quantum_force.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ndex2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import math\n",
    "import json\n",
    "import igraph\n",
    "import networkx as nx\n",
    "from random import randint\n",
    "from cyjupyter import Cytoscape\n",
    "from timeit import default_timer as timer\n",
    "from operator import itemgetter\n",
    "\n",
    "import io\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## get test networks from NDEx in as edge tables in csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is used to prompt users for NDEx credentials in this notebook\n",
    "#import getpass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Watch out for typos and be sure to hit enter in the field to set the value!!!\n",
    "#NDEXUSER = getpass.getpass()\n",
    "NDEXUSER = \"dexterpratt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Watch out for typos and be sure to hit enter in the field to set the value!!!\n",
    "# NDEXPASSWORD = getpass.getpass()\n",
    "NDEXPASSWORD = \"cytoscaperules\"\n",
    "SERVER = \"http://www.ndexbio.org\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: catch server errors, network not found, be graceful\n",
    "def get_network_as_edge_table(uuid, server=SERVER):\n",
    "    edge_url = server+'/v3/networks/'+uuid+'/export?type=edge'\n",
    "    #print(edge_url)\n",
    "    r = requests.get(edge_url)\n",
    "    df = pd.read_csv(io.StringIO(r.text),sep='\\t')\n",
    "    return df[[\"source_id\", \"target_id\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get an edge table from the NDEx test server or from a CX file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>target_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>56</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>59</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>72</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     source_id  target_id\n",
       "0            7          1\n",
       "1           16         17\n",
       "2           16          1\n",
       "3           17          1\n",
       "4           18         16\n",
       "..         ...        ...\n",
       "141         56         30\n",
       "142          1         56\n",
       "143         59         61\n",
       "144         61         58\n",
       "145         72         61\n",
       "\n",
       "[146 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_uuid = \"d1258a93-c81c-11eb-9060-0660b7976219\"\n",
    "edges = get_network_as_edge_table(test_uuid, \"https://test.ndexbio.org\")\n",
    "edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy Fields\n",
    " - An energy field is a square numpy array with an odd number dimension.\n",
    " - We define it by its \"radius\", r, such that the field function is symetric around a center coordinate of (r+1,r+1)\n",
    " - The energy in a repulsion field falls off as the square of the distance from the center\n",
    " - The energy in an attraction field increases linearly with distance from the center\n",
    " - A \"gameboard\" is the field in which the network can be arranged, initialized to zeros.\n",
    " - The area of the gameboard is based on the number of nodes times a scaling factor, \"sparsity\".\n",
    " - Low values of sparsity lead to layouts in which the network shape is strongly influenced by the boundaries of the gameboard. High values lead to greater memory consumption and computation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "def repulsion_field(radius, scale=1, dtype=np.int32, center_spike=False):\n",
    "    dimension = (2*radius)+1\n",
    "    ef = np.zeros((dimension, dimension), dtype=dtype)\n",
    "    energy = int(scale * radius)\n",
    "    center_energy = 1000 if center_spike == True else energy\n",
    "    for x in range(0, dimension):\n",
    "        dx = abs(radius - x)\n",
    "        for y in range(0, dimension):\n",
    "            dy = abs(radius - y)\n",
    "            distance = math.sqrt(dx**2 + dy**2)\n",
    "            #energy = 1000 if distance == 0 else int(scale * (abs(distance - radius)**2))\n",
    "            ef[x,y] = center_energy if distance == 0 else int(energy / distance**2) + int(0.1 * (energy / distance))\n",
    "    return ef\n",
    "\n",
    "def attraction_field(radius, scale, dtype=np.int32):\n",
    "    dimension = (2*radius)+1\n",
    "    ef = np.zeros((dimension, dimension), dtype=dtype)\n",
    "    energy = int(scale * radius)\n",
    "    slope = energy/radius\n",
    "    for x in range(0, dimension):\n",
    "        dx = abs(radius - x)\n",
    "        for y in range(0, dimension):\n",
    "            dy = abs(radius - y)\n",
    "            distance = math.sqrt(dx**2 + dy**2)\n",
    "            ef[x,y] = -1 * energy if distance == 0 else min( 0, int((slope * distance) - energy))\n",
    "    return ef\n",
    "\n",
    "def make_gameboard (node_count, sparsity=30, dtype=np.int32):\n",
    "    radius = round(math.sqrt(node_count * sparsity))\n",
    "    dimension = (2*radius)+1\n",
    "    return np.zeros((dimension, dimension), dtype=dtype)\n",
    "\n",
    "def plot_field(field):\n",
    "    center_x = int(field.shape[0]/2)\n",
    "    plt.plot(field[center_x])\n",
    "    plt.show()\n",
    "    \n",
    "def show_field(field):\n",
    "    sns.heatmap(field)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JwEURsTMi7gQ2AavaHauoj/gC4OfAV4G3Sno98KaI2Am8uE0BvXiomdUipctB0hpgTdNTn8rrr7Lx64CTgUfI1vMEWA5c05RsIn9uVkUV8bMi4vX5429IWgv8i6QTyhbUzKyXUoavNTcaW5F0JbB/i11rI+LSiFgLrJV0BvAu4H9Ayz6Ptg3Soop4oaSRiGjkhV4naQL4IfC0glgzs56b7uDotYg4tmTSLwHfJquIJ4ADm/atADa3Cy6qiL8JvBy4sqlg/yzpAeDjJQuYZGS/lTQe2ZoWNLY4OR8tHU+OGd3noaT0y44Ott86mRTz6K/TZ4ktm6pyzbXttYMneRaLuW80rZdpssKMtyrr76XOkgNYNpU2yGnp/J3JeYzvvyM5ZnSfseSY5HO5wueFscGZWderCR2SDo2In+c/ngDcmj++DPiSpI8By4BDgWvbHavoNpjvn+X570n626RSl5RcCfex1Eq4n6VWwmZ16eHMurMkPSfP8m7gHQARsVHSxcDNwBRwSkS0/cs/lwkdZ+LFQ82sz/RqKbqm62et9q0D1pU9ltesM7OhMoz3mvCadWY2UAZxirPXrDOzoTJ0N4b3mnVmNmiGsWvCzGyguCI2M6vZIA60dEVsZkNl6PqIzcwGzTCOmui5kSX7JsfE+D7pGY0/mBwysmLvpPRjj92fnMf+O35VnGim+9JDxnalTT9+xnT6qbJjJG0aNcCiRvoXyyoLe6ZOWd5/efr7MnZg+utPPccANL40MX3656XK57IujQHsnOi7itjMbC58sc7MrGaD1x52RWxmQ8YtYjOzmk1p8NrEyTd/lVTYa+8168ysLr1as66Tiu6+9oyZTwHXSnoBoIj4Zas4r1lnZnUZxq6Jh8hueNxsOXAD2R+UQ7pRKDOzqgZx+FpR18T7gduAEyJiZUSsBCbyx66EzazvDF3XRER8VNJFwLmS7iVbGK+fym9m9gTD2DVBREwAJ0n6T8AVQIWVB7tL+65MjonJx5JjRibTFoMc3ZG+4OSetOx2b2v+om3p+WxZlJT+IODxnWmDbHbuSp9ZtnAsfYLqgoVTyTGpC3tWmSU3etDMSyzFRg5otXJ7gWXPTEpe5fMySKYHsK1YetRERHwTeBlwLICkt3SrUNZ/Uiths7o0ErZ+kTR8LSJ2RMSG/Mczu1AeM7M5iYR//cKLh5rZUOmnlm5ZXjzUzIbKIA5f8+KhZjZUBq8a9uKhZjZkpgawKvalcDMbKv10Ea4sV8RmNlSG8WKdmdlAcYvYzKxmbhHXpNLChhWmeTYmtyelT77ZMzC6KG1RT4DFezyUHLNgr7QpvtPb01sZ0zvS1zWftyg9n3mL0/MZ3WcsKX2VRT2rTVc+KD2fxHN5kBYCrWI63CI2M6vVMI4jNjMbKO4jNjOrmfuIzcxqNohdE1UWD92rRBovHmpmtej13dckvU9SSNq76bkzJG2SdJuk44qOUXT3tbOAj0bEQ5KOAi4GGpLmAydHxNWt4rx4qJnVpZejJiQdCPwRcE/Tc4cDq4EjgGXAlZIOi4hZVz0oahG/OiJ2j436CPCGiHh2nvE5cyi/mVlXNIjSWwecS7a2Z/PBTgQuioidEXEnsAlY1e4gRRXxfEm7W82LIuI6gIi4HUgf8Gpm1mUpK3Q0d6Pm25qy+Ug6AbgvItbP2LUcuLfp54n8uVkVXaz7e+A7eRfF9yT9HfA14BjgSbfGNDOrW0rfb3M3aiuSrgRazcxZC/xX4BWtwloWq42i22B+XNLPgHcCh+XpDwO+AXyoXWy/G9mv+wsoNsbS11kdGUtb1BNAS8fT83k4ccHRCguhRoUYVZhZSIWY1N+Zxpcm55G6qCekz5KD3pzLg6SToyYi4thWz0t6HrASWC8JYAVwg6RVZC3gA5uSrwA2t8unzCrOVwFXtSjIW4ALiuLNzHopenCxLiJ+Bvxmrriku4Cj8oENlwFfkvQxsot1hwLXtjveXMYRn4krYjPrM9M1jyOOiI2SLgZuBqaAU9qNmAAvHmpmQ6aOCR0RcfCMn9cB68rGe/FQMxsqveia6DQvHmpmQ2UQpzh78VAzGyq++5qZWc18Y3gzs5oNXdeEmdmgcUU85JJnMI3tkZxHVIhh24PJIUpcfw+A7Y+lpZ9MWxcPgAozC1lc4XeWOOtR4/skZ6Eqs+SGfD25XhjGURNmmdRK2KwmbhGbmdXMoybMzGo2HYO3ap0rYjMbKu4jNjOr2SD2EScvHlqGFw81s7r0evHQTii6+9pRZGvV3QecAZxPtvbS7cCaiPhpqzgvHmpmdWkMYNdEUYv4PODDwLfJ7rb2yYhYApye7zMz6yuD2CIuXDw0Ir4bERcCERGXkD34ATDW9dKZmSWajkbprV8UXayblPQKYAkQkl4bEd+Q9FKg7R3nzczqMIhdE0UV8TvIuiYaZDeIf6ekfyLrM/6L7hZt8FWarlohpvHI1vR8JtNmysXOCjPrqsQsTJ+urAoxqdPPPfV4cPRTl0NZbbsmImJ9RBwXEa+MiFsj4tSIGI+II4Dn9KiMZmalNSJKb/1iLsPXzuxYKczMOmQQL9Z58VAzGyrT7RdM7ktePNTMhsowTnH24qFmNlAGcYqzFw81s6EyjC1iM7OB0k+jIcpyRWxmQ6WfRkOU5YrYzIZKP01dLssV8RCoNoOv8+Uw6wfuIzYzq5n7iM3MauYWsZlZzYZuHLGZ2aAZxBZx25v+SFoi6SxJt0r6t3y7JX9uvFeFNDMraxBvDF9097WLye4zcXRE7BURewEvy5/7ymxBXjzUzOoyiLfBVLtmvKTbIqLlfYfb7WvmxUPNrKz5ex+iuR5jbOyg0nXO5OQ9c86vE4paxHdLer+k39zyUtJ+kk4D7u1u0czM0vXqfsSSPijpPkk35turmvadIWmTpNskHVd0rKKLdW8gW7H56rwyDuAB4DLgT+bwGszMuqLHF+vOjYiPNj8h6XBgNXAEsAy4UtJhEbPfKLno7msPS7oAuAK4JiJ+3ZTZ8cD35vACzMw6rg/6fk8ELoqIncCdkjYBq4AfzRoREbNuwLuB24BvAHcBJzbtu6FdbJkNWNPN9P0c06/l8mvpz3I91V9LtzZgDXB901a6XMAH83rxJuB8YGn+/CeAP2tK91ngP7c9VkFGPyO7MTzAwXlBT81//mkHfgnXdzN9P8f0a7n8WvqzXE/111LXBlwJbGixnUi2gtE8smtt64Dz85i/b1ERv75dPkV9xPMi746IiLskHQ1cIumZZMslmZkNrYg4tkw6SZ8mW9EIYAI4sGn3CmBzu/iiURNbJB3ZVKhfA68B9gaeV6aAZmbDSNIBTT++jqylDNlghtWSFkpaCRwKXNvuWEUt4pOBqeYnImIKOFnSJ5NK3dqnupy+n2P6tVxVYvq1XFVi+rVcVWL6tVxVY/rNh/OGapD1Ff8lQERslHQxcDNZ/XlKtBkxAQUTOszMrPuKuibMzKzLXBGbmdWslopY0vH51L9Nkk4vkf58SVslbShK2xRzoKR/ze8Wt1HSqQXpxyRdK2l9nv7MhLzmSfqppG8VpwZJd0n6WT4t8vqSMeOSLsnvhHeLpP/QJu1zmqZd3ijpV5LeUyKP9+avfYOkCyWNlYg5NU+/cbY8Wr1/kp4h6QpJP8//X1oi5qQ8n4ako0qk/0j++7pJ0tdn3jFwlpgP5elvlHS5pGVFMU373icpJO1dIp9202Nb5iHpr/LPzUZJHy6Rx5ebjn+XpBtLxBwp6Zrd56akVSVi/p2kH+Xn9DclPb1pX8vPYdH7/5RTw7i8ecAvgEOABcB64PCCmJcALwQ2JORzAPDC/PGewO3t8iEbjrd7zPR84MfAi0vm9V+ALwHfKpn+LmDvxN/bPwNvzx8vAMYTft9bgGcWpFsO3Aksyn++GPjzgpjnkl0pXkx24fdK4NAy7x/wYeD0/PHpwNklYn4XeA5wFXBUifSvAEbzx2eXzOPpTY/fDfxjmXORbLjS94G7Z763s+TzQeB9Zc93srseXgkszH/eN+UzApwDfKBEPpcDr8wfvwq4qkTMdcBL88dvBT7UtK/l57Do/X+qbXW0iFcBmyLijoh4HLiIbHD0rCLih8AvUzKJiPsj4ob88aPALWSVzWzpI347hXt+vhVeyZS0Ang18JmU8qXIWxgvIRsYTkQ8HhHbSoYfA/wiIu4ukXYUWCRplKxybTv2kaxivCYitkc2muZqsmE8TzDL+3ci2R8X8v9fWxQTEbdExG2tCjJL+svzcgFcQzaesyjmV00/7sGMc6DNuXgu8P6Z6QtiWpol/TuBsyKbNktEbC2bhySR3RvmwhIxAexu0S5hxjkwS8xzgB/mj68AXt+UfrbPYdv3/6mmjop4OU+8c9sEbSrITpB0MPACslZuu3Tz8q9vW4ErIqJt+tzfkX0AU+4yHcDlkn4iaU2J9IcADwIX5F0gn5G0R8m8VjPjA9iyQBH3AR8F7gHuBx6JiMsLwjYAL5G0l6TFZC2oAwtidtsvIu7P874fqLAUdZK3At8tk1DSOkn3An8KfKBE+hOA+yJifWKZ3pV3g5xf4qv5YcAfSvqxpKsl/fuEfP4QeCAifl4i7XuAj+Sv/6PAGSViNgAn5I9PYpZzYMbnsNfvf1+royJuNSOva2PoJD0N+CrwnhmtnScXImI6Io4kazmtkvTcgmO/BtgaET9JLNbvR8QLgVcCp0h6SUH6UbKvg/8QES8AHiP7OteWpAVkH5BZb+LflHYpWStlJdkdo/aQ9GftYiLiFrKv/FeQ3QBqPTPGnfcDSWvJyvXFMukjYm1EHJinf1fBsRcDaylRYc/wD8CzgCPJ/vCdU5B+FFgKvBj4a+DivKVbxhsp8cc4907gvfnrfy/5t7ACbyU7j39C1v3w+MwEKZ/Dp6I6KuLk6X9VSZpP9uZ/MSK+VjYu/9p/FXB8QdLfB06QdBdZF8vLJX2hxPE35/9vBb5O1l3TzgQw0dRCv4SsYi7ySrKbMz1QIu2xwJ0R8WBE7AK+BvzHoqCI+GxEvDAiXkL2lbVMqwvgAeUzk/L/txakr0TSm8lmg/5pRKT+wf8STV+zZ/Essj9e6/PzYAVwg6T92wVFxAP5H/4G8GnKnQNfy7vQriX7BrZ3QQx5N9MfA18uSpt7M9l7D9kf8KJyERG3RsQrIuJFZBX+L2aUodXnsCfv/6CooyK+DjhU0sq8xbaabEpgR+Wthc8Ct0TEx0qk32f3VXVJi8gqplvbxUTEGRGxIiIOJnsd/xIRbVuRkvaQtOfux2QXlNqOBomILcC9knaviHIM2aydIiktoXuAF0tanP/ujiHrz2tL0r75/weRfeDL5ncZ2Yee/P9LS8aVpuxWracBJ0TE9pIxhzb9eALF58DPImLfiDg4Pw8myC5ObSnIZ7bpsbP5BvDyPPYwsgu2DxXEQH4eR8REibSQNYpemj9+OSX+sDadAyPAfwP+sWnfbJ/Drr//A6WOK4RkfYm3k/3lXFsi/YVkX992kZ3obysR8wdkXR43ATfm26vapH8+8NM8/QZmXGEukd/RlBg1Qdbfuz7fNpZ5/XnckWR3v7uJ7EO5tCD9YuDfgCUJr+FMsopnA/B58iv0BTH/m+yPwnrgmLLvH7AX8AOyD/oPgGeUiHld/ngn2QIF3y9Iv4nsesTu93/mCIhWMV/NX/9NwDeB5SnnIi1GxMySz+fJ7m54E1mldEBB+gXAF/Ky3QC8vEy5gH8C3pHwvvwB8JP8/fwx8KISMaeSfZ5vB84in7Hb7nNY9P4/1TZPcTYzq5ln1pmZ1cwVsZlZzVwRm5nVzBWxmVnNXBGbmdXMFbGZWc1cEZuZ1ez/A336N/jGeH/kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_field(repulsion_field(10, scale=10))\n",
    "show_field(repulsion_field(10, scale=10))\n",
    "plot_field(attraction_field(10, scale=5))\n",
    "show_field(attraction_field(10, scale=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Node Dictionaries\n",
    " - node_dict objects serve both as\n",
    " - an adjacency list form of the network \n",
    " - the structure holding the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_node_dict_from_edge_array(edge_array, sparsity=30, dtype=np.int32, initialize_coordinates=True):\n",
    "    # for each edge, add the nodes to node_dict, updating the adjancy set\n",
    "    node_dict = {}\n",
    "    for edge in edge_array:\n",
    "        # print(edge[0], \" \", edge[1])\n",
    "        if edge[0] not in node_dict:\n",
    "            node_dict[edge[0]] = {\"adj\":set()}\n",
    "        node_dict[edge[0]][\"adj\"].add(edge[1])\n",
    "        node_dict[edge[0]][\"degree\"] = len(node_dict[edge[0]][\"adj\"])\n",
    "        if edge[1] not in node_dict:\n",
    "            node_dict[edge[1]] = {\"adj\":set()}\n",
    "        node_dict[edge[1]][\"adj\"].add(edge[0])\n",
    "        node_dict[edge[1]][\"degree\"] = len(node_dict[edge[1]][\"adj\"])\n",
    "            \n",
    "    gameboard = make_gameboard(len(node_dict), sparsity=sparsity, dtype=dtype)  \n",
    "    \n",
    "    # randomly place the nodes in the center of the board\n",
    "    dimension = gameboard.shape[0]\n",
    "    temp_board = np.zeros((dimension, dimension))\n",
    "    center_left = round(dimension/4)\n",
    "    center_right = dimension - center_left\n",
    "    if initialize_coordinates:\n",
    "        for node_id, node in node_dict.items():\n",
    "            placed = False\n",
    "            while not placed:\n",
    "                x = randint(center_left, center_right)\n",
    "                y = randint(center_left, center_right)\n",
    "                if temp_board[x, y] == 0:\n",
    "                    temp_board[x, y] = 1\n",
    "                    placed = True\n",
    "            node[\"x\"] = x\n",
    "            node[\"y\"] = y\n",
    "\n",
    "    return node_dict, gameboard     \n",
    "\n",
    "def make_node_dict_from_cx (cx_network, sparsity=30, dtype=np.int32, initialize_coordinates=True):\n",
    "    edge_array = np.zeros((len(cx_network.get_edges()), 2), dtype=dtype)\n",
    "    print(\"edge array: \", edge_array.shape)\n",
    "    i = 0\n",
    "    for edge_id, edge in cx_network.get_edges():\n",
    "        edge_array[i, 0] = edge[\"s\"]\n",
    "        edge_array[i, 1] = edge[\"t\"]\n",
    "        i += 1\n",
    "    node_dict, gameboard = make_node_dict_from_edge_array(edge_array,\n",
    "                                                          sparsity=sparsity,\n",
    "                                                          dtype=dtype,\n",
    "                                                          initialize_coordinates=initialize_coordinates)\n",
    "    return node_dict, gameboard, edge_array\n",
    "\n",
    "def make_node_dict_from_cx_old (cx_network, sparsity=30):\n",
    "    gameboard = make_gameboard(len(cx_network.get_nodes()), sparsity=sparsity)\n",
    "    dimension = gameboard.shape[0]\n",
    "    node_dict = {}\n",
    "    nodes = cx_network.get_nodes()\n",
    "    for node_id, node in nodes:\n",
    "        #print(node)\n",
    "        placed = False\n",
    "        while not placed:\n",
    "            x = randint(0,dimension-1)\n",
    "            y = randint(0,dimension-1)\n",
    "            if gameboard[x, y] == 0:\n",
    "                gameboard[x, y] = 1\n",
    "                placed = True\n",
    "        node_dict[node_id]= {\n",
    "            'x' : x,\n",
    "            'y' : y,\n",
    "            'adj' : set()\n",
    "       }\n",
    "    for edge_id, edge in cx_network.get_edges():\n",
    "        #print(edge)\n",
    "        node_dict[edge[\"s\"]]['adj'].add(edge['t'])\n",
    "        node_dict[edge[\"t\"]]['adj'].add(edge['s'])\n",
    "    return node_dict, gameboard\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'foo': {'adj': {'bar', 'baz', 'fred'}, 'degree': 3, 'x': 8, 'y': 11},\n",
       " 'bar': {'adj': {'baz', 'foo'}, 'degree': 2, 'x': 18, 'y': 20},\n",
       " 'baz': {'adj': {'bar', 'foo'}, 'degree': 2, 'x': 11, 'y': 16},\n",
       " 'fred': {'adj': {'alice', 'foo', 'jane'}, 'degree': 3, 'x': 9, 'y': 18},\n",
       " 'jane': {'adj': {'fred'}, 'degree': 1, 'x': 12, 'y': 8},\n",
       " 'alice': {'adj': {'fred'}, 'degree': 1, 'x': 20, 'y': 20}}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_edges_1 = [\n",
    "    [\"foo\", \"bar\"], \n",
    "    [\"foo\", \"baz\"],\n",
    "    [\"bar\", \"baz\"],\n",
    "    [\"foo\", \"fred\"],\n",
    "    [\"fred\", \"jane\"],\n",
    "    [\"fred\", \"alice\"]\n",
    "]\n",
    "\n",
    "test_node_dict_1, test_gameboard_1 = make_node_dict_from_edge_array(test_edges_1)\n",
    "for node_id, node in test_node_dict_1.items():\n",
    "    test_gameboard_1[node[\"x\"], node[\"y\"]]=10\n",
    "\n",
    "show_field(test_gameboard_1)\n",
    "test_node_dict_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding fields:\n",
    "# The two fields are combined at an x-y offset, destructively modifying the target field\n",
    "# It is neccessary to crop the added field to fit within the target field\n",
    "def add_field(source_field, target_field, x, y, remove=False, show_node_dict=False):\n",
    "    if show_node_dict: print(\"node_dict\", x,y)\n",
    "    target_x_max = target_field.shape[0] - 1\n",
    "    target_y_max = target_field.shape[1] - 1\n",
    "    source_x_max = source_field.shape[0] - 1\n",
    "    source_y_max = source_field.shape[1] - 1\n",
    "    #\n",
    "    #   0.0 1.0 2.0\n",
    "    #   0.1 1.1 2.1\n",
    "    #   0.2 1.2 2.2\n",
    "    #\n",
    "    x_offset = int(source_field.shape[0]/2)\n",
    "    y_offset = int(source_field.shape[1]/2)\n",
    "    #print(\"gb shape\", target_field.shape)\n",
    "    #print(\"rf offset\", x_offset, y_offset)\n",
    "    target_top_x = x - x_offset\n",
    "    target_top_y = y - y_offset\n",
    "    target_bottom_x = x + x_offset\n",
    "    target_bottom_y = y + y_offset\n",
    "    #print(\"gb target\", target_top_x,target_top_y,target_bottom_x,target_bottom_y)\n",
    "    source_top_x = 0\n",
    "    source_top_y = 0\n",
    "    source_bottom_x = source_x_max\n",
    "    source_bottom_y = source_y_max\n",
    "    #print(\"rf source\", source_top_x,source_top_y,source_bottom_x,source_bottom_y)\n",
    "    # trim if source_field goes off board negative\n",
    "    if target_top_x < 0:\n",
    "        #print(\"negative x\")\n",
    "        source_top_x = -target_top_x\n",
    "        target_top_x = 0        \n",
    "    if target_top_y < 0:\n",
    "        #print(\"negative y\")\n",
    "        source_top_y = -target_top_y\n",
    "        target_top_y = 0\n",
    "    # trim if source_field goes off board positive\n",
    "    if target_bottom_x > target_x_max:\n",
    "        dx = target_bottom_x - target_x_max\n",
    "        #print(\"positive x\", dx)\n",
    "        target_bottom_x = target_x_max\n",
    "        source_bottom_x = source_x_max - dx\n",
    "    if target_bottom_y > target_y_max:\n",
    "        dy = target_bottom_y - target_y_max\n",
    "        #print(\"positive y\", dy)\n",
    "        target_bottom_y = target_y_max\n",
    "        source_bottom_y = source_y_max - dy\n",
    "    #print(\"adj gb target\", target_top_x,target_top_y,target_bottom_x,target_bottom_y) \n",
    "    #print(\"adj rf source\", source_top_x,source_top_y,source_bottom_x,source_bottom_y)\n",
    "    #gameboard[target_top_x, target_top_y] = 1\n",
    "    #gameboard[target_bottom_x, target_bottom_y] = 1\n",
    "    # add the source_field to the target_field\n",
    "    #print(target_field[target_top_x:target_bottom_x+1, target_top_y:target_bottom_y+1])\n",
    "    #print(source_field[source_top_x:source_bottom_x+1, source_top_y:source_bottom_y+1])\n",
    "    if remove:\n",
    "        target_field[target_top_x:target_bottom_x+1, target_top_y:target_bottom_y+1] -= source_field[source_top_x:source_bottom_x+1, source_top_y:source_bottom_y+1]\n",
    "    else:\n",
    "        target_field[target_top_x:target_bottom_x+1, target_top_y:target_bottom_y+1] += source_field[source_top_x:source_bottom_x+1, source_top_y:source_bottom_y+1]\n",
    "\n",
    "# wrapper for readability\n",
    "def subtract_field(source_field, target_field, x, y, show_node_dict=False):\n",
    "    add_field(source_field, target_field, x, y, remove=True, show_node_dict=show_node_dict)\n",
    "\n",
    "# Layout a NiceCX network to create a node_dict\n",
    "def layout_cx_network(cx_network, rounds=1, sparsity=30, r_radius=4, \n",
    "                      a_radius=10, r_scale=10, a_scale=5, center_attractor_scale=0.01,\n",
    "                     initialize_coordinates=True, dtype=np.int32):\n",
    "    start = timer()\n",
    "    node_dict, gameboard, edge_array = make_node_dict_from_cx(cx_network, sparsity=sparsity,\n",
    "                                                 initialize_coordinates=initialize_coordinates, dtype=dtype)\n",
    "    end = timer()\n",
    "    print(\"make node dict time = \", end - start)\n",
    "    return layout_node_dict(node_dict, gameboard, rounds=rounds, \n",
    "                            r_radius=r_radius, a_radius=a_radius, \n",
    "                            r_scale=r_scale, a_scale=a_scale, \n",
    "                            center_attractor_scale=center_attractor_scale), gameboard, edge_array\n",
    "\n",
    "# layout a node_dict, assigning coordinates to each node\n",
    "def layout_node_dict(node_dict, gameboard, rounds=1, r_radius=10, \n",
    "                     a_radius=10, r_scale=10, a_scale=5, \n",
    "                     center_attractor_scale=0.01):\n",
    "    # nodes are pulled towards the center of the gameboard\n",
    "    # by giving the gameboard an attraction field at its center\n",
    "    # the radius of the field is the distance from the center to the corners\n",
    "    center = int(gameboard.shape[0]/2)\n",
    "    center_attractor_radius = int(math.sqrt(2 * center**2))\n",
    "    add_field(attraction_field(radius=int(gameboard.shape[0]/2), scale=center_attractor_scale),\n",
    "              gameboard,\n",
    "              center, center)\n",
    "    r_field = repulsion_field(r_radius, scale=r_scale, center_spike=True, dtype=gameboard.dtype)\n",
    "    a_field = attraction_field(a_radius, scale=a_scale, dtype=gameboard.dtype)\n",
    "    a_field_med = attraction_field(a_radius, scale=a_scale*5, dtype=gameboard.dtype)\n",
    "    a_field_high = attraction_field(a_radius, scale=a_scale*30, dtype=gameboard.dtype)\n",
    "    \n",
    "    # make a scratchpad board where we add all the attraction fields\n",
    "    # and the use it to update the gameboard\n",
    "    s_field = np.zeros(gameboard.shape, gameboard.dtype)\n",
    "    \n",
    "    # get the nodes as a list, sorted by degree, highest degree first\n",
    "    node_list = sorted(node_dict.values(), key=itemgetter('degree'), reverse=True)\n",
    "    \n",
    "    # if a node does not have coordinates, don't add its repulsion field\n",
    "    for node in node_list:  \n",
    "        if node.get(\"x\"):\n",
    "            add_field(r_field, gameboard, node[\"x\"], node[\"y\"])\n",
    "            \n",
    "    # perform the rounds of layout\n",
    "    start = timer()\n",
    "    for n in range(0, rounds):\n",
    "        # print(\"round \", n)\n",
    "        for node in node_list:\n",
    "            layout_one_node(gameboard, s_field, r_field, a_field, a_field_med, a_field_high, node, node_dict)\n",
    "\n",
    "    end = timer()\n",
    "    print(\"layout time = \", end - start)\n",
    "    return node_dict\n",
    "\n",
    "# update the position of one node\n",
    "def layout_one_node(gb, s_field, source_field, a_field, a_field_med, a_field_high, node, node_dict):\n",
    "    # clear the scratchpad\n",
    "    s_field[...]=0\n",
    "    rank = len(list(node['adj']))     \n",
    "    for adj_node_id in node['adj']:\n",
    "        # if the adjacent node has coordinates,\n",
    "        # add its attraction field to the scratchpad field\n",
    "        adj_node = node_dict[adj_node_id]\n",
    "        if adj_node.get(\"x\"):\n",
    "            if rank == 1:\n",
    "                add_field(a_field_high, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            elif rank < 5:\n",
    "                add_field(a_field_med, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            else:\n",
    "                add_field(a_field, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            \n",
    "    # add s_field to the gameboard\n",
    "    gb += s_field\n",
    "    # if the node has coordinates\n",
    "    # remove it from the gameboard by subtracting it at its current location\n",
    "    if node.get(\"x\"):\n",
    "        subtract_field(source_field, gb, node['x'], node['y'])\n",
    "    \n",
    "    # select the destination\n",
    "    # idea #1: choose a location with the minimum value\n",
    "    # argmin returns the index of the first location containing the minimum value in a flattened \n",
    "    # version of the array\n",
    "    # unravel_index turns the index back into the coordinates\n",
    "    destination = np.unravel_index(np.argmin(gb, axis=None), s_field.shape)\n",
    "    #print(node)\n",
    "    #print(\"destination: \", destination)\n",
    "\n",
    "    # add it at the destination\n",
    "    add_field(source_field, gb, destination[0], destination[1])\n",
    "    \n",
    "    # subtract the s_field to revert the gameboard to just the repulsions\n",
    "    gb -= s_field\n",
    "    \n",
    "    # update the node's coordinates\n",
    "    node[\"x\"] = destination[0]\n",
    "    node[\"y\"] = destination[1] \n",
    "          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Field Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_add_field_positions(small_size=21, large_size=101, dtype=np.int64):\n",
    "    # large target, small source\n",
    "    test_add_field_overlaps(small_size, large_size)\n",
    "    # small target, large source\n",
    "    test_add_field_overlaps(large_size,small_size)\n",
    "    \n",
    "def test_add_field(source_size, target_size, pos=\"center\", dtype=np.int64):\n",
    "    source = np.ones((source_size, source_size), dtype=dtype) \n",
    "    target = np.ones((target_size, target_size), dtype=dtype) \n",
    "    target_max_x = target.shape[0]-1\n",
    "    target_max_y = target.shape[1]-1\n",
    "    target_center_x = int(target.shape[0]/2)\n",
    "    target_center_y = int(target.shape[1]/2)\n",
    "    print(source.shape, target.shape, \" \", pos)\n",
    "    if pos==\"top_left\":\n",
    "        add_field(source, target, 0,0)\n",
    "    elif pos==\"bottom_right\":      \n",
    "        add_field(source, target, target_max_x, target_max_y)\n",
    "    elif pos==\"center\":\n",
    "        add_field(source, target, target_center_x, target_center_y)   \n",
    "    show_field(target)\n",
    "    \n",
    "def test_add_field_overlaps(source_size, target_size):\n",
    "    test_add_field(source_size, target_size, pos=\"top_left\")\n",
    "    test_add_field(source_size, target_size, pos=\"bottom_right\")\n",
    "    test_add_field(source_size, target_size, pos=\"center\")\n",
    "\n",
    "def test_add_field_speed(target_size,  source_size, rounds=1000, target_x=5, target_y=5, dtype=np.int64):\n",
    "    target_field = np.ones((target_size, target_size), dtype=dtype)\n",
    "    source_field = np.ones((source_size, target_size), dtype=dtype)\n",
    "    start = timer()\n",
    "    for i in range(0, rounds):\n",
    "        add_field(source_field, target_field, target_x, target_y)\n",
    "    end = timer()\n",
    "    elapsed_time = end - start\n",
    "    rate = elapsed_time / rounds\n",
    "    print(\"Elapsed time = \", elapsed_time , \" for \", rounds, \" rounds. Rate = \", rate, \" per round\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_gameboard_2 = np.zeros((15, 15), dtype=np.int32)\n",
    "# add a repulsion field and an attraction field to the gameboard at 10,13\n",
    "# The fields will go over the boundaries of the gameboard\n",
    "add_field(repulsion_field(10, scale=10), test_gameboard_2, 10, 13)\n",
    "add_field(attraction_field(10, scale=15), test_gameboard_2, 10, 13)\n",
    "show_field(test_gameboard_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time =  0.4516501109901583  for  40000  rounds. Rate =  1.1291252774753957e-05  per round\n",
      " "
     ]
    }
   ],
   "source": [
    "%prun test_add_field_speed(3001,  15, rounds=40000, target_x=5, target_y=5, dtype=np.int16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layout time =  0.0131862580019515\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting edges to a numpy array, perform a layout and then show the gameboard\n",
    "# The cells containing the nodes are set to zero so that the range to plot \n",
    "# doesn't contain the repulsion center extreme values.\n",
    "test_node_dict_3, test_gameboard_3 = make_node_dict_from_edge_array(edges.to_numpy(), \n",
    "                                                                    sparsity=20,\n",
    "                                                                    initialize_coordinates=False,\n",
    "                                                                    dtype=np.int32\n",
    "                                                                   )\n",
    "test_node_dict_3 = layout_node_dict(test_node_dict_3, test_gameboard_3, rounds=2, \n",
    "                       r_radius=10, a_radius=10,\n",
    "                       r_scale=10, a_scale=5, center_attractor_scale=1\n",
    "                      )\n",
    "for node_id, node in test_node_dict_3.items():\n",
    "    test_gameboard_3[node[\"x\"], node[\"y\"]]=0\n",
    "show_field(test_gameboard_3)\n",
    "plot_field(test_gameboard_3)\n",
    "#print(node_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Layout a network from NDEx and save the modified network. \n",
    "- get the network\n",
    "- do the layout\n",
    "- show the gameboard\n",
    "- make a cx layout from the new coordinates in the node_dict\n",
    "- set it as the layout of the test network\n",
    "- upload the modified network to NDEx\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the network and the edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Genome instability and mutation\n",
      "Nodes: 1043\n",
      "Edges: 1376\n",
      "Node Attributes: 7144\n",
      "Edge Attributes: 6140\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nci_pid_tcr_signaling_uuid = \"0c2862fa-6196-11e5-8ac5-06603eb7f303\"\n",
    "sonic_hedgehog_uuid = \"cfd3528f-a7a4-11eb-9e72-0ac135e8bacf\"\n",
    "nest_uuid = \"60112105-f853-11e9-bb65-0ac135e8bacf\"\n",
    "string_hc_uuid = \"275bd84e-3d18-11e8-a935-0ac135e8bacf\"\n",
    "cptac_genomic_instability_uuid = \"d121e661-4cfc-11e9-9f06-0ac135e8bacf\"\n",
    "\n",
    "test_uuid_4 = cptac_genomic_instability_uuid #nest_uuid\n",
    "test_network_4 = ndex2.create_nice_cx_from_server(SERVER, uuid=test_uuid_4)\n",
    "test_network_4.print_summary()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edge array:  (1376, 2)\n",
      "make node dict time =  0.00830996000149753\n",
      "layout time =  0.1591762219904922\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_node_dict_4, test_gameboard_4 = layout_cx_network(test_network_4, sparsity=20, \n",
    "                                                       rounds=1, r_radius= 20, r_scale=20, \n",
    "                                                       a_radius=100, a_scale=3,\n",
    "                                                      center_attractor_scale=1,\n",
    "                                                      initialize_coordinates=False)\n",
    "show_field(test_gameboard_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{69295: {'adj': {69241}, 'degree': 1, 'x': 121, 'y': 124},\n",
       " 69241: {'adj': {68262,\n",
       "   68267,\n",
       "   68277,\n",
       "   68284,\n",
       "   68292,\n",
       "   68296,\n",
       "   68302,\n",
       "   68303,\n",
       "   68305,\n",
       "   68312,\n",
       "   68316,\n",
       "   68319,\n",
       "   69265,\n",
       "   69270,\n",
       "   69295},\n",
       "  'degree': 15,\n",
       "  'x': 123,\n",
       "  'y': 123},\n",
       " 69294: {'adj': {69247}, 'degree': 1, 'x': 129, 'y': 165},\n",
       " 69247: {'adj': {68256, 68299, 68301, 69240, 69258, 69291, 69293, 69294},\n",
       "  'degree': 8,\n",
       "  'x': 128,\n",
       "  'y': 163},\n",
       " 69293: {'adj': {69247}, 'degree': 1, 'x': 128, 'y': 161},\n",
       " 69292: {'adj': {69244}, 'degree': 1, 'x': 162, 'y': 174},\n",
       " 69244: {'adj': {68266, 68269, 69252, 69284, 69292},\n",
       "  'degree': 5,\n",
       "  'x': 161,\n",
       "  'y': 172},\n",
       " 69291: {'adj': {69247}, 'degree': 1, 'x': 126, 'y': 164},\n",
       " 69290: {'adj': {69254}, 'degree': 1, 'x': 148, 'y': 101},\n",
       " 69254: {'adj': {68254, 68258, 68260, 68261, 69239, 69290},\n",
       "  'degree': 6,\n",
       "  'x': 149,\n",
       "  'y': 103},\n",
       " 69289: {'adj': {69256}, 'degree': 1, 'x': 149, 'y': 123},\n",
       " 69256: {'adj': {68255,\n",
       "   68264,\n",
       "   68265,\n",
       "   68276,\n",
       "   68278,\n",
       "   68282,\n",
       "   68297,\n",
       "   68304,\n",
       "   68322,\n",
       "   69272,\n",
       "   69276,\n",
       "   69277,\n",
       "   69280,\n",
       "   69285,\n",
       "   69289},\n",
       "  'degree': 15,\n",
       "  'x': 147,\n",
       "  'y': 123},\n",
       " 69288: {'adj': {69250}, 'degree': 1, 'x': 95, 'y': 128},\n",
       " 69250: {'adj': {68274, 68281, 68313, 69236, 69263, 69288},\n",
       "  'degree': 6,\n",
       "  'x': 97,\n",
       "  'y': 127},\n",
       " 69287: {'adj': {69179, 69191}, 'degree': 2, 'x': 154, 'y': 120},\n",
       " 69191: {'adj': {68760, 69132, 69167, 69238, 69242, 69248, 69287},\n",
       "  'degree': 7,\n",
       "  'x': 156,\n",
       "  'y': 122},\n",
       " 69286: {'adj': {69258}, 'degree': 1, 'x': 124, 'y': 160},\n",
       " 69258: {'adj': {68273, 68299, 69247, 69286}, 'degree': 4, 'x': 125, 'y': 162},\n",
       " 69285: {'adj': {69256}, 'degree': 1, 'x': 145, 'y': 121},\n",
       " 69284: {'adj': {69244}, 'degree': 1, 'x': 162, 'y': 170},\n",
       " 69283: {'adj': {69245}, 'degree': 1, 'x': 97, 'y': 169},\n",
       " 69245: {'adj': {68274, 68279, 69279, 69283}, 'degree': 4, 'x': 98, 'y': 167},\n",
       " 69282: {'adj': {69239}, 'degree': 1, 'x': 149, 'y': 110},\n",
       " 69239: {'adj': {68257,\n",
       "   68272,\n",
       "   68289,\n",
       "   68317,\n",
       "   68318,\n",
       "   69131,\n",
       "   69147,\n",
       "   69254,\n",
       "   69282},\n",
       "  'degree': 9,\n",
       "  'x': 147,\n",
       "  'y': 111},\n",
       " 69281: {'adj': {69246}, 'degree': 1, 'x': 183, 'y': 159},\n",
       " 69246: {'adj': {68283, 68298, 69281}, 'degree': 3, 'x': 183, 'y': 161},\n",
       " 69280: {'adj': {69256}, 'degree': 1, 'x': 146, 'y': 125},\n",
       " 69279: {'adj': {68316, 68731, 69245}, 'degree': 3, 'x': 120, 'y': 121},\n",
       " 68316: {'adj': {68266, 69241, 69262, 69279}, 'degree': 4, 'x': 123, 'y': 126},\n",
       " 68731: {'adj': {68702, 68736, 68745, 69269, 69279},\n",
       "  'degree': 5,\n",
       "  'x': 141,\n",
       "  'y': 90},\n",
       " 69278: {'adj': {68298, 68556, 69167}, 'degree': 3, 'x': 171, 'y': 164},\n",
       " 68556: {'adj': {68349, 68540, 68588, 69278}, 'degree': 4, 'x': 175, 'y': 168},\n",
       " 69167: {'adj': {68263,\n",
       "   68285,\n",
       "   68286,\n",
       "   68737,\n",
       "   68741,\n",
       "   68748,\n",
       "   68749,\n",
       "   68768,\n",
       "   68872,\n",
       "   68887,\n",
       "   69119,\n",
       "   69129,\n",
       "   69133,\n",
       "   69150,\n",
       "   69154,\n",
       "   69162,\n",
       "   69190,\n",
       "   69191,\n",
       "   69193,\n",
       "   69197,\n",
       "   69200,\n",
       "   69202,\n",
       "   69204,\n",
       "   69205,\n",
       "   69216,\n",
       "   69260,\n",
       "   69278},\n",
       "  'degree': 27,\n",
       "  'x': 137,\n",
       "  'y': 129},\n",
       " 68298: {'adj': {69246, 69252, 69278}, 'degree': 3, 'x': 174, 'y': 162},\n",
       " 69277: {'adj': {69256}, 'degree': 1, 'x': 149, 'y': 121},\n",
       " 69276: {'adj': {69256}, 'degree': 1, 'x': 145, 'y': 118},\n",
       " 69275: {'adj': {69243}, 'degree': 1, 'x': 158, 'y': 151},\n",
       " 69243: {'adj': {68275, 69129, 69229, 69230, 69275},\n",
       "  'degree': 5,\n",
       "  'x': 156,\n",
       "  'y': 152},\n",
       " 69274: {'adj': {69255}, 'degree': 1, 'x': 179, 'y': 138},\n",
       " 69255: {'adj': {68293, 68310, 69234, 69268, 69274},\n",
       "  'degree': 5,\n",
       "  'x': 177,\n",
       "  'y': 137},\n",
       " 69273: {'adj': {69240}, 'degree': 1, 'x': 132, 'y': 164},\n",
       " 69240: {'adj': {68315, 69247, 69248, 69273}, 'degree': 4, 'x': 131, 'y': 162},\n",
       " 69272: {'adj': {69256}, 'degree': 1, 'x': 150, 'y': 125},\n",
       " 69271: {'adj': {69249}, 'degree': 1, 'x': 157, 'y': 138},\n",
       " 69249: {'adj': {68287, 68307, 68311, 68321, 69129, 69271},\n",
       "  'degree': 6,\n",
       "  'x': 155,\n",
       "  'y': 139},\n",
       " 69270: {'adj': {69241}, 'degree': 1, 'x': 123, 'y': 121},\n",
       " 69269: {'adj': {68731, 68761, 69179}, 'degree': 3, 'x': 142, 'y': 94},\n",
       " 68761: {'adj': {68727, 68846, 69269}, 'degree': 3, 'x': 128, 'y': 97},\n",
       " 69268: {'adj': {69255}, 'degree': 1, 'x': 178, 'y': 135},\n",
       " 69267: {'adj': {68726, 69251}, 'degree': 2, 'x': 97, 'y': 105},\n",
       " 69251: {'adj': {68275, 68291, 69264, 69267}, 'degree': 4, 'x': 89, 'y': 105},\n",
       " 68726: {'adj': {68291, 68669, 68670, 68740, 69267},\n",
       "  'degree': 5,\n",
       "  'x': 175,\n",
       "  'y': 115},\n",
       " 69266: {'adj': {69131, 69133}, 'degree': 2, 'x': 153, 'y': 102},\n",
       " 69133: {'adj': {69167, 69266}, 'degree': 2, 'x': 150, 'y': 106},\n",
       " 69265: {'adj': {69241}, 'degree': 1, 'x': 125, 'y': 124},\n",
       " 69264: {'adj': {68729, 69129, 69251}, 'degree': 3, 'x': 146, 'y': 116},\n",
       " 69129: {'adj': {68253,\n",
       "   68737,\n",
       "   68785,\n",
       "   69150,\n",
       "   69167,\n",
       "   69206,\n",
       "   69243,\n",
       "   69249,\n",
       "   69264},\n",
       "  'degree': 9,\n",
       "  'x': 145,\n",
       "  'y': 141},\n",
       " 68729: {'adj': {68683, 68684, 68685, 69264}, 'degree': 4, 'x': 168, 'y': 90},\n",
       " 69262: {'adj': {68316}, 'degree': 1, 'x': 121, 'y': 127},\n",
       " 69261: {'adj': {68314, 69159}, 'degree': 2, 'x': 157, 'y': 104},\n",
       " 68314: {'adj': {69147, 69261}, 'degree': 2, 'x': 151, 'y': 99},\n",
       " 69260: {'adj': {68718, 69167}, 'degree': 2, 'x': 149, 'y': 164},\n",
       " 68718: {'adj': {68691, 68692, 68693, 68708, 68709, 68710, 68711, 69260},\n",
       "  'degree': 8,\n",
       "  'x': 151,\n",
       "  'y': 161},\n",
       " 68299: {'adj': {69230, 69247, 69258}, 'degree': 3, 'x': 126, 'y': 166},\n",
       " 69257: {'adj': {68288, 68294, 68310, 69233}, 'degree': 4, 'x': 164, 'y': 111},\n",
       " 69233: {'adj': {69257}, 'degree': 1, 'x': 165, 'y': 109},\n",
       " 69234: {'adj': {69255}, 'degree': 1, 'x': 176, 'y': 139},\n",
       " 69253: {'adj': {68277, 68307, 68310, 68311, 68321},\n",
       "  'degree': 5,\n",
       "  'x': 156,\n",
       "  'y': 93},\n",
       " 68310: {'adj': {69159, 69238, 69253, 69255, 69257},\n",
       "  'degree': 5,\n",
       "  'x': 161,\n",
       "  'y': 109},\n",
       " 69252: {'adj': {68268, 68271, 68298, 68306, 69244},\n",
       "  'degree': 5,\n",
       "  'x': 163,\n",
       "  'y': 166},\n",
       " 68275: {'adj': {69243, 69251}, 'degree': 2, 'x': 93, 'y': 110},\n",
       " 69263: {'adj': {68308, 69250}, 'degree': 2, 'x': 95, 'y': 133},\n",
       " 68274: {'adj': {68308, 69245, 69250}, 'degree': 3, 'x': 97, 'y': 150},\n",
       " 69248: {'adj': {68280, 68290, 69191, 69240}, 'degree': 4, 'x': 155, 'y': 126},\n",
       " 68256: {'adj': {69230, 69247}, 'degree': 2, 'x': 135, 'y': 164},\n",
       " 69242: {'adj': {68295, 68320, 69172, 69191}, 'degree': 4, 'x': 150, 'y': 118},\n",
       " 68277: {'adj': {69241, 69253}, 'degree': 2, 'x': 151, 'y': 94},\n",
       " 69147: {'adj': {68314, 69142, 69176, 69239}, 'degree': 4, 'x': 146, 'y': 108},\n",
       " 69238: {'adj': {68263, 68270, 68286, 68309, 68310, 69191},\n",
       "  'degree': 6,\n",
       "  'x': 158,\n",
       "  'y': 116},\n",
       " 69237: {'adj': {68259, 68286}, 'degree': 2, 'x': 140, 'y': 104},\n",
       " 68286: {'adj': {68295, 68738, 69131, 69167, 69231, 69232, 69237, 69238},\n",
       "  'degree': 8,\n",
       "  'x': 142,\n",
       "  'y': 116},\n",
       " 68259: {'adj': {68295, 68722, 68738, 69237}, 'degree': 4, 'x': 137, 'y': 105},\n",
       " 69236: {'adj': {68308, 69250}, 'degree': 2, 'x': 97, 'y': 137},\n",
       " 68308: {'adj': {68274, 69236, 69263}, 'degree': 3, 'x': 95, 'y': 153},\n",
       " 69232: {'adj': {68286, 68300}, 'degree': 2, 'x': 145, 'y': 102},\n",
       " 68300: {'adj': {68295, 69231, 69232}, 'degree': 3, 'x': 143, 'y': 105},\n",
       " 69231: {'adj': {68286, 68300}, 'degree': 2, 'x': 143, 'y': 109},\n",
       " 69230: {'adj': {68256, 68299, 69243}, 'degree': 3, 'x': 146, 'y': 163},\n",
       " 69229: {'adj': {68254, 69243}, 'degree': 2, 'x': 160, 'y': 153},\n",
       " 68254: {'adj': {69229, 69254}, 'degree': 2, 'x': 169, 'y': 144},\n",
       " 69228: {'adj': {69126, 69163, 69165, 69221}, 'degree': 4, 'x': 113, 'y': 132},\n",
       " 69126: {'adj': {69048,\n",
       "   69049,\n",
       "   69051,\n",
       "   69103,\n",
       "   69125,\n",
       "   69127,\n",
       "   69140,\n",
       "   69174,\n",
       "   69192,\n",
       "   69212,\n",
       "   69219,\n",
       "   69222,\n",
       "   69228},\n",
       "  'degree': 13,\n",
       "  'x': 123,\n",
       "  'y': 144},\n",
       " 69227: {'adj': {69131, 69175, 69186}, 'degree': 3, 'x': 140, 'y': 122},\n",
       " 69186: {'adj': {68789, 68887, 68931, 69125, 69199, 69227},\n",
       "  'degree': 6,\n",
       "  'x': 117,\n",
       "  'y': 122},\n",
       " 69226: {'adj': {69180, 69203, 69206}, 'degree': 3, 'x': 124, 'y': 155},\n",
       " 69180: {'adj': {69128, 69175, 69203, 69226}, 'degree': 4, 'x': 126, 'y': 157},\n",
       " 69225: {'adj': {69209, 69219}, 'degree': 2, 'x': 102, 'y': 149},\n",
       " 69209: {'adj': {69219, 69225}, 'degree': 2, 'x': 105, 'y': 150},\n",
       " 69219: {'adj': {69126, 69209, 69211, 69218, 69220, 69223, 69225},\n",
       "  'degree': 7,\n",
       "  'x': 112,\n",
       "  'y': 149},\n",
       " 69224: {'adj': {69177}, 'degree': 1, 'x': 152, 'y': 108},\n",
       " 69177: {'adj': {69131, 69159, 69199, 69224}, 'degree': 4, 'x': 152, 'y': 110},\n",
       " 69223: {'adj': {69212, 69219, 69222}, 'degree': 3, 'x': 108, 'y': 150},\n",
       " 69212: {'adj': {69126, 69223}, 'degree': 2, 'x': 111, 'y': 152},\n",
       " 69222: {'adj': {69126, 69223}, 'degree': 2, 'x': 109, 'y': 147},\n",
       " 69221: {'adj': {69228}, 'degree': 1, 'x': 111, 'y': 131},\n",
       " 69220: {'adj': {69219}, 'degree': 1, 'x': 112, 'y': 147},\n",
       " 69211: {'adj': {69193, 69219}, 'degree': 2, 'x': 110, 'y': 139},\n",
       " 69218: {'adj': {69219}, 'degree': 1, 'x': 113, 'y': 151},\n",
       " 69216: {'adj': {69167}, 'degree': 1, 'x': 139, 'y': 128},\n",
       " 69193: {'adj': {68253,\n",
       "   68378,\n",
       "   68379,\n",
       "   68380,\n",
       "   68383,\n",
       "   68389,\n",
       "   68394,\n",
       "   68396,\n",
       "   68399,\n",
       "   68407,\n",
       "   68418,\n",
       "   68750,\n",
       "   68755,\n",
       "   68770,\n",
       "   68787,\n",
       "   68806,\n",
       "   68827,\n",
       "   68849,\n",
       "   68855,\n",
       "   68863,\n",
       "   68864,\n",
       "   68866,\n",
       "   68872,\n",
       "   68873,\n",
       "   68877,\n",
       "   68879,\n",
       "   68887,\n",
       "   68892,\n",
       "   68895,\n",
       "   68899,\n",
       "   68903,\n",
       "   68907,\n",
       "   68945,\n",
       "   69064,\n",
       "   69072,\n",
       "   69073,\n",
       "   69074,\n",
       "   69076,\n",
       "   69081,\n",
       "   69086,\n",
       "   69094,\n",
       "   69098,\n",
       "   69099,\n",
       "   69100,\n",
       "   69101,\n",
       "   69103,\n",
       "   69104,\n",
       "   69106,\n",
       "   69127,\n",
       "   69140,\n",
       "   69142,\n",
       "   69148,\n",
       "   69152,\n",
       "   69167,\n",
       "   69174,\n",
       "   69175,\n",
       "   69176,\n",
       "   69178,\n",
       "   69179,\n",
       "   69192,\n",
       "   69193,\n",
       "   69201,\n",
       "   69211},\n",
       "  'degree': 63,\n",
       "  'x': 135,\n",
       "  'y': 135},\n",
       " 69210: {'adj': {69162, 69217}, 'degree': 2, 'x': 159, 'y': 112},\n",
       " 69217: {'adj': {69128, 69210}, 'degree': 2, 'x': 155, 'y': 116},\n",
       " 69207: {'adj': {69122, 69125, 69141, 69148}, 'degree': 4, 'x': 155, 'y': 148},\n",
       " 69125: {'adj': {69049,\n",
       "   69054,\n",
       "   69055,\n",
       "   69056,\n",
       "   69103,\n",
       "   69120,\n",
       "   69126,\n",
       "   69150,\n",
       "   69153,\n",
       "   69179,\n",
       "   69186,\n",
       "   69196,\n",
       "   69198,\n",
       "   69207},\n",
       "  'degree': 14,\n",
       "  'x': 128,\n",
       "  'y': 148},\n",
       " 69206: {'adj': {69129, 69160, 69170, 69188, 69199, 69226},\n",
       "  'degree': 6,\n",
       "  'x': 115,\n",
       "  'y': 109},\n",
       " 69205: {'adj': {69143, 69150, 69151, 69161, 69167},\n",
       "  'degree': 5,\n",
       "  'x': 137,\n",
       "  'y': 148},\n",
       " 69150: {'adj': {68748,\n",
       "   68749,\n",
       "   68751,\n",
       "   69103,\n",
       "   69125,\n",
       "   69129,\n",
       "   69167,\n",
       "   69197,\n",
       "   69205,\n",
       "   69213},\n",
       "  'degree': 10,\n",
       "  'x': 133,\n",
       "  'y': 146},\n",
       " 69204: {'adj': {69121, 69144, 69145, 69167, 69171},\n",
       "  'degree': 5,\n",
       "  'x': 141,\n",
       "  'y': 101},\n",
       " 69121: {'adj': {69132, 69138, 69204}, 'degree': 3, 'x': 162, 'y': 115},\n",
       " 69203: {'adj': {69117, 69180, 69190, 69226}, 'degree': 4, 'x': 129, 'y': 158},\n",
       " 69117: {'adj': {69203}, 'degree': 1, 'x': 130, 'y': 156},\n",
       " 69190: {'adj': {69167, 69203}, 'degree': 2, 'x': 132, 'y': 159},\n",
       " 69202: {'adj': {69118, 69135, 69143, 69151, 69156, 69158, 69161, 69167},\n",
       "  'degree': 8,\n",
       "  'x': 143,\n",
       "  'y': 147},\n",
       " 69118: {'adj': {68253,\n",
       "   68357,\n",
       "   68358,\n",
       "   68359,\n",
       "   68381,\n",
       "   68389,\n",
       "   68395,\n",
       "   68400,\n",
       "   68414,\n",
       "   68674,\n",
       "   68730,\n",
       "   68853,\n",
       "   68860,\n",
       "   68862,\n",
       "   68863,\n",
       "   68866,\n",
       "   68871,\n",
       "   68873,\n",
       "   68874,\n",
       "   68875,\n",
       "   68877,\n",
       "   68886,\n",
       "   68892,\n",
       "   68901,\n",
       "   68918,\n",
       "   68938,\n",
       "   68939,\n",
       "   68943,\n",
       "   68949,\n",
       "   69097,\n",
       "   69108,\n",
       "   69118,\n",
       "   69178,\n",
       "   69202},\n",
       "  'degree': 34,\n",
       "  'x': 139,\n",
       "  'y': 143},\n",
       " 69200: {'adj': {69139, 69149, 69154, 69164, 69167, 69172},\n",
       "  'degree': 6,\n",
       "  'x': 132,\n",
       "  'y': 106},\n",
       " 69149: {'adj': {69138, 69183, 69200}, 'degree': 3, 'x': 167, 'y': 114},\n",
       " 69199: {'adj': {69134,\n",
       "   69137,\n",
       "   69146,\n",
       "   69152,\n",
       "   69169,\n",
       "   69177,\n",
       "   69186,\n",
       "   69189,\n",
       "   69206},\n",
       "  'degree': 9,\n",
       "  'x': 114,\n",
       "  'y': 115},\n",
       " 69198: {'adj': {69125, 69173, 69194}, 'degree': 3, 'x': 128, 'y': 151},\n",
       " 69197: {'adj': {69123, 69150, 69155, 69167, 69184},\n",
       "  'degree': 5,\n",
       "  'x': 117,\n",
       "  'y': 146},\n",
       " 69196: {'adj': {69125, 69168, 69185, 69214}, 'degree': 4, 'x': 127, 'y': 145},\n",
       " 69214: {'adj': {69196}, 'degree': 1, 'x': 128, 'y': 144},\n",
       " 69194: {'adj': {69198}, 'degree': 1, 'x': 130, 'y': 151},\n",
       " 68383: {'adj': {68399, 69193}, 'degree': 2, 'x': 160, 'y': 136},\n",
       " 69074: {'adj': {68388, 68389, 68808, 68875, 68903, 69099, 69193},\n",
       "  'degree': 7,\n",
       "  'x': 137,\n",
       "  'y': 121},\n",
       " 68879: {'adj': {68389, 68770, 68786, 68939, 69193},\n",
       "  'degree': 5,\n",
       "  'x': 145,\n",
       "  'y': 151},\n",
       " 68394: {'adj': {68860, 68899, 69193}, 'degree': 3, 'x': 143, 'y': 139},\n",
       " 68892: {'adj': {68253, 68849, 69118, 69193}, 'degree': 4, 'x': 139, 'y': 139},\n",
       " 68396: {'adj': {68875, 69193}, 'degree': 2, 'x': 153, 'y': 129},\n",
       " 69148: {'adj': {68794, 68904, 69049, 69050, 69193, 69207},\n",
       "  'degree': 6,\n",
       "  'x': 159,\n",
       "  'y': 146},\n",
       " 68379: {'adj': {68852, 68855, 69193}, 'degree': 3, 'x': 139, 'y': 131},\n",
       " 68873: {'adj': {68253, 68849, 68875, 69118, 69193},\n",
       "  'degree': 5,\n",
       "  'x': 139,\n",
       "  'y': 134},\n",
       " 68407: {'adj': {68253, 69193}, 'degree': 2, 'x': 141, 'y': 140},\n",
       " 68418: {'adj': {68381, 68408, 69193}, 'degree': 3, 'x': 124, 'y': 132},\n",
       " 68378: {'adj': {68852, 68899, 69193}, 'degree': 3, 'x': 148, 'y': 131},\n",
       " 68872: {'adj': {68377, 68385, 68417, 68766, 68810, 68968, 69167, 69193},\n",
       "  'degree': 8,\n",
       "  'x': 146,\n",
       "  'y': 129},\n",
       " 68895: {'adj': {68377, 68860, 68968, 69193}, 'degree': 4, 'x': 135, 'y': 143},\n",
       " 68877: {'adj': {68253, 68849, 69118, 69193}, 'degree': 4, 'x': 134, 'y': 137},\n",
       " 69179: {'adj': {68809, 69125, 69130, 69157, 69193, 69269, 69287},\n",
       "  'degree': 7,\n",
       "  'x': 129,\n",
       "  'y': 154},\n",
       " 69174: {'adj': {69126, 69193, 69208}, 'degree': 3, 'x': 126, 'y': 142},\n",
       " 69201: {'adj': {69124, 69166, 69187, 69193}, 'degree': 4, 'x': 136, 'y': 132},\n",
       " 69176: {'adj': {68521, 69103, 69147, 69193}, 'degree': 4, 'x': 128, 'y': 137},\n",
       " 69142: {'adj': {69147, 69193}, 'degree': 2, 'x': 148, 'y': 114},\n",
       " 69140: {'adj': {69100, 69126, 69193, 69208}, 'degree': 4, 'x': 131, 'y': 149},\n",
       " 69178: {'adj': {68835, 68845, 69118, 69193}, 'degree': 4, 'x': 142, 'y': 143},\n",
       " 69175: {'adj': {69103, 69120, 69136, 69180, 69181, 69193, 69227},\n",
       "  'degree': 7,\n",
       "  'x': 135,\n",
       "  'y': 151},\n",
       " 69152: {'adj': {69104, 69193, 69199}, 'degree': 3, 'x': 128, 'y': 129},\n",
       " 69081: {'adj': {69092, 69102, 69115, 69193}, 'degree': 4, 'x': 141, 'y': 129},\n",
       " 69106: {'adj': {69061,\n",
       "   69065,\n",
       "   69071,\n",
       "   69084,\n",
       "   69085,\n",
       "   69103,\n",
       "   69108,\n",
       "   69166,\n",
       "   69193},\n",
       "  'degree': 9,\n",
       "  'x': 131,\n",
       "  'y': 138},\n",
       " 69064: {'adj': {69092, 69193}, 'degree': 2, 'x': 177, 'y': 120},\n",
       " 69098: {'adj': {69067, 69097, 69105, 69193}, 'degree': 4, 'x': 155, 'y': 157},\n",
       " 69104: {'adj': {69070, 69083, 69152, 69193}, 'degree': 4, 'x': 130, 'y': 130},\n",
       " 69103: {'adj': {69059,\n",
       "   69065,\n",
       "   69066,\n",
       "   69071,\n",
       "   69085,\n",
       "   69088,\n",
       "   69089,\n",
       "   69091,\n",
       "   69094,\n",
       "   69106,\n",
       "   69125,\n",
       "   69126,\n",
       "   69128,\n",
       "   69150,\n",
       "   69166,\n",
       "   69175,\n",
       "   69176,\n",
       "   69193},\n",
       "  'degree': 18,\n",
       "  'x': 130,\n",
       "  'y': 142},\n",
       " 69100: {'adj': {69079, 69082, 69086, 69140, 69181, 69193},\n",
       "  'degree': 6,\n",
       "  'x': 142,\n",
       "  'y': 162},\n",
       " 68863: {'adj': {68382, 68397, 68858, 68887, 68903, 68948, 69118, 69193},\n",
       "  'degree': 8,\n",
       "  'x': 136,\n",
       "  'y': 139},\n",
       " 68855: {'adj': {68379, 68420, 68849, 68854, 69193},\n",
       "  'degree': 5,\n",
       "  'x': 126,\n",
       "  'y': 135},\n",
       " 68866: {'adj': {68377,\n",
       "   68404,\n",
       "   68849,\n",
       "   68858,\n",
       "   68862,\n",
       "   68906,\n",
       "   68968,\n",
       "   69118,\n",
       "   69193},\n",
       "  'degree': 9,\n",
       "  'x': 131,\n",
       "  'y': 127},\n",
       " 68903: {'adj': {68253, 68382, 68849, 68852, 68863, 68948, 69074, 69193},\n",
       "  'degree': 8,\n",
       "  'x': 133,\n",
       "  'y': 132},\n",
       " 68945: {'adj': {69193}, 'degree': 1, 'x': 136, 'y': 134},\n",
       " 68399: {'adj': {68383, 68390, 68419, 68791, 68904, 69193},\n",
       "  'degree': 6,\n",
       "  'x': 163,\n",
       "  'y': 135},\n",
       " 68755: {'adj': {68759, 68773, 69193}, 'degree': 3, 'x': 146, 'y': 145},\n",
       " 68907: {'adj': {68387, 68796, 68940, 69128, 69193},\n",
       "  'degree': 5,\n",
       "  'x': 109,\n",
       "  'y': 155},\n",
       " 68887: {'adj': {68757,\n",
       "   68823,\n",
       "   68863,\n",
       "   68899,\n",
       "   68906,\n",
       "   69120,\n",
       "   69167,\n",
       "   69186,\n",
       "   69193},\n",
       "  'degree': 9,\n",
       "  'x': 141,\n",
       "  'y': 125},\n",
       " 68899: {'adj': {68253,\n",
       "   68378,\n",
       "   68394,\n",
       "   68849,\n",
       "   68851,\n",
       "   68867,\n",
       "   68887,\n",
       "   68905,\n",
       "   68917,\n",
       "   68926,\n",
       "   68944,\n",
       "   69193},\n",
       "  'degree': 12,\n",
       "  'x': 142,\n",
       "  'y': 132},\n",
       " 68864: {'adj': {68752, 68753, 68825, 68849, 68854, 68906, 69120, 69193},\n",
       "  'degree': 8,\n",
       "  'x': 125,\n",
       "  'y': 129},\n",
       " 68750: {'adj': {68847, 69193}, 'degree': 2, 'x': 134, 'y': 118},\n",
       " 68827: {'adj': {69193}, 'degree': 1, 'x': 136, 'y': 137},\n",
       " 68806: {'adj': {69193}, 'degree': 1, 'x': 133, 'y': 136},\n",
       " 68787: {'adj': {69193}, 'degree': 1, 'x': 135, 'y': 131},\n",
       " 69192: {'adj': {69126, 69193}, 'degree': 2, 'x': 124, 'y': 146},\n",
       " 69189: {'adj': {69199}, 'degree': 1, 'x': 114, 'y': 113},\n",
       " 69188: {'adj': {69206}, 'degree': 1, 'x': 113, 'y': 109},\n",
       " 69187: {'adj': {69201}, 'degree': 1, 'x': 137, 'y': 131},\n",
       " 68931: {'adj': {68785, 68931, 68947, 69128, 69186},\n",
       "  'degree': 5,\n",
       "  'x': 105,\n",
       "  'y': 146},\n",
       " 68789: {'adj': {69186}, 'degree': 1, 'x': 115, 'y': 122},\n",
       " 69185: {'adj': {69196}, 'degree': 1, 'x': 126, 'y': 147},\n",
       " 69184: {'adj': {69197}, 'degree': 1, 'x': 119, 'y': 145},\n",
       " 69183: {'adj': {68722, 68733, 69132, 69149, 69182},\n",
       "  'degree': 5,\n",
       "  'x': 170,\n",
       "  'y': 118},\n",
       " 69132: {'adj': {68679, 68733, 68739, 69121, 69183, 69191},\n",
       "  'degree': 6,\n",
       "  'x': 164,\n",
       "  'y': 120},\n",
       " 68733: {'adj': {68679, 68706, 69132, 69183}, 'degree': 4, 'x': 168, 'y': 121},\n",
       " 68722: {'adj': {68259, 68730, 69183}, 'degree': 3, 'x': 160, 'y': 121},\n",
       " 69182: {'adj': {69183}, 'degree': 1, 'x': 172, 'y': 118},\n",
       " 69181: {'adj': {68332, 68334, 68335, 68337, 68343, 69100, 69175},\n",
       "  'degree': 7,\n",
       "  'x': 136,\n",
       "  'y': 160},\n",
       " 68343: {'adj': {68323, 68348, 68353, 68354, 68355, 69181},\n",
       "  'degree': 6,\n",
       "  'x': 132,\n",
       "  'y': 168},\n",
       " 68337: {'adj': {68324, 68353, 68354, 68355, 69079, 69181},\n",
       "  'degree': 6,\n",
       "  'x': 122,\n",
       "  'y': 167},\n",
       " 68335: {'adj': {68325, 68353, 68354, 68355, 69181},\n",
       "  'degree': 5,\n",
       "  'x': 129,\n",
       "  'y': 178},\n",
       " 68334: {'adj': {68327, 68350, 68353, 68354, 68355, 69181},\n",
       "  'degree': 6,\n",
       "  'x': 147,\n",
       "  'y': 170},\n",
       " 68332: {'adj': {68328, 68349, 68353, 68354, 68355, 69181},\n",
       "  'degree': 6,\n",
       "  'x': 133,\n",
       "  'y': 174},\n",
       " 69128: {'adj': {68753, 68785, 68907, 68931, 69103, 69180, 69217},\n",
       "  'degree': 7,\n",
       "  'x': 115,\n",
       "  'y': 156},\n",
       " 69157: {'adj': {68799, 68837, 69179, 69208}, 'degree': 4, 'x': 132, 'y': 153},\n",
       " 69130: {'adj': {69179, 69208}, 'degree': 2, 'x': 136, 'y': 157},\n",
       " 68809: {'adj': {69179}, 'degree': 1, 'x': 127, 'y': 154},\n",
       " 68845: {'adj': {69178}, 'degree': 1, 'x': 142, 'y': 145},\n",
       " 68835: {'adj': {69178}, 'degree': 1, 'x': 143, 'y': 142},\n",
       " 69159: {'adj': {68295, 68310, 69162, 69177, 69261},\n",
       "  'degree': 5,\n",
       "  'x': 155,\n",
       "  'y': 107},\n",
       " 69131: {'adj': {68286, 69131, 69177, 69227, 69235, 69239, 69266},\n",
       "  'degree': 7,\n",
       "  'x': 152,\n",
       "  'y': 114},\n",
       " 68521: {'adj': {68515, 68523, 68524, 68525, 69176},\n",
       "  'degree': 5,\n",
       "  'x': 85,\n",
       "  'y': 135},\n",
       " 69136: {'adj': {69175}, 'degree': 1, 'x': 135, 'y': 153},\n",
       " 69120: {'adj': {68754, 68802, 68864, 68887, 69125, 69175, 69215},\n",
       "  'degree': 7,\n",
       "  'x': 125,\n",
       "  'y': 139},\n",
       " 69208: {'adj': {69130, 69140, 69157, 69174}, 'degree': 4, 'x': 137, 'y': 154},\n",
       " 69173: {'adj': {69198}, 'degree': 1, 'x': 127, 'y': 150},\n",
       " 69172: {'adj': {68735, 68737, 69200, 69242}, 'degree': 4, 'x': 139, 'y': 113},\n",
       " 68737: {'adj': {68320,\n",
       "   68707,\n",
       "   69129,\n",
       "   69135,\n",
       "   69143,\n",
       "   69151,\n",
       "   69161,\n",
       "   69167,\n",
       "   69172},\n",
       "  'degree': 9,\n",
       "  'x': 150,\n",
       "  'y': 135},\n",
       " 68735: {'adj': {68685, 68686, 68734, 68978, 69172},\n",
       "  'degree': 5,\n",
       "  'x': 105,\n",
       "  'y': 110},\n",
       " 69171: {'adj': {69204}, 'degree': 1, 'x': 141, 'y': 99},\n",
       " 69170: {'adj': {68785, 69206}, 'degree': 2, 'x': 106, 'y': 120},\n",
       " 68785: {'adj': {68931, 69128, 69129, 69170}, 'degree': 4, 'x': 115, 'y': 152},\n",
       " 69169: {'adj': {69199}, 'degree': 1, 'x': 114, 'y': 117},\n",
       " 69168: {'adj': {69196}, 'degree': 1, 'x': 125, 'y': 144},\n",
       " 69154: {'adj': {68295, 68738, 69167, 69200}, 'degree': 4, 'x': 135, 'y': 108},\n",
       " 68263: {'adj': {68295, 69167, 69238}, 'degree': 3, 'x': 143, 'y': 112},\n",
       " 69162: {'adj': {69159, 69167, 69210}, 'degree': 3, 'x': 156, 'y': 111},\n",
       " 69119: {'adj': {69167}, 'degree': 1, 'x': 136, 'y': 128},\n",
       " 68749: {'adj': {69150, 69167}, 'degree': 2, 'x': 134, 'y': 148},\n",
       " 68748: {'adj': {69150, 69167}, 'degree': 2, 'x': 137, 'y': 141},\n",
       " 68768: {'adj': {69167}, 'degree': 1, 'x': 141, 'y': 127},\n",
       " 69166: {'adj': {69099, 69103, 69106, 69201}, 'degree': 4, 'x': 131, 'y': 135},\n",
       " 69099: {'adj': {69061,\n",
       "   69065,\n",
       "   69071,\n",
       "   69074,\n",
       "   69085,\n",
       "   69090,\n",
       "   69113,\n",
       "   69166,\n",
       "   69193},\n",
       "  'degree': 9,\n",
       "  'x': 133,\n",
       "  'y': 121},\n",
       " 69165: {'adj': {68628,\n",
       "   68994,\n",
       "   68997,\n",
       "   68998,\n",
       "   69007,\n",
       "   69015,\n",
       "   69016,\n",
       "   69023,\n",
       "   69228},\n",
       "  'degree': 9,\n",
       "  'x': 113,\n",
       "  'y': 129},\n",
       " 69023: {'adj': {68987, 69030, 69043, 69165}, 'degree': 4, 'x': 119, 'y': 136},\n",
       " 69016: {'adj': {68978, 68979, 68987, 69043, 69165},\n",
       "  'degree': 5,\n",
       "  'x': 107,\n",
       "  'y': 125},\n",
       " 69015: {'adj': {68970,\n",
       "   68972,\n",
       "   68974,\n",
       "   68975,\n",
       "   68976,\n",
       "   68980,\n",
       "   68987,\n",
       "   68988,\n",
       "   68989,\n",
       "   68990,\n",
       "   69029,\n",
       "   69043,\n",
       "   69165},\n",
       "  'degree': 13,\n",
       "  'x': 153,\n",
       "  'y': 145},\n",
       " 69007: {'adj': {68971,\n",
       "   68978,\n",
       "   68979,\n",
       "   68980,\n",
       "   68985,\n",
       "   68986,\n",
       "   68987,\n",
       "   69028,\n",
       "   69043,\n",
       "   69165},\n",
       "  'degree': 10,\n",
       "  'x': 119,\n",
       "  'y': 130},\n",
       " 68998: {'adj': {68987, 69043, 69165}, 'degree': 3, 'x': 115, 'y': 137},\n",
       " 68997: {'adj': {68970,\n",
       "   68972,\n",
       "   68974,\n",
       "   68975,\n",
       "   68976,\n",
       "   68987,\n",
       "   68988,\n",
       "   68989,\n",
       "   68990,\n",
       "   69028,\n",
       "   69043,\n",
       "   69165},\n",
       "  'degree': 12,\n",
       "  'x': 111,\n",
       "  'y': 135},\n",
       " 68994: {'adj': {68987, 69029, 69030, 69043, 69165},\n",
       "  'degree': 5,\n",
       "  'x': 103,\n",
       "  'y': 135},\n",
       " 68628: {'adj': {68607, 68608, 68653, 68987, 69043, 69165},\n",
       "  'degree': 6,\n",
       "  'x': 113,\n",
       "  'y': 140},\n",
       " 69164: {'adj': {68738, 69200}, 'degree': 2, 'x': 134, 'y': 98},\n",
       " 68738: {'adj': {68259, 68286, 68295, 69154, 69164},\n",
       "  'degree': 5,\n",
       "  'x': 135,\n",
       "  'y': 101},\n",
       " 69163: {'adj': {69228}, 'degree': 1, 'x': 113, 'y': 134},\n",
       " 69161: {'adj': {68737, 69202, 69205}, 'degree': 3, 'x': 140, 'y': 146},\n",
       " 69160: {'adj': {69206}, 'degree': 1, 'x': 115, 'y': 107},\n",
       " 69158: {'adj': {69202}, 'degree': 1, 'x': 143, 'y': 149},\n",
       " 68799: {'adj': {68253, 68762, 69157}, 'degree': 3, 'x': 139, 'y': 152},\n",
       " 68837: {'adj': {69157}, 'degree': 1, 'x': 133, 'y': 151},\n",
       " 69156: {'adj': {69202}, 'degree': 1, 'x': 145, 'y': 147},\n",
       " 69155: {'adj': {69197}, 'degree': 1, 'x': 115, 'y': 146},\n",
       " 68295: {'adj': {68259, 68263, 68286, 68300, 68738, 69154, 69159, 69242},\n",
       "  'degree': 8,\n",
       "  'x': 140,\n",
       "  'y': 108},\n",
       " 69153: {'adj': {69125}, 'degree': 1, 'x': 130, 'y': 148},\n",
       " 69151: {'adj': {68737, 69202, 69205}, 'degree': 3, 'x': 146, 'y': 148},\n",
       " 69213: {'adj': {69150}, 'degree': 1, 'x': 135, 'y': 146},\n",
       " 68751: {'adj': {68754, 69150}, 'degree': 2, 'x': 121, 'y': 160},\n",
       " 68904: {'adj': {68399, 68850, 68859, 68865, 68870, 68884, 69148},\n",
       "  'degree': 7,\n",
       "  'x': 165,\n",
       "  'y': 142},\n",
       " 69050: {'adj': {69041, 69042, 69058, 69141, 69148},\n",
       "  'degree': 5,\n",
       "  'x': 166,\n",
       "  'y': 149},\n",
       " 69049: {'adj': {69041,\n",
       "   69042,\n",
       "   69043,\n",
       "   69044,\n",
       "   69045,\n",
       "   69054,\n",
       "   69055,\n",
       "   69058,\n",
       "   69125,\n",
       "   69126,\n",
       "   69141,\n",
       "   69148},\n",
       "  'degree': 12,\n",
       "  'x': 121,\n",
       "  'y': 150},\n",
       " 68794: {'adj': {69148}, 'degree': 1, 'x': 160, 'y': 144},\n",
       " 69146: {'adj': {68727, 68732, 68744, 69199}, 'degree': 4, 'x': 93, 'y': 119},\n",
       " 68744: {'adj': {68690, 69146}, 'degree': 2, 'x': 90, 'y': 118},\n",
       " 68732: {'adj': {68675, 68690, 68725, 69146}, 'degree': 4, 'x': 99, 'y': 121},\n",
       " 69145: {'adj': {69204}, 'degree': 1, 'x': 139, 'y': 100},\n",
       " 69144: {'adj': {69204}, 'degree': 1, 'x': 143, 'y': 100},\n",
       " 69143: {'adj': {68737, 69202, 69205}, 'degree': 3, 'x': 149, 'y': 146},\n",
       " 69141: {'adj': {69049, 69050, 69207}, 'degree': 3, 'x': 161, 'y': 150},\n",
       " 69139: {'adj': {69200}, 'degree': 1, 'x': 130, 'y': 107},\n",
       " 69138: {'adj': {69121, 69149}, 'degree': 2, 'x': 165, 'y': 116},\n",
       " 69137: {'adj': {69199}, 'degree': 1, 'x': 116, 'y': 115},\n",
       " 69135: {'adj': {68646, 68737, 69202}, 'degree': 3, 'x': 142, 'y': 151},\n",
       " 68646: {'adj': {68602, 68603, 68604, 68606, 68651, 69135},\n",
       "  'degree': 6,\n",
       "  'x': 100,\n",
       "  'y': 157},\n",
       " 69134: {'adj': {69199}, 'degree': 1, 'x': 112, 'y': 116},\n",
       " 68739: {'adj': {69132}, 'degree': 1, 'x': 164, 'y': 122},\n",
       " 68679: {'adj': {68676, 68733, 69132}, 'degree': 3, 'x': 167, 'y': 124},\n",
       " 69235: {'adj': {69131}, 'degree': 1, 'x': 154, 'y': 113},\n",
       " 68253: {'adj': {68384,\n",
       "   68387,\n",
       "   68389,\n",
       "   68397,\n",
       "   68403,\n",
       "   68404,\n",
       "   68407,\n",
       "   68409,\n",
       "   68784,\n",
       "   68799,\n",
       "   68850,\n",
       "   68852,\n",
       "   68856,\n",
       "   68865,\n",
       "   68873,\n",
       "   68876,\n",
       "   68877,\n",
       "   68880,\n",
       "   68882,\n",
       "   68883,\n",
       "   68890,\n",
       "   68892,\n",
       "   68894,\n",
       "   68899,\n",
       "   68903,\n",
       "   68934,\n",
       "   68939,\n",
       "   68940,\n",
       "   69118,\n",
       "   69129,\n",
       "   69193},\n",
       "  'degree': 31,\n",
       "  'x': 141,\n",
       "  'y': 137},\n",
       " 69127: {'adj': {69126, 69193}, 'degree': 2, 'x': 123, 'y': 141},\n",
       " 69051: {'adj': {69044, 69057, 69126}, 'degree': 3, 'x': 121, 'y': 142},\n",
       " 69048: {'adj': {69043, 69044, 69057, 69058, 69126},\n",
       "  'degree': 5,\n",
       "  'x': 111,\n",
       "  'y': 144},\n",
       " 69056: {'adj': {69125}, 'degree': 1, 'x': 123, 'y': 148},\n",
       " 69054: {'adj': {69049, 69125}, 'degree': 2, 'x': 118, 'y': 157},\n",
       " 69055: {'adj': {69049, 69125}, 'degree': 2, 'x': 123, 'y': 150},\n",
       " 69124: {'adj': {69201}, 'degree': 1, 'x': 138, 'y': 133},\n",
       " 69123: {'adj': {69197}, 'degree': 1, 'x': 118, 'y': 148},\n",
       " 69122: {'adj': {69207}, 'degree': 1, 'x': 157, 'y': 148},\n",
       " 69215: {'adj': {69120}, 'degree': 1, 'x': 127, 'y': 139},\n",
       " 68754: {'adj': {68751, 69120}, 'degree': 2, 'x': 121, 'y': 163},\n",
       " 68802: {'adj': {69120}, 'degree': 1, 'x': 124, 'y': 138},\n",
       " 68875: {'adj': {68396, 68873, 68881, 68900, 69074, 69118},\n",
       "  'degree': 6,\n",
       "  'x': 151,\n",
       "  'y': 127},\n",
       " 68359: {'adj': {68363, 69118}, 'degree': 2, 'x': 163, 'y': 146},\n",
       " 68358: {'adj': {68360, 69118}, 'degree': 2, 'x': 179, 'y': 147},\n",
       " 68357: {'adj': {68372, 69118}, 'degree': 2, 'x': 152, 'y': 149},\n",
       " 69108: {'adj': {69063, 69077, 69106, 69118}, 'degree': 4, 'x': 133, 'y': 141},\n",
       " 69097: {'adj': {69062, 69093, 69098, 69116, 69118},\n",
       "  'degree': 5,\n",
       "  'x': 157,\n",
       "  'y': 161},\n",
       " 68943: {'adj': {68923, 68924, 69118}, 'degree': 3, 'x': 151, 'y': 152},\n",
       " 68938: {'adj': {68884, 68911, 68953, 69118}, 'degree': 4, 'x': 125, 'y': 149},\n",
       " 68949: {'adj': {68920, 68921, 68922, 69118}, 'degree': 4, 'x': 140, 'y': 149},\n",
       " 68730: {'adj': {68677, 68691, 68697, 68698, 68722, 69118},\n",
       "  'degree': 6,\n",
       "  'x': 150,\n",
       "  'y': 155},\n",
       " 69116: {'adj': {69097, 69111, 69114}, 'degree': 3, 'x': 157, 'y': 165},\n",
       " 69114: {'adj': {69086, 69116}, 'degree': 2, 'x': 154, 'y': 167},\n",
       " 69115: {'adj': {69072, 69081, 69087}, 'degree': 3, 'x': 151, 'y': 122},\n",
       " 69113: {'adj': {69099}, 'degree': 1, 'x': 132, 'y': 119},\n",
       " 69111: {'adj': {69079, 69105, 69116}, 'degree': 3, 'x': 161, 'y': 162},\n",
       " 69105: {'adj': {69078, 69080, 69098, 69111}, 'degree': 4, 'x': 158, 'y': 157},\n",
       " 69109: {'adj': {69076}, 'degree': 1, 'x': 126, 'y': 119},\n",
       " 69076: {'adj': {69109, 69193}, 'degree': 2, 'x': 126, 'y': 121},\n",
       " 69107: {'adj': {69075, 69092, 69095}, 'degree': 3, 'x': 187, 'y': 113},\n",
       " 69075: {'adj': {69107, 69112}, 'degree': 2, 'x': 191, 'y': 112},\n",
       " 69102: {'adj': {69060, 69068, 69081, 69110}, 'degree': 4, 'x': 144, 'y': 126},\n",
       " 69110: {'adj': {69072, 69102}, 'degree': 2, 'x': 147, 'y': 120},\n",
       " 69101: {'adj': {69069, 69070, 69193}, 'degree': 3, 'x': 134, 'y': 129},\n",
       " 69095: {'adj': {69107}, 'degree': 1, 'x': 186, 'y': 111},\n",
       " 69094: {'adj': {69103, 69193}, 'degree': 2, 'x': 133, 'y': 139},\n",
       " 69093: {'adj': {69097}, 'degree': 1, 'x': 159, 'y': 160},\n",
       " 69092: {'adj': {69064, 69081, 69107}, 'degree': 3, 'x': 181, 'y': 115},\n",
       " 69091: {'adj': {68519, 69103}, 'degree': 2, 'x': 120, 'y': 102},\n",
       " 68519: {'adj': {68522, 68529, 68530, 68531, 68532, 68535, 69091},\n",
       "  'degree': 7,\n",
       "  'x': 124,\n",
       "  'y': 103},\n",
       " 69090: {'adj': {69099}, 'degree': 1, 'x': 135, 'y': 120},\n",
       " 69089: {'adj': {69103}, 'degree': 1, 'x': 131, 'y': 141},\n",
       " 69088: {'adj': {69103}, 'degree': 1, 'x': 128, 'y': 142},\n",
       " 69087: {'adj': {69115}, 'degree': 1, 'x': 153, 'y': 122},\n",
       " 69086: {'adj': {68844, 69100, 69114, 69193}, 'degree': 4, 'x': 141, 'y': 159},\n",
       " 69085: {'adj': {69099, 69103, 69106}, 'degree': 3, 'x': 129, 'y': 140},\n",
       " 69084: {'adj': {69106}, 'degree': 1, 'x': 130, 'y': 137},\n",
       " 69083: {'adj': {69104}, 'degree': 1, 'x': 129, 'y': 131},\n",
       " 69082: {'adj': {69100}, 'degree': 1, 'x': 144, 'y': 161},\n",
       " 69080: {'adj': {69105}, 'degree': 1, 'x': 160, 'y': 157},\n",
       " 69079: {'adj': {68337, 69100, 69111}, 'degree': 3, 'x': 142, 'y': 166},\n",
       " 69078: {'adj': {69105}, 'degree': 1, 'x': 158, 'y': 155},\n",
       " 69077: {'adj': {69108}, 'degree': 1, 'x': 133, 'y': 143},\n",
       " 69112: {'adj': {69075}, 'degree': 1, 'x': 193, 'y': 111},\n",
       " 68388: {'adj': {69074}, 'degree': 1, 'x': 138, 'y': 119},\n",
       " 68389: {'adj': {68253, 68861, 68878, 68879, 69074, 69118, 69193},\n",
       "  'degree': 7,\n",
       "  'x': 145,\n",
       "  'y': 136},\n",
       " 68808: {'adj': {69074}, 'degree': 1, 'x': 139, 'y': 116},\n",
       " 69073: {'adj': {69193}, 'degree': 1, 'x': 138, 'y': 138},\n",
       " 69072: {'adj': {69110, 69115, 69193}, 'degree': 3, 'x': 148, 'y': 126},\n",
       " 69071: {'adj': {69099, 69103, 69106}, 'degree': 3, 'x': 131, 'y': 144},\n",
       " 69070: {'adj': {69101, 69104}, 'degree': 2, 'x': 135, 'y': 123},\n",
       " 69069: {'adj': {69101}, 'degree': 1, 'x': 133, 'y': 127},\n",
       " 69068: {'adj': {69102}, 'degree': 1, 'x': 144, 'y': 128},\n",
       " 69067: {'adj': {69098}, 'degree': 1, 'x': 153, 'y': 158},\n",
       " 69066: {'adj': {69103}, 'degree': 1, 'x': 135, 'y': 141},\n",
       " 69065: {'adj': {69099, 69103, 69106}, 'degree': 3, 'x': 127, 'y': 132},\n",
       " 69063: {'adj': {69108}, 'degree': 1, 'x': 137, 'y': 143},\n",
       " 69062: {'adj': {69097}, 'degree': 1, 'x': 156, 'y': 163},\n",
       " 69061: {'adj': {69099, 69106}, 'degree': 2, 'x': 130, 'y': 122},\n",
       " 69060: {'adj': {69102}, 'degree': 1, 'x': 143, 'y': 125},\n",
       " 69059: {'adj': {69103}, 'degree': 1, 'x': 125, 'y': 141},\n",
       " 69058: {'adj': {69048, 69049, 69050}, 'degree': 3, 'x': 118, 'y': 150},\n",
       " 69057: {'adj': {69046, 69048, 69051}, 'degree': 3, 'x': 108, 'y': 144},\n",
       " 69046: {'adj': {69043, 69057}, 'degree': 2, 'x': 105, 'y': 143},\n",
       " 69045: {'adj': {69049}, 'degree': 1, 'x': 120, 'y': 152},\n",
       " 69044: {'adj': {69048, 69049, 69051}, 'degree': 3, 'x': 115, 'y': 148},\n",
       " 69043: {'adj': {68628,\n",
       "   68994,\n",
       "   68997,\n",
       "   68998,\n",
       "   69007,\n",
       "   69015,\n",
       "   69016,\n",
       "   69023,\n",
       "   69046,\n",
       "   69048,\n",
       "   69049},\n",
       "  'degree': 11,\n",
       "  'x': 119,\n",
       "  'y': 139},\n",
       " 69042: {'adj': {69049, 69050}, 'degree': 2, 'x': 166, 'y': 152},\n",
       " 69041: {'adj': {69049, 69050}, 'degree': 2, 'x': 170, 'y': 149},\n",
       " 69034: {'adj': {68980}, 'degree': 1, 'x': 128, 'y': 115},\n",
       " 68980: {'adj': {68562,\n",
       "   68566,\n",
       "   68567,\n",
       "   68630,\n",
       "   69007,\n",
       "   69014,\n",
       "   69015,\n",
       "   69032,\n",
       "   69034},\n",
       "  'degree': 9,\n",
       "  'x': 127,\n",
       "  'y': 117},\n",
       " 69032: {'adj': {68980, 69011, 69014, 69031}, 'degree': 4, 'x': 125, 'y': 114},\n",
       " 69014: {'adj': {68978, 68979, 68980, 68985, 68986, 69030, 69032},\n",
       "  'degree': 7,\n",
       "  'x': 125,\n",
       "  'y': 111},\n",
       " 69031: {'adj': {68969, 69020, 69022, 69032}, 'degree': 4, 'x': 126, 'y': 107},\n",
       " 69022: {'adj': {68978, 68979, 69031}, 'degree': 3, 'x': 118, 'y': 106},\n",
       " 69030: {'adj': {68994, 69014, 69023}, 'degree': 3, 'x': 107, 'y': 135},\n",
       " 69029: {'adj': {68994, 68996, 69013, 69015}, 'degree': 4, 'x': 127, 'y': 126},\n",
       " 69028: {'adj': {68993, 68997, 69007, 69018}, 'degree': 4, 'x': 116, 'y': 127},\n",
       " 69020: {'adj': {69031}, 'degree': 1, 'x': 126, 'y': 105},\n",
       " 69018: {'adj': {68972, 68974, 68975, 68976, 69028},\n",
       "  'degree': 5,\n",
       "  'x': 119,\n",
       "  'y': 126},\n",
       " 69013: {'adj': {68972, 68974, 68975, 68976, 69029},\n",
       "  'degree': 5,\n",
       "  'x': 135,\n",
       "  'y': 126},\n",
       " 69011: {'adj': {68985, 68986, 69032}, 'degree': 3, 'x': 118, 'y': 111},\n",
       " 69010: {'adj': {68985, 68986, 69037}, 'degree': 3, 'x': 113, 'y': 105},\n",
       " 69037: {'adj': {69010}, 'degree': 1, 'x': 111, 'y': 104},\n",
       " 68999: {'adj': {68977, 68981, 68982, 68983, 68984, 69036},\n",
       "  'degree': 6,\n",
       "  'x': 99,\n",
       "  'y': 112},\n",
       " 69036: {'adj': {68999}, 'degree': 1, 'x': 97, 'y': 112},\n",
       " 68996: {'adj': {68970, 68988, 68989, 68990, 69029},\n",
       "  'degree': 5,\n",
       "  'x': 123,\n",
       "  'y': 119},\n",
       " 68993: {'adj': {68970, 68988, 68989, 68990, 69028},\n",
       "  'degree': 5,\n",
       "  'x': 130,\n",
       "  'y': 111},\n",
       " 68992: {'adj': {68978, 68979, 69039}, 'degree': 3, 'x': 110, 'y': 112},\n",
       " 69039: {'adj': {68992}, 'degree': 1, 'x': 109, 'y': 110},\n",
       " 68990: {'adj': {68562, 68642, 68993, 68996, 68997, 69015},\n",
       "  'degree': 6,\n",
       "  'x': 131,\n",
       "  'y': 115},\n",
       " 68562: {'adj': {68537,\n",
       "   68580,\n",
       "   68588,\n",
       "   68598,\n",
       "   68970,\n",
       "   68972,\n",
       "   68974,\n",
       "   68975,\n",
       "   68976,\n",
       "   68980,\n",
       "   68988,\n",
       "   68989,\n",
       "   68990},\n",
       "  'degree': 13,\n",
       "  'x': 135,\n",
       "  'y': 113},\n",
       " 68642: {'adj': {68654, 68970, 68988, 68989, 68990},\n",
       "  'degree': 5,\n",
       "  'x': 130,\n",
       "  'y': 119},\n",
       " 68989: {'adj': {68562, 68642, 68993, 68996, 68997, 69015},\n",
       "  'degree': 6,\n",
       "  'x': 128,\n",
       "  'y': 123},\n",
       " 68988: {'adj': {68562, 68642, 68993, 68996, 68997, 69015},\n",
       "  'degree': 6,\n",
       "  'x': 107,\n",
       "  'y': 131},\n",
       " 68987: {'adj': {68628,\n",
       "   68971,\n",
       "   68994,\n",
       "   68997,\n",
       "   68998,\n",
       "   69007,\n",
       "   69015,\n",
       "   69016,\n",
       "   69023},\n",
       "  'degree': 9,\n",
       "  'x': 122,\n",
       "  'y': 135},\n",
       " 68971: {'adj': {68987, 69007, 69038}, 'degree': 3, 'x': 120, 'y': 132},\n",
       " 68986: {'adj': {68734, 69007, 69010, 69011, 69014},\n",
       "  'degree': 5,\n",
       "  'x': 112,\n",
       "  'y': 102},\n",
       " 68734: {'adj': {68735, 68979, 68986}, 'degree': 3, 'x': 109, 'y': 106},\n",
       " 68985: {'adj': {69007, 69010, 69011, 69014}, 'degree': 4, 'x': 121, 'y': 112},\n",
       " 68984: {'adj': {68630, 68999}, 'degree': 2, 'x': 103, 'y': 114},\n",
       " 68630: {'adj': {68654, 68977, 68980, 68981, 68982, 68983, 68984},\n",
       "  'degree': 7,\n",
       "  'x': 120,\n",
       "  'y': 115},\n",
       " 68983: {'adj': {68630, 68999}, 'degree': 2, 'x': 101, 'y': 109},\n",
       " 68982: {'adj': {68630, 68999}, 'degree': 2, 'x': 100, 'y': 116},\n",
       " 68981: {'adj': {68630, 68999}, 'degree': 2, 'x': 104, 'y': 106},\n",
       " 68567: {'adj': {68544, 68581, 68980}, 'degree': 3, 'x': 129, 'y': 103},\n",
       " 68566: {'adj': {68538, 68973, 68980}, 'degree': 3, 'x': 108, 'y': 164},\n",
       " 68979: {'adj': {68734, 68992, 69007, 69014, 69016, 69022},\n",
       "  'degree': 6,\n",
       "  'x': 121,\n",
       "  'y': 108},\n",
       " 68978: {'adj': {68735, 68992, 69007, 69014, 69016, 69022},\n",
       "  'degree': 6,\n",
       "  'x': 107,\n",
       "  'y': 116},\n",
       " 68977: {'adj': {68630, 68999}, 'degree': 2, 'x': 110, 'y': 115},\n",
       " 68976: {'adj': {68562, 68638, 68997, 69013, 69015, 69018},\n",
       "  'degree': 6,\n",
       "  'x': 116,\n",
       "  'y': 134},\n",
       " 68638: {'adj': {68654, 68972, 68974, 68975, 68976},\n",
       "  'degree': 5,\n",
       "  'x': 117,\n",
       "  'y': 142},\n",
       " 68975: {'adj': {68562, 68638, 68997, 69013, 69015, 69018},\n",
       "  'degree': 6,\n",
       "  'x': 143,\n",
       "  'y': 120},\n",
       " 68974: {'adj': {68562, 68638, 68997, 69013, 69015, 69018},\n",
       "  'degree': 6,\n",
       "  'x': 107,\n",
       "  'y': 140},\n",
       " 68973: {'adj': {68566, 68650, 69009, 69019, 69033, 69040},\n",
       "  'degree': 6,\n",
       "  'x': 108,\n",
       "  'y': 169},\n",
       " 69040: {'adj': {68973}, 'degree': 1, 'x': 110, 'y': 170},\n",
       " 69033: {'adj': {68973}, 'degree': 1, 'x': 106, 'y': 168},\n",
       " 69019: {'adj': {68973}, 'degree': 1, 'x': 109, 'y': 167},\n",
       " 69009: {'adj': {68973}, 'degree': 1, 'x': 107, 'y': 171},\n",
       " 68650: {'adj': {68625, 68633, 68973}, 'degree': 3, 'x': 108, 'y': 173},\n",
       " 68972: {'adj': {68562, 68638, 68997, 69013, 69015, 69018},\n",
       "  'degree': 6,\n",
       "  'x': 149,\n",
       "  'y': 142},\n",
       " 69038: {'adj': {68971}, 'degree': 1, 'x': 118, 'y': 133},\n",
       " 68970: {'adj': {68562, 68642, 68993, 68996, 68997, 69015},\n",
       "  'degree': 6,\n",
       "  'x': 137,\n",
       "  'y': 117},\n",
       " 68969: {'adj': {69031, 69035}, 'degree': 2, 'x': 124, 'y': 99},\n",
       " 69035: {'adj': {68969}, 'degree': 1, 'x': 124, 'y': 97},\n",
       " 68968: {'adj': {68866, 68872, 68895}, 'degree': 3, 'x': 138, 'y': 126},\n",
       " 68960: {'adj': {68967}, 'degree': 1, 'x': 112, 'y': 204},\n",
       " 68967: {'adj': {68960}, 'degree': 1, 'x': 110, 'y': 204},\n",
       " 68958: {'adj': {68965}, 'degree': 1, 'x': 75, 'y': 174},\n",
       " 68965: {'adj': {68958}, 'degree': 1, 'x': 75, 'y': 176},\n",
       " 68957: {'adj': {68964}, 'degree': 1, 'x': 99, 'y': 65},\n",
       " 68964: {'adj': {68957}, 'degree': 1, 'x': 97, 'y': 65},\n",
       " 68956: {'adj': {68963}, 'degree': 1, 'x': 156, 'y': 207},\n",
       " 68963: {'adj': {68956}, 'degree': 1, 'x': 154, 'y': 207},\n",
       " 68955: {'adj': {68962}, 'degree': 1, 'x': 170, 'y': 69},\n",
       " 68962: {'adj': {68955}, 'degree': 1, 'x': 172, 'y': 69},\n",
       " 68954: {'adj': {68961}, 'degree': 1, 'x': 211, 'y': 135},\n",
       " 68961: {'adj': {68954}, 'degree': 1, 'x': 211, 'y': 137},\n",
       " 68953: {'adj': {68883, 68911, 68938, 68951}, 'degree': 4, 'x': 121, 'y': 147},\n",
       " 68883: {'adj': {68253, 68854, 68953}, 'degree': 3, 'x': 145, 'y': 154},\n",
       " 68911: {'adj': {68938, 68942, 68953}, 'degree': 3, 'x': 122, 'y': 153},\n",
       " 68951: {'adj': {68925, 68941, 68953}, 'degree': 3, 'x': 110, 'y': 159},\n",
       " 68925: {'adj': {68380, 68861, 68951}, 'degree': 3, 'x': 114, 'y': 166},\n",
       " 68950: {'adj': {68952}, 'degree': 1, 'x': 57, 'y': 145},\n",
       " 68952: {'adj': {68950}, 'degree': 1, 'x': 57, 'y': 147},\n",
       " 68947: {'adj': {68891, 68926, 68930, 68931}, 'degree': 4, 'x': 114, 'y': 161},\n",
       " 68891: {'adj': {68387, 68854, 68927, 68929, 68940, 68946, 68947},\n",
       "  'degree': 7,\n",
       "  'x': 162,\n",
       "  'y': 156},\n",
       " 68926: {'adj': {68888, 68899, 68909, 68942, 68947},\n",
       "  'degree': 5,\n",
       "  'x': 118,\n",
       "  'y': 162},\n",
       " 68946: {'adj': {68891, 68932, 68933}, 'degree': 3, 'x': 165, 'y': 158},\n",
       " 68942: {'adj': {68849, 68884, 68898, 68909, 68911, 68926},\n",
       "  'degree': 6,\n",
       "  'x': 122,\n",
       "  'y': 157},\n",
       " 68884: {'adj': {68849, 68904, 68937, 68938, 68942},\n",
       "  'degree': 5,\n",
       "  'x': 125,\n",
       "  'y': 152},\n",
       " 68941: {'adj': {68951}, 'degree': 1, 'x': 108, 'y': 160},\n",
       " 68940: {'adj': {68253, 68891, 68907}, 'degree': 3, 'x': 148, 'y': 158},\n",
       " 68939: {'adj': {68253, 68851, 68861, 68878, 68879, 68919, 69118},\n",
       "  'degree': 7,\n",
       "  'x': 149,\n",
       "  'y': 149},\n",
       " 68851: {'adj': {68849, 68899, 68939}, 'degree': 3, 'x': 143, 'y': 135},\n",
       " 68919: {'adj': {68854, 68939}, 'degree': 2, 'x': 148, 'y': 153},\n",
       " 68937: {'adj': {68849, 68884, 68890, 68896, 68909, 68913, 68914},\n",
       "  'degree': 7,\n",
       "  'x': 111,\n",
       "  'y': 123},\n",
       " 68909: {'adj': {68849, 68910, 68926, 68937, 68942},\n",
       "  'degree': 5,\n",
       "  'x': 119,\n",
       "  'y': 154},\n",
       " 68890: {'adj': {68253, 68937}, 'degree': 2, 'x': 111, 'y': 126},\n",
       " 68934: {'adj': {68253}, 'degree': 1, 'x': 143, 'y': 137},\n",
       " 68933: {'adj': {68946}, 'degree': 1, 'x': 167, 'y': 159},\n",
       " 68932: {'adj': {68946}, 'degree': 1, 'x': 164, 'y': 160},\n",
       " 68930: {'adj': {68947}, 'degree': 1, 'x': 112, 'y': 162},\n",
       " 68929: {'adj': {68891}, 'degree': 1, 'x': 164, 'y': 155},\n",
       " 68927: {'adj': {68891}, 'degree': 1, 'x': 162, 'y': 158},\n",
       " 68861: {'adj': {68389, 68770, 68786, 68925, 68939},\n",
       "  'degree': 5,\n",
       "  'x': 159,\n",
       "  'y': 140},\n",
       " 68924: {'adj': {68943}, 'degree': 1, 'x': 153, 'y': 152},\n",
       " 68923: {'adj': {68943}, 'degree': 1, 'x': 149, 'y': 151},\n",
       " 68922: {'adj': {68949}, 'degree': 1, 'x': 141, 'y': 153},\n",
       " 68921: {'adj': {68949}, 'degree': 1, 'x': 139, 'y': 150},\n",
       " 68920: {'adj': {68949}, 'degree': 1, 'x': 141, 'y': 148},\n",
       " 68854: {'adj': {68855, 68864, 68883, 68891, 68919},\n",
       "  'degree': 5,\n",
       "  'x': 145,\n",
       "  'y': 157},\n",
       " 68918: {'adj': {69118}, 'degree': 1, 'x': 140, 'y': 142},\n",
       " 68917: {'adj': {68899, 68944}, 'degree': 2, 'x': 163, 'y': 128},\n",
       " 68944: {'adj': {68899, 68908, 68915, 68916, 68917},\n",
       "  'degree': 5,\n",
       "  'x': 161,\n",
       "  'y': 126},\n",
       " 68916: {'adj': {68944}, 'degree': 1, 'x': 163, 'y': 125},\n",
       " 68915: {'adj': {68944}, 'degree': 1, 'x': 160, 'y': 128},\n",
       " 68914: {'adj': {68937}, 'degree': 1, 'x': 109, 'y': 122},\n",
       " 68913: {'adj': {68937}, 'degree': 1, 'x': 113, 'y': 124},\n",
       " 68910: {'adj': {68909}, 'degree': 1, 'x': 117, 'y': 154},\n",
       " 68849: {'adj': {68850,\n",
       "   68851,\n",
       "   68853,\n",
       "   68855,\n",
       "   68856,\n",
       "   68857,\n",
       "   68859,\n",
       "   68860,\n",
       "   68864,\n",
       "   68865,\n",
       "   68866,\n",
       "   68870,\n",
       "   68871,\n",
       "   68873,\n",
       "   68874,\n",
       "   68876,\n",
       "   68877,\n",
       "   68880,\n",
       "   68881,\n",
       "   68882,\n",
       "   68884,\n",
       "   68885,\n",
       "   68886,\n",
       "   68889,\n",
       "   68892,\n",
       "   68893,\n",
       "   68894,\n",
       "   68897,\n",
       "   68899,\n",
       "   68900,\n",
       "   68901,\n",
       "   68902,\n",
       "   68903,\n",
       "   68908,\n",
       "   68909,\n",
       "   68937,\n",
       "   68942,\n",
       "   69193},\n",
       "  'degree': 38,\n",
       "  'x': 129,\n",
       "  'y': 133},\n",
       " 68908: {'adj': {68849, 68944}, 'degree': 2, 'x': 158, 'y': 125},\n",
       " 68387: {'adj': {68253, 68891, 68907}, 'degree': 3, 'x': 154, 'y': 154},\n",
       " 68796: {'adj': {68907}, 'degree': 1, 'x': 107, 'y': 156},\n",
       " 68906: {'adj': {68864, 68866, 68868, 68887}, 'degree': 4, 'x': 132, 'y': 124},\n",
       " 68868: {'adj': {68906}, 'degree': 1, 'x': 130, 'y': 124},\n",
       " 68382: {'adj': {68384, 68863, 68903}, 'degree': 3, 'x': 138, 'y': 136},\n",
       " 68852: {'adj': {68253, 68378, 68379, 68858, 68903},\n",
       "  'degree': 5,\n",
       "  'x': 151,\n",
       "  'y': 131},\n",
       " 68948: {'adj': {68863, 68903}, 'degree': 2, 'x': 133, 'y': 134},\n",
       " 68902: {'adj': {68849}, 'degree': 1, 'x': 126, 'y': 133},\n",
       " 68901: {'adj': {68774, 68849, 69118}, 'degree': 3, 'x': 103, 'y': 140},\n",
       " 68774: {'adj': {68752, 68756, 68848, 68901}, 'degree': 4, 'x': 83, 'y': 145},\n",
       " 68900: {'adj': {68849, 68875}, 'degree': 2, 'x': 152, 'y': 125},\n",
       " 68905: {'adj': {68403, 68885, 68897, 68899}, 'degree': 4, 'x': 146, 'y': 133},\n",
       " 68898: {'adj': {68942}, 'degree': 1, 'x': 120, 'y': 158},\n",
       " 68897: {'adj': {68849, 68905}, 'degree': 2, 'x': 147, 'y': 135},\n",
       " 68896: {'adj': {68937}, 'degree': 1, 'x': 109, 'y': 124},\n",
       " 68377: {'adj': {68866, 68872, 68895}, 'degree': 3, 'x': 144, 'y': 130},\n",
       " 68894: {'adj': {68253, 68849}, 'degree': 2, 'x': 122, 'y': 129},\n",
       " 68893: {'adj': {68849}, 'degree': 1, 'x': 131, 'y': 133},\n",
       " 68888: {'adj': {68926}, 'degree': 1, 'x': 117, 'y': 164},\n",
       " 68757: {'adj': {68810, 68887}, 'degree': 2, 'x': 158, 'y': 132},\n",
       " 68823: {'adj': {68887}, 'degree': 1, 'x': 142, 'y': 123},\n",
       " 68886: {'adj': {68849, 69118}, 'degree': 2, 'x': 144, 'y': 144},\n",
       " 68885: {'adj': {68849, 68905}, 'degree': 2, 'x': 140, 'y': 119},\n",
       " 68882: {'adj': {68253, 68849}, 'degree': 2, 'x': 132, 'y': 130},\n",
       " 68881: {'adj': {68849, 68875}, 'degree': 2, 'x': 149, 'y': 128},\n",
       " 68880: {'adj': {68253, 68849}, 'degree': 2, 'x': 141, 'y': 134},\n",
       " 68786: {'adj': {68770, 68861, 68878, 68879}, 'degree': 4, 'x': 157, 'y': 142},\n",
       " 68770: {'adj': {68786, 68861, 68878, 68879, 69193},\n",
       "  'degree': 5,\n",
       "  'x': 157,\n",
       "  'y': 135},\n",
       " 68878: {'adj': {68389, 68770, 68786, 68939}, 'degree': 4, 'x': 152, 'y': 140},\n",
       " 68876: {'adj': {68253, 68849}, 'degree': 2, 'x': 124, 'y': 136},\n",
       " 68874: {'adj': {68849, 69118}, 'degree': 2, 'x': 130, 'y': 146},\n",
       " 68385: {'adj': {68400, 68872}, 'degree': 2, 'x': 165, 'y': 132},\n",
       " 68417: {'adj': {68384, 68872}, 'degree': 2, 'x': 151, 'y': 138},\n",
       " 68810: {'adj': {68757, 68843, 68872}, 'degree': 3, 'x': 154, 'y': 132},\n",
       " 68766: {'adj': {68872}, 'degree': 1, 'x': 146, 'y': 131},\n",
       " 68871: {'adj': {68849, 69118}, 'degree': 2, 'x': 129, 'y': 135},\n",
       " 68870: {'adj': {68849, 68904}, 'degree': 2, 'x': 167, 'y': 139},\n",
       " 68867: {'adj': {68899}, 'degree': 1, 'x': 144, 'y': 133},\n",
       " 68862: {'adj': {68395, 68866, 69118}, 'degree': 3, 'x': 137, 'y': 145},\n",
       " 68865: {'adj': {68253, 68849, 68904}, 'degree': 3, 'x': 153, 'y': 136},\n",
       " 68752: {'adj': {68758, 68774, 68820, 68864}, 'degree': 4, 'x': 91, 'y': 141},\n",
       " 68753: {'adj': {68864, 69128}, 'degree': 2, 'x': 112, 'y': 156},\n",
       " 68825: {'adj': {68864}, 'degree': 1, 'x': 125, 'y': 127},\n",
       " 68397: {'adj': {68253, 68863}, 'degree': 2, 'x': 146, 'y': 139},\n",
       " 68858: {'adj': {68852, 68863, 68866}, 'degree': 3, 'x': 144, 'y': 123},\n",
       " 68395: {'adj': {68862, 69118}, 'degree': 2, 'x': 137, 'y': 150},\n",
       " 68860: {'adj': {68394, 68413, 68849, 68895, 69118},\n",
       "  'degree': 5,\n",
       "  'x': 133,\n",
       "  'y': 156},\n",
       " 68859: {'adj': {68849, 68904}, 'degree': 2, 'x': 164, 'y': 138},\n",
       " 68857: {'adj': {68849}, 'degree': 1, 'x': 126, 'y': 130},\n",
       " 68856: {'adj': {68253, 68849}, 'degree': 2, 'x': 129, 'y': 126},\n",
       " 68420: {'adj': {68411, 68855}, 'degree': 2, 'x': 189, 'y': 165},\n",
       " 68853: {'adj': {68849, 69118}, 'degree': 2, 'x': 116, 'y': 131},\n",
       " 68850: {'adj': {68253, 68849, 68904}, 'degree': 3, 'x': 162, 'y': 141},\n",
       " 68889: {'adj': {68849}, 'degree': 1, 'x': 122, 'y': 132},\n",
       " 68844: {'adj': {68784, 69086}, 'degree': 2, 'x': 139, 'y': 164},\n",
       " 68843: {'adj': {68810}, 'degree': 1, 'x': 155, 'y': 134},\n",
       " 68784: {'adj': {68253, 68844}, 'degree': 2, 'x': 139, 'y': 161},\n",
       " 68848: {'adj': {68774}, 'degree': 1, 'x': 85, 'y': 145},\n",
       " 68762: {'adj': {68451, 68799}, 'degree': 2, 'x': 115, 'y': 189},\n",
       " 68451: {'adj': {68473, 68511, 68762}, 'degree': 3, 'x': 113, 'y': 194},\n",
       " 68846: {'adj': {68761}, 'degree': 1, 'x': 128, 'y': 95},\n",
       " 68727: {'adj': {68690, 68705, 68728, 68761, 69146},\n",
       "  'degree': 5,\n",
       "  'x': 87,\n",
       "  'y': 121},\n",
       " 68760: {'adj': {68758, 69191}, 'degree': 2, 'x': 158, 'y': 119},\n",
       " 68758: {'adj': {68752, 68760}, 'degree': 2, 'x': 93, 'y': 138},\n",
       " 68756: {'adj': {68774}, 'degree': 1, 'x': 81, 'y': 146},\n",
       " 68759: {'adj': {68755}, 'degree': 1, 'x': 148, 'y': 144},\n",
       " 68773: {'adj': {68755}, 'degree': 1, 'x': 146, 'y': 143},\n",
       " 68820: {'adj': {68752}, 'degree': 1, 'x': 89, 'y': 142},\n",
       " 68847: {'adj': {68750}, 'degree': 1, 'x': 134, 'y': 116},\n",
       " 68747: {'adj': {68668, 68678, 68715}, 'degree': 3, 'x': 125, 'y': 74},\n",
       " 68678: {'adj': {68716, 68747}, 'degree': 2, 'x': 124, 'y': 70},\n",
       " 68741: {'adj': {69167}, 'degree': 1, 'x': 136, 'y': 125},\n",
       " 68736: {'adj': {68281, 68313, 68700, 68701, 68720, 68731},\n",
       "  'degree': 6,\n",
       "  'x': 135,\n",
       "  'y': 92},\n",
       " 68745: {'adj': {68731}, 'degree': 1, 'x': 141, 'y': 88},\n",
       " 68691: {'adj': {68711, 68718, 68730}, 'degree': 3, 'x': 153, 'y': 163},\n",
       " 68685: {'adj': {68685, 68721, 68729, 68735}, 'degree': 4, 'x': 162, 'y': 92},\n",
       " 68728: {'adj': {68671, 68703, 68704, 68727}, 'degree': 4, 'x': 83, 'y': 121},\n",
       " 68740: {'adj': {68726}, 'degree': 1, 'x': 176, 'y': 113},\n",
       " 68725: {'adj': {68673, 68687, 68688, 68689, 68711, 68732},\n",
       "  'degree': 6,\n",
       "  'x': 174,\n",
       "  'y': 153},\n",
       " 68723: {'adj': {68667, 68671, 68674, 68695, 68696},\n",
       "  'degree': 5,\n",
       "  'x': 86,\n",
       "  'y': 155},\n",
       " 68671: {'adj': {68723, 68728}, 'degree': 2, 'x': 83, 'y': 129},\n",
       " 68720: {'adj': {68672, 68694, 68699, 68712, 68736},\n",
       "  'degree': 5,\n",
       "  'x': 129,\n",
       "  'y': 88},\n",
       " 68712: {'adj': {68713, 68720}, 'degree': 2, 'x': 130, 'y': 84},\n",
       " 68716: {'adj': {68678, 68742}, 'degree': 2, 'x': 120, 'y': 69},\n",
       " 68742: {'adj': {68716}, 'degree': 1, 'x': 118, 'y': 68},\n",
       " 68715: {'adj': {68747}, 'degree': 1, 'x': 126, 'y': 76},\n",
       " 68714: {'adj': {68713, 68746}, 'degree': 2, 'x': 93, 'y': 191},\n",
       " 68746: {'adj': {68714}, 'degree': 1, 'x': 95, 'y': 190},\n",
       " 68713: {'adj': {68712, 68714}, 'degree': 2, 'x': 89, 'y': 191},\n",
       " 68711: {'adj': {68691, 68711, 68718, 68725}, 'degree': 4, 'x': 169, 'y': 156},\n",
       " 68710: {'adj': {68718}, 'degree': 1, 'x': 149, 'y': 161},\n",
       " 68709: {'adj': {68718}, 'degree': 1, 'x': 153, 'y': 161},\n",
       " 68708: {'adj': {68718}, 'degree': 1, 'x': 151, 'y': 165},\n",
       " 68707: {'adj': {68737}, 'degree': 1, 'x': 152, 'y': 134},\n",
       " 68706: {'adj': {68733}, 'degree': 1, 'x': 170, 'y': 122},\n",
       " 68705: {'adj': {68727}, 'degree': 1, 'x': 87, 'y': 123},\n",
       " 68704: {'adj': {68728}, 'degree': 1, 'x': 81, 'y': 121},\n",
       " 68703: {'adj': {68728}, 'degree': 1, 'x': 83, 'y': 119},\n",
       " 68702: {'adj': {68731}, 'degree': 1, 'x': 139, 'y': 91},\n",
       " 68701: {'adj': {68736}, 'degree': 1, 'x': 135, 'y': 90},\n",
       " 68700: {'adj': {68736}, 'degree': 1, 'x': 136, 'y': 94},\n",
       " 68699: {'adj': {68720}, 'degree': 1, 'x': 127, 'y': 87},\n",
       " 68698: {'adj': {68730}, 'degree': 1, 'x': 150, 'y': 157},\n",
       " 68697: {'adj': {68730}, 'degree': 1, 'x': 152, 'y': 155},\n",
       " 68696: {'adj': {68723}, 'degree': 1, 'x': 84, 'y': 156},\n",
       " 68695: {'adj': {68723}, 'degree': 1, 'x': 86, 'y': 153},\n",
       " 68694: {'adj': {68674, 68720}, 'degree': 2, 'x': 124, 'y': 91},\n",
       " 68693: {'adj': {68718}, 'degree': 1, 'x': 151, 'y': 159},\n",
       " 68692: {'adj': {68718}, 'degree': 1, 'x': 148, 'y': 166},\n",
       " 68690: {'adj': {68727, 68732, 68744}, 'degree': 3, 'x': 92, 'y': 123},\n",
       " 68689: {'adj': {68725}, 'degree': 1, 'x': 176, 'y': 153},\n",
       " 68688: {'adj': {68725}, 'degree': 1, 'x': 173, 'y': 155},\n",
       " 68687: {'adj': {68725}, 'degree': 1, 'x': 173, 'y': 151},\n",
       " 68686: {'adj': {68735}, 'degree': 1, 'x': 106, 'y': 108},\n",
       " 68721: {'adj': {68669, 68685}, 'degree': 2, 'x': 161, 'y': 88},\n",
       " 68684: {'adj': {68729}, 'degree': 1, 'x': 168, 'y': 88},\n",
       " 68683: {'adj': {68729}, 'degree': 1, 'x': 170, 'y': 91},\n",
       " 68677: {'adj': {68730}, 'degree': 1, 'x': 148, 'y': 155},\n",
       " 68676: {'adj': {68679}, 'degree': 1, 'x': 169, 'y': 125},\n",
       " 68675: {'adj': {68732, 68743}, 'degree': 2, 'x': 101, 'y': 124},\n",
       " 68743: {'adj': {68675}, 'degree': 1, 'x': 101, 'y': 126},\n",
       " 68674: {'adj': {68694, 68723, 69118}, 'degree': 3, 'x': 90, 'y': 155},\n",
       " 68673: {'adj': {68725}, 'degree': 1, 'x': 176, 'y': 156},\n",
       " 68672: {'adj': {68720}, 'degree': 1, 'x': 131, 'y': 88},\n",
       " 68670: {'adj': {68726}, 'degree': 1, 'x': 177, 'y': 116},\n",
       " 68669: {'adj': {68721, 68726}, 'degree': 2, 'x': 168, 'y': 96},\n",
       " 68668: {'adj': {68747}, 'degree': 1, 'x': 123, 'y': 74},\n",
       " 68667: {'adj': {68723}, 'degree': 1, 'x': 87, 'y': 157},\n",
       " 68666: {'adj': {68659, 68662, 68663}, 'degree': 3, 'x': 169, 'y': 188},\n",
       " 68663: {'adj': {68665, 68666}, 'degree': 2, 'x': 97, 'y': 178},\n",
       " 68665: {'adj': {68660, 68661, 68663}, 'degree': 3, 'x': 87, 'y': 177},\n",
       " 68662: {'adj': {68666}, 'degree': 1, 'x': 170, 'y': 190},\n",
       " 68661: {'adj': {68665}, 'degree': 1, 'x': 85, 'y': 177},\n",
       " 68660: {'adj': {68657, 68658, 68665}, 'degree': 3, 'x': 90, 'y': 179},\n",
       " 68659: {'adj': {68666}, 'degree': 1, 'x': 168, 'y': 186},\n",
       " 68658: {'adj': {68660}, 'degree': 1, 'x': 91, 'y': 181},\n",
       " 68657: {'adj': {68660}, 'degree': 1, 'x': 91, 'y': 177},\n",
       " 68656: {'adj': {68635, 68644, 68648}, 'degree': 3, 'x': 172, 'y': 127},\n",
       " 68648: {'adj': {68651, 68653, 68656}, 'degree': 3, 'x': 173, 'y': 130},\n",
       " 68655: {'adj': {68625, 68649, 68654}, 'degree': 3, 'x': 112, 'y': 119},\n",
       " 68625: {'adj': {68650, 68655}, 'degree': 2, 'x': 104, 'y': 171},\n",
       " 68649: {'adj': {68655}, 'degree': 1, 'x': 110, 'y': 119},\n",
       " 68654: {'adj': {68629, 68630, 68638, 68642, 68655},\n",
       "  'degree': 5,\n",
       "  'x': 117,\n",
       "  'y': 118},\n",
       " 68653: {'adj': {68628, 68631, 68640, 68648}, 'degree': 4, 'x': 114, 'y': 144},\n",
       " 68640: {'adj': {68652, 68653}, 'degree': 2, 'x': 189, 'y': 151},\n",
       " 68652: {'adj': {68624, 68629, 68640}, 'degree': 3, 'x': 194, 'y': 149},\n",
       " 68629: {'adj': {68652, 68654}, 'degree': 2, 'x': 184, 'y': 142},\n",
       " 68651: {'adj': {68627, 68643, 68646, 68648}, 'degree': 4, 'x': 177, 'y': 129},\n",
       " 68633: {'adj': {68650}, 'degree': 1, 'x': 109, 'y': 175},\n",
       " 68644: {'adj': {68656}, 'degree': 1, 'x': 174, 'y': 126},\n",
       " 68643: {'adj': {68619, 68620, 68621, 68622, 68651},\n",
       "  'degree': 5,\n",
       "  'x': 183,\n",
       "  'y': 127},\n",
       " 68635: {'adj': {68599, 68602, 68603, 68604, 68605, 68606, 68656},\n",
       "  'degree': 7,\n",
       "  'x': 169,\n",
       "  'y': 128},\n",
       " 68631: {'adj': {68600,\n",
       "   68601,\n",
       "   68609,\n",
       "   68610,\n",
       "   68611,\n",
       "   68612,\n",
       "   68613,\n",
       "   68614,\n",
       "   68615,\n",
       "   68616,\n",
       "   68653},\n",
       "  'degree': 11,\n",
       "  'x': 141,\n",
       "  'y': 155},\n",
       " 68627: {'adj': {68651}, 'degree': 1, 'x': 179, 'y': 129},\n",
       " 68624: {'adj': {68617, 68618, 68652}, 'degree': 3, 'x': 193, 'y': 145},\n",
       " 68622: {'adj': {68643}, 'degree': 1, 'x': 185, 'y': 127},\n",
       " 68621: {'adj': {68643}, 'degree': 1, 'x': 182, 'y': 125},\n",
       " 68620: {'adj': {68643}, 'degree': 1, 'x': 183, 'y': 129},\n",
       " 68619: {'adj': {68643}, 'degree': 1, 'x': 185, 'y': 124},\n",
       " 68618: {'adj': {68624}, 'degree': 1, 'x': 194, 'y': 143},\n",
       " 68617: {'adj': {68624}, 'degree': 1, 'x': 191, 'y': 145},\n",
       " 68616: {'adj': {68631}, 'degree': 1, 'x': 140, 'y': 157},\n",
       " 68615: {'adj': {68631}, 'degree': 1, 'x': 143, 'y': 156},\n",
       " 68614: {'adj': {68631}, 'degree': 1, 'x': 139, 'y': 155},\n",
       " 68613: {'adj': {68631}, 'degree': 1, 'x': 143, 'y': 158},\n",
       " 68612: {'adj': {68631}, 'degree': 1, 'x': 138, 'y': 158},\n",
       " 68611: {'adj': {68631}, 'degree': 1, 'x': 143, 'y': 154},\n",
       " 68610: {'adj': {68631}, 'degree': 1, 'x': 145, 'y': 159},\n",
       " 68609: {'adj': {68631}, 'degree': 1, 'x': 137, 'y': 162},\n",
       " 68608: {'adj': {68628}, 'degree': 1, 'x': 111, 'y': 141},\n",
       " 68607: {'adj': {68628}, 'degree': 1, 'x': 112, 'y': 138},\n",
       " 68606: {'adj': {68635, 68646}, 'degree': 2, 'x': 98, 'y': 160},\n",
       " 68605: {'adj': {68635}, 'degree': 1, 'x': 167, 'y': 128},\n",
       " 68604: {'adj': {68635, 68646}, 'degree': 2, 'x': 100, 'y': 154},\n",
       " 68603: {'adj': {68635, 68646}, 'degree': 2, 'x': 102, 'y': 161},\n",
       " 68602: {'adj': {68635, 68646}, 'degree': 2, 'x': 168, 'y': 131},\n",
       " 68601: {'adj': {68631}, 'degree': 1, 'x': 138, 'y': 156},\n",
       " 68600: {'adj': {68631}, 'degree': 1, 'x': 143, 'y': 164},\n",
       " 68599: {'adj': {68635}, 'degree': 1, 'x': 170, 'y': 130},\n",
       " 68598: {'adj': {68562, 68570}, 'degree': 2, 'x': 111, 'y': 93},\n",
       " 68570: {'adj': {68539, 68580, 68588, 68590, 68598},\n",
       "  'degree': 5,\n",
       "  'x': 107,\n",
       "  'y': 90},\n",
       " 68597: {'adj': {68568, 68575}, 'degree': 2, 'x': 77, 'y': 144},\n",
       " 68575: {'adj': {68545, 68591, 68597}, 'degree': 3, 'x': 70, 'y': 135},\n",
       " 68568: {'adj': {68543, 68591, 68597}, 'degree': 3, 'x': 119, 'y': 185},\n",
       " 68596: {'adj': {68543}, 'degree': 1, 'x': 117, 'y': 180},\n",
       " 68543: {'adj': {68555, 68557, 68568, 68596}, 'degree': 4, 'x': 117, 'y': 182},\n",
       " 68595: {'adj': {68548}, 'degree': 1, 'x': 105, 'y': 180},\n",
       " 68548: {'adj': {68542, 68592, 68593, 68594, 68595},\n",
       "  'degree': 5,\n",
       "  'x': 107,\n",
       "  'y': 181},\n",
       " 68594: {'adj': {68548}, 'degree': 1, 'x': 109, 'y': 180},\n",
       " 68593: {'adj': {68548}, 'degree': 1, 'x': 107, 'y': 183},\n",
       " 68592: {'adj': {68548}, 'degree': 1, 'x': 107, 'y': 178},\n",
       " 68591: {'adj': {68568, 68575}, 'degree': 2, 'x': 79, 'y': 150},\n",
       " 68590: {'adj': {68564, 68570}, 'degree': 2, 'x': 101, 'y': 88},\n",
       " 68564: {'adj': {68536, 68589, 68590}, 'degree': 3, 'x': 96, 'y': 86},\n",
       " 68589: {'adj': {68564}, 'degree': 1, 'x': 95, 'y': 88},\n",
       " 68588: {'adj': {68556, 68562, 68570}, 'degree': 3, 'x': 133, 'y': 111},\n",
       " 68587: {'adj': {68573}, 'degree': 1, 'x': 98, 'y': 141},\n",
       " 68573: {'adj': {68541, 68582, 68583, 68584, 68585, 68586, 68587},\n",
       "  'degree': 7,\n",
       "  'x': 98,\n",
       "  'y': 143},\n",
       " 68586: {'adj': {68573}, 'degree': 1, 'x': 99, 'y': 145},\n",
       " 68585: {'adj': {68573}, 'degree': 1, 'x': 100, 'y': 142},\n",
       " 68584: {'adj': {68573}, 'degree': 1, 'x': 97, 'y': 146},\n",
       " 68583: {'adj': {68573}, 'degree': 1, 'x': 95, 'y': 141},\n",
       " 68582: {'adj': {68573}, 'degree': 1, 'x': 101, 'y': 144},\n",
       " 68581: {'adj': {68567}, 'degree': 1, 'x': 128, 'y': 101},\n",
       " 68580: {'adj': {68562, 68570}, 'degree': 2, 'x': 115, 'y': 95},\n",
       " 68545: {'adj': {68550, 68558, 68575}, 'degree': 3, 'x': 70, 'y': 139},\n",
       " 68541: {'adj': {68550, 68560, 68573}, 'degree': 3, 'x': 95, 'y': 144},\n",
       " 68539: {'adj': {68557, 68559, 68570}, 'degree': 3, 'x': 108, 'y': 86},\n",
       " 68544: {'adj': {68546, 68547, 68567}, 'degree': 3, 'x': 131, 'y': 100},\n",
       " 68538: {'adj': {68547, 68566, 68569}, 'degree': 3, 'x': 104, 'y': 164},\n",
       " 68536: {'adj': {68552, 68559, 68564}, 'degree': 3, 'x': 92, 'y': 85},\n",
       " 68537: {'adj': {68546, 68558, 68562}, 'degree': 3, 'x': 137, 'y': 110},\n",
       " 68560: {'adj': {68541}, 'degree': 1, 'x': 93, 'y': 145},\n",
       " 68559: {'adj': {68536, 68539}, 'degree': 2, 'x': 102, 'y': 83},\n",
       " 68558: {'adj': {68537, 68540, 68545}, 'degree': 3, 'x': 77, 'y': 135},\n",
       " 68540: {'adj': {68556, 68557, 68558}, 'degree': 3, 'x': 176, 'y': 172},\n",
       " 68557: {'adj': {68539, 68540, 68543}, 'degree': 3, 'x': 122, 'y': 175},\n",
       " 68555: {'adj': {68542, 68543}, 'degree': 2, 'x': 111, 'y': 184},\n",
       " 68554: {'adj': {68542}, 'degree': 1, 'x': 103, 'y': 186},\n",
       " 68542: {'adj': {68548, 68554, 68555}, 'degree': 3, 'x': 104, 'y': 184},\n",
       " 68550: {'adj': {68541, 68545}, 'degree': 2, 'x': 81, 'y': 140},\n",
       " 68547: {'adj': {68538, 68544}, 'degree': 2, 'x': 102, 'y': 130},\n",
       " 68546: {'adj': {68537, 68544}, 'degree': 2, 'x': 133, 'y': 103},\n",
       " 68569: {'adj': {68538}, 'degree': 1, 'x': 102, 'y': 165},\n",
       " 68552: {'adj': {68536}, 'degree': 1, 'x': 90, 'y': 84},\n",
       " 68535: {'adj': {68519}, 'degree': 1, 'x': 122, 'y': 104},\n",
       " 68534: {'adj': {68513, 68520, 68524}, 'degree': 3, 'x': 196, 'y': 121},\n",
       " 68520: {'adj': {68514, 68526, 68534}, 'degree': 3, 'x': 197, 'y': 125},\n",
       " 68524: {'adj': {68521, 68534}, 'degree': 2, 'x': 197, 'y': 117},\n",
       " 68513: {'adj': {68522, 68534}, 'degree': 2, 'x': 187, 'y': 119},\n",
       " 68533: {'adj': {68522}, 'degree': 1, 'x': 119, 'y': 95},\n",
       " 68522: {'adj': {68513, 68514, 68519, 68527, 68528, 68533},\n",
       "  'degree': 6,\n",
       "  'x': 121,\n",
       "  'y': 96},\n",
       " 68532: {'adj': {68519}, 'degree': 1, 'x': 123, 'y': 101},\n",
       " 68531: {'adj': {68519}, 'degree': 1, 'x': 126, 'y': 102},\n",
       " 68530: {'adj': {68519}, 'degree': 1, 'x': 123, 'y': 106},\n",
       " 68529: {'adj': {68519}, 'degree': 1, 'x': 121, 'y': 100},\n",
       " 68528: {'adj': {68522}, 'degree': 1, 'x': 122, 'y': 94},\n",
       " 68527: {'adj': {68522}, 'degree': 1, 'x': 119, 'y': 97},\n",
       " 68526: {'adj': {68514, 68516, 68520}, 'degree': 3, 'x': 198, 'y': 129},\n",
       " 68516: {'adj': {68526}, 'degree': 1, 'x': 199, 'y': 131},\n",
       " 68525: {'adj': {68521}, 'degree': 1, 'x': 86, 'y': 133},\n",
       " 68523: {'adj': {68521}, 'degree': 1, 'x': 83, 'y': 135},\n",
       " 68514: {'adj': {68520, 68522, 68526}, 'degree': 3, 'x': 193, 'y': 126},\n",
       " 68515: {'adj': {68521}, 'degree': 1, 'x': 86, 'y': 137},\n",
       " 68511: {'adj': {68421, 68451, 68494}, 'degree': 3, 'x': 125, 'y': 193},\n",
       " 68494: {'adj': {68511}, 'degree': 1, 'x': 125, 'y': 195},\n",
       " 68421: {'adj': {68470, 68476, 68478, 68511}, 'degree': 4, 'x': 135, 'y': 191},\n",
       " 68510: {'adj': {68453}, 'degree': 1, 'x': 72, 'y': 110},\n",
       " 68453: {'adj': {68435, 68477, 68510}, 'degree': 3, 'x': 74, 'y': 110},\n",
       " 68508: {'adj': {68424,\n",
       "   68427,\n",
       "   68429,\n",
       "   68432,\n",
       "   68433,\n",
       "   68434,\n",
       "   68436,\n",
       "   68437,\n",
       "   68439,\n",
       "   68443,\n",
       "   68444},\n",
       "  'degree': 11,\n",
       "  'x': 157,\n",
       "  'y': 130},\n",
       " 68443: {'adj': {68454, 68490, 68508}, 'degree': 3, 'x': 143, 'y': 171},\n",
       " 68439: {'adj': {68486, 68508}, 'degree': 2, 'x': 163, 'y': 99},\n",
       " 68436: {'adj': {68488, 68505, 68508}, 'degree': 3, 'x': 168, 'y': 108},\n",
       " 68434: {'adj': {68486, 68488, 68508}, 'degree': 3, 'x': 162, 'y': 104},\n",
       " 68429: {'adj': {68486, 68508}, 'degree': 2, 'x': 157, 'y': 99},\n",
       " 68424: {'adj': {68456, 68462, 68476, 68497, 68508},\n",
       "  'degree': 5,\n",
       "  'x': 170,\n",
       "  'y': 134},\n",
       " 68507: {'adj': {68427, 68442}, 'degree': 2, 'x': 175, 'y': 93},\n",
       " 68427: {'adj': {68458, 68507, 68508}, 'degree': 3, 'x': 161, 'y': 131},\n",
       " 68504: {'adj': {68426, 68432, 68437}, 'degree': 3, 'x': 155, 'y': 195},\n",
       " 68426: {'adj': {68478, 68479, 68504}, 'degree': 3, 'x': 158, 'y': 193},\n",
       " 68437: {'adj': {68479, 68504, 68508}, 'degree': 3, 'x': 155, 'y': 186},\n",
       " 68432: {'adj': {68478, 68504, 68508}, 'degree': 3, 'x': 154, 'y': 175},\n",
       " 68503: {'adj': {68449, 68452}, 'degree': 2, 'x': 114, 'y': 79},\n",
       " 68452: {'adj': {68431, 68501, 68502, 68503}, 'degree': 4, 'x': 116, 'y': 82},\n",
       " 68449: {'adj': {68441, 68501, 68502, 68503}, 'degree': 4, 'x': 159, 'y': 181},\n",
       " 68502: {'adj': {68449, 68452, 68454}, 'degree': 3, 'x': 146, 'y': 175},\n",
       " 68454: {'adj': {68443, 68461, 68466, 68482, 68496, 68501, 68502},\n",
       "  'degree': 7,\n",
       "  'x': 140,\n",
       "  'y': 171},\n",
       " 68501: {'adj': {68449, 68452, 68454}, 'degree': 3, 'x': 151, 'y': 170},\n",
       " 68500: {'adj': {68445}, 'degree': 1, 'x': 182, 'y': 98},\n",
       " 68445: {'adj': {68457, 68479, 68499, 68500}, 'degree': 4, 'x': 181, 'y': 100},\n",
       " 68498: {'adj': {68425, 68440}, 'degree': 2, 'x': 73, 'y': 162},\n",
       " 68440: {'adj': {68460, 68498, 68509}, 'degree': 3, 'x': 140, 'y': 74},\n",
       " 68496: {'adj': {68454}, 'degree': 1, 'x': 140, 'y': 173},\n",
       " 68495: {'adj': {68455}, 'degree': 1, 'x': 143, 'y': 186},\n",
       " 68455: {'adj': {68422, 68464, 68484, 68495}, 'degree': 4, 'x': 145, 'y': 185},\n",
       " 68493: {'adj': {68425}, 'degree': 1, 'x': 75, 'y': 157},\n",
       " 68425: {'adj': {68485, 68493, 68498}, 'degree': 3, 'x': 75, 'y': 159},\n",
       " 68492: {'adj': {68428}, 'degree': 1, 'x': 96, 'y': 94},\n",
       " 68428: {'adj': {68467, 68469, 68489, 68492}, 'degree': 4, 'x': 97, 'y': 96},\n",
       " 68491: {'adj': {68450}, 'degree': 1, 'x': 178, 'y': 86},\n",
       " 68450: {'adj': {68442, 68487, 68491}, 'degree': 3, 'x': 176, 'y': 86},\n",
       " 68490: {'adj': {68431, 68443}, 'degree': 2, 'x': 120, 'y': 89},\n",
       " 68431: {'adj': {68452, 68488, 68490}, 'degree': 3, 'x': 122, 'y': 84},\n",
       " 68489: {'adj': {68428}, 'degree': 1, 'x': 97, 'y': 98},\n",
       " 68488: {'adj': {68431, 68433, 68434, 68436, 68441, 68444},\n",
       "  'degree': 6,\n",
       "  'x': 166,\n",
       "  'y': 104},\n",
       " 68444: {'adj': {68481, 68488, 68508}, 'degree': 3, 'x': 170, 'y': 104},\n",
       " 68441: {'adj': {68449, 68476, 68488}, 'degree': 3, 'x': 154, 'y': 179},\n",
       " 68433: {'adj': {68466, 68488, 68508}, 'degree': 3, 'x': 171, 'y': 110},\n",
       " 68487: {'adj': {68450}, 'degree': 1, 'x': 174, 'y': 87},\n",
       " 68486: {'adj': {68429, 68434, 68439}, 'degree': 3, 'x': 160, 'y': 100},\n",
       " 68485: {'adj': {68425}, 'degree': 1, 'x': 77, 'y': 160},\n",
       " 68484: {'adj': {68455}, 'degree': 1, 'x': 145, 'y': 183},\n",
       " 68483: {'adj': {68448}, 'degree': 1, 'x': 158, 'y': 70},\n",
       " 68448: {'adj': {68438, 68459, 68483}, 'degree': 3, 'x': 160, 'y': 71},\n",
       " 68482: {'adj': {68454}, 'degree': 1, 'x': 138, 'y': 171},\n",
       " 68481: {'adj': {68444, 68456}, 'degree': 2, 'x': 176, 'y': 108},\n",
       " 68456: {'adj': {68424, 68465, 68474, 68481}, 'degree': 4, 'x': 171, 'y': 138},\n",
       " 68480: {'adj': {68430}, 'degree': 1, 'x': 178, 'y': 182},\n",
       " 68430: {'adj': {68467, 68469, 68480}, 'degree': 3, 'x': 180, 'y': 183},\n",
       " 68479: {'adj': {68426, 68437, 68445}, 'degree': 3, 'x': 159, 'y': 187},\n",
       " 68478: {'adj': {68421, 68426, 68432}, 'degree': 3, 'x': 150, 'y': 190},\n",
       " 68477: {'adj': {68453}, 'degree': 1, 'x': 75, 'y': 112},\n",
       " 68476: {'adj': {68421, 68424, 68441, 68442}, 'degree': 4, 'x': 137, 'y': 185},\n",
       " 68442: {'adj': {68450, 68476, 68507}, 'degree': 3, 'x': 174, 'y': 83},\n",
       " 68475: {'adj': {68435, 68460}, 'degree': 2, 'x': 91, 'y': 98},\n",
       " 68435: {'adj': {68453, 68471, 68475}, 'degree': 3, 'x': 77, 'y': 108},\n",
       " 68474: {'adj': {68456}, 'degree': 1, 'x': 172, 'y': 140},\n",
       " 68473: {'adj': {68451}, 'degree': 1, 'x': 113, 'y': 196},\n",
       " 68472: {'adj': {68422, 68438}, 'degree': 2, 'x': 149, 'y': 71},\n",
       " 68438: {'adj': {68448, 68460, 68472}, 'degree': 3, 'x': 149, 'y': 75},\n",
       " 68471: {'adj': {68422, 68435}, 'degree': 2, 'x': 76, 'y': 104},\n",
       " 68422: {'adj': {68446, 68447, 68455, 68471, 68472},\n",
       "  'degree': 5,\n",
       "  'x': 149,\n",
       "  'y': 184},\n",
       " 68470: {'adj': {68421}, 'degree': 1, 'x': 135, 'y': 193},\n",
       " 68467: {'adj': {68428, 68430}, 'degree': 2, 'x': 180, 'y': 187},\n",
       " 68466: {'adj': {68433, 68454}, 'degree': 2, 'x': 173, 'y': 122},\n",
       " 68465: {'adj': {68456}, 'degree': 1, 'x': 173, 'y': 137},\n",
       " 68464: {'adj': {68455}, 'degree': 1, 'x': 146, 'y': 187},\n",
       " 68461: {'adj': {68454}, 'degree': 1, 'x': 142, 'y': 174},\n",
       " 68460: {'adj': {68423, 68438, 68440, 68475}, 'degree': 4, 'x': 139, 'y': 78},\n",
       " 68423: {'adj': {68460, 68463, 68506}, 'degree': 3, 'x': 136, 'y': 80},\n",
       " 68459: {'adj': {68448}, 'degree': 1, 'x': 162, 'y': 72},\n",
       " 68458: {'adj': {68427}, 'degree': 1, 'x': 161, 'y': 133},\n",
       " 68457: {'adj': {68445}, 'degree': 1, 'x': 180, 'y': 102},\n",
       " 68499: {'adj': {68445}, 'degree': 1, 'x': 179, 'y': 99},\n",
       " 68509: {'adj': {68440}, 'degree': 1, 'x': 140, 'y': 72},\n",
       " 68505: {'adj': {68436}, 'degree': 1, 'x': 170, 'y': 107},\n",
       " 68469: {'adj': {68428, 68430}, 'degree': 2, 'x': 183, 'y': 180},\n",
       " 68497: {'adj': {68424}, 'degree': 1, 'x': 172, 'y': 134},\n",
       " 68462: {'adj': {68424}, 'degree': 1, 'x': 169, 'y': 136},\n",
       " 68506: {'adj': {68423}, 'degree': 1, 'x': 134, 'y': 79},\n",
       " 68463: {'adj': {68423}, 'degree': 1, 'x': 137, 'y': 82},\n",
       " 68447: {'adj': {68422}, 'degree': 1, 'x': 151, 'y': 183},\n",
       " 68446: {'adj': {68422}, 'degree': 1, 'x': 150, 'y': 186},\n",
       " 68411: {'adj': {68398, 68402, 68420}, 'degree': 3, 'x': 195, 'y': 169},\n",
       " 68419: {'adj': {68399, 68410}, 'degree': 2, 'x': 186, 'y': 106},\n",
       " 68410: {'adj': {68386, 68406, 68419}, 'degree': 3, 'x': 190, 'y': 101},\n",
       " 68381: {'adj': {68408, 68418, 69118}, 'degree': 3, 'x': 122, 'y': 138},\n",
       " 68408: {'adj': {68381, 68418}, 'degree': 2, 'x': 116, 'y': 139},\n",
       " 68384: {'adj': {68253, 68382, 68412, 68417}, 'degree': 4, 'x': 148, 'y': 138},\n",
       " 68416: {'adj': {68401, 68415}, 'degree': 2, 'x': 63, 'y': 120},\n",
       " 68401: {'adj': {68416}, 'degree': 1, 'x': 61, 'y': 120},\n",
       " 68415: {'adj': {68416}, 'degree': 1, 'x': 65, 'y': 121},\n",
       " 68414: {'adj': {69118}, 'degree': 1, 'x': 139, 'y': 145},\n",
       " 68413: {'adj': {68860}, 'degree': 1, 'x': 134, 'y': 158},\n",
       " 68412: {'adj': {68384}, 'degree': 1, 'x': 149, 'y': 140},\n",
       " 68386: {'adj': {68406, 68410}, 'degree': 2, 'x': 191, 'y': 97},\n",
       " 68406: {'adj': {68386, 68410}, 'degree': 2, 'x': 194, 'y': 99},\n",
       " 68409: {'adj': {68253}, 'degree': 1, 'x': 140, 'y': 136},\n",
       " 68404: {'adj': {68253, 68866}, 'degree': 2, 'x': 138, 'y': 124},\n",
       " 68403: {'adj': {68253, 68905}, 'degree': 2, 'x': 151, 'y': 143},\n",
       " 68402: {'adj': {68411}, 'degree': 1, 'x': 197, 'y': 169},\n",
       " 68400: {'adj': {68385, 69118}, 'degree': 2, 'x': 166, 'y': 135},\n",
       " 68390: {'adj': {68399}, 'degree': 1, 'x': 162, 'y': 137},\n",
       " 68791: {'adj': {68399}, 'degree': 1, 'x': 163, 'y': 133},\n",
       " 68398: {'adj': {68411}, 'degree': 1, 'x': 193, 'y': 170},\n",
       " 68391: {'adj': {68380}, 'degree': 1, 'x': 116, 'y': 170},\n",
       " 68380: {'adj': {68391, 68925, 69193}, 'degree': 3, 'x': 117, 'y': 168},\n",
       " 68376: {'adj': {68361}, 'degree': 1, 'x': 193, 'y': 135},\n",
       " 68361: {'adj': {68356, 68368, 68375, 68376}, 'degree': 4, 'x': 191, 'y': 135},\n",
       " 68375: {'adj': {68361}, 'degree': 1, 'x': 189, 'y': 135},\n",
       " 68374: {'adj': {68362, 68370}, 'degree': 2, 'x': 206, 'y': 151},\n",
       " 68362: {'adj': {68374}, 'degree': 1, 'x': 208, 'y': 151},\n",
       " 68372: {'adj': {68357}, 'degree': 1, 'x': 152, 'y': 147},\n",
       " 68371: {'adj': {68356, 68365, 68373}, 'degree': 3, 'x': 133, 'y': 205},\n",
       " 68373: {'adj': {68371}, 'degree': 1, 'x': 132, 'y': 207},\n",
       " 68370: {'adj': {68374}, 'degree': 1, 'x': 204, 'y': 151},\n",
       " 68369: {'adj': {68360}, 'degree': 1, 'x': 184, 'y': 151},\n",
       " 68360: {'adj': {68358, 68364, 68366, 68367, 68369},\n",
       "  'degree': 5,\n",
       "  'x': 185,\n",
       "  'y': 149},\n",
       " 68368: {'adj': {68361}, 'degree': 1, 'x': 191, 'y': 137},\n",
       " 68367: {'adj': {68360}, 'degree': 1, 'x': 186, 'y': 147},\n",
       " 68366: {'adj': {68360}, 'degree': 1, 'x': 183, 'y': 148},\n",
       " 68365: {'adj': {68356, 68371}, 'degree': 2, 'x': 136, 'y': 202},\n",
       " 68356: {'adj': {68361, 68365, 68371}, 'degree': 3, 'x': 139, 'y': 199},\n",
       " 68364: {'adj': {68360}, 'degree': 1, 'x': 187, 'y': 150},\n",
       " 68363: {'adj': {68359}, 'degree': 1, 'x': 165, 'y': 146},\n",
       " 68355: {'adj': {68332, 68334, 68335, 68337, 68342, 68343},\n",
       "  'degree': 6,\n",
       "  'x': 128,\n",
       "  'y': 172},\n",
       " 68342: {'adj': {68329, 68353, 68354, 68355}, 'degree': 4, 'x': 135, 'y': 171},\n",
       " 68354: {'adj': {68332, 68334, 68335, 68337, 68342, 68343},\n",
       "  'degree': 6,\n",
       "  'x': 137,\n",
       "  'y': 176},\n",
       " 68353: {'adj': {68332, 68334, 68335, 68337, 68342, 68343},\n",
       "  'degree': 6,\n",
       "  'x': 137,\n",
       "  'y': 167},\n",
       " 68352: {'adj': {68333}, 'degree': 1, 'x': 82, 'y': 96},\n",
       " 68333: {'adj': {68326, 68351, 68352}, 'degree': 3, 'x': 83, 'y': 98},\n",
       " 68351: {'adj': {68333}, 'degree': 1, 'x': 83, 'y': 100},\n",
       " 68350: {'adj': {68334}, 'degree': 1, 'x': 148, 'y': 172},\n",
       " 68349: {'adj': {68332, 68556}, 'degree': 2, 'x': 167, 'y': 172},\n",
       " 68348: {'adj': {68343}, 'degree': 1, 'x': 131, 'y': 170},\n",
       " 68323: {'adj': {68338, 68341, 68343}, 'degree': 3, 'x': 104, 'y': 158},\n",
       " 68329: {'adj': {68330, 68340, 68342}, 'degree': 3, 'x': 134, 'y': 179},\n",
       " 68341: {'adj': {68323, 68324, 68327, 68328}, 'degree': 4, 'x': 88, 'y': 165},\n",
       " 68327: {'adj': {68326, 68334, 68341}, 'degree': 3, 'x': 93, 'y': 165},\n",
       " 68340: {'adj': {68325, 68329}, 'degree': 2, 'x': 148, 'y': 91},\n",
       " 68325: {'adj': {68335, 68338, 68340}, 'degree': 3, 'x': 151, 'y': 87},\n",
       " 68338: {'adj': {68323, 68324, 68325, 68328}, 'degree': 4, 'x': 153, 'y': 81},\n",
       " 68328: {'adj': {68332, 68338, 68341}, 'degree': 3, 'x': 123, 'y': 171},\n",
       " 68324: {'adj': {68337, 68338, 68341}, 'degree': 3, 'x': 104, 'y': 153},\n",
       " 68326: {'adj': {68327, 68331, 68333}, 'degree': 3, 'x': 84, 'y': 111},\n",
       " 68330: {'adj': {68329}, 'degree': 1, 'x': 133, 'y': 181},\n",
       " 68331: {'adj': {68326}, 'degree': 1, 'x': 84, 'y': 109},\n",
       " 68322: {'adj': {69256}, 'degree': 1, 'x': 148, 'y': 118},\n",
       " 68321: {'adj': {69249, 69253}, 'degree': 2, 'x': 155, 'y': 96},\n",
       " 68320: {'adj': {68737, 69242}, 'degree': 2, 'x': 152, 'y': 117},\n",
       " 68319: {'adj': {69241}, 'degree': 1, 'x': 121, 'y': 119},\n",
       " 68318: {'adj': {69239}, 'degree': 1, 'x': 145, 'y': 111},\n",
       " 68317: {'adj': {69239}, 'degree': 1, 'x': 149, 'y': 112},\n",
       " 68266: {'adj': {68316, 69244}, 'degree': 2, 'x': 158, 'y': 171},\n",
       " 68315: {'adj': {69240}, 'degree': 1, 'x': 133, 'y': 162},\n",
       " 68313: {'adj': {68736, 69250}, 'degree': 2, 'x': 131, 'y': 93},\n",
       " 68312: {'adj': {69241}, 'degree': 1, 'x': 119, 'y': 123},\n",
       " 68311: {'adj': {69249, 69253}, 'degree': 2, 'x': 159, 'y': 95},\n",
       " 68309: {'adj': {69238}, 'degree': 1, 'x': 160, 'y': 117},\n",
       " 68307: {'adj': {69249, 69253}, 'degree': 2, 'x': 155, 'y': 90},\n",
       " 68306: {'adj': {69252}, 'degree': 1, 'x': 165, 'y': 167},\n",
       " 68305: {'adj': {69241}, 'degree': 1, 'x': 122, 'y': 117},\n",
       " 68304: {'adj': {69256}, 'degree': 1, 'x': 151, 'y': 120},\n",
       " 68303: {'adj': {69241}, 'degree': 1, 'x': 121, 'y': 122},\n",
       " 68302: {'adj': {69241}, 'degree': 1, 'x': 125, 'y': 122},\n",
       " 68301: {'adj': {69247}, 'degree': 1, 'x': 128, 'y': 168},\n",
       " 68297: {'adj': {69256}, 'degree': 1, 'x': 146, 'y': 122},\n",
       " 68296: {'adj': {69241}, 'degree': 1, 'x': 117, 'y': 125},\n",
       " 68294: {'adj': {69257}, 'degree': 1, 'x': 166, 'y': 112},\n",
       " 68293: {'adj': {69255}, 'degree': 1, 'x': 181, 'y': 136},\n",
       " 68292: {'adj': {69241}, 'degree': 1, 'x': 124, 'y': 117},\n",
       " 68291: {'adj': {68726, 69251}, 'degree': 2, 'x': 103, 'y': 100},\n",
       " 68290: {'adj': {69248}, 'degree': 1, 'x': 157, 'y': 127},\n",
       " 68289: {'adj': {69239}, 'degree': 1, 'x': 148, 'y': 108},\n",
       " 68288: {'adj': {69257}, 'degree': 1, 'x': 162, 'y': 111},\n",
       " 68287: {'adj': {69249}, 'degree': 1, 'x': 155, 'y': 141},\n",
       " 68285: {'adj': {69167}, 'degree': 1, 'x': 137, 'y': 127},\n",
       " 68284: {'adj': {69241}, 'degree': 1, 'x': 118, 'y': 120},\n",
       " 68283: {'adj': {69246}, 'degree': 1, 'x': 183, 'y': 163},\n",
       " 68282: {'adj': {69256}, 'degree': 1, 'x': 143, 'y': 118},\n",
       " 68281: {'adj': {68736, 69250}, 'degree': 2, 'x': 116, 'y': 100},\n",
       " 68280: {'adj': {69248}, 'degree': 1, 'x': 155, 'y': 124},\n",
       " 68279: {'adj': {69245}, 'degree': 1, 'x': 97, 'y': 165},\n",
       " 68278: {'adj': {69256}, 'degree': 1, 'x': 149, 'y': 116},\n",
       " 68276: {'adj': {69256}, 'degree': 1, 'x': 148, 'y': 124},\n",
       " 68273: {'adj': {69258}, 'degree': 1, 'x': 123, 'y': 164},\n",
       " 68272: {'adj': {69239}, 'degree': 1, 'x': 146, 'y': 113},\n",
       " 68271: {'adj': {69252}, 'degree': 1, 'x': 164, 'y': 164},\n",
       " 68270: {'adj': {69238}, 'degree': 1, 'x': 158, 'y': 114},\n",
       " 68269: {'adj': {69244}, 'degree': 1, 'x': 159, 'y': 174},\n",
       " 68268: {'adj': {69252}, 'degree': 1, 'x': 161, 'y': 167},\n",
       " 68267: {'adj': {69241}, 'degree': 1, 'x': 119, 'y': 117},\n",
       " 68265: {'adj': {69256}, 'degree': 1, 'x': 146, 'y': 127},\n",
       " 68264: {'adj': {69256}, 'degree': 1, 'x': 144, 'y': 115},\n",
       " 68262: {'adj': {69241}, 'degree': 1, 'x': 128, 'y': 120},\n",
       " 68261: {'adj': {69254}, 'degree': 1, 'x': 151, 'y': 103},\n",
       " 68260: {'adj': {69254}, 'degree': 1, 'x': 147, 'y': 104},\n",
       " 68258: {'adj': {69254}, 'degree': 1, 'x': 148, 'y': 98},\n",
       " 68257: {'adj': {69239}, 'degree': 1, 'x': 144, 'y': 107},\n",
       " 68255: {'adj': {69256}, 'degree': 1, 'x': 154, 'y': 118}}"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_node_dict_4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform node_dict coordinates into igraph or CX formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def igraph_layout_from_node_dict(g, node_dict):\n",
    "    igraph_layout = [(0,0)] * len(node_dict)\n",
    "    print(len(g.vs), len(node_dict))\n",
    "\n",
    "    for node_id, node in node_dict.items():\n",
    "        igraph_node_id = g.vs.find(cx_id=node_id).index\n",
    "        igraph_layout[igraph_node_id] = (node[\"x\"],node[\"y\"])\n",
    "    # print(igraph_layout)\n",
    "    return igraph.Layout(igraph_layout)\n",
    "\n",
    "def draw_igraph_with_node_dict(g, node_dict):\n",
    "    l = igraph_layout_from_node_dict(g, node_dict)\n",
    "    fig, ax = plt.subplots()\n",
    "    igraph.plot(g, layout=l, target=ax)\n",
    "    #igraph.plot(g, l)\n",
    "    \n",
    "def cx_layout_from_node_dict(node_dict, node_dimension = 40):\n",
    "    cx_layout = []\n",
    "    for node_id, node in node_dict.items():\n",
    "        cx_node = {\"node\": node_id,\n",
    "                   \"x\": node[\"x\"] * node_dimension,\n",
    "                   \"y\": node[\"y\"] * node_dimension}\n",
    "        cx_layout.append(cx_node)\n",
    "    return cx_layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating CX\n",
      "Network's URL (click to view!): http://www.ndexbio.org/viewer/networks/621815ba-7705-11ec-b3be-0ac135e8bacf\n"
     ]
    }
   ],
   "source": [
    "new_cx_layout = cx_layout_from_node_dict(test_node_dict_4)\n",
    "test_network_4.set_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT, new_cx_layout)\n",
    "new_network_url = test_network_4.upload_to(SERVER, NDEXUSER, NDEXPASSWORD)\n",
    "new_network_uuid = new_network_url.split('/')[-1]\n",
    "print(\"Network's URL (click to view!): \" + SERVER + \"/viewer/networks/\" + new_network_uuid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: NCI PID - Complete Interactions\n",
      "Nodes: 2794\n",
      "Edges: 25296\n",
      "Node Attributes: 5260\n",
      "Edge Attributes: 50592\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#test_network_5 = ndex2.create_nice_cx_from_file('nectin-adhesion.cx')\n",
    "#test_network_5 = ndex2.create_nice_cx_from_file('hierarchy2.cx')\n",
    "#test_network_5 = ndex2.create_nice_cx_from_file('hnscc_dis.cx')\n",
    "#test_network_5 = ndex2.create_nice_cx_from_file('MDA231_All_PPI>=0.9.cx')\n",
    "#test_network_5 = ndex2.create_nice_cx_from_file('budzikhierarchy1.cx')\n",
    "#test_network_5 = ndex2.create_nice_cx_from_file('bioid_big.cx')\n",
    "test_network_5 = ndex2.create_nice_cx_from_file('nci_pid_all.cx')\n",
    "\n",
    "\n",
    "test_network_5.print_summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "def layout_cx_network_2(cx_network, rounds=1, sparsity=30, r_radius=4, \n",
    "                      a_radius=10, r_scale=10, a_scale=5, center_attractor_scale=0.01,\n",
    "                     initialize_coordinates=True, dtype=np.int32):\n",
    "    start = timer()\n",
    "    node_dict, gameboard, edge_array = make_node_dict_from_cx(cx_network, sparsity=sparsity,\n",
    "                                                 initialize_coordinates=initialize_coordinates, dtype=dtype)\n",
    "    end = timer()\n",
    "    print(\"make node dict time = \", end - start)\n",
    "    return layout_node_dict_2(node_dict, gameboard, rounds=rounds, \n",
    "                            r_radius=r_radius, a_radius=a_radius, \n",
    "                            r_scale=r_scale, a_scale=a_scale, \n",
    "                            center_attractor_scale=center_attractor_scale), gameboard, edge_array\n",
    "\n",
    "def add_center_attractor(gameboard, scale):\n",
    "    # nodes are pulled towards the center of the gameboard\n",
    "    # by giving the gameboard an attraction field at its center\n",
    "    # the radius of the field is the distance from the center to the corners\n",
    "    center = int(gameboard.shape[0]/2)\n",
    "    center_attractor_radius = int(math.sqrt(2 * center**2))\n",
    "    attractor = attraction_field(radius=int(gameboard.shape[0]/2), scale=scale)\n",
    "    add_field(attractor,gameboard, center, center)\n",
    "    \n",
    "# layout a node_dict, assigning coordinates to each node\n",
    "def layout_node_dict_2(node_dict, gameboard, rounds=1, r_radius=10, \n",
    "                     a_radius=10, r_scale=10, a_scale=5, \n",
    "                     center_attractor_scale=0.01):\n",
    "   \n",
    "    add_center_attractor(gameboard, center_attractor_scale)\n",
    "    r_field = repulsion_field(r_radius, scale=r_scale, center_spike=True, dtype=gameboard.dtype)\n",
    "    a_field = attraction_field(a_radius, scale=a_scale, dtype=gameboard.dtype)\n",
    "    a_field_med = attraction_field(a_radius, scale=a_scale, dtype=gameboard.dtype)\n",
    "    a_field_high = attraction_field(a_radius, scale=a_scale*10, dtype=gameboard.dtype)\n",
    "    \n",
    "    # make a scratchpad board where we add all the attraction fields\n",
    "    # and the use it to update the gameboard\n",
    "    s_field = np.zeros(gameboard.shape, gameboard.dtype)\n",
    "    \n",
    "    # get the nodes as a list, sorted by degree, highest degree first\n",
    "    node_list = sorted(node_dict.values(), key=itemgetter('degree'), reverse=True)\n",
    "    \n",
    "    # if a node does not have coordinates, don't add its repulsion field\n",
    "    for node in node_list:  \n",
    "        if node.get(\"x\"):\n",
    "            add_field(r_field, gameboard, node[\"x\"], node[\"y\"])\n",
    "            \n",
    "    # perform the rounds of layout\n",
    "    start = timer()\n",
    "    number_of_nodes = len(node_list)\n",
    "    nodes_per_round = int(number_of_nodes/rounds)\n",
    "    for n in range(1, rounds+1):\n",
    "        if n < rounds:\n",
    "            nodes_this_round = nodes_per_round * n\n",
    "        else:\n",
    "            nodes_this_round = number_of_nodes\n",
    "        print(\"nodes this round = \", nodes_this_round)\n",
    "        for i in range(0, nodes_this_round):\n",
    "            node = node_list[i]\n",
    "            layout_one_node_2(gameboard, s_field, r_field, a_field, a_field_med, a_field_high, node, node_dict)\n",
    "\n",
    "    end = timer()\n",
    "    print(\"layout time = \", end - start)\n",
    "    return node_dict\n",
    "\n",
    "# update the position of one node\n",
    "def layout_one_node_2(gb, s_field, source_field, a_field, a_field_med, a_field_high, node, node_dict):\n",
    "    # clear the scratchpad\n",
    "    s_field[...]=0\n",
    "    degree = math.log(node['degree'], 2)   \n",
    "    for adj_node_id in node['adj']:\n",
    "        # if the adjacent node has coordinates,\n",
    "        # add its attraction field to the scratchpad field\n",
    "        adj_node = node_dict[adj_node_id]\n",
    "        if adj_node.get(\"x\"):\n",
    "            if degree < 2:\n",
    "                add_field(a_field_high, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            #elif degree < 2:\n",
    "            #    add_field(a_field_med, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            else:\n",
    "                add_field(a_field, s_field, adj_node[\"x\"], adj_node[\"y\"])\n",
    "            \n",
    "    # add s_field to the gameboard\n",
    "    gb += s_field\n",
    "    # if the node has coordinates\n",
    "    # remove it from the gameboard by subtracting it at its current location\n",
    "    if node.get(\"x\"):\n",
    "        subtract_field(source_field, gb, node['x'], node['y'])\n",
    "    \n",
    "    # select the destination\n",
    "    # idea #1: choose a location with the minimum value\n",
    "    # argmin returns the index of the first location containing the minimum value in a flattened \n",
    "    # version of the array\n",
    "    # unravel_index turns the index back into the coordinates\n",
    "    destination = np.unravel_index(np.argmin(gb, axis=None), s_field.shape)\n",
    "    #print(node)\n",
    "    #print(\"destination: \", destination)\n",
    "\n",
    "    # add it at the destination\n",
    "    add_field(source_field, gb, destination[0], destination[1])\n",
    "    \n",
    "    # subtract the s_field to revert the gameboard to just the repulsions\n",
    "    gb -= s_field\n",
    "    \n",
    "    # update the node's coordinates\n",
    "    node[\"x\"] = destination[0]\n",
    "    node[\"y\"] = destination[1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edge array:  (25296, 2)\n",
      "make node dict time =  0.12374668798292987\n",
      "nodes this round =  1397\n",
      "nodes this round =  2794\n",
      "layout time =  1.3423238709801808\n",
      " "
     ]
    },
    {
     "data": {
      "image/png": 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IuFJVT4+7l2VmNfAe4EtVdQ/wG+CW/t1VkjXMXQHYAvw+cHuSj463q8VZyeHuIw7mkeS1zAX7Y1X1zXH3swzcB3w4yU+Yu3z3/iRfG29Ly8IMMFNVL/2f3ePMhf2t7APA+aqarar/Ab4J/NGYe1qUlRzuPuKgjyRh7hrqmar6/Lj7WQ6qan9Vbayqzcz9O/leVa3I2dgwVdVPgYtJ3t6VduBjuS8A9yZ5Y/e9tIMV+kvmUT0VcuTG8IiDleI+4GPAj5I829U+U1X/MMaetHx9AnismyC9AHx8zP2MVVU9leRx4IfMffLsGVboYwh8/IAkNWglX5aRJM3DcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkN+l81ZsxkKKyQlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def layout_2(t): \n",
    "    return layout_cx_network_2(t,sparsity=10,rounds=2,r_radius=20,r_scale=10,\n",
    "                               a_radius=10,a_scale=10,center_attractor_scale=1,initialize_coordinates=False,dtype=np.int16)\n",
    "\n",
    "%prun test_node_dict_5, test_gameboard_5, test_edge_array_5 = layout_2(test_network_5)\n",
    "\n",
    "degrees=[]\n",
    "for node in test_node_dict_5.values():\n",
    "    degrees.append(math.log(node['degree'], 2))\n",
    "num_bins = 10\n",
    "n, bins, patches = plt.hist(degrees, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "show_field(test_gameboard_5)\n",
    "\n",
    "distances=[]\n",
    "for row in test_edge_array_5:\n",
    "    source = test_node_dict_5[row[0]]\n",
    "    target = test_node_dict_5[row[1]]\n",
    "    distance = math.sqrt((source['x'] - target['x'])**2 + (source['y'] - target['y'])**2)\n",
    "    distances.append(distance)\n",
    "    \n",
    "num_bins = 20\n",
    "n, bins, patches = plt.hist(distances, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: NCI PID - Complete Interactions\n",
      "Nodes: 2794\n",
      "Edges: 25296\n",
      "Node Attributes: 5260\n",
      "Edge Attributes: 50592\n",
      "\n",
      "starting upload\n",
      "Generating CX\n",
      "upload time =  5.276256849989295\n",
      "Network's URL (click to view!): http://www.ndexbio.org/viewer/networks/ecdc517e-77cf-11ec-b3be-0ac135e8bacf\n"
     ]
    }
   ],
   "source": [
    "new_cx_layout_5 = cx_layout_from_node_dict(test_node_dict_5)\n",
    "test_network_5.set_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT, new_cx_layout_5)\n",
    "test_network_5.print_summary()\n",
    "upload=True\n",
    "if upload:\n",
    "    print(\"starting upload\")\n",
    "    start=timer()\n",
    "    new_network_url_5 = test_network_5.upload_to(SERVER, NDEXUSER, NDEXPASSWORD)\n",
    "    end=timer()\n",
    "    print(\"upload time = \", end - start)\n",
    "    new_network_uuid_5 = new_network_url_5.split('/')[-1]\n",
    "    print(\"Network's URL (click to view!): \" + SERVER + \"/viewer/networks/\" + new_network_uuid_5)\n",
    "else:\n",
    "    display_cx = test_network_5.to_cx()\n",
    "    Cytoscape(data=test_network_5.to_cx(), format='cx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def igraph_from_nice_cx(nice_cx):\n",
    "    g = igraph.Graph(len(nice_cx.get_nodes()))\n",
    "    # network attributes\n",
    "    for name in nice_cx.get_network_attribute_names():\n",
    "        value = nice_cx.get_network_attribute(name)[\"v\"]\n",
    "        #print(value)\n",
    "        g[name]=value      \n",
    "    # attribute declarations\n",
    "    # nodes and attributes and coordinates\n",
    "    # make a node_id list\n",
    "    node_id_list = []\n",
    "    n_id = 0\n",
    "    for node_id, node in nice_cx.get_nodes():\n",
    "        g.vs[n_id][\"cx_id\"] = node_id\n",
    "        #atts = nice_cx.get_node_attributes(node_id)\n",
    "        #for att in atts:\n",
    "        #    att_name = att[\"n\"]\n",
    "        #    att_value = att[\"v\"]\n",
    "        #    g.vs[n_id][att_name] = att_value\n",
    "        n_id += 1\n",
    "        \n",
    "    # edges and attributes\n",
    "    for edge_id, edge in nice_cx.get_edges():\n",
    "        cx_source_id = edge[\"s\"]\n",
    "        cx_target_id = edge[\"t\"]\n",
    "        g_source = g.vs.find(cx_id=cx_source_id)\n",
    "        g_target = g.vs.find(cx_id=cx_target_id)\n",
    "        g.add_edges([(g_source, g_target)])\n",
    "        #e_id = g.get_eid(g_source, g_target)\n",
    "        #g.es[e_id][\"cx_id\"] = edge_id\n",
    "        #atts = nice_cx.get_edge_attributes(node_id)\n",
    "        #for att in atts:\n",
    "        #    att_name = att[\"n\"]\n",
    "         #   att_value = att[\"v\"]\n",
    "         #   g.es[e_id][att_name] = att_value\n",
    "\n",
    "    # cartesian coordinates\n",
    "    g[\"cartesian_layout\"] = nice_cx.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT)\n",
    "    #visual styles\n",
    "    # bypasses\n",
    "    # rules\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<igraph.Graph at 0x7fa4f9d41130>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g1 = igraph_from_nice_cx(test_network_5)\n",
    "g1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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