diff --git a/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
index 4e945fd..808aed1 100644
--- a/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ b/doc_source/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
@@ -32,8 +32,8 @@
       "Requirement already satisfied: six>=1.5 in /Users/liz/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->ect==0.1.4) (1.16.0)\n",
       "Building wheels for collected packages: ect\n",
       "  Building wheel for ect (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40056 sha256=af622bb010c7e5244594248cd5b6d2a3f6a1c553d074f4e6e3abf593d02cc8ae\n",
-      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-jv4cz1t4/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
+      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40205 sha256=1a297b65949d477c6ceeae6d0cff6e6e6c6840b5d46b262bd72a8e083698bcf2\n",
+      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-b8z93fr2/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
       "Successfully built ect\n",
       "Installing collected packages: ect\n",
       "  Attempting uninstall: ect\n",
@@ -132,10 +132,10 @@
      "data": {
       "text/plain": [
        "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  4,  4,  4,  4,  4,\n",
-       "        4,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,\n",
-       "        1,  1,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  4,  4,  4,\n",
-       "        4,  4,  4, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
+       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,\n",
+       "        3,  3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
+       "        2,  2,  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": 5,
@@ -156,7 +156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -202,7 +202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -282,7 +282,8 @@
     }
    ],
    "source": [
-    "K = EmbeddedCW()\n",
+    "# K = EmbeddedCW()\n",
+    "K = EmbeddedGraph()\n",
     "\n",
     "K.add_node('A', 0,0)\n",
     "K.add_node('B', 1,0)\n",
@@ -299,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -335,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +345,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -366,12 +367,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -387,12 +388,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -410,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +423,7 @@
        " ('C', 'D'): 0.7071067811865475}"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,23 +434,228 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#....... check on the list of sorted edges, something is wrong"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out = myect.calculateECC(K, theta, r, return_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'EmbeddedCW' object has no attribute 'get_sorted_edges'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mK\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_sorted_edges\u001b[49m()\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'EmbeddedCW' object has no attribute 'get_sorted_edges'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 4]\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x135b2aa70>]"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
-    "#....... check on the list of sorted edges, something is wrong"
+    "r, r_thresh = myect.get_radius_and_thresh(K, r)\n",
+    "print(r_thresh)\n",
+    "print(out[2])\n",
+    "plt.plot(r_thresh,out[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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,draw_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A -0.71\n",
+      "B 0.0\n",
+      "C 0.71\n",
+      "D -0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "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": [
+    "funcdict = K.g_omega(theta)\n",
+    "for key in funcdict:\n",
+    "    print(key, round(funcdict[key],2))\n",
+    "K.plot(color_nodes_theta=theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_below_threshold(func_list, thresh):\n",
+    "    \"\"\" \n",
+    "    Returns the number of entries in func_list that are below the threshold thresh. \n",
+    "    Warning: func_list must be sorted in ascending order.\n",
+    "\n",
+    "    Parameters\n",
+    "        func_list (list): sorted list of function values\n",
+    "        thresh (float): threshold value\n",
+    "\n",
+    "    Returns\n",
+    "        int \n",
+    "    \"\"\"\n",
+    "    # If the list is empty, return 0\n",
+    "    if len(func_list) == 0:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        func_max = func_list[-1]\n",
+    "        if thresh < func_max:\n",
+    "            return np.argmin(func_list < thresh)\n",
+    "        else:\n",
+    "            return len(func_list)\n",
+    "# --"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = K\n",
+    "r,r_threshes = myect.get_radius_and_thresh(G, r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_list, g = G.sort_vertices(theta, return_g=True)\n",
+    "g_list = [g[v] for v in v_list]\n",
+    "\n",
+    "vertex_count = np.array([num_below_threshold(\n",
+    "    g_list, thresh) for thresh in r_threshes])\n",
+    "# print(vertex_count)\n",
+    "\n",
+    "e_list, g_e = G.sort_edges(np.pi/2, return_g=True)\n",
+    "g_e_list = [g_e[e] for e in e_list]\n",
+    "edge_count = np.array([num_below_threshold(\n",
+    "    g_e_list, thresh) for thresh in r_threshes])\n",
+    "# print(edge_count)\n",
+    "\n",
+    "if type(G) == EmbeddedCW:\n",
+    "    f_list, g_f = G.sort_faces(theta, return_g=True)\n",
+    "    g_f_list = [g_f[f] for f in f_list]\n",
+    "    face_count = np.array([num_below_threshold(\n",
+    "        g_f_list, thresh) for thresh in r_threshes])\n",
+    "    # print(face_count)\n",
+    "else:\n",
+    "    face_count = np.zeros_like(vertex_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.49999999999999994, 0.49999999999999994, 0.5, 0.5]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g_e_list"
    ]
   },
   {
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index 8ad3bcf..752bf99 100644
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diff --git a/docs/_modules/ect_graph.html b/docs/_modules/ect_graph.html
index b660fe9..3953ee9 100644
--- a/docs/_modules/ect_graph.html
+++ b/docs/_modules/ect_graph.html
@@ -184,7 +184,8 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
         <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">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Bounding radius must be a positive number.&#39;</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>
 
@@ -211,7 +212,7 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="ECT.calculateECC">
 <a class="viewcode-back" href="../ect_on_graphs.html#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="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 of an `EmbeddedGraph`, that is, a graph with coordinates for each vertex.</span>
 
@@ -222,6 +223,8 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
 <span class="sd">                The angle (in radians) to use for the direction function when computing the Euler Characteristic Curve.</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">            return_counts (bool):</span>
+<span class="sd">                Whether to return the counts of vertices, edges, and faces below the threshold. 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">G</span><span class="p">,</span> <span class="n">bound_radius</span><span class="p">)</span>
@@ -239,11 +242,15 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
 <span class="sd">            Returns</span>
 <span class="sd">                int </span>
 <span class="sd">            &quot;&quot;&quot;</span>
-            <span class="n">func_max</span> <span class="o">=</span> <span class="n">func_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="n">thresh</span> <span class="o">&lt;</span> <span class="n">func_max</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">func_list</span> <span class="o">&lt;</span> <span class="n">thresh</span><span class="p">)</span>
+            <span class="c1"># If the list is empty, return 0</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">func_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">return</span> <span class="mi">0</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">func_list</span><span class="p">)</span>
+                <span class="n">func_max</span> <span class="o">=</span> <span class="n">func_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="n">thresh</span> <span class="o">&lt;</span> <span class="n">func_max</span><span class="p">:</span>
+                    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">func_list</span> <span class="o">&lt;</span> <span class="n">thresh</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">func_list</span><span class="p">)</span>
         <span class="c1"># --</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>
@@ -253,7 +260,7 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
             <span class="n">g_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span> <span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">r_threshes</span><span class="p">])</span>
         <span class="c1"># print(vertex_count)</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">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">return_g</span><span class="o">=</span><span class="kc">True</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="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">edge_count</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">num_below_threshold</span><span class="p">(</span>
             <span class="n">g_e_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span> <span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">r_threshes</span><span class="p">])</span>
@@ -271,7 +278,10 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
         <span class="c1"># print(vertex_count - edge_count)</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="k">return</span> <span class="n">ecc</span></div>
+        <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="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">
@@ -339,7 +349,7 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="ECT.plotECC">
 <a class="viewcode-back" href="../ect_on_graphs.html#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="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>
 
@@ -353,13 +363,23 @@ <h1>Source code for ect_graph</h1><div class="highlight"><pre>
 <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">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="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="bp">self</span><span class="o">.</span><span class="n">threshes</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">set_bounding_radius</span><span class="p">(</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">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">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">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>
diff --git a/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt b/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt
index 0dc5921..808aed1 100644
--- a/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt
+++ b/docs/_sources/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb.txt
@@ -18,6 +18,7 @@
       "Requirement already satisfied: networkx in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.2.1)\n",
       "Requirement already satisfied: matplotlib in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.7.0)\n",
       "Requirement already satisfied: numba in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (0.56.4)\n",
+      "Requirement already satisfied: scipy in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (1.10.0)\n",
       "Requirement already satisfied: contourpy>=1.0.1 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (1.0.5)\n",
       "Requirement already satisfied: cycler>=0.10 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (0.11.0)\n",
       "Requirement already satisfied: fonttools>=4.22.0 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (4.49.0)\n",
@@ -31,8 +32,8 @@
       "Requirement already satisfied: six>=1.5 in /Users/liz/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->ect==0.1.4) (1.16.0)\n",
       "Building wheels for collected packages: ect\n",
       "  Building wheel for ect (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=39481 sha256=945a865dca4ccb5f217f0a29d5144245bca97342c60f4d3a2dcdaedba9e337e3\n",
-      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-32vyxgih/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
+      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40205 sha256=1a297b65949d477c6ceeae6d0cff6e6e6c6840b5d46b262bd72a8e083698bcf2\n",
+      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-b8z93fr2/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
       "Successfully built ect\n",
       "Installing collected packages: ect\n",
       "  Attempting uninstall: ect\n",
@@ -131,10 +132,10 @@
      "data": {
       "text/plain": [
        "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  4,  4,  4,  4,  4,\n",
-       "        4,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,\n",
-       "        1,  1,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  4,  4,  4,\n",
-       "        4,  4,  4, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
+       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,\n",
+       "        3,  3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
+       "        2,  2,  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": 5,
@@ -155,7 +156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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hCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsPDYcJSUl6bbbbpO/v7+CgoJ033336fDhw1cdl5GRoejoaPn6+uqmm25ScnJyA1QLAAA8hceGo4yMDE2bNk179uxRWlqaysvLFR8fr/Pnz1c7Ji8vT2PGjNGgQYOUm5uruXPnasaMGUpNTW3AygEAQFPWsrELqK8tW7Y4rS9fvlxBQUHKycnR4MGDqxyTnJyszp07a/HixZKknj17Kjs7WwsXLtS4cePcXTLQ7BljdLGswqX7vFDq2v019HH8vL1ks9ncsm8A7uGx4ehKRUVFkqTAwMBq+2RmZio+Pt6pbeTIkUpJSVFZWZm8vb0rjSkpKVFJSYljvbi42EUVA82LMUYPJGcq5+iZxi6lXmL+61P37LdLO62bEktAAjyIx95WszLGKDExUXfccYeioqKq7VdYWKjg4GCntuDgYJWXl+vUqVNVjklKSlJAQIBjCQ8Pd2ntQHNxsazCrcEopks7+Xl7uXSfft5eiunSzqX7vFL20TMuv5oGwL2axZWjhIQEffHFF9q1a9dV+1757s0YU2X7ZXPmzFFiYqJjvbi4mIAEXEX2b/+fWvm4Psi4+uqLzWbTuimxbgkvF0or3HY1CoB7eXw4mj59uj7++GPt2LFDnTp1qrFvSEiICgsLndpOnjypli1bqn379lWOsdvtstvtLqsXuB608vFSKx/PeHmx2WweUyuAhuGxt9WMMUpISND69ev12WefKSIi4qpjYmNjlZaW5tS2bds2xcTEVPm8EQAAuP54bDiaNm2aVq5cqVWrVsnf31+FhYUqLCzUxYsXHX3mzJmjxx57zLE+ZcoUHT16VImJiTp06JDef/99paSkaPbs2Y0xBQAA0AR5bDhaunSpioqKNHToUIWGhjqWtWvXOvoUFBQoPz/fsR4REaHNmzcrPT1d/fr10+9//3u9+eab/Bo/AABw8Ngb7ZcfpK7JihUrKrUNGTJEe/fudUNFAACgOfDYK0cAAADuQDgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABaEIwAAAAvCEQAAgAXhCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAICFR4ejHTt2aOzYsQoLC5PNZtPGjRtr7J+eni6bzVZp+eqrrxqmYAAA0OS1bOwCrsX58+d166236vHHH9e4ceNqPe7w4cNq27atY71jx47uKA8AAHggjw5Ho0eP1ujRo+s8LigoSDfccIPrCwIAAB7Po2+r1Vf//v0VGhqq4cOHa/v27TX2LSkpUXFxsdMCAACar+sqHIWGhmrZsmVKTU3V+vXrFRkZqeHDh2vHjh3VjklKSlJAQIBjCQ8Pb8CKAQBAQ/Po22p1FRkZqcjISMd6bGysjh07poULF2rw4MFVjpkzZ44SExMd68XFxQQkAACasevqylFVBg4cqCNHjlS73W63q23btk4LAABovq77cJSbm6vQ0NDGLgMAADQRHn1b7dy5c/rnP//pWM/Ly9O+ffsUGBiozp07a86cOTp+/Lg+/PBDSdLixYvVtWtX9e7dW6WlpVq5cqVSU1OVmpraWFMAAABNjEeHo+zsbN15552O9cvPBk2cOFErVqxQQUGB8vPzHdtLS0s1e/ZsHT9+XH5+furdu7c2bdqkMWPGNHjtAACgafLocDR06FAZY6rdvmLFCqf1Z555Rs8884ybqwIAAJ7sun/mCAAAwIpwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALBoeS2Dy8rKVFhYqAsXLqhjx44KDAx0VV0AAACNos5Xjs6dO6d33nlHQ4cOVUBAgLp27apevXqpY8eO6tKliyZPnqysrCx31AoAAOB2dQpHr7/+urp27ap3331Xw4YN0/r167Vv3z4dPnxYmZmZmjdvnsrLyzVixAiNGjVKR44ccVfdAAAAblGn22q7d+/W9u3b1adPnyq333777frFL36h5ORkpaSkKCMjQ927d3dJoQAAAA2hTuFo3bp1tepnt9v11FNP1asgAACAxnRND2RblZeXa+fOnfL19VWvXr0UEBDgql0DAAA0GJeFowceeEDt27fXxo0b1bZtW126dEl9+vTRn//8Z1cdAgAAwO1cFo7y8vK0ceNG5eTkaN++fXrjjTd05swZV+0eAACgQbjsQyD9/PwkST4+PiotLdXTTz+tjIwMV+0eAACgQbjsylFCQoL+/e9/6/7779e0adMUFxenb775xlW7BwAAaBB1vnK0ZMmSKtt//vOfKzAwUM8++6z+4z/+QwcPHtRHH310zQUCAAA0pDpfOfr1r3+t/v37KzY2tto+o0aN0qRJk66lLgAAgEZR5ytHCxYs0Lhx4/Tdd99VuT03N1e33377NRdWGzt27NDYsWMVFhYmm82mjRs3XnVMRkaGoqOj5evrq5tuuknJycnuLxQAAHiMOoejmTNn6s4779S4ceNUXl7utO2jjz7SoEGDFBcX57ICa3L+/Hndeuuteuutt2rVPy8vT2PGjNGgQYOUm5uruXPnasaMGUpNTXVzpQAAwFPU64Hs9957T3FxcZo+fbqWLl0qSXrttdc0d+5c/e53v9Pzzz/v0iKrM3r0aI0ePbrW/ZOTk9W5c2ctXrxYktSzZ09lZ2dr4cKFGjdunJuqBHC9u1Ba4Zb9+nl7yWazuWXfwPWsXuHIz89P69ev12233aa+ffsqJydHa9as0Zo1a5p0yMjMzFR8fLxT28iRI5WSkqKysjJ5e3tXGlNSUqKSkhLHenFxsdvrBNC8xPzXp+7Zb5d2WjclloAEuFidw9GTTz6p6Oho9e/fX++9954eeOAB3Xjjjdq1a5f69evnhhJdp7CwUMHBwU5twcHBKi8v16lTpxQaGlppTFJSkl544YWGKhFAM+Hn7aWYLu2UfdR9H4abffSMLpZVqJWPyz6VBYDqEY7+8Y9/aN26dTp79qxatmwpm82mqKgo7dy5U+fPn1e/fv3UunVrd9TqEle+wzLGVNl+2Zw5c5SYmOhYLy4uVnh4uPsKBNAs2Gw2rZsSq4tlrr+ldqG0wm1XowDUIxzt2LFDknTkyBHl5ORo7969ysnJ0bx58/T999+rRYsW6tGjhw4ePOjyYq9VSEiICgsLndpOnjypli1bqn379lWOsdvtstvtDVEegGbGZrNxVQfwQPX+X9u9e3d1795dDz/8sKMtLy9P2dnZys3NdUlxrhYbG6tPPvnEqW3btm2KiYmp8nkjAABw/XHpW5qIiAhFRETowQcfdOVuq3Xu3Dn985//dKzn5eVp3759CgwMVOfOnTVnzhwdP35cH374oSRpypQpeuutt5SYmKjJkycrMzNTKSkpWr16dYPUCwAAmr46fc5Rfn5+nXZ+/PjxOvWvq+zsbPXv31/9+/eXJCUmJqp///6OjxIoKChwqjkiIkKbN29Wenq6+vXrp9///vd68803m/Rv2AEAgIZVpytHt912m+655x5Nnjy52k/BLioq0p/+9Ce98cYb+uUvf6np06e7pNCqDB061PFAdVVWrFhRqW3IkCHau3ev22oCAACerU7h6NChQ3rppZc0atQoeXt7KyYmRmFhYfL19dWZM2d08OBBffnll4qJidFrr71Wpw9oBAAAaArqdFstMDBQCxcu1IkTJ5ScnKwePXro1KlTOnLkiCTpZz/7mXJycvTXv/6VYAQAADxSvR7I9vX1lZ+fn15//XVX1wMAANCo6vyHZy+777779PTTTzv9aQ0AAABPV+9wtGvXLm3dulXR0dH64osvquxz4sQJ3XvvvfUuDgAAoKHVOxzFxMQoNzdXcXFxGjBggBYtWuTYdunSJR08eFDPP/+8MjMzXVIoAABAQ7imD4H08/PTggUL5OPjo1//+tdavXq1IxiVlJSoS5cuSkpKclWtAAAAblfvK0fvvPOOwsLCFBISohUrVui2225Ty5YtlZubqyeffFJnzpxRXl6ennjiCVfWCwAA4Fb1Dke//e1vde+99+rgwYM6e/as9uzZo8zMTP3hD3/Qe++9p1mzZunChQuurBUAAMDt6h2Ohg4dqvnz5ysyMlI2m83RPmvWLP3tb39Tdna2+vbtq88//9wlhQIAADSEeoejdevWKTg4uMptffr0UVZWlu6++24NHjy43sUBAAA0tGt6ILsmdrtdixcv1l133eWuQwAAALhcva8c1daIESPcfQgAAACXcXs4AgAA8CSEIwAAAAvCEQAAgAXhCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALjw9HS5YsUUREhHx9fRUdHa2dO3dW2zc9PV02m63S8tVXXzVgxQAAoCnz6HC0du1azZw5U88995xyc3M1aNAgjR49Wvn5+TWOO3z4sAoKChxL9+7dG6hiAADQ1Hl0OFq0aJGeeOIJPfnkk+rZs6cWL16s8PBwLV26tMZxQUFBCgkJcSxeXl4NVDEAAGjqPDYclZaWKicnR/Hx8U7t8fHx2r17d41j+/fvr9DQUA0fPlzbt2+vsW9JSYmKi4udFgAA0Hx5bDg6deqUKioqFBwc7NQeHByswsLCKseEhoZq2bJlSk1N1fr16xUZGanhw4drx44d1R4nKSlJAQEBjiU8PNyl8wAAAE1Ly8Yu4FrZbDandWNMpbbLIiMjFRkZ6ViPjY3VsWPHtHDhQg0ePLjKMXPmzFFiYqJjvbi4mIAEAEAz5rFXjjp06CAvL69KV4lOnjxZ6WpSTQYOHKgjR45Uu91ut6tt27ZOCwAAaL48Nhz5+PgoOjpaaWlpTu1paWmKi4ur9X5yc3MVGhrq6vIAAICH8ujbaomJiZowYYJiYmIUGxurZcuWKT8/X1OmTJH04y2x48eP68MPP5QkLV68WF27dlXv3r1VWlqqlStXKjU1VampqY05DQAA0IR4dDgaP368Tp8+rRdffFEFBQWKiorS5s2b1aVLF0lSQUGB02celZaWavbs2Tp+/Lj8/PzUu3dvbdq0SWPGjGmsKQAAgCbGZowxjV2EJykuLlZAQICKiop4/giwuFBarl7Pb5UkHXxxpFr5ePR7ryaNrzVQd3X5+e2xzxwBAAC4A+EIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABaEIwAAAAvCEQAAgAXhCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWHh+OlixZooiICPn6+io6Olo7d+6ssX9GRoaio6Pl6+urm266ScnJyQ1UKQAA8AQeHY7Wrl2rmTNn6rnnnlNubq4GDRqk0aNHKz8/v8r+eXl5GjNmjAYNGqTc3FzNnTtXM2bMUGpqagNXDgAAmqqWjV3AtVi0aJGeeOIJPfnkk5KkxYsXa+vWrVq6dKmSkpIq9U9OTlbnzp21ePFiSVLPnj2VnZ2thQsXaty4cQ1ZeiXGGF0sq2jUGoBrcaGU718AzYPHhqPS0lLl5OTo2WefdWqPj4/X7t27qxyTmZmp+Ph4p7aRI0cqJSVFZWVl8vb2rjSmpKREJSUljvXi4mIXVF/ZxbIK9Xp+q1v2DQAAas9jb6udOnVKFRUVCg4OdmoPDg5WYWFhlWMKCwur7F9eXq5Tp05VOSYpKUkBAQGOJTw83DUTAJqpmC7t5Oft1dhlAEC9eeyVo8tsNpvTujGmUtvV+lfVftmcOXOUmJjoWC8uLnZLQPLz9tLBF0e6fL9AQ/Pz9qrx/yAANHUeG446dOggLy+vSleJTp48Wenq0GUhISFV9m/ZsqXat29f5Ri73S673e6aomtgs9nUysdjTwcAAM2Gx95W8/HxUXR0tNLS0pza09LSFBcXV+WY2NjYSv23bdummJiYKp83AgAA1x+PDUeSlJiYqPfee0/vv/++Dh06pFmzZik/P19TpkyR9OMtsccee8zRf8qUKTp69KgSExN16NAhvf/++0pJSdHs2bMbawoAAKCJ8ej7OOPHj9fp06f14osvqqCgQFFRUdq8ebO6dOkiSSooKHD6zKOIiAht3rxZs2bN0ttvv62wsDC9+eabjf5r/AAAoOmwmctPJKNWiouLFRAQoKKiIrVt27axywFwHbpQWu746I+DL47keUWgFury89ujb6sBAAC4GuEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABaEIwAAAAvCEQAAgAXhCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWHhuOzpw5owkTJiggIEABAQGaMGGCvv/++xrHTJo0STabzWkZOHBgwxQMAAA8QsvGLqC+Hn30UX377bfasmWLJOk///M/NWHCBH3yySc1jhs1apSWL1/uWPfx8XFrnQAAwLN4ZDg6dOiQtmzZoj179mjAgAGSpHfffVexsbE6fPiwIiMjqx1rt9sVEhLSUKUCAAAP45G31TIzMxUQEOAIRpI0cOBABQQEaPfu3TWOTU9PV1BQkHr06KHJkyfr5MmTNfYvKSlRcXGx0wIAAJovjwxHhYWFCgoKqtQeFBSkwsLCaseNHj1af/zjH/XZZ5/pD3/4g7KysjRs2DCVlJRUOyYpKcnxXFNAQIDCw8NdMgcAANA0NalwNH/+/EoPTF+5ZGdnS5JsNlul8caYKtsvGz9+vO666y5FRUVp7Nix+stf/qJ//OMf2rRpU7Vj5syZo6KiIsdy7Nixa58oAABosprUM0cJCQl6+OGHa+zTtWtXffHFF/ruu+8qbfvXv/6l4ODgWh8vNDRUXbp00ZEjR6rtY7fbZbfba71PAADg2ZpUOOrQoYM6dOhw1X6xsbEqKirS3/72N91+++2SpM8//1xFRUWKi4ur9fFOnz6tY8eOKTQ0tN41AwCA5qVJ3VarrZ49e2rUqFGaPHmy9uzZoz179mjy5Mm6++67nX5T7ZZbbtGGDRskSefOndPs2bOVmZmpb775Runp6Ro7dqw6dOign/70p401FQAA0MR4ZDiSpD/+8Y/q06eP4uPjFR8fr759++p//ud/nPocPnxYRUVFkiQvLy/t379f9957r3r06KGJEyeqR48eyszMlL+/f2NMAQAANEFN6rZaXQQGBmrlypU19jHGOP7t5+enrVu3urssAADg4Tz2yhEAAIA7EI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABaEIwAAAAvCEQAAgAXhCAAAwIJwBAAAYEE4AgAAsCAcAQAAWBCOAAAALAhHAAAAFoQjAAAAC8IRAACABeEIAADAgnAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGDhseFowYIFiouLU6tWrXTDDTfUaowxRvPnz1dYWJj8/Pw0dOhQffnll+4tFAAAeBSPDUelpaV68MEHNXXq1FqPefXVV7Vo0SK99dZbysrKUkhIiEaMGKGzZ8+6sVIAAOBJWjZ2AfX1wgsvSJJWrFhRq/7GGC1evFjPPfec7r//fknSBx98oODgYK1atUq//OUv3VUqALjNhdKKxi4BcDk/by/ZbLZGO77HhqO6ysvLU2FhoeLj4x1tdrtdQ4YM0e7du6sNRyUlJSopKXGsFxcXu71WAKitmP/6tLFLAFzu4Isj1cqn8SKKx95Wq6vCwkJJUnBwsFN7cHCwY1tVkpKSFBAQ4FjCw8PdWicAXI2ft5diurRr7DKAZqtJXTmaP3++43ZZdbKyshQTE1PvY1x5mc4YU+Oluzlz5igxMdGxXlxcTEAC0KhsNpvWTYnVxTJuqaF58vP2atTjN6lwlJCQoIcffrjGPl27dq3XvkNCQiT9eAUpNDTU0X7y5MlKV5Os7Ha77HZ7vY4JAO5is9ka9bYD0Jw1qf9ZHTp0UIcOHdyy74iICIWEhCgtLU39+/eX9ONvvGVkZOiVV15xyzEBAIDn8dhnjvLz87Vv3z7l5+eroqJC+/bt0759+3Tu3DlHn1tuuUUbNmyQ9OO7rJkzZ+qll17Shg0bdODAAU2aNEmtWrXSo48+2ljTAAAATUyTunJUF88//7w++OADx/rlq0Hbt2/X0KFDJUmHDx9WUVGRo88zzzyjixcv6qmnntKZM2c0YMAAbdu2Tf7+/g1aOwAAaLpsxhjT2EV4kuLiYgUEBKioqEht27Zt7HIAAEAt1OXnt8feVgMAAHAHwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABaEIwAAAAuP/fMhjeXyB4oXFxc3ciUAAKC2Lv/crs0fBiEc1dHZs2clSeHh4Y1cCQAAqKuzZ88qICCgxj78bbU6unTpkk6cOCF/f3/ZbDaX7ru4uFjh4eE6duxYs/y7bczP8zX3OTI/z9fc58j86s8Yo7NnzyosLEwtWtT8VBFXjuqoRYsW6tSpk1uP0bZt22b5TX8Z8/N8zX2OzM/zNfc5Mr/6udoVo8t4IBsAAMCCcAQAAGBBOGpC7Ha75s2bJ7vd3tiluAXz83zNfY7Mz/M19zkyv4bBA9kAAAAWXDkCAACwIBwBAABYEI4AAAAsCEcAAAAWhKMGtGDBAsXFxalVq1a64YYbquyTn5+vsWPHqnXr1urQoYNmzJih0tLSGvdbUlKi6dOnq0OHDmrdurXuueceffvtt26YQd2kp6fLZrNVuWRlZVU7btKkSZX6Dxw4sAErr72uXbtWqvXZZ5+tcYwxRvPnz1dYWJj8/Pw0dOhQffnllw1Uce198803euKJJxQRESE/Pz9169ZN8+bNu+r3Y1M/f0uWLFFERIR8fX0VHR2tnTt31tg/IyND0dHR8vX11U033aTk5OQGqrRukpKSdNttt8nf319BQUG67777dPjw4RrHVPd/9Kuvvmqgqutm/vz5lWoNCQmpcYynnD+p6tcTm82madOmVdnfE87fjh07NHbsWIWFhclms2njxo1O2+v7epiamqpevXrJbrerV69e2rBhg0vrJhw1oNLSUj344IOaOnVqldsrKip011136fz589q1a5fWrFmj1NRU/epXv6pxvzNnztSGDRu0Zs0a7dq1S+fOndPdd9+tiooKd0yj1uLi4lRQUOC0PPnkk+ratatiYmJqHDtq1CincZs3b26gquvuxRdfdKr1t7/9bY39X331VS1atEhvvfWWsrKyFBISohEjRjj+bl9T8dVXX+nSpUt655139OWXX+r1119XcnKy5s6de9WxTfX8rV27VjNnztRzzz2n3NxcDRo0SKNHj1Z+fn6V/fPy8jRmzBgNGjRIubm5mjt3rmbMmKHU1NQGrvzqMjIyNG3aNO3Zs0dpaWkqLy9XfHy8zp8/f9Wxhw8fdjpf3bt3b4CK66d3795Ote7fv7/avp50/iQpKyvLaW5paWmSpAcffLDGcU35/J0/f1633nqr3nrrrSq31+f1MDMzU+PHj9eECRP097//XRMmTNBDDz2kzz//3HWFGzS45cuXm4CAgErtmzdvNi1atDDHjx93tK1evdrY7XZTVFRU5b6+//574+3tbdasWeNoO378uGnRooXZsmWLy2u/FqWlpSYoKMi8+OKLNfabOHGiuffeexumqGvUpUsX8/rrr9e6/6VLl0xISIh5+eWXHW0//PCDCQgIMMnJyW6o0LVeffVVExERUWOfpnz+br/9djNlyhSntltuucU8++yzVfZ/5plnzC233OLU9stf/tIMHDjQbTW6ysmTJ40kk5GRUW2f7du3G0nmzJkzDVfYNZg3b5659dZba93fk8+fMcY8/fTTplu3bubSpUtVbve08yfJbNiwwbFe39fDhx56yIwaNcqpbeTIkebhhx92Wa1cOWpCMjMzFRUVpbCwMEfbyJEjVVJSopycnCrH5OTkqKysTPHx8Y62sLAwRUVFaffu3W6vuS4+/vhjnTp1SpMmTbpq3/T0dAUFBalHjx6aPHmyTp486f4C6+mVV15R+/bt1a9fPy1YsKDG2055eXkqLCx0Ol92u11DhgxpcuerKkVFRQoMDLxqv6Z4/kpLS5WTk+P0tZek+Pj4ar/2mZmZlfqPHDlS2dnZKisrc1utrlBUVCRJtTpf/fv3V2hoqIYPH67t27e7u7RrcuTIEYWFhSkiIkIPP/ywvv7662r7evL5Ky0t1cqVK/WLX/ziqn/k3JPOn1V9Xw+rO6+ufA0lHDUhhYWFCg4Odmpr166dfHx8VFhYWO0YHx8ftWvXzqk9ODi42jGNJSUlRSNHjlR4eHiN/UaPHq0//vGP+uyzz/SHP/xBWVlZGjZsmEpKShqo0tp7+umntWbNGm3fvl0JCQlavHixnnrqqWr7Xz4nV57npni+rvR///d/+u///m9NmTKlxn5N9fydOnVKFRUVdfraV/V/Mjg4WOXl5Tp16pTbar1WxhglJibqjjvuUFRUVLX9QkNDtWzZMqWmpmr9+vWKjIzU8OHDtWPHjgastvYGDBigDz/8UFu3btW7776rwsJCxcXF6fTp01X299TzJ0kbN27U999/X+ObSU87f1eq7+thdefVla+hLV22p+vU/Pnz9cILL9TYJysr66rP2FxW1TsEY8xV3zm4Ykxt1WfO3377rbZu3ao//elPV93/+PHjHf+OiopSTEyMunTpok2bNun++++vf+G1VJf5zZo1y9HWt29ftWvXTg888IDjalJ1rjw37jxfV6rP+Ttx4oRGjRqlBx98UE8++WSNYxv7/F1NXb/2VfWvqr0pSUhI0BdffKFdu3bV2C8yMlKRkZGO9djYWB07dkwLFy7U4MGD3V1mnY0ePdrx7z59+ig2NlbdunXTBx98oMTExCrHeOL5k358Mzl69GinOwlX8rTzV536vB66+zWUcHSNEhIS9PDDD9fYp2vXrrXaV0hISKUHys6cOaOysrJKKdk6prS0VGfOnHG6enTy5EnFxcXV6rh1VZ85L1++XO3bt9c999xT5+OFhoaqS5cuOnLkSJ3H1se1nNPLv5X1z3/+s8pwdPk3awoLCxUaGupoP3nyZLXn2NXqOr8TJ07ozjvvVGxsrJYtW1bn4zX0+atOhw4d5OXlVendZU1f+5CQkCr7t2zZssbw25imT5+ujz/+WDt27FCnTp3qPH7gwIFauXKlGypzvdatW6tPnz7Vfm954vmTpKNHj+rTTz/V+vXr6zzWk85ffV8PqzuvrnwNJRxdow4dOqhDhw4u2VdsbKwWLFiggoICxzfKtm3bZLfbFR0dXeWY6OhoeXt7Ky0tTQ899JAkqaCgQAcOHNCrr77qkrquVNc5G2O0fPlyPfbYY/L29q7z8U6fPq1jx445/edxp2s5p7m5uZJUba0REREKCQlRWlqa+vfvL+nHZwsyMjL0yiuv1K/gOqrL/I4fP64777xT0dHRWr58uVq0qPud+IY+f9Xx8fFRdHS00tLS9NOf/tTRnpaWpnvvvbfKMbGxsfrkk0+c2rZt26aYmJh6fS+7kzFG06dP14YNG5Senq6IiIh67Sc3N7fRz1VtlZSU6NChQxo0aFCV2z3p/FktX75cQUFBuuuuu+o81pPOX31fD2NjY5WWluZ05X7btm2uvSDgske7cVVHjx41ubm55oUXXjBt2rQxubm5Jjc315w9e9YYY0x5ebmJiooyw4cPN3v37jWffvqp6dSpk0lISHDs49tvvzWRkZHm888/d7RNmTLFdOrUyXz66adm7969ZtiwYebWW2815eXlDT7Hqnz66adGkjl48GCV2yMjI8369euNMcacPXvW/OpXvzK7d+82eXl5Zvv27SY2NtbceOONpri4uCHLvqrdu3ebRYsWmdzcXPP111+btWvXmrCwMHPPPfc49bPOzxhjXn75ZRMQEGDWr19v9u/fbx555BETGhra5OZ3/Phxc/PNN5thw4aZb7/91hQUFDgWK086f2vWrDHe3t4mJSXFHDx40MycOdO0bt3afPPNN8YYY5599lkzYcIER/+vv/7atGrVysyaNcscPHjQpKSkGG9vb/O///u/jTWFak2dOtUEBASY9PR0p3N14cIFR58r5/f666+bDRs2mH/84x/mwIED5tlnnzWSTGpqamNM4ap+9atfmfT0dPP111+bPXv2mLvvvtv4+/s3i/N3WUVFhencubP5zW9+U2mbJ56/s2fPOn7WSXK8Zh49etQYU7vXwwkTJjj9Rulf//pX4+XlZV5++WVz6NAh8/LLL5uWLVuaPXv2uKxuwlEDmjhxopFUadm+fbujz9GjR81dd91l/Pz8TGBgoElISDA//PCDY3teXl6lMRcvXjQJCQkmMDDQ+Pn5mbvvvtvk5+c34Mxq9sgjj5i4uLhqt0syy5cvN8YYc+HCBRMfH286duxovL29TefOnc3EiROb1Hwuy8nJMQMGDDABAQHG19fXREZGmnnz5pnz58879bPOz5gff3113rx5JiQkxNjtdjN48GCzf//+Bq7+6pYvX17l9+uV76k87fy9/fbbpkuXLsbHx8f85Cc/cfpV94kTJ5ohQ4Y49U9PTzf9+/c3Pj4+pmvXrmbp0qUNXHHtVHeurN97V87vlVdeMd26dTO+vr6mXbt25o477jCbNm1q+OJrafz48SY0NNR4e3ubsLAwc//995svv/zSsd2Tz99lW7duNZLM4cOHK23zxPN3+eMGrlwmTpxojKnd6+GQIUMc/S9bt26diYyMNN7e3uaWW25xeSC0GfP/P50GAAAAfpUfAADAinAEAABgQTgCAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIASS+++KL69Omj1q1bKzg4WFOnTlVZWVljlwWgEbRs7AIAoLEZY1RRUaF33nlHN954ow4ePKjHHntMffv21dSpUxu7PAANjD88CwBVePTRR9WxY0e98cYbjV0KgAbGbTUA172jR48qISFBUVFRateundq0aaM//elP6tSpU2OXBqAREI4AXNdOnTql22+/XadOndKiRYu0a9cuZWZmysvLS/369Wvs8gA0Ap45AnBd27x5s8rLy7V69WrZbDZJ0ttvv63S0lLCEXCdIhwBuK4FBgaquLhYH3/8sXr16qVPPvlESUlJuvHGG9WxY8fGLg9AI+CBbADXNWOMpk6dqlWrVsnPz08///nP9cMPP+jo0aP685//3NjlAWgEhCMAAAALHsgGAACwIBwBAABYEI4AAAAsCEcAAAAWhCMAAAALwhEAAIAF4QgAAMCCcAQAAGBBOAIAALAgHAEAAFgQjgAAACwIRwAAABb/H5ypt4DzkemrAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -201,7 +202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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MFgCA+lX5aAlEtupJCQkJ0SWXXCJJSklJ0bZt2/T8889r4cKFtW4fGhqq0NDQpgwRAACPVMmhSjm8PkYgslWSUp1hGC41JwAA2E2V8fPi7TECkW2SlFmzZiktLU1xcXE6efKk3njjDeXl5Sk3N9fs0AAAgB/YJkk5evSoxowZo6KiIrVv3169e/dWbm6urr/+erNDcwrkQtn6UEgLAI1T6YPhHm/3tyrbJCk5OTlmhwAAgM+RpLhnq7t7AABA8LBNTwoAAIGoynCoyvDy7h4v97cqkhQAAEzEcI97DPcAAABLoicFAAATVaqZKr3sM6j0USxWQ5ICAICJDB/UpBgBWpPCcA8AALAkelK8EMyTt9WHyd0AoGEonHWPJAUAABNVGs1UaXhZk8KzewAAgK9VyaEqL6svqhSYWQo1KQAAwJLoSQEAwETUpLhHkuIBCmUbj0JaAKidb2pSGO4BAABoMvSkAABgop8LZ718wCDDPQAAwNeqfDAtfqDe3UOSAlNQowIAqA81KQAAmOhc4ay3iyeys7N11VVXqV27doqKitLo0aO1b9++OvfJy8uTw+GosXz55ZfeXH6d6EkBAMBEVWrW5JO5bdy4Uffff7+uuuoqVVRU6JFHHtGwYcO0Z88etWnTps599+3bp/DwcOd6x44dGxVzQ5CkAAAQZHJzc13WlyxZoqioKG3fvl0DBw6sc9+oqChdcMEFfozuFwz3AABgokrD4ZNFkkpLS12WsrKyBsVQUlIiSYqIiKh32yuuuEKxsbEaOnSoNmzY0PgLbwB6UurBBG4AAH+q9MHdPZX/Gu6Ji4tzac/MzFRWVlad+xqGofT0dPXv31+9evVyu11sbKwWLVqk5ORklZWV6bXXXtPQoUOVl5dXb+9LY5GkAABgoiqjmaq8nHG26l8zzhYWFrrUi4SGhta775QpU7Rr1y797W9/q3O7xMREJSYmOtdTU1NVWFioOXPm+C1JYbgHAIAAER4e7rLUl6RMnTpVa9as0YYNG9S5c2ePz9e3b1/t37+/seHWi54UAABM5MvhnoYyDENTp07VqlWrlJeXp65duzbqvDt27FBsbGyj9m0IkhQAAExUJTkLX705hifuv/9+vf7661q9erXatWun4uJiSVL79u3VqlUrSVJGRoYOHz6sZcuWSZLmzp2rhIQE9ezZU+Xl5Vq+fLlWrlyplStXehV7XUhSqqFQ1hzMQAsATWfBggWSpMGDB7u0L1myROPGjZMkFRUVqaCgwPmz8vJyzZgxQ4cPH1arVq3Us2dPvf/++xo5cqTf4iRJAQDARL6ZzM2z/Q2j/uGhpUuXuqzPnDlTM2fO9Og83iJJAQDARI2Z1r62YwSiwLwqAABge/SkAABgoio5VCVvC2e929+qSFIAADARwz3uBeZVAQAA26MnBQAAE/lmMrfA7HMgSQEAwERVhkNV3k7m5uX+VhX0SQqTt1kTk7sBCBZVPuhJ8XaeFasKzKsCAAC2F/Q9KQAAmKnKaKYqL+/O8XZ/qyJJAQDARJVyqNLLeU683d+qSFIAALb1083XeLV/q1Uf+ygS+ANJCgAAJmK4xz2SFAAATFQp74drKn0TiuUEZuoFAABsj54UAABMxHCPe0GZpBx4+io1CwszOwx4gMndgODgbSGst+c7MuiXYZeqM2ekh1f7PQYeMOieba4qOztbV111ldq1a6eoqCiNHj1a+/btMzssAADgJ7ZJUjZu3Kj7779fW7du1fr161VRUaFhw4bp9OnTZocGAECjGXKoysvFYJ4Uc+Xm5rqsL1myRFFRUdq+fbsGDhxoUlQAAHiH4R73bJOkVFdSUiJJioiIcLtNWVmZysrKnOulpaV+jwsAAE/wFGT3bJmkGIah9PR09e/fX7169XK7XXZ2tmbPnt2EkQEAPNHUhbKwF1v2D02ZMkW7du3SihUr6twuIyNDJSUlzqWwsLCJIgQAoGEq1cwnSyCyXU/K1KlTtWbNGm3atEmdO3euc9vQ0FCFhoY2UWQAAHiO4R73bJOkGIahqVOnatWqVcrLy1PXrl3NDgkAAPiRbZKU+++/X6+//rpWr16tdu3aqbi4WJLUvn17tWrVyuTo0NSY3M03qtcD8ERY+JrVa07On7zNLFVqpiovh2u83d+qbJOkLFiwQJI0ePBgl/YlS5Zo3LhxTR8QAAA+UGk4VOnlcI23+1uVbZIUwzDMDgEAADQh2yQpAAAEIgpn3SNJAQDARIYPnoJsMOMsgEBXX5EjhbWBqdPWcJ8e75unk3x6PAQvkhQAAExUKYcqvXxAoLf7WxVJCgAAJqoyvK8pqQrQe0tIUgAAMFGVD2pSvN3fqgLzqgAAgO3Rk4KAwAy0DePt7J8U1tpD9c9DvdtX+7gM6LvHq/Nf/PDehp+7iYtsrTDDbHVVcqjKy5oST/fPzs7WO++8oy+//FKtWrVSv3799PTTTysxMbHO/TZu3Kj09HR98cUX6tSpk2bOnKlJkyZ5E3qd6EkBAMBE52ac9XbxxMaNG3X//fdr69atWr9+vSoqKjRs2DCdPn3a7T4HDhzQyJEjNWDAAO3YsUOzZs3StGnTtHLlSm9/BW7RkwIAQJDJzc11WV+yZImioqK0fft2DRw4sNZ9XnrpJcXHx2vu3LmSpKSkJOXn52vOnDm69dZb/RInSQoAACbyZeFsaWmpS3toaKhCQ0Pr3b+kpESSFBER4XabLVu2aNiwYS5tw4cPV05Ojs6ePauWLVt6Gna9SFIA+AxPVW4antacWFl99SvBMDFclXwwLf6/alLi4uJc2jMzM5WVlVXnvoZhKD09Xf3791evXr3cbldcXKzo6GiXtujoaFVUVOj48eOKjY1tXPB1IEkBACBAFBYWKjz8lxmEG9KLMmXKFO3atUt/+9vf6t3W4XBNps49/Ld6u6+QpAAAYCLDB3f3GP/aPzw83CVJqc/UqVO1Zs0abdq0SZ07d65z25iYGBUXF7u0HTt2TC1atFCHDh08D7oBSFIAADCRGU9BNgxDU6dO1apVq5SXl6euXbvWu09qaqree+89l7Z169YpJSXFL/UoErcgAwAQdO6//34tX75cr7/+utq1a6fi4mIVFxfrp59+cm6TkZGhsWPHOtcnTZqkQ4cOKT09XXv37tUrr7yinJwczZgxw29x0pOCgHR+YWEwT+zm7eRtvj4/hbSN09SFsh9t7eGy7u3kbt7wtrDWipO3VWfGtPgLFiyQJA0ePNilfcmSJRo3bpwkqaioSAUFBc6fde3aVWvXrtX06dM1f/58derUSS+88ILfbj+WSFIAADCVWcM99Vm6dGmNtkGDBunTTz/16FzeIEkBAMBEZkyLbxfUpAAAAEuiJwVAk6FGpWECabI2f6tes2LHyd/MGO6xC5IUAABMRJLiHsM9AADAkuhJAQDARPSkuEeSAgCAiUhS3CNJQcCrXoQYyJO7mT15m6eCuZDWTsWxVprcrT6b5i9yWb/krd+ZFAl8gSQFAAATGfJ+npP6p2azJ5IUAABMxHCPe9zdAwAALImeFAAATERPinskKQg6wVysaXWB/NrYqVC2PnYqpP363xa6rFuxkJYkxT2SFAAATESS4h41KQAAwJLoSQEAwESG4ZDhZU+It/tbFUkKgs6RQa4f5k6ybx2E3SZv89T512en10UKrBqUQGLFGpUqObyeJ8Xb/a2K4R4AAGBJ9KQAAGAiCmfdI0kBAMBE1KS4x3APAACwJHpSgGrqK0a1WwEnmkYwF8qaObnbkviPvNrfCoW0DPe4R5ICAICJGO5xj+EeAABgSfSkAABgIsMHwz2B2pNCkgIAgIkMSYbh/TECka2SlE2bNunZZ5/V9u3bVVRUpFWrVmn06NEeHyc15UuFtA2RVLPgy0qqF59ZOdZgYvaTegN9lll3zP69VxfMhbKB5J6CAS7r53/vlp8q16EmiKFKDjmYcbZWtqpJOX36tC6//HLNmzfP7FAAAICf2aonJS0tTWlpaWaHAQCAz3B3j3u2SlI8VVZWprKyMud6aWmpidEAAFBTleGQg3lSahXQSUp2drZmz55d5zZWqvtoygmQ8IsaT0Xe6FkJmtVqJfwpkK8NvnP+96ivv9c8nbytes0J7MVWNSmeysjIUElJiXMpLCw0OyQAAFwYhm+WQBTQPSmhoaEKDQ01OwwAANyiJsW9gO5JAQAA9mWrnpRTp07p66+/dq4fOHBAO3fuVEREhOLj402MDACAxqEnxT1bJSn5+fkaMmSIcz09PV2SdPfdd2vp0qU+OUdTFtLWV1DG5G32ZOVCWivFYjdM3mYPdiyUNePuHk8nR83Ly3P5+3vO3r171b17d0/DbTBbJSmDBw+WEajVQQCAoOSLwldP9z83Oeo999yjW2+9tcH77du3T+Hh4c71jh07enZiD9kqSQEAAN5r7OSoUVFRuuCCC3wfkBsUzgIAYKKfe1IcXi4/H6u0tNRlOX9CU1+44oorFBsbq6FDh2rDhg0+PXZt6Empx/l1I97WiDBZmz14O7lbdd7WqHjyQEFqTnyHGhTfqP696en3YPXJ2+xYc1IfXxbOxsXFubRnZmYqKyvLq2NLUmxsrBYtWqTk5GSVlZXptdde09ChQ5WXl6eBAwd6fXx3SFIAAAgQhYWFLjUjvporLDExUYmJic711NRUFRYWas6cOX5NUhjuAQDARIaPFkkKDw93Wfw5oWnfvn21f/9+vx1foicFAABT2XWelB07dig2Ntav5yBJAQAgyNQ3OWpGRoYOHz6sZcuWSZLmzp2rhIQE9ezZU+Xl5Vq+fLlWrlyplStX+jVOkhQ/8rRAjMnbgoO3hbQUx/oHhbLWFIiFsjWcP17jzTE8UN/kqEVFRSooKHD+vLy8XDNmzNDhw4fVqlUr9ezZU++//75GjhzpZeB1I0kBAMBMPhjukYf71zc5avVZ3GfOnKmZM2c2JjKvUDgLAAAsiZ4UAABMZMa0+HZBkgIAgInsendPUyBJ8UB9T0hmRtnA5OsZaKurr5CWQln/oFDWHJ7eIBAU36uGw+OaklqPEYCoSQEAAJZETwoAACaiJsU9khQAAMxkwjwpdkGS4oWv/22hy7qnkw4xeRtq4+1kb4CV1VfTdfHDe5soEtgBSQoAACbi7h73SFIAADBbgA7XeIu7ewAAgCXRkwIAgIkY7nGPJMUDS+I/8ujnQfH0TvgdhbS+YafJ23w9gZmZRfq+nvwwIHF3j1sM9wAAAEuiJwUAAFM5/rV4e4zAQ5ICAICZGO5xiySlHvXVoXiCydsCg78fOFif82tUqE9xz9c1KHZ+0F1Tx+7Jd131z9ORYHxwK0mKW9SkAAAAS6InBQAAMxmOnxdvjxGASFIAADART0F2j+EeAABgSfSk+FH1ottLKJyFjzHR2y+qF8oGRcGlRdRVKFu9MBa1oHDWLZIUAADMRE2KWwz3AAAAS6InBQAAEzmMnxdvjxGISFIAADATNSlukaRU48sZZqs/Bbl6IR8z0AYGs2egDRb1zSBLoWzTacrvrurn4nUOLiQpAACYicJZt0hSAAAwE8M9bpGkAABgJpIUt4I+SfFlDYqnqFGBr9ltcjdvnlRMbULT4bsJZgn6JAUAAFPRk+IWSQoAAGaicNYtZpwFAACWRE8KAAAmYsZZ94IySVkQt1nh7Zr7/LjVJ28DzNbUhbTeFMLWh0JZ/7FTYay/J3c7/2aK0pOVesOnR3eDmhS3bDfc8+KLL6pr164KCwtTcnKyPvrIvLtzAACwo02bNmnUqFHq1KmTHA6H3n333Xr32bhxo5KTkxUWFqZu3brppZde8nucHicplZWVWrhwoaZPn64//OEP+utf/6oTJ074I7Ya3nzzTT3wwAN65JFHtGPHDg0YMEBpaWkqKChokvMDABAITp8+rcsvv1zz5s1r0PYHDhzQyJEjNWDAAO3YsUOzZs3StGnTtHLlSr/G6fFwz9SpU/XnP/9Z119/vebPn69mzZrp7Nmzuuiii9SnTx+tWbPGH3FKkp577jmNHz9eEyZMkCTNnTtXH3zwgRYsWKDs7Gy/nRcAAH9xyAc1KR5un5aWprS0tAZv/9JLLyk+Pl5z586VJCUlJSk/P19z5szRrbfe6uHZG87jnpR33nlHr732mv70pz8pNDRU+fn5euGFF3TmzBl16dLFHzFKksrLy7V9+3YNGzbMpX3YsGHavHlzrfuUlZWptLTUZQEAIFBV/5tXVlbmk+Nu2bKlxt/f4cOHKz8/X2fPnvXJOWrjcU/KqVOn1KPHz4VLLVu2VPPmzXX//fervLxcR44c8XmA5xw/flyVlZWKjo52aY+OjlZxcXGt+2RnZ2v27Nl+i8nXmIE2MFj5qcieFtL6sxC2ISiW9Q++W35h5qzjTj6cJyUuLs6lOTMzU1lZWd4dW1JxcXGtf38rKip0/PhxxcbGen2O2njck9KtWzdnMnLRRRfp8OHDkqRRo0Zp+fLlvo2uFg6H6wtpGEaNtnMyMjJUUlLiXAoLC/0eHwAAHjF8tEgqLCx0+buXkZHhszBr+/tbW7sveZyk3HbbbcrNzZUkDR48WK+88ookac+ePfrpp598G915IiMj1bx58xq9JseOHauR3Z0TGhqq8PBwlwUAAEvxYZJS/W9eaGioT0KMiYmp9e9vixYt1KFDB5+cozYeD/c8+uijzn8/9NBDuvrqq9WxY0eVlpZq/PjxPg3ufCEhIUpOTtb69et18803O9vXr1+vm266yW/nBQAg2KWmpuq9995zaVu3bp1SUlLUsmVLv53Xq8nc4uPj9cUXX2jt2rWKiIjQr3/9a1/FVav09HSNGTNGKSkpSk1N1aJFi1RQUKBJkyb59bzu+HvyNmpU4GvV62U0yNyaEzSNQP7u8HRyN0vUoFRjxoyzp06d0tdff+1cP3DggHbu3KmIiAjFx8crIyNDhw8f1rJlyyRJkyZN0rx585Senq6JEydqy5YtysnJ0YoVK7wLvB5ezzjboUMHjRkzxhex1Ov222/XiRMn9Pjjj6uoqEi9evXS2rVr/XpXEQAAfmXCjLP5+fkaMmSIcz09PV2SdPfdd2vp0qUqKipymYOsa9euWrt2raZPn6758+erU6dOeuGFF/x6+7Fkw2nxJ0+erMmTJ5sdBgAAtjV48GBn4Wttli5dWqNt0KBB+vTTT/0YVU22S1IAAAgoPLvHLZIUAABMxFOQ3SNJsREKae3Jn5O71SiEtTkmb/MNvht+YcVCWTQcSQoAAGby4YyzgYYkBQAAM1GT4pbHM84CAAA0BXpSPODvyds8RY1KYAq0OpO6UIPiG3z2f1Hjd2GDmhQKZ90jSQEAwEwM97hFkgIAgJl80JMSqEkKNSkAAMCS6EkBAMBMDPe4RZISQHxZhEghnv8EU2FsdRTK+gafT/ds+R4jSXGL4R4AAGBJ9KQAAGAibkF2j54UAABgSSQpAADAkhjuqYfVZpltKk1dfObrQsCmjJ8iRvgb7zH36vusV/8Ot+RTkSmcdYskBQAAE1GT4h7DPQAAwJLoSQEAwGwB2hPiLZIUWIKdJmCiPqDh7PS6WgnvMfcC8j1FTYpbJCkAAJiImhT3qEkBAACWRE8KAABmYrjHLZIUAABMxHCPeyQp1QTr5G2ALwRkUaOfUBzbML5+T9licjc4kaQAAGAmhnvcIkkBAMBMJClucXcPAACwJHpSAAAwEYWz7pGkAPWgwNE9CmUbjvdRw9T3nqpe6BoQNzsw3OMWwz0AAMCS6EkBAMBM9KS4RZICAICJqElxL+iTlIAYzwRgOdSgNIy3dU31TcZW33e8JSZ3oyfFLWpSAAAIQi+++KK6du2qsLAwJScn66OP3CdoeXl5cjgcNZYvv/zSrzEGfU8KAABmMmO4580339QDDzygF198Uddee60WLlyotLQ07dmzR/Hx8W7327dvn8LDw53rHTt2bGzIDUJPCgAAZjJ8tHjgueee0/jx4zVhwgQlJSVp7ty5iouL04IFC+rcLyoqSjExMc6lefPmnp3YQyQpAAAEiNLSUpelrKysxjbl5eXavn27hg0b5tI+bNgwbd68uc7jX3HFFYqNjdXQoUO1YcMGn8ZeG4Z7gGooeHSPydt+wfukcbx9D3la6OptYW2T8GHhbFxcnEtzZmamsrKyXNqOHz+uyspKRUdHu7RHR0eruLi41sPHxsZq0aJFSk5OVllZmV577TUNHTpUeXl5GjhwoJfBu0eSAgCAiRz/Wrw9hiQVFha61IyEhoa638fhelbDMGq0nZOYmKjExETnempqqgoLCzVnzhy/JikM9wAAECDCw8NdltqSlMjISDVv3rxGr8mxY8dq9K7UpW/fvtq/f7/XMdeFJAUAADM1ceFsSEiIkpOTtX79epf29evXq1+/fg0+zo4dOxQbG9vwEzdCUA733FfYTyFtQyTZZLwSMEkw16BQc+IbVn8P1fXAwvJT5ZL+4fcYzLgFOT09XWPGjFFKSopSU1O1aNEiFRQUaNKkSZKkjIwMHT58WMuWLZMkzZ07VwkJCerZs6fKy8u1fPlyrVy5UitXrvQu8HrYJkl54okn9P7772vnzp0KCQnRDz/8YHZIAADY0u23364TJ07o8ccfV1FRkXr16qW1a9eqS5cukqSioiIVFBQ4ty8vL9eMGTN0+PBhtWrVSj179tT777+vkSNH+jVO2yQp5eXluu2225SamqqcnByzwwEAwDdMmhZ/8uTJmjx5cq0/W7p0qcv6zJkzNXPmzEYE5h3bJCmzZ8+WVPMXBwCA7QXos3e8ZZskpTHKyspcJrIpLS01MRoAAGriKcjuBXSSkp2d7eyBaSxTnojpBkW88DerFzn6E4WyvhPM7yP4lqm3IGdlZdX6VMXzl/z8/EYfPyMjQyUlJc6lsLDQh9EDAOADJjy7xy5M7UmZMmWK7rjjjjq3SUhIaPTxQ0ND65xtDwAAszHc456pSUpkZKQiIyPNDAEAAFiUbWpSCgoK9M9//lMFBQWqrKzUzp07JUmXXHKJ2rZta25wAAA0lkm3INuBbZKUxx57TK+++qpz/YorrpAkbdiwQYMHD270cT19omZTolC2aVAw+Yvqvws7F0DyujYdM98n3n6HW+F7luEe92zz7J6lS5fKMIwaizcJCgAAsC7b9KQAABCQGO5xiyQFAAAzkaS4RZICwC1v6zq8rVWgrsQa7FybBHsjSQEAwEQUzrpHkgIAgJkY7nGLJAUAABM5DEMOw7ssw9v9rco2tyADAIDgQk9KNWZO7maFSYWCAcWYTYfftT3ZuVC2vu9wS37PMtzjFkkKAAAmonDWPYZ7AACAJdGTAgCAmRjucYskBQAAEzHc4x5JSj3OL7LydRGtJQu4AAQdOxfK1ofvWXsjSQEAwEwM97hFkgIAgIkY7nGPu3sAAIAl0ZPShBgbNQcTigF1q/4ZCaQaFVtcG8M9bpGkAABgskAdrvEWSQoAAGYyjJ8Xb48RgKhJAQAAlkRPCgAAJuLuHvdIUjzg6ROSKZQFYEe2KDZ1w5aF8hTOusVwDwAAsCR6UgAAMJGj6ufF22MEIpIUAADMxHCPWyQpXqDmxLpsOS4NWJSda1SqC6RrCQbUpAAAYKJzd/d4u3jqxRdfVNeuXRUWFqbk5GR99FHdN4Ns3LhRycnJCgsLU7du3fTSSy818oobjiQFAAAznZvMzdvFA2+++aYeeOABPfLII9qxY4cGDBigtLQ0FRQU1Lr9gQMHNHLkSA0YMEA7duzQrFmzNG3aNK1cudIXvwG3SFIAAAgyzz33nMaPH68JEyYoKSlJc+fOVVxcnBYsWFDr9i+99JLi4+M1d+5cJSUlacKECbr33ns1Z84cv8ZJkgIAgIl8OdxTWlrqspSVldU4X3l5ubZv365hw4a5tA8bNkybN2+uNcYtW7bU2H748OHKz8/X2bNnffOLqAWFs16gAAtAMKqvML0pvwsDokjeh3f3xMXFuTRnZmYqKyvLpe348eOqrKxUdHS0S3t0dLSKi4trPXxxcXGt21dUVOj48eOKjY31Ln43SFIAADCRL6fFLywsVHh4uLM9NDTU/T4Oh8u6YRg12urbvrZ2XyJJAQAgQISHh7skKbWJjIxU8+bNa/SaHDt2rEZvyTkxMTG1bt+iRQt16NDBu6DrQE0KAABmauK7e0JCQpScnKz169e7tK9fv179+vWrdZ/U1NQa269bt04pKSlq2bKl59fcQCQpAACYyIx5UtLT0/Xyyy/rlVde0d69ezV9+nQVFBRo0qRJkqSMjAyNHTvWuf2kSZN06NAhpaena+/evXrllVeUk5OjGTNm+PJXUQPDPfXwpCiLQlrzBETxHBAgzv88+vp70Nef9WD93r799tt14sQJPf744yoqKlKvXr20du1adenSRZJUVFTkMmdK165dtXbtWk2fPl3z589Xp06d9MILL+jWW2/1a5wkKQAAmMmkZ/dMnjxZkydPrvVnS5curdE2aNAgffrpp56fyAskKQAAmMiXd/cEGmpSAACAJQV9T4o/axmCdazTajptbNr/YhwZ5L85AwC7sdLEb5ZVZfy8eHuMABT0SQoAAKYyqSbFDhjuAQAAlkRPCgAAJnLIB4WzPonEekhSAAAwk4czxro9RgCyRZJy8OBB/f73v9eHH36o4uJiderUSXfddZceeeQRhYSEeHy8A3MT1aJl2M8rg3wcLJpEfcV4TV0s68m5KawFfuHxZ9nH39nVj//NxiTnvyvOnvHtydzgFmT3bJGkfPnll6qqqtLChQt1ySWXaPfu3Zo4caJOnz6tOXPmmB0eAADwA1skKSNGjNCIESOc6926ddO+ffu0YMECkhQAgL1xd49btkhSalNSUqKIiIg6tykrK1NZWZlzvbS01N9hAQDgEYdhyOFlTYm3+1uVLZOUb775Rn/84x/1hz/8oc7tsrOzNXv27CaKqn5M7gap5hg4NSrAL+qr6TKz3gxNz9R5UrKysuRwOOpc8vPzXfY5cuSIRowYodtuu00TJkyo8/gZGRkqKSlxLoWFhf68HAAAPFfloyUAmdqTMmXKFN1xxx11bpOQkOD895EjRzRkyBClpqZq0aJF9R4/NDRUoaGh3oYJAIDfMNzjnqlJSmRkpCIjIxu07eHDhzVkyBAlJydryZIlataMyXIBAAhktqhJOXLkiAYPHqz4+HjNmTNH3333nfNnMTExJkYGAICXuLvHLVskKevWrdPXX3+tr7/+Wp07d3b5meFlF5eZRYwU0jaclSdv85ansXvzHmWiOcCCmHHWLVuMmYwbN06GYdS6AACAwGSLnhQAAAIV0+K7R5ICAICZGO5xiyQFAAATOap+Xrw9RiAiSamGQlprCORCWW8F87Uj8PB+Rl1IUgAAMBPDPW6RpAAAYCbmSXHLFrcgAwCA4ENPioVRo/ILxq2bBk9ohr/xWa6JZ/e4R5ICAICZqElxi+EeAABgSfSkAABgJkOSt/OcBGZHCkkKAABmoibFPZKUepxf5GV2EWEgF9JWvzaK64DAwGcZ3iBJAQDATIZ8UDjrk0gshyQFAAAzcXePWyQpAACYqUqSt9UEAfqAQW5BBgAAlkRPigesNhunnQtpv3k6yWW9U6AOqNqc1d7zsAeKZT1j9bt7vv/+e02bNk1r1qyRJN1444364x//qAsuuMDtPuPGjdOrr77q0nbNNddo69atHp2bJAUAADNZvCblzjvv1Lfffqvc3FxJ0m9/+1uNGTNG7733Xp37jRgxQkuWLHGuh4SEeHxukhQAAFCrvXv3Kjc3V1u3btU111wjSVq8eLFSU1O1b98+JSYmut03NDRUMTExXp2fmhQAAMx0rifF20VSaWmpy1JWVuZVaFu2bFH79u2dCYok9e3bV+3bt9fmzZvr3DcvL09RUVG69NJLNXHiRB07dszj89OTEkCq16hUZ2bNSo3J2qhBsSVqVCBRc+JzPhzuiYuLc2nOzMxUVlZWow9bXFysqKioGu1RUVEqLi52u19aWppuu+02denSRQcOHNCjjz6q6667Ttu3b1doaGiDz0+SAgBAgCgsLFR4eLhz3V1CkJWVpdmzZ9d5rG3btkmSHI6a/xkxDKPW9nNuv/1257979eqllJQUdenSRe+//75uueWWOs97PpIUAADM5MN5UsLDw12SFHemTJmiO+64o85tEhIStGvXLh09erTGz7777jtFR0c3OLzY2Fh16dJF+/fvb/A+EkkKAACmMuMW5MjISEVGRta7XWpqqkpKSvTJJ5/o6quvliR9/PHHKikpUb9+/Rp8vhMnTqiwsFCxsbEexUnhLAAAqFVSUpJGjBihiRMnauvWrdq6dasmTpyoG264weXOnu7du2vVqlWSpFOnTmnGjBnasmWLDh48qLy8PI0aNUqRkZG6+eabPTo/PSlesFsRYX2Ftf5EoR3gXn2fD7O/W/j8+pnF50n505/+pGnTpmnYsGGSfp7Mbd68eS7b7Nu3TyUlJZKk5s2b6/PPP9eyZcv0ww8/KDY2VkOGDNGbb76pdu3aeXRukhQAAMxUZUgOL5OMKv8lKREREVq+fHmd2xjnJUmtWrXSBx984JNzk6QAAGAmi/ekmImaFAAAYEn0pPiQ3WpU/Ikx7ODAe77xPPmM8HkKdD7oSQnQCTJJUgAAMBPDPW4x3AMAACyJnhQAAMxUZcjr4Ro/3t1jJpIUAADMZFT9vHh7jABEkuJHwVRUSGEfpOB6z3uKzwjgOZIUAADMROGsWyQpAACYiZoUt7i7BwAAWBI9KQAAmInhHrdIUppQIBUVUgQIAD5iyAdJik8isRySFAAAzERPilvUpAAAAEuiJwUAADNVVUnycjK2KiZzg4/ZqUaFGhQ0hp3e477GZwYNxnCPW7YZ7rnxxhsVHx+vsLAwxcbGasyYMTpy5IjZYQEAAD+xTZIyZMgQvfXWW9q3b59Wrlypb775Rr/5zW/MDgsAAO+c60nxdglAthnumT59uvPfXbp00f/7f/9Po0eP1tmzZ9WyZUsTIwMAwAvMOOuWbZKU8/3zn//Un/70J/Xr16/OBKWsrExlZWXO9dLS0qYIDwAA+ICtkpSHH35Y8+bN048//qi+ffvqf//3f+vcPjs7W7Nnz26i6LxXX6GdP4sOKfJDUwjkQlo+Q2gsw6iSYXh3d463+1uVqTUpWVlZcjgcdS75+fnO7R966CHt2LFD69atU/PmzTV27FgZdYzDZWRkqKSkxLkUFhY2xWUBANBwhvHzcI03CzUpvjdlyhTdcccddW6TkJDg/HdkZKQiIyN16aWXKikpSXFxcdq6datSU1Nr3Tc0NFShoaG+DBkAADQRU5OUc0lHY5zrQTm/5gQAANsxfFA4S0+KeT755BN98skn6t+/vy688EL94x//0GOPPaaLL77YbS8KAAC2UFUlObysKQnQmhRbJCmtWrXSO++8o8zMTJ0+fVqxsbEaMWKE3njjjaAazqEwD4HGzoW0fB7hM/SkuGWLJOWyyy7Thx9+aHYYAACgCdkiSQEAIFAZVVUyvBzuCdRbkElSAAAwE8M9bpGkALCMuuo8mrpehZoTwHwkKQAAmKnKkBz0pNSGJAUAADMZhiRvb0EOzCTF1GnxAQAA3KEnBQAAExlVhgwvh3vqeo6dnZGkALAFClkRsIwqeT/c479bkJ944gm9//772rlzp0JCQvTDDz/UH45haPbs2Vq0aJG+//57XXPNNZo/f7569uzp0bkZ7gEAwERGleGTxV/Ky8t122236b777mvwPs8884yee+45zZs3T9u2bVNMTIyuv/56nTx50qNzk6QAAAC3Zs+erenTp+uyyy5r0PaGYWju3Ll65JFHdMstt6hXr1569dVX9eOPP+r111/36NxBNdxzbsyu4uwZkyMBAFjdub8V/q73qDDKvB6uqdBZSVJpaalLe2hoaJM/4+7AgQMqLi7WsGHDXOIYNGiQNm/erN/97ncNPlZQJSnnupm25z5hciQAALs4efKk2rdv7/PjhoSEKCYmRn8rXuuT47Vt21ZxcXEubZmZmcrKyvLJ8RuquLhYkhQdHe3SHh0drUOHDnl0rKBKUjp16qTCwkIZhqH4+HgVFhYqPDzc7LAapbS0VHFxcVyDybgGa+AarCMQruPcNRQUFMjhcKhTp05+OU9YWJgOHDig8vJynxzPMAw5HK4zM7vrRcnKytLs2bPrPN62bduUkpLS6Hiqx1JbfPUJqiSlWbNm6ty5s7M7LDw83LYfonO4BmvgGqyBa7COQLiO9u3b+/0awsLCFBYW5tdz1GbKlCm644476twmISGhUceOiYmR9HOPSmxsrLP92LFjNXpX6hNUSQoAAJAiIyMVGRnpl2N37dpVMTExWr9+va644gpJP98htHHjRj399NMeHYu7ewAAgFsFBQXauXOnCgoKVFlZqZ07d2rnzp06deqUc5vu3btr1apVkn4e5nnggQf05JNPatWqVdq9e7fGjRun1q1b68477/To3EHZkxIaGqrMzMwmr3j2Ja7BGrgGa+AarCMQriMQrsGXHnvsMb366qvO9XO9Ixs2bNDgwYMlSfv27VNJSYlzm5kzZ+qnn37S5MmTnZO5rVu3Tu3atfPo3A4jUOfSBQAAtsZwDwAAsCSSFAAAYEkkKQAAwJJIUgAAgCWRpAAAAEsKuiTlxRdfVNeuXRUWFqbk5GR99NFHZofkkb179yoiIkIXX3yx+vTpo7Zt2zpvAbMLu17DyZMnddVVV6lPnz667LLLtHjxYrNDClotWrRQnz591KdPH02YMMHscDwWCO+lffv2OV+DPn36qFWrVnr33XfNDqvBsrOzddVVV6ldu3aKiorS6NGjtW/fPrPDQnVGEHnjjTeMli1bGosXLzb27Nlj/Od//qfRpk0b49ChQ2aH5pFhw4YZu3btMgzDMC6++GLjp59+Mjkiz9nxGioqKozTp08bhmEYp0+fNrp27WocP37c5KiCU4cOHcwOwSuB9l46efKk0aFDB+PUqVNmh9Jgw4cPN5YsWWLs3r3b2Llzp/HrX//aiI+Pt9U1BIOg6kl57rnnNH78eE2YMEFJSUmaO3eu4uLitGDBArND88hXX32lxMREnTx5Us2bNzfluQ/esuM1NG/eXK1bt5YknTlzRpWVlTIMw7Y9Q927d5fD4ah1eeGFF8wOz2N2eh0C7b20Zs0aDR06VG3atLHNNeTm5mrcuHHq2bOnLr/8ci1ZskQFBQXavn27pMD7fNiW2VlSUykrKzOaN29uvPPOOy7t06ZNMwYOHGhSVJ4rKSkxkpKSDMMwjM2bNxujR482OSLP2fkavv/+e6N3795Gq1atjHnz5jnb7dgztGfPHkOS8de//tUoKioyCgoKjBYtWhhvv/22cebMGbPDq1PLli2NK6+80rj22muNvLw8Z7udXodAei/ddNNNxsqVK53rdryG/fv3G5KMzz//3DAMe38+AknQ9KQcP35clZWVNZ7AGB0dreLiYpOi8twXX3yhHj16SPr5f46XXnqpyRF5zs7XcMEFF+izzz7TgQMH9Prrr+vo0aOS7NkzVFxcrBYtWujaa69VTEyMTpw4oYqKCg0YMMDy04EfPHhQ27dv10svvaSxY8c6n2xup9chUN5LpaWl+vvf/66RI0c62+x2DYZhKD09Xf3791evXr0k2fvzEUiCJkk5x+FwuKwbhlGjzcr27Nmjnj17SpLatGmjv/zlL/rnP/9pclSeCYRriI6OVu/evbVp0yaVlpaqVatWCgkJ0e7du50JmNV9/vnnuvTSS51fuDt37lTHjh09fpS6GTp16iRJ6tWrl3r06KGvvvrKtq+D3d9Lq1ev1vDhw52JiB2vYcqUKdq1a5dWrFjhbLPz5yOQBM0DBiMjI9W8efMavSbHjh2z1Ztu/Pjxzn/ffvvtuv32202MpnHseg1Hjx5Vq1atFB4ertLSUm3atEn33XefbXuGdu3apcsuu8y5vnPnTvXu3dvEiBrm+++/V+vWrRUaGqpvv/1We/bsUbdu3Wz1OgTSe+mtt97Sb3/7W+e63a5h6tSpWrNmjTZt2qTOnTs72+36+Qg0QdOTEhISouTkZK1fv96lff369erXr59JUcFOvv32Ww0cOFCXX365+vfvrylTpqh379627RnatWuXy5euXb6E9+7dq5SUFF1++eW64YYb9PzzzysiIsJWr0OgvJdKSkr0ySefaPjw4c42u1yDYRiaMmWK3nnnHX344Yfq2rWry8/t+vkIOCbXxDSpc7cg5+TkGHv27DEeeOABo02bNsbBgwfNDq3BJNW52EEgXIPdVVZWGq1btzbee+89Z1tcXJwxd+5cE6MCms59991ntG/f3sjLyzOKioqcy48//sjnw0KC7i/C/PnzjS5duhghISHGlVdeaWzcuNHskBrl9OnTRnx8vPHggw+aHYrHCgoKjEGDBhlJSUnGZZddZrz11ltmhxR0vvrqK0OSyxxBN9xwg3HBBRfY9jMBeMLdf5KWLFnC58NCHIZhGE3XbwNfeeSRR7R//37Fx8drzpw5ZofjkaKiIh09elR9+vTRsWPHdOWVV2rfvn1q06aN2aHVqbEF1nzEfCsQXgeuAWiYoKlJCST79+/Xl19+6XLLn53ExsaqT58+kqSoqChFRERYcsy6OuPnnkeX5fTp04qPj9eDDz5Y68/5Qva9QHgdzo+roKBAgwYNUlJSki677DK99dZbtrsGu74OsD6SFBuaMWOGsrOzzQ7DJ/Lz81VVVaW4uDizQ2mUJ554Qtdcc43ZYQQ9O78OLVq00Ny5c7Vnzx793//9n6ZPn67Tp0+bHVaj2Pl1gDWRpNjM6tWrdemll1r+tr6GOHHihMaOHatFixaZHUqj2L1HK1DY/XWwa89idXZ/HWBNJCk2s3XrVr3xxhtKSEjQjBkztHjxYj3++ONmh+WxsrIy3XzzzcrIyLDtLeCB1KNlZ4H0Oti5ZzGQXgdYB0mKzWRnZ6uwsFAHDx7UnDlzNHHiRD322GNmh+URwzA0btw4XXfddRozZozZ4TRKIPVo2VkgvQ527lkMpNcB1hI0M87COv7+97/rzTffVO/evfXuu+9Kkl577TWX2R2t7lyP1ttvv61Tp07p7NmzCg8Pt13CaHeB8jrYvWcxUF4HWA+3IANeWrp0qXbv3m27W8EDjV1fB8MwdOeddyoxMVFZWVlmh+M1u74OsCaGewDAROd6Ft9991316dNHffr00eeff252WIAl0JMCAAAsiZ4UAABgSSQpAADAkkhSAACAJZGkAAAASyJJAQAAlkSSAgAALIkkBQAAWBJJCgAAsCSSFAAAYEkkKQAAwJJIUgAAgCWRpAAAAEsiSQGC1JQpU9S/f/9af5aQkKAnnniiiSMCAFctzA4AQNPbs2ePFixYoE2bNtX686SkJO3cubNpgwKAauhJAYLQs88+q6uuukrXXnttrT+PiIjQ0aNHmzgqAHBFkgIEmYqKCq1cuVK33nqrs+13v/udcnJynOsnT55UmzZtzAgPAJxIUoAg88033+jkyZO67LLLJElVVVV6++231bZtW+c2u3btUlJSklkhAoAkkhQg6Pzwww+S5ExKPvjgA33//fcKCQmRJH3yySc6dOiQRo8ebVKEAPAzCmeBINOlSxc5HA6tWLFCbdq00YMPPqiRI0dq9erVSkhI0O9+9ztdd911GjhwoNmhAghyDsMwDLODANC0srOz9dRTT6lVq1b67//+b1199dW66aabdOzYMY0aNUovvviiIiIizA4TQJAjSQEAAJZETQoAALAkkhQAAGBJJCkAAMCSSFIAAIAlkaQAAABLIkkBAACWRJICAAAsiSQFAABYEkkKAACwJJIUAABgSSQpAADAkv4/XeS6WluyKwkAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -236,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +246,7 @@
        "dict"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,17 +257,406 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 11,
+     "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",
+    "K = EmbeddedGraph()\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_mean_centered_coordinates()\n",
+    "K.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071067811865476\n",
+      "0.7071067811865476 [-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(100,80)\n",
+    "r = K.get_bounding_radius()\n",
+    "print(r)\n",
+    "\n",
+    "r,thresh = myect.get_radius_and_thresh(K,r)\n",
+    "print(r,thresh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 13,
+     "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/4\n",
+    "K.plot(color_nodes_theta=theta)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.plotECC(K,theta,1.2*r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.calculateECT(K,1.2*r)\n",
+    "\n",
+    "myect.plotECT()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('A', 'B'): 5.551115123125783e-17,\n",
+       " ('A', 'D'): -5.551115123125783e-17,\n",
+       " ('B', 'C'): 0.7071067811865475,\n",
+       " ('C', 'D'): 0.7071067811865475}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.g_omega_edges(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#....... check on the list of sorted edges, something is wrong"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "out = myect.calculateECC(K, theta, r, return_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 4]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x135b2aa70>]"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
+    "r, r_thresh = myect.get_radius_and_thresh(K, r)\n",
+    "print(r_thresh)\n",
+    "print(out[2])\n",
+    "plt.plot(r_thresh,out[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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,draw_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A -0.71\n",
+      "B 0.0\n",
+      "C 0.71\n",
+      "D -0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "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": [
+    "funcdict = K.g_omega(theta)\n",
+    "for key in funcdict:\n",
+    "    print(key, round(funcdict[key],2))\n",
+    "K.plot(color_nodes_theta=theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_below_threshold(func_list, thresh):\n",
+    "    \"\"\" \n",
+    "    Returns the number of entries in func_list that are below the threshold thresh. \n",
+    "    Warning: func_list must be sorted in ascending order.\n",
+    "\n",
+    "    Parameters\n",
+    "        func_list (list): sorted list of function values\n",
+    "        thresh (float): threshold value\n",
+    "\n",
+    "    Returns\n",
+    "        int \n",
+    "    \"\"\"\n",
+    "    # If the list is empty, return 0\n",
+    "    if len(func_list) == 0:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        func_max = func_list[-1]\n",
+    "        if thresh < func_max:\n",
+    "            return np.argmin(func_list < thresh)\n",
+    "        else:\n",
+    "            return len(func_list)\n",
+    "# --"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = K\n",
+    "r,r_threshes = myect.get_radius_and_thresh(G, r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_list, g = G.sort_vertices(theta, return_g=True)\n",
+    "g_list = [g[v] for v in v_list]\n",
+    "\n",
+    "vertex_count = np.array([num_below_threshold(\n",
+    "    g_list, thresh) for thresh in r_threshes])\n",
+    "# print(vertex_count)\n",
+    "\n",
+    "e_list, g_e = G.sort_edges(np.pi/2, return_g=True)\n",
+    "g_e_list = [g_e[e] for e in e_list]\n",
+    "edge_count = np.array([num_below_threshold(\n",
+    "    g_e_list, thresh) for thresh in r_threshes])\n",
+    "# print(edge_count)\n",
+    "\n",
+    "if type(G) == EmbeddedCW:\n",
+    "    f_list, g_f = G.sort_faces(theta, return_g=True)\n",
+    "    g_f_list = [g_f[f] for f in f_list]\n",
+    "    face_count = np.array([num_below_threshold(\n",
+    "        g_f_list, thresh) for thresh in r_threshes])\n",
+    "    # print(face_count)\n",
+    "else:\n",
+    "    face_count = np.zeros_like(vertex_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.49999999999999994, 0.49999999999999994, 0.5, 0.5]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g_e_list"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/docs/doctrees/ect_on_graphs.doctree b/docs/doctrees/ect_on_graphs.doctree
index 3b9d0bf..83449ba 100644
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diff --git a/docs/doctrees/embed_cw.doctree b/docs/doctrees/embed_cw.doctree
index da5af94..e48b6c0 100644
Binary files a/docs/doctrees/embed_cw.doctree and b/docs/doctrees/embed_cw.doctree differ
diff --git a/docs/doctrees/embed_graph.doctree b/docs/doctrees/embed_graph.doctree
index e3eda6f..b44d09c 100644
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diff --git a/docs/doctrees/environment.pickle b/docs/doctrees/environment.pickle
index b30a693..fae7f62 100644
Binary files a/docs/doctrees/environment.pickle and b/docs/doctrees/environment.pickle differ
diff --git a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
index 0dc5921..808aed1 100644
--- a/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ b/docs/doctrees/nbsphinx/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
@@ -18,6 +18,7 @@
       "Requirement already satisfied: networkx in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.2.1)\n",
       "Requirement already satisfied: matplotlib in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.7.0)\n",
       "Requirement already satisfied: numba in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (0.56.4)\n",
+      "Requirement already satisfied: scipy in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (1.10.0)\n",
       "Requirement already satisfied: contourpy>=1.0.1 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (1.0.5)\n",
       "Requirement already satisfied: cycler>=0.10 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (0.11.0)\n",
       "Requirement already satisfied: fonttools>=4.22.0 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (4.49.0)\n",
@@ -31,8 +32,8 @@
       "Requirement already satisfied: six>=1.5 in /Users/liz/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->ect==0.1.4) (1.16.0)\n",
       "Building wheels for collected packages: ect\n",
       "  Building wheel for ect (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=39481 sha256=945a865dca4ccb5f217f0a29d5144245bca97342c60f4d3a2dcdaedba9e337e3\n",
-      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-32vyxgih/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
+      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40205 sha256=1a297b65949d477c6ceeae6d0cff6e6e6c6840b5d46b262bd72a8e083698bcf2\n",
+      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-b8z93fr2/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
       "Successfully built ect\n",
       "Installing collected packages: ect\n",
       "  Attempting uninstall: ect\n",
@@ -131,10 +132,10 @@
      "data": {
       "text/plain": [
        "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  4,  4,  4,  4,  4,\n",
-       "        4,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,\n",
-       "        1,  1,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  4,  4,  4,\n",
-       "        4,  4,  4, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
+       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,\n",
+       "        3,  3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
+       "        2,  2,  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": 5,
@@ -155,7 +156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -201,7 +202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -236,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +246,7 @@
        "dict"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,17 +257,406 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 11,
+     "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",
+    "K = EmbeddedGraph()\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_mean_centered_coordinates()\n",
+    "K.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071067811865476\n",
+      "0.7071067811865476 [-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(100,80)\n",
+    "r = K.get_bounding_radius()\n",
+    "print(r)\n",
+    "\n",
+    "r,thresh = myect.get_radius_and_thresh(K,r)\n",
+    "print(r,thresh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 13,
+     "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/4\n",
+    "K.plot(color_nodes_theta=theta)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.plotECC(K,theta,1.2*r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.calculateECT(K,1.2*r)\n",
+    "\n",
+    "myect.plotECT()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('A', 'B'): 5.551115123125783e-17,\n",
+       " ('A', 'D'): -5.551115123125783e-17,\n",
+       " ('B', 'C'): 0.7071067811865475,\n",
+       " ('C', 'D'): 0.7071067811865475}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.g_omega_edges(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#....... check on the list of sorted edges, something is wrong"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "out = myect.calculateECC(K, theta, r, return_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 4]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x135b2aa70>]"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
+    "r, r_thresh = myect.get_radius_and_thresh(K, r)\n",
+    "print(r_thresh)\n",
+    "print(out[2])\n",
+    "plt.plot(r_thresh,out[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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,draw_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A -0.71\n",
+      "B 0.0\n",
+      "C 0.71\n",
+      "D -0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "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": [
+    "funcdict = K.g_omega(theta)\n",
+    "for key in funcdict:\n",
+    "    print(key, round(funcdict[key],2))\n",
+    "K.plot(color_nodes_theta=theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_below_threshold(func_list, thresh):\n",
+    "    \"\"\" \n",
+    "    Returns the number of entries in func_list that are below the threshold thresh. \n",
+    "    Warning: func_list must be sorted in ascending order.\n",
+    "\n",
+    "    Parameters\n",
+    "        func_list (list): sorted list of function values\n",
+    "        thresh (float): threshold value\n",
+    "\n",
+    "    Returns\n",
+    "        int \n",
+    "    \"\"\"\n",
+    "    # If the list is empty, return 0\n",
+    "    if len(func_list) == 0:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        func_max = func_list[-1]\n",
+    "        if thresh < func_max:\n",
+    "            return np.argmin(func_list < thresh)\n",
+    "        else:\n",
+    "            return len(func_list)\n",
+    "# --"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = K\n",
+    "r,r_threshes = myect.get_radius_and_thresh(G, r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_list, g = G.sort_vertices(theta, return_g=True)\n",
+    "g_list = [g[v] for v in v_list]\n",
+    "\n",
+    "vertex_count = np.array([num_below_threshold(\n",
+    "    g_list, thresh) for thresh in r_threshes])\n",
+    "# print(vertex_count)\n",
+    "\n",
+    "e_list, g_e = G.sort_edges(np.pi/2, return_g=True)\n",
+    "g_e_list = [g_e[e] for e in e_list]\n",
+    "edge_count = np.array([num_below_threshold(\n",
+    "    g_e_list, thresh) for thresh in r_threshes])\n",
+    "# print(edge_count)\n",
+    "\n",
+    "if type(G) == EmbeddedCW:\n",
+    "    f_list, g_f = G.sort_faces(theta, return_g=True)\n",
+    "    g_f_list = [g_f[f] for f in f_list]\n",
+    "    face_count = np.array([num_below_threshold(\n",
+    "        g_f_list, thresh) for thresh in r_threshes])\n",
+    "    # print(face_count)\n",
+    "else:\n",
+    "    face_count = np.zeros_like(vertex_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.49999999999999994, 0.49999999999999994, 0.5, 0.5]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g_e_list"
+   ]
   },
   {
    "cell_type": "code",
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diff --git a/docs/doctrees/nbsphinx/notebooks_Tutorial-ECT_for_CW_Complexes_7_0.png b/docs/doctrees/nbsphinx/notebooks_Tutorial-ECT_for_CW_Complexes_7_0.png
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diff --git a/docs/doctrees/notebooks/Tutorial-ECT_for_CW_Complexes.doctree b/docs/doctrees/notebooks/Tutorial-ECT_for_CW_Complexes.doctree
index e5d7b98..4c1c130 100644
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diff --git a/docs/ect_on_graphs.html b/docs/ect_on_graphs.html
index a0e01a9..cdf19e2 100644
--- a/docs/ect_on_graphs.html
+++ b/docs/ect_on_graphs.html
@@ -188,7 +188,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="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><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect_graph.html#ECT.calculateECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect_graph.ECT.calculateECC" title="Link to this definition"></a></dt>
+<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_graph.html#ECT.calculateECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect_graph.ECT.calculateECC" title="Link to this definition"></a></dt>
 <dd><p>Function to compute the Euler Characteristic of an <cite>EmbeddedGraph</cite>, that is, a graph with coordinates for each vertex.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -196,6 +196,7 @@
 <li><p><strong>G</strong> (<em>nx.Graph</em>) – The graph to compute the Euler Characteristic for.</p></li>
 <li><p><strong>theta</strong> (<em>float</em>) – The angle (in radians) to use for the direction function when computing the Euler Characteristic Curve.</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>return_counts</strong> (<em>bool</em>) – Whether to return the counts of vertices, edges, and faces below the threshold. Default is False.</p></li>
 </ul>
 </dd>
 </dl>
@@ -240,7 +241,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="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><span class="sig-paren">)</span><a class="reference internal" href="_modules/ect_graph.html#ECT.plotECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ect_graph.ECT.plotECC" title="Link to this definition"></a></dt>
+<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_graph.html#ECT.plotECC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#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>
diff --git a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html b/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html
index 1e80663..16ec524 100644
--- a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html
+++ b/docs/notebooks/Tutorial-ECT_for_CW_Complexes.html
@@ -103,6 +103,7 @@
 Requirement already satisfied: networkx in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.2.1)
 Requirement already satisfied: matplotlib in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.7.0)
 Requirement already satisfied: numba in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (0.56.4)
+Requirement already satisfied: scipy in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (1.10.0)
 Requirement already satisfied: contourpy&gt;=1.0.1 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib-&gt;ect==0.1.4) (1.0.5)
 Requirement already satisfied: cycler&gt;=0.10 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib-&gt;ect==0.1.4) (0.11.0)
 Requirement already satisfied: fonttools&gt;=4.22.0 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib-&gt;ect==0.1.4) (4.49.0)
@@ -116,8 +117,8 @@
 Requirement already satisfied: six&gt;=1.5 in /Users/liz/anaconda3/lib/python3.10/site-packages (from python-dateutil&gt;=2.7-&gt;matplotlib-&gt;ect==0.1.4) (1.16.0)
 Building wheels for collected packages: ect
   Building wheel for ect (pyproject.toml) ... done
-  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=39481 sha256=945a865dca4ccb5f217f0a29d5144245bca97342c60f4d3a2dcdaedba9e337e3
-  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-32vyxgih/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f
+  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40205 sha256=1a297b65949d477c6ceeae6d0cff6e6e6c6840b5d46b262bd72a8e083698bcf2
+  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-b8z93fr2/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f
 Successfully built ect
 Installing collected packages: ect
   Attempting uninstall: ect
@@ -206,10 +207,10 @@
 <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,
-        2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  4,  4,  4,  4,  4,
-        4,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,
-        1,  1,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  4,  4,  4,
-        4,  4,  4, -1, -1, -1, -1, -1, -1, -1, -1, -1])
+        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,
+        3,  3,  3,  3,  3,  3,  3,  2,  2,  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])
 </pre></div></div>
 </div>
 <div class="nbinput docutils container">
@@ -283,7 +284,7 @@
 </pre></div></div>
 </div>
 <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>[10]:
 </pre></div>
 </div>
 <div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">type</span><span class="p">(</span><span class="n">K</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
@@ -291,7 +292,7 @@
 </div>
 </div>
 <div class="nboutput nblast 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>[10]:
 </pre></div>
 </div>
 <div class="output_area docutils container">
@@ -299,22 +300,358 @@
 dict
 </pre></div></div>
 </div>
+<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"># K = EmbeddedCW()</span>
+<span class="n">K</span> <span class="o">=</span> <span class="n">EmbeddedGraph</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="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"># K.add_face([&#39;A&#39;, &#39;B&#39;, &#39;C&#39;, &#39;D&#39;])</span>
+
+<span class="n">K</span><span class="o">.</span><span class="n">set_mean_centered_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>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</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_CW_Complexes_10_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_10_1.png" />
+</div>
+</div>
+<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">ECT</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">80</span><span class="p">)</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="nb">print</span><span class="p">(</span><span class="n">r</span><span class="p">)</span>
+
+<span class="n">r</span><span class="p">,</span><span class="n">thresh</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">get_radius_and_thresh</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="n">r</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">r</span><span class="p">,</span><span class="n">thresh</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>
+0.7071067811865476
+0.7071067811865476 [-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959
+ -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097
+ -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234
+ -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372
+ -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509
+ -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647
+ -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216
+  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078
+  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941
+  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803
+  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666
+  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528
+  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391
+  0.68920534  0.70710678]
+</pre></div></div>
+</div>
+<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">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">K</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">theta</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>[13]:
+</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_CW_Complexes_12_1.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_12_1.png" />
+</div>
+</div>
+<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><br/><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_13_0.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_13_0.png" />
+</div>
+</div>
+<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><br/><span></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><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">plotECT</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_14_0.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_14_0.png" />
+</div>
+</div>
+<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">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>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+{(&#39;A&#39;, &#39;B&#39;): 5.551115123125783e-17,
+ (&#39;A&#39;, &#39;D&#39;): -5.551115123125783e-17,
+ (&#39;B&#39;, &#39;C&#39;): 0.7071067811865475,
+ (&#39;C&#39;, &#39;D&#39;): 0.7071067811865475}
+</pre></div></div>
+</div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#....... check on the list of sorted edges, something is wrong</span>
 </pre></div>
 </div>
 </div>
 <div class="nbinput nblast docutils container">
-<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
 </pre></div>
 </div>
-<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">out</span> <span class="o">=</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="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>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">r</span><span class="p">,</span> <span class="n">r_thresh</span> <span class="o">=</span> <span class="n">myect</span><span class="o">.</span><span class="n">get_radius_and_thresh</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">r_thresh</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">out</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">plot</span><span class="p">(</span><span class="n">r_thresh</span><span class="p">,</span><span class="n">out</span><span class="p">[</span><span class="mi">2</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>
+[-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959
+ -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097
+ -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234
+ -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372
+ -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509
+ -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647
+ -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216
+  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078
+  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941
+  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803
+  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666
+  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528
+  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391
+  0.68920534  0.70710678]
+[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+ 2 2 2 2 2 4]
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[&lt;matplotlib.lines.Line2D at 0x135b2aa70&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_18_2.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_18_2.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</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><span class="n">draw_counts</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 empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_19_0.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_19_0.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">funcdict</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="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">funcdict</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">funcdict</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="n">K</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">theta</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>
+A -0.71
+B 0.0
+C 0.71
+D -0.0
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</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_CW_Complexes_20_2.png" src="../_images/notebooks_Tutorial-ECT_for_CW_Complexes_20_2.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">num_below_threshold</span><span class="p">(</span><span class="n">func_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the number of entries in func_list that are below the threshold thresh.</span>
+<span class="sd">    Warning: func_list must be sorted in ascending order.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">        func_list (list): sorted list of function values</span>
+<span class="sd">        thresh (float): threshold value</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">        int</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># If the list is empty, return 0</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">func_list</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="mi">0</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">func_max</span> <span class="o">=</span> <span class="n">func_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="n">thresh</span> <span class="o">&lt;</span> <span class="n">func_max</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">func_list</span> <span class="o">&lt;</span> <span class="n">thresh</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">func_list</span><span class="p">)</span>
+<span class="c1"># --</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</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">K</span>
+<span class="n">r</span><span class="p">,</span><span class="n">r_threshes</span> <span class="o">=</span> <span class="n">myect</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">r</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></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="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">vertex_count</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">num_below_threshold</span><span class="p">(</span>
+    <span class="n">g_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span> <span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">r_threshes</span><span class="p">])</span>
+<span class="c1"># print(vertex_count)</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">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">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="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">edge_count</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">num_below_threshold</span><span class="p">(</span>
+    <span class="n">g_e_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span> <span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">r_threshes</span><span class="p">])</span>
+<span class="c1"># print(edge_count)</span>
+
+<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o">==</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="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">face_count</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">num_below_threshold</span><span class="p">(</span>
+        <span class="n">g_f_list</span><span class="p">,</span> <span class="n">thresh</span><span class="p">)</span> <span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">r_threshes</span><span class="p">])</span>
+    <span class="c1"># print(face_count)</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">vertex_count</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>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">g_e_list</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+[-0.49999999999999994, 0.49999999999999994, 0.5, 0.5]
+</pre></div></div>
+</div>
 <div class="nbinput nblast docutils container">
 <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
 </pre></div>
diff --git a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb b/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
index 0dc5921..808aed1 100644
--- a/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
+++ b/docs/notebooks/Tutorial-ECT_for_CW_Complexes.ipynb
@@ -18,6 +18,7 @@
       "Requirement already satisfied: networkx in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.2.1)\n",
       "Requirement already satisfied: matplotlib in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (3.7.0)\n",
       "Requirement already satisfied: numba in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (0.56.4)\n",
+      "Requirement already satisfied: scipy in /Users/liz/anaconda3/lib/python3.10/site-packages (from ect==0.1.4) (1.10.0)\n",
       "Requirement already satisfied: contourpy>=1.0.1 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (1.0.5)\n",
       "Requirement already satisfied: cycler>=0.10 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (0.11.0)\n",
       "Requirement already satisfied: fonttools>=4.22.0 in /Users/liz/anaconda3/lib/python3.10/site-packages (from matplotlib->ect==0.1.4) (4.49.0)\n",
@@ -31,8 +32,8 @@
       "Requirement already satisfied: six>=1.5 in /Users/liz/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->ect==0.1.4) (1.16.0)\n",
       "Building wheels for collected packages: ect\n",
       "  Building wheel for ect (pyproject.toml) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=39481 sha256=945a865dca4ccb5f217f0a29d5144245bca97342c60f4d3a2dcdaedba9e337e3\n",
-      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-32vyxgih/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
+      "\u001b[?25h  Created wheel for ect: filename=ect-0.1.4-py3-none-any.whl size=40205 sha256=1a297b65949d477c6ceeae6d0cff6e6e6c6840b5d46b262bd72a8e083698bcf2\n",
+      "  Stored in directory: /private/var/folders/lm/dn75vz_d72b1cntn3ncjj10c0000gn/T/pip-ephem-wheel-cache-b8z93fr2/wheels/63/e8/b6/c1ed3cda3e641c4df1351d804e411763c0776b1f4494126c2f\n",
       "Successfully built ect\n",
       "Installing collected packages: ect\n",
       "  Attempting uninstall: ect\n",
@@ -131,10 +132,10 @@
      "data": {
       "text/plain": [
        "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
-       "        2,  2,  2,  2,  2,  2,  1,  1,  1,  1,  1,  1,  4,  4,  4,  4,  4,\n",
-       "        4,  0,  0,  0,  0,  0,  0,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,\n",
-       "        1,  1,  3,  3,  3,  3,  3,  3,  1,  1,  1,  1,  1,  1,  4,  4,  4,\n",
-       "        4,  4,  4, -1, -1, -1, -1, -1, -1, -1, -1, -1])"
+       "        2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,\n",
+       "        3,  3,  3,  3,  3,  3,  3,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,\n",
+       "        2,  2,  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": 5,
@@ -155,7 +156,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -201,7 +202,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ZY1MsTiLhAADARYYNNRyGD2o4uEsFAAA4jhYOC1Kp/9lu9GejPul0zXANoKEoGgUAAI6rMZqoxrBYw8GzVAAAQDxhBRS2WOEQlvczDmo4AACA42jhAADARdRwoI50Knizm9kCOjc/a4r77MM18zmKSHE69tRw0KUCAABACwcAAG46WTRq8eFtdKkAAIB4wjYMbe6Hu1TSMuGY/tOn1bJ1hun3zRo/zv5g0lSoIjobD+YdiV5hfJ8kRhOrOmpubr9l7oThQ7M2j4te4Or3GF+oopWr+699znOOedPRwzV6/Tm3o0gd1HAAAOCiU0WjVqfGKikpUSAQ0K233mrfQdUjLVs4AADwirCauDbw14YNG/Too4+qX79+lvbfELRwAACQho4cOaJvf/vbmj9/vtq2bev4/kg4AABwUY0RsGWSpKqqqqgpFAqddr8333yzvvKVr+jLX/5yUo6TLpUE6hTBoVHcLtCzItE5QMGfN3n9nIuKz/nWbHhYjQ13qdT8t0slLy8vanlRUZGKi4vrrL948WL961//0oYNGyzt1wwSDgAAXBQ2mihscaTR8H9HGq2oqFB2dnZkeTAYrLNuRUWFfvCDH+iFF15Q8+bNLe3XDBIOAABSRHZ2dlTCUZ+ysjIdOHBAAwcOjCyrqanR2rVr9dBDDykUCikjw/zQEYmQcAAA4CI7u1Qa4rLLLtOWLVuill1//fU677zzdNdddzmSbEgkHAAAuCosRYo+rWyjoVq3bq0+faIH5cvKylK7du3qLLcTCUcMikTtYbZgL3b9OiOPeljsOZNORaReul68XiQazx1/GR81f9GQbVHzY9v9K5nhAI4g4QAAwEX2DPxl7f2rV6+29P6GIOEAAMBFVocmP7UNr/N+hAAAwPdo4YAt/Nx/brdUrumgZiM5Xl7XK3rBkOhZajpSS1gBhWW1aNTa+5OBhAMAABfRpQIAAGATWjgAAHCRPQN/eb/9gIQDAAAXhY2AwlYH/rL4/mRI+4TDS0VwfuJ0wZ6fBwKLlcpFpMmUykWiZi0/dH7UPEWk/ha2oYXD6jgcyeD9CAEAgO+lfQsHAABusufx9N5vPyDhAADARTUKqMbiOBpW358MJBxoEPrP7eOnmg63a5w4706KHQgs9uFu6VzTEXvsZqXTZ+U2Eg4AAFxElwoAAHBcjax3idTYE4qjvJ8SAQAA36OFAwAAF9GlksJ+/MK1atK8eb2v+XmAqVSWSgOBeRlFov6QykWkVotAre6v9mcbPnZM0hbHY+DhbR5TUlKiCy64QK1bt1b79u01btw4bd++3e2wAABAA/gm4VizZo1uvvlmrVu3TqtWrdKJEyc0atQoVVdXux0aAACNZiigsMXJYBwO+6xcuTJqfuHChWrfvr3Kysp08cUXuxQVAADWpEuXim8SjliVlZWSpJycnNOuEwqFFAqFIvNVVVWOxwUAgBk8LdbDDMPQjBkzNHz4cPXp0+e065WUlGjOnDmmth2vaC2dChUp3kueRCOP2lnI6bVRTTnPkOwiUbjH+20w9Zg+fbo2b96sJ598Mu56M2fOVGVlZWSqqKhIUoQAADRMzX8fT2918jrftXDccsstWrFihdauXavOnTvHXTcYDCoYDCYpMgAAzKNLxWMMw9Att9yipUuXavXq1eratavbIQEAgAbyTcJx880364knntDy5cvVunVr7du3T5LUpk0btWjRIikxpPLgU37rS/fzd9H0ley4rxe9MjF6wXD7ip1j60H8/Dnic14eCMzrNRqxn50bwmqisMUuEavvTwbfJBylpaWSpBEjRkQtX7hwoSZPnpz8gAAAsEGNEVCNxS4Rq+9PBt8kHIZhuB0CAABoJN8kHAAApCKKRgEAgOMMG54WazDSaGqj4A71SVQU6mWJioetnuN+K05OVV4v5ERqIuEAAMBFNQqoxuLD16y+PxlIOAAAcFHYsF6DEfbBfRUkHAAAuChsQw2H1fcng/cjBAAAvkcLh438VkSaSgV8bn72TheJxm7/hIWRR61+52aLSlPpHPOTfz0V/RRts+fM4LPLTa2//sN8U+tb2ZdVXhhZNFZYAYUt1mCYfX9paalKS0u1a9cuSVLv3r01e/ZsFRYWWoojHhIOAABc5MZIo507d9a9996rc845R5L02GOPaezYsXrjjTfUu3dvS7GcDgkHAABpZsyYMVHzc+fOVWlpqdatW0fCAQBAKrKzaLSqKrr7LBgMKhgMxn1vTU2NnnnmGVVXV2vo0KGW4oiHhMNBXqvpoD+9cbw2kFfteKzUcziBcyw5sna7W+9vpWbD7LaSXePhhrBsGNr8vzUceXl5UcuLiopUXFxc73u2bNmioUOH6tixY2rVqpWWLl2qXr2cq3Eh4QAAIEVUVFQoO/vz/5TEa93o0aOHNm3apE8++UTPPvusJk2apDVr1jiWdJBwAADgIsOGu1SM/74/Ozs7KuGIJzMzM1I0OmjQIG3YsEEPPvig5s2bZymW0yHhAADARV55WqxhGAqFQpa3czokHAAApJm7775bhYWFysvL0+HDh7V48WKtXr1aK1eudGyfaZlwDB30tjJbZbodhsa2+1dS97f87OQ9IdLs4DoXDdnmUCTmP+dZm8dFzXutMLO2uf2WxX29zlNBz3YuFokCQKfEfq4n8k6zYgMlOm8SSfZvVzzjsqrjvn59+UVR82Z+a44fOa7djYrKHDeGNt+/f78mTJigvXv3qk2bNurXr59Wrlypyy+/3FIc8aRlwgEAgFe40aWyYMECS/trDBIOAABc5MbQ5m7g4W0AAMBxtHC4KLZ/3e5+0Tr99yYk+wFHVvdnpQYktmbDT2Jjt9o3b5bZAaBi16emo352DqzVGE7/NtlpWXVW1Hyimg4v8spdKk4j4QAAwEXpknDQpQIAABxHCwcAAC5KlxYOEg4AAFxEwoGks1qoZbZINNmFoU6KeyxDomfdLshzkpVCYTekcxGpm+dhbLFxos/dT0WkvV/7dtR8Op1TXkfCAQCAiwxZH0fDsCcUR5FwAADgonTpUuEuFQAA4DhaOAAAcFG6tHCQcHiY1UKtVCoKteJfT/WJmg91CUfNB/OOJDMcR8V+53Y/hdfpQsdULiL1UrFyqKJV9AKTTxH2UxGpH84pEg4AAOC4dEk4qOEAAACOo4UDAAAXGUZAhsUWCqvvTwYSDhtZ7aM1O/hOLGo2Ppe1+/SNd7GvVSu6P9tPNR11+uJjWK3pcLvuoPb+vdj3Ho/bn50ZTtf+eIkXazrCClgeh8Pq+5OBLhUAAOA4WjgAAHBRuhSNknAAAOCidKnhoEsFAAA4jhaOGG4Wepndd9NXsqMXxAxohcZJVIjpp6JSJI+Xi0QTndNWuTkQWOyTb83yQhEpXSoAAMBxdKkAAADYhBYOAABcZNjQpeKHFg4SDgAAXGRIMgzr2/A6XyUca9eu1a9+9SuVlZVp7969Wrp0qcaNG2d6O69tPE9NmjeX5O0CwNii0Da7amLWiJ3PMLX96hQqMo03sqjdYgvwkn0OWSkATDSipJcLH71Q3Feblz8rq0Wi6TTyaOxn9XLF58cePnYsKTGEFVCAkUa9pbq6Wv3799dDDz3kdigAAMAEX7VwFBYWqrCw0O0wAACwTbrcpeKrhMOsUCikUCgUma+qqnIxGgAA6gobAQUYh8PfSkpKNGfOnLjruN0fX1udgbxMiq3xqCyIX9NRu+7Bb/UcdtZs1Hl6rMnPwkvnkNOcHkDKjNp97VJq1xkk4qXvRYoeCMzuQcDMDvTltc8mnfmqhsOsmTNnqrKyMjJVVFS4HRIAAFEMw57J61K6hSMYDCoYDLodBgAAp5UuNRwp3cIBAAC8wVctHEeOHNHOnTsj8++//742bdqknJwc5ed79554AABOJ11aOHyVcGzcuFEjR46MzM+YMUOSNGnSJC1atMiWfSSzADBRkWjdgb6ck6gI0+2i0mQO7GWVl4tIYz/Hd9f0jJoPXeL9H63TiR2sKtHnbnbgMC8P9IXP+bFI1I27VEpKSrRkyRK9/fbbatGihYYNG6Zf/OIX6tGjh6U44vFVwjFixAgZfqiMAQCggewo+jT7/jVr1ujmm2/WBRdcoBMnTmjWrFkaNWqUtm3bpqysLGvBnIavEg4AAGDdypUro+YXLlyo9u3bq6ysTBdffLEj+yThAADARSdbOKzWcJz8M3aAy4berVlZWSlJysnJsRRHPCQcCdTuD7TaF291YC+zzA4EFo/XazzsZHUgsFhWazrM9En7qdbFaYk+90Q1GV6uxUl2nYKVh7nVHgRMMj8QWOxAX36s0UjEzqLRvLy8qOVFRUUqLi5O8F5DM2bM0PDhw9WnTx9LccRDwgEAQIqoqKhQdvbn/7ltSOvG9OnTtXnzZr3yyitOhkbCAQCAm4z/Tla3IUnZ2dlRCUcit9xyi1asWKG1a9eqc+fOFqOIj4QDAAAXuTEOh2EYuuWWW7R06VKtXr1aXbt2tbT/hiDhAAAgzdx888164okntHz5crVu3Vr79u2TJLVp00YtWrRwZJ8kHA4yWyTq9EBfdhaRxrK70DKVix+tFiNa+WwSnWOd1kQ37O7x8UBgZiUqRkz0upNFpalYKNlQaXHsdvapNFBpaamkk+Nb1bZw4UJNnjzZYjD1I+EAAMBNNnSpqBFdKsmWuv+NBAAAnkELBwAALnJjaHM3kHAAAOAinhaLOhIV+yV7JFEvM1tE6uUiUbsLYmPFnlex++MitYfTxYdeHpnUbnaOPAqdrL9Icg2HG7z7Kw8AAFIG/3kCAMBF1HAAAADnuTAOhxvSMuEYOuhtZbbKtLyd2H5MDa+qf8XTiO3zre6S7D64z2sRnO5vjn18UOyxnzD52bnporPLbd1e7FNLq2VfLYDd55iZvnqvi/3cB9v8vSYS76mpdeocznY4GIsW5r/c4HWXVWfFfT322N08544fOa7dru099aRlwgEAgFdwlwoAAEgOH3SJWMVdKgAAwHG0cAAA4CK6VFBHnSLRGH4e+Mft2N3ev5NiixNjmX1KaSp9NsmU6HtIttrFkbEFpHbH6nRB7PXlF0XN1y4iTVQkCqXNXSp0qQAAAMfRwgEAgKsC/52sbsPbSDgAAHBTmnSpkHAkkKhuwwynHx7lZWaP3ct1C4kGjHK6VqD2ZxP7uTh9jll5aFeymf0e3PxeE/3OWD3/k12/0vvDb0f+Prffsrjrzto8Lu7ryR6QzRVpknBQwwEAABxHCwcAAG5Kk8fTk3AAAOCidHlaLF0qAADAcbRwOMjPRaJWizbtPnY/FZEmYudn4+dzzG6x34PVz+bliuhCTrvPOTPxefn8l+LHl6gotOkr2fE3fm1jo/KRNCkaJeEAAMBNaVLDQZcKAABwHC0cAAC4KGCcnKxuw+tIOAAAcBM1HOnJzpFFs3bH77Gq7hK2bV9O81rRWjLjMVt8mMzPJvYcS/Y5lcyRR60+dTeVJDpWp89BM084thrLv57qEzWf6Bz38ui36Y6EAwAAN6VJ0SgJBwAAbqJLBQAAOI6EIz3YWbNhltv973Zyu//czpoOt4/FCr+dU1aeYprs78lrg+GZ2ZfVOgo7Y0840JdFZn/TqflInrRPOAAAcBUtHAAAwHFpUjTKSKMAAMBxtHAAAOAiRhpNYf9e0ksZweaSpKyY16wU2SUa6CuVxBZ+hTxWnGhm4CGni/ns3r6Z8yzZRaSxgzSdGF5l27a9VsybqDDTS/G6GWvstq3+o2P3OR1bZFp7+zWhY5a23WBpUsPhu38hH3nkEXXt2lXNmzfXwIED9fLLL7sdEgAAvrJ27VqNGTNGnTp1UiAQ0LJlyxzfp+mEo6amRvPmzdNtt92m+++/X//4xz906NAhJ2Kr46mnntKtt96qWbNm6Y033tBFF12kwsJClZeXJ2X/AACkgurqavXv318PPfRQ0vZpunXrlltu0Z/+9Cddfvnlevjhh9WkSRN99tlnOvvsszVgwACtWLHCiTglSb/+9a81ZcoUTZ06VZL0wAMP6G9/+5tKS0tVUlLi2H4BAHBKQDbUcJhcv7CwUIWFhdZ2apLpFo4lS5boD3/4g/74xz8qGAxq48aN+s1vfqNjx46pS5cuTsQoSTp+/LjKyso0atSoqOWjRo3Sq6++Wu97QqGQqqqqoiYAAFJV7L95oVDI7ZAiTLdwHDlyRL16nSyyadasmTIyMnTzzTfr+PHj2rNnj+0BnnLw4EHV1NSoQ4cOUcs7dOigffv21fuekpISzZkzx9R+zBTk2V2A57dRIv0itsDV6c/VS8WCscyeY24XQnv5s/Sz2Gsi9h8CO6+RTmui/+teWWDbpm3h9jkuydZxOPLy8qIWFxUVqbi42Nq2bWL6k+7WrVsksTj77LP14YcfSpLGjBmjxx9/3N7o6hEIRH8phmHUWXbKzJkzVVlZGZkqKiocjw8AAFMMmyZJFRUVUf/uzZw5M6mHEo/phOOaa67RypUrJUkjRozQ//3f/0mStm3bpk8//dTe6GrJzc1VRkZGndaMAwcO1Gn1OCUYDCo7OztqAgDAU2xMOGL/zQsGg0k9lHhMd6n85Cc/ifz9zjvv1ODBg3XmmWeqqqpKU6ZMsTW42jIzMzVw4ECtWrVKV199dWT5qlWrNHbsWMf2CwAArLM0Bkt+fr7efPNNPf/888rJydFXvvIVu+Kq14wZMzRhwgQNGjRIQ4cO1aOPPqry8nJ973vfc3S/pxPbNynVxF2/siDD1Pa9VNORqJ/TS7FK8eNNdCxWY7f7s7Czj7nNrpqY+ejXzZ6jicTWCtg5EJjXeHkwPLPnkJPXc+w5aPWc81tdUn3cGGn0yJEj2rlzZ2T+/fff16ZNm5STk6P8/MY/xTkeyyONtmvXThMmTLAjloSuvfZaHTp0SD/96U+1d+9e9enTR88//7yjd8cAAOAoF0Ya3bhxo0aOHBmZnzFjhiRp0qRJWrRokcVg6ue7oc1vuukm3XTTTW6HAQCAb40YMUKGkdzx0H2XcAAAkFLS5FkqJBwAALiIp8XCdrHFUonEFlMlszDTamFVsotI7SwEc7ro08kCPLu3Z3cRaSKpNNCXm4XUdhdGeq0o3AwvFommKxIOAADcZONIo15GwgEAgJvSpIaDtiYAAOA4WjhMsLu/3Pr+ovvX/dSvapab/bCJ+q/tjs3sgHJOsrumI9kP0nOSl2oDkh1Lov0l+7eyNi99Lw1F0SgAAHBemnSpkHAAAOAmG1o4/JBw+K/tCQAA+A4tHAAAuIkuFXhd3cKsxhf01S0ObPSm6pUKT3Q8xXrBXKLvyb2CO6fFfjbVXbw/dsApXhoMz8vXhyS12vFJ5O9HvnBG3HXtLkx2e/C6RkmThMPbZy0AAEgJtHAAAOCidLktlhYOAADgOBIOAADguLTsUrl76pNq2bphhUTLD53vcDTOGdvuX3Ffd/PY1n+YHzU/t98yW7efzGOLPZbu15Ynbd9uS3SOxYr9XrrHvL4w/2WLETlnWXWWY9sel1Xt2r7tMGvzuKj53cOb1ZqLPrbBZ8e/PqyeU4mY2f7RwzWa8LCpzTdOmhSNpmXCAQCAV1DDAQAAYBNaOAAAcJsPWiisIuFIYbF9m2b7Ru0UW+cQy0+1MomOJfb1RH3WfpboHPPT9xrLzboJr9dsWJHo+omVSufUaVHDAQAAnEYNBwAAgE1o4QAAwE10qQAAAKelS5cKCUeMlCxI+i8nj81sIVii96dSoaXZz8bPxx47AJTZY7m+/KKo+WQOBJbsQs3a1+PyQ9GvuVng7TarvyWJeKmYPt2QcAAA4Ca6VAAAgOPSJOHgLhUAAOA4WjgAAHARRaPwvUTFV1aKE50u7PISq8caqmhl7g1nW9pdUjHqasOZKdr2emFjbHGwnWKvl5crekXNB/OORM2nxDlFlwoAAIA9aOEAAMBNadLCQcIBAICLqOFIE6k80FcitfvXY/tB3a7RSOe+/1Q+drPHVnsgMLsHAXN6oC87f1u8XtNhhekapxhWa9U88dmmSQsHNRwAAKShRx55RF27dlXz5s01cOBAvfyysyP7knAAAOCiU10qVicznnrqKd16662aNWuW3njjDV100UUqLCxUeblzLaokHAAAuMmwaTLh17/+taZMmaKpU6eqZ8+eeuCBB5SXl6fS0lJbDqk+JBwAAKSIqqqqqCkUCtVZ5/jx4yorK9OoUaOilo8aNUqvvvqqY7GlfdFoKklUPBWvOCvR4DpuS2YhZdIH+krw/vU6fTzJLii1u5g4md+r3UWi6VRwbudAX3ZfH4l+q5wcANE2NhaN5uXlRS0uKipScXFx1LKDBw+qpqZGHTp0iFreoUMH7du3z2Igp0fCAQCAiwL/naxuQ5IqKiqUnZ0dWR4MBk//nkD0Xg3DqLPMTiQcAACkiOzs7KiEoz65ubnKyMio05px4MCBOq0edqKGAwAANyW5aDQzM1MDBw7UqlWropavWrVKw4YNs3YscaRlC8ePX7hWTZo3l5S4/88T/Xun8fK6XolXaiSz/aRwh9M1EMkeAC7e8dQeBEyyfyCwWF6u0fDEYFUNZLVmw2mx51zt39XwsWOStjgegxsjjc6YMUMTJkzQoEGDNHToUD366KMqLy/X9773PWuBxOGbhGPu3Ln6y1/+ok2bNikzM1OffPKJ2yEBAOBL1157rQ4dOqSf/vSn2rt3r/r06aPnn39eXbp0cWyfvulSOX78uK655hpNmzbN7VAAALCPC+NwSNJNN92kXbt2KRQKqaysTBdffLHlQ4nHNy0cc+bMkSQtWrTI3UAAALCbD56FYpVvEo7GCIVCUYOeVFVVuRgNAAB18bTYFFBSUhJpGWkst5+aWpvXi6+SyUtPVHX6ezFTwGt1kCMvne9S/CcaJ2J2oC8vF4kmYncRqdWBvvitQn1creEoLi5WIBCIO23cuLHR2585c6YqKysjU0VFhY3RAwBgA5dqOJLN1RaO6dOn67rrrou7TkFBQaO3HwwG446yBgCA2+hSSYLc3Fzl5ua6GQIAAEgC39RwlJeX6+OPP1Z5eblqamq0adMmSdI555yjVq3oLwQA+JSND2/zMt8kHLNnz9Zjjz0Wmf/iF78oSXrppZc0YsSIRm/XyyNqeqnwysufk2StiNRrhZJOij3WROeYl77n2Nh7f/jtqPm5/ZZFzfu5CNSqZI9E6uZvldXfJi/8zqZLl4pvBv5atGiRDMOoM1lJNgAAQHL4poUDAICURJcKAABwHAkHcHp+qumwe1Awt/t8a+/f6f5qq8dq9bwws/87KsZHzV80ZJulfeNzbp/zSA0kHAAAuChdikZJOAAAcBNdKgAAwGkBw1DAsJYxWH1/MvjmtlgAAOBftHDEcLMY0s+FWV4vIq0tnQb6cvuccnL/Wbvj/3/pZfWKmk/nItJEA4HFPh3W7fPGikS/RZ48NrpUAACA09KlaJQuFQAA4DhaOAAAcBNdKgAAwGnp0qVCwpGAlVEdzWw71XipiNRqkaifvic/xWpWoiLRRF5e1yvu6+lUVHrHX8YnXilFpPI14TckHAAAuIkuFQAA4LR06VLhLhUAAOC4tGzhyDz7iDJanjD9PqtPHY2tJfDy4FjJZvcTXWtLVMMRu28/fU9eqpVxQu3jq+4StrStRJ9NovNkbr9llvafTLEDeZmVSueRlWuk5ugxu8OpH10qAAAgGfzQJWIVCQcAAG4yjJOT1W14HDUcAADAcbRwAADgonS5S4WEw4TYorJEhY7p9FRSq8x+tk7u2+tSeSAjJ4/NaoGt1UJMM0WnVvdllZ+LkX15faRJ0ShdKgAAwHG0cAAA4KJA+ORkdRteR8IBAICb0qRLhYTDAr/1/fuJ1ZoOM99Noj7fZPdfW+mD9lvfuy/72xvJzboMq5+z386reFLpWPyGGg4AAFx06i4Vq5NT5s6dq2HDhqlly5Y644wzGr0dEg4AANx0auAvq5NDjh8/rmuuuUbTpk2ztB26VAAAwGnNmTNHkrRo0SJL2yHhAADARXYO/FVVVRW1PBgMKhgMWtu4TUg4LKD4KHnMPvHVTnZ/z+lUKBnLS8fO9dt4Xiq09tI51Wg23qWSl5cXtbioqEjFxcUWN24PajgAAHCRnUWjFRUVqqysjEwzZ86sd5/FxcUKBAJxp40bN9p6nLRwAACQIrKzs5WdnZ1wvenTp+u6666Lu05BQYFNUZ1EwgEAgJtceDx9bm6ucnNzre3TJBIOAABc5PWnxZaXl+vjjz9WeXm5ampqtGnTJknSOeeco1atGl5DQ8KRgJmCJIrQ3PPyul5J25efitTcPif5rNzh9udee/92f452H1sqfe9OmT17th577LHI/Be/+EVJ0ksvvaQRI0Y0eDsUjQIA4CbDpskhixYtkmEYdSYzyYZECwcAAK7yepeKXWjhAAAAjkv7Fg4n+zrpG/SGrN3Jzauru4STuj8vcbt2AN7jpUHCPCtsnJysbsPj0j7hAADAVTaONOpldKkAAADH0cIBAICLArKhaNSWSJxFwgEAgJtcGGnUDb5IOHbt2qWf/exnevHFF7Vv3z516tRJ48eP16xZs5SZmWl6e01fy1ZGsLkkKZTGBX5+lqgQLdmFomb27WZRKU++bTg/FX37+Xswey3b/Ztd53rd/flzSAIh8/++NEa63Bbri4Tj7bffVjgc1rx583TOOedo69atuvHGG1VdXa377rvP7fAAAEACvkg4Ro8erdGjR0fmu3Xrpu3bt6u0tJSEAwDgb2lyl4ovEo76VFZWKicnJ+46oVBIoVAoMl9VVeV0WAAAmBIwDAUs1mBYfX8y+DLhePfdd/Xb3/5W999/f9z1SkpKNGfOnCRFlZif+oThnNg+Yy8PFObn2gC7cf0mR6IaKDfrs2CNq99ccXGxAoFA3Gnjxo1R79mzZ49Gjx6ta665RlOnTo27/ZkzZ6qysjIyVVRUOHk4AACYF7Zp8jhXWzimT5+u6667Lu46BQUFkb/v2bNHI0eO1NChQ/Xoo48m3H4wGFQwGLQaJgAAjqFLJQlyc3OVm5vboHU//PBDjRw5UgMHDtTChQvVpAnNagAA+IUvajj27NmjESNGKD8/X/fdd58++uijyGsdO3Z0MTIAACziLhXveOGFF7Rz507t3LlTnTt3jnrNsNiM5GYBH0VoDeflgb6sMhu7lXM03iBHVreN5KGYN8WkyUijvviVnjx5sgzDqHcCAADe54sWDgAAUhVDmwMAAOelSZcKCQcAAC4KhE9OVrfhdSQcMSgi9YZULhK1Kp2P3Uu4Xu3B+Zw+SDgAAHATXSoAAMBxaTIOB21ZAADAcbRweBh9xJ+jnzc5/PQkW69x8npNpYG+uJbr4lkqAADAeWlSw0GqCQAAHEcLBwAAbjIkWe299H4DBwkHAABuooYDkqILnNwuoEvlItLYY6OwDH6XyterGVzLOIWEAwAANxmyoWjUlkgcRcIBAICb0uQuFRIOAADcFJYUsGEbHkfnGgAAcBwtHCZ4bRRGPxelNX0lO3repTgQn9fOeT8zc736fWRRCkXN8fJdKrt27dLPfvYzvfjii9q3b586deqk8ePHa9asWcrMzDS1LX7nAQBwk4drON5++22Fw2HNmzdP55xzjrZu3aobb7xR1dXVuu+++0xti4QDAADUa/To0Ro9enRkvlu3btq+fbtKS0tJOAAA8BUbWziqqqqiFgeDQQWDQWvbjlFZWamcnBzT7yPhSCGJ+n3drPGIjY0Tz5+o6bBP7WvCT/VXEjUatrMx4cjLy4taXFRUpOLiYmvbruXdd9/Vb3/7W91///2m38tZAwBAiqioqFBlZWVkmjlzZr3rFRcXKxAIxJ02btwY9Z49e/Zo9OjRuuaaazR16lTTsfEfTQAA3GTjOBzZ2dnKzs6Ov66k6dOn67rrrou7TkFBQeTve/bs0ciRIzV06FA9+uijjQqRhAMAABe5cVtsbm6ucnNzG7Tuhx9+qJEjR2rgwIFauHChmjRpXOcICQcAAKjXnj17NGLECOXn5+u+++7TRx99FHmtY8eOprZFwmGB3wro3BxMiCIz4PQSDYTn9m8L16/DPDwOxwsvvKCdO3dq586d6ty5c8wuze2TswgAADeFDXsmB0yePFmGYdQ7mUULBwAAbvJwC4edaOEAAACOo4XDRn6r6XASfb7pgXO+8cxcI1xPqc6GFg55v4WDhAMAADfRpQIAAGAPWjgAAHBT2JDlLhGH7lKxEwkHAABuMsInJ6vb8DgSDgelU0EdRW2Q0uucN4trBOmOhAMAADelSdEoCQcAAG5KkxoO2vgAAIDjaOEAAMBNdKnAbqlUUEcBHADYxJANCYctkTiKhAMAADelSQsH/00FAACOo4UDAAA3hcOSLHaxh73fRU/C4SI/1XRQs4HG8NM5bjeuGTQYXSrectVVVyk/P1/NmzfXWWedpQkTJmjPnj1uhwUAABrANwnHyJEj9fTTT2v79u169tln9e677+ob3/iG22EBAGDNqRYOq5PH+aZL5bbbbov8vUuXLvrRj36kcePG6bPPPlOzZs1cjAwAAAvSZKRR3yQctX388cf64x//qGHDhsVNNkKhkEKhUGS+qqoqGeEBAIAYvko47rrrLj300EM6evSohgwZoj//+c9x1y8pKdGcOXOSFJ11iYrMnCy4o8ANyZDKRaRcQ2gswwjLsPh4eavvTwZXr5Di4mIFAoG408aNGyPr33nnnXrjjTf0wgsvKCMjQxMnTpQRp99q5syZqqysjEwVFRXJOCwAABrOME52iViZqOGIb/r06bruuuvirlNQUBD5e25urnJzc3XuueeqZ8+eysvL07p16zR06NB63xsMBhUMBu0MGQAANIKrCcepBKIxTrVs1K7RAADAdwwbikZp4bDH+vXrtX79eg0fPlxt27bVe++9p9mzZ6t79+6nbd0AAMAXwmEpYLEGwwc1HL5IOFq0aKElS5aoqKhI1dXVOuusszR69GgtXrw4rbpMKEpDqvFzESnXI2xDC4d39O3bVy+++KLbYQAAgEbyRcIBAECqMsJhGRa7VPxwWywJBwAAbqJLBQCSK15dRLLrO6jRAOxFwgEAgJvChhSghQMAADjJMCRZvS3W+wkHbYYAAMBxtHAAAOAiI2zIsNilEu+5Yl5BwgHAFyjiRMoywrLepeJcUfVVV12lTZs26cCBA2rbtq2+/OUv6xe/+IU6depkajtcwQAAuMgIG7ZMThk5cqSefvppbd++Xc8++6zeffddfeMb3zC9HVo4AADAad12222Rv3fp0kU/+tGPNG7cOH322Wdq1qxZg7eTVgnHqT6umuPHXI4EAOB1p/6tcLo+4oQRstwlckKfSZKqqqqilgeDQVufOfbxxx/rj3/8o4YNG2Yq2ZAkGWmkoqLi1HBuTExMTExMDZoqKioc+Tfp008/NTp27GhbnK1ataqzrKioyJZYf/jDHxotW7Y0JBlDhgwxDh48aHobAcPwQWmrTcLhsPbs2SPDMJSfn6+KigplZ2e7HVajVFVVKS8vj2NwGcfgDRyDd6TCcZw6hvLycgUCAXXq1ElNmjhT8njs2DEdP37clm0ZhqFAIBC17HQtHMXFxZozZ07c7W3YsEGDBg2SJB08eFAff/yxdu/erTlz5qhNmzb685//XGd/8aRVwnFKVVWV2rRpo8rKSl9fEByD+zgGb+AYvCMVjiMVjiGRgwcP6uDBg3HXKSgoUPPmzess/+CDD5SXl6dXX31VQ4cObfA+06qGAwAASLm5ucrNzW3Ue0+1U4RCIVPvI+EAAAD1Wr9+vdavX6/hw4erbdu2eu+99zR79mx1797dVOuGlKbjcASDQRUVFdlauZtsHIM3cAzewDF4RyocRyocg11atGihJUuW6LLLLlOPHj10ww03qE+fPlqzZo3pzyctazgAAEBypWULBwAASC4SDgAA4DgSDgAA4DgSDgAA4DgSDgAA4Li0SzgeeeQRde3aVc2bN9fAgQP18ssvux2SKW+99ZZycnLUvXt3DRgwQK1atdKIESPcDssUvx7D4cOHdcEFF2jAgAHq27ev5s+f73ZIaatp06YaMGCABgwYoKlTp7odjmmpcC5t37498h0MGDBALVq00LJly9wOq8FKSkp0wQUXqHXr1mrfvr3GjRun7du3ux1WarPlqS4+sXjxYqNZs2bG/PnzjW3bthk/+MEPjKysLGP37t1uh2bKqFGjjM2bNxuGYRjdu3c3Pv30U5cjMs+Px3DixAmjurraMAzDqK6uNrp27dqoBxjBunbt2rkdgiWpdi4dPnzYaNeunXHkyBG3Q2mwK664wli4cKGxdetWY9OmTcZXvvIVIz8/31fH4Ddp1cLx61//WlOmTNHUqVPVs2dPPfDAA8rLy1NpaanboZnyzjvvqEePHjp8+LAyMjLqHeve6/x4DBkZGWrZsqWkkw9cqqmpkWEYvm2xOe+88xQIBOqdfvOb37gdnml++h5S7VxasWKFLrvsMmVlZfnmGFauXKnJkyerd+/e6t+/vxYuXKjy8nKVlZVJSr3rwxPczniSJRQKGRkZGcaSJUuiln//+983Lr74YpeiMq+ystLo2bOnYRiG8eqrrxrjxo1zOSLz/HwM//nPf4x+/foZLVq0MB566KHIcj+22Gzbts2QZPzjH/8w9u7da5SXlxtNmzY1nnnmGePYsWNuhxdXs2bNjPPPP9+48MILjdWrV0eW++l7SKVzaezYscazzz4bmffjMezYscOQZGzZssUwDH9fH16VNi0cBw8eVE1NjTp06BC1vEOHDtq3b59LUZn35ptvqlevXpJO/o/u3HPPdTki8/x8DGeccYb+/e9/6/3339cTTzyh/fv3S/Jni82+ffvUtGlTXXjhherYsaMOHTqkEydO6KKLLvL8kM67du1SWVmZ/vd//1cTJ05UVVWVJH99D6lyLlVVVemf//ynrrzyysgyvx2DYRiaMWOGhg8frj59+kjy9/XhVWmTcJwSCASi5g3DqLPMy7Zt26bevXtLkrKysvTXv/5VH3/8sctRmZMKx9ChQwf169dPa9euVVVVlVq0aKHMzExt3bo1kkx53ZYtW3TuuedGfjw3bdqkM888s05S7kWdOnWSJPXp00e9evXSO++849vvwe/n0vLly3XFFVdEkgo/HsP06dO1efNmPfnkk5Flfr4+vCptnhabm5urjIyMOq0ZBw4c8NUJNGXKlMjfr732Wl177bUuRtM4fj2G/fv3q0WLFsrOzlZVVZXWrl2radOm+bbFZvPmzerbt29kftOmTerXr5+LETXMf/7zH7Vs2VLBYFAffPCBtm3bpm7duvnqe0ilc+npp5/Wd77znci8347hlltu0YoVK7R27Vp17tw5styv14eXpU0LR2ZmpgYOHKhVq1ZFLV+1apWGDRvmUlTwkw8++EAXX3yx+vfvr+HDh2v69Onq16+fb1tsNm/eHPUD6pcf1LfeekuDBg1S//799dWvflUPPvigcnJyfPU9pMq5VFlZqfXr1+uKK66ILPPLMRiGoenTp2vJkiV68cUX1bVr16jX/Xp9eJrLNSRJdeq22AULFhjbtm0zbr31ViMrK8vYtWuX26E1mKS4kx+kwjH4XU1NjdGyZUvjueeeiyzLy8szHnjgARejApJn2rRpRps2bYzVq1cbe/fujUxHjx7l+nBI2v26P/zww0aXLl2MzMxM4/zzzzfWrFnjdkiNUl1dbeTn5xu3336726GYVl5eblxyySVGz549jb59+xpPP/202yGlnXfeeceQFDUGzVe/+lXjjDPO8O01AZhxuv/wLFy4kOvDIQHDMIzktafALrNmzdKOHTuUn5+v++67z+1wTNm7d6/279+vAQMG6MCBAzr//PO1fft2ZWVluR1aXI0tLuYSs1cqfA8cA9JR2tRwpJIdO3bo7bffjroNzU/OOussDRgwQJLUvn175eTkeLKPN5ZxskUwaqqurlZ+fr5uv/32el/nx9V+qfA91I6rvLxcl1xyiXr27Km+ffvq6aef9t0x+PV7QHKRcPjQHXfcoZKSErfDsMXGjRsVDoeVl5fndiiNMnfuXH3pS19yO4y05+fvoWnTpnrggQe0bds2/f3vf9dtt92m6upqt8NqFD9/D3AeCYfPLF++XOeee67nbzVriEOHDmnixIl69NFH3Q6lUfze0pQq/P49+LXFL5bfvwc4j4TDZ9atW6fFixeroKBAd9xxh+bPn6+f/vSnbodlWigU0tVXX62ZM2f69rbkVGpp8rNU+h783OKXSt8DnEHC4TMlJSWqqKjQrl27dN999+nGG2/U7Nmz3Q7LFMMwNHnyZF166aWaMGGC2+E0Siq1NPlZKn0Pfm7xS6XvAc5Jm5FG4R3//Oc/9dRTT6lfv35atmyZJOkPf/hD1Kh+XneqpemZZ57RkSNH9Nlnnyk7O9t3yZ/fpcr34PcWv1T5HuAsbosFLFq0aJG2bt3qu9uTU41fvwfDMPStb31LPXr0UHFxsdvhWObX7wHOo0sFAFx0qsVv2bJlGjBggAYMGKAtW7a4HRZgO1o4AACA42jhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhAAAAjiPhANLU9OnTNXz48HpfKygo0Ny5c5McEYBU1tTtAAAk37Zt21RaWqq1a9fW+3rPnj21adOm5AYFIKXRwgGkoV/96le64IILdOGFF9b7ek5Ojvbv35/kqACkMhIOIM2cOHFCzz77rL7+9a9Hln33u9/VggULIvOHDx9WVlaWG+EBSFEkHECaeffdd3X48GH17dtXkhQOh/XMM8+oVatWkXU2b96snj17uhUigBREwgGkmU8++USSIgnG3/72N/3nP/9RZmamJGn9+vXavXu3xo0b51KEAFIRRaNAmunSpYsCgYCefPJJZWVl6fbbb9eVV16p5cuXq6CgQN/97nd16aWX6uKLL3Y7VAApJGAYhuF2EACSq6SkRPfee69atGihn//85xo8eLDGjh2rAwcOaMyYMXrkkUeUk5PjdpgAUggJBwAAcBw1HAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHEkHAAAwHH/H5DTqsDligO6AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -236,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +246,7 @@
        "dict"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,17 +257,406 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 11,
+     "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",
+    "K = EmbeddedGraph()\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_mean_centered_coordinates()\n",
+    "K.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7071067811865476\n",
+      "0.7071067811865476 [-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n"
+     ]
+    }
+   ],
+   "source": [
+    "myect = ECT(100,80)\n",
+    "r = K.get_bounding_radius()\n",
+    "print(r)\n",
+    "\n",
+    "r,thresh = myect.get_radius_and_thresh(K,r)\n",
+    "print(r,thresh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 13,
+     "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/4\n",
+    "K.plot(color_nodes_theta=theta)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o0aJFHvtv3bpVbdu21aRJkxQVFaWrrrpK999/v3bs2HGOKwcAAL7KZ8JOUVGR0tPTFR8f79YeHx+v1NRUj/v07t1bP/zwg9avXy9jjA4cOKC33npL119/fYWvU1hYqIKCArcHAACwl8+Enby8PJ08eVJhYWFu7WFhYcrNzfW4T+/evfXmm29q+PDhCgwMVIsWLXTBBRfoL3/5S4Wvk5iYqNDQUNcjIiLCq+cBAAB8i8+EnTIOh8PtuTGmXFuZzMxMTZo0SY8//rjS09P1wQcfKCsrSwkJCRUef+bMmcrPz3c99u3b59X6AQCAb/Gv7QLKNGvWTH5+fuVmcQ4ePFhutqdMYmKirrzySj300EOSpG7duqlBgwbq06ePnnjiCbVs2bLcPk6nU06n0/snAAAAfJLPzOwEBgYqJiZGKSkpbu0pKSnq3bu3x32OHz+uevXcT8HPz0/SqRkhAAAAnwk7kjRt2jQtXbpUy5cv1549ezR16lRlZ2e7bkvNnDlTI0eOdPW/8cYbtXbtWi1atEjfffedPvnkE02aNEk9e/ZUeHh4bZ0GAADwIT5zG0uShg8frkOHDmnu3LnKyclRdHS01q9fr8jISElSTk6O22fu3HvvvTpy5Ij++te/6o9//KMuuOAC9e/fX08//XRtnQIAAPAxDnOe3+8pKChQaGio8vPzFRISUtvleN3xohJ1efxDSVLm3EGqH+hT+RYAcBbO59/xVfn77VO3sQAAALyNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsJrPhZ2kpCRFRUUpKChIMTEx2rx582n7FxYW6tFHH1VkZKScTqfatWun5cuXn6NqAQCAr/Ov7QJ+Kzk5WVOmTFFSUpKuvPJKLVmyREOGDFFmZqbatGnjcZ9hw4bpwIEDWrZsmS6++GIdPHhQJSUl57hyAADgq3wq7MyfP19jxozR2LFjJUkLFizQhx9+qEWLFikxMbFc/w8++ECbNm3Sd999pyZNmkiS2rZtey5LBgAAPs5nbmMVFRUpPT1d8fHxbu3x8fFKTU31uM8//vEPxcbG6plnnlGrVq3UoUMHTZ8+XSdOnKjwdQoLC1VQUOD2AAAA9vKZmZ28vDydPHlSYWFhbu1hYWHKzc31uM93332nLVu2KCgoSOvWrVNeXp4mTJign3/+ucJ1O4mJiZozZ47X6wcAAL7JZ2Z2yjgcDrfnxphybWVKS0vlcDj05ptvqmfPnrruuus0f/58rVixosLZnZkzZyo/P9/12Ldvn9fPAQAA+A6fmdlp1qyZ/Pz8ys3iHDx4sNxsT5mWLVuqVatWCg0NdbV17txZxhj98MMPat++fbl9nE6nnE6nd4sHAAA+y2dmdgIDAxUTE6OUlBS39pSUFPXu3dvjPldeeaX279+vo0ePutq+/vpr1atXT61bt67RegEAQN3gM2FHkqZNm6alS5dq+fLl2rNnj6ZOnars7GwlJCRIOnULauTIka7+d911l5o2barRo0crMzNTH3/8sR566CHdd999Cg4Orq3TAAAAPsRnbmNJ0vDhw3Xo0CHNnTtXOTk5io6O1vr16xUZGSlJysnJUXZ2tqt/w4YNlZKSogcffFCxsbFq2rSphg0bpieeeKK2TgEAAPgYnwo7kjRhwgRNmDDB47YVK1aUa+vUqVO5W18AAABlfOo2FgAAgLcRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAamf13VjFxcXKzc3V8ePH1bx5czVp0sRbdQEAAHhFlWd2jh49qiVLluiaa65RaGio2rZtqy5duqh58+aKjIzUuHHjtH379pqoFQAAoMqqFHZeeOEFtW3bVq+88or69++vtWvXateuXfrqq6+UlpamWbNmqaSkRAMHDtTgwYP1zTff1FTdAAAAlVKl21ipqanasGGDunbt6nF7z549dd9992nx4sVatmyZNm3apPbt23ulUAAAgOqoUthZs2ZNpfo5nU5NmDChWgUBAAB401ktUP6tkpISbd68WUFBQerSpYtCQ0O9dWgAAIBq81rYuf3229W0aVO98847CgkJUWlpqbp27ap//vOf3noJAACAKvNa2MnKytI777yj9PR07dq1Sy+++KIOHz7srcMDAABUi9c+VDA4OFiSFBgYqKKiIk2ePFmbNm3y1uEBAACqxWszOxMnTtTPP/+s2267TQ888IB69+6t77//3luHBwAAqJYqz+wkJSV5bL/nnnvUpEkTzZgxQ1deeaUyMzP17rvvnnWBAAAAZ6PKMzsPPfSQevToobi4uAr7DB48WPfee+/Z1AUAAOAVVZ7ZefLJJzV06FAdOHDA4/aMjAz17NnzrAsDAADwhiqHnSlTpqhfv34aOnSoSkpK3La9++676tOnj3r37u21AgEAAM5Gtd6NtXTpUh07dkwPPvigq+3ZZ5/V7bffrocfflirV6/2WoEAAABno1rvxgoODtbatWt1+eWXq1u3bkpPT9fq1au1evVqDR061Ns1AgAAVFuVw87YsWMVExOjHj16aOnSpbr99tvVqlUrbdmyRd27d6+BEgEAAKqvymHn66+/1po1a3TkyBH5+/vL4XAoOjpamzdv1rFjx9S9e3c1aNCgJmoFAACosiqHnY8//liS9M033yg9PV07d+5Uenq6Zs2apV9++UX16tVThw4dlJmZ6fViAQAAqqran6Dcvn17tW/fXnfeeaerLSsrSzt27FBGRoZXigMAADhbXvu6CEmKiopSVFSU7rjjDm8eFgAAoNqq9Nbz7OzsKh38xx9/rFJ/AAAAb6tS2Ln88ss1btw4bdu2rcI++fn5euWVVxQdHa21a9eedYEAAABno0q3sfbs2aN58+Zp8ODBCggIUGxsrMLDwxUUFKTDhw8rMzNT//nPfxQbG6tnn31WQ4YMqam6AQAAKqVKMztNmjTRc889p/3792vx4sXq0KGD8vLy9M0330iS7r77bqWnp+uTTz4h6AAAAJ9QrQXKQUFBCg4O1gsvvODtegAAALyqWt+NJUm33HKLJk+erMLCQm/WAwAA4FXVDjtbtmzRhx9+qJiYGH3++ece++zfv18333xztYsDAAA4W9UOO7GxscrIyFDv3r11xRVXaP78+a5tpaWlyszM1OOPP660tDSvFAoAAFAdZ/WhgsHBwXryyScVGBiohx56SKtWrXIFncLCQkVGRioxMdFbtQIAAFRZtWd2lixZovDwcLVo0UIrVqzQ5ZdfLn9/f2VkZGjs2LE6fPiwsrKyNGbMGG/WCwAAUCXVDjuPPfaYbr75ZmVmZurIkSPaunWr0tLS9Pzzz2vp0qWaOnWqjh8/7s1aAQAAqqzaYeeaa67R7Nmz1bFjRzkcDlf71KlTtW3bNu3YsUPdunXTp59+6pVCAQAAqqPaYWfNmjUKCwvzuK1r167avn27brjhBvXt27faxQEAAJwtr37r+W85nU4tWLBA119/fU29BAAAwBlVe2ansgYOHFjTLwEAAFChGg87AAAAtYmwAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYzefCTlJSkqKiohQUFKSYmBht3ry5Uvt98skn8vf3V/fu3Wu2QAAAUKf4VNhJTk7WlClT9OijjyojI0N9+vTRkCFDlJ2dfdr98vPzNXLkSA0YMOAcVQoAAOoKnwo78+fP15gxYzR27Fh17txZCxYsUEREhBYtWnTa/e6//37dddddiouLO0eVAgCAusJnwk5RUZHS09MVHx/v1h4fH6/U1NQK93v11Vf17bffatasWTVdIgAAqIP8a7uAMnl5eTp58qTCwsLc2sPCwpSbm+txn2+++UYzZszQ5s2b5e9fuVMpLCxUYWGh63lBQUH1iwYAAD7PZ2Z2yjgcDrfnxphybZJ08uRJ3XXXXZozZ446dOhQ6eMnJiYqNDTU9YiIiDjrmgEAgO/ymbDTrFkz+fn5lZvFOXjwYLnZHkk6cuSIduzYoYkTJ8rf31/+/v6aO3euPvvsM/n7++ujjz7y+DozZ85Ufn6+67Fv374aOR8AAOAbfOY2VmBgoGJiYpSSkqJbb73V1Z6SkqKbb765XP+QkBB98cUXbm1JSUn66KOP9NZbbykqKsrj6zidTjmdTu8WDwAAfJbPhB1JmjZtmkaMGKHY2FjFxcXp5ZdfVnZ2thISEiSdmpX58ccf9frrr6tevXqKjo522//CCy9UUFBQuXYAAHD+8qmwM3z4cB06dEhz585VTk6OoqOjtX79ekVGRkqScnJyzviZOwAAAL/lMMaY2i6iNhUUFCg0NFT5+fkKCQmp7XK87nhRibo8/qEkKXPuINUP9Kl8CwA4C+fz7/iq/P32mQXKAAAANYGwAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwms+FnaSkJEVFRSkoKEgxMTHavHlzhX3Xrl2rgQMHqnnz5goJCVFcXJw+/PDDc1gtAADwdT4VdpKTkzVlyhQ9+uijysjIUJ8+fTRkyBBlZ2d77P/xxx9r4MCBWr9+vdLT09WvXz/deOONysjIOMeVAwAAX+VTYWf+/PkaM2aMxo4dq86dO2vBggWKiIjQokWLPPZfsGCBHn74YV1++eVq37695s2bp/bt2+u99947x5UDAABf5TNhp6ioSOnp6YqPj3drj4+PV2pqaqWOUVpaqiNHjqhJkyY1USIAAKiD/Gu7gDJ5eXk6efKkwsLC3NrDwsKUm5tbqWM8//zzOnbsmIYNG1Zhn8LCQhUWFrqeFxQUVK9gAABQJ/jMzE4Zh8Ph9twYU67Nk1WrVmn27NlKTk7WhRdeWGG/xMREhYaGuh4RERFnXTMAAPBdPhN2mjVrJj8/v3KzOAcPHiw32/N7ycnJGjNmjP7+97/r2muvPW3fmTNnKj8/3/XYt2/fWdcOAAB8l8+EncDAQMXExCglJcWtPSUlRb17965wv1WrVunee+/VypUrdf3115/xdZxOp0JCQtweAADAXj6zZkeSpk2bphEjRig2NlZxcXF6+eWXlZ2drYSEBEmnZmV+/PFHvf7665JOBZ2RI0fqxRdfVK9evVyzQsHBwQoNDa218wAAAL7Dp8LO8OHDdejQIc2dO1c5OTmKjo7W+vXrFRkZKUnKyclx+8ydJUuWqKSkRA888IAeeOABV/uoUaO0YsWKc10+AADwQT4VdiRpwoQJmjBhgsdtvw8wGzdurPmCAABAneYza3YAAABqAmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALCaz4WdpKQkRUVFKSgoSDExMdq8efNp+2/atEkxMTEKCgrSRRddpMWLF5+jSgEAQF3gU2EnOTlZU6ZM0aOPPqqMjAz16dNHQ4YMUXZ2tsf+WVlZuu6669SnTx9lZGToT3/6kyZNmqS33377HFcOAAB8lX9tF/Bb8+fP15gxYzR27FhJ0oIFC/Thhx9q0aJFSkxMLNd/8eLFatOmjRYsWCBJ6ty5s3bs2KHnnntOQ4cOPZell2OM0Ynik7VagyQdL6r9GgAANc/Xf98HB/jJ4XDUymv7TNgpKipSenq6ZsyY4dYeHx+v1NRUj/ukpaUpPj7erW3QoEFatmyZiouLFRAQUG6fwsJCFRYWup4XFBR4ofryThSfVJfHP6yRYwMA8HuxT/y7tks4rcy5g1Q/sHZih8/cxsrLy9PJkycVFhbm1h4WFqbc3FyP++Tm5nrsX1JSory8PI/7JCYmKjQ01PWIiIjwzgn4uNjIxgoO8KvtMgAAXhQc4KfYyMa1XYbP85mZnTK/n+Iyxpx22stTf0/tZWbOnKlp06a5nhcUFNRI4AkO8FPm3EFeP2511eb0IQCgZjgcDq1JiPOJZRNnUpv/w+0zYadZs2by8/MrN4tz8ODBcrM3ZVq0aOGxv7+/v5o2bepxH6fTKafT6Z2iT8PhcNTadB0A4PzB35sz85nbWIGBgYqJiVFKSopbe0pKinr37u1xn7i4uHL9//Wvfyk2Ntbjeh0AAHD+8ZmwI0nTpk3T0qVLtXz5cu3Zs0dTp05Vdna2EhISJJ26BTVy5EhX/4SEBO3du1fTpk3Tnj17tHz5ci1btkzTp0+vrVMAAAA+xqfmvYYPH65Dhw5p7ty5ysnJUXR0tNavX6/IyEhJUk5Ojttn7kRFRWn9+vWaOnWqFi5cqPDwcL300ku1/rZzAADgOxymbEXveaqgoEChoaHKz89XSEhIbZcDAAAqoSp/v33qNhYAAIC3EXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKv51NdF1IayD5AuKCio5UoAAEBllf3drswXQZz3YefIkSOSpIiIiFquBAAAVNWRI0cUGhp62j7n/XdjlZaWav/+/WrUqJEcDkdtl1MtBQUFioiI0L59+8777/diLE5hHE5hHP6LsTiFcfivuj4WxhgdOXJE4eHhqlfv9KtyzvuZnXr16ql169a1XYZXhISE1MkLtiYwFqcwDqcwDv/FWJzCOPxXXR6LM83olGGBMgAAsBphBwAAWI2wYwGn06lZs2bJ6XTWdim1jrE4hXE4hXH4L8biFMbhv86nsTjvFygDAAC7MbMDAACsRtgBAABWI+wAAACrEXYAAIDVCDt1wOHDhzVixAiFhoYqNDRUI0aM0C+//HLafRwOh8fHs88+6+pzzTXXlNt+55131vDZnJ3qjMW9995b7jx79erl1qewsFAPPvigmjVrpgYNGuimm27SDz/8UINncnaqOg7FxcV65JFH1LVrVzVo0EDh4eEaOXKk9u/f79avLlwTSUlJioqKUlBQkGJiYrR58+bT9t+0aZNiYmIUFBSkiy66SIsXLy7X5+2331aXLl3kdDrVpUsXrVu3rqbK95qqjMPatWs1cOBANW/eXCEhIYqLi9OHH37o1mfFihUef2f8+uuvNX0qZ60qY7Fx40aP5/nll1+69bP9mvD0e9HhcOiSSy5x9anL10Q5Bj5v8ODBJjo62qSmpprU1FQTHR1tbrjhhtPuk5OT4/ZYvny5cTgc5ttvv3X1ufrqq824cePc+v3yyy81fTpnpTpjMWrUKDN48GC38zx06JBbn4SEBNOqVSuTkpJidu7cafr162cuvfRSU1JSUpOnU21VHYdffvnFXHvttSY5Odl8+eWXJi0tzVxxxRUmJibGrZ+vXxOrV682AQEB5pVXXjGZmZlm8uTJpkGDBmbv3r0e+3/33Xemfv36ZvLkySYzM9O88sorJiAgwLz11luuPqmpqcbPz8/MmzfP7Nmzx8ybN8/4+/ubrVu3nqvTqrKqjsPkyZPN008/bbZt22a+/vprM3PmTBMQEGB27tzp6vPqq6+akJCQcr87fF1Vx2LDhg1Gkvnqq6/czvO3/9bPh2vil19+cTv/ffv2mSZNmphZs2a5+tTVa8ITwo6Py8zMNJLc/pGlpaUZSebLL7+s9HFuvvlm079/f7e2q6++2kyePNlbpda46o7FqFGjzM0331zh9l9++cUEBASY1atXu9p+/PFHU69ePfPBBx94pXZv8tY1sW3bNiPJ7Zehr18TPXv2NAkJCW5tnTp1MjNmzPDY/+GHHzadOnVya7v//vtNr169XM+HDRtmBg8e7NZn0KBB5s477/RS1d5X1XHwpEuXLmbOnDmu56+++qoJDQ31VonnTFXHoizsHD58uMJjno/XxLp164zD4TDff/+9q62uXhOecBvLx6WlpSk0NFRXXHGFq61Xr14KDQ1VampqpY5x4MABvf/++xozZky5bW+++aaaNWumSy65RNOnT3d9C7wvOpux2Lhxoy688EJ16NBB48aN08GDB13b0tPTVVxcrPj4eFdbeHi4oqOjKz3G55I3rglJys/Pl8Ph0AUXXODW7qvXRFFRkdLT091+TpIUHx9f4XmnpaWV6z9o0CDt2LFDxcXFp+3jiz97qXrj8HulpaU6cuSImjRp4tZ+9OhRRUZGqnXr1rrhhhuUkZHhtbprwtmMRY8ePdSyZUsNGDBAGzZscNt2Pl4Ty5Yt07XXXqvIyEi39rp2TVTkvP8iUF+Xm5urCy+8sFz7hRdeqNzc3Eod47XXXlOjRo102223ubXffffdioqKUosWLbR7927NnDlTn332mVJSUrxSu7dVdyyGDBmiO+64Q5GRkcrKytKf//xn9e/fX+np6XI6ncrNzVVgYKAaN27stl9YWFilx/hc8sY18euvv2rGjBm666673L4A0Jeviby8PJ08eVJhYWFu7af7OeXm5nrsX1JSory8PLVs2bLCPr74s5eqNw6/9/zzz+vYsWMaNmyYq61Tp05asWKFunbtqoKCAr344ou68sor9dlnn6l9+/ZePQdvqc5YtGzZUi+//LJiYmJUWFiov/3tbxowYIA2btyovn37Sqr4urH1msjJydH//u//auXKlW7tdfGaqAhhp5bMnj1bc+bMOW2f7du3Szq12Pj3jDEe2z1Zvny57r77bgUFBbm1jxs3zvXf0dHRat++vWJjY7Vz505ddtlllTq2N9T0WAwfPtz139HR0YqNjVVkZKTef//9cgGwKsf1tnN1TRQXF+vOO+9UaWmpkpKS3Lb5yjVxOr8/xzOdt6f+v2+v6jF9QXVrXrVqlWbPnq13333XLTT36tXLbeH+lVdeqcsuu0x/+ctf9NJLL3mv8BpQlbHo2LGjOnbs6HoeFxenffv26bnnnnOFnaoe01dUt+YVK1boggsu0C233OLWXpevid8j7NSSiRMnnvFdLm3bttXnn3+uAwcOlNv2008/lUvxnmzevFlfffWVkpOTz9j3sssuU0BAgL755ptz+oftXI1FmZYtWyoyMlLffPONJKlFixYqKirS4cOH3WZ3Dh48qN69e1f6uGfrXIxDcXGxhg0bpqysLH300Uduszqe1NY14UmzZs3k5+dX7v9UDx48WOF5t2jRwmN/f39/NW3a9LR9qnJNnUvVGYcyycnJGjNmjNasWaNrr732tH3r1aunyy+/3PXvxBedzVj8Vq9evfTGG2+4np9P14QxRsuXL9eIESMUGBh42r514ZqoUO0sFUJllS1G/fTTT11tW7durfRi1FGjRpV7x01FvvjiCyPJbNq0qdr11qSzHYsyeXl5xul0mtdee80Y898FysnJya4++/fv9/kFylUdh6KiInPLLbeYSy65xBw8eLBSr+Vr10TPnj3N+PHj3do6d+582gXKnTt3dmtLSEgot0B5yJAhbn0GDx7s84tRqzIOxhizcuVKExQUZNatW1ep1ygtLTWxsbFm9OjRZ1NqjavOWPze0KFDTb9+/VzPz5drwpj/Ltj+4osvzvgadeWa8ISwUwcMHjzYdOvWzaSlpZm0tDTTtWvXcm8z7tixo1m7dq1bW35+vqlfv75ZtGhRuWP+3//9n5kzZ47Zvn27ycrKMu+//77p1KmT6dGjh8++3dqYqo/FkSNHzB//+EeTmppqsrKyzIYNG0xcXJxp1aqVKSgocO2TkJBgWrdubf7973+bnTt3mv79+/v8W8+rMg7FxcXmpptuMq1btza7du1yextpYWGhMaZuXBNlb69dtmyZyczMNFOmTDENGjRwvYNkxowZZsSIEa7+ZW89nzp1qsnMzDTLli0r99bzTz75xPj5+ZmnnnrK7Nmzxzz11FN15m3GlR2HlStXGn9/f7Nw4cIKP1Zg9uzZ5oMPPjDffvutycjIMKNHjzb+/v5uodoXVXUsXnjhBbNu3Trz9ddfm927d5sZM2YYSebtt9929Tkfroky99xzj7niiis8HrOuXhOeEHbqgEOHDpm7777bNGrUyDRq1Mjcfffd5d42Kcm8+uqrbm1LliwxwcHBHj8nJTs72/Tt29c0adLEBAYGmnbt2plJkyaV+/wZX1PVsTh+/LiJj483zZs3NwEBAaZNmzZm1KhRJjs7222fEydOmIkTJ5omTZqY4OBgc8MNN5Tr40uqOg5ZWVlGksfHhg0bjDF155pYuHChiYyMNIGBgeayyy5zm3UaNWqUufrqq936b9y40fTo0cMEBgaatm3begz/a9asMR07djQBAQGmU6dObn/4fFVVxuHqq6/2+LMfNWqUq8+UKVNMmzZtTGBgoGnevLmJj483qamp5/CMqq8qY/H000+bdu3amaCgINO4cWNz1VVXmffff7/cMW2/Jow5NasdHBxsXn75ZY/Hq8vXxO85jPn/q/UAAAAsxOfsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsArDV37lx17dpVDRo0UFhYmMaPH6/i4uLaLgvAOeZf2wUAQE0wxujkyZNasmSJWrVqpczMTI0cOVLdunXT+PHja7s8AOcQXwQK4Lxx1113qXnz5nrxxRdruxQA5xC3sQBYae/evZo4caKio6PVuHFjNWzYUH//+9/VunXr2i4NwDlG2AFgnby8PPXs2VN5eXmaP3++tmzZorS0NPn5+al79+61XR6Ac4w1OwCss379epWUlGjVqlVyOBySpIULF6qoqIiwA5yHCDsArNOkSRMVFBToH//4h7p06aL33ntPiYmJatWqlZo3b17b5QE4x1igDMA6xhiNHz9eK1euVHBwsO655x79+uuv2rt3r/75z3/WdnkAzjHCDgAAsBoLlAEAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACw2v8DENeh55aAu0gAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.plotECC(K,theta,1.2*r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "myect.calculateECT(K,1.2*r)\n",
+    "\n",
+    "myect.plotECT()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('A', 'B'): 5.551115123125783e-17,\n",
+       " ('A', 'D'): -5.551115123125783e-17,\n",
+       " ('B', 'C'): 0.7071067811865475,\n",
+       " ('C', 'D'): 0.7071067811865475}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "K.g_omega_edges(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "#....... check on the list of sorted edges, something is wrong"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "out = myect.calculateECC(K, theta, r, return_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.70710678 -0.68920534 -0.67130391 -0.65340247 -0.63550103 -0.61759959\n",
+      " -0.59969816 -0.58179672 -0.56389528 -0.54599384 -0.52809241 -0.51019097\n",
+      " -0.49228953 -0.47438809 -0.45648666 -0.43858522 -0.42068378 -0.40278234\n",
+      " -0.38488091 -0.36697947 -0.34907803 -0.33117659 -0.31327516 -0.29537372\n",
+      " -0.27747228 -0.25957084 -0.24166941 -0.22376797 -0.20586653 -0.18796509\n",
+      " -0.17006366 -0.15216222 -0.13426078 -0.11635934 -0.09845791 -0.08055647\n",
+      " -0.06265503 -0.04475359 -0.02685216 -0.00895072  0.00895072  0.02685216\n",
+      "  0.04475359  0.06265503  0.08055647  0.09845791  0.11635934  0.13426078\n",
+      "  0.15216222  0.17006366  0.18796509  0.20586653  0.22376797  0.24166941\n",
+      "  0.25957084  0.27747228  0.29537372  0.31327516  0.33117659  0.34907803\n",
+      "  0.36697947  0.38488091  0.40278234  0.42068378  0.43858522  0.45648666\n",
+      "  0.47438809  0.49228953  0.51019097  0.52809241  0.54599384  0.56389528\n",
+      "  0.58179672  0.59969816  0.61759959  0.63550103  0.65340247  0.67130391\n",
+      "  0.68920534  0.70710678]\n",
+      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 4]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x135b2aa70>]"
+      ]
+     },
+     "execution_count": 19,
+     "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": [
+    "r, r_thresh = myect.get_radius_and_thresh(K, r)\n",
+    "print(r_thresh)\n",
+    "print(out[2])\n",
+    "plt.plot(r_thresh,out[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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,draw_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A -0.71\n",
+      "B 0.0\n",
+      "C 0.71\n",
+      "D -0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "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": [
+    "funcdict = K.g_omega(theta)\n",
+    "for key in funcdict:\n",
+    "    print(key, round(funcdict[key],2))\n",
+    "K.plot(color_nodes_theta=theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_below_threshold(func_list, thresh):\n",
+    "    \"\"\" \n",
+    "    Returns the number of entries in func_list that are below the threshold thresh. \n",
+    "    Warning: func_list must be sorted in ascending order.\n",
+    "\n",
+    "    Parameters\n",
+    "        func_list (list): sorted list of function values\n",
+    "        thresh (float): threshold value\n",
+    "\n",
+    "    Returns\n",
+    "        int \n",
+    "    \"\"\"\n",
+    "    # If the list is empty, return 0\n",
+    "    if len(func_list) == 0:\n",
+    "        return 0\n",
+    "    else:\n",
+    "        func_max = func_list[-1]\n",
+    "        if thresh < func_max:\n",
+    "            return np.argmin(func_list < thresh)\n",
+    "        else:\n",
+    "            return len(func_list)\n",
+    "# --"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G = K\n",
+    "r,r_threshes = myect.get_radius_and_thresh(G, r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_list, g = G.sort_vertices(theta, return_g=True)\n",
+    "g_list = [g[v] for v in v_list]\n",
+    "\n",
+    "vertex_count = np.array([num_below_threshold(\n",
+    "    g_list, thresh) for thresh in r_threshes])\n",
+    "# print(vertex_count)\n",
+    "\n",
+    "e_list, g_e = G.sort_edges(np.pi/2, return_g=True)\n",
+    "g_e_list = [g_e[e] for e in e_list]\n",
+    "edge_count = np.array([num_below_threshold(\n",
+    "    g_e_list, thresh) for thresh in r_threshes])\n",
+    "# print(edge_count)\n",
+    "\n",
+    "if type(G) == EmbeddedCW:\n",
+    "    f_list, g_f = G.sort_faces(theta, return_g=True)\n",
+    "    g_f_list = [g_f[f] for f in f_list]\n",
+    "    face_count = np.array([num_below_threshold(\n",
+    "        g_f_list, thresh) for thresh in r_threshes])\n",
+    "    # print(face_count)\n",
+    "else:\n",
+    "    face_count = np.zeros_like(vertex_count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.49999999999999994, 0.49999999999999994, 0.5, 0.5]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g_e_list"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/docs/searchindex.js b/docs/searchindex.js
index d8fac47..0bc9bcb 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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"embed_graph.EmbeddedGraph.set_coordinates"]], "set_mean_centered_coordinates() (embed_graph.embeddedgraph method)": [[4, "embed_graph.EmbeddedGraph.set_mean_centered_coordinates"]], "sort_edges() (embed_graph.embeddedgraph method)": [[4, "embed_graph.EmbeddedGraph.sort_edges"]], "sort_vertices() (embed_graph.embeddedgraph method)": [[4, "embed_graph.EmbeddedGraph.sort_vertices"]]}})
\ No newline at end of file
diff --git a/ect/ect_on_graphs/ect_graph.py b/ect/ect_on_graphs/ect_graph.py
index 9cb94ce..1e63ea2 100644
--- a/ect/ect_on_graphs/ect_graph.py
+++ b/ect/ect_on_graphs/ect_graph.py
@@ -94,7 +94,8 @@ def get_radius_and_thresh(self, G, bound_radius):
         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.')
+                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)
 
@@ -112,7 +113,7 @@ def get_SECT(self):
         """
         return self.SECT_matrix
 
-    def calculateECC(self, G, theta, bound_radius=None):
+    def calculateECC(self, G, theta, bound_radius=None, return_counts=False):
         """
         Function to compute the Euler Characteristic of an `EmbeddedGraph`, that is, a graph with coordinates for each vertex.
 
@@ -123,6 +124,8 @@ def calculateECC(self, G, theta, bound_radius=None):
                 The angle (in radians) to use for the direction function when computing the Euler Characteristic Curve.
             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.
+            return_counts (bool):
+                Whether to return the counts of vertices, edges, and faces below the threshold. Default is False.
         """
 
         r, r_threshes = self.get_radius_and_thresh(G, bound_radius)
@@ -158,7 +161,7 @@ def num_below_threshold(func_list, thresh):
             g_list, thresh) for thresh in r_threshes])
         # print(vertex_count)
 
-        e_list, g_e = G.sort_edges(np.pi/2, return_g=True)
+        e_list, g_e = G.sort_edges(theta, return_g=True)
         g_e_list = [g_e[e] for e in e_list]
         edge_count = np.array([num_below_threshold(
             g_e_list, thresh) for thresh in r_threshes])
@@ -176,7 +179,10 @@ def num_below_threshold(func_list, thresh):
         # print(vertex_count - edge_count)
         ecc = vertex_count - edge_count + face_count
 
-        return ecc
+        if return_counts:
+            return ecc, vertex_count, edge_count, face_count
+        else:
+            return ecc
 
     def calculateECT(self, graph, bound_radius=None, compute_SECT=True):
         """
@@ -235,7 +241,7 @@ def calculateSECT(self):
 
         return M_SECT
 
-    def plotECC(self, graph, theta, bound_radius=None):
+    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.
 
@@ -249,13 +255,23 @@ def plotECC(self, graph, theta, bound_radius=None):
         """
 
         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())
 
-        ECC = self.calculateECC(graph, theta, r)
+        plt.step(r_threshes, ECC, label='ECC')
 
-        if self.threshes is None:
-            self.set_bounding_radius(graph.get_bounding_radius())
+        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()
 
-        plt.step(r_threshes, ECC)
         theta_round = str(np.round(theta, 2))
         plt.title(r'ECC for $\omega = ' + theta_round + '$')
         plt.xlabel('$a$')