{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import movekit as mkit\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial import Voronoi, voronoi_plot_2d, convex_hull_plot_2d, delaunay_plot_2d\n",
    "import networkx as nx\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Extracting all absolute features: 100%|██████████| 100.0/100 [00:01<00:00, 61.85it/s]\n"
     ]
    },
    {
     "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>time</th>\n",
       "      <th>animal_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>distance</th>\n",
       "      <th>direction</th>\n",
       "      <th>turning</th>\n",
       "      <th>average_speed</th>\n",
       "      <th>average_acceleration</th>\n",
       "      <th>stopped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>312</td>\n",
       "      <td>405.29</td>\n",
       "      <td>417.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210217</td>\n",
       "      <td>-0.006079</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>511</td>\n",
       "      <td>369.99</td>\n",
       "      <td>428.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020944</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>607</td>\n",
       "      <td>390.33</td>\n",
       "      <td>405.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.070235</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>811</td>\n",
       "      <td>445.15</td>\n",
       "      <td>411.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370500</td>\n",
       "      <td>0.007092</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>905</td>\n",
       "      <td>366.06</td>\n",
       "      <td>451.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>(0.0, 0.0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>-0.003975</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  animal_id       x       y  distance   direction  turning  \\\n",
       "0     1        312  405.29  417.76       0.0  (0.0, 0.0)      0.0   \n",
       "1     1        511  369.99  428.78       0.0  (0.0, 0.0)      0.0   \n",
       "2     1        607  390.33  405.89       0.0  (0.0, 0.0)      0.0   \n",
       "3     1        811  445.15  411.94       0.0  (0.0, 0.0)      0.0   \n",
       "4     1        905  366.06  451.76       0.0  (0.0, 0.0)      0.0   \n",
       "\n",
       "   average_speed  average_acceleration  stopped  \n",
       "0       0.210217             -0.006079        1  \n",
       "1       0.020944              0.000041        1  \n",
       "2       0.070235              0.000344        1  \n",
       "3       0.370500              0.007092        1  \n",
       "4       0.118000             -0.003975        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"datasets/fish-5-cleaned.csv\"\n",
    "data = mkit.read_data(path)\n",
    "data = mkit.extract_features(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtaining a list of graphs, based on delaunay triangulations\n",
    "Caclulate a network list for each timestep based on delaunay. trinangulation. Each timestep carries the respective graph with data attached to nodes, edges and graph. Just insert time and animal specific x and y coordinate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Calculating spatial objects: 100%|██████████| 1000/1000 [00:12<00:00, 82.05it/s]\n",
      "Computing euclidean distance: 100%|██████████| 1000/1000 [00:03<00:00, 263.66it/s]\n",
      "Extracting all absolute features: 100%|██████████| 100.0/100 [00:01<00:00, 75.25it/s]\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:05<00:00, 182.21it/s]\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:06<00:00, 162.88it/s]\n",
      "Calculating centroid distances: 100%|██████████| 1000/1000 [00:04<00:00, 210.44it/s]\n",
      "Computing centroid direction: 100%|██████████| 100.0/100 [00:00<00:00, 709.71it/s]\n",
      "Calculating network list: 100%|██████████| 1000/1000 [00:04<00:00, 226.92it/s]\n"
     ]
    }
   ],
   "source": [
    "#Calculates a network list for each timestep based on delaunay triangulation (currently only one available)\n",
    "graphs = mkit.network_time_graphlist(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the graph for time step '3'-\n",
    "\n",
    "pos = nx.spring_layout(graphs[2])\n",
    "\n",
    "nx.draw(graphs[2], pos)\n",
    "node_labels = nx.get_node_attributes(graphs[2], 'animal_id')\n",
    "nx.draw_networkx_labels(graphs[2], pos=pos, labels=node_labels)\n",
    "\n",
    "edge_labels = nx.get_edge_attributes(graphs[2], 'distance')\n",
    "nx.draw_networkx_edge_labels(graphs[2], pos=pos, edge_labels=edge_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': 3,\n",
       " 'x_centroid': 395.392,\n",
       " 'y_centroid': 423.234,\n",
       " 'medoid': 312,\n",
       " 'polarization': 0.30008793109871296,\n",
       " 'total_dist': 1.0251553045775692,\n",
       " 'mean_speed': 0.15561007599508753,\n",
       " 'mean_acceleration': 0.0018184129934008715,\n",
       " 'mean_distance_centroid': 29.691399999999998,\n",
       " 'centroid_direction': (0.01, 0.014)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all graph attributes at time 3\n",
    "graphs[2].graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(312, 811), (312, 607), (312, 511), (312, 905), (811, 905), (811, 607), (607, 511)])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all edges at time 3\n",
    "graphs[2].edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'distance': 40.70507585056191}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the distance of one node pair at time 3\n",
    "graphs[2].edges[312, 811]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': 3,\n",
       " 'animal_id': 312,\n",
       " 'x': 405.31,\n",
       " 'y': 417.07,\n",
       " 'distance': 0.30000000000001137,\n",
       " 'direction': (0.0, -0.3),\n",
       " 'turning': 0.9986876634765885,\n",
       " 'average_speed': 0.17472339975245438,\n",
       " 'average_acceleration': -0.004658902224371936,\n",
       " 'stopped': 1,\n",
       " 'x_centroid': 395.392,\n",
       " 'y_centroid': 423.234,\n",
       " 'medoid': 312,\n",
       " 'distance_to_centroid': 11.677}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all attributes of node 312 at time 3\n",
    "graphs[2].nodes[312]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network analysis\n",
    "\n",
    "The networkx package enables to analyze the extracted graphs over time. See doc: https://networkx.org/documentation/stable/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(graphs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot number of nodes over time\n",
    "num_edges = []\n",
    "for G in graphs: \n",
    "    num_edges.append(nx.number_of_edges(G))\n",
    "\n",
    "plt.plot(num_edges)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot clustering coefficient over time\n",
    "avg_cluster = []\n",
    "for G in graphs: \n",
    "    avg_cluster.append(nx.average_clustering(G))\n",
    "\n",
    "plt.plot(avg_cluster)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gq8rUNNGCvqzIDq8e+yCthlTeC8gqbMwZmw3134RkYjV67zPpoS80494GCVMiYuGVZVNawlQ72qEkFVWyy0rWtqafSrAYb6yUrBG7K4HgheU2bZy4IriOyRp9MxztnvlW/L6Y6aZUOlE3BZ+nHWGjB3zba/2HafCDy5q0k3Zy8DBuq1eWq/W50tiCsFyv/fUfJ67paPRj4IwNlJ2q3s4mWtAHFD1Q4qJOqnqNy0qepqbS6EOqm+vJwcvE1cO7XbEDr2o0hTO2KXgh1mLO2CooK2FKY6JOmlJ5L2/VvbRFzYqKoy87Td7VuRSgQgde1QR9ThoLXkRV/S9OOLzSnLEl0zXdlBB1E3Glq5ptZlSC65PHRp+GQssUF3bkiHM5uqlBfZRWw978XBEoFkNLIDTg2gShslJnjV5ELhSR+0Rkq4hcEbPNuSJyh4hsEZHvhZY/KCJ3+es2uWq4C8qrXjlYphiaM99lp856joSpNDp6kROPlHmdXZ0pCMttitbqmu5UgkO2K74puQneaquaGXLoxCMiMgVcBVyANwfsRhFZr6p3h7ZZAnwOuFBVHxaR/pl836qqu9012xElOWOVaDHXFE0tEJLZ86WindED9JhumhtH76rSZFAfpSkhhK7pJEwlhldWU/Z3VDxnbL01+tOBraq6TVUPANcCl/Rt837g66r6MICq7nTbzIIp+MJHlUCA5vyAgybmGaCjhlc2eSpB1xp9q9UMYeaaoMeJRc0a4qftmuHqa6NfBjwS+r7dXxbmZOBwEfmuiNwmIr8WWqfALf7yNXEnEZE1IrJJRDbt2rUrbftzUaozNuJUVU0UPCpBG4vOjJWYv/MiJTljg3O4i7rxYvK8+ihODtko0kTdNMRE302YohqNPs2csVFXub+ls4A3AOcB84AficiPVfVnwNmqusM353xLRO5V1VsHDqi6DlgHMD09XcqVKCuOPqoEgnf+ZmhqgZAp2hk7bCahPJSaMOUq6ka7E0o3QSFwTbqpBJsR0NB1xtY3jn47sCL0fTmwI2Kbm1R1n2+LvxU4FUBVd/ifO4Hr8ExBtaBcZ+zg8lZDim3ld8amm2GquMzYcl6XXU8m062P0ozsT9d0nLFjNpVgXcsUbwRWi8gqEZkDXAqs79vmBuAcEZklIvOBM4B7RGSBiCwCEJEFwNuBze6an49OYbGi69FrtJmoKZpafmdsSltqUc7Ykn9bLhOmvPoozYjOck3XGRtPU7KGA/NtVebaoaYbVZ0RkcuBm4Ep4GpV3SIil/nr16rqPSJyE/BToA18QVU3i8iJwHW+QJ0FfFlVbyqqM1kp+rLHlUBoSiJMx/ac451z5IQplxp9yXE37pyx3TLFTRgnrukkTA2fYqr2hMsUV3Er09joUdUNwIa+ZWv7vl8JXNm3bBu+CaeOlDVGvPTniPM3JLyynVejJ52w7Zl4JOO5og9crtbnUqOH6hx4VZN2KsEmXJlO3aKKNHrLjKWEWjdxCVM047UzEDjZSyCk26/IZJLmZsZO7sQjAcmZsc1wdAXKnpUproBAyywj6ia6emUzIgZyl0CAVCp6bxy944SpEq+zy1o3QQ3zJowT16Sx0UMzsobbba+CqzcWTaMvl45GX3TCVHwJhCa8kueNuoHRTTFuE6bKNeS6LFPcnZWo/uPENZ3wygQp1RATfU8JBNPox5S4zNiqHDOjEgiZzAM0pv/9tAqy0Zdtx3VnuqnWgVc1qRKmGuKo9kw31U0LOdGCvixFz3NGRp+/CRp9J2Eqx1SCqaIrx6SomTuNHqjQgVc1XWd0PGVlPeelU7dIvBnnymayBb3/WY4zNuL8DQkZCJqYVYCld8YWmRlbJi6dsc3x5bjH6/TwhKn6Xxwvy7m65LfJFvRBwlQJ9ejjTDdN0NQ64ZU5nLGj1rpxabwpu3icK40+sOt6tW7qP05ck2YqwaaYbnorkZrpplSqLoHQlGJVnczYgp2xxZluynbGuoqjDztjnRyyUaQtgdAEAmXPphIcY5I0+ib8frtRN9n3TyNsi0qY8ixk5V1pl+GVVTrwqiZVwlRTfkN42fGtVn3LFI8tZVWvDF7bBhvQjFfybpnirM7Y6vtYrunGnUaPVOfAq5p0E480420nmOi9qiznyRb0QcJUCdc96vWzqkp2o9LR6HPsP3ocvdNiN40QBgPoZGv0QZeHZ0zX/9po2AxXwfknW9B3NPriE6Yii5rRFI0++MxaAoGRJb1b001zbfTS+dvJIRtF1xnb/Dj6tq/sVJXlPNGCPqB4Z2y8Rt+EQRroIHnaOqqwbfRUgk5t9E0aJ27RTnhl/DZN8NOGE79aFWU5m6AvAY2xXTQlEaY7w1SOqJuRNXrX4ZVNTJjy66NMeAmE5OqV9XfGaqgfVUXaTbSgLy0zNkajb1qZ4qITpoqkzBY4M920g/oozci3cE36Egj1vjbBvQv8LeaMLZmuM7bghCmiXz9bZZdVzEhuZyxVFzUr2Y7r8FydiUfcHbIxpI2jr/u1CdoXlCmurY1eRC4UkftEZKuIXBGzzbkicoeIbBGR742yb1WUVY8+LrxSKqpkNyp5wyuhWltq452xDXnzc03aqQTrTjv0ZlLVG8jQGaZEZAq4CrgAbxLwjSKyXlXvDm2zBPgccKGqPiwiR6fdt0rKGiRxphvPydagX3CehKmKnbGlJky5Oo6GNPomjRNHpNLoG+CoDtvoq3o7SzOV4OnAVn9aQETkWuASICys3w98XVUfBlDVnSPsWzlZLvze/TP8yfot7N0/M3TbXc/tj3bGAnc9+iyf++5WfvPckzK0wg2qyqduvJdH9jwfuf7uHc8CsOmhPXzkS7clHmvWVIuPn7+aE49ayKdvupcHd+/jqecPVOuMFXjq+YND256FqZbw2+efzElHL2Tzo951+tKPH+LWn+3qbPPyYxfx2+efDMBtDz3FF3+wLZVw2uNfN8G7B8Pa32oJH3vbSbzi2JcMrLt5y+Nc/y+Ppu9Y1PFF+PA5q3j9ysMzH+PqHzzAxgf39CxbPG82f/zuV3GorfzVP/6cT1xwMofNnmLTQ08Bw5yxsGXHM4Xc235aIqx584mcumLJSPt1BL3/b8++Az3tXTJ/Nn/y7lczZ1ZxlvQ0gn4Z8Ejo+3bgjL5tTgZmi8h3gUXAX6nq36bcFwARWQOsAVi5cmWatuemU9Qsg6S/57Fn+T+3bWfZknksmDuVuO2KI+ZxzuqlA8vPf+UxfPGHD/D579xfqaDfd+AQ627dxtKFczliweyB9UcunMOe5w+weN5s7t+1N/Y4h9rK/bv2cdrKJRy3ZB6f/+79LF04hxOOXMAvnDTY/yh+ZXoF9+/ay/Ij5mXuTz9nn7SU2x96OrHtWQj6+9rli1m1dEFn+cFD7c65du89wLfufqIj6L9x5w5u2vw4Jx29cOjxVx25gLNPWsqKI+Zzwx2PJrZfFX6+cy8vP2ZRpKC/9icP80/3P8nxR84ftZsdfr5zL8cuPiyXoF936zaePzDDsYsPA2DvizPseOZFfvWs47nxrsdZd+s2jl40lw+fcyJP7t0PwIoj4tt8/iuPZv2dO5zf2yh+9sRelh8+b3RBHwoTfdPqpdzxSHcsPvfiDI898yIfOnsVJx+zyHWTO6QR9FHP037ROAt4A3AeMA/4kYj8OOW+3kLVdcA6gOnp6VLebjpFzTLo9G3faHrle1+bWoj187HzVvP0Cwf5+42PDN+4QAIb4mVvOZEPn3Ni5uM88/xBTv1Pt6DafXj+xptO5CPnviz1MT793tdmPn8c73rtcbzrtcc5P+6+/TO86j/ejGr3Gv7O20/m8ret7mzz5zffx+e/d3/nu6qy6LDZ3PLxt4x0ro++NVkRONRWXvb7G2KVlrbCK45dxA2Xv2mk84Z5td/XPLRVueg1L+W//SvvPt+85XH+zd/dhqr3gAQ4eMjP2wCOW3wYi+cNKh8Bl79tdc/1LpJX/OGNmd7+w2GiF596HBef2h2L//enj/HRL99euPkpjaDfDqwIfV8O7IjYZreq7gP2icitwKkp962MPM7YNFl7aWjVIJZe/Toqefsi/ptnWzUUUpbrkLUmsB23tdfh1rtN7/0Nap64b0tw/OixFMTk5yWvr6N/trVW+K26r3lpi+GVRUuko+CNQji8cvCYvdsURRqj0EZgtYisEpE5wKXA+r5tbgDOEZFZIjIfzzxzT8p9KyPPIAoGfN5xWIeJn9NkIKYhvHs3pKw+P1TXhEtohB1u/RuF768SPX9w/rb4AjNhGxf3N/9Y7S3Z3Qpdw+4WOrCsDmStNqsJSmEe8/EoDNXoVXVGRC4HbgamgKtVdYuIXOavX6uq94jITcBPgTbwBVXdDBC1b0F9yUyem5f3R1uH7Ng0U7aloavhakjDzXnQGhN+Iww73MJ0BJmvUXsabXHtiYvOcaLRO2h3f/9FQssjigzWafxkrU3VSfyKOmZJGn0a0w2qugHY0Ldsbd/3K4Er0+xbOzLdPO8zr5ZUh5r0wUBs5exM+DU8SYsZF7r91di3ovA1CRK3iromSTVx1IHJyEWrgyqOnWOGruHAZan6h9FH1sS7JFlR1hvvRGfGQhBjPTquNNay67BE4UqjD2tnSVrMuBD0zbPR+8skbhvfHBGTPOeqPYk2+pxnFgd5H0EVx84xY5YDqaegLIus/Y/z38Dg+CgKE/RkfEoH++d2xtbHRp/bGRths54EZ6yGHmz9GlrwlhTc4rjkOVftiRtKwRtFXvIO1cCEFdC9FtqzTWfbGqkKWZOd2gm/hZYvgYuWARMv6LOSJj07DXWw0cc6EkckbGNN0mLGhbB9dVgUVriMRGGXJGEsuRD0LmoG9bejx0bfFwVXR40+k42e+B9Y8Jsxjb5gvDknsztY8jtjq69jkhT+NQqdULF294qOs0bfE+kSY/7qCR8kKHBXlEYfL4iDicbz4KLV/e3oOPDbXe093IU6DZ+k65tE0ttt+EFXJCboyXfzXAUyVGmnd2VmCQu+SdDoIfjxx+cN9GupRWr0SbWTXDxgsipFg+0IH7O7fECjr50zNptSlhShF2W6KgIT9Lntbu4iVaoizSTMaQgnf7h6ENad4HU+KWEKws7YAsMridcMXTxgsipFg+0IRd2ETBfRztj6DKCsgRNJZl7T6Esiq3DrToqQ7/ytkEZTFc7eTiLCK8c5YQq6r/NxpqpW6C0HBsML3bYlObzSTWZszv37HnStCIW2kzBVYIRSFrIGTnTHRrxGb87YEshykbshiW4iVap0yLqMeQ+Sdlw5q+uOID0lEOKeluFZugq7JonO2PxC00nUjvb+ZoIx1w7Zbnq6UKMBlDVwop0Qv2zhlWUh2VKtu0lGOU8fyiatCpd1aVq+HTNJixknJBg/MX6Ojsbm1xMq1hkbb6N3U2Mnfyiw54ztfg+btvqV+5qZ6HOXQIibThRM0BeOQKYRFeziSqOv0kbvUigLnuALtJgxl/Od1/k4n004twCKdsbGD2UXNXYk64+lpx291yjswO/6JbshSnUaPlk1+qRaUlGmqyIwQZ/ZGetGC66VM9ahRh9QJ2daEQSmqk7SWd/6cIVLwBNeBV2TpDjvYKLxXMfHlTM2dMwejb63gYqbipuuEE+LGZm4rGlvWd/4KAgT9Jmdsf7+rsIrK3xRdWmjR3oTpsY5jh66D7ahGn0oYaqoa5IU5+0qgsVNwlS3Ha3uK093m/C2+U7nlFbWhKmEPJWo6p1FMPGCHnKGTDkKr6wyacplXZogrnxiwivpLYHQfxH7NbZ+Z6Tr1sSNozo4Y6PGWdgZGWXGrNP4yajQJ/62LbyyJPKmdbv68VSaMOV/urHRS0/C1CQ4Y8N5AwMavf9Zlo0+ThSpOkiYIl/CVNQ16qnuGWzXCa/MfKpC6DdLpif+txCu3lkkJujJa6PP6+CqXqN3a6P3wsmqLutQFkFFwzhTVb8Ppn+GJbdt8WzxUThJmMqpFEWNsx4bfZ9Gr9SrqFlS+GoSiTZ6/9Pi6Asm6wxPwQ/KVX2YKjX6bl/yH6ur9UyGRh9EusSZqganiivSRp/gjHWQqJVVKeq2wfuMKoEQ9QAsMos4C62MkRvJUwnWKLxSRC4UkftEZKuIXBGx/lwReUZE7vD//1Fo3YMicpe/fJPLxrsg6zjqhFe6csZWGl7pxt/gHcQPr0yIHR4nAuEa92Pu11LbDkwoSW2JD690l/CUed+IcRZZpjj7KQol6/zOGvGA6x6znKi7oTNMicgUcBVwAd5k3xtFZL2q3t236fdV9V0xh3mrqu7O19TiyGJ3dGXuCOqV1yIz1sGxgrjyiXHGSm8JhMH1vfc3ciYlhySXKXZjZsxK1JiIckaGwujrFV6ZMWGse0+ibPT92xRDGo3+dGCrqm5T1QPAtcAlhbaqTLLaHR2FJA7qM+Xjsi5NfwmEcQ+vDCoaxoXQ9b+xFWmjb7WIHUjqKKyzUGdsX6hh3ZyxmROmEjT6iOjSQkgj6JcBj4S+b/eX9XOWiNwpIjeKyKtCyxW4RURuE5E1cScRkTUisklENu3atStV412Qdey7EmR1KoHgMmGq253xlvS+mEqw0fe+mivFXRGv7k6cjd5RhJgLZ2z4mKF1g47XehU1kwTTWBJJeSrdyXqK/f2nmRw86lr3t+p24HhV3SsiFwHXA6v9dWer6g4RORr4lojcq6q3DhxQdR2wDmB6ero0qZdVu3Jd1KwOmbGuSiC0J0ijb4nQbg9PmAqbbopMmEoqU+yiBEI+Z+zgOItUdIKHYu2csflybpKmEqxDHP12YEXo+3JgR3gDVX1WVff6f28AZovIUv/7Dv9zJ3AdnimoNkjGm5dUv2IU6lACwaXyHWg9TrNta0zwOj80vNL/3l+P3W1bkueMdXF/82ieUQEMUX+Hz1Cn4SMJD9Ikgl0i4+gp5/efRtBvBFaLyCoRmQNcCqwPbyAix4o/ekXkdP+4T4rIAhFZ5C9fALwd2OyyAy7Ico07N9xR1E21zliHGr301n6ZBI0+/GDrHxCDGn1x1yTJhuyiDn7et9eggmdU1E1P9crgWjk4p0uSqoMm0S1hHXXMvm0KYqjpRlVnRORy4GZgCrhaVbeIyGX++rXAe4GPiMgM8AJwqaqqiBwDXOff2FnAl1X1poL6kgkhqzPWjXDs1/iqIMlZNCpBvZVJCa8cptFL3xtbkRp9Upy3RrRtVPKabqIe/t08kkEzZpW5JVEUMZWglPT7T2OjD8wxG/qWrQ39/VngsxH7bQNOzdnGQsk6D6YrQdbR+CpMJXXlb4BuXHlc7Zdxo99BGTeVoIY0+uKcsfGaYbSzc/Tj58uM7R6ne0zprBusXlkz0w3ZBHJSLSnpGx9FYZmxGfdzVQisDjZsjdFGsxAIg0nR6AcTpnrXd2yw/nfPdFOcRh8nL1TJPUkO5NTog2sUukhRgi58reo0erI6Y5Ns9DaVYElkrd/hbnLw4HjVa/SunLHhEgh1+qEWQaDlpS2B4NVYL6gtCTZ6757kffvMt3+kRp9guunZoAZkL5fi/xYiulKWj27iBT1kdca6MU1EZQaWTRHO2MnS6OPLVndNc/5ngRp9kg3ZRVinkDPqJuIaRdV66SRMZT5TMWQtgZBU1KysMuUm6HPOg+kuvLLCqJu+tuShPwpl3KNuulFGHoNx9IHpJrDRV1SmOKJtI5PbGesRmRnLoOPaRQ19l2TV6LtO6ChnrL+NafTFknXsu5p4pHs8J4fJeO74V8tR6Y9CqdUvtQCCH3+cz6YbMuh9Fl6mOGYcOSlTDG4yY0PtCIefJjkr60CSszuJpJ9CWQmTJuiBLKPXlcYaVb2vbNyGVwaTZbszB9WZVufBFnzvj7oZ1FKLLFMcpxm6cgK7qXXTXTZM0NVp9CQ5u5Po9Dvixrf63viKwgR95c7Ycmx0SSRV1xuVjkNwMhT6TkXDWGdsJ8W9a3cuNrwyep0LZ19+Z2zw1hOKugnVeuk3Y2iBbz9Z8PIIstjoh4dXmo2+BPLY3fJS1qtbEl3baf5jBVEo7QQtZpzoN1X1y6X+8EoXNWfi25JQdMuBRh+EzmYl6mHYnRw7ZObqfNbLHZt1KsEkM6+FV5ZE1nkwk7LdRqEO4ZVR0RBZCcwHnUkmch+x3gzU9klRAqHI8Mo4000dphLsCvqQRh+80ba7GcNdM1e9xk/S9U2i875s4ZXVkdkZmxAbO2ILvONVGUfvcCpBEXqqOdbp1bsIgiSauKSz/smfC61HL8llivOHV+abHDwqqayr6AxuX+RDMQvZSyAkRd2UE3Vngp6sphsPd3PG5jpMLtyHV8YLvnGjM6NW8L2vw/33t+gyxbGZsbgxGeXS6P3PqDLFXnhlsF33JPUqapa1BEJ3/6hjQvGhGCbok+yaCSQ5WEY9P1Qr6F2/TYQnHhl3jX7ARt+/PlTLBQJzREE2+iETj7hK7stKUnhlOGa+81CkXrYbTynM4owN9o/X6IuudTXxgh4yavQxURaj0orQYsrGlb8hOEZveGXuQ9aa4HU+zlTVX9Ssreqk5kx0WxLGsrPwyhz7RlyjHmdkSLsPb18XkkxjSWjEA657TH+bPA1LgQl6sglZVw7MOpVAcJUw5dms/e91UskKIIgyir2GfffXU6yLirqJF45xCUmjHT9fFnlUUlmSM7LIUNQs5A3FjnbGlhNePfGCPntmrCvnZQ2csY41+qRww3Gj1fdgi0+Y6mr0xZVASDLduJh4BPLonlHjrCePJBSZFJyqTuOnEGdsq3ebokgl6EXkQhG5T0S2isgVEevPFZFnROQO//8fpd23aiRQyUbElXOrHlMJujOzBA4rlw7eOtN1xkZfw4GJZRyZUGLbErNOI9qWhXzO2MFrFHbAamjLzvoa6fRNLlM8dOIREZkCrgIuwJs/dqOIrFfVu/s2/b6qvivjvpXhhYyNTtuRttF1QFWv0TuRP77W49IcVGc6zthgmrwhcfRFavSdrOQIwjbwPMfPQ+caRTpju8vCztg6OfODLOhRSXq7LSuOPs0MU6cDW/3ZohCRa4FLgDTCOs++pbHruf386P4nR9rn0adecJZgBLD50Wc4eKgaYf/zJ54DXCVMwdPPH+C+x/f63+vzQy0CEeHp5w9y7+PP+t971wfa6907nmVWq8ULBw8Vmhn73IszA2PZ1RubKvzLI0+P/FsJeOjJfZ12BgTX4qEn9zHjj/8nnn2xc7460WrBvgOD13cYP3si+C1EHNPv/4NPPs+P7n+SObOENxx/RO629pNG0C8DHgl93w6cEbHdWSJyJ7AD+B1V3TLCvojIGmANwMqVK1M0yw0L587iB1t384Otu0fe98gFc/Kf/zDvFvzxN6p/9i2cm2pmyaHH+P7Pd/PT7c8AsGDuVO5j1plFc2fxkwf2cPdjnqBfdFjvNVw4dzYAn7rx3tCy/Nc5ri1bd+7lff/zx5Hr85436GPc8dOyKNSOKRHmzZ7iHzZt7yz73s92Ab2x9XVg4dxZbH/qhcz9XxBx/WdNCXNntfjKTx7mKz95mKUL57LpD87P29TB86TYJupS9z9rbweOV9W9InIRcD2wOuW+3kLVdcA6gOnp6dKe5X/zoTeybde+TPsuP3xe7vOfunwx37j8TezdP5P7WHk4fMFsjnnJYbmP85lfeV1Hg1kyfzbLD5+f+5h15s9/+VTufdx7I3rJvFkcf+SCnvUnH7OQb37sTTz3Yvf+vmb54kLa8l/f8xo+cObxkeumWsLrVixxcp6/+OVTOW5JtrE/b84Ur13W7X+rJdz4W+fwzw88ySe/dhcAi+fN7qyvk43+D991Cu95/fJM+y6ZP5uXLh68ZrOnWtz4W+fwxLP7AZgzq5j+phH024EVoe/L8bT2Dqr6bOjvDSLyORFZmmbfqjnmJYc5EXBZEZHCfvhVcOTCuZy1cG7VzSiNwxfM4ayXHRm7XkR49bJy7u/i+bMT2+KK169cwolHLXR2vBOWLuDFmUMDy4ucpCULiw4r5vqeeNRCp9czijRRNxuB1SKySkTmAJcC68MbiMix4hveROR0/7hPptnXMIxmUYSPIXxM7fs08jNUo1fVGRG5HLgZmAKuVtUtInKZv34t8F7gIyIyA7wAXKpe2EXkvgX1xTCMEihCyw4fMly90nBDKu+Mqm4ANvQtWxv6+7PAZ9PuaxhGcylCo4+K+NKY5cboTHxmrGEY1RMOPQznlJiYd4MJesMwRqKIWcMkwkZPzZyxTcYEvWEYI1FERdKeY2rPh+EAE/SGYYxEEbHt4WOGyxSbQu8GE/SGYYxEERp9lImmbrVumowJesMwRqMI003o6WHOWPeYoDcMYyQKCa8M/d1jujFJ7wQT9IZhjEQRsrcnM9YSppxjgt4wjJEoJmFqcFn0dOtGFkzQG4YxEkUL+vBcU2a6cYMJesMwRqOQWjdRppv8E5obHiboDcMYiaITpsKmedPo3WCC3jCMkSgitr0lg5LenLHuMEFvGMZIlJowZcYbJ5igNwxjJAopgdBT1Mycsa4xQW8YxkgUMvFIT5ni7qcJejekEvQicqGI3CciW0XkioTt3igih0TkvaFlD4rIXSJyh4hsctFowzCqowjha1MJFsvQGaZEZAq4CrgAb7LvjSKyXlXvjtju03jTBvbzVlXd7aC9hmFUTOElEHyV3guvNJXeBWk0+tOBraq6TVUPANcCl0Rs9zHga8BOh+0zDKNmFD05eICCJcY6Io2gXwY8Evq+3V/WQUSWAe8B1jKIAreIyG0isibuJCKyRkQ2icimXbt2pWiWYRhVUITsjYiuLOxck0gaQR91rfvNZ58BPqmqhyK2PVtVTwPeCXxURN4cdRJVXaeq06o6fdRRR6VolmEYVVCWMxa1ycFdMdRGj6fBrwh9Xw7s6NtmGrjWvylLgYtEZEZVr1fVHQCqulNErsMzBd2au+WGYVRC4QlTePZ5c8a6I41GvxFYLSKrRGQOcCmwPryBqq5S1RNU9QTgq8Bvqur1IrJARBYBiMgC4O3AZqc9MAyj8fQ/OtpqtW5cMlSjV9UZEbkcL5pmCrhaVbeIyGX++ii7fMAxwHW+BjAL+LKq3pS/2YZhjBP9Gn3bt9+Y5cYNaUw3qOoGYEPfskgBr6q/Hvp7G3BqjvYZhjEB9At0Vc8RaHLeDZYZaxhG5fTb/duqfmasiXoXmKA3DKOWmDvWHSboDcOoHR2NvuqGjAkm6A3DqB3tQJk3Se8EE/SGYdQO7Wj0JuldYILeMIzaEWj05ot1gwl6wzDqh3arWBr5MUFvGEbtaPslEEyhd4MJesMwakfHF2uS3gkm6A3DqB1tc8Y6xQS9YRi1wzPdmI3eFSboDcOoH2qTg7vEBL1hGLXDwivdYoLeMIza0TXcmKR3gQl6wzBqR9tMN04xQW8YRu3wkqXMGeuKVIJeRC4UkftEZKuIXJGw3RtF5JCIvHfUfQ3DMAI00OirbsiYMFTQi8gUcBXwTuAU4H0ickrMdp/Gm3JwpH0NwzDC2FSCbkmj0Z8ObFXVbap6ALgWuCRiu48BXwN2ZtjXMAyjwwev/glPPX/AEqYckWbO2GXAI6Hv24EzwhuIyDLgPcDbgDeOsm/oGGuANQArV65M0SzDMMrk2jVn8uhTLxR2/D+++BQe2vM8Tz9/kP0zh3jVcYt59+uOK+x8k0QaQR/1SO33knwG+KSqHuqb4zHNvt5C1XXAOoDp6WnzwhhGzTjzxCMLPf6vn72q0ONPMmkE/XZgRej7cmBH3zbTwLW+kF8KXCQiMyn3NQzDMAokjaDfCKwWkVXAo8ClwPvDG6hq51EsItcA31TV60Vk1rB9DcMwjGIZKuhVdUZELseLppkCrlbVLSJymb9+7aj7umm6YRiGkQap4ywu09PTumnTpqqbYRiG0RhE5DZVnY5aZ5mxhmEYY44JesMwjDHHBL1hGMaYY4LeMAxjzKmlM1ZEdgEPZdx9KbDbYXOagPV5MrA+jz95+nu8qh4VtaKWgj4PIrIpzvM8rlifJwPr8/hTVH/NdGMYhjHmmKA3DMMYc8ZR0K+rugEVYH2eDKzP408h/R07G71hGIbRyzhq9IZhGEYIE/SGYRhjztgI+nGdhFxEVojId0TkHhHZIiK/5S8/QkS+JSI/9z8PD+3ze/51uE9E3lFd6/MhIlMi8i8i8k3/+1j3WUSWiMhXReRe/36fNQF9/rg/rjeLyFdE5LBx67OIXC0iO0Vkc2jZyH0UkTeIyF3+ur8WGWFGXVVt/H+8Esj3AycCc4A7gVOqbpejvr0UOM3/exHwM7yJ1v8MuMJffgXwaf/vU/z+zwVW+ddlqup+ZOz7J4Av481vwLj3GfhfwIf9v+cAS8a5z3hTjT4AzPO//wPw6+PWZ+DNwGnA5tCykfsI/AQ4C2/mvhuBd6Ztw7ho9GM7CbmqPqaqt/t/Pwfcg/cDuQRPMOB//pL/9yXAtaq6X1UfALbiXZ9GISLLgV8EvhBaPLZ9FpGX4AmELwKo6gFVfZox7rPPLGCeP0nRfLwZ6Maqz6p6K7Cnb/FIfRSRlwIvUdUfqSf1/za0z1DGRdBHTUK+rKK2FIaInAC8Hvhn4BhVfQy8hwFwtL/ZuFyLzwD/HmiHlo1zn08EdgF/45urviAiCxjjPqvqo8CfAw8DjwHPqOotjHGfQ4zax2X+3/3LUzEugj71JORNRUQWAl8DfltVn03aNGJZo66FiLwL2Kmqt6XdJWJZo/qMp9meBnxeVV8P7MN7pY+j8X327dKX4JkojgMWiMgHknaJWNaoPqcgro+5+j4ugn6sJyEXkdl4Qv5/q+rX/cVP+K9z+J87/eXjcC3OBt4tIg/imeHeJiJfYrz7vB3Yrqr/7H//Kp7gH+c+nw88oKq7VPUg8HXgFxjvPgeM2sft/t/9y1MxLoK+M4G5iMzBm4R8fcVtcoLvWf8icI+q/mVo1Xrgg/7fHwRuCC2/VETm+pOyr8Zz4jQGVf09VV2uqifg3ctvq+oHGO8+Pw48IiIv9xedB9zNGPcZz2RzpojM98f5eXg+qHHuc8BIffTNO8+JyJn+tfq10D7Dqdoj7dCzfRFeRMr9wH+ouj0O+/UmvFe0nwJ3+P8vAo4E/hH4uf95RGif/+Bfh/sYwTNfx//AuXSjbsa6z8DrgE3+vb4eOHwC+vwnwL3AZuDv8KJNxqrPwFfwfBAH8TTz38jSR2Dav073A5/Fr2yQ5r+VQDAMwxhzxsV0YxiGYcRggt4wDGPMMUFvGIYx5pigNwzDGHNM0BuGYYw5JugNwzDGHBP0hmEYY87/B+CgbhfYRXxjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot density over time\n",
    "dens = []\n",
    "for G in graphs: \n",
    "    dens.append(nx.density(G))\n",
    "\n",
    "plt.plot(dens)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Plot transitivity over time\n",
    "trans = []\n",
    "for G in graphs: \n",
    "    trans.append(nx.transitivity(G))\n",
    "\n",
    "plt.plot(trans)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Compute features for individiual nodes__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick the first graph \n",
    "G = graphs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{312: 4, 811: 3, 905: 2, 607: 3, 511: 2}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Degree \n",
    "dict(G.degree())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{312: 1.0, 811: 0.75, 905: 0.5, 607: 0.75, 511: 0.5}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Degree centraility \n",
    "nx.degree_centrality(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{312: 0.5,\n",
       " 811: 0.6666666666666666,\n",
       " 905: 1.0,\n",
       " 607: 0.6666666666666666,\n",
       " 511: 1.0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clustering coefficient \n",
    "nx.clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Degree rank \n",
    "degree_sequence = sorted([d for n, d in G.degree()], reverse=True)\n",
    "dmax = max(degree_sequence)\n",
    "\n",
    "plt.loglog(degree_sequence, \"b-\", marker=\"o\")\n",
    "plt.title(\"Degree rank plot\")\n",
    "plt.ylabel(\"degree\")\n",
    "plt.xlabel(\"rank\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__For more methods please check the networkX doc: https://networkx.org/documentation/stable/index.html__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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