graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# generate a 3d graph\n",
+    "list_3d = [\n",
+    "    (\"A\", [0, 0, 0]),\n",
+    "    (\"B\", [1, 0, 0]),\n",
+    "    (\"C\", [0, 1, 0]),\n",
+    "    (\"D\", [1, 1, 0]),\n",
+    "    (\"E\", [0, 0, 1]),\n",
+    "    (\"F\", [1, 0, 1]),\n",
+    "    (\"G\", [0, 1, 1]),\n",
+    "    (\"H\", [1, 1, 1]),\n",
+    "]\n",
+    "graph_3d = EmbeddedGraph()\n",
+    "graph_3d.add_nodes_from(list_3d)\n",
+    "\n",
+    "# add edges\n",
+    "graph_3d.add_edge(\"A\", \"B\")\n",
+    "graph_3d.add_edge(\"B\", \"C\")\n",
+    "graph_3d.add_edge(\"C\", \"D\")\n",
+    "graph_3d.add_edge(\"D\", \"E\")\n",
+    "graph_3d.add_edge(\"E\", \"F\")\n",
+    "graph_3d.add_edge(\"F\", \"G\")\n",
+    "graph_3d.add_edge(\"G\", \"H\")\n",
+    "graph_3d.add_edge(\"H\", \"A\")\n",
+    "\n",
+    "# plot the graph\n",
+    "graph_3d.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " lets center the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph_3d.center_coordinates(center_type=\"mean\")\n",
+    "graph_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Now we can compute the ECT of the 3d graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect_3d = ECT(num_dirs=16, num_thresh=20)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n",
+    "\n",
+    " ECT results inherit from ndarrays but they store the associated directions and thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.48609512, -0.71244342, -0.50609872],\n",
+       "       [ 0.00364975, -0.82190818,  0.56960831],\n",
+       "       [-0.77710663, -0.23260984,  0.5848059 ],\n",
+       "       [ 0.75362655, -0.61431121, -0.23381352],\n",
+       "       [-0.18133219, -0.98270096, -0.03764916],\n",
+       "       [-0.92728701,  0.26549447,  0.26391569],\n",
+       "       [-0.81397529,  0.14802792,  0.56172232],\n",
+       "       [ 0.54073592, -0.29337918,  0.78837385],\n",
+       "       [ 0.48363696,  0.74722911, -0.45578937],\n",
+       "       [ 0.99631892,  0.01735462,  0.08394891],\n",
+       "       [ 0.74179171, -0.62822234, -0.23469503],\n",
+       "       [ 0.09906268, -0.01998319, -0.99488052],\n",
+       "       [ 0.57301878,  0.71110718, -0.40740159],\n",
+       "       [ 0.93645869, -0.11437729, -0.33160663],\n",
+       "       [-0.19170715,  0.53609831, -0.82209913],\n",
+       "       [ 0.69801901,  0.38101835, -0.6062957 ]])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.directions.vectors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n",
+       "       -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n",
+       "        0.04558028,  0.13674085,  0.22790142,  0.31906199,  0.41022256,\n",
+       "        0.50138313,  0.5925437 ,  0.68370427,  0.77486483,  0.8660254 ])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.thresholds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ect import Directions\n",
+    "\n",
+    "directions = Directions.random(16, dim=3)\n",
+    "thresholds = np.cos(np.linspace(0, np.pi, 20))\n",
+    "ect_3d = ECT(directions=directions, thresholds=thresholds)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of direction vectors: 10000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# another example sampling from the half sphere\n",
+    "sample_size = 100\n",
+    "theta = np.linspace(0, np.pi / 2, sample_size)  # Only go up to pi/2 for half sphere\n",
+    "phi = np.linspace(0, 2 * np.pi, sample_size)\n",
+    "theta, phi = np.meshgrid(theta, phi)\n",
+    "\n",
+    "# Flatten the meshgrid arrays and create vectors\n",
+    "half_sphere_vectors = np.column_stack(\n",
+    "    [\n",
+    "        np.sin(theta.flatten()) * np.cos(phi.flatten()),\n",
+    "        np.sin(theta.flatten()) * np.sin(phi.flatten()),\n",
+    "        np.cos(theta.flatten()),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Normalize the vectors\n",
+    "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n",
+    "    half_sphere_vectors, axis=1, keepdims=True\n",
+    ")\n",
+    "\n",
+    "directions = Directions.from_vectors(half_sphere_vectors)\n",
+    "print(f\"Number of direction vectors: {len(directions)}\")\n",
+    "ect_3d = ECT(directions=directions, num_thresh=20)  # Reduced number of thresholds\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_sources/tutorials.rst.txt b/docs/_sources/tutorials.rst.txt
index f7ffaab..5a5d1b1 100644
--- a/docs/_sources/tutorials.rst.txt
+++ b/docs/_sources/tutorials.rst.txt
@@ -5,8 +5,8 @@ Tutorials
    :maxdepth: 2
    :caption: Contents:
 
-   notebooks/Tutorial-ECT_for_embedded_graphs
-   notebooks/Tutorial-ECT_for_CW_Complexes
-   notebooks/Matisse/Matisse_ECT
-   notebooks/Tutorial-ExactECT
+   notebooks/tutorial_graph
+   notebooks/tutorial_cw
+   notebooks/Matisse/example_matisse
+   .. notebooks/Tutorial-ExactECT
 
diff --git a/docs/_static/css/badge_only.css b/docs/_static/css/badge_only.css
index 88ba55b..c718cee 100644
--- a/docs/_static/css/badge_only.css
+++ b/docs/_static/css/badge_only.css
@@ -1 +1 @@
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-      </dl>
-    `;
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-          <span class="fa fa-caret-down"></span>
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-                  ${config.projects.current.slug === language.slug ? 'selected="selected"' : ""}
-                  data-url="${language.urls.documentation}">
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-  document
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\ No newline at end of file
diff --git a/docs/_static/pygments.css b/docs/_static/pygments.css
index 08bec68..84ab303 100644
--- a/docs/_static/pygments.css
+++ b/docs/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #E40000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #008400 } /* Generic.Inserted */
diff --git a/docs/_static/searchtools.js b/docs/_static/searchtools.js
index 92da3f8..b08d58c 100644
--- a/docs/_static/searchtools.js
+++ b/docs/_static/searchtools.js
@@ -178,7 +178,7 @@ const Search = {
 
   htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlinks", "script", "style"]) {
+    for (const removalQuery of [".headerlink", "script", "style"]) {
       htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
     }
     if (anchor) {
@@ -328,13 +328,14 @@ const Search = {
     for (const [title, foundTitles] of Object.entries(allTitles)) {
       if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
-          let score = Math.round(100 * queryLower.length / title.length)
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
           normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
             null,
-            score,
+            score + boost,
             filenames[file],
           ]);
         }
diff --git a/docs/citing.html b/docs/citing.html
index 611440a..20c8317 100644
--- a/docs/citing.html
+++ b/docs/citing.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,18 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>6. Citing &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -56,12 +58,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -71,18 +71,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
diff --git a/docs/contributing.html b/docs/contributing.html
index e486a8c..1e22c8a 100644
--- a/docs/contributing.html
+++ b/docs/contributing.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,23 +5,27 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>4. Contributing to the ect Package &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="5. GPL-3.0 License" href="license.html" />
-    <link rel="prev" title="3.4. Tutorial for exact ECT computation" href="notebooks/Tutorial-ExactECT.html" /> 
+    <link rel="prev" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" href="notebooks/Matisse/example_matisse.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -57,12 +59,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -72,18 +72,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">4. Contributing</a><ul>
@@ -227,7 +224,7 @@ <h2><span class="section-number">4.3. </span>Conclusion<a class="headerlink" hre
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="notebooks/Tutorial-ExactECT.html" class="btn btn-neutral float-left" title="3.4. Tutorial for exact ECT computation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="notebooks/Matisse/example_matisse.html" class="btn btn-neutral float-left" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="license.html" class="btn btn-neutral float-right" title="5. GPL-3.0 License" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
diff --git a/docs/doctrees/citing.doctree b/docs/doctrees/citing.doctree
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diff --git a/docs/doctrees/nbsphinx/notebooks/CodingFiguresFern.ipynb b/docs/doctrees/nbsphinx/notebooks/CodingFiguresFern.ipynb
deleted file mode 100644
index 42c35ca..0000000
--- a/docs/doctrees/nbsphinx/notebooks/CodingFiguresFern.ipynb
+++ /dev/null
@@ -1,1924 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ec2a3c4f",
-   "metadata": {},
-   "source": [
-    "# Figure generation for *Invitation to the ECT*\n",
-    "\n",
-    "This notebook was used to generate the figures for the paper \n",
-    "\n",
-    "> Elizabeth Munch. An Invitation to the Euler Characteristic Transform. [arXiv:2310.10395](https://arxiv.org/abs/2310.10395), 2023. To appear: MAA Monthly, 2024."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7dde292",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "# import teaspoon \n",
-    "\n",
-    "plt.rcParams.update({\n",
-    "    \"text.usetex\": True,\n",
-    "    # \"font.family\": \"Helvetica\",\n",
-    "    \"font.family\": \"serif\",\n",
-    "    \"font.size\": 8\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e26c7194",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4640fcce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'3.8.4'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib\n",
-    "matplotlib.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4188598",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to set colormaps to deal with the transparency issue \n",
-    "\n",
-    "cmap_viridis = matplotlib.cm.viridis\n",
-    "cmap_viridis.set_bad('white',1.)\n",
-    "\n",
-    "\n",
-    "cmap_binary = matplotlib.cm.tab20b\n",
-    "cmap_binary.set_bad('white',1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5192867b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "F = plt.imread('figures/leafbw.tif')\n",
-    "F = F.T\n",
-    "F = F/255.\n",
-    "\n",
-    "F_finite = np.copy(F)\n",
-    "F_finite[F==0] = 2000\n",
-    "\n",
-    "# G = np.ones_like(F)\n",
-    "# G[F==0] = 1\n",
-    "# print(G.shape)\n",
-    "\n",
-    "F[F==0] = np.nan\n",
-    "\n",
-    "\n",
-    "plt.matshow(F, origin = 'lower',cmap = cmap_binary)\n",
-    "# plt.matshow(G, origin = 'lower')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.title('Testing')\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c62385",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d9a8a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "F"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c5e8ecb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPic(F, theta, plot = True):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    DT = np.multiply(F,fMask)\n",
-    "    \n",
-    "    return DT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f80ebea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XtL0g7Z+fyMaO9g1Bak0JFigtxq9SNmxIJ9fR0VGYmpqC4eFhAChf5m1ubjZnsQTV94hkl7FCakOtkwiNTlt4SYeWr0uXKLmFKa4IEhRzRkx67XBSPqrEDMOw+bGuCsVi0Xx4KZvNQktLi/mzPvQrN83NzeYjzqL9dszMzMARRxwB+/fvd/XSMKKXsbc6hMe6Pjrhqp6fsDaxdtDH6WNO9XTZwxJEnJxsQBAZvIzNpG7P45dC0wf/WLre7O/fg5Fz7o9kPpBOrkGByTV83PvWEuGxyz66W7qsqL5qHbfyZW31wyZWN4vfMUAQr+hIrt2PdcHhCsn10O/fg9FPj0UyH+AKTYiQe978xPxf4vselTIE53sklx/7S7Nuub6/91VKxgKLTmIDb+UiUo4kpGr/nCEynLjnzU+YZVlbLj/2l650u7EDQYLCz5+cCxuYXBEhbh6Dv+LYZ+D7b57pWK5cxj/5Vxz7jPk38eP7b54JV8wnHJF+IpvUdxMDVoa4zIKqv+10y/rupBtBakXJUHsCuBSq66pqYHJFhKguUPCV4/5dqt533vhLUHlCmJVPEkdZjj20LjrhXHHsM7b1K8fU7aRlEJtFNtnFgo0jKUvLZH3g6UOQsOB24f4ogskVEaKyQMHVxz8BAAA7Xj8b/L7MS+DZR+ygy1htq5TzY2m1q49/wqKHh6xeUbkdr59t+kTKsH4jSBgpKa4trFI2bGByRbh8+/VPg5sk6eeKKmWbaMq6rjn+MW7Za45/DOZgAVVvgeW4ri8BrH67GKjodZYDpizymRcLBAkLjfQqTnTn3IivvGcc5rhdc/xj5t/feu18AADLPj+3a45/zNRFIDYQ+7/12vlKPrjdvvXa+VW6yeYmriplWV8QBAkHOHNFuMjc6/iHV5NAfz/7h1eT8PUTc77fJ/n6iTmL/vL/AF8/8VGzzN+f8Ki53w7WBy+2VPRVx4QgGxtVu2gdCBJW8J5rBNj2aif0npittRl1i9tH4Le92glufte098TsfF37MjTERrKfrt97Yha+fmLOUaaTHtLPWNk0sj7zyvH8lpWH/R+JGiVQXFsY77kGy5ZXLgSABfP/l7n+pIdqZ1AdUYlpcN8YSduRZEG3Kw9y/PqT2PLVNrv55rvllQurbBLJIX1RRl7viVmub6L9dmB/R6KInz+WHjYimVx5s6rhV5ZB/0m7amBNfaEyY+0/aRcMv7LMkz7SZlY5McsxAinTP59YKnWsNtP13M7AK7p2Vcmh+5qqfFH5OY7PvNg2Wh+XjQGe/9EAfxUnxAy9fDHgc1j+oTLT89oW6ZMfEMohx+x0iWwtlyXo6SsiXaozYyc5Qy9fbPreiMmC+F9pQ/t+QYjyvblGAu+5hpRbXvoseHmpH3FGdeEIFW4+5afzbVim8rdYJylz8ykPcPb73xfsbHRjAx1fazwq++kYkXL1xC0vfbaqL1Q4rCquMv772W8RfTTSzDVSXwvmjAW2G+Kdm0/5qWOc3W4Azm1Ilx148fMwZyzgDq5u7NTpm4ovdN90IyPqDLz4eRh48fPm5zljgdm2uvyvty8g9QpZREJliyqR+VWcm168REnOrR/7sV7DGgzVeANUx9yNDFYeTwbR40W+DhlB6wtzn77pxUu0xtSNr7QNiH50/CrOZx76Kvzxnx4uXe+9/z4EP7vwn/FXcfzk1o/9GNa8cKl0ebrsxlPv98Okuua9kvfLbGSgk2m3jafeb5Yjf5c/i+1Q7RM0lXrV8un+4la+CBJXNzrWvHBpaPoybQvdTk5t5oQX/3T0WcRf8LJwSNl46v0wBwvMjf0s2la/0FVr00PL6he6zI1GNrb0JsKuDtGz+oUu87NdHXbwVbVR1Q/dMlkfiN9O28ZT7w9NYgWoLM1I2y/ri2zburUJCS8kuapsUSUyM1fC5lPHuJ9XPb/cth45vvW0+/wxLKLQnZeO4dbT7oPNp445xlUGIocX+7L8mOUY2cej2h5/Tz5dJ3fF7hjjA19+mPopfe7QfgDoi48Of6M8EDcKjTRzjVxyFbH1tPtg5XMrHMutfG4FbP/4zgAsigaizkviRAY9p9jSMeWXrdZTLle9P0wnlBtbtn98p1RftKsfJugY0H+L2k8W3X6WjBie3yHHALVVl0L1QJAidZNcAcon63WTlzuWo8vc0X6PnyaFHruYWfcfZsaKV54tS7ij/R64bvJyS5xJ2Tva+YNgmF6rENlC/BLBxtUudmyZWlJpG9q/w6hj4WkbljD1G4RPI81c6+4mxR3t9ygNUjLJuN7xMqh7ifd1k5f7Gn+3fnm1ifaLxMdvX6OEap9RBeMcXvCeax1wR/s9cM3EFVJlSblvd3zfT5NCzfsS3/qvmbgCvt3xfUvZSoyr65N4vm8cZlvOjS0ylHW6k8XzVVY23Y9U/A6yH/J00T55iV0tiJq9jUojzVzrNrkClAeOq3d/Ubo8Kbtjyff8Mim0kEFWJl4yP2BMYliWp3aCqLabXzjZvmPJ94R2Xr37i7Bjyfdc/dhzEP2QtctNO8kSxPkU5R/VbiQwudYR5MS+avxK6Tp02btOv1uvQSFHJl5O632SmJVluLvzUOs1Ra0+iNmx5HtmmbtOvxuuGr/S9NuL/7Ru3X2QZ5ef8b5q/Erfz6Na9xdEDsOIgaGQMFXKho2G6ZF3nX637XV90fEv//LLtTa9Joji9OVfflnqHgkpJxN3Ff1BbTJ2sGXI33edfre56bDly7/8snQ/lCmn6qebjfbf78TK65ONet4i4UFp+cNMJgOFQgEmJiagq6sLkskkAAB0dXVBOp0GAICdO3fC8PCwWR4AoFAoQCKRMMvb4WWJLRn+5tmrhMd+cMZdjuXoMo0ILy68uMnEUoSXujr5wRl3SfWDv3n2KrOsqH948UO2z/Fir9sWGrv4yNihG9qWRj9P/UDH8oef/Le/hz/60ybpeu//9yw8/flvRXL5Q+nkOjk5Cfl8HlKpFBSLRVi8eDHs27cPAAA6Ojogn8/DkiVLYGxsDOLxOOTzeRgeHoaRkREAAOjs7IRsNuuox+/kSrj8mauFx+45c4djWbZMo0BioRIju1iL6tjpDgLdtrOwcu85cwdc/szVrvsVLa8WMQzD+cCLKaIPHcn1jB9/TTm5PnvJN6V16pzoeUX6snChUDCTYzweh+bmZpicnAQAgHQ6Dfv27YNsNgvxeBwAAHK5nPk3qZPL5arkzs7OwszMjGULgnvO3OF4uc+u7GVPdwdiZ9goGbGqQeuyp7vN/fRGH5PdLnu629x4uokeUX2i20mPUzlWp4w8Oh4iH0S+AHhLBjJ9U7UtVNut1jidx0jtIfdcVTYV8vk8LF26FPr7+80km8/nIZvNQiqVgu7ubjPh+o30A03JZNKS7QuFArS3twMAwPj4uLkPAKC7uxumpqagpaXFLN/c3AzFYrFK7tDQEKxbt86V8V6595OjAACQeuoay37yOXPWt6vK0sfpenTZeuZ95sGR1FPXQOasUUHpctzY+MrCtgPdBiK5lTqj3P3Vn/nfL1k5dj4Q+5xk0rC+yGDX39h24SFTxo5qP62U+0JtzoOyTRX/GuV8jBqqX3xUvySl02lIpVKWfaKJnt+zV1dPC/f09MCOHZVv2fQ3gba2Nli+nL8eLUm+NOl0Gnp7e83PMzMz0Nra6sYs7YgGi8xZ35ZKyPUOHR/RgOs1Hmx9mUQdZBvw+gJNrb6AySY6p/ZjZYaNMCZ6RIzbp4XZK5pNTU3Q1FR9ednLRE83ysk1k8lAZ2en+e0gk8nA+Pi4mWDJ/da2tjaLA+RaN4soSEFCTsJLnry26hjZ9+NP3cmtw9aj/2brRJ2ybwsoH61/s/z4U3eadbzppJGT5bYegW2790sLzP2XPHmt5XjmrG9z+47IJpV+YZUr9oHYR2B1iNrBrv2cCEP/Zv0mhME2pBpDceZKkis74RoYGIDBwcGq8l4merpRSq5kep1MJmFychLi8TgkEgnLlLtYLEJ7ezvE43Ho7+839+fz+UBuInuBnJCfe+I65Xq8Op974jr4ydl3aLEtDLh5l7BW7x/q0Gtt0wVmWxLZ7HEVuTL9oizfWS6vHK3DTs5Pzr7D4gdtl9N5EIb+je+3RgsDAOTfT6ks3D89PW15oIk3IfM60dON9NPC+XweOjo6zM/FYhFIVfIk1vj4OPT09JiG009oNTc3V10L5xHU08JO/K8n/p67/3+f/S1XdWXqhRniE/FDFJ+w4JedOuU69QknHf/77G/ZlnE67pVa92mZ+CD60PG08KmZVXDYn8hfqZz7wyy8kNoqpXNychIKhYI5iWtra4OpqSnI5/PQ398PY2Plnyft6OiAiYkJJfvdID1zTSQS5qs3LCRpsslTJpmGFdGli4sf/xo8cM43beuSk/rix79mqQcAjnXDSNn2mMX2qDyNqcPOB875pqUtdcm160sk5k7Y2SErQ5aw9V0738NmK1LGzxWa2tvbIZPJmDNY8nZLIpGAFStWmOs0kKeI/UZpEYkgCMvMlXDBL74hPPbwubd7kqNSvxYQm3l22sWlnnj43NsD8ZWOsaw+r7YRnSoywtRnRXaHycZ6QsfM9ZT7VivPXF9avjk0+UCFul9b2CvkRO38+cqqY50/XwnZ87YryaFlsTJlZfkNsSt73u3c/WXcz4iIn7yYBgkbb9qe7Hnbzc+8wVq37aQvleX6v/Yq7R9pZxmf2BjVEpHvFb9qY1+t9YcZw1C85xqqqZ8a+DSAJNnztkMJYlXb0p/3Olf2UZZulv68F0oQ4w4M2fO2C21X2Qhe5eiyg7Une952Mw50XOj28cse2bKkfdzqIv7R/U61fWuNTHyChPQREkekGr8XkQgTOHNV4JHztgEAwKcfud6yn3x+bOkWZVmsPPpvFXleqfiwzaFk5fWHx5ZuqYqFqi5efbdyVeDFlvj16UeuN+NQscP6PfSR8/i2e6EsT+77rsgugmoMrWUXCPue3+0igndeiF7DCRKrXc7nTqPTSL+Kg8nVBY8t3QLn5FZX7T8ntxoeT252JY/UZ+UBgCuZslR0yCdy0uHL/vJjIa+bf/Kwg7tbHfa6WWKcMuKTW9QPguLx5GahftZ20odk7eX1Zad4+Ak7yIps8fNcqdYPXBsQMSUjBjEfV2gKE/hAkwc+le3j7n+yc1OoZdPyVeWJ7NIJzyZVvU92bnJtq2xdXW1BE0R8VSF+srb54b8IN+0flA1BxqGW6Hig6bgfrlF+oOmNL2yMRD5gwZmrB57s3ARnPdxftZ/se+oC9wtEkxOWla9DdllGzJWMIL5J8nx0o1fUPjLI6KNle2kPqzw1P5+6YNi1j7JU5NduFlHrGYyobXS0eyNRfqBJ5bKwj8b4DCZXj5CT64yH1lQdO+OhNfDshRu1yGd10H/L6LCWdz8gzAU4yNHxe+qCYW6MnbBrHxY6jqp+Evm0DNX2dxtbt7EhONnoRbYuvProlorO6F6eDBONdM+19k8E1AmiAUrngGCnw06PaiKuR2S/gNQqkejuJyrtLNt37Mqwm05U5Kn67lavnZ5a9iMkPOA9V80sefAG2+O7L9oQqD7denn6dl+0QdoOt6jo4JUl+0gc7GTRsfLql1PcdcgnMnS0g07fWXlucWOHV71Bn8dRQMc917bvp+GwP/mAdL25PxyEqSuGIpkP8LKwZnZftAE6HlgbqD4AsNU5cfF6rfp4uvz2uyxb7hIRryy9T0WW35elvMonvkxcvF7JLxm82Karz+n2SU4fCHU6+dXxwFqt51u90UiXhTG5+gA5uT7+s+pkQ/Y99xm9JyB9QrN6deucuHg91zfR/jDx8Z+thec+42wnGzO2vIwMWp9Ivgw8G1j9XuPO2uhFns6+XVK8ruZGt64+G/a+HwoMqPzUjWz5iIKXhX3m1J/eZHv8hc/eGrh+XTpP/elNQln0MWID+5mHTBk3Zb3U0VmX4NZmu3h7wYtvdH0ZWB0i3ao2ue3XKn1Npr7f53St0HFZOHH3WligcFm49IeDkL9yfSTzAc5cfcbpFYKP/eRmePFzt/im/4XP3gof+8nNVToBwLNeO9/oAYYdbIhNdvp5druxg6UiU/1yE7G5ZMRM22VtZHFrM9t2XvwR6RDx4udusegX+S5qW54Oqx+xqvIvfu4WYV+ws0EFr6/50PX9PJfrgUZaWxhnrgFxisQg8JJPJybR/dLnbhHa4ZduHcjErlbQcbOzU7acrD4dMVGxSdR3WBkq9umKiZ1cEbL6nGTRcsJ8DulAx8x10XduVJ657vnKbZHMBzhzDQhy4p38bwOB66YfCqAHANqWk/9tAF7+/LpA7ZIlKg81yNhZjrk3f3TIYHGyndZJ+gnbl+m+Jepn1TIJevyx68N0H5ftU3bnhR/tUPcYsfKmUj6i4My1Rpz4o0HhsVf/SnzMqz6RbJ49ftjhB7RvdnGtJSSWXuzTIYMnz0kmT6+bvuF329jZxNouY4usPJny9YCOmesx/3yT8sx171dvjWQ+wEUkaoTqies3r/7VYJVNYU1UPFQGtqgMgsROtm102a/SvmzZE380aG61htd37XCy2UleIyZWbRgutoiCM9cQcPz94ns1r1+q717U8fffoiSPZ5dOe3RB20nsE8WUtt8u7rp5/dKbpfSRcnblZWXJ6CIQeU7xE9VXwavtXvuwFz0y/UoF1XOyluiYuR49erPyzPXX3bdEMh/gPdcQYPf15rjMLfBGSs/Jp/o1ipz0x2UqA4pOe3RB/KLtkvE1yK+VdAydMAz78iqyVHT6JZvtM17jztpo1x/d6uLJ1B0bP2IdCUI1nfMPTK4hgJzIx47x3487duxWeLPL23uMAOWHONzIogeaY8durbJTh21uKdtS/dCD6IEVq+3BPSxBYiRqYwLx582umxzL6oTES9ZOlTZn+53uB9SIraxNor7hBM83O1lu+j/dzo0ErtCE1ARyon10521Vx8i+t1bc6Ek+T7aqDNYuWqYX+1Qp641V6ST7WXtUfCf16Lh7jR0AgFFyHiwquvQMLCLbP7rzNtNP2i5Wt5c2pWURfTIxUIXfzhqXghTYrBob2rYgz5XQ0EArNGFyDSFvrbgR2u71Z31S3d8EyQBB7KXtnrrMv+XgynpiXB10/KxxlPOdlkkPgCR2U5etNeWSsk7tVSlvjZedXl19gMTKTh/xjS6ro/3Y/iayxQu0nary7Xx003ecoPtQYxIDtVhGd+aq9EBTJpOBQqEAExMT0NXVBclk0twPAFAoFCCRSDjut6MRH2iyIyEYYPMuT04iz219VT0EXfpk7BfFzA0qdjvppWXpLCsDT17+srWQuHd9lY+8fSrQ9WVtJ7bIIrLPS6x4cXBrB4tf50OQ6HigqfWfBmHB/6PwQNP/PQjTfzsYyXwgPXOdnJwEAIDu7m4oFouwePFi2LdvH+TzechmszAyMgIAAJ2dnZBMJoX7ETXyl62FxL9W/7wV2Zf/a8Wf43Ixc6X1y+pj7XZtb5UdMWcZNbpPI2orAKvfxA9pNPiT+NcNFRuMcgzL+6oHeU+JlfUt6AUDdMbKRlYQ511dgpeFqykUCpDNZiGVSkE8Hofm5maYnJyE3bt3QzweN8vF43HI5XKQz+e5+zHBqkNO5MU/rB64yb63vyB3shulSj27OjxdqrAD0OIfbqiSK2u3rJ867FbVK6NTVObtL9xg265+YJT8k0/3LxXclqfbxa1PbNvqjo+fbRk5GmiFJulFJJLJpDkLBSgn2/b2dpiamoKWlhZzf3NzMxSLReF+ltnZWZiZmbFsCB8dyfDtL9wgndB0w9PNS7h29euJMPijEv8wUgvbVdotyrFFvOHqgaaenh7YsWOH8HihUJDePzQ0BOvWhXNN2zDy9hdugEXfH+IeY/fvuSJtU47/jbAio/q4SJ4q7OC06PtDXJ/2XJE29++5Qm5As4uPFyp2VMfArU67WPuFXdu7kcXGw+uTwESeSjx1xZH44+QDz2+xXVZZus6hqNJIv4qjvPxhJpOBzs5OSKVSAADQ1tZmOU4eXhLtZ0mn07B//35zm56eVjWp4dhzRbpyecVuE2HEyonrexu5x3ibn4OCyJ9F39uofhnJRp6OjRszFzr9tFGkzxJPHW3LaxeP/pvxDTA2tO2iGKnGSyin0Wmg5Q+VZq65XA7i8Tgkk0mYnJw0/+7v7zfL5PN584Em3n6WpqYmaGpq8uBCY7Lni2sAAOCY7/IHezsMQ1yP901x75fWmH876aPLqkD8oaF1kb9l5fv5jVdkC+uDXazoY3u/tMZVO4ogdrF28mLsFiL7mO9utMRBNu6ysWFhfVOB13doP1TritDZlnWH6peMCH8hkU6u+Xweurq6zM/FYhHIWzwrVqwwX9NJp8vf6hKJBHc/ope9X1oDx9w9rK2O3bHy/uA6uyWpz9tE/t97ZT+3Dl1XNS7aURgYdNlriYsRMz8fc/ewY8yUoHyzyPZ5MKzESF0PHd+9V/ab/Vl7vxbEQCX+2tsrJMSM8qZSPqrgwv11xDHfqR6c936Ff4KSsuT4Md8Zhr1f6efKcINIrw5ENvJ0sn46yVDFzk9ZHTrjTsukbeDpYMvYyWER9TXdftRCh5N+Hk42qZwPoj5ba7S853r7LervuX7j5kjmA1yhqY4wB8u7NjkXZr5dmyfy/P69V/WZx6TkMbB1aHleqUqU87ponUSfaIDiJmJFP+18KsuKmWXsZJOyuqFtYHVU9oGtbl5MAUA8Q1WZrbvoY7piZfXfvlxQNtHtofN8CRV4WRiJNDLXIkRlDIC9X51PCP+snlRF0LKIfF2YCYzSQf5W0cWTIyxrI5db38X1IWUdbD3D+rdsfSnbrurz7KfIBp7fnu3lxdLBVpm+o+sc0XmuhRpcRAKJMnu/2gfHjG6Gvd2rHcvQlD/HqP3UTKd7dVV5txA5dva5Yv4VClouz2YnvdzBfT6edjGo+NXH2e/iHqFNnHjtV20vZYfLV2REseL5RHR67SfcvuvxFR9uLB1ksn5wYyFhl1N/Y2Op/bwIEz4nVzdL7vqF8qs4SDRQPUGdBkSnZO0WXQkboOIzLXNv92pzo3Wq6iX1WVm0TKe6uuEmXc4XANEXDJEvdvJljumAtJGbtnKSS/734oMOm3T7Fgl8fBWHLLmbSqWgu7sbhodr+0AjzlwblGNGNlv+V6njlw17ezQM2DYnIzuYivxRsYOWIarnNW528TG/UIxshr09q83/nfTS5VwnGUGs93av5uom9tUKS/zmbefZKtX+hly7qvYJLedAmPHxnit5VZRQ6yV3MbnWIcd8e34wvsbmRA3hvYxjvr3Z3mYZNFxyIvEDsI+hbLlAYm0w/weh106+zT19P9l7zWrHdqGP82zSdf7UvE+EELev4rDL4vLWR5BdcjcoMLlGnEV3bqnaF5u/f0OO7bn2+uoyPvxgtQ5of3h2O0EGtEV3brGtv+jOLWacWOh6vPgSSH0nO/des9pWjixO7bnozi2w99qK/zL+0XLtyvDg9SESd9ExkU26YHWw/YA+TvwW2WTXF2XOH14ftNPppr9HDpf3XFtbWy27BwYGYHBw0LG6aCneIMDkGlF0DNZ7rr1eixy/cEqQftcHkE+0QSLzxUFWjlsdUcKLL15iEMa+E1Wmp6ct77nyVvVra2uzzFRFS+4GBSbXCLHoDncn6KI7tsCe6663fDYpieuROqzePdddb9lHf2aPeYW1nbaH3W/Bxi8n+1jfzL8FAy1PXpVtNva4gY2LxTYHXbLt4xTnPdfy21pn+3tF1O/ZPquMIMY8WTLxcGVDBImB4mXh+f8XLlzouIiEaCneWoErNEWMxd/c6rru219bZanPfuaV5+ml9weNrL10WZEfqsj6LaODJ0unbSJZpJyqLjvfebLc6vGKyE6v/rJ9SaWNVfpsmNGxQtMxG9fDgg8orNB08CDsXbNWWif9Kk5zc7P5AzO1AGeuEcPLoGWXIGUSbdADpVc7SNlaDvAqunXUo/fpak83CSAMfYVGtf8u/uZWT4k6SkkzUHx+z7WWyZQFZ651wOLb3Q1kb39jlVmf/ps9Hjbs/GVtpn1zqquC29jI6neST+TYlVNtSzZWKniJK6/N2OOq8kV+yMjh1XWqx+tjTnaH9fwSoWXmusHFzPUG+ZlrmMCZax1ATtLEdvGJnF+5qup4YvtWyK9cBTHD+rcTtJz8yuAHCBl/yXH2mUxdv7JBdKv6T/Tz6tH+OPkWk9AtGye2vCq8OMsgsv/tb6yqiq+udrOTYxdPJ/3k/KHL2sWlFucNEiw4c60TEtv4A2i+d5XtcV75xLatZj0n+awevyF2EDvtoH1XjYMqqv7zYixbT6cdXtERT57NbuXa+a9yDni1Q0VHVNAxc120Xn3mumctzlyRGpDYum3+r+rvyPlVvZUPCiudVCXWrdu48oOGtoO1ifhaiQcF7XtYfmXDpR3cRET5TP9taX8fIG1gG3tJOVW2qvRXBz/tzhFHOQI76LLEfhn//W6T0IML9yNhpW2z9QR2GoJIeZWhvG3zNphaXRkEYjavdtDl/MbODp6fxI/8ql7zbzsZXmBj5gRtE08WD5F80YAtkiMr1462zdsgz9QjPrlBpc/x6gLw/WjbvI3b92V9FtlB+0nioKtvqfalSIHJFQkjqgOX24FuarX8IEmX83NAcOtLkNgN8rrkq8imy7LtRH+OQmxlqFVSkomfrF1ElpPMqCZft8sfRhG85xoBPropuMHvrb5eoc63+nodbSH1daDLb9onng92PrPlVGySiQWRJ2uDimyeLq/tI7LPTq5qO9Ky3PQBmfqycVDp73RZkXy/zmWd550IHfdcF6/boHzP9e2BGyKZD3DmGgHMgXejf0n2rTXMycm5xOWnfr90WfwqMf+70PVWX690+Y9u3FYdVxbKFhU76LKOOkg5hwGYp5+VbZtENbSZSJ+MbLouKU/2uYmXnU5Whox8nfER9S3W79CBl4WRMMIbKHQTZALVbcNba5wTn64BTnWw9xPVAZU3MIv8UZFNx8Vu4Fe1TVa/XZ1aJxvd55Vs+4WNRrosjMk1Yhy7gf+Ahi68PpRx7IZt8OYNveb/qnUBvD2XbGf/W2usdhF9spDyb97QqzRYs3po/W/Nx0pHu9L22UHiQOOkW+SDjGxSX6TDzl7VNiJ4iSXR+Zagn9D2kv5k1x9IO9vpcoKNkV37qZ53gYIzVyRsHLs+oG+mGjozsfXY9dvgzbUKJ7qOE8lJhsffO6X9kfGP127svjdv6NXavkSWrW0eY23nO/GHHHfyjT7OyvSaKHi6RXaL7KTb58211W0l03Zsm6i2d1W8Be2ndL7VAsWZKyZXxFeOu83f2SpNLS7DHHeb9xkr4c21vaY8HjGjrO+NG3tNX9+4sddihx10mRjzmcihyzr5RNvyxo32tqtCZPPQ0c7EVp4OEmcAtXZl/afbhtbjFCdSlk02x922TSjLzk66XxGZdvEV9QtSh7ZL5AvbL9m+JyofanDmWk2xWITR0VEAAOjr6zP3d3V1QTqdBgCAnTt3wvDwMABYf50gkUjU9Kd/Ik+QCx9o1nXcrdvNv9+4aWUgOm3lUceqBiOPdhBfiZ9v3Nhr8d++XkyLDTzZ3Lhr1MP6DSDvu7zsXmafvf2ifkfbVSkjGQsqZsQGUXyF/huxatuc2kKirYTnVthooOQq/SpOJpOB8fFxaGlpsSTXjo4OyOfzsGTJEhgbG4N4PA75fB6Gh4dhZGQEAAA6Ozshm81KGYSv4vA5fp33gUqG1wdW+qbr9YHqAUBVF20fTx4rV1Tm+HXbq44FFWM72PjraA+Rn360Na1Lp2yvtrpta5V6XnS4OQ/c2uUWHa/itN2wAQ5TeBVn7uBBmNoQzVdxFsgWTKVS0NbWVrU/nU7Dvn37IJvNQjweBwCAXC5n/g0AEI/HIZfLeTa2UTl+cHvlG5+Pm3kS+qWDhxf7JOJ1/OB2635arwdb/NqOH5xP+rSPHtqzahBmYmPR5bHvVA3iuuV6kFGFH/UYZGMrc35XyZrn+MHtzvWRmuD5nuv4+DgAlC//AgB0d3fD1NQUtLS0mGWam5uhWCxy68/OzsLs7Kz5eWZmxqtJdcUJA9sDud/62rrKQMK7H/faupVwwsB2s9wJA9uVdZA6tAwn32i7CK8Pir+J82QSvTHq79dt5NJ+sjJUbFWNEYkx7YMoRry42EHHgN3vpX+J7PAiVyRT9T6xW9vsYmtnA9u/Acp9ys25wrOH1W3nh2r/CAzVhB/hLwfSM1cRw8PDkEqloLu7G4aHh4VJlCRflqGhITjiiCPMrbW11atJ9YWGb/8y2wk3VwaA19ZZvyWTE9VywnrRJynD1QChYAPtM81r61ZWH2Ptmv+f3lzbQsfFqI6/ltlIAP0GoPz5hJu36+kfjFwVGXZ9xym+pg8u48jWl2pPiRizfY0nV9gXQwJ5z1VliypKyx+Ojo5CsVg077mS+7DkIaaOjg7YsWMH7N6921Kuq6sLenp6uA818Waura2tkbzG7jcn3rgdXr1tpfm3W2gZr9620iKLPcazwW94emXwaptqbEV2qtbntYMX/V5sCgu0b25t99o+bm1xahed/ohkuT2H7NBxz/WjazbAYU0K91xnD8JbG6N5z9XTZeFEImG5t1osFqG9vR3i8Tj09/eb+/P5vPBp4aamJmhqavJiRl1z4trttp9VeXX9SlMG/bedvlfXr9Si284mr7ixjfjv1j+2vOmHw9dV1t9Xb7NvEzv90rGL2AxAR1+jY2pBIRZu6gr1Epm3ybcxK5fXd1i9SDiQnrnmcjkYGRmBYrEIPT09kEqlAKDyys34+Dj09PRAIpGw7C8UCtDc3GyWdwKfFq5Q65PF76TK6vGKqp2s3iDiTevk6VNJrjyZPGrdj8KAU9xl6umq70aODplezzMtM9d+FzPX4WjOXPFXcULMSTfUdlB8ZcPKQOwgetwia98rG1ZaygblH6vPSSexk7VXVraIWvenWsGLjZtYyLafnV6vNjjJ1mGXCB3J9dg+9eT65qZoJlfPDzQh/vHKBu+vSbyyYaV7OQSPNjhtJ6XdDTInpbeX6yr4Y4lFQP5V6XPSCZX/pdtOhqD81LCRfqvjHNAWC8X6Zv8U4MlHEbr6il/oar8IgDPXkHJyv/ikjCovD5e/MfN8I8fcIBsrOx1+x9vOd6eysnVr6Z8f0P7osN+rPLf1Vfq2ql2sbL/sAtAzcz3uevWZ6xtbojlzxbWFQ8jJfbUfCF/etNI3OyyD1LyOk/u2w8ub1E52r/aR+i9vWsm1yRdUvsrOl5W1xzaGofoKLYfFH932u5Dnqz3zeO6HYW9n1dlo2P2xAWeuIeaU1f4n2Zc2rwxEj0g3zSmrt1ftE6FiM5EpqsMe59lgp89OPs9HFXu9tI9dLFXtqDVu4+A1BrLynGTJ9muv0H1YpY1l0DJz7XUxc90WzZkr3nMNKadcv93TfaaXNivcq/NS38N2yvXWk1/lRFeyzyGeph2Cb9WObUHgxZBFtk2Ask1TfFXtqIoL4xvZ/O4nXuLgNQaO7QlyttnaoRGzPcDenqDtIuAiEjWk0WeuH+uV7+wvbltplqf/VuHFbSs96dUFsUMGVVtl6xC/dPlH+ySrX6W8GzvcyJZpG7ofquiQjbWONhH5QctVsYdXX1W334hsc2OPjpnr8d9Qn7m+fns0Z66YXEPIx1ZKDkzbqRNcso6KDHJMdr9qGSd77FCNkUx5VrfbmLrVr9Meuzjq8KsWOojMF7ev1NLfWble5Lg5Z4OGttGNHVqS69ddJNd/iGZyxcvCIcTN5RKZ8i9uX2krgz0uks3uZ+vRJ64fl4D+4hvbpfwkdjiVZ3X/xTe2u7LdTqabusQO1p4Xt680N57PTgOnDr+IfcQ2P3UQiI9e5OuwlRdf3rklUy9I6H5TK3S1WRTA5BoiTv3adjj1a9shVgKp7dSvVQYemfIAAL+6faX5+Ve3V59kbHmebMKvbl9pyiByWZmyvvB8EmFXn7aHxFRWNy2fjZXbTbVNefXp/9n4sm0gg4xeFd+Jj17aXVY+299UNx5031WxR9Vnmb5d9xgutoiCyTVEvPBN58GRLvPCN1dyT3IZGawuJzmkDs9Gu7oyPvHkuYGny43+MBPmATrMthFEfbXe+klYaaSZK95zDRGnXuc8OL1wB5MUbeqQsnQZtj7PBpEON7qdcCNDVIdXXjWmxH82DnQMZGTqxK1OuzZwK8+pHq/P6YbXLl7l0PJU4q2jz0UFHfdcT/w79Xuur/5jNO+54iISISLm4nsOOUlP+7tttmXo4/Tfz/9jr2V/zKE+XZdXltVByxfZZleft5+nV6RHJaa0bLZezDBMHbTNp/3dNnj+H3tt468DN33DD3lsX2Ih8XAq5xa2nd3qEfUX0raycnn9XHdb1RWql3ojHEpMriHhtGudT+Tn76QSoUN5uiwAM5hQHVZGL13n+X/sla/DyGdtskOYlDknm61ciZOT649R3k9kE3tYf8h+py8RKjGj5but6yhboR1V7SBxU+0rsnZU4dcALClXdK750W5RJwb2X8p55XXT1dUF6XQaAAB27txp/h45/UtuiURC+DOpsmByDQlO9xae+yfrCezlXoSbuvQA4lb3x/+2PNiwvqjUp082GTlubKX1EJtNeUw5WVtoO577p94quTK20Hrs6svG9/k75e0AcBdLnffMPv6327i++XVfzqvcKN8v9I0QzFzz+TwsXboUlixZAmNjY+a+bDYLIyMjAADQ2dmJybUeaO/hX+oEAJgc6a0qC+D8ja69Z1tVXcJz/9RrylGpS/aLnrqUhdYtspEH0StTRzZOIiZHei1xsIsXe5xnX6xk3S8TQ7ZfEJsAAJ5TiJsddnbQ9tr1UTuc+poqRJZqLHlynPoRSeQybc+TRX8RYGXI6K9HVB9S8uMLSjqdrvp98VwuB/F43Pwcj8chl8t5SrD4tHBEaO/Zpm2Q0jnYBY3sgKRj4HIrg9dWugdSnf2BRvSlShVeHZ0xcPsFzU0dlS9ziD/MzMxYttnZWdeyxsfHIZPJwOjoKIyOjgIAwNTUFLS0tJhlmpuboVgserIZZ641pv1q/kk5uaPXsYyoTvvV2yz1q7CbrezoFesrMf9rgOiytXcelQFRNmYyciZ39Lry2bYdOPLo9hPC1JPxU6Yv0GUmR5g+4LK9if+WdnMpy6l/VNksaZu0fgn5djLNKyCUDFUb6gKXl4VbW1stuwcGBmBwcNCVCeQeKwBAW1sbLF++nFuuUCi4kk/A5Fpj2EtaE3dZT7aOq/iX49hyNE4nLO8yGi2PV5+2Y3JHL3RcVRkkJu6q/szWdYKUsfNLBa+XrgEqPndctQ0mGR9VZABU+yVqd1F7i+p5RZgMqDZ2uhys0maq9vspW7XPkVjZ9YOOq7ZJnZtEhlP5usTFpd7p6WnLqzhNTU3ccps2bYJ33323an9LSwv09fVBJpOB8fFxM8HG43HI5/PQ1tZmmamSh5q8gMk1RLhJSjJ4lUPXJ4MBSaiszbyBgk4cMrpqPdiwXxZ4+0Vl3OqLMmFoMwC9beK3nqBsDRtu77kuXLhQ6j3Xvr4+2+OJRMJyb7VYLEJ7ezvE43Ho7+839+fzec8PNOEiEjVkyZfLJ9fuf+nl7iewx1Xl64DYsOTL27h/67bLrc8qOmTY/S+9vseR4KTHrS1B9R9ZPX62v4rNXvuYnT4V2W7Po6DRsYjEKVdvgMMOV1hE4tBBeGmH3kUkyCs34+Pj0NPTY85Q6Vdxmpubqx56UgVnrjXi9Cv5l//I/vG7vZ9sMt8Qx+/uhdOvdPc6htsBQcau06/c5ikGOp8yJH6qxElWJo2TzaI+I8JrH1KNoWyb7f4XtT6ngorNxIZa97MoJFZdhOFpYZI02eTpNZmyYHKtEaITWkdSNZHsmON398LpX7If7E7/0jYY/24vgEH9zSljkcspQ/T5DuP7+HedfbSD+KwiQ+S/EIWBxKs/Uvh5Tcsn2aQvq8RH1J9l9REZumTWNSF4zzUo8FWcOuQTX9wKn/jiVogZhuNGkC07/t1eiBkGfOKLW6t0suWJHbWAtoMMcjI+2sVHVYYXm2Vk69ZPI9t/3Oq0k+E1Kam2t44kSM4LXfGvVxpp4X7pmWuxWDTfCaJvGouWjNK9lBQij8pTk2f8TTn5KS1JVgJ49gerzLpO9Um5Z3+wSkGLN0gMaJ1enrQldX/5PavfdpzxN1uVfFa1z8kWVf1ebKF10oj0s7b70Td0P1ntxC+/V/aB+FWLfh96cOZaTS6Xq3rEmSwZlUqloLu723y8WbQf8Z8zvrDV3W8mymyE+b+f/cEqpfpnfGFr2b4gMDiDmkefTdsVfVax2VVbCDZPg7qTXB3+8/zRiR9t5EYvUsHLORgxpGeuqVQKCoWC5V0g0ZJR+Xxe+1JSiBzP/nAVnHm5eLB45p7KgGtXzo6YUa77zD2rXF22IXppW3Tz7A+rZavYSmwjtpKZ+ZmXb4VnFWNIYuWEin2sXc/cY213r7F16keyttrZwWsjnRD5OttIRa8fsqNOGB5oCgpPDzSJloxSWUpqdnbWspTVzMyMF5Mamk9eZn+J9+l7V5ll7MrZyX/63lWWy21eLr0RW56+1/+B55OXbVXylxdLEj9RDIkf9HFanpOfJJZ2cuzsfUZzHEVtS8eStTWItlRFpo/6aTdJrDJ9oO5RnY02anLlIVoySrR/aGgI1q1bp9uMxkTmIQoPD1o8vfP6ahlReXBDl50ScugB9JMrtlj+NmMoI1vRZlqXaYudPick9BOfwpw0nr53FTc2NI5to8mORsfNg29RxVNytVsySnYpqXQ6Db29laf1ZmZmqtaRRJw5a/kW4czsqfuuh7OWlweXp3dW/nbiqfuqB5uzlm+Bp+f32+lU4azlW7i6dKLr8pKdHBJX2heVAZsuqzO2BNUYE3uc+osXHUHh1O/Danfd0UAzV0+v4iSTSRgfHzc/kyWjRPt5NDU1mUtbyS5xhXBweCCADB5ndW1x/SCBWZfS+dR917t7SIHZzuraUpbvFxpslJWjxRcd9nLs8tsW39vRA3Z9Naw21xv4Kg6HXC4H2WwWisUiJBIJSKVSkEgkYMWKFZDJZKBQKJi/7i7aj/gH777Sv99f+Tb+l5eWBw+V2RCpY+rg6PzLS7fAU/dfX1U2bJD4/LtHW4N6vYPV49Vuwl9eusXSL9zYImMT2a+qy2+eGgt/X61rGmjmimsL1wGiwYI3sOkaWFQGe2KHTHm/BmM6qbiJgdvk5tYfWpcXu0W4scut/rAlWAC+L2G0M0zoWFu4/a/XK68tPPmvayOZD3D5w4jzqb/aXDWjfPJHq7nlANSfELaDHoyIfB6f+qvN8OSPVgsHL3LcT2jdsZL1+yTR7eSDztg5QWyk48La7QU3MXfS/+SPVtvGMEzwfAmiHzY8DTRzxeQacehLdk/8m3hg8PtyppP8sz+/WWhf0AMasZW1J+gVfeyIlfyzz66f2CGjn7Tl2Z8Pd5J98kerQ28jEm0wuUad+W92T/xEPGCe/Tn/BpGzP7e5rFviGyZth529fiNMLj58SzbjowjXxlp/i3fQz/azWraxFBx/3LYXIkcjLSKBC/dHmHM+u8nxvTG6jM6N4GZB+5hhwDmf3eR7fFTxI046fdXddn7rD2Mb0zzxk9Va44NI4PYp/QiCyTWinPOZTfD4T/vMTUhJ3/b4T/vMv8/5zCbTBvK/qjwi45zPhGQQlowB7au03wHZpxJ3VVjfZXWFGa4/iK80wms4AHhZOLI8/jObhEph10Efe6APPn2x/OD36Ys3cR/qITpENsno+PTFm+CxB+R88gvih11cyP4Y9fnxebvt/CTHvPgYMyr1ZWJK61JpZztEfYDVqUtfEDz+M7XzAPGAYYDSymMRvpKAM9c65tMXDVc6M2dzOi61wXwyuoj/y0dEx2MP9DnKEskIDGLn/N+yG7Fbxkev9ln+lrDLjOm8b/TmBtpHnr+mTrpMBKB9qXk/rGNwEQmkLojN8XvmLx5eA+desFG7Lp5MMss594KN5t+/eHiNpQxd79wLNlYdD4rHHuw3/xbFTgTxgZ7V6fbDrX0k9nScvdimYkct21MV2pco2R0pGuhVHJy5Nhh+JFZatgznXrCxaqCntyhhZ69fcdZBmG2rFVHre1EkVlLfogom1zrlvORGSwelE9cvHl7jqpPbdfxfPLymSqfTdl5yI5yXDOcgr+J/LXwkbeql7XTYJWNDmNuZhfYH8QF8WhiJMucvHbJ9vYB3XMdrHTHDgJ/n1gT2aoifqNheCx9/nlsjrfvnuTXCdtFBlNuZB4nV+UuHam1K3YH3XJFoM98hH320+gcTzj/fnwGDlvvoI2klPeefP8S1taYwMeT58+ij1X7y9pH6vvgoMfiI2kKHPU7tHLp2lUS1DyOSqD7YF6EvZSw4c61DYoYhHNR0zVgffTRt0UH2Lz1vg+Xzo4+mpeQtPW+DWTcMsLMt2VkrAJix4fnol51uNh32BKGjVkT1i0GYwZkrEmke+fkN4oOCpzsfeXwtAAAsPWe9s/z5sqLyS89ZD488vhaWnrNeSl4YqYohEzfiHwtvn6isFubtcquD1KHbVFW/U98h/QFBGulpYUyuDUTy7PWWV0VyT1QPeE7fFEmd5NnlgZRdUCD3xFpInr2+SpeKjTy7ag0bFxX/SFk//KKTluy3fLoNvdpE9IviEca2RGpHI60tjMm1kaAuc+aevNFyKPmp21zL4h3LPXmjtEzWllCi4d5P8lO3+euroo1aE59At+8+I9EC77ki9UbnWbdCrFROerzBLlYybDe2nqgcQDlZdp51K+SevNFRbqxkQOdZt0LnWbcGFgs30DGQ8cnOVz9tlNn8wC4uYW9bJDga6Z4rJtcGIvvUTVrqyQ6WqoNqmAdhEgMdNobZT79oRJ8RDg30niteFm4QnBKraBm7h5+9WbosAMAFZ9xSLsOpT45FGdVlEVl48dQFaWOnOJPjum1R6UNIY9JI91xx5oqUIfdCqI03KF7wiXXKC/sTHn72ZsfyF3xiXVAeu4MXI5fxCMrGwGyJapsiiA9gckXKsEnjlwNS5SzleQPr6YOWTabuBacPBum5GozdIp+EiYbEIUAb7dpGJyI9wr6ENB4lQ32LKHhZGIELOgblCwvWXFWSweHhcW/1A6ME8PDEoOVz2Hh4fBAu6BiUspO0m6WsF0IYDyREqN5HjW5uxeSKAMRK5RHxoeec74mSsjQPPXcLXPhx9/fV6LoyNtQSNgk9PDHo6HstfGLt5LUbDc8HN3bz9Fz48ZtD365IMMRA8Z6rDzYUi0UYHR0FAIC+vsrvDWcyGQAAKBQKkEgkIJlM2u53Qkty7erqgnS6vFTYzp07YXh42JNRSMAYBjz0vPPTnBeeZvNQVITfR/OMg+8kbjIx9o2A2ueh526p6ic19RsJFyF4zzWXy8G7774LLS0t5r58Pg/ZbBZGRkYAAKCzsxOSyaRwvwxa7rnm83lYunQp9Pf3m0mWGJVKpaC7u9tMuEj4kB38Hnr+1vJlP2p76Plb4cK/uKlqv9vtwr9w97pQTVHwjWxBQ9ruoedv5bajnb1udZkbgswThvdcU6kUtLW1WfblcjmIx+Pm53g8DrlcTrhfBi3JNZ1Ow759+yCbzZqGeDEKCSfLTlkLsVLJ3B76VTkp0/vstod+datlE5Vbdkq0lsyz8yVMPpL2ApBvM2Krir1sP0EQE8PFBgAzMzOWbXZ2VqtZU1NTlplsc3MzFItF4X4ZtFwWHh8fB4Dy5V8AgO7ubmmjZmdnLYGamZnRYRLiA7teWg/LTi4vaL/rZerXTiQv3ZC6FpkvR/dXUywoXr6qtd+7Xqosss9rF6+ydctE6gPV3/YlZVtbWy37BwYGYHBwUKdpVZB8JrufRUtypS/5trW1wfLly6WNGhoagnXr8D24qMBNCg6Py+96tfy7mMtOrP4JL3ofKRdFdr28getfFKDb1M4HckyqnSL8CgXiI6q3CubLTk9Pw8KFC83dTU1N3OKbNm2Cd999t2p/S0uL5eEllra2NsvkjzwnBADC/U54Tq6ZTAbGx8fNBBuPxyGfz9saS5NOp6G3t9f8PDMzU/UtBQk5st9EHcotO2EN7HptowaDaoP5JeKENY5lw+qrig8ivNRF6hu3M9eFCxdakqsIuwRqRzKZhP7+fvNzPp83H2ji7ZfBc3JNJBKWe6vFYhHa29shHo9LGdXU1CT8FoJEBNn7ahLllh3XB7ve2OTRoBojGY8w+0on/mXHWQcs8llk+67XNlbVQRAACMV7rrlcDrLZLBSLRUgkEpBKpSCRSMCKFSsgk8lAoVAwH8wV7ZfBc3Jtb2+HTCZjzmCz2axno5D6Z9cbmxp+AA5rYtVJI/iIKBCCV3GSySR3opdKpbjlRfud0HLPlShnjXBrFBItYnPlmdqDU1vgorbrq46TfTFm366pLZbPovpRwykeBOJz2KETJO2Pnf0kBghCgwv3I4gK9LdRZl3ZB6e2CNfZvSixytzIIB2FZOOE6TOA41rDFyVW1dZYRR6c2mJpU6H9Qf5QARIdVH7kIuJ9CJc/RDzzYH5r5QNzMly0uPKw2oNvb7N85pV78O1t+g2sAWZMIjw42EG3Obft6tRvxBuxUnlTKR9VcOaK6GWuBDBXggff3lYebOc/w1wJLjr6G5bj9DFLmTqCF4d689n0kYb4hyA0DTRzxeSK6MUw4MFf3275zL0c2vr1crk6OplsqdMBREi9+oV4w+UKTVEELwsjWnlw+h+sOxxeS6kqX6802jKApRI8+M63am0FEjLcvucaRXDmiviL3cM8R11Xa+sC48F3vlVONo0SiwgPigiiA0yuiG8s+7NrwZgrmduDv73D8tmYK8GyP7sWlv3ZtbU2NTB4MSBbPfHgb++otQlIGMF7rgiil13/eadtEm2kBLvrP+/k7m+kGCANigFqP0MZ3dyKyRXxh2X/owegNFdOJKU58zO9kWNkW/Y/emptdmCwvjdiDJDGg9xzVdmiCiZXxBd2/Z8R2PV/RsofSgZ/my/H7msUqnxvwBggDYYBipeFa22we/BpYcRXljV/1fHYrsI/B2VO6Nj17igAWOO0rPmrDR0TpI5RvY+KM1cEESCatVLbsvhVtbay9uDsFWkEVO63qv72a8jA5Ir4yq7iXbCreBcYc3O224UfurLWptYUNk6NHg+kPsF7rgiikQs/+CWp+ysXfvBLtTa15jx04G6MB1K/NNCrOHjPFfEfh9WJHvrD9wMyJBo89PvvwoV/cgUAAFz4J1dgfJD6oYHuuWJyRXyn3hZICAKSUC9o+kKNLUEQjTRQcsXLwoj/GCXb7YLD/7rWFoaWh2d/iPFB6ocGeqAJZ66I7xgOT79m3783IEuiycOH/rXWJiCIFhpp4X5Mroj/GNavn9m5+2pkCIIgNaWBLgtjckV8Jzt3H3Qu6IJsaazWpiAIUktKBkBMIWFG+J1vTK5IIGBiRRAEZ64IgiAIoh3Vd1ejm1zxaWEEQRAE0YyvM9dMJgMAAIVCARKJBCSTST/VIQiCIGEGLwt7J5/PQzabhZGR8s+OdXZ2YnJFEARpZEoGKF3qjfADTb5dFs7lchCPx83P8XgccrmcX+oQBEGQsOOwoAx3iyi+zVynpqagpaXF/Nzc3AzFYrGq3OzsLMzOzpqf9+/fDwAAMzMzfpmGIAiCKELGZMPLpVq8LOwPhUKhat/Q0BCsW7euan9ra2sQJiEIgiAKHDhwAI444gh3lRvosrBvybWtrc0yUyUPNbGk02no7e01P5dKJdi7dy+cdtppMD09DQsXLvTLxNAyMzMDra2t6D/635D+A2AMwui/YRhw4MABOOqoo7wIwZmrV5LJJPT395uf8/k894GmpqYmaGpqsuxbsKB8K3jhwoWh6Vi1AP1H/xvZfwCMQdj8dz1jJRigmFy9qaslviXXRCIBK1asgEwmA4VCAdLptF+qEARBkCiAM1c9pFIpP8UjCIIgUaKk+DtyJXxaWCtNTU0wMDBQdbm4UUD/0f9G9h8AY1C3/odg5losFmF0dBQAAPr6+sz9XV1d5hXWnTt3wvDwMAC4XwwpZnh6rhpBEARB7JmZmYEjjjgCkv/vV+CPFhwuXe/90iHI/X/fgf3792u795zJZGB8fBxaWlosybWjowPy+TwsWbIExsbGIB6PQz6fh+HhYctiSNlsVkoPri2MIAiCBEPJUN80k0qloK2trWp/Op2Gffv2QTabNRdA8rIYUigvCyMIgiD1h2GUwFBYdYmUZRcV4r1l4pXx8XEAqKzH0N3dLb0YEg9MrgiCIEgwGIqz0fm7luyiQgMDAzA4OKjRMDDvsQKU12lYvnw5txxvMSQemFwRBEGQYDAUV2iaT67sYhqiWeumTZvg3XffrdrP3l9lIfdhSYIl91tlF0PigckVQRAECYZSCSCm8HrN/GVh2cU07BKoHYlEwnJvtVgsQnt7O8TjcanFkHhgckUQBEGCweXMVSe5XA6y2SwUi0VIJBKQSqWgvb0dMpmMOYMlTwR7WQwJX8VBEARBfIW8irP0g5fDH8UUXsUxDsEjv79H66s4QYEzVwRBECQQjFIJDIXLwipPFocNTK4IgiBIMITgsnBQYHJFEARBgqFkAMQwuSIIgiCIPgwDlBbux+SKIAiCIPYYJQMMhZlrlJ+3xeSKIAiCBIOh+JNz+EATgiAIgtiDM1cEQRAE0cz7xqzSbPR9eM9Ha/wFkyuCIAjiK4cffjgceeSR8OTvHlCue+SRR8Lhh8svPBEWcIUmBEEQxHcOHjwIhw4dUq53+OGHwwc+8AEfLPIXTK4IgiAIopkFtTYAQRAEQeoNTK4IgiAIohlMrgiCIAiiGUyuCIIgCKIZTK4IgiAIohlMrgiCIAiiGUyuCIIgCKKZ/x+TCj0XFoHNbgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cmap = matplotlib.cm.viridis\n",
-    "cmap.set_bad('white',1.)\n",
-    "\n",
-    "plt.matshow(makeFernPic(F,np.pi/2), origin = 'lower', cmap = cmap_viridis)\n",
-    "plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77423552",
-   "metadata": {},
-   "source": [
-    "# Trying Erik's stuff \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "844cd027",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import demeter.euler as euler\n",
-    "import demeter.directions as dirs\n",
-    "import demeter.misc as misc\n",
-    "import time\n",
-    "import tifffile as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e2ec851",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "leaf_file = 'figures/leafbw.tif'\n",
-    "leaf_img = tf.imread(leaf_file)\n",
-    "leaf_img[leaf_img > 0] = 1\n",
-    "\n",
-    "fig, ax = plt.subplots(1,2,figsize=(20,6))\n",
-    "ax[0].imshow(leaf_img, cmap='gray');\n",
-    "ax[0].plot([200,200,250,250,200], [280,310,310,280,280], c='red', lw=4);\n",
-    "ax[1].imshow(leaf_img[280:310, 200:250], cmap='gray');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37c4e19d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Complexify in 1.0271 seconds.\n",
-      "\n",
-      "Cubical complex made of:\n",
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    }
-   ],
-   "source": [
-    "tic = time.perf_counter()\n",
-    "leaf = euler.CubicalComplex(leaf_img).complexify()\n",
-    "toc = time.perf_counter()\n",
-    "\n",
-    "print(\"Complexify in {:.4f} seconds.\\n\\nCubical complex made of:\".format(toc-tic))\n",
-    "\n",
-    "leaf.summary();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b86a4b9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e32dfd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[-271.57746322,   89.89517387],\n",
-       "        [-271.57746322,   90.89517387],\n",
-       "        [-270.57746322,   88.89517387],\n",
-       "        ...,\n",
-       "        [ 259.42253678,  -44.10482613],\n",
-       "        [ 259.42253678,  -43.10482613],\n",
-       "        [ 260.42253678,  -45.10482613]]),\n",
-       " array([[    0,     1],\n",
-       "        [    0,     3],\n",
-       "        [    1,     4],\n",
-       "        ...,\n",
-       "        [53828, 53829],\n",
-       "        [53828, 53831],\n",
-       "        [53829, 53830]]),\n",
-       " array([[    0,     1,     3,     4],\n",
-       "        [    2,     3,     7,     8],\n",
-       "        [    3,     4,     8,     9],\n",
-       "        ...,\n",
-       "        [53821, 53822, 53826, 53827],\n",
-       "        [53825, 53826, 53828, 53829],\n",
-       "        [53826, 53827, 53829, 53830]])]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a52e965",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = np.pi/4\n",
-    "omega = (np.cos(theta),np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4f5d741",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-128.46877882, -127.76167204, -128.46877882, ...,  152.25261331,\n",
-       "        152.9597201 ,  152.25261331])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "heights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75ae5f49",
-   "metadata": {},
-   "source": [
-    "## Making pictures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db2a598d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = 6*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a048e87d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f180e96250>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(leaf.cells[0][:,0],leaf.cells[0][:,1],  \n",
-    "                         s=0.1, c=heights, cmap='magma', label='T = {}'.format(32))\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d438bea4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "177.0\n",
-      "279.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Figuring out centering \n",
-    "size = np.shape(F)\n",
-    "print(np.floor(size[0]/2))\n",
-    "print(np.floor(size[1]/2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c184035d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f16b1855d0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -150, vmax = 150)\n",
-    "ax = plt.gca()\n",
-    "midx = 279\n",
-    "ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "midy = 177\n",
-    "ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "plt.colorbar()\n",
-    "\n",
-    "# plt.savefig('figures/Fern' + str(int(theta*1000)) + '.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec2ad8d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.356194490192345\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "4.71238898038469\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "5.497787143782138\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Version without colorbars \n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    im = plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    # plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.8)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "    height_fig = fig.get_size_inches()[0]\n",
-    "    print(height_fig)\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4e09dc67",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "2.356194490192345\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "4.71238898038469\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "5.497787143782138\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    plt.colorbar(fraction=0.03, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    # fig.set_figwidth(2.4)\n",
-    "    print(height_fig)\n",
-    "    fig.set_figheight(height_fig)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1db8d134",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEEAAADXCAYAAABFyDwbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAJ1klEQVR4nO2dMWwbyRWGv6V8lpLI0oJqnADCxVKKK1JRrq41hRRpKbsPEqkLkEaGggB2gCDGuQmQzlaQXhDbBAiWZXCNTFYprgjpBAYS4wCtaNlJaJK7k4IiT7T3kTvUcJcrzm9MwTcLmvr53pu3/7wdOkopxZwjl/YHmAVYErAkAJYEwJIAWBIASwJgSQAsCQDcSOs/LpfL+L5PtVplZ2eHYrE4sAP4vs/GxsZYuxGoFFCtVtXx8bFSSqmzszPluq5SSql6va52d3cH1xWLxZF2U4jtCa1Wi3a7PY5QHMcZsi0uLrK4uDhk830fz/MolUq4rks+n6dWq/HixQtc1x1c57oulUqFRqMRaTflDbFIaLVa3Pl0mddfByOvW15e5t27d0O2R48e8fjx4yFbsVgc+gN836dQKHB0dMTa2trAns/naTab1Ov1SLspxCKh3W7z+uuAv79YZ+VWdC49fxvyg7uvePXqFSsrKwP7h17wIfb29jg8PBTnfd/Xsk8CrcS4fMth+ZYTORfSs6+srAyRMArlcpnt7W1KpRIAm5ubQ99wPwkCot0EtJbIcMw/HVQqFVzXpVQqUavVaDQaFItFTk5OBtf0bZLdFBylxosq5+fnrK6u8s+vvjcyHD797F+8efNmrCc0Gg22trYGr5vNJv2PcXkpzOfzAy+R7CagRcLLr77LLYGEt29D7nz271gkzBq0ckKIIiSaM8meBWiR0FGKjuA4kj0L0CIhQBEI37hkzwI0PaE3pLmsQjMnOASMrhOyCD0SVG9Ic1mFFgltcrSF+mr0rdVsQ9MTHEIlhINgzwI0Vwc5J0j2LECLhK5aoKOiw6FrPWGOPKGjFuioBWFutOAyy7CegC4JKkcg5IRgXu4duizQITocuhrv02w2ef78OQD7+/sD+87ODgcHBwAcHR3xxRdfAFOW20nJEyqVCqenp0PiKfTElnv37nH37l2Oj48HNs/zePbsGQDb29vGSdCU13IjB/QEmMvj/fv3H71PqVRic3PzI/vBwQFnZ2d4njeQ2PsyXB99ud0k9MpmtcANYXVoXzjC+vr6kD1KcpfQ1xH7SvLu7u7U5XbQLptzhEI4hBfhoCu5X0Y/B0BPeb5//37kdSbldtAMhw452mohcnQu3qovufdHXBLK5TIPHz4cvHZdl0aj8VHYmJbbQVtP+Cb2o+augo2NjaHYbzabFAoFXNcdIse03A5GV4f4JFQqFTzPo9lssrGxQalUolAoUC6XKZfLnJyc4Hke0CPnwYMHg13s/hJqElqS++9efM63lqN5+9+7Lr+4++X1l9wDcgSC20v2LMCKKmjrCTfoCC0N3ezeOti7SNDWE3IsiHqC3q70LMFgxTgnidFUnTBrmEBoFfSE+QkHu0TaYgl64SCtDnMTDoFyCAS3l+xZgB4J4QLdMNoTgnBePMFWjP3+BGl1iP8+kuSeSoc7EyTGnJgYo+1RiJLcJWl95iT3fmKURlxESe6StD5zknuce4fz8/Mhe1SrfxQkaT0JyV3PE8jRVdGjXyytr6+zuro6GE+ePJn4wyXR4Q5TKJsn3XdIq8MdND2he1EnSAMm33dIq8MdJuhjlPoVdfoYoyR3SVqfOcn9x3/5KZ9852bkNZ3/tPnTj/5w/SX3bpjDCYXGLcGeBVg9gQnuIh1RXpsTEqwnYEkAIBiRGIO5SYyG6oRZgw0HbDgAmiQo5aCEb1yyZwHa4RCEcx4OIQ7OvCdGGw7QCwUhHKQwyQJS84S0OtqjkBoJaXW0R0FPaA2dkUMHaXW0R0HTE+RvvK9PxZXc0+poj4J2neCMKZvjtvqn1dEeBeM5IY7k3u9f7hNxuaN92vJ6FPSOHVMXQ5oj3uk6aXa0R0HPE0KHUEiASiMxptnRHgUtyf37f/wVuW8vRV4T/rfFP37ym+svuaOc3pDmMgrNcOgNaS6rsDdQTEKClBjnhYQ4S2QWoXk8qXMxpLlsQo+E8GJIcxmFXSKZ6C5SnssqNMNBltdEewagRYKjekOayyqsJ2DrBMCSANhwAFJMjGnsL0hIJRzS2l+QoLXv4PCNN3w0Lq6Jc7pOWvsLEvQ6K/plszSI1+We1v6CBOM3UJN2uSexvyDBeGKMI7mntb8gQTMcxoyYSKJ9Xwd6nhD2hjQXF2ntL0hITU8weSr/VWHLZlIKh1mDtieI5fG8eIIVWrm+ylJ2G5INwiZGJvmBvAy7vQRbJ2DDAbCrA2DrBMB6AmATIzDjiTGpxwFm2hOSehxAT3KX5PZLuSKO5B4XST0OYHx1uMqB9h8iqccB9HIC49u2rnKg/YdI6nEA44kx7m/IPn36lNPT04/sa2tr7O/vJ/o4QGqJ8fKZa1FI8nGAmb2LTPJxAK1W/x/+7Lcs3Ixu9Q/aLf52+Mvr3+pvy2awZTNYPaEH6wnWEwBwlMIRFhPJngXYcMCGA2DrBMCGA2DDAZjgBirLbi9B0xMUjnBeuWTPAmxOYMZzQlIH3880CUkdfK/Z0apGD8NI6uB748XSpAfax8U0Dr7X23wJRw8we6B9XFz14HvN1WH8I7Jx9x3GSe4SpnHwfWr7DuMkdwnFYjFScm80GhNL8TN9A5XUwfdakvvn27/mxifRknu30+JL79GcSO5SOMxLxXhdzw6Y6YoxKVihFdvCB1g9AbCJEZjxYikp6JEQKBzhr3WC7LJgwwGbGAHrCcAkarNUD2SXgwnCQaiPbTj05wxDktxNd7/rl81SX29CkjuY737XFFrDkcM0oiR3MN/9bjwcpi25g/nudz0SghGbkRcVo8lWfwmmu9+N6wnTltyn0f1uPBymLblPo/tdMxxGqCqB+cQYJblPo/tdS3IvbvycG7lo9+6G76k0fn/9JXd77wAQBKCC6LlQsGcA1hOYJDFKv2EwhYoxKUzQuCV5wtU/TFqw4YBNjID1BEBbTxhxAzUvypIKA5QQDpI9C9APB8kT5iYcggAc4RufF09QYYgS1GaV4R+CsuHAJGWzGA5z4gkqVChhVzqGLKGNvkBSrVbZ2dkZ29KfzL6DGqEsGfaEWq0G9JTkZrPJnTt3ODs7S7/VvxO06HSFEbQAc6fr+L4/kM5c1yWfz1Or1dJr9b958ya3b9/mr6//PPK65eVlY5J7sVgc+iZ936dQKHB0dGS81T8WCUtLS7x8+ZJ2uz3yOqUUjjO8T2di42Vvb4/Dw0NxPrFW/6WlJZaWovuaJ0HcfYdyucz29vbgxO9ptPqjZhie5ynP85RSSlWrVVWv11W9XlelUmlwTaFQUEop0R4HsST3NNBoNNja2hq8bjabg2X48lKYz+cHXiLZx2FmSUgS9txmLAmAJQGwJACWBMCSAFgSAEsCYEkALAkA/B8UWn75HreksAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 80x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots()\n",
-    "plt.colorbar(im,ax=ax)\n",
-    "\n",
-    "ax.remove()\n",
-    "fig.set_figwidth(0.8)\n",
-    "plt.savefig('figures/OnlyColorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "plt.savefig('figures/OnlyColorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17c0ccc0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28dbfb28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'5497'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(int(theta*1000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d379223a",
-   "metadata": {},
-   "source": [
-    "## Thresholded Figure "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08121a32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPicThreshold(F, theta, thresh, both = False):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    \n",
-    "    fMaskBelow = fMask.copy()\n",
-    "    fMaskBelow[fMaskBelow <= thresh] = np.nan\n",
-    "    \n",
-    "    fMaskAbove = fMask.copy()\n",
-    "    fMaskAbove[fMaskAbove > thresh] = np.nan\n",
-    "    \n",
-    "    \n",
-    "    DTdown = np.multiply(F,fMaskAbove)\n",
-    "    DTup = np.multiply(F,fMaskBelow)\n",
-    "    \n",
-    "    # Why the fork is this backwards!?!?!?!?!\n",
-    "    # Must have something to do with meshgrid but i just can't fight it right now\n",
-    "    if both:\n",
-    "    \n",
-    "        return DTdown,DTup\n",
-    "    else:\n",
-    "        return DTdown\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "639dcbdd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "for thresh in [-150,-100,-50,0,50,100,150]:\n",
-    "\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    plt.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "    ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "    \n",
-    "    plt.title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.25)\n",
-    "\n",
-    "    thresh_size_fig = fig.get_size_inches()\n",
-    "    print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d32f8502",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "fig,axes = plt.subplots(1,5,sharey = True, figsize = (6,2))\n",
-    "\n",
-    "for i, thresh in enumerate([-150,-50,0,50,150]):\n",
-    "\n",
-    "    ax = axes[i]\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    ax.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    # midy = 177\n",
-    "    # ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    # ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    ax.set_xticks([])   \n",
-    "    ax.set_yticks([]) \n",
-    "    ax.set_title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    # fig = plt.gcf()\n",
-    "    # fig.set_figwidth(1.25)\n",
-    "\n",
-    "    # thresh_size_fig = fig.get_size_inches()\n",
-    "    # print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.png', bbox_inches = 'tight',dpi = 300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.eps', bbox_inches = 'tight',dpi = 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd8361dd",
-   "metadata": {},
-   "source": [
-    "## ECC in one direction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a416a675",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "numThresh = 64\n",
-    "\n",
-    "threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "\n",
-    "outECC = leaf.ECC(heights, numThresh)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "positions = [-150, -50, 0, 50, 150]\n",
-    "for pos in positions:\n",
-    "    ax.axvline(x=pos, color='grey', linestyle='--')\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.step(threshes,outECC)\n",
-    "plt.title(r'ECC for $\\omega = \\frac{3 \\pi}{4}$')\n",
-    "plt.xlabel('$a$')\n",
-    "plt.ylabel(r'$\\chi(K_a)$')\n",
-    "\n",
-    "# ax.set_xticks([-200,-150,-100,-50,0,50,100,150,200])\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='y', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(5,2)\n",
-    "# fig.set_size_inches(thresh_size_fig)\n",
-    "# print(thresh_size_fig)\n",
-    "# print(fig.get_size_inches())\n",
-    "\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.png', bbox_inches = 'tight',dpi=300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f9c12e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-197\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(outECC[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7017e7c9",
-   "metadata": {},
-   "source": [
-    "## ECT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81210cb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.     0.0625 0.125  0.1875 0.25   0.3125 0.375  0.4375 0.5    0.5625\n",
-      " 0.625  0.6875 0.75   0.8125 0.875  0.9375 1.     1.0625 1.125  1.1875\n",
-      " 1.25   1.3125 1.375  1.4375 1.5    1.5625 1.625  1.6875 1.75   1.8125\n",
-      " 1.875  1.9375]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get all the directions around the circle\n",
-    "numCircleDirs = 32\n",
-    "circledirs =  np.linspace(0, 2*np.pi, num=numCircleDirs, endpoint=False)\n",
-    "circledirs\n",
-    "\n",
-    "print(circledirs/np.pi)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13cdd2ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose number of thresholds for the ECC to stop at \n",
-    "numThresh = 100 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b57d67",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-285.68893688291087 285.68893688291087\n"
-     ]
-    }
-   ],
-   "source": [
-    "ECT_preprocess = {}\n",
-    "\n",
-    "overallmin = 0\n",
-    "overallmax = 0\n",
-    "\n",
-    "for i, omega in enumerate(circledirs):\n",
-    "    \n",
-    "    omegavec = np.array((np.cos(omega),np.sin(omega)))\n",
-    "    \n",
-    "    # Function values of all the pixels\n",
-    "    heights = np.sum(leaf.cells[0]*omegavec, axis=1)\n",
-    "\n",
-    "    # Thresholds implicitly calculated by Erik's code\n",
-    "    threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "    \n",
-    "    if heights.min()<overallmin:\n",
-    "        overallmin = heights.min()\n",
-    "    if heights.max()>overallmax:\n",
-    "        overallmax= heights.max()\n",
-    "            \n",
-    "\n",
-    "    outECC = leaf.ECC(heights, numThresh)\n",
-    "    \n",
-    "    ECT_preprocess[i] = (threshes,outECC)\n",
-    "    \n",
-    "print(overallmin,overallmax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17128b08",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x73f16b12de10>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 2\n",
-    "\n",
-    "plt.step( ECT_preprocess[i][0],  ECT_preprocess[i][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ec3eefe",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_57600/1470381530.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  M[i,j] = ECT_preprocess[omegai][1][index]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Making a matrix of size numThreshes x numCircleDirs\n",
-    "# M[i,j] = \n",
-    "\n",
-    "\n",
-    "# Make meshgrid. The +1 is to draw 2pi on both ends\n",
-    "X,Y = np.meshgrid(range(numCircleDirs+1),np.linspace(-250,250,numThresh))\n",
-    "M = np.ones_like(X)\n",
-    "\n",
-    "for i in range(X.shape[0]):\n",
-    "    for j in range(X.shape[1]):\n",
-    "        omegai = X[i,j]\n",
-    "        \n",
-    "        # Deal with redrawing the 2pi\n",
-    "        omegai = np.mod(omegai,numCircleDirs)\n",
-    "        \n",
-    "        \n",
-    "        omega = i*np.pi/numCircleDirs\n",
-    "        a = Y[i,j]\n",
-    "        threshes = ECT_preprocess[omegai][0]\n",
-    "#         print(threshes)\n",
-    "        try: \n",
-    "            index = np.where(threshes>=a)[0][0]   \n",
-    "        except:\n",
-    "            index = [-1]\n",
-    "\n",
-    "        M[i,j] = ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eac07698",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern_ECT.png', bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern_ECT.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "930cb35f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  1,  1,  1,  6,  1,  1,  1,  1,  2,  1,  4,  1,  1,  3,  3,  2,\n",
-       "        1,  2, 10,  3,  3,  1,  1,  3,  3,  1,  2,  1,  1,  1,  1,  1])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M[0,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5afc592b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4., 11., 19.])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bigvals = np.where(M[0,:] >3)[0]\n",
-    "\n",
-    "\n",
-    "circledirs[bigvals]/np.pi*16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad5f2a1d",
-   "metadata": {},
-   "source": [
-    "## Let's draw a cylinder!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "203aece0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# pip install ipympl\n",
-    "\n",
-    "# %matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b2acd4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "grid = M[:,:-1]\n",
-    "print(grid.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9ab03be",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "color_dimension = grid # change to desired fourth dimension\n",
-    "minn, maxx = color_dimension.min(), color_dimension.max()\n",
-    "norm = matplotlib.colors.Normalize(minn, maxx)\n",
-    "m = plt.cm.ScalarMappable(norm=norm, cmap='viridis')\n",
-    "m.set_array([])\n",
-    "fcolors = m.to_rgba(color_dimension)\n",
-    "\n",
-    "print(fcolors.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7131aa3f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.27318197 0.54636394 0.81954591 1.09272788 1.36590985\n",
-      " 1.63909182 1.91227379 2.18545576 2.45863773 2.7318197  3.00500167\n",
-      " 3.27818364 3.55136561 3.82454758 4.09772955 4.37091152 4.64409349\n",
-      " 4.91727546 5.19045743 5.4636394  5.73682137 6.01000334 6.28318531]\n"
-     ]
-    }
-   ],
-   "source": [
-    "theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "print(theta)\n",
-    "# theta = circledirs[:-1]\n",
-    "# print(theta, theta.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c071d904",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "equation for a circle\n",
-    "\n",
-    "x  =  h + r cosθ\n",
-    "y  =  k + r sinθ\n",
-    "\n",
-    "where h and k are the co-ordinates of the center\n",
-    "\n",
-    "0 <= θ <= 360\n",
-    "\"\"\"\n",
-    "\n",
-    "fig = plt.figure(figsize=(4,3))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "\n",
-    "# theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "radius = 1\n",
-    "\n",
-    "\n",
-    "x = np.linspace(0, numThresh, numThresh)\n",
-    "\n",
-    "# theta = circledirs\n",
-    "theta = np.linspace(0, 2 * np.pi, 32)\n",
-    "\n",
-    "thetas, xs = np.meshgrid(theta, x)\n",
-    "\n",
-    "y = radius * np.cos(thetas)\n",
-    "z = radius * np.sin(thetas)\n",
-    "\n",
-    "#ax.plot_surface(xs, y, z, color='orange')\n",
-    "#ax.plot_surface(xs, y, z, facecolors=fcolors)\n",
-    "surf = ax.plot_surface(xs, y, z, facecolors = fcolors,linewidth = 0.1)\n",
-    "surf.set_edgecolor('grey')\n",
-    "\n",
-    "ax.set_axis_off()\n",
-    "\n",
-    "plt.savefig('figures/fern_ECT_cylinder.png', bbox_inches = 'tight',dpi = 900)\n",
-    "plt.savefig('figures/fern_ECT_cylinder.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b03f0104",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-193.98518858 -190.40235323 -186.81951788 -183.23668253 -179.65384718\n",
-      " -176.07101183 -172.48817648 -168.90534113 -165.32250578 -161.73967042\n",
-      " -158.15683507 -154.57399972 -150.99116437 -147.40832902 -143.82549367\n",
-      " -140.24265832 -136.65982297 -133.07698762 -129.49415227 -125.91131692\n",
-      " -122.32848157 -118.74564621 -115.16281086 -111.57997551 -107.99714016\n",
-      " -104.41430481 -100.83146946  -97.24863411  -93.66579876  -90.08296341\n",
-      "  -86.50012806  -82.91729271  -79.33445735  -75.751622    -72.16878665\n",
-      "  -68.5859513   -65.00311595  -61.4202806   -57.83744525  -54.2546099\n",
-      "  -50.67177455  -47.0889392   -43.50610385  -39.92326849  -36.34043314\n",
-      "  -32.75759779  -29.17476244  -25.59192709  -22.00909174  -18.42625639\n",
-      "  -14.84342104  -11.26058569   -7.67775034   -4.09491499   -0.51207963\n",
-      "    3.07075572    6.65359107   10.23642642   13.81926177   17.40209712\n",
-      "   20.98493247   24.56776782   28.15060317   31.73343852   35.31627387\n",
-      "   38.89910923   42.48194458   46.06477993   49.64761528   53.23045063\n",
-      "   56.81328598   60.39612133   63.97895668   67.56179203   71.14462738\n",
-      "   74.72746273   78.31029808   81.89313344   85.47596879   89.05880414\n",
-      "   92.64163949   96.22447484   99.80731019  103.39014554  106.97298089\n",
-      "  110.55581624  114.13865159  117.72148694  121.3043223   124.88715765\n",
-      "  128.469993    132.05282835  135.6356637   139.21849905  142.8013344\n",
-      "  146.38416975  149.9670051   153.54984045  157.1326758   160.71551116]\n",
-      "59\n",
-      "17.402097119801482\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-83"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "omegai = 5\n",
-    "omega = i*np.pi/numCircleDirs\n",
-    "a = 17\n",
-    "threshes = ECT_preprocess[omegai][0]\n",
-    "print(threshes)\n",
-    "\n",
-    "index = np.where(threshes>=a)[0][0]\n",
-    "print(index)\n",
-    "print(threshes[index])\n",
-    "ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "554bc00c",
-   "metadata": {},
-   "source": [
-    "## Erik's version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e5a2a91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# t = 32\n",
-    "# tic = time.perf_counter()\n",
-    "# ect = leaf.ECT(circle_dirs, T=t)\n",
-    "# toc = time.perf_counter()\n",
-    "# print(\"Complex with {} vertices\\n\".format(len(leaf.cells[0])))\n",
-    "# print(\"ECT with {} directions in {:.4f} seconds.\\n{:.4f}s per direction\".format(len(circle_dirs), toc-tic, (toc-tic)/len(circle_dirs)))\n",
-    "\n",
-    "# plt.figure(figsize=(15,2))\n",
-    "# plt.plot(ect);\n",
-    "# plt.title('ECT for {} directions and {} thresholds each'.format(len(circle_dirs),t), fontsize=24);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "719235ff",
-   "metadata": {},
-   "source": [
-    "# SECT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96766900",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'ECT of Fern Leaf')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# THe original ECT\n",
-    "\n",
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "731fe26b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 33)\n",
-      "[-100.72  -99.01  -96.97  -95.    -93.24  -92.33  -91.34  -90.83  -90.69\n",
-      "  -89.07  -87.98  -87.07  -87.2   -87.64  -88.43  -89.47  -90.97  -90.94\n",
-      "  -91.8   -91.22  -93.    -94.51  -96.85  -98.61  -99.98 -100.05 -100.44\n",
-      " -100.47 -101.04 -101.12 -101.03 -100.91 -100.72]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Following Crawford2019 for the moment. Is this different in the Meng22 version?\n",
-    "print(M.shape)\n",
-    "\n",
-    "# Averages for a fixed direction \n",
-    "AvgOverOmega = np.average(M,axis = 0)\n",
-    "print(AvgOverOmega)\n",
-    "\n",
-    "# Mean centered \n",
-    "\n",
-    "Mcentered = M-AvgOverOmega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33144af2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mcentered, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf with value centered')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern-ECT-centered.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cfbf9ef1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.09992015e-11,  1.23066002e-11, -1.07718279e-11,  0.00000000e+00,\n",
-       "       -1.27613475e-11, -5.48538992e-12,  6.96331881e-12, -5.03064257e-12,\n",
-       "        4.71800377e-12,  7.78754838e-12,  1.33582034e-12,  7.78754838e-12,\n",
-       "       -9.12336873e-12, -5.42854650e-12, -7.56017471e-12, -1.09992015e-11,\n",
-       "       -1.09992015e-11,  4.71800377e-12,  9.35074240e-12, -1.09992015e-11,\n",
-       "        0.00000000e+00,  1.25339739e-11, -6.56541488e-12,  5.54223334e-12,\n",
-       "        1.33582034e-12,  9.40758582e-12,  4.66116035e-12, -1.09992015e-11,\n",
-       "       -2.87059265e-12, -8.29913915e-12,  1.07718279e-11, -7.53175300e-12,\n",
-       "       -1.09992015e-11])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mcentered.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "101671c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [4 5 6]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [5, 7, 9]])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[1,2,3], [4,5,6]])\n",
-    "print(a)\n",
-    "np.cumsum(a,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f047f4f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Mint = np.cumsum(Mcentered,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a6cc372d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mint, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$t$')\n",
-    "\n",
-    "plt.title(r'SECT of Fern Leaf')\n",
-    "\n",
-    "plt.savefig('figures/Fern-SECT.png',bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern-SECT.eps',bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4478b3c",
-   "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/doctrees/nbsphinx/notebooks/Matisse/Matisse_ECT.ipynb b/docs/doctrees/nbsphinx/notebooks/Matisse/Matisse_ECT.ipynb
deleted file mode 100644
index 5b66a5e..0000000
--- a/docs/doctrees/nbsphinx/notebooks/Matisse/Matisse_ECT.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23bd9d9b",
-   "metadata": {},
-   "source": [
-    "# ECT on Matisse's \"The Parakeet and the Mermaid\" \n",
-    "\n",
-    "Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312). \n",
-    "\n",
-    "![matisse.jpg](matisse.jpg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b73a120d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-----------------\n",
-    "# Standard imports \n",
-    "#-----------------\n",
-    "import numpy as np # for arrays\n",
-    "import matplotlib.pyplot as plt # for plotting\n",
-    "from sklearn.decomposition import PCA # for PCA for normalization\n",
-    "from scipy.spatial import distance_matrix\n",
-    "\n",
-    "from os import listdir # for retrieving files from directory\n",
-    "from os.path import isfile, join # for retrieving files from directory\n",
-    "from sklearn.manifold import MDS # for MDS\n",
-    "import pandas as pd # for loading in colors csv\n",
-    "\n",
-    "#---------------------------\n",
-    "# The ECT packages we'll use\n",
-    "#---------------------------\n",
-    "from ect import ECT, EmbeddedGraph # for calculating ECTs\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c149a147",
-   "metadata": {},
-   "source": [
-    "We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
-    "\n",
-    "<img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f7b7fe4c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 150 files in the directory\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_dir = \"doc_source/notebooks/Matisse/outlines/\" # set data directory\n",
-    "file_names = [f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == '.txt'] # create a list of file names\n",
-    "file_names.sort() # sort the list of file names\n",
-    "print(f\"There are {len(file_names)} files in the directory\") # print number of files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b082bf2",
-   "metadata": {},
-   "source": [
-    "Here we have an example of one of these leaves loaded in as an `EmbeddedGraph` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3cb77aa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 3\n",
-    "shape = np.loadtxt(data_dir + file_names[i])\n",
-    "# shape = normalize(shape)\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_cycle(shape)\n",
-    "G.plot(with_labels = False, node_size = 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aef7ef04",
-   "metadata": {},
-   "source": [
-    "We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3d930346",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "G.plot(with_labels = False, node_size = 10, bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49bc6a0c",
-   "metadata": {},
-   "source": [
-    "And then we can compute the ECT of this leaf. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e0e07987",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) # intiate ECT\n",
-    "myect.set_bounding_radius(1) # set bounding radius\n",
-    "myect.calculateECT(G) # calculate ECT on embedded graph\n",
-    "\n",
-    "myect.plotECT() # plot ECT\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebc64b1d",
-   "metadata": {},
-   "source": [
-    "Let's just make a data loader with all of this for ease in a bit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dcf72cc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def matisse_ect(filename, num_dirs, num_thresh):\n",
-    "    shape = np.loadtxt(data_dir + filename)\n",
-    "    G = EmbeddedGraph()\n",
-    "    G.add_cycle(shape)\n",
-    "    G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "    myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) \n",
-    "    myect.set_bounding_radius(1) \n",
-    "    M = myect.calculateECT(G) \n",
-    "    return M"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91cb74c1",
-   "metadata": {},
-   "source": [
-    "And now we can load in all the outlines, compute their ECT and store it in a 3D array. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2dfe86ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "ect_arr = np.zeros((len(file_names),num_dirs,num_thresh))\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    ect_arr[i,:,:] = matisse_ect(file_names[i], num_dirs, num_thresh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be2f273",
-   "metadata": {},
-   "source": [
-    "Here, we are just going to compute the distance between two ECTs using $L_2$ distance. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e1efac55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x3199f6190>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs*num_thresh)\n",
-    "D = distance_matrix(flattened_ect, flattened_ect)\n",
-    "plt.matshow(D)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce93bb3f",
-   "metadata": {},
-   "source": [
-    "For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0984d176",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_components = 2 # select number of components\n",
-    "mds = MDS(n_components=n_components, # initialize MDS\n",
-    "          dissimilarity=\"precomputed\", # we have precomputed the distance matrix\n",
-    "          normalized_stress='auto',\n",
-    "          random_state=5 # select random state for reproducibility\n",
-    "         )\n",
-    "MDS_scores = mds.fit_transform(D) # get MDS scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "551aa2fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# read in color hexcodes\n",
-    "col_df = pd.read_csv(\"doc_source/notebooks/Matisse/colors.csv\", header=None) \n",
-    "\n",
-    "scale_val = 6 # set scale value\n",
-    "plt.figure(figsize=(5,5)) # set figure dimensions\n",
-    "\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    shape = np.loadtxt(data_dir+file_names[i]) # get the current shape\n",
-    "    shape = shape - np.mean(shape, axis=0) # zero center shape\n",
-    "    shape = scale_val * shape / max(np.linalg.norm(shape, axis = 1)) # scale to radius 1 then mult by scale_val\n",
-    "\n",
-    "    trans_sh = (shape + MDS_scores[i]) # translate shape to MDS position\n",
-    "\n",
-    "    plt.fill(trans_sh[:,0], trans_sh[:,1], c=col_df[0][i], lw=0) # plot shape\n",
-    "    plt.gca().set_aspect(\"equal\")\n",
-    "\n",
-    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
-    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3c3d0e7",
-   "metadata": {},
-   "source": [
-    "## Acknowledgements\n",
-    "\n",
-    "This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood). "
-   ]
-  }
- ],
- "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb b/docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb
new file mode 100644
index 0000000..6f0c4f8
--- /dev/null
+++ b/docs/doctrees/nbsphinx/notebooks/Matisse/example_matisse.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ECT on Matisse's \"The Parakeet and the Mermaid\"\n",
+    "\n",
+    "\n",
+    "\n",
+    " Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312).\n",
+    "\n",
+    "\n",
+    "\n",
+    " ![matisse.jpg](matisse.jpg)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 150 files in the directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -----------------\n",
+    "# Standard imports\n",
+    "# -----------------\n",
+    "import numpy as np  # for arrays\n",
+    "import matplotlib.pyplot as plt  # for plotting\n",
+    "from sklearn.decomposition import PCA  # for PCA for normalization\n",
+    "from scipy.spatial import distance_matrix\n",
+    "\n",
+    "from os import listdir  # for retrieving files from directory\n",
+    "from os.path import isfile, join  # for retrieving files from directory\n",
+    "from sklearn.manifold import MDS  # for MDS\n",
+    "import pandas as pd  # for loading in colors csv\n",
+    "import os\n",
+    "import zipfile\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ---------------------------\n",
+    "# The ECT packages we'll use\n",
+    "# ---------------------------\n",
+    "from ect import ECT, EmbeddedGraph  # for calculating ECTs\n",
+    "\n",
+    "# Simple data paths\n",
+    "data_dir = \"outlines/\"\n",
+    "colors_path = \"colors.csv\"\n",
+    "\n",
+    "file_names = [\n",
+    "    f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n",
+    "]\n",
+    "file_names.sort()\n",
+    "print(f\"There are {len(file_names)} files in the directory\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
+    "\n",
+    "\n",
+    "\n",
+    " <img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 3\n",
+    "shape = np.loadtxt(data_dir + file_names[i])\n",
+    "# shape = normalize(shape)\n",
+    "G = EmbeddedGraph()\n",
+    "G.add_cycle(shape)\n",
+    "G.plot(with_labels=False, node_size=10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "G.transform_coordinates(projection_type=\"pca\")  # project with PCA\n",
+    "G.scale_coordinates(1)  # scale to radius 1\n",
+    "G.plot(with_labels=False, node_size=10, bounding_circle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And then we can compute the ECT of this leaf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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mVa2aivdXkA4sVxKgKst8/SRbk0uTLg1TeAC59HWBwvvEKbly32VbsmWatFKFseLvW8AxBanMCj5PAHCT2d+ev4U7DJIOBqWF7BTubysMYIiIiJyUQYUrMKwDQ0RERKQSXoEhIiJyUgbJBQaFWURK97cVBjBEREROqho6VCus46J0f1txzLCKiIiISIBXYIiIiJwUbyFRnbm1aQk311rmULLjbNNKlUUGCNtFadJK2Tq1VG42bBG590U027RScim5cqmlitKsFc7gK/ddlUshvx5lvr3RCXF6eaMTwmZFv9O8x+8Rtst9l2U/MwVp+fZMk5YrhaCU0t+CInK/BXMlB6rLgTPqD6fWQ0H5LSBxsQv7ccywioiIiEiAV2CIiIicFG8hERERkeZwMkciIiIiB8IrMERERE5Kgg4GhQ/xSg5aB4YBDBERkZNy5ltIDGCIiIicFGejJpuz6ZTxMuTqmcjVcfC5YH0dmPIAV6v3VYOoNoeo3gggf95ytTeU1Inxyb5l9b4A4FYorpdiU3K1M2R2D95vfuxytVKE9W0AlHWNFLbbsraP3O/Qkdm61ouIXC0nUZ0Yxb8jRXuTUnz/iYiInFQ1XFCtMF9H6f62wgCGiIjISTnzLSTHDKuIiIiIBBjAEBEROSkDXFRZLHHw4EEMHjwYYWFh0Ol02LFjh9ltJ06cCJ1Oh5UrV1p8bgxgiIiInFS1pFNlsURpaSmioqKwZs0a4Xbbt2/H4cOHERYWZtW58RkYIiIiUs2AAQMwYMAA4TaXLl3C888/j927d2PQoEFWHYcBjMqq/L0BN88a62/KTPmuNJ1PTs7gILNtoYfEKbU+Co99vaP5FMuGl8ynMTs7UZq10nRduZRc7+xC8f6CdGLvjGyrxlSXvgFlKblyqe3eH4vT9gvXitvLA7zMtlXI/FBK+pYJ20M36MUd2JE906RtSa7UgdL9zf2Oq6rqr3yEmg/xFhcXm6zX6/XQ6y3/3hoMBjz77LOYMWMG7r33XqvHxVtIRERETkr672zUShbpv5V4w8PD4efnZ1ySkpKsGtNrr70GNzc3TJkyRdG58QoMERERycrJyYGvr6/xtTVXXzIyMvDWW2/h2LFj0OmUXRniFRgiIiInVQ2dKgsA+Pr6mizWBDDffPMN8vPz0bx5c7i5ucHNzQ0XLlzAtGnTEBERYVFfvAJDRETkpAyS8kJ0BkmlwQB49tlnERsba7IuLi4Ozz77LEaPHm1RXwxgiIiInNSd51iU9mGJkpISnD171vg6Ozsbx48fR2BgIJo3b45GjRqZbO/u7o6QkBC0bdvWouMwgCEiIiLVHD16FI888ojxdWJiIgBg5MiR2Lhxo2rHYQCjMreiMri5VtdYr5dJt7s4QDyLbkiaspmNRanSNyPNp4YCgL6w5vlYwuOmot1tSm4mWxG5mbSVvG9KZ7JWmoYtl2Zty769FWRpZ00SlyvAkubidpkJoUuamv++yH7PjzQUNpcHKCspoOT7Zu9Z4W1JyW9cjlz5C7OzvleX22A0tTNABwMU3kKycP8+ffpAkup+3+n8+fMWjug2BjBEREROyppKurX14Yg0mYW0Zs0aREREwNPTEzExMThy5IjZbTdu3AidTmeyeHqaFpqTJAnz5s1DaGgovLy8EBsbizNnztj6NIiIiMhKmgtgtm3bhsTERMyfPx/Hjh1DVFQU4uLikJ+fb3YfX19fXLlyxbhcuHDBpH3ZsmV4++23kZycjPT0dDRo0ABxcXH47bffbH06RERENqO0iJ0aDwHbimOOSmDFihUYP348Ro8ejQ4dOiA5ORne3t7YsGGD2X10Oh1CQkKMS3BwsLFNkiSsXLkSL7/8MoYMGYLOnTtj8+bNuHz5snAGTSIiIkdngM44nYDVi8JnaGxFUwFMRUUFMjIyTHLIXVxcEBsbi7S0NLP7lZSUoEWLFggPD8eQIUNw6tQpY1t2djZyc3NN+vTz80NMTIywz/LychQXF5ssREREVD80FcBcu3YN1dXVJldQACA4OBi5ubm17tO2bVts2LABn376Kd577z0YDAb07NkTv/76KwAY97OkTwBISkoymRMiPDxcyakRERGpTvpvFpKSReIVGPvo0aMH4uPjER0djYcffhiffPIJmjRpgn/+85+K+p0zZw5u3LhhXHJyclQaMRERkToU3z5SYTZrW9FUGnXjxo3h6uqKvLw8k/V5eXkICQmpUx/u7u7o0qWLsUrgnf3y8vIQGhpq0md0dLTZfsxNI17Wwh9u7p617CHWcov5h5ABoCpAXCdGSd0QpXVebrZwFx+7WFl9CyVsWQNCVBMEkH9fRbU3lH4mcsoixQVPhHVkZL6LbucuiQ9eUCRuD/QXNuf1CTLb1mqr+XpHALD7k83C9tYf/E3YHp5SabbtekeZ34FMnRil31XRd0auzostfydapvR9Mfc7q6r8DWCiq2Ka+tZ6eHiga9euSE1NNa4zGAxITU1Fjx496tRHdXU1fvrpJ2OwEhkZiZCQEJM+i4uLkZ6eXuc+iYiIHJEzZyFp6goMcLsk8ciRI9GtWzd0794dK1euRGlpqXESqPj4eDRt2hRJSUkAgIULF+KBBx5A69atUVRUhNdffx0XLlzAuHHjANzOUJo6dSpeffVVtGnTBpGRkZg7dy7CwsIwdOhQe50mERGRYmrcAuItJJUMGzYMV69exbx585Cbm4vo6GikpKQYH8K9ePEiXFz+Fy0WFhZi/PjxyM3NRUBAALp27YrvvvsOHTp0MG4zc+ZMlJaWYsKECSgqKkLv3r2RkpJSo+AdERGRlthjKoH6orkABgASEhKQkJBQa9v+/ftNXr/55pt48803hf3pdDosXLgQCxcuVGuIREREZEOaDGCIiIhIHm8hERERkeYwgCHFhGmpkE9rlUtV9rlgPr0TAG5GepltU5qyK5cmLUpFtGeKNQBU+Jhvk0t7lWuXS121daq0iNz30a1QnI4sJJMGLVcS4HqUuF30nRF9zwGg5zRxmnSr7FvC9kc37Dfb9u9FA4T7yqXdy37fZH4rou8b06Rrp/QzkStfQbbFAIaIiMhJ8QoMERERaY4zBzC8rkhERESawyswRERETkqC8joukjpDUR0DGCIiIifFW0hEREREDoRXYIiIiJyUM1+BYQCjMu8LRXBz1Vu8n1ztC6XsWnPEhrVelNa3ENV5KA0T79vgsrhdbmyiz0Suhgwgrj8hV+dFjqhWi6IaMXXQ6IS4f1HtDfn3TeziAPHvcNNbg8y2eUD8PW94SdnvQO43LFcrylmJajkB8rVchPsq/NvlnV1Y6/qq6nJF/VqCAQwRERFpjjMHMHwGhoiIiDSHV2CIiIiclCTpICm8gqJ0f1thAENEROSkDNAprgOjdH9b4S0kIiIi0hxegSEiInJSzvwQLwMYlVX5ewNunjXWy6WeBu/PF7b/569BMke2PoXS50KlsF1paqoondiWKdZ1IUrBlEuTVkr0vipNexelGgPK06xtScnYywO8hPsWPFYmbA/c7i1sz48x3+b9q/iCtlw6r9zvMLeH9b9xW3+X7UnufZVLsxbuK1MKQa7do6j2tPyqKmV/Uy3hzM/A8BYSERERqebgwYMYPHgwwsLCoNPpsGPHDmNbZWUlZs2ahU6dOqFBgwYICwtDfHw8Ll+2PMpmAENEROSk7txCUrpYorS0FFFRUVizZk2NtrKyMhw7dgxz587FsWPH8MknnyArKwt//vOfLT433kIiIiJyUva4hTRgwAAMGDCg1jY/Pz/s3bvXZN3q1avRvXt3XLx4Ec2bN6/zcRjAEBERkazi4mKT13q9Hnq95VPn/NGNGzeg0+ng7+9v0X68hUREROSkJBVuH925AhMeHg4/Pz/jkpSUpHh8v/32G2bNmoWnn34avr6+Fu3LKzBEREROSgIgScr7AICcnByTIEPp1ZfKyko89dRTkCQJa9eutXh/BjD1RDS7LyA/G3XLLeI067w+4jRr0czKPheEu8qSTSVUkCotm8aocCZaJTPVyh27Um5sxeJ2EbmZh+VScu1J6WzW54abT5X2zxR/XxqmNhS26wvF71t4ivm23B7K0qjlyhW42/C7rHTmdXtS8juUe0/l+m500nF/Z3cYoINOpUq8vr6+Fl8lMedO8HLhwgV8/fXXVvXLAIaIiIjqzZ3g5cyZM9i3bx8aNWpkVT8MYIiIiJyUPbKQSkpKcPbsWePr7OxsHD9+HIGBgQgNDcVf/vIXHDt2DJ9//jmqq6uRm5sLAAgMDISHh7iQ5e8xgCEiInJSBkkHXT1PJXD06FE88sgjxteJiYkAgJEjR+KVV17Bzp07AQDR0dEm++3btw99+vSp83EYwBAREZFq+vTpA0nw5LCozRIMYIiIiJyUJKmQhaROvKE6BjBEREROipM5EhERETkQXoFxEI1OiGtjlEUGKNq/wsd8nZnrHcU1RRpeEtdxUVLnRSkltS9sfWy5drkaNyL2rPMiV9NIrs6L3HdZrsaNf6awWRG5WiyF7cx/ZiFp4s9Eru+SpuLvg2xdIQW/BUeu8yJHyXkr/fsh95l6FCnrXw3OfAWGAQwREZGTskcWUn1hAENEROSknPkhXk0+A7NmzRpERETA09MTMTExOHLkiNlt169fjwcffBABAQEICAhAbGxsje1HjRoFnU5nsvTv39/Wp0FERERW0lwAs23bNiQmJmL+/Pk4duwYoqKiEBcXh/z82ucK2r9/P55++mns27cPaWlpCA8PR79+/XDp0iWT7fr3748rV64Yl3//+9/1cTpEREQ2c/sKjE7hYu+zqJ3mApgVK1Zg/PjxGD16NDp06IDk5GR4e3tjw4YNtW7//vvv47nnnkN0dDTatWuHf/3rXzAYDEhNTTXZTq/XIyQkxLgEBIgfNCwvL0dxcbHJQkRE5EiUBy/KHwK2FU0FMBUVFcjIyEBsbKxxnYuLC2JjY5GWllanPsrKylBZWYnAwECT9fv370dQUBDatm2LSZMm4fr168J+kpKS4OfnZ1zCw8MtPyEiIiKyiqYe4r127Rqqq6sRHBxssj44OBinT5+uUx+zZs1CWFiYSRDUv39/PP7444iMjMS5c+fw0ksvYcCAAUhLS4Ora+1pcnPmzDHO7wAAxcXFCA8PR4WfBwzudZ+M6g6PogqL97GofwXpgnLpvvZMoybtkfuuy2QLC/1r3Uph+7DFM4Tt+sJqYbvf/eb/w6b8tHhGXdnfkR1LAmhZhQ3Ty5X+bavwr/3fgqrK+vubKf13UdqHI9JUAKPU0qVLsXXrVuzfvx+enp7G9cOHDzf+/06dOqFz585o1aoV9u/fj759+9bal16vh16vt/mYiYiIrOXMdWA0dQupcePGcHV1RV5ensn6vLw8hISECPddvnw5li5dij179qBz587CbVu2bInGjRubTAdOREREjkNTAYyHhwe6du1q8gDunQdye/ToYXa/ZcuWYdGiRUhJSUG3bt1kj/Prr7/i+vXrCA0NVWXcREREdiGptDggTQUwAJCYmIj169dj06ZNyMzMxKRJk1BaWorRo0cDAOLj4zFnzhzj9q+99hrmzp2LDRs2ICIiArm5ucjNzUVJSQkAoKSkBDNmzMDhw4dx/vx5pKamYsiQIWjdujXi4uLsco5ERESqUCMDyUFvIWnuGZhhw4bh6tWrmDdvHnJzcxEdHY2UlBTjg70XL16Ei8v/4rK1a9eioqICf/nLX0z6mT9/Pl555RW4urrixx9/xKZNm1BUVISwsDD069cPixYt4jMuREREDsqqAKa6uhr/+te/kJWVhWbNmiEqKgrR0dFo1Ej8FL5aEhISkJCQUGvb/v37TV6fP39e2JeXlxd2796t0siIiIgchzNPJWBVAPP888/j448/RmxsLFavXg2dToeqqio0bdoU0dHR2Llzp9rjJCIiIgs5cxaSVQHMJ598gs2bNyMuLg47d+7Ed999hwMHDmDhwoVo0aKF2mN0CnK1L84N9xK2B6WL+7/ZooGw3edCpWBfd3HnMpTUiZHb926VHyNu9/5V/Jk1Omn+8waAm5Hi75tP9i3xAASqAsTfRXO1Me6Q+z6WhplvGzdhqnDf4Ozapxy5I69PkLC94usmZtsaQlzbQ65eSaVMu7tMPRPR+9LgsnhfpUTn5sz1bUqaiv9+ydUVqhdqPMPiTAFMSUkJOnToAABwd3eHm5sbEhISUFlZicuXbfxLISIiorueVf/527JlS2Og0rRpU+PEiIMHD8Z7772n3uiIiIjIaneegVG6OCKrApjHH38cX375JQDg4YcfNk6k+PPPP+PWLesvPRMREZGKnLgOjFW3kF555RXj/585cybuv/9+NGnSBMXFxRg7dqxaYyMiIiKqleI6MM2bN8epU6fw2WefoVGjRhg8eLAa4yIiIiKFmIUko3HjxsZKuERERORAHPQWkFKaq8SrVXJpq+Ep4nQ77+xCYXvOYHH6pxJy6Z8NL8mkjwpSpeX6duYUTBH/TOtT0wGgPMBVzeGoSq6kgIfM2H0uWJ+aKpfiXdBdnH7eMNN8irdc6ntQuvgzK2wn85nL/Bbs+VvR6rFFqee3iT8TuXIFZFsMYIiIiJwUbyERERGR9qiRReSgt6BYBpWIiIg0h1dgiIiInJbuv4vSPhxPnQOYxMTEOne6YsUKqwZDREREKrLDLaSDBw/i9ddfR0ZGBq5cuYLt27dj6NCh/+tOkjB//nysX78eRUVF6NWrF9auXYs2bdpYdJw6BzA//PCDyetjx46hqqoKbdu2BQD88ssvcHV1RdeuXS0aABEREdmIHQKY0tJSREVFYcyYMXj88cdrtC9btgxvv/02Nm3ahMjISMydOxdxcXH4+eef4enpWefj1DmA2bdvn/H/r1ixAj4+Pti0aRMCAgIAAIWFhRg9ejQefPDBOh/8biI367Lc7L+F9zUWtod/Jp5l9z9/NZ9mHXBabhZd2z0qdbemSQO2ncFX6Sy4ohmj5dKglfQNyP9WlJzbmfHiYwceEc+ELU5fF4+7pKmwWbG7dUZoOaL3RfZvn8x3kWo3YMAADBgwoNY2SZKwcuVKvPzyyxgyZAgAYPPmzQgODsaOHTswfPjwOh/Hqk/njTfeQFJSkjF4AYCAgAC8+uqreOONN6zpkoiIiNQm6dRZABQXF5ss5eXlFg8nOzsbubm5iI2NNa7z8/NDTEwM0tLSLOrLqgCmuLgYV69erbH+6tWruHnzLg71iYiIHIias1GHh4fDz8/PuCQlJVk8ntzcXABAcHCwyfrg4GBjW11ZlYX02GOPYfTo0XjjjTfQvXt3AEB6ejpmzJhR6/0uIiIi0racnBz4+voaX+v1ejuOxsoAJjk5GdOnT8df//pXVFZWQpIkuLu7Y+zYsXj99dfVHiMRERFZQ8WHeH19fU0CGGuEhIQAAPLy8hAaGmpcn5eXh+joaIv6suoWkre3N/7xj3/g+vXr+OGHH3D8+HEUFBTgH//4Bxo0EM81QkRERPVExWdg1BAZGYmQkBCkpqYa1xUXFyM9PR09evSwqC/WgSEiIiLVlJSU4OzZs8bX2dnZOH78OAIDA9G8eXNMnToVr776Ktq0aWNMow4LCzOpFVMXVteBMUenc8yKfURERHcbnXR7UdqHJY4ePYpHHnnE+PrOBZCRI0di48aNmDlzJkpLSzFhwgQUFRWhd+/eSElJsagGDGBlHRiynKgWAQB4LxM/fV2xpLmwvSpAfOuu+ZelZtuuR4n3rZQZO9W/kqbK6lMoqaUiV8dFrk6MXHsjmfZzw73Mtvlnit+X/wxYK2yPWx8vbBfVkQk8Ij623N+ABpfF7XIctdaL3HnL/X1R+r4o4XOh0n4HV4sdCtn16dMHkmR+J51Oh4ULF2LhwoWKhsUqPURERKQ5Vk/mWFRUhHfeeQeZmZkAgA4dOmDs2LHw8/NTbXBERESkgBoP4ar4EK+arLoCc/ToUbRq1QpvvvkmCgoKUFBQgDfffBOtWrXCsWPH1B4jERERWUNSaXFAVl2BefHFF/HnP/8Z69evh5vb7S6qqqowbtw4TJ06FQcPHlR1kERERGQFOzwDU1+sCmCOHj1qErwAgJubG2bOnIlu3bqpNjgiIiKi2lh1C8nX1xcXL16ssT4nJwc+PkxZISIicgi8hWRq2LBhGDt2LJYvX46ePXsCAL799lvMmDEDTz/9tKoDdBYNL4mnbS+bGSJs94A4tfTiAOvTqAu6i1MFQ75yFbYrSel11NRPQD79U+nYlezv0O+bTJr1zRbuwnaPYvFvRZQqLZf22nPa34Tten9xern+V/Njl/u+yLH1981e5MZt6/OS+9vr9Jz4IV6rApjly5dDp9MhPj4eVVVVAAB3d3dMmjQJS5cuVXWARERERH9kVQDj4eGBt956C0lJSTh37hwAoFWrVvD29lZ1cERERGQ9e1TirS9W14EBbk/q2KlTJ7XGQkRERGpy4iwkqx9eSE1NxUsvvYRx48ZhzJgxJoutrVmzBhEREfD09ERMTAyOHDki3P7DDz9Eu3bt4OnpiU6dOuGLL74waZckCfPmzUNoaCi8vLwQGxuLM2fO2PIUiIiISAGrApgFCxagX79+SE1NxbVr11BYWGiy2NK2bduQmJiI+fPn49ixY4iKikJcXBzy8/Nr3f67777D008/jbFjx+KHH37A0KFDMXToUJw8edK4zbJly/D2228jOTkZ6enpaNCgAeLi4vDbb7/Z9FyIiIjIOlbdQkpOTsbGjRvx7LPPqj0eWStWrMD48eMxevRo41h27dqFDRs2YPbs2TW2f+utt9C/f3/MmDEDALBo0SLs3bsXq1evRnJyMiRJwsqVK/Hyyy9jyJAhAIDNmzcjODgYO3bswPDhw+vv5IiIiFSkgwrPwKgyEvVZdQWmoqLCmD5dnyoqKpCRkYHY2FjjOhcXF8TGxiItLa3WfdLS0ky2B4C4uDjj9tnZ2cjNzTXZxs/PDzExMWb7BIDy8nIUFxebLERERFQ/rLoCM27cOGzZsgVz585VezxC165dQ3V1NYKDg03WBwcH4/Tp07Xuk5ubW+v2ubm5xvY768xtU5ukpCQsWLCgzmOv8BXHijcjvRTtH5Imrn8h6r/Fh+LaF7k9xMd2V1DHwZFrX2i17kZdlAeIa/voC8XfCSXk6rz4ZN+yum8l9ZAA4Mx4cQ2bkK/Mj11JPSTAsb9vjvw7VcKW33OHwTowQGJiovH/GwwGrFu3Dl999RU6d+4Md3fT4k4rVqxQb4QOas6cOSbvSXFxMcLDw+04IiIioj9w4iykOgcwP/zwg8nr6OhoADB5GBYAdDrbRWqNGzeGq6sr8vLyTNbn5eUhJKT2SrYhISHC7e/8b15eHkJDQ022uXOOtdHr9dDr9dacBhERUf1gAAPs27fPluOoEw8PD3Tt2hWpqakYOnQogNtXg1JTU5GQkFDrPj169EBqaiqmTp1qXLd371706NEDABAZGYmQkBCkpqYaA5bi4mKkp6dj0qRJtjwdIiIispJVz8DcunULkiQZK+9euHAB27dvR4cOHdCvXz9VB/hHiYmJGDlyJLp164bu3btj5cqVKC0tNWYlxcfHo2nTpkhKSgIAvPDCC3j44YfxxhtvYNCgQdi6dSuOHj2KdevWAbh9xWjq1Kl49dVX0aZNG0RGRmLu3LkICwszBklERERaxEq8fzBkyBA8/vjjmDhxIoqKitC9e3d4eHjg2rVrWLFihU2vXAwbNgxXr17FvHnzkJubi+joaKSkpBgfwr148SJcXP73MF3Pnj2xZcsWvPzyy3jppZfQpk0b7NixAx07djRuM3PmTJSWlmLChAkoKipC7969kZKSAk9PT5udBxERkc3xFpKpY8eO4c033wQAfPTRRwgJCcEPP/yAjz/+GPPmzbP5rZeEhASzt4z2799fY92TTz6JJ5980mx/Op0OCxcuxMKFC9UaIhEREdmQVQFMWVkZfHxu59Xt2bMHjz/+OFxcXPDAAw/gwoULqg5QazxuVMDNrWY6ZaOiCkX9nhsuTrMG3IWtR+evNdsW93i8cN/yZuIjNzgiPrYoBVOr6Zd1IZd6Wilob3BZ2bHlUnobXhKnMtuS3NiCJ5kvXwAARUuam22TS5OWEyjzXS5sZ74t4LT4PZUrheDIbPk7VZqiLbu/HUt0eZj5u+9SpezfA4s48RUYq35RrVu3xo4dO5CTk4Pdu3cbn3vJz8+Hr6+vqgMkIiIi69x5Bkbp4oisCmDmzZuH6dOnIyIiAjExMcaMnj179qBLly6qDpCIiIjoj6y6hfSXv/wFvXv3xpUrVxAVFWVc37dvXzz22GOqDY6IiIgUYCXemkJCQmoUj+vevbviAREREZFK+AxMTd988w2eeeYZ9OjRA5cuXQIA/N///R8OHTqk2uCIiIiIamNVAPPxxx8jLi4OXl5e+OGHH1BeXg4AuHHjBpYsWaLqAImIiMg6zvwQr1W3kF599VUkJycjPj4eW7duNa7v1asXXn31VdUG50wq/MWz3N5sIU7fDE8RzzYNiGdVbfnlOLNtgVHiY4tm4AWAkqbCZqHSMHG7XDqxI8+SK3dsR04hF30ffS7IfRfF5M67bGbt85rdUR5pfiZt72xxGvV//hokbJdNhfbRbiq0s5IrCWDTmdUVlseoF058C8mqACYrKwsPPfRQjfV+fn4oKipSOiYiIiJSgxpXUBw0gLHqPydCQkJw9uzZGusPHTqEli1bKh4UERERaU91dTXmzp2LyMhIeHl5oVWrVli0aBEkSf0oyKorMOPHj8cLL7yADRs2QKfT4fLly0hLS8P06dMxd+5ctcdIRERE1qjnW0ivvfYa1q5di02bNuHee+/F0aNHMXr0aPj5+WHKlCkKB2LKqgBm9uzZMBgM6Nu3L8rKyvDQQw9Br9dj+vTpeP7551UdIBEREVmpngOY7777DkOGDMGgQYMAABEREfj3v/+NI0eOKBxETRbfQqqsrERsbCyeeuopFBQU4OTJkzh8+DCuXr2KRYsWqT5AIiIisr/i4mKT5U4G8u/17NkTqamp+OWXXwAAJ06cwKFDhzBgwADVx2PxFRh3d3f8+OOPAAAPDw906NBB9UERERGRcmqkQd/ZPzw83GT9/Pnz8corr5ismz17NoqLi9GuXTu4urqiuroaixcvxogRI5QNohZW3UJ65pln8M4772Dp0qVqj4eIiIgcUE5OjsmEzXq9vsY2H3zwAd5//31s2bIF9957L44fP46pU6ciLCwMI0eOVHU8VgUwVVVV2LBhA7766it07doVDRo0MGlfsWKFKoNzJnL1AjwCzNe2UEOb9eaPf2a8eN+GmeI6MWXNxHUYgtLNtxW2U1ZXw5FrqdiT3Psi977L1d9RwqNYptaKTM2kgGPXzLblDBbXeQlJE9ewkavHZEuOXNPIlmx9XuUK/rb6ZN9ScSTa5+vraxLA1GbGjBmYPXs2hg8fDgDo1KkTLly4gKSkJMcIYE6ePIn77rsPAIz3ue7Q6Rxz0iciIqK7Tj0/xFtWVgYXF9P/QHJ1dYXBIP4PF2tYFcDs27dP7XEQERGRytR8BqYuBg8ejMWLF6N58+a499578cMPP2DFihUYM2aMskHUwurZqImIiIh+b9WqVZg7dy6ee+455OfnIywsDH/7298wb9481Y9ldQCTmpqK1NRU5Ofn17g0tGHDBsUDIyIiIhXU41QAPj4+WLlyJVauXGnzY1kVwCxYsAALFy5Et27dEBoayudeiIiIHBEnczSVnJyMjRs34tlnn1V7PERERKSS+n4Gpj5ZFcBUVFSgZ8+eao/Fqcmlhlb4itNa5dJe5dJD3QpLzbYFHmlgtg2QT+8UpUkDQElT61Ol5Y5dKdNuy3Rge6a9Kj223PsiSnW+3lGcatzopPi7KJeaem64l7A9KL2x2TZbp+SK+pf7Dculj1s5t67TK2ovft+8fxW/b6Lvo5IUa7I/q34x48aNw5YtW9QeCxEREalJUmlxQHW+ApOYmGj8/waDAevWrcNXX32Fzp07w93d9L/IWMiOiIjI/ngLCcAPP/xg8jo6OhrA7aJ2v8cHeomIiMjW6hzA7Nu3DwsXLsS0adNqTB1AREREDsiJs5AsegZmwYIFKC01/zAoERERORAnfgbGogBGkhz0LIiIiOiuYnEaNZ9xISIi0gY+xPs799xzj2wQU1BQYPWAtM6tqAxurtUW7ydT1gM+F8Tt/1q3Utg+bPEMQd/iuh23phSJD/5/jYTNotoZcnU75Oqd2LLOixxb1xxxVA0vKZtVVq4mUqut4joxov3lasxcj7Ld83vydV6U7S9XZ8ZZydWZ0heK/36JyH1flDJbf6u63KbHNeHEz8BYHMAsWLAAfn5+thgLERERUZ1YHMAMHz4cQUFBthgLERERqYlXYG7j8y9ERETa4czPwGgqC6mgoAAjRoyAr68v/P39MXbsWJSUlAi3f/7559G2bVt4eXmhefPmmDJlCm7cuGGynU6nq7Fs3brV1qdDRERkW06cRm3RFRiDQdlDakqNGDECV65cwd69e1FZWYnRo0djwoQJZudlunz5Mi5fvozly5ejQ4cOuHDhAiZOnIjLly/jo48+Mtn23XffRf/+/Y2v/f39bXkqREREpIBVs1HbQ2ZmJlJSUvD999+jW7duAIBVq1Zh4MCBWL58OcLCwmrs07FjR3z88cfG161atcLixYvxzDPPoKqqCm5u/zt9f39/hISE2P5EiIiI6okz30LSTACTlpYGf39/Y/ACALGxsXBxcUF6ejoee+yxOvVz48YN+Pr6mgQvADB58mSMGzcOLVu2xMSJEzF69GjhMz/l5eUoL/9fKlxxcbHwuHKpozdbuAvb5YjSpOXITSl/4/smwvYQBWmMJU3FdzErZdKo79ZUZqXp57L7C1J25dJ9PYoqxJ3bkdz7IkdpCjlRveNDvPaXm5tbI/vJzc0NgYGByM3NrVMf165dw6JFizBhwgST9QsXLsSf/vQneHt7Y8+ePXjuuedQUlKCKVOmmO0rKSkJCxYssPxEiIiISDG7V0aaPXt2rQ/R/n45ffq04uMUFxdj0KBB6NChA1555RWTtrlz56JXr17o0qULZs2ahZkzZ+L1118X9jdnzhzcuHHDuOTk5CgeIxERkar4EK/tTJs2DaNGjRJu07JlS4SEhCA/P99kfVVVFQoKCmSfXbl58yb69+8PHx8fbN++He7u4ts1MTExWLRoEcrLy6HX62vdRq/Xm20jIiJyBLr/Lkr7cER2D2CaNGmCJk3Ez1gAQI8ePVBUVISMjAx07doVAPD111/DYDAgJibG7H7FxcWIi4uDXq/Hzp074enpKXus48ePIyAggAEKERGRg7J7AFNX7du3R//+/TF+/HgkJyejsrISCQkJGD58uDED6dKlS+jbty82b96M7t27o7i4GP369UNZWRnee+89FBcXGx+2bdKkCVxdXfHZZ58hLy8PDzzwADw9PbF3714sWbIE06dPt+fpEhERKceHeB3D+++/j4SEBPTt2xcuLi544okn8PbbbxvbKysrkZWVhbKyMgDAsWPHkJ5+eyaw1q1bm/SVnZ2NiIgIuLu7Y82aNXjxxRchSRJat26NFStWYPz48fV3YkRERDbANGoHERgYaLZoHQBERESYVAvu06ePbPXg/v37mxSwsxXv7EJhu0eReJbcc8O9hO3+mdY/j60vFM+eHZImbs/tIX6mSDRjtFxaqlyatVKlNcsHGdlzpms5cunAcunnSvhcsHy2dUvIzRgdvD/fbFteH/E8bXLvS0iauCSAqOSA3GzRSmerliP6Tti63IDo2HLvecBp8fsi9/dJrgSFzwXryzzIMTvbNNULTQUwREREZAHeQiIiIiJNctAARCkGMERERE7KmZ+BsXshOyIiIiJL8QoMERGRs3LiZ2B4BYaIiMhJ3bmFpHSxxKVLl/DMM8+gUaNG8PLyQqdOnXD06FHVz41XYIiIiEgVhYWF6NWrFx555BF8+eWXaNKkCc6cOYOAgADVj8UApp5UBYhrW1T4ewjbW229JWy/GSmuE1PwWJnZNo/t3sJ9883P1AAA8P5V3C4iV+OhsJ34IqFcjQm5Wi6OXOtFxNZ1PUT9y9XdaFRUIWyXq50RvF/c/p+/mq/1Ys/PW67Oi9I6MfJ1ZKy/oC5XV0ju+yZqt/V31ZZ1XpxCPd9Ceu211xAeHo53333XuC4yMlLhAGrHW0hEREROSs1bSHem47mzlJeX1zjezp070a1bNzz55JMICgpCly5dsH79epucGwMYIiIikhUeHg4/Pz/jkpSUVGOb//znP1i7di3atGmD3bt3Y9KkSZgyZQo2bdqk+nh4C4mIiMhZqXgLKScnB76+vsbVer2+xqYGgwHdunXDkiVLAABdunTByZMnkZycjJEjRyociClegSEiInJWkkoLAF9fX5OltgAmNDQUHTp0MFnXvn17XLx4UfVTYwBDREREqujVqxeysrJM1v3yyy9o0aKF6sdiAENEROSk6rsOzIsvvojDhw9jyZIlOHv2LLZs2YJ169Zh8uTJqp8bn4FxEB4yqadyadZyAgWp0nKpyv6Z4r7l0zvNKw9wFbaHpIlTJOVSep2V0rRX2f0Fn6lc6rtcmrScvD7m06QBcSq0rVNqRecu911W8juxNbnvS2mYuF1Jerrc90nufZXbX+5vq9Or5zTq+++/H9u3b8ecOXOwcOFCREZGYuXKlRgxYoTCQdTEAIaIiMhJ6SQJOklZBGPp/o8++igeffRRRcesC95CIiIiIs3hFRgiIiJn5cSTOTKAISIiclLWTMZYWx+OiLeQiIiISHN4BYaIiMhZ8RYSERERaY0z30JiAOMgrkc1ELbbsoZE8y/FdTse3bBf2P7vRQOE7SVNzd+plKs/IUfufanwdc67pErfNzmi902u7kZZZICwXa4uh1wtFyW1f5TWFFFC7ruo9Dcu2l/u2HJ1gZTUeVF6Xvas86K0phHZFgMYIiIiZ8VbSERERKQ1znwLyTmvrxMREZFT4xUYIiIiZ8VbSERERKRFjnoLSCkGMERERM5Kkm4vSvtwQAxg1FZ4A3DxqLHaraBIuFtwob+wPa9PkLC90Qlxut/uTzabbWv9wd+E+36S2E/YXtJRJj1UQcqvXPqnXMqt0v6dVcNL1qefy6WtVvjX/P5b0u6dXShs9ygyX3JArm+f7FvCdrn9hfvaOE3alpSm5YvSsH0uiNOg7ZraruDzrgu3c5dqbzDYLvX7bsIAhoiIyEk5cxYSAxgiIiJn5cQP8d6d18+JiIhI03gFhoiIyEnpDLcXpX04Ik1dgSkoKMCIESPg6+sLf39/jB07FiUlJcJ9+vTpA51OZ7JMnDjRZJuLFy9i0KBB8Pb2RlBQEGbMmIGqqipbngoREZHtSSotDkhTV2BGjBiBK1euYO/evaisrMTo0aMxYcIEbNmyRbjf+PHjsXDhQuNrb29v4/+vrq7GoEGDEBISgu+++w5XrlxBfHw83N3dsWTJEpudCxEREVlPMwFMZmYmUlJS8P3336Nbt24AgFWrVmHgwIFYvnw5wsLCzO7r7e2NkJCQWtv27NmDn3/+GV999RWCg4MRHR2NRYsWYdasWXjllVfg4aFSml2gv6Ldg/fnC9uzJjUWtj8yZpzZtiDx5MG43lE8+69cCqYoxVIunVcpJSmYd2uKNaAs5VcuDVputur//FVcMiAkzXzqvNKZiZWk9CqdRduWada2nrVdye/YlmnScuS+q7JkymM4AmfOQtLMX+i0tDT4+/sbgxcAiI2NhYuLC9LT04X7vv/++2jcuDE6duyIOXPmoKyszKTfTp06ITg42LguLi4OxcXFOHXqlNk+y8vLUVxcbLIQERE5lDuF7JQuDkgzV2Byc3MRFGT6X2Zubm4IDAxEbm6u2f3++te/okWLFggLC8OPP/6IWbNmISsrC5988omx398HLwCMr0X9JiUlYcGCBdaeDhERESlg9wBm9uzZeO2114TbZGZmWt3/hAkTjP+/U6dOCA0NRd++fXHu3Dm0atXK6n7nzJmDxMRE4+vi4mKEh4db3R8REZHanPkWkt0DmGnTpmHUqFHCbVq2bImQkBDk55s+B1JVVYWCggKzz7fUJiYmBgBw9uxZtGrVCiEhIThy5IjJNnl5eQAg7Fev10Ov19f5uERERPXOiQvZ2T2AadKkCZo0aSK7XY8ePVBUVISMjAx07doVAPD111/DYDAYg5K6OH78OAAgNDTU2O/ixYuRn59vvEW1d+9e+Pr6okOHDhaeDRERkeNw5iswmnmIt3379ujfvz/Gjx+PI0eO4Ntvv0VCQgKGDx9uzEC6dOkS2rVrZ7yicu7cOSxatAgZGRk4f/48du7cifj4eDz00EPo3LkzAKBfv37o0KEDnn32WZw4cQK7d+/Gyy+/jMmTJ/MKCxERkYPSTAAD3M4mateuHfr27YuBAweid+/eWLdunbG9srISWVlZxiwjDw8PfPXVV+jXrx/atWuHadOm4YknnsBnn31m3MfV1RWff/45XF1d0aNHDzzzzDOIj483qRtDRESkScxCcgyBgYHConURERGQfvdGh4eH48CBA7L9tmjRAl988YUqYzRHrvaFXI0IOeEp4hoU/i9dNNuWtzZSuK9cjYeSpuI4uKK7+WrJ+g32vcolqkGh5TowcnU/FNXH8VdWG0mu1krzL0uF7W6F4naRnMHiGjOhh8R9Kzl3UT0kAPCwYyUGpXVibFnLRWltH9FnJvddkvu77c06MHal3b/QREREdNdiAENEROSs7DwX0tKlS6HT6TB16lTrOzFDU7eQiIiIqO7seQvp+++/xz//+U9j0ozaeAWGiIiIVFVSUoIRI0Zg/fr1CAiQmXDPSgxgiIiInJVBUmcBasz/V15ebvawkydPxqBBgxAbG2uzU2MAQ0RE5KxUfAYmPDwcfn5+xiUpKanWQ27duhXHjh0z264WPgNTT7wzssXt2f6K+q8KaCBsr3jCfJqjvqs4BVIujbHCV3zshtu9Ba22S79UyueCODVdaeq7PSlJTVWSxgwAAecuCdsLY1uL91dw/PDP8oXtSsodyH1fGp0Ut8ullzsrpWnScryzC63fV+bv9t0mJycHvr6+xte1FXvNycnBCy+8gL1798LT09Om42EAQ0RE5KR0UOEh3v/+r6+vr0kAU5uMjAzk5+fjvvvuM66rrq7GwYMHsXr1apSXl8PVVZ1gnQEMERGRs1Kjkq4F+/ft2xc//fSTybrRo0ejXbt2mDVrlmrBC8AAhoiIyGnVdxq1j48POnbsaLKuQYMGaNSoUY31SvEhXiIiItIcXoEhIiJyVgor6Rr7UGD//v0KB1A7BjBEREROSidJ0Cl8Bkbp/rbCAKa+BPoLm+XSoK9HidsbnRCnlla1amq2TXGqYNQ9Vu8ql+Io977IUTpzsog906zlZg8OOHbNZseG3Ay8Mt91OQFfnbV+Z4XHlk+5tU1FUUB+RmdbplnLzTYt9/dF9DuzdZr0zUgvYbso7V7u74ubBmabvpsxgCEiInJWhv8uSvtwQAxgiIiInJQz30JiFhIRERFpDq/AEBEROSsHyEKyFQYwREREzqqeK/HWJ95CIiIiIs3hFRgiIiInVd9TCdQnBjAqq7p2HdDVUv/jqrK6HI0Oi9uv/62nsL00zHxbyy3+lg/od4L35wvbC+9rbFUbIF8bQ67GhFy7PevEiMjV/LBpnRc5cjWNssR1XNyaiD9zRWxco0ZUJyZncJBw30Ynrf8+APK1WuRqA9lqX0D8viit5SRH7rcgOr7buUvKDm7lb6FKUvZdsIgT30JiAENEROSkdIbbi9I+HBGfgSEiIiLN4RUYIiIiZ8VbSERERKQ5TlwHhreQiIiISHN4BYaIiMhJOfNcSAxgnESjf34n3kAmzVpI4ZTyAV+Z3z/v8XuE+8qlUSslSrO2ZYq1nICvxKnIsuTShRV8plUKSwLYcmxKyaX8uhWWmm0LPWS+DQBuRnoJ2+XSpOXS8kWp90rLESghes8AZe95nY6vNFVaQK5kgENw4mdgeAuJiIiINIdXYIiIiJyVBEBpHRfHvADDAIaIiMhZOfMzMLyFRERERJrDKzBERETOSoIKD/GqMhLVMYAhIiJyVsxCcgwFBQUYMWIEfH194e/vj7Fjx6KkpMTs9ufPn4dOp6t1+fDDD43b1da+devW+jglIiIi2zGotDggTV2BGTFiBK5cuYK9e/eisrISo0ePxoQJE7Bly5Zatw8PD8eVK1dM1q1btw6vv/46BgwYYLL+3XffRf/+/Y2v/f39VR+/PQnrxDRpLNy3qlVTYbtsnQVB3Y9GJxTWeFBYY0LElrUxANvWp1BaS0VxrRdR3zK1M9xkvo+KyLwvcn8QsyaZH1urrbeE+/pki9vl6sSI6rzI9S9X00hprRUllNZbsmmdFxv+Dkg5zQQwmZmZSElJwffff49u3boBAFatWoWBAwdi+fLlCAsLq7GPq6srQkJCTNZt374dTz31FBo2bGiy3t/fv8a2IuXl5SgvLze+Li4utuR0iIiIbI5ZSA4gLS0N/v7+xuAFAGJjY+Hi4oL09PQ69ZGRkYHjx49j7NixNdomT56Mxo0bo3v37tiwYQMkmQ8sKSkJfn5+xiU8PNyyEyIiIrK1O8/AKF0ckGYCmNzcXAQFBZmsc3NzQ2BgIHJzc+vUxzvvvIP27dujZ0/TsvoLFy7EBx98gL179+KJJ57Ac889h1WrVgn7mjNnDm7cuGFccnJyLDshIiIisprdbyHNnj0br732mnCbzMxMxce5desWtmzZgrlz59Zo+/26Ll26oLS0FK+//jqmTJlitj+9Xg+9Xq94XERERDbjxFlIdg9gpk2bhlGjRgm3admyJUJCQpCfn2+yvqqqCgUFBXV6duWjjz5CWVkZ4uPjZbeNiYnBokWLUF5eziCFiIi0iwGM7TRp0gRNmjSR3a5Hjx4oKipCRkYGunbtCgD4+uuvYTAYEBMTI7v/O++8gz//+c91Otbx48cREBDA4IWIiMhB2T2Aqav27dujf//+GD9+PJKTk1FZWYmEhAQMHz7cmIF06dIl9O3bF5s3b0b37t2N+549exYHDx7EF198UaPfzz77DHl5eXjggQfg6emJvXv3YsmSJZg+fXq9nZu9yaYKyrTnTO8pbG90stJsm1yqsmx6p8K0WJtSmMqsiCB1HZBPZbYrmbHb9H2V6bvtYkG7wnEHnBPvLtu/gJJSB0rJlTLwzshW1j9TncUMAHQq9FFHSUlJ+OSTT3D69Gl4eXmhZ8+eeO2119C2bVuFg6hJMw/xAsD777+Pdu3aoW/fvhg4cCB69+6NdevWGdsrKyuRlZWFsrIyk/02bNiAZs2aoV+/fjX6dHd3x5o1a9CjRw9ER0fjn//8J1asWIH58+fb/HyIiIhs6U4atdKlrg4cOIDJkyfj8OHDxppt/fr1Q2mp+rWGNHMFBgACAwPNFq0DgIiIiFrTn5csWYIlS5bUuk///v1NCtgRERFRTX+sd1ZbMktKSorJ640bNyIoKAgZGRl46KGHVB2Ppq7AEBERkQVUrAMTHh5uUv8sKSlJ9vA3btwAcPsChNo0dQWGiIiILGCQAJ3CLCLD7f1zcnLg6+trXC2X6GIwGDB16lT06tULHTt2VDaGWjCAISIiclYqplH7+vqaBDByJk+ejJMnT+LQoUPKjm8GAxgiIiJSVUJCAj7//HMcPHgQzZo1s8kxGMCQYqHLBTNdK1Ql0+7WtrV4A1um3MqlntowNVWOQ6dJy5CdrVr0mdszdd3Wx5brX8n3TaZvuRnpRWRnjGcatI2pMZdR3feXJAnPP/88tm/fjv379yMyMlLhsc1jAENEROSs6rkS7+TJk7FlyxZ8+umn8PHxMc5V6OfnBy8vL2Xj+ANmIREREZEq1q5dixs3bqBPnz4IDQ01Ltu2bVP9WLwCQ0RE5KwMEiy5BWS+j7qprRabrTCAISIiclaS4faitA8HxFtIREREpDm8AkNEROSs6vkh3vrEAIaIiMhZ1fMzMPWJAQxpmtJ6J25NGptvtHEdl6qABmbbZGtnaLjOi1Kicxd+no5O4ffNpt8JBbVa5Go5EVmLAQwREZGz4i0kIiIi0hwJKgQwqoxEdQxgiIiInJUTX4FhGjURERFpDq/AEBEROSuDAYDCQnQGxyxkxwCGiIjIWTnxLSQGMHRXqxKkh8r+OGTSXssiAywej9HhE9bvexcTfZ6AndOs5dKkC4qEzXLnRnS3YQBDRETkrHgFhoiIiDTHiSvxMguJiIiINIdXYIiIiJyUJBkgScqyiJTubysMYIiIiJyVJCm/BeSgz8DwFhIRERFpDq/AEBEROStJhYd4HfQKDAMYIjNk627ItHtkqTgYUoXsZ/pAlLDZrbBUxdGYYp0XsgmDAdApfIaFz8AQERFRvXLiKzB8BoaIiIg0h1dgiIiInJRkMEBSeAuJadRERERUv3gLiYiIiMhx8AoMERGRszJIgM45r8BoKoBZvHgxdu3ahePHj8PDwwNFRUWy+0iShPnz52P9+vUoKipCr169sHbtWrRp08a4TUFBAZ5//nl89tlncHFxwRNPPIG33noLDRs2tOHZEJHDOXxC2FxVT8MgUo0kAVCaRu2YAYymbiFVVFTgySefxKRJk+q8z7Jly/D2228jOTkZ6enpaNCgAeLi4vDbb78ZtxkxYgROnTqFvXv34vPPP8fBgwcxYcIEW5wCERERqUBTV2AWLFgAANi4cWOdtpckCStXrsTLL7+MIUOGAAA2b96M4OBg7NixA8OHD0dmZiZSUlLw/fffo1u3bgCAVatWYeDAgVi+fDnCwsJsci5ERES2JhkkSApvIUm8AlP/srOzkZubi9jYWOM6Pz8/xMTEIC0tDQCQlpYGf39/Y/ACALGxsXBxcUF6errZvsvLy1FcXGyyEBERORTJoM7igJw6gMnNzQUABAcHm6wPDg42tuXm5iIoKMik3c3NDYGBgcZtapOUlAQ/Pz/jEh4ervLoiYiIlJEMkiqLpdasWYOIiAh4enoiJiYGR44cUf3c7B7AzJ49GzqdTricPn3a3sOsYc6cObhx44ZxycnJsfeQiIiI7G7btm1ITEzE/PnzcezYMURFRSEuLg75+fmqHsfuz8BMmzYNo0aNEm7TsmVLq/oOCQkBAOTl5SE0NNS4Pi8vD9HR0cZt/vimVlVVoaCgwLh/bfR6PfR6vfH1nXuEVahUXDOIiIicVxUqAdTPsyVVUrniW0B3xvvHRyX++O/gHStWrMD48eMxevRoAEBycjJ27dqFDRs2YPbs2YrGYkLSoHfffVfy8/OT3c5gMEghISHS8uXLjetu3Lgh6fV66d///rckSZL0888/SwCko0ePGrfZvXu3pNPppEuXLtV5TDk5OXfKHXLhwoULFy6yS05OTt3/4bPQrVu3pJCQENXG2rBhwxrr5s+fX+O45eXlkqurq7R9+3aT9fHx8dKf//xnVc/R7ldgLHHx4kUUFBTg4sWLqK6uxvHjxwEArVu3NtZsadeuHZKSkvDYY49Bp9Nh6tSpePXVV9GmTRtERkZi7ty5CAsLw9ChQwEA7du3R//+/TF+/HgkJyejsrISCQkJGD58uEUZSGFhYcjJyYGPjw90Oh2Ki4sRHh6OnJwc+Pr6qv1W2JzWxw9o/xy0Pn6A5+AItD5+wPnOwcfHBzdv3rRplqunpyeys7NRUVGhSn+SJEGn05msq+3qy7Vr11BdXV3rs6dqPw6iqQBm3rx52LRpk/F1ly5dAAD79u1Dnz59AABZWVm4ceOGcZuZM2eitLQUEyZMQFFREXr37o2UlBR4enoat3n//feRkJCAvn37GgvZvf322xaNzcXFBc2aNaux3tfXV7M/OED74we0fw5aHz/Ac3AEWh8/4Fzn4OfnZ/NjeXp6mvxb52w0FcBs3LhRtgaM9Id7ijqdDgsXLsTChQvN7hMYGIgtW7aoMUQiIqK7VuPGjeHq6oq8vDyT9Xl5ecLnSq1h9ywkIiIicg4eHh7o2rUrUlNTjesMBgNSU1PRo0cPVY+lqSswWqLX6zF//vxa7xFqgdbHD2j/HLQ+foDn4Ai0Pn6A56A1iYmJGDlyJLp164bu3btj5cqVKC0tNWYlqUUn/fGeCxEREZECq1evxuuvv47c3FxER0fj7bffRkxMjKrHYABDREREmsNnYIiIiEhzGMAQERGR5jCAISIiIs1hAENERESawwDGBupjGnFyLkVFRejWrRuio6PRsWNHrF+/3t5DumtFRESgc+fOiI6OxiOPPGLv4VjEGb5HWVlZiI6ONi5eXl7YsWOHvYdVJ0lJSbj//vvh4+ODoKAgDB06FFlZWfYeltNiFpLKtm3bhvj4eCQnJyMmJgYrV67Ehx9+iKysLAQFBdl7eLJOnz6NXr16wd/fHz4+Pjh79iyio6Nx6NAhew+tzrR4DtXV1SgvL4e3tzdKS0vRsWNHHD16FI0aNbL30O46EREROHnypHF+NS1xtu9RSUkJIiIicOHCBTRo0MDew5HVv39/DB8+HPfffz+qqqrw0ksv4eTJk/j55581MX7NUXVqSJK6d+8uTZ482fi6urpaCgsLk5KSkuw4KssMGDBA+vHHHyVJkqR77rlHKikpsfOILKflc7h+/brUokUL6erVq1JmZqYUGBgotWzZUoqKipIaNGgg9erVy95DlNWuXTuzs9quWrXK3sMTatGihXTz5k2TdVr8HJzhe/T+++9LTz31lCRJ2vwM8vPzJQDSgQMHJEnS9u/CETGAUVF9TiNuSy1btpTKy8ul0tJSqVWrVvYejlW0eA6FhYVS586dJS8vL2n16tXG9VoMxk6dOiUBkFJTU6UrV65I58+fl1xcXKQPP/xQ+u233+w9PKGIiAjpvvvuk7p16ya99957xvVa+Ryc6Xs0ZMgQ6eOPPza+1to5nDlzRgIg/fTTT5Ikaft34Yj4DIyKRNOI5+bm2mlUlrl58yb0ej08PDxw6tQptG/f3t5DsphWz8Hf3x8nTpxAdnY2tmzZYpwMLSsrC23btkVZWRmqq6s1cSk6Ly8Pbm5u6NWrF0JCQnDt2jUYDAY8+OCDDl9K/dChQ8jIyMDOnTuxZMkS/PjjjwC08zk4y/eouLgY3333HQYOHGhcp6VzMBgMmDp1Knr16oWOHTsC0PbvwhExgCETP//8Mzp06AAAyMzMxD333GPnEVlO6+cQHByMqKgofPPNN5oNxn766Sfcc889xj/KJ06cQFBQUI3g3hE1bdoUABAaGoqBAwfi2LFjmvwctP49+vTTT9GvXz94enoC0N5/mEyePBknT57E1q1bjeu0/LtwRAxgVFSf04jbyqlTp3DvvfcCABo0aIA9e/bg+vXrdh6VZbR4Dnl5ebh58yYA4MaNGzh48CDatm2r2WDsxx9/RKdOnYyvT5w4YfLaUZWWlho/h5KSEnz99de49957NfM5ONP36IMPPsCwYcOMr7V0DgkJCfj888+xb98+NGvWzLheq78LR8UARkX1OY24rYwZMwYLFiwAADzxxBP46aefNJfBoMVzuHDhAh588EFERUXhwQcfxPPPP49OnTppMhgDbv+h7ty5s/H1iRMnTF47qry8PPTu3RtRUVF44IEHEB8fj/vvv18zn4OzfI9u3LiBI0eOIC4uzrhOC+cgSRISEhKwfft2fP3114iMjDRp1+rvwlExjVpl27Ztw8iRI/HPf/7TOI34Bx98gNOnT/MyId0VDAYDfHx8sG3bNjz66KMAgPDwcMyYMQNTpkyx8+iIbOe5557Dli1b8Omnn6Jt27bG9X5+ftDr9fxdqIwBjA3UxzTitqLT6YTtWvi6OMM5aNmZM2dwzz334MKFC2jevDkAYODAgUhLS8Pnn3+OXr162XmERLZh7m/Pu+++i169evF3oTIGMFSrsrIytG/fHk8++SSWL19u7+FYJCcnB88++yzy8/Ph5uaGuXPn4sknn7T3sITkgi5z+PNVl9Y/B62PH3COc6D6wWdgqFaLFy/GAw88YO9hWMXNzQ0rV67Ezz//jD179mDq1KkoLS2197CEpNs1mUyW0tJSNG/eHNOmTau1nX+w1af1z+H3Y7p48SIefvhhtG/fHp06dcIHH3zg8OMHtP8ZUP1hAEM1nDlzBqdPn8aAAQPsPRSrhIaGIjo6GgAQEhKCxo0bo6CgwL6DsoKWg0hnotXPQYuBvDla/QzIthjAUA3Tp09HUlKSvYehioyMDFRXVyM8PNzeQ7GI1oNIZ6Hlz8FZAnktfwZkWwxgyMSnn36Ke+65x6FrLNRVQUEB4uPjsW7dOnsPxWLOFERqmbN8DloN5AHn+QxIfQxgyMThw4exdetWREREYPr06Vi/fj0WLlxo72FZrLy8HEOHDsXs2bPRs2dPew/HIs4URGqZs3wOWg7kneUzINtws/cAyLEkJSUZ/2tn48aNOHnyJObNm2fnUVlGkiSMGjUKf/rTn/Dss8/aezgWuxNEfvjhhygpKUFlZSV8fX019zlonTN8DloO5AHn+AzIdphGTWbdCWC0lkZ96NAhPPTQQyYVLv/v//5PkyW7tfoZOBstfg6SJOGvf/0r2rZti1deecXew1FMi58B2RavwJBZo0aNsvcQrNK7d28YDAZ7D4PIrr799lts27YNnTt3xo4dOwBoN5Anqg2vwBAREZHm8CFeIiIi0hwGMERERKQ5DGCIiIhIcxjAEBERkeYwgCEiIiLNYQBDREREmsMAhoiIiDSHAQwRERFpDgMYIiIi0hwGMERERKQ5DGCIiIhIcxjAENWjPn36YOrUqXfdsS11/fp1BAUF4fz58zbp//fvhT3el+HDh+ONN96o12MSORtO5kikglGjRmHTpk0AADc3NwQGBqJz5854+umnMWrUKLi43P5vhYKCAri7u8PHx8em4+nTpw+io6OxcuVK47r6OrYaEhMTcfPmTaxfv94m/f/+/bHH+3Ly5Ek89NBDyM7Ohp+fX70dl8iZ8AoMkUr69++PK1eu4Pz58/jyyy/xyCOP4IUXXsCjjz6KqqoqAEBgYKDwH8qKigqbjU/u2I6irKwM77zzDsaOHWt2GzXfJ3u8Lx07dkSrVq3w3nvv1etxiZwJAxgilej1eoSEhKBp06a477778NJLL+HTTz/Fl19+iY0bNwKoebuiT58+SEhIwNSpU9G4cWPExcUBAAwGA5KSkhAZGQkvLy9ERUXho48+Mu5nMBiwbNkytG7dGnq9Hs2bN8fixYsB3L4adODAAbz11lvQ6XTQ6XQ4f/58jWOXl5djypQpCAoKgqenJ3r37o3vv//e5Jz69OmDKVOmYObMmQgMDERISAheeeUV2fdi0qRJ6N27d61tzZo1w9KlS83u+8UXX0Cv1+OBBx6QfZ9SUlLQu3dv+Pv7o1GjRnj00Udx7tw5k/5KS0sRHx+Phg0bIjQ0tMatmz++L3Xpsy7vy0cffYROnTrBy8sLjRo1QmxsLEpLS43tgwcPxtatW82+D0QkxgCGyIb+9Kc/ISoqCp988onZbTZt2gQPDw98++23SE5OBgAkJSVh8+bNSE5OxqlTp/Diiy/imWeewYEDBwAAc+bMwdKlSzF37lz8/PPP2LJlC4KDgwEAb731Fnr06IHx48fjypUruHLlCsLDw2scd+bMmfj444+xadMmHDt2DK1bt0ZcXBwKCgpqjK9BgwZIT0/HsmXLsHDhQuzdu9fs+Zw6dQrr1q3DsmXLam1v3749jh8/bnb/b775Bl27dq3T+1RaWorExEQcPXoUqampcHFxwWOPPQaDwWDcb8aMGThw4AA+/fRT7NmzB/v378exY8fMHr8ufcq9L1euXMHTTz+NMWPGIDMzE/v378fjjz+O39+x7969O44cOYLy8nKzYyEiAYmIFBs5cqQ0ZMiQWtuGDRsmtW/fXpIkSXr44YelF154wdj28MMPS126dDHZ/rfffpO8vb2l7777zmT92LFjpaeffloqLi6W9Hq9tH79erPj+eNx/riupKREcnd3l95//31je0VFhRQWFiYtW7bMZJ/evXub9HP//fdLs2bNMnvskSNHSjExMWbbn3rqKenhhx822z5kyBBpzJgxNcb+x/epNlevXpUASD/99JMkSZJ08+ZNycPDQ/rggw+M21y/fl3y8vIyvhe1vVeiPu/sI3pfMjIyJADS+fPnzfZ74sQJ2W2IyDxegSGyMUmSoNPpzLb/8WrD2bNnUVZWhv/3//4fGjZsaFw2b96Mc+fOITMzE+Xl5ejbt6/VYzp37hwqKyvRq1cv4zp3d3d0794dmZmZJtt27tzZ5HVoaCjy8/Nr7beqqgqffPIJnnjiCeO6v/3tb3jnnXeMr2/evAkvLy+zY7t16xY8PT1rrK/tqsyZM2fw9NNPo2XLlvD19UVERAQA4OLFi8bzrKioQExMjHGfwMBAtG3b1uzx5fq8Q/S+REVFoW/fvujUqROefPJJrF+/HoWFhSbb33kPysrKzI6FiMxzs/cAiJxdZmYmIiMjzbY3aNDA5HVJSQkAYNeuXWjatKlJm16vR1FRkepjFHF3dzd5rdPpatxOuePcuXO4efMmOnXqBOD2szoffvihSbD1448/YtiwYWaP17hx4xr/2AM13yfg9nMkLVq0wPr16xEWFgaDwYCOHTsqesi3rn2K3hdXV1fs3bsX3333Hfbs2YNVq1bh73//O9LT043fhTu36po0aWL1WInuZrwCQ2RDX3/9NX766SeTKxJyOnToAL1ej4sXL6J169YmS3h4ONq0aQMvLy+kpqaa7cPDwwPV1dVm21u1amV8nuSOyspKfP/99+jQoUOdx/pHd4Krhg0bAgB2796NwsJC4xWVw4cP49KlS3jsscfM9tGlSxf8/PPPsse6fv06srKy8PLLL6Nv375o3759jcCnVatWcHd3R3p6unFdYWEhfvnlF6v7rCudTodevXphwYIF+OGHH+Dh4YHt27cb20+ePIlmzZqhcePGVvVPdLfjFRgilZSXlyM3NxfV1dXIy8tDSkoKkpKS8OijjyI+Pr7O/fj4+GD69Ol48cUXYTAY0Lt3b9y4cQPffvstfH19MXLkSMyaNQszZ86Eh4cHevXqhatXr+LUqVPG1OOIiAikp6fj/PnzaNiwIQIDA02O0aBBA0yaNAkzZsxAYGAgmjdvjmXLlqGsrEyYviynRYsW0Ol0+Pe//40GDRpg+vTpGDRoED799FOEh4dj4sSJiI2NNZuhBABxcXGYM2cOCgsLERAQYHa7gIAANGrUCOvWrUNoaCguXryI2bNnm2zTsGFDjB07FjNmzECjRo0QFBSEv//978a6PNb0WRfp6elITU1Fv379EBQUhPT0dFy9ehXt27c3bvPNN9+gX79+FvdNRLcxgCFSSUpKCkJDQ+Hm5oaAgABERUXh7bffxsiRI83+g2nOokWL0KRJEyQlJeE///kP/P39janZADB37ly4ublh3rx5uHz5MkJDQzFx4kTj/tOnT8fIkSPRoUMH3Lp1C9nZ2TWOsXTpUhgMBjz77LO4efMmunXrht27dwuDBjkhISFYvHgxli5dio8//hhLlixB165dMWTIEGzbtg2DBw/GP/7xD2EfnTp1wn333YcPPvgAf/vb38xu5+Ligq1bt2LKlCno2LEj2rZti7fffht9+vQx2e71119HSUkJBg8eDB8fH0ybNg03btxQ1KccX19fHDx4ECtXrkRxcTFatGiBN954AwMGDAAA/Pbbb9ixYwdSUlIs6peI/oeVeInI4ezatQszZszAyZMnLQ7+tGDt2rXYvn079uzZY++hEGkWr8AQkcMZNGgQzpw5g0uXLtVaw0br3N3dsWrVKnsPg0jTeAWGiIiINMf5rs0SERGR02MAQ0RERJrDAIaIiIg0hwEMERERaQ4DGCIiItIcBjBERESkOQxgiIiISHMYwBAREZHmMIAhIiIizfn/bRKigVGk498AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh);  # intiate ECT\n",
+    "result = myect.calculate(G);  # calculate ECT on embedded graph\n",
+    "\n",
+    "result.plot();  # plot ECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Let's just make a data loader with all of this for ease in a bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def matisse_ect(filename, ect):\n",
+    "    shape = np.loadtxt(data_dir + filename)\n",
+    "    G = EmbeddedGraph()\n",
+    "    G.add_cycle(shape)\n",
+    "    G.transform_coordinates(projection_type=\"pca\")\n",
+    "    G.scale_coordinates(1)\n",
+    "    result = ect.calculate(G)\n",
+    "    return result\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And now we can load in all the outlines, compute their ECT and store it in a 3D array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "ect_arr = np.zeros((len(file_names), num_dirs, num_thresh))\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh, bound_radius=1)\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    ect_arr[i, :, :] = matisse_ect(file_names[i], myect)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Here, we are just going to compute the distance between two ECTs using $L_2$ distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x32185cbd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs * num_thresh)\n",
+    "D = distance_matrix(flattened_ect, flattened_ect)\n",
+    "plt.matshow(D)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_components = 2  # select number of components\n",
+    "mds = MDS(\n",
+    "    n_components=n_components,  # initialize MDS\n",
+    "    dissimilarity=\"precomputed\",  # we have precomputed the distance matrix\n",
+    "    normalized_stress=\"auto\",\n",
+    "    random_state=5,  # select random state for reproducibility\n",
+    ")\n",
+    "MDS_scores = mds.fit_transform(D)  # get MDS scores\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# read in color hexcodes\n",
+    "col_df = pd.read_csv(colors_path, header=None)\n",
+    "\n",
+    "scale_val = 6  # set scale value\n",
+    "plt.figure(figsize=(5, 5))  # set figure dimensions\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    shape = np.loadtxt(data_dir + file_names[i])  # get the current shape\n",
+    "    shape = shape - np.mean(shape, axis=0)  # zero center shape\n",
+    "    shape = (\n",
+    "        scale_val * shape / max(np.linalg.norm(shape, axis=1))\n",
+    "    )  # scale to radius 1 then mult by scale_val\n",
+    "\n",
+    "    trans_sh = shape + MDS_scores[i]  # translate shape to MDS position\n",
+    "\n",
+    "    plt.fill(trans_sh[:, 0], trans_sh[:, 1], c=col_df[0][i], lw=0)  # plot shape\n",
+    "    plt.gca().set_aspect(\"equal\")\n",
+    "\n",
+    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
+    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "\n",
+    "\n",
+    " This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/doctrees/nbsphinx/notebooks/Playing_with_Spirals.ipynb b/docs/doctrees/nbsphinx/notebooks/Playing_with_Spirals.ipynb
deleted file mode 100644
index 3b1339f..0000000
--- a/docs/doctrees/nbsphinx/notebooks/Playing_with_Spirals.ipynb
+++ /dev/null
@@ -1,1204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.patches import Circle\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Recreating the sprial example from Arya \n",
-    "\n",
-    "# Create the graph\n",
-    "Spiral = EmbeddedGraph()\n",
-    "Spiral.add_node('a', .5, 0)\n",
-    "Spiral.add_node('b', 1, 1)\n",
-    "Spiral.add_node('c', -1.5, -.5)\n",
-    "Spiral.add_node('d', 3, -2.5)\n",
-    "Spiral.add_node('e',1.5,2)\n",
-    "Spiral.add_node('f',-1.5,2)\n",
-    "Spiral.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = list(Spiral.nodes())\n",
-    "nodelist.sort()\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    Spiral.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "Spiral.plot(angle_labels_circle=True, edges_only=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Spiral.plot(angle_labels_circle=True, edges_only=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfadbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>debacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle             Swapped Verts Sorted list\n",
-       "0    0.00                  [(c, f)]     gcfabed\n",
-       "1    0.32                  [(d, e)]     gcfabed\n",
-       "2    0.52                  [(b, d)]     gcfadbe\n",
-       "3    0.79  [(a, d), (f, a), (f, d)]     gcdafbe\n",
-       "4    1.15                  [(c, d)]     gcdafbe\n",
-       "5    1.19                  [(b, f)]     gdcafbe\n",
-       "6    1.57                  [(e, f)]     gdcabfe\n",
-       "7    1.65                  [(d, g)]     gdcabef\n",
-       "8    1.82                  [(a, c)]     dgacbef\n",
-       "9    2.11                  [(b, c)]     dgacbef\n",
-       "10   2.27                  [(c, e)]     dgabecf\n",
-       "11   2.28                  [(a, g)]     dagbecf\n",
-       "12   2.36                  [(b, g)]     dabgecf\n",
-       "13   2.41                  [(e, g)]     dabegcf\n",
-       "14   2.60                  [(c, g)]     dabegcf\n",
-       "15   2.68  [(a, b), (a, e), (b, e)]     debacgf\n",
-       "16   2.85                  [(f, g)]     debacgf\n",
-       "17   3.14                  [(c, f)]     debacfg\n",
-       "18   3.46                  [(d, e)]     debafcg\n",
-       "19   3.66                  [(b, d)]     edbafcg\n",
-       "20   3.93  [(a, d), (f, a), (f, d)]     ebfadcg\n",
-       "21   4.29                  [(c, d)]     ebfadcg\n",
-       "22   4.33                  [(b, f)]     ebfacdg\n",
-       "23   4.71                  [(e, f)]     efbacdg\n",
-       "24   4.79                  [(d, g)]     febacdg\n",
-       "25   4.96                  [(a, c)]     febcagd\n",
-       "26   5.25                  [(b, c)]     febcagd\n",
-       "27   5.41                  [(c, e)]     fcebagd\n",
-       "28   5.42                  [(a, g)]     fcebagd\n",
-       "29   5.50                  [(b, g)]     fcegbad\n",
-       "30   5.55                  [(e, g)]     fcegbad\n",
-       "31   5.74                  [(c, g)]     fcgebad\n",
-       "32   5.82  [(a, b), (a, e), (b, e)]     fgcabed\n",
-       "33   5.99                  [(f, g)]     fgcabed"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(Spiral.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys([5.82, 1.82, 3.93, 2.28, 2.11, 3.66, 1.19, 2.36, 4.29, 5.41, 0.0, 2.6, 0.32, 1.65, 1.57, 2.41, 2.85, 2.68, 4.96, 0.79, 5.42, 5.25, 0.52, 4.33, 5.5, 1.15, 2.27, 3.14, 5.74, 3.46, 4.79, 4.71, 5.55, 5.99])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>angle</th>\n",
-       "      <th>vertex_pair(s)_swapped</th>\n",
-       "      <th>sorted_order_at_next_midpoint</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[g, c, f, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[g, c, f, a, d, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.04</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, c, d, a, f, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, d, c, a, b, f, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[g, d, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.73</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, g, a, c, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.24</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[d, a, g, b, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[d, a, b, g, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.49</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[d, e, b, a, c, g, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[d, e, b, a, c, f, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.02</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, e, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.37</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.59</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[e, b, d, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[e, b, f, a, d, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.47</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.50</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.59</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.72</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[f, e, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.87</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[f, e, b, c, a, g, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[f, c, e, b, g, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.47</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[f, c, g, e, b, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>6.26</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    angle    vertex_pair(s)_swapped sorted_order_at_next_midpoint\n",
-       "0    0.00                  [(c, f)]         [g, c, f, a, b, e, d]\n",
-       "1    0.32                  [(d, e)]         [g, c, f, a, d, b, e]\n",
-       "2    1.04                  [(b, f)]         [g, c, d, a, f, b, e]\n",
-       "3    1.19                  [(b, f)]         [g, d, c, a, b, f, e]\n",
-       "4    1.57                  [(e, f)]         [g, d, c, a, b, e, f]\n",
-       "5    1.65                  [(d, g)]         [d, g, c, a, b, e, f]\n",
-       "6    1.73                  [(c, e)]         [d, g, c, a, b, e, f]\n",
-       "7    1.82                  [(a, c)]         [d, g, a, c, b, e, f]\n",
-       "8    2.11                  [(b, c)]         [d, g, a, b, c, e, f]\n",
-       "9    2.24                  [(e, f)]         [d, g, a, b, c, e, f]\n",
-       "10   2.28                  [(a, g)]         [d, a, g, b, e, c, f]\n",
-       "11   2.36                  [(b, g)]         [d, a, b, g, e, c, f]\n",
-       "12   2.41                  [(e, g)]         [d, a, b, e, g, c, f]\n",
-       "13   2.49                  [(d, g)]         [d, a, b, e, g, c, f]\n",
-       "14   2.52                  [(b, d)]         [d, a, b, e, g, c, f]\n",
-       "15   2.60                  [(c, g)]         [d, e, b, a, c, g, f]\n",
-       "16   2.85                  [(f, g)]         [d, e, b, a, c, f, g]\n",
-       "17   3.02                  [(a, c)]         [d, e, b, a, f, c, g]\n",
-       "18   3.37  [(a, d), (f, a), (f, d)]         [e, d, b, a, f, c, g]\n",
-       "19   3.59                  [(c, f)]         [e, d, b, a, f, c, g]\n",
-       "20   3.66                  [(b, d)]         [e, b, d, a, f, c, g]\n",
-       "21   3.93                  [(b, c)]         [e, b, f, a, d, c, g]\n",
-       "22   4.29                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "23   4.47                  [(a, g)]         [e, f, b, a, c, d, g]\n",
-       "24   4.50                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "25   4.59                  [(d, e)]         [e, f, b, a, c, d, g]\n",
-       "26   4.72                  [(b, g)]         [f, e, b, a, c, d, g]\n",
-       "27   4.87                  [(e, g)]         [f, e, b, c, a, g, d]\n",
-       "28   5.41                  [(c, e)]         [f, c, e, b, g, a, d]\n",
-       "29   5.47                  [(c, g)]         [f, c, g, e, b, a, d]\n",
-       "30   5.82  [(a, b), (a, e), (b, e)]         [g, f, c, a, b, e, d]\n",
-       "31   6.26                  [(f, g)]         [g, f, c, a, b, e, d]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2)\n",
-    "\n",
-    "# add pi + angle for all angles \n",
-    "All_Angles_2pi = {}\n",
-    "for key in All_Angles.keys():\n",
-    "    All_Angles_2pi[round(key,2)] = All_Angles[key]\n",
-    "    other_key = (key + np.pi) % 2*np.pi\n",
-    "    other_key = round(other_key, 2)\n",
-    "    All_Angles_2pi[other_key] = All_Angles[key]\n",
-    "\n",
-    "keys = list(All_Angles_2pi.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns=['angle', 'vertex_pair(s)_swapped', 'sorted_order_at_next_midpoint'])\n",
-    "pd_table['angle'] = keys\n",
-    "pd_table['vertex_pair(s)_swapped'] = [All_Angles_2pi[key] for key in keys]\n",
-    "\n",
-    "# sort the vertex pairs at the midpoint\n",
-    "\n",
-    "for i in range(len(keys)-1):\n",
-    "    angle = np.average([keys[i], keys[i+1]])\n",
-    "    sorted_order = Spiral.sort_vertices(angle)\n",
-    "    pd_table.at[i, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "angle = np.average([keys[-1], keys[0]+2*np.pi])\n",
-    "sorted_order = Spiral.sort_vertices(angle)\n",
-    "pd_table.at[len(keys)-1, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "pd_table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f', 'g']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nodelist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modified version breaking the spiral"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Modified version with vertex added between C and D\n",
-    "\n",
-    "# Create the graph\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_node('a', .5, 0)\n",
-    "G.add_node('b', 1, 1)\n",
-    "G.add_node('c', -1.5, -.5)\n",
-    "G.add_node('X', 1.5, -1)\n",
-    "G.add_node('d', 3, -2.5)\n",
-    "G.add_node('e',1.5,2)\n",
-    "G.add_node('f',-1.5,2)\n",
-    "G.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = ['a', 'b', 'c','X',  'd', 'e', 'f', 'g']\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    G.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "G.plot(angle_labels_circle=True, edges_only=True, ax = ax)\n",
-    "# remove axes \n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x30be90c90>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(G.get_all_angles()[0])\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>gcfabXed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.25</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfaXdbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.41</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdXcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgXacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1.99</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dXgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dXagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dXabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>dXebacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.39</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfaXcdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.55</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.13</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle                                     Swapped Verts Sorted list\n",
-       "0    0.00                                  [(c, f), (X, e)]    gcfabXed\n",
-       "1    0.25                                          [(b, X)]    gcfaXbed\n",
-       "2    0.32                                          [(d, e)]    gcfaXbed\n",
-       "3    0.52                                          [(b, d)]    gcfaXdbe\n",
-       "4    0.79  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    gcdXafbe\n",
-       "5    1.15                                          [(c, d)]    gcdXafbe\n",
-       "6    1.19                                          [(b, f)]    gdcXafbe\n",
-       "7    1.41                                          [(c, X)]    gdXcabfe\n",
-       "8    1.57                                          [(e, f)]    gdXcabfe\n",
-       "9    1.65                                          [(d, g)]    gdXcabef\n",
-       "10   1.82                                          [(a, c)]    dgXacbef\n",
-       "11   1.99                                          [(X, g)]    dXgacbef\n",
-       "12   2.11                                          [(b, c)]    dXgacbef\n",
-       "13   2.27                                          [(c, e)]    dXgabecf\n",
-       "14   2.28                                          [(a, g)]    dXagbecf\n",
-       "15   2.36                                          [(b, g)]    dXabgecf\n",
-       "16   2.41                                          [(e, g)]    dXabegcf\n",
-       "17   2.60                                          [(c, g)]    dXabegcf\n",
-       "18   2.68                          [(a, b), (a, e), (b, e)]    dXebacgf\n",
-       "19   2.85                                          [(f, g)]    dXebacgf\n",
-       "20   3.14                                  [(c, f), (X, e)]    dXebacfg\n",
-       "21   3.39                                          [(b, X)]    debXafcg\n",
-       "22   3.46                                          [(d, e)]    debXafcg\n",
-       "23   3.66                                          [(b, d)]    edbXafcg\n",
-       "24   3.93  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    ebfaXdcg\n",
-       "25   4.29                                          [(c, d)]    ebfaXdcg\n",
-       "26   4.33                                          [(b, f)]    ebfaXcdg\n",
-       "27   4.55                                          [(c, X)]    efbacXdg\n",
-       "28   4.71                                          [(e, f)]    efbacXdg\n",
-       "29   4.79                                          [(d, g)]    febacXdg\n",
-       "30   4.96                                          [(a, c)]    febcaXgd\n",
-       "31   5.13                                          [(X, g)]    febcaXgd\n",
-       "32   5.25                                          [(b, c)]    febcagXd\n",
-       "33   5.41                                          [(c, e)]    fcebagXd\n",
-       "34   5.42                                          [(a, g)]    fcebagXd\n",
-       "35   5.50                                          [(b, g)]    fcegbaXd\n",
-       "36   5.55                                          [(e, g)]    fcegbaXd\n",
-       "37   5.74                                          [(c, g)]    fcgebaXd\n",
-       "38   5.82                          [(a, b), (a, e), (b, e)]    fgcabeXd\n",
-       "39   5.99                                          [(f, g)]    fgcabeXd"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = G.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(G.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
deleted file mode 100644
index c19d638..0000000
--- a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial: ECT for CW complexes\n",
-    "\n",
-    "This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedCW, create_example_cw\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = EmbeddedCW()\n",
-    "\n",
-    "K.add_node('A', 0,0)\n",
-    "K.add_node('B', 1,0)\n",
-    "K.add_node('C', 1,1)\n",
-    "K.add_node('D', 0,1)\n",
-    "\n",
-    "K.add_edges_from((('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')))\n",
-    "\n",
-    "K.add_face(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "K.set_centered_coordinates()\n",
-    "K.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = create_example_cw(centered = True)\n",
-    "K.plot(bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also color the nodes based on a function value for a given direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = np.pi/7\n",
-    "K.plot(bounding_circle=True,color_nodes_theta=theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function value for any direction can be computed and returned for vertices, edges, or faces. The function value for an edge or face $\\sigma$ is given by $g_\\omega(\\sigma) = \\max_{v \\in \\sigma}\\{f(v)\\}$. Here we show the function values for the triangle `KDC` and its faces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "K: 0.18\n",
-      "D: 0.65\n",
-      "C: 2.89\n",
-      "('D', 'K'): 0.65\n",
-      "('C', 'D'): 2.89\n",
-      "('C', 'K'): 2.89\n",
-      "('K', 'D', 'C'): 2.89\n"
-     ]
-    }
-   ],
-   "source": [
-    "vert_g = K.g_omega(theta)\n",
-    "edge_g = K.g_omega_edges(theta)\n",
-    "face_g = K.g_omega_faces(theta)\n",
-    "\n",
-    "for v in ['K','D','C']:\n",
-    "    print(f'{v}: {round(vert_g[v],2)}')\n",
-    "\n",
-    "for edge in [('D','K'),('C','D'),('C','K')]:\n",
-    "    print(f'{edge}: {round(edge_g[edge],2)}')\n",
-    "\n",
-    "for face in [('K','D','C')]:\n",
-    "    print(f'{face}: {round(face_g[face],2)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myect = ECT(num_dirs = 100,num_thresh = 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,\n",
-       "        3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  1,\n",
-       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r = K.get_bounding_radius()\n",
-    "myect.calculateECC(K,0,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(K,theta,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       ...,\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2*r)\n",
-    "myect.calculateECT(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once it's been computed, we can use the internal plotting function to take a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.841874542459709\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECT()\n",
-    "print(myect.bound_radius)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly we can take a look at the SECT. This one was computed when we asked to compute the ECT. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotSECT()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb b/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
deleted file mode 100644
index 0ba510f..0000000
--- a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
+++ /dev/null
@@ -1,623 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial : ECT for embedded graphs \n",
-    "\n",
-    "This jupyter notebook will walk you through using the `ect` package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see [this paper](https://arxiv.org/abs/2310.10395) for a more thorough treatment of details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing the embedded graph\n",
-    "\n",
-    "We assume our input is an undirected graph $G$ with an embedding in 2D given by a map on the vertices $f: V(G) \\to \\mathbb{R}^2$. A graph can be constructed as follows. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Construct an example graph \n",
-    "# Note that this is the same graph that is returned by:\n",
-    "# G = create_example_graph()\n",
-    "\n",
-    "G = EmbeddedGraph()\n",
-    "\n",
-    "G.add_node('A', 1, 2)\n",
-    "G.add_node('B', 3, 4)\n",
-    "G.add_node('C', 5, 7)\n",
-    "G.add_node('D', 3, 6)\n",
-    "G.add_node('E', 4, 3)\n",
-    "G.add_node('F', 4, 5)\n",
-    "\n",
-    "G.add_edge('A', 'B')\n",
-    "G.add_edge('B', 'C')\n",
-    "G.add_edge('B', 'D')\n",
-    "G.add_edge('B', 'E')\n",
-    "G.add_edge('C', 'D')\n",
-    "G.add_edge('E', 'F')\n",
-    "\n",
-    "G.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The coordinates of all vertices, given as a dictionary, can be accessed using the `coordinates` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': (1, 2), 'B': (3, 4), 'C': (5, 7), 'D': (3, 6), 'E': (4, 3), 'F': (4, 5)}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G.coordinates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the `set_mean_centered_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'A': (-2.0, -2.5), 'B': (0.0, -0.5), 'C': (2.0, 2.5), 'D': (0.0, 1.5), 'E': (1.0, -1.5), 'F': (1.0, 0.5)}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_centered_coordinates(type = 'min_max')\n",
-    "print(G.coordinates)\n",
-    "G.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a bounding radius we can use the `get_bounding_radius` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This is actually getting the radius \n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n",
-    "\n",
-    "# plotting the graph with it's bounding circle of radius r.\n",
-    "G.plot(bounding_circle=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also rescale our graph to have unit radius using `rescale_to_unit_disk`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.rescale_to_unit_disk(preserve_center=False)\n",
-    "G.plot(bounding_circle=True)\n",
-    "\n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Directions are given in the code by specifying $\\theta \\in [0,2\\pi]$. We often convert this to $\\omega \\in \\mathbb{S}^1$ by defining the unit vector $\\omega = (\\cos(\\theta), \\sin(\\theta))$. Then the function $g_\\omega$ is defined on the vertices of $G$ by taking the dot product of the embedding coordinates with the unit vector, specifically\n",
-    "$$\n",
-    "g_\\omega(v) = \\langle f(v), \\omega\\rangle.\n",
-    "$$\n",
-    "This is done in the code using the `g_omega` method as shown. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': 0.11043152607484681,\n",
-       " 'B': 0.11043152607484658,\n",
-       " 'C': -0.11043152607484681,\n",
-       " 'D': -0.3312945782245397,\n",
-       " 'E': 0.5521576303742327,\n",
-       " 'F': 0.11043152607484646}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This gives the function value of the nodes as a dictionary\n",
-    "G.g_omega(theta = 7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This input to the plotting function will color the nodes by function value.\n",
-    "G.plot(color_nodes_theta=7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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z2jXrjG5hfdEyqI3H78VArMYSKjUkBiORk66Yf8OW4s/ww7kvUSZfhg56yDUevHOxqgTnyk9hz4Ut2HDqA1wTmowBUfehfbN4l9+LgWiLJVRtcPKNMgYjkRMOX8zF6oK38FuVCQIyANiEooX8+2sAkHcxF4cu5uCmNiMxIGoUAnVB9b4PA7EullC1Izx8uobw0TvfNMhRLVq0CJ06dUJwcDCSk5OxdetWu203bdoESZLqLAcPHrRpt3r1anTr1g0GgwHdunXDmjVrtD4M8lPfnVmLD4/+Hb9VlVpD0RnVISmw5cwaLPn1eZSZf7Pb9vjx45gyZQri4+OxevVqvPDCCzh69CiefPJJvw5FllC1ZYbk8eKLNA/GVatWYfr06XjmmWewa9cu3HjjjRg6dCjy8/Mdbnfo0CEUFhZal/j4P8pR2dnZGD16NNLT07Fnzx6kp6dj1KhR2LFjh9aHQ34m++wGfFGYAQAQEG7uReDE5TxkHP07KuUKm1cYiI6xhEqNQfNgfO211zBx4kQ89NBD6Nq1KxYsWIDY2Fi88847DreLjIxEdHS0ddHr9dbXFixYgNtuuw2zZ89GQkICZs+ejYEDB2LBggUaHw35k1NXjmL9qaWq7EtAxonLh/HN6ZUAGIjOsJRQFyxYwBKqRmTxx3lG95bGPgJtaBqMFRUVyMnJQVpams36tLQ0bNu2zeG2PXv2RExMDAYOHIiNGzfavJadnV1nn4MHD7a7z/LycphMJpuFyBGzqMKn+QsUC0V7Pz+OF69bY11e7v05Fg7YgGUTt2LbkkP47Vy54j4FBLae+RyP/t9EBmI9WEJtGPLv5xg9Wdzhyum1mr7//nsEBATg+uuvd+t9naXp5JuzZ8/CbDYjKirKZn1UVBSKiooUt4mJicHixYuRnJyM8vJyfPzxxxg4cCA2bdqEm266CQBQVFTk0j7nz5+PuXPnqnBE5C/2l/6A4vICh22GzUtC606hMFfJuHy+HAW7ziH7gzzs+DAPd73SB51SIutsI1eZURp7hJNq6sESqu+ynF5btGgR+vfvj/feew9Dhw7F/v370aFDB7vblZaWYvz48Rg4cCBOnz6taR8bZFZq7b/YQgi7f9m7dOmCLl26WH9OTU1FQUEB/vGPf1iD0dV9zp49GzNmzLD+bDKZEBsb6/JxkP/IPrseEnQOJ9u0uToMMd1bWX9OGNQOff58NT6+fwtWz9iBKetuQ4vWwTbb6AJ06NA3HBMTxqNFEENRCWehNhwZEmQPJtC4s23N02tA9amxL7/8Eu+88w7mz59vd7uHH34YY8eOhV6vx3//+193u+wUTUupERER0Ov1dUZyxcXFdUZ8jqSkpCAvL8/6c3R0tEv7NBgMCAsLs1mI7PmtqhTHLx9waQaqhTGmGQY+0QMVv1Vh16fHFNtIkPBzabaHvfRNLKE2LMudbzxZANQ5VVVernw6wd3Tax988AGOHDmC559/Xr2Dd0DTYAwKCkJycjKysrJs1mdlZaFfv35O72fXrl2IiYmx/pyamlpnn1999ZVL+ySy5+SVIx5t3/mGKEh6CQW5Z+20kHDysmfv4atYQm2aYmNjYTQarYu9kZ87p9fy8vIwa9YsLF++HAEBDXPpvebvMmPGDKSnp6NXr15ITU3F4sWLkZ+fjylTpgCoLnOePHkSH330EYDqYXXHjh3RvXt3VFRUYNmyZVi9ejVWr15t3ee0adNw00034eWXX8aIESPw+eef4+uvv8Z3332n9eGQHyi6cqzeMqojQc0C0KxlEC6eKVN8XUDGySu/eNJFn8QSasPzZAKNZXsAKCgosKnEGQwGh9s5eyrMbDZj7NixmDt3Lq655hq3++kqzYNx9OjROHfuHObNm4fCwkIkJiZiw4YNiIuLAwAUFhbaXNNYUVGBmTNn4uTJkwgJCUH37t2xfv163H777dY2/fr1w8qVK/Hss8/iueeeQ+fOnbFq1Sr07dtX68MhP3DFfBk6SYLZg6noop5ty8xX3N+5D2IJtXHI8PCWcL+fY3T2FJWrp9cuXryInTt3YteuXXj00Uer31OWIYRAQEAAvvrqKwwYMMDt/tvTIOPSqVOnYurUqYqvZWRk2Pz81FNP4amnnqp3n/feey/uvfdeNbpHZEMn6eoNNkcqLlfhSmkFIuPtf1HoWCa0wRKqf6h5em3kyJHW9VlZWRgxYkSd9mFhYfjpp59s1i1atAjffvst/vOf/6BTp06a9JP3SiWqJTSglc09T131y9YiCLNAh14RDt4j3O39+xqWUBuP8HBWqnBjW1dOr+l0OiQmJtpsHxkZieDg4Drr1cRgJKqlbchVgJu3fystvIxvX9sHQ2gget6r/NusDnrENmu48yXejCXUxtUYT9dw9fRaY2AwEtUSE9IReikAZlHlsN2ZX0yQzQJylYzffr/Af+/n+dDpgHte64vm4coTEGSYGYy/Ywm1cak1+cZVrpxeq23OnDmYM2eOW+/rLAYjUS2BOgOua3kTdpdsclhS/d//5QIA9IE6GEIDEXFVKFIfiMd1IzvaDUXL/rsbU1Tvd1PDEip5KwYjkYKU1kORW/Kt4mvXjojDtSPi3NqvBB16tRoIgz7Ek+41eSyhegc+qFiZbz5lkshD7Zp1Rs9Wt0JS9Z+IhGB9M9wadZ+K+2yaWEL1DpZbwnmy+CIGI5Edd7R9EM0DwlQMR4G72v8FzQOMKu2vaeLjpMjbMRiJ7AjRN8eETs8iUBekSjgOiByFRGOqCj1rulhC9S6ePYvRszKsN2MwEjnQNuQqTOr8dzQLCHUrHKu3kZAW/WcMiBqtfgebGJZQvQuDURmDkagebUOuwuPXvInrWt4IoPo6xPpIv597aRUUiYc7v4ibI+/2+yBgCZWaCs5KJXJCSEAL3NdhGlIibseOs19gT+lWyMJcHYBCQlVVFQICAiCk6ss72oZchdSI25Fo7I9AXVAj977xsYTqnTgrVRmDkcgFsc3iEdshHneaJ6Gw7ChOXj6CX04exMcrPsL94yYi6apUtA3pjJZB9m8H549YQvVODEZlLKUSucGgD0HH5t3Qv82d6CEPxDf/3Idu4iZ0M/ZlKNbCEio1NRwxEpFmWEL1bgLw8CbivonBSESaYQnVu7GUqozBSESa4L1QvR+DURnPMRKR6lhCpaaMI0YiUh1LqE0DR4zKGIxEpCqWUJsOBqMyllKJSDUsoZIv4IiRiFTDEmrTIoQE4cGoz5NtvRmDkYhUwRJq0+PpMxX5PEYiIjtYQiVfwhEjEXmMJdSmiZNvlDEYicgjLKE2XTzHqIylVCJyG0uo5Is4YiQit7GE2rSxlKqMwUhEbmEJteljKVUZg5GIXMYSqm8QHo4YfTUYeY6RiFzGEir5Mo4YicglLKH6DgFAePC0YT6omIj8HkuovkWGBIl3vqmDpVQictr06dNx5coVvPfeeyyhks/iiJGInFKzhNquXbvG7g6pgLNSlTEYiaheLKH6JllIkHgdYx0spRJRvVhCJX/CESMROcQSqu8SwsNZqT46LbVBRoyLFi1Cp06dEBwcjOTkZGzdutVu288++wy33XYb2rRpg7CwMKSmpuLLL7+0aZORkQFJkuosZWVlWh8KkV9hCdW3Wc4xerL4Is2DcdWqVZg+fTqeeeYZ7Nq1CzfeeCOGDh2K/Px8xfZbtmzBbbfdhg0bNiAnJwe33nor7rzzTuzatcumXVhYGAoLC22W4OBgrQ+HyK+whEr+SPNgfO211zBx4kQ89NBD6Nq1KxYsWIDY2Fi88847iu0XLFiAp556Cr1790Z8fDxefPFFxMfHY926dTbtJElCdHS0zUJE6rGUUBcuXMgSqo9qrBGj2lVEtWkajBUVFcjJyUFaWprN+rS0NGzbts2pfciyjIsXLyI8PNxm/aVLlxAXF4f27dtj2LBhdUaUROQ+llD9g+XpGp4srtKqiqgmTYPx7NmzMJvNiIqKslkfFRWFoqIip/bxz3/+E7/99htGjRplXZeQkICMjAysXbsWK1asQHBwMPr374+8vDzFfZSXl8NkMtksRGQfS6j+wTL5xpPFVVpVEdXUILNSa//DEkI49Y9txYoVmDNnDj7//HNERkZa16ekpCAlJcX6c//+/ZGUlIQ333wTb7zxRp39zJ8/H3PnzvXgCIj8B2ehkqtqDzYMBgMMBkOddpYq4qxZs2zWq1FFVJOmI8aIiAjo9fo6o8Pi4uI6o8jaVq1ahYkTJ+Lf//43Bg0a5LCtTqdD79697Y4YZ8+ejdLSUutSUFDg2oEQ+QmWUP1L9ajPk3OM1fuJjY2F0Wi0LvPnz1d8P62qiGrTdMQYFBSE5ORkZGVlYeTIkdb1WVlZGDFihN3tVqxYgQcffBArVqzAHXfcUe/7CCGwe/du9OjRQ/F1e7+9EJEtllD9i1q3hCsoKEBYWJh1fX3ft2pXEdWmeSl1xowZSE9PR69evZCamorFixcjPz8fU6ZMAVA9mjt58iQ++ugjANUHPn78eCxcuBApKSnW3yJCQkJgNBoBAHPnzkVKSgri4+NhMpnwxhtvYPfu3Xj77be1Phwin8USKrkrLCzMJhjtUaOK+Omnn9ZbRfSU5pdrjB49GgsWLMC8efNw/fXXY8uWLdiwYQPi4uIAAIWFhTazkd577z1UVVXhkUceQUxMjHWZNm2atc2FCxcwefJkdO3aFWlpaTh58iS2bNmCPn36aH04RD6JJVT/JFRYXFGzilhTVlYW+vXrZ3e7FStW4P7778cnn3ziVBXRUw0y+Wbq1KmYOnWq4msZGRk2P2/atKne/b3++ut4/fXXVegZEQH1l1CFENhz/hR2ninATyWFOHHpAiqFjNAAAxJaRqJHeAxuiumMcEOzRug9uasxnq6hRRVRbbxXKpGfc1RCrZJl/OfoHnxweAd+MZ2DDhIkCTDXmKe/82wBqoSMAEmHYR26YXJCKrq01O78DzVto0ePxrlz5zBv3jwUFhYiMTHR6SriI488Yl0/YcKEOgMrtUhC+OptYO0zmUwwGo0oLS11qi5O5Ehubi6Sk5ORk5ODpKSkxu6OS86fP4/u3bsjOTkZ69atsxkt/mI6iye2f459JUWQ4FzZTC9JEAD+2u0GTOnaH0F6vVZd91kN8f1keY+rPvwb9M3cv5Wm+XIZfp3wos99l3LESOTH7JVQN57Kw1++Xw1ZyACcP5dkGUm+8fNWfHf6KJbcNAahgZwR7rU8vRE4byJORL7E3r1Qtxb9ioe/+xRVstmmZOoKAWD3uZO4f/MKXKmqVKnHpLbGuPNNU8BgJPJD9mahnrlyCX/d9hlkIVyecVibWQjsPXcKr+7d6OGeiBoWS6lEfsheCfXZnV/gclWF3VC8uCkXZ979TPE14x390Tp9qM06GQIf5v2IIe0T0Ceyg1rdJ5U0xqzUpoDBSORn7M1C3XvuFL4+ddipfbSZcjcC27WxWRfQKlSxrU6S8I+fNuLfAye432nShpA8O0/IYCSips7RhfzLf8mBXtLB/PuEG0eCYqNg6Ozc3XFkIZBz9gQOXSjmZRzUJPAcI5EfsVdCrZJlrCvY71QoukMvSViX/7Mm+yb3cfKNMo4YifyEowv5j5jOotxc5fS+hCxDmM026yQH1yzKQmDPuVOudZi058593Wpv74MYjER+oL57oe6/cNql/Z167r066zotn2s3HAWAfSWFLr0HUWNhMBL5gWnTpjm8F+qFiivQQYLs5BCgzdR7ENTO9nyhoxEjAFysrHC+w9QgOCtVGYORyMetXbsWy5Ytc/g4KQmufcEFtYt0evKNhc43v0ObPh8th3qCk2+IfNj58+fx8MMP1/s4qdbBzZweLbrLGBSi6f6J1MIRI5EPq6+EapHYKlrTfkgArg2P0fQ9yHUspSpjMBL5KGdKqBZxLcLRItCAS5XlmvRFJ0no2bq9JvsmD3BWqiKWUol8kLMlVAudJOHejtdC72BU6QlZCNzVMVGTfZMnJBUW38MRI5EPcraEWtO4q5ORkfejwzahtyQh9BbXnjmplyTcGH0V2jdv6dJ2RI2FI0YiH2MpodZ+nFR9rgprjT9d1RM6lUeNEiQ8de0AVfdJKhEqLD6IwUjkQ1wtodY26/qBaBPcQtWS6rTEm3iPVG/FYFTEYCTyIe6UUGtqEWjAkhtHw6AP8HjkKAEY0j4BDyekerQfoobGYCTyEe6WUGvr2ioKK25NR1hgsFsjR8sWd3bojgWpd0Gv49eM17I8dsqTxQfxbyyRD/C0hFpbYngMvhr6MNLaJQCA0wGpkyQ0DwjCP/sOx2spIxCoc3ybOGpcfLqGMs5KJfIBnpZQlbQObo63+t+N708fxUeHf8Q3p/IgAARIOshCwGw2I0CvhwwBAaC1oRn+fHUyxl6dhIjgFqr0gagxMBiJmjhXLuR3R/+oTugf1QlnrlzCrnMnsa+kEPsKjmF95gbcNXQY+l/dFYmtYtAjPIYjxKaGF/grYjASNWFql1AdaRPSAmntuyCtfRfkVubigzcfwMP3z0JSvGvXNZIX8fQ8Ic8xEpG30aKESuTvOGIkaqK0LqGS75NE9eLJ9r6IwUjUBDVkCZV8GM8xKmIwEjVBLKGSKniOURGDkaiJYQmVSFsMRqImhCVUUhVLqYoYjERNCEuopCoGoyIGI1ETwRIqUcNgMBI1ASyhkiY4YlTEYCRqAlhCJU1wVqoiBiORl2MJlahhMRiJvBhLqKQl3vlGWYPcK3XRokXo1KkTgoODkZycjK1btzpsv3nzZiQnJyM4OBhXXXUV3n333TptVq9ejW7dusFgMKBbt25Ys2aNVt0najQsoZKmhAqLG7TIBDVpHoyrVq3C9OnT8cwzz2DXrl248cYbMXToUOTn5yu2P3r0KG6//XbceOON2LVrF/72t7/hsccew+rVq61tsrOzMXr0aKSnp2PPnj1IT0/HqFGjsGPHDq0Ph6jBWEqoCxcuZAmVfIYWmaA2SQhtn8Hct29fJCUl4Z133rGu69q1K+666y7Mnz+/Tvunn34aa9euxYEDB6zrpkyZgj179iA7OxsAMHr0aJhMJnzxxRfWNkOGDEGrVq2wYsWKevtkMplgNBpRWlqKsLAwTw6PCLm5uUhOTkZOTg6SktR5BNP58+fRvXt3JCcnY926dV43WtTimKlaQ3w/Wd6jw8t/hy4k2O39yFfKkP/0sy71VYtMUJumI8aKigrk5OQgLS3NZn1aWhq2bdumuE12dnad9oMHD8bOnTtRWVnpsI29fZaXl8NkMtksRN6MJVRqCBL+OM/o1vL7fmp/v5aXlyu+n1aZoDZNg/Hs2bMwm82IioqyWR8VFYWioiLFbYqKihTbV1VV4ezZsw7b2Nvn/PnzYTQarUtsbKy7h0SkOZZQqcFYLtfwZAEQGxtr8x2rNPIDtMsEtTXIrNTav/EKIRz+FqzUvvZ6V/Y5e/ZszJgxw/qzyWRiOJJX4ixUaooKCgpsSqkGg8Fhey0yQU2aBmNERAT0en2d3wSKi4vr/AZgER0drdg+ICAArVu3dtjG3j4NBkO9HxSRN2AJlRqUSne+CQsLc+oco1aZoDZNS6lBQUFITk5GVlaWzfqsrCz069dPcZvU1NQ67b/66iv06tULgYGBDtvY2ydRU8ASKjW4Br5cQ6tMUJvml2vMmDED//rXv7B06VIcOHAAjz/+OPLz8zFlyhQA1WXOmiWjKVOm4Pjx45gxYwYOHDiApUuXYsmSJZg5c6a1zbRp0/DVV1/h5ZdfxsGDB/Hyyy/j66+/xvTp07U+HCJNsIRK/kKLTFCb5ucYR48ejXPnzmHevHkoLCxEYmIiNmzYgLi4OABAYWGhzfUrnTp1woYNG/D444/j7bffRtu2bfHGG2/gnnvusbbp168fVq5ciWeffRbPPfccOnfujFWrVqFv375aHw6RJlhCpcbQGHe+0SIT1Kb5dYzeiNcxkpo8vaZv7dq1GDFiBDIyMjBhwgQNeqg+XseonYa8jrHj31+ALtiD6xjLynDs2Wd87ru0QW4JR0TKWEIl8j68iThRI2IJlRoVn8eoiMFI1Ej4OClqbHy6hjKWUokaAUuoRN6LI0aiRsASKnmFGrd1c3t7H8RgJGpgLKGS1+A5RkUMRqIGxBIqeROeY1TGc4xEDYglVCLvxxEjUQNhCZW8DkupihiMRA2AJVTySh6WUn01GFlKJWoALKESNR0cMRK54eiZ89h9vBD7TxXjcP4JxN7zIN794SBSL8no3j4K13doi6AAPQCWUMmLsZSqiMFI5CRZFvhi7yEs27YLewuqH5waoNOhSpbR4uruyM4/jez8YshCoFWzEIxOuRa3J8SxhErei8GoiMFI5ITjZy9g9qeZ2JNfCF2NUmiVLAMAJEmCLADLN0XJ5StYvPEHLP76e6BdZ7z77rssoRI1ETzHSFSPLQeP4q6FH2HfiepRouzkk9pkIWCGDuG33YPFOw7A/HuIEnkLy3WMniy+iCNGIge+O3wMj378OWRZuFU1knTVv3t+tnMfKs1mvHDvYOh0HDkSeTOOGInsKCq9iMeX/w+ycC8UaxIA1u46gJU79qjRNSLSEEeMRAqEEPi/1Vkor6qCUuX0wt4fcGrDSpt1+pDmMEREo3XfWxB6dXfF/b66YQtu7NIRseEtNeg1kYs4+UYRR4xECn749QS+zzsOs+z4X37b28egY/pj6Jj+GGKG3AfodCj4zxJczPtZsb1ZlrHo6+1adJnIZTzHqIzBSKTgk+zd0DtxLtDQJgbN2nVEs3YdEdblWnS4dyIkfQBKD+QqtjfLAhv2HELJb1fU7jKRe4QHi49iMBLVcqmsHN/uP1LvaFGJFBAISa+HpNPbbWOWZXy177AnXSQiDTEYiWo5cOqM05dkCCFDyGYIsxmVpgs4/fV/IVdWwNgtye42Op2EnwpOq9VdIvd5Mlr04VEjJ98Q1bL/VDF0kuRUOB77aKHNz5I+ANG33Y0WVyXY3cYsC+wtKPS4n0Se4vMYlTEYiWq58NsVp4Ox7bCxMLSOAgCYr1yC6fBPKPrqM0DICE++0e52PMdI5L0YjES1uHLVoqF1FEJiYq0/t7iqKypLS3B64/9g7N4L+uAQO+9B5AV4uYYinmMkqqVlsxCnzzEqCY5sC1FViYrzxQ7eI9jt/ROphZdrKGMwEtXStW2kR8FYdvokAEDfrIXi63qdhGtjY9zePxFpi6VUolq6tYuEJEHxjje1lZ8phJDNAADzlcu4eHgvfjt2GKHX9EBQy9aK28hCILF9lJpdJnIPS6mKGIxEtYQGG3BTl0747vCxeq9lrHlbOJ0hGEHG1ogaMAKtkvrb3UYnSUhLjFetv0RuYzAqYjASKRiX2hObDx61+3rLa/ug5bV9XN6vXlcdihGhzT3pHhFpiOcYiRT0i++A3p3aO3VbOFfoJAmPDEpVdZ9E7uLkG2UMRiIFkiTh7/emIUCn7j+R6YNvQKc24aruk8htvPONIgYjkR3tw40Y1bkNhCw7NxPHASFktLxcgrEp16rUOyIVMBgVMRiJ7Fi3bh3+b/IEtCs6hAC93qOyavdWzbDjnZcw8cEHYTabVewlEamNk2+IFKxbtw733HMP7rzzTqz819s4dq4UT/87E4cKz0CCc78o6yUJgQF6zBp2C+7tnYh/dwzHuHHjAAAZGRnQ6+0/gYOoIfBeqcoYjES12ITiypUIDAxEfHQEVj3yJ/w3Zz+Wfb8LvxSfgyQBOkkHsywDwO8/SzDLAs0NQbi3dyLS+ychpmUoAGD06NEAwHAk78HLNRQxGIlqUApFi0C9Hvf16YF7eyfiwKli7DpeiP2nTuN06SWYZRmhwQYkxLRBt3ZR6Ns5FiFBgXX2z3Ak8n4MRqLfOQrFmiRJQrd2UejWzr271zAcyVt4eym1pKQEjz32GNauXQsAGD58ON588020bNlSsX1lZSWeffZZbNiwAb/++iuMRiMGDRqEl156CW3btnX6fTWdfFNSUoL09HQYjUYYjUakp6fjwoULdttXVlbi6aefRo8ePdC8eXO0bdsW48ePx6lTp2za3XLLLZAkyWYZM2aMlodCPs7ZUFTL6NGjsXz5cqxYsQL3338/J+RQ4/DyWaljx47F7t27kZmZiczMTOzevRvp6el221++fBm5ubl47rnnkJubi88++wyHDx/G8OHDXXpfTUeMY8eOxYkTJ5CZmQkAmDx5MtLT07Fu3TrF9jUP6rrrrkNJSQmmT5+O4cOHY+fOnTZtJ02ahHnz5ll/DglRfrwPUX0aOhQtOHIksu/AgQPIzMzE9u3b0bdvXwDA+++/j9TUVBw6dAhdunSps43RaERWVpbNujfffBN9+vRBfn4+OnTo4NR7axaMWh9Us2bNEB0drVX3yU80VihaMBypUak0+cZkMtmsNhgMMBgMHuwYyM7OhtFotOYHAKSkpMBoNGLbtm2KGaKktLQUkiTZLb8q0ayUWt9BOcveQS1fvhwRERHo3r07Zs6ciYsXL9rdR3l5OUwmk81C1NihaMGyKjUWSYUFAGJjY62nzIxGI+bPn+9x34qKihAZGVlnfWRkJIqKipzaR1lZGWbNmoWxY8ciLCzM6ffWbMSo5UGNGzcOnTp1QnR0NPbt24fZs2djz549dUabFvPnz8fcuXPdOxDySd4SihYcOVJTVlBQYPMd7Wi0OGfOnHq/j3/88UcA1RPdahNCKK6vrbKyEmPGjIEsy1i0aFG97WtyORi94aAmTZpk/f/ExETEx8ejV69eyM3NRVJSUp19zZ49GzNmzLD+bDKZEBsbW28fyDd5WyhaMBypwalUSg0LC3N6RPboo4/WO1myY8eO2Lt3L06fPl3ntTNnziAqyvGM8MrKSowaNQpHjx7Ft99+69JoEXAjGL3xoJKSkhAYGIi8vDzFYFSj3k2+wVtD0YLhSA2pMS7XiIiIQERERL3tUlNTUVpaih9++AF9+lQ/4m3Hjh0oLS1Fv3797G5nyY+8vDxs3LgRrVsrPzDcEZeD0RsP6ueff0ZlZSViYmKcPxDyO94eihYMR2owXnznm65du2LIkCGYNGkS3nvvPQDVVzYMGzbMZuJNQkIC5s+fj5EjR6Kqqgr33nsvcnNz8b///Q9ms9l66i48PBxBQUFOvbdm5xi1OqgjR45g+fLluP322xEREYH9+/fjiSeeQM+ePdG/v/2nppN/ayqhaMFwJKqeZPnYY48hLS0NQPUF/m+99ZZNm0OHDqG0tBQAcOLECevNAK6//nqbdhs3bsQtt9zi1Ptqeh2jFgcVFBSEb775BgsXLsSlS5cQGxuLO+64A88//zy/OEhRUwtFC4YjNQgvvt9peHg4li1b5rCNqPFIuI4dO9r87C5Ng1GLg4qNjcXmzZtV6R/5vqYaihYMR9KSt98SrrHwXqnks5p6KFowHIkaFoORfJKvhKIFw5E04cWTbxoTg5F8jq+FogXDkdTGUqoyBiP5FF8NRQuGI5H2GIzkM3w9FC0YjqQallIVMRjJJ/hLKFowHEkNLKUqYzBSk+dvoWjBcCTSBoORmjR/DUULhiN5hKVURQxGarL8PRQtGI7kNgajIgYjNUkMRVsMR3IHzzEqYzBSk8NQVMZwJFIHg5GaFIaiYwxHcglLqYoYjNRkMBSdw3AkZ0lCQPLgaRSebOvNGIzUJDAUXcNwJHIfg5G8HkPRPQxHqhdLqYoYjOTVGIqeYTiSI5yVqozBSF6LoagOhiORaxiM5JUYiupiOJIillIVMRjJ6zAUtcFwpNpYSlXGYCSvwlDUFsORqH4MRvIaDMWGwXAkK5ZSFTEYySswFBsWw5EAllLtYTBSo2MoNg6GI3HEqIzBSI2Kodi4XAnHS5fKcPBQIQ7nFeHnn4+gS8JIrF13CKeLA3DNNdHo1LENdDqpwfpOpBUGIzUahqJ3qC8c9+8/iTWf52DT5oMwm2Vr+EW3Tcb32/Kx5bvjAIDIyDCMvCsZtw+5DqGhwQ18FOQuXy2HeoLBSI2CoehdlMLxypVKvP3ON/jyq5+g10swm6u/QWW5+r86nR5m+Y9v1eJiExa/vxErVmTjiRlDceMNXRr4KMhlQlQvnmzvgxiM1OAYit6pZjhWVhpQJa7DhQuXAcAaivURArh4qQzPz12DYbdfj2mPpUGv12nWZyItMBipQTEUvdvo0aNRUlKBT1b+goDASwBcP2doGUSs/2I3yisq8fSTw3ju0UtxVqoy/ipHDYah6P3Kyiqx5btLCAwKgTuhWJMQQNbXP2P1Zz+q0zlSn1Bh8UEcMVKDYCg2Df9auhmni012Tx0VnsrBwf3/sbv99UmT0Cr8Kpt17y/ZjL59O6NDbGs1u0qkGQYjaY6h2DScPFWCz9bsdKptQrd70ax5mzrrmzePrLNOlmW8/69N+H9z7/G4j6QuSa5ePNneFzEYSVMMxaZj3f92QaeTrLNOHWneIgphYe2d2q8sC2zLzsOZMya0aRPmaTdJTbzAXxHPMZJmGIpNhxACG77Y41QoukOSJGR9/bMm+yZSG0eMpAmGYtNyqvACLl0qd7q9EDJk2WyzTpIkSJLy79pCAD/vP+lRH0l9nJWqjMFIqmMoNj15eaddap/74zt11kmSDrcMfEGxvRACBw+ecqtvpCFe4K+IpVRSFUOxaTp//hIkyfnLM7p2vw/JfR6xXXpPdbhNqemKp90klVlGjJ4sWiopKUF6ejqMRiOMRiPS09Nx4cIFp7d/+OGHIUkSFixY4NL7csRIqmEoNl3Cxd/8mzWPdHryjbvvQTR27FicOHECmZmZAIDJkycjPT0d69atq3fb//73v9ixYwfatm3r8vsyGEkVDMWmrUWLYM2Dq1lIkKb7Jzd48azUAwcOIDMzE9u3b0ffvn0BAO+//z5SU1Nx6NAhdOli/168J0+exKOPPoovv/wSd9xxh8vvrWkp1Z1h8P333//7Sfw/lpSUFJs25eXl+Otf/4qIiAg0b94cw4cPx4kTJzQ8EnKEodj0Xd257vWHaouPj9b8Pcg1apVSTSaTzVJe7vxELnuys7NhNBqtoQgAKSkpMBqN2LZtm93tZFlGeno6nnzySXTv3t2t99Y0GMeOHYvdu3cjMzMTmZmZ2L17N9LT0+vdbsiQISgsLLQuGzZssHl9+vTpWLNmDVauXInvvvsOly5dwrBhw2A2m+3skbTCUPQNcXERCAx0/iHFv106jdLS/DpLRcUlxfZ6vQ4JCa6XtKhpiI2NtQ6AjEYj5s+f7/E+i4qKEBlZ9xe2yMhIFBUV2d3u5ZdfRkBAAB577DG331uzUqonw2CDwYDoaOXfLktLS7FkyRJ8/PHHGDRoEABg2bJliI2Nxddff43BgwerfzCkiKHoOwIC9Lj5pgRs3LTfqSdp2LstXJeud6Ntu9511pvNMgbc0tXjfpLKVJqVWlBQgLCwP27eYDAY7G4yZ84czJ071+Fuf/yx+v66ShPChBB2J4rl5ORg4cKFyM3NdWkyWW2aBWN9w2BHwbhp0yZERkaiZcuWuPnmm/HCCy9Yf3PIyclBZWUl0tLSrO3btm2LxMREbNu2TTEYy8vLbYb2JpNJjUP0awxF3zNieBK+/sbxRfgxbZMR0zbZpf1KkoQu10Tj6qujPOkeaUCt6xjDwsJsgtGRRx99FGPGjHHYpmPHjti7dy9On657GdGZM2cQFaX8d2nr1q0oLi5Ghw4drOvMZjOeeOIJLFiwAMeOHXOqj5oFo7vD4KFDh+K+++5DXFwcjh49iueeew4DBgxATk4ODAYDioqKEBQUhFatWtlsFxUVZXe/8+fPr/c3FHIeQ9E3devaFr17dUJu7jGbBxB7SgiBBx+4SbX9UdMWERGBiIiIetulpqaitLQUP/zwA/r06QMA2LFjB0pLS9GvXz/FbdLT062VRIvBgwcjPT0dDzzwgNN9dPkc45w5c+pMjqm97NxZfSNiV4fBQPXz4O644w4kJibizjvvxBdffIHDhw9j/fr1DvvlaL+zZ89GaWmpdSkoKHDhiKkmhqLvkiQJM2cMRZBBvd+XdToJd9x+HXold1Jtn6QiL37sVNeuXTFkyBBMmjQJ27dvx/bt2zFp0iQMGzbMpuKYkJCANWvWAABat26NxMREmyUwMBDR0dEOq5S1ufwvQMthsJKYmBjExcUhLy8PABAdHY2KigqUlJTYjBqLi4vt/hZhMBgc1rzJOQxF39emTRj+Nms4/m/OZx5fvqHTSbiqUyT+8vAAlXpHavP2W8ItX74cjz32mPXU2fDhw/HWW2/ZtDl06BBKS0tVfV+Xg1HLYbCSc+fOoaCgADExMQCA5ORkBAYGIisrC6NGjQIAFBYWYt++fXjllVdcPRxyEkPRf/TvF4//e3YE/v7i5xACbt1YXJIkdO4ciVdfGoNmzfhLKbknPDwcy5Ytc9imvl/gnD2vWJNml2u4Mwy+dOkSZs6ciezsbBw7dgybNm3CnXfeiYiICIwcORIAYDQaMXHiRDzxxBP45ptvsGvXLvz5z39Gjx496tSWSR0MRf9z800JePvNCYhtHw5XJvfp9RIkCbjv3t544/U/IywsRLtOkudk4fnigzS9842rw2C9Xo+ffvoJH330ES5cuICYmBjceuutWLVqFUJDQ63bvP766wgICMCoUaNw5coVDBw4EBkZGdDrnb8Oi5zDUPRf18RHY/G7D+K/a3Pw2Wc7cbrYBL1Ogixsf0vX63Uwm2VIkoTUlKvxpzGp6MprFpsGL77zTWOShB/ewNBkMsFoNKK0tNTpKcb+iKFIFrIssHvPcezbdwIHDxWisOgCzFUymjUz4Oqro3BNfDRSUzrzQcQqaIjvJ8t79B80FwEBwW7vp6qqDN9//bzPfZfyXqmkiKFINel0EpJ6dkRSz46N3RUizTEYqQ6GIpGf4PMYFTEYyQZDkch/ePvlGo2FDyomK4YiERFHjPQ7hiKRH+KsVEUMRmIoEvkpSQhIHpwn9GRbb8ZSqp9jKBIR2eKI0Y8xFIn8nPz74sn2PojB6KcYikTEUqoyllL9EEORiMg+jhj9DEORiKw4K1URg9GPMBSJyAbvfKOIwegnGIpEVBvvfKOM5xj9AEORiMh5HDH6OIYiEdnFUqoiBqMPYygSkSOSXL14sr0vYinVRzEUiYjcwxGjD2IoEpFTWEpVxGD0MQxFInIar2NUxFKqD2EoEhF5jiNGH8FQJCJX8V6pyhiMPoChSERu4TlGRSylNnEMRSIidXHE2IQxFInIIwKePVPRNweMDMamiqFIRJ7iOUZlDMYmiKFIRKoQ8PAco2o98So8x9jEMBSJiLTFEWMTwlAkIlVxVqoiBmMTwVAkItXJACQPt/dBLKU2AQxFIqKGwxGjl2MoEpFWOCtVGYPRizEUiUhTPMeoiKVUL8VQJCJqHAxGL8RQJKIGYRkxerJoqKSkBOnp6TAajTAajUhPT8eFCxfq3e7AgQMYPnw4jEYjQkNDkZKSgvz8fKffl8HoZRiKRNRgvDwYx44di927dyMzMxOZmZnYvXs30tPTHW5z5MgR3HDDDUhISMCmTZuwZ88ePPfccwgODnb6fXmOUWVXLl3Bkd3H8MuuYyg9awIAGCPCcHXPjuh8fUeEtAixuy1DkYio2oEDB5CZmYnt27ejb9++AID3338fqampOHToELp06aK43TPPPIPbb78dr7zyinXdVVdd5dJ7MxhVIITA3i37sXZRJr777AfIZhmSJEEXUD0gl6tkCCGg0+vQ/64+GD51MK67pTsk6Y8LiBiKRNTgVLqO0WQy2aw2GAwwGAwe7BjIzs6G0Wi0hiIApKSkwGg0Ytu2bYrBKMsy1q9fj6eeegqDBw/Grl270KlTJ8yePRt33XWX0+/NUqqHzp48h2eGzcfMW+fg+zXVoQhUh6W50gxzpRni93KDbJax7fMf8OTAufjb7S/i7MlzABiKRNQ4LJdreLIAQGxsrPU8oNFoxPz58z3uW1FRESIjI+usj4yMRFFRkeI2xcXFuHTpEl566SUMGTIEX331FUaOHIm7774bmzdvdvq9NQ1Gd06cSpKkuLz66qvWNrfcckud18eMGaPloSj68cvdeLDrdORm7QEAmKvqvw2EpU3uN3vxYNfpeHPOuwxFImocKp1jLCgoQGlpqXWZPXu23becM2eO3e95y7Jz504AsKmq/dFlobgeqB4xAsCIESPw+OOP4/rrr8esWbMwbNgwvPvuu07/sWhaSh07dixOnDiBzMxMAMDkyZORnp6OdevW2d2msLDQ5ucvvvgCEydOxD333GOzftKkSZg3b57155AQ++futLBjQy6ev+tlyLKAkF0/AS1XybjyWxk+n5eFIakMRSJqusLCwhAWFuZU20cffbTegUzHjh2xd+9enD59us5rZ86cQVRUlOJ2ERERCAgIQLdu3WzWd+3aFd99951T/QM0DEZ3T5xGR0fb/Pz555/j1ltvrXPytFmzZnXaNpQTeYWYe+8/IJuFtUzqFgFIkFCVG4jTR8+g/TVt1eskEVF9ZAFIHnyHuTEoiIiIQERERL3tUlNTUVpaih9++AF9+vQBAOzYsQOlpaXo16+f4jZBQUHo3bs3Dh06ZLP+8OHDiIuLc7qPmpVS6ztx6ozTp09j/fr1mDhxYp3Xli9fjoiICHTv3h0zZ87ExYsX7e6nvLwcJpPJZnGXLMt45f63YK4yOwzFfJGHr8V/kC2+qmePEmSzjJcnvAmz2ex2v4iIXObFl2t07doVQ4YMwaRJk7B9+3Zs374dkyZNwrBhw2wGVgkJCVizZo315yeffBKrVq3C+++/j19++QVvvfUW1q1bh6lTpzr93poFozsnTmv78MMPERoairvvvttm/bhx47BixQps2rQJzz33HFavXl2nTU3z58+3OTEcGxvr2sHU8O0n3+FA9mHI9ZxPPIVjAIDfYEKpOOewrblKxsEdv+Db5c4P9YmIfN3y5cvRo0cPpKWlIS0tDddeey0+/vhjmzaHDh1CaWmp9eeRI0fi3XffxSuvvIIePXrgX//6F1avXo0bbrjB6fd1uZQ6Z84czJ0712GbH3/8EYDrJ05rW7p0KcaNG1fnwsxJkyZZ/z8xMRHx8fHo1asXcnNzkZSUVGc/s2fPxowZM6w/m0wmt8NxzRsboNNJkB2UEEziPC6hFBGIxlkU4RSOwYjWDvcr6SSseWMDbht/s1v9IiJynaejPm0v8A8PD8eyZcsc90Ch/w8++CAefPBBt9/X5WDU8sRpTVu3bsWhQ4ewatWqetsmJSUhMDAQeXl5isGoxjU1AHDs5wIc3nmk3nYnfx8tXo0eqEQlilCAa8R10Ev2/7iFLJCX+yuO7stHp8QOHveViKhevIm4IpeDUcsTpzUtWbIEycnJuO666+pt+/PPP6OyshIxMTH1H4AH9m87VG8bszDjNAoQhlZoIRnRVnTEAeTgNE6gLTo63liqfg8GIxFR49HsHKO7J06B6lLnp59+ioceeqjOfo8cOYJ58+Zh586dOHbsGDZs2ID77rsPPXv2RP/+/bU6HABAXu6v0AfoHbYpxglUoRJt0QkAEIVY6BFgPefoiD5A79SIlIhIFbLwfPFBml7g786JUwBYuXIlhBD405/+VGefQUFB+OabbzB48GB06dIFjz32GNLS0vD1119Dr3ccWp46c+IczFWOZ46exFHooEc0qs9hBkgBiER7XMBZXBb2Z84CgLnSjLOnSlTrLxGRQ0L2fPFBml7g7+6J08mTJ2Py5MmK7WNjY126tY+aLLd7s+eyuIQLOItItIOAQKWoAABEoR0KcQyncAxXo4fDfZgrq1TrLxERuY43EXdB85bNHc5IPYWjAIBinEQxTiq8fhydRaLdWbk6nYTmLZur12EiIkc4+UYRg9EFnRI7YMun2VCaoiyEQCGOIwTN0RXJdV4/i0LkIw9nUYg2sHOHG0nCVdc6f3cGIiKPyAIeXXLho+cYGYwuuKZXZ7vl1LMoRDnKcDV6IFyqe2ODFsKIEziCUzhmNxhls4xrkl17bhgRkds4YlTEx0654NqbuqK5sZnia6dwDBJ0di/JCJIMaIN21QEqyhTbNDc2w3W3dFeru0RE5AaOGF0QFByEOybfhv+8tq7OyPE6qf5rM3tIfQH0VXxNp9fhjkmDEBQcpEZXiYjqJ+DhiFG1nngVjhhdNHLa7TCEqB9eQcGBGDntdtX3S0RklxffRLwxMRhdFNE2HI+84f49+Ox5ZOGDiGjn+H6qRESkPQajG9Im3IIBY29w+mbojkiShFvH9MfgB25VoWdERC6QZc8XH8RzjG6QJAlPfvAIzJVmbP4026N93XhPXzz14aOqhCwRkUs4K1URR4xuCggMwOxPpuHhf4xHQFAA9AHO/1HqA3QICArA5FfH428rpiMgkL+fEBF5CwajB/R6Pe6dcSfe3/tP3HB3X+j0Okg6CTp93T/Wmq/1H9kH7+3+B+574k7N7+9KRGQXJ98o4lBFBe2vaYtnV87A+aISbP53Ng79+AsO7MjDxXMXIQCEtQ5F177xuKZXZ9wyuh/Co1s1dpeJiHjnGzsYjCoKj26FkY/xkgsioqaMwUhE5KeEkCE8eHSUJ9t6MwYjEZG/Eh4+bJjnGImIyKcID88x+mgwclYqERFRDRwxEhH5K1kGJA/OE/IcIxER+RSWUhWxlEpERFQDR4xERH5KyDKEB6VUXq5BRES+haVURSylEhER1cARIxGRv5IFIHHEWBuDkYjIXwkBwJPLNXwzGFlKJSIiqoEjRiIiPyVkAeFBKVX46IiRwUhE5K+EDM9Kqbxcg4iIfAhHjMp4jpGIiKgGvxwxWn7LMZlMjdwTIiJblu+lhhiNVYlyj8qhVahUsTfewy+D8eLFiwCA2NjYRu4JEZGyixcvwmg0arLvoKAgREdH47uiDR7vKzo6GkFBQSr0yntIwleLxA7IsoxDhw6hW7duKCgoQFhYWGN3yS0mkwmxsbE8hkbGY/AOvnQM+/fvR5cuXaDTaXe2q6ysDBUVFR7vJygoCMHBwSr0yHv45YhRp9OhXbt2AICwsLAm+4/IgsfgHXgM3sEXjqFdu3aahiIABAcH+1ygqYWTb4iIiGpgMBIREdXgt8FoMBjw/PPPw2AwNHZX3MZj8A48Bu/AYyC1+OXkGyIiInv8dsRIRESkhMFIRERUA4ORiIioBgYjERFRDT4djCUlJUhPT4fRaITRaER6ejouXLjgcBtJkhSXV1991drmlltuqfP6mDFjvOYY7r///jr9S0lJsWlTXl6Ov/71r4iIiEDz5s0xfPhwnDhxotH7X1lZiaeffho9evRA8+bN0bZtW4wfPx6nTp2yaaf1Z7Bo0SJ06tQJwcHBSE5OxtatWx2237x5M5KTkxEcHIyrrroK7777bp02q1evRrdu3WAwGNCtWzesWbNGtf7W5kr/P/vsM9x2221o06YNwsLCkJqaii+//NKmTUZGhuK/i7KyMq84hk2bNin27+DBgzbtGvIzcPUYlP7dSpKE7t27W9s0xufgl4QPGzJkiEhMTBTbtm0T27ZtE4mJiWLYsGEOtyksLLRZli5dKiRJEkeOHLG2ufnmm8WkSZNs2l24cMFrjmHChAliyJAhNv07d+6cTZspU6aIdu3aiaysLJGbmytuvfVWcd1114mqqqpG7f+FCxfEoEGDxKpVq8TBgwdFdna26Nu3r0hOTrZpp+VnsHLlShEYGCjef/99sX//fjFt2jTRvHlzcfz4ccX2v/76q2jWrJmYNm2a2L9/v3j//fdFYGCg+M9//mNts23bNqHX68WLL74oDhw4IF588UUREBAgtm/frkqfPen/tGnTxMsvvyx++OEHcfjwYTF79mwRGBgocnNzrW0++OADERYWVuffh1ZcPYaNGzcKAOLQoUM2/av597khPwN3juHChQs2fS8oKBDh4eHi+eeft7Zp6M/BX/lsMO7fv18AsPlLn52dLQCIgwcPOr2fESNGiAEDBtisu/nmm8W0adPU6qpd7h7DhAkTxIgRI+y+fuHCBREYGChWrlxpXXfy5Emh0+lEZmamKn0XQr3P4IcffhAAbL5QtPwM+vTpI6ZMmWKzLiEhQcyaNUux/VNPPSUSEhJs1j388MMiJSXF+vOoUaPEkCFDbNoMHjxYjBkzRqVe/8HV/ivp1q2bmDt3rvXnDz74QBiNRrW6WC9Xj8ESjCUlJXb32ZCfgRCefw5r1qwRkiSJY8eOWdc19Ofgr3y2lJqdnQ2j0Yi+ffta16WkpMBoNGLbtm1O7eP06dNYv349Jk6cWOe15cuXIyIiAt27d8fMmTOtT+xQkyfHsGnTJkRGRuKaa67BpEmTUFxcbH0tJycHlZWVSEtLs65r27YtEhMTnf6z0br/NZWWlkKSJLRs2dJmvRafQUVFBXJycmz+bAAgLS3Nbp+zs7PrtB88eDB27tyJyspKh23U/PMG3Ot/bbIs4+LFiwgPD7dZf+nSJcTFxaF9+/YYNmwYdu3apVq/a/LkGHr27ImYmBgMHDgQGzdutHmtoT4DQJ3PYcmSJRg0aBDi4uJs1jfU5+DPfPYm4kVFRYiMjKyzPjIyEkVFRU7t48MPP0RoaCjuvvtum/Xjxo1Dp06dEB0djX379mH27NnYs2cPsrKyVOm7hbvHMHToUNx3332Ii4vD0aNH8dxzz2HAgAHIycmBwWBAUVERgoKC0KpVK5vtoqKinP6z0bL/NZWVlWHWrFkYO3aszY2htfoMzp49C7PZjKioKJv1jv5sioqKFNtXVVXh7NmziImJsdtGzT9vd/tf2z//+U/89ttvGDVqlHVdQkICMjIy0KNHD5hMJixcuBD9+/fHnj17EB8f3+jHEBMTg8WLFyM5ORnl5eX4+OOPMXDgQGzatAk33XQTAPufk9qfgbvHUFNhYSG++OILfPLJJzbrG/Jz8GdNLhjnzJmDuXPnOmzz448/AqieSFObEEJxvZKlS5di3Lhxde5AP2nSJOv/JyYmIj4+Hr169UJubi6SkpLq3a/WxzB69Gib/vXq1QtxcXFYv359nZB3Zb8WDfUZVFZWYsyYMZBlGYsWLbJ5zdPPoD61+1dfn5Xa117v6j494e57rVixAnPmzMHnn39u80tNSkqKzQSu/v37IykpCW+++SbeeOMN9TpegyvH0KVLF3Tp0sX6c2pqKgoKCvCPf/zDGoyu7lMN7r5fRkYGWrZsibvuustmfWN8Dv6oyQXjo48+Wu/sw44dO2Lv3r04ffp0ndfOnDlT57c4JVu3bsWhQ4ewatWqetsmJSUhMDAQeXl5Tn0pN9QxWMTExCAuLg55eXkAqh8sWlFRgZKSEptRY3FxMfr16+cV/a+srMSoUaNw9OhRfPvtt/U+RsjVz8CeiIgI6PX6Or/VFxcX2+1zdHS0YvuAgAC0bt3aYRtXPkdnuNN/i1WrVmHixIn49NNPMWjQIIdtdTodevfubf07pSZPjqGmlJQULFu2zPpzQ30GgGfHIITA0qVLkZ6eXu8DgLX8HPxa45za1J5l4seOHTus67Zv3+70xI8JEybUmQlpz08//SQAiM2bN7vdXyWeHoPF2bNnhcFgEB9++KEQ4o/JN6tWrbK2OXXqlGaTb1ztf0VFhbjrrrtE9+7dRXFxsVPvpeZn0KdPH/GXv/zFZl3Xrl0dTr7p2rWrzbopU6bUmXwzdOhQmzZDhgzRbPKNK/0XQohPPvlEBAcHizVr1jj1HrIsi169eokHHnjAk67a5c4x1HbPPfeIW2+91fpzQ34GQrh/DJaJRD/99FO976H15+CvfDYYhaj+S3/ttdeK7OxskZ2dLXr06FHnUoEuXbqIzz77zGZdaWmpaNasmXjnnXfq7POXX34Rc+fOFT/++KM4evSoWL9+vUhISBA9e/ZU/VIHd47h4sWL4oknnhDbtm0TR48eFRs3bhSpqamiXbt2wmQyWbeZMmWKaN++vfj6669Fbm6uGDBggGaXa7jS/8rKSjF8+HDRvn17sXv3bpsp6eXl5UII7T8DyzT7JUuWiP3794vp06eL5s2bW2cHzpo1S6Snp1vbWy7XePzxx8X+/fvFkiVL6lyu8f333wu9Xi9eeuklceDAAfHSSy9pfrmGs/3/5JNPREBAgHj77bftXv4yZ84ckZmZKY4cOSJ27dolHnjgAREQEGDzS09jHsPrr78u1qxZIw4fPiz27dsnZs2aJQCI1atXW9s05GfgzjFY/PnPfxZ9+/ZV3GdDfw7+yqeD8dy5c2LcuHEiNDRUhIaGinHjxtWZzg1AfPDBBzbr3nvvPRESEqJ4XVx+fr646aabRHh4uAgKChKdO3cWjz32WJ3rBBvrGC5fvizS0tJEmzZtRGBgoOjQoYOYMGGCyM/Pt9nmypUr4tFHHxXh4eEiJCREDBs2rE6bxuj/0aNHBQDFZePGjUKIhvkM3n77bREXFyeCgoJEUlKSzUh0woQJ4uabb7Zpv2nTJtGzZ08RFBQkOnbsqPhL1aeffiq6dOkiAgMDRUJCgs2Xttpc6f/NN9+s+Oc9YcIEa5vp06eLDh06iKCgINGmTRuRlpYmtm3bpln/XT2Gl19+WXTu3FkEBweLVq1aiRtuuEGsX7++zj4b8jNw9RiEqK7mhISEiMWLFyvurzE+B3/Ex04RERHV4LPXMRIREbmDwUhERFQDg5GIiKgGBiMREVENDEYiIqIaGIxEREQ1MBiJiIhqYDASERHVwGAkIiKqgcFIRERUA4ORiIioBgYjERFRDf8f22Up2UMbl+QAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Here's an example in another direction.\n",
-    "G.plot(color_nodes_theta=np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Computing the ECT of $G$\n",
-    "\n",
-    "Now we can set up the ECT for the embedded graph. The ECT is defined as \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{ECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ a \\mapsto \\chi(g_\\omega^{-1}(-\\infty,a]) \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "When we initialize the `ECT` object, we specify the number of directions in $\\mathbb{S}^1$ to use with `num_dir`, and the number of thresholds at which to compute the Euler characteristic by `num_thresh`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Directions chosen are: [0.         0.39269908 0.78539816 1.17809725 1.57079633 1.96349541\n",
-      " 2.35619449 2.74889357 3.14159265 3.53429174 3.92699082 4.3196899\n",
-      " 4.71238898 5.10508806 5.49778714 5.89048623]\n",
-      "Thresholds chosen are: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect = ECT(num_dirs = 16, num_thresh=20)\n",
-    "\n",
-    "# The ECT object will automatically choose the directions. \n",
-    "print(f'Directions chosen are: {myect.thetas}')\n",
-    "\n",
-    "# However, because a bounding radius hasn't been chosen yet, the thresholds are not yet set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can set the bounding radius as follows. Note that some methods will automatically set `bound_radius` to be the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Internally set radius is: 1.2\n",
-      "Thresholds chosen are: [-1.2        -1.07368421 -0.94736842 -0.82105263 -0.69473684 -0.56842105\n",
-      " -0.44210526 -0.31578947 -0.18947368 -0.06315789  0.06315789  0.18947368\n",
-      "  0.31578947  0.44210526  0.56842105  0.69473684  0.82105263  0.94736842\n",
-      "  1.07368421  1.2       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2 * G.get_bounding_radius())\n",
-    "\n",
-    "print(f'Internally set radius is: {myect.bound_radius}')\n",
-    "\n",
-    "# Now the thresholds are set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want the Euler characteristic curve for a fixed direction, we use the `calculateECC` function. This outputs a list of integers where each corresponds to $\\chi(f_\\omega^{-1}(-\\infty,a])$ for the $a$ values in `myect.threshes`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.calculateECC(G, np.pi/2,bound_radius=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This can also be calculated on the fly and plotted using the `plotECC` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(G, -np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To calculate the full ECT, we call the `calculateECT` method. This matrix is size `myect.num_dirs x myect.num_thresh`. Once computed, it is stored in the `ECT` class as `ECT.matrix`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n",
-      "16 20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.calculateECT(G)\n",
-    "\n",
-    "# The matrix is passed as an output above but is also saved internally. Get the saved matrix\n",
-    "M = myect.get_ECT()\n",
-    "\n",
-    "print(M.shape)\n",
-    "print(myect.num_dirs, myect.num_thresh)\n",
-    "\n",
-    "# We can use the built in command to plot the matrix. Unlike the plotECC function, this command does not calculate the ECT when called so it must have been run earlier. An equivalent command is myect.plotECT()\n",
-    "myect.plot('ECT')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SECT \n",
-    "\n",
-    "The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
-    "$$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
-    "Then the SECT is defined by \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    " defined from the \n",
-    "TODO: Write intro "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can calculate the SECT directly using the `calculateSECT` method. Note that this both returns the function and sets the SECT internally to the `SECT_matrix` attribute. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n"
-     ]
-    }
-   ],
-   "source": [
-    "M_SECT = myect.calculateSECT()\n",
-    "print(M_SECT.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However this is not technically necessary since, as a default, the SECT was also set internally when the ECT matrix was computed. If you don't want to calculate the SECT at the same time as calculating the ECT, you can use the following command instead.\n",
-    "```python\n",
-    "myect.calculateECT(G, compute_SECT=False)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the internally set plotting functions to easily visualize it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plot('SECT')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb b/docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb
new file mode 100644
index 0000000..575387a
--- /dev/null
+++ b/docs/doctrees/nbsphinx/notebooks/tutorial_cw.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for CW complexes\n",
+    "\n",
+    "\n",
+    "\n",
+    " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedCW\n",
+    "from ect.utils.examples import create_example_cw\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = EmbeddedCW()\n",
+    "\n",
+    "# Add vertices with coordinates\n",
+    "K.add_node(\"A\", [0, 0])\n",
+    "K.add_node(\"B\", [1, 0])\n",
+    "K.add_node(\"C\", [1, 1])\n",
+    "K.add_node(\"D\", [0, 1])\n",
+    "\n",
+    "# Add edges to form a square\n",
+    "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n",
+    "\n",
+    "# Add the square face\n",
+    "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n",
+    "\n",
+    "K.center_coordinates()\n",
+    "K.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = create_example_cw()\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n",
+      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
+      "\n",
+      "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n",
+      "\u001b[1m\n",
+      "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n",
+      "\u001b[1m    @njit(parallel=True, fastmath=True)\n",
+      "\u001b[1m    def shape_descriptor(simplex_counts_list):\n",
+      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\n",
+      "  result[i, j] = shape_descriptor(simplex_counts_list)\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "override_bound_radius = 1.2 * K.get_bounding_radius()\n",
+    "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n",
+    "result.plot();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n",
+    "result.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also look at the Smooth ECT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate SECT and plot\n",
+    "smooth = result.smooth()\n",
+    "smooth.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also compute the ECT in 3D."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = [\n",
+    "    (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n",
+    "]\n",
+    "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n",
+    "faces = [\n",
+    "    (\"a\", \"b\", \"c\"),\n",
+    "    (\"a\", \"b\", \"d\"),\n",
+    "    (\"a\", \"c\", \"d\"),\n",
+    "    (\"b\", \"c\", \"d\"),\n",
+    "    (\"a\", \"b\", \"c\", \"d\"),\n",
+    "]\n",
+    "K = EmbeddedCW()\n",
+    "K.add_nodes_from(vertices)\n",
+    "K.add_edges_from(edges)\n",
+    "\n",
+    "K.add_faces_from(faces)\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n",
+    "result = ect.calculate(K)\n",
+    "result.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb b/docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb
new file mode 100644
index 0000000..7988362
--- /dev/null
+++ b/docs/doctrees/nbsphinx/notebooks/tutorial_graph.ipynb
@@ -0,0 +1,791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for Embedded Graphs\n",
+    "\n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates how to use the `ect` package..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedGraph\n",
+    "\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n",
+    "\n",
+    "\n",
+    "\n",
+    "First, let's create a simple graph\"\"\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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o3fgaLE2PY9WKKlQuXhRT2akUmAos6ppKF12BUvsWNjgpbi2gNSJ6tlqtWF6zEllFJah6YTMysyPPHsnKycGEihmYUDEDfq8Hu7ZsxO/efhubN6yPuqJOKgamgtZ1TamLrkCpfQsbnJRWUmGFaJ6bmppwz/0PonL1kxg7aUrM52Vm52BxzRqcPXUcCxZVYsebv+m1sVkqB6aClnVNrYuuQKl9CxucDoeD1NxWFojk2Wq14p77H8TStc+jqGRMQmWMnTQFS9c+j7vvewCNuxowfPjwlA9MBS3rmloXXYFS+xY2OEeMGMFbAnNE8ry8ZiUqVz+ZcGgqFJWMQeXqJ3Hf0mWYf8ftKR+YClrVNcUuugKl9j2ItwCtsFgsvCUwRxTP9Q07kVVUElf3vD/GTpqC7FGl+POnnyI7OxuzZs3CI488gptvvjklQxPQpq6pdtEVKLXvtDDjJ67xbPquIyffve17/b4IWj5rBoZkZGBIZhb8Xg9Kv3Ut7ny4GhMqpkct0+/14IWqB/DnAx+lbFhqzSeffII//vGPyMrKwooVK0jdbbIgnmyK+46zubkZ9913H0aMGIHs7Gx8+9vfxueff56wWK1obm7mLYE5Ing2Go0oKh8XNTQVfvLSZry4ez/qPvwUt975I/zi3+/H3/7X0ajHZ2bnoGzit3Hq1Cm1JXNB7bqm3EVXoNS+4wpOu92Om266CUOGDMHevXvx5Zdf4oUXXsCwYcO00pcwVGYYsEQEz7sb96Bi9ty4zvnOnO9jzj33451fb+73OMPsudjduCcZeWRQs66pd9EVKLXvuILzl7/8JUpLS7F161bMmDEDV199NebMmYNx48ZppS9hbDYbbwnMEcHz0S++wNXXTY77vGunVOD811/1e0z5hMkwHjuWqDRSqFnXVN+i94VS+44rON955x3ccMMN+OEPf4iioiJMmzYNr732Wr/n+P1+OByOXj8sKCgoYHIdSojg2eX2DNhNj0Qsj+qzcnLgcnsSkUUOteo6FbroCpTad1zBefbsWWzatAnXXHMNPvjgA1RVVWHVqlV44403op5TW1uLgoKCnp/S0lIAgM/ng8lkQigU6nl20dzcjK6uLlgsFrhcLnR0dKC9vR1erxctLS3o7u7udWwgEEBLSws8Hg/a29tht9vhdrvR2toKp9PZ69hgMAiTyQSv14u2tjZ0dnbC6XTCarXC7/f3OjYcDqO5uRl+vx9Wq7Un8G02G7xeb8y6PR5PRN3d3d2X6Xa5XGhtbUUgEOh1bCgUGlC3otfj8fTS7XQ60dnZiba2tqi6A4EAWltb4XK5YLfb49Ld0dEBl8sFi8WCrq6uqLptNlvPZxhJt3KO3+9HKMF3lV+f+AKl10wY8LhQOByTbp/PF1G3yWSKqNtisVz2eZvNZgSDwYift9vtht1u79Vm+9Z9MBiE2WyG1+vt+bydTicsFgscDkdMum02W9Tvms/nw3vvvYf09HSMHz8epaWlquu+tM1aLJaI3zWTyRTxu9ZXt9frVTUjIumOlbjeqmdkZOCGG27Ap59+2vO7VatW4ciRI/jLX/4S8Ry/3w+/39/zb4fDgdLSUs3fqnd0dGDo0KGalU8RETzfOucOrFq/td9jls+agcfqft3Tpf/rH9/H+scfxf/z+lu49vqKfs9dt3IZDnz4vmp6eaFGXafaW3St23c8b9XjGgA/evRoTJw4sdfvrrvuOjQ0NEQ9JzMzk8vwj/R0Ycf2R0UEz3m5OfB5PL0W8IjEi6uX9wxHGjPuGvzsld8OGJo+jwd5uTRmniRLsnWdSl10BUrtOy4lN910E776qvcD+L/97W+95gFTwePxkFqGigUieK6YOhVNp09iQsWMqMds/uivCZXddPokDNOmJSqNFMnUdaq8Re8LpfYd1zPO1atX4/Dhw3jmmWfw9ddf46233sKrr76K6upqrfQlTKp3WRNBBM8L58/D0f17NSnbuH8vFs6fp0nZrEmmrlPlLXpfKLXvuIJz+vTp2LVrF373u99h8uTJ+O///m+8/PLLWLJkiVb6EsZqtfKWwBwRPBsMBliazsDvVfftt8/jgfXcGVRU9N+dTxUSretU7KIrUGrf+pRLHXLUN+zErgN/xuKaNeqVuf45LJ51MxYvuku1MlONUCiELVu2wGQy4ZprrsG9996bMnebLNB0ymWqQGl6FitE8Vy5eBF8lmacPXVclfLOnjqOLptZqNBMpK5TtYuuQKl9Cxuco0eP5i2BOSJ53rxhPepfegaW5gtJlWO5cB71Lz2DzRvWq6SMBvHWdSp30RUotW9hg7OlpYW3BOaI5LmwsBDbf/sGtj21JuE7z7OnjmPb0z/Fjjd/g5EjR6qskC/x1HWqvkXvC6X2TWdglMoMHz6ctwTmiOa5vLwc7+6sx/KalThaWIwFD1fHNB3T7/Xg3dfr0GUzo3FXg3ChCcRX16neRVeg1L6FDU6Xy0VqjxIWiOi5sLAQDTu2o75hJ9avqcLIsrEwzJ6L8gmTI+5yady/F7ZzZ7FqRZVQzzT7Emtdi9BFV6DUvoUNzoyMDN4SmCOy58rFi1C5eBGMRiPe2fMe/uf2bfjmwgUMHjwYBQUFyM/LhWHaNPys+t+EGXLUH7HUtShddAVK7VvY4NQRE4PBAIPBgK6uLtTW1iIrKwurV68m9aWigihddIoI+3Lo0oVFZEFGz5TmL7NkoLoWqYuuQKl9CxucMg6ul9Gzz+fjLYEL/dW1aF10BUrtW9jgVLaBlQkZPefm5vKWwIX+6lrULjql9i1scFIaLMsKGT13dnbylsCFaHUtYhddgVL7FjY4TSYTbwnMkdEzpRVzWBKprkXtoitQat/CBmdJSQlvCcyR0XNHRwdvCVyIVNeidtEVKLVvYYOT0oIArJDRs6x3nH3rWuQuugKl9i1scIo4zW4gZPTsdDp5S+DCpXUtehddgVL7FjY4ZXxpIKPn7Oxs3hK4cGldi95FV6DUvoUNThm/UDJ6DgQCvCVwQalrGbroCpTat7DBGQqFeEtgjoyeBw0Stgn3SygUkqaLrkCpfQvb6rq7u3lLYI6MnmUNzu7ubmm66AqU2rewrS5ngH25RURGz11dXbwlcMHj8UjTRVeg1L6FDU4Zx/fJ6JnScy9WhEIhfPTRR9J00RUotW9hg7OoqIi3BObI6FnG4Uiffvopzp07J00XXYFS+xY2OCntT8IKGT0XFBTwlsAU5S16QUGBNF10BUrtm1lw1tXVYeLEiZg+fTqT61GansUKGT1T6r5pzaVv0QsLC6XpoitQat/MgrO6uhpffvkljhw5wuR6lKZnsUJGzzJNuVTeomdmZmL69OnSdNEVKLVvYbvqlJ6HsEJGzw6Hg7cEJlw60P2OO+5AeXk5Vz08oNS+hQ3O9vZ23hKYI6NnGRYyjjTQXca6puRZ2ODMy8vjLYE5MnqmtA+NVlzaRZ8/fz7S0tKkrGtKnoUNThkHRsvoefDgwbwlaErfLrqy746MdU3Js7DBqSMHIr8gkW0ueiohbHDKuM+2jJ4pzV9Wm0hddAUZ65qSZ2GD0+Vy8ZbAHBk9Z2Zm8pagCdG66Aoy1jUlz8IG5/Dhw3lLYI6Mnt1uN28JqhNLF13GuqbkWdjgtFgsvCUwR0bPfe/ERKC/LrqCjHVNybOwwUlpehYrZPQs2pTLgbroCjLWNSXPwgYnpelZrJDRs0hTLuN5iy5jXVPyLGxwjho1ircE5sjomdIGXskSSxddQca6puRZ2OCk9DyEFTJ6FmVZtVi76JceLxuUPAsbnCJ14WJFRs9er5e3hKRJZKC7jHVNybOwwSnCFypeZPQ8ZMgQ3hKSJp4uuoKMdU3Js7DBKePuhzJ6DofDvCUkRbxddAUZ65qSZzpKVEb0xR8iIaNnSnttx0syc9FlrGtKntN5C9AKr9crzIuDWJHRcyp31ePpohuNRuxu3APjsWNwe7wIhcMYlJaG3JxsGKZNw8L582AwGBiqZw+l9i1scFJ6kMwKGT1Teu4VD7F20esbdmLdxk0oKh+HitlzseQHS5B1yf7iPo8HTadPonbja7A0PY5VK6pQuXgRCwvModS+hQ1Oq9VKaqYBC2T0TOUOJB5i6aJbrVYsr1mJrKISVL2wGZnZORFKArJycjChYgYmVMyA3+vBri0b8bu338bmDetRWFiotRWmUGrfwj7jpPIBs0RGz6k45XKgLnpTUxMWLKrEzLuXYXHNmqih2ZfM7BwsrlmDmXcvw4JFlTh37pwW8rlBqX0LG5yUpmexQkbPlLpvsTBQF91qteKe+x/E0rXPY+ykKQldY+ykKVi69nncfd8DsNlsyUomA6X2LWxwjh49mrcE5sjoOZXuOGPpoi+vWYnK1U+iqGRMUtcqKhmDytVPYnnNyqTKoQSl9i1scLa0tPCWwBwZPRcUFPCWEDMDddHrG3Yiq6gk4TvNvoydNAUZI0ejvmGnKuXxhlL7FvblEKVFT1kho+dUWcg4lrfo6zZuQtULm6OWsXzWDAzJyEBGVhYAYNzk67Hi/3uh3+sueLga69YsF+JNO6X2HVdwPv3001i7dm2v340fPx6nT59WVZQauFwuZP2fBiYLMnpOBb+xdNGNRiOKyscN+CLoJy9txtXXTY752pnZOSgsG4ejR4+ioqIibu2UoNS+477jnDRpEvbv3/9/C0inedNKaWMnVsjoORU2a4tloPvuxj2omD1Xk+sbZs+9WH6KByel9h136qWnp5NaF09HhzKxDnQ/+sUXWLJwyYDlvbh6eU9X/UfV/4Ebvzdw2JZPmIy33n4jdtE6AxJ3cP79739HcXExsrKyMHPmTNTW1uKqq66Kerzf74ff7+/5t8PhSExpnFDavJ4VMnqm2uMB4puL7nJ7YhqvGW9XHbg4SN7l9sR1DkUote+43qrfeOON2LZtG95//31s2rQJ//jHP3DzzTfD6XRGPae2thYFBQU9P6WlpQAAn88Hk8mEUCjUMz6rubkZXV1dsFgscLlc6OjoQHt7O7xeL1paWtDd3d3r2EAggJaWFng8HrS3t8Nut8PtdqO1tRWZmZm9jg0GgzCZTPB6vWhra0NnZyecTiesViv8fn+vY8PhMJqbm+H3+2G1WuFwOOBwOGCz2eD1emPW7fF4Iuru7u6+TLfL5UJraysCgUCvY0Oh0IC6Fb25ubm9dDudTnR2dqKtrS2q7kAggNbWVrhcLtjt9rh0d3R0wOVywWKxoKurK6pum83W8xlG0q2c05/uYDB4me78/HyEQqHLPu++n6Gi2+v1xqzb5/NF1G0ymSLqtlgsl+k+cOAAzGYzRo4cifnz58NkMvV83m63G3a7vafNar1YSTAUitpmnU5nr++a2WyO+HlH0h2pzQaDQZjN5svarMViifhdM5lMEb9rfTMiLy9P1YyIpDtW0sJJrMvV0dGBsrIyvPjii3jooYciHhPpjrO0tBSdnZ2a7lBoMplQXFysWfkUkclzV1dXz/+UV6xYQer5F3Cxi/7qq68iGAxi4cKFmDp1ar/H3zrnDqxav7XfY5bPmoHH6n4d9x0nAKxbuQwHPnw/7vMooXX7djgcKCgoiCmbkurnDB06FNdeey2+/vrrqMdkZmYiMzMzmcskhIzPYWX0THHPoUSWi8vLzYHP4+m1gIda+Dwe5OWqXy5rKLXvpAbAu1wunDlzhtSIfgWz2cxbAnNk9ExxymUiK7pXTJ2KptMn+z1m80d/Tehus+n0SRimTYv7PGpQat9xBeeaNWtw8OBBNDU14dNPP8Vdd92FwYMH495779VKX8JQWhCAFTJ6pjblMtEV3RfOn4ej+/dqosm4fy8Wzp+nSdksodS+4wrOCxcu4N5778X48ePxox/9CCNGjMDhw4dJLl9FaUEAVsjomdIdZzIruhsMBliazsDvVfftt8/jgfXcmZQfwwnQat9xPePcvn27VjpUh2KYa42Mnvsb0cGaRLrol7JqRRV2bdmIxTVrVNPUuKUOj6xYoVp5PKHUvoVd5INaF44FMnrOzs7mLQFA4l30S6lcvAg+SzPOnjquiqazp46jy2bG4kV3qVIebyi1b2GDk8oXiiUyeg4EArwlJNVF78vmDetR/9IzsDRfSEqT5cJ51L/0DDZvWJ9UOZSg1L6FDc54BrOKgoyeKWwZm2wX/VIKCwux/bdvYNtTaxK+8zx76ji2Pf1T7HjzNxg5cmTCWqhBqX3Tna+WJKm8bWyiyOg5mZBSAzW66H0pLy/HuzvrsbxmJY4WFmPBw9UxTcf0ez149/U6dNnMaNzVIFRoArTat7DBSem2nhUyeubZVVezi96XwsJCNOzYjvqGnVi/pgojy8bCMHsuyidMjrjLpXH/XtjOncWqFVXCPNPsC6X2LWxwdnR0kPqgWSCjZ55+1eyiR6Ny8SJULl4Eo9GId/a8h/+5fRu+uXAB6enpuOKKK5CflwvDtGn4WfW/CTHkqD8otW9hg7OoqIi3BObI6JnXcCQtuuj9YTAYYDAYeubop6Wl4fHHHyc3R19LKLVv/k/WNYLS/iSskNEzjz2HtOyix0oq7bWkFpTat7DBSWl6Fitk9MxjbB+LLvpAUBrTyApK7VvY4KQ0PYsVMnpmPeWSdRc9GpSmmrKCUvsWNjgpPQ9hhYyeWe0oANDooiuw9E0FSu1b2OBsb2/nLYE5MnrOzc1ldi0KXXQFlr6pQKl9CxuceXl5vCUwR0bPl+4uoCVUuugKrHxTglL7FjY4KW3sxAoZPbPYrI1SF12B8iZ1WkGpfQsbnDpykMSWWTFDqYuuwMK3TnSEDU6ZBgYryOhZ64UfqHXRFSgteMEKSu1b2OB0uVy8JTBHRs9abgRIsYuuwGMDRN5Qat/CBufw4cN5S2COjJ7dbrdmZVPsoito6ZsqlNq3sMFpsVh4S2COjJ616jpT7aIrUNPDAkrtW9jgpDQ9ixUyetZi6iHlLrqCPuWSL8IGJ6XpWayQ0bMWUw8pd9EV9CmXfBE2OEeNGsVbAnNk9NzZ2alqedS76Apq+04FKLVvYYOT0vMQVsjoOT8/X7WyUqGLrqCm71SBUvtmFpx1dXWYOHEipk+fzuR6MnZlZPTs9XpVKysVuugKavpOFSi1b2bBWV1djS+//BJHjhxhcj2Px8PkOpSQ0bNag6JTpYuuQGkwOCsotW9hu+oyzuWV0bMaOx+mUhddgdKOj6yg1L6FDU4K+22zRkbPagRIKnXRFWQMTkrtm44SlZHxGZCMnocMGZLU+anWRVdI1ncqQql9CxucMm5mJaPnZL5MqdhFV6AUIqyg1L6FDU6bzcZbAnNk9JzMsJxU7KIryDgciVL7FjY4KU3PYoWMnhOdepiqXXQFfcolX4QNTkrTs1gho+dExvalchddgdKYRlZQat/CBmdxcTFvCcyR0XMid16p3EVXkPGOk1L7FjY4zWYzbwnMkdFzvC8MUr2LrkDpRQkrKLVvYYNzxIgRvCUwR0bP8SzoK0IXXUHGhYwptW9hg9PhcPCWwBwZPWdlZcV8rAhddIV4fIsCpfYtbHDKuCeLjJ67u7tjOk6ULrpCrL5FglL7FjY4dXQUROqi69BA2OCktHk9K2T0PHjw4AGPEamLrhCLb9Gg1L6FDc68vDzeEpgjo2e/39/v30XroisM5FtEKLVvYYOzvb2dtwTmyOg5Nzc36t9E7qL351tUKLVvYYOT0v4krJDRc39774jYRVfQ9xziC52VQVXGbDaTmtvKAhk8G41G7G7cgyPGo7jQ3Iz09CH46JNPkZebA8O0aVg4fx4MBoOwXXQFGadcUmrfwgYnlQ+YJSJ7rm/YiXUbN6GofBwqZs/F/T9YgqycnJ6/+zweNJ0+idqNr8HS9DgmT7gWVxYVCddFV5BxyiWl9i1scDY3N5P6oFkgomer1YrlNSuRVVSCqhc2IzM7J+JxWTk5mFAxAxMqZsDv9eD3dS/ir++9j7d/d79QXXQFGe84KbVvYYOzsLCQtwTmiOa5qakJ99z/ICpXP4mxk6bEfF5mdg7uW/NfOHvyOP7lwWXY8eZvUFZWpqFS9jidTt4SmEOpfQv7ckjGroxInq1WK+65/0EsXft8XKF5KWMnT8HStc/j7vseILUIrhpkZ2fzlsAcSu1b2ODMyYncpRMZkTwvr1mJytVPoqhkTFLlFJWMQeXqJ7G8ZqVKymhAaTA4Kyi1b2GDU8a5vKJ4rm/YiayikoTvNPsydtIUZIwcjfqGnaqURwEZZw5Rat/CPuOUcftUUTyv27gJVS9s7veY5bNmYEhGBjKyshDo6sLV101G1X8/3+tN+6UseLga69YsR+XiRVpIZo6IL7wGglL7TuqO89lnn0VaWhoeffRRleSoh4zPgETwbDQaUVQ+Lurb80v5yUub8cIf9uPlxgPwuJz4eNeOqMdmZuegsGwcjh49qqZcbgQCAd4SmEOpfSccnEeOHMErr7yCKVPU6U6pjYwzK0TwvLtxDypmz43rnO5AF/xeL/Ku6H9VdMPsudjduCcZeWSgFCKsoNS+EwpOl8uFJUuW4LXXXsOwYcPU1qQKI0eO5C2BOSJ4PvrFF7j6uskxHfvi6uX4jztn46F/mopBgwbhf8z9Qb/Hl0+YDOOxY2rI5I6Mw5Eote+EgrO6uhrz5s3D7NmzBzzW7/fD4XD0+mFBa2srk+tQQgTPLrcnpm468H+76tv+chKFJWPw2+d/0e/xWTk5cLk9asjkjox7DlFq33EH5/bt23H06FHU1tbGdHxtbS0KCgp6fkpLSwEAPp8PJpMJoVCoZ9vP5uZmdHV1wWKxwOVyoaOjA+3t7fB6vWhpaUF3d3evYwOBAFpaWuDxeNDe3g673Q63243W1lYUFRX1OjYYDMJkMsHr9aKtrQ2dnZ1wOp2wWq3w+/29jg2Hw2hubobf74fVau0JfJvNBq/XG7Nuj8cTUXd3d/dlul0uF1pbWxEIBHodGwqFBtSt6C0uLu6l2+l0orOzE21tbVF1BwIBtLa2wuVywW63x6W7o6MDLpcLFosFXV1dUXXbbLaezzCSbuUcv9+PUDgcb5PE4PR0fGfOPBz75OMBjw2FwzHp9vl8EXWbTKaIui0Wy2Wft9lsRjAYjPh5u91u2O32Xm22b90Hg0GYzWZ4vd5en3deXh6cTmfP5mX96bbZbAN+15xOZ6/vmpq6L22zFosl4nfNZDJF/K711V1SUqJqRkTSHStp4XDsLfX8+fO44YYbsG/fvp5nm7feeiumTp2Kl19+OeI5fr+/19qBDocDpaWl6Ozs1HThBUrTs1ghgudb59yBVeu3Dnjc8lkz8Fjdr3u69W/8ci1aL3yD/1y/pd/z1q1chgMfvq+KVh50dXWhtrYWQ4cORVVVFTIyMnhLYobW7dvhcKCgoCCmbIprOJLRaITFYkFFRUXP74LBIA4dOoQNGzbA7/dfNr4sMzOTy14hRUVFzK/JGxE85+XmwOfxRB1WdCkvrl6OjKwsBINBFBaPwb8//Wy/x/s8HuTl0hlEnQyUNi5jBaX2HVdw3nbbbThx4kSv3y1btgwTJkzAY489RmpQbltbG6n1+1gggueKqVPRdPokJlTM6Pe4zR/9Ne6ym06fhGHatESlkYLSauisoNS+4wrO/Px8TJ7c+41nbm4uRowYcdnveSPa+ouxIILnhfPnoXbjawMGZyIY9+/Fz6r/TfVyeeDz+XhLYA6l9i3slEsZG5YIng0GAyxNZ+D3qvv22+fxwHruTK/HTKlMerqwk/6iQql9J/3pK6tsU0PGKWmieF61ogq7tmzE4po1qpXZuKUOj6xYoVp5vInjna4wUGrfwt5xyvS2UUEUz5WLF8FnacbZk8dVKe/syePospmxeNFdqpRHgXiGzogCpfYtbHC6XC7eEpgjiudwOIwld/8Irz31U1iaLyRVluXCebz21BpU/evDKqmjAY+RKryh1L6FDc7hw4fzlsAcETyHw2F8/PHHOHHiBObOnoXNj9Xg7KnE7jzPnjqOjf9Zje9/bzY++eQTGI1GldXyw+1285bAHErtW9jgtFgsvCUwJ9U9K6H5pz/9CQBwzz33YN97jfjLjq2oX/9czC+M/F4P6tc/h8Nvb8Mf338Pt99+OwCgsbFRmPCk9IaZFZTat7Cv5lJ9Bk0ipLLnvqF5++234zvf+Q4AoGHHdtQ37MT6NVUYWTYWhtlzUT5hcsRdLo3798J27ixWrajqeaapBOdnn32GxsZGABff3qcylLaRYAWl9i1scIow/TBeUtVzf6GpULl4ESoXL4LRaMQ7e97DW2+/AZfbg2AohMGDBvXsq/6z6n+7bMhRWlqacOGp73LJF2GDk8oMA5akoudYQvNSDAZDr8ALBoMxzVgTLTwprU3JCkrtW9hnnJSWoGJFqnmONzQjEY9nJTxvvPFGAKn9zFPGZ5yU2rewwUl1gWUtSSXPaoQmEL9nUcLT4xFjXdF4oNS+hQ1OGRtWqnhWKzSBxDyLEJ6UBoOzglL7FjY4ZZzLmwqe1QxNIHHPqR6eMs4cotS+hQ3OQYOEtRYV6p7VDk0gOc+pHJ4yzlWn1L7pKFEZSiupsIKyZy1CE0jec6qG55AhQ3hLYA6l9i1scMr41pGqZ61CE1DHcyqGp9fr5S2BOZTat7DBabPZeEtgDkXPWoYmoJ7nVAvP/Px83hKYQ6l9CxucVGYYsISaZ61DE1DXcyqFpz7lki/CBqey7adMUPLMIjQB9T2nSnjKOuWSCsIGZ3FxMW8JzKHimVVoAtp4ToXwlPGOk0r7BgQOTpPJxFsCcyh4ZhmagHaeqYdnQUEBbwnModC+FYQNzpEjR/KWwBzenlmHJqCtZ8rhKeNCxrzb96UIG5wOh4O3BObw9MwjNAHtPVMNz6ysLN4SmEPpOy1scMq4Jwsvz7xCE2DjmWJ4BgIBrtfnAaXvtLDBqcMGnqHJEmrhSWmrXBkRNji7urp4S2AOa88UQpOlZ0rhGcvizaJB6TvNLDjr6uowceJETJ8+ncn1cnNzmVyHEiw9UwhNgH09UwlPv9/P/Jq8ofSdZhac1dXV+PLLL3HkyBEm17Pb7UyuQwlWnqmEJsCnnimEJ6UQYQWl77SwXXVK+5OwgoVnSqEJ8Ktn3uFJ6Q0zKyh9p4UNTrPZzFsCc7T2TC00Ab71zDM8ZRwAT+k7LWxwUloQgBVaeqYYmgD/euYVnjJOueRd15cibHBSWhCAFVp5phqaAI165hGe+iIffBE2OAsLC3lLYI4WnimHJkCnnlmHp9Pp1KxsqlCpa0Dg4JSxK6O2Z+qhCdCqZ5bhmZOTo0m5lKFU18IGp4wNS03PqRCaAL16ZhWeMo7jpFTXwgZnd3c3bwnMUctzqoQmQLOeWYSnjDOHKNW1sMEZCoV4S2COGp5TKTQBuvWsdXjKOFedUl0LG5wyLruVrOdUC02Adj1rGZ4yro5Eqa6FDU4ZZ1Yk4zkVQxOgX89ahWd2dnbSZaQalOpa2OCktFo0KxL1nKqhCaRGPWsRni6XSw1pKQWluhY2OFtbW3lLYE4inlM5NIHUqWe1w/OKK65QS1rKQKmuhQ1OStOzWBGv51QPTSC16lnN8KQ0ppEVlOpa2OCkND2LFfF4FiE0gdSrZ7XCU59yyRdhg/PKK6/kLYE5sXoWJTSB1KxnNcKT0osSVlCq63TeArTCZrORWr+PBbF4Fik0gdStZyU8AeCzzz5DY2MjAMBgMEQ83mg0YnfjHhwxHsWF5makp6dj/6FPkJebA8O0aVg4f17Uc0WBUl0LG5wyPjwfyLNooQmkdj3HEp71DTuxbuMmFJWPQ8Xsubj/B0uQdcnUQ5/Hg6bTJ1G78TVYmh7HqhVVqFy8iK0RRlCq67RwOBxmeUGHw4GCggJ0dnZq+kG0t7dj+PDhmpVPkf48ixiagBj1HA6H8cEHH+Czzz4DAMyfPx9XXXUVltesRFZRCeY/tAKZ2QPP0/Z7PWjcshE+SzM2b1hPajUhNdC6ruPJJmHvOGWckhbNs6ihCYhRz33vPH/729/i4KeHseTxtRg7aUrM5WRm52BxzRqcPXUcCxZVYsebv0FZWZlWsplDqa6FfTk0ZMgQ3hKYE8mzyKEJiFPPSniOHz8ee/d/hH9/dn1coXkpYydNwdK1z+Pu+x6AzWZTWSk/KNW1sMHpdrt5S2BOX8+ihyYgVj2npaXh97v+gH9d+xyKSsYkVVZRyRhUrn4Sy2tWqqSOP5TqWtjgTPXnXolwqWcZQhMQq57rG3Yiq6gEYycndqfZl7GTpiBj5GjUN+xUpTzeUKprYZ9xWiwWUjMNWKB4liU0AbHqed3GTah6YXPUvy+fNQNDMjKQcckqQat+uR5l46+Les6Ch6uxbs1yId60U6rruIJz06ZN2LRpE5qamgAAkyZNws9//nPMnTtXC21JQeUDZolsoQmIU89GoxFF5eMGfHv+k5c24+rrJsdcbmZ2DgrLxuHo0aOoqKhIViZXKNV1XF31MWPG4Nlnn4XRaMTnn3+OWbNmYeHChTh16pRW+hKG0vQsVly4cEGq0ATEqefdjXtQMVubGxDD7LnY3bhHk7JZQqmu47rjXLBgQa9//+IXv8CmTZtw+PBhTJo0SVVhyTJ69GjeEpgSDofx1Vdf4ZNPPgEgR2gC4tTz0S++wJKFSwY87sXVy3t11Z/Z/i4ys/pfm7N8wmS89fYbSWvkDaW6TvgZZzAYxO9//3u43W7MnDkz6nF+v7/XxlKs5ti2tLSguLiYybV4o3TPT5w4AUCe0ATEqWeX2xPTIPd4u+oAkJWTA5fbk6g0MlCq67jfqp84cQJ5eXnIzMzE8uXLsWvXLkycODHq8bW1tSgoKOj5KS0tBQD4fD6YTCaEQqGeW/Dm5mZ0dXXBYrHA5XKho6MD7e3t8Hq9aGlpQXd3d69jA4EAWlpa4PF40N7eDrvdDrfbjdbWVuTl5fU6NhgMwmQywev1oq2tDZ2dnXA6nbBarfD7/b2ODYfDaG5uht/vh9VqhcPhgMPhgM1mg9frjVm3x+OJqLu7u/sy3S6XC62trQgEAr2ODYVC/eq+cOECPvroI5w4cQJutxu33HILpk2bBqvVCqfTic7OTrS1tUXVHQgE0NraCpfLBbvdHpfujo4OuFwuWCwWdHV1RdVts9l6PsNLP2/lc1bOUT7vSLqDwWBE3ZmZmZfp7vsZKrq9Xm/Mun0+X0TdJpMpom6LxXKZbrPZHFW32+2G3W7vabNa76cTDIWitlmn09nruxaP7khtNhgMwmw2X9ZmLRZLxO+ayWSK+F3rmxHDhg1TNSMi6Y6VuKdcdnV14ZtvvkFnZyfq6+vx+uuv4+DBg1HDM9IdZ2lpqeZTLtva2jBixAjNyqdA3xdB//zP/4zvfve7nFWxRZR6vnXOHVi1fmu/xyyfNQOP1f067jtOAFi3chkOfPh+ovJIoHVdazrlMiMjA9/61rcAXFyM4MiRI/jVr36FV155JeLxmZmZyMzMjPcySZOeLuxIKwCRx2led130YSmiIko95+XmwOfx9FrAIxJ9n3EufXwtvv2dm/o9x+fxIC+Xzp7kiUKprpNWEgqFet1RUmHQIGHH9kcdcuR0OjkrY48o9VwxdSqaTp/EhIoZUY/Z/NFfEyq76fRJGKZNS1QaGSjVdVxKnnjiCRw6dAhNTU04ceIEnnjiCRw4cABLlgz8NpA1Pp+PtwRN6G+cpqie+0MUzwvnz8PR/Xs1Kdu4fy8Wzp+nSdksoVTXcd1xWiwWPPDAAzCbzSgoKMCUKVPwwQcf4Hvf+55W+hKG0tp9ajHQ4HYRPQ+EKJ4NBgMsTY/D743t7Xqs+DweWM+dSfnB7wCtuo7rjnPLli1oamrqeYu4f/9+kqEJQKhVYYDY5p6L5jkWRPK8akUVGrdsVLXMxi11eGTFClXL5AWluqbz0EBlqIz3UoNYp1GK5DlWRPJcuXgRfJZmnD11XJXyzp46ji6bGYsX3aVKebyhVNfCBqfJZOItQRXimXsuiud4EM3z5g3rUf/SM7A0X0iqHMuF86h/6Rls3rBeJWX8oVTXwgYnpf87JUq8C3aI4DleRPNcWFiI7b99A9ueWpPwnefZU8ex7emfYsebv8HIkSNVVsgPSnUtbHBS+r9TIiSyylGqe04EET2Xl5fj3Z31+MuOrahf/xz83timS/q9HtSvfw6H396Gxl0NQm2bAdCqa2E3a/P7/VwG3qtBokvDpbLnRBHdc33DTqzftBkjy8bCMHsuyidMjrjLpXH/XtjOncWqFVXCPNPsi9Z1HU82CRucVqs1JXf5S2Y9zVT1nAyyeDYajXhnz3swHjsGl9uDUDiMQWlpvfZVF2HIUX9oXdf6LpcAsi6ZlpYqJLsIcSp6ThZZPBsMhl77rTudTuTn53NUxB5KdS3sM06tV5tRGzVWbk81z2ogo2dATt+UPAsbnN3d3bwlxIxa212kkme1kNEzIKdvSp6FDc6cAVaZoYKaewSlimc1kdEzIKdvSp6FDU673c5bwoCovbFaKnhWGxk9A3L6puRZ2OAcNWoUbwn9osVulNQ9a4GMngE5fVPyLGxwms1m3hKiotUWvpQ9a4WMngE5fVPyLOw4TqrItu+5jk6qEE82CXvHSWkPZgWtQ5OiZ62R0TMgp29KnoUNzqKiIt4SesHiTpOaZxbI6BmQ0zclz8IGZ3t7O28JPbDqnlPyzAoZPQNy+qbkWdjgzM3N5S0BANtnmlQ8s0RGz4Ccvil5FjY4A4EAbwnMXwRR8MwaGT0Dcvqm5FnY4GQ8WCDi9Vm/PeftmQcyegbk9E3Js7DByXMlFV5DjiitHsMKGT0Dcvqm5FnY4HQ4HFyuy3OcJi/PPJHRMyCnb0qehQ1OHnut8B7cLtL+MrEio2dATt+UPAsbnK2trUyvxzs0AfaeKSCjZ0BO35Q8MwvOuro6TJw4EdOnT2dyvZKSEibXAWiEJsDWMxVk9AzI6ZuSZ2bBWV1djS+//BJHjhxhcj1W07OohCZAa0oaK2T0DMjpm5JnYbvqV155pebXoBSaABvP1JDRMyCnb0qehQ1Om82mafnUQhPQ3jNFZPQMyOmbkmdhg1PLJesohiagrWeqyOgZkNM3Jc/CBqfP59OkXKqhCWjnmTIyegbk9E3Js7DBOWiQ+tYohyagjWfqyOgZkNM3Jc90lKhMenq6quVRD01Afc+pgIyeATl9U/IsbHB6PB7VykqF0ATU9ZwqyOgZkNM3Jc/CBufQoUNVKSdVQhNQz3MqIaNnQE7flDwLG5xWqzXpMlIpNAF1PKcaMnoG5PRNybO+y2UUUi00dXR0kkPf5RLJTc9K1dCkNCWNFTJ6BuT0TcmzsME5evTohM5L1dAEEvecysjoGZDTNyXPwgZnS0tL3OekcmgCiXlOdWT0DMjpm5JnYYNz2LBhcR2f6qEJxO9ZBGT0DMjpm5JnYYPT7XbHfKwIoQnE51kUZPQMyOmbkmdhgzMjIyOm40QJTSB2zyIho2dATt+UPNOZw5QkRqMRuxv3wHjsGNweL8JhIC0NyM3JhmHaNCycPw8Gg6HXOSKFpo6ODjtSPjjrG3Zi3cZNKCofh4rZc7HkB0uQlZPT83efx4Om0ydRu/E1WJoex6oVVahcvEjI0PT7/bwlMEdGz4Ccvil5TtkB8FarFctrViKrqATzH1qBzOycAc/xez1o3LIRPkszltz9I5w4cQKAGKEJXGxYmZmZvGUwRUbPgJy+tfYs/AD4pqYmLFhUiZl3L8PimjUxhSYAZGbnYHHNGsz80TI88fOn0dHRIUxoArRWyGaFjJ4BOX1T8pxyd5xWqxULFlVi6drnUVQyJmEdluYL2PxYDfbv3UNqv+ZkCIfDSEtL4y2DKTJ6BuT0rbVnoe84l9esROXqJ5MKTQAoKhmD+574f7G8ZqVKyvhjMpl4S2COjJ4BOX1T8pxSwVnfsBNZRSUYO2mKKuWNnTQFGSNHo75hpyrl8YbSvtOskNEzIKdvSp5TKjjXbdyE+Q+tULXMBQ9XY92mTaqWyQtKiyCwQkbPgJy+KXmOKzhra2sxffp05Ofno6ioCHfeeSe++uorrbT1wmg0oqh8XEwvgrwuF5ZUfAt1P/vJgMdmZuegsGwcjh49qoZMrojyrDYeZPQMyOmbkue4gvPgwYOorq7G4cOHsW/fPgQCAcyZM4fJVKjdjXtQMXtuTMf+ee9ujJ04BZ/t2wtvDNoMs+did+OeZCVyx+Fw8JbAHBk9A3L6puQ5ruB8//33sXTpUkyaNAnXX389tm3bhm+++QZGo1ErfT0c/eILXH3d5JiO/WP9dtz5r9WYeMON+PPe3QMeXz5hMozHjiUrkTtZWVm8JTBHRs+AnL4peU5q5lBnZycAYPjw4VGP8fv9vUb8J/p/DZfbE1M3/fzXf4OtxYSp/3QrQsFu7Hp1A2ZX/ku/52Tl5MDlprMRVKKEQiHeEpgjo2dATt+UPCf8cigUCuHRRx/FTTfdhMmTo98J1tbWoqCgoOentLQUwMXN5U0mE0KhUM9D3+bmZnR1dcFiscDlcqGjowPt7e3wer0Ixvih/bH+d7h1YSUGDx6Miu/ehtbm87hw5u8DnqeU39zcjHA4jObmZvj9flitVjgcDjgcDthsNni93ph1ezwetLS0oLu7u9ex3d3daGlpgcfjQXt7O+x2O1wuF1pbWxEIBHodGwqFYDKZ4PV60dbWhs7OTjidTlitVvj9/l56lXMV3U6nE52dnWhra4uqOxAIoLW1FS6XC3a7PS7dHR0dcLlcsFgs6OrqiqrbZrP1fIaRdCvn9Kc7GAxG1O10Oi/T3fczVHR7vd6Ydft8voi6TSZTRN0Wi+Uy3WazOaput9sNu90Ou90Ot9sdse6DwSDMZnMv3U6nExaLBV6vNybdNpttwO+a0+ns9V1TU/elbdZisfTU/aXfNZPJFPG71le30h6jZUSkNhsIBC77rvWnO1YSHgBfVVWFvXv34pNPPsGYMdHHVEa64ywtLY17APytc+7AqvVb+z2mOxDAv95SgfT0IRj8f/Zgdjs6MfuH/4IHH3uq33PXrVyGAx++H7Meini9XmRnZ/OWwRQZPQNy+tbaczwD4BPqqtfU1KCxsRGHDh3qNzQBIDMzU5X5pXm5OfB5PL0W8OjLkY8+xJWlZXh2R2PP7y6c+Tt+/sBiLPnJk0gfMiTieT6PB3m5sU3bpIzdbpfuyySjZ0BO35Q8x9VVD4fDqKmpwa5du/DRRx/h6quv1krXZVRMnYqm0yf7PeaPDb/Dd+ff1et3Y8Zdg+FXjsLnH++Lel7T6ZMwTJumik6eXHnllbwlMEdGz4Ccvil5jis4q6ur8eabb+Ktt95Cfn4+Wlpaep4dac3C+fNwdP/efo/5r1ffxPfvf+iy3z+/80N8Z873o55n3L8XC+fPS1ojbyjtycIKGT0Dcvqm5Dmu4Ny0aRM6Oztx6623YvTo0T0/O3bs0EpfDwaDAZamM/B71X377fN4YD13BhUVFaqWywNKU9JYIaNnQE7flDzH3VWP9LN06VKN5PVm1YoqNG7ZqGqZjVvq8MgKdadx8oLSlDRWyOgZkNM3Jc8pNVe9cvEi+CzNOHvquCrlnT11HF02MxYvumvgg1OAoqIi3hKYI6NnQE7flDynVHACwOYN61H/0jOwNF9IqhzLhfOof+kZbN6wXiVl/Glvb+ctgTkyegbk9E3Jc8oFZ2FhIbb/9g1se2pNwneeZ08dx7anf4odb/6G1MIByZKXl8dbAnNk9AzI6ZuS55QLTgAoLy/Huzvr8ZcdW1G//rmYXxj5vR7Ur38Oh9/ehsZdDSgrK9NYKVu6urp4S2COjJ4BOX1T8pyyu1wWFhaiYcd21DfsxPo1VRhZNhaG2XNRPmFyxF0ujfv3wnbuLFatqBLmmWZfGO+CQgIZPQNy+qbkOeX2HIqG0WjEO3veg/HYMbjcHoSRhjSEkZeb07OvughDjvrD4/Egp5+ZVSIio2dATt9ae9Z8yiVFDAYDDAZDz79bWlowatQojorY43A4pPsyyegZkNM3Jc8p+YwzFkaMGMFbAnN0z/Igo29KnoUNTovFwlsCc3TP8iCjb0qehXnGqaOjo5MMQu+rHiuUpmexQvcsDzL6puRZ2OCktAQVK3TP8iCjb0qehQ1Om83GWwJzdM/yIKNvSp6FDc6CggLeEpije5YHGX1T8ixscLJYXJkaumd5kNE3Jc/CBuegQcJai4ruWR5k9E3JMx0lKpOeLsykqJjRPcuDjL4peRY2OD0edbfYSAV0z/Igo29KnoUNzqFDh/KWwBzdszzI6JuSZ2GD02q18pbAHN2zPMjom5Jnfcqljo6ODvQplwBoTc9ihe5ZHmT0TcmzsME5evRo3hKYo3uWBxl9U/IsbHC2tLTwlsAc3bM8yOibkmdmA6Pq6upQV1eH7u5uABefJ2hJenq65teghu5ZHmT0rbVnpexYXvswfzl04cIFlJaWsrykjo6OTsycP38eY8aM6fcY5sEZCoVgMpmQn5+PtLQ0Ta7hcDhQWlqK8+fPc3lzP336dBw5coTpNXXPcngG+PoW2XM4HIbT6URxcfGA0zuZz2EaNGjQgGmuFldccQWXL9TgwYO5DbXSPbODp2eAj2/RPce6ApOwL4d4Ul1dzVsCc3TPciCj50gw76qzQMZB9rpnOTwDcvqm5lnIO87MzEw89dRTyMzM5C2FGbpneZDRNzXPQt5x6ujo6GiJkHecOjo6OlqiB6eOjo5OnOjBqaOjoxMnenDq6OjoxIlQwXno0CEsWLAAxcXFSEtLwx/+8AfekjSntrYW06dPR35+PoqKinDnnXfiq6++4i1LUzZt2oQpU6b0DIaeOXMm9u7dy1sWU5599lmkpaXh0Ucf5S1FM55++mmkpaX1+pkwYQJvWQAEC063243rr78edXV1vKUw4+DBg6iursbhw4exb98+BAIBzJkzB263m7c0zRgzZgyeffZZGI1GfP7555g1axYWLlyIU6dO8ZbGhCNHjuCVV17BlClTeEvRnEmTJsFsNvf8fPLJJ7wlXSQsKADCu3bt4i2DORaLJQwgfPDgQd5SmDJs2LDw66+/zluG5jidzvA111wT3rdvX/iWW24JP/LII7wlacZTTz0Vvv7663nLiIhQd5w6QGdnJwBg+PDhnJWwIRgMYvv27XC73Zg5cyZvOZpTXV2NefPmYfbs2bylMOHvf/87iouLMXbsWCxZsgTffPMNb0kAOCzyoaMdoVAIjz76KG666SZMnjyZtxxNOXHiBGbOnAmfz4e8vDzs2rULEydO5C1LU7Zv346jR49yWZ2IBzfeeCO2bduG8ePHw2w2Y+3atbj55ptx8uRJ5Ofnc9WmB6dAVFdX4+TJk3SeA2nI+PHj8cUXX6CzsxP19fV48MEHcfDgQWHD8/z583jkkUewb98+ZGVl8ZbDhLlz5/b895QpU3DjjTeirKwMb7/9Nh566CGOyvTgFIaamho0Njbi0KFDzJbt40lGRga+9a1vAQAMBgOOHDmCX/3qV3jllVc4K9MGo9EIi8WCioqKnt8Fg0EcOnQIGzZsgN/vx+DBgzkq1J6hQ4fi2muvxddff81bih6cqU44HMbKlSuxa9cuHDhwAFdffTVvSVwIhULw+/28ZWjGbbfdhhMnTvT63bJlyzBhwgQ89thjwocmALhcLpw5cwb3338/byliBafL5er1f6N//OMf+OKLLzB8+HBcddVVHJVpR3V1Nd566y3s3r0b+fn5PRtaFRQUIDs7m7M6bXjiiScwd+5cXHXVVXA6nXjrrbdw4MABfPDBB7ylaUZ+fv5lz61zc3MxYsQIYZ9nr1mzBgsWLEBZWRlMJhOeeuopDB48GPfeey9vaWINR/r444/DAC77efDBB3lL04xIfgGEt27dyluaZvz4xz8Ol5WVhTMyMsKFhYXh2267Lfzhhx/ylsUc0Ycj3X333eHRo0eHMzIywiUlJeG77747/PXXX/OWFQ6Hw2F9WTkdHR2dONHHcero6OjEiR6cOjo6OnGiB6eOjo5OnOjBqaOjoxMnenDq6OjoxIkenDo6Ojpxogenjo6OTpzowamjo6MTJ3pw6ujo6MSJHpw6Ojo6caIHp46Ojk6c6MGpo6OjEyf/GzIVVjW4sebGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Construct an example graph\n",
+    "# Note that this is the same graph that is returned by:\n",
+    "# G = create_example_graph()\n",
+    "\n",
+    "G = EmbeddedGraph()\n",
+    "\n",
+    "G.add_node(\"A\", [1, 2])\n",
+    "G.add_node(\"B\", [3, 4])\n",
+    "G.add_node(\"C\", [5, 7])\n",
+    "G.add_node(\"D\", [3, 6])\n",
+    "G.add_node(\"E\", [4, 3])\n",
+    "G.add_node(\"F\", [4, 5])\n",
+    "\n",
+    "G.add_edge(\"A\", \"B\")\n",
+    "G.add_edge(\"B\", \"C\")\n",
+    "G.add_edge(\"B\", \"D\")\n",
+    "G.add_edge(\"B\", \"E\")\n",
+    "G.add_edge(\"C\", \"D\")\n",
+    "G.add_edge(\"E\", \"F\")\n",
+    "\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n",
+    "\n",
+    " The coordinates of all vertices can be accessed using the `coord_matrix` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2.],\n",
+       "       [3., 4.],\n",
+       "       [5., 7.],\n",
+       "       [3., 6.],\n",
+       "       [4., 3.],\n",
+       "       [4., 5.]])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.coord_matrix\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.33333333 -2.5       ]\n",
+      " [-0.33333333 -0.5       ]\n",
+      " [ 1.66666667  2.5       ]\n",
+      " [-0.33333333  1.5       ]\n",
+      " [ 0.66666667 -1.5       ]\n",
+      " [ 0.66666667  0.5       ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.center_coordinates(center_type=\"mean\")\n",
+    "print(G.coord_matrix)\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To get a bounding radius we can use the `get_bounding_radius` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This is actually getting the radius\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n",
+    "# plotting the graph with it's bounding circle of radius r.\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also rescale our graph to have unit radius using `scale_coordinates`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 0.9362075413977773\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.scale_coordinates(radius=1)\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(num_dirs=16, num_thresh=20)\n",
+    "\n",
+    "# The ECT object will automatically create directions when needed\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n",
+      " -0.41390228 -0.29564449 -0.17738669 -0.0591289   0.0591289   0.17738669\n",
+      "  0.29564449  0.41390228  0.53216008  0.65041787  0.76867567  0.88693346\n",
+      "  1.00519125  1.12344905]\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_bound_radius = 1.2 * G.get_bounding_radius()\n",
+    "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n",
+    "\n",
+    "print(f\"Thresholds chosen are: {myect.thresholds}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G, theta=np.pi / 2)\n",
+    "print(f\"ECT values for direction pi/2: {result[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT matrix shape: (16, 20)\n",
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G)\n",
+    "\n",
+    "print(f\"ECT matrix shape: {result.shape}\")\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n",
+    "\n",
+    "# We can plot the result matrix\n",
+    "result.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## SECT\n",
+    "\n",
+    "\n",
+    "\n",
+    " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
+    "\n",
+    " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
+    "\n",
+    " Then the SECT is defined by\n",
+    "\n",
+    " $$\n",
+    "\n",
+    " \\begin{matrix}\n",
+    "\n",
+    " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
+    "\n",
+    " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
+    "\n",
+    " \\end{matrix}\n",
+    "\n",
+    " $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The SECT can be computed from the ECT result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sect = result.smooth()\n",
+    "\n",
+    "sect.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## ECT for higher dimensions\n",
+    "\n",
+    "\n",
+    "\n",
+    " The `ECT` class can also be used for higher dimensional graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# generate a 3d graph\n",
+    "list_3d = [\n",
+    "    (\"A\", [0, 0, 0]),\n",
+    "    (\"B\", [1, 0, 0]),\n",
+    "    (\"C\", [0, 1, 0]),\n",
+    "    (\"D\", [1, 1, 0]),\n",
+    "    (\"E\", [0, 0, 1]),\n",
+    "    (\"F\", [1, 0, 1]),\n",
+    "    (\"G\", [0, 1, 1]),\n",
+    "    (\"H\", [1, 1, 1]),\n",
+    "]\n",
+    "graph_3d = EmbeddedGraph()\n",
+    "graph_3d.add_nodes_from(list_3d)\n",
+    "\n",
+    "# add edges\n",
+    "graph_3d.add_edge(\"A\", \"B\")\n",
+    "graph_3d.add_edge(\"B\", \"C\")\n",
+    "graph_3d.add_edge(\"C\", \"D\")\n",
+    "graph_3d.add_edge(\"D\", \"E\")\n",
+    "graph_3d.add_edge(\"E\", \"F\")\n",
+    "graph_3d.add_edge(\"F\", \"G\")\n",
+    "graph_3d.add_edge(\"G\", \"H\")\n",
+    "graph_3d.add_edge(\"H\", \"A\")\n",
+    "\n",
+    "# plot the graph\n",
+    "graph_3d.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " lets center the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph_3d.center_coordinates(center_type=\"mean\")\n",
+    "graph_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Now we can compute the ECT of the 3d graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect_3d = ECT(num_dirs=16, num_thresh=20)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n",
+    "\n",
+    " ECT results inherit from ndarrays but they store the associated directions and thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.48609512, -0.71244342, -0.50609872],\n",
+       "       [ 0.00364975, -0.82190818,  0.56960831],\n",
+       "       [-0.77710663, -0.23260984,  0.5848059 ],\n",
+       "       [ 0.75362655, -0.61431121, -0.23381352],\n",
+       "       [-0.18133219, -0.98270096, -0.03764916],\n",
+       "       [-0.92728701,  0.26549447,  0.26391569],\n",
+       "       [-0.81397529,  0.14802792,  0.56172232],\n",
+       "       [ 0.54073592, -0.29337918,  0.78837385],\n",
+       "       [ 0.48363696,  0.74722911, -0.45578937],\n",
+       "       [ 0.99631892,  0.01735462,  0.08394891],\n",
+       "       [ 0.74179171, -0.62822234, -0.23469503],\n",
+       "       [ 0.09906268, -0.01998319, -0.99488052],\n",
+       "       [ 0.57301878,  0.71110718, -0.40740159],\n",
+       "       [ 0.93645869, -0.11437729, -0.33160663],\n",
+       "       [-0.19170715,  0.53609831, -0.82209913],\n",
+       "       [ 0.69801901,  0.38101835, -0.6062957 ]])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.directions.vectors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n",
+       "       -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n",
+       "        0.04558028,  0.13674085,  0.22790142,  0.31906199,  0.41022256,\n",
+       "        0.50138313,  0.5925437 ,  0.68370427,  0.77486483,  0.8660254 ])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.thresholds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ect import Directions\n",
+    "\n",
+    "directions = Directions.random(16, dim=3)\n",
+    "thresholds = np.cos(np.linspace(0, np.pi, 20))\n",
+    "ect_3d = ECT(directions=directions, thresholds=thresholds)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of direction vectors: 10000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# another example sampling from the half sphere\n",
+    "sample_size = 100\n",
+    "theta = np.linspace(0, np.pi / 2, sample_size)  # Only go up to pi/2 for half sphere\n",
+    "phi = np.linspace(0, 2 * np.pi, sample_size)\n",
+    "theta, phi = np.meshgrid(theta, phi)\n",
+    "\n",
+    "# Flatten the meshgrid arrays and create vectors\n",
+    "half_sphere_vectors = np.column_stack(\n",
+    "    [\n",
+    "        np.sin(theta.flatten()) * np.cos(phi.flatten()),\n",
+    "        np.sin(theta.flatten()) * np.sin(phi.flatten()),\n",
+    "        np.cos(theta.flatten()),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Normalize the vectors\n",
+    "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n",
+    "    half_sphere_vectors, axis=1, keepdims=True\n",
+    ")\n",
+    "\n",
+    "directions = Directions.from_vectors(half_sphere_vectors)\n",
+    "print(f\"Number of direction vectors: {len(directions)}\")\n",
+    "ect_3d = ECT(directions=directions, num_thresh=20)  # Reduced number of thresholds\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+++ b/docs/ect_on_graphs.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,20 +5,24 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>2.3. ECT on Graphs &mdash; ect 0.1.5 documentation</title>
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       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
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-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
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+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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@@ -59,12 +61,10 @@
 <li class="toctree-l1 current"><a class="reference internal" href="modules.html">2. Modules</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">2.3. ECT on graphs</a><ul>
@@ -74,18 +74,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
@@ -133,183 +130,59 @@
 <span id="ect-on-graphs"></span><h1><span class="section-number">2.3. </span>ECT on Graphs<a class="headerlink" href="#module-ect.ect_graph" title="Link to this heading"></a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="ect.ect_graph.ECT">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ect.ect_graph.</span></span><span class="sig-name descname"><span class="pre">ECT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dirs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT" title="Link to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ect.ect_graph.</span></span><span class="sig-name descname"><span class="pre">ECT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">directions=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_dirs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresh=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int32'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT" title="Link to this definition"></a></dt>
 <dd><p>A class to calculate the Euler Characteristic Transform (ECT) from an input <a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph" title="ect.embed_graph.EmbeddedGraph"><code class="xref any py py-class docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a> or <a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW" title="ect.embed_cw.EmbeddedCW"><code class="xref any py py-class docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a>.</p>
-<p>The result is a matrix where entry <code class="docutils literal notranslate"><span class="pre">M[i,j]</span></code> is <span class="math notranslate nohighlight">\(\chi(K_{a_i})\)</span> for the direction <span class="math notranslate nohighlight">\(\omega_j\)</span> where <span class="math notranslate nohighlight">\(a_i\)</span> is the ith entry in <code class="docutils literal notranslate"><span class="pre">self.threshes</span></code>, and <span class="math notranslate nohighlight">\(\omega_j\)</span> is the ith entry in <code class="docutils literal notranslate"><span class="pre">self.thetas</span></code>.</p>
+<p>The result is a matrix where entry <code class="docutils literal notranslate"><span class="pre">M[i,j]</span></code> is <span class="math notranslate nohighlight">\(\chi(K_{a_i})\)</span> for the direction <span class="math notranslate nohighlight">\(\omega_j\)</span> where <span class="math notranslate nohighlight">\(a_i\)</span> is the ith entry in <code class="docutils literal notranslate"><span class="pre">self.thresholds</span></code>, and <span class="math notranslate nohighlight">\(\omega_j\)</span> is the ith entry in <code class="docutils literal notranslate"><span class="pre">self.thetas</span></code>.</p>
 <dl class="simple">
 <dt>Attributes</dt><dd><dl class="simple">
 <dt>num_dirs (int):</dt><dd><p>The number of directions to consider in the matrix.</p>
 </dd>
 <dt>num_thresh (int):</dt><dd><p>The number of thresholds to consider in the matrix.</p>
 </dd>
-<dt>bound_radius (int):</dt><dd><p>Either <code class="docutils literal notranslate"><span class="pre">None</span></code>, or a positive radius of the bounding circle.</p>
+<dt>directions (Directions):</dt><dd><p>The directions to consider for projection.</p>
 </dd>
-<dt>ECT_matrix (np.array):</dt><dd><p>The matrix to store the ECT.</p>
-</dd>
-<dt>SECT_matrix (np.array):</dt><dd><p>The matrix to store the SECT.</p>
+<dt>bound_radius (float):</dt><dd><p>Either <code class="docutils literal notranslate"><span class="pre">None</span></code>, or a positive radius of the bounding circle.</p>
 </dd>
 </dl>
 </dd>
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="ect.ect_graph.ECT.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_dirs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.__init__" title="Link to this definition"></a></dt>
-<dd><p>Constructs all the necessary attributes for the ECT object.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>num_dirs</strong> (<em>int</em>) – The number of directions to consider in the matrix.</p></li>
-<li><p><strong>num_thresh</strong> (<em>int</em>) – The number of thresholds to consider in the matrix.</p></li>
-<li><p><strong>bound_radius</strong> (<em>int</em>) – Either None, or a positive radius of the bounding circle.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.set_bounding_radius">
-<span class="sig-name descname"><span class="pre">set_bounding_radius</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.set_bounding_radius"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.set_bounding_radius" title="Link to this definition"></a></dt>
-<dd><p>Manually sets the radius of the bounding circle centered at the origin for the ECT object.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>bound_radius</strong> (<em>int</em>) – Either None, or a positive radius of the bounding circle.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.get_radius_and_thresh">
-<span class="sig-name descname"><span class="pre">get_radius_and_thresh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.get_radius_and_thresh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.get_radius_and_thresh" title="Link to this definition"></a></dt>
-<dd><p>An internally used function to get the bounding radius and thresholds for the ECT calculation.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>G</strong> (<em>EmbeddedGraph / EmbeddedCW</em>) – The input graph to calculate the ECT for.</p></li>
-<li><p><strong>bound_radius</strong> (<em>float</em>) – If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float <span class="math notranslate nohighlight">\(R\)</span> where the ECC will be computed at thresholds in <span class="math notranslate nohighlight">\([-R,R]\)</span>. Default is None.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>float, np.array</dt><dd><p>The bounding radius and the thresholds for the ECT calculation.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.get_ECT">
-<span class="sig-name descname"><span class="pre">get_ECT</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.get_ECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.get_ECT" title="Link to this definition"></a></dt>
-<dd><p>Returns the ECT matrix.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.get_SECT">
-<span class="sig-name descname"><span class="pre">get_SECT</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.get_SECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.get_SECT" title="Link to this definition"></a></dt>
-<dd><p>Returns the SECT matrix.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.calculateECC">
-<span class="sig-name descname"><span class="pre">calculateECC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">G</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.calculateECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.calculateECC" title="Link to this definition"></a></dt>
-<dd><p>Function to compute the Euler Characteristic Curve (ECC) of an <cite>EmbeddedGraph</cite>.</p>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">directions=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_dirs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresh=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thresholds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=&lt;class</span> <span class="pre">'numpy.int32'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.__init__" title="Link to this definition"></a></dt>
+<dd><p>Initialize ECT calculator with either a Directions object or sampling parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>G</strong> (<em>nx.Graph</em>) – The graph to compute the ECC for.</p></li>
-<li><p><strong>theta</strong> (<em>float</em>) – The angle (in radians) for the direction function.</p></li>
-<li><p><strong>bound_radius</strong> (<em>float</em><em>, </em><em>optional</em>) – Radius for threshold range. Default is None.</p></li>
-<li><p><strong>return_counts</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to return vertex, edge, and face counts. Default is False.</p></li>
+<li><p><strong>directions</strong> – Optional pre-configured Directions object</p></li>
+<li><p><strong>num_dirs</strong> – Number of directions to sample (ignored if directions provided)</p></li>
+<li><p><strong>num_thresh</strong> – Number of threshold values (required if directions not provided)</p></li>
+<li><p><strong>bound_radius</strong> – Optional radius for bounding circle</p></li>
+<li><p><strong>thresholds</strong> – Optional array of thresholds</p></li>
+<li><p><strong>dtype</strong> – Data type for output array</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>ECC values at each threshold.
-(Optional) Tuple of counts: (ecc, vertex_count, edge_count, face_count)</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>numpy.ndarray</p>
-</dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.calculateECT">
-<span class="sig-name descname"><span class="pre">calculateECT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_SECT</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.calculateECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.calculateECT" title="Link to this definition"></a></dt>
-<dd><p>Calculates the ECT from an input either <cite>EmbeddedGraph</cite> or <cite>EmbeddedCW</cite>. The entry <code class="docutils literal notranslate"><span class="pre">M[i,j]</span></code> is <span class="math notranslate nohighlight">\(\chi(K_{a_j})\)</span> for the direction <span class="math notranslate nohighlight">\(\omega_i\)</span> where <span class="math notranslate nohighlight">\(a_j\)</span> is the jth entry in <code class="docutils literal notranslate"><span class="pre">self.threshes</span></code>, and <span class="math notranslate nohighlight">\(\omega_i\)</span> is the ith entry in <code class="docutils literal notranslate"><span class="pre">self.thetas</span></code>.</p>
+<dt class="sig sig-object py" id="ect.ect_graph.ECT.calculate">
+<span class="sig-name descname"><span class="pre">calculate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.calculate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.calculate" title="Link to this definition"></a></dt>
+<dd><p>Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>EmbeddedGraph/EmbeddedCW</em>) – The input graph to calculate the ECT from.</p></li>
-<li><p><strong>bound_radius</strong> (<em>float</em>) – If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float <span class="math notranslate nohighlight">\(R\)</span> where the ECC will be computed at thresholds in <span class="math notranslate nohighlight">\([-R,R]\)</span>. Default is None.</p></li>
-<li><p><strong>compute_SECT</strong> (<em>bool</em>) – Whether to compute the SECT after the ECT is computed. Default is True. Sets the SECT_matrix attribute, but doesn’t return it. Can be returned with the get_SECT method.</p></li>
+<li><p><strong>graph</strong> (<em>EmbeddedGraph/EmbeddedCW</em>) – The input graph to calculate the ECT for.</p></li>
+<li><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2\pi]\)</span> for the direction to calculate the ECT.</p></li>
+<li><p><strong>override_bound_radius</strong> (<em>float</em>) – If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a positive float <span class="math notranslate nohighlight">\(R\)</span> where the ECC will be computed at thresholds in <span class="math notranslate nohighlight">\([-R,R]\)</span>. Default is None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>np.array</dt><dd><p>The matrix representing the ECT of size (num_dirs,num_thresh).</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.calculateSECT">
-<span class="sig-name descname"><span class="pre">calculateSECT</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.calculateSECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.calculateSECT" title="Link to this definition"></a></dt>
-<dd><p>Function to calculate the Smooth Euler Characteristic Transform (SECT) from the ECT matrix.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><dl class="simple">
-<dt>np.array</dt><dd><p>The matrix representing the SECT of size (num_dirs,num_thresh).</p>
-</dd>
-</dl>
-</p>
-</dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.plotECC">
-<span class="sig-name descname"><span class="pre">plotECC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">draw_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.plotECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.plotECC" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the Euler Characteristic Curve (ECC) for a specific direction theta. Note that this calculates the ECC for the input graph and then plots it.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>graph</strong> (<em>EmbeddedGraph/EmbeddedCW</em>) – The input graph or CW complex.</p></li>
-<li><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2\pi]\)</span> for the direction to plot the ECC.</p></li>
-<li><p><strong>bound_radius</strong> (<em>float</em>) – If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float <span class="math notranslate nohighlight">\(R\)</span> where the ECC will be computed at thresholds in <span class="math notranslate nohighlight">\([-R,R]\)</span>. Default is None.</p></li>
-<li><p><strong>draw_counts</strong> (<em>bool</em>) – Whether to draw the counts of vertices, edges, and faces varying across thresholds. Default is False.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.plotECT">
-<span class="sig-name descname"><span class="pre">plotECT</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.plotECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.plotECT" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the Euler Characteristic Transform (ECT) matrix. Note that the ECT matrix must be calculated before calling this function.</p>
-<p>The resulting plot will have the angle on the x-axis and the threshold on the y-axis.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.plotSECT">
-<span class="sig-name descname"><span class="pre">plotSECT</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.plotSECT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.plotSECT" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the Smooth Euler Characteristic Transform (SECT) matrix. Note that the SECT matrix must be calculated before calling this function.</p>
-<p>The resulting plot will have the angle on the x-axis and the threshold on the y-axis.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.ect_graph.ECT.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.plot" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the ECT or SECT matrix. The type parameter should be either ‘ECT’ or ‘SECT’.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>plot_type</strong> – str
-The type of plot to make. Either ‘ECT’ or ‘SECT’.</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.ect_graph.ECT.shape_descriptor">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shape_descriptor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">simplex_counts_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/ect_graph.html#ECT.shape_descriptor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.ect_graph.ECT.shape_descriptor" title="Link to this definition"></a></dt>
+<dd><p>Calculate shape descriptor from simplex counts (Euler characteristic)</p>
 </dd></dl>
 
 </dd></dl>
diff --git a/docs/embed_cw.html b/docs/embed_cw.html
index 65ffaca..a80c28d 100644
--- a/docs/embed_cw.html
+++ b/docs/embed_cw.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,20 +5,24 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>2.2. Embedded CW complex &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -59,12 +61,10 @@
 <li class="toctree-l1 current"><a class="reference internal" href="modules.html">2. Modules</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -74,18 +74,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
@@ -148,61 +145,23 @@
 <dd><p>Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>embedded_graph</strong> (<a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph" title="ect.embed_graph.EmbeddedGraph"><em>EmbeddedGraph</em></a>) – The EmbeddedGraph object to add from.</p>
+<dd class="field-odd"><p><strong>G</strong> (<a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph" title="ect.embed_graph.EmbeddedGraph"><em>EmbeddedGraph</em></a>) – The EmbeddedGraph object to add from.</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="ect.embed_cw.EmbeddedCW.add_face">
-<span class="sig-name descname"><span class="pre">add_face</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">face</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.add_face"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.add_face" title="Link to this definition"></a></dt>
-<dd><p>Adds a face to the list of faces.</p>
-<p>TODO: Do we want a check to make sure the face is legit? (i.e. is a cycle in the graph, and bounds a region in the plane)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>face</strong> (<em>list</em>) – A list of vertices that make up the face.</p></li>
-<li><p><strong>check</strong> (<em>bool</em>) – Whether to check that the face is a valid addition to the cw complex.</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
+<span class="sig-name descname"><span class="pre">add_face</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.add_face" title="Link to this definition"></a></dt>
+<dd></dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_cw.EmbeddedCW.g_omega_faces">
-<span class="sig-name descname"><span class="pre">g_omega_faces</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.g_omega_faces"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.g_omega_faces" title="Link to this definition"></a></dt>
-<dd><p>Calculates the function value of the faces of the graph by making the value equal to the max vertex value</p>
+<dt class="sig sig-object py" id="ect.embed_cw.EmbeddedCW.add_faces_from">
+<span class="sig-name descname"><span class="pre">add_faces_from</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">faces</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.add_faces_from"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.add_faces_from" title="Link to this definition"></a></dt>
+<dd><p>Adds a list of faces to the graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>theta</strong> (<em>float</em>) – The direction of the function to be calculated.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>dict</dt><dd><p>A dictionary of the function values of the faces.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_cw.EmbeddedCW.sort_faces">
-<span class="sig-name descname"><span class="pre">sort_faces</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.sort_faces"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.sort_faces" title="Link to this definition"></a></dt>
-<dd><p>Function to sort the faces of the graph according to the function</p>
-<div class="math notranslate nohighlight">
-\[g_\omega(\sigma) = \max \{ g_\omega(v) \mid  v \in \sigma \}\]</div>
-<p>in the direction of <span class="math notranslate nohighlight">\(\theta \in [0,2\pi]\)</span> .</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2\pi]\)</span> for the direction to sort the edges.</p></li>
-<li><p><strong>return_g</strong> (<em>bool</em>) – Whether to return the <span class="math notranslate nohighlight">\(g(v)\)</span> values along with the sorted edges.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A list of edges sorted in increasing order of the <span class="math notranslate nohighlight">\(g(v)\)</span> values.
-If <code class="docutils literal notranslate"><span class="pre">return_g</span></code> is True, also returns the <code class="docutils literal notranslate"><span class="pre">g</span></code> dictionary with the function values <code class="docutils literal notranslate"><span class="pre">g[vertex_name]=func_value</span></code>.</p>
+<dd class="field-odd"><p><strong>faces</strong> (<em>list</em>) – A list of faces to add.</p>
 </dd>
 </dl>
 </dd></dl>
@@ -231,36 +190,12 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="ect.embed_cw.EmbeddedCW.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounding_circle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_nodes_theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.plot" title="Link to this definition"></a></dt>
-<dd><p>Plots the graph with the faces filled in.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><dl class="simple">
-<dt>matplotlib.axes.Axes</dt><dd><p>The axes object with the plot.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounding_circle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounding_center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bounding_box'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_nodes_theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edge_color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gray'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">azim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">face_color</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lightblue'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">face_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#EmbeddedCW.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.EmbeddedCW.plot" title="Link to this definition"></a></dt>
+<dd><p>Visualize the embedded CW complex in 2D or 3D, including faces</p>
 </dd></dl>
 
 </dd></dl>
 
-<dl class="py function">
-<dt class="sig sig-object py" id="ect.embed_cw.create_example_cw">
-<span class="sig-prename descclassname"><span class="pre">ect.embed_cw.</span></span><span class="sig-name descname"><span class="pre">create_example_cw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_cw.html#create_example_cw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_cw.create_example_cw" title="Link to this definition"></a></dt>
-<dd><p>Creates an example EmbeddedCW object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be <code class="docutils literal notranslate"><span class="pre">mean</span></code>, <code class="docutils literal notranslate"><span class="pre">min_max</span></code> or <code class="docutils literal notranslate"><span class="pre">origin</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p><dl class="simple">
-<dt>EmbeddedCW</dt><dd><p>The example EmbeddedCW object.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
 </section>
 
 
diff --git a/docs/embed_graph.html b/docs/embed_graph.html
index 467ec70..b93f18a 100644
--- a/docs/embed_graph.html
+++ b/docs/embed_graph.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,20 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>2.1. Embedded graphs &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
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-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -59,12 +59,10 @@
 <li class="toctree-l1 current"><a class="reference internal" href="modules.html">2. Modules</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -74,18 +72,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
@@ -134,12 +129,18 @@
 <dl class="py class">
 <dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ect.embed_graph.</span></span><span class="sig-name descname"><span class="pre">EmbeddedGraph</span></span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph" title="Link to this definition"></a></dt>
-<dd><p>A class to represent a graph with 2D embedded coordinates for each vertex.</p>
+<dd><p>A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations.</p>
 <dl class="simple">
 <dt>Attributes</dt><dd><dl class="simple">
 <dt>graph<span class="classifier">nx.Graph</span></dt><dd><p>a NetworkX graph object</p>
 </dd>
-<dt>coordinates<span class="classifier">dict</span></dt><dd><p>a dictionary mapping vertices to their (x, y) coordinates</p>
+<dt>coord_matrix<span class="classifier">np.ndarray</span></dt><dd><p>a matrix of embedded coordinates for each vertex</p>
+</dd>
+<dt>node_list<span class="classifier">list</span></dt><dd><p>a list of node names</p>
+</dd>
+<dt>node_to_index<span class="classifier">dict</span></dt><dd><p>a dictionary mapping node ids to their index in the coord_matrix</p>
+</dd>
+<dt>dim<span class="classifier">int</span></dt><dd><p>the dimension of the embedded coordinates</p>
 </dd>
 </dl>
 </dd>
@@ -147,429 +148,347 @@
 <dl class="py method">
 <dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.__init__">
 <span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.__init__" title="Link to this definition"></a></dt>
-<dd><p>Initializes an empty EmbeddedGraph object.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_node">
-<span class="sig-name descname"><span class="pre">add_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_node" title="Link to this definition"></a></dt>
-<dd><p>Add a vertex to the graph.
-If the vertex name is given as None, it will be assigned via the <code class="docutils literal notranslate"><span class="pre">next_vert_name</span></code> method.</p>
+<dd><p>Initialize a graph with edges, name, or graph attributes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>vertex</strong> (<em>hashable like int</em><em> or </em><em>str</em><em>, or </em><em>None</em>) – The name of the vertex to add.</p></li>
-<li><p><strong>x</strong> (<em>floats</em>) – The function value of the vertex being added.</p></li>
-<li><p><strong>y</strong> (<em>floats</em>) – The function value of the vertex being added.</p></li>
-<li><p><strong>reset_pos</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, will reset the positions of the nodes based on the function values.</p></li>
+<li><p><strong>incoming_graph_data</strong> (<em>input graph</em><em> (</em><em>optional</em><em>, </em><em>default: None</em><em>)</em>) – Data to initialize graph. If None (default) an empty
+graph is created.  The data can be an edge list, or any
+NetworkX graph object.  If the corresponding optional Python
+packages are installed the data can also be a 2D NumPy array, a
+SciPy sparse array, or a PyGraphviz graph.</p></li>
+<li><p><strong>attr</strong> (<em>keyword arguments</em><em>, </em><em>optional</em><em> (</em><em>default= no attributes</em><em>)</em>) – Attributes to add to graph as key=value pairs.</p></li>
 </ul>
 </dd>
 </dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert</span></code></p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>  <span class="c1"># or DiGraph, MultiGraph, MultiDiGraph, etc</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;my graph&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">e</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)]</span>  <span class="c1"># list of edges</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Arbitrary graph attribute pairs (key=value) may be assigned</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="n">day</span><span class="o">=</span><span class="s2">&quot;Friday&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">graph</span>
+<span class="go">{&#39;day&#39;: &#39;Friday&#39;}</span>
+</pre></div>
+</div>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_nodes_from">
-<span class="sig-name descname"><span class="pre">add_nodes_from</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_nodes_from"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_nodes_from" title="Link to this definition"></a></dt>
-<dd><p>Adds multiple vertices to the graph and assigns them the given coordinates.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>nodes</strong> (<em>list</em>) – A list of vertices to be added.</p></li>
-<li><p><strong>coordinates</strong> (<em>dict</em>) – A dictionary mapping vertices to their coordinates.</p></li>
-</ul>
-</dd>
-</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.coord_matrix">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">coord_matrix</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.coord_matrix" title="Link to this definition"></a></dt>
+<dd><p>Return the N x D coordinate matrix</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.next_vert_name">
-<span class="sig-name descname"><span class="pre">next_vert_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_verts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.next_vert_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.next_vert_name" title="Link to this definition"></a></dt>
-<dd><p>Making a simple name generator for vertices.
-If you’re using integers, it will just up the count by one.
-Letters will be incremented in the alphabet. If you reach <code class="docutils literal notranslate"><span class="pre">Z</span></code>, it will return <code class="docutils literal notranslate"><span class="pre">AA</span></code>. If you reach <code class="docutils literal notranslate"><span class="pre">ZZ</span></code>, it will return <code class="docutils literal notranslate"><span class="pre">AAA</span></code>, etc.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>s</strong> (<em>str</em><em> or </em><em>int</em>) – The name of the vertex to increment.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>str or int</dt><dd><p>The next name in the sequence.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.dim">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">dim</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.dim" title="Link to this definition"></a></dt>
+<dd><p>Return the dimension of the embedded coordinates</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_edge">
-<span class="sig-name descname"><span class="pre">add_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">u</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_edge" title="Link to this definition"></a></dt>
-<dd><p>Adds an edge between the vertices u and v if they exist.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>u</strong> (<em>str</em>) – The first vertex of the edge.</p></li>
-<li><p><strong>v</strong> (<em>str</em>) – The second vertex of the edge.</p></li>
-</ul>
-</dd>
-</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.node_list">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">node_list</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.node_list" title="Link to this definition"></a></dt>
+<dd><p>Return ordered list of node names.</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_cycle">
-<span class="sig-name descname"><span class="pre">add_cycle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coord_matrix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_cycle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_cycle" title="Link to this definition"></a></dt>
-<dd><p>Add nodes and edges from a cycle of coordinates.
-Specifically, will add a node for each row and the edges connecting the nodes in the order they appear in the matrix as a closed cycle.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>coord_matrix</strong> – numpy array
-An (n x 2) matrix of coordinates.</p>
-</dd>
-</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.node_to_index">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">node_to_index</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.node_to_index" title="Link to this definition"></a></dt>
+<dd><p>Return a dictionary mapping node ids to their index in the coord_matrix</p>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_coordinates">
-<span class="sig-name descname"><span class="pre">get_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertex</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Returns the coordinates of the given vertex.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>vertex</strong> (<em>str</em>) – The vertex whose coordinates are to be returned.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The coordinates of the vertex.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>tuple</p>
-</dd>
-</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.position_dict">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">position_dict</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.position_dict" title="Link to this definition"></a></dt>
+<dd><p>Return a dictionary mapping node ids to their coordinates</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.edge_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">edge_indices</span></span><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.edge_indices" title="Link to this definition"></a></dt>
+<dd><p>Return edges as array of index pairs</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.set_coordinates">
-<span class="sig-name descname"><span class="pre">set_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vertex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.set_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.set_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Sets the coordinates of the given vertex.</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_node">
+<span class="sig-name descname"><span class="pre">add_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_node" title="Link to this definition"></a></dt>
+<dd><p>Add a single node <cite>node_for_adding</cite> and update node attributes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>vertex</strong> (<em>str</em>) – The vertex whose coordinates are to be set.</p></li>
-<li><p><strong>x</strong> (<em>float</em>) – The new x-coordinate of the vertex.</p></li>
-<li><p><strong>y</strong> (<em>float</em>) – The new y-coordinate of the vertex.</p></li>
+<li><p><strong>node_for_adding</strong> (<em>node</em>) – A node can be any hashable Python object except None.</p></li>
+<li><p><strong>attr</strong> (<em>keyword arguments</em><em>, </em><em>optional</em>) – Set or change node attributes using key=value.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Raises<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ValueError</strong> – If the vertex does not exist in the graph.</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_bounding_box">
-<span class="sig-name descname"><span class="pre">get_bounding_box</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_bounding_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_bounding_box" title="Link to this definition"></a></dt>
-<dd><p>Method to find a bounding box of the vertex coordinates in the graph.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>A list of tuples representing the minimum and maximum <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(y\)</span> coordinates.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>list</p>
-</dd>
 </dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#ect.embed_graph.EmbeddedGraph.add_nodes_from" title="ect.embed_graph.EmbeddedGraph.add_nodes_from"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_nodes_from</span></code></a></p>
+</div>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>  <span class="c1"># or DiGraph, MultiGraph, MultiDiGraph, etc</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;Hello&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">K3</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">K3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">number_of_nodes</span><span class="p">()</span>
+<span class="go">3</span>
+</pre></div>
+</div>
+<p>Use keywords set/change node attributes:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">UTM</span><span class="o">=</span><span class="p">(</span><span class="s2">&quot;13S&quot;</span><span class="p">,</span> <span class="mi">382871</span><span class="p">,</span> <span class="mi">3972649</span><span class="p">))</span>
+</pre></div>
+</div>
+<p class="rubric">Notes</p>
+<p>A hashable object is one that can be used as a key in a Python
+dictionary. This includes strings, numbers, tuples of strings
+and numbers, etc.</p>
+<p>On many platforms hashable items also include mutables such as
+NetworkX Graphs, though one should be careful that the hash
+doesn’t change on mutables.</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_center">
-<span class="sig-name descname"><span class="pre">get_center</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'origin'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_center"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_center" title="Link to this definition"></a></dt>
-<dd><p>Calculate and return the center of the graph. This can be done by either returning the average of the coordiantes (<code class="docutils literal notranslate"><span class="pre">mean</span></code>), the center of the bounding box (<code class="docutils literal notranslate"><span class="pre">min_max</span></code>), or the origin (<code class="docutils literal notranslate"><span class="pre">origin</span></code>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>type</strong> (<em>str</em>) – The type of center to calculate. Options are <code class="docutils literal notranslate"><span class="pre">mean</span></code>, <code class="docutils literal notranslate"><span class="pre">min_max</span></code>, or <code class="docutils literal notranslate"><span class="pre">origin</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The <span class="math notranslate nohighlight">\((x, y)\)</span> coordinates of the center.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>numpy.ndarray</p>
-</dd>
-</dl>
-</dd></dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_nodes_from_dict">
+<span class="sig-name descname"><span class="pre">add_nodes_from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nodes_with_coords</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_nodes_from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_nodes_from_dict" title="Link to this definition"></a></dt>
+<dd></dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_bounding_radius">
-<span class="sig-name descname"><span class="pre">get_bounding_radius</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'origin'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_bounding_radius"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_bounding_radius" title="Link to this definition"></a></dt>
-<dd><p>Method to find the radius of the bounding circle of the vertex coordinates in the graph.</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_nodes_from">
+<span class="sig-name descname"><span class="pre">add_nodes_from</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nodes_with_coords</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_nodes_from"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_nodes_from" title="Link to this definition"></a></dt>
+<dd><p>Add multiple nodes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>type</strong> (<em>str</em>) – The type of center to calculate the radius relative to. Options are <code class="docutils literal notranslate"><span class="pre">mean</span></code>, <code class="docutils literal notranslate"><span class="pre">min_max</span></code>, or <code class="docutils literal notranslate"><span class="pre">origin</span></code>.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The radius of the bounding circle.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>float</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nodes_for_adding</strong> (<em>iterable container</em>) – A container of nodes (list, dict, set, etc.).
+OR
+A container of (node, attribute dict) tuples.
+Node attributes are updated using the attribute dict.</p></li>
+<li><p><strong>attr</strong> (<em>keyword arguments</em><em>, </em><em>optional</em><em> (</em><em>default= no attributes</em><em>)</em>) – Update attributes for all nodes in nodes.
+Node attributes specified in nodes as a tuple take
+precedence over attributes specified via keyword arguments.</p></li>
+</ul>
 </dd>
 </dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<p><a class="reference internal" href="#ect.embed_graph.EmbeddedGraph.add_node" title="ect.embed_graph.EmbeddedGraph.add_node"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_node</span></code></a></p>
+</div>
+<p class="rubric">Notes</p>
+<p>When adding nodes from an iterator over the graph you are changing,
+a <cite>RuntimeError</cite> can be raised with message:
+<cite>RuntimeError: dictionary changed size during iteration</cite>. This
+happens when the graph’s underlying dictionary is modified during
+iteration. To avoid this error, evaluate the iterator into a separate
+object, e.g. by using <cite>list(iterator_of_nodes)</cite>, and pass this
+object to <cite>G.add_nodes_from</cite>.</p>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>  <span class="c1"># or DiGraph, MultiGraph, MultiDiGraph, etc</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="s2">&quot;Hello&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">K3</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">K3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">sorted</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="nb">str</span><span class="p">)</span>
+<span class="go">[0, 1, 2, &#39;H&#39;, &#39;e&#39;, &#39;l&#39;, &#39;o&#39;]</span>
+</pre></div>
+</div>
+<p>Use keywords to update specific node attributes for every node.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">weight</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Use (node, attrdict) tuples to update attributes for specific nodes.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="mi">11</span><span class="p">)),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;color&quot;</span><span class="p">:</span> <span class="s2">&quot;blue&quot;</span><span class="p">})])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
+<span class="go">11</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">H</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s2">&quot;size&quot;</span><span class="p">]</span>
+<span class="go">11</span>
+</pre></div>
+</div>
+<p>Evaluate an iterator over a graph if using it to modify the same graph</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># wrong way - will raise RuntimeError</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># G.add_nodes_from(n + 1 for n in G.nodes)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># correct way</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">G</span><span class="o">.</span><span class="n">nodes</span><span class="p">))</span>
+</pre></div>
+</div>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_centered_coordinates">
-<span class="sig-name descname"><span class="pre">get_centered_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_centered_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_centered_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to find the centered coordinates of the vertices in the graph.</p>
-<p>If type is <code class="docutils literal notranslate"><span class="pre">min_max</span></code>, the coordinates are centered at the mean of the min and max values of the <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(y\)</span> coordinates.
-If type is <code class="docutils literal notranslate"><span class="pre">mean</span></code>, the coordinates are centered at the mean of the <span class="math notranslate nohighlight">\(x\)</span> and <span class="math notranslate nohighlight">\(y\)</span> coordinates.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.set_centered_coordinates">
-<span class="sig-name descname"><span class="pre">set_centered_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.set_centered_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.set_centered_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to set the centered coordinates of the vertices in the graph. Warning: This overwrites the original coordinates.</p>
-</dd></dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_coord">
+<span class="sig-name descname"><span class="pre">get_coord</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_coord"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_coord" title="Link to this definition"></a></dt>
+<dd></dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_scaled_coordinates">
-<span class="sig-name descname"><span class="pre">get_scaled_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_scaled_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_scaled_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to find the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by <code class="docutils literal notranslate"><span class="pre">radius</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>radius</strong> (<em>float</em>) – The radius of the bounding disk.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A dictionary mapping vertices to their scaled coordinates.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
-</dd>
-</dl>
-</dd></dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.set_coord">
+<span class="sig-name descname"><span class="pre">set_coord</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.set_coord"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.set_coord" title="Link to this definition"></a></dt>
+<dd></dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.set_scaled_coordinates">
-<span class="sig-name descname"><span class="pre">set_scaled_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.set_scaled_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.set_scaled_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to set the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by <code class="docutils literal notranslate"><span class="pre">radius</span></code>. Warning: This overwrites the original coordinates</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_cycle">
+<span class="sig-name descname"><span class="pre">add_cycle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coord_matrix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_cycle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_cycle" title="Link to this definition"></a></dt>
+<dd><p>Add nodes in a cyclic pattern from coordinate matrix</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.rescale_to_unit_disk">
-<span class="sig-name descname"><span class="pre">rescale_to_unit_disk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preserve_center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'origin'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.rescale_to_unit_disk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.rescale_to_unit_disk" title="Link to this definition"></a></dt>
-<dd><p>Rescales the graph coordinates to fit within a radius 1 disk.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>preserve_center</strong> (<em>bool</em>) – If True, maintains the current center point of type <code class="docutils literal notranslate"><span class="pre">center_type</span></code>.
-If False, centers the graph at (0, 0).</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>Returns the instance for method chaining.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>self</p>
-</dd>
-<dt class="field-even">Raises<span class="colon">:</span></dt>
-<dd class="field-even"><p><strong>ValueError</strong> – If the graph has no coordinates or all coordinates are identical.</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_center">
+<span class="sig-name descname"><span class="pre">get_center</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bounding_box'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_center"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_center" title="Link to this definition"></a></dt>
+<dd><p>Calculate center of coordinates</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_PCA_coordinates">
-<span class="sig-name descname"><span class="pre">get_PCA_coordinates</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_PCA_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_PCA_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to find the PCA coordinates of the vertices in the graph.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>A dictionary mapping vertices to their PCA normalized coordinates.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p>dict</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_bounding_box">
+<span class="sig-name descname"><span class="pre">get_bounding_box</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_bounding_box"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_bounding_box" title="Link to this definition"></a></dt>
+<dd><p>Get (min, max) for each dimension</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.set_PCA_coordinates">
-<span class="sig-name descname"><span class="pre">set_PCA_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.set_PCA_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.set_PCA_coordinates" title="Link to this definition"></a></dt>
-<dd><p>Method to set the PCA coordinates of the vertices in the graph which is helpful for coarse alignment.
-If you also want to center at zero, the options for <code class="docutils literal notranslate"><span class="pre">center_type</span></code> are <code class="docutils literal notranslate"><span class="pre">mean</span></code> or <code class="docutils literal notranslate"><span class="pre">min_max</span></code>.
-Set <code class="docutils literal notranslate"><span class="pre">scale_radius</span></code> to a value to scale to a specific radius.
-Warning: This overwrites the original coordinates.</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_bounding_radius">
+<span class="sig-name descname"><span class="pre">get_bounding_radius</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bounding_box'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_bounding_radius"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_bounding_radius" title="Link to this definition"></a></dt>
+<dd><p>Get radius of minimal bounding sphere</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.g_omega">
-<span class="sig-name descname"><span class="pre">g_omega</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.g_omega"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.g_omega" title="Link to this definition"></a></dt>
-<dd><p>Function to compute the function <span class="math notranslate nohighlight">\(g_\omega(v)\)</span> for all vertices <span class="math notranslate nohighlight">\(v\)</span> in the graph in the direction of <span class="math notranslate nohighlight">\(\theta \in [0,2\pi]\)</span> . This function is defined by <span class="math notranslate nohighlight">\(g_\omega(v) = \langle \texttt{pos}(v), \omega \rangle\)</span> .</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_normal_angle_matrix">
+<span class="sig-name descname"><span class="pre">get_normal_angle_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">edges_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decimals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_normal_angle_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_normal_angle_matrix" title="Link to this definition"></a></dt>
+<dd><p>Optimized angle matrix computation using vectorized operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2\pi]\)</span> for the direction to compute the <span class="math notranslate nohighlight">\(g(v)\)</span> values.</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>edges_only</strong> – Only compute angles between connected vertices</p></li>
+<li><p><strong>decimals</strong> – Round angles to specified decimal places</p></li>
+</ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A dictionary mapping vertices to their <span class="math notranslate nohighlight">\(g(v)\)</span> values.</p>
+<dd class="field-even"><p>NaN-filled matrix with pair angles
+vertex_labels: Ordered node identifiers</p>
 </dd>
 <dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
+<dd class="field-odd"><p>angle_matrix</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.g_omega_edges">
-<span class="sig-name descname"><span class="pre">g_omega_edges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.g_omega_edges"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.g_omega_edges" title="Link to this definition"></a></dt>
-<dd><p>Calculates the function value of the edges of the graph by making the value equal to the max vertex value</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_normal_angles">
+<span class="sig-name descname"><span class="pre">get_normal_angles</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">edges_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decimals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_normal_angles"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_normal_angles" title="Link to this definition"></a></dt>
+<dd><p>Optimized angle dictionary construction using NumPy grouping.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>theta</strong> (<em>float</em>) – The direction of the function to be calculated.</p>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>edges_only</strong> – Only include edge-connected pairs</p></li>
+<li><p><strong>decimals</strong> – Round angles to specified decimal places</p></li>
+</ul>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>dict</dt><dd><p>A dictionary of the function values of the edges.</p>
-</dd>
-</dl>
-</p>
+<dd class="field-even"><p>Dictionary mapping rounded angles to vertex pairs</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.sort_vertices">
-<span class="sig-name descname"><span class="pre">sort_vertices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.sort_vertices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.sort_vertices" title="Link to this definition"></a></dt>
-<dd><p>Function to sort the vertices of the graph according to the function <cite>g_omega(v)</cite> in the direction of <span class="math notranslate nohighlight">\(\theta \in [0,2\pi]\)</span>.</p>
-<p>TODO: eventually, do we want this to return a sorted list of g values as well? Since we’re already doing the sorting work, it might be helpful.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2 \pi]\)</span> for the direction to sort the vertices.</p></li>
-<li><p><strong>return_g</strong> (<em>bool</em>) – Whether to return the <span class="math notranslate nohighlight">\(g(v)\)</span> values along with the sorted vertices.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>list</dt><dd><p>A list of vertices sorted in increasing order of the <span class="math notranslate nohighlight">\(g(v)\)</span> values.
-If <code class="docutils literal notranslate"><span class="pre">return_g</span></code> is True, also returns the <code class="docutils literal notranslate"><span class="pre">g</span></code> dictionary with the function values <code class="docutils literal notranslate"><span class="pre">g[vertex_name]=func_value</span></code>.</p>
-</dd>
-</dl>
-</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.transform_coordinates">
+<span class="sig-name descname"><span class="pre">transform_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bounding_box'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pca'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.transform_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.transform_coordinates" title="Link to this definition"></a></dt>
+<dd><p>Transform coordinates center and orientation</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.sort_edges">
-<span class="sig-name descname"><span class="pre">sort_edges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_g</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.sort_edges"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.sort_edges" title="Link to this definition"></a></dt>
-<dd><p>Function to sort the edges of the graph according to the function</p>
-<div class="math notranslate nohighlight">
-\[g_\omega(e) = \max \{ g_\omega(v) \mid  v \in e \}\]</div>
-<p>in the direction of <span class="math notranslate nohighlight">\(\theta \in [0,2\pi]\)</span> .</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>theta</strong> (<em>float</em>) – The angle in <span class="math notranslate nohighlight">\([0,2\pi]\)</span> for the direction to sort the edges.</p></li>
-<li><p><strong>return_g</strong> (<em>bool</em>) – Whether to return the <span class="math notranslate nohighlight">\(g(v)\)</span> values along with the sorted edges.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A list of edges sorted in increasing order of the <span class="math notranslate nohighlight">\(g(v)\)</span> values.
-If <code class="docutils literal notranslate"><span class="pre">return_g</span></code> is True, also returns the <code class="docutils literal notranslate"><span class="pre">g</span></code> dictionary with the function values <code class="docutils literal notranslate"><span class="pre">g[vertex_name]=func_value</span></code>.</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.center_coordinates">
+<span class="sig-name descname"><span class="pre">center_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.center_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.center_coordinates" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.scale_coordinates">
+<span class="sig-name descname"><span class="pre">scale_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.scale_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.scale_coordinates" title="Link to this definition"></a></dt>
+<dd><p>Scale coordinates to fit within given radius</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.lower_edges">
-<span class="sig-name descname"><span class="pre">lower_edges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">omega</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.lower_edges"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.lower_edges" title="Link to this definition"></a></dt>
-<dd><p>Function to compute the number of lower edges of a vertex <cite>v</cite> for a specific direction (included by the use of sorted <cite>v_list</cite>).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>v</strong> (<em>str</em>) – The vertex to compute the number of lower edges for.</p></li>
-<li><p><strong>omega</strong> (<em>tuple</em>) – The direction vector to consider given as an angle in [0, 2pi].</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>The number of lower edges of the vertex v.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>int</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.project_coordinates">
+<span class="sig-name descname"><span class="pre">project_coordinates</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">projection_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pca'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.project_coordinates"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.project_coordinates" title="Link to this definition"></a></dt>
+<dd><p>Project coordinates using a function</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_all_normals_matrix">
-<span class="sig-name descname"><span class="pre">get_all_normals_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_rounding_digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_all_normals_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_all_normals_matrix" title="Link to this definition"></a></dt>
-<dd><p>Function to get all angles of normals to any line between vertices in the graph, returned as a matrix. Note this includes both adjacent vertices and non-adjacent. This function is useful for knowing the angle of the circle where two vertices switch in order.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>num_rounding_digits</strong> (<em>int</em>) – The number of digits to round the angles in the matrix. If <cite>None</cite>, no rounding is done.</p>
-</dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A tuple consissting of a matrix of angles, and the sorted label list for the rows/columns.</p>
-</dd>
-</dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.pca_projection">
+<span class="sig-name descname"><span class="pre">pca_projection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.pca_projection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.pca_projection" title="Link to this definition"></a></dt>
+<dd><p>Dimensionality reduction using PCA</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.get_normals_dict">
-<span class="sig-name descname"><span class="pre">get_normals_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_rounding_digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edges_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opposites</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.get_normals_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.get_normals_dict" title="Link to this definition"></a></dt>
-<dd><p>Function to get all angles of normals to any line between vertices in the graph, returned as a dictionary of angles with <code class="docutils literal notranslate"><span class="pre">dict[theta]</span></code> returning the list of pairs of vertices with vector normal to <span class="math notranslate nohighlight">\(\overrightarrow{AB}\)</span> at angle <code class="docutils literal notranslate"><span class="pre">theta</span></code>. Note this includes both adjacent vertices and non-adjacent unless <code class="docutils literal notranslate"><span class="pre">edges_only</span></code> is set to <code class="docutils literal notranslate"><span class="pre">True</span></code>. This function is useful for knowing the angle of the circle where two vertices switch in order, especially when we want to sort the events around the circle. All angles are rounded to the number of digits given by <code class="docutils literal notranslate"><span class="pre">num_rounding_digits</span></code>, and are returned in the range <span class="math notranslate nohighlight">\([0, 2\pi]\)</span> .</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.add_edge">
+<span class="sig-name descname"><span class="pre">add_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.add_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.add_edge" title="Link to this definition"></a></dt>
+<dd><p>Add an edge between u and v.</p>
+<p>The nodes u and v will be automatically added if they are
+not already in the graph.</p>
+<p>Edge attributes can be specified with keywords or by directly
+accessing the edge’s attribute dictionary. See examples below.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>num_rounding_digits</strong> (<em>int</em>) – The number of digits to round the angles in the dictionary. If None, no rounding is done.</p></li>
-<li><p><strong>edges_only</strong> (<em>bool</em>) – If True, the dictionary version only returns the angle of the normals to the lines between vertices sharing an edge. The matrix version is unchanged.</p></li>
-<li><p><strong>opposites</strong> (<em>bool</em>) – If True, will also include both <span class="math notranslate nohighlight">\(\theta\)</span> and <span class="math notranslate nohighlight">\(\theta + \pi\)</span> in the dictionary keys.</p></li>
+<li><p><strong>u_of_edge</strong> (<em>nodes</em>) – Nodes can be, for example, strings or numbers.
+Nodes must be hashable (and not None) Python objects.</p></li>
+<li><p><strong>v_of_edge</strong> (<em>nodes</em>) – Nodes can be, for example, strings or numbers.
+Nodes must be hashable (and not None) Python objects.</p></li>
+<li><p><strong>attr</strong> (<em>keyword arguments</em><em>, </em><em>optional</em>) – Edge data (or labels or objects) can be assigned using
+keyword arguments.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns<span class="colon">:</span></dt>
-<dd class="field-even"><p>A dictionary of angles with <cite>dict[theta]</cite> returning the list of pairs of vertices with vector normal to <span class="math notranslate nohighlight">\(\overrightarrow\{AB\}\)</span> at angle <cite>theta</cite>, e.g. <code class="docutils literal notranslate"><span class="pre">dict[theta]</span> <span class="pre">=</span> <span class="pre">[('A','B'),</span> <span class="pre">('C',</span> <span class="pre">'D')]</span></code>.</p>
-</dd>
-<dt class="field-odd">Return type<span class="colon">:</span></dt>
-<dd class="field-odd"><p>dict</p>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_edges_from</span></code></dt><dd><p>add a collection of edges</p>
 </dd>
 </dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Adding an edge that already exists updates the edge data.</p>
+<p>Many NetworkX algorithms designed for weighted graphs use
+an edge attribute (by default <cite>weight</cite>) to hold a numerical value.</p>
+<p class="rubric">Examples</p>
+<p>The following all add the edge e=(1, 2) to graph G:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>  <span class="c1"># or DiGraph, MultiGraph, MultiDiGraph, etc</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">e</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>  <span class="c1"># explicit two-node form</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="o">*</span><span class="n">e</span><span class="p">)</span>  <span class="c1"># single edge as tuple of two nodes</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)])</span>  <span class="c1"># add edges from iterable container</span>
+</pre></div>
+</div>
+<p>Associate data to edges using keywords:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">weight</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">capacity</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mf">342.7</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>For non-string attribute keys, use subscript notation.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="mi">0</span><span class="p">:</span> <span class="mi">5</span><span class="p">})</span>
+</pre></div>
+</div>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bounding_circle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_nodes_theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.plot" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the graph with the embedded coordinates.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">bounding_circle</span></code> is True, a bounding circle is drawn around the graph.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">color_nodes_theta</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code>, it should be given as a <span class="math notranslate nohighlight">\(theta\)</span> in <span class="math notranslate nohighlight">\([0,2\pi]\)</span>. Then the nodes are colored according to the <span class="math notranslate nohighlight">\(g(v)\)</span> values in the direction of <span class="math notranslate nohighlight">\(\theta\)</span>.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">with_labels</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the nodes are labeled with their names.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">ax</span></code> is not <code class="docutils literal notranslate"><span class="pre">None</span></code>, the plot is drawn on the given axis.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.plot_bounding_circle">
-<span class="sig-name descname"><span class="pre">plot_bounding_circle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bounding_center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'origin'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.plot_bounding_circle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.plot_bounding_circle" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the bounding circle of the graph.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">ax</span></code> is not None, the plot is drawn on the given axis.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">bounding_center_type</span></code> is ‘origin’, the bounding circle is centered at the origin. If it is <code class="docutils literal notranslate"><span class="pre">min_max</span></code>, the bounding circle is centered at the mean of the min and max values of the <span class="math notranslate nohighlight">\(x\)</span> and :math`y` coordinates.</p>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.validate_plot_parameters">
+<span class="sig-name descname"><span class="pre">validate_plot_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.validate_plot_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.validate_plot_parameters" title="Link to this definition"></a></dt>
+<dd><p>Decorator to validate plot method parameters</p>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.plot_angle_circle">
-<span class="sig-name descname"><span class="pre">plot_angle_circle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edges_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.plot_angle_circle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.plot_angle_circle" title="Link to this definition"></a></dt>
-<dd><p>Function to plot the circle of angles for the graph.</p>
-<p>Example Usage:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot_angle_circle</span><span class="p">(</span><span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">)</span>
-</pre></div>
-</div>
-</dd></dl>
-
-</dd></dl>
+<dt class="sig sig-object py" id="ect.embed_graph.EmbeddedGraph.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#EmbeddedGraph.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.EmbeddedGraph.plot" title="Link to this definition"></a></dt>
+<dd></dd></dl>
 
-<dl class="py function">
-<dt class="sig sig-object py" id="ect.embed_graph.create_example_graph">
-<span class="sig-prename descclassname"><span class="pre">ect.embed_graph.</span></span><span class="sig-name descname"><span class="pre">create_example_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min_max'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect/embed_graph.html#create_example_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect.embed_graph.create_example_graph" title="Link to this definition"></a></dt>
-<dd><p>Function to create an example <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> object. Helpful for testing. If <code class="docutils literal notranslate"><span class="pre">centered</span></code> is True, the coordinates are centered using the center type given by <code class="docutils literal notranslate"><span class="pre">center_type</span></code>, either <code class="docutils literal notranslate"><span class="pre">mean</span></code> or <code class="docutils literal notranslate"><span class="pre">min_max</span></code>.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns<span class="colon">:</span></dt>
-<dd class="field-odd"><p>An example <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> object.</p>
-</dd>
-<dt class="field-even">Return type<span class="colon">:</span></dt>
-<dd class="field-even"><p><a class="reference internal" href="#ect.embed_graph.EmbeddedGraph" title="ect.embed_graph.EmbeddedGraph">EmbeddedGraph</a></p>
-</dd>
-</dl>
 </dd></dl>
 
 </section>
diff --git a/docs/genindex.html b/docs/genindex.html
index 2daba50..bca3533 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Index &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" /> 
@@ -54,12 +56,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -69,18 +69,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
@@ -129,14 +126,15 @@ <h1 id="index">Index</h1>
  <a href="#_"><strong>_</strong></a>
  | <a href="#A"><strong>A</strong></a>
  | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
  | <a href="#E"><strong>E</strong></a>
  | <a href="#G"><strong>G</strong></a>
- | <a href="#L"><strong>L</strong></a>
  | <a href="#M"><strong>M</strong></a>
  | <a href="#N"><strong>N</strong></a>
  | <a href="#P"><strong>P</strong></a>
- | <a href="#R"><strong>R</strong></a>
  | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#V"><strong>V</strong></a>
  
 </div>
 <h2 id="_">_</h2>
@@ -161,6 +159,8 @@ <h2 id="A">A</h2>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.add_edge">add_edge() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
       <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.add_face">add_face() (ect.embed_cw.EmbeddedCW method)</a>
+</li>
+      <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.add_faces_from">add_faces_from() (ect.embed_cw.EmbeddedCW method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -169,6 +169,8 @@ <h2 id="A">A</h2>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.add_node">add_node() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.add_nodes_from">add_nodes_from() (ect.embed_graph.EmbeddedGraph method)</a>
+</li>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.add_nodes_from_dict">add_nodes_from_dict() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -176,17 +178,21 @@ <h2 id="A">A</h2>
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.calculateECC">calculateECC() (ect.ect_graph.ECT method)</a>
-</li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.calculateECT">calculateECT() (ect.ect_graph.ECT method)</a>
+      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.calculate">calculate() (ect.ect_graph.ECT method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.calculateSECT">calculateSECT() (ect.ect_graph.ECT method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.center_coordinates">center_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
-      <li><a href="embed_cw.html#ect.embed_cw.create_example_cw">create_example_cw() (in module ect.embed_cw)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.coord_matrix">coord_matrix (ect.embed_graph.EmbeddedGraph property)</a>
 </li>
-      <li><a href="embed_graph.html#ect.embed_graph.create_example_graph">create_example_graph() (in module ect.embed_graph)</a>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.dim">dim (ect.embed_graph.EmbeddedGraph property)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -219,6 +225,8 @@ <h2 id="E">E</h2>
         <li><a href="embed_graph.html#module-ect.embed_graph">module</a>
 </li>
       </ul></li>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.edge_indices">edge_indices (ect.embed_graph.EmbeddedGraph property)</a>
+</li>
       <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW">EmbeddedCW (class in ect.embed_cw)</a>
 </li>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph">EmbeddedGraph (class in ect.embed_graph)</a>
@@ -229,14 +237,6 @@ <h2 id="E">E</h2>
 <h2 id="G">G</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.g_omega">g_omega() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.g_omega_edges">g_omega_edges() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.g_omega_faces">g_omega_faces() (ect.embed_cw.EmbeddedCW method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_all_normals_matrix">get_all_normals_matrix() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_bounding_box">get_bounding_box() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
       <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_bounding_radius">get_bounding_radius() (ect.embed_graph.EmbeddedGraph method)</a>
@@ -245,29 +245,11 @@ <h2 id="G">G</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_centered_coordinates">get_centered_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_coordinates">get_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.get_ECT">get_ECT() (ect.ect_graph.ECT method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normals_dict">get_normals_dict() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_coord">get_coord() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_PCA_coordinates">get_PCA_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normal_angle_matrix">get_normal_angle_matrix() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.get_radius_and_thresh">get_radius_and_thresh() (ect.ect_graph.ECT method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_scaled_coordinates">get_scaled_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.get_SECT">get_SECT() (ect.ect_graph.ECT method)</a>
-</li>
-  </ul></td>
-</tr></table>
-
-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
-  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.lower_edges">lower_edges() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.get_normal_angles">get_normal_angles() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -292,7 +274,11 @@ <h2 id="M">M</h2>
 <h2 id="N">N</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.next_vert_name">next_vert_name() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.node_list">node_list (ect.embed_graph.EmbeddedGraph property)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.node_to_index">node_to_index (ect.embed_graph.EmbeddedGraph property)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -300,59 +286,51 @@ <h2 id="N">N</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.plot">plot() (ect.ect_graph.ECT method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.pca_projection">pca_projection() (ect.embed_graph.EmbeddedGraph method)</a>
+</li>
+      <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.plot">plot() (ect.embed_cw.EmbeddedCW method)</a>
 
       <ul>
-        <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.plot">(ect.embed_cw.EmbeddedCW method)</a>
-</li>
         <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.plot">(ect.embed_graph.EmbeddedGraph method)</a>
 </li>
       </ul></li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.plot_angle_circle">plot_angle_circle() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.plot_bounding_circle">plot_bounding_circle() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
       <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.plot_faces">plot_faces() (ect.embed_cw.EmbeddedCW method)</a>
 </li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.plotECC">plotECC() (ect.ect_graph.ECT method)</a>
-</li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.plotECT">plotECT() (ect.ect_graph.ECT method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.position_dict">position_dict (ect.embed_graph.EmbeddedGraph property)</a>
 </li>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.plotSECT">plotSECT() (ect.ect_graph.ECT method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.project_coordinates">project_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
   </ul></td>
 </tr></table>
 
-<h2 id="R">R</h2>
+<h2 id="S">S</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.rescale_to_unit_disk">rescale_to_unit_disk() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.scale_coordinates">scale_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.set_coord">set_coord() (ect.embed_graph.EmbeddedGraph method)</a>
+</li>
+      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.shape_descriptor">shape_descriptor() (ect.ect_graph.ECT static method)</a>
 </li>
   </ul></td>
 </tr></table>
 
-<h2 id="S">S</h2>
+<h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="ect_on_graphs.html#ect.ect_graph.ECT.set_bounding_radius">set_bounding_radius() (ect.ect_graph.ECT method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.set_centered_coordinates">set_centered_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.set_coordinates">set_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.set_PCA_coordinates">set_PCA_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.transform_coordinates">transform_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
   </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.set_scaled_coordinates">set_scaled_coordinates() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.sort_edges">sort_edges() (ect.embed_graph.EmbeddedGraph method)</a>
-</li>
-      <li><a href="embed_cw.html#ect.embed_cw.EmbeddedCW.sort_faces">sort_faces() (ect.embed_cw.EmbeddedCW method)</a>
-</li>
-      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.sort_vertices">sort_vertices() (ect.embed_graph.EmbeddedGraph method)</a>
+      <li><a href="embed_graph.html#ect.embed_graph.EmbeddedGraph.validate_plot_parameters">validate_plot_parameters() (ect.embed_graph.EmbeddedGraph method)</a>
 </li>
   </ul></td>
 </tr></table>
diff --git a/docs/index.html b/docs/index.html
index 887eb2e..b685aea 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,20 +5,24 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>ect: Euler Characteristic Transform in Python &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -58,12 +60,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -73,18 +73,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
diff --git a/docs/installation.html b/docs/installation.html
index 8d5c26a..b2fb764 100644
--- a/docs/installation.html
+++ b/docs/installation.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,18 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>1. Installation &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -57,12 +59,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -72,18 +72,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
diff --git a/docs/license.html b/docs/license.html
index dbc9aff..73ade2d 100644
--- a/docs/license.html
+++ b/docs/license.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,18 +5,22 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>5. GPL-3.0 License &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -57,12 +59,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -72,18 +72,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
diff --git a/docs/modules.html b/docs/modules.html
index a972e8d..1b8bacd 100644
--- a/docs/modules.html
+++ b/docs/modules.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
@@ -7,20 +5,24 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>2. Table of Contents &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
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-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
-      <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
-      <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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@@ -59,12 +61,10 @@
 <li class="toctree-l1 current"><a class="current reference internal" href="#">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -74,18 +74,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
@@ -134,12 +131,10 @@ <h1><span class="section-number">2. </span>Table of Contents<a class="headerlink
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l2"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
diff --git a/docs/notebooks/CodingFiguresFern.html b/docs/notebooks/CodingFiguresFern.html
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-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../ect_on_graphs.html#ect.ect_graph.ECT"><code class="docutils literal notranslate"><span class="pre">ECT</span></code></a></li>
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-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
-</ul>
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-  <section id="Figure-generation-for-Invitation-to-the-ECT">
-<h1>Figure generation for <em>Invitation to the ECT</em><a class="headerlink" href="#Figure-generation-for-Invitation-to-the-ECT" title="Link to this heading"></a></h1>
-<p>This notebook was used to generate the figures for the paper</p>
-<blockquote>
-<div><p>Elizabeth Munch. An Invitation to the Euler Characteristic Transform. <a class="reference external" href="https://arxiv.org/abs/2310.10395">arXiv:2310.10395</a>, 2023. To appear: MAA Monthly, 2024.</p>
-</div></blockquote>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><br/><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="c1"># import teaspoon</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span>
-    <span class="s2">&quot;text.usetex&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-    <span class="c1"># &quot;font.family&quot;: &quot;Helvetica&quot;,</span>
-    <span class="s2">&quot;font.family&quot;</span><span class="p">:</span> <span class="s2">&quot;serif&quot;</span><span class="p">,</span>
-    <span class="s2">&quot;font.size&quot;</span><span class="p">:</span> <span class="mi">8</span>
-<span class="p">})</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="n">matplotlib</span><span class="o">.</span><span class="n">__version__</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&#39;3.8.4&#39;
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Code to set colormaps to deal with the transparency issue</span>
-
-<span class="n">cmap_viridis</span> <span class="o">=</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span>
-<span class="n">cmap_viridis</span><span class="o">.</span><span class="n">set_bad</span><span class="p">(</span><span class="s1">&#39;white&#39;</span><span class="p">,</span><span class="mf">1.</span><span class="p">)</span>
-
-
-<span class="n">cmap_binary</span> <span class="o">=</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">tab20b</span>
-<span class="n">cmap_binary</span><span class="o">.</span><span class="n">set_bad</span><span class="p">(</span><span class="s1">&#39;white&#39;</span><span class="p">,</span><span class="mf">1.</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">F</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;figures/leafbw.tif&#39;</span><span class="p">)</span>
-<span class="n">F</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">T</span>
-<span class="n">F</span> <span class="o">=</span> <span class="n">F</span><span class="o">/</span><span class="mf">255.</span>
-
-<span class="n">F_finite</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">F</span><span class="p">)</span>
-<span class="n">F_finite</span><span class="p">[</span><span class="n">F</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2000</span>
-
-<span class="c1"># G = np.ones_like(F)</span>
-<span class="c1"># G[F==0] = 1</span>
-<span class="c1"># print(G.shape)</span>
-
-<span class="n">F</span><span class="p">[</span><span class="n">F</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-
-<span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span><span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_binary</span><span class="p">)</span>
-<span class="c1"># plt.matshow(G, origin = &#39;lower&#39;)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="c1"># fig.set_rasterized(True)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Testing&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.png&#39;</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.eps&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">transparent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.pdf&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
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-</pre></div>
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-</pre></div>
-</div>
-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">F</span>
-</pre></div>
-</div>
-</div>
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-array([[nan, nan, nan, ..., nan, nan, nan],
-       [nan, nan, nan, ..., nan, nan, nan],
-       [nan, nan, nan, ..., nan, nan, nan],
-       ...,
-       [nan, nan, nan, ..., nan, nan, nan],
-       [nan, nan, nan, ..., nan, nan, nan],
-       [nan, nan, nan, ..., nan, nan, nan]])
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">makeFernPic</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">plot</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-    <span class="n">maxRow</span><span class="p">,</span> <span class="n">maxCol</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">F</span><span class="p">)</span>
-    <span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
-
-    <span class="c1"># center origin</span>
-    <span class="n">Col</span><span class="p">,</span><span class="n">Row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">maxCol</span><span class="p">),</span><span class="nb">range</span><span class="p">(</span><span class="n">maxRow</span><span class="p">))</span>
-    <span class="n">Col</span> <span class="o">=</span> <span class="n">Col</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">maxCol</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">Row</span> <span class="o">=</span> <span class="n">Row</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">maxRow</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="n">fMask</span> <span class="o">=</span> <span class="n">omega</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">Col</span> <span class="o">+</span> <span class="n">omega</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">Row</span>
-    <span class="n">DT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">fMask</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">DT</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cmap</span> <span class="o">=</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">viridis</span>
-<span class="n">cmap</span><span class="o">.</span><span class="n">set_bad</span><span class="p">(</span><span class="s1">&#39;white&#39;</span><span class="p">,</span><span class="mf">1.</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPic</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">fraction</span><span class="o">=</span><span class="mf">0.035</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.04</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="c1"># fig.set_rasterized(True)</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.png&#39;</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.eps&#39;</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">,</span> <span class="n">transparent</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/test.pdf&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_9_0.png" src="../_images/notebooks_CodingFiguresFern_9_0.png" />
-</div>
-</div>
-</section>
-<section id="Trying-Erik's-stuff">
-<h1>Trying Erik’s stuff<a class="headerlink" href="#Trying-Erik's-stuff" title="Link to this heading"></a></h1>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">demeter.euler</span> <span class="k">as</span> <span class="nn">euler</span>
-<span class="kn">import</span> <span class="nn">demeter.directions</span> <span class="k">as</span> <span class="nn">dirs</span>
-<span class="kn">import</span> <span class="nn">demeter.misc</span> <span class="k">as</span> <span class="nn">misc</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">tifffile</span> <span class="k">as</span> <span class="nn">tf</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">leaf_file</span> <span class="o">=</span> <span class="s1">&#39;figures/leafbw.tif&#39;</span>
-<span class="n">leaf_img</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">leaf_file</span><span class="p">)</span>
-<span class="n">leaf_img</span><span class="p">[</span><span class="n">leaf_img</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">leaf_img</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">);</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">200</span><span class="p">,</span><span class="mi">200</span><span class="p">,</span><span class="mi">250</span><span class="p">,</span><span class="mi">250</span><span class="p">,</span><span class="mi">200</span><span class="p">],</span> <span class="p">[</span><span class="mi">280</span><span class="p">,</span><span class="mi">310</span><span class="p">,</span><span class="mi">310</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">280</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">4</span><span class="p">);</span>
-<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">leaf_img</span><span class="p">[</span><span class="mi">280</span><span class="p">:</span><span class="mi">310</span><span class="p">,</span> <span class="mi">200</span><span class="p">:</span><span class="mi">250</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">);</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_12_0.png" src="../_images/notebooks_CodingFiguresFern_12_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tic</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
-<span class="n">leaf</span> <span class="o">=</span> <span class="n">euler</span><span class="o">.</span><span class="n">CubicalComplex</span><span class="p">(</span><span class="n">leaf_img</span><span class="p">)</span><span class="o">.</span><span class="n">complexify</span><span class="p">()</span>
-<span class="n">toc</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Complexify in </span><span class="si">{:.4f}</span><span class="s2"> seconds.</span><span class="se">\n\n</span><span class="s2">Cubical complex made of:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">toc</span><span class="o">-</span><span class="n">tic</span><span class="p">))</span>
-
-<span class="n">leaf</span><span class="o">.</span><span class="n">summary</span><span class="p">();</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-Complexify in 1.0271 seconds.
-
-Cubical complex made of:
-53832   vertices
-99196   edges
-45167   squares
-----
-Euler Characteristic: -197
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">leaf</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
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-53832   vertices
-99196   edges
-45167   squares
-----
-Euler Characteristic: -197
-</pre></div></div>
-</div>
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-<div class="highlight"><pre>
-0
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[array([[-271.57746322,   89.89517387],
-        [-271.57746322,   90.89517387],
-        [-270.57746322,   88.89517387],
-        ...,
-        [ 259.42253678,  -44.10482613],
-        [ 259.42253678,  -43.10482613],
-        [ 260.42253678,  -45.10482613]]),
- array([[    0,     1],
-        [    0,     3],
-        [    1,     4],
-        ...,
-        [53828, 53829],
-        [53828, 53831],
-        [53829, 53830]]),
- array([[    0,     1,     3,     4],
-        [    2,     3,     7,     8],
-        [    3,     4,     8,     9],
-        ...,
-        [53821, 53822, 53826, 53827],
-        [53825, 53826, 53828, 53829],
-        [53826, 53827, 53829, 53830]])]
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
-<span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omega</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">heights</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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-array([-128.46877882, -127.76167204, -128.46877882, ...,  152.25261331,
-        152.9597201 ,  152.25261331])
-</pre></div></div>
-</div>
-<section id="Making-pictures">
-<h2>Making pictures<a class="headerlink" href="#Making-pictures" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="mi">6</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
-<span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omega</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span><span class="mi">0</span><span class="p">],</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span><span class="mi">1</span><span class="p">],</span>
-                         <span class="n">s</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">heights</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;magma&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;T = </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">32</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.colorbar.Colorbar at 0x73f180e96250&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_20_1.png" src="../_images/notebooks_CodingFiguresFern_20_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Figuring out centering</span>
-<span class="n">size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">F</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="highlight"><pre>
-177.0
-279.0
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPic</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">theta</span><span class="p">),</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">150</span><span class="p">)</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-<span class="n">midx</span> <span class="o">=</span> <span class="mi">279</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">midx</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-
-<span class="n">midy</span> <span class="o">=</span> <span class="mi">177</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">midy</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-<span class="c1"># plt.savefig(&#39;figures/Fern&#39; + str(int(theta*1000)) + &#39;.png&#39;, bbox_inches = &#39;tight&#39;)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.colorbar.Colorbar at 0x73f16b1855d0&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_22_1.png" src="../_images/notebooks_CodingFiguresFern_22_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Version without colorbars</span>
-
-<span class="n">thetaStrs</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;\pi/4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3\pi/4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3\pi/2&#39;</span><span class="p">,</span> <span class="s1">&#39;7\pi/4&#39;</span><span class="p">]</span>
-<span class="n">thetaStrsFile</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;pi_over_4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3pi_over_4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3pi_over_2&#39;</span><span class="p">,</span> <span class="s1">&#39;7pi_over_4&#39;</span><span class="p">]</span>
-<span class="n">thetas</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">]</span>
-
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">theta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">thetas</span><span class="p">):</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-    <span class="n">im</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPic</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">theta</span><span class="p">),</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">200</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">200</span><span class="p">)</span>
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-    <span class="n">midx</span> <span class="o">=</span> <span class="mi">279</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">midx</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
-
-    <span class="n">midy</span> <span class="o">=</span> <span class="mi">177</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">midy</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-
-    <span class="c1"># plt.colorbar(fraction=0.035, pad=0.04)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$f_{\omega}$ for $\omega = &#39;</span> <span class="o">+</span> <span class="n">thetaStrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="sa">r</span><span class="s1">&#39;$&#39;</span><span class="p">)</span>
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mf">1.8</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">())</span>
-    <span class="n">height_fig</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">height_fig</span><span class="p">)</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_&#39;</span> <span class="o">+</span> <span class="n">thetaStrsFile</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;_nocolorbar.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_&#39;</span> <span class="o">+</span> <span class="n">thetaStrsFile</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;_nocolorbar.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-0.7853981633974483
-[1.8 4.8]
-1.8
-2.356194490192345
-[1.8 4.8]
-1.8
-4.71238898038469
-[1.8 4.8]
-1.8
-5.497787143782138
-[1.8 4.8]
-1.8
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_23_1.png" src="../_images/notebooks_CodingFiguresFern_23_1.png" />
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_23_2.png" src="../_images/notebooks_CodingFiguresFern_23_2.png" />
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_23_3.png" src="../_images/notebooks_CodingFiguresFern_23_3.png" />
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_23_4.png" src="../_images/notebooks_CodingFiguresFern_23_4.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><br/><span></span><span class="n">thetaStrs</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;\pi/4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3\pi/4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3\pi/2&#39;</span><span class="p">,</span> <span class="s1">&#39;7\pi/4&#39;</span><span class="p">]</span>
-<span class="n">thetaStrsFile</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;pi_over_4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3pi_over_4&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;3pi_over_2&#39;</span><span class="p">,</span> <span class="s1">&#39;7pi_over_4&#39;</span><span class="p">]</span>
-<span class="n">thetas</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">]</span>
-
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">theta</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">thetas</span><span class="p">):</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPic</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">theta</span><span class="p">),</span> <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">,</span> <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">200</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">200</span><span class="p">)</span>
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-    <span class="n">midx</span> <span class="o">=</span> <span class="mi">279</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">midx</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
-
-    <span class="n">midy</span> <span class="o">=</span> <span class="mi">177</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">midy</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">fraction</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.04</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$f_{\omega}$ for $\omega = &#39;</span> <span class="o">+</span> <span class="n">thetaStrs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="sa">r</span><span class="s1">&#39;$&#39;</span><span class="p">)</span>
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-    <span class="c1"># fig.set_figwidth(2.4)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">height_fig</span><span class="p">)</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">set_figheight</span><span class="p">(</span><span class="n">height_fig</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">())</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_&#39;</span> <span class="o">+</span> <span class="n">thetaStrsFile</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_&#39;</span> <span class="o">+</span> <span class="n">thetaStrsFile</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
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-0.7853981633974483
-1.8
-[7.54478873 1.8       ]
-2.356194490192345
-1.8
-[7.54478873 1.8       ]
-4.71238898038469
-1.8
-[7.54478873 1.8       ]
-5.497787143782138
-1.8
-[7.54478873 1.8       ]
-</pre></div></div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mf">0.8</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/OnlyColorbar.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span> <span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/OnlyColorbar.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
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-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">theta</span><span class="o">*</span><span class="mi">1000</span><span class="p">))</span>
-</pre></div>
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-&#39;5497&#39;
-</pre></div></div>
-</div>
-</section>
-<section id="Thresholded-Figure">
-<h2>Thresholded Figure<a class="headerlink" href="#Thresholded-Figure" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">makeFernPicThreshold</span><span class="p">(</span><span class="n">F</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">thresh</span><span class="p">,</span> <span class="n">both</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
-    <span class="n">maxRow</span><span class="p">,</span> <span class="n">maxCol</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">F</span><span class="p">)</span>
-    <span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
-
-    <span class="c1"># center origin</span>
-    <span class="n">Col</span><span class="p">,</span><span class="n">Row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">maxCol</span><span class="p">),</span><span class="nb">range</span><span class="p">(</span><span class="n">maxRow</span><span class="p">))</span>
-    <span class="n">Col</span> <span class="o">=</span> <span class="n">Col</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">maxCol</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">Row</span> <span class="o">=</span> <span class="n">Row</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">maxRow</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-
-    <span class="n">fMask</span> <span class="o">=</span> <span class="n">omega</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">Col</span> <span class="o">+</span> <span class="n">omega</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">Row</span>
-
-    <span class="n">fMaskBelow</span> <span class="o">=</span> <span class="n">fMask</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">fMaskBelow</span><span class="p">[</span><span class="n">fMaskBelow</span> <span class="o">&lt;=</span> <span class="n">thresh</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-    <span class="n">fMaskAbove</span> <span class="o">=</span> <span class="n">fMask</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">fMaskAbove</span><span class="p">[</span><span class="n">fMaskAbove</span> <span class="o">&gt;</span> <span class="n">thresh</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-
-    <span class="n">DTdown</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">fMaskAbove</span><span class="p">)</span>
-    <span class="n">DTup</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">fMaskBelow</span><span class="p">)</span>
-
-    <span class="c1"># Why the fork is this backwards!?!?!?!?!</span>
-    <span class="c1"># Must have something to do with meshgrid but i just can&#39;t fight it right now</span>
-    <span class="k">if</span> <span class="n">both</span><span class="p">:</span>
-
-        <span class="k">return</span> <span class="n">DTdown</span><span class="p">,</span><span class="n">DTup</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">DTdown</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-
-<span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span><span class="o">-</span><span class="mi">100</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">150</span><span class="p">]:</span>
-
-
-    <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omega</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Plot colors above theshold</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPicThreshold</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">theta</span><span class="p">,</span><span class="n">thresh</span><span class="p">),</span>
-                <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span>
-                <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">,</span>
-                <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">200</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">200</span><span class="p">)</span>
-
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-    <span class="n">midx</span> <span class="o">=</span> <span class="mi">279</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">midx</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_ticks_position</span><span class="p">(</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">labelrotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-
-    <span class="n">midy</span> <span class="o">=</span> <span class="mi">177</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">(</span><span class="n">midy</span><span class="o">+</span><span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$K_{&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">thresh</span><span class="p">)</span> <span class="o">+</span> <span class="sa">r</span><span class="s1">&#39;}$&#39;</span><span class="p">)</span>
-
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">set_figwidth</span><span class="p">(</span><span class="mf">1.25</span><span class="p">)</span>
-
-    <span class="n">thresh_size_fig</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">thresh_size_fig</span><span class="p">)</span>
-
-    <span class="c1"># plt.colorbar()</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_Thresh&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">thresh</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_Thresh&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">thresh</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-
-    <span class="c1"># plt.clf()</span>
-</pre></div>
-</div>
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-[1.25 4.8 ]
-[1.25 4.8 ]
-[1.25 4.8 ]
-[1.25 4.8 ]
-[1.25 4.8 ]
-[1.25 4.8 ]
-[1.25 4.8 ]
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-
-<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="n">sharey</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span><span class="o">-</span><span class="mi">50</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">50</span><span class="p">,</span><span class="mi">150</span><span class="p">]):</span>
-
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-    <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omega</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Plot colors above theshold</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">makeFernPicThreshold</span><span class="p">(</span><span class="n">F</span><span class="p">,</span><span class="n">theta</span><span class="p">,</span><span class="n">thresh</span><span class="p">),</span>
-                <span class="n">origin</span> <span class="o">=</span> <span class="s1">&#39;lower&#39;</span><span class="p">,</span>
-                <span class="n">cmap</span> <span class="o">=</span> <span class="n">cmap_viridis</span><span class="p">,</span>
-                <span class="n">vmin</span> <span class="o">=</span> <span class="o">-</span><span class="mi">200</span><span class="p">,</span> <span class="n">vmax</span> <span class="o">=</span> <span class="mi">200</span><span class="p">)</span>
-
-    <span class="c1"># midx = 279</span>
-    <span class="c1"># ax.set_xticks(midx+100*np.arange(-2,3))</span>
-    <span class="c1"># ax.set_xticklabels(np.arange(-2,3)*100)</span>
-    <span class="c1"># ax.xaxis.set_ticks_position(&#39;bottom&#39;)</span>
-    <span class="c1"># ax.tick_params(axis=&#39;x&#39;, labelrotation=45)</span>
-
-
-    <span class="c1"># midy = 177</span>
-    <span class="c1"># ax.set_yticks(midy+100*np.arange(-2,3))</span>
-    <span class="c1"># ax.set_yticklabels(np.arange(-2,3)*100)</span>
-
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$K_{&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">thresh</span><span class="p">)</span> <span class="o">+</span> <span class="sa">r</span><span class="s1">&#39;}$&#39;</span><span class="p">)</span>
-
-    <span class="c1"># fig = plt.gcf()</span>
-    <span class="c1"># fig.set_figwidth(1.25)</span>
-
-    <span class="c1"># thresh_size_fig = fig.get_size_inches()</span>
-    <span class="c1"># print(thresh_size_fig)</span>
-
-    <span class="c1"># plt.colorbar()</span>
-
-    <span class="c1"># plt.savefig(&#39;figures/Fern_3pi_over_4_Thresh&#39;+str(thresh)+&#39;.png&#39;, bbox_inches = &#39;tight&#39;,dpi = 300)</span>
-    <span class="c1"># plt.savefig(&#39;figures/Fern_3pi_over_4_Thresh&#39;+str(thresh)+&#39;.eps&#39;, bbox_inches = &#39;tight&#39;)</span>
-
-    <span class="c1"># plt.clf()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_AllThresh.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_AllThresh.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span> <span class="o">=</span> <span class="mi">300</span><span class="p">)</span>
-</pre></div>
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-</div>
-</div>
-</section>
-<section id="ECC-in-one-direction">
-<h2>ECC in one direction<a class="headerlink" href="#ECC-in-one-direction" title="Link to this heading"></a></h2>
-<div class="nbinput docutils container">
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">))</span>
-<span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omega</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">numThresh</span> <span class="o">=</span> <span class="mi">64</span>
-
-<span class="n">threshes</span><span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">heights</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">numThresh</span><span class="p">)</span>
-
-<span class="n">outECC</span> <span class="o">=</span> <span class="n">leaf</span><span class="o">.</span><span class="n">ECC</span><span class="p">(</span><span class="n">heights</span><span class="p">,</span> <span class="n">numThresh</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-
-<span class="n">positions</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">150</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>
-<span class="k">for</span> <span class="n">pos</span> <span class="ow">in</span> <span class="n">positions</span><span class="p">:</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pos</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;grey&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-
-
-
-<span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">threshes</span><span class="p">,</span><span class="n">outECC</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECC for $\omega = \frac{3 \pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\chi(K_a)$&#39;</span><span class="p">)</span>
-
-<span class="c1"># ax.set_xticks([-200,-150,-100,-50,0,50,100,150,200])</span>
-    <span class="c1"># midx = 279</span>
-    <span class="c1"># ax.set_xticks(midx+100*np.arange(-2,3))</span>
-    <span class="c1"># ax.set_xticklabels(np.arange(-2,3)*100)</span>
-    <span class="c1"># ax.xaxis.set_ticks_position(&#39;bottom&#39;)</span>
-    <span class="c1"># ax.tick_params(axis=&#39;x&#39;, labelrotation=45)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">labelrotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelrotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-
-
-
-
-
-
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="c1"># fig.set_size_inches(thresh_size_fig)</span>
-<span class="c1"># print(thresh_size_fig)</span>
-<span class="c1"># print(fig.get_size_inches())</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_ECC.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_3pi_over_4_ECC.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
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-<div class="prompt empty docutils container">
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-<img alt="../_images/notebooks_CodingFiguresFern_33_0.png" src="../_images/notebooks_CodingFiguresFern_33_0.png" />
-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">outECC</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
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--197
-</pre></div></div>
-</div>
-</section>
-<section id="ECT">
-<h2>ECT<a class="headerlink" href="#ECT" title="Link to this heading"></a></h2>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get all the directions around the circle</span>
-<span class="n">numCircleDirs</span> <span class="o">=</span> <span class="mi">32</span>
-<span class="n">circledirs</span> <span class="o">=</span>  <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">numCircleDirs</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">circledirs</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">circledirs</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
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-[0.     0.0625 0.125  0.1875 0.25   0.3125 0.375  0.4375 0.5    0.5625
- 0.625  0.6875 0.75   0.8125 0.875  0.9375 1.     1.0625 1.125  1.1875
- 1.25   1.3125 1.375  1.4375 1.5    1.5625 1.625  1.6875 1.75   1.8125
- 1.875  1.9375]
-</pre></div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Choose number of thresholds for the ECC to stop at</span>
-<span class="n">numThresh</span> <span class="o">=</span> <span class="mi">100</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ECT_preprocess</span> <span class="o">=</span> <span class="p">{}</span>
-
-<span class="n">overallmin</span> <span class="o">=</span> <span class="mi">0</span>
-<span class="n">overallmax</span> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">omega</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circledirs</span><span class="p">):</span>
-
-    <span class="n">omegavec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">omega</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">omega</span><span class="p">)))</span>
-
-    <span class="c1"># Function values of all the pixels</span>
-    <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">leaf</span><span class="o">.</span><span class="n">cells</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">omegavec</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="c1"># Thresholds implicitly calculated by Erik&#39;s code</span>
-    <span class="n">threshes</span><span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">heights</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">numThresh</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">heights</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">&lt;</span><span class="n">overallmin</span><span class="p">:</span>
-        <span class="n">overallmin</span> <span class="o">=</span> <span class="n">heights</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">&gt;</span><span class="n">overallmax</span><span class="p">:</span>
-        <span class="n">overallmax</span><span class="o">=</span> <span class="n">heights</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-
-
-    <span class="n">outECC</span> <span class="o">=</span> <span class="n">leaf</span><span class="o">.</span><span class="n">ECC</span><span class="p">(</span><span class="n">heights</span><span class="p">,</span> <span class="n">numThresh</span><span class="p">)</span>
-
-    <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">threshes</span><span class="p">,</span><span class="n">outECC</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">overallmin</span><span class="p">,</span><span class="n">overallmax</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
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--285.68893688291087 285.68893688291087
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">2</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span> <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>  <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
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-[&lt;matplotlib.lines.Line2D at 0x73f16b12de10&gt;]
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Making a matrix of size numThreshes x numCircleDirs</span>
-<span class="c1"># M[i,j] =</span>
-
-
-<span class="c1"># Make meshgrid. The +1 is to draw 2pi on both ends</span>
-<span class="n">X</span><span class="p">,</span><span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">numCircleDirs</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">250</span><span class="p">,</span><span class="mi">250</span><span class="p">,</span><span class="n">numThresh</span><span class="p">))</span>
-<span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
-    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
-        <span class="n">omegai</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
-
-        <span class="c1"># Deal with redrawing the 2pi</span>
-        <span class="n">omegai</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mod</span><span class="p">(</span><span class="n">omegai</span><span class="p">,</span><span class="n">numCircleDirs</span><span class="p">)</span>
-
-
-        <span class="n">omega</span> <span class="o">=</span> <span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">numCircleDirs</span>
-        <span class="n">a</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
-        <span class="n">threshes</span> <span class="o">=</span> <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">omegai</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-<span class="c1">#         print(threshes)</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">threshes</span><span class="o">&gt;=</span><span class="n">a</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="n">M</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">omegai</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="n">index</span><span class="p">]</span>
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-/tmp/ipykernel_57600/1470381530.py:26: DeprecationWarning: Conversion of an array with ndim &gt; 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-  M[i,j] = ECT_preprocess[omegai][1][index]
-</pre></div></div>
-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">M</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">numCircleDirs</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-
-<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-         <span class="p">]</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECT of Fern Leaf&#39;</span><span class="p">)</span>
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_ECT.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">900</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern_ECT.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">M</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span>
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-array([ 1,  1,  1,  1,  6,  1,  1,  1,  1,  2,  1,  4,  1,  1,  3,  3,  2,
-        1,  2, 10,  3,  3,  1,  1,  3,  3,  1,  2,  1,  1,  1,  1,  1])
-</pre></div></div>
-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">bigvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">M</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span> <span class="o">&gt;</span><span class="mi">3</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-
-<span class="n">circledirs</span><span class="p">[</span><span class="n">bigvals</span><span class="p">]</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="mi">16</span>
-</pre></div>
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-array([ 4., 11., 19.])
-</pre></div></div>
-</div>
-</section>
-<section id="Let's-draw-a-cylinder!">
-<h2>Let’s draw a cylinder!<a class="headerlink" href="#Let's-draw-a-cylinder!" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.mplot3d</span> <span class="kn">import</span> <span class="n">Axes3D</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="c1"># pip install ipympl</span>
-
-<span class="c1"># %matplotlib widget</span>
-</pre></div>
-</div>
-</div>
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">grid</span> <span class="o">=</span> <span class="n">M</span><span class="p">[:,:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-</pre></div>
-</div>
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-(100, 32)
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-</div>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">color_dimension</span> <span class="o">=</span> <span class="n">grid</span> <span class="c1"># change to desired fourth dimension</span>
-<span class="n">minn</span><span class="p">,</span> <span class="n">maxx</span> <span class="o">=</span> <span class="n">color_dimension</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">color_dimension</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
-<span class="n">norm</span> <span class="o">=</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">colors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">minn</span><span class="p">,</span> <span class="n">maxx</span><span class="p">)</span>
-<span class="n">m</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-<span class="n">m</span><span class="o">.</span><span class="n">set_array</span><span class="p">([])</span>
-<span class="n">fcolors</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">color_dimension</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">fcolors</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-</pre></div>
-</div>
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-(100, 32, 4)
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">24</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-<span class="c1"># theta = circledirs[:-1]</span>
-<span class="c1"># print(theta, theta.shape)</span>
-<br/></pre></div>
-</div>
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-[0.         0.27318197 0.54636394 0.81954591 1.09272788 1.36590985
- 1.63909182 1.91227379 2.18545576 2.45863773 2.7318197  3.00500167
- 3.27818364 3.55136561 3.82454758 4.09772955 4.37091152 4.64409349
- 4.91727546 5.19045743 5.4636394  5.73682137 6.01000334 6.28318531]
-</pre></div></div>
-</div>
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">equation for a circle</span>
-
-<span class="sd">x  =  h + r cosθ</span>
-<span class="sd">y  =  k + r sinθ</span>
-
-<span class="sd">where h and k are the co-ordinates of the center</span>
-
-<span class="sd">0 &lt;= θ &lt;= 360</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
-<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
-
-<span class="c1"># theta = np.linspace(0, 2 * np.pi, 24)</span>
-<span class="n">radius</span> <span class="o">=</span> <span class="mi">1</span>
-
-
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">numThresh</span><span class="p">,</span> <span class="n">numThresh</span><span class="p">)</span>
-
-<span class="c1"># theta = circledirs</span>
-<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
-
-<span class="n">thetas</span><span class="p">,</span> <span class="n">xs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
-
-<span class="n">y</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span>
-<span class="n">z</span> <span class="o">=</span> <span class="n">radius</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">thetas</span><span class="p">)</span>
-
-<span class="c1">#ax.plot_surface(xs, y, z, color=&#39;orange&#39;)</span>
-<span class="c1">#ax.plot_surface(xs, y, z, facecolors=fcolors)</span>
-<span class="n">surf</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot_surface</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span><span class="p">,</span> <span class="n">facecolors</span> <span class="o">=</span> <span class="n">fcolors</span><span class="p">,</span><span class="n">linewidth</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">)</span>
-<span class="n">surf</span><span class="o">.</span><span class="n">set_edgecolor</span><span class="p">(</span><span class="s1">&#39;grey&#39;</span><span class="p">)</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/fern_ECT_cylinder.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span> <span class="o">=</span> <span class="mi">900</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/fern_ECT_cylinder.eps&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_49_0.png" src="../_images/notebooks_CodingFiguresFern_49_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">omegai</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">omega</span> <span class="o">=</span> <span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">numCircleDirs</span>
-<span class="n">a</span> <span class="o">=</span> <span class="mi">17</span>
-<span class="n">threshes</span> <span class="o">=</span> <span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">omegai</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">threshes</span><span class="p">)</span>
-
-<span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">threshes</span><span class="o">&gt;=</span><span class="n">a</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">threshes</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
-<span class="n">ECT_preprocess</span><span class="p">[</span><span class="n">omegai</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="n">index</span><span class="p">]</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[-193.98518858 -190.40235323 -186.81951788 -183.23668253 -179.65384718
- -176.07101183 -172.48817648 -168.90534113 -165.32250578 -161.73967042
- -158.15683507 -154.57399972 -150.99116437 -147.40832902 -143.82549367
- -140.24265832 -136.65982297 -133.07698762 -129.49415227 -125.91131692
- -122.32848157 -118.74564621 -115.16281086 -111.57997551 -107.99714016
- -104.41430481 -100.83146946  -97.24863411  -93.66579876  -90.08296341
-  -86.50012806  -82.91729271  -79.33445735  -75.751622    -72.16878665
-  -68.5859513   -65.00311595  -61.4202806   -57.83744525  -54.2546099
-  -50.67177455  -47.0889392   -43.50610385  -39.92326849  -36.34043314
-  -32.75759779  -29.17476244  -25.59192709  -22.00909174  -18.42625639
-  -14.84342104  -11.26058569   -7.67775034   -4.09491499   -0.51207963
-    3.07075572    6.65359107   10.23642642   13.81926177   17.40209712
-   20.98493247   24.56776782   28.15060317   31.73343852   35.31627387
-   38.89910923   42.48194458   46.06477993   49.64761528   53.23045063
-   56.81328598   60.39612133   63.97895668   67.56179203   71.14462738
-   74.72746273   78.31029808   81.89313344   85.47596879   89.05880414
-   92.64163949   96.22447484   99.80731019  103.39014554  106.97298089
-  110.55581624  114.13865159  117.72148694  121.3043223   124.88715765
-  128.469993    132.05282835  135.6356637   139.21849905  142.8013344
-  146.38416975  149.9670051   153.54984045  157.1326758   160.71551116]
-59
-17.402097119801482
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
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--83
-</pre></div></div>
-</div>
-</section>
-<section id="Erik's-version">
-<h2>Erik’s version<a class="headerlink" href="#Erik's-version" title="Link to this heading"></a></h2>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># t = 32</span>
-<span class="c1"># tic = time.perf_counter()</span>
-<span class="c1"># ect = leaf.ECT(circle_dirs, T=t)</span>
-<span class="c1"># toc = time.perf_counter()</span>
-<span class="c1"># print(&quot;Complex with {} vertices\n&quot;.format(len(leaf.cells[0])))</span>
-<span class="c1"># print(&quot;ECT with {} directions in {:.4f} seconds.\n{:.4f}s per direction&quot;.format(len(circle_dirs), toc-tic, (toc-tic)/len(circle_dirs)))</span>
-
-<span class="c1"># plt.figure(figsize=(15,2))</span>
-<span class="c1"># plt.plot(ect);</span>
-<span class="c1"># plt.title(&#39;ECT for {} directions and {} thresholds each&#39;.format(len(circle_dirs),t), fontsize=24);</span>
-</pre></div>
-</div>
-</div>
-</section>
-</section>
-<section id="SECT">
-<h1>SECT<a class="headerlink" href="#SECT" title="Link to this heading"></a></h1>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># THe original ECT</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">M</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">numCircleDirs</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-
-<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-         <span class="p">]</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECT of Fern Leaf&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Text(0.5, 1.0, &#39;ECT of Fern Leaf&#39;)
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_54_1.png" src="../_images/notebooks_CodingFiguresFern_54_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Following Crawford2019 for the moment. Is this different in the Meng22 version?</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-<span class="c1"># Averages for a fixed direction</span>
-<span class="n">AvgOverOmega</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">M</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">AvgOverOmega</span><span class="p">)</span>
-
-<span class="c1"># Mean centered</span>
-
-<span class="n">Mcentered</span> <span class="o">=</span> <span class="n">M</span><span class="o">-</span><span class="n">AvgOverOmega</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(100, 33)
-[-100.72  -99.01  -96.97  -95.    -93.24  -92.33  -91.34  -90.83  -90.69
-  -89.07  -87.98  -87.07  -87.2   -87.64  -88.43  -89.47  -90.97  -90.94
-  -91.8   -91.22  -93.    -94.51  -96.85  -98.61  -99.98 -100.05 -100.44
- -100.47 -101.04 -101.12 -101.03 -100.91 -100.72]
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">Mcentered</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">numCircleDirs</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-
-<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-         <span class="p">]</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$a$&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;ECT of Fern Leaf with value centered&#39;</span><span class="p">)</span>
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern-ECT-centered.png&#39;</span><span class="p">,</span> <span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_CodingFiguresFern_56_0.png" src="../_images/notebooks_CodingFiguresFern_56_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Mcentered</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
-<div class="highlight"><pre>
-array([-1.09992015e-11,  1.23066002e-11, -1.07718279e-11,  0.00000000e+00,
-       -1.27613475e-11, -5.48538992e-12,  6.96331881e-12, -5.03064257e-12,
-        4.71800377e-12,  7.78754838e-12,  1.33582034e-12,  7.78754838e-12,
-       -9.12336873e-12, -5.42854650e-12, -7.56017471e-12, -1.09992015e-11,
-       -1.09992015e-11,  4.71800377e-12,  9.35074240e-12, -1.09992015e-11,
-        0.00000000e+00,  1.25339739e-11, -6.56541488e-12,  5.54223334e-12,
-        1.33582034e-12,  9.40758582e-12,  4.66116035e-12, -1.09992015e-11,
-       -2.87059265e-12, -8.29913915e-12,  1.07718279e-11, -7.53175300e-12,
-       -1.09992015e-11])
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]])</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-<span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
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-[[1 2 3]
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-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
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-<div class="output_area docutils container">
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-       [5, 7, 9]])
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-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Mint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">Mcentered</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">Mint</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;viridis&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-
-<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">numCircleDirs</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
-
-<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$0$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\pi$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{5\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{3\pi}</span><span class="si">{2}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$\frac{7\pi}</span><span class="si">{4}</span><span class="s1">$&#39;</span><span class="p">,</span>
-          <span class="sa">r</span><span class="s1">&#39;$2\pi$&#39;</span><span class="p">,</span>
-         <span class="p">]</span>
-
-<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-
-
-<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span>
-<span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$\omega$&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;$t$&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;SECT of Fern Leaf&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern-SECT.png&#39;</span><span class="p">,</span><span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">,</span><span class="n">dpi</span><span class="o">=</span><span class="mi">900</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">&#39;figures/Fern-SECT.eps&#39;</span><span class="p">,</span><span class="n">bbox_inches</span> <span class="o">=</span> <span class="s1">&#39;tight&#39;</span><span class="p">)</span>
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-<img alt="../_images/notebooks_CodingFiguresFern_60_0.png" src="../_images/notebooks_CodingFiguresFern_60_0.png" />
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-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2025, MunchLab.</p>
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-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/docs/notebooks/CodingFiguresFern.ipynb b/docs/notebooks/CodingFiguresFern.ipynb
deleted file mode 100644
index 42c35ca..0000000
--- a/docs/notebooks/CodingFiguresFern.ipynb
+++ /dev/null
@@ -1,1924 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ec2a3c4f",
-   "metadata": {},
-   "source": [
-    "# Figure generation for *Invitation to the ECT*\n",
-    "\n",
-    "This notebook was used to generate the figures for the paper \n",
-    "\n",
-    "> Elizabeth Munch. An Invitation to the Euler Characteristic Transform. [arXiv:2310.10395](https://arxiv.org/abs/2310.10395), 2023. To appear: MAA Monthly, 2024."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7dde292",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "# import teaspoon \n",
-    "\n",
-    "plt.rcParams.update({\n",
-    "    \"text.usetex\": True,\n",
-    "    # \"font.family\": \"Helvetica\",\n",
-    "    \"font.family\": \"serif\",\n",
-    "    \"font.size\": 8\n",
-    "})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e26c7194",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4640fcce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'3.8.4'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib\n",
-    "matplotlib.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4188598",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Code to set colormaps to deal with the transparency issue \n",
-    "\n",
-    "cmap_viridis = matplotlib.cm.viridis\n",
-    "cmap_viridis.set_bad('white',1.)\n",
-    "\n",
-    "\n",
-    "cmap_binary = matplotlib.cm.tab20b\n",
-    "cmap_binary.set_bad('white',1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5192867b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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myy0rj4jD9uo+QQV76L5okG4COerClG3kL5Udsy6YQ+aadwmddS59os5taNIVTLpPZywymRu3i52bDara1PaAoMy66fMgpEtzw3z2iA2CssiaUB2p+/rQ+4yKddT0XrCyUOeqxX196hBNimwbAQ0uaFpKq6/BLMQMnbHI8qhMghM1ivNGKu7fqpUw6nJ4fOu0ROtTt19V6oDI3JtvK/nbCtpoixDr3Cc6ZZHlkQ27poVWjqslnooJ0Kll4vK66FhDolZZ3fGLwi2zQLJPibs2rK06fDlv0h6dtMh04ejLHU3zcC7x6brLiGp+ENDGKiZN4twVunQuXSOohGhddJJvif48WNm+osfN7y9bJxOYXgDZZb1tUWYtuzgv12kXbZVNmBBtFOahfcPkPIzLjs8EpkPeiy5EW4TSvjJ08ZyIHr0SMt1Jcz5Azci0UX5eSeS6yGwPFVdJ1br7t7FMV9n/bSKyKDPxAynX4mKxwGazwf39Pc7PzzEejwEA5+fnmE6nAICPHz9iNptl+wPAZrPBcDjM9q/Dpmsxj+iSRnX7da0TNYFIZGJVYIRsJ9Hm+odVNKUY5EkDNOoCNVwnBpsIWlGhyd3a1rPGlVXaRVQPhKMWHx4eAAAXFxdIkgQ//vgjnp6eAABxHOPnn3/GmzdvcHt7m21bLpe4ubkBAJyengoJmStkIsKA6tEZb+pqyjppW4ITgquyOKckEm2oM59m69600cY+X7c8fN79RNgiW61WuL29zYRpNBrh9vYWx8fHWCwWODs729t/Pp9jvV5n1tn5+TkuLy9fiNl2u8V2u82+Pz8/4+joyLpFlkd2JCiTD9U3mqytpvZU6bjrjiliIckMaJr21w1gEUk3KJ6b7Ko2otZfmy52X54lWmTtYtwiG4/HeyK02WxwfHwMALi7u8u2Ad+stvV6jcPDw2z/wWCAJEleHPf6+hrv378XrYYV6jq7sge/qrOihVZOk7sq3/55d5vO8WXysURcnDY6b1VLVXW+yJcw+RCsZxIWSuH3l5eXOD09fWGFAd8stfv7e1xfX+Pw8BBXV1fZb05OTnBxcbG3vw8WWR5VPz2ttG/kxbwoMHVtJGJBNaE652Y6rF90flVVvFSps/ZkV12p2s+kOLX57Pg4X9dHrIXfLxaLPRFbLBaYTCbZ36MoQhzHGI1Ge79LAz6KHBwc4NWrV3ufNlG9SRnp+A1ZESv+TceqTdMkVNIlTKVY1FmFxf+76BDzlm7Z9jpkBF63/dI5w/yHEFGklqharVaIogjj8RgPDw+IogjD4RBRFGX7JEmC4+NjRFG0J3BxHHsV7FFH1WhZxkUm87suIdthlrVZm+Jvy4Woew/o/N7UOfVtUEbCQdi1GMcxTk5Osu9JkiD9aRpmf3d3h8vLy8zyyoffDwaDUldkEVfh96Louhj6NFlswj3YBi7mbExHbqree2XH0HWB2gioaRtOFfiB8WCP4XCYhdsXSQWqKFQiwuU7up1yncXRpYeiC+dkWoDbtDZ9GUjozFO2QVN9GdDlJ71aa1EXUxPAXbLSTCU3d41iaLzt/LkiujmSNvH5Xhe1YokbuNaix5RNZpsKNnBJF6wwFZrOt5hOYPO66qYKmHJzyg7kQrvXgf7d5yFBi0wB06G5oYX6iiY36+BrrlFTArOLtqmrl0pIvynXuc7xfLrPQ7DK+uLipEVmEZV8sqbjhRC+n46k6+pr+uEK4WEttke+Q89bHzbPpa37pJg6AchbaG3i0/MlQv4ZJP+FFpkmtqwpn6w0lY7Y1PxQ3TFcWm11Aw2RhG7TUYsuMBH8Ijroa6Njrirfx2evrfLbRlQPKGQGsHnjtx0GrCpieWyF47t23zUhEoae4oOYqa4uIlt3kevUtpA10baAtVEHH6CQOcb2KM71ja06WpYJt7Y12m8zCk8lX8qGMIgeR2c+S1UIfOmkfc1t86V9fIBC1hIu3BIuLEDdFShcYlIYXHXsZWXroNruviSv+yIYItioa9ueF19hsEeHsRUc0tdweh1SIfBBDGRxdZ19Df5QqZNLESPi0CKzhOsJY5WQYRvzFG09lDpWmYlBgW7gSdEqtOHOrSu36l4w4fYUOU5TeaafmWKQThM2ytcpry+Ri8aXqCJy1D2YNiyfpo6gLtfHxQNh24Vl6sE21aHrlit6PibqVXYvNEVhmqapDBsdt4iY9EUwQocWmQPaTLBsq+y2BME0Ih25rYhEkVG5iTJMpUoUj5tiqp6mMGHB2ipXdPDSF3HlHFlAuFjxgagRYgCHKEWRtlFeaAMX32F7lkMhc4DIZLdtMfMxVD0UROsssoqHKfen7Pxd0xypSUvMtMtc9ni2chZVziu9VqZdwGQfuhYd06absYjtfJWmTr3KpWVjpQzd4AXdIA6Tod6651A192PSBSgrtDqBR3W/1XVxm0pn0S3H9Zy2LzCPzGN8ScR0HVlpC5tCZoIy4dCJqjRpPbUZZVo3WCkT3yKqA5y6e8F0MJQJIbMRXRwKFLIA8EHQ6qIZXdXBJKKRaG0jI2o2AidMtoGKdSwqQjIDENPtVHf8Okxae30WMYDBHkHg401ZNhcQasJvGSGfh6m6t90GRWun7Dkobk//32S52LpXRcqVpel3bV+nkKBF5gk+hOjLuDds18kWKiP9tjFhQdm0VmStS1NWhotrJ2M1qVqPdcdUqU+XoEUWGE1RUW13uCaCDnwg384h1l+GKivGdGShLjYtKR1sRUuqiJhv18w3aJF5istADNWJ7RCttKpzFRkB+xAYYep4gN3kZ5fRgW0kXNso09YzH3ICNZeo6jC+3JhlnZWIm5LIYVpAXQiyzTJs3P8ugyraTrHpInQteorvrsY8IQaIyFhavp9LGa46y7pyROugmmhsk7prrlJ2+ozU5VTW/ZbUQ9diALgIBDFtSfk2WV3mUtRNli3DRni/DykDdXVQdS2aCPKw3Tamc/hU5pp1hD50EWSwR4dwMdFr+vhVdfahQ87/v6oTVmmPYnCFyTaVsW586bzKBgpNlonsMV3ShovXl2vpO1IW2WKxwGazwf39Pc7PzzEej7PtALDZbDAcDhu310GLrBmbk8K6x5ApJ4+tMlVTC3SwETjQ5AotWkA2IjPrLFlbFnhxPsy2kKm4/lSOJ3pM3XspdCE0Huzx8PAAALi4uECSJPjxxx/x9PSEOI6xXC5xc3MDADg9PcV4PK7cToirIJGuuFdEqBMSV65O0264/HFULWSZOti4T1zeg10TMRmELbLVaoXb29tMmEajEW5vb/H582es12vMZjMAwPn5OS4vLxHHcen2JjGjRSZO275105aVyeOJnkdbI/y0bFudv02qrD8buJwPS8toqofOcW0Iq2/z0SYxPkc2Ho8zEQO+uQuPj4+xXq9xeHiYbR8MBkiSpHJ7ke12i+fn570PEaPNyEZXywCpRAyGZInpJAOHcH4mcZU2UCxHN/hHZ76ViKEU7HF5eYnffvut8u+bzUZ4+/X1NV6/fp19jo6OVKpEKpDtJFUtMVPUBYm07SayTajn5+vKHCZo45x8urahIB1+nwZwnJ2dAQDm8zmSJMHV1RWAfddi2faia3G73WK73Wbfn5+fcXR0RNeiAiZWUaibj3CRBlCH6DxQG6s0NGHLZeWaJveejivYRpSry0CRuvLT8zO50kkfcs+shN+vVitEUYSzszM8PDwgjmOMx2Pc3d1l+6TbqrYXOTg4wKtXr/Y+RA2TOWCyD72LB8fXkH4dQnI7NZXbZkh93UDBl/vDpIjZ/H2ICFtkcRzj5OQk+54kCdKf5sPsB4NBZq1Vba+DwR5mUB2tNVk2TQEZbeTGmJjsdtnZmXTf2gqAqAvnt3n92gh+0U1VqBNRketj2oPQJSHjizWJMzGT7QhcRbnJludKzEwLmcz+MsdMj2s7SrEubNwXa0oGlcGFqUGXiij6LHwUMpIha7G4CnluI7emqeyqztp10nS+Hk0dl+3r05RkLSKmstG1NgS6CpdlidSjDJvJ010QMq5+3wNULKY6MTPVybtI4Kw696qRaFUdTHZ2MiLWhK51LErZcWXKctFp6px72yLmmhBETAZaZD1DNLqv7EZvGpmbpM0ISJPHkz2+yTbWSQYvmxMzdb1FBkK2y/Th2KZTXXQGPr5Ci4yU4mJ1hJBRDePX7ShduXPbxNfgm7bQSRnJ49sccBtw9fseorraQEpZSLONAABXFl/aHmWWgotVHco6NN1rlB5Xp/4mAwHaTjOweS+ZuFdti4xOEFQI0LVISnHpRhTFVrSc6iS7CaHRKV8GFZemahCHiqVR5+r14d4TIV9XmedG15pXuX9CETG6FokRfOpETE9Qq+T45PfXcQ25blcZ60pmXqzsuKY7yarr5IvI6bqgbeFD27iCQtZxbFoUbWFS0NLOUHeOQlQQTc2LqFDXbuk5yLZFldC3mVrhgiYrqqpuJl18IukzMr8LGQpZR9GdGwkBk51mUwcu0zmU7RtKm8okI4tYq13sNAE/EolV2rjtOtuCQtYRdHJnVEPsfXHtmLJyVDsn0cCXsly2uv3Tv5lu47rrXvy/6LFkypLFh3usCZU66oqKqXzOLkAh6wBtu1jKOvK6bS4FsC2xqConT1pmm+7GPPmISZkkZxGaBglVZYbQOZddP5sWqc28tVChkBErVIlZ2d+L+9YJoeicVpX1U6yHCLY60zKLSKSepiJJ69rTlSUYIjL3nuh9JxuMI1KPPsHw+45hovOpcjWKuNB8mDvIoxM6bgIbLjXZ3LQmZOcGVV1apuvtEtMpElVuXZkBhM58Zigw/L6ntD1vFeoDk0emg2hCZ/QsOodVFYVYVx+T1LVX2/eDSes1fzxbqLgIfRV/l9Ai6wm2O7CqstruyFJkLAuTQlaFTlCJKZGSqYMrS1unvUWtJl1xU7HkTVl0sgNVX54/Vay8IZqES9N8i84xUtI8pLJtPo4aq6wcH+sKNLf///7rf1586uhahyjjRjYpYiKYuqd8vTfbhq7FjiI7NySKi3klE4icf1FgTbmhRFB1VYkGu+T3F62L6u9NIJP2IXOcun3qcDEPaXP+NoSBh0koZB1DtgNQyRnSne9pE5nIMFeuNFU3o26QRtX2JuvUdLuU3Vdtz/W2HbTkY5v4DIWsI6jk9NicUzHxOxvUnXPaLr53GE3106170xyhTjSlSJn5bTYtM5FjtD1oq4saNnFsn55NHShkgaP7oLso10Ukm2vhMRkVqBocU9YRiQiczryobOfny4AgX29XXgiTyAaTiJ6jydSJNmHUYsC00UmYmEey5ZoyRf4cq0bCou1g0/LVibL0oWNSnZszmbdl4xiy10+1TJcpFk1l2oJ5ZD3AZXBCvkP2ZZQNuElgLhMjmyHTKi4fnblR03N0ovNqpsWqimI5Ms9NVdBFmcCY7OSbLCwXIpa/b+uCT3wYFFHIOkAb0XY+ENL5yoiZ645BRdRkO1LZMopWsUxZZfvVDVB03bk+dOSAu1xRn/qAFLoWO4Bri8zk8VStAVtUjYR1z99U/pGI29MEpsPPVcuqGvXruPZMBsOoUGXRlR1fJp3AhJVmIk3AJKJ6QCELHFejo7Y7zTw2raQ6IdMpu9g5mRAKF5a4iCiYuDeaOmrVOS5bHa1sWoKIWKXbbUedyhyjTREDKGS9wKWJ74OQ2Zo3qXN7lQmZiY6iqi6yxy1atq7cSyku5ihNl1NsK905sxSZKFLRgYyqtapiwfowQChiPNgjSRLM53MAwNXVVbb9/Pwc0+kUAPDx40fMZjMAwGKxAABsNhsMh0OMx2P5syC9QDfwwCU2UwfyxxYdOOQDcGxaH66ui4sAAh13cdO9KmqhVolNUVRU26PtQadrhNdaXK1W+P33319sj+MYP//8MyaTSSZocRxjuVzi7OwMFxcXmbgRs7i6qXTms2Sx5cr433/9d+1BmePkf2cb3c7HpoVedmyfotZUyIuFScta51gqdUl/oyN6Im5sl5a5LMIW2dnZGTabDZIk2ds+nU5xdna2t221WiGKoux7FEVYrVa0ygzi8sYx7cOvw5RrqapTEZ3zKfu/yO91MTU/YjNIpWp+qO3OTBQZt5vKsXXmpeqiPkXSCEzfP1XbROvoCu3w+7u7OwDfXIgAcHFxgfV6jcPDw2yfwWDwQgBTttsttttt9v35+Vm3Sp2njQ5D1opRraOsf7+q/LL/l5XVtL3p4Syea1sdelpusey6OZo6sZMZUJjswGx2hC5CyWXmNlWP7VosbJ6TKbSFLO82HI1GePv2bel+qdAVub6+xvv373WrQSxT11mZvoFlgxdcWElVwR758ss6mjKxsBUeLhOEUjwP3ShKW+JtwsKoundVju1i7k50v6Kr3FYwVNWxfXIpa72PbLFYYDKZZN+jKEIcxxiNRnv7pQEfZUynU3z58iX7PD4+6lSpF6T+ap9upDwmHihT8xamEHXfFOcrdMqpo0yEfGszFUy6yUwdw8bxTLkAi9t0+wSZevl0r0mF38/ncyRJkkUtPjw8YLPZZHNfo9EI6/UacRxjMpng9vYWAHBycoL7+3uhMhh+L4btMGtVS8jFze3yYS0rWzQyTfR4eVy3u0xb+tRx6dDmtTN5DFuWveyxbQ6ojb8herVaYblcYrlcZqH1x8fHSJIks8yWyyUAYDgc4t27d1gsFpjP51k0IzGD7eg0mTkPl52bz1aoKjrzf6rHMPXbUPHlnG0PyHSO70sbicKE6ADx5SazmYRbV54JdCPLXJ97WnaKSBCHynGr8OWe8w1ZL0bd71WPYzMsXufYJp5Xrn7fUdih6CHruvEl1Fymw5TtXEUs8DaE23dEwuGbKHPRydxfPnsoXEZZagV7EPeYvClCc9XputB8i1KzSVuh2r6SD5DqUpuIDGp0zjmUgQtdiwERyk2lS9PI1pXvvy6XypVVpjLKL8ttEz2GTTeVD+i6ApuOqXNcnbrJPhNtzM2qPLfGgz0IyeNiVGtyBG0qNF00cdgnZM69C2JVh8/n5yLyNP+bLlmntMgCw+cH0QZVI0GbYeMiFkxdgrRouTKWp4olacJyNGWhtbXiSR2+tI3IMX1YGKBI0X1to460yDqITx2Bq4emzJqQLVulriIrY+j8vYk2c+VsHKeOtqwDn54nwJ9rLoqN50oVWmQBoGJR5EdLJkadvjz0ruYCZEaXpsPxTSRJm75eLudIVOf3ZPe3lRidp3guqtGIPlpkRUQ8GLLQIusptju0urJE/+YS1XqEnFtVdPnoXAuTllKxvarm7nTqLbLiRP6cfJp/Db0ebc65UcgCQGU+yMRNlf99Xaegc3wVC8vFw9pmh6ByjulvTNbbRqdUVs86Mav6e1MZKr+zjW/1sYXpgYIIdC16jqlJY1Gzvyn/SNTtU/xb1femutVhwmrStWBNunFtYkuUbGDCjem6nCb3omoaRdmxdOoSGnQtEgBmLRgV/36Z1dWlBy2PbDSlS9pyO/kUEFBWjstnQ9WytFGXrkGLzHNUJrmbLIOq0WFTp1NlrdVZcSKh5fl9Vawa3fB32d8Vf6sTnOCCqjats4ptzhGqBtTYxISHoOxYeVSOa/reDg1aZD2lzRtY5kFtmuQ34Wd3aQ0ULc8QOpK6ebW2AwdcIxIkonKstvChDi6hReY5uj7xppG46P5ND7qpEHSTAiBjDTb9puy3VZaYi3MQwcX1cJVnZqMsnftA9ni6x1eZLwthMNUELbIOoPIw5UfZrufGQhgFmhhtN7VtnYUTQhvJINueOlay6Y7Z9LVwGfxStz2la/daHRSygGkzb6OIyVwjEx1+2ZyPzbaqypEqOxedc1RJV1A5hkx7md7PJW2KmWh79EmQVKFr0VNEw+Wb9hX5vUzZdb9XDdYQQbUDb6Oz0HGFNbltVY+bHrsN60b3fi0ew5Xl48pFbiqkXnXQ4jN0LfaEUNwjbSDzEJt64HWFvMxSM9kZ5UW2ylo0WU4RGwMGU5hq95By9roC3xDtIaJBCipuDFuhxcV9bAmsDTeWj52EStCI6jUWETOVdi/eB7oCrxvFWsTWvSx77USvW9MzUGa12rLAfYNCFiCy+TcmwsJtPAgqHW9ID6YtN2tVWYCfogz4Vy/b909I92kX4ByZZzS5N0yH3Ip2MCKhymnHXexUq77r1KfpODKYtFLz84Rl21XKq7sHZO4P03N3otgsz9W56MxLqmDqOQ9dUDlH1kHqXI4qUXmyllBdpJ2Iu0OkjjLn4dsoX8RFp5pSUUYbc4C+4et5+RJR7LulbgpaZB5RNdI2PaqSscJkLTaTdTVhLZosRxQbnUdbOUMur6NO2aYi/3SOb7rMpnJ9EErb0CILjLwAFG9Q06M7GxaPzZD7NjpxnTY34aosfog+rsLpTcLrLwaDPTzC1Q3bRrRjVdkirkaR/Wxg4pxdBXy4DCwJlbbuIQqRfeha7BEuO1TVjrVtwTKVeFsV+CJ7DFFUj2/bJQeYGxColNFmTpersrsslMZdi0mS4MOHD/jw4cPe9sVigcVigfl8jtVq1bid9AeX0WHET2xZ8kXK7rW2O3gfVrjpC8IW2WKxwN3dHQ4PD3F1dQUAiOMYs9kMNzc3AIDT01Msl8vK7SLQIrOHTGdganI7hHmJKvePSSvHVOh9FSbm5WyN9psCFmy1iW3aSBWpKt+XNjGNcYvs7OwMo9Fob9tqtUIURdn3KIqwWq0qt5P+YGI0WhfubxKXnYDs5L1LC7VYN986RwY+7JNvj757MrSCPdbrNQ4PD7Pvg8EASZJUbi9ju91iu91m35+fn3WqREqQXQnEN9pI6lRth7JgliZBbmp7kYCBvGWjYuXURc3apG5+siuiZfs8utpuMhgPv99sNlLbr6+v8fr16+xzdHRkukq9p60EYx9F0WdMhNubCMwoflwRmqvMx8T9ENrNBlpCVnQ1bjYbDIfDyu1lTKdTfPnyJfs8Pj7qVImUYCvKqqrT5ST3PiIRd2WWnC95ZLqCFvJqLU20fW3IN7Rci+PxGJPJJPsexzHG4zHiOC7dXsbBwQEODg50qkFqkFkZQDfAI9/hmQg/r6tbFzsQXReq7TZx5eINLcScOXztIyxkq9UKy+USSZJgOBzi7OwMw+EQ7969w2KxwGazwXQ6BYDK7cRPTM0H1RHqw97G3KHv8xxtzVn63Cah3t9dgQnRHUY2SdTG2oC2xM6FVdB0Dk311encTCYh2xRjE7mCsu3ks6C5TMzuA1xrsee0KWKiZVbtK7pslU1sliFybFPXow03YJ+hYLUD11rsCXUPmC9rAbYVAi6DiRywNnLjbK3wbwKRObGQBLPsXvfdNRo6tMh6QNsPkGpOk2+47lxtuHp9O1YVxUhJXyI4iZ/QIusgovk4vgoG4FdOUdvl+4bLYI9Qo1VplbmFFllH8XH0qlqfNpJzbSHbBj6fs04gi869EAq+PX9dhkLWMdoc9RWXytFZMqlrpJ13G6HrNun7dZWF7WUHuhY7hqkOUme9vrr66EZN+uieKQp4GXVuJpEgGNNzXCppDm10wqEPgkKvfyjQIuspohGEpqh7oGXnw7rSMbRhnam4Nm0vn1V3nLKAj9AIsc6hwYToHqKzErsKqsdsihL0sYMIJf8LcPPmZhl8SYa3QQj3ro8wIZpIk1pNviRH14Vg+9gRmFydIhSrM5R6to2P92uXoEXWQ5pGhzaTZ3VXUfcdGavCFwvEJ8usK0tV1dG1gB+b0CIjpbieGyujqw9wiNaJjJhWWcJdvZ4kHBi12GPa7IB0oiJD7zhVAi5cB4TURVnaqkvT/RD6dU9hJKN56FrsEU1CYHtJpOJ8l06ema+dmuqq+U3u17bEQxQT9fPF1eoKH1NJfIOuRfICnSAJ3QeuaoRve5TvGpGV+8s67GICeWiYFMSuBMI0EeJ19hVaZASA2GtfRDoQlwvr+t4R+BREIYpunW0Hffh+zYlZRPWAc2SkFNMWWPHYXRlV28ZnK83HOpF+QiEjQitrmJ6I1wnJD60DNSHcnE/5BtuBlME5MpIRmhsrBNpY+aLrsB1IEQoZ0V4pQ2deTGWEHUpHZmNg4OrcfbZ6fK4baQe6FokWKp1KU3i5SGfdRxeTi/OVWRfT5/k70i9okZFGZCLIQozUawOdOcW+E4pFTtxBi4xIo5OLVpVDpfL7UK0y3Xe9uT7nJivNxnWoKzPEa07sQiEjtZgc/fbZWgut3qnYitabbkbSJhQyIoxuQIiJEHQTdfEB0fZoez1MWVwkModqiRN7GBGy8/NzTKdTAMDHjx8xm80AAIvFAgCw2WwwHA4xHo9NFEdagB1HO3TB0rHldiQkxcgSVScnJ4jjGG/evMHt7S2iKEIcx5jNZri5uQEAnJ6eYrlcNh6LS1SFTdViuCG+p8s2qhZqm+ctsihyHSasvNCvOxHH6aLB0+kUT09PWC6XiKIIALBarbL/A0AURVitViaKIx0mv5hw1zusEM/Ph6R5Ri2SIkaE7O7uDovFAvP5HPP5HACwXq9xeHiY7TMYDJAkyYvfbrdbPD8/731INzDR6dUdo68dmi/nbXIOzXa5pNsYmSNL58QAYDQa4e3bt6X7bTabF9uur6/x/v17E9UgHqHT2TQtONx3fGoDlbU5ZYI1uMA0EUHbIlssFphMJtn3dH5sNBrt7ZcGfBSZTqf48uVL9nl8fNStEmkR2+8861qnlj9nnwTKNl27jqRdtC2y4XC4NxeWJAmOj48RRdGewMVxXBq1eHBwgIODA91qkMDJv2CzqZPrWvi16lqTPreByJJjIZwHCQNti+z4+BhJkmSWWRqZOBwO8e7du2zuLA3PJ6QO0ZE6R/Th0GSl81oSXYzMkZ2dne39W9xOiCo6od5dJkQrpu5ailrZIZ43sQ8XDSZOkR2Z591PfQnLFyFkYZe9hrzepAkjCdEmYUJ09ylLcO36W6Jl6UtSeErT8mNdmxclYjhNiCZEB67cQPJWWsjWJmkHChlpFdV3mnW9s5Nddb4rpILWtfMiduHq96QVZBJige512CLw3AkRgxYZcY5KJ8Ugj2r6IHS89qQOWmTEa4pJs33s0PpsmREiAi0yEgR//dvf2ZHXwPYhfYZCRlpFNmKxz5013auElEMhI62hmk/WZzEjhLyEQkZapy7kmlbIS9gehOzDlT1I64hYWMWAB3bm36hqO7YP6QJc2YN0iuKai+QbbAtCKGQkIBiZVw7FnfQdChlpHa5qbwaRl1kS0kUoZMQb+FJNfShmpI9wZQ/iBTIBH6QeLrpL+gYtMkI6CFeRJ32CQka8QMTaYqcsD8WM9AEKGSGEkKDhHBnxEs6HmYNtSboOLTLiDX1+VQshRB0KGfEKihghRBYKGSGEkKChkBFCCAkaChkhhJCgsRq1uFgsAACbzQbD4RDj8dhmcYQQQnqINSGL4xjL5RI3NzcAgNPTUwoZIYQQ41hzLa5WK0RRlH2Pogir1cpWcYQQQnqKNYtsvV7j8PAw+z4YDJAkyYv9ttsttttt9v3Lly8Avr0ZlBBCSH9JdWC329Xu53Rlj81m82Lb9fU13r9//2L70dGRiyoRQgjxnK9fv+L169eVf7cmZKPRaM8CSwM+ikynU/z666/Z9z/++AP//ve/8Ze//AWPj4949eqVrSoGxfPzM46OjtgmBdguL2GbvIRtUo7v7bLb7fD161f88MMPtftZE7LxeIzJZJJ9j+O4NNjj4OAABwcHe9v+9KdvU3evXr3ysnHbhG1SDtvlJWyTl7BNyvG5XeossRRrQjYcDvHu3TssFgtsNhtMp1NbRRFCCOkxVufIzs7ObB6eEEII8XNlj4ODA/zjH/944XLsM2yTctguL2GbvIRtUk5X2uW7XVNcIyGEEOIxXlpkhBBCiCgUMkIIIUFDISOEEBI0FDJCCCFBQyEjhBASNBQyQgghQUMhI4QQEjQUMkIIIUFDISOEEBI0/wc2p80Vb9oFEwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "F = plt.imread('figures/leafbw.tif')\n",
-    "F = F.T\n",
-    "F = F/255.\n",
-    "\n",
-    "F_finite = np.copy(F)\n",
-    "F_finite[F==0] = 2000\n",
-    "\n",
-    "# G = np.ones_like(F)\n",
-    "# G[F==0] = 1\n",
-    "# print(G.shape)\n",
-    "\n",
-    "F[F==0] = np.nan\n",
-    "\n",
-    "\n",
-    "plt.matshow(F, origin = 'lower',cmap = cmap_binary)\n",
-    "# plt.matshow(G, origin = 'lower')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.title('Testing')\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "55c62385",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d9a8a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "F"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c5e8ecb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPic(F, theta, plot = True):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    DT = np.multiply(F,fMask)\n",
-    "    \n",
-    "    return DT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f80ebea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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XtL0g7Z+fyMaO9g1Bak0JFigtxq9SNmxIJ9fR0VGYmpqC4eFhAChf5m1ubjZnsQTV94hkl7FCakOtkwiNTlt4SYeWr0uXKLmFKa4IEhRzRkx67XBSPqrEDMOw+bGuCsVi0Xx4KZvNQktLi/mzPvQrN83NzeYjzqL9dszMzMARRxwB+/fvd/XSMKKXsbc6hMe6Pjrhqp6fsDaxdtDH6WNO9XTZwxJEnJxsQBAZvIzNpG7P45dC0wf/WLre7O/fg5Fz7o9kPpBOrkGByTV83PvWEuGxyz66W7qsqL5qHbfyZW31wyZWN4vfMUAQr+hIrt2PdcHhCsn10O/fg9FPj0UyH+AKTYiQe978xPxf4vselTIE53sklx/7S7Nuub6/91VKxgKLTmIDb+UiUo4kpGr/nCEynLjnzU+YZVlbLj/2l650u7EDQYLCz5+cCxuYXBEhbh6Dv+LYZ+D7b57pWK5cxj/5Vxz7jPk38eP7b54JV8wnHJF+IpvUdxMDVoa4zIKqv+10y/rupBtBakXJUHsCuBSq66pqYHJFhKguUPCV4/5dqt533vhLUHlCmJVPEkdZjj20LjrhXHHsM7b1K8fU7aRlEJtFNtnFgo0jKUvLZH3g6UOQsOB24f4ogskVEaKyQMHVxz8BAAA7Xj8b/L7MS+DZR+ygy1htq5TzY2m1q49/wqKHh6xeUbkdr59t+kTKsH4jSBgpKa4trFI2bGByRbh8+/VPg5sk6eeKKmWbaMq6rjn+MW7Za45/DOZgAVVvgeW4ri8BrH67GKjodZYDpizymRcLBAkLjfQqTnTn3IivvGcc5rhdc/xj5t/feu18AADLPj+3a45/zNRFIDYQ+7/12vlKPrjdvvXa+VW6yeYmriplWV8QBAkHOHNFuMjc6/iHV5NAfz/7h1eT8PUTc77fJ/n6iTmL/vL/AF8/8VGzzN+f8Ki53w7WBy+2VPRVx4QgGxtVu2gdCBJW8J5rBNj2aif0npittRl1i9tH4Le92glufte098TsfF37MjTERrKfrt97Yha+fmLOUaaTHtLPWNk0sj7zyvH8lpWH/R+JGiVQXFsY77kGy5ZXLgSABfP/l7n+pIdqZ1AdUYlpcN8YSduRZEG3Kw9y/PqT2PLVNrv55rvllQurbBLJIX1RRl7viVmub6L9dmB/R6KInz+WHjYimVx5s6rhV5ZB/0m7amBNfaEyY+0/aRcMv7LMkz7SZlY5McsxAinTP59YKnWsNtP13M7AK7p2Vcmh+5qqfFH5OY7PvNg2Wh+XjQGe/9EAfxUnxAy9fDHgc1j+oTLT89oW6ZMfEMohx+x0iWwtlyXo6SsiXaozYyc5Qy9fbPreiMmC+F9pQ/t+QYjyvblGAu+5hpRbXvoseHmpH3FGdeEIFW4+5afzbVim8rdYJylz8ykPcPb73xfsbHRjAx1fazwq++kYkXL1xC0vfbaqL1Q4rCquMv772W8RfTTSzDVSXwvmjAW2G+Kdm0/5qWOc3W4Azm1Ilx148fMwZyzgDq5u7NTpm4ovdN90IyPqDLz4eRh48fPm5zljgdm2uvyvty8g9QpZREJliyqR+VWcm168REnOrR/7sV7DGgzVeANUx9yNDFYeTwbR40W+DhlB6wtzn77pxUu0xtSNr7QNiH50/CrOZx76Kvzxnx4uXe+9/z4EP7vwn/FXcfzk1o/9GNa8cKl0ebrsxlPv98Okuua9kvfLbGSgk2m3jafeb5Yjf5c/i+1Q7RM0lXrV8un+4la+CBJXNzrWvHBpaPoybQvdTk5t5oQX/3T0WcRf8LJwSNl46v0wBwvMjf0s2la/0FVr00PL6he6zI1GNrb0JsKuDtGz+oUu87NdHXbwVbVR1Q/dMlkfiN9O28ZT7w9NYgWoLM1I2y/ri2zburUJCS8kuapsUSUyM1fC5lPHuJ9XPb/cth45vvW0+/wxLKLQnZeO4dbT7oPNp445xlUGIocX+7L8mOUY2cej2h5/Tz5dJ3fF7hjjA19+mPopfe7QfgDoi48Of6M8EDcKjTRzjVxyFbH1tPtg5XMrHMutfG4FbP/4zgAsigaizkviRAY9p9jSMeWXrdZTLle9P0wnlBtbtn98p1RftKsfJugY0H+L2k8W3X6WjBie3yHHALVVl0L1QJAidZNcAcon63WTlzuWo8vc0X6PnyaFHruYWfcfZsaKV54tS7ij/R64bvJyS5xJ2Tva+YNgmF6rENlC/BLBxtUudmyZWlJpG9q/w6hj4WkbljD1G4RPI81c6+4mxR3t9ygNUjLJuN7xMqh7ifd1k5f7Gn+3fnm1ifaLxMdvX6OEap9RBeMcXvCeax1wR/s9cM3EFVJlSblvd3zfT5NCzfsS3/qvmbgCvt3xfUvZSoyr65N4vm8cZlvOjS0ylHW6k8XzVVY23Y9U/A6yH/J00T55iV0tiJq9jUojzVzrNrkClAeOq3d/Ubo8Kbtjyff8Mim0kEFWJl4yP2BMYliWp3aCqLabXzjZvmPJ94R2Xr37i7Bjyfdc/dhzEP2QtctNO8kSxPkU5R/VbiQwudYR5MS+avxK6Tp02btOv1uvQSFHJl5O632SmJVluLvzUOs1Ra0+iNmx5HtmmbtOvxuuGr/S9NuL/7Ru3X2QZ5ef8b5q/Erfz6Na9xdEDsOIgaGQMFXKho2G6ZF3nX637XV90fEv//LLtTa9Joji9OVfflnqHgkpJxN3Ff1BbTJ2sGXI33edfre56bDly7/8snQ/lCmn6qebjfbf78TK65ONet4i4UFp+cNMJgOFQgEmJiagq6sLkskkAAB0dXVBOp0GAICdO3fC8PCwWR4AoFAoQCKRMMvb4WWJLRn+5tmrhMd+cMZdjuXoMo0ILy68uMnEUoSXujr5wRl3SfWDv3n2KrOsqH948UO2z/Fir9sWGrv4yNihG9qWRj9P/UDH8oef/Le/hz/60ybpeu//9yw8/flvRXL5Q+nkOjk5Cfl8HlKpFBSLRVi8eDHs27cPAAA6Ojogn8/DkiVLYGxsDOLxOOTzeRgeHoaRkREAAOjs7IRsNuuox+/kSrj8mauFx+45c4djWbZMo0BioRIju1iL6tjpDgLdtrOwcu85cwdc/szVrvsVLa8WMQzD+cCLKaIPHcn1jB9/TTm5PnvJN6V16pzoeUX6snChUDCTYzweh+bmZpicnAQAgHQ6Dfv27YNsNgvxeBwAAHK5nPk3qZPL5arkzs7OwszMjGULgnvO3OF4uc+u7GVPdwdiZ9goGbGqQeuyp7vN/fRGH5PdLnu629x4uokeUX2i20mPUzlWp4w8Oh4iH0S+AHhLBjJ9U7UtVNut1jidx0jtIfdcVTYV8vk8LF26FPr7+80km8/nIZvNQiqVgu7ubjPh+o30A03JZNKS7QuFArS3twMAwPj4uLkPAKC7uxumpqagpaXFLN/c3AzFYrFK7tDQEKxbt86V8V6595OjAACQeuoay37yOXPWt6vK0sfpenTZeuZ95sGR1FPXQOasUUHpctzY+MrCtgPdBiK5lTqj3P3Vn/nfL1k5dj4Q+5xk0rC+yGDX39h24SFTxo5qP62U+0JtzoOyTRX/GuV8jBqqX3xUvySl02lIpVKWfaKJnt+zV1dPC/f09MCOHZVv2fQ3gba2Nli+nL8eLUm+NOl0Gnp7e83PMzMz0Nra6sYs7YgGi8xZ35ZKyPUOHR/RgOs1Hmx9mUQdZBvw+gJNrb6AySY6p/ZjZYaNMCZ6RIzbp4XZK5pNTU3Q1FR9ednLRE83ysk1k8lAZ2en+e0gk8nA+Pi4mWDJ/da2tjaLA+RaN4soSEFCTsJLnry26hjZ9+NP3cmtw9aj/2brRJ2ybwsoH61/s/z4U3eadbzppJGT5bYegW2790sLzP2XPHmt5XjmrG9z+47IJpV+YZUr9oHYR2B1iNrBrv2cCEP/Zv0mhME2pBpDceZKkis74RoYGIDBwcGq8l4merpRSq5kep1MJmFychLi8TgkEgnLlLtYLEJ7ezvE43Ho7+839+fz+UBuInuBnJCfe+I65Xq8Op974jr4ydl3aLEtDLh5l7BW7x/q0Gtt0wVmWxLZ7HEVuTL9oizfWS6vHK3DTs5Pzr7D4gdtl9N5EIb+je+3RgsDAOTfT6ks3D89PW15oIk3IfM60dON9NPC+XweOjo6zM/FYhFIVfIk1vj4OPT09JiG009oNTc3V10L5xHU08JO/K8n/p67/3+f/S1XdWXqhRniE/FDFJ+w4JedOuU69QknHf/77G/ZlnE67pVa92mZ+CD60PG08KmZVXDYn8hfqZz7wyy8kNoqpXNychIKhYI5iWtra4OpqSnI5/PQ398PY2Plnyft6OiAiYkJJfvdID1zTSQS5qs3LCRpsslTJpmGFdGli4sf/xo8cM43beuSk/rix79mqQcAjnXDSNn2mMX2qDyNqcPOB875pqUtdcm160sk5k7Y2SErQ5aw9V0738NmK1LGzxWa2tvbIZPJmDNY8nZLIpGAFStWmOs0kKeI/UZpEYkgCMvMlXDBL74hPPbwubd7kqNSvxYQm3l22sWlnnj43NsD8ZWOsaw+r7YRnSoywtRnRXaHycZ6QsfM9ZT7VivPXF9avjk0+UCFul9b2CvkRO38+cqqY50/XwnZ87YryaFlsTJlZfkNsSt73u3c/WXcz4iIn7yYBgkbb9qe7Hnbzc+8wVq37aQvleX6v/Yq7R9pZxmf2BjVEpHvFb9qY1+t9YcZw1C85xqqqZ8a+DSAJNnztkMJYlXb0p/3Olf2UZZulv68F0oQ4w4M2fO2C21X2Qhe5eiyg7Une952Mw50XOj28cse2bKkfdzqIv7R/U61fWuNTHyChPQREkekGr8XkQgTOHNV4JHztgEAwKcfud6yn3x+bOkWZVmsPPpvFXleqfiwzaFk5fWHx5ZuqYqFqi5efbdyVeDFlvj16UeuN+NQscP6PfSR8/i2e6EsT+77rsgugmoMrWUXCPue3+0igndeiF7DCRKrXc7nTqPTSL+Kg8nVBY8t3QLn5FZX7T8ntxoeT252JY/UZ+UBgCuZslR0yCdy0uHL/vJjIa+bf/Kwg7tbHfa6WWKcMuKTW9QPguLx5GahftZ20odk7eX1Zad4+Ak7yIps8fNcqdYPXBsQMSUjBjEfV2gKE/hAkwc+le3j7n+yc1OoZdPyVeWJ7NIJzyZVvU92bnJtq2xdXW1BE0R8VSF+srb54b8IN+0flA1BxqGW6Hig6bgfrlF+oOmNL2yMRD5gwZmrB57s3ARnPdxftZ/se+oC9wtEkxOWla9DdllGzJWMIL5J8nx0o1fUPjLI6KNle2kPqzw1P5+6YNi1j7JU5NduFlHrGYyobXS0eyNRfqBJ5bKwj8b4DCZXj5CT64yH1lQdO+OhNfDshRu1yGd10H/L6LCWdz8gzAU4yNHxe+qCYW6MnbBrHxY6jqp+Evm0DNX2dxtbt7EhONnoRbYuvProlorO6F6eDBONdM+19k8E1AmiAUrngGCnw06PaiKuR2S/gNQqkejuJyrtLNt37Mqwm05U5Kn67lavnZ5a9iMkPOA9V80sefAG2+O7L9oQqD7denn6dl+0QdoOt6jo4JUl+0gc7GTRsfLql1PcdcgnMnS0g07fWXlucWOHV71Bn8dRQMc917bvp+GwP/mAdL25PxyEqSuGIpkP8LKwZnZftAE6HlgbqD4AsNU5cfF6rfp4uvz2uyxb7hIRryy9T0WW35elvMonvkxcvF7JLxm82Karz+n2SU4fCHU6+dXxwFqt51u90UiXhTG5+gA5uT7+s+pkQ/Y99xm9JyB9QrN6deucuHg91zfR/jDx8Z+thec+42wnGzO2vIwMWp9Ivgw8G1j9XuPO2uhFns6+XVK8ruZGt64+G/a+HwoMqPzUjWz5iIKXhX3m1J/eZHv8hc/eGrh+XTpP/elNQln0MWID+5mHTBk3Zb3U0VmX4NZmu3h7wYtvdH0ZWB0i3ao2ue3XKn1Npr7f53St0HFZOHH3WligcFm49IeDkL9yfSTzAc5cfcbpFYKP/eRmePFzt/im/4XP3gof+8nNVToBwLNeO9/oAYYdbIhNdvp5druxg6UiU/1yE7G5ZMRM22VtZHFrM9t2XvwR6RDx4udusegX+S5qW54Oqx+xqvIvfu4WYV+ws0EFr6/50PX9PJfrgUZaWxhnrgFxisQg8JJPJybR/dLnbhHa4ZduHcjErlbQcbOzU7acrD4dMVGxSdR3WBkq9umKiZ1cEbL6nGTRcsJ8DulAx8x10XduVJ657vnKbZHMBzhzDQhy4p38bwOB66YfCqAHANqWk/9tAF7+/LpA7ZIlKg81yNhZjrk3f3TIYHGyndZJ+gnbl+m+Jepn1TIJevyx68N0H5ftU3bnhR/tUPcYsfKmUj6i4My1Rpz4o0HhsVf/SnzMqz6RbJ49ftjhB7RvdnGtJSSWXuzTIYMnz0kmT6+bvuF329jZxNouY4usPJny9YCOmesx/3yT8sx171dvjWQ+wEUkaoTqies3r/7VYJVNYU1UPFQGtqgMgsROtm102a/SvmzZE380aG61htd37XCy2UleIyZWbRgutoiCM9cQcPz94ns1r1+q717U8fffoiSPZ5dOe3RB20nsE8WUtt8u7rp5/dKbpfSRcnblZWXJ6CIQeU7xE9VXwavtXvuwFz0y/UoF1XOyluiYuR49erPyzPXX3bdEMh/gPdcQYPf15rjMLfBGSs/Jp/o1ipz0x2UqA4pOe3RB/KLtkvE1yK+VdAydMAz78iqyVHT6JZvtM17jztpo1x/d6uLJ1B0bP2IdCUI1nfMPTK4hgJzIx47x3487duxWeLPL23uMAOWHONzIogeaY8durbJTh21uKdtS/dCD6IEVq+3BPSxBYiRqYwLx582umxzL6oTES9ZOlTZn+53uB9SIraxNor7hBM83O1lu+j/dzo0ErtCE1ARyon10521Vx8i+t1bc6Ek+T7aqDNYuWqYX+1Qp641V6ST7WXtUfCf16Lh7jR0AgFFyHiwquvQMLCLbP7rzNtNP2i5Wt5c2pWURfTIxUIXfzhqXghTYrBob2rYgz5XQ0EArNGFyDSFvrbgR2u71Z31S3d8EyQBB7KXtnrrMv+XgynpiXB10/KxxlPOdlkkPgCR2U5etNeWSsk7tVSlvjZedXl19gMTKTh/xjS6ro/3Y/iayxQu0nary7Xx003ecoPtQYxIDtVhGd+aq9EBTJpOBQqEAExMT0NXVBclk0twPAFAoFCCRSDjut6MRH2iyIyEYYPMuT04iz219VT0EXfpk7BfFzA0qdjvppWXpLCsDT17+srWQuHd9lY+8fSrQ9WVtJ7bIIrLPS6x4cXBrB4tf50OQ6HigqfWfBmHB/6PwQNP/PQjTfzsYyXwgPXOdnJwEAIDu7m4oFouwePFi2LdvH+TzechmszAyMgIAAJ2dnZBMJoX7ETXyl62FxL9W/7wV2Zf/a8Wf43Ixc6X1y+pj7XZtb5UdMWcZNbpPI2orAKvfxA9pNPiT+NcNFRuMcgzL+6oHeU+JlfUt6AUDdMbKRlYQ511dgpeFqykUCpDNZiGVSkE8Hofm5maYnJyE3bt3QzweN8vF43HI5XKQz+e5+zHBqkNO5MU/rB64yb63vyB3shulSj27OjxdqrAD0OIfbqiSK2u3rJ867FbVK6NTVObtL9xg265+YJT8k0/3LxXclqfbxa1PbNvqjo+fbRk5GmiFJulFJJLJpDkLBSgn2/b2dpiamoKWlhZzf3NzMxSLReF+ltnZWZiZmbFsCB8dyfDtL9wgndB0w9PNS7h29euJMPijEv8wUgvbVdotyrFFvOHqgaaenh7YsWOH8HihUJDePzQ0BOvWhXNN2zDy9hdugEXfH+IeY/fvuSJtU47/jbAio/q4SJ4q7OC06PtDXJ/2XJE29++5Qm5As4uPFyp2VMfArU67WPuFXdu7kcXGw+uTwESeSjx1xZH44+QDz2+xXVZZus6hqNJIv4qjvPxhJpOBzs5OSKVSAADQ1tZmOU4eXhLtZ0mn07B//35zm56eVjWp4dhzRbpyecVuE2HEyonrexu5x3ibn4OCyJ9F39uofhnJRp6OjRszFzr9tFGkzxJPHW3LaxeP/pvxDTA2tO2iGKnGSyin0Wmg5Q+VZq65XA7i8Tgkk0mYnJw0/+7v7zfL5PN584Em3n6WpqYmaGpq8uBCY7Lni2sAAOCY7/IHezsMQ1yP901x75fWmH876aPLqkD8oaF1kb9l5fv5jVdkC+uDXazoY3u/tMZVO4ogdrF28mLsFiL7mO9utMRBNu6ysWFhfVOB13doP1TritDZlnWH6peMCH8hkU6u+Xweurq6zM/FYhHIWzwrVqwwX9NJp8vf6hKJBHc/ope9X1oDx9w9rK2O3bHy/uA6uyWpz9tE/t97ZT+3Dl1XNS7aURgYdNlriYsRMz8fc/ewY8yUoHyzyPZ5MKzESF0PHd+9V/ab/Vl7vxbEQCX+2tsrJMSM8qZSPqrgwv11xDHfqR6c936Ff4KSsuT4Md8Zhr1f6efKcINIrw5ENvJ0sn46yVDFzk9ZHTrjTsukbeDpYMvYyWER9TXdftRCh5N+Hk42qZwPoj5ba7S853r7LervuX7j5kjmA1yhqY4wB8u7NjkXZr5dmyfy/P69V/WZx6TkMbB1aHleqUqU87ponUSfaIDiJmJFP+18KsuKmWXsZJOyuqFtYHVU9oGtbl5MAUA8Q1WZrbvoY7piZfXfvlxQNtHtofN8CRV4WRiJNDLXIkRlDIC9X51PCP+snlRF0LKIfF2YCYzSQf5W0cWTIyxrI5db38X1IWUdbD3D+rdsfSnbrurz7KfIBp7fnu3lxdLBVpm+o+sc0XmuhRpcRAKJMnu/2gfHjG6Gvd2rHcvQlD/HqP3UTKd7dVV5txA5dva5Yv4VClouz2YnvdzBfT6edjGo+NXH2e/iHqFNnHjtV20vZYfLV2REseL5RHR67SfcvuvxFR9uLB1ksn5wYyFhl1N/Y2Op/bwIEz4nVzdL7vqF8qs4SDRQPUGdBkSnZO0WXQkboOIzLXNv92pzo3Wq6iX1WVm0TKe6uuEmXc4XANEXDJEvdvJljumAtJGbtnKSS/734oMOm3T7Fgl8fBWHLLmbSqWgu7sbhodr+0AjzlwblGNGNlv+V6njlw17ezQM2DYnIzuYivxRsYOWIarnNW528TG/UIxshr09q83/nfTS5VwnGUGs93av5uom9tUKS/zmbefZKtX+hly7qvYJLedAmPHxnit5VZRQ6yV3MbnWIcd8e34wvsbmRA3hvYxjvr3Z3mYZNFxyIvEDsI+hbLlAYm0w/weh106+zT19P9l7zWrHdqGP82zSdf7UvE+EELev4rDL4vLWR5BdcjcoMLlGnEV3bqnaF5u/f0OO7bn2+uoyPvxgtQ5of3h2O0EGtEV3brGtv+jOLWacWOh6vPgSSH0nO/des9pWjixO7bnozi2w99qK/zL+0XLtyvDg9SESd9ExkU26YHWw/YA+TvwW2WTXF2XOH14ftNPppr9HDpf3XFtbWy27BwYGYHBw0LG6aCneIMDkGlF0DNZ7rr1eixy/cEqQftcHkE+0QSLzxUFWjlsdUcKLL15iEMa+E1Wmp6ct77nyVvVra2uzzFRFS+4GBSbXCLHoDncn6KI7tsCe6663fDYpieuROqzePdddb9lHf2aPeYW1nbaH3W/Bxi8n+1jfzL8FAy1PXpVtNva4gY2LxTYHXbLt4xTnPdfy21pn+3tF1O/ZPquMIMY8WTLxcGVDBImB4mXh+f8XLlzouIiEaCneWoErNEWMxd/c6rru219bZanPfuaV5+ml9weNrL10WZEfqsj6LaODJ0unbSJZpJyqLjvfebLc6vGKyE6v/rJ9SaWNVfpsmNGxQtMxG9fDgg8orNB08CDsXbNWWif9Kk5zc7P5AzO1AGeuEcPLoGWXIGUSbdADpVc7SNlaDvAqunXUo/fpak83CSAMfYVGtf8u/uZWT4k6SkkzUHx+z7WWyZQFZ651wOLb3Q1kb39jlVmf/ps9Hjbs/GVtpn1zqquC29jI6neST+TYlVNtSzZWKniJK6/N2OOq8kV+yMjh1XWqx+tjTnaH9fwSoWXmusHFzPUG+ZlrmMCZax1ATtLEdvGJnF+5qup4YvtWyK9cBTHD+rcTtJz8yuAHCBl/yXH2mUxdv7JBdKv6T/Tz6tH+OPkWk9AtGye2vCq8OMsgsv/tb6yqiq+udrOTYxdPJ/3k/KHL2sWlFucNEiw4c60TEtv4A2i+d5XtcV75xLatZj0n+awevyF2EDvtoH1XjYMqqv7zYixbT6cdXtERT57NbuXa+a9yDni1Q0VHVNAxc120Xn3mumctzlyRGpDYum3+r+rvyPlVvZUPCiudVCXWrdu48oOGtoO1ifhaiQcF7XtYfmXDpR3cRET5TP9taX8fIG1gG3tJOVW2qvRXBz/tzhFHOQI76LLEfhn//W6T0IML9yNhpW2z9QR2GoJIeZWhvG3zNphaXRkEYjavdtDl/MbODp6fxI/8ql7zbzsZXmBj5gRtE08WD5F80YAtkiMr1462zdsgz9QjPrlBpc/x6gLw/WjbvI3b92V9FtlB+0nioKtvqfalSIHJFQkjqgOX24FuarX8IEmX83NAcOtLkNgN8rrkq8imy7LtRH+OQmxlqFVSkomfrF1ElpPMqCZft8sfRhG85xoBPropuMHvrb5eoc63+nodbSH1daDLb9onng92PrPlVGySiQWRJ2uDimyeLq/tI7LPTq5qO9Ky3PQBmfqycVDp73RZkXy/zmWd550IHfdcF6/boHzP9e2BGyKZD3DmGgHMgXejf0n2rTXMycm5xOWnfr90WfwqMf+70PVWX690+Y9u3FYdVxbKFhU76LKOOkg5hwGYp5+VbZtENbSZSJ+MbLouKU/2uYmXnU5Whox8nfER9S3W79CBl4WRMMIbKHQTZALVbcNba5wTn64BTnWw9xPVAZU3MIv8UZFNx8Vu4Fe1TVa/XZ1aJxvd55Vs+4WNRrosjMk1Yhy7gf+Ahi68PpRx7IZt8OYNveb/qnUBvD2XbGf/W2usdhF9spDyb97QqzRYs3po/W/Nx0pHu9L22UHiQOOkW+SDjGxSX6TDzl7VNiJ4iSXR+Zagn9D2kv5k1x9IO9vpcoKNkV37qZ53gYIzVyRsHLs+oG+mGjozsfXY9dvgzbUKJ7qOE8lJhsffO6X9kfGP127svjdv6NXavkSWrW0eY23nO/GHHHfyjT7OyvSaKHi6RXaL7KTb58211W0l03Zsm6i2d1W8Be2ndL7VAsWZKyZXxFeOu83f2SpNLS7DHHeb9xkr4c21vaY8HjGjrO+NG3tNX9+4sddihx10mRjzmcihyzr5RNvyxo32tqtCZPPQ0c7EVp4OEmcAtXZl/afbhtbjFCdSlk02x922TSjLzk66XxGZdvEV9QtSh7ZL5AvbL9m+JyofanDmWk2xWITR0VEAAOjr6zP3d3V1QTqdBgCAnTt3wvDwMABYf50gkUjU9Kd/Ik+QCx9o1nXcrdvNv9+4aWUgOm3lUceqBiOPdhBfiZ9v3Nhr8d++XkyLDTzZ3Lhr1MP6DSDvu7zsXmafvf2ifkfbVSkjGQsqZsQGUXyF/huxatuc2kKirYTnVthooOQq/SpOJpOB8fFxaGlpsSTXjo4OyOfzsGTJEhgbG4N4PA75fB6Gh4dhZGQEAAA6Ozshm81KGYSv4vA5fp33gUqG1wdW+qbr9YHqAUBVF20fTx4rV1Tm+HXbq44FFWM72PjraA+Rn360Na1Lp2yvtrpta5V6XnS4OQ/c2uUWHa/itN2wAQ5TeBVn7uBBmNoQzVdxFsgWTKVS0NbWVrU/nU7Dvn37IJvNQjweBwCAXC5n/g0AEI/HIZfLeTa2UTl+cHvlG5+Pm3kS+qWDhxf7JOJ1/OB2635arwdb/NqOH5xP+rSPHtqzahBmYmPR5bHvVA3iuuV6kFGFH/UYZGMrc35XyZrn+MHtzvWRmuD5nuv4+DgAlC//AgB0d3fD1NQUtLS0mGWam5uhWCxy68/OzsLs7Kz5eWZmxqtJdcUJA9sDud/62rrKQMK7H/faupVwwsB2s9wJA9uVdZA6tAwn32i7CK8Pir+J82QSvTHq79dt5NJ+sjJUbFWNEYkx7YMoRry42EHHgN3vpX+J7PAiVyRT9T6xW9vsYmtnA9u/Acp9ys25wrOH1W3nh2r/CAzVhB/hLwfSM1cRw8PDkEqloLu7G4aHh4VJlCRflqGhITjiiCPMrbW11atJ9YWGb/8y2wk3VwaA19ZZvyWTE9VywnrRJynD1QChYAPtM81r61ZWH2Ptmv+f3lzbQsfFqI6/ltlIAP0GoPz5hJu36+kfjFwVGXZ9xym+pg8u48jWl2pPiRizfY0nV9gXQwJ5z1VliypKyx+Ojo5CsVg077mS+7DkIaaOjg7YsWMH7N6921Kuq6sLenp6uA818Waura2tkbzG7jcn3rgdXr1tpfm3W2gZr9620iKLPcazwW94emXwaptqbEV2qtbntYMX/V5sCgu0b25t99o+bm1xahed/ohkuT2H7NBxz/WjazbAYU0K91xnD8JbG6N5z9XTZeFEImG5t1osFqG9vR3i8Tj09/eb+/P5vPBp4aamJmhqavJiRl1z4trttp9VeXX9SlMG/bedvlfXr9Si284mr7ixjfjv1j+2vOmHw9dV1t9Xb7NvEzv90rGL2AxAR1+jY2pBIRZu6gr1Epm3ybcxK5fXd1i9SDiQnrnmcjkYGRmBYrEIPT09kEqlAKDyys34+Dj09PRAIpGw7C8UCtDc3GyWdwKfFq5Q65PF76TK6vGKqp2s3iDiTevk6VNJrjyZPGrdj8KAU9xl6umq70aODplezzMtM9d+FzPX4WjOXPFXcULMSTfUdlB8ZcPKQOwgetwia98rG1ZaygblH6vPSSexk7VXVraIWvenWsGLjZtYyLafnV6vNjjJ1mGXCB3J9dg+9eT65qZoJlfPDzQh/vHKBu+vSbyyYaV7OQSPNjhtJ6XdDTInpbeX6yr4Y4lFQP5V6XPSCZX/pdtOhqD81LCRfqvjHNAWC8X6Zv8U4MlHEbr6il/oar8IgDPXkHJyv/ikjCovD5e/MfN8I8fcIBsrOx1+x9vOd6eysnVr6Z8f0P7osN+rPLf1Vfq2ql2sbL/sAtAzcz3uevWZ6xtbojlzxbWFQ8jJfbUfCF/etNI3OyyD1LyOk/u2w8ub1E52r/aR+i9vWsm1yRdUvsrOl5W1xzaGofoKLYfFH932u5Dnqz3zeO6HYW9n1dlo2P2xAWeuIeaU1f4n2Zc2rwxEj0g3zSmrt1ftE6FiM5EpqsMe59lgp89OPs9HFXu9tI9dLFXtqDVu4+A1BrLynGTJ9muv0H1YpY1l0DJz7XUxc90WzZkr3nMNKadcv93TfaaXNivcq/NS38N2yvXWk1/lRFeyzyGeph2Cb9WObUHgxZBFtk2Ask1TfFXtqIoL4xvZ/O4nXuLgNQaO7QlyttnaoRGzPcDenqDtIuAiEjWk0WeuH+uV7+wvbltplqf/VuHFbSs96dUFsUMGVVtl6xC/dPlH+ySrX6W8GzvcyJZpG7ofquiQjbWONhH5QctVsYdXX1W334hsc2OPjpnr8d9Qn7m+fns0Z66YXEPIx1ZKDkzbqRNcso6KDHJMdr9qGSd77FCNkUx5VrfbmLrVr9Meuzjq8KsWOojMF7ev1NLfWble5Lg5Z4OGttGNHVqS69ddJNd/iGZyxcvCIcTN5RKZ8i9uX2krgz0uks3uZ+vRJ64fl4D+4hvbpfwkdjiVZ3X/xTe2u7LdTqabusQO1p4Xt680N57PTgOnDr+IfcQ2P3UQiI9e5OuwlRdf3rklUy9I6H5TK3S1WRTA5BoiTv3adjj1a9shVgKp7dSvVQYemfIAAL+6faX5+Ve3V59kbHmebMKvbl9pyiByWZmyvvB8EmFXn7aHxFRWNy2fjZXbTbVNefXp/9n4sm0gg4xeFd+Jj17aXVY+299UNx5031WxR9Vnmb5d9xgutoiCyTVEvPBN58GRLvPCN1dyT3IZGawuJzmkDs9Gu7oyPvHkuYGny43+MBPmATrMthFEfbXe+klYaaSZK95zDRGnXuc8OL1wB5MUbeqQsnQZtj7PBpEON7qdcCNDVIdXXjWmxH82DnQMZGTqxK1OuzZwK8+pHq/P6YbXLl7l0PJU4q2jz0UFHfdcT/w79Xuur/5jNO+54iISISLm4nsOOUlP+7tttmXo4/Tfz/9jr2V/zKE+XZdXltVByxfZZleft5+nV6RHJaa0bLZezDBMHbTNp/3dNnj+H3tt468DN33DD3lsX2Ih8XAq5xa2nd3qEfUX0raycnn9XHdb1RWql3ojHEpMriHhtGudT+Tn76QSoUN5uiwAM5hQHVZGL13n+X/sla/DyGdtskOYlDknm61ciZOT649R3k9kE3tYf8h+py8RKjGj5but6yhboR1V7SBxU+0rsnZU4dcALClXdK750W5RJwb2X8p55XXT1dUF6XQaAAB27txp/h45/UtuiURC+DOpsmByDQlO9xae+yfrCezlXoSbuvQA4lb3x/+2PNiwvqjUp082GTlubKX1EJtNeUw5WVtoO577p94quTK20Hrs6svG9/k75e0AcBdLnffMPv6327i++XVfzqvcKN8v9I0QzFzz+TwsXboUlixZAmNjY+a+bDYLIyMjAADQ2dmJybUeaO/hX+oEAJgc6a0qC+D8ja69Z1tVXcJz/9RrylGpS/aLnrqUhdYtspEH0StTRzZOIiZHei1xsIsXe5xnX6xk3S8TQ7ZfEJsAAJ5TiJsddnbQ9tr1UTuc+poqRJZqLHlynPoRSeQybc+TRX8RYGXI6K9HVB9S8uMLSjqdrvp98VwuB/F43Pwcj8chl8t5SrD4tHBEaO/Zpm2Q0jnYBY3sgKRj4HIrg9dWugdSnf2BRvSlShVeHZ0xcPsFzU0dlS9ziD/MzMxYttnZWdeyxsfHIZPJwOjoKIyOjgIAwNTUFLS0tJhlmpuboVgserIZZ641pv1q/kk5uaPXsYyoTvvV2yz1q7CbrezoFesrMf9rgOiytXcelQFRNmYyciZ39Lry2bYdOPLo9hPC1JPxU6Yv0GUmR5g+4LK9if+WdnMpy6l/VNksaZu0fgn5djLNKyCUDFUb6gKXl4VbW1stuwcGBmBwcNCVCeQeKwBAW1sbLF++nFuuUCi4kk/A5Fpj2EtaE3dZT7aOq/iX49hyNE4nLO8yGi2PV5+2Y3JHL3RcVRkkJu6q/szWdYKUsfNLBa+XrgEqPndctQ0mGR9VZABU+yVqd1F7i+p5RZgMqDZ2uhys0maq9vspW7XPkVjZ9YOOq7ZJnZtEhlP5usTFpd7p6WnLqzhNTU3ccps2bYJ33323an9LSwv09fVBJpOB8fFxM8HG43HI5/PQ1tZmmamSh5q8gMk1RLhJSjJ4lUPXJ4MBSaiszbyBgk4cMrpqPdiwXxZ4+0Vl3OqLMmFoMwC9beK3nqBsDRtu77kuXLhQ6j3Xvr4+2+OJRMJyb7VYLEJ7ezvE43Ho7+839+fzec8PNOEiEjVkyZfLJ9fuf+nl7iewx1Xl64DYsOTL27h/67bLrc8qOmTY/S+9vseR4KTHrS1B9R9ZPX62v4rNXvuYnT4V2W7Po6DRsYjEKVdvgMMOV1hE4tBBeGmH3kUkyCs34+Pj0NPTY85Q6Vdxmpubqx56UgVnrjXi9Cv5l//I/vG7vZ9sMt8Qx+/uhdOvdPc6htsBQcau06/c5ikGOp8yJH6qxElWJo2TzaI+I8JrH1KNoWyb7f4XtT6ngorNxIZa97MoJFZdhOFpYZI02eTpNZmyYHKtEaITWkdSNZHsmON398LpX7If7E7/0jYY/24vgEH9zSljkcspQ/T5DuP7+HedfbSD+KwiQ+S/EIWBxKs/Uvh5Tcsn2aQvq8RH1J9l9REZumTWNSF4zzUo8FWcOuQTX9wKn/jiVogZhuNGkC07/t1eiBkGfOKLW6t0suWJHbWAtoMMcjI+2sVHVYYXm2Vk69ZPI9t/3Oq0k+E1Kam2t44kSM4LXfGvVxpp4X7pmWuxWDTfCaJvGouWjNK9lBQij8pTk2f8TTn5KS1JVgJ49gerzLpO9Um5Z3+wSkGLN0gMaJ1enrQldX/5PavfdpzxN1uVfFa1z8kWVf1ebKF10oj0s7b70Td0P1ntxC+/V/aB+FWLfh96cOZaTS6Xq3rEmSwZlUqloLu723y8WbQf8Z8zvrDV3W8mymyE+b+f/cEqpfpnfGFr2b4gMDiDmkefTdsVfVax2VVbCDZPg7qTXB3+8/zRiR9t5EYvUsHLORgxpGeuqVQKCoWC5V0g0ZJR+Xxe+1JSiBzP/nAVnHm5eLB45p7KgGtXzo6YUa77zD2rXF22IXppW3Tz7A+rZavYSmwjtpKZ+ZmXb4VnFWNIYuWEin2sXc/cY213r7F16keyttrZwWsjnRD5OttIRa8fsqNOGB5oCgpPDzSJloxSWUpqdnbWspTVzMyMF5Mamk9eZn+J9+l7V5ll7MrZyX/63lWWy21eLr0RW56+1/+B55OXbVXylxdLEj9RDIkf9HFanpOfJJZ2cuzsfUZzHEVtS8eStTWItlRFpo/6aTdJrDJ9oO5RnY02anLlIVoySrR/aGgI1q1bp9uMxkTmIQoPD1o8vfP6ahlReXBDl50ScugB9JMrtlj+NmMoI1vRZlqXaYudPick9BOfwpw0nr53FTc2NI5to8mORsfNg29RxVNytVsySnYpqXQ6Db29laf1ZmZmqtaRRJw5a/kW4czsqfuuh7OWlweXp3dW/nbiqfuqB5uzlm+Bp+f32+lU4azlW7i6dKLr8pKdHBJX2heVAZsuqzO2BNUYE3uc+osXHUHh1O/Danfd0UAzV0+v4iSTSRgfHzc/kyWjRPt5NDU1mUtbyS5xhXBweCCADB5ndW1x/SCBWZfS+dR917t7SIHZzuraUpbvFxpslJWjxRcd9nLs8tsW39vRA3Z9Naw21xv4Kg6HXC4H2WwWisUiJBIJSKVSkEgkYMWKFZDJZKBQKJi/7i7aj/gH777Sv99f+Tb+l5eWBw+V2RCpY+rg6PzLS7fAU/dfX1U2bJD4/LtHW4N6vYPV49Vuwl9eusXSL9zYImMT2a+qy2+eGgt/X61rGmjmimsL1wGiwYI3sOkaWFQGe2KHTHm/BmM6qbiJgdvk5tYfWpcXu0W4scut/rAlWAC+L2G0M0zoWFu4/a/XK68tPPmvayOZD3D5w4jzqb/aXDWjfPJHq7nlANSfELaDHoyIfB6f+qvN8OSPVgsHL3LcT2jdsZL1+yTR7eSDztg5QWyk48La7QU3MXfS/+SPVtvGMEzwfAmiHzY8DTRzxeQacehLdk/8m3hg8PtyppP8sz+/WWhf0AMasZW1J+gVfeyIlfyzz66f2CGjn7Tl2Z8Pd5J98kerQ28jEm0wuUad+W92T/xEPGCe/Tn/BpGzP7e5rFviGyZth529fiNMLj58SzbjowjXxlp/i3fQz/azWraxFBx/3LYXIkcjLSKBC/dHmHM+u8nxvTG6jM6N4GZB+5hhwDmf3eR7fFTxI046fdXddn7rD2Mb0zzxk9Va44NI4PYp/QiCyTWinPOZTfD4T/vMTUhJ3/b4T/vMv8/5zCbTBvK/qjwi45zPhGQQlowB7au03wHZpxJ3VVjfZXWFGa4/iK80wms4AHhZOLI8/jObhEph10Efe6APPn2x/OD36Ys3cR/qITpENsno+PTFm+CxB+R88gvih11cyP4Y9fnxebvt/CTHvPgYMyr1ZWJK61JpZztEfYDVqUtfEDz+M7XzAPGAYYDSymMRvpKAM9c65tMXDVc6M2dzOi61wXwyuoj/y0dEx2MP9DnKEskIDGLn/N+yG7Fbxkev9ln+lrDLjOm8b/TmBtpHnr+mTrpMBKB9qXk/rGNwEQmkLojN8XvmLx5eA+desFG7Lp5MMss594KN5t+/eHiNpQxd79wLNlYdD4rHHuw3/xbFTgTxgZ7V6fbDrX0k9nScvdimYkct21MV2pco2R0pGuhVHJy5Nhh+JFZatgznXrCxaqCntyhhZ69fcdZBmG2rFVHre1EkVlLfogom1zrlvORGSwelE9cvHl7jqpPbdfxfPLymSqfTdl5yI5yXDOcgr+J/LXwkbeql7XTYJWNDmNuZhfYH8QF8WhiJMucvHbJ9vYB3XMdrHTHDgJ/n1gT2aoifqNheCx9/nlsjrfvnuTXCdtFBlNuZB4nV+UuHam1K3YH3XJFoM98hH320+gcTzj/fnwGDlvvoI2klPeefP8S1taYwMeT58+ij1X7y9pH6vvgoMfiI2kKHPU7tHLp2lUS1DyOSqD7YF6EvZSw4c61DYoYhHNR0zVgffTRt0UH2Lz1vg+Xzo4+mpeQtPW+DWTcMsLMt2VkrAJix4fnol51uNh32BKGjVkT1i0GYwZkrEmke+fkN4oOCpzsfeXwtAAAsPWe9s/z5sqLyS89ZD488vhaWnrNeSl4YqYohEzfiHwtvn6isFubtcquD1KHbVFW/U98h/QFBGulpYUyuDUTy7PWWV0VyT1QPeE7fFEmd5NnlgZRdUCD3xFpInr2+SpeKjTy7ag0bFxX/SFk//KKTluy3fLoNvdpE9IviEca2RGpHI60tjMm1kaAuc+aevNFyKPmp21zL4h3LPXmjtEzWllCi4d5P8lO3+euroo1aE59At+8+I9EC77ki9UbnWbdCrFROerzBLlYybDe2nqgcQDlZdp51K+SevNFRbqxkQOdZt0LnWbcGFgs30DGQ8cnOVz9tlNn8wC4uYW9bJDga6Z4rJtcGIvvUTVrqyQ6WqoNqmAdhEgMdNobZT79oRJ8RDg30niteFm4QnBKraBm7h5+9WbosAMAFZ9xSLsOpT45FGdVlEVl48dQFaWOnOJPjum1R6UNIY9JI91xx5oqUIfdCqI03KF7wiXXKC/sTHn72ZsfyF3xiXVAeu4MXI5fxCMrGwGyJapsiiA9gckXKsEnjlwNS5SzleQPr6YOWTabuBacPBum5GozdIp+EiYbEIUAb7dpGJyI9wr6ENB4lQ32LKHhZGIELOgblCwvWXFWSweHhcW/1A6ME8PDEoOVz2Hh4fBAu6BiUspO0m6WsF0IYDyREqN5HjW5uxeSKAMRK5RHxoeec74mSsjQPPXcLXPhx9/fV6LoyNtQSNgk9PDHo6HstfGLt5LUbDc8HN3bz9Fz48ZtD365IMMRA8Z6rDzYUi0UYHR0FAIC+vsrvDWcyGQAAKBQKkEgkIJlM2u53Qkty7erqgnS6vFTYzp07YXh42JNRSMAYBjz0vPPTnBeeZvNQVITfR/OMg+8kbjIx9o2A2ueh526p6ic19RsJFyF4zzWXy8G7774LLS0t5r58Pg/ZbBZGRkYAAKCzsxOSyaRwvwxa7rnm83lYunQp9Pf3m0mWGJVKpaC7u9tMuEj4kB38Hnr+1vJlP2p76Plb4cK/uKlqv9vtwr9w97pQTVHwjWxBQ9ruoedv5bajnb1udZkbgswThvdcU6kUtLW1WfblcjmIx+Pm53g8DrlcTrhfBi3JNZ1Ow759+yCbzZqGeDEKCSfLTlkLsVLJ3B76VTkp0/vstod+datlE5Vbdkq0lsyz8yVMPpL2ApBvM2Krir1sP0EQE8PFBgAzMzOWbXZ2VqtZU1NTlplsc3MzFItF4X4ZtFwWHh8fB4Dy5V8AgO7ubmmjZmdnLYGamZnRYRLiA7teWg/LTi4vaL/rZerXTiQv3ZC6FpkvR/dXUywoXr6qtd+7Xqosss9rF6+ydctE6gPV3/YlZVtbWy37BwYGYHBwUKdpVZB8JrufRUtypS/5trW1wfLly6WNGhoagnXr8D24qMBNCg6Py+96tfy7mMtOrP4JL3ofKRdFdr28getfFKDb1M4HckyqnSL8CgXiI6q3CubLTk9Pw8KFC83dTU1N3OKbNm2Cd999t2p/S0uL5eEllra2NsvkjzwnBADC/U54Tq6ZTAbGx8fNBBuPxyGfz9saS5NOp6G3t9f8PDMzU/UtBQk5st9EHcotO2EN7HptowaDaoP5JeKENY5lw+qrig8ivNRF6hu3M9eFCxdakqsIuwRqRzKZhP7+fvNzPp83H2ji7ZfBc3JNJBKWe6vFYhHa29shHo9LGdXU1CT8FoJEBNn7ahLllh3XB7ve2OTRoBojGY8w+0on/mXHWQcs8llk+67XNlbVQRAACMV7rrlcDrLZLBSLRUgkEpBKpSCRSMCKFSsgk8lAoVAwH8wV7ZfBc3Jtb2+HTCZjzmCz2axno5D6Z9cbmxp+AA5rYtVJI/iIKBCCV3GSySR3opdKpbjlRfud0HLPlShnjXBrFBItYnPlmdqDU1vgorbrq46TfTFm366pLZbPovpRwykeBOJz2KETJO2Pnf0kBghCgwv3I4gK9LdRZl3ZB6e2CNfZvSixytzIIB2FZOOE6TOA41rDFyVW1dZYRR6c2mJpU6H9Qf5QARIdVH7kIuJ9CJc/RDzzYH5r5QNzMly0uPKw2oNvb7N85pV78O1t+g2sAWZMIjw42EG3Obft6tRvxBuxUnlTKR9VcOaK6GWuBDBXggff3lYebOc/w1wJLjr6G5bj9DFLmTqCF4d689n0kYb4hyA0DTRzxeSK6MUw4MFf3275zL0c2vr1crk6OplsqdMBREi9+oV4w+UKTVEELwsjWnlw+h+sOxxeS6kqX6802jKApRI8+M63am0FEjLcvucaRXDmiviL3cM8R11Xa+sC48F3vlVONo0SiwgPigiiA0yuiG8s+7NrwZgrmduDv73D8tmYK8GyP7sWlv3ZtbU2NTB4MSBbPfHgb++otQlIGMF7rgiil13/eadtEm2kBLvrP+/k7m+kGCANigFqP0MZ3dyKyRXxh2X/owegNFdOJKU58zO9kWNkW/Y/emptdmCwvjdiDJDGg9xzVdmiCiZXxBd2/Z8R2PV/RsofSgZ/my/H7msUqnxvwBggDYYBipeFa22we/BpYcRXljV/1fHYrsI/B2VO6Nj17igAWOO0rPmrDR0TpI5RvY+KM1cEESCatVLbsvhVtbay9uDsFWkEVO63qv72a8jA5Ir4yq7iXbCreBcYc3O224UfurLWptYUNk6NHg+kPsF7rgiikQs/+CWp+ysXfvBLtTa15jx04G6MB1K/NNCrOHjPFfEfh9WJHvrD9wMyJBo89PvvwoV/cgUAAFz4J1dgfJD6oYHuuWJyRXyn3hZICAKSUC9o+kKNLUEQjTRQcsXLwoj/GCXb7YLD/7rWFoaWh2d/iPFB6ocGeqAJZ66I7xgOT79m3783IEuiycOH/rXWJiCIFhpp4X5Mroj/GNavn9m5+2pkCIIgNaWBLgtjckV8Jzt3H3Qu6IJsaazWpiAIUktKBkBMIWFG+J1vTK5IIGBiRRAEZ64IgiAIoh3Vd1ejm1zxaWEEQRAE0YyvM9dMJgMAAIVCARKJBCSTST/VIQiCIGEGLwt7J5/PQzabhZGR8s+OdXZ2YnJFEARpZEoGKF3qjfADTb5dFs7lchCPx83P8XgccrmcX+oQBEGQsOOwoAx3iyi+zVynpqagpaXF/Nzc3AzFYrGq3OzsLMzOzpqf9+/fDwAAMzMzfpmGIAiCKELGZMPLpVq8LOwPhUKhat/Q0BCsW7euan9ra2sQJiEIgiAKHDhwAI444gh3lRvosrBvybWtrc0yUyUPNbGk02no7e01P5dKJdi7dy+cdtppMD09DQsXLvTLxNAyMzMDra2t6D/635D+A2AMwui/YRhw4MABOOqoo7wIwZmrV5LJJPT395uf8/k894GmpqYmaGpqsuxbsKB8K3jhwoWh6Vi1AP1H/xvZfwCMQdj8dz1jJRigmFy9qaslviXXRCIBK1asgEwmA4VCAdLptF+qEARBkCiAM1c9pFIpP8UjCIIgUaKk+DtyJXxaWCtNTU0wMDBQdbm4UUD/0f9G9h8AY1C3/odg5losFmF0dBQAAPr6+sz9XV1d5hXWnTt3wvDwMAC4XwwpZnh6rhpBEARB7JmZmYEjjjgCkv/vV+CPFhwuXe/90iHI/X/fgf3792u795zJZGB8fBxaWlosybWjowPy+TwsWbIExsbGIB6PQz6fh+HhYctiSNlsVkoPri2MIAiCBEPJUN80k0qloK2trWp/Op2Gffv2QTabNRdA8rIYUigvCyMIgiD1h2GUwFBYdYmUZRcV4r1l4pXx8XEAqKzH0N3dLb0YEg9MrgiCIEgwGIqz0fm7luyiQgMDAzA4OKjRMDDvsQKU12lYvnw5txxvMSQemFwRBEGQYDAUV2iaT67sYhqiWeumTZvg3XffrdrP3l9lIfdhSYIl91tlF0PigckVQRAECYZSCSCm8HrN/GVh2cU07BKoHYlEwnJvtVgsQnt7O8TjcanFkHhgckUQBEGCweXMVSe5XA6y2SwUi0VIJBKQSqWgvb0dMpmMOYMlTwR7WQwJX8VBEARBfIW8irP0g5fDH8UUXsUxDsEjv79H66s4QYEzVwRBECQQjFIJDIXLwipPFocNTK4IgiBIMITgsnBQYHJFEARBgqFkAMQwuSIIgiCIPgwDlBbux+SKIAiCIPYYJQMMhZlrlJ+3xeSKIAiCBIOh+JNz+EATgiAIgtiDM1cEQRAE0cz7xqzSbPR9eM9Ha/wFkyuCIAjiK4cffjgceeSR8OTvHlCue+SRR8Lhh8svPBEWcIUmBEEQxHcOHjwIhw4dUq53+OGHwwc+8AEfLPIXTK4IgiAIopkFtTYAQRAEQeoNTK4IgiAIohlMrgiCIAiiGUyuCIIgCKIZTK4IgiAIohlMrgiCIAiiGUyuCIIgCKKZ/x+TCj0XFoHNbgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 500x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cmap = matplotlib.cm.viridis\n",
-    "cmap.set_bad('white',1.)\n",
-    "\n",
-    "plt.matshow(makeFernPic(F,np.pi/2), origin = 'lower', cmap = cmap_viridis)\n",
-    "plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "# fig.set_rasterized(True)\n",
-    "fig.set_figwidth(5)\n",
-    "\n",
-    "plt.savefig('figures/test.png', dpi = 300)\n",
-    "plt.savefig('figures/test.eps', facecolor='white', transparent=False,bbox_inches='tight')\n",
-    "plt.savefig('figures/test.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "77423552",
-   "metadata": {},
-   "source": [
-    "# Trying Erik's stuff \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "844cd027",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import demeter.euler as euler\n",
-    "import demeter.directions as dirs\n",
-    "import demeter.misc as misc\n",
-    "import time\n",
-    "import tifffile as tf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e2ec851",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "leaf_file = 'figures/leafbw.tif'\n",
-    "leaf_img = tf.imread(leaf_file)\n",
-    "leaf_img[leaf_img > 0] = 1\n",
-    "\n",
-    "fig, ax = plt.subplots(1,2,figsize=(20,6))\n",
-    "ax[0].imshow(leaf_img, cmap='gray');\n",
-    "ax[0].plot([200,200,250,250,200], [280,310,310,280,280], c='red', lw=4);\n",
-    "ax[1].imshow(leaf_img[280:310, 200:250], cmap='gray');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37c4e19d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Complexify in 1.0271 seconds.\n",
-      "\n",
-      "Cubical complex made of:\n",
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    }
-   ],
-   "source": [
-    "tic = time.perf_counter()\n",
-    "leaf = euler.CubicalComplex(leaf_img).complexify()\n",
-    "toc = time.perf_counter()\n",
-    "\n",
-    "print(\"Complexify in {:.4f} seconds.\\n\\nCubical complex made of:\".format(toc-tic))\n",
-    "\n",
-    "leaf.summary();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b86a4b9e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53832\tvertices\n",
-      "99196\tedges\n",
-      "45167\tsquares\n",
-      "----\n",
-      "Euler Characteristic: -197\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e32dfd9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[array([[-271.57746322,   89.89517387],\n",
-       "        [-271.57746322,   90.89517387],\n",
-       "        [-270.57746322,   88.89517387],\n",
-       "        ...,\n",
-       "        [ 259.42253678,  -44.10482613],\n",
-       "        [ 259.42253678,  -43.10482613],\n",
-       "        [ 260.42253678,  -45.10482613]]),\n",
-       " array([[    0,     1],\n",
-       "        [    0,     3],\n",
-       "        [    1,     4],\n",
-       "        ...,\n",
-       "        [53828, 53829],\n",
-       "        [53828, 53831],\n",
-       "        [53829, 53830]]),\n",
-       " array([[    0,     1,     3,     4],\n",
-       "        [    2,     3,     7,     8],\n",
-       "        [    3,     4,     8,     9],\n",
-       "        ...,\n",
-       "        [53821, 53822, 53826, 53827],\n",
-       "        [53825, 53826, 53828, 53829],\n",
-       "        [53826, 53827, 53829, 53830]])]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "leaf.cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5a52e965",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = np.pi/4\n",
-    "omega = (np.cos(theta),np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4f5d741",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-128.46877882, -127.76167204, -128.46877882, ...,  152.25261331,\n",
-       "        152.9597201 ,  152.25261331])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "heights"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "75ae5f49",
-   "metadata": {},
-   "source": [
-    "## Making pictures"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db2a598d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "theta = 6*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a048e87d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f180e96250>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ed8V2r6eTTCa5995HqamuZdGi5TQ3tWDa1gmkQLOtFAcfPI677r65i5vIg4ddAru5+8NTKjzsEli2bDVr164jHk+4x5QykdnFlpQU09DQjGVZttIBIAiHI7z77kf069+H39/wi03aTiQSbvwBOG4Qu/2NdsnvvvsxX3y+xJEAaYGmKypvv89HYWEB7777Ma2t7ViWVLU+sFyrCcDAgf0BtcD++le30NERdtsD0HWdki0Elj7/3Gu0NLdhmhY+n+5e0xmOcPMf78pKG81ksfTs0YMrf3YJV15+PbNmzSMSidCjRylLlqzkwAP342dXXUIotH2Uiwfu/xdPP/kSjU1NaEIjlVIcHkoJUmMfCgXZe++xjBzZ/WWZPXjY5rBkNygVnvvDg4dtjkRCpRjiuAk0zVUp6uoayS/IJxQKufEQjtvBSfv26ZvXoYWwd/wiYwFxzPCFBYVdzr3zjodYvWad7fvXsl6C4uIifvLT8+nVu6fK+rADKB1mTykFbW0dPPPMZCKRKB9OnUlVVa1LjuW4SHJyQhx2+IGbyLlixRo+/mgWiUTKjZNw5DVNi0gkZrtxtC59KC4p4sOpM1m0aBnhcCemadHS0s7HH83i449n8/FHs7pxlr4c777zIc3NbRhpy40hyXB2aPj8PgYO7M/Vv/jRdpPJgwcP3QfPUuFhp4aUktdfe4958xZhGKbLCSGEwOfzk5un3AgP/O1xlixZRSqZdhd5h91S13307NWDn1x+wRbukmG5FMIOlkSSl5fH0ccc0iVzIhqJY6Rtxk1NQ1jStXCkUwYzZ3zO8BGDFXMmwlZ+bFeEZZGfn88+e48hPz8Py7KIRROuAgAquDKdNlm2dJV7zzVr1jH1gxm88uo7LFu6yiby0tyxwL6PtLICPYVypfgDfkKhEPff/wSNja32dcoiUFhYyKmnTGDCxCO6e9q2iGg0bo+zhiY0pDPeNs9Hr549+NGPz6dXry/nFPHgYadFd5BXeeRXHjxsG3z++WLuufsx1lWsJ5Wyd+gS1zJwxZUXApBOm6SThvpbtNkrBdKOTZAEA0GCwcAm7c+dM9+m5VZxCOo6DYRE13SikQQN9U307qNiMaTMYsdEw0LFS0gpiceT1NU1EonEMpTc9v2RGj6fzoABfbnp5l+597dLlrkvTQiKi4o49pjD3XP+ettDTJ0yA8MwiccTtktFc5UfXfNRkB8CIUi3h1HJpTrBYJCxY0cxoH8/liz+xM4IcWIXNPr06c0ppx3HK5PfJZ0y8Pk08vJyOfnUY7s126K5qZWPPp7N4YePdwnG1Bjb8Sgoa0V+Xg5jxoziRz8+t9vu7cHDdocXU+HBw86L2ppG6uua7PRNZzFSwZTFRcUMHToIwzAYM3YUOTmfEIvFslwO0nYNmDTUt/LE4y/wi19e5ra9ZvU6nvz3S5SvrVaKAQ7nhFrsIpE4b/3vQ0pKirn1z9fS0dFJIpG2UzXVbl/TfHa8BBQWFrL3PmOYNXMeyYThWkqy630UFRe7lo+6uiYsC7t4mUBoPvx+PwMG9ue8C86gpqae+V8sZe3aSmKxJFJaGfmEQ/sNBYX5jB41nLVrK8FRUDRBMBDk2GMPZ/36GhLxpK38KItAcUkx++w7hmt+8Sc++3Qhuq6RSCQZOnQQ0Vic8y84o9vm8IH7/83kl99mz71Gu3II4dRBUWNdUJDLMccexi23Xuulj3rwsAvDi6nwsNMikUjyz3/8h0gkmpVWKNygSp9f6cTPPfs6/33xLWKxOCDU/zQniFMtwJFIjM5w1yJdpmUxY8bnhMMRl3jKad9ZvHWfzsTjjkBKyc033UubTaOt5MnESgihYqt8Ph+trWEsmxkzQ+SkYVnQUN/Mq6+8h2VZvPzS20QicbctARiGBRKWL1vNL35+M7/4+c2sXVOFZWVKsWeXdtc0nX79+nL+hWe6fRVCw+/T6dGzjBEjh7BgwXJSNgmXrmv07FnGCSccxefzljBn9nzC4QjhcJR4PElTcyuJRLJb57G5pY3m5lbmzllAXV1jFhmYsBUylUL6i19extChA7v13h48bHfIbuCo8NwfHjx0Px5+6GmWr1irFlo7I0Paro+cnBAT7ViASCRKbU09pmUpfgepuQRYGUXBoqGhhY72MEXFTvClwDRM0oapPolM1ocQEAoFmXjckRx55EFM+WAG8+cvIx5PdiGTcoI7pYRkIkU4HLFTWZXzxc0iQWV16LrO2LEjWTB/GfX1LViWldWWZseJ5PLhh7NZvGgl4XDEzkLJyKXOBWdRHjlyKNi/RUJoBINBhg4dyGU/Pofnnn2DqvU17v0LCvK4+NKzWL1qHeVrqzBNw23X7/fRp3cPTjttYvdOpJRIBIlEEtO03PGTQF5eLvuP25P99hvLXnvv0b339eBhR8Bzf3jwsHOioaGFZCwFbqkse1GVqqT5UUcf4hxFRSboKprAyf7AcT5opJIGb74+FcuUPPSPWwHQBOiaXVnUDqzUsBA2UZTf52eP0cNJpdIccMDeWKaFrvlU+mNXqRBCo7i4kNNOO44PP5gNaGhIhHTtJeTl5fH731/FjTfcw8oVa2lra1fxDWSKgem6zgEH7EPNhnpi0XgmMkQ6rgLLrppqXyV0hBC88NwbxKNxdE2jrKyEy350Ds//5w2WL1+DaVhoQqOwIJ/jTzgSy5B8OnchZlplqGhCkpuby6hRQ/n9H36urDqdUQqL8unRo3Sr5qqpsYVUOk3//n1IJlPEYwmKSwrd8dGkci85fXX4NXJzc7nzrj8wZuyIb/u4ePDgYSeAp1R42GmhTOMCzVQLkq1PqABFCVpWJVHNfuHUz3CDNe1sBwmWJdlr79Fu+4OHDGDkiKG0t3WSThvOTRECdCAWS/LQ/U/T0RHh4EP2w0ybSEvFT2SqgkpbqRD0KCulV68ywFYphMomcRQQM22ycOFy+vXvzRefL8E0nLodUhFuSYkudNrbwnz+2WKMtOXeR2q208O2WuhAKCfEiBGDOfW0CfzlloewTIlAp7Mjxm23PkQ8nsQwnNgOSCbTfDhlDslUimQiqTJThCAvP4cTT/4ev7n+cp7612R+fsUf0TSNIUMH8Px/H6CktOhL52lDdR2XXfxbhBDc+pdf8/ZbH1NTU89d9/6enj3LSKdsGaTM6quFJnSlCHqMmR6+S/AsFR487HxIp9MYaQPb7p9lqcjU6HB8AcefcCRP/etlIpG4nVYpu7gynPTL3NwcTjjpSEDFa4RCQf5y+2+46PxfU1fb6FYIVZAYhklnZ5R15VV8OmchVVV19ndZPgiZ+VxRvoEfnn01neEMBbdrXQHShsH6dTVMPP4I3n9nGnGSXWIzcnNDHHzI/hx+5HjefWeaLY8dcJrVHijCrYED+3Lpjybxj78/S0NDEyAI5QTJL8ijoaHZ9stminOlUgapZJhs5OSE+P7px/HXu37LuWf9gmVLVxGJxFQ9E13Y7pkvx123/5NlS1eTl5/H++9N5603P6SxoZn1lRuYeNzhLFy4wnZNZfhDhFAVSEeMHMygwf2+8h4ePOwy6I7aHbsw+ZWnVHjYKfHjS3/P7Jkq3VMZ/dXCKoSgoCCfYycextg9VSnvocMGMnLUMNatq0EKCTJTatxZ1HXdx8hRQ+nduyfl5VXcf9+TjBu3J5dc9gMGDepHXW2TOtvVF9R1ubk5nHzqMfz1tkcwXaZODSGUXAiJ0ASWaZFMpkgmk5msj6wNuBDK1H/8SUfx5OMvE48ns+Ii7DiR3Bxuv/t3xGJx7GLmbh+cgBK3TSloqG/hlpseIJFIYJgmuqZhpE3FBSEd5UspWJZUrpjstjRdMGToAK67/ifcf9+/WTB/mU3E5dy7ax82hxeff5MpH8xW1UUtSUtrB21tYVIpk+XLy1m7pkrxatj31jRBSWkRAwb0pWfPUq773U/Jzc35Zg+JBw87I6SlXt+2jV0UnlLhYafE2jXrbb4HexEHd4fbv39fHnnsNkDRU198/m9YvHglYBMqYbkLvrPT9/l8HHn0weTkhLjz/x7l/Xdn8P47M/js08UI4XMJrJxdvZN6WVhUyIknH81fb/snLucEAqRiqzQl9r8FhYV5aJpOe3una1HJbquoqJATTzqa2279B4ahuCtw29TpDMe58NxrKSouJBZRmSy6rtO7d5ntghBUrK0ikVQWjlBOiM5wDMtyUm11pVRE4spNo+tuJoujkEkpVcyIzVdRV9PChKMuxrIs0mkTTeggoEePUk448SgKCvO3OEf19c388+EXaWtT3BjxWIo3X/+IVFK5OyxTEjeSriLmJJulEha5uXk88PAtrrvIgwcP3w14KaUedk5Im+AJ3S0QpoqEaayvrOP0U65k1coK0obJmtXriXQmskivdISmu9dqQqdHWSmHH3EALz7/Np989CnJZIqO9gjvvjWd6uo6Av6gu8hnyKh0nMJeThlxgf3uBoM69/AxeMggRo0ejuZmoHSVu7mpgyMOPpemxjYyFUnV9z6fn149S+nTtxftbR0q3kL4KC4q4u77/sAnM1/g2AmHo+s++vTuyd57j+bWP/+K7x1zMMFAQMmBliWjttH46ZmXyMjX0dFJR3uESGcc0AgEQwwZPJA//PEq7nvgxi/ljIhFYzTUN2OZINAxDJNYNGl/3uiethxIjVgswdrV64nHuzd11YOHnQJe6XMPHnYuvPjc2zQ2tOD6392F3C5J7tMpLCwgNzeH3NwQo0YPw+/3Z9XvcCiynXgKnc7OBFf++BbuueNfFBTkEwiEEAgS8RSRcJxQKKSUA5SyoLltaDz3zFu0NneQYbHUXFkc+UCwbm0NK5aVu4yVmRokqoy5kTJobmrPCiB1uCZ89OpVykOP/ImLLz2TjvYooKFrOn379ebzz5Yybu8zeft/nzBocH8OOWwcyYRJOBzl9zdeyaDBA9C1LJZNsbF82cezsi+yaqQgVBZKKBjkzEkn8PKL77P36FM5cJ+zOO+sX9PR3rnpRAmRUYyy23Lbd1xFai58Pj/BYAAhNKKRBI//86Xt8jx58LBd4QRqftvXLgrP/eFhp8OC+ctJxFNqUXdiDuw0z2AwyPe/P4H7H77RPf/kU49mwfwVGGn1h6jYGu3MDCGwLEnUJr8KhQL84OwTeO/t6bQmnXTVTOyCSlnNkEylkwZP/esVkokUmibc87v8Z19jmiZG0rSvx7acqHaxs1YGDOhDJBKnpblNOVKERjDgZ999x/L2m58AEIsmFLM3sH5dLU8+8SqRSBS/30dhUQHTP1aEXffc+W8OP2IcP73yh/z55odIJpJbkE+48RSuPEhbPkdW5a5JJdM8+cSrxGJx0mkDXdNobGzl7w88y423/AxQqb733/MUw0cMsoNQRKav2cGrkkxgLRK/38/oPYYR7ujEsiTFG5WU9+DBw64PT6nwsPNBZBZ119qgSXJzgow/aG+uu75rBcsxY0fQs2cp1VX14FJtO4uZYnrA3jUXFhZwwPg9WbxoNR0dEaSUJBJpN/NDvakF0ufzUVpWTG1NIxI3eRSxGfkCQR/DRwyiYm0VlpXOEOLZ8SAIVdL70h+fyeP/nEw2u4aUgs/nLSMnFOCIo8fjZLf4/YqsyufXSSZTpNNpmpva3CFqbwvzwftzaG5uJ78gj2QyjUBuVj6EijORwl7iXfkc9UsdTxsmaZt51LHUpFJp/vP0m/zv9U/QdUEqZRCLxTjhpCOzlBSlIGXYRt3JhKyx+/7px/KzX5z3rR4PDx52anjZHx487GRwCnYJMG1KZw0YMWoo/331/i6nPvXEqzz97zeIRVNoQkdKaVsAcBdtTahaIb6gzqmnT6C2ppm2ljCa8GFJU1UdFXbcBPZGG8jJyeWQQ/fjtVc+zMQE2Iu006YpBbqmM2LkEA45dF/WldfYmSFdoawFOp9M/YJevXvQ3Nhuc2NopFMGHe1Reo4sZcnCNUgpyM3NYf8DxnLvA9dTUJjHlT/+Ewvnr6CzM5qJJwVSyTTz561QWSi2XNiFyTQEiitUfVbKhK1gkfnRkigiMFtzcscP+1oBRMJROtojaJpAWhb+QACldugquNPtZ0Y2cHhFFL9GSWkxo/YY8nWfBg8edi1Ysht4KnZdpcKLqfCw00HLil1weC4FGsLaNL9x6gdzWbu6irbWsBvLkH2lE0OgCx+jRg7h2t9ewtT3ZtPY0IZlWrhBjVJHt2MDdDu2Ip00+GjKZ4SCQfeYcNvW3X9pQqOjLcL7b8/GMiyb2TMrlgENpCCZSDHv0yWYaYtQKOjGNvj9fkaMGEjffr3YUN1AUUEBhx66P0jBZRfcxPW/vo8H/3Ej48fvhe7EMEj10oSiGjdS5kbyqXs7/wmZ7RDJGl+3X5obB+GMn9O3zJFMICwWfDzlM+JO6fYuc6a58Rm60NGFTjAQYNI5JzDhuEN3wBPlwYOH7QXPUuFhp4QmBJZNQe3GBWib6sAjRw9hzsxFJFNpwLZOaJnNvCYUs6Wu+wh3xLn4hzdSXdWo2tR0e5GVdp0Om8vB1l2MtElbWyfjD9qTLz5bTjpt2JwSWta7wLKgvrYFXVcEW4l4GsNwGDozlg+E2oBEowk0TcfvD1BaVkT/Ab3o06cnM2d8gWlY7LvfHqxdvYG6uiZM06S6sp4H732WUXsMZc6sJaTTKbevLvUEG8ul3hUDZ8YV4qa6Spc/vAscigrlwnAqiWb6qoI6LSwLmpvbs5SUTF8d3g1d86FpkvyCHPbcewS/+NUF3fBkePCwk2M3d394lgoPOwVqNjRy9eV/5bTjr+bjqZ8jLZsG200TFXbKZ1dc/asLKCoucs+TzjXSKfqlrASGYVKzoZFli9fSGY6ptqTjEsi8O+c7sQ66phPpjKtCZVnybPyuCY38/Hwu/ckkCosLbaouDelwSNiugj59enLiSUegCR8lJcW8+/Fj7L3PaKZ//AWdHXEMQzL/85XU1jS5xF/JVJrZMxfzy+suZOSowWhCd/sqEGApuZw+OX3QhE5efj4lxUWUlBRRVFxIcXExPt3vyueklmb3x+ln1z4645qxdrjfb9RXKQXSEm6w7SGH7ccLr9xNXn7u9nqcPHjYgbAyBFjf9IWX/eHBw7fCF/OWM+2jz+lo71RWCtNCaBkrhe7TGDio9ybX+Xw6vXqV0Nrcjmlm6KyFk4Vgycy/AWk5ZFRORomGcMiycHb/me17PJZi2eJyTNO063TYWQ12fIIrn65z8KF7c875x/Pif97BzQqx29KERiDg57TTj6axsZ10ysAwLG76zYO0tYfJyQ1hmCq+I5E23JRWtVhLmhpaueLiW0nEkvh8OqmU4cqsaRkGUSfTQyDw+3UuuPhkfvXbi1zLTUNDC1de9hfWrl6PZVluyfdsdk93/MDO4BBZ52XKuTuhppngVfvaLiYQgU/X8fm8nxoPHnYHeJYKDzsFBPYuWDo8EQ7/gXJRFBUUcOXV52xyXVFRPqee/j1yQzloaCrmwCGksvktNDuYUENH11TbSPt7hLvzd+6p2URSzruqJJo5JkRX+TShMXToAK67/hKCAT89e5Xg0332NSqmoKggn733GcVVvzqPz+cuJZ00SMZSLF9awWlnfI93P36Ew48YR0F+HrrdZw3dLYwWCceZN3cZ69fVY6Sk23Z2XzeWT9d8BAIBCgrzKCzMo6AwjxEjB3Hu+ScyZOgAfHrAvj7TP3f8sttyxi9LLs0Zt43GZhNZ0ChfXcOG6obt/1B58LAj4JFfefCw45Gfn0uPnqX2Iu0QN/lcNsh0WvLZnOVdrqmuauC6n9/Lm6/OsP8GRRZJVmZxdZUATccfCKLr9vea853mvoNuWyQUDXdXeXS7VLjDWKnk8+l+jLTkntuf5Y+/fYShQwfSo2cZQuj4A36Kigs54+wJ/Ou5W1m6cC3hcMJtK52y+GTKF9x92zP89sZLOf6kIwgEg4D6PiNXJk1U02yyKs3ppy8jX1afpSX48L15vPna9C7j9pOf/YB7H7qOMWOHUVJahK757XiVbNKsrLa0bPZPm5HTZRLVlDwbsZgqOZXy1lDXym03PZ5Vj8WDh+8wdnOlwrNJetgp8L2J43npuQ+oqmxASokmbA4HFYFIMpZmyrtz+enPz+TBu19k1bJKqqrqqFrfSCwaU1kL9iJnWhINDVNadhooaEInlOtXsRFWEsNQyZZOzqMQdu6jdJwf0k4ZlXb8gGpDWk4ApHq3AMuEmupGajY0oQmd0WMGMXBQH9qawwwa1JeLfnQK5118Im9MnsZnc5aSThiqb5ZFe2sn0z+aT0FhLgMH92H4yIHk5uRipCK2FFlEXlnOGc2Rz+2rLR+ZNFrTlDQ3tfPh+59y2plHuWMthCAWTXHWuccxbGR/fveL+2lpbseyScOklGiaXYRMaGhStWmJjHsF1zvSlWhM2kXPsuVLJgw+m72cqe99ynEnHbJdnicPHnYYvNLnHjzsWCxdtJb//Osd1q7cgJAqpdTCVioAJ62gprqJKy/6PxZ8sYpIOIZpWoDK2NA0gbSN7lJIu7CYOiKEoFfvMhCQiCWwDHUcMtkK0lYmJKqSpmlaqi170XRSLC07bVXaC62STqjYLADdwjQlpXk5FBbmc+AheyIlXPfzv7Fy6Tr23m8EJSUFNDW2q527HZOVEwpRkJ/DhT8+lRkfzWfhF6sx0oYiqxJOdgpucTJXvuy+CpXRkl+YRzptUFpWxOFH7sOvbrhwkzF/6dkPaG/r5AfnHovf53MLsSklTBIMBkjEU+hZc+FGnWRleaj3bNUiI59P8xHwa+TkhBDA3BlLPKXCg4fvODylwsMOx3NPvstbr03HMiWmIfH57UVbU4HQmh0E2NYSZvrHC7FME6EJNF2xTsbjKXvRs+z0SZcuEk3T6Nu/Jz6fTm1NE+mUYS+GTpijvcO2QGgCKSwcFk5hk0QFgwG7VHkmMNGSAmUEsQMW7bTO3JwcevQoIpQTom//nkw44WBuvf6fGIaJYVhUrK0jmTQUvwTCtpIo28Oa1TXc93/Pcvb5x1GxZgNtrZ3KciI0+10gbZKrbPmcxV4gCOUG2WufkZx8xmH06FHMUceOIxgKuGMtpeTpx95i+dJ1WJbkw/c+zaJEV5aIYNBHSXEBzel2xYflKm0qG8ayzCxa7i3IJ6BPvx5MOOFA9jtwDH6/j/EHjdkuz5MHDzsUu3lKqadUeNjhSCXSWXU7HGIp0NCRGAh0LGkhTVAmAcWBMGRYHwL+ACuWr7MXWBVkKO3iWJYQBAIBQCMWSZKMG2i6ZrtXtAyngr0gak7qp3AWbZ1g0E9ZWTHhjqiiwc5i3tTQMC0ToSklqKAwh/GHjuWnV5/Jn3//BIlomiceeh3d52PUmCH07d+DJQvK6WyPZbgk0EEKOtqi/O+laeQV5NKnbxmm6SgrGSYNt6y7I589TsLh8xA6iXiaNcurmFNayAOPX9clKwOgen0Dzz35HrXVzfh0H4/c/yrRSCrTJ+GjuKiAfcaN5uMPPrczXZScwYAfn08nGo3brhbLTTElSz5N08nJCbHHnkO54c8/xu/3fmY87EbYzZUKL1DTww6Hy8goM5kD2eXHs1kdpVTH8gvy+OHFJ9K7b5mdJZHF6igz7I5GWlJf00JrS8R1YjhZDU5mhUs17dQZkU7WiU5xcSF3PvRLTjztcGXx0PUusjmBi5qmU9qjhDse+AX/fXoqDXVtNNS3IRAcdcwB3H7/L7jljivo2asYv99PNpslCCxTYhgW0XCcitU1RDvjNoeE7p7rEk1l9bVrqXYNaQo6OmIs+nwN0z9csMlYr11VTUdrFGmBaVi0t6h/O3wVGhrBUJC83Nwsp4+6VzplkUqY+HRVbTQYCKDrvkzgqhT4dB/9+vfkxNMO4+bbL/cUCg8edjN4f/Eedjjy8nJUBoVhZiwBdqyA5da0UG4Cn+3HLyrMZ9CQPrzZNNNeXJ16H5ob5KnqWWiYaQOfP7skuO0sERkXgiVBRwNh2mmU4NN0ysqK2X/8aMYdNIYXnv6Axx98nfbWTqws+TSh4w/46N+/J53hOMuXrCPamaRPv1IOO3pfikvyee9/cwkEfFx+9ZmsWlZNa0vYtTJoduyHZYdZmqYdICkc94iST8VQKMuIZnXtq64J/H6/cuMgaGnq5OXnPuLoieO6jPUrz39CLJpEQ8eyJKlkWgWhappdbMxRUIRt9VExExoall10LTcnh4OP3IuysgKmvPMZ4fYIPs1PMOSnpKyAi39yCudddgLBoH/7PkgePOwM8AI1PXjYsTj/shOZ8s7ntLdF3EUxU0siOwNBZRuEQgEGDe5L+epamprCSAt0TXMzHyATWNi1JkUmHsEpCobdvpBZVU3tyqLBnCBlPUp457XZaD6dgvxc/D6/y3GhdukaOXk5DB3Rh6MmjOP9/82lvSVCTk6QMXsOY/5na1i6sNyWT3LqWUdQUlpEe2sUy6XBdup0SNuFo0IjEdItme70LrPoK0It4dPw+XxIKRk8pA+tzWHa26JYhkXN+ibisSQ5uUEA4rEkG9Y3YjmmVSkwDStj+SBjNXLcLmSNoSOfkbJorm9nv3Gj0DUdTfORVxDiuFMO5pjjD2DiSQdut2fHg4edDlLCt02f3oXTrz2lwsMOxdqV1VSsrcPn86tURRx66MyCllEn1Ofi4gJuuesnvP3aLKLhGHaxCrK5HFy6acj63FXhcFgi3f93uC7sNpJxgwWfrWLFkkoSyRShYIBIZwyEhj/go0//Hhxw0CjyC3I59sTx3PPn56he30g8nkQDZn68GE1ALJZE96l7V6yqod+AntRtaCESsTLyIVRkKo4M2MyVjlybypebm8uoMQMZOKQ3/oCPSecfw/VX/4P2tihSQnNDO/95/F0uv+YMAP7z+Ls0N3YgTac8uXTvnYm9yGIMzfp39vgZhsnqFdVUrKklkUiBBDMliUeSjBg14Bs9Bx48ePhuwFMqPOwwJOIp/virx1m/rp5wR1QtlzJTaROpoQl7Jy0kQlOLXF5eDn36laJrylogsmpeaNJOLRVCxV9ItfdHqugDxdjp0HI7u3+ZyaGw61YoHglJKm1hGglMw8JMW1gm5Ob5GTSsN9fedB577DmEyjV1rK+sp76mDTNt4Nd8pFIp0mnLdq8o+SwLli+qxDQsLDt9U0iBLmzWTrl18qn4B9CFxv7jR3PkhH3o078Hg4f1USqCXaMjFk0S6Yy74x0Jx4lHUxlrTlZflSKn2+3bioat1DhxH5pQ8klpYSRN0kkDRwGKx1PM/HgpGyqbePK1G8nLz9n2D5AHDzsjdvNATU+p8LDDIKWksa6dzvYY0gRN19CFtMt6gybt1FKhkZOr6KZbWzpprg/zwF9f5sLLT+D1F6YT7UiAJdWijECTUtFHZ7eFhiUt97PakWfYFbTseh7SJoCS9jlSLas6OqHcID16FnPcSQdzyJF7ceW5d7N2xQYMw7SDK1FplVJDt+8jpURYICxIxNTOvlf/EqLhJLFo3E6bzeKD2Eg+4dT1wCakstTYxKMpXnhiCm+8MJMBQ3rx6Eu/RbcDXR0eDZFVhC3amVTjInVnAmCjvmpOIKhFpp2N5sKVRjr8FMpNk+hMsL68gWceeY+f/fbM7fUYefCwc8FTKroP7e3tPPbYYwD87ne/c49PnjwZgNbWVoYNG8bEiRO/9LiH3QfuIoa0d9jSsVO4C6KQAr/ux+/zYaZM4imTGVMWs9/4UQwe0oeayma1MNrnZrflpolK3AwKh85KCmnvyhWBlhC2jcAx+VvqbA2B0HV69Splz/2GULuhhc72GP+48zUqVtYgNMHwUf1Ys3wD0WgCaTncDdh+Ffv+jnxCMHz4ANZXNBCPJF3rghoLdUfHQmFfjWa3pbnygTQhaZiYRoz6Da3UVjVhpjNKEKYgGVdl0tdXNDB/zmrSCct1oDjuDVDkXY6lCClIJgx3/B25seVDai4fiHAlFeTkBhk1ZiAHHTl2mz83Hjx42DnRrUrF1KlTaWlpoayszD1WUVHBlClTePTRRwE47rjjmDhx4haPe9i9IJ3S4dJd6jPKBLYfXwpinSlikZQbb1Fb2czt1z9LTp4qimU5LgMpyThQLPw+H/hU6XPLctwKjmnfdi/YVN3SvtaOjsTm10RDIycvwLW3nMvLT31C5eoGqtc2UVSaT49eJZx10dEcddy+/OvBt/nwrS9UTINl2ou2dOVy3pGCJZ9XkkwmbYWKzHdklKIMG3YWBbZjWdE1QjmK1Mo0TDqao1x59n2EO6I4JeLNlGTejNVUVzZipA1amzptN5Du3sulAXOsImikExZrltYgTYHQRdZcSJf0yo4hdeXTdJ0hw/rx4DO/pqSsYJs/Nx487LSQ3ZD9IXfd7I9u5amYNGkSw4cP73Js6tSpFBcXu5+Li4uZOnXqFo/vaKRTxo4WYbeBqhGhKapqkQnQdBgZhM0/oaFhmRJp4mYqGIZFW0snsUiCQMBvKwki827/v7QUBwQOJ4XI4rTA4YlwLAOaazVwztd0jR69i5l08bEcf/pBNNS2YaRMJDB0ZF8uvfpk3nppDucf/xc62qJc9otTCIUU4ZbLtpn9LlUZ8XgsgWnHXGjuvTW3YmpmFDaWTxAKhRg9dhDn/WgChYV5WCYYpklHWwQzbboqlZSClqYOUsm0SlOV2e1lt5+RL+D30XdAGe2tEaXSyU3nIsPvIVyZiwrzuPaWcz2FwoMHr6DYtkV5eXkXy0VpaSnt7e1bPL4lJJNJksmk+zkcDne7rKuXVfPC4x+yz4HD+f4PDwOwK1aKr7jSwzeB7tPYa/8htLaESSUNVMwAYJvTNTvjw9lRZ94VpAXh9ngmKyKrIIW0lBXEsiSmYaHrdms27XVBcS5CQLgtBq5lQKWaOmb9YMDPgCE9+dn1ZzDh1AN45h/v09wYRtM19txvCIcdsxcP/mUyzY2dmIbBZ9NWMmrsQEI5QeKxJF0yS7LlAzuWwX63l/ZgToDBw3tjWpIN65qwLBVlkUqlEFIQCPnoO7CM0XsN4te3nMOrz06nrSWCYSiWUdWmkzQrAYm0f5yeeuBdIp0J1wjj1BAJhvwk4mmktBASjLRF5ZoGDMOws082mguRsaiEQn4GDO5JbkGIIyfuy8FHeW4PDx5UPZ9vG1PRLZLsEOyQQM3W1tavdRzg9ttv59Zbb91WIgHw8F9fY96MVUx94wsevu01NE3j0GP25Na/X7ZN77s7YtbUpfzj9tepqmhAmnY8AXZwJZnKmEjLrY3huEMc14VAIG0rhJYVw+A6EYRESN0liFIkT4JA0M85lxxDUWk+706eS+2GZsJtcYRTFcxWTALBIJf98hSOPeUArjn/7yz+ohxNaPQdWMb61U08vvRtOsMxsO/n8/no278MTWYov12hHH+Bc8QJJFW5KWrZNjX22m84v/rTJG6//jnSaYP6Da2sXFyFaVrkhEL85s/nceRx+zD7o2W8/dJcjGSmsJhiBs0Ee2poxCMGzzz0Pv0H98Cn+zAw1b01QV5BiOPPGM87L39GIp5QnhlTkoyncHkwNp4LO7ZC1zVGjBnI31+4xrNOePCwg7AzxjFuc6Vi+PDhXSwQTmeALR7fHG644QauvfZa93M4HGbgwIHdKuuGimZSCQPLsohFkggBa5bXdOs9PCjUVTdTubaeeDRpByA6aY12cKVzzA5OtJyCVTZZlbSVDae6qNpRZ/n6Be6uHdvNIYSgoDCXg44ewyVXn0BhcR4XX3U8PzntLhZ+Wo5lZfFVCEHvPiWM3XcILz72IQvnriUY8rHvYSOoqWyirTmcofoWwi6gVcaxpxzAq8/MpLMjrlw22fKJLFuLcIIzM/KZKZMZ7y3m4KPGcvujlwPwzzv/R/nyWqRpuC4HNX4thFvsNFy3r8rKopQMO+PFsFi3up5Lf3ESrzw1g1Q8rZQ0CRgwZFhfCvJzSMVTOLQYKrY0K9DVmQvUXPiCOsP36M9Vvztjt1UoIuE411/2KJVr6kkk0gwe3ptf3HIm+x8ykuULKnnjudkcecLeHHHc3jtaVA/bG9sx+2NnjGPc5krFxIkTuf76693PFRUVbgc3d3xLCAaDBIPBbSbn/56fTXN9B06pJuWTltRXt/LeK59x4lkHbbN7754QXf5zqK0Adw5cb77uo7g0h1gkoYp6OdkZZBM2SZvimwxhU1YrGgKpCYbv0Y/r/nwOv//x41SuqUdKScxOBQ0G/RSXFZCXH0LXNc67cgL/+OsbLJy7BoHgR786hXkzVlBX3aoUIOFGFRAI+NjvwBHkFYQ4fMJerF5cTdJMZ/UVJU+WfLJLL9XncHuMe/7wIovnlfPb23/Y5ftUPM0rT07j8Il7uffOxG1kMjGyxw9wlS83ykMqRUTTdfILcl0uDU1AUVk+eXkhotEEHS0RnMwODQGaRn5BDuOPGM2N911ISY/dS6H4+O0FPHHPW7Q0daLrOi2NHRiGSSAYIDc/QF6B4uao29DK4nnlDBrey1MqdkNIK+N2/DZtbA0mTZpEa2trlw36luIVKyoqNnu8uxWLbs/+mDJlCu3t7QwbNoxJkyYxbNgwzj33XCZPnkxrays33HADwBaP7whUrqnn+X9MVbvm7J9pCfFIihUL13tKxTaAkE6qIlljnglodCpgCgmYZDFtZuISMimN2PwONmSW2iJVmfSiknzKehTxi0kPsGF9M6lUGsd2oGkag4f14c4nr2DwiN4s+rSch297jaVfVIKAvccPZdJlR/HSEx+7wZBdlnQLpr+ziFlTlhKLJDDSZlf5HC6IrL4jsxZ5+9/SlHS0xlgybx3trRFmTVmKmVYxE0ZaWR2kzLhSBMLtq4aKqXDbdZQK20SSHTgKICyVFaMCU1VrhQV53PqPS6lcW89TD7yHZUnSKQOfXxVcO/6M8Vz1h9O7/VnYGTH1jS+YPXUp9dVt1FQ1E4smCLdHME3AtpiV9Cjg0O/tyZ8evgRNy2QteVFYHroDG8cObs3murviGL8pulWpmDhx4ma1nkmTJm32/C0d396orWqhqS6sfPtCszkLFImS5v1EbBM4GQRu7IFia1JLnpNmaRNVSVMFVKo50dz0z0zchKNWkJ0NCijSJiEEZaVF/OzG7/PiYx9TVd5kL/KZtnShs/8hoxg8ojfplMGjt7/F6sUbyMsLUVCUS11lGxcefTutdWGEZT8jVuZZMZImTXUdasG33QuaHd7o2CEyfbff7T6qXAyHsEpdW72mmUsn3kVTfTuW4fB5kJVu61yrZRQMNz3VkU89t/VVbcyfudZm7cyMdzKe5p0X5hINJ+zj0LChjd9e/BiFRbmMHjuImx++2E1d3R1Qs76Ze65/icrVDSTiCSKRJJZhkU6l7WdFKW/+gEZpryJ+8puTOf2iw7u0oZ4/zVWYPexm6MbaHxu7+G+55Rb+9Kc/fe3mvkkc4zfFbs+oKaXkvRc/I50wXb+7Jh0SIsfw61WI73YIJzhTc3fJDlz/vR186bgGNIRbydNhcRDuH2/WD7gEoanrNV3HSBsYSYt/3f0ezY1hLNNu2+FoEKgCYPsOYsm8dfzztjdZPn89PfqW8MeHLmTFwiqefuADWptaSCdNNM1WhTZ5VrICSLOIq7rIly2urXg4TJpOm5YliUeTxCIJO+U0Q+AVbolz+69fYN70VZgpm9PDdalkFBTstpDKFJuIpdxnWQiB7tcoKMhhQ0Uz6bjlPuNGyqKtsZP25gh1VW38+pxHuOIPp7LfoV1Txb8LsCyLR//vLQYM68nKhdVUlzcidMGC2WtIJtIITc2FJjIptHpAuYCG7tGX39x+NiP27L9pwxKiHUlmvreUH1x25G6llHmgW2MqqqurKSwsdA9vTQhAd8UxflPs9krFJ28tYvbU5Zhp0+ULkFj2j4gFJiyeXU5rUyelPXcvH/K2RP9BZZSUFpCMtoGzJNsKQtZ+m4xh37L/ZWciCInu07EsS/FQbJRyqgmd3LwgCOhMGUQ6EoTbomiahp5lO1BKhaB331IsQ3LrFU/T1hohNy/EDy49kn0PHs6+Bw9nrwOG8uAfX2XlwipMQ91PKQRWF/mkLZ8UymXh8+v4AjrpZNq9LhM9kokf2VxfN1KfkEAyluKzD1fS3NCBZVnudbJLW5mATQQMGNyLw4/fi+ce+hCn/kleToiefYqpXF2vWrB5QrLnwkimWf55JY//9W3ufenKXX5xTKcM7vntS3S0RPn5raezdF4lrz81G59PEI+lME2Vx2emJQ6LycZzkZ+Xwx1P/ZT9Dxuxxfv06ldCKOSntrKFWCSxy4+bhx2HwsLCLkrF1qC74hi/KXZ7pWLOB8tIRtN2IarMT7pT1glLUrGqjqu//xAj9+rHNf/3A0p7fb1J9rApDpkwljH7DqKppsNmugShqSJeGwdqOnOhIdwMBb/fx+BRfWhr6aSlLuzqE06uhQZoIhP46SzZXYIZVZoDAM117Tx+x9vEOpOM3Ks/Z/3kSD763wLef2kefr/OmP0H88hbv+K8Q/5C3frWTeWzSawsx4Vj/79P18nPzyGcNpHCIjc/RCphKB4IyCK/2lJfu8acIKGzI5YVX5KVuCoydjXX6SIljTXtrJi/3lUeNCHw+X1sKG/CMmzFxPUeZbcKlmGxYv56fnf+Y1xx46nsOX5INz8J2xbLvqhk1vtLmfSTo5j21iKmvjYfy7SoWFlHPJYk2hFDIhVvh65hmZbiptnoWUET+AN+hozsQ2Fx7pfec88DhrD/ISNYNr8ym1bFw+6C7Zz9sbPFMe7WSkVjTTsrFlQhLcv1lwKKREg48fqQihtUrWmgobqV2nWt/PTGkznomD12nODfEQwc2QufTyeZVEoFlu2OBJu+m03mQkqBJiCUG+Dnt3yfWVOW88Hkz0kn08SjKTc2Q1qQjBsIzQ7+tAMjcRZQe8H2+XQM0yQeSZJKGAwf25/SnoX8+673aKhpI5000HSN5rowV9x0Ko5Ws8mzIu2vbK3C8cqkEmlaE4Zy32iCA44aTWtjJysXVJJOK16MLvLJjHxOXx1GbWmpviViaVfBQAib0Vf9kLnuXEdOCyLtcRbMXquWSUsgdEhEU6STBtJyskOce2aySCyprCqJRJrFc9bxyK1vcu9LVxDciXfei+dW8Pm01Zx71ff48NX5PPfQR0Q7E9RUtLD8i0oSsTRISc26Znu9l67yJS252WdFaIKcgiDjjxrNnx+/VAWtfgUsKelojjHtrcWc+aMjtm2nPexc2I5Kxc4Yx7hbBwssnVdJc21YlX+2bLOxJewAQeUv16Tufk7FDVYv2sA9107m2fs/3NHi7/K45FfHUVJagC50lFPC5pNw5kJuOhcqhkFDFz6EpvHL287kxU9v4tTzD6WoWKVDqjkTGCnTJYdyKapNZ++po0kdn+bDr6ny6T7dR1tjhM8+WklNRYsqviXtZ8JSq3uvvqXowrflZ8XpA7odE+KUNdfQpEbA7+PiXx1H3wE90IWOsDaivpbZ8jnF1nT3mBMA6BzXpIYunHPscczqq4ZGTm6Qnr2L3RgWaQnMtEVOboi8vByXtlyTGhqbzoWQage/euEGbrzoSVYtrN7Rj85m8dq/ZvHXq1/gw9cWMPv9ZTx17wfUrW8l3Bpl5jtLaa4PK6XPfa40RY7m/qdv8qxoQiO/MJeLfnkctz52yVYpFKCsY5H2ODPeWbqNe+3Bw86F3VqpyC8MUdKjAF2o+gX2zzSayCwNAmVGd1P+LGjc0MYLD3zEHVe/SGtj547uxi4LXdfp2acYv66TyQXJzIUmNzMX2CmiNk+DrmsUleZx9V/O4Klpv+XkCw4hFArYWTyZa9134XwW+H06RlKlfmqajpEwaK3rJJ003XtotgujrFcRmqZx1k+OJD8/Rz0rYlP5dPtZcd41kbmnQLDy82pefXQmZspC13T3O13Tyc0NUVCUR0Fhjq1oZdrS7LZ0oWX1RXTtm3tcz5JPIxjwM2rv/ggEPk0j4PMxas8B3PSPC9hz/BAlh6O2iOy2MnMhpCARS7NkbiUfv75oRz86AITbovzn3qlUlzcB8NZ/5lK/vpXO1hiff7yazpYYmBIsSCcMjJSFJlzVAV3Y6pM7pl3H0afrFBTksP+hI7nwmon4A1tv2HWDYrdV5z3svPBqf+y+OOjYPThkwhimtMaJdSYwbH++rmsIAaZTUyGbAEECliQWTjDzrSVomsbvHjxnh/VhV4BpWjz51/dYu7QWAfTsX8xPbz6ZguJcTr/0UJ7724fUVrYokzu2pmv7/jNEVrhzIaWTvZGBz6/Ta0AJP/3Dycx+axnN9R2AE1eRUUICOX6kaSEtVXvE55P0GVxGS10H0XAcpER35LBdD737FnPKRQeRWxAilONH0+wlI4tjI1u+jSFtN4WwBC11YRpr2pVcprS5DQTBoJ8TfjieH/z4cCrXNPC3a1+hoy1qu4GyqpJIm2EU0FHuCk0TNi23sImslFQOE2k8nGLulBW2IuVjzPhB3PCP89CFRlP12wjkJmMthKrDotrUCAYDaLqguCyPkh753fVofCss/bSS6W8uZuncdfzoxhNpr+9EWJBOmCyaVa4K0GVmB8jMBZaibJdZfc5+VnRdp2f/Io45fV8u/PXEr13/xyl137CujbrKFvoOKfvKazx8N+C40r5tG7sqdmulAuCKm09h4LCevPPsPGoqGtF9PorL8jAti/qq1iz/Ntm1lMCCRCzFrHeWcp9P40c3nkhx2c7xY7uzoWFDG1Nenk9rfQfSgtyiEBXL6hi57wAu/f3xLJtXRUt9GCNlgbRst4Gd7SGcDA1wggfdbInN/N0FQ3569CmkvSlC2jTUeUJZAkI5fsYePISmDe1sKG8iFAowdGwfxn9vJC8+9Amas5C66Z6ABpYhWTi9nAXT1tLWHMVKWUpJEXY2isjOJHFDHVSsg0pUUeuYtOMiTInQhWttwLZAFJfmM2RMX1IpE11Xu2WJcGgvMmmoApBKEVB03Ep50ISqyqppGghFY64DQkoa1rdjpEwCfh8yLfnnTW9St76V9saIcjdpXZ9xR7nOKwxhWZK9DxnK+ddOQNc0Ru67mTTK7YxVC6uZ/M8Z1Fe2EG6J8ca/ZhMNqxTcVCxFk10kTXPGz/67defCeaTsvrqMo0IjmOOn/9AeXHr9CRxywhh8vq1zeWRDKY2KX2XKy/O5+LfHdfcQeNhZsR1jKnZG7PZKRTAnwBk/OZyR+wwgGo4jJZT2LuDdZz9j5rtLaW+MYFoWQmR+lRy+BCklkfYYM95cQtXqJi7/0ymMHT9oR3dpp4MmBPlFIVrqOzClRbwzwcr5VdSsa2H9ykYuuPYYVs2vonptI1Yy68deqB25U9kzu8qnCi7ctJSfP+Dj6NP3pXZdC5GwJK8wRCqepkffIvY9bBh5hbmsW1ZHYUkuB03Yg8v+cCL3XvMyiWha7WqFRGgCYdq1Rixob44w652l+Pw6qZSRlR3g7F4zTCYOSwUI0DLPirIgOFTiwl29s/vqtiZUJoyzO85qUd1BEwgz67jLT+5YG3DlU4Y1werFNSRjaUCyakE1QsNNcXXH05ZTCkEoL0iv/sVceO2x5JfkMWqf/pTs4JTqN5+ay4a1TfzgyiN46PrXWb1oA5YlCeYEWLe8DstSoySlVOnJQnT5u3VsPpm5sDNsRNZsChg8she/eeAcRuz9LZQnoZJzLctCmrtwyUkPHr4mdnulAtQP6l4HD+lybOCInvQf1oM3/j2H+soWRVAkMuZm7J2NaUkiHQlWfl7NfddM5geXH8HJl3iU3tnoM6iUMeOGUF/Z7pYEt0xJtD3O8s8r+edNbzN0dF9MU1JX2QpSYpmKBllYFsImqnIgBBSV5OHz+1g5v5oPnv8cw7AQmqCkRz6tTZ1omk7/IT04++dHkVcYorM9zsJp5XzxSTmJzhS9Bpbwy7vOJCcviJE27QUlEwfhkG2Z0sIywTLU7hYzw+mgaRqWtBOQ7UpmjlVBWSa6PisZdk27sijYRclU1kH50nq33LkiYsuwZ2q2QiKFI58aF2FTZwmp4iekLtF1HctUJF0SiZW2SKUtHJ+OZWLfE3RdAzeNV/Ul4NfY68DBXHrDiYw5YOdQkld8XsV/7p6KT9eJhhM014Qxkha6TyMeSdGwvgMzZdlWmk3nwvm7tbYwF8InyM0PIaVk4Ije306hwFbqhIY0BeVL65Si8zVdKB52UXiWCg+bQ05ekLOuPJKKpfWkoinamqOun0xRKiszvUSVgzYNg/rKVv5z9xTqq1o55xdHk1+cs4N7sfOgZ99CND0TCCftxdBMWtSuaybaEeOkiw+htTHM9NcWEe6Io2sa0lRmfcuylQwBoVw/PfsV8/nUVXz+8Ro2rG1GaGCkTXr0LSKdMtAkHDRhD0679FCMtMkfz3uK1Ys2kJMXoLAknxF79SOUG+Cdpz+laUPYXeRxF3wLh0ZKty0BKmhXc9k4ldlc2G4T6Vb/dKwPOmTaEooqy3HHaNgKAmrhN1MmaxfVMPOtpfQZXEImE0RBuEtgRiFxmDOVfOq+eXkh8gpzaK5pt5UitXhaTgl5e7FFKpq3rvKBT1exKVf93+kMHt17Oz4hW0Y8kuSxP75NuDlKbmGIxup2ErGkGwSbihmYKcddphRWYftzskNcNp0Lm0RNE+QX53LWVUeRX5jDAceM/FbyppIGNeUtaICVtuhojtFY3U7vQSXfbiA87BrwlAoPW4IQgqv+7zRe/nshH768gJa6MKaZ+fFyMkLUoicwUgZtDRGm/ncBlcsb+PEtJzJ4j53jh3lHIxj0k5sTINmZdMfMWfTMtElHU4x5U1fSb2gZAkFeboic/ACt9WEyNESAhGAwQEdjhDf/9SlSWhjJtE2cpYguLMMiNy/EoBG9aKkL8587p1Jb3kJufpAJZ49j3FEjyCsKUbW6kU9eXUy4JaJcEFrGceDI58RvSPuYE8OgFvXMcYeMytmRCk1gOc8KIkOulkU9rpGJixASou0JPn1vBWdccTiakG5dkY3pov0+XSlbrnxqsdQ1jZ59iyntWUBHUyfSBNDw+XWE0EFCOm2oPmbLZ8d0+AIapb0LOPzEsTtUoYiGE3zwwheM3Kcfex06lCdve5/6ylastIU0LMoX15CKGmhk4iGERSbfwlYAnSDMLc+FAKmhSUEoGOCUiw6isDTvW8v/+dRVpBMG0gBfrg9/wOcpFB52G+zWKaVbg/ziHC698QTGjh9MYUmuygzBjrIXGV+4a+60LDqaoyyaWc7frnmFTz9YuaO7sFPglMsOpteAYvx+X1bZ7ky6rmlYVC6rZ+YbS4hH0uQWBOk7pAx/QHdLmmuahj/gJxFNUVvRQiqRxkhbtu9cMR72GVhKn0FllPYp5Niz9+Ph3/+P6f9bTLgtSkFhDkVluUQ7kwzbqy+DRvXi2HP2o6RHQSZOQoiMfHb8g3tcau5xJ1DTNhK4x3y6prIxwG1L2/hZEcK2ujjjoP7z+TVCuUFiEVWnQ9NEVlwFbs2OYMiPruubyOcP+MnLD9pWC4HQNAIhH30GlrDXYUPp0a+IYCiQadMNNhXk5Qc58NjRnP7Tw7n4huN30FOi8MQt7/D6o7P495/fZ92yej6buoqOtihCCJLxNLHOpB1Uupm5yDqmsriy+rrx361QzKL+oJ+c/K+uqbC1GD9xNKFcPz6/jpU2ad7QzqIZ5d3WvoedHA4D3bd97aLwlIqtgBCCq+78Pkd+f2+KyvJt8zE4oXPOu/v/UpKMp1i3vJ7Hbn6bNx6bTSKW2hGi7zQoKM7lot9NoLA01x01d/xsn7eRMkmnTGVtSFuU9Mx3AxAFGj6fRmFxCJ9fxzBMdak9F0II8opCnPuro0knDcp6F/Dfv33C+hUNxDsVW2b12iaeuX0q/7lzClWrGhFCcOIFB9JrQDFOECTYGQK2YNlecOFmoIgs2R13hvpTCuYG3FoPzlmyy7OS3W7mHQHJeJp5U1cx/bXFXcbHtdPYrgyfT89cn9WsmTapXNFAxfJ6TMPCH9AZMqYP/YaWUb2iibaGCEbKyArmtF+aRjA3wB7jB3H21UeRk7f9GDNrK5pVHI2NpXMqWTSjgpa6DjrbY7z+2Cyaa9pxYnKlZcfb2GMqsufCfnfmwoludfu50d9tbm6QPQ8dwoRz9uesq47sFisFQCDoo/+wHqigUWhtjPDR5IXd0raHnR/S6p7XrgrP/bGVKO6Rz8/vOp3Whk4WzahQ5aKF+7uVsVaATfMLRtKkobKN1x6eSW1FCz+747Qd2YUdjgMn7sHYAwez4JM1xCIpnPBLgUBI2WX8YuEklcsa3KA6C5XaGQ8nSafNjK/cUtfpdvDmF1NX07yhnWh7nCWzKpBScVhIU2IkTKy0pL0x0iWPXFqZOAjNXaiF6+ZQgZIZF0ymdLljTscJl0R3XCjO4mVJ2yKT1Ve3ugdujIOQAsuyCLfEaK7psMucO3fMKDuWKUnE0limRNcz5dI1VKaKaVoEgioLxh/wUVicS926VtpskjbpKBQy08dgnp9QyM+i6WsZOKIHA0b1Ysh2cNslYikev+kdhCb4/RM/JBDy89a/59BU2w4WpOJpqlc32a6ezPg5abzu35r9WRNO7RR1npQO/4bMmgvhzoXu0zjmzH04+ZKDu7VfUko6WuJodgCuz6/vFGm4HjxsD3iWiq8BIQQ/v/N0evQpVGZumSng5DI+SvVZtxcWaZi01IVZ+MlaPpm8kHTK2MG92HEQQjBm3CD8AZ87Ztmv7PGTpkUqlnIXQA0QliSVMJCmZS/0aoHUBeTmh9jrkCHUrmshGUsRaYsRbU+QjqVdVkXNuWYjy2IgoGdcExLbPw8CmZHNliF7frUsGTShlItUJE0ykuoin+PHd/sqN31WnLbMpEnVigaMlGEvnnRpS1gSM2mga1ltu2Mkyc0PMnhUL3r2LqJXnyLWLa2jsarNVXy7yGwrLum4QUdThOVz1vPwda9z7+X/ZfGMCjsTZdvh6b+8z/LP1rPysypee2QWpmFSv64VDBUb01bfyfpldUhTZvVVuJYkd/zssXfGQXT5LLuMn08IdE3Dp2n4/fpGtqjuwWcfrKRubZNNvgVW0mDFp+u7/T4edlLs5oyanlLxNVHWt5ALr59AfkEIkOqHOfuHLstP7u5upaR+XStP/fl9nv7z+6xf0YBpm+93Nxx91j4ccMwo8gpCdqpl1rjJruOnFtGsz9nxK1LYNNyK2Kr3wBIGjuhB7ZpmpGFhpEx39y4N6Sotwk6jzE7vO/L0vQnlBHDYJ3CtJmLz8tlBmY4lwf23naUiLZkln71rtiw7cyRj2erSV7dNSWtt2FZM7Haz+ioQhHIC9O5fQjDoc5dERy5NCMLNUSzDYsDInsQ7EmpRltjKTNf/sCxMwyIZTWEZJpGOBFWrGnn092/y33s/pmplwzZh91s4bS0LPikn1h4nEUsRbony5uNzad6gCNKEVJkTyVhmHLb4rMiuc6E58RbZYytVMGpeYYj9jxrB+ONGc8jxYzj05LHd3rdU3CDSHnfH3DIkrXUenf9ug91cqfDcH98Ah39/L6pWNvLh8/NpqQ/bFIQqXdIxvTqLlrMQWKZFW0MnU579gplvLOWYs/fj7F9/j9yC7gsQ2xXQc0AxV939fX45d71aMOwFwnLGzAkyxE4h1QSa6Ywr7rg6f3JCKCVh4MgefDJ5ES21YWyWB8WaKDLtqpLfOv2HltFvWIY2ef+jRxDKCZCIJhVbpbtYSRAZx4cbxCkyTJ/ODrqLfBK3RDs4Zdg1AnkBcvODtDdEgIyZ3inR7txDkTdllKhMfyWaUJk0+x01grnvLCcVN7r0NRlJ0RxNEcoNULlUxVY4rQjhxGY4oQXSjgex3U9Cc10sNeXNvPbgTGa8toQTLjqQ0392eLc+B6/9fRaNla04Dh5N0+hsiZKIprLklZvMhSZVmfnsZ0Vz5lluZi7IflYEBUW53PSfC/AH/N3an2zsc+QwSnoUqNoj9rO8OfZXDx6+i/AsFd8AmqZxwe8n8qO/nOhGjbuhYpvsRJ2FDbAgHk3S3hDh/afnceePXyDWmdgxndiByC0Icsw5+zH2oMHk5IfUwpEdUAdg7+6zxzXzjVpINKFouYfv049U3KBmVROWZbnBi647g0xMhO7TGXPwYPzBjD4twa3nsZGdJHPHzciHIHMvkVnEhC2bs4gLO9jUSJgkIkl35++0CbgWDJ9Po2e/YoI5/ixlhiwlRl0xbO++lPTMdzNEgiEfgYCOZVpICfFYiuaa9izZsnojcC0obotZHRFCBUQm4inqKlqZ+sJ8qlc3bvX8xiPJL/1eSklzTZtNcJZRzgy7FLy2sXx2Bxw5s+fBnYus8cwOXs1+VpAQbony6kMzt7ov3wRr5m/ASGcCYgFMw/zKcfHw3cDuHqjpKRXfAoedthcnXXIQpX0K7ZREhewfPE0ICopzKCzKsytnAlIS60ywfGYlt5z1FMvn7l7+Vk3TuOim47n15UvJLwy6pr7MmAFI4h0pLHun7Zr/3UBDteAEAqpAVuXSevxBHyP3G0BufjAzBxspeQVFOYw7diSJeIpEPIVpWpT2LmDQqN6Kz4GsRcsJlNxIPuffTvXOLm4KMtYA57OqU6L4MxJ2vMWmC6N6fnRdZ7+jhxPMCWSyUFzTfqbt4l75nPf7CeTmqb76gz78AT85uUFG7NufQaN7q/RdubF7oIuqlFFcttRXS1K7upk7L3mBqc998ZVzO23yIp665T0Wz6jY4jlvPTqH1trOzDxKiLTGmP/hGsy00VU+J2B1C/I5ypvzbIBjsVDjqWkaZX2KCOYG7LRbH8P36feV/fimME2LVx+YQVNVRyYtUELV8gZuPecZGqvbttm9PewkkN3g+vBSSndPaJrGJbecwI/+fDK5eYHMD7fI/ulWi9l+Rw+n14Aie+HCJSJat7SOF+/8ENM0d+nKdN8E/qCPkfsPIBDwoSEI+H30GVTK8H36M2BET1Xkyg2aBKdeg6Zp9OhXTO+BJVimxZRn5xELJxg/cRR/ffMn5BflZC0wXeciFk5w1yUvcMV+9/Kz8ffxtytfxhfQufRPJ1DWu9DmmMhabCGrxLg66lSzdJDZVcsu12a7SNx4ENuc76Q2Os+KZvctvyjE2IMHu33dWAnQNUHP/kWMPmAgB524B7mFQXw+3c5ysBhz0CDuePun3PbqZQwc3RNdF13lk5vKl51NIchYbBz5fD6Nsn6FDPqKjJBIR5z/PTKLee+vVIRiW0CdzTGCPYqWYTHn7WXUlje7wY3Zbo2M/M5nW0YhKCjJZeCIXgSCPnscu86FrgnGHzeaYybtx6j9BnDyjw5m/HGjv/rh/IbQNEHPAUXovsyzIiUqxkej29JWPezEsLrptYvCUyq6AUecsRf7HzMCf0B3TbfuD7WEWEeCmjXNXPTH4xm1/wBFQIRarKQhWfVZNT8b9zf+ev5zmLtR8SFN07jwxuPY/9iRDB7Tm32OHsb1T53H3VOu5Mq7TyOY489kQAg1XsGgj2F79+Vn936fEy89CExJOmHQo18R598wMUM2Ba7ZW8U0qLlIJww622N0tsXoaIryxXur+ONp/yIQ9HHaFYepezruBq2rSd1tk0ybrsIjuhKhZcvgEi5ln7tRW85imYykeOeJz1QlUzKLvcN6mZMf5Ie/O4aeA4qRUpKTE6TXgCIKinIp6VnARX88Hp9fp7Asjwv+MJHh+/QjkOPLInzKKEnZO/1smVRfVRCtz6cxavwA/vDshYw6YMCXzuerf5vGhpWNpBMG4kt+WlzFwI5NkVKSiKTAtLYsn5vt0vV5sNIW+UUh+g/vgU8XXb4TgDQlqz6t4uQfH8xd71/BBX+Y+A2f1q2DEILzb5hAv6Fl+LRMVo/Pp7H3EcMIbUcOEA8edgQ8paIbIITgvN9PZJ8jhxPKDbrWCuc/KSV15c0s+ngtN714MQeeMJqC4lzXLG6kDFrrwiyeXs7N3/8XK3Yjd8iAUT35w7MX8sCMX3DT8xczzDZNOy4PIRQzYnGPAnLzQwzeow83Pnchy2ev442HZ2KkFSX32b8+mr7DysCxFmRH/7uWI+G265RON9ImyXia3MIQJ1x6IIeePNYmOMuKsRBd4y3Ibl9sdC/nfo65PssFkuFS2FQ+xxqRjKdpXN/qXp/J2FALvZmyePLG97hi33v44Ml5jDqgvxuHcNoVhzF834xpf/9jRnLTCxczeI8++HSti/xdxpgsemtbeQnlqKDSPcYP4vwbJhLM+erAxvUrGjDTFum4wZSn5202w6lqZQPLZq1TsS/2+LhuJJlt58mST2aNmT1+WICERCTFusX1tNR0sNEsu2Pa0RChoyn6DZ/Qr4/eg0sZdcBAVdwMZVkrKMllr8OGbjcZPOw4SDsD7Nu+dlV42R/dhAGjenLjCxdxz49eZP7UNVgJS/Et2CZmI23wxQcr2e/YEVz3xLm8+sB0Pn5xAeGWKNH2BBKJkTZYu7CG+376EqdffTinXnHYju7WjoO744SC0lwu/fNJrF1Qw56HD2HtghqmvbSISFscoUEwx8+Kz6roO6yM5XPX09matYCIDE+BzLY62DvzvMIQZ/36aMr6FQJwzSOTeOeJT3nvX58SbosSaY1lAvdFZrcsyYo7EGQRYqmXcy8nWM/hX9q0n9K9xo0B6FtAW1Mk0zbqGZJIjFSa5tp2hBC8cv90AMy0xfB9+zHhgnGbNF/UI4+zr/seL9/7MZH2BEiLlrpO0inDlUsTAl9Ix0ha5JeGKCjJY9QBA8grzuGCGycSCH21QjF/6mqqVzYpeQyTuspWLCnRNzqvraFTZb/IzDw4r0xfM+MnJe7WJzsAU32p3oy0QSxsIS3QfYKikjxy8oMIAaYFA0b2oM/Q0q/sQ3fD6YsFJKMpXv/7DAaN6UVRz/ztLouH7YjucF/swgZrT6noRgghOO+GCdSubWHDmkb7mHpJCdGOJG88OJM9DhrED645ih9ccxQv3/sJrz8wnWQijUBRLbc3dvLa32aw6tMqfvnIpC6ZCrsN7EU4EPJz5A/24ciz1OuFv07lo+fnk4wlKeldQCg/QO9BJQjg+dumsm5pLemkgc+nSoerHbF0lRR3AUOZys20xXN/ep9n/vgu/Ub05LK/nszJPzmYk39yMO88Pofn/m+qMufbNT3SltWlLU0ThPKD5BUE6WiJkk4a9s5fWUwcpQHU85FfkoOmaUQ6Yphpy7Flue35/Br7HD2ClZ+tJ96ZQJpq9XTbcN6lJNIWQwhBr0El/PCGCfiDPh655nVWfLqedMKguFc+k677HuNPGM34E1QcQbQjzm3nPMO6JbXofp1AyI9pWux37AhqVjdz/KUHcsKlB212SpprOvjfw7PY+4ihHHjymC7fVS1vINoexeEgjbXF+fjZ+Rx/Wde2pj7zOalEejNzYY9Xl75mxi87LVQ6ZeDtAcluSxOCHn0L+f3zF1LSu+BrP3bdBUd5lQBSkoimWLekjkh73FMqPHyn4bk/uhkDRvXi+1cdTq9+JQT8ehfGTaSkdk0jd5z3LJ+9vRyAU684lL2OGE7vgSUEAj7FDghE2+MsmLKav57zDCt3I3eIAyEU02RuXpDj7UVuwdTVfPz8fCKtMXoPLOXW1y7joTnXcNN/L+aSW08kmOtHSomOwKdp5BcGKSzOVcGKboCfzcxoWxdS8RQNVW201IZZObeSuy96jpmvqNobx196EPt/byR9BpWyx/iBjD5AxcNkt6ULweBRPbn9/SvY69Ch9OpfQlmfAvILQ3bMgGLN0BDoCPy6wO/T0IWWkUdkPSOmZO0X1Zx42cEUluShaY68zj01t12fJgiF/Jxw6YEUlORwx/n/YfbrS2hc30ZLbQcbVjaydsGGLuOaV5TDpOu+x/D9+nPoaXvxg2uO4ntn78ev/nk2937y8y0qFACv3vsJM15ayGv3TyfcnLEGJSJJ5k9ZhZW23KwLI2Gw6rOqTdqoWtaANKwuc6ELu48aWWOWHXch0IXAJzQKinMoKs13g1M151pn/CxJS00HHU1bDhTdHsg8Z6ofPl2jZ/9i8otzdqhcHrYDZDe9dlHshlvgbY9jztufkeP6c/9PX6JmTZMyjQvHVGtSs6aJ1/42nRHjBlDat5Abnr+AVDzNfZe9yOr51cTDSSQWRspi7YIaHr76VU752aGc+ONDdnTXtiMEwRw/hWW55BYEibTFeOW+aaSiaXr0L+LUKw+j34ge7tn+oI/fPXM+D1z+Ep++uRzLtEjF0uTZi7vjStCyzOyW6OoWMVIGjevb+N9DMxm+f3/6DivjN0/9sItUvzz4fpqrO1QJdA0sC+rKW7jj3P9w4CljuPGliwF4/rYP+Pi5+UTDcSzD2ZFLwi0x1wqj2TtrNOnKhWlRs6qJ2jVNHHLKGKa9uJB02lCU78IZGfX/miYoLMmlqCyXey5+gZbaDtKJNJqmqT4JXO6MbBxw/GgOOP7rZ0CsWbCBZCzFhtVNvPHQDC669UQAYp0JGta1KOZOJ1h1C9uVrlaFri4p160kMu4lN4HUtlgEAj6G7tuP2rVNNFa2KTuPkF3mNRlP89iv/8fZ1x/D/hNHfe1+flvUVbSwdn4N2DTvUqhAzQOOG+VZKXYDdEdMhBdT4WETDBjdi5N+egjP/fkDktEUWLbp1k5Brl3bxKv3TeMnd6siY4EcP79/8SJee2A67z46h2g4gWVYWIZFS007r947nWkvLuLoc/flxJ/sDsqFpKA0hwNO2IPqFQ28dOeH1KxuptegYk6/5kim/Xch7zw6G13XMUwTv19n9EGDM7EOmiqn7iziwkkbsFcel61SOEt0ZvE10iaRtjgAtWub+eSF+bTVdVKztpmwHeuQCa6UxDuT1FW0UFfR4rZx/k3HU1CSx+R7PiJlGjiqgGuyh8xq6moA6nM6ZbBidiVX/3MSn729AqPV3Eg5UAGLOQWqzPkLf51KuCmKU07dCbiUlmTaiwv44v1VDBjVk8tuP5n8ktxvPCNOIKWRMlg+J9t6pvqQkVG9Vy6pp2Z1E/1H9cyWvGtwqwBN18jJDdil7E01rraGkZkX9d7RHGHZzHXkFQYRTvE2uz1nXo2UQWN123YNzgT1rDz/5w+oWtlIR2PEZlXLzMXSmes49aoEuQWh7SqXBw/bE55SsQ0x4aLxLJlWztIZ64iG1SKlAvok6aTBZ28t58CTxrDvsSPca8685ihG7N+fF/86lfVL6112tWh7nEhbjNdrOqhaVs/FfzmJUP53l+J74dQ15BaEKOqZz8t3fkTl4noCeX4GjunF5Ds/pqm2HSttsyJJELpGR2MnwfwAEolP1wnmBpCosZOQsRhlvyNtK4BNpSwkqViKJ294C5+m0dkeJ9IRIxk1ME2l5AldkHW6iv3I9bP30cO456LnGXXgQE664lCWTC/HTHeNuLINFplAQykzQZ6OBUPT6DO8lGCOn5LeBUTaYoq6W1ekTgjIKQhxwk8PYunH5axdUKPqZeioAC9NtWkaFq11YdrrO2mr76Sxuv1rKxWLPlrD2vkbGLZvP9obO5XQlqS9LszyWesYe/jQrI5lIC2JaZi01IZdpWLB1NV0tkRBStfyYjNxE8rxYaZNzJSp5sLl2iZjCrbf04k0HSlDWUZ0RdOeV5yDZVhoPrWI733UML533v5fq6/fFmvnb2DFnEpinUmV+ZH1fJmGRUNFC+31nZ5S8V2HF6jpYVvil4+ezXtPfMpr935CPJJ0azRIKYmHEzz5+7c45vxxnP6ro9xr9j5qOKMOHMg9F71A5ZJau8y6xDQl0fYYn76xjOYNHZx9/bGMHD9wB/Zu22HF7Apa6zr55NkvqF/XgmWa6JqfZTMqiLbFsSzpBsAKQNcgEPTTXt8JUvETmCmD3KIQiQ7VpoUyubvvzlxkBf4JCe31aiGWTs0GkcmSEEJVRTVRm2mnLZ8uWD23imWz1rFm3nrmvbuC2tVNaqEjy0XqKDRZJn6R1ZauCfoOL2XSb46htG8hBxw/msZ1rViWRVHPPCJtCYr75HPJbSezzzEj6Nm3iOqVDcSNlF2/I0s+odwzQgMjnuJf173B4L36csEtJ5C3Fb799sZOXr7jI5o2tBPK8RPviLtrfDycZP4Hq5RSISW64/awv9c0wdB9+7LP94YDKuvj5Ts+IhFJ4Nh4nDnAknQ0RVzLRRf3h5UV8Og0LkBYauJ1AXnFIf705o8o7Vfkyu7zb5x3sh3gKBGwyVz4NEFJzzz8W5FJ42HXRnfQbO/KNN2eUrGNoekaJ11+CKvnVrJs1joSNv+/RJH+tNa08/5jcxhxwAD2PHKYe10wJ8Bv/3M+/3toBh8+rRgjpZBICcl4ijWfVvHYL1/npJ8dxrEXHbCDerdtsHJOJU3VYeIdCdeELYBEOIUlVcZEpvCXWq5DuX4KSnNoawjbhEoWqYSFkTTd9EVV/0G6NOBOvQ25UVsKMstF4bhUQDNxeSHcd9t3DoBlEQunqVxcg5S2nGRt5LOs+W4sgf1vTajsj9LehTx9/dtIwBfQCeX6GTi2N5pPY92iWg45Y0/WfFbF23+fSbg1pqqwiuw+bipfKpmmenkjjevaKCzL44c3HfeV8/DafdOoL28mEUsR6xBIUw2kkle6mtL8D1aTiqe69FUA9auaaanpoG5tMy/d8SGN61qUG1DLmgt7DHRdwzKdz4JQviKJsqRFMprOjF9WPq+wuSqMhMFzN3/AOX+YwMCxX876uS3hZAM5xGnus6JBXnEO37tgHD0HFu8w+Tx42B7wlIrtACEEV/79LN5+eCYfPj1P+evtbbZEEO9M8uTv3mTCxQdy0s8y3BSBHD8/uO57DBrTi7cenkVdeQvphIllmFimRVNVK6/e9SEbVjRwzo0TCOV9N9whc19fSrIziUAqs3hKledGZmimM9tZ9dlMW9SuVmXPHb+9tJxUTdGlkqe0FQbH/J7dlkMo4SoU2ZYKNCxhKSZH4Vg9MoGJBSW55BUECTfHVEKpS8WNS+XvWldsa5XbFnb1TQRrv6jGSCllyBfQyCkMkYwkSSdNRo8fzODRvXnq+ndUMTrbvCLsTAMlV+bd9RzYu+hUIs2MFxaS6Exy9h8mkFe0eYuFlJIVM9eRTqgYB7c6qBLUDX6tXdPEu4/MJh0z3DgVaXulOhojzHtrGTNfXsyGlY1uvx35VByEJJQbRFpKNgu1MOcXh+g7rAeaT7B85nqXVwN3SgRSU+OXiqdZ/el6Pntz2Q5VKpTFRpGMZT8rCEHfoaWcsFvEQnnY3d0fXkrpdkIoL8CZ132PUQcOJCc/iK7ZwX4CLMuipaaDtx+exco5lV2u03SNg07bi6v/eTaX3Xkqg/bshT+kTLtSSiLtCea8upiHfvISlUvqdkDPuhfxzgSVi+sQAvLL8hh/0hhyi4JuHKPmUGdn78xRmRtuPQkBmpBdzhNa12uy37u+VGoj4GYwOG4PsVGb7q5dCMyUSUGPXEYcMBCfX8visci05VgmNteW0OxnwbBIJ9JIU/0ySVMSaY1StawBTRPsf8IoXrt3GvFIHJAq1kKIzfQD183i9Ekt+JJoR4z5H6yifH7NFudh7RfVRNvjGTlt+RwXBVJSuaiWWS8vprMtikRmLBXur4pkw8pG2hrCdvVY2WUehABd0wiEfCiflbTbhrb6TlbOXU/tmhZ8fg3N9mZ0GT/7s+7TKOyV962CUL8tVs6u5KNnPlcxN458dl9Bpbm+fPuHGOlNWUY9fLfgVSn1sN2g6Ro/vvd0xp+0B4Gg310opQTLtIiF47z45w+ItMU2ubbXkFIO+8E+/PRvZzDigIHkFYfwB3xgSRLRBKvnVvHY1a/y+C9fY/3SXVO5qFxcy0M//i+ttR34Q35G7N+fYy4ar4LenIUSMotl1rXuV471AWG7PYR7bpfFNutqLau9bDeFcw2QaYvNtQXJWIqPn/6cQ3+wN7mFOZvI56Y82oum2Eg+3HvZSZRCoOka/qAPn8+HP+Rj5EEDWfrJWnuRzpJhK+TLlkNKFYz6wWNzNvusxMIJnr/5fWId8U3kcyw4lilp3tBOe0OnW6dk475ahsWqT9djJI0tyieRpOJpLLvmjeOukZaq6tpW30Eylsq6RhUJyyvOIZjjI7cwRL9RPTnv5uOZ+KMDN+nL9sInz3/B+qX1pJPpTF9lZuwjbXFWzFpHc3X7DpPRw3aC5NsXE9t1M0o998f2RkFZHv1H9WJ1z/W01oaxpKIX1nRlrm+pbucfP32Zs286jqH7bVqieeCY3lz+wJlULavn/X/OZv2yepLxFGbaoHF9Cy0b2qla1sBJVx3GYWftswN6+M2QiKZ47qZ3qVxaR0FZHuNOGMUZvzmG5qp2ND1TUTOTpQHS3glKy+Z7EI47IcNboBZRtdN2/PUyw37gahLOv525sEy7LTZqC2Wel/b3ritFSqKtMd649xMs08xwMGibysfG8pFxVWR7Ykr6FJCOpYmnTfqN7Mkehw7muRvfIx1PuxwXFlJZIayu8rkpzGQCWt2+SkX6tfqzKp676T2ue/4CgrmZQleLP1pDW30n0pToPrGpfFLtxPOKgmi6Gi+xmblIJ9K0VLWD5nBybGYupIWZMrCkhV0qI2sunDouuPPq82n0HFzC9399tCrOJaGkbyFD9u67jZ/QL4dlqO2lECo4s8tcmJBXHGTEuAH0GVa2Q+X04GFbw7NU7ABM/NFBnHzVEfQaVIKuaWSXw453Jimfv4EX/vgesXBis9eX9S9i/+NH8+P7z2CfCSMp7V2oqLylKrHctL6VV27/kDfum0YyltrOvftmeOPeT2iqagML+o/qyQV/OZniXgVYlkSTtg9fZApLOUyFjg9bOCXLXZeEs4ZmWCuFHRjosjba466+63osm0/BrT4qMouh5l6Xac9ImTSvbycZSWXdw55fLdOuJgSaLtB9mhuvkJFP9Sm3MESPfoVIS+IP6OTlBfnffdOJhRPu85LN98BG8gmZCQB172mzcTrfm/az8vjPX6V2TZM7F+sW1GDE024Z8a7jZ7N5+nX8AT+dzYoHxJFby5oLQLk06Dp+mq2ZCEDXFFW43+9TkQhZc7LJXCAIBv3k5QfpN7wH+x83mv2PH73DFQpwnjfhBuxmz4XPr9N/ZC/O3sYVUj3sHHC4iL7ta1eFp1TsAARy/Bxz8XjGHjGU0j5F+DTh7m4EEjNt0lLTxqu3f/il7fQcVMKld53GGdd9j32OHUlJrwLF8pg26GyKMOvFBTzxi1d5/5HZO61yMf/dFbz0lyl88dYy4uEE+cU5nPW7Y/AHfbTVhZnz0kK1C8RxU2TRbdtjlqGyzjpHKMZJTSNL0cgQNDn+f5cSWkMpKCKr3eyXG9WfpbS4bTq9kXbsAK58zry68mqqraKyPApKcgnl+NGceAVbbgC/XyfamlDU17qgekUjnU1RsCx0tla+jJLlEIBlFmz1rEXbY6yYvY6Pn/wMUMRfq2ZXkkqkVFtZsT/Z44cpibRFqVnRgJk0MmOvKXp1Xag+uFTaWmYONM2OpQjq9BxYxMQfHUT/0b0I5frd87c0F+lkmtpVTfzn+rf49PUl2+EJ3Tq4yulGc6FrgtJ+hZx+7dEEtqLS67ZGY2Uriz5YtaPF+E5jd4+p8NwfOxCTbjqO0v5FzHj2C9oaO9EsaWcUSGLtcZZNW0vl4lqG7LOpG8RBTn6QI364PwecMpap/5rLrP8uorO5k1TCINzcydKPI2xY3sC6RTWMPnQIh52z31aVsd4eqF3dxCv/N5Vwc4R0ysQf8FHUI5eyAcWUf17NG/d8zPqldSSjKXdRsZzARE1DWpatZEh024HtpCg6a5+uCfSghq7rJBO2id3KKBWOqd0hYXJ30tnUz1lKgXTeRdd3yJYPhOgqn+vuADS/Rt8RPTjxZ4fz3sOzqFi4AStt2btaDSktkpEU0rQoG1DEQafvBQIWvb+K6hUpsKSiprZlz1aQNpaPrHelWzgBhLZ8EsyUQd3aZuKdSea+uohIS7SLVadrW47rSJKOGwRspUhl2jh9leAT6LpA+jIsHRu3lVeUw0Gn70Ug5OOAE/dgzWdVrF9ca8dyOIrERnOBxEwa1K9t4vW7PqJmZSND9unHuI0KnG1v5BYE8fmU28ztKygrU0GQ8s+r6WyKMv60sRnFbDtjxYwKPnhsDmUDihl58GByizwSLg/dD0+p2IHIyQ9yyi+OpHFdK0s/WkO4JeoGdlmWpKOhk8l//oBTf3U0exwx9MvbKghy6jVH0WNgMVMe/ZT6dYp4yTBM2hrCdEyJULWkjvLPqznjd8fSYwfny6+aU8lb908j3BRBSklhzzzGnbAHQ/cfQEFZHs/89k1qVjUR70ig6S4LhPpBViQf9ufNty/s7/xBnUF79cVIW1Qvq8fMir533ABAVq0MtXA5bTitCZF9LCvg0BZCuPJkzP7Z8mXLn1sU4qw/HMfQffvRd0QP7jzz33S2xPD5NMr6FxHtSGCZkn4je7DPhJEE84MEgj5O+sURvPjH9wk3d25WPpH5lJHNPjHTN7t3WfJZpqS5up2nrn2d+rUtdLZGM4GRWeOZGX8VoJFbEKS4TwGx9jipeKprcKql4kykZSE0zZYvY9MVgGWazH5xAalEmvyyPPY7fjTt9Z1uYKa0NjcXSjFKpwzaasN88vTn9BhYzOB9+1HWv4jtheaqNuLhJAP36gPAMZccyKIpq4i2xbvMhZW2qF/bzAf/mMXAvfoyYGxv+mbVrNkeWDN3PeuX1PLp60vRdI2egzVPodiW2M1TSj2lYifAOTcfT92qJmIdcZUiiNrRmmmTqmX1vHjzu/z4oTMZuOeX+46FEBx61r70G9mTV26bQu2aZjpbImBKDMugrbaDxS1ROpsifP83xzB8B7BxGimTL95axrt/n0l7Qyc5BSH2PGY4Iw8azGFn7+ued8T5+9PR0EkymsQyLYSmqyI7DoWmbV1AZi/0WcyY9r5cExpFvfIZdfBgOho66WjodDUIIaW7zAlEpoiPEF0WMw2HQVPY7NGZ7wQo9sQuy96XySfIL8pxF5ayAcXkFAZJRlL0GdGDvJIcEssbyCsMsOdRw5gzeRHtDZ34/Dp7Hj2c0YcMYtm0cpUhJLrKl1EkshZvmzMC4YyI89k+wVbQOhs7qfjcIJ1W9NeanrEwbKxgaIDPJwjl+CkozVX9gi5FkISdwaHiLWz5ZEbZsaQk3pEEAdK0MOIGn722hNJ+hRT3zCfeaZPESdBRiomORPdpGNJEQ2CaFulEimhbFLmdndDvPzKbpsoWfvKPSeSX5NJneA/yS3KItSfs/tuWIWlhpS38QR85+QF6b8dATTNtMv/t5bz94HQ6W2Lkl+Wxz8RRnHT1EdtNht0RuzujphdTsRMgrziHM39/LPklOXaZbsBmLDQSBs3rW3njro/YsKx+q9obvE8/Lv/n2Rxw6hgKSvNUnr+9IqRiKdYvquG5G95k9ksLqNrO3BbvPDiddx6cTnNlK36/zj4TR3LJ3d/volAA7DtxND9+6AeU9C6goDSXMYcPJb8opMzrEjTbXeEwZDrmcmeRdRZcI5lm8Qer+OiJT+k3qiehHD++gE4g6EPYbglNgK5nxT7IjdoiywqQ5SpR/vOMdUOdL9GkklFNY1f5cgqCDBs3QGUuOG1ayg/fc2ARrRvaEVJS3DufWS8uoGl9K6lYknhHgoaKVvY9YTTFvfLRdeHK18WiYMuXHW8iBGgySz5s10aWfJZlEW2Lkopl2CtdhSm7TXvMpClp2dDOimnlGCnDlcGJm9B1jWDQp+R05BNd2xJYSNNU1hLLJNoao668mVh7nD4jyug3ogf9Rvak95BS+o3owbBxA9j/pLGMOmggwVy/LR84jKnbC2bapHZlA1VL63n1tilIKfGHfPQf3YtAyLfpXOiCwh55nP9/p6hskO0g38qZFUz+y/u89cA0mitbEQhGHDCAs/4wkdxCz0rhYdvBs1TsJNjjiKGccOXhTH/2C1o2tKmDToyFYVE+r5qnr3uDS+47gwFbwRqYX5rLeX85md5Dylg9p5LyedUkIgkME9IJk8byVl69bSol/Qo5+Zqj2Oe40ei+badjWqbFyukVLHpvJa21HeQUBhl36li+/5tjtnhNaf8i/EGddCJNj4FFxNpiqpJlwrC3gSLLMqHgsDQ6CxwWGIZBR1MnPYeW4gvoNi+C6bpSgrkBCsry6GyOKgItzd5VO20JgWb79aW9aHbJCBEC6Q5dtg+gq3xCExQU53DAqWNY+1mVfR9ppyNKKubXkE4YFPcrINIcVRwQTqyCrmIU9jpmJAveWkbjuhYsS7oLtZuy6bo6bKVD2HvmLconbeOPsGNAMooWbl/tNrWsQE1Uyiju2G/UVyCY4ycVT7spoZqu2QqZQLpZHtkySYx4GhA0VbRSNrCIkQcN4ahLxrPo/VUc/sP9ySvOYdrT86hd3ZQVfJuRYntg+rNf0FrTTqIzScXn1cx5eSF9hvegsbwFK210mQtNVwGnPQeVUNKvcJvKVb+2mUhbjEXvr2LJh6tpq+nAsixCBTkccMoYvv/bY7fp/T0odEf2xq6c/eEpFTsJhBBMvPxQ+o3uxVO/fJV4JEn2D6URN2hc28w7933C2X85iZK+W/cDdeyPD+bYHx/MR//6lBUzyqlcUEsiksQ0TJKdCRrWpHj9tilULqjhxKuPIGcb7GISkSRTHpnN/LeX01bfSVm/Io6+5CCOueygL73u7fumEW7oJBlN88XrSwnk+tE1gaUJ102kXPzC9fM7C70y9Qt7vRJYKYvyTysx0tINoHTWokDIz6m/OZoV09YRbYthpA2qFtWRiCSxUNkhMqttxzHgrNNulVE25YPAkUoqy1OkJcK/r3oVf46PdMLAH/QR70wAgnhbjPye+QRDARrWtLhFs5y2ywYU01rTzl4TRtFaE6axooVkNGUrP0oaJztGZltXyCz27ii5nbD5J5Cqr4IufRX2+VklN9w2fXZxrHQi27qR6WsyklRKkaaha5L8khyKeubTWt2uUmM3ks+FlCQjSepXNaMLQd+RZRxz2UH4gz4S0RRzXlpI2u63I5/Yjr/C8XCCRGcSISVttR28e/90cvKD1K9tAmmnPSPw+XSKeuUzYM8+HHrOfttUpg3LG3j2N2/QuqGdVMrEsKu45hSGGHfSHpz311O26f09ZMESdqW8b9nGLortplScffbZ3HDDDQD897//5c477wRg8uTJALS2tjJs2DAmTty9c7nHHDWMCT89lFkvzqetpsNdmEDtaFfOWscTV7zMZQ/9gB6DS7a63YxyMZfpz8yjdUOHKk9tmrTWhZn9wnwayps49y8nkVuc24UM6dsg0hbjpZveZcW0clKJNDmFIS686/uMOGjQl163fmENSz9cTaIzCUKQjKVIxdPK128rFBLwBXwIC1L2DhGyFj/H/AxIyyKVkG7KJmQtkBKKexVyyX2nq+ul5Imfvcyyj9cqq4i9QDvuhuxrnQ/ODtytiS43Wijta5KxNNKUpJP2e8JAWha+oI/SASUM3LMPyz5Zg2WYXUz60pSs+KSc5R+vofewMg4/fxwV86rpaIwAkqrFdSRjapcvtAz3hZMt47gINCEIFYYYuGcfp2VA0FDeTEdjZ6ZGid3XzfXZeVd05BpGIr3Zvhop043xEAiMWBohYdThQ1g9q5JEJKWIyLIUMEcipCqdXrOqkTfv+pjGdW384KbjmPLILBrWNqm4D0cWma1CbXsot4u6n2lYqqot0i1dKwCfX1DSr5gJlx/KHkcNo7Asf5vIkoqnibRGeeeBadStbsI0TFex1XWNHoOKOe23W7YGevDQ3dhuSkVFRQUTJkxg/PjxvPzyy+6xKVOm8OijjwJw3HHH7fZKhRCCE64+ggF79eapX75OMpJ00/AsS+0KW6rb6GyNUjao+Gunpx3740MYOLYP/7vnI2pXNCr+CkuSiCRZNXMdd532L4bs258L/3a6IiUKfvNHpLGihWd+/Tq1qxoxDYvivoUcedH4r1QoQNFedzZH3UBHZ80x7cVW2oukZZjKzmzDsWAAGTu880/7x1YdljZDpGJ1THYmSUSThPKCCCEYfdgwVs6owMD1tChdQai0ymyFxFFgcoty0HQUv4TNrujePFu+rONO33Rd44BTxxBpjZGOpzeST2kpiagKAqxd1cjclxZy+b/PQ9cgpyDElEdms3JGhdu+JR27jZPeogZMaIL9TtyDoy/taiVa9MFK3rzrE1qqW5Wi44iY7dLIngsB6XiaYF4QX0BXBdDs4xk9OKOMSSTJeJq61Y2014dt10n295m+Oqm7an4tYuEEiz9YyeB9+9JY0YKRNrPmAjdGY3tB2Dd1YnqkRZfnTAqVUdNe38mbd3/Em3d9xJ7HjOCi+87oVjmklDz969co/7SqC9W5w5WRUxDkjBsmUlCW16339fDl2N0DNbebUnHDDTcwadKkLsemTp1KcXGx+7m4uJipU6fu9ooFwNijR7DvxJEseHcFMu5U3lS7s2Rnkv/88lWO+cmhHHnJ1693MPLQIVzx+A95/f+msGFZPc1VbSRjKayUSbQ1yupZFdw+4REG7tOXnzx6Drpf/9r3MNMmb9/3CbUr6hG6xuB9+nHKdd9j9OFDt+r6DCOhw/+gdszq99t1biBNlaqYHZxp/xOwr3F979JtQ2VyqDZS0STPXvsGvUf24Ac3H8+Q/Qd0Cb50Cl1lduzCjT1wqpUiVDroAd/fk3cfmJ5Ji6SrUtRFPgFuwS4gXN9JxfwNWKbVRT5nJ++4LizDonZpPXce/w+wBIedP45TfnMMJ3yLqP7qRTVEmzqV0kZmcRT2HLjj586FAAvyi3Mw8wO014WxrIwFKVuRysyFxDIl8Y4ElmVXeyV7XrPmMEtZQ0J7bQcvXv+W0h8thyLdkU9m65XbHG5QKhnlp4siZcuYjqcwkwZSWqyaXs6c/y7g0HP37xYZLNNi+tPzKJ9TSTycspVYJZgmNIr7FXLoOfuzxxHDuuV+HrYeUgrlkv2Wbeyq2G5Kxbx58wDl5gC4/PLLKS8vp6wsk2JVWlpKe3v7Zq9PJpMkk0n3czgc3nbC7gQQQnDydcdQu7KRmpX17gInhNqtt9V2MPO5zxl91FB6Df36ee/5pblceO/pRNtivPrnD1j20SqSnWksS2KkDDpbI6yeWcGD5z7FGX88niH7Ddgqq4iUksZ1Lbzw2/9Ru7KRQE6APY8ZwZk3n0B+6derIun8eGdM8M7OVmZZHHBN+7LLyp3ZBasddtZnuw2nTTMtSRgJGsubibXHsu7tMElmZbJuti3nmOTIiw9k2UdrWDe/GmlYarGUWQuPO4TSbksQDPko6VvIkqmriYcTdnBmps/ZY+F00jBMOptjaJqgfm2TWqQ1NxrTTbHcWktWQ3mLiu2QmR24kltusc++oMbeE0eyYXkjHXXhDHPnRnORScEVdvpt5ll22nYYPrMtJAC+oI/ivoUkoyk6myLZGcW24iEzCsh2wpijhjPnhfm014UVF0dW9snmnhUA3a/Tpxv5KZ655jVWzSgnbsemZD8rugZ7HjWcE395ZLfdz8PWY3e3VGy3lNI777yTSZMmcfnll3PnnXduUXlwlI6Ncfvtt1NUVOS+Bg7c/hwL2xul/Ys48Zqj6DuyF4GgL5OaaH/fsr6Vxy55nk9fWviN75FXkstFfzuDCVccQb8xvcktDrkmZcswqVlaz+OXvsiDZz9F5RfVX9pWw9pm/n7u0zw46Sk2LK1DWpKDz96Xi+4/85spFDaro1Osqgs1N5mF1k3vFDYN9sbnamSoqR2TuZ01kE0BbSYNZjyllN9h4wdS3DvfLlG/mRTNrLkQTiEsoSjYT/vtsYw6ZAjFfQrxZ5dBz5YL1ZbPrzFk/36cev0x9BpSSmGPPDsNU2xyzeb6KoDyOZXcesj9vHvfJ+74vXLzu7x990dbP95sftycDA1tI8uRo2x01IUZe/QwN5Vyc3Ox8fi5bTn9EgJ/0EfPYWX0G9OLvqN70X9MH/qO6sXYo0dw1dMXMOyAAeia6DJfLgW7tn2j5Qft04/ew3ts2rfN9NVxRfQYVMzQA7rnN2vui/NZOX0tiUhykzHXBYTyg4w9dkS33MuDh6+L7WKpmDx5MvPmzXODM4uLi6moqGD48OFdlAsnWHNzuOGGG7j22mvdz+FweLdQLPY5YQ8GjO3Dv698idpV9cr8bZvxzZRJW22Yaf+ey7CDBtFzSOk3vs9xVx3OcVcdztR/zGT2s5/T0aSqVJqGSbwzTvXiGh7/yQuE8oP0HdWLSx85B18g4xb59KWFvP/gNDqbophpg5yCECMOG8pxPzv8G8uUrUxYZO0GbX+AwA6n2Kxvwf6nyHqRCV50zPhuW0IxPDZXqXTefnv0pt+Y3rRWtWFlsU86wY6qemlmLrIzI4aOH8jP/nMhlV9U859fvUpbbUcmK4SuJwspqVveyOu3vk+vYWXsOXEUn/53AdK0Mq4ekRWHsQkk8c4EyaigZrniMfn0pQUsemc5fUf3+soxTsXTPPmzl1i/sKZrvIctp+PiyV64LDso1TJMmipaOOPmE5j2r7luiqk/oJNXmkeoIEBLVYcqB541B13G0Z4LDcWvcuHfztxExjWz11GzrE7J53JyZaUNo56VF69/k9UzK7BME2mnGwdy/Ey86ggOOnu/rxyLrcH/7pjKig9Xu4SHXaYl6/lzxiq3OIfCnnnsNXH0t7733BfnM/WRmSQ6U6SiyQwZmzItoekaBT3yGHfaXpvcz0ybPHnVy0Rb45x07dGMOtxzi2wrSNkNlgovpfTLMWzYsC6xE+3t7YwbN47i4mKuv/5693hFRcUW4ymCwSDBYHBbi7pTonRgMROvOoL37v+Etup20pbl/nAhJc2VLTx+6fOccM1RHHDmtyt3PvGqIxi4d18+eHA6zevbiLZFsUyVeRLvSJAIJ+lsjHDXcf+gz6ieXPTQWUy+8R1WTltLpC2KpusU9irg4HP246Rrj/nGcggyCxA4C5FA9+uE8gLEwwmkZXVdzTfGZsz2TltsfMzWTqLNUT59aQEHn7M/ow4dwqpP1iruimyZHHmy/P5+v86QcQOItsV4/tev0VjRipkySCRSaEIjpzSErmlE22KYdoE0JEjTIt4ZJxlNYqZM9jlpDDkFQWLtcddtki1flwHK/reUrP+imofPfZpoW5R4R5zG8maWfrCKvY7f8oI27Ym5VHy6nnTS6BLD4DYtZRdlwJ0LW36EYObT81SKpQB/0E9xnwJSsTT5xbnk5IeoXlqHmTZxGE83NxdmymDVtLXMf2Mp407fq4uMTetaiLbGNjvNQkpS4QQPT3qKaFuMVNJE2vEFUko0TeOduz6k4rMqfnj397c4DluL6gUbFJmUL4tDNVuJ1YSqPKxrIHSOuuxgjr+6e9wQG5bW014bdrOLshU1pLKI7HfSGL7/h+M2udayJE0VzYDotswuD5vH7h5TsV3cH+PGjaO9vZ3Jkydz/fXXM2XKFEApG+eeey6TJ0/msccec1NOPWyK/U4Zyzl/PZVgnt/2oWYYFc2USfuGdt66Yyov/+Gtb32v0UcO5xcvX8b5957OoL37UVCai+4TrhJjpk3aatpZPbOCe09+lEXvLifWEUMgyC/J5Zz/O+VbKRSQFSQILtGUBuTmBdjn+NGKJVQ6BE+ZRS+bCTPbQuEsaBt/35U5E9LxFO/c/SF3TnwYpCp6lTHVZ2I61P2c2Af1Q91rWBmPXvQc5XPX07qhjc7mCGbKQNcEe35vJCf/5lj8QV9X+TS6tDn6iGHse+Ie+AP6pvJu1Fd3fAAkJCIJqhZsoLW6HWlJkp1JNiyt3eIYr52zjk9fno9lp35qApcJdBP5HEbOrLlwZKlaVIs0TAIBHwP37INlmnQ2dVK1sIamipauMRRbmAtQAchv3v4Br97yjiujkTRY9NYyzJSRcXs597blM9ImHQ1hUon0JuMlLEm0Lcb6BV/uuttq2EETZtJ0x2TjuXDHy5LMn7yQO479Ox88MO1b3baxvJnKz6tAWpufCw1CuQEG77956+28lxcSCPo58uIDGbz/gG8liwcPX4btFqjpZH5snAGy8WcPW0Zhr3z6j+1D1fwNpO1y026MhZREWyIs+X/2zjpMsuM6+7+6t3EYd2dmmZkFK5ZWbFuybFmSGWKKKXH8JXGcxIkdchInjmNmkEG2JFuWZPFKq9UKlpl3h5mb4VJ9f1zo2zOzJG1sy5r3eWZ7p6dv3apT1bdOHXjPo4dZcMlsVt+y/EzNnRGLrpjHoivmceKFFh777030HPYX5JKYmsFo5yiWZZ8IlZDCmluWsvTaha/43pVTyu2x7ulG15zNwtl96mbXUloRJamlUHy8Ci78lggvK8TbxGSR3IR/o7KHRWYkQ3Y0y8avbiaf0oo3DWE/zP1MlgKJnsnz9Ne3kIlnvSqoHtumkAy1DNGyrd3bHMf2TwkoVE+rIFoe4dJ3XsDBJ45iOvTX7qarjBlfkbkdJ6PFNFGk4o3xdOg+1Ed6OA2OTNyb+Mm8PPkpoFjF8kMI9EyekUSOUDRIeV0pIx0jpGO2VcEyTHKJrD0+4RurKz+3LadtpCQ9lGLfw4eYf/FsVr5uKS/9dCedB3rA9GXC+OSgMH4u/G0KRVBSFWXW6mmnF8ZZ4PiWZkbaR3xul0KNmMIClM4cCCxLMtodRyJp3toGf37VOd+z7/gAv/3CU/QdG7TlKieYC1VQWlnC8hsWs+aWZePa6DrQw/M/2oaUkgvvWPPyBj+Js4clkK9h8qvJ2h+vItTNquHDP3kX9XNqCjUnfD9g1/Z45N838uDnHz9vRZYWXDaXj9zzHhZcOptoZRhFcRkX8QIKhZBEy8Nc/t6Lz8s96+fU8uGfvou62dWFUyn2w3zuRTOZMq/G7oe7mUwgD7uPvs9M+Pfin8K9JOmRTKGuRdFYx7dh6ibZWNbO3HDblXaZbqRF77F+4r1xpGlN2L9gUGHZdQsprSmhpDJK1dRyFHV8v8a+nmocQtiEX3bKYW6cfPWcztFNJ5Cmdeq2/PIbcw9FgZKKMHpGIzOSoqwqirQs0sMZO83X3xbFbXlptMrYv9t90zIaJ19s5Rf/7zc88+0tmDljwvGddj6d/6sBweo3LOWt/3XbK16Tbbs6ySYyxVaxU62tCdbKy0H/iUE69nSS6E8gDWvC+wUCChfevpK3/Ot41kxTN3niS5uI98Y9OU7i/xYuTfcr/Tlb3HHHHezevZvdu3cXhRPcf//9nhdg48aN/wcjnRiTNN2vQjQsmkKsN0k2mSnyf4O9kSQGkux+YB+zL5zJqtctPS/3DIYDvP8H7+CZbz7Pi3dvR8/pDpV4Afl0nnv/6jfc+BfXMOeiWeflvnVzahhsHcbSjMKbUjJ1wRTad3djWdY4NsfTQeAT2QQPWDepcVybE9xESM7Aj2BfZFkSYViEIkF0Tccyx06a/aOlNX7+yV8z1DpM/bw68jmdeHccwyiUa0cIAkEFU/O/x4SlkqVhMdw2THIgVUS/3r6niye+9Ayd+7rtuiOngjM2NaBSUhUll8xhajbxlKIq1M2poc8hNksNp525mCDuo0jovj9LX4ytYqePRspCBEIBamZUseWHW0mPZCeUlVBEkdIsGD8XalClvLaMhoVnDlgFOPz0MTr39bDhY1ecFenb2awVoQgipWEaF5+5Xo8Ly7R46J+eINYbx9QMm1jMHdupFvsp3t95/17adnZi6taE630Sr378oRFLTloqXoW4679u4+a/vIZoWcTx39onay9w0JJoGY3HvvAkj/7HU+e1LPSGj1zO377wSa758OVU1JQSKQt57gMzb9C5t5v7/+YhXrx7x3m531UfuIRIadCX829HqF33iSuZOq/WS6sU2KyUiiIchsVCES7hPE89s76fR8DlY3Dkp/j+7v7fjTMQSFRFECkN+XznTDAHcsz7UNVYwcVvX2tXR3XvQ+Gz0jTZ99ABjjx9nN7DfbTtaOfm/3cN01c2EY4ECv0TUDuzmkBInbB/wiq06WHMZtL8Qgsde7owcnrxWP1jFsIbS7QizMd/9T5qZ1bbp39VEK2IMNQyjJBQWh1FDap2TZUzyO90cxEpCfLW/76NTz/7CULRIHpGH9/WGLl5r7K4LVdO7/vBW1n/tnUTri0pJY/950a+cfv3+Opt3+Whzz3OgUcOkY1l2fztF/jGHT/gm3f8gBd/vM294JzWipBQVh3l1s/eyK2fvfFslzx63uDw00c58Xwzrds7kLo1nivEsucoUhqickoZdXPGl1TvOzbAc997CT2noSiCyoZymw10Ev+ncAM1X+nP2eIzn/kMo6OjPPXUU15CxKmIJX8XmLRUvAohhODit62j+cUWjjx9wjHRO9RBjukXKUkOpjj46BGu/+Q1r4hueywUReGqD1/KJe+6kBd+vI0Xf7yNbCJnV800LUa7YjzztefoP9rPzZ+5nkj5y8/aCZeGqagrIxvLoigKlfVlhEtDlNWWcvn71rP5e88z0joKAqqnVaHrBvGeBEJApDxMtDLKSOeozXGgANKthwFShUAwQKgkiKGZaFltHJOjm8LomtRLa0qYf+kc9j962Dk1O205Jm/pVDX15kJAMBLkgjevomJquWe+dudJIO1+mZAaTGHX6ZDkYjme/OIzlNWVUTm1nGw8h64ZGHnbhF1aESE5lPbiTNzNxlaypBe8Z2kGh548ypSP2GybRt6gZVt7wQ3jH6t/zL5MDVURCKGgKgqqIqiZVUNZbSmj3TGaljZy1/+8mee+8wLPffcF9JxR6JDbphjTP1mohOqmq0pAT2kceeo4RzeeYP/jhzGcwMuJ5gLw3DCKonhzoSiCQGmQ0qoo6+9aS+PCU1sIDj15hO2/2I2W1rAsCzWgMGX+FJSAwt6HDjLQPIi0JMNto/QfG2Tq4il2Zs5Ippika4L+KY7yuez6Ray97dwysmzXmcDSC9Yof7yIggBFEggprH/rOq775NWEosGiNizT4okvPk28N4YAQhGVC9+y5rw+ByYxMeR5iKlwrx9L8jhRFuQrJZY835hcYa9iXPfn1zB4cpiBFrs6omchdR/iliSfyPHAZx7ihr+8lqqmyvN2byEE4dIQ13zkcqYtbeDRLzzFUOuw/TcLcvEcBx49RN+xfpqWNnDjX1/3spSLqQvqWfPGlWz5/ktIKVl96woaHP6FdbevYvaFM7n3Uw9gWRYbPnoFrTs62P3r/QhFUDurmtkXzmT3/XsxTYtoRdSuPGra9UKUgMKM1U285d9vZe9DB9n83RdJD6YKWaquQBWXJVGgCgVFVWyOBcum07Y3EuEwKzrEVe71QlBaGWWoZZhDjx1BGtLzt0tHjq4Fxi1SpWC3PdodI9YdJxQNsvq2lQy1DtO+p4tINAy6ndXg8lvbfXbXgE2pjbSr2+68ZxeLrphH0/JGEgMpBpuHwJB2NVNnrbivftINt39GVufeT/2aRH8SRbELg8VycRZcNo9gWOUnH7qHRF8SaRW36Xd/eLKxF49XVVQp9BhpSY5tOkEmlkVzaM7xy9Lpn3CCCSzLorymDMvJ8FAQqAGFtW9cyXV/fhXHnzvJPZ+4n8vedzEz1xayIo4+c5xtP91J38kB9LTmKIaCstoy1r9tHSPtI3YAq2VbVnIJey2Pdo4ybWkTrdvbvGJp7loRAYEaULEMC9O0UFWFGcun8fq/veGs1/qRjcdIDqRYddsKFNUJaB0zF+5aUbCroSqqGKdQAOx58AAduzqxdPuz5XVllNf/3xQ1m8T/HcZyMf3jP/4jn/vc54rec/mfAObNm8edd945YVunIpY835hUKl7FmDK/jlW3LuelH28jPZzxHrx+HVlL5zm26QTSgjv/502cawGyM0EIwaKrF3DwscPEukaxDIkUEqSFltXoPdTHUOsww+0j3PD/NjB91blH4V/xoUsZbB5ESsmVHy4m06qdWc27v/tWTN2kYko5C6+azyXvugApBZHyMJHyMNOXN9L8Qgu1c+tYev1iZxe1N7XSmlLCpSEufc9F9Bzq5ejG4+SzeXuz9naMwinbyBsYOYNQJIiW0WzLhKd0OCj+hVw8y9FnjmHkTKSXPeGUJ/esHCAVVznxRWpJu6LpwccOE4wGqJtZxdu/cQfNW5rZ9M3niXXFvLYUAZYT/OhZTqTE1EzadnbQtLyRE1tOome0glVi7JrxrACF8ul6Vqd7Xw+maaEogsxIhmhVhI7dnaQGUuQzuSKyH8/C7t1DIBXpG6NvrGPqxif640jL+Yz0teW2HVAory8nl8yhpTVAIg0LxwiFIqD1pVbu6xyl/+gAuWSe/mP9XPKe9Vz8jnXEeuI88Z9PM9w+gpQ2z4kiBEpQYeHlc7jwbWv57T89gZbKeZk77lru2N1FWV0pgYCCpZtF/QsEVaJVUXLxHDJnIUxJz6Ee7v7gLwhFAqy5fTXLb1pyyjU+0jnKk//9DFpaIx1Lo6d1n0WmeK0oikCNBiivL6NiSvm4tobbR3juG1vIZ/IoCgRDARZfPZ8l5yEraxJnxrkGWp6qDYDOzk4qKiq898daKc4HseT5xqRS8SrHlR+6lO4DPbS81IqW1cYxuUkJRs7g5JZmfv3XD3L9X15LxdTxD6JXipoZ1QTCQTRDQw0oqCEVPadjWRZaJk/Hri5+9dcPctmfrOeCu9aeU9uKInj9Z2/yzNtjUVpdoABXgyrV06uL/r785qVMW9lEZWPlhNeD/eC+9XOvo2Z6FXt+tY9YbwKXytNVGiR2ZkLf0X5KqqK2u8Q95uNj+ARvQ0XaPnJEcVtFQQ9+H8SYy+1TviSXyJJN2sGcD3/2EYLRIFPn15FP5LBMi3xaK7CLuiZ5XFO8wrxL5wBw6PEjHvNlwbRFYazC7YUs6ohleQZ/qqZVogYVRjpGC2Pz5Ohr0+vPmCesb6hKQCEYDaIgMA0TXdMnbsvpX1VDBWVTysnEMkgpycZymKaJIhSCJUHUoEKiP8loV5xgJIBQBLHuOJu+8iwnNh8n3pck1hfDMizPXaEEBHWzatjwZ1ejZXR6DvQgrcL4XR3UNEziAwnvb57rw7KzLFL9SU/LsJDk0xoduzpRAgqldWWnVSoOPnqYZH8SPafz/HdfQsvqvr/61oUiKK0pZfWbVrL6jSuonze+nsjBRw+THsl4Aa2lNSVc+8lrTnnvSZxfnE/yq4qKiiKlYizOB7Hk+cakUvEqh6Iq3PYvb+DZr2/h+OYTjHbFwC1ypGAXt5GSfCrH8WdPYOQN7vzf28+7xeLyD17K/ocOMZIdJlIZYc5Fs+je30MmlrXpvnWD0a5Rnvnyswy1DHHNJ64iXHb27hB/9sLLQfW0qjN+Jlwa4ppPXEXbzg4SA7Yv094T7WOzIgTStMgMp+1TO4WTts+IX3ApyIIS4bdIgGPCd6ttOimWhW0bx+wvC3uzU2Qsn8rT/GILilBYcuNi3vGtO9l57x4OPXoYy5SedcGzfligZfPse/AAF75tHbHOUdcrY1sEFNe07ru/sxnZ/fO1BQRCCtHyMCMdo5i64ekvXhu+ded38RQqi/qULwHRygg3/vV1VE+vYvd9ezj0xFH0nF5oSxTaRrFLoQ+3DGNpJopiu+D0jEa0MsKSG5ew/OYlPPu1LYz2xJi7fjaBUJDjz50k2Zekc283+bSGZZpFaZ6BkIpA8uS/P0V6JMNIx4gdc4IYMxe2O8S18LgGKXu+ZcFA5Vg/LNNCqJKqpkoufff4asLHN5/g6JPHWP/ei2xFRUqkaWHk9ILsx8yFuz7CkQD18+pQ1OJY+5Nbmjn8xBF7jNg6jhpQJmMpfoewLIH1CmMqzvb6tWvXeqmjO3bsmJBYcmRk5HdKLDm50v4IUFpTwo2fvo7pa6ax5VvPE++Oo2U075RlVwcDPaPTsbODE5tPsvDqBee1D8FwgMrGUuI9owhTMu/iWay7Yw2GZrDtx9vpPtiDntPIxjIc+O1B+o/0M21lE1f86eXnpFz8X0NRBFPm1NJ3qA8jb1taXMUgHA1gGjYFtJXXURQFNRhEURVM3fSIwbyNlfEbLMKOd3DfV5B2+W/v7/gPpsWbtbCVCyyQwmTgSB8HHjzAmttW0r6tndRQCsu0x2CZziauSISUWLrJI597lGw8a2e8uP1ThccrIZ2NG58S5K9zIh1f/nDbEHrWJvHy3Ble/wrXemP12nLG6slEEFAVpi1vpH5eHc9/+wUUJ3C1oPTYsRZuHEZmJIVp2BVZI1UlXP/X13Lw4QMEo2Fu/swNBEIqNf9Rw8CJAWZfNAuhKMxYPY1DjxyCgGC4eZiRzlEsy/L6Z+kmwy0jxDpiGIaJTVpWmAshQKgqlmH64kMm6N+YsaJAWW0pi6+ZT8OShnFrbeuPt9N7sJdQSZCSulKPIdOroKsIjybdnQspLbRkjpPPt7Dy1hXUzCrU+0n2J3n83560x2eavkJn5/cAMYk/LPyhEUtOKhV/JAiEVFa9YTnVjZVs+sqzdO7uxDItLKVQ+AppkYtn2fyVzUxfNY2S6nOrHHomrLptJUPHBsml8xzdeIx3/fCdADQunsrT/7OJrt1dJIdS5BM5Ond3MNI6TN/hXmasmcH6963/g1EurvrYlUgJ7dvbiffGCYRUpCWZvnoaK25dyf7f7Kd9ZzuBkMrs9bOZdcFsXvzei45JvmAWFwKPT0EI4cyFnfGg4Lw6m5IiwLTsDdeFP2DU86oIEIpEWoLRrlFi3TG69nVhOUyd7oYmsV9N59ScjWcYbRvBzGrePdzTuht/UdQ/BSyHGVQ6vwtVJRBSMA2zEGjpXi99Y1XsomNujIfXpmOVUQR2vxTpEakNtw6jpXJYhlnIjhHFlh5UQSCoYumWQyluUTmljJv/7iaCkYBX4K6ysYLKxoLJeNWtK6idVUPt7Bpe+O6LHHj4IPH+RCH7REosS9quQ0uiqGDat7PlGFBtV4nlWFp8niGhyMJYReFVVRSCYYUpC+rZMIHrITWYIt49iqkZ9B/tZ876OahKgYm1aC68NgWoKqX1ZTQsnlqkUEhL8ug/PUZqKIk0bSuOXfsEL0NoEr8bnM+YilcjJpWKPzLMXDeDG//meu7/5K9IDiTQsgaFQk4SaUrivTF+/Ze/5tq/vJbGJY3n7d7V06pQggrCkhj5gk+4aloVt/zT6zn06CGatzTTfaCbRF+S9GiKzPY0Q81D9B7p5dpPXUv9/Prz1p+Xi4qp5bzuszdy8JFDjrncIhBUWXDlfKqmVzF0YoCe/V0EoyGaljXSubMdPa/ZkfmepUF6ZnMAIaST/uie0n31Qyhs8AJnQ1akl9kBEqQoWBWc47I0TSwLRttHMDUTobon3UIAoeLcAykxNKdaqKsAWLKof+61wtloFdVVcgSldaU0Lm1g7vq57P3NXkbahjHyhr3hyzFjddxA48dYfA8ASzfoP97P8tcvp6qpkoHj/ehZwzb5OxYL17oRqSohGAliajGQFvlUnqe/+DTzr5yPlJLaObWsetOqCV17boDw1Z+4ir7DfWRGUrYVSoztnyxkqeA83C1bzn6eFKEUXDmujN22VBUqppbRtKKJDZ/aMKHrYec9O8nFs5iawUjHMIGw6s1/QU6F762qQPmUMhqXNbLgqvksu7mYjvvIU0foPdiDns4XQmqEXeCtpCJ6hhU/ifOJyYJik/ijQ8OSBq7/9HVMXTiVQFBxTrpuWWuJlsrTtauTx//pMfJjWDFfCYRnLy6crF2ESkKsecsabv23W7ngbRdQ2ViBGlSxLIvUcIq2l1r57Wd/y4lnT5y3/rwSBEIBVr9pFRe/80Iuec/FXPj2C6iaXgXAxe+5mMpplVQ0lFNaW0rf0T7MnIEaUEG6NS0U5/+2TFRFIVoRoaS6hKKCZPg3Y2cj9jFD2RuE8P7vBoR6PhIpsQzTthAgCte68+0LknSVEX8b/rYFEjWgogh7/OGSEMFwkEhFmBWvX8rMtdMRKtzw6etpWt6EGlTtTdUmAPHG6rbljemUYwUjp3Pi2RMIIaiaUeVZJ4KhAJGKMKFoyGvTzOmYml5w15gWI23DbP3RVnb+fCd7f72XYSet+ZTzGg5w3V9fS/nUcgLhgK9Am9s/V1bSUzKkE+vgydCVl2Jv2mpQIRAOoAYEoUiIyqZK1r11Hbf+663UzKwpun82luXEs8cxHM4RpCQ7mqX3YK8X1OrJyfneKoogVBpk6qIp3PzZm1jzljWESosrje799V7SI2ncMugCm6Ssbl4tG/7iGtSgelq5TITBk4P0HDh1QbpJTGIiTFoq/kixaMMiyqeUc/8n7iM9msa0CkF1SNuSEOsc5eG/fZANn7qWmtnjGfnOHZJQNEh2JEM+mSOfyo9zaYRKQ1z2wcuobKyka28XxzYeJR3LYOZ0Bo738+S/P8HgyUGi5WFmXzqX6hnV56Ff5xdqULXZOxHs+cUu8rEsZXWlRCujjHaOYuQM8FJHbatBSXUJS25cSve+LrKjaVf3KpxGPUuF8Hzq0pfv6ZZBd0+uwudC8SwD7snZc2ngzbmWzmMahp39YmH3D9+mjxPQFwmgKFDeUIaW0cnHcgRDIfoP9zFwfAA1qDLn0rlMW9FE78Fup+5RYaxn7J8o7l8wEmD+FfPtYTr9DQQV6ubVUVoTpWtvt60cSYme1TBzutdf6VOogmUhympKqZ2AWXIsGhY3cO3/u5aO3R0cfvQwuVi2qH+FH1tJs2NHiudCUQTBaIjpq6dTO6/OmU/bejFtxTSWvX58YS8pJU994QkysQy5RM4br7Qs8qksCFGYE5/8kBIzazB0YoAXvrWFm/7hdUVtnnz2BHEnQLtoLhSFqQumMPeyc08lPPncCXb/YhflU8ppWtF0zte/ljFpqZjEHy2aljdx1Z9dTe2sGtSAUjDVOj/5eI7WF1p49HOPFNIMXwGmr5lBVVMlgYBCZiTNjru3nfKzy9+wnJv+/iau/vNrqJtVgxoOIA2LRFeMl77zPBu/uJFH/v5hunafp5LV5xHbf7SVeNcow62DjHaOUlpXysXvvph5V8xzggz9JbElakChsqmCa/7iGmpmVTvzILy5UHw/rlJQtEE7bSEtx9RefO1pX51Nv+9Ad8E07p3O8TYvm9hLEC4LIRCYWQMtkUNaFloiR+/BHrKjGTLDGbr3dNF3qBfFUxZcZfUs+ycKvwdDARqXOkGMDqeFkIJIaZBYx6jNqul8VjgZEoqjqLjvR8rCLL5+CTd99uYJXR9+9B/tIxvLsuzmZdzwVzcQiYaKS6r750JSsGRQ/N1BWhgZjWRvDFUIpi1rZPqqJuaun82SGydOHe071Evf4V7aX2pj4Gg/Vt7wXFNY0ldKfaz8BNKys44Gjg6Q6CuwLDY/d5Jn/vtp4t0xcGnSvblwrZPnhraXWnj6Pzcy3DxE9RhLyyTODEuK8/LzasWkpeKPHKvetIq6ObX86hP3kk1ksSzTt0lJzJzO8MkBNn/pGa7727OvTzAR1KDK1IVT6NnbhZ7RGO04M4PbqjetYsrCKey6Zyc9+7qIdYxi5g0sw6TvYA+Pf/4RLnjXRTQsaaBh2R/GiSkXz2FpJliScEWUSz94GWvuWMtv//YhryaES88gJU7xrVq693SSHU77SoA7n1X8yoT0grTcYEgvDdPn3hDeP/6gSpvN0m3fTUmVmkkulnPSJH1tuf1zPy+lPS7TREvmvLRN07Qwc4azwVoku2OkB5OYumGn1frbxOdecfvnWCZcgirX6qII2yrhQnH6hGUycLgPLa97ZeTxKUJutoyl4LmVLvvQZZSdgTEyM5rhpe++QDAc4HX/eqstR1/ZdDvjonAfxT8X/hgZl2tEWoy2j7K7ayeBSAAjb1DZVEn7tjYWXLOQ6etmFqV8pgZTZEcyWIYBpi2xcWulYBCxFSQFRECAVCmfWs7CaxdS7vDMGHmDbT94kXjnCNIwnROi22G77o0aODe3h6EZvPSd54l3jlJaW0Lj8vMXczWJ1wYmlYrXAKatns7lH72SnT/ZRqxz1Hvu2A80ST6eo+2FZrp2dTB93cxXdC9VsdPxLN1i8HAfA0f7mLJ4fDqdH43LGnnDv9xC74EedvxkGwOH+hjtHsHKG8Rah3n2ixupmlbJBe9dT8OyJurm/X6DObVkzj4FhlQalkxl1ZtWM9wyxMChnkIWhuPfUIRddyE9kOLBT/0KaVlgSoSKt1G6J2N/wLfAl07qvbqfsLxr3Q3JNZuPtQx4nBl+3gNf//y+eyww0nk7ZiEiCEXsTAu/QgBgmabNGiml1wcotOUSNnj9o6AI+a0CakChYkq553qz91A7mwIBQVVFN/VieVAYqyoEZfWlrHzTaioaK888b+k83bs6CEaD7P/1HpbfssorKOfeY6xFwh2zOw63D16FUtPENEws3bCDoDtG2fuLnbRuaWbZrSu45EOXe/ff+8ud6KlCIOWEcyEL9wmGA5RNKaO0vpxwSYgL3nMxcy4puDKe+/IzDB3vt0ui+2OEgVA4SP3iqay+fc0Z5eLHlv/dxMCxARRFUDWtiqaV586A+1rH+az98WrEpPvjNYK1b7uAm//5FqLlEc/8jfMQw5LEu2K8+K3niPfEXtF9lt+2ikh5FJBkRtIed8PZoHFFE7f+55t4/b/fyvQ1MwmXhjBNEy2dZ+jEEBv/5XF+++nfcPTxQ7YP+feAE88cZ+BoL5YhUVWFaSunkRxIMNo2RHoojVtHwh/PYGR0evZ1kU/k0LOFmACvqiaAb+MVivDNkWvOpvi6oo1QFt4f+yySdvXWyoYKgpHg+Lac69w8OEu3UzrL68qIVpV4n3XXivD97lojitoqatP9XXipo/6xSsMil8wxeHygME5suVbPqCJUFhonJ2/jFwJVUahfMIXLP3rlWVXf3Pq9F8mOZkj0xNnz812kR9NONo0sats36MLB/1Rz4Y4VV3mzMHIGsc4RTmw8SmYkDUCyP0FqIOWRUuEoZOPa8v3N0g3S/UliLUNoqTwNvkyt/sO9tD7fQj6ZL54LV7kwLYIhlYZzsDR07+mkefMJtGSOkqoo1//dTQQj4+uKTOL0cFNKX+nPqxWTSsVrCDPWzWTRTUsJRoPeZuA+yUzTpGtXBw998n569ne97HtMWdxA+dSy4ofwOaJxxTTe/NU7WXLzMhpXNBEqCTt03xpDJwZ4/B9+y4Ofup+Tm44V6JR/B7BMi+3ff554dwxFsYMvG5Y38shf/4ZH/+4h8okcwUiQqUsaKastQ1EVJBJpWegupTcUy77wFghQAyoVUyvsrAfhmMBFQVkQfsH6a4wUdsSC9cE5VgtVMOOimYRKQkVteK+Krw1spQYpySey4/rn/nhcHIxpa4LdNxBWKW+oIBANFLdlSRJdMbZ974WCXIQgXBHhso9eWSBtUvD4L8AeT1l9ObMuncOqt5zdSVxKSax9BGlKLNNipHWIhz55P9lYpnhshRdfn84wVoVxc6EGVUpqS4lURunc1cELX99MZihV2PwVX9v+Nn1zIS2JntPRszqRyigRh1U2G8ui53QyIymHA8XXP6dNNahSO7f+jDEmfvm8+K3niHfFCEaCrL5z7RktjJOYxESYdH+8xnD5x6/CzBucePooejrvnZQk9il18EQ/L35jM7d/8+1n/UAai/lXLSDWNmz7/19mG+GyMDd87vXkU3k2/9dG+o70MtoyhJbVMDIWg8f7ePIfH2H1W/tY+86LUAKqt2n+X2Hrt59n8Fg/iqJQNbua9R+6gpnr59C6pZmR1iHAAgtKqyOsvmsthx4+wGjrENnRtE2s5Gy0XoDkmFcVQVltKa/7zzex5cvP0Hewh2BJEKEKcqNZu9AYBWXB/sW+1nVzeG26cQsA0s7KqZ1VQz6ZRepmwWXhP4kLO+WypLYUM284pccV7x6Ku1aczV96URS+OI+xUy5sS8mMC2bQuqUZw+0nzucsi6QTeCidgmUlVVFan7VPzIrTsWAkSEVTJSXVJShBlTVvu4AF1y4+67k7/PABRtvsORKANEwSvTFwKLsVn/zs6qBj6M6lf8yOAcOnR/jnAlVQPqWcDX99A0IRvPTN5+je04llmL45GtOWfy68RqWjICjMung2QhF07+nk4G/2EogGJ2jLYT1VFGpm13LZx648a/ns+MGL9O7vRhomwYowc688v4y7ryVYvPJAS+tlHcf+MDCpVLzGUFJTys3/eiuh0iCHHtiHltVwSgt4D9u+vd3s+slW1r7j4nG1Bc4G8zYsYt8vdiItsF6hHc9VLrS0xqb/eIKBw32MtA2j5zVyo2l2/WQru+/eRtWsai79+NXMWj/3ZeXknwkdW1s5+sgBTM2gorGKW/7rzd5J7obPvZ7yKWXs+OFL6DmNrp0dDB7pxzRMTNO0a4c4pchdeIdcUWDYVKMBVt21lqYV07jqU9ey4wcvMv3CWTQ/c4zu3Z0oYZVoRQmZWBppWKiRAEIKDMO0gyqF9JgrLScV0pIgTMmxRw8xdVkTMy6YRe+eTru+BgVlBCEIRoPMvHgOa95+Ift/tZu255q9OhwKY9p0x+Hsiu7m6MaAeIOUYOZ1mjcew3Lql/jZOgPhAPOuWsBo2whtW05iaQbJnhhH++NYul0NtryxkimLpnLFX1xL5VnUcHEhLZtjQlEV0oNJ8slcIXbFkkjDJBBUMTTTmwupQCCiYuTtWAtvrKJ4rPbYHYWqEGjhFbS75KNXUjOnll13b6V/fzdSM5HSsudmjIHJJyoUVRCtK6OivhwRUJCmpLyhgjmXzwPgpW9spmdPJ1LYcUuC4rkQQKQ8wlV/sYGSmtKzktPAkT4OP7gfPZVHDShUz6im2sfWOYlzw2s9pXRSqXiNYv2HriA3mqV1ywn0jI7i5LgjQc/k2fHt5+nd3cUtX77jnNt2vw5GNs+eH71E03+/cg76UGmIG//pFvSMxtP/9jgDh3uJdYygp2xry9Cxfh77q19TM7uWS//sGmZdOu8V39OFZVps/dZzJHviBMNBLnjP+nGm4QXXL2HvPTswshqWbpCNGd7fvAd/0Q5S8OMr0jF76xZ7f7qN/T/fwazL53PbV+8CIBgJ2pkF06pYcfsa9vxsO9nRLI2rpyNNSfuLJxhpHUEgUFzyrSJLhYWezpOLZbj1f+/gnrf9ADOvF6wK0ia7mrqkgfRgksf/5jcse/Nqqt9Rw5GH95McSBTaBCd2YwwZl6ucTGCpwLQwTYuS2jKMrI6e0+1AV2yeCgE88Kc/s1MlTcuuy6HbXBDVM2u47WtvpaLpzIGYY7HlS0+jZ/Jc+w+v9071nsEIiakZBMIBTK3A4SIkCAsnCFU4cvTCMseMtZCyqYYDlNaX2zEen9xArRNMHGsfwcjr+INo7Z8xbeEoBRJkXidaGeGNX3/bOAU52RPH1EykZXmpp16/3FfDYLR1iFmXnh0/xUtff5Z416jNoqoIpq+d8QdDmf9qhDwPKaGTSsUkXnUorSvj9V98M5v+7VEOPrAPI+fUTHc2vFwyS8dLLey/dxcr71x3jq07Zl3TYrT99AyH54pgSYib/uVWjJzO0//8KANHbOXCyBtoqTwDx/p47NMPsPqdF7P+w1e84vt17Wxny5eeYujYAJGKCHOunM+KCfz4u370Inom7zzc/UZtB95Rfszbzi4jEFiGSS6WBQHDJ/u9z6y6cx2rfHMw65LizeLoo008/fnfYuSMIkuF8HUjEAow86JZ2Hca40IBpGUyeKTPpvK2YKR5kFu/cheNK6bxxGcfREtpuMd0xR+DISYYq3/M3jgFDcsbqZ1Xz7HHDpLsT3gbbLInhp7VqJpeSSAcwLIk2ZEM0pJc+tGrXpZCAdB3oIuRliGmLG3Epd72lDsJpmYgNbPIEmHHrFoEQgEs3bI1PnxMpRM864UiqJpezS3/cwc1cwulyAeP9tG1o83mj/CKmJwhkE1K8skcnTvauffdP+KyP7+GmesL8y0Un3I0VnlzXpWASvlZZMMAHLhvN5072uwMEmFXjT2fCvkkXnuYVCpe47jog1eSGkjRubUVPaN5FReREiOrsfWrm+ja1srN/3Uu5dId8h5Lku5NcOSh/Sy5deUZr9r/y53s+sGLduyAJVn4uhVc/qlrJ/xsIBLkxn99I4ZmsPEfHqbtxWZyoxmkbpGPZdh790scum8Xy29fy8UfOXvfsh+mZrL1a88yeKQfJahy0YcuZ807LprwsyPNQ0jDRzbkM4l7KHKgM+6U6r5t72xnf1JZdPMyTjx1hPYtJ3x00q76IBGqoG5+PRd+4HK2f3sLWjo/LtRFGhaGodl6g1eiE7KjGTsF1j8EN77AdW1NoCyNHauQkr49XWBYXPbn1/LsFx7zMmE6t7YwdVkTN/zzrZTU2ib7fb/YSWY4xfzrzj5uwo8TTxwm1jpkZw4dH6C8sYLCid7pn+GUM/fLHjsGJBAJkk/k7Oxdr15LcVKpe4EQMHVpQ5FCAZAeSpEdTju1Vgr3sAuQ2cXVQBQHeTrylIbJ0NF+tn3jOYabBznwi10svHmZw4bqs3755kKoCiW1pSy6eRnzNiw6o4yGTw6w92fbMLIaAgUhBBWNleOU1kmcGybdH5N4TaO0voxbvnwnm/7tUQ7/ei9GzvAC05CQi2do3XycBz7wEy7/y+uZchYFyMrqy6mcVkk+mUXPagwe6T2tUiEtyeOf/jUdW1vIxbOe8tJ/oPuM9wqEAtz0729i3z072PezHUgsUn0J8skcWiLHvp9t48iDe1l86yrWf/Sqs5YLwL5fbKf/UA+qotC4Yhor75jYYnPiicMku2Mo0uZYcAMngcLT369MeAN3eBmEn9La/kOifYgf3vi/BKMhLvzg5Sx6/Ypx9937820cvHc3y25fw2Ufvxozo9Ozqw1DNwsxAAJUINUT4xd3fc8L1Cx6ZPlJMoRtmu/d3cb3N/wPlmGhZ/IoQin0z23bHas3sDGbo18GFujpHINH+7j+X2+lrL6cePsIRlZDVRVWvmWtp1AArHrrBaealrPC4NF+8uk8wpScfPIQZVMrCoaVifrnmwsMy2MfdS0LnmVgAqOMkDBwoIe+A900rPDzOogi1lJ3nj35WWPWChTNhaLYMScDh3qId46w/+fbPYbRieZCAUqqoqy868yyO/zAXrZ9azOZoTFKz6s4lfEPBRae7veK2ni1YjKldBIAXPShK5lx0RzCZWGPoEhxggstzaBvXyfbvvbsWaVwltaXMWP9HNSAHZHXs7ONxCn4LwYO9fDAB++mddMxtHi2mBr5HJ5wq952Ie/+7Ud514MfZd6GxUSroggFtFSOZE+MAz/bxk/e8FW2fePZs2qvb18X+3+xA6kblDeUc8nHr0YNTRwAOnCwx3N9uKyY3v/xUVOP+SlQcQNIL/NASJvbItUbJ94xckrlqndXJ/G2YXZ9ZwsPf+SnWHmNcGXUd1/pZX/kYhky/XGMtF1NVQg7ViNSGR7XPyFBS+Ts4MZExtlopLMmCgRWilKIDVD8Yx47VnxjdRRWBTsZX+omFU2VzD2Lk/W5wK3ZgbTIjaYZPdmPdDg4Tts/sAnKnBLsHpmYM1b3e+Efq0CS7BnliU/dx4Ff7PD6cOSB3Vh5l5dEFssZOX6tOPdTQyolNaU0rJrO+o9f7XB8SLRUzi5C5lsrRXMhOGtrYt/+LtIDCaSbkeL2b3JHmMQrxOQSmgRgx1jc+o23sfiWlahBpfCwczYNSzPo3tbKPW/+Jhv/7jdYxul16SlLGqmaXoWqKKT64qR89QpcWIbF1q9tom9PB5bmBLONue+5QlEVbvrPN3PJx6+hoqmKaEUUIewHcrxjhP0/287Pb/vGGZWLoWP95EfSBEIqy+9cS+OaGaf5dKG6qBDOxoN/HNILoPT/KE4VTMX5sYMDHSptYf9+Ok6nqSuaUIMKuXiGZF+C/gPdaKOZovsW/TibmAIEQyqzLplD0+rp9ubk75+Q4+ZiXHtu/2DMnJ1prLZbxt6MLZAW8bZBfnrL17j3rd+l5Zmj5zTfp4N3PyntTAmHpvys50KOn4ux3wu3LWmYJAcS7P3JNvoP9nD80YN0vNhsk4mdYS7GthUOB7jqb27k9h+8m8ZV052xUDwX/rWCRA0qdnXU915C1RnqdYy2DtG3r9MLFvb3TzlVfMwkzhqu++OV/rxaMen+mEQRLvrwFfTt7WDoRD/CKjbNGprOaOsgyZ4YmaEkl3zyOupP4Q5ZcNMyRlsHOXjvrgnTUgeP9vHS/zxF9+7OYgXFeaapQZW6RS+ffGfFnRew4s4LOHjfLnZ9bwt6RiOXyKKlcmjJLPt/to3mjYcJl0ZY98HLmX3lQgD6D3az7WubGGkeRAhB/dJGVr394jPfcOyzWJziVY75P45pQPje9zdrWjQ/dZiurc1Uza7j8r+6kQonrXLVOy6mbfNx+vbZMpRO5oRwTRWnQTASZPrFc9j3k60UuS3G9Y9C307TpsBdK6JgQx8TayEUhdKGCgLhgO1ec8wWWlojnxpGURR2fmcLU1dMo7S+/PQDOCNkcXqr20n/6znOhdtYYaxjPiclqb4Yj//FL7EsiZ7RPPKy02JMW4ZmsP0bm5CmxaI3rPQsWY5vanz/BCiKwszL57HoltPHLh1/7AA7vvkcyd64E9xRaEsJqFTPm3KGzk7iTLAkr5yn4lWs200qFZMoQkldGWvfdynP/9cT5IYzvgdaAWZOp2dXO49/6pfUzJ3CRR+7hvql45WL3l3tqEGFSGW06H3LsNj+lWfo3ulEneM81xwSBEUV1Myr44IPvfLsjeV3rGP5Hes4eO9Odv/gBbRkDi2dQ0/nGT2ZQ1EVnvuXR9heuYnKGdVkRtL0HehCGpKKGdVc/LGrz8h7UTO3jlA0hJbKeXEGE8nNfcfde8H+rFcgSxTiINxXpCQ7mCTn0D3n41lwlAo1qLLs9rUMHevFSGnefYt4Kty28O1dwuaO2PP958nGM97J+az7J4v7KXz73YRkHM7bwbDKnCvms/mfHybTn/BiGLy0zJBKIBwgn8idk1Kx98cvES4Ps+TNa323drI9zmJPn3Csiq14qeEglmGip525PdVY3WEaJun+BEJVvBTNcfKjeC7GtiUNk0x/krbNxzn6wB4Mw/A+d6q5sDSd5icOMeeqhcy8bP4ph9q+5QTxzmFPPh5PiqpQPaeWiz969ZmkNYlJnBaTSsUkxmHh61bQt7uDE48dQHNYN/1B7wDoJumeOPlYju7trRMqFdK0sLIaoiJK1wsnaVprFyvTMnmGj/eBS97jbxeIVkVZ/a5LiFaXnLcxLb/zApa+eS2H79/Fnh+9iJbIomXyCMsi3Z8gM5Ak1j5oE1XpFooqaFgxjWkXzj5j20vetIaOl5ppf+Yopmba5vBTZH0UNhNhF/lCepuVyy3hF3Uh30ASDAcoGVOJs7SujJLqEpJpzd7EpGP29/FUOLqJzbcgbIZGEVBsRcUqTj0d2z/XxXGm/uGMw/eHQpvONeGyMP37Ounf121nHCiFEt8ioDDzsnnc9N93nlUdDxedLzaz/+4XUcMBahdMZYoXKCmdQmvuXPjNDMVzowQVLN0qmgvVUYJmXzWfYGmEQ/ft9DGRnmGs/jlw5cfYufDJb0x/hCKoWzQVpKRvbydqUPWKtymnWCuoCoFwgETn6ClllegaZfhoX8Gt5rYFRCrCrHr7xZQ1VJy17CcxMSazPyYxiQlw+Wdupm5xAzu/9SzZoZT95hiTK87D81TVbyzNxMwbpHpidG9tgU9sAGxTbWldGdmRpH308p6MEiWkMmfDYha+YSW9u9rZ+8Pn0VOat7VWz6lj/aeuJ1QeOecxKQGF5W+9kEW3ruLor3dz4OfbyMUydl0OKR3WQzfAUjJ6vJ9Ub5yys8j5X/cnl9O/vY3saBqEWwLcJyufzGy4tSzcuIkCfbdXWdTRuFwZJ9qHefKTv+TCj1/D9EtsLoHp6+ey+A2rOHzvDiSQT+Y8zoGJ2lJUQUlNKaZukHPYMs/UP1fxk742wbVWOP3zr40ACBQvvRVhW1XmXruE7peasQyDUEmQQCSI5lRFrZxZw0Ufu+acFAqAtmeOkotlUBXBti9v5Oavv51AJOgFL1r+/vndHe76VQSRqhKyw7YlyJ0LAD2Zo+XJw0QqS0BaDtdFwT0iVJVQRRgza2DkNY9Zy8ukcOU3di4UUMMqgXAQSzfR8zpCKDbjpqJQs2AKS29fw94fvQimzcqqSEBxLUpj2lSgclYN1/zLm5iyrGliOT17jF1f30Sqe9Sl3nD4RiRCEVTPqmXJm9eck+wnMTFs98crb+PVikmlYhITQlEVlr5lHYOHemh98jBaJjdhOp1X9GkCuAFspmFiZHXv/VBZmAWvW0GsfQgzq/se+IJIRZS1H7wCLZlj2/88xeCRXvtgKG0CoeFjfQwd6yNUFmb21YtYfjbxDmMQLAmx/B0XM/91Kzj6mz0c+80eckNpTMvCzOmeZSbZPcqTf/FLLvj4Ncy8/PS1EIIlIaK1JeTi9uZUdII9hdXCe8Of5eJWr3Q2c+n9LjBzOkNHe9n25aepWTiVklrbarHuw1ex9M4LEELh6U/fz8jJfvKpvJc6Kn1tKaqCFs86LI+AamebeJwT407yTv+8qpqF14nHA8GSMNGqUtIjScyMjlAVqufUM2P9XLpeOEnVrBoW3rqahbeu5rnPPUSsbYiFb1hF7fxz8+fnYhkGD3aDJbEsSbxtiFRfnKrZdTbjpOum8fdv7BqW0ilHXuTXsD9nScycTjqfQFjSp5PYjYZKglzxd68n2THCru8+V7yW/fKTcty8BkIBZl65gMqZtbQ8cciuexJUUUIBpl04m3wiR3YoNYakzLdWBKihAEpQIVQWYeEbVp1SoQA4+ch+RpoH7e9R0V/s3xTl7GP2O7acINE5wrK3XXQO3DWTeK1gUqmYxGlx2d/cTMWMag7fu5N0X7yQR+8+SyyLvl1tyPdcghjzYCpvqmT4WD/SNMnH0gwe7KZ+uW2ejtaWEq0uIZ2NF518hQqjx/vZ96MXibUO2add50EtpYWZ0xg+0otQBLHmAaYsb2LKytNlZkwMIQTRmlJWv+8y5t24DGlKdn7zWTq2nECoAj2Vx8zrjJzo58V/e4RjC6ey+I4LmHHZxMpF5cwa5t6wnIO/2EZ+NHOK0z/F7+O3ADhkUmOsQYVfnUZMi8xAgt4dbcy7abn9MUV4CsYNX7qT7Giaxz9xD4mOkYJS4p6SdRNDFsrRh0rDhCsiZEbTWBl9XACi38rBWIsEuLb+omtKastZ95GraH50P50vNFMytZz5b1jOnu8+hxpSuerzt1K/bBpCCK7999vJjqQpb6qaUK6nQ/++TtIDCdvKJMDIa3S9eJKq2XWeXMb1zz8Xzlo2NcOxWvjmwg3YtPDGPXYujIzGjv99imAkVGytG2uZmODeelqj49mjVM+pJ1IVJlQWYf4tqzn54F6anziAIhSkYXpWEa8Jp39ISbg8zJWfu5Wq2XXnJj9//wSIgDgraxxAuj/Ojq9sRKgqi29fRyA8uYWMxaT7YxKTOA0C4QCr33cZ8ZYB2p89ip7VkaZ0TigSaRjEmgfIjqQpqSsOrlvxrkvp3dlGPpZBT+bY+p+Pc+M330moNEzTRbMJfEtFKAW/siLAzGq89J+PkeqLYxmWzZXhHTedk5oFWBZaIsvmzz5A3ZImlr19PVNWTj/n8QkhqJhWDcAVf/96Ep0j6GmNl/7rMVJ9CfREjsxAgvRAgpET/dQtaWLRm9YyfYJguDUfuJyhQ910vXTS3hAQBEqCGDkDpIU0nY1L4snP3iicTUux5VrE2ujaGqRNFS0RWJpO786CUuFHqDxCqDxCeWMl6Z5RJ7OmuC3hyFEiUAOCVe+7jNaNR+jd3uLNhXcAFfjcG6fpH45rQBEEAjD9krkMH+mmf08H1bNryQ2lMPM6wZDKsV/tZu93twCSmnlTWPsR2+2R6Bxhz3c2oyVzSEvaFqV3XuIpouMhQRbWiJnVOfyzbcy4dL4df6BIR0ny1/B05Gj55gLAoTUfNxfKacYqLVI9Mcft4bqJHBmPacuz+LhrWVoYmTxDh3uQ2BakWPMAuXgOI5PHsvDSV4VPofPPhZnOseNLT1K7tInVf3I5VXPrx0moe1sLR+/fydDhHgRWkfvE7V+kPMrK91xyChn7pC0lW//rCbLDScqbqpnQdDkJu0rpOO373Nt4tWJSqZjEGSGE4JK/vpnSqZW0PHGAZFfMjhp34iGMdJ7+PR3MuX5Z0XWh0hCKanMSSMvCNEx2/PcTBEpCpPsT5OIpO1DQNXBIMNMa6bxhk/IoovDcKjrBOw9WwyLZPUqmL0moNPyylIri/oapW2wHnF77H3cQbx9m51efJj2QQE/lyQ6m6Og/xsjRXmqXNhGpiLDy/VdQ1lgF2C6jtR++kkTnMInOYQQKDatmkBlMkR5MYGR1LNMCy/Lk556mPRZTd6ieGd7ZTHyvlqbTuekoA7esYsqqia00dYumMnSwC920UAIK0rB8W4DTlmVzEwzt7yTTPYI/DkRgW04UxaGA9vfPPxey0BaKPTW54RSb/vJelr/3ckaP95MbTtHRPkzZjBrKm6ro2HSMfCKLoghGj/cz9+YVpHpi7P7GJhLdI5g5w4kVUBg53s+U5U0suesi6pYVKxeF+BdHXpZNdPXSFx6lZtFUu7IqY/qH9CwU7jC8uXCVrrOcC6REjYYKLjNHNtIaLz+vMa8t59VyNnrdIt0XR5oFObpydvs3di5MzSThFNXrmFc/TqnIDqfY/t9P2BY/h/peUUVhrNKmcC+pKyV8FjFKnc8fp3dHGwBzrl9KIBw84zWvRchTh5mdUxuvVkwqFZM4K4Qroqz76DWkO0fQYllMTbcfUgLMrE7LowfGKRVCEZRUlaDFMwhhBwiOtgyS6Y2hZTTMnIYaUAiWhDA1AyWg2IyPzoNT8U6KE8A5cdm+b5PBfR08/7nfgCWZsmYWC9+0duLrzhKVM2upnFlLeVMVIycHOPD958gMpNDTObKDSTo2HSEUDTNytI+1H99A03o7cLJ+2TRmXjafjs0melqjfmkTs69fRqJzmIE9HXRtbSbhFFlTHfOznSboOxW7ZdL9Znsfn4BwNrTOLccnVCri7UO0PXUIqRmES0MESsJoiaxdmdTfpqOYdT57DCNv0z+rJSEs3bRdCro9D5YY07+xJ1R//6RET+cZOtBN+8ZDNF08l8M/34aWylG/tImeF09gpHK2hSEQJFwZ5eQDu2nffIzMUBLLMO0NX4AwJanuEdI9o4wc7WPZO9Yz/41rim8rXCpsW26WZpDsGiE6pQxFsRkn8f/4x+64ORRVoAQUAsGATc8t3IJszlyMHSuAIlBDAeoWNTJyog89a9fNcd0KBcZUN232NG05cnPVGr9VQzoWFG+tuHPhtKUogvLpVSx96/iaNM2PHSA3nAJHQR/LJmoJiFaXMu3ieWd0n+RG0+z5+rMY6Rxl06opbzg7d8kkXnuYVComcdZQVIWLP30zke8+R+fzx0l3x5zUNImlG+M+XzWnntpFjY6JWFLRVIkSqqU7niE3kkZBECwJsvhtF1ExrYYTD+9lYG97sYve8XX7LRZOTKd9cHS4LZJdoyTah1GCKn07WqmeP4X6Fa/McgFQPW8K1fOmUDWrhpGjfRz82VYyfXGstIGR1Rg92c+2f3+EpksXMPOaxTReOId1H7+WKSumIy3JtEvnEa4soXZRA7OuXkz904fZ/+3nyAwlMTQDNaCgp7WisUqnJkQhGEJ6Y5WmJFwaZvYNy1g6QZCqqRts+8Jj5EczhMsjTF03m1BpiJYnDtpBtaLg3pDC5quQppPZoAjCpREiFWHb9aPnPUZPt39qUGCZwlN6PHeCr39CSqRlITWD7EASDIvSujIy/QnyozYvhqooVEyvpnHtLNqfOUJmOOUpqV7/pATLQlqQ7Bxmz9c3keweZfl7LiNYGnYo3QtpqXaXbOpvm3XS3did+AG3fxQ2awmEIiGaLluAmdXo29uOldGcuhxy/FwoztYvIBAIoMUzmE49DqkU+uFWElX87hC3LVeghU4jFQhGQhg5HZCFTBuK2wQIhFSkVFBUhZIpFax87+UEoqGidTB8pIfuzcewdKMggzFtCVVQPrWCdR/fcNrvgJSS7V98nEx/HAGUN1Ux67qlZ/zuvFZhnYfS56/0+t8nJpWKSZwTorVlXPT/biTdGyM3lLTNta4JegKUTi23zbaW/Uhb+7ENlE+rZv/3t9gmcFUhP5QmHVAwsvkihaJAFOQLYnSqRbouA8BWakwLTAtTSrIjabb/56M0rp9HuCzCgtvXESo79xRUP2oXN1G7uImqefXs/eazDB7qtoMe8wbp3gQnf7Ob3m3NzLxmMYFIiPm3rqZkSnHOvxJQmXvjCkrrKxg61INQBd1bTjB4qAtpSgzNKBxmnbG6u4AnF2FbjRrXzSJaU8xZAXDkZ1tJtA0idZOSujIu/fs38Pzf/7ooR83dFIWb1SGxN0sLtESG/IidQqyogkA4iGmYqKqCBYRKwlimiZHVMDWjoAz5lQ9hK6DStOjacpxgSYiqOfXEWgbtUuMBxR6WbtD94kmbL8Ms7Ljuad1zx0iJaZhkR5K0PnaQ0ikVLHzLBXbmQ2mY3EjKt26E04xSUAB8a2Vs2zaplERPZKicVUd2ME6yM4aVytltKQIlpHqWBCWgoAZVTM3EyOZJ9uh231VRNEfC950oMpK4yoUCaijoKWMioFCzoIF426BdVM/XP3zXq0Gb9XL2TcsQEkobq5l+RXHwsJbMse0LjzByoh/TIZdD+Nryf690A0sz4BSuDC2Z5dBPXqJ/dztaKocqBKV1ZROy5E7Chkug/krbeLViUqmYxDlDCapc9Jc38dTH7iYzkChEyJ8Chdx+SSAaYt4bVnL4Jy9iRQJUz58CiuDoPdvsU5opEQFn03P94WLMyc09glpuoKN9f/dv0jCJtQwweqLfZmYUUDm7jqnrZhMsDb+isdevmMFln3sjzY/sp/O5Ywwf7sayTDAlqe5RjtyzDTWkMrC7jcaL5xKpKWPOzStQAgVWzqlrZzF17SwAZl27lJZH9mNqOq1PHCQ7mMAy7PFbDt+EZfk2JVWhdGoFM64uLsBlGSatjx7gxIO7yccyKEEFRRXs+/YmYi0DSOfE6smPwgbuHoqktANvpQlKQBAqjzL3ltUEokFnkxTUr5nJyOEeurYcZ+hwt+0qcZU8CtkJro9ei2eonFPP7BuXset/nvRtthaZ/gSmaRb4HYQ7h97C8V6FEA7jU+EDDRfORY0GUAKuecNeY6auk2gbBKfWh8vUOuFawc4a6d/VytCBTk+xc10XgfIIs65dQrS6zHFhCFJ9MTo3HQXTolA6XfpkO0axcBU4V7sQIII2ZfnsG5aDFKjhANOvnM8zf/ZzxJj+CUWC5bgtwirVC6aAKVEjARovmj0urfPEr3eS6Y9j5Q2E6ibLjmnLKZcqpeTkQ3tZ+o6JAzV3fukJerY2kx1OorjK6ISfnMQkbEwqFZN4WSifUcPsG5Zz8oHdmBmNYHTik44aVO2TnWl4p690fwKpG0hdJzeUZMrqGbaZ2zARQqIqAtOXYSIQdsEn14wtnRh84fqh7XalKMT4S91EGhZaPEPX5qOMHKkg3jzAsve9curvkikVrHjf5WjxNLHjfVia4T24MS2srMnQ/i4G93ZQ1lDJ6JFupqydzfSrFxcpFwClUytY8SeXA7ay0bXlOCAZOtiDkc2THUiST2URzsarKIKK6dWoweKv7rFfbOPkA7vJ9MYIlkcoqS1HVRVaHt6LmTfsLBs5Rn6cQn5CoqCgKFBWV0bN4gamXjjXu1fTRXMx0nlizf0YujmuLaFAuCqClsiiqgqVs2sJhALOfmoX77IsW/kTTq0SzzQvsN0nrjwdRKpKqF7cQP2qmcy52c566d/RgtQNW3lQFW+tmCmNVMewxxx52rUiJEIKpG5iWRIMCwKKPQ4EKlAxtYpFb72IoGPteunzDyLcrBN8+SBCImzy8cJYFXus/rUsBSiGbVkTlsWSd15KqCJKPp6x1zpyTP/cNiUyq9O58RDthkW4MsrIkV7W/8MbixQLUzNtl4wY/71w2wqEA1QvbGTqhbOZ94ZVE67z/p2tDOxuJzecQrEkIqCgBFQCp/iuT8LGZKDmJCbxMlG3pImRg12YmsmSd1w64WcWvuVC2p84QE5PIaTA0k32f20jeioLpiTTM8qJ+3ZgZvLeSUgNKChKwKb5Bs9nD+4pyUmLBGx/yHifsbuBSs1g5HA3yfYhymfU0L+zBaGqTFkz6xWPf9EdF9G16SiZ/jhKKEDdihmUTLGVl2TnMHrGJD0Qp/nB3fTvsE/CtUunUT67jppF42nNGy+aS+NF9uad7o/T/fxxep47Tqo3RnYgiZ7XUBWFgK8Eu2Va9L14nOYH95DpjREujzDjmqWseP8VtPx2H/kHdpIdTI0JBCzIr2AFchMuC7EJRiLLoR9spmxaNVf+99somVoIzlt4x4V0PHOIdN6wgyt9cxEujxIuCRGMBqlb1sS8W1bT8/xxhCWLgwUVgVTstGLLlJ41wX8aFopABBXKp1Vz5RfuKLI0SUtiOfEMBSsJSGkRLA0TKg2THUr4slYmjlVwr1V893YtJmY6z/Ff7SDe3M+Ma5eSG0kxuK8dM6eNiVXwyU84pd3dNov6J71skGx/nJMP7CIzkODCv30DvS+eQBHScxf6++H2z3II2qRpkR+xGDnUTbx5gKr5UzHzOqPH++yrpBzTP4rYVVVVMP3y+Sx773glO90bY/hgF8fu2062P25nuwChaJDaFTNYdOf4oNBJFDAZUzGJSbxMNF4yj0xfnFk3rSBaO96/D1BSX07plHLyQ0mEkDT/Zhexk70226OwA+fMdM7ej5y9MhgNoIQC6Mk8upn3kQi6dSZgrLvF+4znN5b2ZyxpKydBg9jhbnq2HENVFdb/0+1UTpDXfy4om1bNgtsv4PAPNmPmDQJBhUv+8TYyAwkOfn8zWjLPyKEu0n1xkp3DpO4bpTW6l2h9Gcs/tIFANIgaClC/auYEFoxKFt5+IdMvX0j7xkOYWY1Y8yCBSJD5b77A+9yJ+7bT/MAuUp1DBKJhpq6ayUWfeT1CCJa//0os3eDYPS+hZ0zPAlAkP8dt4W4cni8facd5pPNk+hMM7utgxjVLUZziaiVTKrjgUzex7xvPkOoctqnOhR1zUFpfjpHKER9OsfRPrqDtt3vp3HwEK2d4J3ghBOGKCFPWzUYogsF9HeRG0nZsjLT7JxRBqCJC1fwG1vzZ9eNcVw0XzqGsoZL8cAppWc46sFCEQrgsRKSmlNxwwjk6+sfqXyPumKXPO+HGCdlrRxtO0vP8MXqePw6qxMgYPnlRcNVRWHduW26Qpvd3X1qqNE30eIZEywA7/vkhRo72YGY1giVB9ETeF/vgtimL+yckRibvxCvB8V9uJ94yQG445ZRt9ylnY74Xp9uydn/pcQZ2tmJoOtI0PY9TMBpi/m1rqJqsZDqJ02BSqZjEy0awJMziU/hiXVimS30tyY+mibcOkk/k7IeryxvgBs45D349mbNz6AxZtMkhx9TUKApnx2ujYIq3PySExMob9O9pRxoWofIIo8d7EaqgYlbdK5LB1LWzOH5PCCOjefUjSqZUcNFnbgGgd1sz+7+20bZcpPLopomeyrHjXx/CyhtEGyqYceVipm1YSrgySvmM2qL2S6ZWTmgFklIyfKib5gd3keoYJlASYvoVC1n58euKTOGjR7qx8qazCYoCxwIUXv3yc10EnqwFeiLDvm8+TfxkPys/ep3XdtNlC6mYU8/erzxJ70snQUL1kkYC0RDD+7somVrhKCTtGImcXblTFNoWSMqnV7Pq49fT9cxhWh/fz/DBLq8ia6gszKzrV7DiIxsmrPUSiIa44K9fx6ZP/IR8LOP1V0qL3FCKshk1hErCaKmsT3A41OR+d0jxWinIwFEsJBhZrSAzWdikiwJd/W1JOU6O3qvPaqSoKkgYPd5HqnOESH05wdIQ8fRgoQ3sgFGhKAhp2imlKkRryph900oq59jKcbp3lO7njmLmdEzDmXOPVr24f4oo2Pr8SHQMk+oaQUvnCtcCQlWI1pXRcNG8Ca6ahB8F9e+VtfFqxaRSMYmXBUs3SPfE7MDBadXjgsVcnPjlVrL9MaRm0L+tBWmaXpqbRyaEz3QtsK0LTm79ROlwnl5RZFt2T2+umVd65l5wToaajhACI5Vj93/8lrJp1az9q9dTt2rmy5ZD5fyplE2rRk8UIvb9aLx4HpWz69j3zWfIj6SQEkb2d2Ikc0jLItM1yon7ttPy4G5Km6pY9YnriE6pIFpfedqg0pYHdnLivh0kW4dQwwEaL5zLxZ9707h5yA2lCwRLnnWC4mi7ieQnCvKTpkWuJ0bPc8eY+8Z1lDkMpABlTdWs/eRNPLX/O1i6RcX0GrLDKarmTSFaV8bJe7ehZwpl2b3bIdETWXqfP8bcW9cyfcNSpm9YSuczh2n57R4wJU2XL2LBHReeVv7VixqJVEbRY5mCq0BKzLxO2dQKrCWNDOztsGMvnJuroSDh6ij50QxmVhu/VmC8m+R0z3j/GvZdi+KWoReFewjhvarRIDVLp7PwLRey4z8eBilRVYUVf7qB7Z//DUZas+NbQioldRVE68ogqKAGAyiqwuzXr2bmdTY3TKp7lKE97egpnzLg67M9r05mTEAhXFPKrBtWFA0jdrKfHf/yIMmOYY+N3xWAIgTVixpeViG/1xomC4r9nnH//fcDMDIywty5c7nuuuvOcMUkfp/IjaaxdIMTP99K+6N7CdWUMufWtSy44yKU4PjlpI1mbEIrITGzOQCvSqL70CsoAmOUCxjjX8cLWCw6Zfs/K4rbGKtkAEjTxEhbJFoGOfS9Taz/lzsIV768MuuBSJD65dNIHO9DmJbDeFi8A5VMreSSz70JsJWxR2//ClYu7wUlWjkNqRkkTvbz0mfuRSgKdatmsubTb/Dak5YkEA4Qriol1TVM8692kGgdQEhJ6dQKVn/yxgkVO5ua2T11j5Gdq6WdzVxISW4kRXYwQWlTVdG9InVlVM2bSqpzhMEdLVimZNWfX0/XpsOY6Zy9QYkxbWPPiRbLeDEZUkrqVs6gdsV0orVl42rJnAoKdqyGpRSGpgi7Km39qhmMHOjENApjDQQFU1ZMp+fFE6dfK65VQsFJZfbJbaz8XDeHXxkTglBVxK7Wm9GwsANOK+bVI4DyWXWs/MQN7P3y45jJvK0jWyaVs+sIloSwMs4aMU20kSRmJkdpYyWrPnMjoZpSu3qqA2mY5EdTXixH0ffCVy1VDQUom1HL8j/dQKmP8Cofz5AZiJPuHnGUb2e8wifPV+/heRK/Q/xelYqWlhaeeuopvv3tbwNw/fXXTyoVf6AwMhrdm49w/KcvkOkewRJgpDX0ZJaj39tEqnWQdX/3xnHX2T7ZMcY8z+TgfmiMr9jnMy4QIRVbH6S/La8df1sU2sJ5QFogFLdNCYbByJ52nv/zu1nxZzdSs2TaOBKhs8GM65bT+dRB8kMJjv/kBRa9+/JTflZ3GUP9/RN2cKp0ZIq0GNh2kqfe9nWbXloFaUjKZtUy5YK59Dx/lFT7CIGAQtmsepZ9+BpKHarwsbAPzE4QoUvzPFZ+ZzkXZjLLzs//mvl3rmfB2wpuLyWgMnXtLNvVktUpn11H/ZrZdG085Lk7iufCflUDKhUz6yhtqsIyTJrv307zvdsw8zpzbr+Qxe+6fEJFdfwYbUuAQmGtKNKxdgk3KwRvrZhZjd7njmLqxqnXirve8L0KH+X2KeRX0DdsV4rMaXY/sFAVQWlTBVf+zztRI4UMinTnMDixC9pQik0f+j5GMl+YC9PCNDWsvE48mWXLJ34MCBovX8SF//gm78Zuxm1RRyjun6oqzLppBU2XLfTeix3vZfe/PUyifRAzbzhy9NOLC4JlYWaOsWxMYmK81gM1f68MJhs3bqSqqsr7vaqqio0bN/7+OjSJcbAMk84n9vPsB7/D3v94iERzn13wKJ23WQuxAy37XzxG+2/3FF2b6Y0x8NJJMAw7JRTpXaMI6Zwwfb87J3fFecArQhSu8Yoruf/3XQvj23KuUxQKbQt343GzH2wm0PjxPrb+1c/Z8rEfMnyg45xlVLN8OnNuW4s0Ldof2U38RN+En+t74ThbPvEj8sNJu2/+saqC+tUzqV0+nar5U203TSKDns6hJbIYmRzx47203LuVVJu9CalBhYV3Xcy0Kxefsm8Nly4kEFY9mRXJ7xznQpomub4YbQ/uJNk6WHQfoQjQDQKRAMs/ci2xIz3EjnXb9UWkNW4uApEgdWtmseLj16OoCrv/9Tcc/e4zpHtGyA8nOfnzF3npr35uV6k9E4Tdtn+tYJmM7G5Di6XsUzaFtSJMEyuvO9koxWtFCN+YKawVT36nWHee/JxUU0Wx5Sfzhs3wKexYinTrIM999Ick24YA6Hh0L6n2AQQWSAtpGBixjKNkjJ8LaZoYyRxGMku6c6ggA2nL4Uz9s/IaJ+7ewsFvPGVfZtpp1+meYYx0zk5zHTPWYCTAjGuXMsWXVjyJU6MgvVf282rF71WpaG5upra2EJhWU1NDLBab8LP5fJ5EIlH0M4n/W3Q/fZBn3v1N9vz7QyRaBjAzhVO230eOhPxompP3vGTXGnBg5HRyw0mUgM1V4U8ndE+IguKiUK6JVXEadkt/2GmD0ksftK8R3gYwri33aykLv9ttCvtE5yoYSDBNO1vheB9HvrPpZclqxrXLUFSFTPcoh7/zzISfyQ4nSXUOg2HimqP9Y6tfPYtrvvcBrvzWnzD9miVUL26iamED4coS293gBL26kf2WZtD20C67dPcpMH3DEtRIYGL5nW4uHJeC2z9XftKSpDqHeeEvfkLbg7u8+9jX2tkadetmc/RHm8kNJMetFbftYDTIus/cSsuvtvP4G79E19OH0FM5L7jQSOUY3NnK5g9/n+H9p1f0XAuDf61gWeSGU04she1COuNa8Y9VgBIOOAXxCmvFvcZra4z8xo/VF/pjScycTvx4L0e+a6+RdO+oXW+Es58LRRWUNFYyw2c5iNaXUzGrDsVhKx3XP7ctKbF0k9JpNcSO9bD5T7/P1r/5JWY6P+574d5fDQWYdvWS087BJApwYype6c+rFX9wXKsjIyMTvv+FL3yByspK72fGjImrM07ilaN740E2vu2r7P7X35BoHcBI58H0Bfn54CkWliTVPsjR7xU21JM/ew4jkyMQCVA+sw78VUeLLgYRVBGq/UYh1a/4M0X3L4rY9H1ubP9E8Z9dc34Ru4w7NMNgeE8rmz/4XUYOdXFOcPpjGaZtzh4DUzPoeGgXVl4v7p97uWHS/PMXeOx1/8HTb/0qajjIhrs/wrU/+ShL3n81FfOmUjFnCmo4UBijYRI/0sVTt/8PL3z8R4UMBR9KGqvswEpfJVjvdaK5OJ38XDnpJrnBBMk221qhxTN0bTyIpRtYWY3nPvR9Ykd7bPrqCW7hbnLdTx+kb8tRcsMprJxefH/LLg42eriLbZ++h5M/f3Hc2Lx2vcyGYkhLMrS7DamZnNVa8fcPWwnAkhOulTP9XrTeYEzWjSBSX05+JEXvpsN2/85mLpx/wjVlXPAPb2beneu9vwVKwky5cJ73HRoHJ8YiUl3K9OuXM/vWteRH06TaBjGSWaRhjXPlOD4fShoqKZ89mUY6ibPD7zWmYt68eUWWCTdYcyJ85jOf4VOf+pT3eyKRmFQs/g9w8MuP0fHYXrRExiPgOeXGLtyHphNMaBgkmwe8jyVabKpkRQiql00n1TWEZRWi8L02VIVQWZhARZRM57B9CkeiqArByihGJm+zQgqBCKkES8JoyQzSAiWkIjWzUBtkTP9s/kAfo6EibCpwx2fskj6BwC2MlmodIDeQAKfoasu9Wzn5s+cRQlA2q44L/u2t4zIzInXllE2rJp7Oe0qRH5ZhkukbRTh8Cu4tFWH3z67umcPIaIAkfqzHu3b+XZcw/65LMPM6u//lN8RP9GJpJiKokO0dJTeUQBtN88zbvgqWxYzXr2XJh68FIFxVypR1c4mf6AXDoZVGeFUux87FaeUnfHKTFkPbT5LuHkEJqujJLKHKKDUrZqInMqiqzYp6qraMVJYTP3kOM5O1XSu+uSjMCWCaaKMp2h7YTs2KGdSsGP+dr10xg2x/DNPUvewPIQRmLke2N4+QPuXmLMcqHR4JIX2xKP614imnTPj9cL8XirDLr3vrThVUzZvCovdcydHvP0OyfRCBhRoNoYQDGIksEpuOu6SxGsuwUIMKlm6iBoNIJLNuWUfdmtnj5OBak6xCJ2wognBVKaHqUua/9RJmv9HmOWm9bxtWzs5+KXwvCvKTQhIqi7D4fVdRMlmV9KwxmVL6e8R1113Hpz/9ae/3lpaWUwZqhsNhwuFXVrdhEmcBBays5vibxwSm+de5/7nqKhdC2LUFCk3ZwWmNlYQqIh5jof+5jGtiDarULG0i1z0M0jZqqAGVGTevZnh3K4kTvSAhFA2x4L1X0fP0QYxMnvL5DQzvabXTNS07/c7SDMDO61cUu+xnIBxwyqurdvnogIJlWnZ6YV0Z0rAQARVLNyifVU/ZrDosw2T3P9zHwM5m9GQWgUJuKMnmd36N8jn1rP38HQTLowCEKktouHg+yeZ+tMEkXY/vY/pNqzxZtP96O1YyV4ig98nPNTM7B0O77xM8U9RwkAv/+Q7vd2la7P6nXzF6pBsjlUcbSWFqOm33baXzkd1EaspY+CdX4zefS0CogmBVKULaBcSkSzg1wT2L+icoZEhYkkzHEMe/v4loYxXhiijz33EZ029chaUbvPTJuxne21Z0Avab5aVuYTrEZkIZmxXCmBROSbZ3lB1/9VPKZtez5CPXU7NqlvfXxR+4hviJXpIn+8BRmATYc6oqtnXHr3iOHeup5gKKM2F8c+QZyk5h6/V/Lzy3RjhA+ex6Fr//asLVpSSO9SJ0C1XYtVKEKrASWVy3nzBMAhbMvnUd894+Pvi3Z+NBhve0seB9VxGpK/cCVVVRbFwRApsOPa9j5QrWrFTHgKP0TzC/zoXRunKmbVg28SAnMSEmU0p/j5g7dy533XUX999/PyMjI3zmM5/5fXZnEsDCd1/F6O42Ysd7imiTi+C3Mgjf2U1aZLtG6H/hGNK0yPaM2A9VWYjMV8Zcb7/Yf69ZPoP4vg5yIymsvB3cFyoNs/A9V3L8h8+iJ3OUz66n8eolzHtbgRBqaHcrR7+5kfxIksol04gf7kYoEJlSCQiyvaNULmoi3T1CSWMV2f44kfoKcsMpqpdNZ8VfvmFCWViGiZ7IYmXyKBYIVSJ1g1xfjPxwgq1/9iMWfXADUy61i3tVLZlGqDSMnsjQ8tPnqF4xg9JpNYzsb6ft19uwNH3c+AtFtApyRFpkO4Z45vb/Bikpmz2F5X95CyVN1cXToCqs+7ytZCRO9nHoy48xsr8DM2MH8ukjKfZ/4Te2sue4IuyNUzD9mmU0XLWUY9/ZSLYvDkEVbSjhFAhzbuCcuKUzR/4NFgGWYdC36RBKSCVSX8n0G20lSgkGmHHjSmKHO22GTP9YBQRLIyglQaRmoiXSOPoohfPZBOc0w0SLZ8gNJhircZU0VbPwXVdw4IsPOdwOeMqqIgTls+tJtg0VXE9jFYqxc1Fsj/DaKuqfLL52vNvIN9ZoECWgUj67ntX/cDvh6lL6XzhGtnvEDoqUEiOWsmuEOA1J3SDbPQICTvx4Mx0PbGfmbRcy7x02rXa2P87JuzeTdtpY+MFrGXjpOMKN1/GPTUqMRAYjmaH1vq1UL51O9YqZKDh1RnxjtvuOzWZaVULd6llMYhLngt87T8Vb3vKW33cXJuFDqKqEBe+7iuPf30Smb9SOCLcAv3vBhXNsE45ZHCnRYmn2f+EBAPRUFjUUIFhZgnQrRgoBQQV002tTOG3MeN0a6tbO5dCXfsvowU5vo2m6djlN1y4/ZZ/r1s7h8u9+8LzLQgmoLP7wtez+/H1ku0e9jUcIgdQMkid7Of6DTVQtn0moIkrDVUsY3NlM54M7SHYM8dLHvo8aDWIkc+Rjabx4Dp/87DLk0ntFWPYmkMtjdOdACPR0nmzf6Dilwo+K+Q1c8rX3sfsf7mX0YAdWXscyLbS4s2m7ubn2LwgB9RfNo95hSLR0g11/+wvizX2YqTx6JgeG5XN74MyXVYgPsCRmTkOaKqXTa4r6UzqjjnBNKbmemH1PZ3cWiqBqSRPrv/o+uh7fy8EvPYyRykNR/0Thd/deAZVwRQlz7riYmpXjycqm3bCSk3dvJtXS7woWpERikR1M4PF1+Nv0w+2fKLh6EJbPlTWmf/5+2pcWQ9hjrV7axMVfed+4/lp5m2XWdZNI0yfXMZYUI5HBSGRpu28bVUtnULtmNrnBuO3yyeToeeYAueEEyeY+2702To6FPiElZt4N7HXmVbjfw8JYFVUw7YaVLPvk68b1fRKnx2s9pfT3rlRM4g8PDVctpeGqpfRtPsyBLz6MNpwsmHtdiMJL0SHNsuw6H469PVJbRs3SaUSn1RAsL8GIpymdVUe6Y9h5sFJkq62YN5VLvv5+dn/2l6Tbhyif+/sNEKteMZPZt6/n+LefwtLN4jg20yJ1so/Wnz/Poj+9HoC5d11K38YDaKksZiZvp+zF0nb1Sz/GWGv8b4/xDmFmNQ782wOUz2tg6SdfR7Tx1MrF2n+60/t/5293c+JHz2JmNfR0Hqnp9h4jJX2bDzG8swU1FKBm9WyW/MXruPCL7wRg9EAHh7/6OKmOIdRQAFM3UAMqpmbYJE6WUdS/QCTE7LdcXNSP2jWzqVoynf7emK+gl21dqVgwFYDpN61maFcLQ9tO2vVZ8gZ6NmfrVc4FgfIIQhGUzahjycdupGb17FNPlnc8L3BxCEuix1L4zA02JlrLovC2CATseB6/3MbMzTj4lAFbH5Ykjvey5V1fY/rr1zLnrQXrWuOG5bT+8kVG45mCjcDX/3H9kxItkWb0YAe1TjyFo3NjJLIMvHgcqRWsUcX9EwTKIgQiQWbeegF1F8ylb9Mhe136b+HKQAITxAVN4uwgOcX6OMc2Xq2YVComcUo0XLWU0f0ddD+2B1PTsdL5gnLhHoZc86nPFyuFfcqXpok2mKT36QOs/OxbqJxbz+jeDJXzGzBGU+Q12xztT8tzsfaf7/odj/bUmHPHejrue5HcQGKcq8LSDYZ3NgPXe58XQhIqCbP6n++ietkMtn38+ySO93iFn/xPjFO5F4pedYNs9yjacJIT33+alX9/dta9GW9Yy4w3rAVg7+fvpf/pQzbhkyXRhpLoQ0kQgkznIKP7W5GmRAQEiqoy5y3rabppTVF7qY4hDvzrr0l3DCN1Az2bRwnYcQKhqtJx96+cO5XhLUcwHYrsQDRExYJGFrz3Gu8zq//uzd7/ux/fy5GvPIoey6CWRQlEQ8x9x+XMvmviCrhjUTatlmz7EJZmTaAA+C0OE18vkBBQCVWWUDqjljX/+la2f+rHpE72FeIMgnZ6tJnXbTr5U7ZlBzwayQzJeJq2e7ZQtWQa1b5YkLLZdSSOdGHpfvfDBNYsVUEJBylprKZ0WsEipLhpspZEmoZNcjbGRRUoi6KGA8y6Yz36SIrkoQ4Gtx7n5A+eQabzXkJWkYEkqBKpqaBizisruDeJ1yYmlYpJnBaLP34ji/70Oo5/5ym6frsbKSRmIudkEjjmeyTC9pGgBlUsw0RRwbIk0tDJjyQ5/vXHMTQdYZmkjveAZaG4xz8hiE4phzG0zMO7mmm5ezNmOo9QFBquW8GsO89ugzmfEEJQNqMWM2XHKtjw+/0LWkL/poPInGanyAKB0jDTb1rN0bZ+ZK5w4sXNePCdrN1y1eN+lzahk5XXGHj2EFvbB1FCAWbddRlTr1x6VmNY+IFrSR3vJdXaDwLUaMjmbggG7DiRwYSdeWDa1WOPf/0J2u97CSEUpLQQioJ0aoiUNFaijaaR0qJm3RzWfeGdCHV8xOLcd17O6O5mEq39WBmN6pUzuOCL757wswDTblrN6J4Whne1MOPN65l9x/qzYtR0UTa9mhHVySDBkaOQHsW2dIJCcajevbkIKijBAIGSENGmGtZ84R2Eq0oRqkLZ9Boyrf1YpoVQIdJUSag0QuJED14xMYq9N847+OdRT2QY3Hq8SKmY966rGNxyFG0kVUjdFYX+oQhbmZhew7x3X83Uq5d51WxtncF2KU60VnDGGiwJ0nj9KqKNVbT99Dmb7O1oN0YyZ9eEUX3rT9hZICUzaljzL2+jfO7Us5b9JAqQvHL3x2T2xyT+aCGEQAQDLPrIjcx/zzWYhsG+v/058eO9WJk83lNUSpSgSrSxmkz3MFIz7EBPISCvkzjSjQwpICX5oSSBsjCGaqetqpEg025e41Fkj+xqofVnW0i3D5Ltj+Gag9MdQww8e5gp1yxj1h2nr456XmWgKqz7r3fTcvdmWn+6BTOvFwXsaYMJ4oe7EIqg89db7c1Xqgw+f5S6i+Y7/uqCeRkoxFdI/wboc4fbH/JtNrYSYmY04oc6EaodHHm2SkXJtFpm3HYRJ3/wNGY6T+OG5Sz+s9fZ6Q6WxNQN9n3mHlIdg1h5A8uySLcNIg23AJxiZ08oOKHpws5mmFV/SiVBDQW54EvvxdB0jvzHg0Qaq075WRfL/vo2jJxGoCTMqYrUnQrD2096xeo8+XkxK+7v/uwG+zpFUYhOrWTOO6+i8boVqOEChfb8919LunWQdFs/QlWIVJaRau5zKuj6TvfuPCmuslKwSglh80hEp1YV9dcj5PJ5ZjwrAyACCiUNVcx951U0Xrey6FpbiVEKFr4xawWkbZEaTtD1m20oJSHMdB4EaEN2sKunjOAPwxCESiPj+jqJs4eFm9b7ytp4tWJSqZjE2UEI2n/xPKP72hBAsDRM3iFbEopAiQQpnVHrpKDKwmbpONSltCBnk/xIwyA6ZQqWZaH1JwhXlRKuKQdAi6c5/vXHSJzotd0FVsEcbCQzjO5vJd05QOWSaVQtf/nVRc8WucEEJ7/9BNpomulvvIhwbRnZvtHCbmKBHktx4PO/AKlgpHNEp1Zj5XVGdrcgLYuRXc027bIoXAO+zch9qvtLVeM7eeL7vCtPy2J0Xyu7/9+PmPGWS6i/ZNEZxzLzLesZ3dfG0EvHSTf3gQXBMrvqZBBY96X3YOZ0W+6KIL6/nZa7N5NuH0QEFaQpkZaFldXsfugGPb/dRepYt2MFENSuX8jst13h3VMJBQiFAiz72zejBNUz9lGoCsHSc6uEqadynPj6Y2QHYkXrrkhuwik57lfYwFmPJtnOIdru3kTNypmUzKjz2i6fO5WaC+aS6R4CS5I82Yv0lMoxbfnfw9moFYEaCVN74TymO/wQLgZfOGrThZ+ifxgWmY5BWn/4DNXLZxBtsl0fsYMdnPz2E5j5/HgrxZg+YNqKqJnJF3mAPCXEDUr1WTzMnE5uIEbZnElLxSTOHZNKxSROC2lZtP7wGUb3t5E40o2R0xCmRCp20SYRUBGRIHXrF9Jw7UqOfvE3KKble1jZRyC3iJdEgKajDSeYevFCBrceo+7iBUy9xs6Fb/3hM2S6hhCOGR43Yt/3RJSaQWxv6+9EqTj5zScY2HIYK6eRbh3wqm6iSJRwCKkK0Jz0PwmByiiLP/k62u/ZQq5rmOHtJ0ke60IYpsdB4fAhOwqTO0aKx+q8SiF8cSvSZ2aXaEMJRoeTBErCZ6VUCCGY9yfXENvTQn4gTvxwJ3XrC4WlAiVhAiUFLpjI1cuoWDLNs8xIIeh5eCd9G/ehJ3MoigDLInakG+nwHySPdzOy/QRCQKS+krkfuoFwbfnLKtR2tmj7ybP0P3MAK6sRKAsj8zpSt1Mri2JghERajgvEFkjBB+LEmez/u58x7wPXU++zAAXCATv2QFqg5UEKFEUipTM30temoqAEVJRoCCuvE55SQcO1q5j9jis9y0vvk3vpf2of8WPdWLm83RaMb8vpoxINMbqvjWhTDUY6z4mvPGKToxkmiuL7jikCNRIC7O+lmcmDIuwATjeD55TrTkEpCRCoLGXqlUsmFYpXACkF8pW6P87h+j+0St+TSsUkJoS0LNp+vInYwXYSh7sw0jk7kM81GSNAsesjNN60mvkfvpGOXz5vl032PxTdtFH8aWsSM5EBabLis2+hctlMhBDED3XQ//R+OyZBOKZk17/stCUCAcrnNzDzrsv+z2Vg5nViB9uQ+TxCSvSRBNIwURQFNRKhas0cwvUV9D2xFzNrm5ZVRSFQapvutVia5m8/YfuuPUZFZ2MTOEU/3Ad7IbjOf+x10wy9V/zysIWTPNJJ+882M/PthY3rVAiWRIjUlpIfTDC6u7lIqXChJzK0fPcpcoPxohN42bwG5v7JBhquW+kQZtl9av3xZmJ7mkFRwdAZ3dMCpkWgNELqZC/h+gqnzDuUL57O7HdffdZlzc+E5LFu+p7YA9KifO4U5rz3Wroe2Epsb6vHiOmtP5wN250LxaekKYBukOkYpOvBbUVKhcAO97Gkbw3717Qv0lENqVSumMXcD15vW/ACAcrmTfXGm24boPnbT9gZVb400qK2HJeUCAiiM+tZ/Kk3ULXMVqAThzvJ9Y8iDKPg73CgKIJwZZTGm9dSvXoOnb96iXTnINmuEaRhFnRVtwCMb92JoKBsdj2L/+o2yuc3npe5ea3id+n++EOs9D2pVExiHHL9MVq+/TjDW0+gZ3I2K6LibmjS871Kh6xH64vR/uNn6H1iL1ZeQ/FsuK6d1TnVOa8IsHIaqRO9DEVC1KybD0DvI7swEhncappQeIC7kWfRxmpmv+PKcwrge7lIHu7ETGacE6QEy3JiRxRC1SWoCgxvOYzMayjuzmIYJPa2Eq4rQ0iL/FACqRuFGArcglZOoKZSsMZ4Y/VyMB1S7bHyUwDT/ruQEn04Sc9D22m85UJCFSWnHVOkoYq6SxYx8PQBRnecAG4e95mW7z1F/9P7MZJZlIBib36KQupELw03rqFiYVPR55f+zZvsDAlpMfzScUa2HSPbOwqqIN3SR6qlz+a8CCjED7STOtqJEgk547R345Lp9cx67zXnNK9mVuPElx9GajpVy2cw/2Ovp3xeA6HKKEe/GCPbNeQocBJpOfIrClopnguwwJRk2/ppu3sTs955FUJRiovonWYukIBpkT7RQ/9vdzLng9cTctx6Lnof3YURT4NherwZbltKaRh0EyUSxMppRJtqabx5HdXLC8GdEnsdCj93CO79Jfpwgr6HdpDrHKJq+UymXL2MY196CNM0ir+3isSlRBfCjr9QFUEgGvaCQcdCSknfIzspW9BE+aJpZz1PrzX8Lhk1T1Xp+/epWEwqFZMowsjOk7R++wkyXYMY6VwhiIvC5ueaUqUAoRvE9reSPNKJHkshhFI4QfkcvC59t0CCohCqKqVkei3p5l4O/8u9CFUhebgTm6RIeDwFdoCj01BAQUiTwWcPkDraxcx3Xo0S+r9Zwnoyy8lvPGqzabpjFXbKnmJaaH2jjAzGfaRegAQrrzH0whEWfOpWYjtOIE2LQGkYmcsjrYJbyAvoE45jxxG0CKhgFvgwPNEr0idH31xICdLESGZo+/5GZr/v2gnTO/2Y+4HrSRxoRxtJkm4foHRWgQskcbSLoc2HsDJZu2aGBVgWUoCZydH+k2eZ88EbHLZSG6GqUmousEm0qtfMZeSi+Zh5DStv0PnTZ8kNJrDyGkgwExlGdzYjAipKQPEqdKrlEZLNPQQrSrygVDfWwIX/OSuA/GCcXN8o4aoy5n3wBsrnNXh9qFo6g/xAHJl34n6EHWQq/DU8JpgLpEQfSdH3yE6mbFhBpKGa1Mke21ohJpiLMS46IU3MRIahLQfJtPcz5yM3U7VidmEMznpx42vc0SkBhaplM2h648W2hUVVUMMhai5cUDx5Dn2+IsQYBQnbcmWBHk8x+PwhYvtaCJRFkTkNxfveyiI6bvv7IwlWllGxZDqlsyZOI9Viadp/+DQjO06ghAKUL5pWlP2FEHaAsqrQcONaqlbPOc0KnMTZYmw17rHlKs6l0vfvCpNKxSQ8aMNJTn7lYbJdw47pGNvuK60iBcGGG/0ukekclqpS9PgvUizc/xe2hUA0yNwPXM/hz/+SwecOIjUToQiEKSEgCpdZBZcLpkWuP0b+qX0Eq0tQQgFmvvPq8yqDvkd2kjrRQ7ptgFzvqE1vrYpC8CR4Vgs7JtVhnXR3CMuyo+tNi1BVGUYyQ+mcqRiZHJaRK5afIlCjYSxNRwQDICWBsihGOmsHQ3pwH9ynmAspMVN5hjbtx0xlWfz3d57WDaIEA6iRINpompNfepAV//lelHAQM6tx8qu/RU+kiyjaPfGn8wy/eAQzkWHZF941oQtDCajUrS/Ed5TOqCN1opdUay8jLxwj1zOKUhKmctUc0A3ih9qxcjpSM0gc7EBRFaRhIoIBm63TsuwqngHn/YDq1PQQtisqEiI6vZbyxdOL+hGqKfcpw4yTnxc065tXV8zSsjBSWVLHe4jtbiHbMYSiCqQIIA3DF6TrWgl8P858GOk8iaPdNH/5YVZ+6U8IVpY6H3UtcH6h2V2LTK1iylWnZo717uX22c/W5f+jZYEB+mgaPZG1WTYVUfwpASKoUr6oiYab1qGGAwTKo5z834dwY63Bcv0+pFv7SbcOosdTEFCdbBjVVpJUBWmaqIEAliWJH2ij5sIFTNmwkkqfQvVawfksKDa2aOY//uM/8rnPfe60156q0vfvCpNKxSQASBzpoPVbj5MfiNnmeu90XqgJUGR5wH2euRHr1rhgQtvl70u9c0+gQiJzeZvG+IK5ZHuHMDMaqIXbSEV4p3ghsOM5FPukJyUYsTTDWw6fV6UitqeFth9uREuk7Y3M851TGJvbn3Fjdc3RYCTTdN79DJXLZ5AfTCAUCETDaKlcQX4CAhUlNL35EvSRFNmOQaQC4fpKhl88isw58kPam7dNhVnwGIydC8vCSGaJ72mm44dPMe3OKwiURU851kB5FHSdbOcgx/7zV8z5wA2M7mkh3zdqm+aVwt5ZCCKUWOkc6ZPdDGzcx9Qb1pxRphVLZlCxZAaWYXKoL46RzBCpL2fJZ25H6gb9T+/HyusFV5DvpomDHSSPdqGNJG0+BVM4TK3SLqtuStAMct1DNP/vQ6hlEQLRMA1vuBA1EiBYGsawbNeNZdruF5nTC5ahggjHjBWsTI6OHz8NoQBKULE3R2kRP2grQa7Vqmhf938vpATDJD8wSvNXHmbR398FUpLtHHImtqCUC+e8fzZRJrZRpGD181u7hFNJTOJU73UUYumUPEGxFUqpmCjBIMGaMub96c1ULJ1B36M7aP7x0+QH40hDFsYlQSgCy3TKs0sJpmkTf0nLdv1IE0ywMGyluneUvod3EN99kvorl1N96RIqlrx2KkqfT/dHZ2cnFRUV3vtji2qeS6Xv3xUmlYpJAND+vSdJHe10TKX4TqjCe4j5fdFewBquWdWpDum+gvN74UFt/1/arutUlp5fvYAIBe2iRj7/shASEQ2gqCpGKmtfpxTKMnt9O89Uwn0Pb0Ufsa0MQkpQRCGAzt8/p7+FMVP4HQl5nUznIHNefwE9972AmdcJVUYwRuI4T2YQgkA4wNRrVnD4H3+K1h/H0g3SqoKlGyiO/AAURxZmOme/J/EUHv9cCCz0kQR9j+xEG4iz4G/uOOVYp73xIuK7m9FHk8S3H6erLIKRyGJlc05MTGEuiufVQh9N0X3PZrThBIoCoZoK6q5dddrgSyWgsuCTt3L4sz9BCQZI7G6m5vKlTH/zeL4RM5MnvreFxjdcSN8jOzBSOa8cNxKyXYMkDrWjDadQAwpWMsPQM/swMnkCZRHie05SvmwWZfMbwLBQoiFK5zZg5nSGNx9AT6TtwmmOoubJzx2rYluicl1DBGvLqb1sGTPffQ29D2wlfbIbI6/h5eS4m/zYuRB2zIKVypE63k2+P0ZifyvZtj6EbhQxWdrrWZLt6Cexv5WKladxHQi76qnirjv3eyEEqlO4zNJNSuc3ULFiNoNP7SWvx72+lc6so/bSpYCkZG4DlStmEd/XQs/9L5DrHrL742cKldjfA6uwJrw/+Fw//jFjmiAtcl3DdN37HMMvHGbGO6+h7pqVZwwknkQxKioqipSKsTiXSt+/K0wqFZMAsN0dlrRLhTs+dOFYGvyxFOC85/r1PTKkQnEk7yHrM527bgNFARG0g9+MRIZMW7+jyBQ2LgTeg1NxPbcKeA9tCUpYpXLZ+Tv9xHadJNsxiLBML1Nj3Fjd3xWch6wbUCqcQFbnBOmcUtVwCLUkRKall2BVGYoq7BO225au0frVh8n3jiJ1A0wLaSkoXuyF/dFAKEB01hSyrf0YibSXvTCufwIwLYzRJPF9LcT3tVC5auJTS3RGPYGQgiVABBSMWIqRbceQmm4HnfpSDoWzBoQ7F5ZJrnOArh8/BRZEptUSrC2nau3808o4MrWK+quWM7zpAB0/eoqSuVOJNNUWfSbXM0L7dx7DMkxiu05SMquecFUJVRcvJlRdBthZOd33Po8+krRP5gLSRzrJtPVjpfOkjnSSPNDmySk0tZLSGbV2OfsL55HrsSnPtaG4Q+A2ZqxIW+GwTBRF0PD6CwhVl9H4pvUMb9lPNpt3MqHkKedCCJABgRJQ7DlJZ+m+/3mMeAola4GsmQAAriFJREFUoCAEWJblfMdsced7Rui8+xmW/uf7Tqmg5ftGEaZZpPjZ8R2CUEWUmitXoKiCxlvXM7r1qP09ctwfQlUpn9/I7A9cX9RmtnsYYySJ4qYlKWOtN8730B3rKZ4FQoBUHQujaX8Mw44/6n94O6HqMrShBFJaVK9fQvAMQcWvVpxP98eZ8IdY6XtSqZgEADPefjVDmw4Q23MSfShhBxVatpkWGGcVKFggxpRO9n3O22SdR5OdzqYQbazB0nS04Tj6SNL2+RZ93naPWKrq+J6dBz2F2AZ0ncS+Fox0jsA5kiWNRfJIB61feZBs74itLIwLfnf6JcaM1R/sJwv9E0GFSH0llavn0vvA81h5HTUaIBAO2twBThtmIkPyUDvkNNt07Tvt+uWhCEHTmy6l8+6NmKmM159x/fPNhTGaZOCxnadUKtzPCySBkpB9Os1rhb1i7Lz6rVRIpGF68QXaUJzY7maQksq18097Gp32lsuJbz+OmdPIDyWLlApLMzj5X78iebjDjp8QIJxA3KrnD1N/01oqVswhWFnKzHddU9RurmeY7nu3YGY14nua7fTfvE0rrvXHGXhku5NVoSNCQYxkxosbGbuWvbEGVELVpZQ6nA2h6jLm/vlttH/rUTIdA1gZJ54Aab8aFiIgkIYgWBYhPK2WkllTic5toO/+F9AH44Sqy6hYPY9scw/ZrmGsvIZaXkLZwmmEasqpunDhaS0+Q0/twczkC98Lz6JgYaUyBCMBZr7/RlueeR0jk3OsIYJASZiGWy4qas/M5Oj7zUuYmZwvDGrM99b7tBzz6nze+V6ooQDBunKsVB5LWlSsmINaFrGVZaD/0R0kD7RiZjSybQPM+tD4zKM/Bvwusz/gD6/S96RSMQkAqi9aRPVFixjdeoTBp/YQ39eKPhR3/irtAIsiz7NzcpXuQ0gUTu2ueR73kkLWtRCC0kVNJPe32UyMWa2gKDhkQnZNBdsEXaA79m9qAkyTXNcwPb/Y7D1EXy567nuebPcQGHYsCU4sgxck4Y7ZHassWChEKGD7nLN5Z4ASgUrpvAaUUAChqgjTtNMbcwV6bzcuwMprBbm5ch47Vssk3z9M/XVr6P3lZox03vbLq87jPiBs2mjFPmWq0RCRxlrqNqw65ZiNVMbhSQCpGwVOB98ZdexYOcVcmJkcffc+R+ylQ9RtWEPl6rmULZ05oXKhhIPM/YvbOPlvv6T3l88SKL2B0nl2imrH9x4n3dyLzNoBrVJi070Do7uOE9t9kuqLFtJw63pKFzQRKC+cdCNNtcz75G0AjLxwmI7vPU62ewglHKRy9Tw7wFNKFASJo50wahbGCNhJS66JzA7oFKaJmcww8PgOwlOrveXQdMfltH/nMYiGKV8xCy8LRwiEBEtAqLKU6e++llBNOSPPH2Lw4a0IKZn+9qtpuHU9ub5Run/6DEYqR7ihmhnvu76IHnwimDkNbTBu923sWpESSzMwEnZtGiORIdc7bAe+2mY/1GiI0gWFdGApJe3ffIRc56Azz47JxHLneAxbwqm+FygIRaKEAtRvWI2lG6iREE13Xkn6WCcDT+0htvME+mgKqeuIQMBTrifxx4dJpWISRahev4TozHqOff7nGMMxX8lnx5rgpqThey0ylzr571AcREbhVYDtd83lvYeZd61zYPfaxvfMRha9ounkOweRUpLvGSbcVHtOPlsjmSHbM0yuawhhGoV+ulwcbn8EY8Zc+F0NqSAEVrbYkuGi/vq1JPc2Y6ZzTpyG4iQgnGqsxWMUQmJlcnT/aCPRmfXUXLoELZ4pRP77dBHhCDxYUcL0915PpKEaPZ4m3zuCE81CoDRKdEY9Pb98DiuVRQgLM5Yic7LHO/3aOuE5zIVpIg2TXFs/3Xc/xcimOhpuv4IpN184odxLZk8FJIk9J2n+wihN79yAEILY1iNYiVSx6V1IezPLaUgpiW0/SnzHMaouWkTjW68iOr1+nKWq5rKlpA610/vACyiWRdmCJqa/2/YzSyk58pffQ+8bAd8JO9RQRbCyFLU0jBAKeipDrqUfrX+Ezm8/Ck7KJGCzdSoKakUJjXdeYRf+ciXkUmJKSX5glPxAjJ57NqENJyiZ3cDU19kyiTRUM+8vbz/rtaqPJOm6eyNmMm3H2zjuQH+dEUURdlaMZdHyP78mdaidcF054eUzybYN2MG5DizNYPDJXST2tSA1jVBNOaULpyGlIHO8E3006SUaefM+7nthv6qRAOGGWsoWTWPq6y8kWFNOtrWPzu8+yvCWgxjJDFbOsGWjCIJlUabcfMEEo/zjwO/aUvGHhkmlYhIepGWR2NdCxzd/S659oMgz6KXgOQ+TQvS7dDgFpBcP4QU14nt1LxeWZ3K2OSmcNhV3U5Zem/7AsELWZuF3IU3SzT20/Pf9CARli6dTd+MFZ02g1PrlB4jvOIaZNzyCI9e/7bkginL73aBFt3+OC0EIX3Cr9K4BqL1yOcNP7SK28ziKqqKWRdAG47aFQEww1rHyAzBtS4I+GKf2PddTecFCtKE4oSlVpx2rNC2a/+0eUkc7bRO0hMj0WpresYF896Dd13AQ8jp6LOEoPS9vLux+2spFtn2Avl8/T/mK2USnT8x7UH3RIvq6B8m29tH2pV85NUV0BE7K59i5cGueZO0YiNi2IyR2Hqfq4sXM/NgthKqLSabqb1jL0NO7MUZTaD3D3vv9D7xAtmPAuY8dqyMUCfk8ZkpQdcECajeswsxpDDy4lXzfiKcZa/0j5Prs+BehCMwRnROfvRsrp3mEVUokhJXTUCMhrJyOEg6ipzL2od/QSR7t9NJL3VO/Gg4Sqq+aUE5GIoMeS9HyxfvQRpJEGmsI1ZSTOtaJmcx6LKwiYgejVq6eS/dPnyZ1qA0zqzH9gzdTvmQGyUPtaD3DaAMxpBAMPvQSg0/uxtJ0Ig3VTH/PDdTfsBZpWXR+/3H6fv0CVs511Y39Xvh4NhRBsKqE+Z+5g7KFdlpv53cfY/DJnZjpPGYuX7DwKbYLpv7GNZQtmj7heP8Y8LuMqfhDxKRSMQnA9r/23fssA49uRxuI26XJoRAFDkUPdy8o01n8hTQ392TjO8lSsFgoAE4ApvA362+LwoZdsH4UWzvc9/XeIUYHRpFIYs/tQ+sfZfr7z85Xq/WOYKUyOM87z2sj3U65J0Ak1tj+OR9RAgqhukqsTM4jrXJZkG25CCpXzyW59ySBsggzP/5GWv79l8hMzqtw7bZZNDZPfs7viiBYU0a4qZaOr/+G+NZjVF21nMa3bkBRFQJVZePG1/vzZ0gdasPK5Dxq8FxLL+3/8UtMy0IRkpJ5DWRb+wqxHlIU5sKvOBT1T46fC1HwtAvTJN/aR/Pnf0LjW6+h9to14/rW+Larie8+TuZEN1LT7XgE0/Q2r3Fz4dzIXoIWZDUsIP7iIU50DTD7r++kZE6jZ6mKzp5K0x1X0v3jJ0kdaSex9yQl85oYemw7xkjMmV5nbLqOPhhDCIX+e55h4FfPgYSKdfOZ9zd32msgFEQbStDxnUfJHOuEUMCJB7ELgVngKRtYThpwQEEgiDjyDNWU0/XNh8l3DTpWKIFlmERm1NP0nuupungxQlEw4ikswwLTovmff0q2axAjmUMJqtS96TKm3nEF3T99huFHd2Bm8whFRQ0o6F2DtP/P/Vh5Hcvhfen65sM242skhJXVGHjoJZtKX1UwkznbPRJPEd96iMp18xh8ZDsjT+9BaLarTijOenDnwhfuYX/lJTKRpv1Lv2LuZ96G1jvM6PP70YcTToy3jyhPVSiZWc+0d1w7bj1M4o8Hk0rFJABo+497iO84juEUzJLuxu7uHBS/uqZp7/SOb7MR9kPUS0WUvlckCvZG7afzFt5mMkFbTqpmgR/CpVXG3shNEwDLMMn3DJ1xrNIwsTQdpJPa5z74ZGFsY8eqjB2rE+2uKoKyBU3kO/qd3H2KFCbADahHSIvSVXMIloXQczkCwRCB8hK04ZjjbvDLjwKLp5QoWBgDwxz7+FcwpYSczvCj2xl5dDuhxhoa334tNRsKm3f6WCcjT+9GZnJeeqoQAmkYDueA/bs5mkB1gg3HzYUc2x9ZmKOxc+FfK1KCaZFv66P/vmepunQJarTYRREojVCxdCb5lh7UaAgjnQOForUy0VyM7Z/M5cmd7KblH35Mw1uupP62Qk2YKbesJ/bCQTLHu+i9+0lm/dVdGCMJJ7vGN78O/boQEitnIPIaIElsPczhXccBQXh6HTM+cRsLPv8uWv/l51i6RuNbN1C+ymYS7frWwww/scMOc8gbBKdWMfUtV1J99Wo7YSgSwsprdH7jYbv8uwRTN9Da+8g1d9HxX/eSv+NKwg219Px8I1rPMDKoYiVztpLi9G/owRcYeXy7XeLdsDN1FGGBpkEkTHhavR1vo9j3cJUcS0ryrX3ITI5gXQWB6goiAoyhOPpgnMRLhzi885jNd5K1A3bVcNBWnIIB9L5hCASQmRxmTitSumU2T+5kF8c++TUszVZoFGkVfW9FKETpwulMe//NRW6YP0bI8+D+kK/w+t8nJpWKSQCQ6x7CyuS8tDJ/XIC3Y/jf8twFsuDm8DYX3+8Un3aFsAMK7Zi4MW2Ou9bHE+H4qoX0ZYq4x2S3f0JiWRaWU2xJUSeuYdD67/eQ3H0CI5N3DsEFy8N4e8hpxqqodpzDhjUkdhzFGE0hpK8ugzssJ8fOjKcYuG+zY+FQKV81hym3XUbrv/28EHPhyXHMWKVEpnL2mdeRm8zkkECuvZ++Xz5L+YULCZbbpvWeHz9BvnfYnk+nfsXYuQCJHkuhKApKKECgosSuS6Eb4+di3OuY/k20VkyTXGsf3T/dyIz3/3/2/jvOjqu+/8ef58zcsr1qtSqraslyt+Xem9wLNrYxhF5CSEhIQvKB+JdPPmnfTwik55MQICEQIASDMGBsA5ZscO/GtqwurepK2l7v3jYz5/fHmXLm7t3VSlrJEr4vPa7u3rkzZ97nfc6d8z7vevOEqAbtFKtwM1mE406kz5T0YveIzxVcl8KBAboffJa6C1dop0opkakE9WcvJbNpF5k3d7LxN/8JL5M9+LwTeiFWBUcvoFIyvm0vnX/6X7TdexXVy+bS88BT7P+vR6n9u08w/Ox6+n/xGs5QJqSzsLePPf/6I/b9x09QStF42Rks+N07WfD770QIoRNy5Qts/aP/YGz9TlTRoWrJXJJtjbjjeR3qGsw7Gcw3gd1QQ6KpNhS+cr5pJz1/Fo2Xn0G+q4+Rl7fQ+o5LaL/7ipDnSik6/+KbDL+0CXdwFCudYO77r8OqrWbf137CeOd+3EKRqvmzSPsmNWd0HGdgGCUEKptHJV1wHGTSItnWhFVbRW5Xt06F7nq6SKCfbCz+TJDY6QTzP3ErtacuLPub/FXCsSwodjxiZkoFVvArhmDXKiLVs/9xgtZiAvwvAlE7vEaFXwlFpGko11bpPWKZeMrRFy3+Y69sYd1df8bmT/4zw89tKEthbnePXjzzBcCb2FezzSnoE5ag7swlNJx/MsnWhvBrVbrN8Hf3ynEp7O2j6fIzqT93GfM/fgsNF52K3VhThp8B/6YYiwCeR27HPrZ9+ksMPb0OgJqTF5BoqtURIpOOhYBCES9bQCiFlUpCwgp5WbbvqvSPqehTqKLD4I+fZ+sffklXcp3ASA+cIBFVKX1MeyxQisLeXjZ/8p/Z8vtfJL9fL7b15y3HbqhGuS7eyJjOExLjxRR9NTtddHEGhun+xhrGfrkNuyaNMzKGm82z/7/X4vQNxehWnocaz+OMZHCHxxh6/FXevOcvePOev9D07etHppLM//VbSLbUAYrsjn3s+ecHcIbHJvQNtOBj11Vx0l99jFO++Hus+NffJTWnGSEFrTddQLKlnsGn11E4MEDPtx+j80++Fs5FIQTzPnoTidYGvHyBwoEBCt2D1J+3nHmfuA2ZSiA8j2L/CIXuQdyRDADu6Dje8Jiucuqb+ISCZFMdy//+EyQajFwT5eaKAKsmRd15yyfkJPlVRVD6/EhfJyoqmooKAGi6/Azc4TGKfSNaferbSzVMrQCxB36kpg+UBir+wCb+wJYJi8z6ToSr6zjg71DNxSRoM7xv6aIS0lJyXCnUWBYXyOcKdH/rUWrPXopVFU9tW3fOUgp7uvHypnu7ito0lRVG+0KASiVI1FVRHBoj0VBD3Upd8Kn+nGXkdx3AK3hYtWnqz9f1L9xsnrGXN5NqbcQdGkVKQccn3xGjJ4hkVEFfywpuZcbCXHQcl/yuA+z52/sZ37iLub9+K97YOD0/egY/33cJr/y23GDBAqd3EKsqgSejwQ7s4RHvJwpMU84VFF4my/iGnXR/+zHmfvRmAHJ7ehh7ZTMyYWPXJykOZSI6Jxvf2N9l5orj4A6PkVcebkanRK89bRFV81oZ6xue2GbYJ+O2pYKl2XelcMcyZDfsRCUsRJ/Lxg/9Nc5Qxk+GFp0b/i78O3q5gg47FgKUh5vRoZ81py5k1u2X0v3ttXR/ey1etqjTpJejT3nkdx3gwLceZf4n7wSg7swl9D/8PPu+9CNA4I1rjZc3Os7Ya1vp/eHTtN15OaC1GbUnz2dwbw+i6ND932vo/f6TJNubSM1qILtjXAsTSuGNSL/mjTGuAUmuQ3bzLjZ+0O97MBbCGAsJsjZNsq2RpivPYs77dMItZyTDvi89iDM8TqKljvm/f88hRWxVcPyjIlRUAEDrDecz8uTruMGOC+KL1yRaaAiqdEYRD7EFxrxICKyUhTs0hlWdItnWgHOgH18THyEwM4QZ/Ih2sqUaDnNxjd8KkU4iDBNIfl8fe//+uzpVsvK0T0dpf8oIFLK+Bi9XAMclUZtizodvpP+RF2i49HTqztZZJJtvOJeBnz4PxSLJ5loaLzlNc6DoUugeoO6UBeC4OIMjjK3rpPaMKCmV9sFQ8Qf4VH2dbCw8F3c0Q/9DzzD46Isox0O6TsnJU/DPdUi3zyZf7EH5dnOrtgqvUND5IkrHolTYm2QssC3sxloSLVGExoGv/5TCvl7sqhQNF53CwJqXwZlEYAmFpzL9DucKIX0ql6PvB0+y4H+9x+cvfmZWFRvXqebyRCHJONkSJJpqcXqGcPuGQx+PeHtBjpUS+vDpe+BJFnz21wBou/MyRl/YQGb9DoTnlWnLINhxyHXuC4+0v/saxl/fzvi2PbqCacgjncxs4EdPU3vaIqqX6+yzdSuXM/LMOlS+gDfm4GXzuKNjpDtmIy38MVBawBMWdkM17mhWJ8Izx6Lo4PYPRwVaffqEUMi6KhJN9TRdcw4tN13I7s9/m/UPPROm/3ZHsv65gtFXNlO9bD4L/+SD2tfkVwAV80cFb3sMP/EanZ/9N/K7D/g+AYR+AUJGIZLaHyL+Lv1zZeAvEVwriZ/vH1fZHNKCmhUdVJ80T9uXg7aDhxLxzxE9pZ+ZcG1IV0LSdMWZsdLoztAo2c4u3MERrcYtbUtOvJdMJ2l//3Ukm2qRtiQ9r5Wma1ay/F9+l9nvibzYB370NOTyCBRu9wA7Pvtv5Pf20v/g0zh9wwgJdectJ9/VS8/XH8ErOtEAyMgRdMq+ltJXbixQWu0+MII7Nq75IqfPP8azVC+Zo1OpS0HNyfNItjUcAn3GWCQlieZaak9fxOL//X5m3XE5+T097PjsvzHy/Js62iOfx+kfxqpL+742k/QVFZ+LE+ZKMN8Edm0VdkMUDaPPLTdXSsY6OAdvYt+EQlgCu7mOlhvO4+R/+h3sutTE30bp70KU0ucLvKloXsp0ksZLTkUmZbyvJb89iUImLWpOjwRSu7GWunOXIfzU3zH+oXAGRygciEJqW64/j/pzliFtK2yTokNhb48WaIzfrSWU/zzwJvJPRjyUtVXItO2PhU4rP+83bqVqQRudn/k3Mm9ux+kd0nOybxiKRSgWUYUCzoCueTO56enEQ5Cn4khfJyoqmoq3OYq9Q/T8z1oKe3ujXUfpbjPYOkWZj/SX/gVhzLqpOqbkPdjtuYrUrEbaP3QzB775M0JNuyDWvhCAJZC1Vai8gyp4xj1L2y/ZvgvAcRl+8jWab7oQmdSZCgceeg6CgmkmXbG++p+lRNZVU3vWSbTeegm5jbso9g0x+wM3ItPJiH99w3T94/cYfX0bOLomg8oXGHtjO51/+C84+SKM58lt3k1yTgsqmyff1adt1L5DXPXiOYx2D4bJlUhamhDzYTtBE3AYYyEM/pSOj22F88Edzfq7DUVuyx7cILww5I3Q0QxCQL6IqE2jxvM+vQJsC6u2ivTC2bR/7FaqT1kEwNBjr3Dgv36K0zeEKBR1hELRIbd1L4maKooj48ZcKTOPMD6XzhUBJG1S89uY/YEbaLj8LABGX95Ebm9vFGk05VwO2hKRFkb4/a2vJdFUR9uvXUfjNSvp/tbPdGSNSQtmW5OPhVWVpOWWS8LueNk8I8++iXBchKmmMOe1UGBLqpbOo+2eq+LsEIR5UkrniioUOfDlB/HGcjTffBFCCOZ8+CaK+3p1xJLAr1XjIAMnVUMboRw3Oh6jKRqH6jMXU9zZTbFnEOF6uCMZ9v7td0AI3OGxKZ4rkuScFuZ86KYpU5NXcGKhIlS8zVHoG6LYO6jVm0GyhvK61/jxsqYO4930UTAhBTWnL6Zq6VyddAiih0xgm03aqGIRu76GljuvoO97P8ctFCdv06Qp+Mp1yG7cxc77vsTsD9+COzTKyHNv6noVU9GHQNZXY1WlaL3nalrv0PboBX/8gbK3dAaGGd+4A7I5Y9XVuz+nf4QgrWhhfx+DDz2DSFiIRDwqZfaHbia/u4f87gMgBam5s7Dqqsjv78MbyepiYxOgIr5JwLKxm+r1LjBh4wyNwYS+etG1pb1OJUg011PY14c3mgnbdsey0dwI2gKSHW3UnLGEsVe30HLbpYw88ya5Pd2ookNqfhtzPvEOqk9eELaf39tDz3ceo3jADPlV2n4/lsHN5srSNZFQSsZN80FWJak6eSEdf/Q+Es3azOKOZen5xk/xhkbK8y9sU5WYvIw5bNsk25uZ/f4baLzm3PCSzJudYQrxsm2V+10kbOz6GlILZmOHCbCg74EnyG7d4xebC/hMJKjYEplOkZzbStv7rscySto7w2OM/XKL74Bahn9Fh2LvIN1ff5jxDZ3M+/17SS+ZS83pi8nv7TGEoYP8bsvyTSBsi+rlHQzv6o711c1kwxLpE58rwW8sTeudV1C17FcrEZZvQDriNk5UVISKtzn0Bs1XfYvggLGjFSU/kZLdhpBRdklEuXdzd6wAj+ymHRT291G1ZB6ZFzegHA+Ero4qbJvq0xdR7B6k7sLTqD3nJPpXP37o9KGgWCC3dQ/ZDTsp9Aygcjm/nUnasiV2Qy2z3nMdLe+4fFr8Uyb/goqt5egrerhDoyQXzkYKwcgTr9J0oy77nV4wm9a7rqDnWz/DG81g1yZZ+Je/jld02HXfl8jt3KdvZMmA6ch0CpFOQqGISCVJdbSx8M8+iqxKMfrcOvb87bdRY8Uwy2m8r6X06aZrz1rCcGYclSugCkVdAr7c+CqF2zdI/fkrmPubdwLQcvtlU/Ipv+sAbv+gDkOVIj4GykMU3EhDEMwVg74YfO2BTjIldBkUW5Jsqkb6ZoXMa1vo/trD5Dq7EJ7R9mRzGQjykOj2FSJhYzfUsPBPP0x60ZzwsvENOyjs7Y7mUiltRL8LIdBp7KuSVJ00n477PkDCiBQCcEYzPl/K0yelwqpJkGxIY5dU9nT6hike6Iv6WO53oRTu0AgjT7yKOzDM7I/eTnrpPBKNtbijGS20Hux3W3YsFNKGZGMNVUvmguOg8kXs5jpkVZLctq5425ZEVlVh1aSY9e5VNN96Kb+K0OaLUvXaobdxoqIiVLzNYdVVkV7QpnctjouXzfm7F1MQIHoAR5sN/z0QSEoWiuBdanU4Rdd/KLkU9/fR841HaP/UvQw/8gxOJou0LVSuQGpROwv+5MNYNVWMvbKJA19cDUVdGj24X+xBN5VML0B4DoMPPaHzASm/7HgpncFutzrFvD94N3Xnnzpt/o088eoh0OeBU0S5Hr3feJjc1j20/+ZdCNui+aaLSS9sZ8//+Qr5zi52/uE/IpJJ7IYqZNKCggNViVDdPud330XDFedMoKfn6w8xtPYlyIyHdvByfcXW9Sn000vnPyjs2o+lXOyOWeT39UImV358lcLLjDP26mZqzztlmpwKfA2mmCtlF7CpxlePpcDDy2QYfe4Nkh1ttNx5FT3/9Qi5rbsQriEwB9oW14j6Cdsi3lcUwtLRSqaTppcv0PP1h/AGhnXSqQk0xfuLb02wa1O0f+SWCQIFwKx7rmH05y/jDo2W76fj4PYOMj48yr7Pf4OWd11L082X+ndRfuTQJPMupEeA6/oRXkO03H4Z1acu5MC/fp/crgM610SpcFEqVJQbi3yBgQd+QceffIT0knnh4fFNO9nzJ1/GHS76t9djlZ7XxIK/+A3s5vryfa3ghEfFkPU2R6pjNov+9lMs//Zf0PbR27DqqxFJC9FQpctxBwJG8E78c8zDvfTdFtgtddjN9QiJn7YYKBQYf3E9mRff1M5eroOQkFw4m5Z7rsaqqcIZHqPv6w+R37ZHZwsscw/w4nSJMn+7Lk73IF7/EHjupHTKmhSpjlmxypcHw/gbWxle+yIUp0mf8vAGhvHGxvGGxhhZ+yJdf/Ef5Dq7GH9tC73//kOtISgUKOw+QH7rbvKbdoLrAB6iWNC8KBbJPL9uYj4MYOTJX+INDBkVXsuPjUjbJOfPQlSnEGmb2ivPwekdxBvNUNy1H+E4U4+v8shv6MQdHZ82v4S/ME05Z4J3VYZ/xkuI0muAQpHhB59k9x/+E/nOvX5oZjQWIpXAqkoyYT5PaMv/vljEGxym54urGV/fCcDgQ8+Q27Jbm5bEFL+LoE2BHvfhMXq/9uOomq2BsWdeR/nF9cr21S8ehutSPNBP3389woF/+o6ucAsTeBHyzaRPKOzGGjq+8NvUXagjk6pO6mDR336KRZ//LaqWzde/+3K8KDeXzTk9NELmtS0lvYoEkbAtz6O4+wB7/+zfybyyadrz5kRD+VE89NeJioqmogJyW3bT/52f4Y7lEEJgVaWw25op7jmgHQrLIdjpBVn/gge78j3ckwmSi+bQdNvl9H79YYQQus6A6+nQsvEcPV/5oc5w6HpYiQQdf/kJki16J5ffuZ/CgT6Eo9XlodObMt7N7eZk2kblP1iVlqB1ymtCZ0NhWSTmtdL2sTtIL1+AlU5N0lAc7ug4Pf/xIGosE3NEOxh9wvUQloVCQcEh++Z2uv7yqwhPad8W1zUsRfocnVZaIBwnrBkx9tw6dv/BPyGTNo3vuJK6i8/Q7fvaBxmMRajaj9OXaGqk/Xfexdjzb+L0DlG1qJ2RTA7hKUSxGO6wJ4xv0Jbnkd+5n/7vPUbbR247KL/07VXoACqCsShHH2WG0zxgzjuDPjydn6HgJ26SxMdCSBFTWIRP7knmsvI8KBTJbtzBgb/9Fk3vvJqx59ch8gWMVB4l9Ilwzpn0Cdcht20PXX/+H8z6yG2kl0f+JqPPrUMUCtEOr5Q+47eFp31Qspt24Q5nkOkkicY6PL/PsXln0qcUKpOn5x/+h1kfvZ3knFZ9viWpWraABX/9Sfb91dcYf3UzynOmNxZB+47LyE+fI9HSQP2VKwGw0mnSc1vJuTpyROWL2hSSL5DfvpfRp1+n5twVpS3/SqBSpbSCtzW8bJ7erzxAdssePxZd+zcURzN6s1Fid46vLGriE8cP2k90tNF43YUMfHcNXiYb7iRFkBHI87T2wL9GKcXIj5+k4caLSbS3knlpvS7QZKpdzVwVgjJPO3ynMDWRvnDxEjqqpLmBxhsuouGmS5DJBHZ9TZnGJkeucy/F7j5EoEo36Stz3xB+RkdZlQIkVkM1xf19+kRP10sIBSF/lxt6zofNK11vYWMnCEGhq4f81l20vP+WMGQxHItwd+nTl7AQddXUX3k2NWcuo/rUJRS6etj7l1+FfB6RtMFxEOaqEiC2sOsol9E1z1N3yRlUrVg0NcPMITPHIuhrWf6Vo8FgROmD1+eXeXnUJtrmb3YjmCsx5pahz3EpdvfT/81HdMRLeMoUPCoddwXk8mTXb6P3Kw8w7//7rTCKSCkd2SRK2yqlz++/qK2m/trzSLQ1AVB19nIK+3oN84VBjNlmPk/m5Q2gPOb+749iJp2y0ila338z+/f3U9xzYHpj4bv44LgU9hyg/xsPkT5pPsl5baQWzWHeX3xC19gBuv7i3yls2WMw40TOxFDBVKgIFW9zDH7/MfK794NbjB7Yge9B8CBRwTvRg850SpSKcOvmP4jUwBAD3/4J7siYzpxpgyoYDyplnC9AOEUK+3vZ/3+/itVYR3bLbq3qN23wlHvIltAXtFlKn0Fncv4sZn38ndSsXHHYoWwylcRurKUw4qdUDiUwg57geOyzh7RA1tVCwaF65QpGH3sBL+9HtwijLWWOAREPMD574A2OMPTgE+Q37cAdHvP7X9KW/56Y28qsD94KUrDvz7+MclycvmHcvn7Aw2ptwhsYQRWL+ppgXH3HSD1ewVi4eGMZ+r78feb9308iq+NFw0xkX9+kzTjmWJT21eQfZfhnCpiTzLuyc8XnnygW/UPmFtyfy5h0ldKHFoLHMtrkFLQRvvvnB21O9rtQClyX/M4uhh78Bc3vut6/RIDw4nPZpC8YT/+3Z6dsai8+PeRtcnYzsjqFN+rE50YpfUqBUyT3xhbGnnmNusvOiY1R1bIF1F1+NkM/fgpvLDONsQigTSNu/xC9X1rN3D/7BMKSMUE9NaeVQmeXH5GkyL6+ma7/80UCKU8oSJ+xjKZ7VnGiZ9h8uye/qggVb2Pkd3Qx/LNnIJ9Dhg/F6GGkgHgJc72biqpUBs5oKnooBkLFaAblefqQ4/mlt402/YCPYC2RaRurtorsvh4Ku/ahHAdZGg1QSp8y6BMYbfpq/5A2g76kRfXpS6k9b/rOmOXgdHVDoYC0he/4Zzx4mYI+oZApm5ozl1LYuZ+aM5eSfXUDTnd/1FcxjbEQhGOAcmDcIfumnyuj1BESQIKoSlN3wamkl3XQ9cf/D6d7QGscvCBhkqRqyVzyThF3sOgvjuXHN6BPuC7Frm4Gv/coLR+8vSyvlOdR2NuDtKW/WHtT9jXg48S5ojArX+oF13+FQuQkc8VvC0RsLMK2ys2VCfR5YRuUG4sg2kOYvwtFnG8eFIvkt+4O+ZOY1UjeEoAX9dUzfmNh3zQNamyMnv/7VZo/cAs1F51F0x1XMv76ZrK/1ILbxN9tNFeCrK25N7dPECoAWt97I+MvvEEhMxbxcbpj4TjkN+9g9OcvUr/qoli7TXddQ/b1TXgjRVAubk8/2e7+SFi1JLktO8lt2EbVaUtpvPu6E1a4MJVvR9LGiYqKo+bbGKNrnkONZhAqyBrov/xshjoboJ9G2hJhdkDpnxe8C4LriNTvQbVOP0wuzL4nyrQtFFJKmt99I7KuSl+LitoMaVOT0xd+ViV0GX2T+lznQG9YLv1wUNi1n/5v/FiHSHru1PTZFjJhxflnSZrfexN4DgPfeggvMx7j3bTGgnhfQWnziYp4HWvPtkgvnkvz+2+h94v34w7orKIo/xpA4OF292I31SETUjvuWWaWy4ljgfBQuRzjL6+flF9CSto+9R6qTluiK1hOaCve19hckcLouz5nwvjKGZwrZecd2h/DzzIqDjIWUpTQUTIWsjpF/Q3Rott81zUk57TG++pfG2/LF4yKRQp799P75dUM/Pcjuo17r8eqTR/8d5u0SC2Zq+dfubGyLETankD/xN9tyVgEm4lcjrEnX5nQrlVThbSEz2stHIXmVs/TwtDYOLnXNjHy02dwRzOH9ds8HqAQeEf4ijyYTjxUhIq3MWQ6BdUpvXBA/BU8UDDegwcuxvHgoZJMIBJ26AwXHi/9G73DCVMlG23JqhQyYRvnEhd4KKFTlNBRlj4RHffVz8WdXYyuff6weJZ55lV6/u7ruEMjOq1xrH+lfFRY1SnSZ56E1dxo0AmyKo2XzeH2DEAg2CkV59m0x8JYDIM+h4KCvt6qTtP4zqtxh0Yp7OmGQr5kLEB4Cm84gz27iUR7q37Na0NYMj4WxPuKUpM79PpIzmnVUUAiGouy88NcQJlk3pX2dcK8LT8WUUr4KeZKQEc47wQiYSGr01hNdcj6mhIhxGhLlYwFJWMRCBWWxGqI6qCkFs2l6uzliHRqcvqI+KXNKB5e/zCjjz9PYed+7PqaeKru8PdV0pbjUNy7n8H/eQQ3NN3FkWhq0FFBVUnj2VDmd6tK2haAp3C6+3AGhmNtCgFWQ20klJXhH8rTjtxhZtEKTkRUhIq3MWqvWEly7izfYUwZ9Qb8HRHoh4rUuxICLUTwffAudb6LqpUroraE+SD1tRXGbg3Lf/d3capQZPzVDaic79QpSu4V7sCNnaRJn5iCPozPKFQmS6bMbmo6GPzuzyjuPaCdGfUTfgJ9CKOvtkXDDReTWtTuh+xp+or7+1DjudB/JaI3anNaY0FJn82+hm2B3VCD3dpEoXMPKjsejoMQIKzgGgWFAmosg8pkEMql6Z2rSC5oR1YljXuUoW98nGJ3P5NBFYu4+7qj80v7asyVSGjQ44xVck3Z8Z3GWIQagynmSil9AhJtzbR86A7a//fHSbTo8GgR7LolJW2VmbslfaVYZPSRJ/1y6RqJ5ga9k58ufb4pRSZs/5iueRIKWVagESppS3kwMkbmqVfo+fxXyW3dNWGsWj58B7M+eieNd9+IPbs5xr/S320pXQKFNzxK/5fuj4U8J+a2kV46H5lMxH+3ZZ4rqpBn+IE1uGPTD1c+nhAGnB3h60RFRah4m0K5HoPffBBnzz4o5P1iVETqc3RpBnPnJSdThQqlHS03btN+BuFuMWhL6LbChcIvBBW0hV6UBv79e6ixLEGSpPi9/Gt9P4VwkQgWoXJ0WQKZSiJt/dANTQXKw+nr106NhwiRsLSa2n+olqMv/Dvgn2XR8pE7sWc1I9NJ7NoqRn/2NCqXN/jq0xeolGNj4d8PJo5FqXo/VtAKpBTImjSJRXMp7tnP0LcfhmwuNhYm/yjkcfb3IlIJVCaLs6eL2itXkj55EbIqWdLXaNeqMuMMfOk7k2osxl9ej9M/5JuLStX88blSasYI50ppX0UJLw42FlPNlVhbwVj41ysHKylJL+0gMXuWr7mJBKryY6EmpY9cjvEX3mDgy98J671EWpJDoC9hYflja89rI7loHjJlR/NusvsrDzWaobBtN2OPPjthrJLz2qi/8TJa3n0D1acvw25qCAVieRD6BB7k8hS27WT8+ddi7SZmNWsB1hyL0jZQkBln7OfPM/jV7x/y7/N4gDdDrxMVFaHibYrRR5+muHsvIpslVhUx2M2F1QrB3KmEVS9DjYG/+8pmIZtF4BptmeeWXme0mbAADzU+jnCL0XXm/YP2LKM9UdJWQI9EZ0Osq6bu+kuw21uQqQQiIfxduYcaHWPw3+O7qemg9srzsRtrELaYmj6f/kAwSy2cS+tH76L+1iupuXwlzu4urJoUdkON3rXZMs5TWdK2MI6ZYyFKeBqjx0NYYDfU0nDblYz+YA3Ovm6EcsqPhQCEh7QlVnM95HOMPfo0mbXPklzUjkzb/g6zhD6hoFiguGcfg//xXdwS1TcAnovK5w3a0PQdylyJ9VXF2zqSuVK2zeAaD29giKHv/YShb/2QmsvPQdakJ9I36VhMpA/las1O1wHym3RSrUBbNSl9E94V0nNxR4YY+Z8fozLjJOa0+rv+cr9bYjQjFbI6Tc3lK6ec780fu4uGd1xF1RnLsRprtVCdNOfBRP6Bhzc8ythjJSZGqbUrE8aipG8oDzWSIb91B04Qdl7BCYNK9MfbEO5ohrGfPoE3OEKo9pYQheb5FUH93U7gza78h5X2TBfxz/hOV1ISersHbeI3Z0WqaSX0NcoS2HNacIdHYWw8dq+YN77ZlvEeo89/uKngoWVBor2ZmgvPwBvN4uXyFDZ34g6NQC5HYdsOxp99lZpLo0JRU/JtZIzM2qe1acBzjb6W0Kf8Y7bUCbZ8O0XV+aehinmG738Yp28IWVuNPasFWZ1CSIHT0693+hYIS6I87XxZtq/C7GswFkEkR8A/vetPzG5Cjedxh4cRnp8JksjsEo6FoeVItrfgbt8JjkL1D5J97FmdxwJRMleiPqvBITJPvojT20/15edTdfYpWI06HbOQAitl6zoYtqVzM2h1zMS5wiT0BX2WRp9nZK6IGD9jUSXKT9bWN0Bm7fMkFu1GSoVXSp/Zliw3FiX0Se0XEWQ5EgkLmZR44yX0+b4MYcSVSR8e9A8xtuZZ3L5BrLZWZDqBcooIKVBFkz4vaksqRNLCaqnDGx5BFYoIv5JvKazaahruup7aGy5l9MHH8cayKKdI9qV1OsTWcfVcxYnoEwqhXNT4RPOFlJTM4Unmsi+YZF98nZprLkamkmWoOz5RSX5VwdsOYw//AndgKHKQIrDR+jpjFRzDj8TQn4V/ThCWFtqvg89BO8K8W7BoWLrUt5eP2hJ6t2831UOhgDc2Hm9L6/vjiNGnwkiRCfQpD0bHGPnOQ+B5pE5ZSusf/jqjDz5GfttOClt24Q6MkFn77LSECqUUQ1/9Hu6+HnB1ifNJ+wpQlSa1bBH2gjkoz2X82VfJbdxG9pcb8Lq6EUIilaLxA3cw+B/fRdZUoQoFvKFRRFWK5MlLKO7swusfjASL0rEQOkskltS2ctQE/smaaqovP5fxJ1+AbF6bODyfjybfhHYoDHgsEjbCtrAaG3DHs6hMFhkL2TTnig/XhfEc+fXbKGzcTu7c02j5/Y8gpCS98nTsBx9DJG2SC+aR3bQdRsaM6w3h0xxffyHUfXSi8RUcZN4Zc0UEhupJ5gr+XA55QdTPoE3XRY2OUti0XYftSgFuCf+ESc8U9FkS2dRA7dUXkjp5MQC111xM5vHnNE9M+vxrJqXPU6iRUYp7u6m67DxqMxlUJkt203ZUz0DUZ2N8BQqKDu7OvQx/+8e4/UPU33EdU8GqraHx16LMqWOPPk1+wzYQAk8p8q9vRI2N682Fn/ultBqvdu4MNCXCj9adfCwYG2P0wbWoXJ76O6+fkr7jCcHMPdI2TlRUhIq3IaouOhunu5f8xq24vYN6ATYz6EH0AAzfjSe2ucMMj6voeGhSiB6kQgLVKVQ+T+wnoxTu0DBeLkss10NM2AmOCcrSp4zP5neeh8pmQSkKO/cw9K0HaLj3NuprbmT4/ofJPvWS7vs0kHtlHU7Xfl3nI3JwKOlrRJ9MWNTddjWZx59j8IvfAil10SbX1UKCJVCug9VYB8Ui7v4RUqctg4JLYn479ffezMiPH2fk/ocglyvps/+HFIjqKpKnLiO/fjOEdSWUdgZIJUkunk/tNRdD0SH3yjqUUygR1kofX/rBX33pueR/uR6rrQkGLdzx8ZKxMK416fI8KOR1boqtOxi+/8fU334dsqaapo+/m8LmHdTccDkj33uE0R+uNXazKj6m5rimU8j6aryeAYNMY/5JocfEcwnnjDlXyrVpHi/bLzVxfJWKxt+y/bmjyrelvHibwe/CsrBbm2n44Dupvvic8FKroY7E3DbcrgOxsZj4GyilD0jYyFSCqjNPpvpcnRQr+8Ymhv7juzjdfeDmDR4Y9Dke3uAQbq9Zjn56qL3+MmqvjyrTZh57luwr6yN+WZKaKy+MXSMsCywtIMpZTXjDY5A3aqGUjoXy8AaHcXt7D5m+Ct46VISKtyES82aTPn0ZxW2deMp3rBPGQyp48JgPYlyttrWU3qH5mQdFykbhP8wl0YPL3NEG/wtBmNnPeMAJATKZQDHutxu8ShIulaUvOO76n41iSkFbyoORUbJPPI8az9Lyex+l8b23kzppwbTzVaRWnERy+SKc7h4dtVGOvgCWrnOSfe4Vclu2Q2ZcCwCeikwmeDCeYexHj4LnoMYyyGSCxl9/N7KmmuKBXvLrNiAcs8qjMRYCSFgkly+i/p7rGejcqSvMBn2uSpM+8xQaP3AnALKpHpGQkDXHx/g7yCBpgbAE+XUbITuOu68bVSzoHeh0xwLNU9XXz/jDj0M2R+NH7iW5uIPk4g4A0qctI/PI43hZgxZTAxLMPeEhPAeZSKDMAndSnytb6kksnI9Akd+6AzWaieuOy/U1nCtq4lwxX6XXB/Qpw53OoHNCW8G7IVgI5UJCt6FcVy+0PqqvvIDChi2ooFJwyNMp6EsnSSzpoO6u6xGJyISRXDRf+/04QVZaL847v03hFils7sTp7cee1VJu6k8LNddeQs21l0x9zjUXk/nFcyhvhKpTlpJ7cxNefnwS/qH9QzyH4tadOD392G2HT9+xRMX8UcHbDiPf+RHjjz8bmRu08TZ6UJVCKBASqtLYSxbg7u5CJGzsue0o16G4pVP/ChK2tonm8xN3wsIjMWcWTj4bLcq4IC2stmbY3YVn3l+UvMrRZ6ZhDnatsYe50ZbyIF/A2deNc6AH2VBP1YVnT59pAmpvX0Xul2/qxTtGn7mTFIiaKtJnn0Jh+y7E0HC0sEhBaFMXCjwPZ383QgqEUyT3/CsMOQ7VV1zAyH//kOLuLoTrxAU0Eb0Lt4izYTMD/98OlF/qHAHYNsnli2j5w48hLIuxBx9l9OHHYXw8VNFHY+O3mbSx57RhNTaQPOUkCru6wCnCyAiBH4wfejPJXCmhT2qeq1weZ393mIExPDufN1Tzpe0YbSnAdZD11STnnU5h115ddryxnuTC+VRfdzlVF5wFQPcf/CXOWCYal6C5UvrCueLF54ppVph03vnf+emmY21KyrSFIWwDeKgDvYx87X6cXXupf887wparL16Ju7uL3MZtFLft1Boqs82gXYM+4Tp4u/cy+u0fIlyP1DmnIyyp04oPD2uzWDBHy9EnJbK2Gqu5kaMNe3YryYVzcXYpaq6/HG98nPzIqF+zpIS+pMSa1Ybd0kRicQdWy9Gnb6bwds+oecyEinvuuYf77rsPgPvvv5/Pf/7zAKxevRqAgYEBlixZwqpVq44VSW9LFDZsIf/y66iRUX8RKtmpmhACEjquHMdB1laRXDiH/PZO8Iq4OzoBiQiqaPo27wkPZ38tSnTMxkonyb+5UT9IAIGHt2kLqugghU6bHNn7gaSNkLJEUPEhMcw2RItQuND7AobvCyBQqK4u+u77HKkzT6Hp9z8+LZ4p12Xw776Es7tLZyANaQz4F3QShBBYLQ3Uve8O+j7zV/qhHsRYCYW2HEf0uPsPYM9pp9jf7/skjDP0b/+FGhxBuK7RNxE3UUm0ur/gQrGg7eW+FkTYgvSpS8NdsDc4hBoe1rtkUaYtATJpU/+O66m68iJyL73G+BPP+udL3bYpQJWdK0xoU4+7h7f/ALlnX6Lq0gvC0zOPPIbIZeNzJWwrTp/Ag+Fham5bRW11FZmfPkHqknNJn7ECWRuVqq8670wy3T16p29byJZmpBQ4vf2REDDZXBEgG2pRxaK+3qxzIoxJDJrPtgVFP5W5MQ8D58q4RiEudOI5qKEhcs+/Qur05SRPX4EOKRXUv/t2anM5ej/1J3j5nE4BnkqgCgV9vVfCY89B5bRgMfyVb1Jz6ypqb79B90egBXVJJP+U0CcsQXLJfD9V9kF/CkeMmqsvxsuMkzrlJIrbd1LcsAnlFo2x0H0TlqT26ouovbN85s8Kjl8cM6Gis7OTa6+9lvPOO4/vfe974bE1a9bw5S9/GYDrrruuIlQcZXjjWbyREaTwk/gHO2hPRZ+Dh7olkPPasOpqKW7ejhoaIv+LZ/1CX/gPUokMnLM8D6Slw8Kk8B0CowVXCkHijJMpbtxi7PSAXF4n8bFsRFBETPltVKewmhtx9+zzdYIBfX6ESrhzZ6I63rRJS6HpKxah6OAc6MHL5xG2HVNBl4WUWM2NFDdvR7qOzycI602YffWFBau+juqLVzL+zEswOhppVTCrjioYHsH1nSKFbSHrqmF4BBFWJw0WWHNxwui70VbYXw+vWMTL5XXWVCH8MuDBWEzUVgg8vEIBJzPO2A9+An0DekyCcRVCj3vY14CMEgHFpM8SkEySWNRBYsnCGEvdvkHttFc6V5RJX9Se6h9g7Ec/o/F/f4rqVZcy8t0HGf3P/yF18bk0fvheRCJB7Tuup/jmBorbdyPramj81IfJ/3IdmR+v8S0ywRiYWpWoL8ICsBBFSw+u6/tMSEAJqK7G7piLrEphzWsnu/Yp398l3lbkmGmMW6wwmz7VO9DD0D9/lZrbrqf29hJHRKHHREgJ1WlEsRDxRpS0JYTvSDqG29MfXe8LFvqzQZ85V9wi2ceeQmXGafzND3K0UXVx5BSdXnk6hQ2bKbyxUftWmEXlYmanEwtv94JixyxPxX333cfg4CBr1qyhsbERgLVr14Z/AzQ2NrJ27dpjRdLbEoklC7FbmyNnQyAyUZgLk9LqaadI9bWXkTjjFLCl/xD1F/3Y7iJoylwQjB1RVYr0heeQOvs0RGNdfOHwHQ7l7NZwYQ53ukBy8QJC00ap4xroJ2dTA6SMsLhJ6QOUh7d3H72fvI/h//fVg/JMCEHq3DMQCTtqM+BB0KihHRFC4I1nKe7cBUXTMVXFG1aaTpXNYrU2IdIpkqevgNrqiUJROEZMbEsZ3wnAdcj95HH6P/uXZH/xjP+dmji+xhioXI6xb95P36f+GKdzp9aCVKWwZrciGuux2tt8r34V3WcCjcS+E40NVF93BY2f/g3sObNLeGo6OZb2M6DP6Kvr4u7cRf/v/QlD//hlvF17YHSUwtPP0//nf4szMIhIp6h7710kzzqFmnfciNXWQv6V17UZJzbHS/hnac2DGh2DsTFQLpETZvQSeFRdei7Nf/y71N5xk84eK9BRTbYdtWvKLqVjZPLedVCjY3g9JY6IKlj40XRkMr5gFzCPiW0JENVVpC88B9BRP/bC+ZCw4oSUzhWlIJvD3d/NsYY9t52mP/xNqq+9HLlkEaSTeiwSmpcnaqruwKfiSF8nKo6ZpuKll14CtJkD4OMf/zjbt2+npSVyvmlubmZoaKjs9fl8nrzhKTwyMnL0iP0VhtXcSHLZItw9e43drbnjjz/M1fAIoipN82c+ydAX/oXCG+u1lqF0Bx282wIamvTzanAo1DhYjXWkzjoNgNo7bmDsG9+FnD+eCYvkSQsR9XXk9+3HfFgKoUietpzcL9+A0TFtDqmpQY2O6gdtwsZqn031O25i7L9XowqBp/sk9AWOm8UiFIs4e/cdlGfK9cj99Be6XkbM+U5EGgjbIjAtJE8/mczqB3G270AUAxNGyVNCENrzhRQkly4k/9IvcTZtwW5qwB0dJbawipJrMdos7avyIJvF63MpbttJceNmbUqJmW1EvC3PhayLyPqRJlIg8jmkbERls9gL5+INDcLYOCRtZFsr2LauRjs8rBfVZEI7pTbUY7c0U33rdVRdcn55pgoiPw1TgjTNDLG+elpzMDyiD0l/rubzeNt3MvjZv8SeM5v63/s4zfd9SnMnl0dUV+ndPm5cIDRh++HO2ay+h+VrKUKnWl8zlsuSW/MLUqefgtXehr1wPsVNW3S/HUc7MAfOyIGZSKrouDL6CoQOlKVrpy9Ah/4vTjEuFIbCYfB7FZCwqbrsAlJnnKKbsCzq33cXI/+9GmfjFsgXys8VoSCRIHX2aeXH6ShDSEn9h++lTilGv/YdnJ5eZF0NamRMC9gnIAwR+4jaOFFxzISKwIcCYOnSpbzrXe8qe14gdJTic5/7HH/+539+VGh7u0Apxcg/fJHChi2hvTxMxBPmLjA/A8U8uUcfJ7XyTOp+7U5GPBdn0xatCofIXhu8W5Lqqy8huWI5Q//6HzA8Cgmb5IqTQjqqr76MwguvUly/SfsRJCzSF5xDfvO20LIQtCkEJE4+iaorLyH36OOIZJKau29h7IePwOAQpJLU/tqdJE9dQebb3zPsxypOn6BsX9VAP7lfPE36qig8rhTjP1lDsbMz8kkw+wxgC6yOOVitrWBJEovmM3r/D3SiKfP+Jgz6RLDAFgs4XfvCuhJxeoPrmPjEKe1r8K5ccs88CwVH+yWEPgEYtvky7QUHHQe3u1sLXzt2kjptBe6BHuy5s6n72PuQdbUUNmwm88BD2IsWIttayD/1AtU3XUt6MmHC6IYwxypwZqRMn821OOy/isbCdVCjozjZcTKrH6T+4x/U90inSJ95CpnOHVBUE8cgQOAjErRXbq74CbW87h6G//ofqH7nbdS9504y3/0hVFVR2LkLunsjTUDpWEjjXiEDBFRXkTozvqCLRILE4g6KY6P6nKoqGBmdfC77WpTCs88xtGsXtR97P/b8uSQWddD06U/Q//v/G1UslP9dSBB4qEk2c8cKQgjqP/Ket5SGCmYGMyJUfOELX6C/f2IxoZaWFj7zmc+wevVqXnrppVCwaGxspLOzk6VLl8Y0E4GzZjncd999fPrTnw4/j4yM0NHRMRPkv22glMLZtQeyfhSANHbc0thNhVEUgOfidnfj5QuMf+8B3AP7ELjGjtfYkSkFlk5ukzzrNKy2ZryxUUjbVF1phJt5HhRzCGmEkUGYflg/7LTWw54zC1lfR+07bkT1dINSpM4/h9yLr+CODGO1NpGYNweRsEksmEtxdEQLKuHu16fNKtdXvfsc/8mjJFYsw2qPq+gDiFqdShuXeF8D/lmC1NmnUfueuwHIPPBjRHac0BmwrJEx2GUGfgX64e71dGMvXICXtLQTpjkWJRvdiU2WjAVaYxH1GV+YKNNmOfrwwC3q90wGZ8sm0pdfQu377g3PSp56MslTTw4/11x/9STElbQepmT26Sp9l0AqpTUImcxEOkXEv0gjUKTw3AsM9/VS+4mPYjU3aTYHC/Bk/HMd/TLbKvu7AJwi3uAAme9+n+Tpp9Jw3+8hhGDwH7+I09sT6a1LxyJsk5ijq8BFZeNlvoUlSV90Lu6ePVhNjbiZMdTYSHRtOfrcImrMobhtOyNf+Eeq33m7FpSVMCJ3zJvE+ZZ/6WWSp68gdcH0sstWMDkqIaUzgM985jNTfr9kyZKY78TQ0BArV66ksbGRz372s+Hxzs7OSR01U6kUqVRqJsh920IIgb1wHsXBQf0QNXwewsU9cJBM2OC42ou+sYHhz/0tXudOhONGgoepsg4dv1wKr71B1Q3XkjppKbmu/VitzciWJgAKb25g7P4H8HbujqIb3CL5Z56D1hZtR61OQyqN1dRAzTtvQ9ZoD/+GP/htMt/5PsN//td4Y2M6BfXypaEwUHPvnQz/v69AT09k2imlr7SvKLz93WS+90Pqf+c3yjPOKRLmaSi1+ydsrHmzSSyLhOHE0kWI2moYCnIEGIsShvrZX+QEwTNfQTaL1dKIm0pqU07wZelWPdA6TDUWYZ9LhEVK2jwIfaC0eWRklNzjT+J27aPuNz6CbGw4tAloICgoNYG+wEQgBaK5ETmnHfe1N7QAKADL0t+7LjE1fmD2yecpbtrK+PcfpO7XP+grIIIntNlno6+e35ZlRW2V+10E/FPA6BjF7Z1hqGxQHTQy4UxjLHwz1di3v0dx42bqfuMjIX/sjnmkLzwXUVdL9sc/8aOIDkYfWugZGKS4vVMLFULFHYPLzRXlwXgWZ+fuilAxA9AzezK12PTbOFFxTMwfK1euZPXq1aHGYs2aNYAWNu69915Wr17NwMBAGHJawdFB/qlncXfvjQ74m9GYM6Ovl5Yd81H9A2Bb2PPmkn/mWS1khA8m/1wpIF0DTkHboZWH17WP8R89RPWdt4LnklixHGtWq75GKaPugxZeZHMToqYa+6TFOK++hqxKU/cbHyFx2ikhqd7ICGNf+TqFTVtgPEtQZ8TZshV3/wGsOe0kli6h6vpryH73AT+6xFwszf4Zf6PAKVJ8fR35514gdXE8C6CzczfZR34GhSKhE6nRlqiuou5Tv4k9d054OHnWGVhz5+AODRM+wFV4w5DH2BLR1IQ9b6626wfNJlPI1mbc8UxEf1nzR8lYlGJCX0uPGfwppS/0s1HaX0II7YeSy+Gs38jwX/8dNXffQfK8lahcjtF/+yrOXp3bwprTTs1778XumF+GqIB0ZdyLaCyE8ffgAO7YWJymRBKrtQl3f48vGJf0D8ApUnj5VQpnnxF1y1xMg4OxcOQS/njETTJl6Rti9Av/QM373k3ipCU4r77mz5MyNJUbC/x2RkYovPgK+dNPJXXpRQAkli4msXQxuedejN8/+Lvc7xYIMqkmli3Vh22bxNLFFHbtgaEhPycEE+G5FN94E++m65B1tWVOqKCC6eGY+VTcfffdsffS4xUcfRQ2bEL19etnymS2Y/+71PKlJM64ncJr6xDptNYgmA/U4OGcsEldcznO6+twu/YReLU727YjG+qp/cj7YzQkzziNpiWLGPpff4LKjGEvO4n6P/o0wrbxPI/8I4+ihgYZ//4PqV+6BJHW2qnsjx+muH49Il8wHooKr+cAI3//T1Tf+Q5Sl1xE9Q3X4m7crBP/WBbuwCAqX0AoF5Xzw/KUF/U12MHlsmR//DD20iVYbbNCelVBh7qJUnOBn6gnnkwK1HiWsa9+DW/P7uiagN8+zYDOXFlXS+Nf/R9ULs/o3/8zIiERtbV4/X1UXX8N4w//DNXb4y8EQnvyO26JOcUYi5CuiL5I00G8z8HnyegLNRVCe+WDNoVILYR5XV2Mfe0byPtX4+GhegdCwcsZHmbkb/4ee/58qt99D/aCiWbKRMc8Cn29UPDK0OWPSbGoQ4wDdQ4KLEisWI43MAhZJ97XcC4ryI1T3LwFUV1tOISW9NXkQ8C/4FWWfyX0OUWczVvIrv4BNZ/8DYqvvoa7ZavWopSZK+XHwr93Pkf2oYexly6OmeGE78iLbWnH0UAgL/e7DWRCS+jIFEAkEtT/we/gFQoM/eF9eqNQbq4oF69rL9mHHqHmPeX93SqYHhRHbr44wsvfUlQyar6NYC/qwHnpRb3jRIH0w+ZiPhQqtPkmV55NcuXZFF57nfyb66BrX7Q7DBYyqbAaG7BvXMX4Dx6E0RFwdabE7EOPUHjhBVCKxNlnU333OwHIP/s85DOIlE36qssQdhRCZrfPwhkbRo2PMfZvX8Lt6dUm474BnbJaKL0bM9S96sABxr9zP862rVS//33U/8HvTOh77udPUHztDbyhIdyeHoSQKCEQykMJAYUianQUd//+mFAha2t0euswdXDEH1IprPlzkVXp8HxvdBRn4yZEZjSiE4hUzYTtBAJJ9gc/wuvtxZ47G+ukpbhbtmKftITGP/5fjPzN3+Md6EbU1CDnz8XbvRclJeSzkUc/+AtZkFfBv1dw/3BcjXdVsriV0heMcZCKPIi2CE0oHgwN4Y0M+0KXiO7rFGBgACebw925q6xQkb5pFc7GDdqBsJSuUJugSugD3CLu3j1YHXPxdu70ozTMayP6nC2bSV19FaI6beQKMdozBQnFRP5N+F2Y9Pnfp3Tkh7Asau6+g9F//yr09BJm6wzHwr8myAlj5jgJTBiOM6Gyp2xpwe6Yq7/LZfH2d0/xu/XvkRsn861vk334J1TdejPJ88/zh9MQbMrNFaeIs3HThLGq4NBQ8amo4G0Dq7UZYVtGiFrwUDIefAJIJbDnGer8s8+i8eTlDH/mPhga9I8K7VxYU401q4XkBeeTOHkZma9+HbenB3vBPAovvYS3ezcoKPT24qxfh9WxgMKb63Uyn0RVbAcphCB94yqyq0ewly/H2bABtW+fXvw9jzCBUKgtMXawQ0MUn3qKke3bwFPIlhaq774La75WwaevvpL01VdOypvC8y9QXL8BWV8XO+507kAMDaLrPFjRQ9mySF5yITUf+VC8If97IQz6TMFCCpAWoq4O0dLM+Ff/k+Kb6xGei6hOI20LJzNK4YUXqL77LqpuuRFn02bS16/CMhyTi6+/wfj3f+AnHBN43T2IZBKVyWhzhZQIy9IF1Vwjm2SYCrlk8Q7oC00f+iXq6lAoCOqKmHOl7FgYbQbnloGsqUU21uONZ+L0YNInJtLnFlF7dyPa2/0qmK5fLVTE6VMu3v4u3H1dJC88j+ITT/omMYO+oK/BPUtfk/VVCqiqRiSTpG++ifSNNwCQOOVkEsuX4vT3RW2GTrMl9AUCqgAsC1FXS3rV1dhLFsf4lFh2Eg1/+scAZL72dQq9PZEQMhl9KBgaxBsZpvjaayTPPw8hJfa8OTi5rO+8awgYhiOnCKJTKqjgMFERKt4m8Pr7yf3gh3qHa6p5zReAbWGtWE7yyisY/8Y3cXbt8v0gFATFf4DgoS875pG8QIcPWvPmUffHf4TyPIRlMfyXf+kvXDrKwtuxE2/PXr+Ud1CqeyJULovavw/75OUUeronJv6Jqa2NBadQQO3cBYDb20tx48ZQqDgYkhddSOL888pk1zQXvGDXLhFz20nf+Y6ybQkpUMEDu9SnA6XNGM2NqHwOZ/16yOdASrzuA6jZbTA+TvGpp3DOPIPUpZeQvOhCvK4uRv/pn3W6bSmxTzmF2j/5/yGl1GPjaY1L9j//E1HfQNXddwEw9sUv4r6xDopG8SvL8iNkgv5NGAF9Xn0dtX/xZ2Q+/3kdxVM6VyYbi3Lfl8CaN5fUtVeT/f4PIDtunOsLExifw4ssLTAVCqg9e3yBSkbCkulnINChsM89R/qDH8TduhVvz57y4xEO3CSvCX0F0dpC7e/9LpaRZwfQY1E67iZ9Mb8M/92W2CvPJn3D1OW9TRnooGMhJdTUIOe060O2Te0ffprMgw9SfPgRP9eMIQwK9BwaHMTZuhV72bIpaalgckwuSh9aGycqKkLF2wTOtm2ogX7tRS5ktIMO3qWARBLmzaP6zjtRY2M427bpB7Gnk8bGHmqBnVvG4yWFZYULc3h+uIPVSafCImZCTCicI6urkQkbt7sbq6nRX1eCB3G4tY4/TEMYjbkO+ccfo/jKK9iLFpK+510HTcdd+r1SCmfdOr+2iX9/KaGtjfSqa7GamiY24ndIxOgz6Aad42P3bh114KdTDoQit7sblIfKjJL5j3/Ham7WD6mhIby+/tC/wtu7F2fLJkQiQXLVKpJnnwNAzUc+EmopAKpuvoXMlq2+dsqnv7kZhocMp8IS+sIOKPL3f0cnQAtOE0Aqqc0OpinM7Ks/l0RbG7I2rvkxkVq1iuKGjbibNmnBInb/knfLho4OZEsL3quv+iYX45zSkFQ/ukJls7ibNpI871zy3Qd0nyc8skvnVZm2TE2KUojeHrL/9m9U3XUX9qmnRi3JIAqkpM0JbRnfOw7uK6/gXHQR9imRc3IpVHC5tAirVpWjDyBhY599Fulbbolosyyqb7+dkaefgf7+sn1V4+MUXnmlIlQcAd7u5o9jlqa7grcWzpvrwAsc20xVuP8uBcxpo/Z3P0Xx+ecY//cvo/Ljun5D6Ghm7LKCa3v24/X0lL2nMB9aovSeWkXtbnwzdo29fDnW0iWIuhp/ETSuCbIPytI2jd1W8LfrwIEDeNu2UHzxBbzDyMCqCgXc7dvA8xdfWyDmzaH2d36b1FVXTTjf3buX7Le/BcXcRP7G6FOEZbNN2gt5VNdevWhLC4aHcbduwduyGa+7WwsGEn1tPou3dSvuxg3k/vub5H7wgA5vTCRiwpGsTiOb6nWK9YSFWDAfq7ZatzEpffqYyOdwXn8dsr6JwpJQlcZaeQ5yYQfUVOt2pdDal/o6qEohFi2k6kMfpPYPPk3inLMn5a8QgtpP/AaJiy7QIczh7tuYK76Tolgwn+qbb0KmkvE5UW4uB3NFCp39s6UZ2dwEtTWT9nUCH0rbMukSClXI4+3aSfa//pP8Tx6J+iTF1PSFbRhtojV52a//J/mf/WRSflmzWhHNzYh5c3TY9RT04RTxXnuV7Ne+Gpv7QggdiVVfq8eslAe4eJ3bdQG/Cio4DFQ0FW8DFDesx3n9l9rRMUiWE7wD2AlIJbFnz0bkcjgvPY8aGgYgrEEgFbpkIsR2N5lRii+/SOrmWyfcV7a2oPbs8ne0/m5KGW25Bdxt21DFIiIR1e0QCRuRzcBgfzzqAowdrCrfZij8eOF3Kp8l99UvI1IpEpdcRuLcqbM9hnQIgQoKe9kS2ttJXX459iQmleILz+Ft2Ywo5AmTLcnJ6CNuGxcCXXPCRaSrEPNaEdU1eBveBNf1o29K2gz6PjxE8fE1uJ3bsE86ieRtd/ipqcGaNx972TKKI8PIjgWk3nEn2S/9K8Itav5NSp+nHS494acmF5BOa8fVrt0krroWObud/Orv4g0OIua0U/3+D6GEQCYSWG1t0+NxKoWsSusEVWHuBaOvtoWY10H1b/4msqqa3MMPRtEcgbNkaeI2Y66IpI29YgXW4iXkn38WNTLk7/IPNhbR9IopckL6PPAUqr+fwk8fwVq8GHvFKVitLbgJ208aVo6+kraCA56DGuin8MQvSF53Qzh+JlI33khi5UpEQwNj//cvUd0HpvhduNrkuGsn7s4dyDPP0l9LSc1v/zbe8DDjX/9P1M6d+tyAPuWi+npw9+/DXrR4WmNYQRzK/3ekbZyoqAgVbwMUn3vGz6LpHwiqG0pASJg7h+r3fgCkIPfD1ah8DoSKnk9m+Fv4wBWhqn+y6W8vXoS3fh3k3QnXREmLvAktCClgbBRPeTqTZakdXEHs6RxLMFVCnxSQz+Ft3aIvLRSmLVQACFvqXWHLLKrf8z6sKdTCoroaUkktVJT2NaRPRnQGTn+ev7v1FzpRW43EI3HZpeR2dcLYaNTnCWOhtC08m8XbupnC7p04O3ZgL11K8pbbEVJizZqFOPNMEpddgWhq1OYnORl9Bv8wTGUSramxLVRvD8WfPoS1eCmyrZXkDTeROO00RH09xSd+DosWAdMTKnSvfFNGMB+l1P4T+TxUp0ldeSX27Hay//nvWtBUfn0Oz+CfIOJpOFcUIp8j/93vUP37f6gFVwna4VZqs57nxudK8LsIhLZwsY6ojc0/5UJ2nPx3v4P16f9F6sabKb7xGuzfp/1YpIiEcSXibSmjcQGkU8iG+sn5ZNtYc+cCIGe14vX3GL4xGPM+aFOhPA9r6dJYO7K2FllbS9WNN5L7wfdRB/Yb9CnUCV0j863H2938UREq3gZQnksYThYKCgISCbBtrNmzsRYvJvtPf4fXuR0RqD7Dhx+EoXCgH/jJZORFXuoY4cN5cx2iWNJW4BgWpouelGq9W0smtaYjXOjMe/kXT9gBEi0Mwb18vxCvZz/Zr34J+/yLSJx59kF5Zy9ejEKSvv2O8IE+6bkrz6Xw2KPRgi3K0BfLjxF0IfwDhEDOnQejw5p/lox1NTYWoZCI9nHwBIyP423eQPHAPuyLLsGa1Ubyuhvwurux5s/H7ek2fBEMTU9wMJHQIccx+oIO2oj2dlR/H4yO4L7xGtgWhd4DuBtewysUUV17EQkb2bEAFNjLTiZ55TVT8k2Z/hGWBfMXQE0NbNqg/UNqa3C2bMJZ9zoil4sbbUO1/yTaBdfB695H4acPYTU146bT0NyMaGpGbd+uTTvmXDFh8rnMtNN/69wRnvJwNq4ncf6FpK66hsLPfgI9ByKNS7lrTVgW8qSTSL/n/WW1FKVIXnEV+V07IDOmGy2XyEspGB2i8OAPSL/n/RPasFeeh73+TZyhISjkIh44RdytmyuaisNEsF060jZOVFSEil9xqFwWr+cAmKFiQmlfiTntpG68BXvJSRSeeQJ33x79cIntXPEXMv9aS0BtLWLhYtSm9VBVhQiSI5XCLUYLfPDECnaECEhaExw9far1Na3NMCShkCV0TAvoN9sKdpdgRAH4bYTqfUB5MDKC8+pLeMNDBxUqRDJJ6tY7EA31yLrJd5AAyvPIf/e/Na3CK7/7NekLEzAFdBLlGkgnUf15vAN7S/hHfCzK9dX1wAOVzVD46UOkbnsnsrExioIRAqyAHypaOKXSC3pDAwwP6nZK6fNDiLHQu3DQybh6unG7D/hj4qGkxD2wHywLb/NGvN5uUjffjqiuKc+8wA/BApqbSV53PVb7HLL/72/DbheefiLy7Qj6HtAX8CjQXAR8EhD4KxTXvU71b38a59RTkekq3P5eCju3E/OfMOdKIEwE7imlT/lUWgu7vtnOmj0b29eAJS+/EnfLJtzhgSjKYoKgGdDn/yEUamgAb/fOaZmOZFMjoqVZ+z25Xrwt83ebz+K89jLupZdjLVgUa0MIQfqed5PZvQPVc0BrhgAKeYpPPEZi5XnIltaD0lJBBSYqjpq/4nBeeRFGh6MHWvgAFci6OhLnXoC77pcUHn0ExsfiTmsSrWa2hNYYWEJf19BI+l2/hly0BGv+fJJXXF3+5qUOcEGbtg1JG+rrscqkctaqcIG0bURLiy94GIub2ZYUcfrC+4kSp7ygdV/l3d9D7vv/g7Nl6mQ/1vz5BxUoALx9e/G690NQr8OkL6RbxflRypvgmHJhdBgxPKTLagf9smTUt1ieAUP977clCjncN36J8+TjE4k1HWAD+mwL0TqL5B13aUfRMvQJKSKNQnA9SvPTczVvlReZFVwHxrQQV3zh2Ul5F5pjbBtr/jySF1yEbGoK04MrJ493oIuYc2k5/gW8KZ0rQoFbRDY2krzgIuwzz9IaoKDvYrI2/bbCY0pntqypxlq+nMS97yV1592k3vVrpN/zvlDDIKQk/e73Ql1dyZwkWvxL5zIe9PVQeOB/KPzsIVTBSGxWBtbipaRuuwPmzfOdZUv4EMwVpSAzRuHJn5fnfVUV6Q99DGv5CkinI0FsZJj8//wXyjSvVDAtBOaPI32dqKhoKn7F4e7fiyjk4g+xpN5dyeoq3B3bKT7xGGKwj1BxJ0WkTk4kQHmIufNQmTFQHvYZZ2DPbke88x5EXQMiMYmmAuWXtw4WQanbra1BNreSeOe9miTjeuW6qP4enZ5YgH3BxRT37YaxMUMw8P8wQmHFvIWo4QEYGYain8rbI9ohmipupWBkCPfJx3E3von1u59B1DUcMa9FUJAq5J95X2Mhj2kuSs6T6KqcvqOfnDUbVRjXx2ICkoh25aEWiagt14VCDqdzM9buneEuNTQ1mPcVEpqasM9ZifvC0zoxWTn6/PuHJcu9kr6WCnBSay4o5HyhYxK+pVKIqhR4CmvOXO2UCsjqavBc3FdehOEhBF6k2QoEmmDhFOgF3LYj01qgXrAs3Vbspv4pE+gtmSumFsQS0NiAfeZKkjffjqyffM7IunqtwRuDeGl1YZhUTP55OspoeJDiUz9H5XKk3jF5CQMhBImzz0UVCxR+tBoGB/x8LlILpTHaXby9O3DWv4592lkT2rLnL8D64K+T+cfPQfcBrV1xCni93bidW7GXrZiUjgomwlSqHkkbM4l77rknrK11//33hxXDV69eDUQVwicr6HkoqAgVv8JQ+Rzu5o3aryFQ7woLZs/GPv1srJNPpfjGK6jMqJ+/wt/166u1Orx9tnYQW7QUedIK1EAfiSuuBcBaujx2P2fPTrytmyN/tMxY9NAHgqQ81tJlJK64BmvZyZSi+ORavIFehOeixkeRLbO0yn18TD+Aq2t1e+NjeoFpaEAuXU7q9rtx9+ymsOYh2Ocn2HL8na3yF+BQte3pvx0XBvvJf/dbpD78m9OyZU8GMXc+orYKhuwoc2OAcFcPYTRAYI4y6ZOWFvj27tTRGZ4HSRvR0qoFJdcldGIsGjVAwrbx2xG6Tc9B7eok/52vU/U7n0FUVQfpQQzhRvMwcdUqqKrCffVFhOfEBZbA0TAwgQQLljlXwvsTp8tD+xz0lQ87BkhetUoLA0qRuOwq3VR9A8mbbsPdswv3tZf8tOdE2gPpr/qmo2siCfMWIhcsRiRsggEXSmGdevrEMQu1NWXGIjQzGSaVRALZPofUPb920JwnACKdjsYjbMvg1QRnDXwT3SDOxjdI3nJHmMJ+MiTOvRCRz1N47KfQ1xMJb+ZYKA+691P48WqsZSsQyYnVnkVdHclrb6D4yA911lyloJDTybwqOOHR2dnJtddey3nnncf3vve98NiaNWv48pe/DMB1111XESoqmBpqdBjGRohyIlhQW0v6ne9Bnnwq+W98BW/965AzsmwiCLMU2jb2shVYi09CdixEzp5T/j5K4a5/g/yPV0N/r178hPAXV+OhpHTKQ5Ubxx0ZxHv+SeS8BVgdiwDwRkcoPvukTvCUTOhcDDu2IlpnoYYGtYq+YwEikcTbsRUSSRKXXU3i2hsRiSSytQ1rdjuFZ5/A3boJDuwjqvegDFpERFM+i7dnB85Lz5K48LLDZ/aBfahc3vdFCKQqfK2BcV5QBCpYUAJSbBva5yKKBdTwsB4D20aMDGNdeg3uGy+j9u8PzQQMDxFz+jRV62E/lc590ddD4YFvk/y1j/rnK8LCVLU12OddTHLVTRSe/oWRcVUCZhVQEfoPhMJC4OSJIXSYgQOBicYp4G3fghrPlPWrEPUNpG5+x4TjifMvxtm8Qft4mDVAwgU58PvwX04RBvuQc9pJvetjE9qL3TPoj/D7JoyxKDdX/HNke/u0BAoA+4yzcIYHYHjYoJlISAnSdwdfSfR/lkSUCqaT9UNKEpddhUomKf7gf3TtHb+ZcCyUNv+o8cyUbSUuuhyy4xSffAwG+sFzUYP906KjgghBvuAjbWMmcd99900o3rl27VoaGxvDz42Njaxdu/aIBYuKUPErjMLPfwpZ3ztcKqiq0juVhYsprn0Yb9c2GM/4u2dfmKiqRixYjKip03kdLr8G2T551IPKZik+uRb31edhf5d/0BAkROkFLmpPJ87OLZBMIRoasa+/DVJp3BefgaF+REMj8vwL8Tasg2IRu2MRxd07EOkq0u/5ECSSFNc8hFyynMTKC2LNy7nzSd/9XryuPeR/eD/enp36QRvuQiFyDND0qaFBnKfWYJ99HiKV5nCglPL9KcwFCSI/ABWZLQIhLyDD/07WVOMNZqGQRcyZp+ttDA/ivfQU8rRzcEeGtNAR7NaRJe0QmVkCFbsEcuO4nVvwNr4Bs9qj8bYTyFmzSd50u+7D2JAv+KHbN9uyLazZ7VEIY7BImsqKsKS90Vfh+bvew0ymND6q1fnBvWKmlpK+eh6MDuHu7MTr2o2ct2DSZu2VF1B85nEteJsI/V5M/xW/k7Z9UM2BieR1t+JtXo+XGdUHJsgiJZqKVArRsQjZ2IK1fMUh3Stx3kW4rzyHt32rFgzLjYXroMbHymoqQAsoyWtvgnSa4kMPaL+cp9aijuB38XbETIaUjpQk7UulUqRS5cdvKrz00kuANnMAfPzjH2f79u20GGnmm5ubGRoaOjyCDVSEil9RKM9D9RxAeK5WU6drsc5cSeKWu3Ae/C7Oay9BPoew0A/NVBWiYzHWgsUkbrojloxqKjiPP4zz9OOQzWqbd2jLNh6Y4c4XrZ7NZfV3joPKjeN87xtQLKCkhSgWkC2t2KeeSbFzM2RHsa++CXf9LxHNLchW7Rmfuvt9U9Il53WQ/ugnyX/xb/By4/GHrOFxr5nlwoH9FB+8n8TdHwht+ocC2T4X2digF2bHia8XgTNl8Lfp5BgIO66D2rEF4er6IiKRQFVXw3A/9HajnnxU01UsIALawwVDEcvcCESZO/3vB/spPrWWxF3v93Nv1CAXLCb5jnchUmm8kSHcV1+IEncF7aJ8k4tASYHq2qXNI8GiHuzai8XoXmZfhYSqFNbChVBV4tcwHQTJxwJeBQmqSukL+65gbAhvdGRKL3RRXYOcOx9v/95ImyUBJY154r/bNrS0YS1agn3JldMmXSSTWPM6UHt2lPiUBG2L+E/FtpAnn0Li1DORHYunfR/QAkHq3g+S/9F3UZvW6bTngUkTAA+yGYqP/YTUXVP/dhIXXQFDgzgvPgXDgzhPPEri+tsPiZ4KZgYdRhFBgD/90z/lz/7szw65ncCHAmDp0qW8613ly9sHQseRoCJU/IrCeWoN9HbrbIipNPKU00m+5yN4Wzbg7d6OyI1DXQPMW6Cfx+1zSdxy9yHtjgDtj5HLxVXxwXu4qErtWZ5IakfKUpNIdhxQCBzfbFLQ54wM4u0sgLyF5K13H7L3kkhXkbzrfeT//e+1oyNEQo+AINmPFnDyuFvXI9e9gn3meYd0HwDV1wPFPEK5xi7dv59yI6e8gDehoKGiz8Vi5LjnFrHPuxi3v1vTXizoKIlgFTL9KMy+hIurr3kKq7t6kBnTDrLnX4rKjZO49R5EugqA4mMPI/q79Xlh1IOIfGI8B7ZtRLhuXEjCj/aIreA+HakUtLZjdSwkcfNdhyWsgRfdKhRWytBnzrtCDufptdgrJvpRmEhcfBWF3m7Yv1cXdSMQ1ETkUxGwe8lSUu+Z2qRSFkHETLnfBSXHC1nUYw9ReONl7KuuJ3HBFYd2q+ZW0h/8BPmv/D1q+2ZfO2TwRjmo3v2+s+7kYyEsi+Qt70R2LMR97SWsU848JDre9lAz4GjpX79nzx7q66Pos3Jaii984Qv09080U7W0tPCZz3yG1atX89JLL4WCRWNjI52dnSxdujSmmQicNY8UFaHiVxAqm8F9+SkYG4SqauTpK0neeg9CCGTHIuSCJXgNTdgXXoF91qEvoCascy/Bfe1FrX0woxtK1PtCKFQ6CRkMk79pxw6ph4EevI2v+yGJw3j58Ql0qmIBb3go9KYXiQSyoWkCfXLhEqzzLsF94UndnpA6GiH0wpcRHUP9uM+sPTyhwnMhMxKpz1Hah6WuUTuVBqGmmiE+cRjCgaHBUAoxMoAaHkDU1aM8PzIgZuoXhjBBnN+2DU0tiKaWKIhBWljnXKBt8LdOjCrwhvoJTTVhpzCEFE87tpq1IpThmxHmDjHoS1gkrrgW+6Lp7+4nIDBDeGXmikmfGVWjPBg++I7LOulkElesovjAN6FI+bHwAFxU1068fXuQczsma64s7PMvxX3jRW1mdFUkqIQdMP5WrvYn6tmH+9RarLMuQB6i2UFISfLu95N/8Luw5U1tCjHNVH0H8H75PNbKiw9O+5nnYp957iHdv4KZ9amor6+PCRXl8JnPfGbK75csWRLznRgaGmLlypU0Njby2c9+Njze2dlZcdSsoDyUp8B1EI3N2BdcgXXd7eHORFTXkLz3wzNzn0Ied/M6hOtEi1r4CkwL6MXHKSJaZml1eX9vVOES/1yzymRuHJXL6hoPuRzOow8i3/0xpK8+V/kc+e9/Aza8phe5YhFmtWNdfQty2anIRCJmA05c/w5EfhxlJXSGxb7ucMMY7tiUzuApTznrsPkhTLW2AKqrYcEi2Ph6tLv3nVVDM0wp3wKmFfKosVHIjGo+JBK6umkgsMTSgPt8M/JgyLPPJ3nz5CGJpbDOuRBvy5t6x66Ymj6JnwdBalNPEMIZLPIxE8ihcjEOETj9muaIUBidgj7lHXRHHtAX1hIp15YApEQkU3r+HiLkopMQtQ1aqLSU9kmJZVgNxtHgl+dCzz6cxx46pDEM79k6m/SHPknhP/8BtWWDdmAN5sp4RoeGV3DUcLyFlK5cuZLVq1eHGos1a9YAWti49957Wb16NQMDA2HI6ZGiIlT8CkJUVWOfexlibgfWESySU8FzXYr/+Q+ovTvByRNT9ws/n0CQxyB4iNY2QGbUr7cAMYOy+SBXHqK2Hlpmobp2w9Z1FP79b7Dv+QjCTuD8z1dg3x4dZhnYjffvwf3eV3GTKUTrbBIf/19I3yFNVNeQuPcwVNeHhJJFKZlCnnOhDqUM8gYAExbdQOUuZKR693flQiiU5+icFQnbz79hagN8/gnjCSS0qcJb/yruhVdgtUyvBofq2uXb/VU0jhPoM8dVQlWVThMdW+QN+pSH5xant7iXgfvL56BnL9qxdZK5Uo4+pWC4n+KTP8G+6FrkVI5tE4QhFQmFUoCdRCxYQuLmuw/bWdG68HLUji3af2OgN2rb1L6EgqF/b9tGzlt4WPcDrbFI3P5rFH58P2xdr7VzaKHW8xyU5x1RCHUFJxaCyI/SCJDSzzOByqz6FYSQEvvaW4+aQAHgPPEwavdWPy11oC5W0d+mWloqwIVNv4SeLqIS5kROd+ZLuaitbyDmLdBOpkrBgT24X/oriv/yF6iuTnByhOWtBaAcTUtmGNW1g+LaHx3TGPuwiqhE0zy7ncR174ir0s2+lqS/jipkquizZQgaza06m6MI2inDc9MBtG8fzn/8jTaDTQOqd782sZSORSm95ntrmxZ2QlpEnD4nj/rZ93EefeDweNo+3y/PXYZ/U9EnFOSzqLU/pPh3f0Tx6UcnnQs6Z4dX0haQSsCc+chzLiD5kd9DLpq8kNzBkLjqJpIf/l1Il5ZtJ+JV+FlAQyPyoiuxDsMMZ0K2zSH53o9DTVV0L6+AevxHFB/57hG1XcHk8GbodaKioqmo4LCgevZrs4fpMKhK3001r9KLPhA6LE64yP8THRoo8KuUNrVov4TMWJSkK2wDoy10O14RXnycYtd2Eh/4/WMWDieCJEfV1dg33qWzjVr+MZO+kF41Od/8BVL411oXXY37i4d0NIgy+25cbPqzKBdGBlD93YdAu2FSCUicir7Zc2Cg23esNboY/uFCfhzV0zUtGkqhBnoQXuCzUUJIaW2aCfNOCxYU83iP/ZDijk0k3/+pCfeQHUsQjc3Qu9+/KVqYW7CE5Ef/AJGcubkjwBAeA/oFE6qVegXo7dKaI+vIHtEikcI6dSXe+pe1llB52ry47nncxcuwTqv4TMw0lFJHvKE5kZOOVTQVFRwyvF1bYc+2aBcd7M7NOhfhS5S8T/YyvvcXYrFoua5WWdeAOPdynRArPFdNfj+B3qnu2krxK3+Ft+m1Y8MYCSQTyDPPx1pxVnQsprURZftafvft98UtoLa/CW1zpuCj0W4wFnh4m19DjQxNj/awjWnSp200k4+FJSGZRC4/47DYqQZ6ITc2+VxJ+DVkpppXAsiNoba8QeELn6b43/8ce2CL1tmwYInuZ8C3+gYSN9w1owIFMAkvS/gnlF70d27CeebRI76lsCwSd30IedM9Ohon5Mm45m8FFcwwKkJFBYcMNdiLyIyEeYGEEP4Lv+iURKSrELPmIOrqEdIuOde8Rkx6TC4/HdHchrAt7SOSSsXPlaVtGn+D1qT07MV5/EeoXPboMkUITV/7POxr3xEdVqV9LMMzIXy++e92EpFMI/btRBRyCMdBdW5E1NQgGpoRljUN/qEdR3sP4Dz+w4OTj++WUNoWZegTAqEUYtOriEJWj3fCRlgy3s9EArH4ZKzzDi00MoDsWKK1PVJO7JuUmkd2sjx9MT6AcIowNIDat3vCLtA66TRE21zErHZE21ysS69HLpn5ehdy+RmI+kaEtMqMmzEvfHrViz/H27l5Ru5tn3s58szz9O9RCITn4K17AZU/yr+LtyEqBcUqqOAQIdrm6lDJwPmrFFLArDkkf+tP8Ta9hvOD/9Tpwk11NUR/xI6VwHVgqA9v+5sGASVq+uiLiepwT8GB3RT/5U8QbfOw7/k4omqSEtxHAFFThzxlJXLxyYiG5hhJoUo+pM/kQwn/pISmWZq3vQci58lCDnr3YV1/F+5LT2h1fTYTOb1Oyr8iasPLeMtOR542hY2+1CIVHheTtK2i+itSQE29fh8b0WOmgIYW7OveOe201qWQi5bD7Hm+yafMXCkz9WL9UT79AHYSGpoRC0+a4DRqr7wUVl56WDQeChI33Yu34CScH3xNZ7qdYLYJzlT678yo9nVZNLFGzqFCSEni7o/jvPQE3pOPQG4cuvdS/OrnsVe9E7m8kotipqCY/Od4KG2cqKgIFRUcEpTn4T73KOSD9N7+F2ahJMvWVUPHR3FfeUJHLwQmgAkCgYi9hW0FEQ2uA7kMXneX0YaapC2YEM4o0fcf6kMVcqhs5ugIFbX12LfFMxV6OzZp23hAR0hfcJFxPOwbkB3VIYheIeKxUNB/AHftapg9H3nJtXgvPK7DaYsFnZyq3FigfQu8vdunFiog8lsQQtvyXSfeVmjKEfGxAHCyiOVnQaGAygzD6DDy0uuR85ccChsnwryXOVeE0r4cMbomea+pRyw/k8Tdv35ktMwA5GnnwhM/hn1jJXSac1mAlYD2+VqAn0HY518J51+J+8qTuA/9N/Tuw+vaWREqKpgxVISKCg4J7i+fRm14WS96gU0YjMVPQDqNnLuI4n/9PfTs0XHygX43Fk4ZvCtiK60Q4OTxXntGx/abC4tZaju4b6wtE4bQIwQUc7jf/HvcOR3Yt33oqAgXJryuHbp2RSwDpUlrCX0KwIPcqP9dwDPfzuM5MDoI2TG8/Tu1liJp+4tsSmsNspn4WCgAhdq5CZUZQdRMkkhn/iLY9oau7GonoGWODucMSRVxR8jgmBDa+a+Qh94u7Pd8CtE8vTDWaSEYd1XKP6UjfpD+vJCQrNZf5bI6AViqGpIprCtvw1p5+czRdKQIHI2Vr8YSJXNZCEhayPOuQC5cPmkzRwLr3Cvwtr2JGuiBvn24z6/Buui6o3KvtxtmsvbHiYiKUFHBIUGNjWhbfZjKmWjRD0pkC4X30lpET1cUKRBb9FXJ5wCm1kLB2LCvqvZg/46SnX25Rbq0rRL6vCIM9fiJpPJwlIUKEURvTIhUmYQ+FSzSIvoeIJWOFkv8fmSNCrCJJCxagTzpDLxnfqp5Xszrl19LhN4u3Kcfxr7hPWVptS++Afeln+sIgbkLobkN+rr0PQ5GXyAsDhzA+ebfQKpKJ3myLJACeeH1WGcdnnlBhAtwcCT4Q0b8U0I7hc6ZhzzjYrwX1iLmL8W+46OHdc+jDTF7DvTt0QJcXI0VwSmgnnsE1bEE0Tb/qNCRuPe39K0e+gZqdOio3OPtiIpQUUEFhwDR3IKyZFTOu1RREGRitKTeLbrFcLc8IZwyalXvNKtrtZ1eKV0rZP5SWP+8Do8c6CZmOjCvLWuBNE0AxntVNfLi6+N+D0cByilqPxAVJG6aJn1mDRVz/ayq034VZhXRAG4Rdm/GEwL5nk8h2hegerrwHv669ssIKtBmRqYm2ragsQlx7pWo5x+FsEDcQegL6XBgMIgoiAQOb8138V56HHn+NYcsXIjZ81Bd23UfTafCMLOmT5hyYP8u1ElnYP3W/zfBb+J4gnX1nbiDPbB/l9biTfhd+K+xYbw927COklAR0nPL+49q+xW8vVARKiqYNtRAD+qJB7UNvxQCQGpbfH0T1l2/hfvAl/SDM5aPQUx02BRAdR38xl/AD78C3Xuhvhn1/E/1vZIp/R6aA4K2MGL9S1e4klVP+P8Jgff60+AWsc67ZqZYMxH5nDZFKHdq+mJmBZNuU5NhwcLl2vThFifyL9BM7NqI+vn3ke/5PWR7B/JD9+m6IUHX5eQ/dyEEdCyDplmIZAo10j99+qYaC6VgfATGR/CeHEPOXYSYNW/abLSuvRt1xe243/8SbHktEqpK12EhtFPqi2twt61DVFUjL70FueDomA+OBLJpFuJDf4Tztc9B1474HAn5JyBdi6htPOr0HM8C2IkILRIemarhBFZUVISKCqYPb8d6XaQsVsobrX6WAhpaoX0xYu4C1Is/g5F+HZvvCWNxKlmkAmHEtrCSScS7fxd3xwbU0w9Bz36tsVh0Gmx+FaprdMGlXIYwPbcyaIjJEeYO23ho5jOwdyvewH7UppcRp5yHde7MCxeipg5xyrmo/v2RtmYy+iz/b9M/JTAz+Op/4ZceR5XhX1Ch1PNQXdtwV/8r1tV3IWbN06Xtp0NvIol924fATuBteCnKVyFERJ9ljKFJX2xcS4WQAAoyg7gPfBF52e3I0y6cHl1CIJIpXMvy55I0Ok6cPpSeG13bUJaF178fb9ZcrEtuQXQcfkbMowFh2dDSDj27tRmklH9SQNJGzD66WooKZh4V80cFFUwDSinUro2ABxbEd9pK+ymcch7W0tPxnnsEta8zUldLiEwX/rupsQAo5vBW/z+tph/sQwz36sJoqZROcmQJqK3Xgsu21/2Kj9Lf1SmjrZKdtPkeZFpUSidV2rMFlR+HoyBUANoEZEl9ezvtCxeeobYvMQHFNBAGb/wmwraCxcfcriulIzWKOdi5AXdtAeve30fI6YdziqBWSpDULLjU1DAFa3ppFEs4rkRsLx0D5UL/frzH7kd170Je+U69uE4HYcIt854mfcF7MB88GO6DsUHcgW7EBauwzr122rw4FrCuuA23eyf07yeq9krUj9wYqnMdYuXVby2hFRwSjreCYscaleRXFUwLKp+FfZ0I5eoEQ+HL0kmIGloQdQ14T/8Q9m6BYj46x5I6WZGU/jEZJTAKEv84edizFXZuhJE+wNPJmCyhd/3pNCI7ot/9hFj65SfWMpMemYmQwnuKOD1C6Af3+Aiqe/fRYZoQCFsiEknEaRciaht0oihLRjTHXjKiL3gFfenrQqCMvqLbMtsRUjuGKgf696NGBg+f9FJ+JZM6mVVIn4joM3k81VgEkQ7ZUdSGF1AjBy9PHtEjjTEN+hrQltYJwSzjmMAXgDwY7kU98xBq/47D5sfRgGxpR5x+oZ/gy/xdiChB1Z7NJ3TK5grefqgIFRVMD13b9C442IEGMyeZhDMuQd76MeS8pdqnwrK1FiHw3A/egzDH8N34Dl+LEJagFroN14HGNl3hNJeB7W/o/ASWpUMqA3u0eT9pa7MJJfcKEjUFaRYRoFzUjjfLdHiGkEzBrLlw8llaUxGYOEKTgu+HYpobwjSQUod3plIweEALC6V8E7Ikf4TfbjGP2rPpsEhWoHmF36adgIUrdFKugM/CoKGUt+ZYYGg8grFAQSGH98QDqKFppoqub4Z0NaSr4jxIJLUZIZGcSFc4zgryGdy1/3PcZZC0LrkV5i3WPA7nqs8/v34LxfxbTWYFhwCFwjvC15H6ZLyVqAgVFRwUanwU78kHdPRBsOAjtANhXRNi1jzkvCXI+Schr7wLTr1ACxsx9TjGQ19G7ZSeE5wXHHMKsPUVaJilD7gFyAz7mSaJVMZBW1Jop89Fp+qQUV+lH56HiM5PpaC5HXGU1OIimYalZ8A5V8Oml32hTEX8ExJqGiCZNgQJ410KaGzRAlWxGPVBWnoRCvKUIybysphHvfAT1Oj0tQERlLE4S2hsRV5xp7Fw+zcpO76l7yZ9xrgW87DzTbw134qcSaeAddltiHf8BsxZ7C+6wRdSzwfXKZlTJfR5Hgx14z3748Pgx9GDkBJ5+Z3QOGvi78K2obZ+5muQVHBUEZg/jvR1oqIiVFRwUKgNz2kP/iDEUKA1Bc1tiEtuRp59pT7PdVB7NyN2b9SLf2xXTfQ+IfOhKH9uYKLwHMTZV8CsOZCq8UuCEyU9MtsUAoSHOPtKxI0fgEWn+MJFWi/EyYS+pqoalpyOvPkjiETqqPBNnnMl1hmXYZ11mV70wsXa73MqBW3ztdAQ+J3EeAA4eZ1hM6hsKQWkkjr8NhDcSnfnEj1W2THU608eOuHhOPkC2tIzdKKw7Gg0NhYTx7V0fGOvMvQ5BRjsRvXvOzhJ6RqsJadDfUOUN0MIv52c5g++z4dt6XEOtTj+PCrkYNcGlDq4EHMsIds6YNEKnY/E5J/rIA7sxOva+hZTWEEF00fFUbOCg0J5HsJzInW2lNDQBMvOxjrtkui8fZ3w5jMwOmgEXfgLQCwyQMYXycDWHgoaxqIWLB51jXrHNq8F9m33aycYO2GCttAl1p/5AeL8G5A3fABv62tayEFC5zro3QO1jcgbP4hIVR81vgnLhg4d0hg6Pyo0/+qbYO5S5OXvxPv+PxmLbnCx/0duLGowuN4p6pciWoACZ0WJ/k9o05AKNDqHQ3siBa1zkJe/E/XMjxCB6cDcTZeOq/JPiI2riJxQpX+ebWvBNJki6vQ06DI1TUppjZXrxc1HySponQNDfXqeeG7k0+oWD4sfRxuiabZOGlbMRXPBdyhWrz8J846v6JUKJofnv460jRMVFaGigoMjFl5oaZX9ymuRZ14ZO02hdKZKPL1wekE0Q7ADl74PBH4YZJAVUYDybfJB+GnMtwD/P09HlNi2NgGEYaV+256xgA0eQD39AOrMK5FnXx2qkNWK81Ebn4f5Jx9VgWICAlOFJaGmEc65Rmt48lm9Y5dS90cKox+e30dZwj/hFxLzJYqAb0HIafAZBcM9KKU4lFwEwrJQ808CTyGveCcikcQLaFcl9IV0mWGR5ljIEvqk1hy1LYS5JyGa2hCth1DfIvDfQPl9xBdcTB8eYFYHLDod9m2D3r1aOLMS2i/jOIQ86wq8zS9AdhgweYyeHxWcMFBKHbFz7YnsnFsRKio4OIb7ojDIqlpYthLr3HidAJUdgx3rIqEBIsEglrQglBKM80rON4UJYZwwNqSFiVh7xvfhDtbTO/nRQXj9Cbze3chL70Q0tiGq6xDnvgU1DgR6Ua6qg6VnYp1/PQDesw/q3XQgXGH23f9jsgJspqBQ6rsg0Kmy+/ej+vcf0sItlp4NnotYenYojInTLkatf8b3XfAMXsOUY2Fk1iRwnLUTcOZlWMsPUuCsHMJbTTFXinnth9O+GBas0JqL+hZ9SccKhDj+rL4imYbG2VoACrROAsIIJaeIsBNvMZUVVHBwHH+/rgqOK3h9e2H3Rj/iwtZOe5e+I3aOUgrvifth/TO++taMsvAf+kGER2AzlsbxmB8Fhno9WohEsEA5Bb1A2EmjjdI28XfxHmRHtEPg2m++tbbpVLVeNE67BHn5O8PDqpiLtA8m7aUCxlT8C/1KSvnnq88zh+asKewE8pSL4g6C23/pf6ni9AUmp4ONRXhMaPPUiz9BDU8z8iNGXGk/iXgXzCPlQX4c9m2GFx6CgX2QTCIvvBm5+PRDv+cxgjjzcqipi/NKKMgMaYGughMCQfKrI32dqKgIFRVMCfXqWsgM+maPOsSV9070Rt+7GYa6oTBO3NlQRA9I8zXBsbBk8ZQl10L0vSW1vTxhT3T0NNsMHAkDQaRnF+rn38Zb9xTejjdQY4efw+FwIM+8AnHxLViX3RnnX6zPcmq+hQJEabguceHNbMvJw8uPopwj8yUQy8/X0R9B6OgE+oiPhZyEPoGutjo+jOraijpUHwc7oeeiX6xsIm+C+yodMeMWYaQX3vgF6qnVx7VaWXSsgI4VWgAN+oGCYvaIx6+CY4cjDScNXicqKkJFBZNCeR6M9usHeGMr4qJbkXMWTzyxfbHebVuSCQJBuVe4+JW++39bIr5oOHlUzx69UEgBc5Zqu7wsdz+iRczcMXtFGO6G5x6AtV9HPfndY8VGAET7YuTJF5T5Qvh8Y3J+BS9L+guqbfCnpK8mDyyhd+2D+1CvPXZk9DfNhtq6g9M4gSYoO8bFcXjpYdTzhxbiKc+4HJadCw0tPg9K+CbK0KM8KI6jDmxDbX7xiPhwNCGEQF75Lu0ELY2+gU4vX0EFJwAqQkUFk2PD04iaesScxcjrPog844ry5/XuRuAhUH5mQIzMhyVZI0XJ9+FL+sclQliIuiZEMoUQCjHai3jmewjX1Rve/Vu1g1/smtL2ML7376u8qCT4SN+08iMcbURuJCLKIDqBX37m0oZWRMscnVVU6poYwrZL+FeSyVIona10tH8GaFXl7yGJvwtzfK3y9CkPMkOwZz1qqHv6NLQvxrrxI4i2Ds2rUjomow8QowOw4anjLqTUhKiuR1x8ZzTGUiKUg9izHpWrCBYnAhQzkKfire7EEaAiVFQwOeavQLTOQ9z4cUT7kklPU8WCLlkeOA76b6G6O5nUEQ+WXeJUp0quCVZYz9+lWfocp6gFgVRK1/4Y7oWTztG5J6SlQ/FqmyKVf2DzjzkvGnQpD8YGUK/8ZCa5dZgIaPPV95ZvYvC/isxC6IgGgc4sKgW0ztXHgqRUsb6a5gYX9m/D69555LTGHEmZeC/zWCIFrfN0NszAwdakTymdsts9tLBXtXsD9HdFYaql4zuBLv9deTA+fIQ8OAaYt1zPaSCMnsqOHtfCUAUR3u7mjxmP/hgaGuIrX/kKAJ/5zGfC46tXrwZgYGCAJUuWsGrVqimPV/DWQzS2wXk3H/xEzyFKr02UPyKIGJGWTkgUOCROuFFwDZGIPj5MtAj5Ddc0gLRhaL+OmDj1Ep1zYsGpUNcCT39POwEG2RWDtsFYdP17ODnY/greojORszoOn0kzBcuCumb96toabVkCPqKga5MuAS98JmVH4cxr4MAO2P1mlM458CUJBTW0puKXP4UbP3EERCpNZ7pBO0I6eYO+MuMqJZxysR6bV34Cg936Gim0J1roC3OIVBSykB+LzGhB+CXGvJMWpGu143Ax738ny8+/4wwikUS1L4Hx1/3fFhAUZKvguMdMaBqOY9efg2LGNRVr166lvz+uau3s7GTNmjXcfffdfPzjH+fzn//8lMcrOHGglII3f66TS0lK/Bz8l1vQQsJkfgPB4mDa3/GM48Zr0Rn6u+0vw5Jz4NZPwplX6+MLTtHaDCl06Ga6yrDrB23J6J6jffDqI28N4wJI9EJd24C4/qPQMnei02FAr+On+Q4+ZwY1/XVNsPgM35ES42W0gQeHlbLbQDKls39efIfO6lnWh8G4p1uAVx7WNM5dpp1rEylo7dB+OLMXw+KzEHUth0SGaF+q+xz4l9Q1aW1Y6Vzx8oCraUpV6RTfKy46LkNKTQghEBfdobVyUhJWn/3lmreatAoqOChmXFNx9913MzAwwNDQUHhs7dq1NDY2hp8bGxtZu3YtnZ2dZY9XtBUnENb/Anp3E6bw9iM5tQNiQod/jo9oDUKo9sYwRRhJfswdrGe0JYWfLAtYchase0x79P/s33Q0gOtoDYZT0LtSCSjHPy5KNCcGfUpp34pDTA41o5BCpww/42pEawdq20vGzt3YxZv0hfxTOuJm8zP+QTda5L2SsRDA+CBq41OIUy4/PFqXX4iwbVTL3EiDFGh+zHENU5J4ejEcOqDHy/NzmOSGoL4NrvkwsrbpkMkQtY2otoUw3KO1V2dfDy89GGlOAiYVC5FgWlOPeMen37pxPkSI+lbUrPkw2uvz1NWOxhUc95gJ80XF/HEQbN++nZaWaDfS3NzM0NDQpMfLIZ/Pk89H1fpGRkaOGr0VTB9qcL+vohXxnasQWmuw7Hx48xeRrwNEC2KgrjZV9WHkQElbQkFmQOceaJoN3Z2+ajsXJygwDTh53a60QCT0rg+h1faZ4YiGsT7UQ/8Il70H0dR+1PlVCrHgdFRVHeJUY6EPBCFl8si8yPzs72KDvgb8SiShvhWQkQCVqoK61sOmVZ5+pf4jP45qbNO8LDWzxOhTkS+D62r6PFf7hBR2wU//BXX2jYiTzj9kWsTCM1Bj/bD0PJ3/w4z+CHTHJl3ZYdRD/wAX3YWYtfCweXBMseAM6Nqoa5ZIYKgbtfN1xKKz3mrKKpgCnpoBoeIEtn+8ZRk1BwbKq2InO/65z32OP//zPz+aJFVwODB9FUJHPqmzGCbT2jSSHy+xmwvjvdQeL4w2RdRmkG9iuBfmrYCRPr1Y5TLEM+WLknf0tYUsNLTDWdfBxichn/NvrXSW0LeoEqRYeCZi4ZmaypFe7TehPOL9n6AC0G8yoUNlMQ6LON/EuTchFp8zszSnqlFzlkHfnqhabIw+kyDj3aTPc2G4D7XuMZizDFHTeGg0LD4bsfhsALz9flKz2LwJ1TSaLteBvj2oVx6GG37zhNBYiGXnoXa9Brs3aM1bYRw10nOoLigVVHBMcUhCxRe+8IUJ/hIALS0tMafMUixdujSmgQicMoFJj5fivvvu49Of/nT4eWRkhI6O48DB7m0MNdClNQbCqPsgAQQ0tkHbYtjkq+aFjDsexh7qxq7cFDKCjzI4rIUXMXsxat8mndiomNcLq9lmYOZQfiOeozNrei60LUYexs74WEBlxyA34mtbRNTnUr6Fjog1fq0I3zNMGQKeV4TRXtQba6B9KaKqfmaJLXWMLPcO8bFQJeMqlK5N8vrPEJfce/ik1LWgZi+B/Vu1H0dMthERDZ4DPTtQm59FrLj0sO93rCCEhPNuQw33aNPHCSAIVQDK/3ekbZyoOCShYirBYSqsWrWKz372s+Hnzs5OVq1aRWdnZ9nj5ZBKpUiljk6J6goOE6P9iNxotAO1JCSq9GI3ZxksuxA6X9YLnBCGbwMli495DGIRJFaws43U22LeCu1XMbgXXbzMuG5CW8Z93Tw8+q+oxna49NcQVXVvDd8mgahpgJomrVUBg1+l7xiOrf6OPPRJwTAbKe3P8OgXUWffHGpEZoRWAaTTegxL6cVP3OR5Bx9X5cCuN1DzViAWHp5aX9Q2I67/DdQP/1pHBnmT8U2BV4CNv0C1LUI0z5sRXhxNiKY5cMY18OIPtFC0+03UissQyaq3mrQKJoHiyKuMnrgixVEwf6xdu5Y1a9YwNDTEkiVLuPvuu1myZAn33nsvq1evZmBggPvuuw9g0uMVHP9QSkHnS3pRCLz+lYKWeYibPqXPeemHWqCQZRbFcCcr4lqFQECJLQjEo0M0AdpMYFn+LthjgqASa0vqh3JmUJtRchkdIXI8QcooU6ZgisURw+cEYoJUMBZhlUtHO/v17YIZFCpo6dDtDeyFgS59LKAvqMtSyBJWo7UlVDVoTYznGWGgaOHnMEu0xxDk+gjyn8T4Jv05AuRGYaQbTgChAoDlF8OeN+HAFhjeBxt/AWfd9FZTVUEFZTHjQsWqVavKahvuvvvusudPdryC4xx++udQUyAFJKqhdUF0Tk+n9lqfIFQYgkToqBkcK10UzYXUCAW00AtwfRuM9PgmEBFpK4KtcRAJEe6S0RqL57+NOusmxNxTjgm7poXMEGQH9SJrOqkqk35fYLJsnaq8MBoJUpJIEDGvRUHPVlRuDJGunRFSxaKzUc3z4Kf/GNEbLN52IorECIRA24bTr4LNz2pTlLT0C6DjdMTilUdEj9q7HnLDkQBq8ktJza9UrfataGiDmuYjut+xhBACtfJmeKpfC4g9299qkiqYApXojwoqOBzsfgMKGf3wthNQNwvOvAGx8GwA1IEtkB3y/SGCHWnwTvyz6Xrmr5llBZFEEprmoHp3wFifPq9lnk6ElBsxBArjvWxbjlaTH9gKx5NQAQa9ls6u6RYif5Oga9KGpnbtbBpoYxCQSGs/E+WVCHAKxvq1lmaGhIqQ2FBwMOhzi5EwiXHszbV6UbdtSNVpc1l9O5xz6xFRobIj8MZPdRRMMN8CQRQIo2Euuhsx//itUjoVRPN81CmXw+s/iYSxCo5LKDUDPhUncPTH8Z0FpoLjF13r9UJhJ6G5A1b9ViRQFLL64ZcfK3NhIDUwxXsJZAJSNTBnBSw8B157WO92lafLWqdr0KGTEqx0yT3K3R997Z43UBt/fkjdPqpIpiFdBwiwU7DiSh1FE4PQ/Giap3lgpjpvXQTty7VwUdp/z4E9r88svUEWVZM28CNSTIdbX1tSGNcLfz4Dw/u1pmvvOnjiq6gj2X2P9GiBybRkx57JSicOe/kHqNff4mRnRwCx/DJoPxmcAqpv11tNTgUVlEVFqKjg8NAwW9fzWHoBXPfJuNPjWL/OKSEgLNVtvuy0TowVy8BovAe2cSn1eR1nwB3/By59H2x5Ri9GwQ49O6B9I5rnQdtJcPIlkU2/tC3zJdBZPne++tbwrwxEQzvMXaEFqLnLYcm5fgXTkld1nc4Gihfv4+gBaJgFp16jhZHwGj8/xIEtM0uwwjdjlMmiWm48YeJ3XhH6d8Prj6BKc44cCqTUGpxUrR7/Ur4htOZs23OofRtmoPNvEU65EprmVhJhHceo1P6ooILDwWnXwkkXQ7IKMUEdq4jSSQdq6GDXasG8U6B/h7/TNmH4QgRIVsPp1+l2XvsR7HodVDFSuXtFvZguuVifP9ar64w47sS2QvOLiO5VGEP170K0HCcJkWqboeM0OOVq/TlYgIN+WElonAN92wlDecO+ZGDnyzofhyX8SBBfgzMhjHcGYNlQVa81ENLPDR5qKUyUGVdzLISCkQOw+Qk4/YbDo0WgNVaXfxhefUCbt5RXMv+U9qfZ8wbMPfXw7vMWQ7QuRDW0IxKVSLjjFRWfigoqOAwIISe3zx/YrHfGQahnkCHSTmt7en0rDO4ybO6li47vwW8ldDEqOwHbnoZdr0LRD180w0iLedj2jD7mejpsMGg78Pova2pRur3Xfoy64mOIxFuTACuG5ZdBYRyRqkWN9kQ+FgrfCdKCoS6/MFeYwENfK9A+GEN7I/5LW2stPFdXcp1BiJom1KJzYNcrkPTnQm+nITuUjOukY4EWRna8hJp3GqJp/qERYiejTKHFcW1eCTRZ4fwD7Co9/2pmlg/HGhWB4vhGoGs40jZOVFSEigpmHvs3RLkpQGsnUlWw4CxYfKFWUe96hYmJsMydtISGOXDBu2D7s7D7l/HaDsL/T/h5Gtw8OEYT5q483M0b9wgjKhSMD8FgF7QtnWFGHDqEkJo/EAkSZp+dgp+CHGPNNvomVDysVnk6hHPuCjjjlpkneMVVetefroMXvk0UxRPrVMnfAh1mmvI1G77wY1nQsw0OUagQLQtQF71XCw/bnvUFTxEfcmHBnOVw6nXaqbiCCio4KqgIFRXMKNRIjy4gZiY8qm2B5ZfDonN17oJ1P9GLo/ngB8LVKFiEEkld92DvG346buLXSD/ZVmFcL0rBAiVK2jITMAWCiJ3wj3lahX88etSbO/4gy2ZYM8WMCDH7Kkr6rMDNaX+Kxnmw/IqZJVFIbW4BVJgiHIPeUvp8upLVcNbtsPd1/XnZ5XpcDlVLEdzBr3Qaet2HQqd/ggRGe2BwL6JxzmHdo4IKpoOK+aOCCmYSO1/SWgMh9O4wWa01AEsu1PH2e56Fnq2+iSKw9WMsgv4OVghI10faBAlGvm59r6CktXK1cJEf1X+b4ZRS+tUxZbTQWQlobIeOc7WTZ7IKjhefilIEjpb4/FS+MDYhNbbvkOh5kfYm7DM6+mHH86i5pyFqD63U+LQhZGSSmZQ+fyykrRNozVoMQiCqGmaOjsC/o9Q0ND4E259BLTqX4738eQUnLt7uQkXll1XBjEF5rg4VFJ524qtvhWWXwpm3EBZwUkrniQizP4q4l74ldUKrc+6AM27UmoSyUSK+xqFxHtQ2QnUNpKpLIhHM6A8R+SfgaIfOno3QfjKidTHHbYGpIMtmQ5t+BZVIy72C/pU7JtC+Bm/8SI/T0aJ1MtrCDKD+OZY2g4jqxpkVKBJVWgtVOldAz8viuB77Ciqo4KigIlRUMHMY2K0zPMqEVomfdgOcfFXcsUzg71SDxQbjHe1I19CGWHAOoroJRrvRu24iQcRMR51I6BwMuUyUEjxss9wLwK8BMXwAdr9yzNk0fQgtZFXXw6LzQXqE9T7K8s8U0EqENgFhyu6dLx4lcsU06DPG8GjgpEu1T0qpIBn8XczCuodQbvFgLVVQwWHBm6F/JyoqQkUFM4u6WdCyAM65EzH3tPIaAIFh8w52kb7mIVULiy4AQOUzsON57X8ROAASJFyS0WcUFDO+at2OnAEDQSRmXxeRSaWYga51R48XRwqltCamYY4Ooy2MR9/F+Oe/h33FePdtSwGvCmOw7yj1OaApdKCdjD6hNVBHQWMgklV+Gfug3wI9V0R0zC1C9+YZv3cFFQAooVDCO8LXiWv+qPhUVDBzaF6gEwy1LUekasqfY2obwtoUvmybqIZZJyFaFunPO57Rpb2DH1hp+XQpfT8KPx9Bskr7YWT69PeeE1/UwsJSyvc3AHIjqGIWkTgOqz7WzYL5Z0HzQtjzik5xbfIt1icMXwrzuO/DoHwfi6PpkGqntOkhUaV9JvJjvn+NmSfDp9vLw9ZfaOfOtuUza34KtBQq2DMFTq1Sz7H2U2D2yTN3vwoqqCBERaioYMYgpAUd50x9Un7UsPP7D/5gEaxtgtNuBvzc95k+wI0WiFhUg9ALZG4InKxeSGwbTrsJDmzQu9HerVrd7ZkLLVFbAE4GNv4Edcadx51fhRAC5unKoirQsMgSIcLkR9mibTLis/TzOSy78ugQvPRSSNboqJ3erZAfjvxalDSEGwF4MNYNW9ZCdRPUtc0cHYG5QxnaCdD3Tdiw4FyElZi5+1VQgQE1A46aR1o75K1ERaio4JhB5Ud10itVNBYbQFh6d7viOq2+Bn1epr+86QIip85ClrAGhZOFPS/D/HP0jhm0M6bjRgsbUjvsBYuNV9DlpId2Q9NxGgECkUARVmK1te9KEGlTTogw+xoU1TrpSsRR2qWLpg5o6kA5eTiwzjA/mcKOIUQqTwuZ+bGZFSqCFPBBIbtYcrA87H4Bll07c/eroAIDHh7iCH0iTmSfiopQUcGxg8JPTGSqwiVUN0LH+dBqJJ/yPH2uaQsHogVCavOGqVbHg75t0LcV0o06SZRyo4UN384fvvttFjKTFD87jhD4IwT9SKShulkLRMJQ75t+BGZfBVqY63wSZSUQbcuPHq3K0AyAH+Jp63fHi9PnFqHzaVTzwjLp3g8Tc06FTG+UfdWEtGHWUex7BRW8zVFx1Kzg2MH160KETntSO9WdegtiyaVx84O5ySx9Sam1G0GNEdNB0StqYWN8QEeimBk7g+uthI4aCepiuEXY+9rR7/8RwRDCEimoa9eOm0IROSJiCGvEX/g+JmN9sOuFY0CuT5OwtJ/L7BVas1JKn3JhZD9sXXtkBcVMLDgf6mbre8f4IHXelIZ5M3OfCioog5kpJ1bRVFRQwcGx61mtfoZIsEhUQcviMicbgoKZnTPMZ+Tvhs3MkTGhROmCWlIY7/45MqGdSoOwQgG0HeeOewKdCttK6XDdmlbY9gRhQS5PGH3FLyYmDP4Q8S0/jFLq6PmQ2CmdGbN3u17IV9yox2egU38f0BeMiZeDrl/CWC/qtNuNhGgC0rWHnKhKSBvVOB+Gu7SJxUzuqbzIabWCCo4CPOEhxPFl/hgaGuIrX/kKAJ/5zGfC46tXrwZgYGCAJUuWsGrVqimPTwcVoaKCY4fa2TqREy4g9SI59yxf61CCQEMe870whAfll/02FyDTShIkPAoycQalt4UAipDpgfnnwsKLZ07tfjQx+1RAQcf5iGQNas/LkWAm/Y6X62vo0OlFQpeTgd3Pw8KLjwqpQgjU8utBPKZNNG0roHcTUfIrgz7pv7sF7dfy/Jd1X4J+zF2JWnwZwjrER1Uwb0ortLpZ2PkcLLl8BntcQQURjkefirVr19Lf309LS5RNt7OzkzVr1vDlL38ZgOuuu45Vq1ZNeny6qAgVFRw7jPcCSpfvrmmFpVchWpeVPzcIGQ2jHfxFUSS0ySQ/DnhlhA1fqjCrlEovWnyDtopjsOc5HYFwxl3HoPNHBlE7C2qvig60LNE1S3IDaIFClO+rsKC6RUfKBEJGYD45mvRWNcKZEV8VIp74Kjau/jsuOE40jlLCvpdhrAt11nsOTWMRZtIsEUjxIDsws52toILjHHfffTcDAwMMDQ2Fx9auXUtjY2P4ubGxkbVr19LZ2Vn2+HQFi4pQUcGxQ3ZQaxNqWuHMdyHSU6Rn7npR+wCYIYkIHdVhJ/1EUCLagZa+Q5mwVfMdbf7I9h+dvh5liOpmVH07FIYAZSycJX0VaM1EdQucchuiquktIjgQesqNhTG+wtA8gZ4DiRoi+8U00bJMR6AUxgzHUannzqzj3NRVwQmNmdRUjIyMxI6nUilSqVS5Sw4Z27dvj2kumpubGRoamvT4dFExLFZwTKB6N+kFPJGGxVdMLVCAFkDCHXiwy/UdK/Oj5VNx20m9AMXSQcuSd2O3LBTkR1H7XzsGHDgKaF2mnU7FFH3F06G2I/vg1a+jfvlN1PhbIEjVzoaqhsnHwkzlLYzxQ8HQTth7aKnFRWMHzD0nCi8NXslqaK1Ef1Rw9DCTjpodHR00NDSEr8997nNHlfaBgfJavMmOl0NFU1HBsUGiWu+Wa9sQ09kpNnRAfsRPXIWvtq/SQknWd/aMJTaS2qwibe2ygS+AYDjqxUwl/mevAF0voRo6ENVHqXrn0ULbKbDzCcg7gNGnsn11dYjl2H7Y+SSceucxJVVUNaIa5sJ4nx4nK6VNUJ5jDGNpB0ALReOw+1nU8G449Z3T94GZfTrse1VHBIWEzGCnKqjgKGPPnj3U19eHn8tpKb7whS/Q3z9xo9DS0hJzyizF0qVLYxqIwCkTmPT4dFARKio4JhCNC+Ds907/AuWbPsw8C1UNURbN0L4uonevAG4uCjm1U2CndU0Q5eh8FLG6FP4CVhjT351oQoW0oLZNV94MSp0Hi3PYx5K+Kg+GdqJ63kS0nX5s6W1cqH1YZq2A+vmw/vs6b4VJX2k+kgBuXjvXKg+YplCx/9Uo0VrQppODA69rLUYFFRwFeLgIjqwSsOdfX19fHxMqymEqwWEqrFq1is9+9rPh587OztBRs9zx6aIiVFRwfCLjO3UGuQ7sKmhcAKP7/KgPIytmCF+rEaa0tuDsDyASVaieDbD9Z9qPIuYcKAAXet7Q7Z9AEEKiZp8OI11aoDITigWOrbHoD//dK8LeF1F1846pj4VoOw3aTgNA5Ya1o+lYkBE0GAuYOK7+MScHG76HWnQlom4auSbG9hNLtBbOp4rVt4KjB+Un6j7SNmYSa9euZc2aNQwNDbFkyRLuvvtulixZwr333svq1asZGBjgvvvuA5j0+HRRESoqOD5R2wbFUb0AJtKwZBWidQXqjW+VFIuaRJ8tfM3Fm99GKY8wEZKd1Or32LlBMq0TFFIQmnsmOGwG5iNDcyFt7Vugjmw3dSQQ6QbU/Itg97N6jIs5wImcKsuOqwtjB2D3M6hT7jq4GaR2Doz3ED3fJSRrYfYZM9aPCio4EbBq1aqy2oa777677PmTHZ8OKkJFBccn5pwDo7vBFdByMqJ1hT4eZMEU5TQVpXC1qQTjXCutF9SWkxHzj06ehmOKWK4KEe3KQ41NifkDAVWNiNPf/RYSrSFmnQqzTgVAda6Bwe3ah8bN+pqLclCQ69fmrIOZQeadBwObtU9GIKRUlBQVHGUcj8mvjiUqQkUFxycS1VDVAgjouMT4wk/NXVosyoSQIGzfJABRMSu/gNh4PxReQeUGYeHVx2fZ8+kiWad5VRiJq/VLM2kqg0/FLGqoE9E4feerow2x5DrgOm0WefNb5et2BHDysPUhVMfliJopCpEFTqphvZhA6KqggqMH7VNxZNKrd4Q+GW8lKnJ7BcclRHUrnPYeOO3diGSt8YW/+46Fhpa87GqYc24UbmnWGglyN6gCDG2HAy+/ZX2cCYiGDmiYr/ta1awjZCbwpIRfqgB7n0UVM281+RNh2VqYtKu1o23pGEsAF0b3wt6nUd4UD9/BrYQF5YJQ1lR9RbCooIKjiIpQUcFxCyHExPoUZg6DIOOmZR6ztAPg3PMgVWssKJTkrxB+Matdb0nfZhTVLdB8Evz/27u/5jau847j37MgAFIUSYiQJUoWY5lI7NiN24ZW3SROkyai3gEl9QV0pHtfSMMr2Vce6R1QfgUa4rIXnQGnF+1M2ykru4kdp3FKprZqWpZNEPwn/gP29OLs4g9JyZS4BLjA7+PBQFiD4NkjCHj2nOc857XLtaBiZw2P+v4yuKJZXz1b7YdmMMleeG0cfvz3cP5XkDzutriv/3szwVTW2lew8Psnv9hG0Y1qheeeSMHQ6OHteSICEEmNCk1/iDSHl9i95wfUZkG8lJunX3voEgA9j121D6pfKga217Bf/BO8+DYmEU2luqY78xb425hEChtWrdx5rvX3YUC18qAlzf0uxutylzsv/Ai79ghKn0GlHORR1LFl+OrfsZuL8OLPMDuTbYf+ClY+d1MpBrfvjKfrKDlcvg32Njrwa8ST/oVJvFTjgfovSeqGxxNudcPiZ65GQf3xXdMCgL8JpblgX5J4MsZgwhUt9X3xpFu4YVeyt4Wt3qfv/QJ++HfQ92LdJmF1uRHldfd3vddUTiJMUg0SVJO9u1f+iESs07c+V1AhMWN2BBL197jh7vJ2bfOy8P939bib8eqei3tO9dYGntgv9TfjltaebHLxq+dgvC5Md8btduqlwCQbzwvrRqRW/m/3D9vgSQb3996TxfQNN7P5Ih1H0x8SL2HSYbUug99Ydtt4sDwH5VUgmEu3uKHvntOu9PfWcm3HyobiSG3AhNND4TTHE654DFD6A7Yni+k52dQmPpczP4H+HBQ/gaXP3SiU77sRKFuBbz7C9g1jUn21nzHGLR/e2HTviziv8pHYsFSwB7xet1r9IdIkiWAqI9kNx88G+0iESYieG43YXg7KfNetGPA3YP2R+7m+M9B3FvqG3WqAVButCEh2B/3T60Yjwn5puBmgDI+/gvl/wdbvjXFEmUQa038Ozv0tnPoL6B50O9Z6wchT+THM/zPW1o04pfpdIJFIulUlXQoq5PD5Ef0XVwoqJF6SPe7LMt3nbom6L8pEwh0zNtjxsm6rbSzYTdj8Fta/diMZyRRkfwRn/hp6z7b6zKIx+Br0n4fe08GSWhqDiYY+8d226CtftLjR+2e6umHoLTjzU8j+OXRnguTLCmx8C0t/rD15a7k2JZbqgRf+sjWNFukgmv6QeBn6iVu10NUDW0tUt9HGc9Uy8es2kQoSEq2tTZWE+ROVx7C2AZUtzKnRVp5RtAZfd6WoLTD/TS2ACPsAqE312KBUd7zySYzxYOBlbP9LsPQCfPOhC478bfj2I6ytQOaHmPQANt0Pm4uQSLuAROSQHcW9P5pJQYXEikmfgPQJAOyj+3X5EMblE5QfuyvT6u6miVpeQcMUR8V90ZYfY8sbbfOFY4yBvpewa/Nuashfp5qs2PjM4N66K/oYMsaDzCvYZB+sfQnLs241yMJvwEvCwPch2a9lpNJU1lawB8zRslpSKtIC1VUAntviPHnMjVJUM/7DZMwguKjuUGlqq0D8dXj0r41z8e3g2BCkM8GKiQTVcyYY1YEgyCrD2hdu07WYMr1nMKcuwNDfuIDTVmDpv7GVrbq8mjbJmRE54jRSIfHlEXwxdkGqH2vLQKW2uVa1AFQ4NYKrS2HDYli4528WMesP4diZJp/A4THGww79HIofY9cfuikA/LrRmrov2fJjqGxiYp7IaI6fxaYvwjf/6UanSp8CBhsEnworpBn8CCpiKlFTpCU8bDoDvWewp34KfrjiI0xGrFvxkEhAzwtuRCNcdhmOdFQ2iVtewX6YVB9m6GfQe64xQbNhLw3j9gL5dqYtRmtM8jic+QUcOw19LwcBRbDBnEgTuCWlB7/Flf6lSXz1/wDjJWDgVQzGhQVhcaedl6b+lttHIn3CrRLYKrpRi6CWRf3YRdsxtjZCsefSWR82F2Dtczh+vpktOxTGeHDiz4AgVEz2wPFzLW2TSKdQUCGxZZK9cOINAPxw/tyGqz3Mju2/LWw8gqFfYbKj2IX7sFlykUSqH9P9QgvP5JDVFwzbczQiWF7afbrJDWsCAyTScKxNlgzLkWftwctsxznHSUGFtI9dQcWOe38VFj/CnnwLc/LC7g2o2pTpG8Fur8DWYlBpdEdgEUwD2cdfQt+Iu9JvF9UN1tp2HEqOmE7PqYg8qCiVSty9exeAGzduVI9fvnyZiYkJAO7du8ft27cByOfzABSLRUZGRhgbG4u6SdIJwsChuudFOEIR1qcwbrRicx7m/wHbPYQ59csWN7o5TDqLOfNr/MWPoPQ7quXJ69nHsHQfW1nBnPhxK5p5KMzx8+49kM62uinSITp9SWnkQcX09DQLCwtks43/iOfm5rh48SIXLlxgamqqeqxQKDA5OQnApUuXFFTI81n+HbBVV5MgTEb0gi2zw9UgFtiGza/xV/6A1/dqq1rcdObYMHZ11pUsD5fa1meTWB+SmZa17zCY1AlInWh1M0Q6RuTjnOPj4+RyuV3HJyYmWFxcpFAokMlkABeAhH8GyGQyTE9PR90k6QTlpaA8d7CiIZF2t+oxavcGYBtWP8Nux7Pw0/Mw6ZOQeQO6eoMt4sOy3UGfGaC80upmisRaWFHzYLf4rsRqWk7FzMwM4KY5AK5du8bs7GzDiMbg4CClUmnPn9/c3GRzc7P6eHm5c74M5Ons+rzLF6heeSfAJINqkiGPxqtzoLKKXf4tJvvzJre4dby+V/H9LVj+mNooRfgBZmH9T9juU5juodY1UiTGXKLmQac/lFPxncIcCoBcLseVK1f2fF4YdOz0/vvv89577x1K2yTebHkVKLtaBIleYBsq63XlunH3Jo0rzx1OhwDbnXdlbrpPYVePgV2nYfoDwN/GlleV1igiz+WZgoo7d+6wsLCw63g2m21Iytwpn88zMzNTDSwymQxzc3PkcrmGkYkwWXMvExMTvPPOO9XHy8vLDA8PP0vzpU2Z9CC2+yQkjkHXCVj9uK6qZliWugt6c7D5NZRLtR/uwPLNJn0a2/8KrP0xyK+AWvVRC5sPsL0vd8zqGJFoVSKYvOiQRM2nBQ5PMzIy0pA7USqVGB0dJZPJcPPmzerxubm5JyZqptNp0un0c/1+aW8mdRJz8tfY7SJ28d/A+Dt25Az+iW89gFQW8MFfc8c7dLMpr+91bCqLLf1HMGJRF1xVFrGP/wfT2zlJrCJRseFqswO/RjwdyuqPQqFAqVRiZGSE8fFxRkdHyefz1RGLQqEAuGDj6tWr5PN5isVidcmpyLOy1seufQI83jH6UPdnuwFbjyCZBbrd9uepzl1qaNKnsb05WJ91fVNVga1vQUGFiDwjY2Na8H95eZmBgQGWlpbo7+9vdXOkxXx/Gxb+cceXIzQumwzuTQJ6vo93/I2mt/Mo8tdnYeW/qBXsMZA6g5d5u4WtEolOM74vwt8xcOz1A08dWlth6fGnsfx+U0VNaRMG63muombweFcwUX+/+QU2PYRJtnF57n0yiWP4iQGwK1RXyPhr2PISpmug1c0TiRUfH3PQ6Y8YV9TszAllaTvGAIk+IO1uJkVQnGKPmwHK+Ou/x/rbLWrx0WFSZyDzS0icoNo/dgN//dNYz+2KSPNppELagjFdeANv41cqsPEb2H4Y7ANC4+Zi9SMW/mOsv4rxVHExkUji976CXfst2KAeTGUZ629jEkqQFtkvJWqKtAljEq4qdyUojOZ5tbpOBnY8AHzs9iO3DFXwUmepbC/C9v+6kt3Gx5YfQUJLt0X2K4p9O7T3h8hRUVkAtmsTew0XDOGDuqkQv/OKXz2N6XkVW1kAu4RbBTKLTWYx3rFWN00kFlyJ7QNufa4y3SJHg93+Elc4xntCjqYHySFIvQxYPK+3dY09gjyvi0r6Zdj6E9hVYBN/81O87jcxpvMKhYnIs1FQIe2nWtCqbjv0ajVqD0wZL9GPMcpT3ouXOofvHYetT9yW6HYV629gEj2tbprIkRdFPoRyKkSOCq8bbILaNudBRGHCe4BV/PI8ieS5Fjb06DLGkEieoMJ52P7cVShlGVBQIfJdOj2o0KWatBWTOg9esrad986bAShD5WGLW3r0eV3nMKlXwKSxla9b3RwRiQGNVEhbMSaNNV3ANrXEzJ33O/8sezHGYLpOUfHn0fWHyP5EUbgqzsWvFFRI+zEmqKxZn6WZwCVwJoAkdJ1tYQPjxet6CX1UiOxPp09/6JNC2o/XVRdUJIFjYPrcYwsm0YvxhlrbxhhRcTAR2S8FFdJWjPHAOwE2KL9tTuN55zEm1dqGiUhH0EiFSJsxZgBr1oA0nvmB6iuISBNFERAoqBA5MozJAlsYTiugEBFpIgUV0naMSWL4XqubISIdSNMfIiIiEgktKRUREZFIWBvBhmI2vhuKqaKNiIiIREIjFSIiIpGpcPCKvfEdqVBQISIiEhGXZHmwoCLO0x8KKkRERNpYqVTi7t27ANy4caN6/PLly0xMTABw7949bt++DUA+nwegWCwyMjLC2NjYvn+XggoREZHIHHykIurpj+npaRYWFshmsw3H5+bmuHjxIhcuXGBqaqp6rFAoMDk5CcClS5eeKahQoqaIiEhUrB/NLULj4+PkcrldxycmJlhcXKRQKJDJZAAXgIR/BshkMkxPT+/7d2mkQkRE5AhaXl5ueJxOp0mn05G9/szMDOCmOQCuXbvG7Oxsw4jG4OAgpVJp36+poEJERCQiNoKpi/A1hoeHG47funWLd99998CvHwpzKAByuRxXrlzZ83lh0LEfCipEREQiE11OxYMHD+jv768e3WuU4s6dOywsLOw6ns1mG5Iyd8rn88zMzFQDi0wmw9zcHLlcrmFkIkzW3C8FFSIiIkdQf39/Q1Cxl6cFDk8zMjLSkDtRKpUYHR0lk8lw8+bN6vG5uTmt/hAREWkNG8HijehXfxQKBUqlEiMjI4yPjzM6Oko+n6+OWBQKBcAFG1evXiWfz1MsFqtLTvfL2JhW2VhaWiKTyewaHhIREam3vLzM8PAwpVKJgYGBQ/sd7rUTEb1ihaWlpdh9v8V2pGJlZQXYncgiIiKyl5WVlUMLKlKpFENDQzx8+DCS1xsaGiKVSkXyWs0U25EK3/eZn5+nr68PYw6aFBNfYQSuERtH/dFI/dFI/VHTSX1hrWVlZYWzZ8/ieYdXnmljY4Otra1IXiuVStHd3R3JazVTbEcqPM/j3LlzrW7GkbGfhJ5Oov5opP5opP6o6ZS+OKwRinrd3d2xDASipIqaIiIiEgkFFSIiIhIJBRUxl06nuXXrVqSlW+NM/dFI/dFI/VGjvpDDENtETRERETlaNFIhIiIikVBQISIiIpFQUCEiIiKRiG2dik4X1mW/f/8+ly9frm74ks/ngdrOct91vB2USiXu3r0LNG6u04l9Ua9TzrOe3guN9DkhTWcldu7fv2+npqastdYuLi7aTCZjrbV2dnbWXrt2rfq8sbGxpx5vF1NTU/bGjRv29u3b1WOd2hehTjnPnfReqNHnhLSCpj9iqFgsVneUy2QyDA4O8uGHHzI9Pd2wlW0mk2F6evqJx9vF+Pg4uVyu4Vin9kWoU85zJ70XavQ5Ia2goCKGxsbGmJycrD4uFouMjo4yOztLNputHh8cHKRUKj3xeDvr9L7olPPcj059L+hzQlpBQUXMXb9+nQ8++OCJ/79YLD7T8XbW6X3RKee5H532XtDnhDSLEjWPoDt37rCwsLDreDab3ZV8dunSJcbHxwHI5XINVxZhshXwxONH3X77Yqd27Itn8bTz7zSd/l7ohM8JOUJandQhz6dQKNhCoWCtdQlZs7OzdnZ21o6Pj1efMzo6aq21TzzeTiYnJ3cl53VqX1jbOee5F70XavQ5Ic2mMt0xNDc3x5tvvll9XCqVCP8a65eEDQ4OVq9OnnS8HUxPTzM5OUmpVOL69evfec7t3Bf1OuU86+m9UKPPCWkFBRUiIiISCSVqioiISCQUVIiIiEgkFFSIiIhIJBRUiIiISCQUVIiIiEgkFFSIiIhIJBRUiIiISCQUVIiIiEgkFFSIiIhIJBRUiIiISCQUVIiIiEgk/h+WzjotTFhK9AAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(leaf.cells[0][:,0],leaf.cells[0][:,1],  \n",
-    "                         s=0.1, c=heights, cmap='magma', label='T = {}'.format(32))\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d438bea4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "177.0\n",
-      "279.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Figuring out centering \n",
-    "size = np.shape(F)\n",
-    "print(np.floor(size[0]/2))\n",
-    "print(np.floor(size[1]/2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c184035d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x73f16b1855d0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -150, vmax = 150)\n",
-    "ax = plt.gca()\n",
-    "midx = 279\n",
-    "ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "midy = 177\n",
-    "ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "plt.colorbar()\n",
-    "\n",
-    "# plt.savefig('figures/Fern' + str(int(theta*1000)) + '.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ec2ad8d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.356194490192345\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "4.71238898038469\n",
-      "[1.8 4.8]\n",
-      "1.8\n",
-      "5.497787143782138\n",
-      "[1.8 4.8]\n",
-      "1.8\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 180x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Version without colorbars \n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    im = plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    # plt.colorbar(fraction=0.035, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.8)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "    height_fig = fig.get_size_inches()[0]\n",
-    "    print(height_fig)\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '_nocolorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4e09dc67",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7853981633974483\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "2.356194490192345\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "4.71238898038469\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n",
-      "5.497787143782138\n",
-      "1.8\n",
-      "[7.54478873 1.8       ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 754.479x180 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "thetaStrs = [r'\\pi/4', r'3\\pi/4', r'3\\pi/2', '7\\pi/4']\n",
-    "thetaStrsFile = [r'pi_over_4', r'3pi_over_4', r'3pi_over_2', '7pi_over_4']\n",
-    "thetas = [np.pi/4, 3*np.pi/4, 3*np.pi/2, 7*np.pi/4]\n",
-    "\n",
-    "\n",
-    "for i, theta in enumerate(thetas):\n",
-    "    print(theta)\n",
-    "    plt.matshow(makeFernPic(F,theta), origin = 'lower', cmap = cmap_viridis, vmin = -200, vmax = 200)\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    plt.colorbar(fraction=0.03, pad=0.04)\n",
-    "    plt.title(r'$f_{\\omega}$ for $\\omega = ' + thetaStrs[i] + r'$')\n",
-    "    fig = plt.gcf()\n",
-    "    # fig.set_figwidth(2.4)\n",
-    "    print(height_fig)\n",
-    "    fig.set_figheight(height_fig)\n",
-    "\n",
-    "    print(fig.get_size_inches())\n",
-    "\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.png', bbox_inches = 'tight', dpi = 300)\n",
-    "    plt.savefig('figures/Fern_' + thetaStrsFile[i] + '.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1db8d134",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 80x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig,ax = plt.subplots()\n",
-    "plt.colorbar(im,ax=ax)\n",
-    "\n",
-    "ax.remove()\n",
-    "fig.set_figwidth(0.8)\n",
-    "plt.savefig('figures/OnlyColorbar.png', bbox_inches = 'tight', dpi = 300)\n",
-    "plt.savefig('figures/OnlyColorbar.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17c0ccc0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28dbfb28",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'5497'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(int(theta*1000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d379223a",
-   "metadata": {},
-   "source": [
-    "## Thresholded Figure "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08121a32",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def makeFernPicThreshold(F, theta, thresh, both = False):\n",
-    "    maxRow, maxCol = np.shape(F)\n",
-    "    omega = (np.cos(theta), np.sin(theta))\n",
-    "    \n",
-    "    # center origin \n",
-    "    Col,Row = np.meshgrid(range(maxCol),range(maxRow))\n",
-    "    Col = Col - np.floor(maxCol/2)\n",
-    "    Row = Row-np.floor(maxRow/2)\n",
-    "    \n",
-    "    fMask = omega[0]*Col + omega[1]*Row\n",
-    "    \n",
-    "    fMaskBelow = fMask.copy()\n",
-    "    fMaskBelow[fMaskBelow <= thresh] = np.nan\n",
-    "    \n",
-    "    fMaskAbove = fMask.copy()\n",
-    "    fMaskAbove[fMaskAbove > thresh] = np.nan\n",
-    "    \n",
-    "    \n",
-    "    DTdown = np.multiply(F,fMaskAbove)\n",
-    "    DTup = np.multiply(F,fMaskBelow)\n",
-    "    \n",
-    "    # Why the fork is this backwards!?!?!?!?!\n",
-    "    # Must have something to do with meshgrid but i just can't fight it right now\n",
-    "    if both:\n",
-    "    \n",
-    "        return DTdown,DTup\n",
-    "    else:\n",
-    "        return DTdown\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "639dcbdd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n",
-      "[1.25 4.8 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 125x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "for thresh in [-150,-100,-50,0,50,100,150]:\n",
-    "\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    plt.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    ax = plt.gca()\n",
-    "    midx = 279\n",
-    "    ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    ax.xaxis.set_ticks_position('bottom')\n",
-    "    ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    midy = 177\n",
-    "    ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "    \n",
-    "    plt.title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    fig = plt.gcf()\n",
-    "    fig.set_figwidth(1.25)\n",
-    "\n",
-    "    thresh_size_fig = fig.get_size_inches()\n",
-    "    print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d32f8502",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 600x200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "\n",
-    "fig,axes = plt.subplots(1,5,sharey = True, figsize = (6,2))\n",
-    "\n",
-    "for i, thresh in enumerate([-150,-50,0,50,150]):\n",
-    "\n",
-    "    ax = axes[i]\n",
-    "\n",
-    "    heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "\n",
-    "    # Plot colors above theshold\n",
-    "    ax.matshow(makeFernPicThreshold(F,theta,thresh), \n",
-    "                origin = 'lower', \n",
-    "                cmap = cmap_viridis, \n",
-    "                vmin = -200, vmax = 200)\n",
-    "\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "    # midy = 177\n",
-    "    # ax.set_yticks(midy+100*np.arange(-2,3))\n",
-    "    # ax.set_yticklabels(np.arange(-2,3)*100)\n",
-    "\n",
-    "    ax.set_xticks([])   \n",
-    "    ax.set_yticks([]) \n",
-    "    ax.set_title(r'$K_{' + str(thresh) + r'}$')\n",
-    "\n",
-    "    # fig = plt.gcf()\n",
-    "    # fig.set_figwidth(1.25)\n",
-    "\n",
-    "    # thresh_size_fig = fig.get_size_inches()\n",
-    "    # print(thresh_size_fig)\n",
-    "\n",
-    "    # plt.colorbar()\n",
-    "\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.png', bbox_inches = 'tight',dpi = 300)\n",
-    "    # plt.savefig('figures/Fern_3pi_over_4_Thresh'+str(thresh)+'.eps', bbox_inches = 'tight')\n",
-    "\n",
-    "    # plt.clf()\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.png', bbox_inches = 'tight',dpi = 300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_AllThresh.eps', bbox_inches = 'tight',dpi = 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd8361dd",
-   "metadata": {},
-   "source": [
-    "## ECC in one direction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a416a675",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = 3*np.pi/4\n",
-    "omega = (np.cos(theta), np.sin(theta))\n",
-    "heights = np.sum(leaf.cells[0]*omega, axis=1)\n",
-    "numThresh = 64\n",
-    "\n",
-    "threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "\n",
-    "outECC = leaf.ECC(heights, numThresh)\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "ax = plt.gca()\n",
-    "\n",
-    "positions = [-150, -50, 0, 50, 150]\n",
-    "for pos in positions:\n",
-    "    ax.axvline(x=pos, color='grey', linestyle='--')\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.step(threshes,outECC)\n",
-    "plt.title(r'ECC for $\\omega = \\frac{3 \\pi}{4}$')\n",
-    "plt.xlabel('$a$')\n",
-    "plt.ylabel(r'$\\chi(K_a)$')\n",
-    "\n",
-    "# ax.set_xticks([-200,-150,-100,-50,0,50,100,150,200])\n",
-    "    # midx = 279\n",
-    "    # ax.set_xticks(midx+100*np.arange(-2,3))\n",
-    "    # ax.set_xticklabels(np.arange(-2,3)*100)\n",
-    "    # ax.xaxis.set_ticks_position('bottom')\n",
-    "    # ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='x', labelrotation=45)\n",
-    "ax.tick_params(axis='y', labelrotation=45)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(5,2)\n",
-    "# fig.set_size_inches(thresh_size_fig)\n",
-    "# print(thresh_size_fig)\n",
-    "# print(fig.get_size_inches())\n",
-    "\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.png', bbox_inches = 'tight',dpi=300)\n",
-    "plt.savefig('figures/Fern_3pi_over_4_ECC.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3f9c12e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-197\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(outECC[-1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7017e7c9",
-   "metadata": {},
-   "source": [
-    "## ECT\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81210cb6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.     0.0625 0.125  0.1875 0.25   0.3125 0.375  0.4375 0.5    0.5625\n",
-      " 0.625  0.6875 0.75   0.8125 0.875  0.9375 1.     1.0625 1.125  1.1875\n",
-      " 1.25   1.3125 1.375  1.4375 1.5    1.5625 1.625  1.6875 1.75   1.8125\n",
-      " 1.875  1.9375]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get all the directions around the circle\n",
-    "numCircleDirs = 32\n",
-    "circledirs =  np.linspace(0, 2*np.pi, num=numCircleDirs, endpoint=False)\n",
-    "circledirs\n",
-    "\n",
-    "print(circledirs/np.pi)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13cdd2ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Choose number of thresholds for the ECC to stop at \n",
-    "numThresh = 100 "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "04b57d67",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-285.68893688291087 285.68893688291087\n"
-     ]
-    }
-   ],
-   "source": [
-    "ECT_preprocess = {}\n",
-    "\n",
-    "overallmin = 0\n",
-    "overallmax = 0\n",
-    "\n",
-    "for i, omega in enumerate(circledirs):\n",
-    "    \n",
-    "    omegavec = np.array((np.cos(omega),np.sin(omega)))\n",
-    "    \n",
-    "    # Function values of all the pixels\n",
-    "    heights = np.sum(leaf.cells[0]*omegavec, axis=1)\n",
-    "\n",
-    "    # Thresholds implicitly calculated by Erik's code\n",
-    "    threshes= np.linspace(heights.min(), heights.max(), numThresh)\n",
-    "    \n",
-    "    if heights.min()<overallmin:\n",
-    "        overallmin = heights.min()\n",
-    "    if heights.max()>overallmax:\n",
-    "        overallmax= heights.max()\n",
-    "            \n",
-    "\n",
-    "    outECC = leaf.ECC(heights, numThresh)\n",
-    "    \n",
-    "    ECT_preprocess[i] = (threshes,outECC)\n",
-    "    \n",
-    "print(overallmin,overallmax)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17128b08",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x73f16b12de10>]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 2\n",
-    "\n",
-    "plt.step( ECT_preprocess[i][0],  ECT_preprocess[i][1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4ec3eefe",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_57600/1470381530.py:26: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  M[i,j] = ECT_preprocess[omegai][1][index]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Making a matrix of size numThreshes x numCircleDirs\n",
-    "# M[i,j] = \n",
-    "\n",
-    "\n",
-    "# Make meshgrid. The +1 is to draw 2pi on both ends\n",
-    "X,Y = np.meshgrid(range(numCircleDirs+1),np.linspace(-250,250,numThresh))\n",
-    "M = np.ones_like(X)\n",
-    "\n",
-    "for i in range(X.shape[0]):\n",
-    "    for j in range(X.shape[1]):\n",
-    "        omegai = X[i,j]\n",
-    "        \n",
-    "        # Deal with redrawing the 2pi\n",
-    "        omegai = np.mod(omegai,numCircleDirs)\n",
-    "        \n",
-    "        \n",
-    "        omega = i*np.pi/numCircleDirs\n",
-    "        a = Y[i,j]\n",
-    "        threshes = ECT_preprocess[omegai][0]\n",
-    "#         print(threshes)\n",
-    "        try: \n",
-    "            index = np.where(threshes>=a)[0][0]   \n",
-    "        except:\n",
-    "            index = [-1]\n",
-    "\n",
-    "        M[i,j] = ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "eac07698",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern_ECT.png', bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern_ECT.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "930cb35f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1,  1,  1,  1,  6,  1,  1,  1,  1,  2,  1,  4,  1,  1,  3,  3,  2,\n",
-       "        1,  2, 10,  3,  3,  1,  1,  3,  3,  1,  2,  1,  1,  1,  1,  1])"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "M[0,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5afc592b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 4., 11., 19.])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bigvals = np.where(M[0,:] >3)[0]\n",
-    "\n",
-    "\n",
-    "circledirs[bigvals]/np.pi*16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad5f2a1d",
-   "metadata": {},
-   "source": [
-    "## Let's draw a cylinder!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "203aece0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# pip install ipympl\n",
-    "\n",
-    "# %matplotlib widget"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b2acd4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32)\n"
-     ]
-    }
-   ],
-   "source": [
-    "grid = M[:,:-1]\n",
-    "print(grid.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f9ab03be",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 32, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "color_dimension = grid # change to desired fourth dimension\n",
-    "minn, maxx = color_dimension.min(), color_dimension.max()\n",
-    "norm = matplotlib.colors.Normalize(minn, maxx)\n",
-    "m = plt.cm.ScalarMappable(norm=norm, cmap='viridis')\n",
-    "m.set_array([])\n",
-    "fcolors = m.to_rgba(color_dimension)\n",
-    "\n",
-    "print(fcolors.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7131aa3f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.27318197 0.54636394 0.81954591 1.09272788 1.36590985\n",
-      " 1.63909182 1.91227379 2.18545576 2.45863773 2.7318197  3.00500167\n",
-      " 3.27818364 3.55136561 3.82454758 4.09772955 4.37091152 4.64409349\n",
-      " 4.91727546 5.19045743 5.4636394  5.73682137 6.01000334 6.28318531]\n"
-     ]
-    }
-   ],
-   "source": [
-    "theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "print(theta)\n",
-    "# theta = circledirs[:-1]\n",
-    "# print(theta, theta.shape)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c071d904",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 400x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "equation for a circle\n",
-    "\n",
-    "x  =  h + r cosθ\n",
-    "y  =  k + r sinθ\n",
-    "\n",
-    "where h and k are the co-ordinates of the center\n",
-    "\n",
-    "0 <= θ <= 360\n",
-    "\"\"\"\n",
-    "\n",
-    "fig = plt.figure(figsize=(4,3))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "\n",
-    "# theta = np.linspace(0, 2 * np.pi, 24)\n",
-    "radius = 1\n",
-    "\n",
-    "\n",
-    "x = np.linspace(0, numThresh, numThresh)\n",
-    "\n",
-    "# theta = circledirs\n",
-    "theta = np.linspace(0, 2 * np.pi, 32)\n",
-    "\n",
-    "thetas, xs = np.meshgrid(theta, x)\n",
-    "\n",
-    "y = radius * np.cos(thetas)\n",
-    "z = radius * np.sin(thetas)\n",
-    "\n",
-    "#ax.plot_surface(xs, y, z, color='orange')\n",
-    "#ax.plot_surface(xs, y, z, facecolors=fcolors)\n",
-    "surf = ax.plot_surface(xs, y, z, facecolors = fcolors,linewidth = 0.1)\n",
-    "surf.set_edgecolor('grey')\n",
-    "\n",
-    "ax.set_axis_off()\n",
-    "\n",
-    "plt.savefig('figures/fern_ECT_cylinder.png', bbox_inches = 'tight',dpi = 900)\n",
-    "plt.savefig('figures/fern_ECT_cylinder.eps', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b03f0104",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-193.98518858 -190.40235323 -186.81951788 -183.23668253 -179.65384718\n",
-      " -176.07101183 -172.48817648 -168.90534113 -165.32250578 -161.73967042\n",
-      " -158.15683507 -154.57399972 -150.99116437 -147.40832902 -143.82549367\n",
-      " -140.24265832 -136.65982297 -133.07698762 -129.49415227 -125.91131692\n",
-      " -122.32848157 -118.74564621 -115.16281086 -111.57997551 -107.99714016\n",
-      " -104.41430481 -100.83146946  -97.24863411  -93.66579876  -90.08296341\n",
-      "  -86.50012806  -82.91729271  -79.33445735  -75.751622    -72.16878665\n",
-      "  -68.5859513   -65.00311595  -61.4202806   -57.83744525  -54.2546099\n",
-      "  -50.67177455  -47.0889392   -43.50610385  -39.92326849  -36.34043314\n",
-      "  -32.75759779  -29.17476244  -25.59192709  -22.00909174  -18.42625639\n",
-      "  -14.84342104  -11.26058569   -7.67775034   -4.09491499   -0.51207963\n",
-      "    3.07075572    6.65359107   10.23642642   13.81926177   17.40209712\n",
-      "   20.98493247   24.56776782   28.15060317   31.73343852   35.31627387\n",
-      "   38.89910923   42.48194458   46.06477993   49.64761528   53.23045063\n",
-      "   56.81328598   60.39612133   63.97895668   67.56179203   71.14462738\n",
-      "   74.72746273   78.31029808   81.89313344   85.47596879   89.05880414\n",
-      "   92.64163949   96.22447484   99.80731019  103.39014554  106.97298089\n",
-      "  110.55581624  114.13865159  117.72148694  121.3043223   124.88715765\n",
-      "  128.469993    132.05282835  135.6356637   139.21849905  142.8013344\n",
-      "  146.38416975  149.9670051   153.54984045  157.1326758   160.71551116]\n",
-      "59\n",
-      "17.402097119801482\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-83"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "omegai = 5\n",
-    "omega = i*np.pi/numCircleDirs\n",
-    "a = 17\n",
-    "threshes = ECT_preprocess[omegai][0]\n",
-    "print(threshes)\n",
-    "\n",
-    "index = np.where(threshes>=a)[0][0]\n",
-    "print(index)\n",
-    "print(threshes[index])\n",
-    "ECT_preprocess[omegai][1][index]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "554bc00c",
-   "metadata": {},
-   "source": [
-    "## Erik's version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e5a2a91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# t = 32\n",
-    "# tic = time.perf_counter()\n",
-    "# ect = leaf.ECT(circle_dirs, T=t)\n",
-    "# toc = time.perf_counter()\n",
-    "# print(\"Complex with {} vertices\\n\".format(len(leaf.cells[0])))\n",
-    "# print(\"ECT with {} directions in {:.4f} seconds.\\n{:.4f}s per direction\".format(len(circle_dirs), toc-tic, (toc-tic)/len(circle_dirs)))\n",
-    "\n",
-    "# plt.figure(figsize=(15,2))\n",
-    "# plt.plot(ect);\n",
-    "# plt.title('ECT for {} directions and {} thresholds each'.format(len(circle_dirs),t), fontsize=24);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "719235ff",
-   "metadata": {},
-   "source": [
-    "# SECT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96766900",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'ECT of Fern Leaf')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# THe original ECT\n",
-    "\n",
-    "plt.pcolormesh(X,Y,M, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "731fe26b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100, 33)\n",
-      "[-100.72  -99.01  -96.97  -95.    -93.24  -92.33  -91.34  -90.83  -90.69\n",
-      "  -89.07  -87.98  -87.07  -87.2   -87.64  -88.43  -89.47  -90.97  -90.94\n",
-      "  -91.8   -91.22  -93.    -94.51  -96.85  -98.61  -99.98 -100.05 -100.44\n",
-      " -100.47 -101.04 -101.12 -101.03 -100.91 -100.72]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Following Crawford2019 for the moment. Is this different in the Meng22 version?\n",
-    "print(M.shape)\n",
-    "\n",
-    "# Averages for a fixed direction \n",
-    "AvgOverOmega = np.average(M,axis = 0)\n",
-    "print(AvgOverOmega)\n",
-    "\n",
-    "# Mean centered \n",
-    "\n",
-    "Mcentered = M-AvgOverOmega"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33144af2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mcentered, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$a$')\n",
-    "\n",
-    "plt.title(r'ECT of Fern Leaf with value centered')\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.savefig('figures/Fern-ECT-centered.png', bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cfbf9ef1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-1.09992015e-11,  1.23066002e-11, -1.07718279e-11,  0.00000000e+00,\n",
-       "       -1.27613475e-11, -5.48538992e-12,  6.96331881e-12, -5.03064257e-12,\n",
-       "        4.71800377e-12,  7.78754838e-12,  1.33582034e-12,  7.78754838e-12,\n",
-       "       -9.12336873e-12, -5.42854650e-12, -7.56017471e-12, -1.09992015e-11,\n",
-       "       -1.09992015e-11,  4.71800377e-12,  9.35074240e-12, -1.09992015e-11,\n",
-       "        0.00000000e+00,  1.25339739e-11, -6.56541488e-12,  5.54223334e-12,\n",
-       "        1.33582034e-12,  9.40758582e-12,  4.66116035e-12, -1.09992015e-11,\n",
-       "       -2.87059265e-12, -8.29913915e-12,  1.07718279e-11, -7.53175300e-12,\n",
-       "       -1.09992015e-11])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Mcentered.sum(axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "101671c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1 2 3]\n",
-      " [4 5 6]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [5, 7, 9]])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([[1,2,3], [4,5,6]])\n",
-    "print(a)\n",
-    "np.cumsum(a,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f047f4f3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Mint = np.cumsum(Mcentered,axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a6cc372d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.pcolormesh(X,Y,Mint, cmap = 'viridis')\n",
-    "plt.colorbar()\n",
-    "\n",
-    "ax = plt.gca()\n",
-    "ax.set_xticks(np.arange(0,numCircleDirs+1,4))\n",
-    "\n",
-    "labels = [r'$0$',\n",
-    "          r'$\\frac{\\pi}{4}$',\n",
-    "          r'$\\frac{\\pi}{2}$',\n",
-    "          r'$\\frac{3\\pi}{4}$',\n",
-    "          r'$\\pi$',\n",
-    "          r'$\\frac{5\\pi}{4}$',\n",
-    "          r'$\\frac{3\\pi}{2}$',\n",
-    "          r'$\\frac{7\\pi}{4}$',\n",
-    "          r'$2\\pi$',\n",
-    "         ]\n",
-    "\n",
-    "ax.set_xticklabels(labels)\n",
-    "\n",
-    "\n",
-    "fig = plt.gcf()\n",
-    "fig.set_size_inches(3,2)\n",
-    "\n",
-    "plt.xlabel(r'$\\omega$')\n",
-    "plt.ylabel(r'$t$')\n",
-    "\n",
-    "plt.title(r'SECT of Fern Leaf')\n",
-    "\n",
-    "plt.savefig('figures/Fern-SECT.png',bbox_inches = 'tight',dpi=900)\n",
-    "plt.savefig('figures/Fern-SECT.eps',bbox_inches = 'tight')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f4478b3c",
-   "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/notebooks/Matisse/Matisse_ECT.ipynb b/docs/notebooks/Matisse/Matisse_ECT.ipynb
deleted file mode 100644
index 5b66a5e..0000000
--- a/docs/notebooks/Matisse/Matisse_ECT.ipynb
+++ /dev/null
@@ -1,386 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "23bd9d9b",
-   "metadata": {},
-   "source": [
-    "# ECT on Matisse's \"The Parakeet and the Mermaid\" \n",
-    "\n",
-    "Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312). \n",
-    "\n",
-    "![matisse.jpg](matisse.jpg)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b73a120d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#-----------------\n",
-    "# Standard imports \n",
-    "#-----------------\n",
-    "import numpy as np # for arrays\n",
-    "import matplotlib.pyplot as plt # for plotting\n",
-    "from sklearn.decomposition import PCA # for PCA for normalization\n",
-    "from scipy.spatial import distance_matrix\n",
-    "\n",
-    "from os import listdir # for retrieving files from directory\n",
-    "from os.path import isfile, join # for retrieving files from directory\n",
-    "from sklearn.manifold import MDS # for MDS\n",
-    "import pandas as pd # for loading in colors csv\n",
-    "\n",
-    "#---------------------------\n",
-    "# The ECT packages we'll use\n",
-    "#---------------------------\n",
-    "from ect import ECT, EmbeddedGraph # for calculating ECTs\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c149a147",
-   "metadata": {},
-   "source": [
-    "We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
-    "\n",
-    "<img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f7b7fe4c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "There are 150 files in the directory\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_dir = \"doc_source/notebooks/Matisse/outlines/\" # set data directory\n",
-    "file_names = [f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == '.txt'] # create a list of file names\n",
-    "file_names.sort() # sort the list of file names\n",
-    "print(f\"There are {len(file_names)} files in the directory\") # print number of files"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b082bf2",
-   "metadata": {},
-   "source": [
-    "Here we have an example of one of these leaves loaded in as an `EmbeddedGraph` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3cb77aa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "i = 3\n",
-    "shape = np.loadtxt(data_dir + file_names[i])\n",
-    "# shape = normalize(shape)\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_cycle(shape)\n",
-    "G.plot(with_labels = False, node_size = 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aef7ef04",
-   "metadata": {},
-   "source": [
-    "We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "3d930346",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "G.plot(with_labels = False, node_size = 10, bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "49bc6a0c",
-   "metadata": {},
-   "source": [
-    "And then we can compute the ECT of this leaf. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e0e07987",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) # intiate ECT\n",
-    "myect.set_bounding_radius(1) # set bounding radius\n",
-    "myect.calculateECT(G) # calculate ECT on embedded graph\n",
-    "\n",
-    "myect.plotECT() # plot ECT\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebc64b1d",
-   "metadata": {},
-   "source": [
-    "Let's just make a data loader with all of this for ease in a bit. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "dcf72cc4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def matisse_ect(filename, num_dirs, num_thresh):\n",
-    "    shape = np.loadtxt(data_dir + filename)\n",
-    "    G = EmbeddedGraph()\n",
-    "    G.add_cycle(shape)\n",
-    "    G.set_PCA_coordinates( center_type='min_max', scale_radius=1)\n",
-    "    myect = ECT(num_dirs = num_dirs, num_thresh=num_thresh) \n",
-    "    myect.set_bounding_radius(1) \n",
-    "    M = myect.calculateECT(G) \n",
-    "    return M"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "91cb74c1",
-   "metadata": {},
-   "source": [
-    "And now we can load in all the outlines, compute their ECT and store it in a 3D array. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2dfe86ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_dirs=50 # set number of directional axes\n",
-    "num_thresh=50 # set number of thresholds each axis\n",
-    "\n",
-    "ect_arr = np.zeros((len(file_names),num_dirs,num_thresh))\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    ect_arr[i,:,:] = matisse_ect(file_names[i], num_dirs, num_thresh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7be2f273",
-   "metadata": {},
-   "source": [
-    "Here, we are just going to compute the distance between two ECTs using $L_2$ distance. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e1efac55",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x3199f6190>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs*num_thresh)\n",
-    "D = distance_matrix(flattened_ect, flattened_ect)\n",
-    "plt.matshow(D)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce93bb3f",
-   "metadata": {},
-   "source": [
-    "For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0984d176",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_components = 2 # select number of components\n",
-    "mds = MDS(n_components=n_components, # initialize MDS\n",
-    "          dissimilarity=\"precomputed\", # we have precomputed the distance matrix\n",
-    "          normalized_stress='auto',\n",
-    "          random_state=5 # select random state for reproducibility\n",
-    "         )\n",
-    "MDS_scores = mds.fit_transform(D) # get MDS scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "551aa2fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# read in color hexcodes\n",
-    "col_df = pd.read_csv(\"doc_source/notebooks/Matisse/colors.csv\", header=None) \n",
-    "\n",
-    "scale_val = 6 # set scale value\n",
-    "plt.figure(figsize=(5,5)) # set figure dimensions\n",
-    "\n",
-    "for i in range(len(file_names)): # for each leaf\n",
-    "    shape = np.loadtxt(data_dir+file_names[i]) # get the current shape\n",
-    "    shape = shape - np.mean(shape, axis=0) # zero center shape\n",
-    "    shape = scale_val * shape / max(np.linalg.norm(shape, axis = 1)) # scale to radius 1 then mult by scale_val\n",
-    "\n",
-    "    trans_sh = (shape + MDS_scores[i]) # translate shape to MDS position\n",
-    "\n",
-    "    plt.fill(trans_sh[:,0], trans_sh[:,1], c=col_df[0][i], lw=0) # plot shape\n",
-    "    plt.gca().set_aspect(\"equal\")\n",
-    "\n",
-    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
-    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b3c3d0e7",
-   "metadata": {},
-   "source": [
-    "## Acknowledgements\n",
-    "\n",
-    "This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood). "
-   ]
-  }
- ],
- "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": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/notebooks/Matisse/Matisse_ECT.html b/docs/notebooks/Matisse/example_matisse.html
similarity index 60%
rename from docs/notebooks/Matisse/Matisse_ECT.html
rename to docs/notebooks/Matisse/example_matisse.html
index 5d1465c..c7644ad 100644
--- a/docs/notebooks/Matisse/Matisse_ECT.html
+++ b/docs/notebooks/Matisse/example_matisse.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
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@@ -7,26 +5,30 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>3.3. ECT on Matisse’s “The Parakeet and the Mermaid” &mdash; ect 0.1.5 documentation</title>
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-    <link rel="next" title="3.4. Tutorial for exact ECT computation" href="../Tutorial-ExactECT.html" />
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+    <link rel="next" title="4. Contributing to the ect Package" href="../../contributing.html" />
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 </head>
 
 <body class="wy-body-for-nav"> 
@@ -60,12 +62,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -75,18 +75,15 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="../../tutorials.html">3. Tutorials</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="../Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="../Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">4. Contributing</a><ul>
@@ -122,7 +119,7 @@
           <li class="breadcrumb-item"><a href="../../tutorials.html"><span class="section-number">3. </span>Tutorials</a></li>
       <li class="breadcrumb-item active"><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet and the Mermaid”</li>
       <li class="wy-breadcrumbs-aside">
-            <a href="../../_sources/notebooks/Matisse/Matisse_ECT.ipynb.txt" rel="nofollow"> View page source</a>
+            <a href="../../_sources/notebooks/Matisse/example_matisse.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
   </ul>
   <hr/>
@@ -134,43 +131,45 @@
 <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet and the Mermaid”<a class="headerlink" href="#ECT-on-Matisse's-%22The-Parakeet-and-the-Mermaid%22" title="Link to this heading"></a></h1>
 <p>Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse’s 1952 <a class="reference external" href="https://www.moma.org/audio/playlist/6/312">“The Parakeet and the Mermaid”</a>.</p>
 <p><img alt="matisse.jpg" src="../../_images/matisse.jpg" /></p>
-<div class="nbinput nblast docutils container">
+<div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#-----------------</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># -----------------</span>
 <span class="c1"># Standard imports</span>
-<span class="c1">#-----------------</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="c1"># for arrays</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> <span class="c1"># for plotting</span>
-<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span> <span class="c1"># for PCA for normalization</span>
+<span class="c1"># -----------------</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>  <span class="c1"># for arrays</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>  <span class="c1"># for plotting</span>
+<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>  <span class="c1"># for PCA for normalization</span>
 <span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">distance_matrix</span>
 
-<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">listdir</span> <span class="c1"># for retrieving files from directory</span>
-<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">join</span> <span class="c1"># for retrieving files from directory</span>
-<span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">MDS</span> <span class="c1"># for MDS</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="c1"># for loading in colors csv</span>
+<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">listdir</span>  <span class="c1"># for retrieving files from directory</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">isfile</span><span class="p">,</span> <span class="n">join</span>  <span class="c1"># for retrieving files from directory</span>
+<span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">MDS</span>  <span class="c1"># for MDS</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>  <span class="c1"># for loading in colors csv</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">zipfile</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
 
-<span class="c1">#---------------------------</span>
+<span class="c1"># ---------------------------</span>
 <span class="c1"># The ECT packages we&#39;ll use</span>
-<span class="c1">#---------------------------</span>
-<span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedGraph</span> <span class="c1"># for calculating ECTs</span>
+<span class="c1"># ---------------------------</span>
+<span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedGraph</span>  <span class="c1"># for calculating ECTs</span>
+
+<span class="c1"># Simple data paths</span>
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;outlines/&quot;</span>
+<span class="n">colors_path</span> <span class="o">=</span> <span class="s2">&quot;colors.csv&quot;</span>
+
+<span class="n">file_names</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">listdir</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">f</span><span class="p">))</span> <span class="ow">and</span> <span class="n">f</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:]</span> <span class="o">==</span> <span class="s2">&quot;.txt&quot;</span>
+<span class="p">]</span>
+<span class="n">file_names</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> files in the directory&quot;</span><span class="p">)</span>
 <br/></pre></div>
 </div>
 </div>
-<p>We’ve taken care of the preprocessing in advance by extracting out the shapes from the image. You can download these outlines here: <a class="reference external" href="outlines.zip">outlines.zip</a>.</p>
-<p><img alt="Matisse Numbered" src="../../_images/matisse_numbered.jpg" /></p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;doc_source/notebooks/Matisse/outlines/&quot;</span> <span class="c1"># set data directory</span>
-<span class="n">file_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">listdir</span><span class="p">(</span><span class="n">data_dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">isfile</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">f</span><span class="p">))</span> <span class="ow">and</span> <span class="n">f</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="p">:]</span> <span class="o">==</span> <span class="s1">&#39;.txt&#39;</span><span class="p">]</span> <span class="c1"># create a list of file names</span>
-<span class="n">file_names</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span> <span class="c1"># sort the list of file names</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> files in the directory&quot;</span><span class="p">)</span> <span class="c1"># print number of files</span>
-</pre></div>
-</div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
@@ -179,9 +178,10 @@ <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet
 There are 150 files in the directory
 </pre></div></div>
 </div>
-<p>Here we have an example of one of these leaves loaded in as an <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> class.</p>
+<p>We’ve taken care of the preprocessing in advance by extracting out the shapes from the image. You can download these outlines here: <a class="reference external" href="outlines.zip">outlines.zip</a>.</p>
+<p><img alt="Matisse Numbered" src="../../_images/matisse_numbered.jpg" /></p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">3</span>
@@ -189,12 +189,12 @@ <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet
 <span class="c1"># shape = normalize(shape)</span>
 <span class="n">G</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
 <span class="n">G</span><span class="o">.</span><span class="n">add_cycle</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">with_labels</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">node_size</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>
-</pre></div>
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -206,21 +206,22 @@ <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/notebooks_Matisse_Matisse_ECT_5_1.png" src="../../_images/notebooks_Matisse_Matisse_ECT_5_1.png" />
+<img alt="../../_images/notebooks_Matisse_example_matisse_3_1.png" src="../../_images/notebooks_Matisse_example_matisse_3_1.png" />
 </div>
 </div>
 <p>We’re going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">set_PCA_coordinates</span><span class="p">(</span> <span class="n">center_type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">,</span> <span class="n">scale_radius</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">with_labels</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">node_size</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="n">G</span><span class="o">.</span><span class="n">transform_coordinates</span><span class="p">(</span><span class="n">projection_type</span><span class="o">=</span><span class="s2">&quot;pca&quot;</span><span class="p">)</span>  <span class="c1"># project with PCA</span>
+<span class="n">G</span><span class="o">.</span><span class="n">scale_coordinates</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># scale to radius 1</span>
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">with_labels</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">node_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -232,132 +233,143 @@ <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/notebooks_Matisse_Matisse_ECT_7_1.png" src="../../_images/notebooks_Matisse_Matisse_ECT_7_1.png" />
+<img alt="../../_images/notebooks_Matisse_example_matisse_5_1.png" src="../../_images/notebooks_Matisse_example_matisse_5_1.png" />
 </div>
 </div>
 <p>And then we can compute the ECT of this leaf.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">50</span> <span class="c1"># set number of directional axes</span>
-<span class="n">num_thresh</span><span class="o">=</span><span class="mi">50</span> <span class="c1"># set number of thresholds each axis</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_dirs</span> <span class="o">=</span> <span class="mi">50</span>  <span class="c1"># set number of directional axes</span>
+<span class="n">num_thresh</span> <span class="o">=</span> <span class="mi">50</span>  <span class="c1"># set number of thresholds each axis</span>
 
-<span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span> <span class="o">=</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="n">num_thresh</span><span class="p">)</span> <span class="c1"># intiate ECT</span>
-<span class="n">myect</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># set bounding radius</span>
-<span class="n">myect</span><span class="o">.</span><span class="n">calculateECT</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="c1"># calculate ECT on embedded graph</span>
+<span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="n">num_thresh</span><span class="p">);</span>  <span class="c1"># intiate ECT</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">G</span><span class="p">);</span>  <span class="c1"># calculate ECT on embedded graph</span>
 
-<span class="n">myect</span><span class="o">.</span><span class="n">plotECT</span><span class="p">()</span> <span class="c1"># plot ECT</span>
-<br/></pre></div>
+<span class="n">result</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>  <span class="c1"># plot ECT</span>
+</pre></div>
+</div>
 </div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
+</pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/notebooks_Matisse_Matisse_ECT_9_0.png" src="../../_images/notebooks_Matisse_Matisse_ECT_9_0.png" />
+<img alt="../../_images/notebooks_Matisse_example_matisse_7_1.png" src="../../_images/notebooks_Matisse_example_matisse_7_1.png" />
 </div>
 </div>
 <p>Let’s just make a data loader with all of this for ease in a bit.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">matisse_ect</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">):</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">matisse_ect</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">ect</span><span class="p">):</span>
     <span class="n">shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="n">filename</span><span class="p">)</span>
     <span class="n">G</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
     <span class="n">G</span><span class="o">.</span><span class="n">add_cycle</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
-    <span class="n">G</span><span class="o">.</span><span class="n">set_PCA_coordinates</span><span class="p">(</span> <span class="n">center_type</span><span class="o">=</span><span class="s1">&#39;min_max&#39;</span><span class="p">,</span> <span class="n">scale_radius</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span> <span class="o">=</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="n">num_thresh</span><span class="p">)</span>
-    <span class="n">myect</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">M</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculateECT</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">M</span>
-</pre></div>
+    <span class="n">G</span><span class="o">.</span><span class="n">transform_coordinates</span><span class="p">(</span><span class="n">projection_type</span><span class="o">=</span><span class="s2">&quot;pca&quot;</span><span class="p">)</span>
+    <span class="n">G</span><span class="o">.</span><span class="n">scale_coordinates</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">result</span> <span class="o">=</span> <span class="n">ect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">result</span>
+<br/><br/></pre></div>
 </div>
 </div>
 <p>And now we can load in all the outlines, compute their ECT and store it in a 3D array.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">50</span> <span class="c1"># set number of directional axes</span>
-<span class="n">num_thresh</span><span class="o">=</span><span class="mi">50</span> <span class="c1"># set number of thresholds each axis</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_dirs</span> <span class="o">=</span> <span class="mi">50</span>  <span class="c1"># set number of directional axes</span>
+<span class="n">num_thresh</span> <span class="o">=</span> <span class="mi">50</span>  <span class="c1"># set number of thresholds each axis</span>
 
-<span class="n">ect_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">),</span><span class="n">num_dirs</span><span class="p">,</span><span class="n">num_thresh</span><span class="p">))</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)):</span> <span class="c1"># for each leaf</span>
-    <span class="n">ect_arr</span><span class="p">[</span><span class="n">i</span><span class="p">,:,:]</span> <span class="o">=</span> <span class="n">matisse_ect</span><span class="p">(</span><span class="n">file_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">)</span>
-</pre></div>
+<span class="n">ect_arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">),</span> <span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="p">))</span>
+<span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="n">num_thresh</span><span class="p">,</span> <span class="n">bound_radius</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)):</span>  <span class="c1"># for each leaf</span>
+    <span class="n">ect_arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">matisse_ect</span><span class="p">(</span><span class="n">file_names</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">myect</span><span class="p">)</span>
+<br/></pre></div>
 </div>
 </div>
 <p>Here, we are just going to compute the distance between two ECTs using <span class="math notranslate nohighlight">\(L_2\)</span> distance.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flattened_ect</span> <span class="o">=</span> <span class="n">ect_arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">),</span> <span class="n">num_dirs</span><span class="o">*</span><span class="n">num_thresh</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">flattened_ect</span> <span class="o">=</span> <span class="n">ect_arr</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">),</span> <span class="n">num_dirs</span> <span class="o">*</span> <span class="n">num_thresh</span><span class="p">)</span>
 <span class="n">D</span> <span class="o">=</span> <span class="n">distance_matrix</span><span class="p">(</span><span class="n">flattened_ect</span><span class="p">,</span> <span class="n">flattened_ect</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">D</span><span class="p">)</span>
-</pre></div>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-&lt;matplotlib.image.AxesImage at 0x3199f6190&gt;
+&lt;matplotlib.image.AxesImage at 0x32185cbd0&gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/notebooks_Matisse_Matisse_ECT_15_1.png" src="../../_images/notebooks_Matisse_Matisse_ECT_15_1.png" />
+<img alt="../../_images/notebooks_Matisse_example_matisse_13_1.png" src="../../_images/notebooks_Matisse_example_matisse_13_1.png" />
 </div>
 </div>
 <p>For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates.</p>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_components</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># select number of components</span>
-<span class="n">mds</span> <span class="o">=</span> <span class="n">MDS</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span> <span class="c1"># initialize MDS</span>
-          <span class="n">dissimilarity</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span> <span class="c1"># we have precomputed the distance matrix</span>
-          <span class="n">normalized_stress</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-          <span class="n">random_state</span><span class="o">=</span><span class="mi">5</span> <span class="c1"># select random state for reproducibility</span>
-         <span class="p">)</span>
-<span class="n">MDS_scores</span> <span class="o">=</span> <span class="n">mds</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">D</span><span class="p">)</span> <span class="c1"># get MDS scores</span>
-</pre></div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n_components</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># select number of components</span>
+<span class="n">mds</span> <span class="o">=</span> <span class="n">MDS</span><span class="p">(</span>
+    <span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span>  <span class="c1"># initialize MDS</span>
+    <span class="n">dissimilarity</span><span class="o">=</span><span class="s2">&quot;precomputed&quot;</span><span class="p">,</span>  <span class="c1"># we have precomputed the distance matrix</span>
+    <span class="n">normalized_stress</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+    <span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>  <span class="c1"># select random state for reproducibility</span>
+<span class="p">)</span>
+<span class="n">MDS_scores</span> <span class="o">=</span> <span class="n">mds</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">D</span><span class="p">)</span>  <span class="c1"># get MDS scores</span>
+<br/></pre></div>
 </div>
 </div>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># read in color hexcodes</span>
-<span class="n">col_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;doc_source/notebooks/Matisse/colors.csv&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">col_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">colors_path</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
-<span class="n">scale_val</span> <span class="o">=</span> <span class="mi">6</span> <span class="c1"># set scale value</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span> <span class="c1"># set figure dimensions</span>
+<span class="n">scale_val</span> <span class="o">=</span> <span class="mi">6</span>  <span class="c1"># set scale value</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>  <span class="c1"># set figure dimensions</span>
 
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)):</span> <span class="c1"># for each leaf</span>
-    <span class="n">shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">data_dir</span><span class="o">+</span><span class="n">file_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="c1"># get the current shape</span>
-    <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># zero center shape</span>
-    <span class="n">shape</span> <span class="o">=</span> <span class="n">scale_val</span> <span class="o">*</span> <span class="n">shape</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">))</span> <span class="c1"># scale to radius 1 then mult by scale_val</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">file_names</span><span class="p">)):</span>  <span class="c1"># for each leaf</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">data_dir</span> <span class="o">+</span> <span class="n">file_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>  <span class="c1"># get the current shape</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># zero center shape</span>
+    <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">scale_val</span> <span class="o">*</span> <span class="n">shape</span> <span class="o">/</span> <span class="nb">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+    <span class="p">)</span>  <span class="c1"># scale to radius 1 then mult by scale_val</span>
 
-    <span class="n">trans_sh</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape</span> <span class="o">+</span> <span class="n">MDS_scores</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="c1"># translate shape to MDS position</span>
+    <span class="n">trans_sh</span> <span class="o">=</span> <span class="n">shape</span> <span class="o">+</span> <span class="n">MDS_scores</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>  <span class="c1"># translate shape to MDS position</span>
 
-    <span class="n">plt</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">trans_sh</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">trans_sh</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">col_df</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># plot shape</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">trans_sh</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">trans_sh</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">col_df</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">],</span> <span class="n">lw</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># plot shape</span>
     <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s2">&quot;equal&quot;</span><span class="p">)</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;MDS of Matisse&#39;s Leaves via ECT distances&quot;</span><span class="p">)</span>
 <span class="c1"># plt.savefig(&quot;Matisse_MDS.png&quot;, bbox_inches = &#39;tight&#39;, dpi=300)</span>
-</pre></div>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -369,7 +381,7 @@ <h1><span class="section-number">3.3. </span>ECT on Matisse’s “The Parakeet
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../../_images/notebooks_Matisse_Matisse_ECT_18_1.png" src="../../_images/notebooks_Matisse_Matisse_ECT_18_1.png" />
+<img alt="../../_images/notebooks_Matisse_example_matisse_16_1.png" src="../../_images/notebooks_Matisse_example_matisse_16_1.png" />
 </div>
 </div>
 <section id="Acknowledgements">
@@ -382,8 +394,8 @@ <h2><span class="section-number">3.3.1. </span>Acknowledgements<a class="headerl
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../Tutorial-ECT_for_CW_Complexes.html" class="btn btn-neutral float-left" title="3.2. Tutorial: ECT for CW complexes" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="../Tutorial-ExactECT.html" class="btn btn-neutral float-right" title="3.4. Tutorial for exact ECT computation" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="../tutorial_cw.html" class="btn btn-neutral float-left" title="3.2. Tutorial: ECT for CW complexes" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../../contributing.html" class="btn btn-neutral float-right" title="4. Contributing to the ect Package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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   <hr/>
diff --git a/docs/notebooks/Matisse/example_matisse.ipynb b/docs/notebooks/Matisse/example_matisse.ipynb
new file mode 100644
index 0000000..6f0c4f8
--- /dev/null
+++ b/docs/notebooks/Matisse/example_matisse.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ECT on Matisse's \"The Parakeet and the Mermaid\"\n",
+    "\n",
+    "\n",
+    "\n",
+    " Here, we are going to give an example of using the ECT to classify the cutout shapes from Henri Matisse's 1952 [\"The Parakeet and the Mermaid\"](https://www.moma.org/audio/playlist/6/312).\n",
+    "\n",
+    "\n",
+    "\n",
+    " ![matisse.jpg](matisse.jpg)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 150 files in the directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "# -----------------\n",
+    "# Standard imports\n",
+    "# -----------------\n",
+    "import numpy as np  # for arrays\n",
+    "import matplotlib.pyplot as plt  # for plotting\n",
+    "from sklearn.decomposition import PCA  # for PCA for normalization\n",
+    "from scipy.spatial import distance_matrix\n",
+    "\n",
+    "from os import listdir  # for retrieving files from directory\n",
+    "from os.path import isfile, join  # for retrieving files from directory\n",
+    "from sklearn.manifold import MDS  # for MDS\n",
+    "import pandas as pd  # for loading in colors csv\n",
+    "import os\n",
+    "import zipfile\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# ---------------------------\n",
+    "# The ECT packages we'll use\n",
+    "# ---------------------------\n",
+    "from ect import ECT, EmbeddedGraph  # for calculating ECTs\n",
+    "\n",
+    "# Simple data paths\n",
+    "data_dir = \"outlines/\"\n",
+    "colors_path = \"colors.csv\"\n",
+    "\n",
+    "file_names = [\n",
+    "    f for f in listdir(data_dir) if isfile(join(data_dir, f)) and f[-4:] == \".txt\"\n",
+    "]\n",
+    "file_names.sort()\n",
+    "print(f\"There are {len(file_names)} files in the directory\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We've taken care of the preprocessing in advance by extracting out the shapes from the image.  You can download these outlines here: [outlines.zip](outlines.zip).\n",
+    "\n",
+    "\n",
+    "\n",
+    " <img src=\"matisse_numbered.jpg\" alt=\"Matisse Numbered\" style=\"width:50%;\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "i = 3\n",
+    "shape = np.loadtxt(data_dir + file_names[i])\n",
+    "# shape = normalize(shape)\n",
+    "G = EmbeddedGraph()\n",
+    "G.add_cycle(shape)\n",
+    "G.plot(with_labels=False, node_size=10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We're going to align the leaf using the PCA coordinates, min-max center, and scale it to fit in a ball of radius 1 for ease of comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "G.transform_coordinates(projection_type=\"pca\")  # project with PCA\n",
+    "G.scale_coordinates(1)  # scale to radius 1\n",
+    "G.plot(with_labels=False, node_size=10, bounding_circle=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And then we can compute the ECT of this leaf."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh);  # intiate ECT\n",
+    "result = myect.calculate(G);  # calculate ECT on embedded graph\n",
+    "\n",
+    "result.plot();  # plot ECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Let's just make a data loader with all of this for ease in a bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def matisse_ect(filename, ect):\n",
+    "    shape = np.loadtxt(data_dir + filename)\n",
+    "    G = EmbeddedGraph()\n",
+    "    G.add_cycle(shape)\n",
+    "    G.transform_coordinates(projection_type=\"pca\")\n",
+    "    G.scale_coordinates(1)\n",
+    "    result = ect.calculate(G)\n",
+    "    return result\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " And now we can load in all the outlines, compute their ECT and store it in a 3D array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_dirs = 50  # set number of directional axes\n",
+    "num_thresh = 50  # set number of thresholds each axis\n",
+    "\n",
+    "ect_arr = np.zeros((len(file_names), num_dirs, num_thresh))\n",
+    "myect = ECT(num_dirs=num_dirs, num_thresh=num_thresh, bound_radius=1)\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    ect_arr[i, :, :] = matisse_ect(file_names[i], myect)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Here, we are just going to compute the distance between two ECTs using $L_2$ distance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x32185cbd0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 480x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "flattened_ect = ect_arr.reshape(len(file_names), num_dirs * num_thresh)\n",
+    "D = distance_matrix(flattened_ect, flattened_ect)\n",
+    "plt.matshow(D)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " For visualization purposes, we can project this data into 2D using Multi Dimensional Scaling (MDS). Here we plot each figure at the MDS coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_components = 2  # select number of components\n",
+    "mds = MDS(\n",
+    "    n_components=n_components,  # initialize MDS\n",
+    "    dissimilarity=\"precomputed\",  # we have precomputed the distance matrix\n",
+    "    normalized_stress=\"auto\",\n",
+    "    random_state=5,  # select random state for reproducibility\n",
+    ")\n",
+    "MDS_scores = mds.fit_transform(D)  # get MDS scores\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, \"MDS of Matisse's Leaves via ECT distances\")"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# read in color hexcodes\n",
+    "col_df = pd.read_csv(colors_path, header=None)\n",
+    "\n",
+    "scale_val = 6  # set scale value\n",
+    "plt.figure(figsize=(5, 5))  # set figure dimensions\n",
+    "\n",
+    "for i in range(len(file_names)):  # for each leaf\n",
+    "    shape = np.loadtxt(data_dir + file_names[i])  # get the current shape\n",
+    "    shape = shape - np.mean(shape, axis=0)  # zero center shape\n",
+    "    shape = (\n",
+    "        scale_val * shape / max(np.linalg.norm(shape, axis=1))\n",
+    "    )  # scale to radius 1 then mult by scale_val\n",
+    "\n",
+    "    trans_sh = shape + MDS_scores[i]  # translate shape to MDS position\n",
+    "\n",
+    "    plt.fill(trans_sh[:, 0], trans_sh[:, 1], c=col_df[0][i], lw=0)  # plot shape\n",
+    "    plt.gca().set_aspect(\"equal\")\n",
+    "\n",
+    "plt.title(\"MDS of Matisse's Leaves via ECT distances\")\n",
+    "# plt.savefig(\"Matisse_MDS.png\", bbox_inches = 'tight', dpi=300)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "\n",
+    "\n",
+    " This notebook was written by [Liz Munch](https://elizabethmunch.com/) based on [original code](https://github.com/DanChitwood/matisse_test) from [Dan Chitwood](https://www.canr.msu.edu/people/dr_dan_chitwood)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/Playing_with_Spirals.html b/docs/notebooks/Playing_with_Spirals.html
deleted file mode 100644
index a11daff..0000000
--- a/docs/notebooks/Playing_with_Spirals.html
+++ /dev/null
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-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">1. Getting Started</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#prerequisites">1.1. Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-ect">1.2. Installing <cite>ect</cite></a></li>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#uninstalling-ect">1.3. Uninstalling <cite>ect</cite></a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
-</ul>
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-<li class="toctree-l2"><a class="reference internal" href="../contributing.html#getting-started">4.1. Getting Started</a></li>
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-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#testing-guidelines">4.2.3. Testing guidelines</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#issue-tracking-and-pull-requests">4.2.4. Issue tracking and pull requests</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contributing.html#conclusion">4.3. Conclusion</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../license.html">5. License</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../citing.html">6. Citing</a></li>
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-             
-  <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedGraph</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">,</span><span class="n">create_example_graph</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Circle</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-</pre></div>
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Recreating the sprial example from Arya</span>
-
-<span class="c1"># Create the graph</span>
-<span class="n">Spiral</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.5</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;f&#39;</span><span class="p">,</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">Spiral</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
-
-<span class="n">nodelist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">Spiral</span><span class="o">.</span><span class="n">nodes</span><span class="p">())</span>
-<span class="n">nodelist</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nodelist</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-    <span class="n">Spiral</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">nodelist</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nodelist</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-
-<span class="n">Spiral</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">angle_labels_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">edges_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Playing_with_Spirals_1_1.png" src="../_images/notebooks_Playing_with_Spirals_1_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Spiral</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">angle_labels_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">edges_only</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
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-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Playing_with_Spirals_2_1.png" src="../_images/notebooks_Playing_with_Spirals_2_1.png" />
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-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">All_Angles</span> <span class="o">=</span> <span class="n">Spiral</span><span class="o">.</span><span class="n">get_all_angles</span><span class="p">(</span><span class="s1">&#39;dict&#39;</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">opposites</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
-<span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">All_Angles</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="n">keys</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-
-<span class="n">pd_table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Angle&#39;</span><span class="p">,</span> <span class="s1">&#39;Swapped Verts&#39;</span><span class="p">,</span> <span class="s1">&#39;Sorted list&#39;</span><span class="p">])</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Angle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">keys</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Swapped Verts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">All_Angles</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>
-
-
-
-<span class="n">pd_table</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;Angle&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Sorted list&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">Spiral</span><span class="o">.</span><span class="n">sort_vertices</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>
-<span class="n">pd_table</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Angle</th>
-      <th>Swapped Verts</th>
-      <th>Sorted list</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.00</td>
-      <td>[(c, f)]</td>
-      <td>gcfabed</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.32</td>
-      <td>[(d, e)]</td>
-      <td>gcfabed</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>0.52</td>
-      <td>[(b, d)]</td>
-      <td>gcfadbe</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.79</td>
-      <td>[(a, d), (f, a), (f, d)]</td>
-      <td>gcdafbe</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1.15</td>
-      <td>[(c, d)]</td>
-      <td>gcdafbe</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1.19</td>
-      <td>[(b, f)]</td>
-      <td>gdcafbe</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1.57</td>
-      <td>[(e, f)]</td>
-      <td>gdcabfe</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1.65</td>
-      <td>[(d, g)]</td>
-      <td>gdcabef</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1.82</td>
-      <td>[(a, c)]</td>
-      <td>dgacbef</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>2.11</td>
-      <td>[(b, c)]</td>
-      <td>dgacbef</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>2.27</td>
-      <td>[(c, e)]</td>
-      <td>dgabecf</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>2.28</td>
-      <td>[(a, g)]</td>
-      <td>dagbecf</td>
-    </tr>
-    <tr>
-      <th>12</th>
-      <td>2.36</td>
-      <td>[(b, g)]</td>
-      <td>dabgecf</td>
-    </tr>
-    <tr>
-      <th>13</th>
-      <td>2.41</td>
-      <td>[(e, g)]</td>
-      <td>dabegcf</td>
-    </tr>
-    <tr>
-      <th>14</th>
-      <td>2.60</td>
-      <td>[(c, g)]</td>
-      <td>dabegcf</td>
-    </tr>
-    <tr>
-      <th>15</th>
-      <td>2.68</td>
-      <td>[(a, b), (a, e), (b, e)]</td>
-      <td>debacgf</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>2.85</td>
-      <td>[(f, g)]</td>
-      <td>debacgf</td>
-    </tr>
-    <tr>
-      <th>17</th>
-      <td>3.14</td>
-      <td>[(c, f)]</td>
-      <td>debacfg</td>
-    </tr>
-    <tr>
-      <th>18</th>
-      <td>3.46</td>
-      <td>[(d, e)]</td>
-      <td>debafcg</td>
-    </tr>
-    <tr>
-      <th>19</th>
-      <td>3.66</td>
-      <td>[(b, d)]</td>
-      <td>edbafcg</td>
-    </tr>
-    <tr>
-      <th>20</th>
-      <td>3.93</td>
-      <td>[(a, d), (f, a), (f, d)]</td>
-      <td>ebfadcg</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>4.29</td>
-      <td>[(c, d)]</td>
-      <td>ebfadcg</td>
-    </tr>
-    <tr>
-      <th>22</th>
-      <td>4.33</td>
-      <td>[(b, f)]</td>
-      <td>ebfacdg</td>
-    </tr>
-    <tr>
-      <th>23</th>
-      <td>4.71</td>
-      <td>[(e, f)]</td>
-      <td>efbacdg</td>
-    </tr>
-    <tr>
-      <th>24</th>
-      <td>4.79</td>
-      <td>[(d, g)]</td>
-      <td>febacdg</td>
-    </tr>
-    <tr>
-      <th>25</th>
-      <td>4.96</td>
-      <td>[(a, c)]</td>
-      <td>febcagd</td>
-    </tr>
-    <tr>
-      <th>26</th>
-      <td>5.25</td>
-      <td>[(b, c)]</td>
-      <td>febcagd</td>
-    </tr>
-    <tr>
-      <th>27</th>
-      <td>5.41</td>
-      <td>[(c, e)]</td>
-      <td>fcebagd</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>5.42</td>
-      <td>[(a, g)]</td>
-      <td>fcebagd</td>
-    </tr>
-    <tr>
-      <th>29</th>
-      <td>5.50</td>
-      <td>[(b, g)]</td>
-      <td>fcegbad</td>
-    </tr>
-    <tr>
-      <th>30</th>
-      <td>5.55</td>
-      <td>[(e, g)]</td>
-      <td>fcegbad</td>
-    </tr>
-    <tr>
-      <th>31</th>
-      <td>5.74</td>
-      <td>[(c, g)]</td>
-      <td>fcgebad</td>
-    </tr>
-    <tr>
-      <th>32</th>
-      <td>5.82</td>
-      <td>[(a, b), (a, e), (b, e)]</td>
-      <td>fgcabed</td>
-    </tr>
-    <tr>
-      <th>33</th>
-      <td>5.99</td>
-      <td>[(f, g)]</td>
-      <td>fgcabed</td>
-    </tr>
-  </tbody>
-</table>
-</div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
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-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">All_Angles</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
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-dict_keys([5.82, 1.82, 3.93, 2.28, 2.11, 3.66, 1.19, 2.36, 4.29, 5.41, 0.0, 2.6, 0.32, 1.65, 1.57, 2.41, 2.85, 2.68, 4.96, 0.79, 5.42, 5.25, 0.52, 4.33, 5.5, 1.15, 2.27, 3.14, 5.74, 3.46, 4.79, 4.71, 5.55, 5.99])
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-</pre></div>
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-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">All_Angles</span> <span class="o">=</span> <span class="n">Spiral</span><span class="o">.</span><span class="n">get_all_angles</span><span class="p">(</span><span class="s1">&#39;dict&#39;</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-
-<span class="c1"># add pi + angle for all angles</span>
-<span class="n">All_Angles_2pi</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">All_Angles</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-    <span class="n">All_Angles_2pi</span><span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="mi">2</span><span class="p">)]</span> <span class="o">=</span> <span class="n">All_Angles</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-    <span class="n">other_key</span> <span class="o">=</span> <span class="p">(</span><span class="n">key</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-    <span class="n">other_key</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">other_key</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="n">All_Angles_2pi</span><span class="p">[</span><span class="n">other_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">All_Angles</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-
-<span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">All_Angles_2pi</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="n">keys</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-
-<span class="n">pd_table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">,</span> <span class="s1">&#39;vertex_pair(s)_swapped&#39;</span><span class="p">,</span> <span class="s1">&#39;sorted_order_at_next_midpoint&#39;</span><span class="p">])</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;angle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">keys</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;vertex_pair(s)_swapped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">All_Angles_2pi</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>
-
-<span class="c1"># sort the vertex pairs at the midpoint</span>
-
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-    <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">([</span><span class="n">keys</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">keys</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]])</span>
-    <span class="n">sorted_order</span> <span class="o">=</span> <span class="n">Spiral</span><span class="o">.</span><span class="n">sort_vertices</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-    <span class="n">pd_table</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="s1">&#39;sorted_order_at_next_midpoint&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sorted_order</span>
-
-<span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">([</span><span class="n">keys</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">keys</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">])</span>
-<span class="n">sorted_order</span> <span class="o">=</span> <span class="n">Spiral</span><span class="o">.</span><span class="n">sort_vertices</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-<span class="n">pd_table</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;sorted_order_at_next_midpoint&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sorted_order</span>
-
-<span class="n">pd_table</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>angle</th>
-      <th>vertex_pair(s)_swapped</th>
-      <th>sorted_order_at_next_midpoint</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.00</td>
-      <td>[(c, f)]</td>
-      <td>[g, c, f, a, b, e, d]</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.32</td>
-      <td>[(d, e)]</td>
-      <td>[g, c, f, a, d, b, e]</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1.04</td>
-      <td>[(b, f)]</td>
-      <td>[g, c, d, a, f, b, e]</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1.19</td>
-      <td>[(b, f)]</td>
-      <td>[g, d, c, a, b, f, e]</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1.57</td>
-      <td>[(e, f)]</td>
-      <td>[g, d, c, a, b, e, f]</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1.65</td>
-      <td>[(d, g)]</td>
-      <td>[d, g, c, a, b, e, f]</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1.73</td>
-      <td>[(c, e)]</td>
-      <td>[d, g, c, a, b, e, f]</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1.82</td>
-      <td>[(a, c)]</td>
-      <td>[d, g, a, c, b, e, f]</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>2.11</td>
-      <td>[(b, c)]</td>
-      <td>[d, g, a, b, c, e, f]</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>2.24</td>
-      <td>[(e, f)]</td>
-      <td>[d, g, a, b, c, e, f]</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>2.28</td>
-      <td>[(a, g)]</td>
-      <td>[d, a, g, b, e, c, f]</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>2.36</td>
-      <td>[(b, g)]</td>
-      <td>[d, a, b, g, e, c, f]</td>
-    </tr>
-    <tr>
-      <th>12</th>
-      <td>2.41</td>
-      <td>[(e, g)]</td>
-      <td>[d, a, b, e, g, c, f]</td>
-    </tr>
-    <tr>
-      <th>13</th>
-      <td>2.49</td>
-      <td>[(d, g)]</td>
-      <td>[d, a, b, e, g, c, f]</td>
-    </tr>
-    <tr>
-      <th>14</th>
-      <td>2.52</td>
-      <td>[(b, d)]</td>
-      <td>[d, a, b, e, g, c, f]</td>
-    </tr>
-    <tr>
-      <th>15</th>
-      <td>2.60</td>
-      <td>[(c, g)]</td>
-      <td>[d, e, b, a, c, g, f]</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>2.85</td>
-      <td>[(f, g)]</td>
-      <td>[d, e, b, a, c, f, g]</td>
-    </tr>
-    <tr>
-      <th>17</th>
-      <td>3.02</td>
-      <td>[(a, c)]</td>
-      <td>[d, e, b, a, f, c, g]</td>
-    </tr>
-    <tr>
-      <th>18</th>
-      <td>3.37</td>
-      <td>[(a, d), (f, a), (f, d)]</td>
-      <td>[e, d, b, a, f, c, g]</td>
-    </tr>
-    <tr>
-      <th>19</th>
-      <td>3.59</td>
-      <td>[(c, f)]</td>
-      <td>[e, d, b, a, f, c, g]</td>
-    </tr>
-    <tr>
-      <th>20</th>
-      <td>3.66</td>
-      <td>[(b, d)]</td>
-      <td>[e, b, d, a, f, c, g]</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>3.93</td>
-      <td>[(b, c)]</td>
-      <td>[e, b, f, a, d, c, g]</td>
-    </tr>
-    <tr>
-      <th>22</th>
-      <td>4.29</td>
-      <td>[(c, d)]</td>
-      <td>[e, f, b, a, c, d, g]</td>
-    </tr>
-    <tr>
-      <th>23</th>
-      <td>4.47</td>
-      <td>[(a, g)]</td>
-      <td>[e, f, b, a, c, d, g]</td>
-    </tr>
-    <tr>
-      <th>24</th>
-      <td>4.50</td>
-      <td>[(c, d)]</td>
-      <td>[e, f, b, a, c, d, g]</td>
-    </tr>
-    <tr>
-      <th>25</th>
-      <td>4.59</td>
-      <td>[(d, e)]</td>
-      <td>[e, f, b, a, c, d, g]</td>
-    </tr>
-    <tr>
-      <th>26</th>
-      <td>4.72</td>
-      <td>[(b, g)]</td>
-      <td>[f, e, b, a, c, d, g]</td>
-    </tr>
-    <tr>
-      <th>27</th>
-      <td>4.87</td>
-      <td>[(e, g)]</td>
-      <td>[f, e, b, c, a, g, d]</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>5.41</td>
-      <td>[(c, e)]</td>
-      <td>[f, c, e, b, g, a, d]</td>
-    </tr>
-    <tr>
-      <th>29</th>
-      <td>5.47</td>
-      <td>[(c, g)]</td>
-      <td>[f, c, g, e, b, a, d]</td>
-    </tr>
-    <tr>
-      <th>30</th>
-      <td>5.82</td>
-      <td>[(a, b), (a, e), (b, e)]</td>
-      <td>[g, f, c, a, b, e, d]</td>
-    </tr>
-    <tr>
-      <th>31</th>
-      <td>6.26</td>
-      <td>[(f, g)]</td>
-      <td>[g, f, c, a, b, e, d]</td>
-    </tr>
-  </tbody>
-</table>
-</div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">nodelist</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-[&#39;a&#39;, &#39;b&#39;, &#39;c&#39;, &#39;d&#39;, &#39;e&#39;, &#39;f&#39;, &#39;g&#39;]
-</pre></div></div>
-</div>
-<section id="Modified-version-breaking-the-spiral">
-<h1>Modified version breaking the spiral<a class="headerlink" href="#Modified-version-breaking-the-spiral" title="Link to this heading"></a></h1>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Modified version with vertex added between C and D</span>
-
-<span class="c1"># Create the graph</span>
-<span class="n">G</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;c&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">.5</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;X&#39;</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mf">2.5</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;e&#39;</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;f&#39;</span><span class="p">,</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
-
-<span class="n">nodelist</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;a&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;c&#39;</span><span class="p">,</span><span class="s1">&#39;X&#39;</span><span class="p">,</span>  <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;e&#39;</span><span class="p">,</span> <span class="s1">&#39;f&#39;</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">]</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">nodelist</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
-    <span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">nodelist</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">nodelist</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-
-<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">angle_labels_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">edges_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">ax</span><span class="p">)</span>
-<span class="c1"># remove axes</span>
-<span class="n">ax</span><span class="o">.</span><span class="n">set_axis_off</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Playing_with_Spirals_11_0.png" src="../_images/notebooks_Playing_with_Spirals_11_0.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">get_all_angles</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;matplotlib.colorbar.Colorbar at 0x30be90c90&gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Playing_with_Spirals_12_1.png" src="../_images/notebooks_Playing_with_Spirals_12_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">All_Angles</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_all_angles</span><span class="p">(</span><span class="s1">&#39;dict&#39;</span><span class="p">,</span> <span class="n">num_rounding_digits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">opposites</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
-<span class="n">keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">All_Angles</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-<span class="n">keys</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
-
-<span class="n">pd_table</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Angle&#39;</span><span class="p">,</span> <span class="s1">&#39;Swapped Verts&#39;</span><span class="p">,</span> <span class="s1">&#39;Sorted list&#39;</span><span class="p">])</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Angle&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">keys</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Swapped Verts&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">All_Angles</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>
-
-
-
-<span class="n">pd_table</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;Angle&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">pd_table</span><span class="p">[</span><span class="s1">&#39;Sorted list&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">sort_vertices</span><span class="p">(</span><span class="n">key</span><span class="p">))</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">]</span>
-<span class="n">pd_table</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area rendered_html docutils container">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
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-    .dataframe tbody tr th {
-        vertical-align: top;
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-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>Angle</th>
-      <th>Swapped Verts</th>
-      <th>Sorted list</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>0.00</td>
-      <td>[(c, f), (X, e)]</td>
-      <td>gcfabXed</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>0.25</td>
-      <td>[(b, X)]</td>
-      <td>gcfaXbed</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>0.32</td>
-      <td>[(d, e)]</td>
-      <td>gcfaXbed</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>0.52</td>
-      <td>[(b, d)]</td>
-      <td>gcfaXdbe</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>0.79</td>
-      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>
-      <td>gcdXafbe</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1.15</td>
-      <td>[(c, d)]</td>
-      <td>gcdXafbe</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1.19</td>
-      <td>[(b, f)]</td>
-      <td>gdcXafbe</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1.41</td>
-      <td>[(c, X)]</td>
-      <td>gdXcabfe</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1.57</td>
-      <td>[(e, f)]</td>
-      <td>gdXcabfe</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>1.65</td>
-      <td>[(d, g)]</td>
-      <td>gdXcabef</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>1.82</td>
-      <td>[(a, c)]</td>
-      <td>dgXacbef</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>1.99</td>
-      <td>[(X, g)]</td>
-      <td>dXgacbef</td>
-    </tr>
-    <tr>
-      <th>12</th>
-      <td>2.11</td>
-      <td>[(b, c)]</td>
-      <td>dXgacbef</td>
-    </tr>
-    <tr>
-      <th>13</th>
-      <td>2.27</td>
-      <td>[(c, e)]</td>
-      <td>dXgabecf</td>
-    </tr>
-    <tr>
-      <th>14</th>
-      <td>2.28</td>
-      <td>[(a, g)]</td>
-      <td>dXagbecf</td>
-    </tr>
-    <tr>
-      <th>15</th>
-      <td>2.36</td>
-      <td>[(b, g)]</td>
-      <td>dXabgecf</td>
-    </tr>
-    <tr>
-      <th>16</th>
-      <td>2.41</td>
-      <td>[(e, g)]</td>
-      <td>dXabegcf</td>
-    </tr>
-    <tr>
-      <th>17</th>
-      <td>2.60</td>
-      <td>[(c, g)]</td>
-      <td>dXabegcf</td>
-    </tr>
-    <tr>
-      <th>18</th>
-      <td>2.68</td>
-      <td>[(a, b), (a, e), (b, e)]</td>
-      <td>dXebacgf</td>
-    </tr>
-    <tr>
-      <th>19</th>
-      <td>2.85</td>
-      <td>[(f, g)]</td>
-      <td>dXebacgf</td>
-    </tr>
-    <tr>
-      <th>20</th>
-      <td>3.14</td>
-      <td>[(c, f), (X, e)]</td>
-      <td>dXebacfg</td>
-    </tr>
-    <tr>
-      <th>21</th>
-      <td>3.39</td>
-      <td>[(b, X)]</td>
-      <td>debXafcg</td>
-    </tr>
-    <tr>
-      <th>22</th>
-      <td>3.46</td>
-      <td>[(d, e)]</td>
-      <td>debXafcg</td>
-    </tr>
-    <tr>
-      <th>23</th>
-      <td>3.66</td>
-      <td>[(b, d)]</td>
-      <td>edbXafcg</td>
-    </tr>
-    <tr>
-      <th>24</th>
-      <td>3.93</td>
-      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>
-      <td>ebfaXdcg</td>
-    </tr>
-    <tr>
-      <th>25</th>
-      <td>4.29</td>
-      <td>[(c, d)]</td>
-      <td>ebfaXdcg</td>
-    </tr>
-    <tr>
-      <th>26</th>
-      <td>4.33</td>
-      <td>[(b, f)]</td>
-      <td>ebfaXcdg</td>
-    </tr>
-    <tr>
-      <th>27</th>
-      <td>4.55</td>
-      <td>[(c, X)]</td>
-      <td>efbacXdg</td>
-    </tr>
-    <tr>
-      <th>28</th>
-      <td>4.71</td>
-      <td>[(e, f)]</td>
-      <td>efbacXdg</td>
-    </tr>
-    <tr>
-      <th>29</th>
-      <td>4.79</td>
-      <td>[(d, g)]</td>
-      <td>febacXdg</td>
-    </tr>
-    <tr>
-      <th>30</th>
-      <td>4.96</td>
-      <td>[(a, c)]</td>
-      <td>febcaXgd</td>
-    </tr>
-    <tr>
-      <th>31</th>
-      <td>5.13</td>
-      <td>[(X, g)]</td>
-      <td>febcaXgd</td>
-    </tr>
-    <tr>
-      <th>32</th>
-      <td>5.25</td>
-      <td>[(b, c)]</td>
-      <td>febcagXd</td>
-    </tr>
-    <tr>
-      <th>33</th>
-      <td>5.41</td>
-      <td>[(c, e)]</td>
-      <td>fcebagXd</td>
-    </tr>
-    <tr>
-      <th>34</th>
-      <td>5.42</td>
-      <td>[(a, g)]</td>
-      <td>fcebagXd</td>
-    </tr>
-    <tr>
-      <th>35</th>
-      <td>5.50</td>
-      <td>[(b, g)]</td>
-      <td>fcegbaXd</td>
-    </tr>
-    <tr>
-      <th>36</th>
-      <td>5.55</td>
-      <td>[(e, g)]</td>
-      <td>fcegbaXd</td>
-    </tr>
-    <tr>
-      <th>37</th>
-      <td>5.74</td>
-      <td>[(c, g)]</td>
-      <td>fcgebaXd</td>
-    </tr>
-    <tr>
-      <th>38</th>
-      <td>5.82</td>
-      <td>[(a, b), (a, e), (b, e)]</td>
-      <td>fgcabeXd</td>
-    </tr>
-    <tr>
-      <th>39</th>
-      <td>5.99</td>
-      <td>[(f, g)]</td>
-      <td>fgcabeXd</td>
-    </tr>
-  </tbody>
-</table>
-</div></div>
-</div>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
-</pre></div>
-</div>
-</div>
-</section>
-
-
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-          </div>
-          <footer>
-
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-    <p>&#169; Copyright 2025, MunchLab.</p>
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-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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\ No newline at end of file
diff --git a/docs/notebooks/Playing_with_Spirals.ipynb b/docs/notebooks/Playing_with_Spirals.ipynb
deleted file mode 100644
index 3b1339f..0000000
--- a/docs/notebooks/Playing_with_Spirals.ipynb
+++ /dev/null
@@ -1,1204 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, EmbeddedCW,create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.patches import Circle\n",
-    "import numpy as np\n",
-    "import networkx as nx\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Recreating the sprial example from Arya \n",
-    "\n",
-    "# Create the graph\n",
-    "Spiral = EmbeddedGraph()\n",
-    "Spiral.add_node('a', .5, 0)\n",
-    "Spiral.add_node('b', 1, 1)\n",
-    "Spiral.add_node('c', -1.5, -.5)\n",
-    "Spiral.add_node('d', 3, -2.5)\n",
-    "Spiral.add_node('e',1.5,2)\n",
-    "Spiral.add_node('f',-1.5,2)\n",
-    "Spiral.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = list(Spiral.nodes())\n",
-    "nodelist.sort()\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    Spiral.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "Spiral.plot(angle_labels_circle=True, edges_only=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "Spiral.plot(angle_labels_circle=True, edges_only=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfadbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>debacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>debacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfadcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebad</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabed</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle             Swapped Verts Sorted list\n",
-       "0    0.00                  [(c, f)]     gcfabed\n",
-       "1    0.32                  [(d, e)]     gcfabed\n",
-       "2    0.52                  [(b, d)]     gcfadbe\n",
-       "3    0.79  [(a, d), (f, a), (f, d)]     gcdafbe\n",
-       "4    1.15                  [(c, d)]     gcdafbe\n",
-       "5    1.19                  [(b, f)]     gdcafbe\n",
-       "6    1.57                  [(e, f)]     gdcabfe\n",
-       "7    1.65                  [(d, g)]     gdcabef\n",
-       "8    1.82                  [(a, c)]     dgacbef\n",
-       "9    2.11                  [(b, c)]     dgacbef\n",
-       "10   2.27                  [(c, e)]     dgabecf\n",
-       "11   2.28                  [(a, g)]     dagbecf\n",
-       "12   2.36                  [(b, g)]     dabgecf\n",
-       "13   2.41                  [(e, g)]     dabegcf\n",
-       "14   2.60                  [(c, g)]     dabegcf\n",
-       "15   2.68  [(a, b), (a, e), (b, e)]     debacgf\n",
-       "16   2.85                  [(f, g)]     debacgf\n",
-       "17   3.14                  [(c, f)]     debacfg\n",
-       "18   3.46                  [(d, e)]     debafcg\n",
-       "19   3.66                  [(b, d)]     edbafcg\n",
-       "20   3.93  [(a, d), (f, a), (f, d)]     ebfadcg\n",
-       "21   4.29                  [(c, d)]     ebfadcg\n",
-       "22   4.33                  [(b, f)]     ebfacdg\n",
-       "23   4.71                  [(e, f)]     efbacdg\n",
-       "24   4.79                  [(d, g)]     febacdg\n",
-       "25   4.96                  [(a, c)]     febcagd\n",
-       "26   5.25                  [(b, c)]     febcagd\n",
-       "27   5.41                  [(c, e)]     fcebagd\n",
-       "28   5.42                  [(a, g)]     fcebagd\n",
-       "29   5.50                  [(b, g)]     fcegbad\n",
-       "30   5.55                  [(e, g)]     fcegbad\n",
-       "31   5.74                  [(c, g)]     fcgebad\n",
-       "32   5.82  [(a, b), (a, e), (b, e)]     fgcabed\n",
-       "33   5.99                  [(f, g)]     fgcabed"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(Spiral.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys([5.82, 1.82, 3.93, 2.28, 2.11, 3.66, 1.19, 2.36, 4.29, 5.41, 0.0, 2.6, 0.32, 1.65, 1.57, 2.41, 2.85, 2.68, 4.96, 0.79, 5.42, 5.25, 0.52, 4.33, 5.5, 1.15, 2.27, 3.14, 5.74, 3.46, 4.79, 4.71, 5.55, 5.99])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>angle</th>\n",
-       "      <th>vertex_pair(s)_swapped</th>\n",
-       "      <th>sorted_order_at_next_midpoint</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[g, c, f, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[g, c, f, a, d, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.04</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, c, d, a, f, b, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>[g, d, c, a, b, f, e]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[g, d, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.73</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[d, g, c, a, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, g, a, c, b, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.24</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>[d, g, a, b, c, e, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[d, a, g, b, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[d, a, b, g, e, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.49</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[d, a, b, e, g, c, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[d, e, b, a, c, g, f]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[d, e, b, a, c, f, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>3.02</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>[d, e, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>3.37</td>\n",
-       "      <td>[(a, d), (f, a), (f, d)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>3.59</td>\n",
-       "      <td>[(c, f)]</td>\n",
-       "      <td>[e, d, b, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>[e, b, d, a, f, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>[e, b, f, a, d, c, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>4.47</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>4.50</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.59</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>[e, f, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.72</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>[f, e, b, a, c, d, g]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.87</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>[f, e, b, c, a, g, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>[f, c, e, b, g, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>5.47</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>[f, c, g, e, b, a, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>6.26</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>[g, f, c, a, b, e, d]</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    angle    vertex_pair(s)_swapped sorted_order_at_next_midpoint\n",
-       "0    0.00                  [(c, f)]         [g, c, f, a, b, e, d]\n",
-       "1    0.32                  [(d, e)]         [g, c, f, a, d, b, e]\n",
-       "2    1.04                  [(b, f)]         [g, c, d, a, f, b, e]\n",
-       "3    1.19                  [(b, f)]         [g, d, c, a, b, f, e]\n",
-       "4    1.57                  [(e, f)]         [g, d, c, a, b, e, f]\n",
-       "5    1.65                  [(d, g)]         [d, g, c, a, b, e, f]\n",
-       "6    1.73                  [(c, e)]         [d, g, c, a, b, e, f]\n",
-       "7    1.82                  [(a, c)]         [d, g, a, c, b, e, f]\n",
-       "8    2.11                  [(b, c)]         [d, g, a, b, c, e, f]\n",
-       "9    2.24                  [(e, f)]         [d, g, a, b, c, e, f]\n",
-       "10   2.28                  [(a, g)]         [d, a, g, b, e, c, f]\n",
-       "11   2.36                  [(b, g)]         [d, a, b, g, e, c, f]\n",
-       "12   2.41                  [(e, g)]         [d, a, b, e, g, c, f]\n",
-       "13   2.49                  [(d, g)]         [d, a, b, e, g, c, f]\n",
-       "14   2.52                  [(b, d)]         [d, a, b, e, g, c, f]\n",
-       "15   2.60                  [(c, g)]         [d, e, b, a, c, g, f]\n",
-       "16   2.85                  [(f, g)]         [d, e, b, a, c, f, g]\n",
-       "17   3.02                  [(a, c)]         [d, e, b, a, f, c, g]\n",
-       "18   3.37  [(a, d), (f, a), (f, d)]         [e, d, b, a, f, c, g]\n",
-       "19   3.59                  [(c, f)]         [e, d, b, a, f, c, g]\n",
-       "20   3.66                  [(b, d)]         [e, b, d, a, f, c, g]\n",
-       "21   3.93                  [(b, c)]         [e, b, f, a, d, c, g]\n",
-       "22   4.29                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "23   4.47                  [(a, g)]         [e, f, b, a, c, d, g]\n",
-       "24   4.50                  [(c, d)]         [e, f, b, a, c, d, g]\n",
-       "25   4.59                  [(d, e)]         [e, f, b, a, c, d, g]\n",
-       "26   4.72                  [(b, g)]         [f, e, b, a, c, d, g]\n",
-       "27   4.87                  [(e, g)]         [f, e, b, c, a, g, d]\n",
-       "28   5.41                  [(c, e)]         [f, c, e, b, g, a, d]\n",
-       "29   5.47                  [(c, g)]         [f, c, g, e, b, a, d]\n",
-       "30   5.82  [(a, b), (a, e), (b, e)]         [g, f, c, a, b, e, d]\n",
-       "31   6.26                  [(f, g)]         [g, f, c, a, b, e, d]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = Spiral.get_all_angles('dict', num_rounding_digits=2)\n",
-    "\n",
-    "# add pi + angle for all angles \n",
-    "All_Angles_2pi = {}\n",
-    "for key in All_Angles.keys():\n",
-    "    All_Angles_2pi[round(key,2)] = All_Angles[key]\n",
-    "    other_key = (key + np.pi) % 2*np.pi\n",
-    "    other_key = round(other_key, 2)\n",
-    "    All_Angles_2pi[other_key] = All_Angles[key]\n",
-    "\n",
-    "keys = list(All_Angles_2pi.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns=['angle', 'vertex_pair(s)_swapped', 'sorted_order_at_next_midpoint'])\n",
-    "pd_table['angle'] = keys\n",
-    "pd_table['vertex_pair(s)_swapped'] = [All_Angles_2pi[key] for key in keys]\n",
-    "\n",
-    "# sort the vertex pairs at the midpoint\n",
-    "\n",
-    "for i in range(len(keys)-1):\n",
-    "    angle = np.average([keys[i], keys[i+1]])\n",
-    "    sorted_order = Spiral.sort_vertices(angle)\n",
-    "    pd_table.at[i, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "angle = np.average([keys[-1], keys[0]+2*np.pi])\n",
-    "sorted_order = Spiral.sort_vertices(angle)\n",
-    "pd_table.at[len(keys)-1, 'sorted_order_at_next_midpoint'] = sorted_order\n",
-    "\n",
-    "pd_table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'd', 'e', 'f', 'g']"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nodelist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modified version breaking the spiral"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Modified version with vertex added between C and D\n",
-    "\n",
-    "# Create the graph\n",
-    "G = EmbeddedGraph()\n",
-    "G.add_node('a', .5, 0)\n",
-    "G.add_node('b', 1, 1)\n",
-    "G.add_node('c', -1.5, -.5)\n",
-    "G.add_node('X', 1.5, -1)\n",
-    "G.add_node('d', 3, -2.5)\n",
-    "G.add_node('e',1.5,2)\n",
-    "G.add_node('f',-1.5,2)\n",
-    "G.add_node('g', -3,-3)\n",
-    "\n",
-    "nodelist = ['a', 'b', 'c','X',  'd', 'e', 'f', 'g']\n",
-    "for i in range(len(nodelist)-1):\n",
-    "    G.add_edge(nodelist[i], nodelist[i+1])\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "G.plot(angle_labels_circle=True, edges_only=True, ax = ax)\n",
-    "# remove axes \n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x30be90c90>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 480x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.matshow(G.get_all_angles()[0])\n",
-    "plt.colorbar()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Angle</th>\n",
-       "      <th>Swapped Verts</th>\n",
-       "      <th>Sorted list</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.00</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>gcfabXed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.25</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.32</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>gcfaXbed</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>gcfaXdbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.79</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.15</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>gcdXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.19</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>gdcXafbe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.41</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.57</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>gdXcabfe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.65</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>gdXcabef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1.82</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>dgXacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1.99</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>2.11</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>dXgacbef</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>2.27</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>dXgabecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>2.28</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>dXagbecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>2.36</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>dXabgecf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>2.41</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>2.60</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>dXabegcf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>2.68</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>2.85</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>dXebacgf</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>3.14</td>\n",
-       "      <td>[(c, f), (X, e)]</td>\n",
-       "      <td>dXebacfg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>3.39</td>\n",
-       "      <td>[(b, X)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>3.46</td>\n",
-       "      <td>[(d, e)]</td>\n",
-       "      <td>debXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>3.66</td>\n",
-       "      <td>[(b, d)]</td>\n",
-       "      <td>edbXafcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>3.93</td>\n",
-       "      <td>[(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>4.29</td>\n",
-       "      <td>[(c, d)]</td>\n",
-       "      <td>ebfaXdcg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>4.33</td>\n",
-       "      <td>[(b, f)]</td>\n",
-       "      <td>ebfaXcdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>4.55</td>\n",
-       "      <td>[(c, X)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>4.71</td>\n",
-       "      <td>[(e, f)]</td>\n",
-       "      <td>efbacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>4.79</td>\n",
-       "      <td>[(d, g)]</td>\n",
-       "      <td>febacXdg</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>4.96</td>\n",
-       "      <td>[(a, c)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>5.13</td>\n",
-       "      <td>[(X, g)]</td>\n",
-       "      <td>febcaXgd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>5.25</td>\n",
-       "      <td>[(b, c)]</td>\n",
-       "      <td>febcagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>5.41</td>\n",
-       "      <td>[(c, e)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>5.42</td>\n",
-       "      <td>[(a, g)]</td>\n",
-       "      <td>fcebagXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>5.50</td>\n",
-       "      <td>[(b, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>5.55</td>\n",
-       "      <td>[(e, g)]</td>\n",
-       "      <td>fcegbaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>5.74</td>\n",
-       "      <td>[(c, g)]</td>\n",
-       "      <td>fcgebaXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>5.82</td>\n",
-       "      <td>[(a, b), (a, e), (b, e)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>5.99</td>\n",
-       "      <td>[(f, g)]</td>\n",
-       "      <td>fgcabeXd</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Angle                                     Swapped Verts Sorted list\n",
-       "0    0.00                                  [(c, f), (X, e)]    gcfabXed\n",
-       "1    0.25                                          [(b, X)]    gcfaXbed\n",
-       "2    0.32                                          [(d, e)]    gcfaXbed\n",
-       "3    0.52                                          [(b, d)]    gcfaXdbe\n",
-       "4    0.79  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    gcdXafbe\n",
-       "5    1.15                                          [(c, d)]    gcdXafbe\n",
-       "6    1.19                                          [(b, f)]    gdcXafbe\n",
-       "7    1.41                                          [(c, X)]    gdXcabfe\n",
-       "8    1.57                                          [(e, f)]    gdXcabfe\n",
-       "9    1.65                                          [(d, g)]    gdXcabef\n",
-       "10   1.82                                          [(a, c)]    dgXacbef\n",
-       "11   1.99                                          [(X, g)]    dXgacbef\n",
-       "12   2.11                                          [(b, c)]    dXgacbef\n",
-       "13   2.27                                          [(c, e)]    dXgabecf\n",
-       "14   2.28                                          [(a, g)]    dXagbecf\n",
-       "15   2.36                                          [(b, g)]    dXabgecf\n",
-       "16   2.41                                          [(e, g)]    dXabegcf\n",
-       "17   2.60                                          [(c, g)]    dXabegcf\n",
-       "18   2.68                          [(a, b), (a, e), (b, e)]    dXebacgf\n",
-       "19   2.85                                          [(f, g)]    dXebacgf\n",
-       "20   3.14                                  [(c, f), (X, e)]    dXebacfg\n",
-       "21   3.39                                          [(b, X)]    debXafcg\n",
-       "22   3.46                                          [(d, e)]    debXafcg\n",
-       "23   3.66                                          [(b, d)]    edbXafcg\n",
-       "24   3.93  [(a, X), (a, d), (f, a), (X, d), (f, X), (f, d)]    ebfaXdcg\n",
-       "25   4.29                                          [(c, d)]    ebfaXdcg\n",
-       "26   4.33                                          [(b, f)]    ebfaXcdg\n",
-       "27   4.55                                          [(c, X)]    efbacXdg\n",
-       "28   4.71                                          [(e, f)]    efbacXdg\n",
-       "29   4.79                                          [(d, g)]    febacXdg\n",
-       "30   4.96                                          [(a, c)]    febcaXgd\n",
-       "31   5.13                                          [(X, g)]    febcaXgd\n",
-       "32   5.25                                          [(b, c)]    febcagXd\n",
-       "33   5.41                                          [(c, e)]    fcebagXd\n",
-       "34   5.42                                          [(a, g)]    fcebagXd\n",
-       "35   5.50                                          [(b, g)]    fcegbaXd\n",
-       "36   5.55                                          [(e, g)]    fcegbaXd\n",
-       "37   5.74                                          [(c, g)]    fcgebaXd\n",
-       "38   5.82                          [(a, b), (a, e), (b, e)]    fgcabeXd\n",
-       "39   5.99                                          [(f, g)]    fgcabeXd"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "All_Angles = G.get_all_angles('dict', num_rounding_digits=2, opposites = True)\n",
-    "keys = list(All_Angles.keys())\n",
-    "keys.sort()\n",
-    "\n",
-    "pd_table = pd.DataFrame(columns = ['Angle', 'Swapped Verts', 'Sorted list'])\n",
-    "pd_table['Angle'] = keys\n",
-    "pd_table['Swapped Verts'] = [All_Angles[key] for key in keys]\n",
-    "\n",
-    "\n",
-    "\n",
-    "pd_table.sort_values(by='Angle', inplace=True)\n",
-    "pd_table['Sorted list'] = [''.join(G.sort_vertices(key)) for key in keys]\n",
-    "pd_table.head(100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
deleted file mode 100644
index c19d638..0000000
--- a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial: ECT for CW complexes\n",
-    "\n",
-    "This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedCW, create_example_cw\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = EmbeddedCW()\n",
-    "\n",
-    "K.add_node('A', 0,0)\n",
-    "K.add_node('B', 1,0)\n",
-    "K.add_node('C', 1,1)\n",
-    "K.add_node('D', 0,1)\n",
-    "\n",
-    "K.add_edges_from((('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'A')))\n",
-    "\n",
-    "K.add_face(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "K.set_centered_coordinates()\n",
-    "K.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "K = create_example_cw(centered = True)\n",
-    "K.plot(bounding_circle=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also color the nodes based on a function value for a given direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "theta = np.pi/7\n",
-    "K.plot(bounding_circle=True,color_nodes_theta=theta)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The function value for any direction can be computed and returned for vertices, edges, or faces. The function value for an edge or face $\\sigma$ is given by $g_\\omega(\\sigma) = \\max_{v \\in \\sigma}\\{f(v)\\}$. Here we show the function values for the triangle `KDC` and its faces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "K: 0.18\n",
-      "D: 0.65\n",
-      "C: 2.89\n",
-      "('D', 'K'): 0.65\n",
-      "('C', 'D'): 2.89\n",
-      "('C', 'K'): 2.89\n",
-      "('K', 'D', 'C'): 2.89\n"
-     ]
-    }
-   ],
-   "source": [
-    "vert_g = K.g_omega(theta)\n",
-    "edge_g = K.g_omega_edges(theta)\n",
-    "face_g = K.g_omega_faces(theta)\n",
-    "\n",
-    "for v in ['K','D','C']:\n",
-    "    print(f'{v}: {round(vert_g[v],2)}')\n",
-    "\n",
-    "for edge in [('D','K'),('C','D'),('C','K')]:\n",
-    "    print(f'{edge}: {round(edge_g[edge],2)}')\n",
-    "\n",
-    "for face in [('K','D','C')]:\n",
-    "    print(f'{face}: {round(face_g[face],2)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "myect = ECT(num_dirs = 100,num_thresh = 80)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        0,  0,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,\n",
-       "        3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  1,\n",
-       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1,\n",
-       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r = K.get_bounding_radius()\n",
-    "myect.calculateECC(K,0,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(K,theta,1.2*r)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       ...,\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.],\n",
-       "       [ 0.,  0.,  0., ..., -1., -1., -1.]])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2*r)\n",
-    "myect.calculateECT(K)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once it's been computed, we can use the internal plotting function to take a look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.841874542459709\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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PJMVCfN0Y6835aKoFgMbhm54UhntMNW/ePPXq1UthYWEKCwtTWlqaPvroI7PDAgAAfmKbSkqHDh301FNP6eKLL5Ykvf766xo5cqS2b9+u7t27mxwdAAANc65x1ssPGGS4x1wjRoxwefzEE09o3rx52rx5M0kKAMC2KnwwLX6g3t1jmyTlfOXl5Xrvvfd0+vRppaWlud2upKREJSUlzsfFxcWNEV69NXYPiifqio2eFQCAv9mmJ0WSdu3apTZt2ig4OFgTJ07U8uXL1a1bN7fb5+TkKDw83LnEx8c3YrQAANStsnHW28UTOTk5uvLKK9W2bVtFRUVp1KhR2rdvX6375ObmyuFwVFu+/vprby6/VrZKUpKSkrRjxw5t3rxZkyZN0l133aU9e/a43T4zM1NFRUXOJT8/vxGjBQCgbhVq5pPFE+vXr9d9992nzZs3a926dSorK9OQIUN0+vTpOvfdt2+fCgoKnMsll1zS0Euvk62Ge4KCgpyNs6mpqdqyZYuef/55vfLKKzVuHxwcrODg4MYMEQAAy1uzZo3L40WLFikqKkrbtm3TtddeW+u+UVFRateunR+j+xdbVVKqMgzDpecEAAC7KTccPlmkc72X5y/1/RtZVFQkSYqIiKhz28svv1yxsbEaPHiwPv3004ZfeD3YppIyc+ZMpaenKz4+XidPntTbb7+t3NzcatmglVi5MdZbVa+NRloAaJhyH9zdU/7L3T1Vey+zsrKUnZ1d676GYSgjI0P9+vVTjx493G4XGxur+fPnKyUlRSUlJXrjjTc0ePBg5ebm1ll9aSjbJClHjx7V6NGjVVBQoPDwcPXq1Utr1qzR9ddfb3ZoAAA0WIXRTBVezjhb8cuMs/n5+QoLC3Our0/Lw5QpU7Rz50795S9/qXW7pKQkJSUlOR+npaUpPz9fs2fPJklZuHCh2SEAAGBplbOy19f999+vVatWacOGDerQoYPH5+vTp4+WLl3q8X71ZZskBQCAQOTL4Z76MgxD999/v5YvX67c3FwlJiY26Lzbt29XbGxsg/atD5IUAABMVCE5G1+9OYYn7rvvPr355ptauXKl2rZtq8LCQklSeHi4QkNDJZ2bxuPw4cNasmSJJGnOnDnq1KmTunfvrtLSUi1dulTLli3TsmXLvIq9NiQpPhTIjbJ1oZEWAOxj3rx5kqSBAwe6rF+0aJHGjh0rSSooKFBeXp7zudLSUk2fPl2HDx9WaGiounfvrg8//FDDhw/3W5wkKQAAmKghk7HVdAxPGEbdw0OLFy92eTxjxgzNmDHDo/N4iyQFAAATNWRa+5qOEYgC86oAAIDtUUmBX9CjAgD1UyGHKuRt46x3+1sVSQoAACZiuMe9wLwqAABge1RSAAAwkW8mcwvMmgNJCgAAJqowHKrwdjI3L/e3KpIULzTlyds8RSMtANSswgeVFG/nWbGqwLwqAABge1RSAAAwUYXRTBVe3p3j7f5WRZICAICJyuVQuZfznHi7v1WRpHiAHhTfoUcFAFAXkhQAAEzEcI97JCkAAJioXN4P15T7JhTLCczUCwAA2B6VFAAATMRwj3skKXWgWRYA4E98wKB7gXlVAADA9qikAABgIkMOVXjZOGswTwoAAPA1hnvcI0kBAMBEfAqyeyQpVdAoaw5moAUAVEWSAgCAicrVTOVe3sfi7f5WRZICAICJGO5xLzBTLwAAYHtUUmBJ9KgAaCoq1EwVXtYMvN3fqkhSAAAwUbnhULmXwzXe7m9VgZl6AQAA26OSAgCAiWicdY8kBQAAExk++BRkgxlnAxOTtwEAYE1NPkkBAMBM5XKo3MsPCPR2f6siSQEAwEQVhvc9JRWGj4KxGNsMYuXk5OjKK69U27ZtFRUVpVGjRmnfvn1mhwUAgFcqfulJ8XYJRLa5qvXr1+u+++7T5s2btW7dOpWVlWnIkCE6ffq02aEBAAA/sM1wz5o1a1weL1q0SFFRUdq2bZuuvfZaj4514Okr1SwkxJfhwc+YgdY/fr7papfHocs/NykSoGZVf0b97ciAfw27VJw5Iz280u/nrJBDFV72lHi6f05Ojt5//319/fXXCg0NVd++ffX0008rKSmp1v3Wr1+vjIwMffXVV4qLi9OMGTM0ceJEb0KvlW0qKVUVFRVJkiIiItxuU1JSouLiYpcFAAArqZxx1tvFEw0ZnThw4ICGDx+u/v37a/v27Zo5c6amTp2qZcuWefstcMs2lZTzGYahjIwM9evXTz169HC7XU5OjmbNmtWIkQEAYH0NGZ14+eWXlZCQoDlz5kiSkpOTtXXrVs2ePVu33HKLX+K0ZSVlypQp2rlzp956661at8vMzFRRUZFzyc/Pb6QIAQCoH182zlYdPSgpKalXDPUZndi0aZOGDBnism7o0KHaunWrzp4928Crr53tKin333+/Vq1apQ0bNqhDhw61bhscHKzg4OBGigyNiR4V/6hr/J+eFXirsXtM7KBCPpgW/5eelPj4eJf1WVlZys7OrnXf+o5OFBYWKjo62mVddHS0ysrKdPz4ccXGxjYs+FrYJkkxDEP333+/li9frtzcXCUmJpodEgAAlpKfn6+wsDDn4/r8o145OvGXv/ylzm0dDtdkyjCMGtf7im2SlPvuu09vvvmmVq5cqbZt26qwsFCSFB4ertDQUJOjAwCgYQwf3N1j/LJ/WFiYS5JSF09GJ2JiYpx/eysdO3ZMLVq0UPv27T0Puh5s05Myb948FRUVaeDAgYqNjXUu77zzjtmhAQDQYJWfguzt4gnDMDRlyhS9//77+uSTT+o1OpGWlqZ169a5rFu7dq1SU1PVsmVLj85fX7appFSWlAAAgHfqMzqRmZmpw4cPa8mSJZKkiRMn6sUXX1RGRoYmTJigTZs2aeHChXXexOIN2yQpALznbdMijbVNQ9zm2ocLjvSpfc4pOzXHnj95m1l8Ma29p/vPmzdPkjRw4ECX9YsWLdLYsWMlSQUFBcrLy3M+l5iYqNWrV2vatGmaO3eu4uLi9MILL/jt9mOJJAUAAFM1ZLimpmN4oj6jE4sXL662bsCAAfrb3/7m0bm8QZICAICJzJgW3y5s0zgLAACaFiopCAhM7mYNfGChNVR9P3gqTntqf76OnhVpr9tn/v50cgMiCmxmDPfYBUkKAAAmIklxj+EeAABgSVRSAAAwEZUU90hSAAAwEUmKeyQpQAAze1ItGml9w9tGWCvp8rD7plrJ/421Vpi8DfVHkgIAgIkMeT/PSaB+cAxJCgAAJmK4xz3u7gEAAJZEJQUAABNRSXGPJAUB6fxGQ2aftQ4+Rbl+zG6U/WxzN5fH/fvUPgOtL5ndWGsGkhT3SFIAADARSYp79KQAAABLopICAICJDMMhw8tKiLf7WxVJCgJeU/qEZLMnb/NWU5r8zey+E0+Y2aNSlac9K3aYvK1CDq/nSfF2f6tiuAcAAFgSlRQAAExE46x7JCkAAJiInhT3GO4BAACWRCUFTU4gNWfavVG2Ludfn51fJ8lejbJ2tmHufJfHF797r0mR1B/DPe6RpAAAYCKGe9xjuAcAAFgSlRQAAExk+GC4J1ArKSQpAACYyJBkGN4fIxCRpKDJqToDZZwCp5E2kNmt4TmQG2WtNANtXb7991dcHluxkbZCDjmYcbZG9KQAAABLopICAICJuLvHPZIUAABMVGE45GCelBqRpABV1DVBmtm9EIE+gZtdBXIPSl3M7FFZlPCZR9vboUcF/0KSAgCAiQzDB3f3BOjtPSQpAACYiJ4U97i7BwAAWJKtkpQNGzZoxIgRiouLk8Ph0IoVK8wOCQAAr1RWUrxdApGthntOnz6tyy67THfffbduueUWs8NpdFWb0ao2q6Fhqk3utr72wV2rN9Z6w86x+1tTbowNZFZopDXj7p4NGzbo2Wef1bZt21RQUKDly5dr1KhRbrfPzc3VoEGDqq3fu3evunbt6mm49WarJCU9PV3p6elmhwEAgM+Y0Tjb0H/69+3bp7CwMOfjCy+80LMTe8hWSYqnSkpKVFJS4nxcXFxsYjQAAFhDQ//pj4qKUrt27XwfkBu26knxVE5OjsLDw51LfHy82SEBAODiXCXF256Uc8cqLi52Wc7/R90XLr/8csXGxmrw4MH69NNPfXrsmgR0JSUzM1MZGRnOx8XFxYqPj1da6tcKahMkydp9HfSg2JOvPwjPm8nb6DFpOHpQGu7831W+ntjN08nbqro7r3+tz58fb+mpUh3y6mz148tbkKv+M56VlaXs7Gyvji1JsbGxmj9/vlJSUlRSUqI33nhDgwcPVm5urq699lqvj+9OQCcpwcHBCg4ONjsMAAAaRX5+vkvPiK/+BiYlJSkpKcn5OC0tTfn5+Zo9e7Zfk5SAHu4BAMDqDB8tkhQWFuay+PMf9T59+mj//v1+O75ks0rKqVOn9O233zofHzhwQDt27FBERIQSEhJMjAwAgIax64yz27dvV2xsrF/PYaskZevWrS73aVf2m9x1111avHixSVEBAGAvdf3Tn5mZqcOHD2vJkiWSpDlz5qhTp07q3r27SktLtXTpUi1btkzLli3za5y2SlIGDhwow8efomSl5tTG/ORQuOfp5G518XUjrb+O1dTRKBsY6mqMtaTzx2u8OYYH6vqnv6CgQHl5ec7nS0tLNX36dB0+fFihoaHq3r27PvzwQw0fPtzLwGtnqyQFAICA44tp7T3cv65/+quOTsyYMUMzZsxoSGReoXEWAABYEpUUAABMZMa0+HZBkgIAgInsendPYyBJqaIxG2nrapRlhtnAVFcjrT8bbfEvNMo2jqq/xzy9QaDqDLO2bIyti+HwuKekxmMEIHpSAACAJVFJAQDARPSkuEeSAgCAmUyYJ8UuSFLqcP74qac9Ip6OvdKDYk2+ntytKm8+5Riwm7p6VKo+f7ffI4KVkaQAAGAi7u5xjyQFAACzBehwjbe4uwcAAFgSlRQAAEzEcI97JCkeqKvBi08xhj8wuZtvMHmbNfF7VNzdUwuGewAAgCVRSQEAwFSOXxZvjxF4SFIAADATwz1ukaR44dt/f8XlsacffMXkbfbk78nd6nJ+jwr9Ke552oPSJHsh3PDl7yaP3x9NsXWIJMUtelIAAIAlUUkBAMBMhuPc4u0xAhBJCgAAJuJTkN1juAcAAFgSlRQPLEr4zKPnPW2kBTzVlCd6q9oYW7XxNU40wjaUt03EnjTeVm1E7+LVmW2Kxlm3SFIAADATPSluMdwDAAAsiUoKAAAmchjnFm+PEYhIUgAAMBM9KW6RpNShrmZZT/a9+N17vQ0HFmT2DLTns3sjrSezxDJDrHm8aYz19Ni8zk0bSQoAAGaicdYtkhQAAMzEcI9bJCkAAJiJJMUtkpQqvOlBqarqZG5Vx1b5FGT4W2P3qHj6ycOeoDfBPGb+rqJHpWkjSQEAwExUUtwiSQEAwEw0zrrFjLMAAMCSqKQAAGAiZpx1r8knKb5slAUka03uVpWnjbT+bIStD5okzUFTfyOjJ8Ut2w33vPTSS0pMTFRISIhSUlL02WckGQAAeGLDhg0aMWKE4uLi5HA4tGLFijr3Wb9+vVJSUhQSEqLOnTvr5Zdf9nucHicp5eXleuWVVzRt2jT9/ve/15///GedOHHCH7FV88477+iBBx7QI488ou3bt6t///5KT09XXl5eo5wfAIBAcPr0aV122WV68cUX67X9gQMHNHz4cPXv31/bt2/XzJkzNXXqVC1btsyvcXo83HP//ffrT3/6k66//nrNnTtXzZo109mzZ3XRRRepd+/eWrVqlT/ilCQ999xzGjdunMaPHy9JmjNnjj7++GPNmzdPOTk5fjsvAAD+4pAPelI83D49PV3p6en13v7ll19WQkKC5syZI0lKTk7W1q1bNXv2bN1yyy0enr3+PK6kvP/++3rjjTf0xz/+UcHBwdq6dateeOEFnTlzRh07dvRHjJKk0tJSbdu2TUOGDHFZP2TIEG3cuLHGfUpKSlRcXOyyAAAQqKr+zSspKfHJcTdt2lTt7+/QoUO1detWnT171ifnqInHlZRTp06pW7dzTVUtW7ZU8+bNdd9996m0tFRHjhzxeYCVjh8/rvLyckVHR7usj46OVmFhYY375OTkaNasWdXWz4vfqLC2zX0eY9UZZuvCDLRobNU+kXaAuY2xVdEo2zjs/LvG3zPQnn8zRfHJcr3t06O74cN5UuLj411WZ2VlKTs727tjSyosLKzx729ZWZmOHz+u2NhYr89RE48rKZ07d3YmIxdddJEOHz4sSRoxYoSWLl3q2+hq4HC4vpCGYVRbVykzM1NFRUXOJT8/3+/xAQDgEcNHi6T8/HyXv3uZmZk+C7Omv781rfclj5OUW2+9VWvWrJEkDRw4UK+99pokac+ePfr55599G915IiMj1bx582pVk2PHjlXL7ioFBwcrLCzMZQEAwFJ8mKRU/ZsXHBzskxBjYmJq/PvbokULtW/f3ifnqInHwz2PPvqo8+uHHnpIV111lS688EIVFxdr3LhxPg3ufEFBQUpJSdG6det00003OdevW7dOI0eO9Nt5AQBo6tLS0vTBBx+4rFu7dq1SU1PVsmVLv53Xq8ncEhIS9NVXX2n16tWKiIjQr371K1/FVaOMjAyNHj1aqampSktL0/z585WXl6eJEyf69byNhR4VeKpajwlQA36XuGeFCT3NmHH21KlT+vbbb52PDxw4oB07digiIkIJCQnKzMzU4cOHtWTJEknSxIkT9eKLLyojI0MTJkzQpk2btHDhQr311lveBV4Hr2ecbd++vUaPHu2LWOp022236cSJE3r88cdVUFCgHj16aPXq1X69qwgAAL8yYcbZrVu3atCgQc7HGRkZkqS77rpLixcvVkFBgcscZImJiVq9erWmTZumuXPnKi4uTi+88IJfbz+WbDgt/uTJkzV58mSzwwAAwLYGDhzobHytyeLFi6utGzBggP72t7/5MarqbJekAAAQUPjsHrdIUgAAMBGfguweSYoXPJ28zVM00gaGuj4VuSk3vzJ5m2805d8Nnk7uZoVGWdQfSQoAAGby4YyzgYYkBQAAM9GT4pbHM84CAAA0BiopNkKPSmCgBwXe4r1ff3boQaFx1j2SFAAAzMRwj1skKQAAmMkHlZRATVLoSQEAAJZEJQUAADMx3OMWSYoH/D15m6e8bUKk+e4cvo/+Q6Nsw/Az1cSQpLjFcA8AALAkKikAAJiIW5Ddo5ICAAAsiSQFAABYEsM9dbBas6wv0dTYMDQ1usfPVMPwM9Vwdf3MVf0dbskZaGmcdYskBQAAE9GT4h7DPQAAwJKopAAAYLYArYR4iyQFQIPRg1J/9J34RkD+zNGT4hZJCgAAJqInxT16UgAAgCVRSQEAwEwM97hFkgIAgIkY7nGPJKWKQJ68DQ1Dw+O/BGTTop/wc9MwVX/Gqk6+5u3vaFtM7gYnkhQAAMzEcI9bJCkAAJiJJMUt7u4BAACWRCUFAAAT0TjrHkkKALdolHWPxljf8PWnGNvy5geGe9xiuAcAAFgSlRQAAMxEJcUtkhQAAExET4p7TT5JseX4JfyqKfcaNOUelKb8ujcmf/+MedqzYonJ3aikuEVPCgAATdBLL72kxMREhYSEKCUlRZ995j5By83NlcPhqLZ8/fXXfo3RNknKE088ob59+6pVq1Zq166d2eEAAOATlcM93i6eeOedd/TAAw/okUce0fbt29W/f3+lp6crLy+v1v327dungoIC53LJJZd4ceV1s02SUlpaqltvvVWTJk0yOxQAAHzH8NHigeeee07jxo3T+PHjlZycrDlz5ig+Pl7z5s2rdb+oqCjFxMQ4l+bNm3t2Yg/ZJkmZNWuWpk2bpp49e5odCgAAllRcXOyylJSUVNumtLRU27Zt05AhQ1zWDxkyRBs3bqz1+JdffrliY2M1ePBgffrppz6NvSYB3ThbUlLi8gIVFxdLkibl91VQmyCzwgJgAhpjzeHrRllvG109aawtPVUq6R8eHb9BfNg4Gx8f77I6KytL2dnZLuuOHz+u8vJyRUdHu6yPjo5WYWFhjYePjY3V/PnzlZKSopKSEr3xxhsaPHiwcnNzde2113oZvHsBnaTk5ORo1qxZZocBAIBbjl8Wb48hSfn5+QoLC3OuDw4Odr+Pw/WshmFUW1cpKSlJSUlJzsdpaWnKz8/X7Nmz/ZqkmDrck52dXWO38PnL1q1bG3z8zMxMFRUVOZf8/HwfRg8AgLWEhYW5LDUlKZGRkWrevHm1qsmxY8eqVVdq06dPH+3fv9/rmGtjaiVlypQpuv3222vdplOnTg0+fnBwcK1ZJAAApmvkeVKCgoKUkpKidevW6aabbnKuX7dunUaOHFnv42zfvl2xsbGeROkxU5OUyMhIRUZGmhmCORP3NBATz/kHvQr/wvcCDdGUJwH0BTNmnM3IyNDo0aOVmpqqtLQ0zZ8/X3l5eZo4caKkcyMRhw8f1pIlSyRJc+bMUadOndS9e3eVlpZq6dKlWrZsmZYtW+Zd4HWwTU9KXl6e/vnPfyovL0/l5eXasWOHJOniiy9WmzZtzA0OAAAbue2223TixAk9/vjjKigoUI8ePbR69Wp17NhRklRQUOAyZ0ppaammT5+uw4cPKzQ0VN27d9eHH36o4cOH+zVO2yQpjz32mF5//XXn48svv1yS9Omnn2rgwIEmRQUAgJdMmhZ/8uTJmjx5co3PLV682OXxjBkzNGPGjAYE5h3bzJOyePFiGYZRbSFBAQDYXiNO5GYntqmkAAAQiPgUZPeafJJiiU/ABAALs3JjrLe/w7khwdqafJICAICpTOpJsQOSFAAATMRwj3u2aZwFAABNC5UUAADMxHCPWyQpVVipkZaGLv9gVlWgdlZulPWWFX+vMtzjHsM9AADAkqikAABgJoZ73CJJAQDATCQpbpGkWIgVx0oBwE6s1FcI75GkAABgIhpn3SNJAQDATAz3uEWSAgCAiRyGIYfhXZbh7f5WxS3IAADAkqik1OH8JixfN2DRKNs4mLwN8EzV94ydJ3e7+N17XR5b8loY7nGLJAUAABPROOsewz0AAMCSqKQAAGAmhnvcIkkBAMBEDPe4R5LiAU9nMqQx1jw0ywK+Y6dGWt77gYUkBQAAMzHc4xZJCgAAJmK4xz3u7gEAAJZEJcWH6EEB0BTYqUelKkvGznCPWyQpAACYLFCHa7xFkgIAgJkM49zi7TECED0pAADAkqikAABgIu7ucY8kxQu2+HTNJsKTCZzi1ru+m48McHi0fVV17Q8EOjObUQNi8jYaZ91iuAcAAFgSlRQAAEzkqDi3eHuMQESSAgCAmRjucYskpQ6ejHdacpIg1NlTUtfz3h6fnhU0NbX93vT296Kve1D4vW1t9KQAAGCiyrt7vF089dJLLykxMVEhISFKSUnRZ599Vuv269evV0pKikJCQtS5c2e9/PLLDbzi+iNJAQDATJWTuXm7eOCdd97RAw88oEceeUTbt29X//79lZ6erry8vBq3P3DggIYPH67+/ftr+/btmjlzpqZOnaply5b54jvgFkkKAABNzHPPPadx48Zp/PjxSk5O1pw5cxQfH6958+bVuP3LL7+shIQEzZkzR8nJyRo/frzuuecezZ49269x2iJJOXjwoMaNG6fExESFhoaqS5cuysrKUmlpqdmhAQDgFV8O9xQXF7ssJSUl1c5XWlqqbdu2aciQIS7rhwwZoo0bN9YY46ZNm6ptP3ToUG3dulVnz571zTeiBrZonP36669VUVGhV155RRdffLF2796tCRMm6PTp015ncf6cCIiGLP+p63XzthnWlzyNhUZbBLKAmHzN13x4d098fLzL6qysLGVnZ7usO378uMrLyxUdHe2yPjo6WoWFhTUevrCwsMbty8rKdPz4ccXGxnoXvxu2SFKGDRumYcOGOR937txZ+/bt07x58/xeagIAwJ98OS1+fn6+wsLCnOuDg4Pd7+Nw/YfIMIxq6+ravqb1vmSLJKUmRUVFioiIqHWbkpISl1JXcXGxv8MCAMA0YWFhLklKTSIjI9W8efNqVZNjx45Vq5ZUiomJqXH7Fi1aqH379t4FXQtb9KRU9fe//11/+MMfNHHixFq3y8nJUXh4uHOpWgYDAMB0jXx3T1BQkFJSUrRu3TqX9evWrVPfvn1r3CctLa3a9mvXrlVqaqpatmzp+TXXk6lJSnZ2thwOR63L1q1bXfY5cuSIhg0bpltvvVXjx4+v9fiZmZkqKipyLvn5+f68HAAAPGbGPCkZGRl69dVX9dprr2nv3r2aNm2a8vLynP/8Z2ZmasyYMc7tJ06cqEOHDikjI0N79+7Va6+9poULF2r69Om+/FZUY+pwz5QpU3T77bfXuk2nTp2cXx85ckSDBg1SWlqa5s+fX+fxg4ODaxyPOzAnSS1ahpx7MMCjkL1CI23D2alR1ltmXgtNuzCb2e/lv69Pdn5ddvaMiZH412233aYTJ07o8ccfV0FBgXr06KHVq1erY8eOkqSCggKXOVMSExO1evVqTZs2TXPnzlVcXJxeeOEF3XLLLX6N09QkJTIyUpGRkfXa9vDhwxo0aJBSUlK0aNEiNWtmy5EqAABcmfTZPZMnT9bkyZNrfG7x4sXV1g0YMEB/+9vfPD+RF2zROHvkyBENHDhQCQkJmj17tr7//nvnczExMSZGBgCAd3x5d0+gsUWSsnbtWn377bf69ttv1aFDB5fnDA+nAgYAAPZgiyRl7NixGjt2rF+OXXX8szHH5OlRaTizx60DlZnvBzRNvJclVRjnFm+PEYBskaQAABCwTOpJsQO6TwEAgCVRSQEAwEQO+aBx1ieRWA9JCgAAZvJwxli3xwhAJCkWQiPtv1T9XtBcBwQG3svVcQuye/SkAAAAS6KSAgCAmbi7xy2SFAAATOQwDDm87Cnxdn+rIkmpwkqTWTWlHhV6UKzJSu8H2BPvZXiDJAUAADNV/LJ4e4wARJICAICJGO5xj7t7AACAJVFJAQDATNzd4xZJSh3Ob/oyu2kwkBtpaa6zBxppUR+8nz3EjLNuMdwDAAAsiUoKAAAmYlp890hSAAAwE8M9bpGkAABgIkfFucXbYwQikhQPWK1psGojbVVWaqxlRtnAZLX3hF3V9X6w2veV9y8aC0kKAABmYrjHLZIUAADMxDwpbnELMgAAsCQqKV6w+nh8XT0r5/O0f8WTY0uMYTcVVn9PWIkn7wlP3z+eft95f5qLz+5xjyQFAAAz0ZPiFsM9AADAkqikAABgJkOSt/OcBGYhhSQFAAAz0ZPiHkkKJHneCFsXGvEAV435nuD9h0BBkgIAgJkM+aBx1ieRWA5JCgAAZuLuHrdIUgAAMFOFJG+nFArQDxjkFmQAAGBJVFJ8qCnPtkmjHmrCewKom9Xv7vnhhx80depUrVq1SpJ044036g9/+IPatWvndp+xY8fq9ddfd1l39dVXa/PmzR6dmyQFAAAzWbwn5Y477tB3332nNWvWSJJ++9vfavTo0frggw9q3W/YsGFatGiR83FQUJDH5yZJAQAANdq7d6/WrFmjzZs36+qrr5YkLViwQGlpadq3b5+SkpLc7hscHKyYmBivzk9PCgAAZqqspHi7SCouLnZZSkpKvApt06ZNCg8PdyYoktSnTx+Fh4dr48aNte6bm5urqKgoXXrppZowYYKOHTvm8fltU0m58cYbtWPHDh07dkwXXHCB/u3f/k1PP/204uLizA7NrbrGpO08Ps94OxqC9wRQAx8O98THx7uszsrKUnZ2doMPW1hYqKioqGrro6KiVFhY6Ha/9PR03XrrrerYsaMOHDigRx99VNddd522bdum4ODgep/fNknKoEGDNHPmTMXGxurw4cOaPn26fv3rX9eZyQEA0FTk5+crLCzM+dhdQpCdna1Zs2bVeqwtW7ZIkhyO6v88GIZR4/pKt912m/PrHj16KDU1VR07dtSHH36om2++udbzns82Scq0adOcX3fs2FH/7//9P40aNUpnz55Vy5YtTYwMAAAv+HCelLCwMJckxZ0pU6bo9ttvr3WbTp06aefOnTp69Gi1577//ntFR0fXO7zY2Fh17NhR+/fvr/c+ko2SlPP985//1B//+Ef17du31gSlpKTEZTyuuLi4McIDAKDezLgFOTIyUpGRkXVul5aWpqKiIn3xxRe66qqrJEmff/65ioqK1Ldv33qf78SJE8rPz1dsbKxHcdqqcfbhhx9W69at1b59e+Xl5WnlypW1bp+Tk6Pw8HDnUnWsDgAAuJecnKxhw4ZpwoQJ2rx5szZv3qwJEybohhtucLmzp2vXrlq+fLkk6dSpU5o+fbo2bdqkgwcPKjc3VyNGjFBkZKRuuukmj85vaiWlvmNiqampkqSHHnpI48aN06FDhzRr1iyNGTNG//u//+t2XCwzM1MZGRnOx8XFxZZKVKw80RVNgDAD7wk0SRafJ+WPf/yjpk6dqiFDhkg6dyPLiy++6LLNvn37VFRUJElq3ry5du3apSVLlujHH39UbGysBg0apHfeeUdt27b16NymJin1HROrVFmeuvTSS5WcnKz4+Hht3rxZaWlpNe4bHBzsURcxAACNrsKQHF4mGRX+S1IiIiK0dOnSWrcxzkuSQkND9fHHH/vk3KYmKfUdE6tJ5TfE23vAAQAwlcUrKWayRePsF198oS+++EL9+vXTBRdcoH/84x967LHH1KVLF7dVFAAAYG+2SFJCQ0P1/vvvKysrS6dPn1ZsbKyGDRumt99+O6CGcxp7oivG2GF1tf2M8n5A4PBBJUWB+fNriySlZ8+e+uSTT8wOAwAA32O4xy1b3YIMAACaDltUUgAACFgVhrwervHj3T1mIkkBAMBMRsW5xdtjBCCSFBuhsQ/4F94PQOAjSQEAwEw0zrpFkgIAgJnoSXGLu3sAAIAlUUkBAMBMDPe4RZICAICZDPkgSfFJJJZDkgIAgJmopLhFTwoAALAkKikAAJipokKSl5OxVTCZGwAA8DWGe9xiuAcAAFgSlRQAAMxEJcUtkhQAAMzEjLNuMdwDAAAsiUoKAAAmMowKGYZ3d+d4u79VkaQAAGAmw/B+uCZAe1IY7gEAAJZEJQUAADMZPmicDdBKCkkKAABmqqiQHF72lNCTAgAAfI5Kilv0pAAAAEuikgIAgImMigoZXg73cAsyAADwPYZ73GK4BwAAWBKVFAAAzFRhSA4qKTUhSQEAwEyGIcnbW5ADM0lhuAcAAFgSlRQAAExkVBgyvBzuMaikAAAAnzMqfLP4yRNPPKG+ffuqVatWateuXf0uyTCUnZ2tuLg4hYaGauDAgfrqq688PjdJCgAAJjIqDJ8s/lJaWqpbb71VkyZNqvc+zzzzjJ577jm9+OKL2rJli2JiYnT99dfr5MmTHp2bJAUAALg1a9YsTZs2TT179qzX9oZhaM6cOXrkkUd08803q0ePHnr99df1008/6c033/To3E2qJ6VyzK7s7BmTIwEAWF3l3wp/93uUGSVeD9eU6awkqbi42GV9cHCwgoODvTq2pw4cOKDCwkINGTLEJY4BAwZo48aNuvfee+t9rCaVpFSWmbatecLkSAAAdnHy5EmFh4f7/LhBQUGKiYnRXwpX++R4bdq0UXx8vMu6rKwsZWdn++T49VVYWChJio6OdlkfHR2tQ4cOeXSsJpWkxMXFKT8/X4ZhKCEhQfn5+QoLCzM7rAYpLi5WfHw812AyrsEauAbrCITrqLyGvLw8ORwOxcXF+eU8ISEhOnDggEpLS31yPMMw5HA4XNa5q6JkZ2dr1qxZtR5vy5YtSk1NbXA8VWOpKb66NKkkpVmzZurQoYOzHBYWFmbbN1ElrsEauAZr4BqsIxCuIzw83O/XEBISopCQEL+eoyZTpkzR7bffXus2nTp1atCxY2JiJJ2rqMTGxjrXHzt2rFp1pS5NKkkBAABSZGSkIiMj/XLsxMRExcTEaN26dbr88sslnbtDaP369Xr66ac9OhZ39wAAALfy8vK0Y8cO5eXlqby8XDt27NCOHTt06tQp5zZdu3bV8uXLJZ0b5nnggQf05JNPavny5dq9e7fGjh2rVq1a6Y477vDo3E2ykhIcHKysrKxG73j2Ja7BGrgGa+AarCMQriMQrsGXHnvsMb3++uvOx5XVkU8//VQDBw6UJO3bt09FRUXObWbMmKGff/5ZkydP1g8//KCrr75aa9euVdu2bT06t8MI1Ll0AQCArTHcAwAALIkkBQAAWBJJCgAAsCSSFAAAYEkkKQAAwJKaXJLy0ksvKTExUSEhIUpJSdFnn31mdkge2bt3ryIiItSlSxf17t1bbdq0cd4CZhd2vYaTJ0/qyiuvVO/evdWzZ08tWLDA7JCarBYtWqh3797q3bu3xo8fb3Y4HguEn6V9+/Y5X4PevXsrNDRUK1asMDusesvJydGVV16ptm3bKioqSqNGjdK+ffvMDgtVGU3I22+/bbRs2dJYsGCBsWfPHuM///M/jdatWxuHDh0yOzSPDBkyxNi5c6dhGIbRpUsX4+effzY5Is/Z8RrKysqM06dPG4ZhGKdPnzYSExON48ePmxxV09S+fXuzQ/BKoP0snTx50mjfvr1x6tQps0Opt6FDhxqLFi0ydu/ebezYscP41a9+ZSQkJNjqGpqCJlVJee655zRu3DiNHz9eycnJmjNnjuLj4zVv3jyzQ/PIN998o6SkJJ08eVLNmzc35XMfvGXHa2jevLlatWolSTpz5ozKy8tlGIZtK0Ndu3aVw+GocXnhhRfMDs9jdnodAu1nadWqVRo8eLBat25tm2tYs2aNxo4dq+7du+uyyy7TokWLlJeXp23btkkKvPeHbZmdJTWWkpISo3nz5sb777/vsn7q1KnGtddea1JUnisqKjKSk5MNwzCMjRs3GqNGjTI5Is/Z+Rp++OEHo1evXkZoaKjx4osvOtfbsTK0Z88eQ5Lx5z//2SgoKDDy8vKMFi1aGO+9955x5swZs8OrVcuWLY0rrrjCuOaaa4zc3Fzneju9DoH0szRy5Ehj2bJlzsd2vIb9+/cbkoxdu3YZhmHv90cgaTKVlOPHj6u8vLzaJzBGR0ersLDQpKg899VXX6lbt26Szv3neOmll5ockefsfA3t2rXTl19+qQMHDujNN9/U0aNHJdmzMlRYWKgWLVrommuuUUxMjE6cOKGysjL179/f8tOBHzx4UNu2bdPLL7+sMWPGOD/Z3E6vQ6D8LBUXF+uvf/2rhg8f7lxnt2swDEMZGRnq16+fevToIcne749A0mSSlEoOh8PlsWEY1dZZ2Z49e9S9e3dJUuvWrfXRRx/pn//8p8lReSYQriE6Olq9evXShg0bVFxcrNDQUAUFBWn37t3OBMzqdu3apUsvvdT5C3fHjh268MILPf4odTPExcVJknr06KFu3brpm2++se3rYPefpZUrV2ro0KHORMSO1zBlyhTt3LlTb731lnOdnd8fgaTJfMBgZGSkmjdvXq1qcuzYMVv90I0bN8759W233abbbrvNxGgaxq7XcPToUYWGhiosLEzFxcXasGGDJk2aZNvK0M6dO9WzZ0/n4x07dqhXr14mRlQ/P/zwg1q1aqXg4GB999132rNnjzp37myr1yGQfpbeffdd/fa3v3U+tts13H///Vq1apU2bNigDh06ONfb9f0RaJpMJSUoKEgpKSlat26dy/p169apb9++JkUFO/nuu+907bXX6rLLLlO/fv00ZcoU9erVy7aVoZ07d7r80rXLL+G9e/cqNTVVl112mW644QY9//zzioiIsNXrECg/S0VFRfriiy80dOhQ5zq7XINhGJoyZYref/99ffLJJ0pMTHR53q7vj4Bjck9Mo6q8BXnhwoXGnj17jAceeMBo3bq1cfDgQbNDqzdJtS52EAjXYHfl5eVGq1atjA8++MC5Lj4+3pgzZ46JUQGNZ9KkSUZ4eLiRm5trFBQUOJeffvqJ94eFNLm/CHPnzjU6duxoBAUFGVdccYWxfv16s0NqkNOnTxsJCQnGgw8+aHYoHsvLyzMGDBhgJCcnGz179jTeffdds0Nqcr755htDksscQTfccIPRrl07274nAE+4+ydp0aJFvD8sxGEYhtF4dRv4yiOPPKL9+/crISFBs2fPNjscjxQUFOjo0aPq3bu3jh07piuuuEL79u1T69atzQ6tVg1tsOYt5luB8DpwDUD9NJmelECyf/9+ff311y63/NlJbGysevfuLUmKiopSRESEJcesqzLOVR5dltOnTyshIUEPPvhgjc/zC9n3AuF1OD+uvLw8DRgwQMnJyerZs6feffdd212DXV8HWB9Jig1Nnz5dOTk5ZofhE1u3blVFRYXi4+PNDqVBnnjiCV199dVmh9Hk2fl1aNGihebMmaM9e/bo//7v/zRt2jSdPn3a7LAaxM6vA6yJJMVmVq5cqUsvvdTyt/XVx4kTJzRmzBjNnz/f7FAaxO4VrUBh99fBrpXFquz+OsCaSFJsZvPmzXr77bfVqVMnTZ8+XQsWLNDjjz9udlgeKykp0U033aTMzEzb3gIeSBUtOwuk18HOlcVAeh1gHSQpNpOTk6P8/HwdPHhQs2fP1oQJE/TYY4+ZHZZHDMPQ2LFjdd1112n06NFmh9MggVTRsrNAeh3sXFkMpNcB1tJkZpyFdfz1r3/VO++8o169emnFihWSpDfeeMNldkerq6xovffeezp16pTOnj2rsLAw2yWMdhcor4PdK4uB8jrAergFGfDS4sWLtXv3btvdCh5o7Po6GIahO+64Q0lJScrOzjY7HK/Z9XWANTHcAwAmqqwsrlixQr1791bv3r21a9cus8MCLIFKCgAAsCQqKQAAwJJIUgAAgCWRpAAAAEsiSQEAAJZEkgIAACyJJAUAAFgSSQoAALAkkhQAAGBJJCkAAMCSSFIAAIAlkaQAAABLIkkBAACWRJICNFFTpkxRv379anyuU6dOeuKJJxo5IgBw1cLsAAA0vj179mjevHnasGFDjc8nJydrx44djRsUAFRBJQVogp599lldeeWVuuaaa2p8PiIiQkePHm3kqADAFUkK0MSUlZVp2bJluuWWW5zr7r33Xi1cuND5+OTJk2rdurUZ4QGAE0kK0MT8/e9/18mTJ9WzZ09JUkVFhd577z21adPGuc3OnTuVnJxsVogAIIkkBWhyfvzxR0lyJiUff/yxfvjhBwUFBUmSvvjiCx06dEijRo0yKUIAOIfGWaCJ6dixoxwOh9566y21bt1aDz74oIYPH66VK1eqU6dOuvfee3Xdddfp2muvNTtUAE2cwzAMw+wgADSunJwcPfXUUwoNDdV///d/66qrrtLIkSN17NgxjRgxQi+99JIiIiLMDhNAE0eSAgAALImeFAAAYEkkKQAAwJJIUgAAgCWRpAAAAEsiSQEAAJZEkgIAACyJJAUAAFgSSQoAALAkkhQAAGBJJCkAAMCSSFIAAIAl/X9KS5keSDvFbAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECT()\n",
-    "print(myect.bound_radius)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similarly we can take a look at the SECT. This one was computed when we asked to compute the ECT. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
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diff --git a/docs/notebooks/Tutorial-ECT_for_embedded_graphs.html b/docs/notebooks/Tutorial-ECT_for_embedded_graphs.html
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--- a/docs/notebooks/Tutorial-ECT_for_embedded_graphs.html
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-              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="../installation.html">1. Getting Started</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#prerequisites">1.1. Prerequisites</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-ect">1.2. Installing <cite>ect</cite></a></li>
-<li class="toctree-l2"><a class="reference internal" href="../installation.html#uninstalling-ect">1.3. Uninstalling <cite>ect</cite></a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../ect_on_graphs.html#ect.ect_graph.ECT"><code class="docutils literal notranslate"><span class="pre">ECT</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l1 current"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul class="current">
-<li class="toctree-l2 current"><a class="current reference internal" href="#">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="#SECT">3.1.3. SECT</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
-</ul>
-</li>
-<li class="toctree-l1"><a class="reference internal" href="../contributing.html">4. Contributing</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../contributing.html#getting-started">4.1. Getting Started</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../contributing.html#contributing-guidelines">4.2. Contributing Guidelines</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#code-style-and-formatting">4.2.1. Code style and formatting</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#documentation-guidelines">4.2.2. Documentation guidelines</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#testing-guidelines">4.2.3. Testing guidelines</a></li>
-<li class="toctree-l3"><a class="reference internal" href="../contributing.html#issue-tracking-and-pull-requests">4.2.4. Issue tracking and pull requests</a></li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="../contributing.html#conclusion">4.3. Conclusion</a></li>
-</ul>
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-<li class="toctree-l1"><a class="reference internal" href="../license.html">5. License</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../citing.html">6. Citing</a></li>
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-  <section id="Tutorial-:-ECT-for-embedded-graphs">
-<h1><span class="section-number">3.1. </span>Tutorial : ECT for embedded graphs<a class="headerlink" href="#Tutorial-:-ECT-for-embedded-graphs" title="Link to this heading"></a></h1>
-<p>This jupyter notebook will walk you through using the <code class="docutils literal notranslate"><span class="pre">ect</span></code> package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see <a class="reference external" href="https://arxiv.org/abs/2310.10395">this paper</a> for a more thorough treatment of details.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedGraph</span><span class="p">,</span> <span class="n">create_example_graph</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
-</pre></div>
-</div>
-</div>
-<section id="Constructing-the-embedded-graph">
-<h2><span class="section-number">3.1.1. </span>Constructing the embedded graph<a class="headerlink" href="#Constructing-the-embedded-graph" title="Link to this heading"></a></h2>
-<p>We assume our input is an undirected graph <span class="math notranslate nohighlight">\(G\)</span> with an embedding in 2D given by a map on the vertices <span class="math notranslate nohighlight">\(f: V(G) \to \mathbb{R}^2\)</span>. A graph can be constructed as follows.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Construct an example graph</span>
-<span class="c1"># Note that this is the same graph that is returned by:</span>
-<span class="c1"># G = create_example_graph()</span>
-
-<span class="n">G</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
-
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;F&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;E&#39;</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s1">&#39;E&#39;</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">)</span>
-
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_3_1.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_3_1.png" />
-</div>
-</div>
-<p>The coordinates of all vertices, given as a dictionary, can be accessed using the <code class="docutils literal notranslate"><span class="pre">coordinates</span></code> attribute.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">coordinates</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;A&#39;: (1, 2), &#39;B&#39;: (3, 4), &#39;C&#39;: (5, 7), &#39;D&#39;: (3, 6), &#39;E&#39;: (4, 3), &#39;F&#39;: (4, 5)}
-</pre></div></div>
-</div>
-<p>Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the <code class="docutils literal notranslate"><span class="pre">set_mean_centered_coordinates</span></code> method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">set_centered_coordinates</span><span class="p">(</span><span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;min_max&#39;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">coordinates</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;A&#39;: (-2.0, -2.5), &#39;B&#39;: (0.0, -0.5), &#39;C&#39;: (2.0, 2.5), &#39;D&#39;: (0.0, 1.5), &#39;E&#39;: (1.0, -1.5), &#39;F&#39;: (1.0, 0.5)}
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_7_2.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_7_2.png" />
-</div>
-</div>
-<p>To get a bounding radius we can use the <code class="docutils literal notranslate"><span class="pre">get_bounding_radius</span></code> method.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is actually getting the radius</span>
-<span class="n">r</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The radius of bounding circle centered at the origin is </span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># plotting the graph with it&#39;s bounding circle of radius r.</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The radius of bounding circle centered at the origin is 3.2015621187164243
-</pre></div></div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_9_2.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_9_2.png" />
-</div>
-</div>
-<p>We can also rescale our graph to have unit radius using <code class="docutils literal notranslate"><span class="pre">rescale_to_unit_disk</span></code></p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">rescale_to_unit_disk</span><span class="p">(</span><span class="n">preserve_center</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<span class="n">r</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The radius of bounding circle centered at the origin is </span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<br/></pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-The radius of bounding circle centered at the origin is 1.0
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_11_1.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_11_1.png" />
-</div>
-</div>
-<p>Directions are given in the code by specifying <span class="math notranslate nohighlight">\(\theta \in [0,2\pi]\)</span>. We often convert this to <span class="math notranslate nohighlight">\(\omega \in \mathbb{S}^1\)</span> by defining the unit vector <span class="math notranslate nohighlight">\(\omega = (\cos(\theta), \sin(\theta))\)</span>. Then the function <span class="math notranslate nohighlight">\(g_\omega\)</span> is defined on the vertices of <span class="math notranslate nohighlight">\(G\)</span> by taking the dot product of the embedding coordinates with the unit vector, specifically</p>
-<div class="math notranslate nohighlight">
-\[g_\omega(v) = \langle f(v), \omega\rangle.\]</div>
-<p>This is done in the code using the <code class="docutils literal notranslate"><span class="pre">g_omega</span></code> method as shown.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This gives the function value of the nodes as a dictionary</span>
-<span class="n">G</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">theta</span> <span class="o">=</span> <span class="mi">7</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-{&#39;A&#39;: 0.11043152607484681,
- &#39;B&#39;: 0.11043152607484658,
- &#39;C&#39;: -0.11043152607484681,
- &#39;D&#39;: -0.3312945782245397,
- &#39;E&#39;: 0.5521576303742327,
- &#39;F&#39;: 0.11043152607484646}
-</pre></div></div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This input to the plotting function will color the nodes by function value.</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color_nodes_theta</span><span class="o">=</span><span class="mi">7</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_14_1.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_14_1.png" />
-</div>
-</div>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here&#39;s an example in another direction.</span>
-<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color_nodes_theta</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-&lt;Axes: &gt;
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_15_1.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_15_1.png" />
-</div>
-</div>
-</section>
-<section id="Computing-the-ECT-of-G">
-<h2><span class="section-number">3.1.2. </span>Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span><a class="headerlink" href="#Computing-the-ECT-of-G" title="Link to this heading"></a></h2>
-<p>Now we can set up the ECT for the embedded graph. The ECT is defined as</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{matrix}
-\text{ECT}(G): &amp; \mathbb{S}^1 &amp; \to &amp; \text{Func}(\mathbb{R}, \mathbb{Z})\\
-&amp; \omega &amp; \mapsto &amp; \{ a \mapsto \chi(g_\omega^{-1}(-\infty,a]) \}
-\end{matrix}\end{split}\]</div>
-<p>When we initialize the <code class="docutils literal notranslate"><span class="pre">ECT</span></code> object, we specify the number of directions in <span class="math notranslate nohighlight">\(\mathbb{S}^1\)</span> to use with <code class="docutils literal notranslate"><span class="pre">num_dir</span></code>, and the number of thresholds at which to compute the Euler characteristic by <code class="docutils literal notranslate"><span class="pre">num_thresh</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-
-<span class="c1"># The ECT object will automatically choose the directions.</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Directions chosen are: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">thetas</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># However, because a bounding radius hasn&#39;t been chosen yet, the thresholds are not yet set.</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Thresholds chosen are: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">threshes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Directions chosen are: [0.         0.39269908 0.78539816 1.17809725 1.57079633 1.96349541
- 2.35619449 2.74889357 3.14159265 3.53429174 3.92699082 4.3196899
- 4.71238898 5.10508806 5.49778714 5.89048623]
-Thresholds chosen are: None
-</pre></div></div>
-</div>
-<p>We can set the bounding radius as follows. Note that some methods will automatically set <code class="docutils literal notranslate"><span class="pre">bound_radius</span></code> to be the bounding radius of the input <code class="docutils literal notranslate"><span class="pre">G</span></code> if not already set. I’m choosing the radius to be a bit bigger than the bounding radius of <code class="docutils literal notranslate"><span class="pre">G</span></code> to make some better pictures.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="mf">1.2</span> <span class="o">*</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">())</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Internally set radius is: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">bound_radius</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="c1"># Now the thresholds are set.</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Thresholds chosen are: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">threshes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-Internally set radius is: 1.2
-Thresholds chosen are: [-1.2        -1.07368421 -0.94736842 -0.82105263 -0.69473684 -0.56842105
- -0.44210526 -0.31578947 -0.18947368 -0.06315789  0.06315789  0.18947368
-  0.31578947  0.44210526  0.56842105  0.69473684  0.82105263  0.94736842
-  1.07368421  1.2       ]
-</pre></div></div>
-</div>
-<p>If we want the Euler characteristic curve for a fixed direction, we use the <code class="docutils literal notranslate"><span class="pre">calculateECC</span></code> function. This outputs a list of integers where each corresponds to <span class="math notranslate nohighlight">\(\chi(f_\omega^{-1}(-\infty,a])\)</span> for the <span class="math notranslate nohighlight">\(a\)</span> values in <code class="docutils literal notranslate"><span class="pre">myect.threshes</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">calculateECC</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span><span class="n">bound_radius</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-array([0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])
-</pre></div></div>
-</div>
-<p>This can also be calculated on the fly and plotted using the <code class="docutils literal notranslate"><span class="pre">plotECC</span></code> method.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">plotECC</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_23_0.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_23_0.png" />
-</div>
-</div>
-<p>To calculate the full ECT, we call the <code class="docutils literal notranslate"><span class="pre">calculateECT</span></code> method. This matrix is size <code class="docutils literal notranslate"><span class="pre">myect.num_dirs</span> <span class="pre">x</span> <span class="pre">myect.num_thresh</span></code>. Once computed, it is stored in the <code class="docutils literal notranslate"><span class="pre">ECT</span></code> class as <code class="docutils literal notranslate"><span class="pre">ECT.matrix</span></code>.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">calculateECT</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
-
-<span class="c1"># The matrix is passed as an output above but is also saved internally. Get the saved matrix</span>
-<span class="n">M</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">get_ECT</span><span class="p">()</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">myect</span><span class="o">.</span><span class="n">num_dirs</span><span class="p">,</span> <span class="n">myect</span><span class="o">.</span><span class="n">num_thresh</span><span class="p">)</span>
-
-<span class="c1"># We can use the built in command to plot the matrix. Unlike the plotECC function, this command does not calculate the ECT when called so it must have been run earlier. An equivalent command is myect.plotECT()</span>
-<span class="n">myect</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="s1">&#39;ECT&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(16, 20)
-16 20
-</pre></div></div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_25_1.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_25_1.png" />
-</div>
-</div>
-</section>
-<section id="SECT">
-<h2><span class="section-number">3.1.3. </span>SECT<a class="headerlink" href="#SECT" title="Link to this heading"></a></h2>
-<p>The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius <span class="math notranslate nohighlight">\(R\)</span> bounding the graph. The average ECT in a direction <span class="math notranslate nohighlight">\(\omega\)</span> defined on function values <span class="math notranslate nohighlight">\([-R,R]\)</span> is given by</p>
-<div class="math notranslate nohighlight">
-\[\overline{\text{ECT}_\omega} = \frac{1}{2R} \int_{t = -R}^{R} \chi(g_\omega^{-1}(-\infty,t]) \; dt.\]</div>
-<p>Then the SECT is defined by</p>
-<div class="math notranslate nohighlight">
-\[\begin{split}\begin{matrix}
-\text{SECT}(G): &amp; \mathbb{S}^1 &amp; \to &amp; \text{Func}(\mathbb{R}, \mathbb{Z})\\
-&amp; \omega &amp; \mapsto &amp; \{ t \mapsto \int_{-R}^t \left( \chi(g_\omega^{-1}(-\infty,a]) -\overline{\text{ECT}_\omega}\right)\:da \}
-\end{matrix}\end{split}\]</div>
-<p>defined from the TODO: Write intro</p>
-<p>We can calculate the SECT directly using the <code class="docutils literal notranslate"><span class="pre">calculateSECT</span></code> method. Note that this both returns the function and sets the SECT internally to the <code class="docutils literal notranslate"><span class="pre">SECT_matrix</span></code> attribute.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">M_SECT</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculateSECT</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">M_SECT</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-(16, 20)
-</pre></div></div>
-</div>
-<p>However this is not technically necessary since, as a default, the SECT was also set internally when the ECT matrix was computed. If you don’t want to calculate the SECT at the same time as calculating the ECT, you can use the following command instead.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">calculateECT</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">compute_SECT</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>We can also use the internally set plotting functions to easily visualize it.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="s1">&#39;SECT&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
-</div>
-<div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_31_0.png" src="../_images/notebooks_Tutorial-ECT_for_embedded_graphs_31_0.png" />
-</div>
-</div>
-</section>
-</section>
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diff --git a/docs/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb b/docs/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
deleted file mode 100644
index 0ba510f..0000000
--- a/docs/notebooks/Tutorial-ECT_for_embedded_graphs.ipynb
+++ /dev/null
@@ -1,623 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial : ECT for embedded graphs \n",
-    "\n",
-    "This jupyter notebook will walk you through using the `ect` package to compute the Euler characteristic transform of a 2D embedded graph. This tutorial assumes you already know what an ECT is; see [this paper](https://arxiv.org/abs/2310.10395) for a more thorough treatment of details."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ect import ECT, EmbeddedGraph, create_example_graph\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import networkx as nx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Constructing the embedded graph\n",
-    "\n",
-    "We assume our input is an undirected graph $G$ with an embedding in 2D given by a map on the vertices $f: V(G) \\to \\mathbb{R}^2$. A graph can be constructed as follows. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Construct an example graph \n",
-    "# Note that this is the same graph that is returned by:\n",
-    "# G = create_example_graph()\n",
-    "\n",
-    "G = EmbeddedGraph()\n",
-    "\n",
-    "G.add_node('A', 1, 2)\n",
-    "G.add_node('B', 3, 4)\n",
-    "G.add_node('C', 5, 7)\n",
-    "G.add_node('D', 3, 6)\n",
-    "G.add_node('E', 4, 3)\n",
-    "G.add_node('F', 4, 5)\n",
-    "\n",
-    "G.add_edge('A', 'B')\n",
-    "G.add_edge('B', 'C')\n",
-    "G.add_edge('B', 'D')\n",
-    "G.add_edge('B', 'E')\n",
-    "G.add_edge('C', 'D')\n",
-    "G.add_edge('E', 'F')\n",
-    "\n",
-    "G.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The coordinates of all vertices, given as a dictionary, can be accessed using the `coordinates` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': (1, 2), 'B': (3, 4), 'C': (5, 7), 'D': (3, 6), 'E': (4, 3), 'F': (4, 5)}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "G.coordinates"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Because of the rotational aspect of the ECT, we often want our graph to be centered, so you can use the `set_mean_centered_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'A': (-2.0, -2.5), 'B': (0.0, -0.5), 'C': (2.0, 2.5), 'D': (0.0, 1.5), 'E': (1.0, -1.5), 'F': (1.0, 0.5)}\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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wFoyEyzcauCcVYMlMl6FnEYQEg0G43W40NTXh5MmTHMVGwcfPkOFNLMzF2gWlWItSeH1+7P39R/ibJd/FG6//EnfeMs3wd3INxjKBGCwXkCFkWy0oHpWB3s9qUTwqgwE7CMsE4jBkiQyOswnE4v/OiQyOZQKxOJIlMjCWCcRjyBIZFMsEqcFyAZFBsUyQGhzJEhkQywSpw5AlMhiWCVKL5QIig2GZILU4kiUyEJYJUo8hS2QQLBOkB8sFRAbBMkF6CB3JHjx4EHfffTfGjBkDSZLwzjvviGyOiMJgmSB9hIas1+vFDTfcgM2bN4tshogiYJkgvYSWC+bPn4/58+eLbIKIomCZIL1UVZP1+Xzw+Xx97zs7O9PYGyLtY5kg/VQ1u2Djxo3Iy8vrezmdznR3iUizWCZQB1WF7JNPPomOjo6+V1NTU7q7RKRZoTLB1q1bWSZII1WVC6xWK6xWa7q7QaR5LBOoh6pGskSUPJYJ1EXoSLa7uxv19fV978+ePYsTJ07AbrfD5TL2o5eJROFsAnURGrLHjh2D2+3ue79q1SoAwLJly7B9+3aRTRMZEssE6iM0ZOfOnQtZlqPvSERJY5lAnVR14YuIEscygTrxwheRDrBMoF4MWSKNY5lA3VguINI4lgnUjSNZIg1jmUD9GLJEGsUygTawXECkUSwTaANHskQaxDKBdjBkiTSGZQJtYbmASGNYJtAWjmSJNIRlAu3hSJZII+ItE3h9fjS0edHrDyLTYkJRfjayrfwrn2r8iRNpRCxlgrrzXdhZ5UFFTQs87T3ovzyTBMBlt8E9uQD3z3BhYmFuSvptdAxZIg2IViZoau/B6vJqHKpvhdkkIRAcuvqdDKCxvQc7qhqx/YMGzClxYMPCKXDabeK/gIGxJkukctHKBLuOejDvuUocPtMGAMMGbH+h7YfPtGHec5XYddSjfKepD0eyRCoXqUywuaIOm/bVJnTeQFBGICjjid3VaO32YaV7ohLdpUE4kiVSsUhlgl1HPQkH7GCb9tXiTY5oheBIlkilIpUJmtp7sGbPqbDHdp88gLb3nh9228ibF+KKry8f8vnTe05h9gQHa7QKY8gSqVSkMsHq8mr4o9ReASD/rkeQkT92wGfmHPuw+/qDMlaXV2PH8hmJd5qGYMgSqVCkMkHd+S4cqm+N6TwZV46DdXRstdZAUMah+lbUt3ShpIDTu5TCmiyRykSbTbCzygOzSRLSttkk4fUjrM0qiSFLpDKhMsHWrVuHvemgoqYl6jStPnIQcjAw4BVJICijorYlkW5TGCwXEKlItJsOun1+eNp7Yj7fuV8+NuQz1z++C8lkDnuMp60HXp+ft+AqhD9FIpWIZW2CxjYvYhzDAgDyv7kKGfnOAZ9FCljg8p1hDW1elI7Ji6MlCochS6QSsaxN0OsPxnXOjHxnzBe+kmmHwmNNlkgFYl3CMNOSmr+yqWrHCPiTJEqzeJYwLMrPhph5BV+RvmyHlMFyAVGaxfOkg2yrBS67DY1xXPyKlyvfxoteCuJIliiNEnnSgXtygdB5su5JBULObVQMWaI0SfSBiPfPcEWdJ5szdR7GPfHruC96BYIylsx0xXUMRcaQJUqTaDcdhDOxMBdzShyKj2bNJglzShy8pVZhDFmiNEj2gYgbFk6BReGQtZgkbFg4RdFzEkOWKOUSLRP057TbsG5BqaL9Wr+glMscCsBLiEQpFs9sgkgWT3ehtdunyMLdPyqbjPumsxYrAkOWKIWSLRMMttI9EY4cK9bsOQX/l4+TiZXZJMFikrB+QSkDViCWC4hSRIkywXAWT3fhwKO3Y/b4fACIekEstH32+HwcePR2BqxgHMkSpYhSZYLhOO027Fg+A3Xnu7CzyoOK2hZ42noGLCYj4fKNBu5JBVgy08VZBCnCkCVKAaXLBOFMLMzF2gWlWItSeH1+7P39R/ibJd/FG6//EnfeMo13cqUBywVEgokqE0STbbWgeFQGej+rRfGoDAZsmvCnTiSYyDIBqR9HskQCpapMQOrFkCUSJF1lAlIXlguIBGGZgACOZImEYJmAQhiyRApjmYD6Y7mASGEsE1B/HMkSKYhlAhqMIUukEJYJaDgsFxAphGUCGg5HskQKYJmAwklJyL700ksoLi7GiBEjMG3aNBw6dCgVzRKlBMsEFInwkH3zzTfxyCOP4KmnnsLx48cxZ84czJ8/Hx6PR3TTRCmR6AMRyRiEh+zPfvYzLF++HA888ACuvfZaPP/883A6nXj55ZdFN00kHMsEFI3QkO3t7cVHH32EsrKyAZ+XlZXh8OHDQ/b3+Xzo7Owc8CJSK5YJKBZCQ7a1tRWBQACFhYUDPi8sLMS5c+eG7L9x40bk5eX1vZxOp8juESWFZQKKRUoufEnSwGcOybI85DMAePLJJ9HR0dH3ampqSkX3iOLGMgHFSug8WYfDAbPZPGTU2tLSMmR0CwBWqxVWq1Vkl4iSxjIBxUPoSDYzMxPTpk3D/v37B3y+f/9+zJ49W2TTRMKwTEDxEH7H16pVq7B06VLcdNNNmDVrFl599VV4PB48+OCDopsmUhzLBBQv4SF73333oa2tDevXr8dnn32G66+/Hu+99x7GjRsnumkiRbFMQIlIydoFDz30EB566KFUNEUkDNcmoERw7QKiGLBMQIliyBJFwTIBJYNLHRJFwTIBJYMjWaIIWCagZDFkicJgmYCUwHIBURgsE5ASOJIlGgbLBKQUhizRICwTkJJYLiAahGUCUhJHskT9sExASmPIEn2JZQISgeUCMgSvz4+zn19C5uhJOPv5JUz2+ZFtHfjrzzIBicCQJd2qO9+FnVUeVNS0wNPeAxnA6GU/w2P7W/H4/v+Gy26De3IB7p/hgqm7hWUCEoIhS7rT1N6D1eXVOFTfCrNJQiAoD9lHBtDY3oMdVY3Y/kEDRnx+FgXjr2OZgBTHmizpyq6jHsx7rhKHz7QBwLAB219o+xe5TlgWrMWv/9QuvI9kLBzJkm5srqjDpn21CR0rmS24FASe2F2N1m4fVronKtw7MiqOZEkXdh31JBywg23aV4s3j3oUORcRR7KkeU3tPViz59Sw27pPHkDbe88P+MyUNRIZDhdGzrgXtpKbhz3u6T2nMHuCA067TenuksFwJEuat7q8Gv4otdf8ux7BVUs34aqlzyL/zpWQTCZc+K/16KmrGnZ/f1DG6vJqEd0lg2HIkqbVne/CofrWqBe4Mq4cB+vV18B69bWwTZ6NK7+zBjBnwPung8PuHwjKOFTfivqWLhHdJgNhyJKm7azywGyS4j5OsmRCMlsgmcxh9zGbJLx+hLVZSg5DljStoqYl6igWACAHIQcDkAN++Dtb8ZcDr0K+5EP2dbeHPSQQlFFR26Jgb8mIeOGLNKvb54envSemfc/98rGBH5gzYL/jQWSNnxbxOE9bD7zD3IJLFCv+5pBmNbZ5EcMYFgCQ/81VyMh3AgCCX3Sip/YDtO97GbIcwMhpd4c9TgbQ0OZF6Zi85DtMhsSQJc3q9Qdj3jcj3wnr6K9uMMgaPw3+jhZ8XrEdOaVumEaEXxAmnnaIBmNNljQr05Lcr29mQRFkvw+X2j8V2g4ZG397SLOK8rMR/7yCr/SePwsAMNnClwKkL9shShTLBaRZ2VYLXHYbGmO4+HXpQiMQDAAAAl90oaf2MC42HEfWpFnIGHVV2ONc+TZe9KKk8LeHNM09uQA7qhqjTuPqf2utZM2GJa8QV3z9AeR+7f+EPcZskuCeVKBUV8mgGLKkaffPcGH7Bw1ht+dMnYecqfMSOncgKGPJTFeCPSO6jDVZ0rSJhbmYU+JI6K6vSMwmCXNKHCgpyFX0vGQ8DFnSvA0Lp8BikoCYZ81GZzFJ2LBwimLnI+NiyJLmOe02fH1kK5DUXIOB1i0o5TKHpAiGLGneK6+8glf+8bu4prdOkfP9pfI1VL+zBbKs3MiYjIshS5r2yiuv4Ic//CH+/u//Hr/d9A/46b1TYLWY4q7Rmk0SrBYT/uXeKXj62zOwYcMG/OQnP2HQUtI4u4A0q3/APv/885AkCYunu3DLBEfUp9WGhLbPHp+PDQunXC4RTL+8mMzjjz8OAPjnf/5nSJKyF9bIOBiypEnDBWyI027DjuUzUHe+CzurPKiobYGnrWfAZTEJl280cE8qwJKZriGzCB57jEFLymDIkuZECtj+JhbmYu2CUqxFKbw+PxravOj1B5FpMaEoPzvqnVwMWlICQ5Y0JdaAHSzbaklouUIGLSWLIUuakWjAJotBS8lgyJImpCtgQxi0lCiGLKleugM2hEFLiWDIkqqpJWBDGLQUL4YsqZbaAjaEQUvxYMiSKqk1YEMYtBQrhiypjtoDNoRBS7FgyJKqaCVgQxi0FA1DllRDawEbwqClSBiypApaDdgQBi2Fw5CltNN6wIYwaGk4QkP2mWeewW9+8xucOHECmZmZ+Pzzz0U2Rxqkl4ANYdDSYEJDtre3F4sWLcKsWbPwi1/8QmRTpEF6C9gQBi31JzRk161bBwDYvn27yGZIg/QasCEMWgphTZZSTu8BG8KgJUBlIevz+eDz+fred3Z2prE3JIJRAjaEQUtxP0hx7dq1kCQp4uvYsWMJdWbjxo3Iy8vrezmdzoTOQ+pktIANeeyxx7Bp0yY+nNGg4h7Jrly5EosXL464T1FRUUKdefLJJ7Fq1aq+952dnQxanTBqwIZwRGtccYesw+GAw+EQ0RdYrVZYrVYh56b0MXrAhjBojUloTdbj8aC9vR0ejweBQAAnTpwAAJSUlCAnJ0dk06QSDNiBGLTGIzRkn376abz22mt972+88UYAQEVFBebOnSuyaVIBBuzwGLTGIjRkt2/fzjmyBsWAjYxBaxyqmsJF+sCAjQ2D1hgYsqQoBmx8GLT6x5AlxTBgE8Og1TeGLCmCAZscBq1+MWQpaQxYZTBo9YkhS0lhwCqLQas/DFlKGANWDAatvjBkKSEMWLEYtPrBkKW4MWBTg0GrDwxZigsDNrUYtNrHkKWYMWDTg0GrbQxZigkDNr0SCVqvz4+zn19C5uhJOPv5JUz2+ZFt5V/5VONPnKJiwKpDLEFbd74LO6s8qKhpgae9BzKA0ct+hsf2t+Lx/f8Nl90G9+QC3D/DhYmFuan+CobEkKWIGLDqEi5om9p7sLq8GofqW2E2SQgEhz7iRgbQ2N6DHVWN2P5BA+aUOLBh4RQ47bZUfgXDYchSWAxYdRoctNd/6++w9len4P8yWIcL2P5C2w+facO85yqxbkEpFk93CeyxsTFkaVgMWHULBe36tz/EFcHqhM4RCMoIBGU8sbsard0+rHRPVLKL9CWGLA3BgNWGq29bhCtar1HkXJv21eLKHCvu44hWcQxZGoABqw1N7T1Ys+dU2O3dJw+g7b3nw24v/L8bMGLc1AGfPb3nFGZPcLBGqzCGLPVhwGrH6vLqvhpsJPl3PYKM/LFDPs9wDB2x+oMyVpdXY8fyGYr0kS5jyBIABqyW1J3vwqH61pj2zbhyHKyjY6u1BoIyDtW3or6lCyUFnN6lFFO6O0Dpx4DVlp1VHphNYv6MzCYJrx/xCDm3UTFkDY4Bqz0VNS1Rp2n1kYOQg4Ehr3ACQRkVtS0K9ZQAlgsMjQGrPd0+PzztPTHvf+6Xjw39UDJh3I/3hD3G09YDL2/BVQx/igbFgNWmxjYvYhzDAgDyv7kKGfnOgR9G+bOWATS0eVE6Ji/u/tFQDFkDYsBqV68/GNf+GfnOmC98JdMOhcearMEwYLUt05Kav7KpascI+JM0EAas9hXlZ0P0n5r0ZTukDJYLDIIBqw/ZVgtcdhsaY7z4delCIzDMbALLFaNhtg1fc3Xl23jRS0H8SRoAA1Zf3JMLsKOqMaZpXOFurbXPfxi5N3xjyOdmkwT3pIJku0j9MGR1jgGrP/fPcGH7Bw0R98mZOg85U+fFfe5AUMaSmVwkRkmsyeoYA1afJhbmYk6JQ/G7vswmCXNKHLylVmEMWZ1iwOrbhoVTYFE4ZC0mCRsWTlH0nMSQ1SUGrP457TasW1Cq6DnXLyjlMocCMGR1hgFrHIunu/B42SRFzvWjsslcsFsQXvjSEQas8ax0T4Qjx4o1ey4/4yvmhWNwuQZrMUlYv6CUASsQR7I6wYA1rsXTXTjw6O2YPT4fAKJeEAttnz0+HwcevZ0BKxhHsjrAgCWn3YYdy2eg7nwXdlZ5UFHbAk9bz4DFZCRcvtHAPakAS2a6OIsgRSRZluNZ1CelOjs7kZeXh46ODowcOTLd3VElBiyF4/X50dDmRa8/iEyLCUX52byTS0Gx5hN/4hrGgKVIsq0WLleoAqzJahQDlkgbGLIaxIAl0g6GrMYwYIm0hSGrIQxYIu1hyGoEA5ZImxiyGsCAJdIuhqzKMWCJtI0hq2IMWCLtY8iqFAOWSB8YsirEgCXSD4asyjBgifRFWMg2NDRg+fLlKC4uRlZWFiZMmIA1a9agt7dXVJOax4Al0h9hC8R88sknCAaD2LJlC0pKSvDxxx/jBz/4AbxeLzZt2iSqWc1iwBLpU0qXOnz22Wfx8ssv48yZMzHtb5SlDhmwRNoTaz6ltCbb0dEBu92eyiZVjwFLpG8pW0/29OnTeOGFF/Bv//ZvYffx+Xzw+Xx97zs7O1PRtbRhwBLpX9wj2bVr10KSpIivY8eODTimubkZd955JxYtWoQHHngg7Lk3btyIvLy8vpfT6Yz/G2kEA5bIGOKuyba2tqK1tTXiPkVFRRgxYgSAywHrdrsxY8YMbN++HSZT+FwfbiTrdDp1V5NlwBJpn7DHzzgcDjgcjpj2/fTTT+F2uzFt2jRs27YtYsACgNVqhdVqjbdLmsKAJTIWYTXZ5uZmzJ07Fy6XC5s2bcKFCxf6tl111VWimlU1BiyR8QgL2X379qG+vh719fUYO3bsgG0qfkCuMAxYImMSNoXre9/7HmRZHvZlNAxYIuPi2gWCMWCJjI0hKxADlogYsoIwYIkIYMgKwYAlohCGrMIYsETUH0NWQQxYIhqMIasQBiwRDYchqwAGLBGFw5BNEgOWiCJhyCaBAUtE0TBkE8SAJaJYpOzJCGrl9fnR0OZFrz+ITIsJRfnZyLZG/rEwYIkoVoYM2brzXdhZ5UFFTQs87T3ov2SNBMBlt8E9uQD3z3BhYmHugGMZsEQUj5Q+rTZeSj+ttqm9B6vLq3GovhVmk4RAMPxXD22fU+LAhoVT4LTbGLBE1CfWfDJMyO466sGaPafgD8oRw3Uws0mCxSTh6yNb8co/fpcBS0QAGLIDbK6ow6Z9tUn0RAYg4ZreOvx20z8wYIko5nzS/eyCXUc9SQYscLlSC3ySORFvHWtKvlNEZBi6Dtmm9h6s2XMq4j6dx/ag8affRPPPH4rpnE/vOYWm9h4lukdEBqDrkF1dXg1/lPpr98n9AIBLrR74mmuintMflLG6vFqR/hGR/uk2ZOvOd+FQfWvEi1y+z+pwqeUssiZMBwB0/2Ff1PMGgjIO1beivqVLsb4SkX7pNmR3VnlgNkW+QNV98nKojpq7DNarr4X3TwcRvHQx6rnNJgmvH/Eo0k8i0jfdhmxFTUvEUWzwkg/ePx5E5uiJyLyyCNlT74Dc+wV6Pvl91HMHgjIqaluU7C4R6ZQuQ7bb54cnysWpnprfQ/Z5kTO1DACQfe0cSJlZMZUMAMDT1gOvz590X4lI33QZso1tXkSb/Nv9h32QLFZkX3sbAMCUmQXb5Fvg+/MpXGr/NGobMoCGNm/ynSUiXdNlyPb6gxG3X/pLM3xNp5A14SYAMoIXuxG82I3sa24B8NWMg2TbISLS5QIxmZbI/++4HKIyemp+j56aoTVY78fvY9RtSyGZzEm1Q0Sky5Atys+GBAxbMpCDAXir/weWUaORP//hIdu/OH0UnR+W44szH8FWcnPYNqQv2yEiikSXIZtttcBlt6FxmItfX5z5CIHudoya+z2MGDd1yPaMK8eh86Nfo/sP+yKGrCvfFnXdWSIi3f571z25YNh5st1/2AeYLciZesewx5ltebBNmoUvTh9FwPuX4fcxSXBPKlC0v0SkT7pdhavufBfueP6goJ4BBx69DSUFudF3JCJdMvwqXBMLczGnxBH1rq94mU0S5pQ4GLBEFBPdhiwAbFg4BRaFQ9ZikrBh4RRFz0lE+qXrkHXabVi3oFTRc65fUAqn3aboOYlIv3QdsgCweLoLj5dNUuRcPyqbjPumuxQ5FxEZgyHmIK10T4Qjx5rUM77WLyhlwBJR3HQ/kg1ZPN2FA4/ejtnj8wEg6gWx0PbZ4/Nx4NHbGbBElBBDjGRDnHYbdiyfgbrzXdhZ5UFFbQs8bT0D7gyTcPlGA/ekAiyZ6eIsAiJKim7nycbK6/Ojoc2LXn8QmRYTivKzeScXEUUVaz4ZPk2yrRaUjslLdzeISKcMU5MlIkoHhiwRkUAMWSIigRiyREQCMWSJiARiyBIRCcSQJSISiCFLRCQQQ5aISCCGLBGRQAxZIiKBGLJERAIJDdkFCxbA5XJhxIgRGD16NJYuXYrm5maRTRIRqYrQkHW73XjrrbdQU1ODt99+G6dPn8Z3vvMdkU0SEalKSteT3bNnD+655x74fD5kZGRE3T8V68kSESVCdevJtre3Y+fOnZg9e3bYgPX5fPD5fH3vOzs7U9U9IiIhhIfsj3/8Y2zevBk9PT2YOXMmfv3rX4fdd+PGjVi3bt2Qzxm2RKQ2oVyKWgyQ47RmzRoZQMTX0aNH+/a/cOGCXFNTI+/bt0++5ZZb5LvuuksOBoPDnvvixYtyR0dH3+uPf/xj1Lb44osvvtL5ampqipiZcddkW1tb0draGnGfoqIijBgxYsjnf/7zn+F0OnH48GHMmjUralvBYBDNzc3Izc2FJEV+umwyOjs74XQ60dTUZJjaL78zv7Nepeo7y7KMrq4ujBkzBiZT+DkEcZcLHA4HHA5Hwp0CMKDuGonJZMLYsWMTaisRI0eONMwvYgi/szHwO4uRl5cXdR9hNdkPP/wQH374IW699VZcccUVOHPmDJ5++mlMmDAhplEsEZEeCJsnm5WVhd27d+Ov//qvMXnyZHz/+9/H9ddfj8rKSlitVlHNEhGpirCR7JQpU/D++++LOr2irFYr1qxZY6jw53c2Bn7n9EvpzQhEREbDBWKIiARiyBIRCcSQJSISiCFLRCQQQ7afhoYGLF++HMXFxcjKysKECROwZs0a9Pb2prtrQj3zzDOYPXs2bDYbRo0ale7uCPHSSy+huLgYI0aMwLRp03Do0KF0d0mogwcP4u6778aYMWMgSRLeeeeddHdJuI0bN2L69OnIzc1FQUEB7rnnHtTU1KS7WwzZ/j755BMEg0Fs2bIFp06dwnPPPYdXXnkFq1evTnfXhOrt7cWiRYvwwx/+MN1dEeLNN9/EI488gqeeegrHjx/HnDlzMH/+fHg8nnR3TRiv14sbbrgBmzdvTndXUqayshIrVqzAkSNHsH//fvj9fpSVlcHr9aa3Y/EuEGM0//qv/yoXFxenuxspsW3bNjkvLy/d3VDczTffLD/44IMDPrvmmmvkJ554Ik09Si0Acnl5ebq7kXItLS0yALmysjKt/eBINoqOjg7Y7fZ0d4MS1Nvbi48++ghlZWUDPi8rK8Phw4fT1CtKhY6ODgBI+99fhmwEp0+fxgsvvIAHH3ww3V2hBLW2tiIQCKCwsHDA54WFhTh37lyaekWiybKMVatW4dZbb8X111+f1r4YImTXrl0LSZIivo4dOzbgmObmZtx5551YtGgRHnjggTT1PHGJfGc9G7xUpizLQpfPpPRauXIlTp48if/4j/9Id1dS9/iZdFq5ciUWL14ccZ+ioqK+/25ubobb7casWbPw6quvCu6dGPF+Z71yOBwwm81DRq0tLS1DRrekDw8//DD27NmDgwcPpnSp1HAMEbLxrIH76aefwu12Y9q0adi2bVvExXjVLJl1f/UkMzMT06ZNw/79+7Fw4cK+z/fv349vfetbaewZKU2WZTz88MMoLy/H7373OxQXF6e7SwAMErKxam5uxty5c+FyubBp0yZcuHChb9tVV12Vxp6J5fF40N7eDo/Hg0AggBMnTgAASkpKkJOTk97OKWDVqlVYunQpbrrppr5/nXg8Hl3X2ru7u1FfX9/3/uzZszhx4gTsdjtcLlcaeybOihUr8MYbb+Ddd99Fbm5u379e8vLykJWVlb6OpXVug8ps27Yt7HN89GzZsmXDfueKiop0d00xL774ojxu3Dg5MzNT/trXvpb2aT2iVVRUDPtnumzZsnR3TZhwf3e3bduW1n5xqUMiIoG0WXAkItIIhiwRkUAMWSIigRiyREQCMWSJiARiyBIRCcSQJSISiCFLRCQQQ5aISCCGLBGRQAxZIiKBGLJERAL9f04iVcylSOMZAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.set_centered_coordinates(type = 'min_max')\n",
-    "print(G.coordinates)\n",
-    "G.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a bounding radius we can use the `get_bounding_radius` method. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This is actually getting the radius \n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n",
-    "\n",
-    "# plotting the graph with it's bounding circle of radius r.\n",
-    "G.plot(bounding_circle=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also rescale our graph to have unit radius using `rescale_to_unit_disk`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The radius of bounding circle centered at the origin is 1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G.rescale_to_unit_disk(preserve_center=False)\n",
-    "G.plot(bounding_circle=True)\n",
-    "\n",
-    "r = G.get_bounding_radius()\n",
-    "print(f'The radius of bounding circle centered at the origin is {r}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Directions are given in the code by specifying $\\theta \\in [0,2\\pi]$. We often convert this to $\\omega \\in \\mathbb{S}^1$ by defining the unit vector $\\omega = (\\cos(\\theta), \\sin(\\theta))$. Then the function $g_\\omega$ is defined on the vertices of $G$ by taking the dot product of the embedding coordinates with the unit vector, specifically\n",
-    "$$\n",
-    "g_\\omega(v) = \\langle f(v), \\omega\\rangle.\n",
-    "$$\n",
-    "This is done in the code using the `g_omega` method as shown. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': 0.11043152607484681,\n",
-       " 'B': 0.11043152607484658,\n",
-       " 'C': -0.11043152607484681,\n",
-       " 'D': -0.3312945782245397,\n",
-       " 'E': 0.5521576303742327,\n",
-       " 'F': 0.11043152607484646}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This gives the function value of the nodes as a dictionary\n",
-    "G.g_omega(theta = 7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# This input to the plotting function will color the nodes by function value.\n",
-    "G.plot(color_nodes_theta=7*np.pi/4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Here's an example in another direction.\n",
-    "G.plot(color_nodes_theta=np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Computing the ECT of $G$\n",
-    "\n",
-    "Now we can set up the ECT for the embedded graph. The ECT is defined as \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{ECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ a \\mapsto \\chi(g_\\omega^{-1}(-\\infty,a]) \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "\n",
-    "When we initialize the `ECT` object, we specify the number of directions in $\\mathbb{S}^1$ to use with `num_dir`, and the number of thresholds at which to compute the Euler characteristic by `num_thresh`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Directions chosen are: [0.         0.39269908 0.78539816 1.17809725 1.57079633 1.96349541\n",
-      " 2.35619449 2.74889357 3.14159265 3.53429174 3.92699082 4.3196899\n",
-      " 4.71238898 5.10508806 5.49778714 5.89048623]\n",
-      "Thresholds chosen are: None\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect = ECT(num_dirs = 16, num_thresh=20)\n",
-    "\n",
-    "# The ECT object will automatically choose the directions. \n",
-    "print(f'Directions chosen are: {myect.thetas}')\n",
-    "\n",
-    "# However, because a bounding radius hasn't been chosen yet, the thresholds are not yet set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can set the bounding radius as follows. Note that some methods will automatically set `bound_radius` to be the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Internally set radius is: 1.2\n",
-      "Thresholds chosen are: [-1.2        -1.07368421 -0.94736842 -0.82105263 -0.69473684 -0.56842105\n",
-      " -0.44210526 -0.31578947 -0.18947368 -0.06315789  0.06315789  0.18947368\n",
-      "  0.31578947  0.44210526  0.56842105  0.69473684  0.82105263  0.94736842\n",
-      "  1.07368421  1.2       ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "myect.set_bounding_radius(1.2 * G.get_bounding_radius())\n",
-    "\n",
-    "print(f'Internally set radius is: {myect.bound_radius}')\n",
-    "\n",
-    "# Now the thresholds are set.\n",
-    "print(f'Thresholds chosen are: {myect.threshes}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want the Euler characteristic curve for a fixed direction, we use the `calculateECC` function. This outputs a list of integers where each corresponds to $\\chi(f_\\omega^{-1}(-\\infty,a])$ for the $a$ values in `myect.threshes`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "myect.calculateECC(G, np.pi/2,bound_radius=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This can also be calculated on the fly and plotted using the `plotECC` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.plotECC(G, -np.pi/2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To calculate the full ECT, we call the `calculateECT` method. This matrix is size `myect.num_dirs x myect.num_thresh`. Once computed, it is stored in the `ECT` class as `ECT.matrix`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n",
-      "16 20\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "myect.calculateECT(G)\n",
-    "\n",
-    "# The matrix is passed as an output above but is also saved internally. Get the saved matrix\n",
-    "M = myect.get_ECT()\n",
-    "\n",
-    "print(M.shape)\n",
-    "print(myect.num_dirs, myect.num_thresh)\n",
-    "\n",
-    "# We can use the built in command to plot the matrix. Unlike the plotECC function, this command does not calculate the ECT when called so it must have been run earlier. An equivalent command is myect.plotECT()\n",
-    "myect.plot('ECT')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SECT \n",
-    "\n",
-    "The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
-    "$$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
-    "Then the SECT is defined by \n",
-    "$$\n",
-    "\\begin{matrix}\n",
-    "\\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
-    "& \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
-    "\\end{matrix}\n",
-    "$$\n",
-    "\n",
-    " defined from the \n",
-    "TODO: Write intro "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can calculate the SECT directly using the `calculateSECT` method. Note that this both returns the function and sets the SECT internally to the `SECT_matrix` attribute. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(16, 20)\n"
-     ]
-    }
-   ],
-   "source": [
-    "M_SECT = myect.calculateSECT()\n",
-    "print(M_SECT.shape)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However this is not technically necessary since, as a default, the SECT was also set internally when the ECT matrix was computed. If you don't want to calculate the SECT at the same time as calculating the ECT, you can use the following command instead.\n",
-    "```python\n",
-    "myect.calculateECT(G, compute_SECT=False)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the internally set plotting functions to easily visualize it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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diff --git a/docs/notebooks/Tutorial-ExactECT.html b/docs/notebooks/Tutorial-ExactECT.html
index 40d6481..57bc074 100644
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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-    <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="4. Contributing to the ect Package" href="../contributing.html" />
-    <link rel="prev" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" href="Matisse/Matisse_ECT.html" /> 
+    <link rel="search" title="Search" href="../search.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -50,7 +50,7 @@
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
               <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
-<ul class="current">
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation.html">1. Getting Started</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../installation.html#prerequisites">1.1. Prerequisites</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-ect">1.2. Installing <cite>ect</cite></a></li>
@@ -60,12 +60,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -74,19 +72,16 @@
 </li>
 </ul>
 </li>
-<li class="toctree-l1 current"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contributing.html">4. Contributing</a><ul>
@@ -119,8 +114,7 @@
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
       <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
-          <li class="breadcrumb-item"><a href="../tutorials.html"><span class="section-number">3. </span>Tutorials</a></li>
-      <li class="breadcrumb-item active"><span class="section-number">3.4. </span>Tutorial for exact ECT computation</li>
+      <li class="breadcrumb-item active">Tutorial for exact ECT computation</li>
       <li class="wy-breadcrumbs-aside">
             <a href="../_sources/notebooks/Tutorial-ExactECT.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
@@ -131,7 +125,7 @@
            <div itemprop="articleBody">
              
   <section id="Tutorial-for-exact-ECT-computation">
-<h1><span class="section-number">3.4. </span>Tutorial for exact ECT computation<a class="headerlink" href="#Tutorial-for-exact-ECT-computation" title="Link to this heading"></a></h1>
+<h1>Tutorial for exact ECT computation<a class="headerlink" href="#Tutorial-for-exact-ECT-computation" title="Link to this heading"></a></h1>
 <p><em>Warning: This is a work in progress. Proceed at your own risk.</em></p>
 <p>The goal of this section is so show available tools for exact computation of the ECT.</p>
 <div class="nbinput nblast docutils container">
@@ -312,10 +306,7 @@ <h1><span class="section-number">3.4. </span>Tutorial for exact ECT computation<
 
            </div>
           </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-        <a href="../contributing.html" class="btn btn-neutral float-right" title="4. Contributing to the ect Package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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diff --git a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html b/docs/notebooks/tutorial_cw.html
similarity index 53%
rename from docs/notebooks/Tutorial-ECT_for_CW_Complexes.html
rename to docs/notebooks/tutorial_cw.html
index 4b1f1f8..e17089a 100644
--- a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html
+++ b/docs/notebooks/tutorial_cw.html
@@ -1,5 +1,3 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="../">
 <head>
@@ -7,26 +5,30 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>3.2. Tutorial: ECT for CW complexes &mdash; ect 0.1.5 documentation</title>
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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-    <link rel="prev" title="3.1. Tutorial : ECT for embedded graphs" href="Tutorial-ECT_for_embedded_graphs.html" /> 
+    <link rel="next" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" href="Matisse/example_matisse.html" />
+    <link rel="prev" title="3.1. Tutorial: ECT for Embedded Graphs" href="tutorial_graph.html" /> 
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@@ -60,12 +62,10 @@
 <li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -75,18 +75,15 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../contributing.html">4. Contributing</a><ul>
@@ -122,7 +119,7 @@
           <li class="breadcrumb-item"><a href="../tutorials.html"><span class="section-number">3. </span>Tutorials</a></li>
       <li class="breadcrumb-item active"><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes</li>
       <li class="wy-breadcrumbs-aside">
-            <a href="../_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt" rel="nofollow"> View page source</a>
+            <a href="../_sources/notebooks/tutorial_cw.ipynb.txt" rel="nofollow"> View page source</a>
       </li>
   </ul>
   <hr/>
@@ -137,9 +134,10 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedCW</span><span class="p">,</span> <span class="n">create_example_cw</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedCW</span>
+<span class="kn">from</span> <span class="nn">ect.utils.examples</span> <span class="kn">import</span> <span class="n">create_example_cw</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-</pre></div>
+<br/></pre></div>
 </div>
 </div>
 <p>The CW complex is the same as the <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!</p>
@@ -149,18 +147,21 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">K</span> <span class="o">=</span> <span class="n">EmbeddedCW</span><span class="p">()</span>
 
-<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Add vertices with coordinates</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
 
-<span class="n">K</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(((</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;D&#39;</span><span class="p">,</span> <span class="s1">&#39;A&#39;</span><span class="p">)))</span>
+<span class="c1"># Add edges to form a square</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">([(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">)])</span>
 
-<span class="n">K</span><span class="o">.</span><span class="n">add_face</span><span class="p">([</span><span class="s1">&#39;A&#39;</span><span class="p">,</span> <span class="s1">&#39;B&#39;</span><span class="p">,</span> <span class="s1">&#39;C&#39;</span><span class="p">,</span> <span class="s1">&#39;D&#39;</span><span class="p">])</span>
+<span class="c1"># Add the square face</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_face</span><span class="p">([</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">])</span>
 
-<span class="n">K</span><span class="o">.</span><span class="n">set_centered_coordinates</span><span class="p">()</span>
+<span class="n">K</span><span class="o">.</span><span class="n">center_coordinates</span><span class="p">()</span>
 <span class="n">K</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
-</pre></div>
+<br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
@@ -176,7 +177,7 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_3_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_3_1.png" />
+<img alt="../_images/notebooks_tutorial_cw_3_1.png" src="../_images/notebooks_tutorial_cw_3_1.png" />
 </div>
 </div>
 <p>Just to have something a bit more interesting, let’s make a more complicated example that’s built into the class.</p>
@@ -184,9 +185,9 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">K</span> <span class="o">=</span> <span class="n">create_example_cw</span><span class="p">(</span><span class="n">centered</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">K</span> <span class="o">=</span> <span class="n">create_example_cw</span><span class="p">()</span>
 <span class="n">K</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-</pre></div>
+<br/><br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
@@ -202,181 +203,176 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_5_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_5_1.png" />
+<img alt="../_images/notebooks_tutorial_cw_5_1.png" src="../_images/notebooks_tutorial_cw_5_1.png" />
 </div>
 </div>
-<p>We can also color the nodes based on a function value for a given direction.</p>
-<div class="nbinput docutils container">
+<p>As with the <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> class, we can initialize the <code class="docutils literal notranslate"><span class="pre">ECT</span></code> class by deciding how many directions and how many thresholds to use.</p>
+<div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">7</span>
-<span class="n">K</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">color_nodes_theta</span><span class="o">=</span><span class="n">theta</span><span class="p">)</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<p>Then we can compute the ECC for a single direction. In this case, the <span class="math notranslate nohighlight">\(x\)</span>-axis will be computed for the <code class="docutils literal notranslate"><span class="pre">num_thresh=80</span></code> stopping points in the interval <span class="math notranslate nohighlight">\([-1.2r,1.2r]\)</span> where <span class="math notranslate nohighlight">\(r\)</span> is the minimum bounding radius for the input complex.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
 </pre></div>
 </div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">override_bound_radius</span> <span class="o">=</span> <span class="mf">1.2</span> <span class="o">*</span> <span class="n">K</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">ect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">theta</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">override_bound_radius</span><span class="o">=</span><span class="n">override_bound_radius</span><span class="p">)</span>
+<span class="n">result</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
+<br/></pre></div>
+</div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
-</pre></div>
+<div class="prompt empty docutils container">
 </div>
-<div class="output_area docutils container">
+<div class="output_area stderr docutils container">
 <div class="highlight"><pre>
-&lt;Axes: &gt;
+/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: <span class="ansi-bold">
+The keyword argument &#39;parallel=True&#39; was specified but no transformation for parallel execution was possible.
+
+To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.
+</span><span class="ansi-bold">
+File &#34;src/ect/ect_graph.py&#34;, line 216:</span>
+<span class="ansi-bold">    @njit(parallel=True, fastmath=True)
+</span><span class="ansi-bold">    def shape_descriptor(simplex_counts_list):
+</span>    <span class="ansi-bold">^</span>
+
+  result[i, j] = shape_descriptor(simplex_counts_list)
+OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_7_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_7_1.png" />
+<img alt="../_images/notebooks_tutorial_cw_9_1.png" src="../_images/notebooks_tutorial_cw_9_1.png" />
 </div>
 </div>
-<p>The function value for any direction can be computed and returned for vertices, edges, or faces. The function value for an edge or face <span class="math notranslate nohighlight">\(\sigma\)</span> is given by <span class="math notranslate nohighlight">\(g_\omega(\sigma) = \max_{v \in \sigma}\{f(v)\}\)</span>. Here we show the function values for the triangle <code class="docutils literal notranslate"><span class="pre">KDC</span></code> and its faces.</p>
+<p>But of course it’s easier to see this in a plot. This command calculates the ECC and immediately plots it.</p>
+<p>Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the <span class="math notranslate nohighlight">\(y\)</span> axis of the plot.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">vert_g</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">g_omega</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-<span class="n">edge_g</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">g_omega_edges</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-<span class="n">face_g</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">g_omega_faces</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;K&#39;</span><span class="p">,</span><span class="s1">&#39;D&#39;</span><span class="p">,</span><span class="s1">&#39;C&#39;</span><span class="p">]:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">v</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">vert_g</span><span class="p">[</span><span class="n">v</span><span class="p">],</span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">edge</span> <span class="ow">in</span> <span class="p">[(</span><span class="s1">&#39;D&#39;</span><span class="p">,</span><span class="s1">&#39;K&#39;</span><span class="p">),(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span><span class="s1">&#39;D&#39;</span><span class="p">),(</span><span class="s1">&#39;C&#39;</span><span class="p">,</span><span class="s1">&#39;K&#39;</span><span class="p">)]:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">edge</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">edge_g</span><span class="p">[</span><span class="n">edge</span><span class="p">],</span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<span class="k">for</span> <span class="n">face</span> <span class="ow">in</span> <span class="p">[(</span><span class="s1">&#39;K&#39;</span><span class="p">,</span><span class="s1">&#39;D&#39;</span><span class="p">,</span><span class="s1">&#39;C&#39;</span><span class="p">)]:</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">face</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">round</span><span class="p">(</span><span class="n">face_g</span><span class="p">[</span><span class="n">face</span><span class="p">],</span><span class="mi">2</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-</pre></div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">ect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">override_bound_radius</span><span class="o">=</span><span class="n">override_bound_radius</span><span class="p">)</span>
+<span class="n">result</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/></pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
-<div class="prompt empty docutils container">
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-K: 0.18
-D: 0.65
-C: 2.89
-(&#39;D&#39;, &#39;K&#39;): 0.65
-(&#39;C&#39;, &#39;D&#39;): 2.89
-(&#39;C&#39;, &#39;K&#39;): 2.89
-(&#39;K&#39;, &#39;D&#39;, &#39;C&#39;): 2.89
+&lt;Axes: xlabel=&#39;Direction $\\omega$ (radians)&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
 </pre></div></div>
 </div>
-<p>As with the <code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code> class, we can initialize the <code class="docutils literal notranslate"><span class="pre">ECT</span></code> class by deciding how many directions and how many thresholds to use.</p>
-<div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
-</pre></div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span><span class="n">num_thresh</span> <span class="o">=</span> <span class="mi">80</span><span class="p">)</span>
-</pre></div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_cw_12_1.png" src="../_images/notebooks_tutorial_cw_12_1.png" />
 </div>
 </div>
-<p>Then we can compute the ECC for a single direction. In this case, the <span class="math notranslate nohighlight">\(x\)</span>-axis will be computed for the <code class="docutils literal notranslate"><span class="pre">num_thresh=80</span></code> stopping points in the interval <span class="math notranslate nohighlight">\([-1.2r,1.2r]\)</span> where <span class="math notranslate nohighlight">\(r\)</span> is the minimum bounding radius for the input complex.</p>
+<p>We can also look at the Smooth ECT:</p>
 <div class="nbinput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span> <span class="o">=</span> <span class="n">K</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
-<span class="n">myect</span><span class="o">.</span><span class="n">calculateECC</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mf">1.2</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
-</pre></div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Calculate SECT and plot</span>
+<span class="n">smooth</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">smooth</span><span class="p">()</span>
+<span class="n">smooth</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/><br/></pre></div>
 </div>
 </div>
-<div class="nboutput nblast docutils container">
+<div class="nboutput docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
-        0,  0,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,
-        3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  1,
-        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1, -1, -1, -1, -1, -1, -1,
-       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
+&lt;Axes: xlabel=&#39;Direction $\\omega$ (radians)&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
 </pre></div></div>
 </div>
-<p>But of course it’s easier to see this in a plot. This command calculates the ECC and immediately plots it.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">plotECC</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="n">theta</span><span class="p">,</span><span class="mf">1.2</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_15_0.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_15_0.png" />
+<img alt="../_images/notebooks_tutorial_cw_14_1.png" src="../_images/notebooks_tutorial_cw_14_1.png" />
 </div>
 </div>
-<p>Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the <span class="math notranslate nohighlight">\(y\)</span> axis of the plot.</p>
+<p>We can also compute the ECT in 3D.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">set_bounding_radius</span><span class="p">(</span><span class="mf">1.2</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
-<span class="n">myect</span><span class="o">.</span><span class="n">calculateECT</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
-</pre></div>
-</div>
-</div>
-<div class="nboutput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
-</pre></div>
-</div>
-<div class="output_area docutils container">
-<div class="highlight"><pre>
-array([[ 0.,  0.,  0., ..., -1., -1., -1.],
-       [ 0.,  0.,  0., ..., -1., -1., -1.],
-       [ 0.,  0.,  0., ..., -1., -1., -1.],
-       ...,
-       [ 0.,  0.,  0., ..., -1., -1., -1.],
-       [ 0.,  0.,  0., ..., -1., -1., -1.],
-       [ 0.,  0.,  0., ..., -1., -1., -1.]])
-</pre></div></div>
-</div>
-<p>Once it’s been computed, we can use the internal plotting function to take a look.</p>
-<div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">plotECT</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">myect</span><span class="o">.</span><span class="n">bound_radius</span><span class="p">)</span>
-</pre></div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">vertices</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="n">letter</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">)</span> <span class="k">for</span> <span class="n">letter</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="s2">&quot;abcde&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="p">]</span>
+<span class="n">edges</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">)]</span>
+<span class="n">faces</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">),</span>
+    <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span>
+    <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span>
+    <span class="p">(</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span>
+    <span class="p">(</span><span class="s2">&quot;a&quot;</span><span class="p">,</span> <span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="s2">&quot;c&quot;</span><span class="p">,</span> <span class="s2">&quot;d&quot;</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">K</span> <span class="o">=</span> <span class="n">EmbeddedCW</span><span class="p">()</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">vertices</span><span class="p">)</span>
+<span class="n">K</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">edges</span><span class="p">)</span>
+
+<span class="n">K</span><span class="o">.</span><span class="n">add_faces_from</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span>
+<span class="n">K</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<br/><br/></pre></div>
 </div>
 </div>
 <div class="nboutput docutils container">
-<div class="prompt empty docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-3.841874542459709
+&lt;Axes3D: xlabel=&#39;X&#39;, ylabel=&#39;Y&#39;, zlabel=&#39;Z&#39;&gt;
 </pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_19_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_19_1.png" />
+<img alt="../_images/notebooks_tutorial_cw_16_1.png" src="../_images/notebooks_tutorial_cw_16_1.png" />
 </div>
 </div>
-<p>Similarly we can take a look at the SECT. This one was computed when we asked to compute the ECT.</p>
 <div class="nbinput docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span><span class="o">.</span><span class="n">plotSECT</span><span class="p">()</span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">80</span><span class="p">)</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">ect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
+<span class="n">result</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
 </pre></div>
 </div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction Index&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
 </div>
 <div class="nboutput nblast docutils container">
 <div class="prompt empty docutils container">
 </div>
 <div class="output_area docutils container">
-<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_21_0.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_21_0.png" />
+<img alt="../_images/notebooks_tutorial_cw_17_1.png" src="../_images/notebooks_tutorial_cw_17_1.png" />
 </div>
 </div>
 </section>
@@ -385,8 +381,8 @@ <h1><span class="section-number">3.2. </span>Tutorial: ECT for CW complexes<a cl
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="Tutorial-ECT_for_embedded_graphs.html" class="btn btn-neutral float-left" title="3.1. Tutorial : ECT for embedded graphs" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="Matisse/Matisse_ECT.html" class="btn btn-neutral float-right" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="tutorial_graph.html" class="btn btn-neutral float-left" title="3.1. Tutorial: ECT for Embedded Graphs" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="Matisse/example_matisse.html" class="btn btn-neutral float-right" title="3.3. ECT on Matisse’s “The Parakeet and the Mermaid”" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
diff --git a/docs/notebooks/tutorial_cw.ipynb b/docs/notebooks/tutorial_cw.ipynb
new file mode 100644
index 0000000..575387a
--- /dev/null
+++ b/docs/notebooks/tutorial_cw.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for CW complexes\n",
+    "\n",
+    "\n",
+    "\n",
+    " This tutorial walks you through how to build a CW complex with the `EmbeddedCW` class, and then use the `ECT` class to compute the Euler characteristic transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedCW\n",
+    "from ect.utils.examples import create_example_cw\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The CW complex is the same as the `EmbeddedGraph` class with that additional ability to add faces. Faces are added by passing in a list of vertices. Note that we are generally assuming that these vertices follow around an empty region (as in, no other vertex is in the interior) in the graph bounded by the vertices, and further that all edges are already included in the graph. However the class does not yet check for this so you need to be careful!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = EmbeddedCW()\n",
+    "\n",
+    "# Add vertices with coordinates\n",
+    "K.add_node(\"A\", [0, 0])\n",
+    "K.add_node(\"B\", [1, 0])\n",
+    "K.add_node(\"C\", [1, 1])\n",
+    "K.add_node(\"D\", [0, 1])\n",
+    "\n",
+    "# Add edges to form a square\n",
+    "K.add_edges_from([(\"A\", \"B\"), (\"B\", \"C\"), (\"C\", \"D\"), (\"D\", \"A\")])\n",
+    "\n",
+    "# Add the square face\n",
+    "K.add_face([\"A\", \"B\", \"C\", \"D\"])\n",
+    "\n",
+    "K.center_coordinates()\n",
+    "K.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Just to have something a bit more interesting, let's make a more complicated example that's built into the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = create_example_cw()\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " As with the `EmbeddedGraph` class, we can initialize the `ECT` class by deciding how many directions and how many thresholds to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Then we can compute the ECC for a single direction. In this case, the $x$-axis will be computed for the `num_thresh=80` stopping points in the interval $[-1.2r,1.2r]$ where $r$ is the minimum bounding radius for the input complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lizliz/Library/CloudStorage/Dropbox/Math/Code/ect/src/ect/ect_graph.py:211: NumbaPerformanceWarning: \u001b[1m\u001b[1m\n",
+      "The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.\n",
+      "\n",
+      "To find out why, try turning on parallel diagnostics, see https://numba.readthedocs.io/en/stable/user/parallel.html#diagnostics for help.\n",
+      "\u001b[1m\n",
+      "File \"src/ect/ect_graph.py\", line 216:\u001b[0m\n",
+      "\u001b[1m    @njit(parallel=True, fastmath=True)\n",
+      "\u001b[1m    def shape_descriptor(simplex_counts_list):\n",
+      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0m\n",
+      "  result[i, j] = shape_descriptor(simplex_counts_list)\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "override_bound_radius = 1.2 * K.get_bounding_radius()\n",
+    "result = ect.calculate(K, theta=0, override_bound_radius=override_bound_radius)\n",
+    "result.plot();\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " But of course it's easier to see this in a plot. This command calculates the ECC and immediately plots it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Similarly, we can compute the ECT and return the matrix. We make sure to internally set the bounding radius to use to control the $y$ axis of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = ect.calculate(K, override_bound_radius=override_bound_radius)\n",
+    "result.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also look at the Smooth ECT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate SECT and plot\n",
+    "smooth = result.smooth()\n",
+    "smooth.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also compute the ECT in 3D."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "vertices = [\n",
+    "    (letter, coordinates) for letter, coordinates in zip(\"abcde\", np.random.randn(5, 3))\n",
+    "]\n",
+    "edges = [(\"a\", \"b\"), (\"a\", \"c\"), (\"a\", \"d\"), (\"b\", \"c\"), (\"b\", \"d\"), (\"c\", \"d\")]\n",
+    "faces = [\n",
+    "    (\"a\", \"b\", \"c\"),\n",
+    "    (\"a\", \"b\", \"d\"),\n",
+    "    (\"a\", \"c\", \"d\"),\n",
+    "    (\"b\", \"c\", \"d\"),\n",
+    "    (\"a\", \"b\", \"c\", \"d\"),\n",
+    "]\n",
+    "K = EmbeddedCW()\n",
+    "K.add_nodes_from(vertices)\n",
+    "K.add_edges_from(edges)\n",
+    "\n",
+    "K.add_faces_from(faces)\n",
+    "K.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect = ECT(num_dirs=100, num_thresh=80)\n",
+    "result = ect.calculate(K)\n",
+    "result.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/tutorial_graph.html b/docs/notebooks/tutorial_graph.html
new file mode 100644
index 0000000..cd0cafc
--- /dev/null
+++ b/docs/notebooks/tutorial_graph.html
@@ -0,0 +1,748 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>3.1. Tutorial: ECT for Embedded Graphs &mdash; ect 0.1.5 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css" />
+
+  
+    <link rel="shortcut icon" href="../_static/ECT_Logo_128x128.png"/>
+  <!--[if lt IE 9]>
+    <script src="../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
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+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="3.2. Tutorial: ECT for CW complexes" href="tutorial_cw.html" />
+    <link rel="prev" title="3. Tutorials" href="../tutorials.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search"  style="background: #cececf" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            ect
+              <img src="../_static/ECT_Logo.png" class="logo" alt="Logo"/>
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">1. Getting Started</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#prerequisites">1.1. Prerequisites</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#installing-ect">1.2. Installing <cite>ect</cite></a></li>
+<li class="toctree-l2"><a class="reference internal" href="../installation.html#uninstalling-ect">1.3. Uninstalling <cite>ect</cite></a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../modules.html">2. Modules</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../embed_graph.html">2.1. Embedded graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../embed_cw.html">2.2. Embedded CW complex</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../ect_on_graphs.html">2.3. ECT on graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../ect_on_graphs.html#ect.ect_graph.ECT"><code class="docutils literal notranslate"><span class="pre">ECT</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1 current"><a class="reference internal" href="../tutorials.html">3. Tutorials</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#Basic-Usage">3.1.1. Basic Usage</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../contributing.html">4. Contributing</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="../contributing.html#getting-started">4.1. Getting Started</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../contributing.html#contributing-guidelines">4.2. Contributing Guidelines</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="../contributing.html#code-style-and-formatting">4.2.1. Code style and formatting</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../contributing.html#documentation-guidelines">4.2.2. Documentation guidelines</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../contributing.html#testing-guidelines">4.2.3. Testing guidelines</a></li>
+<li class="toctree-l3"><a class="reference internal" href="../contributing.html#issue-tracking-and-pull-requests">4.2.4. Issue tracking and pull requests</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../contributing.html#conclusion">4.3. Conclusion</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../license.html">5. License</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../citing.html">6. Citing</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu"  style="background: #cececf" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../index.html">ect</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content style-external-links">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../tutorials.html"><span class="section-number">3. </span>Tutorials</a></li>
+      <li class="breadcrumb-item active"><span class="section-number">3.1. </span>Tutorial: ECT for Embedded Graphs</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/notebooks/tutorial_graph.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Tutorial:-ECT-for-Embedded-Graphs">
+<h1><span class="section-number">3.1. </span>Tutorial: ECT for Embedded Graphs<a class="headerlink" href="#Tutorial:-ECT-for-Embedded-Graphs" title="Link to this heading"></a></h1>
+<p>This tutorial demonstrates how to use the <code class="docutils literal notranslate"><span class="pre">ect</span></code> package…</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[120]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">ECT</span><span class="p">,</span> <span class="n">EmbeddedGraph</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<br/></pre></div>
+</div>
+</div>
+<section id="Basic-Usage">
+<h2><span class="section-number">3.1.1. </span>Basic Usage<a class="headerlink" href="#Basic-Usage" title="Link to this heading"></a></h2>
+<p>First, let’s create a simple graph”””</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[121]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Construct an example graph</span>
+<span class="c1"># Note that this is the same graph that is returned by:</span>
+<span class="c1"># G = create_example_graph()</span>
+
+<span class="n">G</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
+
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">])</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="s2">&quot;F&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">)</span>
+
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[121]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: &gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_3_1.png" src="../_images/notebooks_tutorial_graph_3_1.png" />
+</div>
+</div>
+<p>The embedded graph class inherits from the networkx graph class with the additional attributes <code class="docutils literal notranslate"><span class="pre">coord_matrix</span></code> and <code class="docutils literal notranslate"><span class="pre">coord_dict</span></code>.</p>
+<p>The coordinates of all vertices can be accessed using the <code class="docutils literal notranslate"><span class="pre">coord_matrix</span></code> attribute.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[122]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">coord_matrix</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[122]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[1., 2.],
+       [3., 4.],
+       [5., 7.],
+       [3., 6.],
+       [4., 3.],
+       [4., 5.]])
+</pre></div></div>
+</div>
+<p>It’s often useful to center the graph, so you can use the <code class="docutils literal notranslate"><span class="pre">center_coordinates</span></code> method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[123]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">center_coordinates</span><span class="p">(</span><span class="n">center_type</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">coord_matrix</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[[-2.33333333 -2.5       ]
+ [-0.33333333 -0.5       ]
+ [ 1.66666667  2.5       ]
+ [-0.33333333  1.5       ]
+ [ 0.66666667 -1.5       ]
+ [ 0.66666667  0.5       ]]
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[123]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: &gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_7_2.png" src="../_images/notebooks_tutorial_graph_7_2.png" />
+</div>
+</div>
+<p>To get a bounding radius we can use the <code class="docutils literal notranslate"><span class="pre">get_bounding_radius</span></code> method.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[124]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is actually getting the radius</span>
+<span class="n">r</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The radius of bounding circle centered at the origin is </span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="c1"># plotting the graph with it&#39;s bounding circle of radius r.</span>
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The radius of bounding circle centered at the origin is 3.2015621187164243
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[124]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: &gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_9_2.png" src="../_images/notebooks_tutorial_graph_9_2.png" />
+</div>
+</div>
+<p>We can also rescale our graph to have unit radius using <code class="docutils literal notranslate"><span class="pre">scale_coordinates</span></code></p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[125]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">G</span><span class="o">.</span><span class="n">scale_coordinates</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">G</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bounding_circle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="n">r</span> <span class="o">=</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The radius of bounding circle centered at the origin is </span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+The radius of bounding circle centered at the origin is 0.9362075413977773
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_11_1.png" src="../_images/notebooks_tutorial_graph_11_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[126]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">myect</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+
+<span class="c1"># The ECT object will automatically create directions when needed</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of directions: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">num_dirs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of thresholds: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">num_thresh</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Number of directions: 16
+Number of thresholds: 20
+</pre></div></div>
+</div>
+<p>We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input <code class="docutils literal notranslate"><span class="pre">G</span></code> if not already set. I’m choosing the radius to be a bit bigger than the bounding radius of <code class="docutils literal notranslate"><span class="pre">G</span></code> to make some better pictures.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[127]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">custom_bound_radius</span> <span class="o">=</span> <span class="mf">1.2</span> <span class="o">*</span> <span class="n">G</span><span class="o">.</span><span class="n">get_bounding_radius</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">override_bound_radius</span><span class="o">=</span><span class="n">custom_bound_radius</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Thresholds chosen are: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">thresholds</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008
+ -0.41390228 -0.29564449 -0.17738669 -0.0591289   0.0591289   0.17738669
+  0.29564449  0.41390228  0.53216008  0.65041787  0.76867567  0.88693346
+  1.00519125  1.12344905]
+</pre></div></div>
+</div>
+<p>If we want the Euler characteristic curve for a fixed direction, we use the <code class="docutils literal notranslate"><span class="pre">calculate</span></code> function with a specific angle. This returns an ECTResult object containing the computed values.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[128]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">theta</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ECT values for direction pi/2: </span><span class="si">{</span><span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]
+</pre></div></div>
+</div>
+<p>To calculate the full ECT, we call the <code class="docutils literal notranslate"><span class="pre">calculate</span></code> method without specifying theta. The result returns the ECT matrix and associated metadata.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[129]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">G</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ECT matrix shape: </span><span class="si">{</span><span class="n">result</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of directions: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">num_dirs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of thresholds: </span><span class="si">{</span><span class="n">myect</span><span class="o">.</span><span class="n">num_thresh</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="c1"># We can plot the result matrix</span>
+<span class="n">result</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ECT matrix shape: (16, 20)
+Number of directions: 16
+Number of thresholds: 20
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[129]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction $\\omega$ (radians)&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_18_2.png" src="../_images/notebooks_tutorial_graph_18_2.png" />
+</div>
+</div>
+<p>## SECT</p>
+<p>The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius <span class="math notranslate nohighlight">\(R\)</span> bounding the graph. The average ECT in a direction <span class="math notranslate nohighlight">\(\omega\)</span> defined on function values <span class="math notranslate nohighlight">\([-R,R]\)</span> is given by</p>
+<div class="math notranslate nohighlight">
+\[\overline{\text{ECT}_\omega} = \frac{1}{2R} \int_{t = -R}^{R} \chi(g_\omega^{-1}(-\infty,t]) \; dt.\]</div>
+<p>Then the SECT is defined by</p>
+<p>$$</p>
+<p>$$</p>
+<p>The SECT can be computed from the ECT result:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[130]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sect</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">smooth</span><span class="p">()</span>
+
+<span class="n">sect</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[130]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction $\\omega$ (radians)&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_21_1.png" src="../_images/notebooks_tutorial_graph_21_1.png" />
+</div>
+</div>
+<p>## ECT for higher dimensions</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">ECT</span></code> class can also be used for higher dimensional graphs.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[131]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="c1"># generate a 3d graph</span>
+<span class="n">list_3d</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;F&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;G&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+    <span class="p">(</span><span class="s2">&quot;H&quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+<span class="p">]</span>
+<span class="n">graph_3d</span> <span class="o">=</span> <span class="n">EmbeddedGraph</span><span class="p">()</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="n">list_3d</span><span class="p">)</span>
+
+<span class="c1"># add edges</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;A&quot;</span><span class="p">,</span> <span class="s2">&quot;B&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;B&quot;</span><span class="p">,</span> <span class="s2">&quot;C&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;C&quot;</span><span class="p">,</span> <span class="s2">&quot;D&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;D&quot;</span><span class="p">,</span> <span class="s2">&quot;E&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="s2">&quot;F&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;F&quot;</span><span class="p">,</span> <span class="s2">&quot;G&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;G&quot;</span><span class="p">,</span> <span class="s2">&quot;H&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="s2">&quot;H&quot;</span><span class="p">,</span> <span class="s2">&quot;A&quot;</span><span class="p">)</span>
+
+<span class="c1"># plot the graph</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[131]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes3D: xlabel=&#39;X&#39;, ylabel=&#39;Y&#39;, zlabel=&#39;Z&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_23_1.png" src="../_images/notebooks_tutorial_graph_23_1.png" />
+</div>
+</div>
+<p>lets center the graph</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[132]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">graph_3d</span><span class="o">.</span><span class="n">center_coordinates</span><span class="p">(</span><span class="n">center_type</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+<span class="n">graph_3d</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[132]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes3D: xlabel=&#39;X&#39;, ylabel=&#39;Y&#39;, zlabel=&#39;Z&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_25_1.png" src="../_images/notebooks_tutorial_graph_25_1.png" />
+</div>
+</div>
+<p>Now we can compute the ECT of the 3d graph.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[133]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">ect_3d</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">num_dirs</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+<span class="n">result_3d</span> <span class="o">=</span> <span class="n">ect_3d</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">graph_3d</span><span class="p">)</span>
+<span class="n">result_3d</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[133]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction Index&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_27_1.png" src="../_images/notebooks_tutorial_graph_27_1.png" />
+</div>
+</div>
+<p>Note that the each of the directions are appended in a list for the ECT result, so we won’t see the same periodic behavior as in the 2d case.</p>
+<p>ECT results inherit from ndarrays but they store the associated directions and thresholds.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[134]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result_3d</span><span class="o">.</span><span class="n">directions</span><span class="o">.</span><span class="n">vectors</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[134]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[ 0.48609512, -0.71244342, -0.50609872],
+       [ 0.00364975, -0.82190818,  0.56960831],
+       [-0.77710663, -0.23260984,  0.5848059 ],
+       [ 0.75362655, -0.61431121, -0.23381352],
+       [-0.18133219, -0.98270096, -0.03764916],
+       [-0.92728701,  0.26549447,  0.26391569],
+       [-0.81397529,  0.14802792,  0.56172232],
+       [ 0.54073592, -0.29337918,  0.78837385],
+       [ 0.48363696,  0.74722911, -0.45578937],
+       [ 0.99631892,  0.01735462,  0.08394891],
+       [ 0.74179171, -0.62822234, -0.23469503],
+       [ 0.09906268, -0.01998319, -0.99488052],
+       [ 0.57301878,  0.71110718, -0.40740159],
+       [ 0.93645869, -0.11437729, -0.33160663],
+       [-0.19170715,  0.53609831, -0.82209913],
+       [ 0.69801901,  0.38101835, -0.6062957 ]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[135]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">result_3d</span><span class="o">.</span><span class="n">thresholds</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[135]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,
+       -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,
+        0.04558028,  0.13674085,  0.22790142,  0.31906199,  0.41022256,
+        0.50138313,  0.5925437 ,  0.68370427,  0.77486483,  0.8660254 ])
+</pre></div></div>
+</div>
+<p>We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[136]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">ect</span> <span class="kn">import</span> <span class="n">Directions</span>
+
+<span class="n">directions</span> <span class="o">=</span> <span class="n">Directions</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">thresholds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+<span class="n">ect_3d</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">directions</span><span class="o">=</span><span class="n">directions</span><span class="p">,</span> <span class="n">thresholds</span><span class="o">=</span><span class="n">thresholds</span><span class="p">)</span>
+<span class="n">result_3d</span> <span class="o">=</span> <span class="n">ect_3d</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">graph_3d</span><span class="p">)</span>
+<span class="n">result_3d</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[136]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction Index&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_32_1.png" src="../_images/notebooks_tutorial_graph_32_1.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[137]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># another example sampling from the half sphere</span>
+<span class="n">sample_size</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>  <span class="c1"># Only go up to pi/2 for half sphere</span>
+<span class="n">phi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
+<span class="n">theta</span><span class="p">,</span> <span class="n">phi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">phi</span><span class="p">)</span>
+
+<span class="c1"># Flatten the meshgrid arrays and create vectors</span>
+<span class="n">half_sphere_vectors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">column_stack</span><span class="p">(</span>
+    <span class="p">[</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">phi</span><span class="o">.</span><span class="n">flatten</span><span class="p">()),</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">phi</span><span class="o">.</span><span class="n">flatten</span><span class="p">()),</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="o">.</span><span class="n">flatten</span><span class="p">()),</span>
+    <span class="p">]</span>
+<span class="p">)</span>
+
+<span class="c1"># Normalize the vectors</span>
+<span class="n">half_sphere_vectors</span> <span class="o">=</span> <span class="n">half_sphere_vectors</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
+    <span class="n">half_sphere_vectors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<span class="n">directions</span> <span class="o">=</span> <span class="n">Directions</span><span class="o">.</span><span class="n">from_vectors</span><span class="p">(</span><span class="n">half_sphere_vectors</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Number of direction vectors: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">directions</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">ect_3d</span> <span class="o">=</span> <span class="n">ECT</span><span class="p">(</span><span class="n">directions</span><span class="o">=</span><span class="n">directions</span><span class="p">,</span> <span class="n">num_thresh</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>  <span class="c1"># Reduced number of thresholds</span>
+<span class="n">result_3d</span> <span class="o">=</span> <span class="n">ect_3d</span><span class="o">.</span><span class="n">calculate</span><span class="p">(</span><span class="n">graph_3d</span><span class="p">)</span>
+<span class="n">result_3d</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Number of direction vectors: 10000
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[137]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;Axes: xlabel=&#39;Direction Index&#39;, ylabel=&#39;Threshold $a$&#39;&gt;
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_tutorial_graph_33_2.png" src="../_images/notebooks_tutorial_graph_33_2.png" />
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
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diff --git a/docs/notebooks/tutorial_graph.ipynb b/docs/notebooks/tutorial_graph.ipynb
new file mode 100644
index 0000000..7988362
--- /dev/null
+++ b/docs/notebooks/tutorial_graph.ipynb
@@ -0,0 +1,791 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial: ECT for Embedded Graphs\n",
+    "\n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates how to use the `ect` package..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ect import ECT, EmbeddedGraph\n",
+    "\n",
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Usage\n",
+    "\n",
+    "\n",
+    "\n",
+    "First, let's create a simple graph\"\"\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Construct an example graph\n",
+    "# Note that this is the same graph that is returned by:\n",
+    "# G = create_example_graph()\n",
+    "\n",
+    "G = EmbeddedGraph()\n",
+    "\n",
+    "G.add_node(\"A\", [1, 2])\n",
+    "G.add_node(\"B\", [3, 4])\n",
+    "G.add_node(\"C\", [5, 7])\n",
+    "G.add_node(\"D\", [3, 6])\n",
+    "G.add_node(\"E\", [4, 3])\n",
+    "G.add_node(\"F\", [4, 5])\n",
+    "\n",
+    "G.add_edge(\"A\", \"B\")\n",
+    "G.add_edge(\"B\", \"C\")\n",
+    "G.add_edge(\"B\", \"D\")\n",
+    "G.add_edge(\"B\", \"E\")\n",
+    "G.add_edge(\"C\", \"D\")\n",
+    "G.add_edge(\"E\", \"F\")\n",
+    "\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The embedded graph class inherits from the networkx graph class with the additional attributes `coord_matrix` and `coord_dict`.\n",
+    "\n",
+    " The coordinates of all vertices can be accessed using the `coord_matrix` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 2.],\n",
+       "       [3., 4.],\n",
+       "       [5., 7.],\n",
+       "       [3., 6.],\n",
+       "       [4., 3.],\n",
+       "       [4., 5.]])"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G.coord_matrix\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " It's often useful to center the graph, so you can use the `center_coordinates` method shift the graph to have the average of the vertex coordinates be 0. Note that this does overwrite the coordinates of the points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-2.33333333 -2.5       ]\n",
+      " [-0.33333333 -0.5       ]\n",
+      " [ 1.66666667  2.5       ]\n",
+      " [-0.33333333  1.5       ]\n",
+      " [ 0.66666667 -1.5       ]\n",
+      " [ 0.66666667  0.5       ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.center_coordinates(center_type=\"mean\")\n",
+    "print(G.coord_matrix)\n",
+    "G.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To get a bounding radius we can use the `get_bounding_radius` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 3.2015621187164243\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This is actually getting the radius\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n",
+    "# plotting the graph with it's bounding circle of radius r.\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also rescale our graph to have unit radius using `scale_coordinates`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The radius of bounding circle centered at the origin is 0.9362075413977773\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G.scale_coordinates(radius=1)\n",
+    "G.plot(bounding_circle=True)\n",
+    "\n",
+    "r = G.get_bounding_radius()\n",
+    "print(f\"The radius of bounding circle centered at the origin is {r}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(num_dirs=16, num_thresh=20)\n",
+    "\n",
+    "# The ECT object will automatically create directions when needed\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can override the bounding radius as follows. Note that some methods will automatically use the bounding radius of the input `G` if not already set. I'm choosing the radius to be a bit bigger than the bounding radius of `G` to make some better pictures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thresholds chosen are: [-1.12344905 -1.00519125 -0.88693346 -0.76867567 -0.65041787 -0.53216008\n",
+      " -0.41390228 -0.29564449 -0.17738669 -0.0591289   0.0591289   0.17738669\n",
+      "  0.29564449  0.41390228  0.53216008  0.65041787  0.76867567  0.88693346\n",
+      "  1.00519125  1.12344905]\n"
+     ]
+    }
+   ],
+   "source": [
+    "custom_bound_radius = 1.2 * G.get_bounding_radius()\n",
+    "result = myect.calculate(G, override_bound_radius=custom_bound_radius)\n",
+    "\n",
+    "print(f\"Thresholds chosen are: {myect.thresholds}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " If we want the Euler characteristic curve for a fixed direction, we use the `calculate` function with a specific angle. This returns an ECTResult object containing the computed values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT values for direction pi/2: [0 0 0 0 1 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G, theta=np.pi / 2)\n",
+    "print(f\"ECT values for direction pi/2: {result[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " To calculate the full ECT, we call the `calculate` method without specifying theta. The result returns the ECT matrix and associated metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ECT matrix shape: (16, 20)\n",
+      "Number of directions: 16\n",
+      "Number of thresholds: 20\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "result = myect.calculate(G)\n",
+    "\n",
+    "print(f\"ECT matrix shape: {result.shape}\")\n",
+    "print(f\"Number of directions: {myect.num_dirs}\")\n",
+    "print(f\"Number of thresholds: {myect.num_thresh}\")\n",
+    "\n",
+    "# We can plot the result matrix\n",
+    "result.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## SECT\n",
+    "\n",
+    "\n",
+    "\n",
+    " The Smooth Euler Characteristic Transform (SECT) can be calculated from the ECT. Fix a radius $R$ bounding the graph. The average ECT in a direction $\\omega$ defined on function values $[-R,R]$ is given by\n",
+    "\n",
+    " $$\\overline{\\text{ECT}_\\omega} = \\frac{1}{2R} \\int_{t = -R}^{R} \\chi(g_\\omega^{-1}(-\\infty,t]) \\; dt. $$\n",
+    "\n",
+    " Then the SECT is defined by\n",
+    "\n",
+    " $$\n",
+    "\n",
+    " \\begin{matrix}\n",
+    "\n",
+    " \\text{SECT}(G): & \\mathbb{S}^1 & \\to & \\text{Func}(\\mathbb{R}, \\mathbb{Z})\\\\\n",
+    "\n",
+    " & \\omega & \\mapsto & \\{ t \\mapsto \\int_{-R}^t \\left( \\chi(g_\\omega^{-1}(-\\infty,a]) -\\overline{\\text{ECT}_\\omega}\\right)\\:da \\}\n",
+    "\n",
+    " \\end{matrix}\n",
+    "\n",
+    " $$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " The SECT can be computed from the ECT result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction $\\\\omega$ (radians)', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sect = result.smooth()\n",
+    "\n",
+    "sect.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ## ECT for higher dimensions\n",
+    "\n",
+    "\n",
+    "\n",
+    " The `ECT` class can also be used for higher dimensional graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# generate a 3d graph\n",
+    "list_3d = [\n",
+    "    (\"A\", [0, 0, 0]),\n",
+    "    (\"B\", [1, 0, 0]),\n",
+    "    (\"C\", [0, 1, 0]),\n",
+    "    (\"D\", [1, 1, 0]),\n",
+    "    (\"E\", [0, 0, 1]),\n",
+    "    (\"F\", [1, 0, 1]),\n",
+    "    (\"G\", [0, 1, 1]),\n",
+    "    (\"H\", [1, 1, 1]),\n",
+    "]\n",
+    "graph_3d = EmbeddedGraph()\n",
+    "graph_3d.add_nodes_from(list_3d)\n",
+    "\n",
+    "# add edges\n",
+    "graph_3d.add_edge(\"A\", \"B\")\n",
+    "graph_3d.add_edge(\"B\", \"C\")\n",
+    "graph_3d.add_edge(\"C\", \"D\")\n",
+    "graph_3d.add_edge(\"D\", \"E\")\n",
+    "graph_3d.add_edge(\"E\", \"F\")\n",
+    "graph_3d.add_edge(\"F\", \"G\")\n",
+    "graph_3d.add_edge(\"G\", \"H\")\n",
+    "graph_3d.add_edge(\"H\", \"A\")\n",
+    "\n",
+    "# plot the graph\n",
+    "graph_3d.plot()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " lets center the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes3D: xlabel='X', ylabel='Y', zlabel='Z'>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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yRYxkaOzbtw/Dw8OYn5/HG2+8gQ9+8IObukHRjgZ0u8vlcnm03AYGBqDX68Hn81e13EK1mXI4HNL2a2pqArB85jQ/P+9R+RgMBhIa19nZCeDt7J6MjAxoNBro9fqoyO6JxopmamoKHA7HQ03IwD8wRBMFuHr1KrZv3w6RSBTS6wZb0VitVvT19WFubg6VlZUoLi7GyMgIDAZDCFcZGDgcDp5++mn88Ic/xNjYGDo7O8lmGwmw2WyIRCKIRCIUFhYCWG4zajQaLC4uQq1WY3R0FDabDQKBAHa7HTKZDMnJyUhISAgZScbFxaGoqAhFRUUAlisEnU7nUfXMzc2tyu75zne+A6FQ6FH1ZGdnb/oNRTTa4SwuLkblkO3DBIZoogA2mw1OpzPk1w20oqEoCjKZDH19fUhNTcWhQ4eIuiua7jbT09Nx5MgRXLhwARcvXkRxcbHHEOdKbHaUc0xMDPFRA5ZfV7PZjOnpaYyMjGBqagpSqRQcDmdVy229GSR/wGKxCAFu3boVwPJmrlQqMTMzg6mpKYyMjMBqtUKn00Gn02FgYIA8Nz09nRBPbm7uhnHZwSIaVWfRWGU9bGCIJgoQDnUYENjGqtPpIJVKYTab0dDQQNRY7teMppCsnTt3YmhoCJOTk3jttdfw0Y9+NGo3BRaLhfj4eKSlpWFqagr79u2Dy+Uidjq0xNpoNBI7HZqARCJRyKTjtEw6IyMD5eXl4PF4OHr0qIfKbXZ2FjqdDgqFAgqFAmKxGMAyea7M7hEKhSGrQqJxU4/GNT1sYIgmChBOovH1ug6HA8PDwxgfH0dRURGx8fd2zWiavWCxWHjqqafw4osvYnp6Gnfu3MHevXsjvSyfwWazSRVDw2azkZabQqHA0NAQnE4nRCKRR9UTHx8f9AZPnxfxeDwUFhaSth8A6PV6j7MeuuU2NTWFqakp8riEhASPqic7OzvgiiwaW2cul+uRiIOIJBiiiQKEi2h8HdiUy+Xo7u5GbGws9u3bt+5ZUbQRDQAkJSXh+PHjeOONN3D16lWUlZUhIyNj4ydGCBuJAXg8HkkLpR9vNBpJ1TM2NgatVouYmBgP4klOTvbbTme9tSQmJqKqqgpVVVUA3o7Ldq965HI5DAbDquwe95YbHZfty2YdjdWD0+mMujU9bGCIJgoQTqJZ77pmsxm9vb1QKpVeDTC9IRqJBgDq6+vJZvfqq6/iE5/4xKqKLFrXvhFYLBYSEhKQkJCAvLw8AMubn3vLjXawTkhI8CAeoVC47ibpjwLOPS67oaEBwNvZPe6Vj1ar9RqXnZWV5VH5eIvLZs5oVuPOnTuYn5/Hu9/97oitIVgwRBMFCIdVDLD2xkpRFMbHx4kB5qFDh3ye94jWzZrFYuHxxx/H9PQ05HI5rl27hiNHjkR6WV4RCnkzh8NZ18F6fn4e/f39cLlcXu103IPxglmLt+weg8GwykjUarVienoa09PT5HECgWCVq0GkN3VvcDqd61ZjBoMBg4ODGJmYxMzsHBxOB3gxPOTlZKOsuAiVlZVBqdauXbuG/v5+hmgYBIdwHbBvZIC5Y8cO0p4J5prRgoSEBDz++OP43//9X9y5cwcVFRWkAngnwBcHa51OBx6PR0gn1CadAEhUd0VFBVmHWq32qHrouOzh4WEMDw+T5wqFQiQkJJC5lWiIy16L/Kanp/HGmXNQ6Q3IKa1EVmEF9u88AA6XC4fdDsXCPNrHp/DaxTbkpKfiyZMnyHvjD0wm06Y4eYcTDNFEATajdeZugFlaWoqysrKAvsDRTDQAUFVVhbq6OkilUpJdE+nh0kjBm4O1w+HwaLmp1WpYrVZcuXIlbA7WLBaLOCu4x2WvzO5ZWloiEuu5uTkAbw+lulc9ycnJmyoYWEk0DocDv3/1NYzMzKPlsRPYlZq26jkxPB5y8guQk1+Axt37oVyYx0u/eAUNVeV44tRJv15bs9n80M/xMEQTBQi36ow2wExMTAzaADPaiQYAjh8/jomJCSwtLeHSpUs4depUpJfkgUjarHC5XBLYBiy7PnR1daGqqorIq2m36HA5WAPLLbe8vDyPitNoNKK9vR1zc3Pkc+sel02Dzu5xV7mF847fvXVmNBrxwg/+C4V1zTj5ft9bs+mZWXj8Q8+ip+M+vvWf38Nff/ovfVbmmUwmJCUlBbL0qAFDNFGAcBGN0+nE9PQ0HA4HampqkJubG/QG9zAQDZ1d88tf/hIdHR2oqKhAaWlp1M0ARQPow/fNdrD2BoFAgKysLCQmJqKxsZHEZbtXPQsLC16ze1JSUjyqnszMzJCtja5orFYrvvW972P74RPIys3f+IkrwGKxsG3HLkwmp+C7P3gRz//1cz6t0Ww2r5pne9jAEE0UINRE43K5MDo6ivn5eSQmJuLAgQMhax89DEQDAMXFxWhubsaDBw9Idk00IVpmRbxVV744WI+NjcFms3kNjQs2H4duK7nHZW/bto2sg87umZubw8zMjEdcdk9PD4Dl71RmZqZH5RNoXDZNNL985TfY2nIgIJJxR2FpOYx6PV574028511Pb/h45oyGQUgQys1brVZDKpWCxWIhMzMTAoEgpGcUDwvRAMCRI0cwNjYGtVqNs2fPorKyMtJLAhBdYWO+tvFWOlhTFOURGhcqB+uNVGdcLpeQBw2z2bxK5WYymTA3N4e5uTm0t7cDWK50V6rcBALBhmtyOp0YGRnBotWOhvLQfIaqGxpx5pVfYGpqipD5WmDOaN5B0Ov1cDqdYemVhqKisVqt6O/vx+zsLDHApOWtoUSoiMblcsHlcoVF9USDzq756U9/it7eXggEAo9wMQaBnxexWKw1HawXFxextLSE/v5+GAwG8Pl8pKSkEOJZz04nEGcAPp+P0tJSkpxKURQ0Gg0JjKOzeywWC8bGxjA2Nkaem5SU5FH1ZGZmegy9UhQFl8uFMxcv48j7P+Lvy7Qu9p18Eq++eQZ/+9z/WfdxTEXzDsLMzAysVitRzYQSwRANbYDZ39+PlJQUDwNMOg0zlAgF0cjlcuKnJhQKySaUkpICgUAQ0rZSTk4O9u3bhxs3bkAsFntYvUQK0ZS5EkrLF3cHa9o92m63k6pHpVJheHgYdrvdIzTO/X0PxcAmi8Ui13bP7pHL5R5Vj0qlgkajgUajIdk97nHZOTk5yMrKglKpBE+YjLgQVxXCpCTorMsO3+vdwDIVzTsIbDY7LA7L9LUDIRqdTofu7m4YjUbU19evOjAMxyBoMNe0Wq3o7e3FwsICtm7diszMTCwtLWFxcRGTk5OQSqXgcrkeG1AoZj3279+PkZERzM/PQywWo7m5OWo2+mhAOF+LmJiYVXY6JpOJkA/9vnM4HCQnJ8NqtUIgEMBms4W05esel71jxw4Ay9k99DkP/X+NRuOquOzRiQk0HXsKc/NzEMQLEC+IRwzXP6uftVBSW4/OLjEOH2pd8zFMRfMOAofDCZtiyV8SczqdGBoawvj4OAoLC7Fr165NNcD093WgKAozMzPo7e0lVRd9h+beenG3VaHJh45MDqbq4XA4OH36NH74wx9CLpejq6sLjY2Nfv0NoUQ0VTSbvRYWiwWBQACBQECCxNwdrMfGxqBUKnHu3DniYO1upxPK4c24uDgUFxejuLgYwNtx2Suze/RGM2IFCZDLFeS5PF4M4uMFEMTHI14Qj3h+fECVWHZeAYbuXMFhrE00TEXzDkK4JMj0tR0Oh0+PpQ0weTzehgaY4Vizv5uSyWSCVCqFVqtFXV0dsrKy1ryGu61KSUkJgOUvGd3zX1n1uPf9N6p6tmzZgpqaGnR3d+PChQsoLi6OijZapBENbsnuDtYajQYCgQBFRUWk6pHL5RgcHAybgzUN97js6upqAMtzM//n7/4BRcVFMBlNMJpMsFgssNnssNmW224AwAIQx48Dn8+Hw+FAVlY24n0gh0SRCCr10rqPYSqadxDCTTQbXZs2wFQoFNi6dSsKCwsjYoDp6+tA+6kNDAwgJycHjY2NAbVC3IfzgLerHpp8JiYmYLVaSc+fJh9vVU95eTlmZ2ehVqvx2muv4SMf+UhEfLWiTXUWTd5itOqMx+ORzBwgvA7W64GiKPBiY5GWmgYsz7jC6XLCZDLBZDTBZFomH7vdDrPZArPZAgCw26dQWbGxQo3FYoHagCeZiuYdhHASzXqDhBRFYWJiAgMDA8jIyMDhw4d9louGq6LZaKPU6XQQi8Ww2+3YuXMnSZgMBbyZSdJVz+LiIiYmJiAWixETE+PRbktKSgKLxcL27dtx/fp1yGQy3L17F3v27AnZ2vxBpKsIGtFEesDa8uZwOlhvtB7A8zXisDlITEhEYkIiAMBitUAmk8FoNAEA2CwWRKIk33/JBu8BU9G8g8DhcDZdDKDRaCCVSmG326PGAHO9azqdTgwPD2NsbAzFxcWoqKjYFEPE9aoemnysVit4PB5iYmLQ3NyMW7du4erVqygtLY3q7JpwI5rOiwD/1uOrgzVFUatabu4O1uvB5XKBRbm8rsvlcmFBvgCFQgGKWiaYzMxMpG/ZAraPf4PVYkH8OjeOdPw3U9G8Q7CZrTO73Y7BwUFMTU2hpKQE5eXlUWOAudY1VSoVJBIJuFzuhmdH4cbKDYj+svb29sJgMIDNZkMoFEKn0+Hll1/GiRMnkJ6eHnJLlfUQLZt7tLbOAkUgDta0nY63lpvT6USKSAS1UoG0LW/fkGh1WszMzMBmswMAREIhcnJzEetne3hhdgYlhWsPbFosy604pqJ5h2AziIaiKMzPz6OnpwcJCQk4cOAAEhMTg75uKLGSaOx2O/r7+zE9PU0GRaNp4wKW1xwfH4/ExETweDzU19ejoaEBL730EvR6PTo7O5GWlgabzbZK4RbKw2Ya0dSuiraKJtTiBF8crCcnJ2E2m1fZ6QiFQrhcLhTm5WJ8sA9pWzJgs9kwMzsDrVYHYFl9lpuTC6FIhEBWPTncj6cOrN2+NZvNAMBUNO8UhLt1Zrfbcf/+fSwtLUW1Aab7Nefm5tDd3Q2hUIjW1laf7DyiBSKRCE888QR+97vfYXx8HAcOHEBqaioRGYyPj0MsFpM7X5p8NrPq2QxEE+kBm1NhrXSwBkDsdGgTT9rBOj4+Hnl5ebh86y5yyyqhVCjhoiiwWMCW9C3IzMwMeL0OhwM6+TyRV3sDQzTvMISronG5XFAoFFhaWkJBQQEOHz4cskG1cFU0LpcLDx48gEqlQk1NDfLy8qLqrngtrCTerVu3ora2Fj09PXjttdfwF3/xF8jNzSXzHU6nExqNhpAPbSRJVz00+fhb9URTFfGotc4CRVxcnFcH64mJCQwODsKwqMKdK5dRUr0NsbE8bNmyZVkMEMT72HX3Jg7v27vuZ8FkMoHH40U8/C1YMETjI8KxadMGmA6HA4mJiSG3twl1RUNRFMkKYbPZOHz4sM+ZGtGKEydOYHJyEouLi7h8+TJOnjxJ/o3D4Xjc+dJnPbTIgJbYulc9tMLtYdkYoon0gOiY6wGWvztsNhs9PT0YGxtDdmYGpA/uYPvO3Ujdkg6b1QalSgWXy4mYGB5ieTzwYnng8WIRExOzYRtNrVRAOz2Jve89ve7jzGbzQ38+AzBE4zNCaUFjs9nQ19eH2dlZVFRUID4+HiMjIyG5tjtCSY4GgwESiQQGgwEA0NTUFJLrRhp8Ph9PPfUUfvWrX6G9vR0VFRVkWHQl6LOe+Ph4r1UPTT42m42onGjycVc5RdPmHk1rAaKjwnK5XOjs7ERbWxusVisAoLm5GR/60IfwP//7Rzz+oWchEopAYfn9t1mtsNqsMBqMWLItASyAx4tFbCwPsbxY8GJ54LDfvvEwm4y4/sbv8fyn/2LD195kMj30bTPgESMatVpNDnxDjVBY0FAUhenpafT19SE5OZkYYMrl8rAlbAZb0dDZNsPDw8jPz8e2bdtw9erVEK0wOlBSUoIdO3agvb0dr7/+Oj796U/7/OX2perp6uoCj8cjrTaHwxE1ZyPRRjSRap3RmJubw5kzZ0iUdGpqKsrKynDs2DEAwJ+960m88vLPcex9f4Z4QQK4HA64f7r5AAAKFOw2O6w2K2xWG5ZMS3DYHeBwuYiN5cFqNuPmm3/Apz/6YY8zorXAVDRRiK6uLjQ0NJDMjFCCNpMM9Iup1+shlUphNBpXWbGE6/wn2OsuLS1BIpGAoijs3r0bKSkp5HAy2jaoYEFn1ywuLuL8+fN4+umNA6m8wVvVQ6ucaPJRqVRwOBy4fv26h8LN19mOUCLa3sdIEY3FYkFbWxsx0oyNjcWhQ4eQnJyMpaW3LWK2VlXhE3w+fvbyL1HVvAflNds8rsMCCzweb/lm90+J6S6XCxarFZL7dzFw/xb27GhEb28vpqenPVRu3twsmIomChFuCTKw/KHxp//uPsRYUFCAnTt3rtLrh2vdgVY0DocDg4ODmJycRFlZGcrKyjxSD4Ho26B8wXqvB4/Hw9NPP42f/exn6O7uRkVFBbZu3RqS37tS5TQ1NYWpqSkUFxdjcXERo6OjZLbDXWSwGWc90fY+bvYZDUVR6OnpwcWLF2E0GgEAtbW1OHr0KBISEjA8PLzqPSgsLMQ//3//gD++/gZe++8foqS2AWXVtV5jBMwmIwalYkwN9GJHXS3+6tvfBIvF8nCwnpiYgEQiIQ7W9H8AU9FEJcItQQb8IxqFQoHu7m7ExMRg7969a2ZOhCvLPhCiUSgUkEqliIuL8zrH4040jxpyc3OxZ88e3Lp1C2+99Rby8/ORkJAQ8t/DYrHA4XA8FG7uUclqtRqjo6PkrMedfEJd9UQb0WzmGY1SqcTZs2cxOTkJAEhLS8PJkydJlg6wdoUVExODZ97zbjxts+HO3Xt4cPZV6AxGUGw22BwOnE4HOBQFUUICdmyvx0ef/AcPWfx6DtZLS0vo6enB+973PjJI+r3vfQ+7du1CXV1dwEcDP/jBD/CNb3wDCwsLqKurw/e+9z00Nzdv+Lzf/OY3+MAHPoCnnnoKr732WkC/myEaP64NLFcoG5n2WSwW9Pb2Qi6X+2SAGY7cGPq6vhLYyqyYtdb8KBMNABw8eBAjIyOQy+V488038f73vz8sA5srr+ktKtlkMhFpNT3RHhsb6yEyWC+tMtC1RBKb0Tqz2Wy4ceMG7t69S1Je9+/fj927d696LTdaD4/Hw8ED+3HwwH7yeIfDAS6X69ff4e5gDQCNjY2Ym5vDF77wBVy/fh1nz57Fl770JZhMJvT09KCsrMyvv/m3v/0tnn/+ebz00kvYuXMnXnjhBRw7dgxDQ0PrWltNTk7iH/7hH7Bv3z6/ft9KPFJEE85wMvrLuN7GTVEUJicn0d/f75cBZiRbZxRFYXZ2Fj09PauyYta6Jv28RxEcDgdPP/00fvzjH2N4eBhisRjbt2/f9HW457bQJpJ01bO4uAi1Wo2RkRHY7XYkJSWtUrj5imgkmnCuZ2hoCOfOnYNWqwWw7Oh94sSJNbsNTqfTLyKnnadDgaysLNTU1ECj0eAPf/gDKIrC6OioR8XlK7797W/jk5/8JJ599lkAwEsvvYQzZ87gZz/7GT73uc95fY7T6cQHP/hBfPnLX8bNmzdJJEIgeKSIJpzhZLSufq3r0waYNpvNbwPMSIkB3LNitm3bhuzsbJ+iB4CHl2h8WXdGRgZaW1tx+fJlXLhwAUVFRVGRXbNe1bO4uBhQ1RNtRBOu1plGo8G5c+cwPDwMYNkZ4sSJE6ioqFj3eS6XK6SxA/7C/YyGxWL5XckAyxVcZ2cnPv/5z5OfsdlsHDlyBHfv3l3zef/6r/+KLVu24OMf/zhu3rzp/+Ld8MgRTbgqGsD7xh0KA0x3r7NQ+zx521iDyYpxP6t62ODPa9vS0oLh4WHIZDK8/vrr+MhHPhKy9yZU77OvVY/D4fA610OvJdJzK+4IdevM6XTizp07uHHjBhwOB9hsNlpaWrB//36fPvORlluHQnWmUqngdDpXuZRnZGRgcHDQ63Nu3bqFn/70p5BIJEH9bhoM0QR4fXcDTIFAEJQBJv1BDjXReCNGnU4HiUQCm80WUFbMw17R+Ao2m43Tp0/jxRdfxNTUFO7du4eWlpZIL2tDeKt66MAwuurRarWIi4sjcnWBQOB3iygcoD9TofoOTExM4OzZs1CpVACW1WInT5706zPvr8o01IhERIBer8ef//mf48c//nHIRkUeCaIxGo3LvdcLF6A3mpCamoqUpCRUV1WisrJyzf6rv6A3bpPJhO7ubiwtLaG6ujpory93ogkl3Csad5l1UVERKisrI76xRDuSk5Nx7NgxvPXWW2hra0NJSYnfmUDesJntKm+BYQ6Hg6iblpaWoNVqMTc3RxRu7nM9mwn6pijYCsJgMODixYvo6ekBsKzwOnr0KGpra/1+3Z1OZ8QrmmD3r7S0NHA4HMjlco+fy+VyEqfgjrGxMUxOTuKJJ54gP6PfGy6Xi6GhoTXdM9bCQ000PT09+PXv/gClzoCcknIkl21DTvKytbteq8Fl6SB+8cc3EEM58L7TT2H37t1BfcFZLBamp6cxMzODnJyckBlgugsNQrn502o2lUoFqVQKDoezrszaF7BYrLDJsaMR27dvx+DgIEZHR/Hqq6/iE5/4xENP0FwuF+np6UhPT4fRaERcXBzy8vKIwm14eBg6nY5UPTT5BKtw2wjBEo3L5UJHRweuXLlCrGOampr8SqX1ds1IEo3ZbCZGn4GCx+OhsbERbW1tOH36NIDlv6utrQ3PPffcqsdXVlYSkqbxhS98AXq9Ht/97nfJDYs/eCiJRq/X4xvfeQFGVgz2PPFepPwpkMhsNoOiXIiPF2BLdg5KqqoBAAadDufbzuG3f3wN//TZv/fK4htBrVbDYrFAoVCgpaXFJ/sIXxGucw+HwwEAuHfvXkizYsIRPxCtYLFYePLJJ/Hiiy9iYWEBN27cQGtra1DXjKYDePqMhq566MwWu91OznqUSiWGh4fJWc/KuZ5Qgf78B/LazM7O4syZM5ifnwewrNh6/PHHkZ2dHdSaIt1SDFWM8/PPP4+PfOQjaGpqQnNzM1544QUYjUaiQvvwhz+MnJwcfPWrX0VcXBxqamo8nk/fnK78ua946IhmbGwM//q1b6L1fR9EYVnlqn/3tv8lCIU4/PQzUMzN4nNf/n/49LMfxk4fBpWAZcVGf38/ZmZmwOPxsHXr1pCSDBAeopmfn4dUKgWAkCdePqxEE+i6ExMTcerUKfz+97/HzZs3UVZWRgbtHnasRXoxMTGk6qEfZzQaSdUzNDQEnU4HPp+/Kq8n0JsZ+r3x5/lmsxlXrlzxsI45fPgwGhsbQ3JTFQ0VTSjI/JlnnoFSqcQXv/hFLCwsoL6+HufPnycCAZlMFta/86EimsnJSXz569/C+577ByR42ThZLBYorL2RbMnOwQef/yf86PvfAoB1yYaiKMzMzKC3txdJSUlobW1FV1dX8H+EF4SyHWWxWNDT0wOlUomqqip0d3eH3MIiFETjcDig0+kgFAofiiCx6upqDA4Oore3l2TXRFL2Gir4Wl25n/WsVfUMDQ3B6XR6zPX4U/X4U9FQFIXu7m5cunSJWMds27YNjz32WEjdHCJNNKGqaADgueee89oqA4Br166t+9yf//znQf3u6P+G/wlWqxVf/o+v471/9bxXkgH+lEG0wf4Xw+Phmb96Hv/17X9HaUmJ1+pEr9eju7sbBoPBwwBzM+KcAwVFUZDJZOjr68OWLVtw6NAhcLlcdHd3h1VkEAhUKhXEYjFsNhucTqdHOyYSh9C+4uTJk5iamoJarcbly5dx4sSJgK4Tba2zQNfiT9XjLjIQiUReN296U99oPUqlEmfOnMHU1BSA5cPuU6dOobCwMKC/Yz1EunUWCdVZOPDQEM1//fCH2HH0CSSKktZ5lG8bIC82Fsc/9DH8+9e/iW/+x7+TD7bT6cTIyAhGR0dRUFCA5uZmj7vWcBNNoJu3wWCAVCqFwWDA9u3byRkUvdZwpGwGsla73Y7+/n5MT0+jqqoKxcXFHpb6tPSWz+cvKwf/RDxCoTAqNmY+n48nn3wSL7/8Mh48eICKiop1Y3gfBoRyEt/fqsedfOLi4jZcizfrmAMHDqClpSVsZPAoVTSRxENBNGq1GgMT0/izp/5s3cexfClp/oSsvAJwRKno7u5GXV2dTwaY4Tbt9JcQVmbFrCTGcM28BGrWKZFIIBAI0NraCoFAAACrLPXtdjuZ+Zifn0dfXx9YLJbHsGFycnJAbatQtPxKS0vR2NiIzs5Okl0TqKIpWhBOEvdW9RgMBvIeu1c9iYmJoCgKS0tLHlUPRVEYGhrC+fPniXVMRUUFjh8/HrLRhbXwTpyjCQceCqL5w6uvofHwMZ8e688+suuxE3j5f38Du90OuVyOqqoqFBUVrfnFC2dF4+8ZjbesGG/XDIcU2Z8N2263o6+vDzMzM6iurt7QYDQmJgZbtmwh8yoURUGn00GtVmNxcREymQxms3lVu20z7/qOHj2K8fFxLC0t4fz580Qy6iseldZZIGCxWEhMTERiYqJH1bO0tETCxu7evUuqntjYWPT19UEmkwHw3TomVIiGORqmotkkPBBL8MHPPrnh4/y9YxWIkjAyOQW73b6hmSQQ/taZL9demRVTWlq67h1XOBRivl5TLpdDIpEgMTGRpIkG8rtEIhFEIhFpU7m32+gsl9jYWEI6qampEAqFYdsgeDweTp8+jZ///OeQSqWoqKhAVVVVWH5XuLHZ+S/eQN9ccLlcyOVyHD16FBqNBrdu3YJUKoXT6QSLxUJ2djZqa2sRExOzquoJFyLdOmMqmk2CyWQCJ863DA5fvy9OpxNGoxEuyoXS6m0+H0BHunXmnhWzf/9+CIXCDa8biYrGZrOht7cX8/PzqKmpQX5+fkg3Mz6fj5ycHOTk5AB4e9J9cXERCoUCg4ODoCjKo93mreILBvn5+di9ezdu376Nt956C3l5eT6rnd7JFc16oDf1yclJnDlzBmq1GsCydczRo0fB4XCwtLQEuVyOwcFBuFyuVQq3ULcxo4FomIpmEzA2NoYteYU+PpoFq9UKi8Xi9QNHgYLZZIbVakVcXBzi+HHILS3D4NCwT6V4pCoa943bl3ybldfdzIpmYWEBEokEIpHIpyoxFHCfdAeWN0+9Xk+MJWdmZmAymRAbGwsOh4Pp6WnSbgtmkz148CBGR0chl8vx1ltv4ZlnnomaTdsfRMuaDQYDhoeHcefOHQDerWPcW6oGg4Eo3AYGBqDX6xEfH+8hMgimsqUoKqJnNLQ7N1PRbAIMBgP4At918VarFUqlEiwWCzExMYiP5yM2dpl0TCYTOBw2hEIh+fDwBQkw6BQ+XTuceTfeiMY9KyY5OTmgjXuzKhqbzYaenh7I5XLU1NQE7f8W7PqEQiGEQiGRvFosFvT392NxcRETExMQi8UkOjk1NTWgYUMul4unn34aP/rRjzA0NASpVIr6+voNnxdtVUSk10Jbx7S1tcFmswEAduzYgUOHDq1Zobif9RQUFADwFJLI5XIMDAyAoqhVeT2xsbE+rwsI3nstUNA2OkxFswnwp4qge7kURYGiKNhstj99cLUAC+DF8MDn84ldOIvFgsvpe2nMZrNht9uD+GvWv7b75k0bd2o0Gp+zYny5biiwkmjm5ubQ3d2N5ORktLa2RuUdWFxcHEQiERwOB5qbm+F0Osmm5C67Xdlu28jLjs6uaWtrw7lz51BYWBh2JVQoEemYgJXWMYmJiXj/+98fkHWMNyGJXq8n77O/VU+kicZkMgFAVH6f/EXUE82WLVugU9/w6bHLFUw8hEIh9Ho9TCYjXK4/bYgU3IhnebPk8WIwMzWBeFGcT4dum1HRUBSFiYkJ9Pf3IycnB4cOHQrKuDOcYgCr1Yqenh4oFArU1tYiNzc34nfHvoLD4ayy06dbMYuLi+jt7YXRaERCQoLHTI9AIFj1N+7evRvDw8OYnp7G66+/jg9/+MPrvg7RZN8TqerKbDajra0NnZ2dAJatY+hcpGD9yWi4V7beqp6FhQWPqsedfGJjY8l3PVKtM7PZDIAhmk1BQUEBFudnfXqs+xeGzWEjISERMTFcWCxWmEwmjy/48kZpw5C4E5aURExMTGDLli3Iz89Hfn4+CgoKVh22hzvB02g04ubNm7BarQFlxXhDuM6V6On+1NTUdVscDwu8tWKsVishnqmpKUilUnC5XA91G+1ofPr0abz00kuYnJzE/fv3sWvXrg1/XzRgs4mGoihIpVJcunSJ3LHT1jFLS0uYnJwM6+/3teoRCATk+09bJW12ZWMymcDj8R56t3DgISAaNpsNPi8GZpMR/HjBuo+liUSv14PP55PNLz5egOTkJJhMZhgMBlLVUBQF1dw0Gsv3w2q1QqFQQKFQEIO+pKQkD+IJlz2+0+mEyWSCSqVCSUkJKioqQub/FeqKxmKxwGw2Y2pqCvX19QG39CIFf16L2NhYZGVlEZt2p9MJrVZLZnpGR0fhcDjI3fDOnTtx69YtXL58GSUlJSG5UQg3NpNoFAoFzpw5Q2Zi0tPTcfLkSXKOtri4uOmb+XpVz8LCAgDg9u3bRMXornDz9awnUDwqijPgISAaAHjq5HE8uHUNu4+eWvMxNpsNJtOyuV5iYuKqjZrFYpPYW7vdDqPRiJ6OB0hOFJBDt7S0NAiFQphMJsjlcmg0Gmg0GnR3dwNY7vMLhUJQFIX8/HxkZmYG/cVQq9WQSCSwWq0oLi5GdXV1UNdbiVBVNLTJaE9PD1gsFqqqqoi8+GFBsBsqh8PxkEq7e3stLi6CoigkJiZCr9fj5ZdfxlNPPYW0tDQkJCSs+t3RQs6bQTQ2mw3Xr1/HvXv34HK5EBMTgwMHDmDXrl0ed+uRlhLToKue2NhYzMzM4MSJEx5VT19fHwwGAwQCgceZXmJiYkjX/6gozoCHhGgO7N+PX/72r9F04Ah4K+4iXC4njEYTnE4n4uMFMBgMG77ZMTExEIlEmBDfx2effx4TExMYHR2FSqWCSqUCj8dDbW0tMjMzYTKZSNiZxWKBxWLBhQsXACwP7uXl5ZGqJycnx2drFHffr4qKCmg0mrDcIYWiorFYLJBKpVhaWkJ9fT3Gx8cfCsflcMObt1dDQwN+/OMfQ6vV4ubNm0hOTvYgqJSUlLDGjfuLcBINRVEYHBzE+fPnodPpACyHah07dsyrYCJaiIaGu8nnyqrHZrORhNL5+Xn09/eHvOphKppNRkxMDD75kQ/h9d+/glMffJb83Gw2w2KxIDY2ltw1+rqxPrhyCQdbmtHS0oKWlhbodDpIpVJIJBIsLi6iu7sb3d3dSEtLQ0NDA06fPo2xsTH09/eDw+FAJpPBarVibGwMY2NjAJarh5ycHEI8+fn5Xs8u5ufn0d3djYSEBOL71dnZGZa2XDBEQ1EUpqen0dvbi4yMDCJMmJiYiKoD7WhCamoqHn/8cfzhD3/A5OQkDh06BIFAQGZ6xsfHYbVawePx0NvbS8gnUmdc4SKapaUlnDt3DiMjIwCW29AnTpxAeXn5pq8lUKw3Q8Pj8ZCRkUHyXNxnt5aWljyqHneRgT9VD1PRRAB79+xB27Xr6Ou4j4r67TAaTcsHuMJEcDlv/xm+fE5nJsYg6+7AZ7/9TfIzoVCIffv2Ye/evZDJZBCLxejv74dKpcKlS5fQ1tZGpr/f9a53AVjuOctkMshkMkxNTcFgMGB6ehrT09O4ffs2gGX5K006GRkZmJychFKpXDVrEg4ZMn3dQAjMbDZDIpFAq9WioaHBI072YQ0+2yzU1NRgcHAQfX19eP311/GpT30KycnJKCkpITkqer0eTqeTmErSklta4ZaYmLgpm26oN3eHw4E7d+7g5s2bZIxgz5492Ldv34bVfrRVNP74nHmb3aKrHtog1t+qh6loIoTn//ozeO5vn8fS0hKa9h1c4y5w/U1QNjaCm394Gd/66r97/RCxWCwUFBSgoKAAJ06cQG9vL8RiMWZnZ0n+xdTUFOrq6lBfX4/m5mY0NzcT11madGQyGRkck8vlaG9vB7A8fFVcXAyVSkXudsKZdeOvgIHOtent7UVWVpZXeTVDNBuDzq5RqVRoa2vD8ePHAdCyeh4SEhJQV1cHYLmNSp/zzM7Oore3FywWy6PdlpycHJZ2ZSiJZnx8HGfPniXWMUVFRTh58iSRkG+EaBgedUewxLde1bPyrGflXA+LxWIqms0GRVGYmppCf38//uLjz6Lt2nVc+PX/4PgHPozYOM83Yq1N0Ol04vqbf4Bpfgbf+dp/+ORLRWv7GxsboVAocOfOHfT398NgMOD27du4ffs28vLy0NDQgOrqarIp0NPhtKWGRCKBWq2G2WyGyWRCb28vent7ASzbbOTn5yMmJgZpaWkhv6vzp1IymUyQSCTQ6/VoamoiX5CVeFiJZjPXHR8fjyeffBKvvPIK7t+/j4qKChQVFQFYvbnHxMR4bEgul4s4VtOSX6vVCqFQ6DHTE4pNKBREo9frcfHiRY/P9LFjx1BTU+PXtSM9PLoSof4ublT1zM3Noa+vD3q9Ht/61rcQHx8Po9EItVoddHz8D37wA3zjG9/AwsIC6urq8L3vfQ/NayQM//jHP8YvfvEL8n42Njbi3//939d8vC+IeqLRarWQSqWwWq0k1Gv37t140N6OF7/zVeRWbMWOQ8cgTEpefsKKz7XZZETXzasY7ryPZ04/iRN//emAvlhbtmzB/v37ERsbi8LCQojFYoyOjpJW2fnz51FdXY2Ghgbk5uaCoijMz89jbm4OTU1NqKqqgtPpxMzMDKl4ZmdnYTQaMTAwQH7PrVu3iMCgoKAAOTk5Qd3J+rK5UhSFyclJMiS6Y8eOddscDyvRbDbKysqwfft2dHV14fXXX8df/uVf+nQWw2azkZSU5HFgbjKZvAbErXSs9vezHQzRuFwutLe34+rVq7BarWCxWNixYwdaW1sDOnOKxtZZuGdYvFU9MzMzmJiYwMsvv4ypqSmkpaWhvLwcLS0t+Pa3v+23Qexvf/tbPP/883jppZewc+dOvPDCCzh27BiGhobIPJE7rl27hg984APYvXs34uLi8LWvfQ1Hjx5FX19fwEpTFhWlO4bD4cDQ0BAmJiZQXFyM8vLyVRsu7ZH0xzfegkqrRZxACIrDRRw/DnaLGcalRSTEx+GJ48dw8MCBoFsPGo0Gd+/eJRG+er0eUqkUYrEYi4uL5HF0DzYtLQ3Nzc1r3o04HA7Mzs5CJpOR8yCHw+HxGA6H4yEwyMvL8+tLfP/+faSnp6+ZBGk0GiGRSGA0GlFfX+/1g7cSDx48QGpqKkpKSnxeRzRgYmICcrl8w2HKUMJms+Gll14iir2nnnoKfX19cDgcpHUWCNwn3OkDaAB+B8SdO3cOLS0tftvmzMzM4MyZM2TWJDs7G6dOnQpqqr+3txculwvbtm0L+BqhxNTUFGZnZ7F79+6I/P5///d/x8DAAL7zne/g3r17uH//Pr70pS/5vY/t3LkTO3bswPe//30Ay/tmXl4ePvOZz+Bzn/vchs+nrZm+//3v48Mf/nBAf0tUVjTz8/Po6ekBn89f1w6fzWaTMxJgmQguXryIrKwsVFZWIi0tLaQ935XnKImJidi7dy/27NkDmUyGrq4u9PX1EdnjxMQEOUz3lhvD5XLJedCWLVtgNBqRlZXlITAwGo3kf9PIzMz0GCRdrw24VvXhbnWTl5e3Kp1zPTAVje+gs2v++7//GxKJhLiEB/u5XCsgjiaetQLi+HzPyA1/Kxqz2YzLly+jq6sLwPJs2eHDh7F9+/agq5Foa51tRkWzHugzmrS0NDz++ON4/PHH/b6GzWZDZ2cnPv/5z5OfsdlsHDlyBHfv3vV5HXa7PaiojagiGrPZjO7ubqjValRXV/udY5KUlIS8vDxkZWWFZSp7rQN7FosFPp+PhIQE7N69G7GxsRgcHMTMzAyGhoYwNDSEhIQEbNu2DQ0NDV4PR+nNm55E37lzJyiKIpsGTTz0xPLCwgIePHgAAEhJSfEgnuTkZA8128o1GwwGSCQSmM3mgKxuHlaiidS66eyaO3fu4M0338ShQ4cCiqJeD+4BcfRZkHtA3NjYGLq6ujwC4lJSUnw+gPdmHVNXV4fHHnuMxHIHC3qYM1oQ6VZeKFRnKpUKTqdz1XlrRkYGBgcHfbrGP/7jPyI7OxtHjhwJeB1RQTQulwvj4+MYHBxEdnY2Dh8+HPCgUzjDyWivM/e7QPesGPco6D179kCpVEIsFqO7uxsGgwF37tzBnTt3PAQEtKLLGyGwWCykpqYiNTUVDQ0NAJbbde7KNrlcTjYTiUQCAGSAMD8/nxhDAsubxfj4OAYGBpCfn49du3YF1E58WIkmkmhtbcXo6CgUCgW6urqwc+fOsP9ObwFxGo0GarUaCoWCOFaLxWJs2bKFkM/Kzd6bdcypU6fI8GKoEOmNfSUivR6TyRRxJ/D/+I//wG9+8xtcu3YtqFmviBPN4uIipFIpXC4Xdu3a5bMUci2EOwUTePsDSEtRk5KSvGbFpKen4+jRozh8+DBGRkYgFosxMjJCBATnzp0jAgJfZciJiYmorq4mVjUWiwXT09OEeObm5mAwGNDf34/+/n4Ay22WnJwcsNnLNjx79+716SxmLTBE4z/o7Jof//jHmJubg0wm2/SzCC6Xu8qx+s0330RmZuayJVNPD4xGIxITE5GamorExEQMDAygs7MTFEWtaR0TKkSbvDnSrTOz2Ry0k3VaWho4HA7kcrnHz+VyOTIzM9d97je/+U38x3/8By5fvhz0ZzWiRKNSqXDv3j2UlZV5PcMIBOFOwQSWD9D7+/uh0WhQW1u7obEkh8NBZWUlKisriYCAljxLJBJIJBIIhUJkZGSgurra50hgYLlHXlZWhrKyMgBvCwxo4pmamoLdbvdwxaXVZXSrLS8vz68KkiGawJCZmYmDBw/iypUrkEql2LdvH0QiUcTWQ39mc3NzSfvLYrFArVajt7cX586dIz6AGRkZaGlpCWugXbSd0URDRROshJ3H46GxsRFtbW04ffo0gOW/q62tDc8999yaz/v617+Of/u3f8OFCxfQ1NQU1BqACBMNbTEfyunXzahobty4EXBWjLuAYHp6GmKxGH19fdDpdNDpdPj2t7+N8vJy1NfXo6yszG/ydRcY6HQ63Lp1C0ajEampqVCpVJDJZB4Cg1u3boHFYhEHg4KCAuTn5wckMHgYEOl179mzh9gcvf766/jzP//ziN3F0wGB7r/fbDbj+vXrHtYxe/fuRWJiIpaWlnD//n04HA6/A+J8QaQ39pWI9Hp8ycjyBc8//zw+8pGPoKmpCc3NzXjhhRdgNBrx7LPLdl4f/vCHkZOTg69+9asAgK997Wv44he/iFdeeQWFhYVEWUj7+gWCiBINi8UKucVCuIhGp9ORM5C6ujrk5eUFdT0Wi0XOUY4fP45bt26hp6cHWq2WCAgEAgHq6urWFBCsBZfLhZGREYyMjCAhIQG5ubmk9KUFBnTFI5PJ1hUY0MTjLjAIRVyCWq3GyMgIBAIBGUIMt99XNLRl2Gw2mpqa0NbWhomJCTx48GBTzmvWA4vFgsPhwO3bt3Hr1q11rWNWBsTR0+0JCQkeMz3eAuI2QjR6nYWCQAOFyWQKyf74zDPPQKlU4otf/CIWFhZQX1+P8+fPE4GATCbzINQXX3wRNpsN73nPezyu86UvfQn/8i//EtAaIn5GE2qEOm7Z6XRiZGQEo6OjKCoqgkajQXJycsiuDyw7EFRVVSEmJgZVVVWQSCSQSqUwGo0eAoL6+npUV1ev2+bSarUQi8VwuVzYs2cPZmdnPe7i3QUG27dvB7BMou7KNoVCsUpgkJiYSIjRarUG/Bo4nU4MDg5iYmICJSUlsNlsxO/LnXQC3aweBggEAmzbtg1isZhk1wR7NhkI6M/F1NQULl26RGbB1rOO2SggTiaTobu72yMgLiUlBUlJSRtW55GuIFbCH6+zcCBUFQ0APPfcc2u2yq5du+bxv8MRPvfIEU0oKxr6DIXD4WDv3r1ISkrC5ORkWD3J0tPT8dhjj+HQoUNeBQTuDgTu/XKXy4Xh4WGMjo6ipKQE5eXl4HA4mJub2/D1EAqFqKmpQU1NDYDlD/hKgYFer0dfXx/6+voALAsMuru7SdWTnZ294Uai0WjQ1dUFDoeDAwcOIDExkfwbbcWhVqvJZhUTE+NBPCKR6JEhnuLiYmi1WoyPj+PVV1/Fxz/+8U3f1HQ6HSYnJz3UiseOHUN1dbVfr/NaAXG0Y/XKgDj6v5U3TNEmBog08TGmmlGMUBDNyqyYkpIS8oELV2tupYhhpYCgu7sbYrHYQ0CQmpqK+vp6FBUVYXh4GAAIIbpf199zCT6fj/LycmLpbrfbPQQGMpkMdrudtOeA5bMhd4FBbm4u2UjcSZAWLqz8Aq+04nA6nR5SXFrzn5ycTCoyOuvlYQPdInrqqafwX//1X5ibm8PNmzdx4MCBTfn9LpcLDx48wNWrV2Gz2YK2jlkJ9/yd0tLSVQFx7nHJ9E1EcnJyxDf2lYi06owx1YxiBKs6c8+KOXjw4KrDr3Cp2ta7bmJiIvbs2YPdu3d7CAjUajXa2toALFuA7Nmzx6NKCNV6Y2JiUFhYSIwAe3p6oFarERsbS4jHZDJhamoKU1NTuHnzJlgsFjIzM5GZmQmr1UpEEL7OBXA4HEIowNuT73SMMm00mZSURB7ny6F0pMUANGiDxZMnT+LVV1/FjRs3UFZWFrScdSOstI6Jj4/H+9///qDPHNeDt4A4m81GiGd6eho9PT1wuVxwOp0wm82k3RbJgL1IEx9T0UQxAq04LBYLenp6oFQq13UliATR0HAXEOzatQttbW2Qy+XQ6XSYm5vD7373O9L/b2hoQHp6elgUYlwuF0lJSaivr0dLSwsoioJarfaoeDQaDebn5zE/P0+eNzo66iEwSEpK8rlV4j75XlxcDIqiYDKZCPHQh9L0DAh9p+z+RY2mtgyN2tpaDA0Nob+/H6+++io+9alPhWU63mQyoa2tzcM65uDBg5ifn/fIGtos8Hg8ciMCLG/qV65cgUgkIi1Fm80GkUjk4Vi9mQFxkSYapqKJYvhLNHT+Sl9fH9LT03Ho0KF1P8yRzo1xP0xvbGxEWVkZFhcXIRaLiYDg7t27uHv3LnJzc5GdnR1y8cJK8mKxWGQQsLGxkYgYVCoVYmNjIZfLoVAooFaroVarIRaLAYAcKNPkuWXLFr+IRyAQkJgFYPlQmiae8fFxiMVixMXFkY3KZrNFRUXjrq5isVg4deoUya65cuUKjh07FtLfJZFIcPnyZWIdU19fjyNHjoDD4WBhYSEq2lVsNhtsNhtZWVnIzMwkNxJ01bMyII6+kQhnQFykW2dMRRPF8IcIaOdig8GwKkVyveuH64xmo02QJhQul+thNpqWluYhIJBIJBgeHsbMzAxmZmbIZHB9fb3f/nHesJ5R5+TkJPr6+pCfn4/W1lbS+jCbzaTacRcYuGfzxMXFecRg+yIwcEdsbCyys7NJ+8nhcBCBwdzcHBYXF0FRFO7du0fabUlJSRHfaOnsml//+te4d+8eKioqSJsyGMjlcpw5cwbT09MAlqMuTp48SdRiZrMZQPRUeu4Dm+43EnRbj3aspt/Pvr6+sAbERbKioYmWqWiiFL5UNC6XC2NjYxgaGvLbuZj2Ows11iNIh8OBwcFBTE5OrhInrFwbLSAwGAyQSqV48OABmQGSSCRISUlBQ0MD6urqVp3n+ApvRGM2myEWi2EwGNDc3LzK4obP56OiooK4F9vtdszMzBDimZ6ehsViwfDwMBE2cLlc5ObmeggM/Jlr4HK5SE9PJ6ahU1NTmJiYQFpamocaaqXAIBLGjuXl5WhoaIBYLCbZNYH6/VmtVly/fh337t0j1jEHDx7Ezp07o1o8sdHGvtKxmg6Io6se94A491jsQDdrl8sVsdeLdmRgKpooxUZEo9FoIJFI4HQ60dLS4ndy3Waf0dCtJh6Pt0oSvB4SEhKwZ88eZGdnY2BgAHa7HX19fVhcXERbWxuuXLmCsrIy1NfXEym0r3AnGoqiyGFudnb2hqFpNGJiYlBUVESchp1OJxYWFjyqHpPJhMnJSaLrZ7FYyMrK8jjn8eeLyGKxEBMTg9LSUqKGoocP1Wo1pFIpTCaTx7lAampqyM8F1hpMPHbsGCYmJqDRaHDhwgU8+eSTfl93YGAA58+fh16vBwBUVVXh2LFjXq1uaDlxtFQ0/lYQ7gFxdN6S2Wwm7VNvAXEpKSk+y+QjOUdDtzmZiiZKsdaG7R6kVlpaGpC9y3rXDxb0delNyOFwYGBgAFNTU6isrERJSUlAGwL9Zdy9ezdOnDiBvr4+iMViTE9Pk+phpYBgI9BEY7VaIZFIsLS0hO3btwd1qEwHvOXk5BCBAW2ZQw+SarVazM3NYW5uDvfu3QOw3DZcKTDYaN3u/3vl8CFtra9WqzE8PEwGSWnSCecgaWxsLJ566in8z//8D8RisUcFuBEWFxdx7tw5jI6OAliWgZ84cYJ44HlDtE3ih2I9fD4fubm5yM3NBfB2+3RxcRELCwsYGBgARVGr2m3ebo4i2Tqj25oM0UQpvFU0SqUSEokEsbGx6wap+YJwntEAIAou+iDbm8TaH7iLDHg8HhoaGtDQ0ACVSrWmgKC+vh41NTVrtm5YLBZMJhOuXLmCtLQ0tLa2BtzmWW/ddNursbERwLLrgbuyTalUQqVSQaVSETWVUCj0EBjQyjtfsdJa3263E+KZnp4mg6TuxCMUCv3akNbbUAsLC9HS0oK7d+/izTff9DC89AbaOubmzZvk8HrPnj3Yu3fvhpVltBFNODb2le3TlQFx09PTMJlMXtttkSQak8kEHo8X1a1Of/BIE81aWTHBXj9cFQ0ASKVSzM7OYuvWrSFZ71oiA3cBwejoKMRisYeA4MKFC9i6dSsaGho8BAQ2mw0zMzPQarVobGwMOEM8EIhEImzbto34tplMJo9W2/z8PHQ6HXp6etDT0wNgmTho0omNjfX7vYuJiVlzkFSpVGJwcJDcIdObVLAH0vR7olQqcebMGbz3ve/1+jkYGxvD2bNniXVMcXExTp486XM7OBqJJtzrWSsgjhYZuAfE2e12zM3NgaIoiESiTSWdR0lxBjyCRENvrDMzMyQrprW1NWRvWrhaZ2q1GsBy+mVra2vIUgs3Wi+HwyEtGlpAQDsQSKVSSKVSpKSkoL6+Hjk5ORgZGSHWMJtJMt4QHx9PxA/A2yRIE8/MzAzMZjMxKQWWX4/JyUlCPv4KDNYaJKWrnpWDpDQBuf+OjdSFdHbNT37yEwwMDKCnp8cjD0Sn0+HixYvEDihQ65hoI5pIxQTw+Xzw+XwPtaJGo8Ht27eJyMDlcq1yrA6naORRUpwBjyDR2Gw2AMvT67W1tcjJyQnplynUREMf0s/OzgIAduzYEdLDZ38GNmkBwe7duzEzM0McCBYXF3HlyhUAy9klpaWlIVtfKMHj8VBcXEwOhp1OJ+bn5wnx0CQwMTGBiYkJACCzG+6yan8FBu53yBRFkQNptVqN/v5+MkhKk47D4djwullZWThw4ACuXr2Ks2fPorCwEAkJCbh37x5+/etfY2JyCgr1IlhsFhISEnHj7j00NjRgz66dOHDggE8VVTQRDUVRER+QpMHlcsmNRGNjI+Li4qDX60m7zT0gzn2mJz4+PmSvJ1PRRCkoisLExAQGBgYAgGRohBqhJBq5XA6JRILExEQcPHgQly9fDvlAYSDOACwWC3l5ecjLy0NTUxOuXLkCtVoNjUZDWmsxMTEwGAzYvn27TwKCSIDD4ZCDYdq+p7e3F+np6URgoNPpMDs7i9nZWdy9exfAcjIqTToFBQV+hZPR0Rfx8fFk/oN2N1ar1ZiYmMDS0hJUKhX0ej2pjrwNHu7duxfDw8OYnZ3Fr3/9a9y+cxftYgmKaurQ8Ph7UVy9Dcnpy1Jfk0GPif5e/PL8VfzfL/8rjh46hH94/u/WPY+MNqIBomemh26/czgcYhckFArJfJPFYvGQVUskEvB4vFWO1YESJ1PRRCF0Oh2kUiksFguamppw7969sHkkhcJU0/3sqKamhpyBhCLnZSUCJUan04mBgQFMTk6ipaUFxcXFZGC0q6sLZrMZ9+7dw71795CTk4OGhoZ1BQTRANpzq6mpiaQGajQaQjoymQwqlQpKpRJKpRKdnZ0Als+G3IknLS0tKHfjBw8egMfjgc/nY35+Hn19fWCz2R53x7St/vHjx/H1r38dv/nDH7H7iXfjS3//JfBiV1e88QmJqG5uQXVzCx7/2KchvXUNj516Al/+wj/h+BpOA9FINNFQ0QAg35m11hMXF+cxHEyf3dE3FCMjI0EFxDEVTRRhZVZMRUUFuFxu2JRhQPB5NwsLC5BIJBCJRDh06JDHXUs4zn8CqWiWlpbQ1dVFMuLpyjA1NRVHjhxBWVkZurq6YLPZyB337OysR4RBKBwINgP0HMZKgQFNPPPz89BqtR4Cg/j4eI9WW1ZWlt/zHwKBgEiPXS6Xh63+2NgY7HY7LBYLLl66hPuSHvztd3+C9Oxcn6/fsP8Qqpp24Qff/Fd0donxfz//uVWPiyai2Whj32z4ux5vZ3cbBcSlpKQgISHB63vAVDRRAjqUi81mr3IFDneccyBkYLPZ0NPTA7lcjpqaGq/Z64FY+m8Ef9brcrkwNDSEsbExlJeXo7S0dE0HgrS0NOzbtw8Gg4FEGKhUqlUCgrq6uqDk5KHGRq/vWgIDmnhmZmZgMpkwODhIogtiYmKQl5fnITDY6KDY/b1ns9lITk5GcnIySkpKsLCwgDfffBN3792DeHAE//STVxAb5/+mExcfj4/+81fxu//8Gr7xrW/js3///KrXgiEa73A6nWCxWAGvZ62AuJWZS2sFxDEVTYRht9sxMDAAmUy2ph1LOCuaQEhsbm4O3d3dSE5ORmtr65p3KpGsaLRaLZlF2bdv37rnEu7rTEhIwO7du9HS0uJVQHD16lWUlpaioaHBbweCUCOQTXUtgYH7PI/FYsH4+DjGx8cBLL8+2dnZHlWP+3u+1vthtVpx7do13L9/HyaTCXfaO/HPP/99QCRDg8Vi4b1//Y946fN/g4P727Fjxw6PdUQL0UTbGU04hAmxsbEejtXuAXGLi4sYGxuDXC7HN77xDSIsUCgUq+yc/MUPfvADfOMb38DCwgLq6urwve99D83NzWs+/ne/+x3++Z//GZOTkygrK8PXvvY1nDx5Mqg1PFREs7CwAKlUumZWDI1wzboA/pGB1WpFT08PFAoFamtrkZubu+4XKRxnNBtd0+VyYXR0FMPDwygpKUFFRYVPX7CVm6W7gOD48ePo7++HWCyGTCYjAWnx8fHEgSDYL0+k4C4w2LNnDyiKglKpJMQzNTUFvV5PRBN37twBsGxoSZOO2Wz2cNT2Zh3TIZHiw5//MvghkLmzWCx88B+/hL/5h+dw7dJFck4QTURDf0ajaT3hvilyD4gDlt+P+fl5LC4u4qc//SlmZmaQmZmJkpIS7NmzB//1X//ld5Xz29/+Fs8//zxeeukl7Ny5Ey+88AKOHTuGoaEhr9/BO3fu4AMf+AC++tWv4vHHH8crr7yC06dPo6uriyTwBoKHgmgsFgt6e3uhUCjWzYqhEenWGUVRpIpJTU3dMHrAn2v7i/XacQaDAV1dXbDb7di9ezf5wAdzTWC5Cqivr0d9fT1xOZBKpTAYDKsEBNXV1ZuaMRJqsFgsYvS4Y8cOUBS1SmBAp4QqFAp0dHQAAAQCAUpKSpCamorR0VHisJycnIxt27bhtrQXldvXvuv0F8LkVNS1Hsfvf/8H/NmffQBA9BENm82OmvVEwueMxWIhOzsbf/d3f0c+M1/72tdw9+5ddHZ2BnRm8+1vfxuf/OQn8eyzzwIAXnrpJZw5cwY/+9nP8LnPrT63++53v4vjx4/js5/9LADgK1/5Ci5duoTvf//7eOmllwL+26KaaPzNiqERbjHAemRgsVjQ3d0NtVqNbdu2ITs72+cvT7haZyuvSUvB+/v7UVBQgK1btwZsqrkRaAHBSgcCdwEB7UBQUFAQNRtNoGCxWOS8pa6uDsByHMVKgYHRaER3d7fHc1NTU9HQ0ICXf/NbHHjPB0O+tr1PvBsv/PXHsG1bLZnniZbXezNcAfxBpGd66DOa5ORknDx5MqDWlc1mQ2dnJz7/+c+Tn7HZbBw5coRI+Vfi7t27eP55z7O8Y8eO4bXXXvP797sjaokmkKwYGuFsna1VLdFuBD09PYQU/ZX6bkZFYzKZIBaLYTKZsGvXLqSlpfl9zUCUbGw2G+Xl5SgvL18lIOju7iZnWHQlFA4BQTjSRn2BQCBAVVUVqqqqAACvv/46BgcHYbFYPNalVqtx+fJlXL91G//6V6vvNoMFPyEBialpUKlUUCgU0Ov1YLPZJD4i1EOH/iBSrgBrIdJEYzKZfI49XwsqlQpOp5PYJ9HIyMggQpaVWFhY8Pp4Ovo7UEQd0QSTFUNjs1tnFosFUqkUS0tLqK+vDzj3PZwVDV0d9vb2IicnJ6DX1f2awWzY7gKC2dlZiMVi9Pb2YmlpCVevXsW1a9dQUlKChoYGVFRUPDLGgjqdDhcuXEB/fz+A5dfh+PHjKC8vx8LCAqamppbdtEW+R1z7i+La7bDZbDh69ChkMhmGh4cRExNDhg5jY2M9rHOEQuGmEE+kN/aViIZ0zUD3kWhEVBFNsFkxNDaLaOgslt7eXmRkZODQoUN++WZ5u3Y45M0AcP/+fWg0GjQ1Na26Y/EXoaoMWCwWOVg/duwY+vv7IZFIMDU1hdHRUYyOjiI+Ph61tbXYvn37QysgcLlcuH//Pq5du0YskiorK3H69GlS9dJCiri4OJTXDYRtLTllFRBLu3H06FFwOBzweDxUV1cDeNvjS61WY2FhAf39/STB0j2RNBwbcDSdFwGRJz6z2Rz0HE1aWhpJ13WHXC4nyreVyMzM9OvxviIqiMY9K6akpCRoGWy4jC/dr202myGRSKDVav1u7W107VCCLnm5XG7QREgjFETjdDoxPj5OBt2EQqGHgIBOBDUYDLh//z7u37+P7Oxs4kDwsAgIZDIZzp49S768ubm5yMvLQ1lZmdfWqsFgQFx8aAxVvYEvSIBh0ghg9ebO5XKRlpZG2ql0giXt2zY+Pg673U4MQ+nKJxTmkpHe2Fci0hWNyWQKeo6Gx+OhsbERbW1tOH36NIDl17mtrQ3PPfec1+e0tLSgra0Nf/u3f0t+dunSJbS0tAS1logTjclkwp07d8Dj8YLOiqER7orGarXiypUryMrKCtnmDYRW3my1WtHd3Q2lUgkAqK+vD5ktT7BEo9Pp0NnZCRaLhdjYWPT394PNZpPNKzU1Fa2trWhtbcXo6CgkEgmGhoZI6Jl7hIG/AoLNOqMxmUy4dOkSJBIJgGWH4CNHjqChoQH3799f83k8Hg/OIJwnNoLDbiPEsFEV4Z5gWVJSAoqiYDQaSYIlbS4pFAo92m2B3IlHG9FEej2hqGgA4Pnnn8dHPvIRNDU1obm5GS+88AKMRiNRoX34wx9GTk4OvvrVrwIA/uZv/gYHDhzAt771LZw6dQq/+c1v0NHRgR/96EdBrSPiRENnyW80Y+IPwkU0JpMJfX19cDgc2LVrV9AtqJUIVUVD29wkJydj//79aGtrC+kGGyjRUBSF8fFxDAwMeMzs0BYs9J3z8PAwXC4XIZ3Dhw/j+PHjJB1UqVRumoAgkL9RLBbj8uXLJCWxoaEBR44cIXeo623wxcXFUExPhm19C5MT2F/+tvWNP9852isuISGBTLvT5pJqtRqjo6Po7OxEfHy8R7ttLZsVdzCqM0+EoqIBgGeeeQZKpRJf/OIXsbCwgPr6epw/f57sXTKZzOPv3L17N1555RV84QtfwD/90z+hrKwMr732WlAzNEAUEA096BdKhLoFRVEUJicn0d/fj/T0dHA4nJCTDBD8uu12O3p7ezE3N4fa2lrk5eURwg3l6xEI0ZjNZnR1dcFkMq06f3O3YCktLSUZLytbNikpKWhtbSVtt76+Pq8CgvLycq/VW7g3soWFBZw5cwYzMzMAloc0T506hfz8fJ+vkZmZCf2iKlxLxPRgD5o+cJr872Bfk5Xmkna7ndiszM7Ooqenh9is0MTjLUQs2lRnkW6dhaqiAYDnnntuzVbZtWvXVv3sve99L9773veG5HfTiDjRhAMcDico40t30DJro9GIHTt2ID4+ftVhWagQDNEolUqIxWIkJCR4mHW6R0SHCv4SzezsLKRSKbKysnxSu7lnvBQXFxODQpp4VKrljbilpQUmkwkzMzOYn58nAgI+n08cCMJxQ7AS7tYxFEWBx+Ph4MGD2LlzZ0CbZ05mBuQzMmTk+k5QvsDpdGJhcpxY3YfjAD4mJoYMsALLNzi0wMC9Wk1OTibttpSUlIhXECsR6fWEqqKJFjyyREPPKAQK96FGd5m12WwmcuFQf0kDOaNxOBzo7++HTCZDdXU1CgsLPdZF//+RIBq73Y7u7m4oFIqgZN/uBoWFhYWgKAomk4lsXjweD0lJSTAajVAoFDCbzV4FBKEGRVHo7+/HhQsXiHXM1q1bcezYsaByYP76/3wa3/2fX+F9f/dPIV2v5OZVPHHyOPndm7GZuscfAMt/u16vJ+c8tFccn8+Hy+XC3NwcUlNTIx43EWmiCWVFEw14JIkm2BaUwWCARCKB2WzGzp07PYK96A9fOLyQ/JU3Ly4uknzztbzfwpFz4wvRqFQqdHV1ITExEa2trSFPDRUIBBAIBKQtRadaKpVKjI6OYnZ2FjqdzkNAUFhYiPj4+JDcJKjVapw7dw5jY2MAQCa4Q5E+umvXLvzTl/4Favk8UjOCVzMCgNPhwJVf/xxv/v635GeRGF51DxErKioCsHz3Pjw8DLlcjsHBQej1emKnT7fbNnuQdDO8ztYDExPwECBQMQBFURgbG8Pg4CDy8/Oxa9euVb3+cBONL4TgdDoxNDSE8fFxVFZWoqSkZFPNOtcjGqfTicHBQUxMTGDr1q0oKiralA2Cz+eTmZyGhgZYrVbMzMxAKpVibGwMJpMJo6OjAICJiQnU1dWhqanJr/RMYLlKu3XrFm7fvk36+Hv37sXevXt9VvVttMGzWCx8/zvfxl999nP4zLd/GJLX79wvfoSPf/iDq5wgouEAPj4+HklJSTCbzWhpaYHNZiMCA/f0SndVYrgHSaPhjIZpnUU5AiEavV4PsVgMm8227rCoO9GEGr4QjUajQVdXF9hsts9y8FAPgq5FNDqdjkQNuAemRQKxsbEoKSkhslyZTIYbN24Qd+Vbt27h1q1bSE9PR3V1Nerq6iASidbdvEZHR3H27FksLS0BAEpKSnDixImABos32iSrq6vx+JFDeO2H/4mn//Jv/L6+O7pvX4N2Yhgf/49/9fh5pNtD7nBfC4/HW2WnTwsM5HI5iWt3TyRNTk4OKTG4XK6QzAcFAro1zFQ0UQ5/w75oy5vCwkJUVlaue2cabqJZiyBdLhex2y8tLUV5ebnPm0SoPb5o4qJbUO6y5eLiYlRWVvq9gZnNZuj1ehI4JRAIfJLF+gIWi4WCggI89thj6O3tRXp6Orq6uiCTyaBUKnHt2jXcvHkT6enpKCsrQ1FREVJTU5GYmAgWiwWdTofz58+TDS4xMRHHjh3D1q1bw3pX/Q/P/x0+/4V/xu/+8+t411/9fUAb6YNLZ9F98Q388X9/u2qt0TSNv95a6KA9b4Oki4uLmJiYIIOkNPH4E5vsDZGsaKxWKwAwFU20w9eKRqfTQSwWw+Fw+GyTT6fuhWNOZ62YaL1ej66uLjidzlVpor5eN1xOCWazGWKxGAaDwW/bIJ1Oh5GxcWgNRnB5ceAnJoDFZgMUBcvsAqwmE2JjOCgpyEdmZmZINkUOh4O6ujrU1dWRlFaJRAK9Xo+FhQWSeURvWhaLBaOjo8TpeOfOnTh48GBQh9X+bPBf/X9fwY9/+lN857mP4gOf/RJyin07AzJoNfjNt/4fSrIz8Nrvf+d1040movGnulprkJRut/X29pJBUvdzHn8qhEhWeyaTCQCYiibasRHRuFcHRUVFqKys9OvuJVwb98rrup8ZBbJOGqGuaOjNaXZ2Ft3d3cjMzMSOHTt8bjVYLBaIpd1wsLnIystH9hoBdsDymcj07Az6R0ZRX701YP87Gu6vQ0pKCg4dOoSDBw9ibGwMYrEYQ0ND0Ol00Ol0kMlk5LHx8fEoLCyESCSCTCYjvl+bsRl98uMfx7HHHsPz/98/QmOyYs/p96GqaSd4sZ4CC6fTiamhftx+7X+hXZjBV774z9izZ8+a1402ogl0Le6DpLQ4xNsgKZ/P97DOoSvWtdYTKaKhB30ZoolyrEcEWq0WYrEYLpcLe/bs8Ug6DMX1g4E7IRiNRojFYnJAGswGG2oxAE3i3d3dqKurQ05Ojs/PXVhYQM/QKArLK5DgwxlOTEwM8gqLYM/JRc/QENLkctRs3RrQutfaVNhsNsrKylBWVgaVSoVXX30Vc3NzHo/h8XgQCoWIj4/H0tISRkdH4XQ6kZycjLS0NL/OCQLZ4PPz8/H73/wac3Nz+PHP/hv/839/DaPFArDYAIsFuFyI4bBRU12Ff3n+MyQLJ9TrCBdCPbC50SBpb28vSbikicf9xiGSrTOTyQQej/fIuJYDjyjReKtoXC4XhoeHMTo6GrRxZ7gsbuiW3OTkJHp7e5GXl+dV+RbIdUNV0ajVanR2dgIA9u3b59eB//z8PAbGZdjasN3vTSUmJgYVNTWYnZahSyLF9vqNN1J/QFEUurq60NbWRu4oy8vLwefzMTg4CI1Gg3v37gFYFgHQc0G0dQ59TkAPItKbV6j85WhkZ2fjS1/4vx7rDnSTjiaiCXcFsdYgKV31uA+S0i3TSMi/gUdPcQa8Q4hGo9FALBYDQEBnHCsRrorG6XRCrVZjaWkJzc3NIbPFD0XrzOVyYXBwEOPj46ioqEB/f79f5xQ6nQ59oxOoqqsPakPJycvH9OQERkbHUFZaEvB13LHSOiYjIwOnTp0i1kh2ux0DAwMQi8WYnJzE2NgYxsbGwOfzUVtbi4aGBjQ2NhLDSdp92mKxQCQSkYonNTU15Eomek4qELyTiGYl3AdJadsjvV5PiMdoNEIqlWJyctLjxmEzXMMfNcUZ8IgSDU0ETqcTw8PDGBsb81up5cv1QwU6nXNkZAQxMTE4dOhQSDekYNer1+vR2dkJiqKwf/9+CAQC9Pf3+0xeLpcLXd3dKN1aG5LXP6+wCH3iLmRnZUIg8M9S333NVqsVV69exYMHD4h1TGtrK5qbmz3WGRMTg23btmHbtm2rBAQPHjzAgwcPkJWVRRwIaMNJd/cC+oBaJBLBYrFAo9EgPT09ohPw0UQ0kV6L+yBpYWEhdDodiouLweFwoFarydmdQCDwcKoWCAQhXzdT0TwkoCua69evg81mY9++fX4P5q2HUKrOrFYrpFIpFhcXkZ+fD71eH5a73kCIxt2Gx12M4B785gsmp6aQmJYR0k21uLIKkp5e7Nm10+fnuNvx9PX14cKFCzAYDACW51aOHj264VySu4BgfHwcYrEYg4ODmJ+fx/z8PC5evIiqqirU19ejqKiIBJoBywfUarUaPT09mJubw9jYGBISEjwqns28k4305u6OSM6teIPL5QKfz8eWLVuQm5sLAB6DpDKZDN3d3YiJifEgno1msXwBU9E8BHA6nWQCPDs7O2RVjDvcN9tgMD8/D4lEQvJXlEoldDpdCFboiUDOaCwWC8RiMfR6PXbt2uUxUe6vf9rkzCwq6rb79fs3Ap/Ph41i+W0+aDab8atf/Qrj4+MAlonj5MmTKCnxrw3HZrNRWlqK0tJSmEwmdHd3QywWQ6FQoKenBz09PUhKSiIRBiKRCHFxccjJycHo6CjKysqQlpZGKh5aGUXfMW+G9Uo0OSZH0/Ao4H093gZJacNQhUKBwcFBAPA4pwtkkJSpaKIci4uLEIvF5I0tKSkJy4c3FHb+3d3dkMvl2LZtG3Jycsh8TrjVbL5gbm4OUqkUW7ZsQWtr66o7TX+IRqPRIC5BGJb3ISMnFxNTMlRXVW74WLvdjgcPHqCrqwsURYHD4WDfvn3Ys2dP0Af28fHx2LVrF3bu3In5+Xl0dXWht7cXGo0G165dIxEG9fX1qKx8e608Hg9ZWVkkndVut5M75qmpKUgkEsTGxnpUPKEaYgWir6KJlrUAvqnO6HRYWhG6Mt5iYmICNpttVSLpRoOkTEUTpXA4HBgcHMTk5CQqKipQVFSEM2fOwOl0hqUcD4YQFAoFxGIxhEIhWltbPT5QoZYh0/B1vXSezfz8PLZt20ZaBt7gK3mpFxeRGKT4Yi2IkpIwPivb8HEjIyM4d+6ch3XMyZMnfRrQ9QcsFotIao8dO7amgIA+hF7pZh0TE4OMjAwSbeBwOFZlu9CtmlB4fkUT0URTdQUEVmF5i7egz+kWFxfR19cHg8GAxMREj3bbyuqFqWiiEGq1GmKxGDwez8Nfi8VihTXO2d9rOxwO9PX1YWZmBtXV1V4jiCNZ0SwuLpJ0xJUEGOg1AUC9uISM4jKf1ul0OqHV6pCYmODTDQKbzYbdsfb7oNVqceHCBWIdIxAIkJubi2eeeSbsG6y7gGBpaQlisRhSqRQ6nQ6zs7P44x//iLt376K+vh61tbVeX28ul4v09HTiHu7eqpHL5V4jsL2Fiq2FaCKah6F15i+8uYxbrVZCPPQZX1xcHLlpUKvV0Ov1YaloFhcX8ZnPfAZvvvkm2Gw23v3ud+O73/2uV9d3+vFf+tKXcPHiRchkMqSnp+P06dP4yle+4veZ90NLNO45LN4cjMNlEwP4f0ajVqvR1dUFPp+PgwcPrqmUCifRrHVdl8uFoaEhjI2NoaqqCsXFxT5tPr4SjcVm89lzqqurC6Ojo4iNjUV+fj62bMnAli3p60pKKRZ71YbpdDpx//59XLt2DXa7HSwWC7t27cLWrVsxODi46ZtrcnKyh4Dg4sWLUKvVqwQEDQ0N67pdr2zVbBSBTbsXrNUCiiaiiba1hGtgMzY21mOQ1OFwYHFxEYuLi+jo6MCzzz4LiqKQnp6Or3/969i3bx8aGxuD8m2j8cEPfhDz8/O4dOkS7HY7nn32WXzqU5/CK6+84vXxdMTGN7/5TWzduhVTU1P4y7/8S8zNzeH3v/+9X7/7oSQalUoFsVhMNm5vjByqA3tv8MfOf2BgAJOTkz5t4uG0tvFGCrSHmsvl8tkJmkaobW2Atx0HrFYrsQgCAJFI9Kdhu42JRyaT4cyZM1AoFACAvLw8nDp1ChkZGVAqlSFdr7+gBQTT09MoLCyEXC4nAoLe3l709vZCJBIRAcFG814bRWCPjY3B4XB4+H0lJyeTM6lo2tyjqaKhP9ebsR4ul0sGSSsrK/Gud70LH/vYxzA6Ooq7d+/iG9/4Bux2O5RKZVDHAAMDAzh//jza29vR1NQEAPje976HkydP4pvf/KbXUMKamhr84Q9/IP+7pKQE//Zv/4YPfehDcDgcfp1tPlREY7fb0d/fj+np6Q2zTsI1vQ/4RghLS0vo6uoCl8v12TJ/s1pnFEVhcnISfX19KCwsRFVVld93b74STQyXC7vd7tOXZNeuXTCZTJDL5YiPj0dMDA9arQZarRZardaDeNLTtyAjYwssZhNYLBaMRiMuX74MiUQCYFmV9thjj6G+vj5qNlN38Pl8DwGBWCxGT08PtFotrl+/juvXr6O4uBgNDQ0bOorT2CgCe2pqClarlaiizGaz33NI4UI0EQ39HYyEBUxsbCwyMzORnp6OF154ARRFYWpqKuiz5rt37yIpKYmQDAAcOXIEbDYb9+/fx9NPP+3TdbRaLYRCod8CmoeGaBQKBSQSCQQCAVpbWzf8goSzdbYeIbhb3dD+WZGy86fhvl6LxQKJRAKtVrsqPdQf+LrWtJQU6DQapPr4e5qaduDcubMwmUzYt68RaWlpUCiUUCoVkMsVK4hnGKM9Egx2L6eh0u/39u3bcfjw4YfiQNVdQHD06FEMDAxAIpFgYmIC4+PjGB8fR1xcHGpra7F9+3YirfX12utFYNPnPQsLCx7KtlC0afxFNFVX9Ocokqaa9BkIi8VCYWFh0NdcWFhY5TTC5XKRkpKChYUFn66hUqnwla98BZ/61Kf8/v1RTzR2ux19fX2YnZ3F1q1bUVhY6NMHMpyts7WqJTr4i6KogIZEw31GQ8/tpKeno7W1NagNxVeiSU1JxpBszmeiEQoTUVFRgYGBAXR1deHkyZPIz89Dfv7y0KPVaoVCoYBCocDY6ChiOGwydElDJpPhypUrKCwsREFBQUQD2LxhrU11pYCAdiDQ6XRob29He3s7MjMz0dDQsKaAYD2sPJy22+0QCoVISEiAWq1Gf38/DAYDhEKhxznPZtiuRGNFE8mYAFryvhE+97nP4Wtf+9q6j6HFMMFAp9Ph1KlT2Lp1K/7lX/7F7+dHNdHI5XJIJBKSO+/PHWq4W2fuuTEURWF0dBRDQ0MoLi5GRUVFQGV3OHNj5ufnMTo6irq6unVly77CV6JJTk6GqbfPrzvWmpoaTE5Owmg0YnBwEDU1NeTfYmNjkZGRAYVCganxUSTEx4PL5SI7OxtWqxVyuRwqlQoqlYqYf6ampqKgoACpqakkVCrakZycjNbWVhw4cADj4+OQSCQYHBzEwsICzp0757OAYD3Qtjt0BDbwtiqKFhfQtivuFU84KsVoIxo2mx2xCstsNvt8E/H3f//3+OhHP7ruY4qLi5GZmUnOLWnQQoSNqmS9Xo/jx48jMTERr776akBtvKgkGpvNRuY5ampqkJ+f7/ebvllnNAaDAWKxGFar1efwtI2uG8o2wuLiIhYWFhATE+M3Wa8HX4mGxWIhPzsbCvkCMjJ9u0vjcrmor2/A3bt3iP2NQCD4U79athy0ptfBrNNi94H9OHbsGKlazGYzpqamMDk5iampKSwsLJCNk8b4+DgKCwtJxeOPCCIU8Of9XelA0NPTA7FYDLlcHpCAYKN1rFRFuduu0HJc91yXUPl9RRPROJ3OiK7FH7cLd/n7emhpaYFGo0FnZycaGxsBAFeuXIHL5cLOnWvbOOl0Ohw7dgyxsbF44403Aq5uo45oFhYWIJFIIBKJcOjQoYD15OGsDujzn/HxcfT396OgoABVVVUhsfMHQtOvdj8ropMGQ3kn6s/rW1xUiGu37yAlNc3nu6GCgnyMjY3+acBVgm3batHR0QG5XA4A0KoU+IuPfwz19fUez+Pz+aisrCQT+GazGTKZjAxNKpVKIift6uoCsGxDU1BQQMhns4nHV8THx2Pnzp1obm7GwsICurq6ghIQ+PI5W2m7Qt8Fr/T7cq941gsUC2Ytm4VIk54/FY2vqKqqwvHjx/HJT34SL730Eux2O5577jm8//3vJzcVs7OzOHz4MH7xi1+gubkZOp0OR48ehclkwq9+9SsSCAgsE5w/XZuoIRqbzYaenh7I5XLU1NQgLy8vqA9eOCsap9MJlUqFpaWloA7UV4L+cAf7QTcYDOjs7ITT6cS+ffs8kiJDCV+FCxwOBw011ZD096Ny2zaf53QaGxtx/vx5TE/LMDMzA4pafl3SU1NxZNcObKut2fA6fD4fFRUVqKiogEqlQnt7O4qLi0nVs7CwQIiHjpJITk4m1Q6dqhlKBCv4YLFYyMrKwqlTp3D06FEMDg5CLBZ7FRA0NDSs2e8PZHN3l+MCy98F2r1gbm4OfX19HvM+vhpNRnpzd4fL5Ypo6Ji//n2+4uWXX8Zzzz2Hw4cPk4HN//zP/yT/brfbMTQ0RKKku7q6cP/+fQBAaalnhPjExIRfIoWoIBq73Y4rV66QvnQo2DwcRENRFKanpzEyMkIs5UNpceOvWaW39U1NTaG3txcFBQXYunUrOBxOWKxt/FXIpaSkoDg3E8P9fSjfWu3TBmc0GsHhcOBwOEBRLmRlZiEvNwcxDitqa6r9Wq/dbsf4+Dji4+MJ8QDLKjy64pmamsL8/DyWlpbIND+wTDzuFU8oiCdUd+8xMTGora1FbW2t3wKCUFQRHA4HaWlpSEtLQ0VFBQkUU6vVUCqVxGhy5RDpSlKJJqKJdOssHBUNsPwdXGs4EwBRJtI4ePBgyFSwUUE0MTEx2LlzJ5KSkkL2BQx168xisUAqlWJpaYnkVYTaR829ovEXVqsVEokEGo1mVWhaOKTegThCFxYUgM1mo18iRnFl1ZpfJqPRiK6uLhJERtsJWU1GxLNcaGhq9OtzsrS0hI6ODggEglX96Li4OJSXl6O8vBzA8vs8PT2NyclJTE5OehAPPaOTlJTkUfEEG6QXKrgLCCYmJkiEgbuAoLKyEg0NDWTOJtTtKvdAsbKyMlAURdwLVCoVRkZG4HK5Vg2RMq2ztxGuiiaSiAqiAZa/JKFEKCua2dlZdHd3Iz09HYcOHYJCoYBGownJtd0RKNHQ51p03MBK2XI45nMCvWZ+Xh5SU1LQIZGCJxAiMzeXHDDSdji9vb1wOBxgsVgoLS2FxWTClQtnsZQgwHtOHfN5Q6IoCuPj4xgYGEBFRQVKS0s3fG5cXByZfwKWCdy94pmbm4NGoyGVA7A8QEpXO74QT7g3VTabjZKSEpSUlMBsNpMIA7lcjr6+PvT19UEkEiE5Odkj/iEcYLFYSEpKQlJSEkpKSkiSpbvDsd1uJ9W43W4PSwS2P3hUK5pIImqIJtQIBdHYbDZ0d3dDqVQSO/9QXdsb6FheX4nG3aiTdlv2toGFQxgRDHkJBAIc2LMbSqUSI2PDMNscMFosGBkdh8FkBEVRSODzUVZShMQYFqqryqCWVUIul6OtrQ1PPfXUhr/DZrNBLBZDq9UGpQaMjY1dRTx0xTM1NYXZ2VlotVpIpVJIpVIAbxOPe8UTqbt1Pp+PnTt3enUg0Gq1mJycRFFREREQhDt8zD3JsqioCBRFwWg04sqVK2SY2GKxeFjrhyMCez08qmc0kcQjSzRsNhs2my3g59M+VElJSWhtbfWQ9YVb0ebLtZeWltDZ2Ym4uLgNZcvRVNG4Iz09HfHx8bhw4QLa29thtVoRGxeHffv2oXnHDohEIrLBPP744/jpT38KiUSCxsbGdWeB1Go1Ojo6kJSUhIMHD4Z00j02NpbIjYHVxDM3N7eKeOh4YJp8IpW9Qmff0A4EV65cgUajwcTEBCYmJhAXF4eamhriQLAZa2SxWCRjp6amBgkJCWtGYLsTTzgjsCPdOmMqmocIgToDuDsR1NbWelW/RZJoXC4XMZwsLy9HWVnZhhtCONbLZrMxOjoKk8mEtLQ0v+zpgeW/o6urC21tbbBYLIiLi8OePXtw+PBhr1+y3Nxc1NXVQSqV4ty5c/jEJz6x6u+mKArDw8MYGRnZ0AsvVFhJPDabbVXFo9Pp0N3dje7ubgDLkuGlpSWUl5ejsLAQycnJm0o8XC4XtbW1UKvVyMnJwezsLLEl6ujoQEdHBzIyMoiAINx31xRFeWzu8fHxiI+PXxWBrVKpMDg4CL1eH9YI7HA5N/sC2iaIqWgeEgTS3lIqlRCLxcRPba03O1KGnUajEZ2dnbDb7di7d6/Ph9DhqGgaGhqwsLBADngpikJqaipRIK0naZ2fn8eZM2cwOzsLAMjMzMSpU6c2dCw4cuQIBgYGMDc3B7FYjO3b346Htlgs6OzshNls9uu1CTV4PB45HwHeJh5aTj07OwubzYahoSEMDQ0BAPEio1ttKSkpm0I8LpcLQqEQZWVlHg4EAwMDkMvlOH/+PC5dukQEBEVFRWG509/ILZmOwKZb11arlczyhCMCO5IVDe1cwVQ0Dwn8UVo5HA4MDAxgamrKpzvhcFY03kiBoijIZDL09vYiLy8P1dXVft1xhUPeLBAIyIZKK4to65ehoSGwWCwP4hEKhbBarbhy5Qo6OjqI/cmhQ4ewY8cOn77YCQkJOHjwIC5evIi2tjZUVS0r1xQKBbq6upCeno7m5uZN7edvhJXEY7fb8eqrryIhIQFyuRwzMzPQ6/Xo6elBT08PgGXicZdTh4t43EUJLBbLQ0BAOxAsLCwQAYFQKCQOBKEU79Cfd1//xtjYWK8R2CqVCpOTk5BIJCRMLJAI7EgSDT3DwlQ0Dwl8bZ3RE+I8Hm/NbJuV2MzWGS1bXlpawo4dO1Y5sPp6zXC4QtNwVxaVlpaSQC6VSgWFQoH+/n5oNBrMzMyQc7Pq6moP6xhf0dzcjK6uLqhUKly9ehWFhYUYHx9HbW1tQFZFmw2bzQaBQICWlhYkJyfDbrdjZmaGyKlnZ2eh1+uJvQywTLDuFU9qampI/s611G98Ph/Nzc1obm72EBDodDrcuHEDN27cCKmAIFgTy/UisGdmZvyOwI6kGMBsNgNgKpqHBhu1t5xOJ4aGhjA+Pu6z9JXGZhENLUhISUnBoUOHAj7UDkdFsx7cA7mSk5MxPDyMyclJAMt3atnZ2UhISMDQ0BCpeHz1y+JwODhx4gR++ctfoqOjA06nEwcOHIha2xh30LYxeXl5pLUXExODoqIiFBUVAYAH8UxNTWFmZgYGg2EV8bhXPIESjy8ya3cBAe1AMD4+vkpAQDsQBLKOULslrxWBrVKpsLCwsGEEdiTlzSaTCTweL2qGV0OFR5Zo1mudabVadHV1gcVi+Z0sCbxdLYVr4M1ut0MqlWJmZmZNQYK/1wxnReMNdrsdN27cwJ07d+ByucDlcrFv3z7s3r0bLBYLS0tLUKlU5I6Tx+MR0qGJZy3w+XwkJyeTa0RbDMBKuFwukrRaX19Pzhq8wRvxzM7OEuKZnp6GwWAg7SxguY3pXvGkpaX59Hnx5/PL5XJRU1ODmpoajzmiUAgIwm3L728EttFoDKuqbT2YzeZHrm0GPMJE46115q7YKikpQUVFRUAfbvfBylCX2C6XC729vYiPj8fBgwdDkoC42RXN8PAwzp07R4Zay8rKcOLECY++Pk0owPIdJN1jl8lkkEqliIuLI49JT08Hn8+H0+lEf38/ZDIZHnvsMfzxj3+ETCZDX1+fR5RANMFisaCjowM2mw379+/3mxRjYmJI5QIst4XcK57p6WkYjcZVxEOTTkFBAdLT070SSqA3SrRs3N2BwJuAoL6+HsXFxRt+x/w9owkWa0Vgq1QqqNVqyOVyMljqLQI7nDCZTI9c2wx4xInGvaLR6/Xo6uqCw+HAnj17gjrMDAfR0NJcg8GAnJwcNDb6Z7OyHjarotFqtTh//jzxtxIKhTh+/DgqKyvX/Vs4HI5Hq4N2CF55uEvnlO/atQupqanYs2cPrl+/jkuXLqG8vDwiyZDrQaVSoaOjA+np6di1a1dINioul7uKeFZWPEajEf39/ejv7wew3K50r3ho4gm2ImexWCguLkZxcXFQAoJIz624R2CXlJSgq6sLHA4HIpEIKpVqVQR2amoqUlJSwiI6YSqahwx068zdhqSwsBBVVVVBk0MwnmTeQHt72Ww2iEQiZGRkhPTuLlwR0TScTifu3r2LGzduwG63g81mY9euXThw4EBAm/9Kh+CpqSl0d3eTauDWrVtISEhAcnIyEhISoNPpcPPmTRw+fDikf1egcA/Cq6mpQUFBQdju1rlcLgoKClBQUABgmXjm5uaIuGB6ehomk2kV8RQUFMBoNEKtVofEucBXAUF9fT2qqqo8NulIDbCuBYqiwOfzCaHTsy10xdPd3Q2TyQSRSBTyCGymonnIQLv+3rlzByaTCbt27QqZr1OoiIZ2g+7p6UFubi6qq6vx4MGDsAxXhqt1Njk5ibNnz0KpVAIA8vPzcerUqYDUcSvhcDjQ29uLubk5NDU1echZaSl1fn4++vv7cfv2bbI5pKWlRay6oa1vdDpdROZ5uFwu8vPzkZ+fj/3798PpdK6qeEwmE4n3ffnll8Hn8z3EBVu2bAlq499IQHD27FmPCAOKoqLq8HvlwKZ7BDZN6GazmZzxhDICm6loHiJQFIWFhQW4XC4kJCRg586dIe2vslisoB2RbTYbJBIJFhcX0dTURKSZ0eZLthYMBgMuXbpEpt3j4+Nx9OhRbPMxb2Yj6HQ6dHR0ICYmBgcPHvT48sXExJDNrKamBr/4xS8wOTmJnp4eWCwW6PV6CIVCD3HBZszWaDQatLe3IzExMeBqLtTgcDiEeIDlTZSueDo6OmAymWA2mzE4OEhanjTx0OQTaIW9UkAglUqJw7i7gKCioiJsA9CBwJdWHp/P9xqBrVKpvEZgp6Wl+VSpPIr2M8AjSDRmsxlSqZQcRNfW1oblbikYQqAHDGlbd3eFS7iIJlTXdLlc6OzsJCaIANDY2LimdYy/oIdTe3p6UFxcjMrKynXfPxaLhVOnTuHFF1/EwsICDh06hPz8fFLx0HebIpEI6enpSEtLC3l/3T0HyF+p/GaDw+EgLy8Pubm50Gq12Lt3L/R6Pal4ZDLZKuKJi4vzEBdkZGT4/Z1KSkrCgQMHsH//fkxMTEAikaC/vx9yuRxyuRwsFgsWi4VEGESywgnkzChUEdiPov0M8AgRDUVRxM4/IyMD+/btw+XLl8OmiQ+EEJxOJ/r6+iCTydYcMAyXL1koKpq5uTmcOXMGc3NzAHy3jvEVtKxbpVKtytRZD2lpadi5cyfu3r2L8+fP49Of/rSHZQnd5lCpVOjp6YHJZEJSUhKpdoKxpXc4HJBKpVAqlSFtz4YTTqcTUqkUXC4XAoEASUlJyMvLw759++B0OjE/P+9BPBaLxcMyhyYe94rH1++Yu4DgxIkT6O3tRXt7O5RKJTlHEgqFqKurQ0NDQ8jjQ3xBKLzOVkZg2+12IsenI7B5PJ6HuCAxMZGpaKIZVqsV3d3dUKlUqK+vR3Z2NinFI+2yTEOj0aCzsxMxMTFobW1dU7YcrjTMYK5psVhw5coVtLe3A1i+ezt06BCamppCRuJ0O4XP5+PgwYN+97cPHDiAnp4eLC4u4t69e9i7dy/5t5VtDrPZDKVSCZVKRWzp6WwWmnh82Wj0ej3a29uJq0QgPfnNhslkwoMHD8Bms3HgwIFVlR2HwyGv1d69ewnx0F5t3ognNjbWg3gyMzN9+lzw+Xzs2LEDBQUFuHv3Lng8HhEQ3Lx5Ezdv3kRhYSEaGhpWCQjCiXCo4GJiYtaNwL506RI++9nPEim1WCzGtm3bQqZqXVxcxGc+8xm8+eabJMb5u9/9rk9OKBRF4eTJkzh//jxeffVVnD592u/f/9ATzfz8PKRSKZmep9tQ7lO+4YCvxpoURWFkZATDw8Mk12S9D3E4pMg0KforZ6UoCj09Pbh48SKMRiOA5VbkY489FrIhSXdVYFlZGcrLywNqO8XGxuLIkSN47bXXcOPGDWzbtm3NQVw+n+9xbmE0GkmrjVb/uRNPcnLyqi887XhMKxmj6TB7LSiVSnR0dCA7O9vnlrI78ezZswcul8uj4qGlv8PDwxgeHgaw/F7k5+cTccFGxEObe7a2tuKxxx7D0NAQxGIxxsbGiHru7NmzxIEgOzs7rK3JzZBbr4zAbmpqQn5+Pr785S9jZmYGBw4cIDcDr776atDr+eAHP4j5+XlcunQJdrsdzz77LD71qU+tG+1M44UXXgj69X5oicZut6OnpwcLCwuora1dFfoVigP79eBLRWMymdDV1QWLxeLz7E44Wmc0+Z47d85jCHI9o0GlUomzZ88S65jU1FScPHkSxcXFIVsXrdDSaDRoaWkhk9uBYtu2bejs7MT09DQuXbqEd7/73T49z11RRAdx0cQzOTkJh8NBiCc1NRWzs7OYnZ1FY2MjaY1EMyiKwtjYGAYHB1FbW0uUU4GAzWaTtiRNPAsLC4QQZDIZrFYrGYwG3iYeuuLJysry2Djdb4C4XC6qq6tRXV0NrVZLHAjojkBnZye2bNmChoYGbNu2LSznGZGICYiNjcXx48dx6dIlOJ1OfPOb34RUKkVfX1/QJDMwMIDz58+jvb0dTU1NAIDvfe97OHnyJL75zW+ScyVvkEgk+Na3voWOjg6i+gwEDyXRKBQKiMViche0Vk8z0EwaX7AeIVAUhZmZGXR3dyM3N9evYb1wEA2fz8fx48eh1WqhVCqJ3xPtCUUTT3x8/LrWMaFU7i0uLqKjowMikchr/HQgYLFYOHHiBH70ox+ht7cXTU1Nfm+qdBAXbWRJURQMBgPxyRoaGiKRCHq9HrGxsX5n8WwmHA4HJBIJ1Gp10IPK3sBms8kh+O7duz2Ix73icSceHo/nUfGsVUGIRCIiIJicnIRYLEZ/fz8UCgUuXLjg4UBQUlISsvcgkgOkZrMZIpEIXC4XjY2NaGxsDPqad+/eRVJSEiEZYDlyg81m4/79+3j66ae9Ps9kMuHP/uzP8IMf/CDoG6qHimjo6OLp6WmfBuEikRtjs9nIgXYgd7zhqsLcbTfKy8tJj9jd9sVsNmNqaoo4yHqzjgkW7q3EqqoqFBcXh7QNkpWVhcbGRnR2duLcuXP41Kc+FdSmwWKxkJiYCJPJBI1GQ+7MaecCf7N4NhNGoxEPHjwgEvHN8O/yRjxyudyj4rFYLBgdHcXo6CiA5SqGJmu64lk5x0L7v9ECArFYjPn5eSIgSExMJA4EgcZ209iIaFwuF7nx0P+ppSxKTERWVlbQkQ4mkymoysEbFhYWVglruFwuUlJSsLCwsObz/u7v/g67d+/2KTp9Izw0RKNWqyEWi0l0sS8eYOFsnXkjMbrSSkpK8jgv8ge0qWa44d4jzszMxNmzZz1aHVlZWcjIyMD09DQsFktIZlEsFgu6urpgNBrDcndN49ChQ+jr64NcLkdHRweam5sDvhZFURgcHMTY2Bjq6upI6mNycrJfWTybTTxyuRydnZ0kvyhSd+hsNpvMPLW0tHgQD13x0AmabW1tAJYPzumKp6CgANnZ2YR4aAHBjh07sLCwQBwI9Hq9h4Cgvr4eW7duDegzu1brTKFQ4M79B1hQLyIxJQ1JaVsQFy8EKAqLGhPEo3dgWFpEQXYmWpqbA/p8+6M6+9znPoevfe1r6z6GHsz1F2+88QauXLkCsVgc0PNXIuqJxul0YnBwEBMTE6isrERJSYnPX9rNap3RZo9TU1NBW46Ec4p/JWjrmOvXr8PhcIDNZqOlpQX79+8HgFWzKLQkOD093W9JsFKpRGdnJ9LS0rBjx46wKoji4+Nx6NAhnD17FlevXkV1dXVA5qRWqxUdHR2wWCxrunxvlMUzMDBA3INp4klMTAwb8bhXjO7EGC3wRjwSiQTDw8NgsViYnJyExWLB2NgYxsbGALxNPPQZD008mZmZOHHixJoCgnPnzgUkIFhZ0VgsFpy9eBE6mxMV2xpQueZZYhUAQDE/j9+fu4jctBQ8dqjVr++JP3M0f//3f4+PfvSj6z6muLgYmZmZUCgUHj+n/QTX6rhcuXIFY2Njq5wt3v3ud2Pfvn24du2aT2ukEdVEo9FoiMHdgQMH/FY6bUbrTKvVorOzExwOx+fgtPUQbl8yGrQViEqlAgAUFBTg1KlTxNgSgMcAmtlsJsRDS4JTUlII8SQnJ3u9a3a5XBgaGsLY2FjYfb/cQbfP5HI5rly5gieeeMKv56vVanR0dCAlJcWv1E73FmVZWRlcLteqLBQul7sqEiEUr4ndbodYLIZWq8W+ffsgEomCvma4wWazERcXh6KiIuzcuRMURUEulxM5Nd3KXUk8eXl5hHhycnJ8EhDU19dj27Zt6950UBTlYZYrl8vxh7fOompHC7auE+/gji1ZWdiSlYWpsTH89Jcv4/3vOu3ze+FPReNuRLseWlpayGtBn/lcuXIFLpcLO3fu9Pqcz33uc/jEJz7h8bPa2lp85zvf8fu7BAAsarODSnyAy+XC8PAwRkdHfZIErwW6jA7HXV17ezscDgfUajVKS0tRXl4ekvbE6OgoSdMMB0JhHUObDNKzKCqVCg6Hg9y1p6enQyQSwWKxoLOzE1arFTt27Nj0cLKpqSn8/Oc/BwB88pOfXFddQ8Ndbu1LrLe/cA/hUqlUWFxcXJXFE0jevV6vx4MHD8Dn89HU1BQV9jcbgaIo0globm72OuxKURQUCgUhncnJSXKGSIPL5SIvL4+IC+iKhxYQDAwMwOFwAFgmtoqKCjQ0NHgVEDidTrz11ls4fvw4NBoNfn/mHPYcPYm4AIcoDXodHrRdxAff8y6fyGb37t34m7/5GzzzzDMB/b61cOLECcjlcrz00ktE3tzU1ETkzbOzszh8+DB+8YtfrNlqZrFYj84cjU6nQ1dXF1wuV9CmhOFqnZlMJqjVarhcLuzevTvow0d3hKt1RlvHtLW1wWq1AgCamppw6NAhvyeR3U0GaWWWXq8nxDM8PEzuDJOSkrB9+/aIhJMVFBSgtrYWPT09OHfuHD72sY+tu4HTFYFGown5+0rDPYSL9vhaL4uHJp71MD8/j66uLhQWFmLr1q1RIUTYCHa7HZ2dnTAajdi/f/+anQAWi0VimumKR6lUkvbY1NQUTCYTMewE3iaegoICNDY24ujRoxgYGIBEIsHc3BwGBgYwMDCAxMRE4kBAv9f0d89ut+MPb53FnmOnAiYZAEhIFGLHocfwv6++jo9/+EMb3oyGyxng5ZdfxnPPPYfDhw+Tgc3//M//JP9ut9sxNDQEk8kU8t8NRFFF4671LyoqQmVlZdBa9vv37yM9PT2ksx8zMzNkM8jMzER1dXXIrg0suyHPz8+jpaUlZNecm5vDW2+9hfn5eQDLyqxTp06tm/QYKFwuF/r6+jA1NYWMjAzY7XYsLi56lVJvBnQ6Hb7//e/DbrfjqaeeQn19vdfHabVatLe3QyAQYPv27RFLWHTP4lGpVNBoNODz+R7EQ29E7kKFhoaGsLyf4YDBYMD9+/cRHx+PpqamoM7r3ImHrnhWbpZcLhe5ubkoLCyEUCjE/Pw8ent7PSqjgoICUuW0tbXBCRaSCsuQHSJ7pbGhQQjsZhw8sH/dx1VUVOAHP/gBjhw5EpLfGy2IqopGq9WG9E4ylGc0NpsN3d3dUCqV2L59O7HFDzVCWdFYLBa0tbWho6MDQHisY9xhNBrR0dEBiqI8zqu8San5fD4hnrS0tLDZtwiFQuzfvx9tbW24fPkyKisrV/2uqakp9PT0BOVMECqszOKhiVqlUmF8fBxdXV0QCARISUmBTqeD3W4PKI48UlAqlWhvb0d+fj6qq6uDfq1ZLBZ5vZqbm0FRFBm0pYnHaDSSCgh42+1AIBBAq9VidnaWKODolqNgSza27gkNyQBASUUlrp95AzuMxnXPiB5Vr7OoqWjCAfpLWVFREdR1lEolurq6IBKJUF9fj7i4OPT19cHpdGLbtm0hWu0y6Jhed68uf7GWdczRo0eDFiusBdqShZbTrleN0mdbKpUKSqUSWq0WiYmJpNoJta2/w+HAiy++iMXFRezatQvHjh0DsEyA3d3dWFhYQGNjY0gydMINu92O6elpIlt1OBzktaP/i8bzGYqiMDExgf7+fmzbto3Y/2zG76VTMmmyob8TNNhsNhITE2G1WmGxWDA+JcPWPQeRmZWFrKxsZGZmgscL/vM4PTGBWIsO+9f5bqelpeHSpUtoaGgI+vdFE6Kqogk1gq1onE4nBgYGMDk5ierqahQWFpI7sHDNuwRb0SiVSpw5cwZTU1MAlj+4J0+eRFFRUaiW6AGn04ne3l7Mzs4SGelG4HK5pO8OLFeLoZRSe/t9x48fxyuvvIIHDx5g+/bt4PP5aG9vB5fLxcGDBx+au0iFQoH+/n6UlpaioqLCIwRucHAwYlk868HlcqGnpwfz8/NhO/taCywWi6izmpqaQFEU1Gq1R8VjMBig1WoBLBOTk82BUCiC0WjC6OgoxsbGkJaWhqysLKSmBj6QmVtYiJtnXl+XaB7VioYhmjWg0+nQ2dlJjO1WHmaH69A+UKdlm82GGzdu4O7du8Q6Zv/+/di9e3fYfJv0ej06OjqItDvQcxcejxcSKfV6oNtiw8PDeP3115GZmUkOz6PVPsYdLpeL3PQ0NjaS6fGVr53VavWaxUOTTmpq6qYSj9VqJQrN/fv3RzxrhcVikdeCJp7FxUVS7fT29kKUmg73Ng99DqRUKhETw0VWVjZysrPBj/dfRMPicmGz2bxWnbSaM9KvUTjwSBNNIFWHuyihpKQEFRUVXjeicBFNINcdHBzE+fPnyV1ZeXk5jh8/HrbJezqCuru7G0VFRSF3L+bz+cjLy0NeXt4qKTVtcrlSSu3LXeZjjz2G0dFRzM7OoqqqCjU1NSFbczhhs9nQ0dEBs9mM/fv3r6vgi42NXTOLp7e31yPrPhTV4nrQ6XS4f/8+RCKRX35/mwnaxSE1NZW0TzvHZ1BZUQGNRoMlzRKsVht5vN3ugEwmg0wmQ2xsLDIyMpCfn+dzu1KUkg65XO515IJWgzIVzUMGDodDUiB9gdlsRldXF0wm04Ylfjg9yXwlGo1Gg3PnzhF7dpFIhOPHj6OysjLk66Jht9vR3d0NhUKBHTt2kPZXuOCLlNr9LnWtyXuz2YyBgQHk5uZCJpOhvb3dr0HMSIGOhxaJRNi/f7/f6w1XFs9GoCXX9M3awyC5pigKcwsLSBSJkJ2TjeycbIBafs2WNBosLi5icXGRzORYrVZCOk1NjT4JMmL58avmgGjQajmmonnI4E/rjHZbzsrK8mkDCteMDo/Hg8lkwrVr10hvOTU11ePL73Q6cefOHdy4cWOVdUw4D4JpCTAdThaJOy8WiwWhUAihUIiSkhLizrCeKzWthsvMzMQHPvABvPjii9Bqtbh9+zYOHjy46X+Dr5ienoZUKkV5eTnKyspCslmHIotnPbhb4DxMkmv6HEkul6Ow3m1gkQXw4/ngx/ORnZ1FiEehVEIul8NoMAAsFoSJvqn+KKydCUUTEFPRPGTwpTpwv0On0zlDde1AIBKJcPToUaLIkkgksFqtSElJQXp6OiwWC27evEmsYwoLC3Hy5EmfrCgChbtiKBokwO7YyJVaIpEAAHn9AODo0aP4/e9/j9u3b6O+vj6ooeBwgJ5Fmp6eDnvVuFEWj91u9zgfS0pKWrNN6nQ6SSRBsMPWmwm73U5ak3t378a0bp0uyJ+Ip6AgHwUF+cutXaMJ8PHrYDEaIBB4F+aYTCbExsY+FGeG/uKRJpqNKhr6Li4xMRGtra1+zXKE0/wyLi6OtDvoL79MJsP169cxNzcHYLnyaW5uRnNzc9gky8Db0/JLS0vYtWuXV5uQaALtSi0UCrG0tIT4+HiUlJTAbDZjdHQUnZ2dSEhIQFpaGlQqFc6dO4cPfOADkV42gcViQUdHB+x2Ow4cOBCQGWigWC+Lh57jcTqdqyIR2Gw2zGYzHjx4ABaLhf379z8UsdbA8uZ+//59xMXFYd++fTCZTOi5fNXn57NYLAgSfH+PtIvqNWX0j6riDHiHEo27I3SgflbhjCBwB+0HdeXKFXJYWFtbi/Lycmi1Wly7dg2xsbGkzRbK4Uc6nEwoFG5ankkoQK87KSkJBw8e9GiD0lLq2NhYtLW1YXh4GK+++irKy8vDfji+EZaWlvDgwQOkpqZGxeE5ncWTmJiIoqKiVedjdBaPUCiETqdDamoqmpqaIr5uX7G0tIT79+8jKyuLRFuLRCKYdNqw/D6n0wkO5Vrz9TGbzY/k+QzwiBONNzKgZcssFisgR2ga4YwgoDE7O4szZ854WMc8/vjjHu09p9MJtVoNpVKJsbExdHZ2IjEx0eN8x98DZIqiMDo6iqGhIb+jGSIJ9xbfWut2lwPTVihTU1PIz88PmZQ6ENDuBNH8eq88H6PPYwYHByEQCKBWq3Hx4sWIZ/H4gvn5eXR2dnp9vUvz8zA/O4OsnNA5AwDA5MgI6rZWrfnvJpOJqWgeRriTgbsrb3FxMSorK4PaQMLZOjObzbhy5YqHdczhw4fR2Ni4as0cDsfDsoS+Y1cqlUTOmpycTIhno43TarWiq6sLBoMhrOFkoYbdbodUKoVarUZLSwtS18wMeRsHDx5ET08PtFotrFYrHnvssZBJqX0FPfA6NzeHnTt3hvWsLZSgKAoDAwOYmJjAzp07kZGREfEsHl/XTY8vbN++3euZ7M4dTfjN62dCSjQURWFqqA+PfejP1nwM0zp7SEG3zsxmM8RiMQwGg8+b0EYIB9FQFIXu7m5cvHiRSB23bduGxx57zOdzmJUDfPTGSRsPOhwOsmmmp6d7fPFVKhU6OzuRkpKyquUUzdDpdGhvb0dcXJxfLb64uDgcOXIEb7zxBq5fv47a2lokJiZuKKVOTU0lbcpgNk6z2Yz29na4XC4cOHDgoWmb2O12dHV1Qa/Xe8z1RDKLxxe4OxSsdxMlFApRmpuF8aEhFAdpX0Wjt6sTuxrq11WFajSakPyuaMQj7XW2uLiIu3fvgsViITMzE7W1tSHbPOn+7vHjx0NyPYVCgbNnz3pYx5w6dQqFhYUhuT4Aj42T3jzpL77T6YRCoUBNTY2H1U60g5YAl5SUoLKy0u91UxSFn/70p5idnUVdXd26WRvuUmo6SyZQV2q1Wo329nZs2bIFdXV1YXNvCDWMRiPu37+P2NhY7Nixwy85fbiyeHyBu7Js165dG75PTqcTP/vly6jdewBJycFZ5sjn5zHTJ8EHn3nfmo9Rq9XYs2cPdDodEfw8SnhkiYbOu5DL5dixY4fPsmVfodPpcPPmTZw6dSqo69hsNly/fh337t0j1jEHDhxAS0tL2Dcfl8uFhYUF9Pb2wmq1gqIoxMfHewgLotGgEfD0WGtsbAxKAjw7O4uf/OQnAICPfexjPgfluUuplUollpaWiKX/WsIMiqIwOTmJvr6+sASrhRMqlQrt7e3IyclBTU1N0GdX7lk8KpUKS0tLfmfx+AKz2Yx79+4RcvT1ZlOv1+NXv/sDGva3Bkw28vl5jHbdx4eeed+alfbk5CSOHz+O6upq/OpXv3ok22ePJNGo1Wp0dnaCz+djaWkJTz75ZMh/h8FgwNWrVwOKNQWWN5z/v73zjm+q3v//K0n33i1CSymjjC7apGUVEJS2LP05QODKUrgo4MDr93K9CHqvAxQFWTIEkatcXCyRppZREKUjHdBSKHbvJm3aJk3azPP7o55zk9KVNLOc5+PRhw+Tc5LPCcl5fz7vz+v9ehcVFSE5ORkikQhAZy+KxMREk9UfNDQ0ICcnB/7+/oiIiKAMB8kVj1gshoeHBxV4DFU1PlAkEgmysrLAYDDA4XAMcjM6d+4c8vLyEBAQgDVr1uh1E+3LldrT0xOFhYWUq4IhUrimgvQBI1e8xkCXXjz9paWlBenp6VrKMl1oa2vDd2fOwjsoBKFh4f0+T61W405ONhStTXj6iYU9Bpnc3FzMnTsXzzzzDHbv3m0Rvy9jMKgCjVqtxr1791BaWopx48ZhyJAhSE1NxcKFCw0+a5RKpXq/dnNzM5KTk/HHH38A6CzSTEpKGnA7g/5CGjSWlZX1atne0dFBBR2BQAC5XE4VPpLFe6aejdfX1yMnJwfDhg3rsx2BLkgkEuzduxcymQzz5s0Dm80e8GtqulLz+XxIJBKwWCwEBgZiyJAhZpVS9xe1Wk2tHDkcjknrqDR78ZCBx9nZmVotent79yrl701ZpgsEQeD39HTcLvoDwWMnYPjIUT0GLKVSibI/7qPq/l1Mjp6IiVGRPb5uamoqnn76aWzZsgVvvvmm1axs9WHQBBqyBTRBEIiJ6fQdkslk4HK5mD9/vsFnCvq8tlKpxO+//45ff/2Vso6ZMmWKXh5W+iKVSsHj8aBSqcBms/st7yYLRzUDD+kxRgYeY27skpOIsrIyREZGUt5dhiQ9PR0pKSlwdHTExo0bDZbCEAgE4PF48PPzg4+PD4RCIQQCgdmk1P2FNPPs6OhAXFycSYtHu0OhUFArxsbGRq0VIxl47O3ttRSmPSnL9KGjowO5t27jTtF9qBhMuHt5w86h8zvSLm1DW0szbAFEhU1AeNiEXn/T//nPf7Bu3TocPnwYy5YtM8j4LBmrDzSatRNdW0ArlUr8/PPPSEpKMvheg0KhwMWLFzF37tx+BYnS0lJcvHgRTU1NAExjHdOV2tpa5OXlUTn2gQRfgiDQ0tJCBR2hUGi0wtGOjg5kZ2dDJpOBw+HoXfvUFyqVCocOHYJAIACbzR7w/pvmDS88PBzDhw/Xek5TSt3Y2Gh0KbUuiMViZGRkwNXVFdHR0RapQNRcMTY2NlK9eAiCQHt7OzgcjtGa2SmVSvD5fLS3t1PGr76+vn1OFAiCwPbt27F9+3acPn0as2fPNsr4LA2rDjTt7e3Iy8uDWCxGdHT0A8t6tVqNn376CXPmzDH4BptKpcKFCxeQmJjYq5xWLBbjl19+QUFBAYBOb6k5c+YgPDzcZDcRcuO8urq6383J9HkPzf2d1tZWqgGXvoWjQOcGNI/Hg6+vLyIjI42eaiorK8OJEyfAYDCwdu1aBAQE6PU6SqWS8v3icDh9NvvqKqVubGw0qJRaFxoaGsDj8agWENaS0mlra0NWVhba29thZ2cHiURi1l48XVGpVHjllVdw5swZJCcnIzKy57TaYMOyE8S9QM7O/f398eijj3b7BWIymXo3EusLcubS02ur1WpkZWXh6tWrlHUMh8PBrFmzTOoDRf74mEwmZs6cabT0h6ELRzXdCbp2NzUmI0aMwPjx41FYWIjk5GSsXLlS5/eVSCTIzMyEra0tZsyY0a9/b11dqckaFEOiWcwYFRVllPSksSBrkuzt7TFt2jTY2tqio6ODCtqm7sXT3fiWLl2KoqIi3Lx5U2t1+zBgdYFGoVAgPz8f9fX1iIyM7NOGfKDtnHuCwWCAyWSiqakJQ4YM0UpD1dTU4MKFC6ivrwcAPPLII5g3b55RVhK9QTYnCw4ONnhzsr7orXC0rKwMKpWqx8JRuVyO3NxciEQis7gTzJkzB3/88QcqKytRUFCA8PD+q434fD54PB4CAwMxYcIEvT/zvlypb9261aeUWhdUKhVu3boFPp9vVY4QwP+UZQEBAYiIiKA+c01zWkC7c+utW7fQ3t5ulF48XWlqasKCBQvAYrFw48YNk7aythSsKnXW1NSEnJwcODk5UX3f+4LL5SIuLs4oPxxSjSOXy+Ht7Q13d3cUFRUhPz8fQO/WMcZEqVTi9u3baGhoQHR0tNGbk+kKQRAQiURU4GlqaqJm687OzqioqIC7uzuio6PNVsdz/fp1XL16Fa6urtiwYUOf49DswxIZGdnvWhx96UtK7e3t3e/PrqOjg3IoiI2Ntao6joEoy7rukQ20F093lJWVISkpCWFhYfj666+txtXa0FhFoFGr1SgqKkJJSQnGjRuHkJCQfn+hfvnlF8TExBitZoHMraenpyM7OxtyeWfb16FDhyI+Ph5BQUEm/eG2traCx+PB3t4eMTExVnHTUKvVEAqFKC4uRkNDAwBQm6vmKhxVKpU4cOAAmpubMXXqVDz22GM9Hku2UmhpaUFsbKxZ+rBobowLBAK0tbXBw8OjzzRRS0sL5RgdFRVlNXUchlaWde3F09jYqNWLhww8ukwYc3JyMHfuXCxatAi7du2yms/WGFh8oBGLxcjOztaSLevC5cuXER4ebjT1CZ/Px88//4zKykoAndYx06ZNg52dHVUtTrop+/n5wdvb2yh5Yc2K81GjRmHMmDEWJZXtDaVSiVu3blFqL3d3d4soHC0qKsKpU6fAZDLx8ssvdztZaWtrQ2ZmJuzt7cFmsy2mlYJmmqgnKTVZk2TIDp6mgKztqa2tRWxsrFFSUV178TQ2NkKlUmkFnt6awP3yyy945pln8Pbbb+Nvf/ub1Xy2xsJiA43mjZPcY9Dn5pKWlobQ0FAMGTLEoOPrah1ja2uL6dOnP2Ado7kpTsohvby84OfnZ7CiR4VCQSmcYmJirMYBGOicSGRlZcHOzg5sNrvb1EJ3haOaaixjFY4SBIGTJ0+iuLgYo0aNwtKlS7Xep76+HtnZ2WbZA9OFrlJqgUAAhUIBABg2bBhGjBhhluJbfSCtpaRSab88ywwFmbnQDDwEQVBydFdXV2oSeeLECbz00ks4cuQIli7t2a35YcIiA01HRwe1GRwdHT2gG+f169cxcuRIg/UuJwgC9+7dA5fL1cs6RiKRgM/nUz96ANRM3c/PT+cfTnNzM3g8HlxcXBAdHW0xM+r+UFNTg7y8PJ1u1ORMU9MY1JiFo01NTThw4ADUajWee+45hIaGUvZBxcXFmDhxosG+W6ZAqVQiOzsbLS0tCAwMpG6e5pJS64K+nmXGgNxnJIPOyZMncfToUfj6+qK+vh579uzBCy+8YLGTD1NjcYGmrq4OeXl58PX1RUREhN65eZlMhsrKSvx8MRkOTk6dqhIPD4wMGYGhQ4fqlb7qah3j4eGBxMREva1jSBt1zaJH0tSSrCLv6cekKUUNDQ3FqFGjLO7G0BNqtRp37txBVVUVJk6cOKDVpqYM2FiFo5cuXcJvv/0GT09PrFmzBrdu3UJbWxtiY2N1TuWaE6lUiszMTNjY2IDD4VCTkr5cqY0hpdaVlpYWZGRkUL58lnYDl8lkWLRoEa5evYrIyEjk5eXB3t4eK1euxM6dO809PLNjUYGGIAhkZWXhkUce0UvDT840uZeuQNgmge/QILAcneDp5QVHJ2e0tbagsbYaTbXVCBrij7kJc/o1G+3OOmbq1KmIj4836KyKtNggVzwSiQQeHh5Umo3cjJTJZNSKj81mW5VckrTAUavV4HA4Br+BkcaMXQtHNTuO6jrJkMvl2LdvH8RiMYKCgjB27FjExMRYrLN1dwiFQmRmZvbLXFIfV2pjUl9fDx6PZ7ETKqlUiqVLl+L+/fvgcrkICgqiUnwikQhz5swx9xDNjkUFmoHQ2tqKoyf+A8LJFVGTp8P9Tzkz2R++q20Jv64WOb9exTBPdyxZ/GyPKaeu1jEjRozA3LlzTWIu2N7eTgUdgUAAtVpN9Wf39PRETEyMVaXKSLdo8mZnig19zT0ygUCgc+EoCSl3ZrFY2LBhg1mUZfpSWVmJ27dv692WwJBSal0pKSkxuGeZIWlsbMSCBQtga2uLs2fPWtWkz5QMikBTVFSEr777AVOSnkBAl/arTU1NsLW17THFUVpUiPwbV7Fx7RotZVp31jEJCQkICwszy4yK7A5YUVEBJycntLe3UykicsVjqTNszT2N3tyiTYFm4ahAIOi1cJQc+927d1FaWoqamhrU19djwoQJeOaZZ8x2Df2FIAjcuXMHlZWV4HA4BhOJdCeldnd311IFDlRZaQpl2UApKytDYmIiIiIi8J///OehrZHpD1YfaO7fv4+vfjiLeUtXwK6b2b1QKASLxYS7u0ePryFqacHlH77Ba+vWwtvbG1lZWbhy5QrkcjnV8+TRRx812xeJNJVsb28Hm82Gh4cHlSIiVzwikQju7u5U0LGU3jEymYxSCVnankZvhaOkqeWdO3fQ3t6O2NhYSCQSHD58GARBYPny5RgxYoS5L6FHyI6SUqkUcXFx/W4Frg/9kVLrsqdiLmWZLpA1MosXL8ann35qEb81S8aqA41IJML23Xswf/maboMM0LmBz2Aw+kx1tAiF4H5zDCMDh4LP5wMwn3WMJnw+Hzk5OZQ4oqc9IU0JMJ/Pp5yAyRWPOVREQqEQWVlZ8PLyQlRUlEU6AGtCFo6S/mIikQgsFgtDhw6Fv78/fHx8kJqaSln+r1271iJvMG1tbcjIyICTkxPYbLZJP/eeXKnJPkZ9ydFJZZmdnR1iY2Mt8juTkpKCZ599Ftu2bcOmTZssbs/IErHqQLNr336MiZsO/2E92320tLSAIIheLWhUKhVqa2uReeMaKnMzERE2AbNnz0Z0dLTZ1C2aTdzCw8MRFBTU7y+0phMwn8/XmqmTKx5jrs40q7atrV0xAFRXVyMvLw8hISHw9PSkZupisRjOzs7IysqCXC7HnDlzMHnyZHMPVwvSa2348OEYP3682T93XVypSWWZn58fIiMjLU5ZBgDHjx/H+vXr8cUXX2DJkiXmHo7VYLWBpqSkBKcvXcOjC5/q9bjW1laoVEp4eXVvQSMUNqG2tg5KpRIEQSD94jl88PY/zOoPRiqzlEol2Gz2gNNNpIqITLO1tLRQSiw/Pz+Duthq2rFYmyJOrVajsLAQlZWViImJeeA7QKaIsrKykJ+fDxaLhWnTpmHo0KFm7x+j2ZfJ3PtgvdGTlNrFxQVCoRAjR460iADZFYIg8MEHH+Djjz/G6dOnMWvWLHMPyaqw2kCz9/NDGBs/G57evau/RCIRFArFA/YhHR3tqK6uRlubBECn02tg4DDUVpTBuV2EhfMH1vRKX+rq6pCbm4tHHnnEaMosuVyulWaTyWRUasPPz0/vG2ZrayuysrLg5ORkdYo4mUwGHo8HmUyG2NjYXvc01Go1vvjiC9TV1WHkyJEYPXq00QtHe0OtVuP27duor6+32I3znlCpVLhz5w7Ky8vh7OwMiURiVil1dyiVSmzYsAE//fQTkpOTERERYdbxWCNWGWiUSiXe/fhTPLFybZ/HisViyGQySo6sVqtRX18HgUAAgui0Yw8ICICvry/Vu+biicN45x+bjX0ZWpA/uKqqKkRFRZms2lyzRTOfz0djYyOYTCZ1s/T19e3XZiwpoR09ejTGjBljcTPS3mhubkZWVhY8PT0xceLEfq3uqqqqcOzYMQDAiy++iCFDhlDFt+RM3VgdRzWRyWTIysqCUqlEXFycVZiokhAEgfz8fNTU1CAuLg5eXl5mlVJ3h1QqxZIlS1BcXIzk5GSLXSlaOlbXjwbotC7xDujfBj2DwYBKpYJarYZYJEJNbQ3k8k6fJw93dwwdNhS2tv/74jKZTBAsW8jlcpN9odva2sDj8cBgMIzanKw7GAwGXFxc4OLighEjRlBuBXw+n+p70tVJWXODVqVSac2mjWVeaiwqKiqQn5+vs818YGAgIiIicPv2bVy8eBEvvvgivLy84OXlhdDQUK3C0eLiYmRnZw+4cLQrIpEIGRkZ8PDwwKRJk0zWxMsQKJVK8Hg8SCQSTJ8+nfrO29jYwN/fn0pbakqpCwsLjSKl7gmyRsbOzg43btywqh49loZVrmiuXb+OSqkK4yIn9nmsXC5HdXU12tulUCiUAAA7O1sMGzYMbm7u3Z7ze2oy5k+LQ0hIiEHH3R3V1dW4desWtXlraRugCoVCyxSULHj08/ODi4sLioqKKEsTa5pNk3VJtbW1iImJ0StAisVi7Nu3D3K5HAsXLsTEiT1/H2UymVbhKGmuSt4we3MC7o66ujrk5ORQTt3WtIJsb29HRkYGbG1tdVaWGVpK3ROlpaVITExEVFQUTpw4Yfb0nbVjlYHmp58vQubqg+DRY/o8trW1BWVl5dT/29vbwcHBAba2nf91dHSAg4Oj1l5I9m/XMXVsCMLCwowxfACdM7r8/HzU1dUhOjpa7970poaUrlZUVKC5uRlMJpNq4ezn52d2T6z+QDb6UqlUiI2NHVCdxu+//47U1FQ4OTlh48aN/b4hkb1P+ls4SqLZYM1Sq+V7o7W1Fenp6QZRlg1USt0T2dnZmDt3LpYuXYpPPvnE4iZ/1oj1rLU1YDGZINC/+GhjYwsGgwEynspkcshkcrBYLDg4OEAstgGDwYCDvT0c/gw6apXKqF8ukUgEHo8HW1tbPProo1a1Euj8zMQQi8Vgs9lwcnKCQCBAbW0t8vPz4ejoqGUKamluBaTnl6+vr0EafcXFxSE3NxeNjY1IS0tDYmJiv85zdnaGs7Mzhg8frlU4yufzcffuXa3CUV9fXzg6OkKlUiE3NxdCoRDx8fFwd+9+RW6pkG0VRo8ebZD+NwwGg/ocg4ODH5BS379/X2dXai6Xi0WLFuGdd97Bpk2bBjQ+mv9hlSua9PR0FPJbEMGZ1K/jFQpFp728WAyRWIw2sRhKlUrrGHs7O9jZ24PFZCL9EhfxkeMxYcIEgxY7EgSBiooKFBQUYOTIkQgNDbWq2VJ7ezslu+ZwOA8os8iNXPKGSTYs03QrMNf1avY3MnRtT0lJCb7++mswGAysW7duwPtUpBydXO20tLTA0dERSqWSKmTs6t1n6ZSWlqKwsNCkbRV6klJrKto0V+BkjczRo0fx3HPPmWSMDwtWGWgaGhpw4uwFPLrwaf1e4M8lt+jPmblUKoHmp5D8zZd4fNpkuLu7w8bGBq6urvD39x+Qp5hCocCtW7fQ2NiI6Ohoq9s0FwgEyM7Ohp+fHyIiIvq1Adve3q7lK6ZUKvuVHjI0pGChoaEBbDbbKIao3377Le7du4fg4GAsX77coNclEAiQmZlJpeXINs2m7jiqDwRBoKCgANXV1ZSyzFx050p94sQJqobu6tWrOHfuHB599FGzjXGwYpWpMz8/P7TyG0AQhH4/aAYDTs7OcHJ2RkBAAFQq1Z/pIBGETU1QytrR0NCg1b++oaEBDg4O1IyI3JPoz+ZjS0sLeDwenJycMHPmTKvaWCQIAvfv38cff/yB8PBwDB8+vN/nOjo6IigoCEFBQQ+khwoLC2FnZ6flVmCMupv29nZkZmaCwWBgxowZRktTJiQkoLi4GOXl5SgsLMSECRMM8rpVVVW4desWxo0bh5CQEDAYDGpDXCAQICcnR6vjqLkLRzXpSVlmLlgsFtVfZ+zYsVAqlZDJZHj77bdRUlICtVqNDRs2YNasWVixYgXYbLZZxzuYsMoVDQCc/PY7OAWOwrARhlWG5d68gWGOLHh6eqKkpATl5eVQKpVax5CbjLa2tnByctLaDNfcWNa0YrG2vuxAp2IvOzsbEokEHA7HoHsCKpVKyxS0tbVVS7bq7e094Fk6WcVvqrYEaWlpuHbtGtzc3LB+/foB7U+RrtFlZWVgs9k9OlX01HFUU45uqsJRTTSVZRwOx+L26oBOYctzzz2H0tJSJCcnw9nZGWlpabhy5Qoef/xxPPHEE+Ye4qDBagNNU1MT9h79CvP+sspgPyKlUomfjh/Cu//4O5UaUiqVqKioQElJCUpKSijDTRI7OztKbcVisSjpr5eXF6qqqiAWixETE/OAM4GlQxYxuru7Izo62ujmhjKZTMutQHOW7ufnBzc3N5283sgAHxYWhuDgYKOOnUShUGD//v1obW1FfHy83jYlCoUCOTk5EIvFiIuL02k/RrNra3eFo8ZaOWpiSGWZsRAIBFiwYAEcHBxw9uxZq+ovZI1YbaABgO9/PI0OJw+Mi4o2yOvd4F7AlLBQxHI4PR4jEomooFNaWor29nat593d3am9HbLQkdzfsZSURm9oembpWsRoyDFoztIFAgFYLBaVYiNVWN2hVCqpvTAOh2PyPYG7d+/iu+++A4vFwvr163Uu8pNIJMjIyICDgwPYbPaAVwLG6DjaGw0NDeDxeAZTlhmDkpISJCUlYeLEiThx4oRZrJL279+Pjz/+GPX19YiMjMTevXsRGxvb53mnTp3CkiVL8MQTT+Ds2bPGH6iBsOpAo1Qq8cHHnyB27pPw8RuYCWbZ/btouHsbG9b9td/nqNVq1NXVUYGnqqoKmh8ni8WCr68v3NzcwGKx4OrqSqXYjO2grA9KpRJ5eXnUTdpSVmFqtVrLFLS5uRkuLi7U5+jj4wMbGxtIJBJkZWWBxWKBw+GY5fMlCAJff/01SktLERoaqpN6iUz1DRs2DBMmTDDKSsDQhaOamENZpis8Hg/z5s3DsmXLsHPnTrOstr799lssX74cBw8eRFxcHHbv3o3vv/8eRUVFvYqEysvLMW3aNISEhMDLy4sONKakpaUFn+zbjynznoJvwBC9XqPs/l2U8tLx+kb98+odHR3IyMhAfX09bGxsUF1djZaWFq1jHB0d4efnB0dHR2pjUtNB2ZzKIZFIhKysLDg4OCAmJsbigqAm3bVndnV1RVtbGwICAhAdHW3Wz1IgEODgwYNQq9VYtmwZRo0a1ec55eXlKCgo0FlwMVBInzsy1aZWq6kN8/4qAy1JWdYbycnJWLRoEd59912z1sjExcWBw+Fg3759ADonUoGBgdi4cSM2b+7eY1GlUmH69OlYvXo1fv31V7S0tNCBxtSIRCLsO3QYPiFjMHFyfL+X60qFAr+lXoSDUoa1q1fpvQ9BNifz8fFBZGQkbG1tQRAEhEIhiouLKVGBQqHQOs/b2xuenp6wtbWFg4PDAxX2pko7kMqmkSNHYuzYsRaZ7ugJgiBQWFiIkpISuLu7QyLpdOPW3JMwh9opJSUF6enp8Pb2xksvvdRj4CNbFtfU1CA2Ntasq8i+Oo52l7JUKpXIzs5GW1sbJk2aZHZlWU98+eWX2LBhg9lrZORyOZycnPDDDz/gySefpB5fsWIFWlpacO7cuW7P27ZtG27fvo0zZ85g5cqVVhdorFLe3BU3Nzf8429v4JdLl3D22OcYHcXB2Igo2PTUjVIqRUF2JqqK7uCpeUmIidZvj0etVqOoqAglJSUPNCcjK5K9vb0RFxcHpVKJqqoqKs1WX1+PpqYmNDU1AQBsbW0p/zAWi6VV6Ojr62uUzXiVSkXd5Dgcjll78OiDUqlEbm4umpubMX36dHh4eIAgCKrYsbq6Grdv34aTk5PWzdIUXRtnzpyJ/Px8NDU1IT09HVOnTn3gGLlcTjVRmzFjhtlbFjMYDGqPcdSoUVqFo+Xl5cjLy9MyWHV2dkZOTg5sbW0RHx9vkcoygiDw3nvv4ZNPPsHPP/+MmTNnmnU8jY2NUKlUD/zW/P39ce/evW7PuXHjBo4ePYq8vDwTjNA4WNyKpqOjA0wmU+8vrUwmw2+/38TvWTzIVWq4+/jBwdkFIAhIRC1oaxbC2cEej8ZPRcwAUixklbxCodCrOVlbWxsVdEpKSiCVSrWed3Nzg7e395++bLZatTv6+Dd1RSqVUvUlHA7H7Dc5XWlra0NmZibs7e3BZrN73NBVKBRabgUSiUQriBvKhLE78vLycO7cOdjZ2WHDhg1a6jGRSITMzEy4uroiJibGKpyXNQ1WGxoaIJVKYWdnh6CgIItI/3ZFqVTi5ZdfxsWLF8Hlco3qXdhfamtrMXToUPz+++9a3Vn/7//+D9euXUNGRobW8WKxGBEREThw4ACSkpIAwCpXNBYXaE6ePIm1a9di9uzZSExMRGJiIkaMGKHXa3X2nqmHRCKhZms+Pj4DvknX19cjJyeHqs8Y6E2CIAjU19dTabaqqiqo1WrqebI/DKlm61q7o2sRIjl+ctPZkm4O/YH0zNLH8bq9vZ0SFQgEAmpPgpylu7i4GCx1SBAEjh07hurqaoSHh+Oppzq7wZLKrJCQEKtLVQL/G39wcDBcXV2p/R2FQqElLDCnylIikeC5555DeXk5kpOTMWzYMLOMoyu6ps7y8vIwceJErd8oeW9gMpkoKirCyJEjTTL2gWBxgYZshpScnAwul4sbN24gJCQESUlJSExMNGp1d1+o1WrcuXMHlZWViIyMNNqXVyaToby8nFrtCIVCrefJ/RwnJyewWCx4eXlRQae3Qke1Wo179+6htLQUUVFRFvPj6y+aLgWGGD9BEJQXFrknQdackKagA5W+1tbW4siRIwA6byZKpRL37t2zys8fAMrKynDnzp0HlGXdFY4ymUytIO7k5GSSwCMQCDB//nw4OjpaZI1MXFwcYmNjsXfvXgCdv8ugoCBs2LDhATFAR0cHiouLtR7bsmULxGIxPvvsM4wZM8YiU5ZdsbhA0xWRSISrV69SgaehoQEzZsxAYmIikpKSTNaLQyKRgMfjgSAIsNnsXlv9Gprm5mZqtVNWVga5XK71PNlwy87OTsvWRdMQtKOjA9nZ2ejo6LBKU0ayiFEkEiE2NtYozsUqlUorzSYSiSi3goGkhn766Sfk5OTAzc0N48aNQ1xcnNU10dJVWaZZOCoQCCAUCilnb1KSboz6lZKSEiQmJiI6OtpsNTJ98e2332LFihU4dOgQYmNjsXv3bnz33Xe4d+8e/P39sXz5cgwdOhQffvhht+fTqTMjQxAEioqKwOVyweVykZaWhiFDhlAptlmzZhnlBlpTU4O8vDwEBQVh/PjxZk01qVQqVFdXU4Gnrq5O63kbGxsqwNjY2MDFxQXu7u4QCoXw9fVFdHS0VewHaCIWi5GRkQFnZ2fExMSYbAbX0dGhVTSqUCi03Ar6awoqFArx+eefQ6lU4vHHH8eUKVNMMHrDYQhlmSkKR7OysjBv3jw8//zz+Pjjjy3SkYBk3759VMFmVFQU9uzZg7i4OACdQpLg4GAcP36823PpQGNipFIprl27RgWesrIyTJ06lUqzhYeHD2i1o1KpqC6MEydOxJAh+tXpGBOJRILS0lIqzdbW1qb1vKOjI5ycnODl5QV7e3udDUHNTW1tLXJzczFixAiMGzfObDl/zV4nZGqIlP72VoDb0tKCjIwMtLW1oaCgAA4ODti4caPViC+M5Vlm6MLRixcvYvHixfjXv/6F119/3SBjpDEcVh1oulJaWoqUlBQkJyfjypUrcHd3R0JCApKSkvDYY4/plK7QbE4WExNjFTcGgiDA5/NRXFyM4uJiVFRUaDkVkGaLHh4esLGxoQpIuzMENTeappITJ060uE6SpPSXFBa0tLRQzg/kDL2hoQG5ubkIDQ1FSEgIjhw5goaGBsTExGD+/PnmvoQ+IT3LyCZxxpyUDKRw9NixY3jllVdw7NgxLFq0yGhjpNGfQRVoNJHJZPjtt9+o1U5hYSHi4uKoNFtMTEy3PxyCIFBZWYn8/HxKFWTps/6utLS0ICsrC46OjvDy8qJMQcmaHRJSVODs7Awmk0nJfsmNcHOl2EjXaKlUajX7SXK5XMsUtKOjAwAwbNgwhISEwN3dHZWVlVQ6ZO3atRa5QiYhlWWjRo0y2T4oSVeRBtmwTHO14+bmBoIg8O9//xu7du3C2bNnMWPGDJONkUY3Bm2g6Up1dTVSUlLA5XKRmpoKW1tbJCQkIDExEXPmzIGfnx+EQiH+85//IDQ01Cqbk2l28OyuLUFLS4uWIahMJtM639PTk0qx2draUj9sUxqCtra2IjMzE25ubiZxjTY05H5GS0sLgoKCIBKJtBRY5CotMDAQq1YZznnckJDKMktRxnXtOLp161aUlpbC3t4ezc3NOH/+vNXtez1sPDSBRhOlUomMjAxKyUamN6qrqxEYGIibN2/qXIBpbkjXYoFAgJiYGPj6+vZ6vFqtRk1NDSUqqK2t1UqzkTNI0hCUNLE0piFodXU18vLyMHr0aJPPog2BVCpFRkYG7OzstPYzSAUWn89HdXU1bt68CbVajZiYGMTExMDHx8ciAipBELhz5w6qqqrMbofTGxUVFVi4cCEqKyvh4+ND9ex56aWXsGLFCnMPj6YbHspAowlBENixYwe2bduGMWPGoK6ujlIHJSYmIiEhwSJmdb0hFouRlZUFOzs7xMTE6FVn1N7ejtLSUirwiMVireddXFzg4+NDGYJqqq8GWhGuVqtRWFiIyspKREdHIyAgQO/XMhdNTU3IzMzEI488gvDw8F7TrdeuXUNaWhrs7e0RFRUFuVwOT09P6vMciIOyvpArMbFYjEmTJplUvq8LfD4f8+fPh7OzM86cOQMPDw/U1NTg8uXL8Pb2xrx588w9RJpueOgDzf79+/Hhhx/iv//9L+Lj46FWq5GdnU3t7WRkZGD8+PHU3s7UqVMtSptPSq+Dg4Mxbtw4g9ygCIKAQCCg0mwVFRVaXUYZDIZWl1EHBwet2h1dDEFlMhl4PB5kMhliY2Mt9gbXGxUVFcjPz8eECRP65WKhVCrx+eefQygUYvLkyZg2bRq1tyMQCEAQBKUOJD3FjLm66+joQHp6OmxsbBAbG2uxBYDFxcVITEwEm83GV199ZfF9ZI4cOYITJ06goKAAABATE4MPPvigX31nBhsPfaBpa2tDR0cHfHx8un2+ubkZqampVOARiUSYNWsWJaHW1x5noJAuBVVVVUaXXisUClRWVlKrHYFAoPW8nZ0d/P39qS6j7u7u/TIEbWlpQWZmJjw9PTFx4kSrq+/R/DfgcDh9pis1+eOPP3Dy5EkwmUy89NJL1PePIIgHCh01A7mPj49BA0FraysyMjLg4+NjdGXZQCBrZJYvX46PPvrIKvrILFu2DFOnTsWUKVPg4OCAHTt24MyZM7hz547F9usxFg99oNEF0h6HDDo3btzAiBEjqNXOzJkzTWKPI5VKwePxoFarweFwTG7NrtlltKSkhFJYkXh4eMDb2xv29vawsbHp1hC0srISt2/fRmhoKEaNGmV1+zEKhQI8Hg9SqVTvIsb//ve/uH//PkaOHIlly5Z1+xkolUottwKxWAwPDw+ttKW+N11zKst04eeff8Zzzz2H9957D6+++qrZxqFPHxlNVCoVPD09sW/fPixfvtzYw7Uo6EAzAMRiMa5cuQIul4vk5GQte5zExESEhoYa/MfL5/ORnZ1NGXqa2xBTrVajtraWCjrV1dUPdBn18/ODm5sbVbtjb2+Pjo4OREZGIjAw0Iyj14+2tjakp6fDxcUFMTExem/kC4VCHDhwACqVCosXL8bYsWP7PId0KyDTbEqlUstPrL9uBZamLOuJo0eP4tVXX8WXX36JZ5991mzj0LePjCZisRh+fn74/vvvraKOypDQgcZAkIaPpJLN0PY4pP1OcXExIiIiEBQUZMDRG46Ojg6UlZVRabbW1lat521tbSkXbRsbG3h6evbLENRS4PP54PF4lHP0QCcSly9fxo0bN+Dh4YGXX35Zp6BFuhWQQaepqUlLlt6dOtBalGUEQeDdd9/FZ599ZhE1Mrra+3fHyy+/jJSUFNy5c8eiO9gaAzrQGAmpVIrr169TgYe0xyEDT0REhE4b5poFjNYivSYIAk1NTSgpKcHdu3cfaH8AAD4+PvD09KQMQTXTbP2dnZsCgiBQWlqKu3fvGnQlJpfLsX//fohEIsycOXNAN1SVSgWhUEgFHtJPjNwvc3d3R15ensUry5RKJdatW0fVvU2YMMHcQxpwoNm+fTs++ugjpKWlISIiwtjDtTjoQGMiSHscLpeLy5cvw83NjQo6jz/+eI/2OEKhEFlZWfDy8kJUVJRF1FvoSnl5OVVEamNjQ6XZGhoatI6zs7OjAkx3tTvmUkOp1WrcunULDQ0NiI2N7dO5WFcKCgrw448/wsbGBuvXrzeYrb1MJnvArcDW1hYhISEICAgwa7+YnpBIJFi0aBGqqqpw8eJFi0nrDSR1tnPnTrz33nu4dOkS2Gy2CUZredCBxgz0xx4HAD799FOEhIQgMjISISEhFndT6AvSlLSurg4cDucBZZ9YLNYyBO2uyyiZ/mEymfD29ja5IahMJkNmZiZUKhXi4uKMIvYgCAInTpxAeXk5xo0bZ3C/LpFIhJs3b1IpS1JcwGKxtNwfzNXniYSskXFxccGZM2eM0gpiIOjSR4bko48+wvvvv4+UlBRMmjTJlMO1KOhAYwHU1NRQZqCpqalgsViws7ODRCLBd999h8TERHMPUWfa29uRlZUFgiAQGxvb502MIAjU1dVRQae7LqOkFY6NjQ3l06ZZa2JoSOmvKeTXDQ0NOHToEAiCwPPPP4+QkBCDvC6fz0dWVtYDyjK1Wk3ZuvD5fLS0tMDZ2Zn6PE3tdUfWyHA4HBw/ftyiatVIdO0js2PHDmzduhUnT57E1KlTqddxcXGx2LSlsaADjYWRk5ODJ554Ara2tnBzc0N+fj4mTpxIrXYmTZpk8fUmjY2N4PF48Pf3R0REhF4b/DKZDGVlZVTgaW5u1nqedJ4mDUHJ2h1DGYLW1tYiJyfHpHY4ycnJyMzMhI+PD9atWzdgYQSZsuzPnpJCodDqvSOVSrVs+z09PY32GWRmZmL+/PlYuXIltm/fbrG1PIBufWSCg4NRUVHxwGts27YN77zzjglHbX7oQGNBCAQCjBo1Cps2bcKWLVvAYrHA5/Pxyy+/gMvlIiUlBQqFwmLtcTQ3zMPCwjB8+HCD3ZyEQiEVdLrrMurt7U11GdV0+tXVEFSzXXR0dLRJ2xO0t7dj3759kEqlmDNnjtamsy4YQllG2vbz+Xw0NjYCwANuBYbgwoULWLJkidlrZGiMCx1oLIyKigoMHz682+fUajVycnKoup2MjAyMGzeOamttTnsclUqFvLw8CAQCo2yYd32vqqoqKvB07TJqa2tL1e4wmUwqJdSXIahSqUReXh6EQiHi4uLMskeQk5ODn376CXZ2dti4caPOKRalUkm1vDaUsox0KyDVbEKhEE5OTloyan1EKkeOHMHrr7+O48eP45lnnhnwOGksFzrQWDHNzc24dOkSJSpobW3FrFmzqDSbofL8fSGVSpGZmQkWiwUOh2PyGgGJREK1PiguLoZEItF63tXVlQowLBaLSglpGoKSnSRZLBZiY2PNFrAJgsAXX3yB2tpaREZGaimc+qKjo0PrGoyl0lMqlVR3TD6fD4lEouVW0JdQgyAIvPPOO9izZw/OnTuH6dOnG2WcNJYDHWgGCX3Z48yYMcMoHTRJp4KhQ4ciLCzM7Pl1giDQ0NBArXYqKyuhUqmo5xkMBmWFY2NjA3t7e3h4eEAkEsHX1xcxMTFmLxqtrq7G0aNHAQCrV6/uV82OSCRCeno6vL29ERUVZdJraG9v13IrUKlUWqsdFxcXKnWpUCiwbt06pKamIjk52SJqZGiMDx1oBild7XHq6+spe5ykpKQB2+MQBIHi4mIUFRVZtFOBXC5HeXk5FXi6dhm1s7ODk5MTfHx84ODgoFXgqG9KyBCcO3cOeXl5GDJkCF588cVeAzipLBs5cqRRbI90gSAIiEQiLbcCsViM7777DjNmzMCFCxcgEAiQnJz80BlLPszQgeYhgNzgJlc7aWlp8Pf3p1Y7s2fP1skeR6lUIjc3F83NzeBwOD0Wm1oiZJfR4uJiFBcXa7U/AAAvLy94e3tTogLN2h3SENQUtLW1Yd++fZDJZJg/fz5VWwX8r41De3s7amtrUV9fDzabbZG+cSqVCgUFBdi+fTvOnDkDmUyGyMhIqlA5Pj7eYtsS0BgOOtA8hLS3t+PatWtU4CkpKcG0adP6ZY/T1taGzMxM2Nvbg81mW2S9Q18oFAqqyVdgYCBVv1NTU6N1nI2NDaVaY7FYWrU7pihwTE9PR0pKCpycnLB69WqkpKbilytp6JAr4OLpBQaLBam0HXKJGLZMBsaPGYO/LFmM4OBgo45LV8gambi4OOzYsQPXr19HamoqLl++jNzcXIv1W6MxHHSgoUFZWRkVdLra4zz22GOUguzixYtQqVQYMWIExo8fb/b9GH2QSCTIyMiAg4MD2Gy21mxaKpVqORV012XU19cXTk5OYDKZ8PDwMKohqEqlwoEDB3Dp6lW0q4AZC5/GxGkzYWNri46ODqjVKjg6do6FIAhU/FGEDO552Chk2PrWZpNKs3siIyMD8+fPx6pVqx6okSEIwurcLmj0gw40NFrI5XLKHic5ORmFhYXgcDhgMpng8Xg4e/YskpKSzD1MvRAIBMjKykJgYCAmTJjQpzJKIBBQLtQVFRUPiArIQkZbW1vY2toa3BC0oqICr775d4zkTEX09Fl/KuSYaG9vB8CAo6Njt+/RUFOFc4f3YtET8/Hs008PaAwDgayRef/99/HKK6+YbRy6dMUEgO+//x5vv/02ysvLMXr0aOzYsQNz58414YgHH3SgoemVe/fuYenSpSgqKgKTyYSDgwPmzJmDxMREzJkzB/7+/uYeYr8g+6+Eh4f3WKfUGwqFAhUVFdRqp2uXUXt7e/j7+8PFxQUsFuuB2h1d9yFKSkrwf1vfweJXN4Nl7wCZTAZbWxvY2NjCxoYFe3uHXgOZSqXCT8cPIWL4UPx1zYs6X+9AOXz4MDZt2oSvvvoKT5sx2OnaFfP333/H9OnT8eGHH2L+/Pk4efIkduzYgZycHISFhZnhCgYHdKCh6RGJRAI2m42goCCcPHkS7u7uyMjIoNJsOTk5Fm+Po1arkZ+fj9raWoP2X2ltbaWCTmlpabddRkklG5PJhJeXV78NQYVCIdZseBVL33wbbh6eUKlUaG4WgiA6rXdcXJwB9L1aIggCZ48ewJy4aDy5cOFAL7lfEASBbdu2Yd++fTh37hzi4+NN8r49oWtXzMWLF0MikeDChQvUY5MmTUJUVBQOHjxosnEPNuhAQ9MrFy9eREJCQrf7D3w+n6qHIO1xHnvsMSrwmNseRy6XIysrC3K5HHFxcUapIwL+12WUTLPV1NQ80GXU39+fEhXY29v3agj6yqY3EJ7wJILHdHbcVCjkaGuTQKlUUkGrv2k5lUqFL979O/bv/Mjoq0+FQoG//vWvuHz5MpKTkzF+/Hijvl9f6GPtHxQUhE2bNuG1116jHtu2bRvOnj2LW7dumWDUgxPLmn4aAKFQiI0bN+Knn34Ck8nE008/jc8++6xfVhwEQWDu3Lngcrk4c+aMTlXZg5XectN+fn5YtmwZli1bpmWPc/z4caxbt46yx0lMTMS0adNMqlATiUTIyMiAu7s74uLijLrSYjKZGDZsGIYNG4aZM2eio6NDS1TQ2tqK2tpa1NbWAgCcnZ0hFApRWVkJJpNJ1e74+fnh3r17IFw9/wwyBGQyORQKOVxdXSEStUKlUkMqlfbba4zFYmHBC+vx/kcfY88nO432GbS1tWHRokWoqanBzZs3LUKI0NjYCJVK9UCA9ff3x71797o9p76+vtvj6+vrjTbOh4FBF2iWLVuGuro6pKamQqFQYNWqVVi7di1OnjzZ57m7d++mVTB6wmQywWazwWazsWXLFi17nOXLl5vUHqe+vh7Z2dkICQnB2LFjTf5v6uDggPHjx2P8+PFUl1FytVNeXg6JRIKysjLqeF9fXwgEAvzxxx/48uuTWPLmNshkMqhUKhCEGk5Ozn96trlAJBKhvV1K2en0h0eGj8DldjkaGhqMsqppaGjAvHnz4OHhgevXr1tcHxka8zOoAs3du3fB5XKRlZVFdbLbu3cv5s6di507d/Y6y8rLy8Mnn3wCHo+HIUOGmGrIgxZPT088++yzePbZZ0EQBAoKCsDlcvHDDz/gtddeQ3BwMOVSYCh7HE23gokTJ1pE5TmDwYCPjw98fHwwadIkKJVKVFZWanUZJa35xWIxCDtH2Dk6oL29HUwmE0wmE3K5DCyWDWxtbWFnZwe5XI62tjadbuicx+fi1Pc/4NUN6w16fffv30dSUhImT56MY8eOWVTxpY+PD1gs1gOdXBsaGhAQENDtOQEBATodT9M/rK8Qohdu3rwJDw8PrXapjz32GJhMZq89vaVSKZYuXYr9+/fTXygjwGAwEB4ejjfffBNXrlxBU1MTdu7cCaVSiZdffhleXl5ISEjArl27cPfuXeizbahSqZCTk4PS0lJMmzbNIoJMd9jY2CAkJASPP/441q1bh02bNuGJJ55AWFgYhM3NCGXHQqFQQqFQQCaTQS6XQSaTo729HZK2NrBYnT9ZuVwOmUzW7/cdHRaJ3Fv5Br2W9PR0TJkyBc888wxOnDhhUUEG6LQXiomJweXLl6nH1Go1Ll++3GMLhsmTJ2sdDwCpqal6t2yg6WRQrWjq6+sfkCza2NjAy8ur1xzr66+/jilTpuCJJ54w9hBp0OmmvHDhQixcuPABe5y33npLyx5n1qxZcHNz6/X1SNdiBoOBGTNmmNw9eiC4uroiKioKUVFR4OXdQuD4cDjY20OlVkOhUEClUkOl+l/vHRsbG7BYLKhUKojFYqhUKtjY2MDGxgZMJgM9qdFYNjZol8sNViT5008/YenSpfjggw+wcePGAb+esdi0aRNWrFgBNptNdcWUSCRYtWoVADzQFfPVV1/FjBkz8Mknn2DevHk4deoUeDweDh8+bM7LsHqsItBs3rwZO3bs6PWYu3fv6vXa58+fx5UrV5Cbm6vX+TQDg8FgIDQ0FKGhoXj11VfR3t6O69evIzk5GZs3b0ZJSQmmTp1Kpdm62uPw+Xzk5ubCx8fH5K7FhqSiogJ3Cu8ibtFKOLt0+s4RhBpyuQIKhRxyuQIqlUrLm40gCHR0dMDBobPOhslkgMWygY0NCyyWzQMBxc7RCR0dHQO2zjl06BDeeOMNs9fI9IfFixdDIBBg69atVFdMLpdL7VWRggySKVOm4OTJk9iyZQveeustjB49GmfPnqVraAaIVcibSRfY3ggJCcHXX3+NN954Q6vtr1KphIODA77//nv8v//3/x4477XXXsOePXu0vmwqlQpMJhPx8fFIS0sz2HXQ6E5ZWRlSUlIoexwXFxdqtVNVVYU9e/ZQUlprFHIQBIHCwkJUVFTg1OkzWPJ/78KmB8dolUoFuVwOubxTiUYQ5B6QNwii83mVSgmlUgW1Wg0Wi0UFHRaLia93vo9929/TyUC161jJGpnz589j2rRpA7l0mocIq1jRkJbtfTF58mS0tLQgOzubcru9cuUK1Go11de7K5s3b8aLL2pXToeHh2PXrl1YsGDBwAdPMyBGjBiBdevWYd26dZQ9TnJyMl555RXw+XyMGjUKP/74IyQSiUX0ktEFcl+ptbUV8fHxuJR2HVJJG9w8unfDZrFYcHR0hKOjIwiCgEQi+XNfhAEGA1QKzd6+cy+CXAHJ5VIAQEtTE/h8PmxsbHRe1WjWyPz2228YN27cQC+f5iHCKgJNfyHrNtasWYODBw9CoVBgw4YNeO655yjFWU1NDWbPno0TJ04gNjYWAQEB3QoAgoKCMGLECFNfAk0v2NnZgcPhYO/evXB1dcWpU6coQ9Bdu3aBxWJhzpw5SEpKsnh7nI6ODmRmZoLBYCA+Ph729vYIGz8WVcX3MYHd/aRIEwaD0WttGKlY6+ynQ0ClUoNBqFBTU4M7d+7AxcWl34agbW1tePbZZ1FXV2cxNTI01sWgCjQA8M0332DDhg2YPXs2VbC5Z88e6nmFQoGioiJIpVIzjpJGXxoaGigVobe3Nx599FGsXr0aSqUSmZmZ4HK52LNnD1auXImoqCgqzTZ58mSLscchi0m9vLy09pWiIiLwDfdyvwKNbjAgEjZhZHAwpk+fDrlcjsbGRvD5fOTl5UEmk2n13dE0BK2vr8e8efPg6emJ69ev9ynMoKHpDqvYo7FmdHUqEAqF2LZtG3755RdUVlbC19cXTz75JP7973/ThXA6QNrjcLlcpKSkQC6XW4Q9Tm/dMFUqFRY9vxJr/73T4C0YUk6dwJPxcQ/sqxAEgba2NvD5fPD5fDQ1NSEnJwc8Hg/R0dH48ssvER8fb3E1MpYEQRB4/PHHwWKxkJKSovXcgQMH8NZbb6GgoMDslkzmhA40RiYpKQl1dXU4dOgQ5VTA4XB6dCooKCjAtm3bsHLlSowfPx4VFRVYt24dIiIi8MMPP5h49IMDtVqN3NxcJCcng8vlIj093Sz2OBUVFcjPz++19fX+zw9C7jUUYbGTDPa+SoUCx/61Gd+eON5nAFOpVPjtt9/w8ccf4+LFiyAIAmw2GwkJCUhISMCUKVOssg+RsamqqkJ4eDh27NiBv/71rwA6hSzh4eH4/PPP8fzzz5t5hOaFDjRG5O7duxg/fryWUwGXy8XcuXNRXV3d71z3999/j7/85S+QSCQWk/6xZpqbmynjRy6Xi5aWFsoeJykpyeD2OARB4O7duygvL0dsbCx8fHx6PFYsFmPlX1/C6q0fws7eMPVAF78+hsfZkZiblNiv48+fP49ly5Zh+/bteOqpp5CamoqUlBQUFhYiJyfHKtV9puCrr77Chg0bcPv2bQQHB2P27Nnw8PDA6dOnzT00s0NPTYyIvk4FXWltbYWbmxsdZAyEp6cnnnnmGRw9ehTV1dVIT0/H9OnTcfr0aYwdOxZjxozBK6+8gosXLw54L0+lUoHH46Gmpgbx8fG9Bhmgs4Bz41/X4PyXhwb0viTlRXehaub3O8gcOnQIS5cuxYkTJ7B+/XoMGTIEy5cvxzfffIPc3FyzBBmhUIhly5bBzc0NHh4eeOGFF9DW1tbr8Rs3bkRoaCgcHR0RFBSEV155Ba2trUYd54oVKzB79mysXr0a+/btQ0FBAQ4dMsy/o7VD37mMiL5OBZo0Njbi3//+N9auXWuMIT70kPY4pEWOWCzG1atXweVysX79etTV1WH69OlISkpCYmKiTiadMpmMciyYPn16v9NzM2fMQE7eLfzy3deYs+gvel9bTVkpLn3zBY7s39fnsQRBYOvWrThw4ABSUlIwdepUvd/X0OhqlEs6Ze/cuVMr/VxbW2v09PPhw4cxYcIEXL9+HT/++GO/yjIeBujUmR7016ng9OnT+Oqrr1BUVKT1nJ+fH95991289NJLvb6GSCTC448/Di8vL5w/f/5PqSqNqSAIAn/88QfV1jotLQ1+fn7U3s7s2bN7VGGRyjJPT09MnDhR5/oegiCwe+8+FFbWYuHqdXBw1M10NPfXq7h1hYu9n+6El5dXr8cqFAqsWbMGaWlpSE5OtqgaGWtMP2/ZsgVnz55FQUGBUd/HmqBXNHrwxhtvYOXKlb0eExISgoCAAPD5fK3HlUolhEJhn+adYrEYiYmJcHV1xZkzZ+ggYwYYDAbGjBlDpdJIexwul4t//OMfWvY4iYmJiIyMBIPBwNmzZyGRSBAdHa13mwIGg4HXX9mI32/exKfvbQE7YT6ipkwHq4+bZE1ZKa788A0ix4zE8SOH+vzeiMViPPvss2hoaMDNmzctzrm8r/Rzd24f3WHK9DNZOEvzP+hPQw+M6VQAdM6GExISYG9vj/Pnz1uVSeRgxtHRkVJf7dq1S8se5/3334eLiwsCAwOplhOGWBlMmTwZUZGR+P7HH3Hs3b/DNygEj4wcg2EjR8HRyRlKpRL82mrUFBeh4u4djA4OxDt/e61fggayRsbLywvXrl2zyBoZOv08OKBTZ0YmKSkJDQ0NlFPBqlWrwGazqfxyV6cCkUiEOXPmQCqV4syZM1qdFH19fa3KYuVhoqOjAy+88AJOnz6NgIAAVFZWIjY2llKyGcIehyAIlJSUIL+gAHfuFqFN2pkGGhEUiMjwcEyYMKHf1jJFRUWUtPvo0aMmr5EZzOnnd955B2fPnkVeXp7R38taoFc0RkZXp4KcnBxKkTZq1Cit1yorK0NwcLDJxk7Tf1599VWkp6cjLy8PoaGhqK2tpVY7u3fvpuxxEhMTkZCQoJc9DoPBwKhRozBq1Cj8vyf1H+vNmzexYMECrFmzBh988IFZlGR0+vnhgl7R0NAYgMzMTIwYMaLblKqmPQ6Xy0V2draWPc6kSZNMdhM8d+4cli1bho8++ggvv/yySd5zIJBiAB6PR6Wff/nlFyQmJvYqBtBMP1+8eNEgHVxpBgBBM6jZt28fMXz4cMLe3p6IjY0lMjIyej3+u+++I0JDQwl7e3siLCyM+Pnnn0000ocHPp9PfP3118Rf/vIXwtfXl3BzcyOeeuop4siRI0RlZaXR3vfAgQOEs7Mzcfr0aaO9hzFITEwkJk6cSGRkZBA3btwgRo8eTSxZsoR6vrq6mggNDaW+262trURcXBwRHh5OFBcXE3V1ddSfUqk012U81NCBZhBz6tQpws7Ojjh27Bhx584dYs2aNYSHhwfR0NDQ7fG//fYbwWKxiI8++ogoLCwktmzZQtja2hL5+fkmHvnDg0qlIng8HvHee+8RU6dOJVgsFjFhwgTijTfeIFJTU4mOjo4Bv4darSbeeustwsvLi/jtt98MMGrT0tTURCxZsoRwcXEh3NzciFWrVhFisZh6vqysjABAXL16lSAIgrh69SoBoNu/srIy81zEQw6dOhvExMXFgcPhYN++zoI9tVqNwMBAbNy4EZs3b37g+MWLF0MikeDChQvUY5MmTUJUVBQOHjxosnE/zJD2OGSarbm5mbLHSUxMxMiRI3V6PYVCgRdffBHXrl0Dl8vF2LFjjTRyGpqeoS1oBilyuRzZ2dl47LHHqMeYTCYee+wx3Lx5s9tzbt68qXU8ACQkJPR4PI3hIe1xvvjiC1RVVSEjI4Oyxxk3bhxGjx7db3scsViM+fPn4/bt27h58yYdZGjMBh1oBimNjY1QqVQPqJv8/f17rD+or6/X6Xga48JgMBAWFoY333wTly9fhlAoxKeffgqlUon169fDy8sLc+bMwaeffoq7d+9CMzlRX1+PGTNmgCAIXLt2zeIKMWkeLmh5Mw2NleDi4oIFCxZgwYIFWvY4XC4X//znPyl7nPDwcHz88ceYOXMmjhw5QveRoTE7dKAZpPj4+IDFYqGhoUHr8YaGhh7rDwICAnQ6nsZ89GWPM2PGDBw/fpy29KexCOjU2SDFzs4OMTExuHz5MvWYWq3G5cuXMXny5G7PmTx5stbxAJCamtrj8TSWA2mPs2vXLjQ1NeH8+fN0kKGxGOgVzSBm06ZNWLFiBdhsNmJjY7F7925IJBKsWrUKALB8+XIMHToUH374IYDO6vYZM2bgk08+wbx583Dq1CnweDwcPnzYnJdBoyN0qozG0qADzSBm8eLFEAgE2Lp1K+rr6xEVFQUul0tt+FdWVmq15Z0yZQpOnjyJLVu24K233sLo0aNx9uxZhIWFmesSaGhoBgNmreKheSjRxa3g8OHDxLRp0wgPDw/Cw8ODmD17dp/uBjTmp6mpiVi6dCnh6upKuLu7E6tXr9YqsuwNtVpNJCYmEgCIM2fOGHegNCaB3qOhMSnffvstNm3ahG3btiEnJweRkZFISEh4wDiRJC0tDUuWLMHVq1dx8+ZNBAYGYs6cOaipqTHxyGl0YdmyZbhz5w5SU1Nx4cIFXL9+vd82/bt376b3lwYb5o50NA8XsbGxxPr166n/V6lUxCOPPEJ8+OGH/TpfqVQSrq6uxFdffWWsIdIMkMLCQgIAkZWVRT2WnJxMMBgMoqamptdzc3NziaFDhxJ1dXX0imYQQa9oaEyGPm4FXZFKpVAoFH22J6YxH311xewJqVSKpUuXYv/+/bSkfpBBBxoak6GPW0FX/v73v+ORRx55wCqHxnLQtyvm66+/jilTpuCJJ54w9hBpTAwdaGishu3bt+PUqVM4c+YM3d7aDGzevBkMBqPXv3v37un12ufPn8eVK1ewe/duww6axiKg5c00JkMftwKSnTt3Yvv27bh06RIiIiKMOUyaHjBmV8wrV66gpKQEHh4eWo8//fTTiI+PR1pa2gBGTmNu6DYBNCYlLi4OsbGx2Lt3L4BOt4KgoCBs2LCh29YFAPDRRx/h/fffR0pKCiZNmmTK4dLogT5dMevr69HY2Kj1WHh4OD777DMsWLAAI0aMMMnYaYyEudUINA8Xp06dIuzt7Ynjx48ThYWFxNq1awkPDw+ivr6eIAiCeP7554nNmzdTx2/fvp2ws7MjfvjhB61Oif2tyaAxD7p2xewO0KqzQQO9R2PFqFQqTJkyBU899ZTW462trQgMDMQ///lPM42sZxYvXoydO3di69atiIqKQl5e3gNuBXV1ddTxn3/+OeRyOZ555hkMGTKE+tu5c6e5LoGmH3zzzTcYO3YsZs+ejblz52LatGlaVkYKhQJFRUV99tShGRzQqTMr5/79+4iKisKRI0ewbNkyAJ0eZrdu3UJWVhbte0VDQ2N26BWNlTNmzBhs374dGzduRF1dHc6dO4dTp07hxIkTdJDRgf379yM4OBgODg6Ii4tDZmZmv847deoUGAwGnnzySeMOkIbGiqFXNIMAgiAwa9YssFgs5OfnY+PGjdiyZYu5h2U1fPvtt1i+fDkOHjyIuLg47N69G99//z2KiooeqAfRpLy8HNOmTUNISAi8vLxw9uxZ0w2ahsaKoAPNIOHevXsYN24cwsPDkZOTAxsbWrneX+Li4sDhcLBv3z4AnUq4wMBAbNy4sUclnEqlwvTp07F69Wr8+uuvaGlpoQMNDU0P0KmzQcKxY8fg5OSEsrIyVFdXm3s4VoO+tjj/+te/4OfnhxdeeMEUw6ShsWroQDMI+P3337Fr1y5cuHABsbGxeOGFF0AvVPuHPrY4N27cwNGjR3HkyBFTDJGGxuqhA42VI5VKsXLlSrz00kt49NFHcfToUWRmZuLgwYPmHtqgRCwW4/nnn8eRI0fg4+Nj7uHQ0FgFdCLfyvnHP/4BgiCwfft2AEBwcDB27tyJv/3tb0hKSkJwcLB5B2jh6GqLU1JSgvLycixYsIB6TK1WA+g0jiwqKsLIkSONO2gaGiuDFgNYMdeuXcPs2bORlpaGadOmaT2XkJAApVKJS5cu0U2k+kAXW5yOjg4UFxdrPbZlyxaIxWJ89tlnGDNmDC0rp6HpAr2isWJmzJgBpVLZ7XMpKSkmHo31smnTJqxYsQJsNhuxsbHYvXs3JBIJVq1aBaCzAHbo0KH48MMP4eDggLCwMK3zSSPIro/T0NB0QgcamoeexYsXQyAQYOvWraivr0dUVNQDtjhMJr2dSUOjL3TqjIaGhobGqNDTNBoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao0IHGhoaGhoao/L/AfgT7U3tQdD9AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph_3d.center_coordinates(center_type=\"mean\")\n",
+    "graph_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Now we can compute the ECT of the 3d graph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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4+vri1q1bWLx4MS5duoSxY8cCuP8E1MSJE/Huu++iS5cu8PHxwYwZM+Dp6YmYmBhzNZOIiEhvDyb26luHGIgjyn8YPnw4lixZgpkzZyIoKAgFBQXYu3evalLv5cuXUVxcrCp/8+ZNJCQkoFu3bhg0aBDkcjmOHj0Kf39/VZkpU6Zg/PjxGDduHB577DGUlZVh7969NRbgIyIiEhMlJAY5NLVmzRr07NlTNXIRHh6Ob775pt5rPv/8c3Tt2hX29vYICAjA119/rVNbJYIgiGR0rHGTy+X3H/ceMg/NbMSbCF3rK47JX42BZ45x/3Qs5bMw9u8JsJzflbFZymdhinYYU3XVPRzfPQO3b9822nSFB99JrxwaCdsWtnrVVVlWif8+9alG8e7evRvW1tbo0qULBEHAxo0bsXjxYpw+fRrdu3evUf7o0aN48sknkZKSgn/961/IyMjAokWLkJ+fjx49emgVp+h6aIiIiEgzpl4peMiQIRg0aBC6dOmCRx55BPPnz0eLFi1w7NixWsu///77iI6OxuTJk9GtWzfMmzcPjz76KFatWqV1W5nQEBERWSh9F9XTZw6OQqHAli1bUF5eXudDNrm5uWoL2wJAVFSUTgvbim5SMBEREZnewwvI2tnZwc7Orka5M2fOIDw8HPfu3UOLFi2wY8cOtXmr/ySTybRe2LYuTGiIdNR56jmj1n/tWO3/AFBN51/Sb6FMTfh+9v+MWr+lzG+xFMb8XSnvSYDdRqte/V7Qfy+mB5OCH15AdtasWZg9e3aN8n5+figoKMDt27fxxRdfYPTo0cjJyakzqTEUJjREREQWStDyKaW66gCAoqIitUnBtfXOAPfXjPP19QUABAcH4+TJk3j//fdrXaFfKpXqtVjuP3EODRERkYUy5G7bDy8mW1dCUyMGpRIVFRW1vhceHq62sC0AZGZmarxY7j+xh4aIiIgMYvr06Rg4cCA6dOiAO3fuICMjA9nZ2di3bx+AmovfTpgwAX379sXSpUsxePBgbNmyBadOncK6deu0vjcTGiIiIgtl6pWCS0tLERsbi+LiYri4uKBnz57Yt28fnnnmGQD3F7+1svpffREREcjIyMA777yDt99+G126dMHOnTu1XoMGYEJDRERksf45ZKRPHZpav359ve9nZ2fXODds2DAMGzZM27Bq4BwaIiIiEj320BAREVkobfdiqqsOMWBCQ0REZKFMPeRkThxyIiIiItFjDw0REZGFako9NExoiIiILFRTSmg45ERERESixx4aIiIiC9WUemiY0BAREVkoAfo/dm38feANgwkNERGRhWpKPTScQ0NERESixx4aIiIiC9WUemiY0BiY7HEJrOyN8+F75ohlJJPof671Fcc/hmQYT/T+yej3uJDTzej3sBRNKaHhkBMRERGJHntoiIiILFRT6qFhQkNERGShBEECQc+ERN/rTYVDTkRERCR67KEhIiKyUEpI9F5YT9/rTYUJDRERkYVqSnNoOOREREREosceGiIiIgvVlCYFM6EhIiKyUE1pyIkJDRERkYVqSj00nENDREREosceGiIiIgslGGDISSw9NExoiIiILJQAQNBzX2OxbIvMISciIiISPfbQEBERWSglJJBwpWAiIiISMz7lRERERCQi7KEhIiKD2dDhW6Pfw7evv9Hv8UTvn4xWd2VZJS4ZrXZ1SkECSRNZWE+UPTSrV6+Gt7c37O3tERYWhhMnTtRZ9qOPPsITTzyBVq1aoVWrVoiMjKxRfsyYMZBIJGpHdHS0sZtBRERkVIJgmEMMRJfQbN26FcnJyZg1axby8/MRGBiIqKgolJaW1lo+OzsbI0eOxKFDh5CbmwsvLy8MGDAAV69eVSsXHR2N4uJi1fHpp5+aojlERERkAKJLaJYtW4aEhATExcXB398fqampcHR0RFpaWq3lN2/ejP/85z8ICgpC165d8fHHH0OpVCIrK0utnJ2dHaRSqepo1aqVKZpDRERkNA8mBet7iIGoEprKykrk5eUhMjJSdc7KygqRkZHIzc3VqI67d++iqqoKrq6uauezs7Ph5uYGPz8/vP766/jzzz/rraeiogJyuVztICIiakyY0DRS169fh0KhgLu7u9p5d3d3yGQyjeqYOnUqPD091ZKi6OhobNq0CVlZWVi0aBFycnIwcOBAKBSKOutJSUmBi4uL6vDy8tKtUUREREbyYLdtfQ8xEFVCo6+FCxdiy5Yt2LFjB+zt7VXnR4wYgWeffRYBAQGIiYnBnj17cPLkSWRnZ9dZ1/Tp03H79m3VUVRUZIIWEBERNV4pKSl47LHH4OTkBDc3N8TExKCwsLDea9LT02s8mPPP72hNiSqhadOmDaytrVFSUqJ2vqSkBFKptN5rlyxZgoULF2L//v3o2bNnvWU7deqENm3a4Pz583WWsbOzg7Ozs9pBRETUmJj6KaecnBwkJibi2LFjyMzMRFVVFQYMGIDy8vJ6r3N2dlZ7MOfSJe0fbBfVOjS2trYIDg5GVlYWYmJiAEA1wTcpKanO69577z3Mnz8f+/btQ0hISIP3uXLlCv788094eHgYKnQiIiKTu5+Q6LtSsOZl9+7dq/Y6PT0dbm5uyMvLw5NPPlnndRKJpMGOiYaIqocGAJKTk/HRRx9h48aNOHfuHF5//XWUl5cjLi4OABAbG4vp06eryi9atAgzZsxAWloavL29IZPJIJPJUFZWBgAoKyvD5MmTcezYMfz+++/IysrC0KFD4evri6ioKLO0kYiIqLF5+EGYioqKBq+5ffs2ANR4EOdhZWVl6NixI7y8vDB06FD8+OOPWscnuoRm+PDhWLJkCWbOnImgoCAUFBRg7969qonCly9fRnFxsar8mjVrUFlZiRdffBEeHh6qY8mSJQAAa2tr/PDDD3j22WfxyCOPID4+HsHBwfj2229hZ2dnljYSEREZgiGfcvLy8lJ7GCYlJaXeeyuVSkycOBF9+vRBjx496izn5+eHtLQ07Nq1C5988gmUSiUiIiJw5coVrdoqqiGnB5KSkuocYnp4Iu/vv/9eb10ODg7Yt2+fgSIjIiJqPIS/D33rAICioiK1+aIN/Z/+xMREnD17FkeOHKm3XHh4OMLDw1WvIyIi0K1bN6xduxbz5s3TOE5RJjRERERkWto8AJOUlIQ9e/bg8OHDaN++vVb3sbGxQa9evep9MKc2ohtyIiIiIs2YemE9QRCQlJSEHTt24ODBg/Dx8dE6ZoVCgTNnzmj9YA57aIiIiCyVIcecNJCYmIiMjAzs2rULTk5OqkVvXVxc4ODgAOD+wzvt2rVTzcGZO3cuevfuDV9fX9y6dQuLFy/GpUuXMHbsWK3CZEJDRERkqQyxdYEW169ZswYA0K9fP7XzGzZswJgxYwDcf3jHyup/A0Q3b95EQkICZDIZWrVqheDgYBw9ehT+/v5ahcmEhoiIiAxC0GDRmocf3lm+fDmWL1+u9711SmgUCgU+/vhjFBYWon379ggMDERQUBBat26td0BERERkGNqu9FtXHWKgU0Izfvx4bNu2DZGRkVi1ahUkEgmqq6vRrl07BAUF4csvvzR0nERERKQlQ+yWLZbdtnVKaLZv345NmzYhKioKX375JY4ePYqcnBzMnTsXHTt2NHSMohIe8jNsW9gape4LOd2MUu8/PdH7J6PfwxS+Pabd2KsuNnT41qj1xxm19vtM8XuyFMb+2zDF3zdpzph/G8p794xWd1OmU0JTVlammqxjY2ODZs2aISkpCVVVVbh27ZpBAyQiIiIdCRKtJvXWWYcI6LQOTadOnVSJS7t27XD16lUAwJAhQ/DJJ58YLjoiIiLSmal32zYnnRKa559/Ht988w0AoG/fvkhLSwMA/PTTT/jrr78MFx0RERGRBnQacpo9e7bq5ylTpuCxxx5D27ZtIZfLER8fb6jYiIiISB8mXljPnPReh6ZDhw748ccfsXv3brRu3RpDhgwxRFxERESkJz7lpKU2bdogLs4Uz2QQERER1cSVgomIiCyZSIaM9MWEhoiIyEJxyImIiIjErwlNCtbpsW0iIiKixoQ9NERERBZL8vehbx2Nn8YJTXJyssaVLlu2TKdgiIiIyICa0JCTxgnN6dOn1V7n5+ejuroafn5+AIBffvkF1tbWCA4ONmyERERERA3QOKE5dOiQ6udly5bByckJGzduRKtWrQAAN2/eRFxcHJ544gnDR0lERETaa0I9NDpNCl66dClSUlJUyQwAtGrVCu+++y6WLl1qsOCIiIhIDw9229b3EAGdEhq5XI4//vijxvk//vgDd+7c0TsoIiIiIm3olNA899xziIuLw/bt23HlyhVcuXIF27ZtQ3x8PJ5//nlDx0hEREQ6EATDHGKg02PbqampeOutt/Dvf/8bVVVVEAQBNjY2iI+Px+LFiw0dI/3tWl/jd/t1NvodSFMbOnxr9Hv4HvM3+j2IDM0zx/jfsKb499YkmtAcGp0SGkdHR3z44YdYvHgxLly4AADo3LkzmjdvbtDgiIiIiDTBdWiIiIgslSEm9YpkUrDO69DURSIRR8OJiIgsnUS4f+hbhxjotA4NERERiUATmkPDzSmJiIhI9HTenPLWrVtYv349zp07BwDw9/dHfHw8XFxcDBYcERER6aEJzaHRqYfm1KlT6Ny5M5YvX44bN27gxo0bWL58OTp37oz8/HxDx0hERES6EAx0iIBOPTRvvvkmnn32WXz00Udo1ux+FdXV1Rg7diwmTpyIw4cPGzRIIiIiovrolNCcOnVKLZkBgGbNmmHKlCkICQkxWHBERESkB04Krp+zszMuX75c43xRURGcnJz0DoqIiIgMoAkNOemU0AwfPhzx8fHYunUrioqKUFRUhC1btmDs2LEYOXKkoWMkIiIiqpdOQ05LliyBRCJBbGwsqqurAQA2NjZ4/fXXsXDhQoMGSERERDpqQk856ZTQ2Nra4v3330dKSoraXk6Ojo4GDY6IiIh0x5WCNeTo6IiAgABDxUJERESkE51XCs7KysLbb7+NsWPH4tVXX1U7jG316tXw9vaGvb09wsLCcOLEiXrLf/755+jatSvs7e0REBCAr7/+Wu19QRAwc+ZMeHh4wMHBAZGRkfj111+N2QQiIiLjM/Gk4JSUFDz22GNwcnKCm5sbYmJiUFhY2OB1DX1Pa0KnhGbOnDkYMGAAsrKycP36ddy8eVPtMKatW7ciOTkZs2bNQn5+PgIDAxEVFYXS0tJayx89ehQjR45EfHw8Tp8+jZiYGMTExODs2bOqMu+99x5WrlyJ1NRUHD9+HM2bN0dUVBTu3btn1LYQERFZkpycHCQmJuLYsWPIzMxEVVUVBgwYgPLy8jqv0eR7WhM6DTmlpqYiPT0dr7zyii6X62XZsmVISEhAXFycKpavvvoKaWlpmDZtWo3y77//PqKjozF58mQAwLx585CZmYlVq1YhNTUVgiBgxYoVeOeddzB06FAAwKZNm+Du7o6dO3dixIgRpmscERGRAUlggDk0WpTdu3ev2uv09HS4ubkhLy8PTz75ZK3XNPQ9rSmdemgqKysRERGhy6V6qaysRF5eHiIjI1XnrKysEBkZidzc3Fqvyc3NVSsPAFFRUaryFy9ehEwmUyvj4uKCsLCwOusEgIqKCsjlcrWDiIjIUj38nVdRUdHgNbdv3wYAuLq61lmmoe9pTemU0IwdOxYZGRm6XKqX69evQ6FQwN3dXe28u7s7ZDJZrdfIZLJ6yz/4X23qBO6PE7q4uKgOLy8vrdtDRERkVA8e29b3AODl5aX2vZeSklLvrZVKJSZOnIg+ffqgR48edZZr6HtaUxoPOSUnJ6sFuW7dOhw4cAA9e/aEjY2NWtlly5ZpFYQYTZ8+Xe13IpfLmdQQEVHjYsCtD4qKiuDs7Kw6bWdnV+9liYmJOHv2LI4cOaJnAJrROKE5ffq02uugoCAAqDFpRyIx3gI8bdq0gbW1NUpKStTOl5SUQCqV1nqNVCqtt/yD/y0pKYGHh4damQdtrI2dnV2DHyYREZGlcHZ2Vkto6pOUlIQ9e/bg8OHDaN++fb1lG/qe1pTGCc2hQ4e0qtgYbG1tERwcjKysLMTExAC431uUlZWFpKSkWq8JDw9HVlYWJk6cqDqXmZmJ8PBwAICPjw+kUimysrJUCYxcLsfx48fx+uuvG7M5RERExmXizSkFQcD48eOxY8cOZGdnw8fHp8FrGvqe1pROTzn99ddfEARBtTLwpUuXsGPHDvj7+2PAgAG6VKmx5ORkjB49GiEhIQgNDcWKFStQXl6ueuopNjYW7dq1U43tTZgwAX379sXSpUsxePBgbNmyBadOncK6desA3O9RmjhxIt5991106dIFPj4+mDFjBjw9PVVJExERkRiZeqXgxMREZGRkYNeuXXByclLNg3FxcYGDgwMA7b+nNaVTQjN06FA8//zzeO2113Dr1i2EhobC1tYW169fx7Jly4zaszF8+HD88ccfmDlzJmQyGYKCgrB3717VhKLLly/Dyup/c50jIiKQkZGBd955B2+//Ta6dOmCnTt3qk1QmjJlCsrLyzFu3DjcunULjz/+OPbu3Qt7e3ujtYOIiMjSrFmzBgDQr18/tfMbNmzAmDFjAOj2Pa0JnRKa/Px8LF++HADwxRdfQCqV4vTp09i2bRtmzpxp9KGapKSkOoeYsrOza5wbNmwYhg0bVmd9EokEc+fOxdy5cw0VIhERkfmZYcipIbp8T2tCp4Tm7t27cHJyAgDs378fzz//PKysrNC7d29cunRJr4CIiIjIQEyc0JiTTuvQ+Pr6YufOnSgqKsK+fftU82ZKS0s1ngFNREREZCg6JTQzZ87EW2+9BW9vb4SFhalmIu/fvx+9evUyaIBERESkmweTgvU9xECnIacXX3wRjz/+OIqLixEYGKg6379/fzz33HMGC46IiIj08I+VfvWqQwR0SmiA+wvhPLzoTWhoqN4BERERkYFwDk3Dvv32W7z88ssIDw/H1atXAQD//e9/TbbEMREREdEDOiU027ZtQ1RUFBwcHHD69GnVjpu3b9/GggULDBogERER6aYpzaHRKaF59913kZqaio8++khtY8o+ffogPz/fYMERERGRHgQDHSKgU0JTWFiIJ598ssZ5FxcX3Lp1S9+YiIiIiLSiU0IjlUpx/vz5GuePHDmCTp066R0UERERGYAhhpssuYcmISEBEyZMwPHjxyGRSHDt2jVs3rwZb731FneoJiIiaiya0JCTTo9tT5s2DUqlEv3798fdu3fx5JNPws7ODm+99RbGjx9v6Bjpb545Jvivqrfxb0Gaibv8hLlDIGqUrvUVx7ooZFpaJzRVVVWIjo5GamoqJk+ejPPnz6OsrAz+/v5o0aKFMWIkIiIiXTShdWi0TmhsbGzwww8/AABsbW3h7+9v8KCIiIhIf4Z47NqiH9t++eWXsX79ekPHQkRERKQTnebQVFdXIy0tDQcOHEBwcDCaN2+u9v6yZcsMEhwRERGRJnRKaM6ePYtHH30UAPDLL7+ovSeRcLIWERFRo8A5NPU7dOiQoeMgIiIiA+McGiIiIiIR0amHBgCysrKQlZWF0tJSKJVKtffS0tL0DoyIiIgMQCQ9LPrSKaGZM2cO5s6di5CQEHh4eHDeDBERUWPEOTT1S01NRXp6Ol555RVDx0NERESkNZ0SmsrKSkRERBg6FiIiIjIgTgpuwNixY5GRkWHoWIiIiMiQuDllTcnJyaqflUol1q1bhwMHDqBnz56wsbFRK8uF9YiIiMiUNE5oTp8+rfY6KCgIwP1F9v6JE4SJiIgah6Y05KRxQnPo0CHMnTsXkyZNqrHVARERETVCTegpJ63m0MyZMwfl5eXGioWIiIhIJ1o95SQIIknTiIiIqEn10Gj92DbnyBAREYkD59DU45FHHmkwqblx44bOAYld7qmusLK3N0rdniZIk7895m/0e1iKuMtPGLV+fhaNi7E/D1P8fZPmnuj9k9HqriyrxCWj1f4Q9tDUbc6cOXBxcTFGLEREREQ60TqhGTFiBNzc3IwRCxERERkSe2hqx/kzRERE4tGU5tBo9dg2n3IiIiKixkirhEapVHK4iYiISCzMsJfT4cOHMWTIEHh6ekIikWDnzp31ls/OzoZEIqlxyGQyre6r0+aURERE1Pg9GHLS99BGeXk5AgMDsXr1aq2uKywsRHFxserQtgNF60nBRERERHUZOHAgBg4cqPV1bm5uaNmypc73ZQ8NERGRpTLgkJNcLlc7KioqDBpqUFAQPDw88Mwzz+C7777T+npRJTQ3btzAqFGj4OzsjJYtWyI+Ph5lZWX1lh8/fjz8/Pzg4OCADh064I033sDt27fVytU2drdlyxZjN4eIiMi4DJjQeHl5wcXFRXWkpKQYJEQPDw+kpqZi27Zt2LZtG7y8vNCvXz/k5+drVY+ohpxGjRqF4uJiZGZmoqqqCnFxcRg3bhwyMjJqLX/t2jVcu3YNS5Ysgb+/Py5duoTXXnsN165dwxdffKFWdsOGDYiOjla91qfbi4iIyNIUFRXB2dlZ9drOzs4g9fr5+cHPz0/1OiIiAhcuXMDy5cvx3//+V+N6RJPQnDt3Dnv37sXJkycREhICAPjggw8waNAgLFmyBJ6enjWu6dGjB7Zt26Z63blzZ8yfPx8vv/wyqqur0azZ/5rfsmVLSKVS4zeEiIjIRCR/H/rWAQDOzs5qCY0xhYaG4siRI1pdI5ohp9zcXLRs2VKVzABAZGQkrKyscPz4cY3ruX37NpydndWSGQBITExEmzZtEBoairS0NK65Q0RE4meGx7YNoaCgAB4eHlpdI5oeGplMVuMRrmbNmsHV1VXjZ9WvX7+OefPmYdy4cWrn586di6effhqOjo7Yv38//vOf/6CsrAxvvPFGnXVVVFSoTYiSy+VatIaIiMj4zLFScFlZGc6fP696ffHiRRQUFMDV1RUdOnTA9OnTcfXqVWzatAkAsGLFCvj4+KB79+64d+8ePv74Yxw8eBD79+/X6r5mT2imTZuGRYsW1Vvm3Llzet9HLpdj8ODB8Pf3x+zZs9XemzFjhurnXr16oby8HIsXL643oUlJScGcOXP0jouIiMiSnDp1Ck899ZTqdXJyMgBg9OjRSE9PR3FxMS5fvqx6v7KyEpMmTcLVq1fh6OiInj174sCBA2p1aMLsCc2kSZMwZsyYest06tQJUqkUpaWlauerq6tx48aNBue+3LlzB9HR0XBycsKOHTtgY2NTb/mwsDDMmzcPFRUVdU56mj59uupDAu4nTF5eXvXWS0REZFJm2JyyX79+9U7bSE9PV3s9ZcoUTJkyRYfA1Jk9oWnbti3atm3bYLnw8HDcunULeXl5CA4OBgAcPHgQSqUSYWFhdV4nl8sRFRUFOzs7fPnll7C3t2/wXgUFBWjVqlW9M7jt7OwMNsObiIjIaJrIlFCzJzSa6tatG6Kjo5GQkIDU1FRUVVUhKSkJI0aMUD3hdPXqVfTv3x+bNm1CaGgo5HI5BgwYgLt37+KTTz5RLQYE3E+krK2tsXv3bpSUlKB3796wt7dHZmYmFixYgLfeesuczSUiIiItiCahAYDNmzcjKSkJ/fv3h5WVFV544QWsXLlS9X5VVRUKCwtx9+5dAEB+fr7qCShfX1+1ui5evAhvb2/Y2Nhg9erVePPNNyEIAnx9fbFs2TIkJCSYrmFERBYi7vITRr+HZ47xuxy+hb/R6lbeu2e0uh9mjknB5iKqhMbV1bXORfQAwNvbW23crqFxPACIjo5WW1CPiIjIYphhDo25iGYdGiIiIqK6iKqHhoiIiDTHISciIiISPw45EREREYkHe2iIiIgsFIeciIiISPya0JATExoiIiJL1YQSGs6hISIiItFjDw0REZGF4hwaIiIiEj8OORERERGJB3toiIiILJREECBpYE9DTeoQAyY0RERElopDTkRERETiwR4aIiIiC8WnnIiIiEj8OOREREREJB7soTEw6REBzWxEks4SmYBnjgn+Hl4y/i1IM98e8zf6PTzF0mXQCHDIiYiIiMSvCQ05MaEhIiKyUE2ph4ZzaIiIiEj02ENDRERkqTjkRERERJZALENG+uKQExEREYkee2iIiIgslSDcP/StQwSY0BAREVkoPuVEREREJCLsoSEiIrJUfMqJiIiIxE6ivH/oW4cYcMiJiIiIRI89NERERJaqCQ05sYeGiIjIQj14yknfQxuHDx/GkCFD4OnpCYlEgp07dzZ4TXZ2Nh599FHY2dnB19cX6enpWreVCQ0REZGlerAOjb6HFsrLyxEYGIjVq1drVP7ixYsYPHgwnnrqKRQUFGDixIkYO3Ys9u3bp9V9OeREREREBjNw4EAMHDhQ4/Kpqanw8fHB0qVLAQDdunXDkSNHsHz5ckRFRWlcD3toiIiILJQhh5zkcrnaUVFRYZAYc3NzERkZqXYuKioKubm5WtXDHhoiHV1Y1M24N+hr3Ootie9n/8/cIejtWl+J0e/hmWP82Z2maIcpGPN3VV0l4JLRan+IAScFe3l5qZ2eNWsWZs+erWflgEwmg7u7u9o5d3d3yOVy/PXXX3BwcNCoHiY0RERE1KCioiI4OzurXtvZ2ZkxmpqY0BAREVkoQ+7l5OzsrJbQGIpUKkVJSYnauZKSEjg7O2vcOwMwoSEiIrJcIthtOzw8HF9//bXauczMTISHh2tVDycFExERkcGUlZWhoKAABQUFAO4/ll1QUIDLly8DAKZPn47Y2FhV+ddeew2//fYbpkyZgp9//hkffvghPvvsM7z55pta3VdUCc2NGzcwatQoODs7o2XLloiPj0dZWVm91/Tr1w8SiUTteO2119TKXL58GYMHD4ajoyPc3NwwefJkVFdXG7MpRERERmeOhfVOnTqFXr16oVevXgCA5ORk9OrVCzNnzgQAFBcXq5IbAPDx8cFXX32FzMxMBAYGYunSpfj444+1emQbENmQ06hRo1BcXIzMzExUVVUhLi4O48aNQ0ZGRr3XJSQkYO7cuarXjo6Oqp8VCgUGDx4MqVSKo0ePori4GLGxsbCxscGCBQuM1hYiIiKjM8PWB/369YNQzzBVbasA9+vXD6dPn9YyMHWiSWjOnTuHvXv34uTJkwgJCQEAfPDBBxg0aBCWLFkCT0/POq91dHSEVCqt9b39+/fjp59+woEDB+Du7o6goCDMmzcPU6dOxezZs2Fra2uU9hAREZHhiGbIKTc3Fy1btlQlMwAQGRkJKysrHD9+vN5rN2/ejDZt2qBHjx6YPn067t69q1ZvQECA2jPwUVFRkMvl+PHHH+uss6KiosYiQ0RERI2JOYaczEU0PTQymQxubm5q55o1awZXV1fIZLI6r/v3v/+Njh07wtPTEz/88AOmTp2KwsJCbN++XVVvbQv6PHivLikpKZgzZ46uzSEiIjI+pXD/0LcOETB7QjNt2jQsWrSo3jLnzp3Tuf5x48apfg4ICICHhwf69++PCxcuoHPnzjrXO336dCQnJ6tey+XyGqsoEhERmZUZ5tCYi9kTmkmTJmHMmDH1lunUqROkUilKS0vVzldXV+PGjRt1zo+pTVhYGADg/Pnz6Ny5M6RSKU6cOKFW5sECP/XVa2dn1+hWSSQiImqqzJ7QtG3bFm3btm2wXHh4OG7duoW8vDwEBwcDAA4ePAilUqlKUjTx4Ll4Dw8PVb3z589HaWmpakgrMzMTzs7O8Pf317I1REREjYcEBlgp2CCRGJ9oJgV369YN0dHRSEhIwIkTJ/Ddd98hKSkJI0aMUD3hdPXqVXTt2lXV43LhwgXMmzcPeXl5+P333/Hll18iNjYWTz75JHr27AkAGDBgAPz9/fHKK6/g+++/x759+/DOO+8gMTGRPTBERCRuD1YK1vcQAdEkNMD9p5W6du2K/v37Y9CgQXj88cexbt061ftVVVUoLCxUPcVka2uLAwcOYMCAAejatSsmTZqEF154Abt371ZdY21tjT179sDa2hrh4eF4+eWXERsbq7ZuDRERETVuZh9y0oarq2u9i+h5e3urLebj5eWFnJycBuvt2LFjjX0kiIiIxM6Qm1M2dqJKaIiIiEgLTegpJ1ENORERERHVhj00REREFkoiCJDoOalX3+tNhQkNqfHMEcd/uGQYlvJ5d554zOj3+Os5zZeH0IXDjvq3cDEEY7cBsJz/piyG8u9D3zpEgENOREREJHrsoSEiIrJQHHIiIiIi8WtCTzkxoSEiIrJUhljpVyQ9NJxDQ0RERKLHHhoiIiILxZWCiYiISPw45EREREQkHuyhISIislAS5f1D3zrEgAkNERGRpeKQExEREZF4sIeGiIjIUnFhPSIiIhK7prT1AYeciIiISPTYQ0NERGSpmtCkYCY0RERElkoAoO9j1+LIZ5jQEBERWaqmNIeGCQ1ZJIcdx41+j7+eCzNq/Z454vhHhIioMWBCQ0REZKkEGGAOjUEiMTomNERERJaqCU0K5mPbREREJHrsoSEiIrJUSgASA9QhAuyhISIislAPnnLS99DW6tWr4e3tDXt7e4SFheHEiRN1lk1PT4dEIlE77O3ttb4nExoiIiIymK1btyI5ORmzZs1Cfn4+AgMDERUVhdLS0jqvcXZ2RnFxseq4dOmS1vdlQkNERGSpHkwK1vfQwrJly5CQkIC4uDj4+/sjNTUVjo6OSEtLq/MaiUQCqVSqOtzd3bVuKhMaIiIiS2XihKayshJ5eXmIjIxUnbOyskJkZCRyc3PrvK6srAwdO3aEl5cXhg4dih9//FHrpjKhISIiogbJ5XK1o6KiokaZ69evQ6FQ1OhhcXd3h0wmq7VePz8/pKWlYdeuXfjkk0+gVCoRERGBK1euaBUfExoiIiJLZcAeGi8vL7i4uKiOlJQUg4QYHh6O2NhYBAUFoW/fvti+fTvatm2LtWvXalUPH9smIiKyVAZ8bLuoqAjOzs6q03Z2djWKtmnTBtbW1igpKVE7X1JSAqlUqtHtbGxs0KtXL5w/f16rMNlDQ0REZKEM+di2s7Oz2lFbQmNra4vg4GBkZWWpzimVSmRlZSE8PFyjmBUKBc6cOQMPDw+t2soeGiIiIjKY5ORkjB49GiEhIQgNDcWKFStQXl6OuLg4AEBsbCzatWunGrKaO3cuevfuDV9fX9y6dQuLFy/GpUuXMHbsWK3uy4SGiIjIUplhL6fhw4fjjz/+wMyZMyGTyRAUFIS9e/eqJgpfvnwZVlb/GyC6efMmEhISIJPJ0KpVKwQHB+Po0aPw9/fX6r5MaIiIiCyVUgAkeiY0Su2vT0pKQlJSUq3vZWdnq71evnw5li9frktkajiHhoiIiESPPTRERESWygxDTubChIaIiMhiGSChARMag7tx4wbGjx+P3bt3w8rKCi+88ALef/99tGjRotbyv//+O3x8fGp977PPPsOwYcMA3N9D4mGffvopRowYYbjgDcBhx3Gj3+Ov58KMfg9LYYrPw9j4eZOh8d8pMhdRJTSjRo1CcXExMjMzUVVVhbi4OIwbNw4ZGRm1lvfy8kJxcbHauXXr1mHx4sUYOHCg2vkNGzYgOjpa9bply5YGj5+IiMikOOTU+Jw7dw579+7FyZMnERISAgD44IMPMGjQICxZsgSenp41rrG2tq6xMuGOHTvw0ksv1ejVadmypcarGBIREYmCUoDeQ0Y6POVkDqJ5yik3NxctW7ZUJTMAEBkZCSsrKxw/rlkXZ15eHgoKChAfH1/jvcTERLRp0wahoaFIS0uD0EBGWlFRUWOjLiIiIjIP0fTQyGQyuLm5qZ1r1qwZXF1d69zB82Hr169Ht27dEBERoXZ+7ty5ePrpp+Ho6Ij9+/fjP//5D8rKyvDGG2/UWVdKSgrmzJmjfUOIiIhMRVDeP/StQwTM3kMzbdo0SCSSeo+ff/5Z7/v89ddfyMjIqLV3ZsaMGejTpw969eqFqVOnYsqUKVi8eHG99U2fPh23b99WHUVFRXrHSEREZFAG3G27sTN7D82kSZMwZsyYest06tQJUqkUpaWlauerq6tx48YNjea+fPHFF7h79y5iY2MbLBsWFoZ58+ahoqKi1s23gPu7jNb1HhERUaPQhObQmD2hadu2Ldq2bdtgufDwcNy6dQt5eXkIDg4GABw8eBBKpRJhYQ0/wrd+/Xo8++yzGt2roKAArVq1YsJCREQkEmZPaDTVrVs3REdHIyEhAampqaiqqkJSUhJGjBihesLp6tWr6N+/PzZt2oTQ0FDVtefPn8fhw4fx9ddf16h39+7dKCkpQe/evWFvb4/MzEwsWLAAb731lsnaRkREZBR8bLtx2rx5M5KSktC/f3/VwnorV65UvV9VVYXCwkLcvXtX7bq0tDS0b98eAwYMqFGnjY0NVq9ejTfffBOCIMDX1xfLli1DQkKC0dtDRERkVAIMkNAYJBKjE1VC4+rqWuciegDg7e1d6+PWCxYswIIFC2q9Jjo6Wm1BPSIiIhIfUSU0REREpAUOOREREZHoKZUA9FxHRsl1aIiIiIhMgj00RERElopDTkRERCR6TSih4ZATERERiR57aIiIiCwVtz4gXTnsPoVmEhtzh6Ezhx3HzR0CmRA/b81Zwu/KEtoAiL8d1UKVye4lCEoIeu6Wre/1psKEhoiIyFIJgv49LJxDQ0RERGQa7KEhIiKyVIIB5tCIpIeGCQ0REZGlUioBiZ5zYEQyh4ZDTkRERCR67KEhIiKyVBxyIiIiIrETlEoIeg45ieWxbQ45ERERkeixh4aIiMhScciJiIiIRE8pAJKmkdBwyImIiIhEjz00RERElkoQAOi7Do04emiY0BAREVkoQSlA0HPISWBCQ0RERGYlKKF/Dw0f2yYiIqImaPXq1fD29oa9vT3CwsJw4sSJest//vnn6Nq1K+zt7REQEICvv/5a63syoSEiIrJQglIwyKGNrVu3Ijk5GbNmzUJ+fj4CAwMRFRWF0tLSWssfPXoUI0eORHx8PE6fPo2YmBjExMTg7NmzWt1XIohlcKyRk8vlcHFxQT8MRTOJjbnDISKiRqpaqEI2duH27dtwdnY2yj0M+Z2kbbxhYWF47LHHsGrVKgCAUqmEl5cXxo8fj2nTptUoP3z4cJSXl2PPnj2qc71790ZQUBBSU1M1jpNzaAzkQV5YjSq91zAiIiLLVY0qAKaZbGuI76QH8crlcrXzdnZ2sLOzUztXWVmJvLw8TJ8+XXXOysoKkZGRyM3NrbX+3NxcJCcnq52LiorCzp07tYqTCY2B3LlzBwBwBNqP+xERUdNz584duLi4GKVuW1tbSKVSHJEZ5jupRYsW8PLyUjs3a9YszJ49W+3c9evXoVAo4O7urnbe3d0dP//8c611y2SyWsvLZDKtYmRCYyCenp4oKiqCk5MTJBJJg+Xlcjm8vLxQVFRktC5HU7CEdlhCGwDLaIcltAFgOxqTxtgGQRBw584deHp6Gu0e9vb2uHjxIiorKw1SnyAINb7bHu6dMTcmNAZiZWWF9u3ba32ds7Nzo/kj04cltMMS2gBYRjssoQ0A29GYNLY2GKtn5p/s7e1hb29v9Pv8U5s2bWBtbY2SkhK18yUlJZBKpbVeI5VKtSpfFz7lRERERAZha2uL4OBgZGVlqc4plUpkZWUhPDy81mvCw8PVygNAZmZmneXrwh4aIiIiMpjk5GSMHj0aISEhCA0NxYoVK1BeXo64uDgAQGxsLNq1a4eUlBQAwIQJE9C3b18sXboUgwcPxpYtW3Dq1CmsW7dOq/syoTETOzs7zJo1q9GNQWrLEtphCW0ALKMdltAGgO1oTCyhDWIzfPhw/PHHH5g5cyZkMhmCgoKwd+9e1cTfy5cvw8rqfwNEERERyMjIwDvvvIO3334bXbp0wc6dO9GjRw+t7st1aIiIiEj0OIeGiIiIRI8JDREREYkeExoiIiISPSY0REREJHpMaMxA223VG5uUlBQ89thjcHJygpubG2JiYlBYWGjusPSycOFCSCQSTJw40dyhaO3q1at4+eWX0bp1azg4OCAgIACnTp0yd1haUSgUmDFjBnx8fODg4IDOnTtj3rx5JtnrRh+HDx/GkCFD4OnpCYlEUmPvGUEQMHPmTHh4eMDBwQGRkZH49ddfzRNsHeprQ1VVFaZOnYqAgAA0b94cnp6eiI2NxbVr18wXcB0a+iz+6bXXXoNEIsGKFStMFh8ZHxMaE9N2W/XGKCcnB4mJiTh27BgyMzNRVVWFAQMGoLy83Nyh6eTkyZNYu3Ytevbsae5QtHbz5k306dMHNjY2+Oabb/DTTz9h6dKlaNWqlblD08qiRYuwZs0arFq1CufOncOiRYvw3nvv4YMPPjB3aPUqLy9HYGAgVq9eXev77733HlauXInU1FQcP34czZs3R1RUFO7du2fiSOtWXxvu3r2L/Px8zJgxA/n5+di+fTsKCwvx7LPPmiHS+jX0WTywY8cOHDt2zKjbDpCZCGRSoaGhQmJiouq1QqEQPD09hZSUFDNGpZ/S0lIBgJCTk2PuULR2584doUuXLkJmZqbQt29fYcKECeYOSStTp04VHn/8cXOHobfBgwcLr776qtq5559/Xhg1apSZItIeAGHHjh2q10qlUpBKpcLixYtV527duiXY2dkJn376qRkibNjDbajNiRMnBADCpUuXTBOUDupqx5UrV4R27doJZ8+eFTp27CgsX77c5LGR8bCHxoQebKseGRmpOtfQtupicPv2bQCAq6urmSPRXmJiIgYPHqz2mYjJl19+iZCQEAwbNgxubm7o1asXPvroI3OHpbWIiAhkZWXhl19+AQB8//33OHLkCAYOHGjmyHR38eJFyGQytf+2XFxcEBYWJvq/d4lEgpYtW5o7FK0olUq88sormDx5Mrp3727ucMgIuFKwCemyrXpjp1QqMXHiRPTp00frVR3NbcuWLcjPz8fJkyfNHYrOfvvtN6xZswbJycl4++23cfLkSbzxxhuwtbXF6NGjzR2exqZNmwa5XI6uXbvC2toaCoUC8+fPx6hRo8wdms5kMhkA1Pr3/uA9sbl37x6mTp2KkSNHNqqNHjWxaNEiNGvWDG+88Ya5QyEjYUJDeklMTMTZs2dx5MgRc4eilaKiIkyYMAGZmZkm343WkJRKJUJCQrBgwQIAQK9evXD27FmkpqaKKqH57LPPsHnzZmRkZKB79+4oKCjAxIkT4enpKap2WLKqqiq89NJLEAQBa9asMXc4WsnLy8P777+P/Px8SCQSc4dDRsIhJxPSZVv1xiwpKQl79uzBoUOH0L59e3OHo5W8vDyUlpbi0UcfRbNmzdCsWTPk5ORg5cqVaNasGRQKhblD1IiHhwf8/f3VznXr1g2XL182U0S6mTx5MqZNm4YRI0YgICAAr7zyCt58803V5nVi9OBv2hL+3h8kM5cuXUJmZqboeme+/fZblJaWokOHDqq/90uXLmHSpEnw9vY2d3hkIExoTEiXbdUbI0EQkJSUhB07duDgwYPw8fExd0ha69+/P86cOYOCggLVERISglGjRqGgoADW1tbmDlEjffr0qfHI/C+//IKOHTuaKSLd3L17V22zOgCwtraGUqk0U0T68/HxgVQqVft7l8vlOH78uKj+3h8kM7/++isOHDiA1q1bmzskrb3yyiv44Ycf1P7ePT09MXnyZOzbt8/c4ZGBcMjJxBraVl0MEhMTkZGRgV27dsHJyUk1H8DFxQUODg5mjk4zTk5ONeb8NG/eHK1btxbVXKA333wTERERWLBgAV566SWcOHEC69atw7p168wdmlaGDBmC+fPno0OHDujevTtOnz6NZcuW4dVXXzV3aPUqKyvD+fPnVa8vXryIgoICuLq6okOHDpg4cSLeffdddOnSBT4+PpgxYwY8PT0RExNjvqAfUl8bPDw88OKLLyI/Px979uyBQqFQ/b27urrC1tbWXGHX0NBn8XAiZmNjA6lUCj8/P1OHSsZi7sesmqIPPvhA6NChg2BrayuEhoYKx44dM3dIWgFQ67FhwwZzh6YXMT62LQiCsHv3bqFHjx6CnZ2d0LVrV2HdunXmDklrcrlcmDBhgtChQwfB3t5e6NSpk/B///d/QkVFhblDq9ehQ4dq/VsYPXq0IAj3H92eMWOG4O7uLtjZ2Qn9+/cXCgsLzRv0Q+prw8WLF+v8ez906JC5Q1fT0GfxMD62bXkkgtDIl+IkIiIiagDn0BAREZHoMaEhIiIi0WNCQ0RERKLHhIaIiIhEjwkNERERiR4TGiIiIhI9JjREREQkekxoiERMIpFg586dTe7e2urXrx8mTpxo7jCIyIiY0BA1MmPGjIFEIoFEIoGNjQ3c3d3xzDPPIC0trcbeRsXFxRg4cKBR45k9ezaCgoJqnDfFvdPT09GyZUuj3oOILAMTGqJGKDo6GsXFxfj999/xzTff4KmnnsKECRPwr3/9C9XV1apyUqkUdnZ2ddZTVVVltBgbujcRkSkxoSFqhOzs7CCVStGuXTs8+uijePvtt7Fr1y588803SE9PV5X757DP77//DolEgq1bt6Jv376wt7fH5s2bAQAff/wxunXrBnt7e3Tt2hUffvih2v2uXLmCkSNHwtXVFc2bN0dISAiOHz+O9PR0zJkzB99//72q1+jB/R8ecjpz5gyefvppODg4oHXr1hg3bhzKyspU748ZMwYxMTFYsmQJPDw80Lp1ayQmJmqVdD3oLfrvf/8Lb29vuLi4YMSIEbhz546qTHl5OWJjY9GiRQt4eHhg6dKlNeqpqKjAW2+9hXbt2qF58+YICwtDdnY2AODevXvo3r07xo0bpyp/4cIFODk5IS0tTeNYici0mNAQicTTTz+NwMBAbN++vd5y06ZNw4QJE3Du3DlERUVh8+bNmDlzJubPn49z585hwYIFmDFjBjZu3Ajg/i7Fffv2xdWrV/Hll1/i+++/x5QpU6BUKjF8+HBMmjQJ3bt3R3FxMYqLizF8+PAa9ywvL0dUVBRatWqFkydP4vPPP8eBAweQlJSkVu7QoUO4cOECDh06hI0bNyI9PV0tQdPEhQsXsHPnTuzZswd79uxBTk4OFi5cqHp/8uTJyMnJwa5du7B//35kZ2cjPz9frY6kpCTk5uZiy5Yt+OGHHzBs2DBER0fj119/VSWCGzduxK5du6BQKPDyyy/jmWeeafS7fxM1aebeHZOI1I0ePVoYOnRore8NHz5c6Natm+o1AGHHjh2CIAiqnZFXrFihdk3nzp2FjIwMtXPz5s0TwsPDBUEQhLVr1wpOTk7Cn3/+Wes9Z82aJQQGBtY4/897r1u3TmjVqpVQVlamev+rr74SrKysBJlMpmpXx44dherqalWZYcOGCcOHD6/1voIgCBs2bBBcXFzUYnF0dBTkcrnq3OTJk4WwsDBBEAThzp07gq2trfDZZ5+p3v/zzz8FBwcH1U7qly5dEqytrYWrV6+q3at///7C9OnTVa/fe+89oU2bNkJSUpLg4eEhXL9+vc44icj8mpk5nyIiLQiCAIlEUm+ZkJAQ1c/l5eW4cOEC4uPjkZCQoDpfXV0NFxcXAEBBQQF69eoFV1dXneM6d+4cAgMD0bx5c9W5Pn36QKlUorCwEO7u7gCA7t27w9raWlXGw8MDZ86c0epe3t7ecHJyUqujtLQUwP3em8rKSoSFhaned3V1hZ+fn+r1mTNnoFAo8Mgjj6jVW1FRgdatW6teT5o0CTt37sSqVavwzTffqL1HRI0PExoiETl37hx8fHzqLfPPpOLBHJaPPvpI7UsegCqxcHBwMHCUdbOxsVF7LZFIajy5Zew6ysrKYG1tjby8PLXkCgBatGih+rm0tBS//PILrK2t8euvvyI6OlqrOInItDiHhkgkDh48iDNnzuCFF17Q+Bp3d3d4enrit99+g6+vr9rxIDHq2bMnCgoKcOPGjVrrsLW1hUKhqPc+3bp1w/fff4/y8nLVue+++w5WVlZqvSPG1rlzZ9jY2OD48eOqczdv3sQvv/yiet2rVy8oFAqUlpbW+J1IpVJVuVdffRUBAQHYuHEjpk6dinPnzpmsHUSkPSY0RI1QRUUFZDIZrl69ivz8fCxYsABDhw7Fv/71L8TGxmpV15w5c5CSkoKVK1fil19+wZkzZ7BhwwYsW7YMADBy5EhIpVLExMTgu+++w2+//YZt27YhNzcXwP0hnosXL6KgoADXr19HRUVFjXuMGjUK9vb2GD16NM6ePYtDhw5h/PjxeOWVV1TDTabQokULxMfHY/LkyTh48CDOnj2LMWPGwMrqf//UPfLIIxg1ahRiY2Oxfft2XLx4ESdOnEBKSgq++uorAMDq1auRm5uLjRs3YtSoUYiJicGoUaNQWVlpsrYQkXaY0BA1Qnv37oWHhwe8vb0RHR2NQ4cOYeXKldi1a1eNYZKGjB07Fh9//DE2bNiAgIAA9O3bF+np6aoeGltbW+zfvx9ubm4YNGgQAgICsHDhQtV9XnjhBURHR+Opp55C27Zt8emnn9a4h6OjI/bt24cbN27gsccew4svvoj+/ftj1apV+v8ytLR48WI88cQTGDJkCCIjI/H4448jODhYrcyGDRsQGxuLSZMmwc/PDzExMTh58iQ6dOiAn3/+GZMnT8aHH34ILy8vAMCHH36I69evY8aMGSZvDxFpRiIIgmDuIIiIiIj0wR4aIiIiEj0mNERERCR6TGiIiIhI9JjQEBERkegxoSEiIiLRY0JDREREoseEhoiIiESPCQ0RERGJHhMaIiIiEj0mNERERCR6TGiIiIhI9JjQEBERkej9f1VQ2hWxYprGAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ect_3d = ECT(num_dirs=16, num_thresh=20)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Note that the each of the directions are appended in a list for the ECT result, so we won't see the same periodic behavior as in the 2d case.\n",
+    "\n",
+    " ECT results inherit from ndarrays but they store the associated directions and thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.48609512, -0.71244342, -0.50609872],\n",
+       "       [ 0.00364975, -0.82190818,  0.56960831],\n",
+       "       [-0.77710663, -0.23260984,  0.5848059 ],\n",
+       "       [ 0.75362655, -0.61431121, -0.23381352],\n",
+       "       [-0.18133219, -0.98270096, -0.03764916],\n",
+       "       [-0.92728701,  0.26549447,  0.26391569],\n",
+       "       [-0.81397529,  0.14802792,  0.56172232],\n",
+       "       [ 0.54073592, -0.29337918,  0.78837385],\n",
+       "       [ 0.48363696,  0.74722911, -0.45578937],\n",
+       "       [ 0.99631892,  0.01735462,  0.08394891],\n",
+       "       [ 0.74179171, -0.62822234, -0.23469503],\n",
+       "       [ 0.09906268, -0.01998319, -0.99488052],\n",
+       "       [ 0.57301878,  0.71110718, -0.40740159],\n",
+       "       [ 0.93645869, -0.11437729, -0.33160663],\n",
+       "       [-0.19170715,  0.53609831, -0.82209913],\n",
+       "       [ 0.69801901,  0.38101835, -0.6062957 ]])"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.directions.vectors\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-0.8660254 , -0.77486483, -0.68370427, -0.5925437 , -0.50138313,\n",
+       "       -0.41022256, -0.31906199, -0.22790142, -0.13674085, -0.04558028,\n",
+       "        0.04558028,  0.13674085,  0.22790142,  0.31906199,  0.41022256,\n",
+       "        0.50138313,  0.5925437 ,  0.68370427,  0.77486483,  0.8660254 ])"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_3d.thresholds\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " We can also define custom directions and thresholds for the ECT in case we need finer control. We use random sampling from the unit sphere for the directions and cosine sampling for the thresholds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from ect import Directions\n",
+    "\n",
+    "directions = Directions.random(16, dim=3)\n",
+    "thresholds = np.cos(np.linspace(0, np.pi, 20))\n",
+    "ect_3d = ECT(directions=directions, thresholds=thresholds)\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of direction vectors: 10000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Direction Index', ylabel='Threshold $a$'>"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# another example sampling from the half sphere\n",
+    "sample_size = 100\n",
+    "theta = np.linspace(0, np.pi / 2, sample_size)  # Only go up to pi/2 for half sphere\n",
+    "phi = np.linspace(0, 2 * np.pi, sample_size)\n",
+    "theta, phi = np.meshgrid(theta, phi)\n",
+    "\n",
+    "# Flatten the meshgrid arrays and create vectors\n",
+    "half_sphere_vectors = np.column_stack(\n",
+    "    [\n",
+    "        np.sin(theta.flatten()) * np.cos(phi.flatten()),\n",
+    "        np.sin(theta.flatten()) * np.sin(phi.flatten()),\n",
+    "        np.cos(theta.flatten()),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Normalize the vectors\n",
+    "half_sphere_vectors = half_sphere_vectors / np.linalg.norm(\n",
+    "    half_sphere_vectors, axis=1, keepdims=True\n",
+    ")\n",
+    "\n",
+    "directions = Directions.from_vectors(half_sphere_vectors)\n",
+    "print(f\"Number of direction vectors: {len(directions)}\")\n",
+    "ect_3d = ECT(directions=directions, num_thresh=20)  # Reduced number of thresholds\n",
+    "result_3d = ect_3d.calculate(graph_3d)\n",
+    "result_3d.plot()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/objects.inv b/docs/objects.inv
index 1c88534..bb21658 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 53395e6..e06d2b2 100644
--- a/docs/py-modindex.html
+++ b/docs/py-modindex.html
@@ -1,23 +1,25 @@
-
-
 <!DOCTYPE html>
 <html class="writer-html5" lang="en" data-content_root="./">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Python Module Index &mdash; ect 0.1.5 documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=e59714d7" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
 
   
     <link rel="shortcut icon" href="_static/ECT_Logo_128x128.png"/>
-      <script src="_static/jquery.js?v=5d32c60e"></script>
-      <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-      <script src="_static/documentation_options.js?v=59fadc99"></script>
-      <script src="_static/doctools.js?v=9a2dae69"></script>
-      <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
-      <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=59fadc99"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -57,12 +59,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.EmbeddedGraph"><code class="docutils literal notranslate"><span class="pre">EmbeddedGraph</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_graph.html#ect.embed_graph.create_example_graph"><code class="docutils literal notranslate"><span class="pre">create_example_graph()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="embed_cw.html">2.2. Embedded CW complex</a><ul>
 <li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.EmbeddedCW"><code class="docutils literal notranslate"><span class="pre">EmbeddedCW</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="embed_cw.html#ect.embed_cw.create_example_cw"><code class="docutils literal notranslate"><span class="pre">create_example_cw()</span></code></a></li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="ect_on_graphs.html">2.3. ECT on graphs</a><ul>
@@ -72,18 +72,15 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials.html">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
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@@ -72,18 +72,15 @@
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-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
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 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">4. Contributing</a><ul>
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\ No newline at end of file
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"websit": 5, "weight": 4, "well": [1, 10], "when": [1, 4, 12], "where": [2, 3, 11], "whether": [1, 10], "which": 1, "who": 5, "why": 11, "with_label": [3, 9], "within": 4, "without": 12, "won": 12, "work": [1, 3, 10], "would": 1, "write": 1, "written": [1, 5, 9], "wrong": 4, "x": [4, 11, 12], "xlabel": [11, 12], "xtick": 10, "y": [11, 12], "yemeen": 5, "yet": [3, 11], "ylabel": [11, 12], "you": [1, 4, 5, 6, 9, 11, 12], "your": [1, 10], "ytick": 10, "z": [11, 12], "zero": 9, "zip": [9, 11], "zipfil": 9, "zlabel": [11, 12]}, "titles": ["<span class=\"section-number\">6. </span>Citing", "<span class=\"section-number\">4. </span>Contributing to the <cite>ect</cite> Package", "<span class=\"section-number\">2.3. </span>ECT on Graphs", "<span class=\"section-number\">2.2. </span>Embedded CW complex", "<span class=\"section-number\">2.1. </span>Embedded graphs", "ect: Euler Characteristic Transform in Python", "<span class=\"section-number\">1. </span>Installation", "<span class=\"section-number\">5. </span>GPL-3.0 License", "<span class=\"section-number\">2. </span>Table of Contents", "<span class=\"section-number\">3.3. </span>ECT on Matisse\u2019s \u201cThe Parakeet and the Mermaid\u201d", "Tutorial for exact ECT computation", "<span class=\"section-number\">3.2. </span>Tutorial: ECT for CW complexes", "<span class=\"section-number\">3.1. </span>Tutorial: ECT for Embedded Graphs", "<span class=\"section-number\">3. </span>Tutorials"], "titleterms": {"": 9, "0": 7, "3": 7, "The": 9, "acknowledg": 9, "author": 5, "basic": 12, "characterist": 5, "cite": 0, "code": 1, "complex": [3, 11], "comput": 10, "conclus": 1, "contact": 5, "content": [5, 8, 13], "contribut": 1, "cw": [3, 11], "depend": 5, "descript": 5, "document": [1, 5], "ect": [1, 2, 5, 6, 9, 10, 11, 12], "embed": [3, 4, 12], "euler": 5, "exact": 10, "format": 1, "get": [1, 5], "gpl": 7, "graph": [2, 4, 12], "guidelin": 1, "inform": 5, "instal": [5, 6], "issu": 1, "licens": [5, 7], "matiss": 9, 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@@ -7,24 +5,28 @@
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>3. Tutorials &mdash; ect 0.1.5 documentation</title>
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+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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-    <link rel="next" title="3.1. Tutorial : ECT for embedded graphs" href="notebooks/Tutorial-ECT_for_embedded_graphs.html" />
+    <link rel="next" title="3.1. Tutorial: ECT for Embedded Graphs" href="notebooks/tutorial_graph.html" />
     <link rel="prev" title="2.3. ECT on Graphs" href="ect_on_graphs.html" /> 
 </head>
 
@@ -59,12 +61,10 @@
 <li class="toctree-l1"><a class="reference internal" href="modules.html">2. Modules</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="embed_graph.html">2.1. Embedded graphs</a><ul>
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@@ -74,18 +74,15 @@
 </ul>
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 <li class="toctree-l1 current"><a class="current reference internal" href="#">3. Tutorials</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
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@@ -133,18 +130,15 @@ <h1><span class="section-number">3. </span>Tutorials<a class="headerlink" href="
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 <ul>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html">3.1. Tutorial : ECT for embedded graphs</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Constructing-the-embedded-graph">3.1.1. Constructing the embedded graph</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#Computing-the-ECT-of-G">3.1.2. Computing the ECT of <span class="math notranslate nohighlight">\(G\)</span></a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Tutorial-ECT_for_embedded_graphs.html#SECT">3.1.3. SECT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/tutorial_graph.html">3.1. Tutorial: ECT for Embedded Graphs</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/tutorial_graph.html#Basic-Usage">3.1.1. Basic Usage</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/Tutorial-ECT_for_CW_Complexes.html">3.2. Tutorial: ECT for CW complexes</a></li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/Matisse_ECT.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/tutorial_cw.html">3.2. Tutorial: ECT for CW complexes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/Matisse/example_matisse.html">3.3. ECT on Matisse’s “The Parakeet and the Mermaid”</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/Matisse/example_matisse.html#Acknowledgements">3.3.1. Acknowledgements</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="notebooks/Tutorial-ExactECT.html">3.4. Tutorial for exact ECT computation</a></li>
 </ul>
 </div>
 </section>
@@ -154,7 +148,7 @@ <h1><span class="section-number">3. </span>Tutorials<a class="headerlink" href="
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="ect_on_graphs.html" class="btn btn-neutral float-left" title="2.3. ECT on Graphs" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="notebooks/Tutorial-ECT_for_embedded_graphs.html" class="btn btn-neutral float-right" title="3.1. Tutorial : ECT for embedded graphs" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="notebooks/tutorial_graph.html" class="btn btn-neutral float-right" title="3.1. Tutorial: ECT for Embedded Graphs" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
diff --git a/ect/ect/__init__.py b/ect/ect/__init__.py
deleted file mode 100644
index 6c1db5d..0000000
--- a/ect/ect/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# __init__.py
-# import .ect_graph as ect_graph
-# import .embed_graph as embed_graph
-
-from .ect_graph import ECT
-from .embed_graph import EmbeddedGraph, create_example_graph
-from .embed_cw import EmbeddedCW, create_example_cw
diff --git a/ect/ect/ect_graph.py b/ect/ect/ect_graph.py
deleted file mode 100644
index 3c21945..0000000
--- a/ect/ect/ect_graph.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import numpy as np
-from itertools import compress, combinations
-from numba import jit
-import matplotlib.pyplot as plt
-from ect.embed_cw import EmbeddedCW
-import time
-
-
-class ECT:
-    """
-    A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedGraph` or :any:`EmbeddedCW`.
-
-    The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.threshes``, and :math:`\omega_j` is the ith entry in ``self.thetas``.
-
-    Attributes
-        num_dirs (int):
-            The number of directions to consider in the matrix.
-        num_thresh (int):
-            The number of thresholds to consider in the matrix.
-        bound_radius (int):
-            Either ``None``, or a positive radius of the bounding circle.
-        ECT_matrix (np.array):
-            The matrix to store the ECT.
-        SECT_matrix (np.array):
-            The matrix to store the SECT.
-
-    """
-
-    def __init__(self, num_dirs, num_thresh, bound_radius=None):
-        """
-        Constructs all the necessary attributes for the ECT object.
-
-        Parameters:
-            num_dirs (int):
-                The number of directions to consider in the matrix.
-            num_thresh (int):
-                The number of thresholds to consider in the matrix.
-            bound_radius (int):
-                Either None, or a positive radius of the bounding circle.
-        """
-        self.num_dirs = num_dirs
-
-        # Note: This version doesn't include 2pi since its the same as the 0 direction.
-        self.thetas = np.linspace(0, 2*np.pi, self.num_dirs, endpoint=False)
-
-        self.num_thresh = num_thresh
-        self.set_bounding_radius(bound_radius)
-
-        self.ECT_matrix = np.zeros((num_dirs, num_thresh))
-        self.SECT_matrix = np.zeros((num_dirs, num_thresh))
-
-    def set_bounding_radius(self, bound_radius):
-        """
-        Manually sets the radius of the bounding circle centered at the origin for the ECT object.
-
-        Parameters:
-            bound_radius (int): 
-                Either None, or a positive radius of the bounding circle.
-        """
-        self.bound_radius = bound_radius
-
-        if self.bound_radius is None:
-            self.threshes = None
-        else:
-            self.threshes = np.linspace(-bound_radius,
-                                        bound_radius, self.num_thresh)
-
-    def get_radius_and_thresh(self, G, bound_radius):
-        """
-        An internally used function to get the bounding radius and thresholds for the ECT calculation.
-
-        Parameters:
-            G (EmbeddedGraph / EmbeddedCW):
-                The input graph to calculate the ECT for.
-            bound_radius (float):
-                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.
-
-        Returns:
-            float, np.array
-                The bounding radius and the thresholds for the ECT calculation.
-
-        """
-       # Either use the global radius and the set self.threshes; or use the tight bounding box and calculate
-        # the thresholds from that.
-        if bound_radius == None:
-            # First try to get the internally stored bounding radius
-            if self.bound_radius is not None:
-                r = self.bound_radius
-                r_threshes = self.threshes
-
-            # If the bounding radius is not set, use the global bounding radius
-            else:
-                r = G.get_bounding_radius()
-                r_threshes = np.linspace(-r, r, self.num_thresh)
-
-        else:
-            # The user wants to use a different bounding radius
-            if bound_radius <= 0:
-                raise ValueError(
-                    f'Bounding radius given was {bound_radius}, but must be a positive number.')
-            r = bound_radius
-            r_threshes = np.linspace(-r, r, self.num_thresh)
-
-        return r, r_threshes
-
-    def get_ECT(self):
-        """
-        Returns the ECT matrix.
-        """
-        return self.ECT_matrix
-
-    def get_SECT(self):
-        """
-        Returns the SECT matrix.
-        """
-        return self.SECT_matrix
-
-    def calculateECC(self, G, theta, bound_radius=None, return_counts=False):
-        """
-        Function to compute the Euler Characteristic Curve (ECC) of an `EmbeddedGraph`.
-
-        Parameters:
-            G (nx.Graph): The graph to compute the ECC for.
-            theta (float): The angle (in radians) for the direction function.
-            bound_radius (float, optional): Radius for threshold range. Default is None.
-            return_counts (bool, optional): Whether to return vertex, edge, and face counts. Default is False.
-
-        Returns:
-            numpy.ndarray: ECC values at each threshold.
-            (Optional) Tuple of counts: (ecc, vertex_count, edge_count, face_count)
-        """
-        r, r_threshes = self.get_radius_and_thresh(G, bound_radius)
-
-        r_threshes = np.array(r_threshes)
-
-        # Sort vertices and edges based on projection
-        v_list, g = G.sort_vertices(theta, return_g=True)
-        g_list = np.array([g[v] for v in v_list])
-        sorted_g_list = np.sort(g_list)
-
-        e_list, g_e = G.sort_edges(theta, return_g=True)
-        g_e_list = np.array([g_e[e] for e in e_list])
-        sorted_g_e_list = np.sort(g_e_list)
-
-        vertex_count = np.searchsorted(sorted_g_list, r_threshes, side='right')
-        edge_count = np.searchsorted(sorted_g_e_list, r_threshes, side='right')
-
-        if isinstance(G, EmbeddedCW):
-            f_list, g_f = G.sort_faces(theta, return_g=True)
-            g_f_list = np.array([g_f[f] for f in f_list])
-            sorted_g_f_list = np.sort(g_f_list)
-            face_count = np.searchsorted(
-                sorted_g_f_list, r_threshes, side='right')
-        else:
-            face_count = np.zeros_like(r_threshes, dtype=np.int32)
-
-        ecc = vertex_count - edge_count + face_count
-
-        if return_counts:
-            return ecc, vertex_count, edge_count, face_count
-        else:
-            return ecc
-
-    def calculateECT(self, graph, bound_radius=None, compute_SECT=True):
-        """
-        Calculates the ECT from an input either `EmbeddedGraph` or `EmbeddedCW`. The entry ``M[i,j]`` is :math:`\\chi(K_{a_j})` for the direction :math:`\omega_i` where :math:`a_j` is the jth entry in ``self.threshes``, and :math:`\omega_i` is the ith entry in ``self.thetas``.
-
-        Parameters:
-            graph (EmbeddedGraph/EmbeddedCW):
-                The input graph to calculate the ECT from.
-            bound_radius (float):
-                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.
-            compute_SECT (bool):
-                Whether to compute the SECT after the ECT is computed. Default is True. Sets the SECT_matrix attribute, but doesn't return it. Can be returned with the get_SECT method.
-
-        Returns:
-            np.array
-                The matrix representing the ECT of size (num_dirs,num_thresh).
-        """
-
-        r, r_threshes = self.get_radius_and_thresh(graph, bound_radius)
-
-        # Note... this overwrites the self.threshes if it's not set.
-        self.set_bounding_radius(r)
-
-        M = np.zeros((self.num_dirs, self.num_thresh))
-
-        for i, theta in enumerate(self.thetas):
-            M[i] = self.calculateECC(graph, theta, r)
-
-        self.ECT_matrix = M
-
-        if compute_SECT:
-            self.SECT_matrix = self.calculateSECT()
-
-        return self.ECT_matrix
-
-    def calculateSECT(self):
-        """
-        Function to calculate the Smooth Euler Characteristic Transform (SECT) from the ECT matrix. 
-
-        Returns:
-            np.array
-                The matrix representing the SECT of size (num_dirs,num_thresh).
-        """
-
-        # Calculate the SECT
-        M = self.ECT_matrix
-
-        # Get average of each row, corresponds to each direction
-        A = np.average(M, axis=1)
-
-        # Subtract the average from each row
-        M_normalized = M - A[:, np.newaxis]
-
-        # Take the cumulative sum of each row to get the SECT
-        M_SECT = np.cumsum(M_normalized, axis=1)
-
-        return M_SECT
-
-    def plotECC(self, graph, theta, bound_radius=None, draw_counts=False):
-        """
-        Function to plot the Euler Characteristic Curve (ECC) for a specific direction theta. Note that this calculates the ECC for the input graph and then plots it.
-
-        Parameters:
-            graph (EmbeddedGraph/EmbeddedCW):
-                The input graph or CW complex.
-            theta (float):
-                The angle in :math:`[0,2\pi]` for the direction to plot the ECC.
-            bound_radius (float):
-                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a postive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None. 
-            draw_counts (bool):
-                Whether to draw the counts of vertices, edges, and faces varying across thresholds. Default is False.
-        """
-
-        r, r_threshes = self.get_radius_and_thresh(graph, bound_radius)
-        if not draw_counts:
-            ECC = self.calculateECC(graph, theta, r)
-        else:
-            ECC, vertex_count, edge_count, face_count = self.calculateECC(
-                graph, theta, r, return_counts=True)
-
-        # if self.threshes is None:
-        #     self.set_bounding_radius(graph.get_bounding_radius())
-
-        plt.step(r_threshes, ECC, label='ECC')
-
-        if draw_counts:
-            plt.step(r_threshes, vertex_count, label='Vertices')
-            plt.step(r_threshes, edge_count, label='Edges')
-            plt.step(r_threshes, face_count, label='Faces')
-            plt.legend()
-
-        theta_round = str(np.round(theta, 2))
-        plt.title(r'ECC for $\omega = ' + theta_round + '$')
-        plt.xlabel('$a$')
-        plt.ylabel(r'$\chi(K_a)$')
-
-    def plotECT(self):
-        """
-        Function to plot the Euler Characteristic Transform (ECT) matrix. Note that the ECT matrix must be calculated before calling this function.
-
-        The resulting plot will have the angle on the x-axis and the threshold on the y-axis.
-        """
-
-        # Make meshgrid.
-        # Add back the 2pi to thetas for the pcolormesh
-        thetas = np.concatenate((self.thetas, [2*np.pi]))
-        X, Y = np.meshgrid(thetas, self.threshes)
-        M = np.zeros_like(X)
-
-        # Transpose to get the correct orientation
-        M[:, :-1] = self.ECT_matrix.T
-        M[:, -1] = M[:, 0]  # Add the 2pi direction to the 0 direction
-
-        plt.pcolormesh(X, Y, M, cmap='viridis')
-        plt.colorbar()
-
-        ax = plt.gca()
-        ax.set_xticks(np.linspace(0, 2*np.pi, 9))
-
-        labels = [r'$0$',
-                  r'$\frac{\pi}{4}$',
-                  r'$\frac{\pi}{2}$',
-                  r'$\frac{3\pi}{4}$',
-                  r'$\pi$',
-                  r'$\frac{5\pi}{4}$',
-                  r'$\frac{3\pi}{2}$',
-                  r'$\frac{7\pi}{4}$',
-                  r'$2\pi$',
-                  ]
-
-        ax.set_xticklabels(labels)
-
-        plt.xlabel(r'$\omega$')
-        plt.ylabel(r'$a$')
-
-        plt.title(r'ECT of Input Graph')
-
-    def plotSECT(self):
-        """
-        Function to plot the Smooth Euler Characteristic Transform (SECT) matrix. Note that the SECT matrix must be calculated before calling this function.
-
-        The resulting plot will have the angle on the x-axis and the threshold on the y-axis.
-        """
-
-        # Make meshgrid.
-        # Add back the 2pi to thetas for the pcolormesh
-        thetas = np.concatenate((self.thetas, [2*np.pi]))
-        X, Y = np.meshgrid(thetas, self.threshes)
-        M = np.zeros_like(X)
-
-        # Transpose to get the correct orientation
-        M[:, :-1] = self.SECT_matrix.T
-        M[:, -1] = M[:, 0]  # Add the 2pi direction to the 0 direction
-
-        plt.pcolormesh(X, Y, M, cmap='viridis')
-        plt.colorbar()
-
-        ax = plt.gca()
-        ax.set_xticks(np.linspace(0, 2*np.pi, 9))
-
-        labels = [r'$0$',
-                  r'$\frac{\pi}{4}$',
-                  r'$\frac{\pi}{2}$',
-                  r'$\frac{3\pi}{4}$',
-                  r'$\pi$',
-                  r'$\frac{5\pi}{4}$',
-                  r'$\frac{3\pi}{2}$',
-                  r'$\frac{7\pi}{4}$',
-                  r'$2\pi$',
-                  ]
-
-        ax.set_xticklabels(labels)
-
-        plt.xlabel(r'$\omega$')
-        plt.ylabel(r'$t$')
-
-        plt.title(r'SECT of Input Graph')
-
-    def plot(self, plot_type):
-        """
-        Function to plot the ECT or SECT matrix. The type parameter should be either 'ECT' or 'SECT'.
-
-        Parameters:
-            plot_type : str
-                The type of plot to make. Either 'ECT' or 'SECT'.
-        """
-
-        if plot_type == 'ECT':
-            self.plotECT()
-        elif plot_type == 'SECT':
-            self.plotSECT()
-        else:
-            raise ValueError('plot_type must be either "ECT" or "SECT".')
diff --git a/ect/ect/embed_cw.py b/ect/ect/embed_cw.py
deleted file mode 100644
index 6be4687..0000000
--- a/ect/ect/embed_cw.py
+++ /dev/null
@@ -1,204 +0,0 @@
-import numpy as np
-from itertools import compress, combinations
-import matplotlib.pyplot as plt
-import networkx as nx
-from ect.embed_graph import EmbeddedGraph, create_example_graph
-from scipy.optimize import linprog
-
-
-class EmbeddedCW(EmbeddedGraph):
-    """
-    A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges. 
-
-    Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane.
-
-    """
-
-    def __init__(self):
-        """
-        Initializes an empty EmbeddedCW object.
-        """
-
-        # The super class initializes the graph and the coordinates dictionary.
-        super().__init__()
-        self.faces = []
-
-    def add_from_embedded_graph(self, G):
-        """
-        Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.
-
-        Parameters:
-            embedded_graph (EmbeddedGraph):
-                The EmbeddedGraph object to add from.
-        """
-        self.add_nodes_from(G.nodes(), G.coordinates)
-        self.add_edges_from(G.edges())
-
-    def add_face(self, face, check=True):
-        """
-        Adds a face to the list of faces.
-
-        TODO: Do we want a check to make sure the face is legit? (i.e. is a cycle in the graph, and bounds a region in the plane)
-
-        Parameters:
-            face (list):
-                A list of vertices that make up the face.
-            check (bool):
-                Whether to check that the face is a valid addition to the cw complex.
-        """
-        if check:
-
-            # Make sure all edges are in the graph
-            edges = list(zip(face, face[1:] + [face[0]]))
-            for edge in edges:
-                if edge not in self.edges:
-                    raise ValueError(f"Edge {edge} not in graph.")
-
-            # TODO: The goal here is to check that none of the other vertices are in the polygon defined by the face.
-            # Problem is that the face could be concave, so we can't just check if the point is in the convex hull of the face.
-            # This is a bit of a tricky problem, so I'm going to leave it for now.
-
-            # def in_hull(points, x):
-            #     # Solution for checking if a point is in a convex hull
-            #     # from Nils answer here:
-            #     # https://stackoverflow.com/questions/16750618/whats-an-efficient-way-to-find-if-a-point-lies-in-the-convex-hull-of-a-point-cl
-            #     n_points = len(points)
-            #     n_dim = len(x)
-            #     c = np.zeros(n_points)
-            #     A = np.r_[points.T,np.ones((1,n_points))]
-            #     b = np.r_[x, np.ones(1)]
-            #     lp = linprog(c, A_eq=A, b_eq=b)
-            #     return lp.success
-
-            # points = np.array([self.coordinates[v] for v in face])
-            # if not in_hull(points.T, self.coordinates[face[0]]):
-            #     raise ValueError(f"Face {face} does not bound an empty region in the plane.")
-
-        # Note: faces need to be tuples to make
-        # the face hashable so it can be used as a key in a dictionary
-        self.faces.append(tuple(face))
-
-    def g_omega_faces(self, theta):
-        """
-        Calculates the function value of the faces of the graph by making the value equal to the max vertex value 
-
-        Parameters:
-
-            theta (float): 
-                The direction of the function to be calculated.
-
-        Returns:
-            dict
-                A dictionary of the function values of the faces.
-        """
-        g = super().g_omega(theta)
-
-        g_faces = {}
-        for face in self.faces:
-            g_faces[tuple(face)] = max([g[v] for v in face])
-
-        return g_faces
-
-    def sort_faces(self, theta, return_g=False):
-        """
-        Function to sort the faces of the graph according to the function
-
-        .. math ::
-
-            g_\\omega(\\sigma) = \\max \\{ g_\\omega(v) \\mid  v \\in \\sigma \\}
-
-        in the direction of :math:`\\theta \\in [0,2\\pi]` .
-
-        Parameters:
-            theta (float):
-                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.
-            return_g (bool):
-                Whether to return the :math:`g(v)` values along with the sorted edges.
-
-        Returns:
-            A list of edges sorted in increasing order of the :math:`g(v)` values. 
-            If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. 
-
-        """
-        g_f = self.g_omega_faces(theta)
-
-        f_list = sorted(self.faces, key=lambda face: g_f[face])
-
-        if return_g:
-            # g_sorted = [g[v] for v in v_list]
-            return f_list, g_f
-        else:
-            return f_list
-
-    def plot_faces(self, theta=None, ax=None, **kwargs):
-        """
-        Plots the faces of the graph in the direction of theta.
-
-        Parameters:
-            theta (float):
-                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.
-            ax (matplotlib.axes.Axes):
-                The axes to plot the graph on. If None, a new figure is created.
-            **kwargs:
-                Additional keyword arguments to pass to the ax.fill function.
-
-        Returns:
-            matplotlib.axes.Axes
-                The axes object with the plot.
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        for face in self.faces:
-            face_coords = np.array([self.coordinates[v] for v in face])
-            ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs)
-
-        return ax
-
-    def plot(self, bounding_circle=False, color_nodes_theta=None, ax=None, **kwargs):
-        """
-        Plots the graph with the faces filled in.
-
-        Returns:
-            matplotlib.axes.Axes
-                The axes object with the plot.
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        ax = self.plot_faces(0, facecolor='lightblue', ax=ax)
-        ax = super().plot(bounding_circle=bounding_circle,
-                          color_nodes_theta=color_nodes_theta, ax=ax)
-        return ax
-
-
-def create_example_cw(centered=True, center_type='min_max'):
-    """
-    Creates an example EmbeddedCW object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be ``mean``, ``min_max`` or ``origin``.
-
-
-    Returns:
-        EmbeddedCW
-            The example EmbeddedCW object.
-    """
-    G = create_example_graph(centered=False)
-    K = EmbeddedCW()
-    K.add_from_embedded_graph(G)
-    K.add_node('G', 2, 4)
-    K.add_node('H', 1, 5)
-    K.add_node('I', 5, 4)
-    K.add_node('J', 2, 2)
-    K.add_node('K', 2, 7)
-    K.add_edges_from([('G', 'A'), ('G', 'H'), ('H', 'D'), ('I', 'E'),
-                     ('I', 'C'), ('J', 'E'), ('K', 'D'), ('K', 'C')])
-    K.add_face(['B', 'A', 'G', 'H', 'D'])
-    K.add_face(['K', 'D', 'C'])
-
-    if centered:
-        K.set_centered_coordinates(type=center_type)
-
-    return K
diff --git a/ect/ect/embed_graph.py b/ect/ect/embed_graph.py
deleted file mode 100644
index dee5293..0000000
--- a/ect/ect/embed_graph.py
+++ /dev/null
@@ -1,785 +0,0 @@
-import networkx as nx
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.decomposition import PCA  # for PCA for normalization
-
-
-class EmbeddedGraph(nx.Graph):
-    """
-    A class to represent a graph with 2D embedded coordinates for each vertex.
-
-    Attributes
-        graph : nx.Graph
-            a NetworkX graph object
-        coordinates : dict
-            a dictionary mapping vertices to their (x, y) coordinates
-
-    """
-
-    def __init__(self):
-        """
-        Initializes an empty EmbeddedGraph object.
-
-        """
-        super().__init__()
-        self.coordinates = {}
-
-    def add_node(self, vertex, x, y):
-        """Add a vertex to the graph. 
-        If the vertex name is given as None, it will be assigned via the ``next_vert_name`` method.
-
-        Parameters:
-            vertex (hashable like int or str, or None) : The name of the vertex to add.
-            x, y (floats) : The function value of the vertex being added.
-            reset_pos (bool, optional) 
-                If True, will reset the positions of the nodes based on the function values.
-        """
-        if vertex in self.nodes:
-            raise ValueError(
-                f'The vertex name {vertex} is already used in the graph.')
-
-        if vertex is None:
-            if len(self.nodes) == 0:
-                vertex = 0
-            else:
-                vertex = self.next_vert_name(max(self.nodes))
-
-        super().add_node(vertex)
-        self.coordinates[vertex] = (x, y)
-
-    def add_nodes_from(self, nodes, coordinates):
-        """
-        Adds multiple vertices to the graph and assigns them the given coordinates.
-
-        Parameters:
-            nodes (list):
-                A list of vertices to be added.
-            coordinates (dict):
-                A dictionary mapping vertices to their coordinates.
-
-        """
-        super().add_nodes_from(nodes)
-        self.coordinates.update(coordinates)
-
-    def next_vert_name(self, s, num_verts=1):
-        """ 
-        Making a simple name generator for vertices. 
-        If you're using integers, it will just up the count by one. 
-        Letters will be incremented in the alphabet. If you reach ``Z``, it will return ``AA``. If you reach ``ZZ``, it will return ``AAA``, etc.
-
-        Parameters:
-            s (str or int): The name of the vertex to increment.
-
-        Returns:
-            str or int
-                The next name in the sequence.
-        """
-
-        if type(s) == int:
-            if num_verts > 1:
-                return [s+1+i for i in range(num_verts)]
-            else:
-                return s+1
-        elif type(s) == str and len(s) == 1:
-            if not s == 'Z':
-                if num_verts > 1:
-                    return [chr(ord(s)+1+i) for i in range(num_verts)]
-                else:
-                    return chr(ord(s)+1)
-            else:
-                if num_verts > 1:
-                    return [chr(ord('AA')+1+i) for i in range(num_verts)]
-                else:
-                    return 'AA'
-        elif type(s) == str and len(s) > 1:
-            if s[-1] == 'Z':
-                if num_verts > 1:
-                    return [s[:-1] + chr(ord('A')+1+i) for i in range(num_verts)]
-                else:
-                    return (len(s)+1) * 'A'
-            else:
-                if num_verts > 1:
-                    return [s[:-1] + chr(ord(s[-1])+1+i) for i in range(num_verts)]
-                else:
-                    return len(s) * chr(ord(s[-1])+1+1)
-        else:
-            raise ValueError('Input must be a string or an integer')
-
-    def add_edge(self, u, v):
-        """
-        Adds an edge between the vertices u and v if they exist.
-
-        Parameters:
-            u (str):
-                The first vertex of the edge.
-            v (str):
-                The second vertex of the edge.
-
-        """
-        if not self.has_node(u) or not self.has_node(v):
-            raise ValueError("One or both vertices do not exist in the graph.")
-        else:
-            super().add_edge(u, v)
-
-    def add_cycle(self, coord_matrix):
-        """
-        Add nodes and edges from a cycle of coordinates. 
-        Specifically, will add a node for each row and the edges connecting the nodes in the order they appear in the matrix as a closed cycle.
-
-        Parameters:
-            coord_matrix : numpy array
-                An (n x 2) matrix of coordinates.
-        """
-        n = len(coord_matrix)
-        if len(self.nodes) == 0:
-            last_name = 0
-        else:
-            last_name = max(self.nodes)
-
-        nodes = self.next_vert_name(last_name, num_verts=n)
-        coords = {nodes[i]: coord_matrix[i] for i in range(n)}
-        self.add_nodes_from(nodes, coords)
-        edges = [(nodes[i], nodes[(i+1) % n]) for i in range(n)]
-        self.add_edges_from(edges)
-
-    def get_coordinates(self, vertex):
-        """
-        Returns the coordinates of the given vertex.
-
-        Parameters:
-            vertex (str):
-                The vertex whose coordinates are to be returned.
-
-        Returns:
-            tuple: The coordinates of the vertex.
-
-        """
-        return self.coordinates.get(vertex)
-
-    def set_coordinates(self, vertex, x, y):
-        """
-        Sets the coordinates of the given vertex.
-
-        Parameters:
-            vertex (str):
-                The vertex whose coordinates are to be set.
-            x (float):
-                The new x-coordinate of the vertex.
-            y (float): 
-                The new y-coordinate of the vertex.
-
-        Raises:
-            ValueError: If the vertex does not exist in the graph.
-
-        """
-        if vertex in self.coordinates:
-            self.coordinates[vertex] = (x, y)
-        else:
-            raise ValueError("Vertex does not exist in the graph.")
-
-    def get_bounding_box(self):
-        """
-        Method to find a bounding box of the vertex coordinates in the graph.
-
-        Returns:
-            list: A list of tuples representing the minimum and maximum :math:`x` and :math:`y` coordinates.
-
-        """
-        if not self.coordinates:
-            return None
-
-        x_coords, y_coords = zip(*self.coordinates.values())
-        return [(min(x_coords), max(x_coords)), (min(y_coords), max(y_coords))]
-
-    def get_center(self, type='origin'):
-        """
-        Calculate and return the center of the graph. This can be done by either returning the average of the coordiantes (``mean``), the center of the bounding box (``min_max``), or the origin (``origin``).
-
-        Parameters:
-            type (str): The type of center to calculate. Options are ``mean``, ``min_max``, or ``origin``.
-
-        Returns:
-            numpy.ndarray: The :math:`(x, y)` coordinates of the center.
-        """
-        if not self.coordinates:
-            return np.array([0.0, 0.0])
-
-        if type == 'origin':
-            return np.array([0.0, 0.0])
-        elif type == 'mean':
-            coords = np.array(list(self.coordinates.values()))
-            return np.mean(coords, axis=0)
-        elif type == 'min_max':
-            x_coords, y_coords = zip(*self.coordinates.values())
-            min_x, max_x = min(x_coords), max(x_coords)
-            min_y, max_y = min(y_coords), max(y_coords)
-            return np.array([(max_x+min_x)/2, (max_y+min_y)/2])
-
-    def get_bounding_radius(self, type='origin'):
-        """
-        Method to find the radius of the bounding circle of the vertex coordinates in the graph. 
-
-        Parameters:
-            type (str): The type of center to calculate the radius relative to. Options are ``mean``, ``min_max``, or ``origin``.
-
-        Returns:
-            float: The radius of the bounding circle.
-
-        """
-        if not self.coordinates:
-            return 0
-
-        center = self.get_center(type)
-        coords = np.array(list(self.coordinates.values()))
-        distances = np.linalg.norm(coords - center, axis=1)
-        return np.max(distances)
-
-    # ------
-    # Methods for normalizing the coordinates in various ways
-    # ------
-
-    def get_centered_coordinates(self, type='min_max'):
-        """
-        Method to find the centered coordinates of the vertices in the graph.
-
-        If type is ``min_max``, the coordinates are centered at the mean of the min and max values of the :math:`x` and :math:`y` coordinates.
-        If type is ``mean``, the coordinates are centered at the mean of the :math:`x` and :math:`y` coordinates.
-        """
-
-        if not self.coordinates:
-            return None
-
-        center = self.get_center(type)
-        return {v: (x - center[0], y - center[1]) for v, (x, y) in self.coordinates.items()}
-
-    def set_centered_coordinates(self, type='min_max'):
-        """
-        Method to set the centered coordinates of the vertices in the graph. Warning: This overwrites the original coordinates.
-        """
-
-        self.coordinates = self.get_centered_coordinates(type=type)
-
-    def get_scaled_coordinates(self, radius=1):
-        """
-        Method to find the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by ``radius``.
-
-        Parameters:
-            radius (float):
-                The radius of the bounding disk.
-
-        Returns:
-            dict: A dictionary mapping vertices to their scaled coordinates.
-
-        """
-        if not self.coordinates:
-            return None
-
-        x_coords, y_coords = zip(*self.coordinates.values())
-        max_norm = max(np.linalg.norm(point)
-                       for point in zip(x_coords, y_coords))
-        x_coords = x_coords * radius / max_norm
-        y_coords = y_coords * radius / max_norm
-
-        return {v: (x, y) for v, x, y in zip(self.coordinates.keys(), x_coords, y_coords)}
-
-    def set_scaled_coordinates(self, radius=1):
-        """
-        Method to set the scaled coordinates of the vertices in the graph to fit in the disk centered at 0 with radius given by ``radius``. Warning: This overwrites the original coordinates
-
-        """
-
-        self.coordinates = self.get_scaled_coordinates(radius)
-
-    def rescale_to_unit_disk(self, preserve_center=True, center_type='origin'):
-        """
-        Rescales the graph coordinates to fit within a radius 1 disk.
-
-        Parameters:
-            preserve_center (bool): If True, maintains the current center point of type ``center_type``.
-                                    If False, centers the graph at (0, 0).
-
-        Returns:
-            self: Returns the instance for method chaining.
-
-        Raises:
-            ValueError: If the graph has no coordinates or all coordinates are identical.
-        """
-        if not self.coordinates:
-            raise ValueError("Graph has no coordinates to rescale.")
-
-        center = self.get_center(center_type)
-        coords = np.array(list(self.coordinates.values()))
-
-        coords_centered = coords - center
-
-        max_distance = np.max(np.linalg.norm(coords_centered, axis=1))
-
-        if np.isclose(max_distance, 0):
-            raise ValueError("All coordinates are identical. Cannot rescale.")
-
-        scale_factor = 1 / max_distance
-
-        new_coords = (coords_centered * scale_factor) + \
-            (center if preserve_center else 0)
-
-        for vertex, new_coord in zip(self.coordinates.keys(), new_coords):
-            self.coordinates[vertex] = tuple(new_coord)
-
-        return self
-
-    def get_PCA_coordinates(self):
-        """
-        Method to find the PCA coordinates of the vertices in the graph.
-
-        Returns:
-            dict: A dictionary mapping vertices to their PCA normalized coordinates.
-
-        """
-
-        if not self.coordinates:
-            return None
-        x_coords, y_coords = zip(*self.coordinates.values())
-        M = np.array((x_coords, y_coords)).T
-
-        pca = PCA(n_components=2)  # initiate PCA
-        pca.fit_transform(M)  # fit PCA to coordinates to find longest axis
-        pca_scores = pca.transform(M)  # retrieve PCA coordinates
-
-        nodes = list(self.coordinates.keys())
-        n = len(nodes)
-        out = {nodes[i]: pca_scores[i] for i in range(n)}
-
-        return out
-
-    def set_PCA_coordinates(self, center_type=None, scale_radius=None):
-        """
-        Method to set the PCA coordinates of the vertices in the graph which is helpful for coarse alignment. 
-        If you also want to center at zero, the options for ``center_type`` are ``mean`` or ``min_max``.
-        Set ``scale_radius`` to a value to scale to a specific radius.
-        Warning: This overwrites the original coordinates.
-        """
-        self.coordinates = self.get_PCA_coordinates()
-
-        if center_type:
-            self.set_centered_coordinates(center_type)
-
-        if scale_radius:
-            self.set_scaled_coordinates(radius=scale_radius)
-
-    # ================
-    # Functions for computing the function g(v) for vertices and edges
-    # ================
-
-    def g_omega(self, theta):
-        """
-        Function to compute the function :math:`g_\\omega(v)` for all vertices :math:`v` in the graph in the direction of :math:`\\theta \\in [0,2\\pi]` . This function is defined by :math:`g_\\omega(v) = \\langle \\texttt{pos}(v), \\omega \\rangle` .
-
-        Parameters:
-
-            theta (float):
-                The angle in :math:`[0,2\\pi]` for the direction to compute the :math:`g(v)` values.
-
-        Returns:
-
-            dict: A dictionary mapping vertices to their :math:`g(v)` values.
-
-        """
-
-        omega = (np.cos(theta), np.sin(theta))
-
-        g = {}
-        for v in self.nodes:
-            g[v] = np.dot(self.coordinates[v], omega)
-        return g
-
-    def g_omega_edges(self, theta):
-        """
-        Calculates the function value of the edges of the graph by making the value equal to the max vertex value 
-
-        Parameters:
-
-            theta (float): 
-                The direction of the function to be calculated.
-
-        Returns:
-            dict
-                A dictionary of the function values of the edges.
-        """
-        g = self.g_omega(theta)
-
-        g_edges = {}
-        for e in self.edges:
-            g_edges[e] = max(g[e[0]], g[e[1]])
-
-        return g_edges
-
-    def sort_vertices(self, theta, return_g=False):
-        """
-        Function to sort the vertices of the graph according to the function `g_omega(v)` in the direction of :math:`\\theta \\in [0,2\\pi]`.
-
-        TODO: eventually, do we want this to return a sorted list of g values as well? Since we're already doing the sorting work, it might be helpful.
-
-        Parameters:
-            theta (float):
-                The angle in :math:`[0,2 \\pi]` for the direction to sort the vertices.
-            return_g (bool):
-                Whether to return the :math:`g(v)` values along with the sorted vertices.
-
-        Returns:
-            list
-                A list of vertices sorted in increasing order of the :math:`g(v)` values. 
-                If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. 
-
-        """
-        g = self.g_omega(theta)
-
-        v_list = sorted(self.nodes, key=lambda v: g[v])
-
-        if return_g:
-            # g_sorted = [g[v] for v in v_list]
-            return v_list, g
-        else:
-            return v_list
-
-    def sort_edges(self, theta, return_g=False):
-        """
-        Function to sort the edges of the graph according to the function
-
-        .. math ::
-
-            g_\\omega(e) = \\max \\{ g_\\omega(v) \\mid  v \in e \\}
-
-        in the direction of :math:`\\theta \\in [0,2\\pi]` .
-
-        Parameters:
-            theta (float):
-                The angle in :math:`[0,2\\pi]` for the direction to sort the edges.
-            return_g (bool):
-                Whether to return the :math:`g(v)` values along with the sorted edges.
-
-        Returns:
-            A list of edges sorted in increasing order of the :math:`g(v)` values. 
-            If ``return_g`` is True, also returns the ``g`` dictionary with the function values ``g[vertex_name]=func_value``. 
-
-        """
-        g_e = self.g_omega_edges(theta)
-
-        e_list = sorted(self.edges, key=lambda e: g_e[e])
-
-        if return_g:
-            # g_sorted = [g[v] for v in v_list]
-            return e_list, g_e
-        else:
-            return e_list
-
-    def lower_edges(self, v, omega):
-        """
-        Function to compute the number of lower edges of a vertex `v` for a specific direction (included by the use of sorted `v_list`).
-
-        Parameters:
-            v (str):
-                The vertex to compute the number of lower edges for.
-            omega (tuple): 
-                The direction vector to consider given as an angle in [0, 2pi].
-
-        Returns:
-            int: The number of lower edges of the vertex v.
-
-        """
-        L = [n for n in self.neighbors(v)]
-        gv = np.dot(self.coordinates[v], omega)
-        Lg = [np.dot(self.coordinates[v], omega) for v in L]
-        return sum(n >= gv for n in Lg)  # includes possible duplicate counts
-
-    # ================
-    # Functions for getting the angles where vertices switch order
-    # ================
-
-    def get_all_normals_matrix(self, num_rounding_digits=None):
-        """
-        Function to get all angles of normals to any line between vertices in the graph, returned as a matrix. Note this includes both adjacent vertices and non-adjacent. This function is useful for knowing the angle of the circle where two vertices switch in order. 
-
-        Parameters:
-            num_rounding_digits (int):
-                The number of digits to round the angles in the matrix. If `None`, no rounding is done. 
-
-        Returns:
-            A tuple consissting of a matrix of angles, and the sorted label list for the rows/columns.
-        """
-        P = np.array(list(self.coordinates.values()))
-        labels = list(self.coordinates.keys())
-
-        # Make rows of repeated copies of first column of P
-        X_Cols = np.tile(P[:, 0], (P.shape[0], 1)).T
-        X_diff = X_Cols - X_Cols.T  # The x value of the vector from A to B
-
-        # Make rows of repeated copies of second column of P
-        Y_Cols = np.tile(P[:, 1], (P.shape[0], 1)).T
-        Y_diff = Y_Cols - Y_Cols.T
-
-        # Convert to float to allow NaN assignment
-        X_diff = X_diff.astype(float)
-        Y_diff = Y_diff.astype(float)
-
-        # Set diagonals to nan
-        np.fill_diagonal(X_diff, np.nan)
-        np.fill_diagonal(Y_diff, np.nan)
-
-        angle_matrix = np.arctan2(X_diff, -Y_diff)
-        # Puts all entries between 0 and 2pi
-        angle_matrix = angle_matrix % (2*np.pi)
-        if num_rounding_digits != None:
-            angle_matrix = np.round(angle_matrix, num_rounding_digits)
-
-        return angle_matrix, labels
-
-    def get_normals_dict(self,
-                         num_rounding_digits=None,
-                         edges_only=False,
-                         opposites=False):
-        """
-        Function to get all angles of normals to any line between vertices in the graph, returned as a dictionary of angles with ``dict[theta]`` returning the list of pairs of vertices with vector normal to :math:`\\overrightarrow{AB}` at angle ``theta``. Note this includes both adjacent vertices and non-adjacent unless ``edges_only`` is set to ``True``. This function is useful for knowing the angle of the circle where two vertices switch in order, especially when we want to sort the events around the circle. All angles are rounded to the number of digits given by ``num_rounding_digits``, and are returned in the range :math:`[0, 2\\pi]` .
-
-        Parameters:
-            num_rounding_digits (int):
-                The number of digits to round the angles in the dictionary. If None, no rounding is done.
-            edges_only (bool):
-                If True, the dictionary version only returns the angle of the normals to the lines between vertices sharing an edge. The matrix version is unchanged.
-            opposites (bool):
-                If True, will also include both :math:`\\theta` and :math:`\\theta + \\pi` in the dictionary keys.
-
-        Returns:
-            dict: A dictionary of angles with `dict[theta]` returning the list of pairs of vertices with vector normal to :math:`\\overrightarrow\\{AB\\}` at angle `theta`, e.g. ``dict[theta] = [('A','B'), ('C', 'D')]``.
-        """
-
-        angle_matrix, labels = self.get_all_normals_matrix(
-            num_rounding_digits=num_rounding_digits)
-
-        angle_dict = {}
-        for i in range(len(labels)):
-            for j in range(i+1, len(labels)):
-                # Skip this edge if edges_only is True and the vertices are not connected
-                if edges_only and not self.has_edge(labels[i], labels[j]):
-                    continue
-
-                if angle_matrix[i, j] in angle_dict.keys():
-                    angle_dict[angle_matrix[i, j]].append(
-                        (labels[i], labels[j]))
-                elif angle_matrix[j, i] in angle_dict.keys():
-                    angle_dict[angle_matrix[j, i]].append(
-                        (labels[j], labels[i]))
-                else:
-                    angle_dict[angle_matrix[i, j]] = [
-                        (labels[i], labels[j])]
-
-        if opposites:
-            for key in list(angle_dict.keys()):
-                other_key = key + np.pi % (2*np.pi)
-                if num_rounding_digits != None:
-                    other_key = round(other_key, num_rounding_digits)
-                angle_dict[other_key] = angle_dict[key]
-
-        # Make sure all keys are in the range [0, 2pi]
-        angle_dict = {key % (2*np.pi): value for key,
-                      value in angle_dict.items()}
-
-        # Make sure all keys are rounded to the correct number of digits
-        if num_rounding_digits != None:
-            angle_dict = {round(key, num_rounding_digits)                          : value for key, value in angle_dict.items()}
-
-        return angle_dict
-
-    # ================
-    # Plotting functions
-    # ================
-    def plot(self,
-             bounding_circle=False,
-             color_nodes_theta=None,
-             ax=None,
-             with_labels=True,
-             **kwargs):
-        """
-        Function to plot the graph with the embedded coordinates.
-
-        If ``bounding_circle`` is True, a bounding circle is drawn around the graph.
-
-        If ``color_nodes_theta`` is not ``None``, it should be given as a :math:`theta` in :math:`[0,2\\pi]`. Then the nodes are colored according to the :math:`g(v)` values in the direction of :math:`\\theta`.
-
-        If ``with_labels`` is ``True``, the nodes are labeled with their names.
-
-        If ``ax`` is not ``None``, the plot is drawn on the given axis.
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        pos = self.coordinates
-        # center = self.get_center(type = 'min_max')
-        # r = self.get_bounding_radius(type = 'min_max')
-
-        if color_nodes_theta is None:
-            nx.draw(self, pos, with_labels=with_labels, ax=ax, **kwargs)
-        else:
-            g = self.g_omega(color_nodes_theta)
-            color_map = [g[v] for v in self.nodes]
-            # Some weird plotting to make the colorbar work.
-            pathcollection = nx.draw_networkx_nodes(
-                self, pos, node_color=color_map, ax=ax)
-            nx.draw_networkx_labels(self, pos=pos, font_color='black', ax=ax)
-            nx.draw_networkx_edges(self, pos, ax=ax, width=1, **kwargs)
-            fig.colorbar(pathcollection, ax=ax, **kwargs)
-
-        plt.axis('on')
-        ax.tick_params(left=True, bottom=True,
-                       labelleft=True, labelbottom=True)
-
-        if bounding_circle:
-            self.plot_bounding_circle(ax=ax)
-
-        ax.set_aspect('equal', 'box')
-
-        return ax
-
-    def plot_bounding_circle(self, ax=None, bounding_center_type='origin', **kwargs):
-        """
-        Function to plot the bounding circle of the graph. 
-
-        If ``ax`` is not None, the plot is drawn on the given axis.
-
-        If ``bounding_center_type`` is 'origin', the bounding circle is centered at the origin. If it is ``min_max``, the bounding circle is centered at the mean of the min and max values of the :math:`x` and :math`y` coordinates.
-
-
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        circle_center = self.get_center(bounding_center_type)
-        r = self.get_bounding_radius(type=bounding_center_type)
-        circle = plt.Circle(circle_center, r, fill=False,
-                            linestyle='--', color='r')
-        ax.add_patch(circle)
-
-        # Always adjust the plot limits to show the full graph
-        ax.set_xlim(circle_center[0] - r, circle_center[0] + r)
-        ax.set_ylim(circle_center[1] - r, circle_center[1] + r)
-
-    def plot_angle_circle(self, ax=None, edges_only=False):
-        """
-        Function to plot the circle of angles for the graph. 
-
-        Example Usage: 
-
-        .. code-block:: python
-
-            fig, ax = plt.subplots()
-            G.plot(ax = ax)
-            G.plot_angle_circle(ax = ax)
-
-        """
-        if ax is None:
-            fig, ax = plt.subplots()
-        else:
-            fig = ax.get_figure()
-
-        circle_center = self.get_center(type='min_max')
-        r = 1.3 * self.get_bounding_radius(type='min_max')
-        circle = plt.Circle(circle_center, r, fill=False,
-                            linestyle='--', color='black')
-        ax.add_patch(circle)
-
-        # Get all the angles with the labels to be drawn
-        angles_dict = self.get_normals_dict(
-            edges_only=edges_only, opposites=True)
-        angles_dict_labels = {key: ', '.join(
-            [f"{a[0]}{a[1]}" for a in value]) for key, value in angles_dict.items()}
-
-        # Draw hash marks on the circle
-        hash_length = 0.1*r  # Length of the hash marks
-        for angle in angles_dict_labels.keys():
-            x_start = circle_center[0] + \
-                (r-hash_length) * np.cos(angle)
-            y_start = circle_center[1] + \
-                (r-hash_length) * np.sin(angle)
-            x_end = circle_center[0] + \
-                (r + hash_length) * np.cos(angle)
-            y_end = circle_center[1] + \
-                (r + hash_length) * np.sin(angle)
-            ax.plot([x_start, x_end], [y_start, y_end], color='black')
-
-            # Add labels near the hash marks
-            scaling = 3
-            label_x = circle_center[0] + \
-                (r + scaling * hash_length) * np.cos(angle)
-            label_y = circle_center[1] + \
-                (r + scaling * hash_length) * np.sin(angle)
-            text_angle = angle if angle <= np.pi/2 or angle >= 3*np.pi/2 else angle - np.pi
-            ax.text(label_x, label_y,
-                    angles_dict_labels[angle], ha='center', va='center', rotation=np.degrees(text_angle), fontsize=8)
-        # Always adjust the plot limits to show the full graph
-        scale_factor = 1.5
-        ax.set_xlim(circle_center[0] - scale_factor*r,
-                    circle_center[0] + scale_factor*r)
-        ax.set_ylim(circle_center[1] - scale_factor*r,
-                    circle_center[1] + scale_factor*r)
-
-
-def create_example_graph(centered=True, center_type='min_max'):
-    """
-    Function to create an example ``EmbeddedGraph`` object. Helpful for testing. If ``centered`` is True, the coordinates are centered using the center type given by ``center_type``, either ``mean`` or ``min_max``.
-
-    Returns:
-        EmbeddedGraph: An example ``EmbeddedGraph`` object.
-
-    """
-    graph = EmbeddedGraph()
-
-    graph.add_node('A', 1, 2)
-    graph.add_node('B', 3, 4)
-    graph.add_node('C', 5, 7)
-    graph.add_node('D', 3, 6)
-    graph.add_node('E', 4, 3)
-    graph.add_node('F', 4, 5)
-
-    graph.add_edge('A', 'B')
-    graph.add_edge('B', 'C')
-    graph.add_edge('B', 'D')
-    graph.add_edge('B', 'E')
-    graph.add_edge('C', 'D')
-    graph.add_edge('E', 'F')
-
-    if centered:
-        graph.set_centered_coordinates(center_type)
-
-    return graph
-
-
-if __name__ == "__main__":
-    # Example usage of the EmbeddedGraph class
-
-    # Create an instance of the EmbeddedGraph class
-    graph = EmbeddedGraph()
-
-    # Add vertices with their coordinates
-    graph.add_node('A', 1, 2)
-    graph.add_node('B', 3, 4)
-    graph.add_node('C', 5, 6)
-
-    # Add edges between vertices
-    graph.add_edge('A', 'B')
-    graph.add_edge('B', 'C')
-
-    # Get coordinates of a vertex
-    coords = graph.get_coordinates('A')
-    print(f'Coordinates of A: {coords}')
-
-    # Set new coordinates for a vertex
-    graph.set_coordinates('A', 7, 8)
-    coords = graph.get_coordinates('A')
-    print(f'New coordinates of A: {coords}')
-
-    # Get the bounding box of the vertex coordinates
-    bbox = graph.get_bounding_box()
-    print(f'Bounding box: {bbox}')
diff --git a/pyproject.toml b/pyproject.toml
index 072cb87..b5aa6dc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,12 +1,12 @@
 [project]
 name = "ect"
-version = "0.1.11"
+version = "1.0.0"
 authors = [
   { name="Liz Munch", email="muncheli@msu.edu" },
 ]
 description = "A python package for computing the Euler Characteristic Transform"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
@@ -20,8 +20,12 @@ dependencies = ["numpy",
             "scikit-learn"
             ]
 
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
 [tool.setuptools.packages.find]
-where = ["ect"]
+where = ["src"]
 
 
 [project.urls]
diff --git a/src/ect/__init__.py b/src/ect/__init__.py
new file mode 100644
index 0000000..c45984d
--- /dev/null
+++ b/src/ect/__init__.py
@@ -0,0 +1,23 @@
+"""
+ECT: A Python package for computing the Euler Characteristic Transform
+
+Main classes:
+    ECT: Calculator for Euler Characteristic Transform
+    EmbeddedGraph: Graph representation for ECT computation
+    EmbeddedCW: CW complex representation for ECT computation
+    Directions: Direction vector management for ECT computation
+"""
+
+from .ect_graph import ECT
+from .embed_graph import EmbeddedGraph
+from .embed_cw import EmbeddedCW
+from .directions import Directions
+from .utils import examples
+
+__all__ = [
+    'ECT',
+    'EmbeddedGraph',
+    'EmbeddedCW',
+    'Directions',
+    'examples',
+]
diff --git a/src/ect/directions.py b/src/ect/directions.py
new file mode 100644
index 0000000..f945a1a
--- /dev/null
+++ b/src/ect/directions.py
@@ -0,0 +1,171 @@
+from typing import Optional, Sequence
+from enum import Enum
+
+import numpy as np
+
+
+class Sampling(Enum):
+    UNIFORM = "uniform"
+    RANDOM = "random"
+    CUSTOM = "custom"
+
+
+class Directions:
+    """
+    Manages direction vectors for ECT calculations.
+    Supports uniform, random, or custom sampling of directions.
+
+    Examples:
+        # Uniform sampling in 2D (default)
+        dirs = Directions.uniform(num_dirs=8)
+
+        # Uniform sampling in 3D
+        dirs = Directions.uniform(num_dirs=10, dim=3)
+
+        # Random sampling in 2D
+        dirs = Directions.random(num_dirs=10, seed=42)
+
+        # Custom angles (2D only)
+        dirs = Directions.from_angles([0, np.pi/4, np.pi/2])
+
+        # Custom vectors in any dimension
+        dirs = Directions.from_vectors([(1,0,0), (0,1,0), (0,0,1)])
+    """
+
+    def __init__(
+        self,
+        num_dirs: int = 360,
+        sampling: Sampling = Sampling.UNIFORM,
+        dim: int = 2,
+        endpoint: bool = False,
+        seed: Optional[int] = None,
+    ):
+        self.num_dirs = num_dirs
+        self.sampling = sampling
+        self.dim = dim
+        self.endpoint = endpoint
+
+        self._rng = np.random.RandomState(seed)
+        self._thetas = None
+        self._vectors = None
+        self._initialize_directions()
+
+    def _initialize_directions(self):
+        """
+        Initialize direction vectors using the chosen sampling strategy.
+        For 2D, the angles may be stored; for n-dim (n>2) the vectors are generated from
+        random normal samples and normalized to lie on the unit sphere.
+        """
+        if self.sampling == Sampling.UNIFORM:
+            if self.dim == 2:
+                self._thetas = np.linspace(
+                    0, 2 * np.pi, self.num_dirs, endpoint=self.endpoint
+                )
+            else:
+                # generate random normal samples and normalize to lie on the unit sphere
+                self._vectors = self._rng.randn(self.num_dirs, self.dim)
+                self._vectors /= np.linalg.norm(self._vectors,
+                                                axis=1, keepdims=True)
+        elif self.sampling == Sampling.RANDOM:
+            if self.dim == 2:
+                self._thetas = self._rng.uniform(0, 2 * np.pi, self.num_dirs)
+                self._thetas.sort()
+            else:
+                self._vectors = self._rng.randn(self.num_dirs, self.dim)
+                self._vectors /= np.linalg.norm(self._vectors,
+                                                axis=1, keepdims=True)
+
+    @classmethod
+    def uniform(
+        cls,
+        num_dirs: int = 360,
+        dim: int = 2,
+        endpoint: bool = False,
+        seed: Optional[int] = None,
+    ) -> "Directions":
+        """
+        Factory method for uniform sampling.
+
+        Parameters:
+            num_dirs: Number of direction vectors.
+            dim: Dimension of the space (default 2).
+            endpoint: Whether to include the endpoint (for 2D angles).
+            seed: Optional random seed.
+        """
+        return cls(num_dirs, Sampling.UNIFORM, dim, endpoint, seed)
+
+    @classmethod
+    def random(
+        cls, num_dirs: int = 360, dim: int = 2, seed: Optional[int] = None
+    ) -> "Directions":
+        """
+        Factory method for random sampling.
+
+        Parameters:
+            num_dirs: Number of direction vectors.
+            dim: Dimension of the space.
+            seed: optional random seed.
+        """
+        return cls(num_dirs, Sampling.RANDOM, dim, seed=seed)
+
+    @classmethod
+    def from_angles(cls, angles: Sequence[float]) -> "Directions":
+        """
+        Create an instance for custom angles (2D only).
+        """
+        instance = cls(len(angles), Sampling.CUSTOM, dim=2)
+        instance._thetas = np.array(angles)
+        return instance
+
+    @classmethod
+    def from_vectors(cls, vectors: Sequence[tuple]) -> "Directions":
+        """
+        Create an instance from custom direction vectors.
+        Works in any number of dimensions.
+        """
+        vectors = np.array(vectors, dtype=float)
+        norms = np.linalg.norm(vectors, axis=1, keepdims=True)
+        if np.any(norms == 0):
+            raise ValueError("Zero-magnitude vectors are not allowed")
+        normalized = vectors / norms
+        instance = cls(len(vectors), Sampling.CUSTOM, dim=vectors.shape[1])
+        instance._vectors = normalized
+        if instance.dim == 2:
+            instance._thetas = np.arctan2(normalized[:, 1], normalized[:, 0])
+        return instance
+
+    @property
+    def thetas(self) -> np.ndarray:
+        """Get the angles for 2D directions. Raises an error if dim > 2."""
+        if self.dim != 2:
+            raise ValueError(
+                "Angle representation is only available for 2D directions."
+            )
+        if self._thetas is None:
+            # Compute the angles from the vectors.
+            self._thetas = np.arctan2(self.vectors[:, 1], self.vectors[:, 0])
+        return self._thetas
+
+    @property
+    def vectors(self) -> np.ndarray:
+        """Get unit direction vectors.
+        For 2D, they are computed from thetas if not already created.
+        For n-dim (n>2), they should be available.
+        """
+        if self._vectors is None:
+            if self.dim == 2:
+                self._vectors = np.column_stack(
+                    (np.cos(self._thetas), np.sin(self._thetas))
+                )
+            else:
+                raise ValueError(
+                    "Direction vectors for dimensions >2 should be generated during initialization."
+                )
+        return self._vectors
+
+    def __len__(self) -> int:
+        return self.num_dirs
+
+    def __getitem__(self, idx) -> np.ndarray:
+        """Return the direction vector at index idx."""
+        return self.vectors[idx]
diff --git a/src/ect/ect_graph.py b/src/ect/ect_graph.py
new file mode 100644
index 0000000..4eec3b6
--- /dev/null
+++ b/src/ect/ect_graph.py
@@ -0,0 +1,221 @@
+import numpy as np
+from numba import prange, njit
+from typing import Optional, Union
+
+from .embed_cw import EmbeddedCW
+from .embed_graph import EmbeddedGraph
+from .directions import Directions
+from .results import ECTResult
+
+
+class ECT:
+    """
+    A class to calculate the Euler Characteristic Transform (ECT) from an input :any:`EmbeddedGraph` or :any:`EmbeddedCW`.
+
+    The result is a matrix where entry ``M[i,j]`` is :math:`\chi(K_{a_i})` for the direction :math:`\omega_j` where :math:`a_i` is the ith entry in ``self.thresholds``, and :math:`\omega_j` is the ith entry in ``self.thetas``.
+
+    Attributes
+        num_dirs (int):
+            The number of directions to consider in the matrix.
+        num_thresh (int):
+            The number of thresholds to consider in the matrix.
+        directions (Directions):
+            The directions to consider for projection.
+        bound_radius (float):
+            Either ``None``, or a positive radius of the bounding circle.
+    """
+
+    def __init__(
+        self,
+        directions: Optional[Directions] = None,
+        num_dirs: Optional[int] = None,
+        num_thresh: Optional[int] = None,
+        bound_radius: Optional[float] = None,
+        thresholds: Optional[np.ndarray] = None,
+        dtype=np.int32,
+    ):
+        """Initialize ECT calculator with either a Directions object or sampling parameters
+
+        Args:
+            directions: Optional pre-configured Directions object
+            num_dirs: Number of directions to sample (ignored if directions provided)
+            num_thresh: Number of threshold values (required if directions not provided)
+            bound_radius: Optional radius for bounding circle
+            thresholds: Optional array of thresholds
+            dtype: Data type for output array
+        """
+        self.directions = directions
+        self.num_dirs = num_dirs
+        self.num_thresh = num_thresh
+        self.bound_radius = bound_radius
+        self.thresholds = thresholds
+        self.dtype = dtype
+
+    def _ensure_directions(self, graph_dim, theta=None):
+        """Ensures directions is a valid Directions object of correct dimension"""
+        if self.directions is None:
+            if self.num_dirs is None:
+                raise ValueError(
+                    "Either 'directions' or 'num_dirs' must be provided.")
+            self.directions = Directions.uniform(self.num_dirs, dim=graph_dim)
+        elif isinstance(self.directions, list):
+            # if list of vectors, convert to Directions object
+            self.directions = np.array(self.directions)
+            self.directions = Directions.from_vectors(self.directions)
+        elif not isinstance(self.directions, Directions):
+            raise TypeError(
+                "directions must be a Directions object, ndarray, or list of vectors."
+            )
+
+        if theta is not None and graph_dim != 2:
+            raise ValueError(
+                "Theta must be provided for 2D graphs. "
+                "Use 'directions' or 'num_dirs' to specify directions."
+            )
+
+        if self.directions.dim != graph_dim:
+            raise ValueError(
+                "Dimension mismatch: directions dimension does not match graph dimension."
+            )
+
+    def _ensure_thresholds(self, graph, override_bound_radius=None):
+        """Ensures thresholds is a valid 1-dimensional ndarray."""
+
+        # determine if we need to generate thresholds
+        if self.thresholds is None or override_bound_radius is not None:
+            if self.num_thresh is None:
+                raise ValueError(
+                    "Either 'thresholds' or 'num_thresh' must be provided."
+                )
+            # determine the radius based on priority
+            if override_bound_radius is not None:
+                radius = override_bound_radius
+            elif self.bound_radius is not None:
+                radius = self.bound_radius
+            else:
+                radius = graph.get_bounding_radius()
+
+            self.thresholds = np.linspace(-radius, radius, self.num_thresh)
+        else:
+            # validate existing thresholds are valid
+            if not isinstance(self.thresholds, np.ndarray):
+                raise TypeError("thresholds must be a numpy ndarray")
+
+            if self.thresholds.ndim != 1:
+                raise ValueError("thresholds must be a 1-dimensional array")
+
+            self.thresholds = self.thresholds.astype(float)
+
+    def calculate(
+        self,
+        graph: Union[EmbeddedGraph, EmbeddedCW],
+        theta: Optional[float] = None,
+        override_bound_radius: Optional[float] = None,
+    ):
+        """Calculate Euler Characteristic Transform (ECT) for a given graph and direction theta
+
+        Args:
+            graph (EmbeddedGraph/EmbeddedCW):
+                The input graph to calculate the ECT for.
+            theta (float):
+                The angle in :math:`[0,2\pi]` for the direction to calculate the ECT.
+            override_bound_radius (float):
+                If None, uses the following in order: (i) the bounding radius stored in the class; or if not available (ii) the bounding radius of the given graph. Otherwise, should be a positive float :math:`R` where the ECC will be computed at thresholds in :math:`[-R,R]`. Default is None.
+        """
+        self._ensure_directions(graph.dim, theta)
+        self._ensure_thresholds(graph, override_bound_radius)
+
+        # override with theta if provided
+        directions = (
+            self.directions if theta is None else Directions.from_angles([
+                                                                         theta])
+        )
+
+        simplex_projections = self._compute_simplex_projections(
+            graph, directions)
+
+        ect_matrix = self._compute_directional_transform(
+            simplex_projections, self.thresholds, self.shape_descriptor, self.dtype
+        )
+
+        return ECTResult(ect_matrix, directions, self.thresholds)
+
+    def _compute_node_projections(self, coords, directions):
+        """Compute inner products of coordinates with directions"""
+        return np.matmul(coords, directions.vectors.T)
+
+    def _compute_simplex_projections(
+        self, graph: Union[EmbeddedGraph, EmbeddedCW], directions
+    ):
+        """Compute projections of each simplex (vertices, edges, faces)"""
+        simplex_projections = []
+        node_projections = self._compute_node_projections(
+            graph.coord_matrix, directions
+        )
+        edge_maxes = np.maximum(
+            node_projections[graph.edge_indices[:, 0]],
+            node_projections[graph.edge_indices[:, 1]],
+        )
+
+        simplex_projections.append(node_projections)
+        simplex_projections.append(edge_maxes)
+
+        if isinstance(graph, EmbeddedCW) and len(graph.faces) > 0:
+            node_to_index = {n: i for i, n in enumerate(graph.node_list)}
+            face_indices = [[node_to_index[v] for v in face]
+                            for face in graph.faces]
+            face_maxes = np.array(
+                [np.max(node_projections[face, :], axis=0)
+                 for face in face_indices]
+            )
+            simplex_projections.append(face_maxes)
+
+        return simplex_projections
+
+    @staticmethod
+    @njit(parallel=True, fastmath=True)
+    def _compute_directional_transform(
+        simplex_projections_list, thresholds, shape_descriptor, dtype=np.int32
+    ):
+        """Compute ECT by counting simplices below each threshold
+
+        Args:
+            simplex_projections_list: List of arrays containing projections for each simplex type
+                [vertex_projections, edge_projections, face_projections]
+            thresholds: Array of threshold values to compute ECT at
+            dtype: Data type for output array (default: np.int32)
+
+        Returns:
+            Array of shape (num_directions, num_thresholds) containing Euler characteristics
+        """
+        num_dir = simplex_projections_list[0].shape[1]
+        num_thresh = thresholds.shape[0]
+        result = np.empty((num_dir, num_thresh), dtype=dtype)
+
+        sorted_projections = []
+        for proj in simplex_projections_list:
+            sorted_proj = np.empty_like(proj)
+            for i in prange(num_dir):
+                sorted_proj[:, i] = np.sort(proj[:, i])
+            sorted_projections.append(sorted_proj)
+
+        for j in prange(num_thresh):
+            thresh = thresholds[j]
+            for i in range(num_dir):
+                simplex_counts_list = []
+                for k in range(len(sorted_projections)):
+                    projs = sorted_projections[k][:, i]
+                    simplex_counts_list.append(
+                        np.searchsorted(projs, thresh, side="right")
+                    )
+                result[i, j] = shape_descriptor(simplex_counts_list)
+        return result
+
+    @staticmethod
+    @njit(parallel=True, fastmath=True)
+    def shape_descriptor(simplex_counts_list):
+        """Calculate shape descriptor from simplex counts (Euler characteristic)"""
+        chi = 0
+        for k in range(len(simplex_counts_list)):
+            chi += (-1) ** k * simplex_counts_list[k]
+        return chi
diff --git a/src/ect/embed_cw.py b/src/ect/embed_cw.py
new file mode 100644
index 0000000..042e625
--- /dev/null
+++ b/src/ect/embed_cw.py
@@ -0,0 +1,151 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d.art3d import Poly3DCollection
+from .embed_graph import EmbeddedGraph
+from .utils.face_check import point_in_polygon
+from typing import Optional
+
+
+class EmbeddedCW(EmbeddedGraph):
+    """
+    A class to represent a straight-line-embedded CW complex. We assume that the coordinates for the embedding of the vertices are given, the 1-skeleton is in fact a graph (so not as general as a full CW complex) with straight line embeddings, and 2-Cells are the interior of the shape outlined by its boundary edges.
+
+    Faces should be passed in as a list of vertices, where the vertices are in order around the face. However, the ECT function will likely still work if the ordering is different. The drawing functions however might look strange. Note the class does not (yet?) check to make sure the face is valid, i.e. is a cycle in the graph, and bounds a region in the plane.
+
+    """
+
+    def __init__(self):
+        """
+        Initializes an empty EmbeddedCW object.
+        """
+
+        super().__init__()
+        self.faces = []
+
+    def add_from_embedded_graph(self, G):
+        """
+        Adds the edges and coordinates from an EmbeddedGraph object to the EmbeddedCW object.
+
+        Parameters:
+            G (EmbeddedGraph):
+                The EmbeddedGraph object to add from.
+        """
+        nodes_with_coords = [
+            (node, G.coord_matrix[G.node_to_index[node]]) for node in G.nodes()
+        ]
+        self.add_nodes_from(nodes_with_coords)
+        self.add_edges_from(G.edges())
+
+    @EmbeddedGraph._validate_node(exists=True)
+    def add_face(self, face, check=False):
+        """
+        Adds a face to the list of faces.
+
+        Parameters:
+            face (list):
+                A list of vertices that make up the face.
+            check (bool):
+                Whether to check that the face is a valid addition to the cw complex.
+        """
+        if len(face) < 3:
+            raise ValueError("Face must have at least 3 vertices")
+
+        if check:
+            edges = list(zip(face, face[1:] + [face[0]]))
+            for u, v in edges:
+                if not self.has_edge(u, v):
+                    raise ValueError(f"Edge ({u},{v}) missing")
+
+            polygon = np.array(
+                [self.coord_matrix[self._node_to_index[v]] for v in face]
+            )
+            for node in self.nodes:
+                if node in face:
+                    continue
+                if point_in_polygon(
+                    self.coord_matrix[self._node_to_index[node]], polygon
+                ):
+                    raise ValueError(f"Node {node} inside face {face}")
+
+        self.faces.append(tuple(face))
+
+    def add_faces_from(self, faces):
+        """
+        Adds a list of faces to the graph.
+
+        Parameters:
+            faces (list):
+                A list of faces to add.
+        """
+        for face in faces:
+            self.add_face(face)
+
+    def plot_faces(self, theta=None, ax=None, **kwargs):
+        """
+        Plots the faces of the graph in the direction of theta.
+
+        Parameters:
+            theta (float):
+                The angle in :math:`[0,2\pi]` for the direction to sort the edges.
+            ax (matplotlib.axes.Axes):
+                The axes to plot the graph on. If None, a new figure is created.
+            **kwargs:
+                Additional keyword arguments to pass to the ax.fill function.
+
+        Returns:
+            matplotlib.axes.Axes
+                The axes object with the plot.
+        """
+        if ax is None:
+            fig, ax = plt.subplots()
+        else:
+            fig = ax.get_figure()
+
+        for face in self.faces:
+            face_coords = np.array(
+                [self.coord_matrix[self.node_to_index[v]] for v in face]
+            )
+
+            if self.dim == 2:
+                ax.fill(face_coords[:, 0], face_coords[:, 1], **kwargs)
+            else:  # 3D case
+                verts = [face_coords]
+                collection = Poly3DCollection(verts, **kwargs)
+                ax.add_collection3d(collection)
+
+        return ax
+
+    def plot(
+        self,
+        bounding_circle: bool = False,
+        bounding_center_type: str = "bounding_box",
+        color_nodes_theta: Optional[float] = None,
+        ax: Optional[plt.Axes] = None,
+        with_labels: bool = True,
+        node_size: int = 300,
+        edge_color: str = "gray",
+        elev: float = 25,
+        azim: float = -60,
+        face_color: str = "lightblue",
+        face_alpha: float = 0.3,
+        **kwargs,
+    ) -> plt.Axes:
+        """
+        Visualize the embedded CW complex in 2D or 3D, including faces
+        """
+        # plot faces then use parent class
+        ax = self._create_axes(ax, self.dim)
+        self.plot_faces(ax=ax, facecolor=face_color, alpha=face_alpha)
+
+        return super().plot(
+            bounding_circle=bounding_circle,
+            bounding_center_type=bounding_center_type,
+            color_nodes_theta=color_nodes_theta,
+            ax=ax,
+            with_labels=with_labels,
+            node_size=node_size,
+            edge_color=edge_color,
+            elev=elev,
+            azim=azim,
+            **kwargs,
+        )
diff --git a/src/ect/embed_graph.py b/src/ect/embed_graph.py
new file mode 100644
index 0000000..9429d8d
--- /dev/null
+++ b/src/ect/embed_graph.py
@@ -0,0 +1,672 @@
+from collections import defaultdict
+from typing import Dict, List, Tuple, Optional, Union
+
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+
+from .utils.naming import next_vert_name
+
+
+CENTER_TYPES = ["mean", "bounding_box", "origin"]
+TRANSFORM_TYPES = ["pca"]
+
+
+class EmbeddedGraph(nx.Graph):
+    """
+    A class to represent a graph with embedded coordinates for each vertex with simple geometric graph operations.
+
+    Attributes
+        graph : nx.Graph
+            a NetworkX graph object
+        coord_matrix : np.ndarray
+            a matrix of embedded coordinates for each vertex
+        node_list : list
+            a list of node names
+        node_to_index : dict
+            a dictionary mapping node ids to their index in the coord_matrix
+        dim : int
+            the dimension of the embedded coordinates
+
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._node_list = []
+        self._node_to_index = {}
+        self._coord_matrix = None
+
+    @property
+    def coord_matrix(self):
+        """Return the N x D coordinate matrix"""
+        if self._coord_matrix is None:
+            return np.empty((0, 0))
+        return self._coord_matrix
+
+    @property
+    def dim(self):
+        """Return the dimension of the embedded coordinates"""
+        if self._coord_matrix is None:
+            return 0
+        return self._coord_matrix.shape[1]
+
+    @property
+    def node_list(self):
+        """Return ordered list of node names."""
+        return self._node_list
+
+    @property
+    def node_to_index(self):
+        """Return a dictionary mapping node ids to their index in the coord_matrix"""
+        return self._node_to_index
+
+    @property
+    def position_dict(self):
+        """Return a dictionary mapping node ids to their coordinates"""
+        return {node: self._coord_matrix[i] for i, node in enumerate(self._node_list)}
+
+    @property
+    def edge_indices(self):
+        """Return edges as array of index pairs"""
+        edges = np.array(
+            [(self._node_to_index[u], self._node_to_index[v])
+             for u, v in self.edges()],
+            dtype=int,
+        )
+        return edges if len(edges) > 0 else np.empty((0, 2), dtype=int)
+
+    # ======================================
+    # Node Management
+    # ======================================
+    @staticmethod
+    def _validate_coords(func):
+        """Validates if coordinates are nonempty and have valid dimension"""
+
+        def wrapper(self, *args, **kwargs):
+            coords = next(
+                (arg for arg in args if isinstance(arg, (list, np.ndarray))), None
+            )
+            if coords is not None:
+                coords = np.asarray(coords, dtype=float)
+                if coords.ndim != 1:
+                    raise ValueError("Coordinates must be a 1D array")
+
+                # Skip dimension check for first node
+                if len(self._node_list) > 0:
+                    if coords.size != self._coord_matrix.shape[1]:
+                        raise ValueError(
+                            f"Coordinates must have dimension {self._coord_matrix.shape[1]}"
+                        )
+
+            return func(self, *args, **kwargs)
+
+        return wrapper
+
+    @staticmethod
+    def _validate_node(exists=True):
+        """Validates if nodes exist or not already"""
+
+        def decorator(func):
+            def wrapper(self, *args, **kwargs):
+                # Handle both positional and keyword arguments
+                if args:
+                    nodes = args[0] if isinstance(
+                        args[0], (list, tuple)) else [args[0]]
+                else:
+                    node_id = kwargs.get("node_id") or kwargs.get("node_id1")
+                    nodes = [node_id] if node_id else []
+
+                for node_id in nodes:
+                    node_exists = node_id in self._node_to_index
+                    if exists and not node_exists:
+                        raise ValueError(f"Node {node_id} does not exist")
+                    if not exists and node_exists:
+                        raise ValueError(f"Node {node_id} already exists")
+                return func(self, *args, **kwargs)
+
+            return wrapper
+
+        return decorator
+
+    @_validate_coords
+    @_validate_node(exists=False)
+    def add_node(self, node_id, coord):
+        """Add a vertex to the graph.
+
+        Args:
+            node_id: Identifier for the node
+            coord: Array-like coordinates for the node
+        """
+        coord = np.asarray(coord, dtype=float)
+
+        if len(self._node_list) == 0:
+            self._coord_matrix = coord.reshape(1, -1)
+        else:
+            coord_reshaped = coord.reshape(1, -1)
+            self._coord_matrix = np.vstack(
+                [self._coord_matrix, coord_reshaped])
+
+        self._node_list.append(node_id)
+        self._node_to_index[node_id] = len(self._node_list) - 1
+        super().add_node(node_id)
+
+    def add_nodes_from_dict(self, nodes_with_coords: Dict[Union[str, int], np.ndarray]):
+        for node_id, coordinates in nodes_with_coords.items():
+            self.add_node(node_id, coordinates)
+
+    def add_nodes_from(
+        self, nodes_with_coords: List[Tuple[Union[str, int], np.ndarray]]
+    ):
+        for node_id, coordinates in nodes_with_coords:
+            self.add_node(node_id, coordinates)
+
+    # ======================================
+    # Coordinate Access
+    # ======================================
+
+    @_validate_node(exists=True)
+    def get_coord(self, node_id):
+        """Return the coordinates of a node"""
+        return self._coord_matrix[self._node_to_index[node_id]].copy()
+
+    @_validate_coords
+    @_validate_node(exists=True)
+    def set_coord(self, node_id, new_coords):
+        """Set the coordinates of a node"""
+        idx = self._node_to_index[node_id]
+        self._coord_matrix[idx] = new_coords
+
+    # ======================================
+    # Graph Operations
+    # ======================================
+
+    def add_cycle(self, coord_matrix):
+        """Add nodes in a cyclic pattern from coordinate matrix"""
+        n = coord_matrix.shape[0]
+        new_names = next_vert_name(
+            self._node_list[-1] if self._node_list else 0, n)
+        self.add_nodes_from(zip(new_names, coord_matrix))
+        self.add_edges_from(
+            [(new_names[i], new_names[(i + 1) % n]) for i in range(n)])
+
+    # ======================================
+    # Geometric Calculations
+    # ======================================
+
+    def get_center(self, method: str = "bounding_box") -> np.ndarray:
+        """Calculate center of coordinates"""
+
+        coords = self._coord_matrix
+        if method == "mean":
+            return np.mean(coords, axis=0)
+        elif method == "bounding_box":
+            return (np.max(coords, axis=0) + np.min(coords, axis=0)) / 2
+        elif method == "origin":
+            return np.zeros(self.dim)
+        raise ValueError(f"Unknown center method: {method}")
+
+    def get_bounding_box(self):
+        """Get (min, max) for each dimension"""
+        return [(dim.min(), dim.max()) for dim in self._coord_matrix.T]
+
+    def get_bounding_radius(self, center_type: str = "bounding_box") -> float:
+        """Get radius of minimal bounding sphere"""
+        center = self.get_center(center_type)
+        coords = self._coord_matrix
+        return np.max(np.linalg.norm(coords - center, axis=1))
+
+    def get_normal_angle_matrix(
+        self, edges_only: bool = False, decimals: Optional[int] = None
+    ) -> Tuple[np.ndarray, List[str]]:
+        """
+        Optimized angle matrix computation using vectorized operations.
+
+        Args:
+            edges_only: Only compute angles between connected vertices
+            decimals: Round angles to specified decimal places
+
+        Returns:
+            angle_matrix: NaN-filled matrix with pair angles
+            vertex_labels: Ordered node identifiers
+        """
+        coords = self._coord_matrix
+        vertices = self._node_list
+        n = len(vertices)
+
+        angle_matrix = np.full((n, n), np.nan, dtype=np.float64)
+
+        if edges_only:
+            edges = np.array(list(self.edges()))
+            if edges.size == 0:
+                return angle_matrix, vertices
+
+            u_indices = np.vectorize(self._node_to_index.get)(edges[:, 0])
+            v_indices = np.vectorize(self._node_to_index.get)(edges[:, 1])
+
+            dx = coords[v_indices, 0] - coords[u_indices, 0]
+            dy = coords[v_indices, 1] - coords[u_indices, 1]
+
+            angles = np.arctan2(dx, -dy) % (2 * np.pi)
+            rev_angles = (angles + np.pi) % (2 * np.pi)
+
+            if decimals is not None:
+                angles = np.round(angles, decimals)
+                rev_angles = np.round(rev_angles, decimals)
+
+            angle_matrix[u_indices, v_indices] = angles
+            angle_matrix[v_indices, u_indices] = rev_angles
+
+        else:
+            x = coords[:, 0]
+            y = coords[:, 1]
+
+            # compute all pairwise differences
+            dx = x[:, None] - x[None, :]
+            dy = y[:, None] - y[None, :]
+
+            # Compute angles and mask invalid pairs
+            angle_matrix = np.arctan2(dx, -dy) % (2 * np.pi)
+            angle_matrix[np.isclose(dx**2 + dy**2, 0)] = np.nan  # Zero vectors
+
+            if decimals is not None:
+                angle_matrix = np.round(angle_matrix, decimals)
+
+            # mask diagonal since we don't want
+            np.fill_diagonal(angle_matrix, np.nan)
+
+        return angle_matrix, vertices
+
+    def get_normal_angles(
+        self, edges_only: bool = False, decimals: int = 6
+    ) -> Dict[float, List[Tuple[str, str]]]:
+        """
+        Optimized angle dictionary construction using NumPy grouping.
+
+        Args:
+            edges_only: Only include edge-connected pairs
+            decimals: Round angles to specified decimal places
+
+        Returns:
+            Dictionary mapping rounded angles to vertex pairs
+        """
+        angle_matrix, vertices = self.get_angle_matrix(edges_only, decimals)
+        n = len(vertices)
+
+        # Extract upper triangle indices
+        rows, cols = np.triu_indices(n, k=1)
+        angles = angle_matrix[rows, cols]
+        valid_mask = ~np.isnan(angles)
+
+        if not valid_mask.any():
+            return defaultdict(list)
+
+        # Filter valid pairs
+        valid_rows = rows[valid_mask]
+        valid_cols = cols[valid_mask]
+        valid_angles = angles[valid_mask]
+
+        # Group pairs by rounded angle
+        angle_dict = defaultdict(list)
+        unique_angles, inverse = np.unique(valid_angles, return_inverse=True)
+
+        for idx, angle in enumerate(unique_angles):
+            mask = inverse == idx
+            pairs = [
+                (vertices[i], vertices[j])
+                for i, j in zip(valid_rows[mask], valid_cols[mask])
+            ]
+            angle_dict[float(angle)].extend(pairs)
+
+        return angle_dict
+
+    # ============================
+    # Coordinate transformations
+    # ============================
+
+    def transform_coordinates(self, center_type="bounding_box", projection_type="pca"):
+        """Transform coordinates center and orientation"""
+        if projection_type not in TRANSFORM_TYPES:
+            raise ValueError(f"Unknown transform type: {projection_type}")
+        self.project_coordinates(projection_type)
+        if center_type not in CENTER_TYPES:
+            raise ValueError(f"Unknown center method: {center_type}")
+        self.center_coordinates(center_type)
+
+    def center_coordinates(self, center_type="mean"):
+        if center_type not in CENTER_TYPES:
+            raise ValueError(f"Unknown center method: {center_type}")
+
+        center = self.get_center(center_type)
+        self._coord_matrix -= center
+
+    def scale_coordinates(self, radius=1):
+        """Scale coordinates to fit within given radius"""
+        current_max = np.linalg.norm(self._coord_matrix, axis=1).max()
+        if current_max > 0:
+            self._coord_matrix *= radius / current_max
+
+    def project_coordinates(self, projection_type="pca"):
+        """Project coordinates using a function"""
+        if projection_type == "pca":
+            self.pca_projection()
+        else:
+            raise ValueError(f"Unknown projection type: {projection_type}")
+
+    def pca_projection(self, target_dim=2):
+        """Dimensionality reduction using PCA"""
+        if self.dim <= target_dim:
+            return
+
+        pca = PCA(n_components=target_dim)
+        self._coord_matrix = pca.fit_transform(self._coord_matrix)
+        self.dim = target_dim
+
+    @_validate_node(exists=True)
+    def add_edge(self, node_id1, node_id2):
+        """Add an edge between two nodes"""
+        super().add_edge(node_id1, node_id2)
+
+    # ===================
+    # Visualization
+    # ===================
+    def validate_plot_parameters(func):
+        """Decorator to validate plot method parameters"""
+
+        def wrapper(self, *args, **kwargs):
+            bounding_center_type = kwargs.get(
+                "bounding_center_type", "bounding_box")
+
+            if self.dim not in [2, 3]:
+                raise ValueError(
+                    "At least 2D or 3D coordinates required for plotting")
+
+            if bounding_center_type not in CENTER_TYPES:
+                raise ValueError(
+                    f"Invalid center type: {bounding_center_type}. "
+                    f"Valid options: {CENTER_TYPES}"
+                )
+
+            return func(self, *args, **kwargs)
+
+        return wrapper
+
+    @validate_plot_parameters
+    def plot(
+        self,
+        bounding_circle: bool = False,
+        bounding_center_type: str = "bounding_box",
+        color_nodes_theta: Optional[float] = None,
+        ax: Optional[plt.Axes] = None,
+        with_labels: bool = True,
+        node_size: int = 300,
+        edge_color: str = "gray",
+        elev: float = 25,
+        azim: float = -60,
+        **kwargs,
+    ) -> plt.Axes:
+        """
+        Visualize the embedded graph in 2D or 3D
+        """
+        ax = self._create_axes(ax, self.dim)
+
+        pos = {node: self._coord_matrix[i]
+               for i, node in enumerate(self._node_list)}
+
+        if self.dim == 2:
+            self._draw_2d(ax, pos, with_labels,
+                          node_size, edge_color, **kwargs)
+        else:
+            self._draw_3d(ax, pos, node_size, edge_color, elev, azim, **kwargs)
+
+        if color_nodes_theta is not None:
+            # calculate directional projection values
+            direction = np.array(
+                [np.sin(color_nodes_theta), -np.cos(color_nodes_theta)]
+            )
+            node_colors = np.dot(self._coord_matrix, direction)
+
+            self._add_node_coloring(
+                ax, pos, node_colors, node_size, self.dim, **kwargs)
+
+        if bounding_circle:
+            self._add_bounding_shape(ax, bounding_center_type, self.dim)
+
+        self._configure_axes(ax)
+
+        return ax
+
+    def _create_axes(self, ax, dim=None):
+        """Create appropriate axes if not provided
+
+        Parameters:
+            ax (matplotlib.axes.Axes, optional):
+                The axes to use. If None, creates new axes
+            dim (int, optional):
+                Dimension of the plot. If None, uses self.dim
+
+        Returns:
+            matplotlib.axes.Axes:
+                The configured axes object
+        """
+        if dim is None:
+            dim = self.dim
+
+        if ax is None:
+            fig = plt.figure()
+            ax = fig.add_subplot(111, projection="3d" if dim == 3 else None)
+        elif dim == 3 and not hasattr(ax, "zaxis"):
+            raise ValueError("For 3D plots, provide axes with 3D projection")
+        return ax
+
+    def _draw_2d(self, ax, pos, with_labels, node_size, edge_color, **kwargs):
+        """2D visualization components"""
+        nx.draw_networkx_edges(
+            self, pos=pos, ax=ax, edge_color=edge_color, width=1.5, **kwargs
+        )
+        nx.draw_networkx_nodes(
+            self,
+            pos=pos,
+            ax=ax,
+            node_size=node_size,
+            node_color="lightblue",
+            edgecolors="black",
+            linewidths=0.5,
+            **kwargs,
+        )
+        if with_labels:
+            nx.draw_networkx_labels(
+                self, pos=pos, ax=ax, font_size=8, font_color="black", **kwargs
+            )
+
+    def _draw_3d(self, ax, pos, node_size, edge_color, elev, azim, **kwargs):
+        """3D visualization components"""
+        ax.view_init(elev=elev, azim=azim)
+
+        coords = np.array(list(pos.values()))
+        ax.scatter3D(
+            coords[:, 0],
+            coords[:, 1],
+            coords[:, 2],
+            s=node_size,
+            c="lightblue",
+            edgecolors="black",
+            linewidth=0.5,
+        )
+
+        for u, v in self.edges():
+            x = [pos[u][0], pos[v][0]]
+            y = [pos[u][1], pos[v][1]]
+            z = [pos[u][2], pos[v][2]]
+            ax.plot3D(x, y, z, color=edge_color, linewidth=1.5)
+
+    def _add_node_coloring(self, ax, pos, node_colors, node_size, dim=None, **kwargs):
+        """Add node coloring based on provided values
+
+        Parameters:
+            ax (matplotlib.axes.Axes):
+                The axes to add coloring to
+            pos (dict):
+                Dictionary of node positions
+            node_colors (array-like):
+                Values to use for coloring nodes
+            node_size (int):
+                Size of nodes in visualization
+            dim (int, optional):
+                Dimension of the plot. If None, uses self.dim
+            **kwargs:
+                Additional keyword arguments for plotting
+        """
+        if dim is None:
+            dim = self.dim
+
+        if dim == 2:
+            nodes = nx.draw_networkx_nodes(
+                self,
+                pos=pos,
+                ax=ax,
+                node_size=node_size,
+                node_color=node_colors,
+                cmap=plt.cm.viridis,
+                edgecolors="black",
+                linewidths=0.5,
+                **kwargs,
+            )
+        else:
+            coords = np.array(list(pos.values()))
+            nodes = ax.scatter3D(
+                coords[:, 0],
+                coords[:, 1],
+                coords[:, 2],
+                s=node_size,
+                c=node_colors,
+                cmap=plt.cm.viridis,
+                edgecolors="black",
+                linewidth=0.5,
+                **kwargs,
+            )
+
+        norm = plt.Normalize(vmin=min(node_colors), vmax=max(node_colors))
+        sm = plt.cm.ScalarMappable(norm=norm, cmap=plt.cm.viridis)
+        sm.set_array([])
+        cbar = plt.colorbar(sm, ax=ax, orientation="vertical", shrink=0.8)
+        cbar.set_label("Node Values")
+
+    def _add_bounding_shape(self, ax, center_type="bounding_box", dim=None):
+        """Add bounding circle/sphere visualization
+
+        Parameters:
+            ax (matplotlib.axes.Axes):
+                The axes to add the bounding shape to
+            center_type (str, optional):
+                Method to compute center ("mean", "bounding_box", or "origin")
+            dim (int, optional):
+                Dimension of the plot. If None, uses self.dim
+        """
+        if dim is None:
+            dim = self.dim
+
+        center = self.get_center(center_type)
+        radius = self.get_bounding_radius(center_type)
+
+        if dim == 2:
+            circle = plt.Circle(
+                center[:2],
+                radius,
+                fill=False,
+                linestyle="--",
+                color="darkred",
+                linewidth=1.2,
+                alpha=0.7,
+            )
+            ax.add_patch(circle)
+            padding = radius * 0.1
+            ax.set_xlim(center[0] - radius - padding,
+                        center[0] + radius + padding)
+            ax.set_ylim(center[1] - radius - padding,
+                        center[1] + radius + padding)
+        else:
+            # sphere wireframe
+            u = np.linspace(0, 2 * np.pi, 30)
+            v = np.linspace(0, np.pi, 30)
+            x = radius * np.outer(np.cos(u), np.sin(v)) + center[0]
+            y = radius * np.outer(np.sin(u), np.sin(v)) + center[1]
+            z = radius * np.outer(np.ones(np.size(u)), np.cos(v)) + center[2]
+
+            ax.plot_wireframe(
+                x, y, z, color="darkred", linewidth=0.5, alpha=0.3, rstride=2, cstride=2
+            )
+            padding = radius * 0.1
+            ax.set_xlim3d(center[0] - radius - padding,
+                          center[0] + radius + padding)
+            ax.set_ylim3d(center[1] - radius - padding,
+                          center[1] + radius + padding)
+            ax.set_zlim3d(center[2] - radius - padding,
+                          center[2] + radius + padding)
+
+    def _configure_axes(self, ax):
+        """Finalize plot appearance"""
+        if hasattr(ax, "zaxis"):
+            # 3D plot configuration
+            ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7)
+            ax.xaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+            ax.yaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+            ax.zaxis.set_pane_color((1.0, 1.0, 1.0, 0.0))
+            ax.set_xlabel("X")
+            ax.set_ylabel("Y")
+            ax.set_zlabel("Z")
+        else:
+            # 2D plot configuration
+            ax.set_aspect("equal")
+            ax.grid(True, linestyle=":", linewidth=0.5, alpha=0.7)
+
+            xlim = ax.get_xlim()
+            ylim = ax.get_ylim()
+
+            x_interval = self._get_nice_interval(xlim[1] - xlim[0])
+            y_interval = self._get_nice_interval(ylim[1] - ylim[0])
+
+            ax.xaxis.set_major_locator(plt.MultipleLocator(x_interval))
+            ax.yaxis.set_major_locator(plt.MultipleLocator(y_interval))
+
+            ax.xaxis.set_minor_locator(plt.MultipleLocator(x_interval / 2))
+            ax.yaxis.set_minor_locator(plt.MultipleLocator(y_interval / 2))
+
+        ax.tick_params(
+            axis="both",
+            which="both",
+            bottom=True,
+            left=True,
+            labelbottom=True,
+            labelleft=True,
+        )
+
+    def _get_nice_interval(self, range_size):
+        """Calculate a nice interval for tick spacing
+
+        Args:
+            range_size: Size of the axis range
+
+        Returns:
+            float: Nice interval value for tick spacing
+        """
+        # Calculate rough interval size (aim for ~5 major ticks)
+        rough_interval = range_size / 5
+
+        # Get magnitude
+        magnitude = 10 ** np.floor(np.log10(rough_interval))
+
+        # Normalize rough interval to between 1 and 10
+        normalized = rough_interval / magnitude
+
+        # Choose nice interval
+        if normalized < 1.5:
+            nice_interval = 1
+        elif normalized < 3:
+            nice_interval = 2
+        elif normalized < 7:
+            nice_interval = 5
+        else:
+            nice_interval = 10
+
+        return nice_interval * magnitude
diff --git a/src/ect/results.py b/src/ect/results.py
new file mode 100644
index 0000000..095e7c7
--- /dev/null
+++ b/src/ect/results.py
@@ -0,0 +1,115 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from ect.directions import Sampling
+
+
+class ECTResult(np.ndarray):
+    """
+    A numpy ndarray subclass that carries ECT metadata and plotting capabilities
+    Acts like a regular matrix but with added visualization methods and metadata about directions and thresholds
+    """
+
+    def __new__(cls, matrix, directions, thresholds):
+        # allow float arrays for smooth transform otherwise int
+        if np.issubdtype(matrix.dtype, np.floating):
+            obj = np.asarray(matrix, dtype=np.float64).view(cls)
+        else:
+            obj = np.asarray(matrix, dtype=np.int32).view(cls)
+        obj.directions = directions
+        obj.thresholds = thresholds
+        return obj
+
+    def __array_finalize__(self, obj):
+        if obj is None:
+            return
+        self.directions = getattr(obj, "directions", None)
+        self.thresholds = getattr(obj, "thresholds", None)
+
+    def plot(self, ax=None):
+        """Plot ECT matrix with proper handling for both 2D and 3D"""
+        ax = ax or plt.gca()
+
+        if self.thresholds is None:
+            self.thresholds = np.linspace(-1, 1, self.shape[1])
+
+        if len(self.directions) == 1:
+            directions = (
+                self.directions.thetas
+                if self.directions.dim == 2
+                else self.directions.vectors
+            )
+            self._plot_ecc(directions)
+            return ax
+
+        if self.directions.dim == 2:
+            # 2D case - use angle representation
+            if (
+                self.directions.sampling == Sampling.UNIFORM
+                and not self.directions.endpoint
+            ):
+                plot_thetas = np.concatenate(
+                    [self.directions.thetas, [2 * np.pi]])
+                ect_data = np.hstack([self.T, self.T[:, [0]]])
+            else:
+                plot_thetas = self.directions.thetas
+
+            X = plot_thetas
+            Y = self.thresholds
+
+        else:
+            X = np.arange(self.shape[0])
+            Y = self.thresholds
+            ect_data = self.T
+
+            ax.set_xlabel("Direction Index")
+
+        mesh = ax.pcolormesh(
+            X[None, :], Y[:, None], ect_data, cmap="viridis", shading="nearest"
+        )
+        plt.colorbar(mesh, ax=ax)
+
+        if self.directions.dim == 2:
+            ax.set_xlabel(r"Direction $\omega$ (radians)")
+            if self.directions.sampling == Sampling.UNIFORM:
+                ax.set_xticks(np.linspace(0, 2 * np.pi, 9))
+                ax.set_xticklabels(
+                    [
+                        r"$0$",
+                        r"$\frac{\pi}{4}$",
+                        r"$\frac{\pi}{2}$",
+                        r"$\frac{3\pi}{4}$",
+                        r"$\pi$",
+                        r"$\frac{5\pi}{4}$",
+                        r"$\frac{3\pi}{2}$",
+                        r"$\frac{7\pi}{4}$",
+                        r"$2\pi$",
+                    ]
+                )
+
+        ax.set_ylabel(r"Threshold $a$")
+        return ax
+
+    def smooth(self):
+        """Calculate the Smooth Euler Characteristic Transform"""
+        # convert to float for calculations
+        data = self.astype(np.float64)
+
+        # get average for each direction
+        direction_avgs = np.average(data, axis=1)
+
+        # center each direction's values
+        centered = data - direction_avgs[:, np.newaxis]
+
+        # compute cumulative sum to get SECT
+        sect = np.cumsum(centered, axis=1)
+
+        # create new ECTResult with float type
+        return ECTResult(sect.astype(np.float64), self.directions, self.thresholds)
+
+    def _plot_ecc(self, theta):
+        """Plot the Euler Characteristic Curve for a specific direction"""
+        plt.step(self.thresholds, self.T, label="ECC")
+        theta_round = str(np.round(theta, 2))
+        plt.title(r"ECC for $\omega = " + theta_round + "$")
+        plt.xlabel("$a$")
+        plt.ylabel(r"$\chi(K_a)$")
diff --git a/src/ect/utils/examples.py b/src/ect/utils/examples.py
new file mode 100644
index 0000000..475a5e7
--- /dev/null
+++ b/src/ect/utils/examples.py
@@ -0,0 +1,98 @@
+from ect.embed_graph import EmbeddedGraph
+from ect.embed_cw import EmbeddedCW
+import numpy as np
+
+
+def create_example_cw(centered=True, center_type="bounding_box"):
+    """
+    Creates an example EmbeddedCW object with a simple CW complex. If centered is True, the coordinates are centered around the center type, which could be ``mean``, ``bounding_box`` or ``origin``.
+
+
+    Returns:
+        EmbeddedCW
+            The example EmbeddedCW object.
+    """
+    G = create_example_graph(centered=False)
+    K = EmbeddedCW()
+    K.add_from_embedded_graph(G)
+
+    extra_coords = {"G": [2, 4], "H": [1, 5], "I": [5, 4], "J": [2, 2], "K": [2, 7]}
+
+    for node, coord in extra_coords.items():
+        K.add_node(node, coord)
+
+    extra_edges = [
+        ("G", "A"),
+        ("G", "H"),
+        ("H", "D"),
+        ("I", "E"),
+        ("I", "C"),
+        ("J", "E"),
+        ("K", "D"),
+        ("K", "C"),
+    ]
+    K.add_edges_from(extra_edges)
+
+    K.add_face(["B", "A", "G", "H", "D"])
+    K.add_face(["K", "D", "C"])
+
+    if centered:
+        K.center_coordinates(center_type)
+
+    return K
+
+
+def create_example_graph(centered=True, center_type="mean"):
+    """
+    Function to create an example ``EmbeddedGraph`` object. Helpful for testing. If ``centered`` is True, the coordinates are centered using the center type given by ``center_type``, either ``mean``, ``bounding_box`` or ``origin``.
+
+    Returns:
+        EmbeddedGraph: An example ``EmbeddedGraph`` object.
+
+    """
+    graph = EmbeddedGraph()
+
+    coords = {
+        "A": [1, 2],
+        "B": [3, 4],
+        "C": [5, 7],
+        "D": [3, 6],
+        "E": [4, 3],
+        "F": [4, 5],
+    }
+
+    for node, coord in coords.items():
+        graph.add_node(node, coord)
+
+    edges = [("A", "B"), ("B", "C"), ("B", "D"), ("B", "E"), ("C", "D"), ("E", "F")]
+    graph.add_edges_from(edges)
+
+    if centered:
+        graph.center_coordinates(center_type)
+
+    return graph
+
+
+def create_random_graph(n_nodes=100, n_edges=200, dim=2):
+    """Creates a random graph with random node positions in [0,1]^dim
+
+    Args:
+        n_nodes: Number of nodes
+        n_edges: Number of random edges to add
+        dim: Dimension of embedding space
+    """
+    G = EmbeddedGraph()
+
+    coords = np.random.random((n_nodes, dim))
+    nodes_with_coords = [(i, coords[i]) for i in range(n_nodes)]
+    G.add_nodes_from(nodes_with_coords)
+
+    edges = set()
+    while len(edges) < n_edges:
+        u = np.random.randint(0, n_nodes)
+        v = np.random.randint(0, n_nodes)
+        if u != v:
+            edges.add(tuple(sorted([u, v])))
+
+    G.add_edges_from(edges)
+    return G
diff --git a/src/ect/utils/face_check.py b/src/ect/utils/face_check.py
new file mode 100644
index 0000000..e567951
--- /dev/null
+++ b/src/ect/utils/face_check.py
@@ -0,0 +1,16 @@
+import numpy as np
+
+
+def point_in_polygon(point: np.ndarray, polygon: np.ndarray) -> bool:
+    """Ray casting algorithm for point-in-polygon test"""
+    x, y = point
+    n = polygon.shape[0]
+    inside = False
+    for i in range(n):
+        p1 = polygon[i]
+        p2 = polygon[(i+1) % n]
+        if ((p1[1] > y) != (p2[1] > y)):
+            xinters = (y - p1[1]) * (p2[0] - p1[0]) / (p2[1] - p1[1]) + p1[0]
+            if x <= xinters:
+                inside = not inside
+    return inside
diff --git a/src/ect/utils/naming.py b/src/ect/utils/naming.py
new file mode 100644
index 0000000..1e8ba75
--- /dev/null
+++ b/src/ect/utils/naming.py
@@ -0,0 +1,23 @@
+def next_vert_name(s, num_verts=1):
+    """Generate sequential vertex names (alphabetical or numerical)."""
+    if isinstance(s, int):
+        return [s + i + 1 for i in range(num_verts)] if num_verts > 1 else s + 1
+
+    def increment_char(c):
+        return "A" if c == "Z" else chr(ord(c) + 1)
+
+    def increment_str(s):
+        chars = list(s)
+        for i in reversed(range(len(chars))):
+            chars[i] = increment_char(chars[i])
+            if chars[i] != "A":
+                break
+            elif i == 0:
+                return "A" + "".join(chars)
+        return "".join(chars)
+
+    # handle multiple increments
+    names = [s]
+    for _ in range(num_verts):
+        names.append(increment_str(names[-1]))
+    return names[1:] if num_verts > 1 else names[1]
diff --git a/tests/test_directions.py b/tests/test_directions.py
new file mode 100644
index 0000000..99bd165
--- /dev/null
+++ b/tests/test_directions.py
@@ -0,0 +1,88 @@
+import unittest
+import numpy as np
+from ect import Directions
+from ect.directions import Sampling
+
+
+class TestDirections(unittest.TestCase):
+    def test_uniform_2d(self):
+        num_dirs = 8
+        dirs = Directions.uniform(num_dirs, dim=2)
+        
+        self.assertEqual(len(dirs), num_dirs)
+        self.assertEqual(dirs.dim, 2)
+        self.assertEqual(dirs.sampling, Sampling.UNIFORM)
+        
+        # test vector properties
+        vectors = dirs.vectors
+        self.assertEqual(vectors.shape, (num_dirs, 2))
+        norms = np.linalg.norm(vectors, axis=1)
+        np.testing.assert_allclose(norms, 1.0)
+
+    def test_uniform_3d(self):
+        num_dirs = 10
+        dirs = Directions.uniform(num_dirs, dim=3)
+        
+        self.assertEqual(len(dirs), num_dirs)
+        self.assertEqual(dirs.dim, 3)
+        
+        vectors = dirs.vectors
+        self.assertEqual(vectors.shape, (num_dirs, 3))
+        norms = np.linalg.norm(vectors, axis=1)
+        np.testing.assert_allclose(norms, 1.0)
+
+    def test_random_sampling(self):
+        num_dirs = 10
+        seed = 42
+        dirs = Directions.random(num_dirs, seed=seed)
+        
+        self.assertEqual(len(dirs), num_dirs)
+        self.assertEqual(dirs.sampling, Sampling.RANDOM)
+        
+        # test reproducibility
+        dirs2 = Directions.random(num_dirs, seed=seed)
+        np.testing.assert_array_equal(dirs.vectors, dirs2.vectors)
+
+    def test_custom_angles(self):
+        angles = [0, np.pi/4, np.pi/2]
+        dirs = Directions.from_angles(angles)
+        
+        self.assertEqual(len(dirs), 3)
+        self.assertEqual(dirs.dim, 2)
+        self.assertEqual(dirs.sampling, Sampling.CUSTOM)
+        np.testing.assert_array_equal(dirs.thetas, angles)
+
+    def test_custom_vectors(self):
+        vectors = [(1,0,0), (0,1,0), (0,0,1)]
+        dirs = Directions.from_vectors(vectors)
+        
+        self.assertEqual(len(dirs), 3)
+        self.assertEqual(dirs.dim, 3)
+        self.assertEqual(dirs.sampling, Sampling.CUSTOM)
+
+    def test_invalid_vectors(self):
+        with self.assertRaises(ValueError):
+            Directions.from_vectors([(0,0), (0,0)])  
+
+    def test_angle_access_3d(self):
+        dirs = Directions.uniform(8, dim=3)
+        with self.assertRaises(ValueError):
+            _ = dirs.thetas  # angles not available in 3D
+
+    def test_endpoint_behavior(self):
+        num_dirs = 4
+        dirs_with_endpoint = Directions.uniform(num_dirs, endpoint=True)
+        dirs_without_endpoint = Directions.uniform(num_dirs, endpoint=False)
+        
+        self.assertNotEqual(dirs_with_endpoint.thetas[-1], 
+                           dirs_without_endpoint.thetas[-1])
+
+    def test_indexing(self):
+        dirs = Directions.uniform(10)
+        vector = dirs[0]
+        self.assertEqual(vector.shape, (2,))
+        self.assertAlmostEqual(np.linalg.norm(vector), 1.0)
+
+
+if __name__ == '__main__':
+    unittest.main() 
\ No newline at end of file
diff --git a/tests/test_ect_graph.py b/tests/test_ect_graph.py
index 68e1964..219f89d 100644
--- a/tests/test_ect_graph.py
+++ b/tests/test_ect_graph.py
@@ -1,57 +1,82 @@
 import unittest
-from ect import embed_graph, ect_graph
-
+import numpy as np
+from ect.utils.examples import create_example_graph, create_example_cw
+from ect import ECT, Directions
 
 
 class TestECT(unittest.TestCase):
-    def test_example_graph_ect(self):
-        G = embed_graph.create_example_graph()
-        num_dirs = 8
-        num_thresh = 10
-        myect = ect_graph.ECT(num_dirs, num_thresh)
-        self.assertEqual( myect.ECT_matrix.shape, (8,10))
-
-        r = G.get_bounding_radius()
-        myect.set_bounding_radius(1.2*r)
-        ecc = myect.calculateECC(G, 0)
-        self.assertEqual( len(ecc), num_thresh)
-
-    def test_check_bounding_radius(self):
-        
-        # make an example graph 
-        G = embed_graph.create_example_graph()
-        num_dirs = 8
-        num_thresh = 10
-
-        myect = ect_graph.ECT(num_dirs, num_thresh)
-
-        # At this point, there shouldn't be a radius set 
-        self.assertIs( myect.bound_radius, None)
-
-        # Try to calculate the ECC without a radius set
-        myect.calculateECC(G, 0, bound_radius=None)
-
-        # Try to calculate the ECC with a negative radius.
-        # It should throw an error.
-        with self.assertRaises(ValueError):  
-            myect.calculateECC(G, 0, bound_radius= -1)
-
-        # Try to calculate the ECC with tightbbox set to True 
-        # This should  work fine 
-        ecc = myect.calculateECC(G, 0, bound_radius=None)
-        self.assertEqual( len(ecc), num_thresh)
-
-        # Now set the bounding radius 
-        r = G.get_bounding_radius()
-        myect.set_bounding_radius(1.2*r)
-        ecc = myect.calculateECC(G, 0, bound_radius=None)
-        self.assertEqual( len(ecc), num_thresh)
-
-        # TODO: write a test where we check that if None is passed and the radius is set internally, it will use that one.
-
-
-    
-
-
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+    def setUp(self):
+        self.graph = create_example_graph()
+        self.num_dirs = 8
+        self.num_thresh = 10
+        self.bound_radius = 2.0
+        self.ect = ECT(
+            num_dirs=self.num_dirs,
+            num_thresh=self.num_thresh,
+            bound_radius=self.bound_radius,
+        )
+
+    def test_initialization(self):
+        self.assertEqual(self.ect.bound_radius, self.bound_radius)
+        self.assertEqual(self.ect.num_dirs, self.num_dirs)
+        self.assertEqual(self.ect.num_thresh, self.num_thresh)
+        self.assertIsNone(self.ect.directions)
+
+    def test_calculate_basic(self):
+        result = self.ect.calculate(self.graph)
+        self.assertEqual(result.shape, (self.num_dirs, self.num_thresh))
+        self.assertTrue(isinstance(result.directions, Directions))
+        self.assertIsNotNone(result.thresholds)
+
+    def test_calculate_single_direction(self):
+        result = self.ect.calculate(self.graph, theta=0)
+        self.assertEqual(result.shape, (1, self.num_thresh))
+        self.assertEqual(len(result.directions), 1)
+
+    def test_threshold_priority(self):
+        graph_radius = self.graph.get_bounding_radius()
+        override_bound_radius = 3 * graph_radius
+
+        no_radius_ect = ECT(num_dirs=self.num_dirs, num_thresh=self.num_thresh)
+        # test graph radius is used when no other radius specified
+        result1 = no_radius_ect.calculate(self.graph)
+        self.assertAlmostEqual(abs(result1.thresholds).max(), graph_radius)
+
+        # test override radius takes precedence over both
+        result2 = self.ect.calculate(
+            self.graph, override_bound_radius=override_bound_radius
+        )
+        self.assertAlmostEqual(abs(result2.thresholds).max(), override_bound_radius)
+
+    def test_different_graph_types(self):
+        cw = create_example_cw()
+        result_graph = self.ect.calculate(self.graph)
+        result_cw = self.ect.calculate(cw)
+
+        self.assertEqual(result_graph.shape, result_cw.shape)
+        self.assertEqual(len(result_graph.directions), len(result_cw.directions))
+
+    def test_directions_matching(self):
+        # test that ect raises error when dimensions don't match
+        G2d = create_example_graph()
+        directions_3d = Directions.uniform(self.num_dirs, dim=3)
+        ect = ECT(directions=directions_3d)
+
+        with self.assertRaises(ValueError):
+            ect.calculate(G2d)
+
+    def test_result_properties(self):
+        result = self.ect.calculate(self.graph)
+
+        # test smooth transform
+        smooth = result.smooth()
+        self.assertEqual(smooth.shape, result.shape)
+        self.assertEqual(smooth.directions, result.directions)
+        self.assertEqual(smooth.thresholds.tolist(), result.thresholds.tolist())
+
+        # verify result is integer-valued
+        self.assertTrue(np.issubdtype(result.dtype, np.integer))
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_ect_result.py b/tests/test_ect_result.py
new file mode 100644
index 0000000..15212c1
--- /dev/null
+++ b/tests/test_ect_result.py
@@ -0,0 +1,83 @@
+import unittest
+import numpy as np
+import matplotlib.pyplot as plt
+from ect import ECT, Directions
+from ect.utils.examples import create_example_graph
+
+
+class TestECTResult(unittest.TestCase):
+    def setUp(self):
+        self.graph = create_example_graph()
+        self.ect = ECT(num_dirs=8, num_thresh=10)
+        self.result = self.ect.calculate(self.graph)
+
+    def test_array_behavior(self):
+        # test numpy array operations
+        self.assertTrue(isinstance(self.result + 1, np.ndarray))
+        # original array should be integer type
+        self.assertTrue(np.issubdtype(self.result.dtype, np.integer))
+        # mean operation always returns float type in numpy
+        self.assertTrue(np.issubdtype(self.result.mean().dtype, np.floating))
+        self.assertEqual(self.result.shape, (8, 10))
+
+    def test_metadata_preservation(self):
+        # test metadata is preserved after operations
+        result2 = self.result.copy()
+        self.assertEqual(result2.directions, self.result.directions)
+        self.assertTrue(np.array_equal(result2.thresholds, self.result.thresholds))
+
+    def test_smooth_transform(self):
+        smooth = self.result.smooth()
+        
+        # test shape preservation
+        self.assertEqual(smooth.shape, self.result.shape)
+        
+        # test metadata preservation
+        self.assertEqual(smooth.directions, self.result.directions)
+        self.assertTrue(np.array_equal(smooth.thresholds, self.result.thresholds))
+        
+        # test each step of SECT calculation
+        data = self.result.astype(np.float64)
+        
+        # 1. test that row averages are subtracted correctly
+        row_avgs = np.average(data, axis=1)
+        for i in range(len(row_avgs)):
+            row = data[i] - row_avgs[i]
+            self.assertTrue(np.allclose(np.average(row), 0))
+        
+        # 2. test that result is cumulative sum of centered data
+        centered = data - row_avgs[:, np.newaxis]
+        expected_smooth = np.cumsum(centered, axis=1)
+        self.assertTrue(np.allclose(smooth, expected_smooth))
+
+    def test_plotting(self):
+        # test basic plotting
+        ax = self.result.plot()
+        self.assertTrue(isinstance(ax, plt.Axes))
+        plt.close()
+        
+        # test plotting with custom axes
+        fig, ax = plt.subplots()
+        self.result.plot(ax=ax)
+        plt.close()
+
+    def test_single_direction_result(self):
+        result = self.ect.calculate(self.graph, theta=0)
+        
+        # test shape
+        self.assertEqual(result.shape, (1, self.ect.num_thresh))
+        
+        # test plotting single direction
+        ax = result.plot()
+        self.assertTrue(isinstance(ax, plt.Axes))
+        plt.close()
+
+    def test_array_finalize(self):
+        # test metadata preservation in array operations
+        sliced = self.result[2:5]
+        self.assertEqual(sliced.directions, self.result.directions)
+        self.assertTrue(np.array_equal(sliced.thresholds, self.result.thresholds))
+
+
+if __name__ == '__main__':
+    unittest.main() 
\ No newline at end of file
diff --git a/tests/test_embed_cw.py b/tests/test_embed_cw.py
index a69d4b2..65732ad 100644
--- a/tests/test_embed_cw.py
+++ b/tests/test_embed_cw.py
@@ -1,37 +1,55 @@
 import unittest
-from ect import EmbeddedCW, create_example_cw
+from ect.utils.examples import create_example_cw
 import numpy as np
 
 
 class TestEmbeddedCW(unittest.TestCase):
     def test_example_cw(self):
-        # Make sure we can build a grpah in the first place
-        K = create_example_cw()
-        self.assertEqual( len(K.nodes), 11)  # assuming my_function squares its input
-
-    def test_add_face(self):
-        # Make sure adding a vertex updates the coordiantes list 
         G = create_example_cw()
-        G.add_face(['D','B','C'])
-        self.assertEqual( len(G.faces), 3)
-
-    # TODO: Add test to check that adding an invalid face throws an error
+        self.assertEqual(len(G.nodes), 11)
+        self.assertEqual(len(G.faces), 2)
 
     def test_get_coordinates(self):
-        # Make sure we can get the coordinates of a vertex
         G = create_example_cw(centered=False)
-        coords = G.get_coordinates('A')
-        self.assertEqual( coords, (1, 2))
+        coords = G.get_coord("A")
+        self.assertTrue(np.array_equal(coords, np.array([1, 2])))
 
     def test_mean_centered_coordinates(self):
-        # Make sure the mean centered coordinates are correct
         G = create_example_cw(centered=False)
-        G.set_centered_coordinates(type = 'mean')
-        x_coords = [x for x, y in G.coordinates.values()]
+        G.center_coordinates("mean")
+        x_coords = G.coord_matrix[:, 0]
+        self.assertAlmostEqual(np.average(x_coords), 0, places=1)
+
+    def test_add_face(self):
+        G = create_example_cw()
+        face = ["A", "B", "C"]
+        G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)])
+        G.add_face(face)
+        self.assertIn(tuple(face), G.faces)
 
-        self.assertAlmostEqual( np.average(x_coords), 0, places = 1)
+    def test_non_existent_vertex(self):
+        G = create_example_cw()
+        face = ["A", "B", "C"]
+        G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)])
+
+        # test non-existent vertex
+        with self.assertRaises(ValueError):
+            G.add_face(["A", "B", "Z"])
+
+    def test_face_with_missing_edges(self):
+        G = create_example_cw()
+        face = ["A", "B", "C"]
+        G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)])
+        with self.assertRaises(ValueError):
+            G.add_face(["A", "D", "E"], check=True)
+
+    def test_too_short_face(self):
+        G = create_example_cw()
+        face = ["A", "B", "C"]
+        G.add_edges_from([(face[i], face[(i + 1) % 3]) for i in range(3)])
+        with self.assertRaises(ValueError):
+            G.add_face(["A", "B"])
 
-    
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_embed_graph.py b/tests/test_embed_graph.py
index ec5bc36..c7b37fd 100644
--- a/tests/test_embed_graph.py
+++ b/tests/test_embed_graph.py
@@ -1,140 +1,101 @@
 import unittest
-from ect import embed_graph
+from ect.utils.examples import create_example_graph
 import numpy as np
 
 
 class TestEmbeddedGraph(unittest.TestCase):
     def test_example_graph(self):
-        # Make sure we can build a grpah in the first place
-        G = embed_graph.create_example_graph()
-        self.assertEqual( len(G.nodes), 6)  # assuming my_function squares its input
+        G = create_example_graph()
+        self.assertEqual(len(G.nodes), 6)
+        self.assertEqual(G.dim, 2)
 
+    def test_coord_matrix(self):
+        G = create_example_graph()
+        self.assertEqual(G.coord_matrix.shape, (6, 2))
+        self.assertTrue(isinstance(G.coord_matrix, np.ndarray))
+
+    def test_node_list(self):
+        G = create_example_graph()
+        self.assertEqual(len(G.node_list), 6)
+        self.assertEqual(set(G.node_list), set(G.nodes))
 
     def test_add_node(self):
-        # Make sure adding a vertex updates the coordiantes list 
-        G = embed_graph.create_example_graph()
-        G.add_node('G', 1, 2)
-        self.assertEqual( len(G.nodes), 7)
-        self.assertEqual( len(G.coordinates), 7)
+        G = create_example_graph()
+        G.add_node("G", [1, 2])
+        self.assertEqual(len(G.nodes), 7)
+        self.assertEqual(G.coord_matrix.shape, (7, 2))
+        self.assertEqual(G.get_coord("G").tolist(), [1, 2])
 
     def test_add_edge(self):
-        # Make sure adding an edge updates the edge list
-        G = embed_graph.create_example_graph()
-        G.add_edge('A', 'B')
-        self.assertEqual( len(G.edges), 6)
-
-    def test_get_coordinates(self):
-        # Make sure we can get the coordinates of a vertex
-        G = embed_graph.create_example_graph(centered=False)
-        coords = G.get_coordinates('A')
-        self.assertEqual( coords, (1, 2))
-
-    def test_coords_list(self):
-        # Make sure the keys in the coordinates list are the same as the nodes
-        G = embed_graph.create_example_graph(centered=False)
-        self.assertEqual( len(G.nodes), len(G.coordinates))
-        self.assertEqual( set(G.nodes), set(G.coordinates.keys()))
+        G = create_example_graph()
+        G.add_edge("A", "B")
+        self.assertEqual(len(G.edges), 6)
+
+    def test_get_coord(self):
+        G = create_example_graph(centered=False)
+        coords = G.get_coord("A")
+        self.assertTrue(np.array_equal(coords, np.array([1, 2])))
+
+    def test_invalid_node_operations(self):
+        G = create_example_graph()
+        with self.assertRaises(ValueError):
+            G.add_node("A", [1, 2])
+        with self.assertRaises(ValueError):
+            G.get_coord("Z")
 
     def test_mean_centered_coordinates(self):
-        # Make sure the mean centered coordinates are correct
-        G = embed_graph.create_example_graph(centered=False)
-        G.set_centered_coordinates(type = 'mean')
-        x_coords = [x for x, y in G.coordinates.values()]
-
-        self.assertAlmostEqual( np.average(x_coords), 0, places = 1)
+        G = create_example_graph(centered=False)
+        G.center_coordinates("mean")
+        x_coords = G.coord_matrix[:, 0]
+        self.assertAlmostEqual(np.average(x_coords), 0, places=1)
 
     def test_get_center(self):
-        G = embed_graph.create_example_graph()
-        center = G.get_center(type = 'mean')
+        G = create_example_graph()
+        center = G.get_center("mean")
         self.assertIsInstance(center, np.ndarray)
         self.assertEqual(len(center), 2)
-        
-        # Check if center is correctly calculated
-        coords = np.array(list(G.coordinates.values()))
+
+        coords = G.coord_matrix
         expected_center = np.mean(coords, axis=0)
         np.testing.assert_almost_equal(center, expected_center)
 
     def test_rescale_to_unit_disk(self):
-        G = embed_graph.create_example_graph()
-        original_center = G.get_center()
-        G.rescale_to_unit_disk(preserve_center=True)
-        
-        self.assertAlmostEqual(G.get_bounding_radius(), 1.0, places=6)
-        np.testing.assert_almost_equal(G.get_center(), original_center)
-
-        G = embed_graph.create_example_graph()
-        G.rescale_to_unit_disk(preserve_center=False)
-        self.assertAlmostEqual(G.get_bounding_radius(), 1.0, places=6)
-        np.testing.assert_almost_equal(G.get_center(), np.array([0, 0]), decimal=6)
-
-    def test_min_max_centered_coordinates(self):
-        # Make sure the min-max centered coordinates are correct
-        G = embed_graph.create_example_graph(centered=False)
-        G.set_centered_coordinates(type = 'min_max')
-        x_coords = [x for x, y in G.coordinates.values()]
-        y_coords = [y for x, y in G.coordinates.values()]
-
-        self.assertAlmostEqual( np.max(x_coords) + np.min(x_coords), 0, places = 1)
-        self.assertAlmostEqual( np.max(y_coords) + np.min(y_coords), 0, places = 1)
+        G = create_example_graph()
+        G.scale_coordinates(1.0)
+
+        self.assertAlmostEqual(G.get_bounding_radius(center_type="mean"), 1.0, places=6)
+
+        coords_before = G.coord_matrix.copy()
+        G.scale_coordinates(2.0)
+        coords_after = G.coord_matrix
+        self.assertTrue(np.allclose(coords_after / 2.0, coords_before))
+
+    def test_bounding_box_centered_coordinates(self):
+        G = create_example_graph(centered=False)
+        G.center_coordinates("bounding_box")
+        x_coords = G.coord_matrix[:, 0]
+        y_coords = G.coord_matrix[:, 1]
+
+        self.assertAlmostEqual(np.max(x_coords) + np.min(x_coords), 0, places=1)
+        self.assertAlmostEqual(np.max(y_coords) + np.min(y_coords), 0, places=1)
 
     def test_PCA_coords(self):
-        # Make sure the PCA coordinates are running
-        # Note this doesn't check correctness
-        G = embed_graph.create_example_graph(centered=False)
-        G.set_PCA_coordinates()
-        self.assertEqual( len(G.coordinates), 6)
+        G = create_example_graph(centered=False)
+        G.project_coordinates("pca")
+        self.assertEqual(G.coord_matrix.shape, (6, 2))
 
     def test_add_cycle(self):
-        # Make sure we can add a loop of input 
-        G = embed_graph.create_example_graph(centered=False)
+        G = create_example_graph(centered=False)
         num_verts = len(G.nodes)
         num_edges = len(G.edges)
         verts_to_add = 8
-        loop_coords = 3*np.random.rand(verts_to_add, 2)
+        loop_coords = 3 * np.random.rand(verts_to_add, 2)
 
         G.add_cycle(loop_coords)
         G.plot()
-        self.assertEqual( len(G.nodes), num_verts + verts_to_add)
-        self.assertEqual( len(G.edges), num_edges + verts_to_add)
-
-    def test_get_angles(self):
-        # Make sure we can get the angles of the vertices
-        G = embed_graph.create_example_graph(centered=False)
-        M,L = G.get_all_normals_matrix()
-        self.assertEqual( M.shape, (6, 6))
-
-        # Check that all entries in the matrix are between 0 and 2pi other 
-        # than the diagonal which should be nan
-
-        self.assertTrue( np.nanmin(M) >= 0)
-        self.assertTrue( np.nanmax(M) <= 2*np.pi)
-
-        # Check that all keys in dictionary have the same property 
-        M_dict = G.get_normals_dict(opposites = True)
-        keys = list(M_dict.keys())
-        self.assertTrue( all([0 <= x <= 2*np.pi for x in keys]))
-        
-
-        #----
-        # Check that the values the dictionary show up in pairs 
-        # when asking for opposites
-        M_dict = G.get_normals_dict(opposites=True)
-        vals = list(M_dict.values())
-
-        # Convert to strings to make this hashable for the counter 
-        vals = [','.join([''.join(x) for x in A]) for A in vals]
-        vals
-
-        # Count all the instaces of each value. 
-        # These should come in pairs, so should always be 2. 
-        from collections import Counter
-        counter = Counter(vals)
-        for k,v in counter.items():
-            self.assertEqual(v, 2) 
-
-         
-
+        self.assertEqual(len(G.nodes), num_verts + verts_to_add)
+        self.assertEqual(len(G.edges), num_edges + verts_to_add)
 
 
-if __name__ == '__main__':
-    unittest.main()
\ No newline at end of file
+if __name__ == "__main__":
+    unittest.main()